IPCC Guidelines for National Greenhouse Gas Inventories [5 Volume Set] 4887880324

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A report prepared by the Task Force on National Greenhouse Gas Inventories (TFI) of the IPCC and accepted by the Panel but not approved in detail

Whilst the information in this IPCC Report is believed to be true and accurate at the date of going to press, neither the authors nor the publishers can accept any legal responsibility or liability for any errors or omissions. Neither the authors nor the publishers have any responsibility for the persistence of any URLs referred to in this report and cannot guarantee that any content of such web sites is or will remain accurate or appropriate.

Published by the Institute for Global Environmental Strategies (IGES), Hayama, Japan on behalf of the IPCC © The Intergovernmental Panel on Climate Change (IPCC), 2006. When using the guidelines please cite as: IPCC 2006, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). Published: IGES, Japan.

IPCC National Greenhouse Gas Inventories Programme Technical Support Unit ℅ Institute for Global Environmental Strategies 2108 -11, Kamiyamaguchi Hayama, Kanagawa JAPAN, 240-0115 Fax: (81 46) 855 3808 http://www.ipcc-nggip.iges.or.jp

Printed in Japan ISBN 4-88788-032-4

Contents

Contents Foreword Preface Overview Glossary and List of Contributors

Volume 1

General Guidance and Reporting

Volume 2

Energy

Volume 3

Industrial Processes and Product Use

Volume 4

Agriculture, Forestry and Other Land Use

Volume 5

Waste

2006 IPCC Guidelines for National Greenhouse Gas Inventories

iii

Foreword

Foreword Recognizing the problem of potential global climate change, the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP) co-established in 1988 the Intergovernmental Panel on Climate Change (IPCC). One of the IPCC’s activities is to support the UN Framework Convention on Climate Change (UNFCCC) through its work on methodologies for National Greenhouse Gas Inventories. This report is the culmination of three years of work by the IPCC National Greenhouse Gas Inventories Programme, to update its own previous guidance on National Greenhouse Gas Emission Inventories. The task was started in response to an invitation made at the seventeenth session of the Subsidiary Body for Scientific and Technological Advice (SBSTA) of the UNFCCC, held in New Delhi in 2002. At the time, the IPCC was invited to revise the 1996 IPCC Guidelines, taking into consideration the relevant work made under the Convention and the Kyoto Protocol1, with the aim to complete this task by early 2006. In response to this invitation by the UNFCCC, the IPCC initiated a process at its 20th session (Paris, February 2003) that led to an agreement at its 21st session (Vienna, November 2003) on the Terms of Reference, Table of Contents and a Workplan2 for the 2006 IPCC Guidelines. The Workplan aimed to complete the task in time for its acceptance and adoption at the 25th session of the IPCC, to be held in April 2006. The 1996 guidelines comprised the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories3, together with the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories 4 and the Good Practice Guidance for Land Use, Land-Use Change and Forestry 5 . The 2006 Guidelines have built upon this body of work in an evolutionary manner to ensure that the transition from the previous guidelines to these new ones will be as straightforward as possible. These new guidelines include new sources and gases as well as updates to the previously published methods whenever scientific and technical knowledge have improved since the previous guidelines were issued. The development of these guidelines has depended on the expertise, knowledge and co-operation of the Coordinating Lead Authors, Lead Authors and Contributing Authors – the contribution over 250 experts worldwide. We wish to thank these authors for their commitment, time and efforts in preparing this report throughout all the drafting and reviewing stages of the IPCC process. As indicated, this report has built upon the work of the previous IPCC inventory reports as well as on reports of the inventory experts’ experiences in using the IPCC inventory guidelines without which the task would have been much more demanding and we are pleased to acknowledge our debt with all those who contributed to these reports. The steering group, consisting of IPCC TFI Co-Chairs Taka Hiraishi (Japan) and Thelma Krug (Brazil) together with Michael Gytarsky (Russian Federation), William Irving (USA) and Jim Penman (UK) has guided the development of these guidelines, ensuring consistency across all the volumes and continuity with the earlier IPCC inventory reports. We would therefore wish to thank them for their considerable efforts in leading and guiding the report preparation. Authors and experts meetings were held in Oslo (Norway); Le Morne (Mauritius); Washington (USA); Arusha (Tanzania); Ottawa (Canada); Manila (The Philippines); Moscow (Russian Federation); and Sydney (Australia). We would therefore like to thank the host countries and agencies for organizing these meetings. We would also 1

Including, inter alia, work by the Subsidiary Body for Scientific and Technological Advice and the Subsidiary Body for Implementation, and by the Consultative Group of Experts on National Communications from Parties not included in Annex I to the Convention, and the technical review of greenhouse gas inventories of Annex I Parties.

2

The Terms of Reference, Table of Contents and Work plan can be found at http://www.ipcc-nggip.iges.or.jp/ .

3

Intergovernmental Panel on Climate Change (IPCC) (1997). Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. and Callander B.A. (Eds). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. IPCC/OECD/IEA, Paris, France.

4

Intergovernmental Panel on Climate Change (IPCC) (2000). Penman J., Kruger D., Galbally I., Hiraishi T., Nyenzi B., Emmanuel S., Buendia L., Hoppaus R., Martinsen T., Meijer J., Miwa K., and Tanabe K. (Eds). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. IPCC/OECD/IEA/IGES, Hayama, Japan.

5

Intergovernmental Panel on Climate Change (IPCC) (2003), Penman J., Gytarsky M., Hiraishi T., Krug, T., Kruger D., Pipatti R., Buendia L., Miwa K., Ngara T., Tanabe K., Wagner F., Good Practice Guidance for Land Use, land-Use Change and Forestry IPCC/IGES, Hayama, Japan

iv

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Foreword

like to thank all governments that supported authors and reviewers, for without their contributions, the production of this report might not have been possible. Two reviews of these guidelines were made in 2005. The first, an expert review, produced over 6000 comments, while the second, a combined governmental and expert review, resulted in an additional 8600 comments. The efforts of the reviewers and their comments have contributed greatly to the quality of the final report and we wish to thank them accordingly. Furthermore, the review editors have ensured the appropriate consideration of all the comments received, so we would also like to thank them for their work. In addition, the NGGIP Technical Support Unit (TSU Head: Simon Eggleston; Programme officers: Leandro Buendia, Kyoko Miwa, Todd Ngara and Kiyoto Tanabe; Administrative Assistant: Ayako Hongo; Project Secretary: Masako Abe; and IT Officer: Toru Matsumoto) has provided guidance and assistance as well as technical and organisational support for the project. They worked extensively with the authors especially in the editing of the various drafts and preparation of the final version, and we wish to congratulate them for their exemplary work. We would also like to express our gratitude to the Government of Japan, for its generous support for the TSU, without which this report might not have been completed. We would also like to thank the IPCC Secretariat (Jian Liu, Rudie Bourgeois, Annie Courtin, and Joelle Fernandez) for their assistance and support in enabling this project to meet its tight deadlines. Finally we would like to thank IPCC Chair Rajendra Pachauri, IPCC Secretary Renate Christ and the Task Force Bureau: the TFI Co-Chairs and Soobaraj Nayroo Sok Appadu (Mauritius), Dari N. Al-Ajmi (Kuwait), Ian Carruthers (Australia), Sergio Gonzalez-Martineaux (Chile), Art Jaques (Canada), Jamidu H.Y. Katima (Tanzania), Sadeddin Kherfan (Syria), Dina Kruger (USA), Kirit Parikh (India), Jim Penman (UK, since 2006), Helen Plume (New Zealand), Audun Rosland (Norway until 2005) and Freddy Tejada (Bolivia) for their support.

Michel Jarraud

Achim Steiner

Secretary-General World Meteorological Organisation

Executive Director United Nations Environment Programme

2006 IPCC Guidelines for National Greenhouse Gas Inventories

v

Preface

Preface These 2006 IPCC Guidelines for National Greenhouse Gas Inventories build on the previous Revised 1996 IPCC Guidelines and the subsequent Good Practice reports in an evolutionary manner to ensure that moving from the previous guidelines to these new guidelines is as straightforward as possible. These new guidelines cover new sources and gases as well as updates to previously published methods where technical and scientific knowledge have improved. This guidance assists countries in compiling complete, national inventories of greenhouse gases. The guidance has been structured so that any country, regardless of experience or resources, should be able to produce reliable estimates of their emissions and removals of these gases. In particular, default values of the various parameters and emission factors required are supplied for all sectors, so that, at its simplest, a country needs only supply national activity data. The approach also allows countries with more information and resources to use more detailed country-specific methodologies while retaining compatibility, comparability and consistency between countries. The guidance also integrates and improves earlier guidance on good practice in inventory compilation so that the final estimates are neither over- nor under-estimates as far as can be judged and uncertainties are reduced as far as possible. Guidance is also provided to identify areas of the inventory whose improvement would most benefit the inventory overall. Hence limited resources can be focused on those areas most in need of improvement to produce the best practical inventory. The IPCC also manages the IPCC Emission Factor Database (EFDB). The EFDB was launched in 2002, and is regularly updated as a resource for inventory compilers to use to assist them by providing a repository of emission factors and other relevant parameters that may be suitable for use in more country-specific methodologies. The 2006 Guidelines are the latest step in the IPCC development of inventory guidelines for national estimates of greenhouse gases. In the opinion of the authors, they provide the best, widely applicable default methodologies and, as such, are suitable for global use in compiling national greenhouse gas inventories. They may also be of use in more narrowly-defined project based estimates, although here they should be used with caution to ensure they correctly include just the emissions and removals from within the system boundaries. We would also like to thank all the authors (over 250) as well as reviewers, review editors, the steering group and the TFB for their contributions and experience. We would also like to thank all the governments who contributed by hosting meetings (Oslo, Norway; Le Morne, Mauritius; Washington, USA; Arusha, Tanzania; Ottawa, Canada; Manila, The Philippines; Moscow, Russian Federation; and Sydney, Australia) as well as those who supported authors and other contributors. Finally we would like to express our gratitude to the NGGIP TSU and the IPCC Secretariat for their invaluable support throughout the entire process of drafting and producing these guidelines.

vi

Taka Hiraishi (Japan)

Thelma Krug (Brazil)

IPCC TFI Co-Chair

IPCC TFI Co-Chair

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2006 IPCC GUIDELINES FOR NATIONAL GREENHOUSE GAS INVENTORIES

OVERVIEW

Overview

Authors Jim Penman (UK), Michael Gytarsky (Russia), Taka Hiraishi (Japan), William Irving (USA), and Thelma Krug (Brazil)

2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Overview

Contents Overview 1

Introduction ............................................................................................................................................ 4

2

Coverage of the Guidelines .................................................................................................................... 5

3

Approach to developing the Guidelines ................................................................................................. 8

4

Structure of the Guidelines ..................................................................................................................... 9

5

Specific developments in the 2006 IPCC Guidelines ........................................................................... 10

Figures Figure 1

Main categories of emissions by sources and removals by sinks ........................................... 6

Figure 2

Example Decision Tree (for CH4 and N2O from Road Transport) ......................................... 9

Tables Table 1

Contents of 2006 Guidelines .................................................................................................. 5

Table 2

Gases for which GWP values are available in the TAR ......................................................... 7

Table 3

Additional gases for which GWP values are not available in the TAR .................................. 7

Table 4

General structure of sectoral guidance chapters ................................................................... 10

2006 IPCC Guidelines for National Greenhouse Gas Inventories

3

Overview

1 INTRODUCTION The 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006 IPCC Guidelines) provide methodologies for estimating national inventories of anthropogenic emissions by sources and removals by sinks of greenhouse gases. The 2006 IPCC Guidelines were prepared in response to an invitation by the Parties to the UNFCCC. They may assist Parties in fulfilling their commitments under the UNFCCC on reporting on inventories of anthropogenic emissions by sources and removals by sinks of greenhouse gases not controlled by the Montreal Protocol, as agreed by the Parties. The 2006 IPCC Guidelines are in five volumes. Volume 1 describes the basic steps in inventory development and offers the general guidance in greenhouse gas emissions and removals estimates based on the authors’ understanding of accumulated experiences of countries over the period since the late 1980s, when national greenhouse gas inventories started to appear in significant numbers. Volumes 2 to 5 offer the guidance for estimates in different sectors of economy. The IPCC has previously developed the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories1 (1996 IPCC Guidelines), together with the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories2 (GPG2000) and the Good Practice Guidance for Land Use, Land-Use Change and Forestry3 (GPG-LULUCF). Taken together, they provide internationally agreed4 methodologies that countries currently use to estimate greenhouse gas inventories to report to the United Nations Framework Convention on Climate Change (UNFCCC). The three-volume 1996 IPCC Guidelines define the coverage of the national inventory in terms of gases and categories of emissions by sources and removals by sinks, and the GPG2000 and GPG-LULUCF provide additional guidance on choice of estimation methodology, improvements of the methods, as well as advice on cross-cutting issues, including estimation of uncertainties, time series consistency and quality assurance and quality control. At its seventeenth session, held in New Delhi in 2002, the Subsidiary Body for Scientific and Technological Advice (SBSTA) under the UNFCCC invited the IPCC to revise the 1996 IPCC Guidelines, taking into consideration the relevant work under the Convention and the Kyoto Protocol5, with the aim of completing the work by early 2006. In response to the UNFCCC’s invitation, the IPCC, at its 20th session in Paris, in February 2003, initiated a process that led to an agreement at its 21st session (in Vienna, November 2003) of Terms of Reference, Table of Contents and a Workplan6 for the 2006 IPCC Guidelines. The Workplan aimed to complete the task in time for adoption and acceptance at the 25th session of the IPCC, in April 2006. The Terms of Reference specified that the revision should be based on, inter alia, the 1996 IPCC Guidelines, GPG2000, GPG-LULUCF, and experiences from the UNFCCC technical inventory review process.

1

Intergovernmental Panel on Climate Change (IPCC) (1997). Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. and Callander B.A. (Eds). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. IPCC/OECD/IEA, Paris, France.

2

Intergovernmental Panel on Climate Change (IPCC) (2000). Penman J., Kruger D., Galbally I., Hiraishi T., Nyenzi B., Emmanuel S., Buendia L., Hoppaus R., Martinsen T., Meijer J., Miwa K., and Tanabe K. (Eds). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. IPCC/OECD/IEA/IGES, Hayama, Japan.

3

Intergovernmental Panel on Climate Change (IPCC) (2003). Penman J., Gytarsky M., Hiraishi T., Krug, T., Kruger D., Pipatti R., Buendia L., Miwa K., Ngara T., Tanabe K., and Wagner F (Eds). Good Practice Guidance for Land Use, landUse Change and Forestry IPCC/IGES, Hayama, Japan.

4

See the Report of the Fourth Session of the Subsidiary Body for Scientific and Technological Advice (FCCC/SBSTA/1996/20), paragraph 30; decisions 2/CP.3 and 3/CP.5 (UNFCCC reporting guidelines for preparation of national communications by Parties included in Annex I to the Convention, part I: UNFCCC reporting guidelines on annual inventories), decision 18/CP.8, revising the guidelines adopted under decisions 3/CP.5, and 17/CP.8 adopting improved guidelines for the preparation of national communications from Parties not included in Annex I to the Convention, and subsequent decisions 13/CP.9 and Draft Decision /CP.10.

5

Including, inter alia, work by the Subsidiary Body for Scientific and Technological Advice and the Subsidiary Body for Implementation, and by the Consultative Group of Experts on National Communications from Parties not included in Annex I to the Convention, and the technical review of greenhouse gas inventories of Annex I Parties.

6

The Terms of Reference, Table of Contents and Work plan can be found at http://www.ipcc-nggip.iges.or.jp/.

4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Overview

2 COVERAGE OF THE GUIDELINES Table 1 shows the contents of the five volumes that make up the 2006 IPCC Guidelines. Estimation methods are provided for the gases shown in Tables 2 and 3, and cover the categories shown in Figure 1. Reporting is described in Chapter 8 of Volume 1. Coverage is complete for all greenhouse gases not covered by the Montreal Protocol, for which the IPCC, at the time of writing, provided a global warming potential (GWP)7. TABLE 1 CONTENTS OF 2006 GUIDELINES Volumes

1 - General Guidance and Reporting

2 - Energy

3 - Industrial Processes and Product Use

4 - Agriculture, Forestry and Other Land Use

5 - Waste

7

Chapters

1. 2. 3. 4. 5. 6. 7. 8. 1. 2. 3. 4. 5. 6. 1. 2. 3. 4. 5. 6. 7. 8. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. 2. 3. 4. 5. 6.

Introduction to the 2006 Guidelines Approaches to Data Collection Uncertainties Methodological Choice and Identification of Key Categories Time Series Consistency Quality Assurance/Quality Control and Verification Precursors and Indirect Emissions Reporting Guidance and Tables Introduction Stationary Combustion Mobile Combustion Fugitive Emissions CO2 Transport, Injection and Geological Storage Reference Approach Introduction Mineral Industry Emissions Chemical Industry Emissions Metal Industry Emissions Non-Energy Products from Fuels and Solvent Use Electronics Industry Emissions Emissions of Fluorinated Substitutes for Ozone Depleting Substances Other Product Manufacture and Use Introduction Generic Methodologies Applicable to Multiple Land-use Categories Consistent Representation of Lands Forest Land Cropland Grassland Wetlands Settlements Other Land Emissions from Livestock and Manure Management N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application Harvested Wood Products Introduction Waste Generation, Composition and Management Data Solid Waste Disposal Biological Treatment of Solid Waste Incineration and Open Burning of Waste Wastewater Treatment and Discharge

Climate Change 2001: The Scientific Basis Contribution of Working Group I to the Third Assessment Report of the IPCC, (TAR), (ISBN 0521 80767 6), Section 6.12.2, Direct GWPs.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5

Overview

Figure 1

Main categories of emissions by sources and removals by sinks

1A1

Energy Industries

1A2 Manufacturing Industries and Construction 1A3a Civil Aviation 1A3b Road Transportation

1A Fuel Combustion Activities

1A3 Transport

1A3c Railways 1A3d Water-borne Navigation 1A3e Other Transportation

1A4 Other Sectors

1 ENERGY

1A5 Non-Specified 1B Fugitive Emissions from Fuels

1B1 Solid Fuels 1B2

Oil and Natural Gas

1B3 Other Emissions from Energy Production 1C Carbon Dioxide Transport and Storage

1C1 Transport of CO2 1C2 Injections and Storage 1C3 Other

2 INDUSTRIAL PROCESSES AND PRODUCT USE

2A

Mineral Industry

2B

Chemical Industry

2C

Metal Industry

2D Non-Energy Products from Fuels and Solvent Use 2E

Electronics Industry

2F Product Uses as Substitutes for Ozone Depleting Substances

National Greenhouse Gas Inventory

2G Other Product Manufacture and Use 2H Other

3A1 Enteric Fermentation 3A Livestock

3A2 Manure Management 3B1 Forest Land

3 AGRICULTURE, FORESTRY, AND OTHER LAND USE

3B2 Cropland 3B3 3B Land

Grassland

3B4 Wetlands 3B5 Settlements 3B6 Other Land

3C Aggregate Sources and Non-CO2 Emissions Sources on Land 3D Other

4A Solid Waste Disposal 4B Biological Treatment of Solid Waste

4 WASTE

4C Incineration and Open Burning of Waste 4D Wastewater Treatment and Discharge 4E Other

5 OTHER

6

5A Indirect N2O Emissions from the Atmospheric Deposition of Nitrogen in NOx and NH3 5B Other

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Overview

Volume 3 of the 2006 IPCC Guidelines also provides estimation methods and/or emission factors for some direct greenhouse gases not covered by the Montreal Protocol for which GWP values were not available from the IPCC at the time of writing (Table 3). These gases are sometimes used as substitutes for gases included in Table 2, for industrial and product applications. Until GWP values are made available from the IPCC, countries will be unable to incorporate these gases in key category analysis (see Section 3 below) or include them in national total GWP weighted emissions. However, optionally, countries may wish to provide estimates of these greenhouse gases in mass units, using the methods provided in the 2006 IPCC Guidelines. Reporting tables are provided for this purpose.

TABLE 2 GASES FOR WHICH GWP VALUES ARE AVAILABLE IN THE TAR8 Name

Symbol

Carbon Dioxide

CO2

Methane

CH4

Nitrous Oxide

N2O

Hydrofluorocarbons

HFCs (e.g., HFC-23 (CHF3), HFC-134a (CH2FCF3), HFC-152a (CH3CHF2))

Perfluorocarbons

PFCs (CF4, C2F6, C3F8, C4F10, c-C4F8, C5F12, C6F14)

Sulphur Hexafluoride

SF6

Nitrogen Trifluoride

NF3

Trifluoromethyl Sulphur Pentafluoride

SF5CF3

Halogenated Ethers

e.g., C4F9OC2H5, CHF2OCF2OC2F4OCHF2, CHF2OCF2OCHF2

Other halocarbons

e.g., CF3I, CH2Br2, CHCl3, CH3Cl, CH2Cl29

TABLE 3 ADDITIONAL GASES FOR WHICH GWP VALUES ARE NOT AVAILABLE IN THE TAR

C3F7C(O)C2F510 C7F16 C4F6 C5F8 c-C4F8O The 2006 IPCC Guidelines contain links to information on methods used under other agreements and conventions11, for the estimation of emissions of tropospheric precursors which may be used to supplement the reporting of emissions and removals of greenhouse gases for which methods are provided here.

8

Third Assessment Report of the IPCC. See also footnote 7.

9

For these gases, emissions can be estimated following the methods described in Section 3.10.2 of Volume 3 if necessary data are available, and then reported under sub-category 2B10 “Other”.

10

This gas is traded as Novec™612 which is a fluorinated ketone produced by 3M (Milbrath, 2002).

11

See, for example, Volume 1 Sections 7.1 and 7.2, where inventory developers are referred to the material developed by the Task Force on Emission Inventories and Projections of the UNECE’s Convention on Long-Range Transboundary Air Pollution for the purpose of estimating emissions of sulphur dioxide (SO2); carbon monoxide (CO); oxides of nitrogen (NOx); ammonia (NH3) and non-methane volatile organic compounds (NMVOCs).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Overview

3 APPROACH TO DEVELOPING THE GUIDELINES The 2006 IPCC Guidelines are an evolutionary development starting from the 1996 IPCC Guidelines, GPG2000 and GPG-LULUCF. A fundamental shift in methodological approach would pose difficulties with time series consistency in emissions and removals estimation, and incur additional costs, since countries and the international community have made significant investments in inventory systems. An evolutionary approach helps ensure continuity, and allows for the incorporation of experiences with the existing guidelines, new scientific information, and the results of the UNFCCC review process. The most significant changes occur in Volume 4, which consolidates the approach to Land Use, Land-Use Change and Forestry (LULUCF) in GPGLULUCF and the Agriculture sector in GPG2000 into a single Agriculture, Forestry and Other Land Use (AFOLU) Volume. This, and other important developments and changes, are summarised in Section 5 below. The 2006 IPCC Guidelines retain the definition of good practice that was introduced with GPG2000. This definition has gained general acceptance amongst countries as the basis for inventory development. According to this definition, national inventories of anthropogenic greenhouse gas emissions and removals consistent with good practice are those, which contain neither over- nor under-estimates so far as can be judged, and in which uncertainties are reduced as far as practicable. These requirements are intended to ensure that estimates of emissions by sources and removals by sinks, even if uncertain, are bona fide estimates, in the sense of not containing any biases that could have been identified and eliminated, and that uncertainties have been reduced as far as practicable, given national circumstances. Estimates of this type are presumably the best attainable, given current scientific knowledge and available resources. The 2006 IPCC Guidelines generally provide advice on estimation methods at three levels of detail, from tier 1 (the default method) to tier 3 (the most detailed method). The advice consists of mathematical specification of the methods, information on emission factors or other parameters to use in generating the estimates, and sources of activity data to estimate the overall level of net emissions (emission by sources minus removals by sinks). Properly implemented, all tiers are intended to provide unbiased estimates, and accuracy and precision should, in general, improve from tier 1 to tier 3. The provision of different tiers enables inventory compilers to use methods consistent with their resources and to focus their efforts on those categories of emissions and removals that contribute most significantly to national emission totals and trends. The 2006 IPCC Guidelines apply the tiered approach by means of decision trees (see the example in Figure 2). A decision tree guides selection of the tier to use for estimating the category under consideration, given national circumstances. National circumstances include the availability of required data, and contribution made by the category to total national emissions and removals and to their trend over time. The most important categories, in terms of total national emissions and the trend, are called key categories12. Decision trees generally require tier 2 or tier 3 methods for key categories. The 2006 IPCC Guidelines provide for exceptions to this, where evidence demonstrates that the expense of data collection would significantly jeopardize the resources available for estimating other key categories. The 2006 IPCC Guidelines also provide advice on; i) ensuring data collection is representative and time series are consistent, ii) estimation of uncertainties at the category level, and for the inventory as a whole, iii) guidance on quality assurance and quality control procedures to provide cross-checks during inventory compilation, and iv) information to be documented, archived and reported to facilitate review and assessment of inventory estimates. Reporting tables and worksheets for tier 1 methods are provided. The use of tiered methodologies and decision trees and the cross cutting advice ensure that the finite resources available for inventory development and updating are deployed most effectively, and that the inventory is checked and reported in a transparent manner.

12

8

In the GPG2000 and GPG-LULUCF these were called key sources, or key categories where there could be removals.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Overview

Figure 2

Example Decision Tree (for CH 4 and N 2 O from Road Transport) Start

VKT by fuel and technology type available?

Yes

Are Country-specific technology based emission factors available?

Yes

Use vehicle activity based model and country-specific factors e.g. COPERT. Box 1: Tier 3

No

No

Can you allocate fuel data to vehicle technology types?

Use default factors and disaggregation by technology.

Yes

Box 2: Tier 2

No

Is this a key category?

Yes

Collect data to allocate fuel to technology types.

No

Use fuel-based emission factors. Box 3: Tier 1

4 STRUCTURE OF THE GUIDELINES The structure of the 2006 IPCC Guidelines improves upon the structure of the 1996 IPCC Guidelines, GPG2000 and GPG-LULUCF in two respects. Firstly, whereas a user of the 1996 IPCC Guidelines, GPG2000 and GPG-LULUCF may need to cross reference between four or five volumes13 to make an emission or removal estimate, the 2006 IPCC Guidelines may require cross referencing between two volumes: Volume 1 (General Guidance and Reporting), and the relevant sectoral volume (one of Volume 2 (Energy), Volume 3 (Industrial Processes and Product Use), Volume 4 (Agriculture, Forestry and Other Land Use), and Volume 5 (Waste)). This represents a considerable simplification. Secondly, the 2006 IPCC Guidelines present Agriculture, Forestry and Other Land Use in a single volume, rather than two volumes comprising Agriculture, on the one hand, and Land-use Change and Forestry on the other. This allows for better integration of information on the pattern of land use and should facilitate more consistent use of activity data (for example, fertilizer application), that affects both agriculture and other land uses, thus reducing or avoiding the possibilities for double counting or omission.

13

That is, three volumes of the IPCC 1996 Guidelines plus at least one of GPG2000 or GPG-LULUCF.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

9

Overview

The 2006 IPCC Guidelines retain the standardised layout of methodological advice at the category level that was introduced in GPG2000 and was maintained in GPG-LULUCF. Table 4 shows the general structure used for each category. Any user familiar with GPG2000 and GPG-LULUCF should be able to shift to the 2006 IPCC Guidelines without difficulty.

•

• • •

TABLE 4 GENERAL STRUCTURE OF SECTORAL GUIDANCE CHAPTERS

Methodological Issues o Choice of Method, including decision trees and definition of tiers. o Choice of Emission Factor o Choice of Activity Data o Completeness o Developing a Consistent Time Series Uncertainty Assessment o Emission Factor Uncertainties o Activity Data Uncertainties Quality Assurance/Quality Control, Reporting and Documentation Worksheets

The previous IPCC inventory guidance has been reviewed and, where needed, clarified and expanded to improve its user friendliness. Across all the volumes, some additional categories have been identified and included. The guidance focuses on inventory methodologies rather than on scientific discussions of the background material, for which references are provided.

5 SPECIFIC DEVELOPMENTS IN THE 2006 IPCC GUIDELINES The 2006 IPCC Guidelines are based on a thorough scientific review and a structural enhancement of the IPCC’s inventory methodology across all categories, including the following specific developments: •

Volume 1 (General Guidance and Reporting)

• •

•

Introductory advice: A new section has been included, providing for an overview of greenhouse gas inventories and the steps needed to prepare an inventory for the first time. Extended advice on data collection: The 2006 IPCC Guidelines introduce systematic cross-cutting advice on data collection from existing sources and by new activities, including design of measurement programmes. Key category analysis: General principles and guidance are provided. In the 2006 IPCC Guidelines, the integration of Agriculture and LULUCF into the AFOLU volume has been addressed, and key category analysis is better integrated across emission and removal categories.

Volume 2 (Energy)

•

10

Treatment of CO2 capture and storage: These emissions are covered comprehensively, including fugitive losses from CO2 capture and transport stages (which are estimated using conventional inventory approaches) plus any losses from carbon dioxide stored underground (estimated by a combination of modelling and measurement techniques, given the amounts injected - which would also be monitored for management purposes). The inventory methods reflect the estimated actual emissions in the year in which they occur. The inventory methods for geological CO2 capture, transport and storage (CCS) provided in Volume 2 are consistent with the IPCC Special Report on Carbon Dioxide Capture and Storage (2005). Amounts of CO2 captured from combustion of biofuel, and subsequently injected into underground storage are included in the inventory as a negative emission. No distinction is made between any subsequent leakage of this CO2 and that of CO2 from fossil sources. Methane from abandoned coal mines: A methodology for estimating these emissions is included in the 2006 IPCC Guidelines for the first time.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Overview

•

Volume 3 (Industrial Processes and Product Use)

•

•

•

New categories and new gases: The 2006 IPCC Guidelines have been expanded to include more manufacturing sectors and product uses identified as sources of greenhouse gases. These include production of lead, zinc, titanium dioxide, petrochemicals, and liquid crystal display (LCD) manufacturing. Additional greenhouse gases identified in the IPCC Third Assessment Report are also included where anthropogenic sources have been identified. These gases include nitrogen trifluoride (NF3), trifluoromethyl sulphur pentafluoride (SF5CF3), and halogenated ethers. Non-Energy Uses of Fossil Fuels: Guidance on demarcation with the energy sector has been improved, and emissions from non-energy uses of fossil fuels are now reported under Industrial Processes and Product Use, rather than in Energy. A method has been introduced for checking the completeness of carbon dioxide emission estimates from the non-energy uses. Actual emissions of fluorinated compounds: The potential emissions approach used as a tier 1 method in the 1996 IPCC Guidelines is no longer considered appropriate, as it does not provide estimates of true emissions, and is not compatible with higher tiers. The Tier 1 methods proposed in this volume are therefore actual emission estimation methods, although these are often based on default activity data where better data are not available. Simplified mass balance approaches have also been proposed in appropriate sectors, such as refrigeration.

Volume 4 (Agriculture, Forestry and Other Land Use)

•

•

• •

•

Integration between agriculture and land use, land-use change and forestry: This integration removes the somewhat arbitrary distinction between these categories in the previous guidance, and promotes consistent use of data between them, especially for more detailed methods. Managed land is used in these guidelines as a proxy for identifying anthropogenic emissions by sources and removals by sinks. In most AFOLU sectors anthropogenic GHG emissions by source and removals by sinks are defined as those occurring on managed land. The use of managed land as a proxy for anthropogenic effects was adopted in the GPG-LULUCF. The preponderance of anthropogenic effects occurs on managed lands and, from a practical standpoint, the information needed for inventory estimation is largely confined to managed lands. Consolidation of previously optional categories: Emissions by sources and removals by sinks associated with all fires on managed land are now estimated, removing the previous optional distinction between wildfires and prescribed burning. This is consistent with the concept of managed land as a proxy for identifying anthropogenic emissions by sources and removals by sinks, as discussed above. Wildfires and other disturbances on unmanaged land cannot, in general, be associated to an anthropogenic or natural cause, and hence are not included in the 2006 IPCC Guidelines, unless the disturbance is followed by a land-use change. In this case, the land affected by disturbance is considered to be managed, and all the greenhouse gas emissions by sources and removals by sinks associated to the fire and other events are now estimated, irrespective of whether of a natural origin or not. Carbon dioxide emissions and removals associated with terrestrial carbon stocks in settlements and managed wetlands, which were previously optional, have been incorporated into the main guidance. Harvested wood products (HWP): The 2006 IPCC Guidelines provide detailed methods that can be used to include HWP in greenhouse gas inventories using any of the approaches that are currently under discussion within the UNFCCC process. Emissions from managed wetlands: The 2006 IPCC Guidelines now contain methods to estimate CO2 emissions due to land use change in wetlands. However, due to limited availability of scientific information, methods for CH4 emissions are contained in an Appendix – Basis for future methodological development.

Volume 5 (Waste)

•

Revised methodology for methane from landfills: The previous Tier 1 method, based on the maximum potential release of methane in the year of placement, has been replaced by a simple first order decay model that provides the option to use data available from the UN and other sources. This approach includes regional and country-specific defaults on waste generation, composition and management, and provides a consistent basis for estimating greenhouse gas emissions across all tiers. This gives a more accurate time series for estimated emissions and should avoid the situation in which usage of landfill gas apparently exceeds the amount generated in a particular year. Carbon accumulation in landfills: This is provided as an output from the decay models, and can be relevant for the estimation of HWP in AFOLU.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

11

Overview • •

Biological treatment and open burning of waste: Guidance on estimation of emissions from composting and biogas facilities has been included to ensure a more complete coverage of sources.

Relevant to all volumes

•

•

12

CO2 resulting from the emissions of other gases: The 2006 IPCC Guidelines estimate carbon emissions in terms of the species which are emitted. Most of the carbon emitted as these non-CO2 species eventually oxidises to CO2 in the atmosphere; and this amount can be estimated from the emissions estimates of the non-CO2 gases. In some cases the emissions of these non-CO2 gases contain very small amounts of carbon compared to the CO2 estimate and it may be more accurate to base the CO2 estimate on the total carbon. See Volume 1 Section 7.2.1.5 for an approach to estimating these inputs of CO2 to the atmosphere. Examples are fossil fuel combustion (where the emission factor is derived from the carbon content of the fuel) and a few IPPU sectors where the carbon mass balance can be estimated much better than individual gases. Treatment of nitrogen (N) deposition: The GPG2000 lists sources of anthropogenic nitrogen deposition that subsequently give rise to anthropogenic emissions of nitrous oxide (N2O), but provides estimation methods only for a subset of these, associated with agricultural sources of ammonia (NH3) and nitrogen oxides (NOx). The 2006 IPCC Guidelines extend this approach to all significant sources of N deposition, including agriculture, industrial and combustion sources, with the ultimate N2O emission attributed to the country responsible for the nitrogen originally emitted. Relationship to entity- or project level estimates: The Guidelines are intended to help prepare national inventories of emissions by sources and removals by sinks. Nonetheless, the Guidelines can also be relevant for estimating actual emissions or removals at the entity or project level.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

GLOSSARY

Glossary

Accuracy Accuracy is a relative measure of the exactness of an emission or removal estimate. Estimates should be accurate in the sense that they are systematically neither over nor under true emissions or removals, so far as can be judged, and that uncertainties are reduced so far as is practicable. Appropriate methodologies conforming to guidance on good practices should be used to promote accuracy in inventories. Accuracy should be distinguished from precision as illustrated below. Illustration of Accuracy and Precision: (a) inaccurate but precise; (b) inaccurate and imprecise; (c) accurate but imprecise; and (d) precise and accurate.

(a)

(b)

(c)

(d)

Activity A practice or ensemble of practices that take place on a delineated area over a given period of time.

Activity data Data on the magnitude of a human activity resulting in emissions or removals taking place during a given period of time. Data on energy use, metal production, land areas, management systems, lime and fertilizer use and waste arisings are examples of activity data.

Anaerobic Conditions in which oxygen is not readily available. These conditions are important for the production of methane emissions. Whenever organic material decomposes in anaerobic conditions (in landfills, flooded rice fields, etc.) methane is likely to be formed.

Andosol A soil developed in volcanic ash. Generally andosols have good drainage and are prone to fertility problems.

Arithmetic mean The sum of the values divided by the number of values.

Auto producer An enterprise which generates electricity or heat for its own use and/or sells it as a secondary activity i.e., not as its main business.

Back-casting The opposite of forecasting. Predicting conditions in the past from current conditions.

Backflows By-product oils from petrochemical processing of refinery products which are generally returned to the refinery for further processing into petroleum products.

Base year The starting year for the inventory. Currently this is typically 1990.

Bias A systematic error of the observation method, whose magnitude in most cases is unknown. It can be introduced by using measuring equipment that is improperly calibrated, by selecting items from a wrong population or by favouring certain elements of a population, etc. For example: Estimating the total fugitive emission from gas transport and distribution using only measurements of leakage from high/medium pressure pipelines can lead to bias if the leakage in the lower pressure distribution network (which is significantly more difficult to measure) is neglected.

G.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Glossary

Biofuels Any fuels derived from biomass, either deliberately grown or from waste products. Peat is not considered a biofuel in these guidelines due to the length of time required for peat to re-accumulate after harvest.

Biogenic carbon Carbon derived from biogenic (plant or animal) sources excluding fossil carbon. Note that peat is treated as a fossil carbon in these guidelines as it takes so long to replace harvested peat.

Biological treatment of waste Composting and anaerobic digestion of organic wastes, such as food waste, garden/park waste and sludge, to reduce volume in the waste material, stabilisation of waste, and destruction of pathogens in the waste material. This includes mechanical-biological treatment.

Biomass (1) The total mass of living organisms in a given area or of a given species usually expressed as dry weight. (2) Organic matter consisting of or recently derived from living organisms (espically regarded as fuel) excluding peat. Includes products, by-products and waste derived from such material.

Blowing agent (for foam production) A gas, volatile liquid, or chemical that generates gas during the foaming process. The gas creates bubbles or cells in the plastic structure of a foam.

Bootstrap technique Bootstrap technique is a type of computationally intensive statistical methods which typically uses repeated resampling from a set of data to assess variability of parameter estimates.

Boreal See polar/boreal.

Calcium carbide Calcium carbide is used in the production of acetylene, in the manufacture of cyanamide (a minor historical use), and as a reductant in electric arc steel furnaces. It is made from calcium carbonate (limestone) and carboncontaining reductant (e.g., petroleum coke).

Carbon budget The balance of the exchanges of carbon between carbon pools or within one specific loop (e.g., atmosphere – biosphere) of the carbon cycle.

Carbon dioxide equivalent A measure used to compare different greenhouse gases based on their contribution to radiative forcing. The UNFCCC currently (2005) uses global warming potentials (GWPs) as factors to calculate carbon dioxide equivalent (see below).

Category Categories are subdivisions of the four main sectors Energy; Industrial Processes and Product Use (IPPU); Agriculture, Forestry and Other Land Use (AFOLU); and waste. Categories may be further divided into subcategories.

Census Data collected by interrogation or count of an entire population.

Chlorofluorocarbons (CFCs) Halocarbons containing only chlorine, fluorine, and carbon atoms. CFCs are both ozone-depleting substances (ODSs) and greenhouse gases.

Chronosequence Chronosequences consist of measurements taken from similar but separate locations that represent a temporal sequence in land use or management, for example, years since deforestation. Efforts are made to control all other between-site differences (e.g., by selecting areas with similar soil type, topography, previous vegetation). Chronosequences are often used as a surrogate for experimental studies or measurements repeated over time at the same location.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

G.3

Glossary

Coefficient of variation

Statistical definition: The coefficient of variation, vx is the ratio of the population standard deviation, σx, and mean, μx, where vx = σx /µx. It also frequently refers to the sample coefficient of variation, which is the ratio of the sample standard deviation and sample mean.1

Cogeneration See: Combined Heat and Power (CHP) generation.

Combined heat and power (CHP) Combined heat and power (CHP), also known as cogeneration, is the simultaneous production of both electricity and useful heat for application by the producer or to be sold to other users with the aim of better utilisation of the energy used. Public utilities may utilise part of the heat produced in power plants and sell it for public heating purposes. Industries as auto-producers may sell part of the excess electricity produced to other industries or to electric utilities.

Comparability Comparability means that estimates of emissions and removals reported by countries in inventories should be comparable among countries. For this purpose, countries should use agreed methodologies and formats for estimating and reporting inventories.

Completeness Completeness means that an inventory covers all sources and sinks and gases included in the IPCC Guidelines for the full geographic coverage in addition to other existing relevant source/sink categories which are specific to individual countries (and therefore may not be included in the IPCC Guidelines).

Confidence The term ‘confidence’ is used to represent trust in a measurement or estimate. Having confidence in inventory estimates does not make those estimates more accurate or precise; however, it will eventually help to establish a consensus regarding whether the data can be applied to solve a problem. This usage of confidence differs substantially from the statistical usage in the term confidence interval.

Confidence interval The value of the quantity for which the interval is to be estimated is a fixed but unknown constant, such as the annual total emissions in a given year for a given country. The confidence interval is a range that encloses the true value of a unknown fixed quantity with a specified confidence (probability). Typically, a 95 percent confidence interval is assumed. From a traditional statistical perspective, the 95 percent confidence interval has a 95 percent probability of enclosing the true but unknown value of the quantity. An alternative interpretation is that the confidence interval is a range that may safely be declared to be consistent with observed data or information. The 95 percent confidence interval is enclosed by the 2.5th and 97.5th percentiles of the PDF.

Consistency Consistency means that an inventory should be internally consistent in all its elements over a period of years. An inventory is consistent if the same methodologies are used for the base year and all subsequent years and if consistent data sets are used to estimate emissions or removals from sources or sinks. An inventory using different methodologies for different years can be considered to be consistent if it has been estimated in a transparent manner taking into account the guidance in Volume 1 on good practice in time series consistency.

Correlation Mutual dependence between two quantities. See correlation coefficient.

Correlation coefficient A number lying between –1 and +1, which measures the mutual dependence between two variables that are observed together. A value of +1 means that the variables have a perfect linear relationship; a value of –1 means that there is a perfect inverse linear relation; and a value of 0 means that there is no straight line relation. It is defined as the covariance of the two variables divided by the product of their standard deviations.

Country-specific data Data for either activities or emissions that are based on research carried out on sites either in that country or otherwise representative of that country. 1

‘Coefficient of variation’ is the term, which is frequently replaced by ‘error’ in a statement like ‘the error is 5%’.

G.4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Glossary

Cruise (When applied to aircraft) All aircraft activities that take place at altitudes above 914 metres (3000 feet) including any additional climb or descent operations above this altitude. There is no upper limit.

Decision tree A decision tree is a flow chart describing the specific ordered steps which need to be followed to develop an inventory or an inventory component in accordance with the principles of good practice.

Distribution function A distribution function or cumulative distribution function F(x) for a random variable X specifies the probability P(X ≤ x) that X is less than or equal to x.

Emission factor A coefficient that quantifies the emissions or removals of a gas per unit activity. Emission factors are often based on a sample of measurement data, averaged to develop a representative rate of emission for a given activity level under a given set of operating conditions.

Emissions The release of greenhouse gases and/or their precursors into the atmosphere over a specified area and period of time. (UNFCCC Article 1.4)

Energy recovery A form of resource recovery in which the organic fraction of waste is converted to some form of usable energy. Recovery may be achieved through the combustion of processed or raw refuse to produce steam through the pyrolysis of refuse to produce oil or gas; and through the anaerobic digestion of organic wastes to produce methane gas.

Enhanced coal bed methane (recovery) Increased CH4 recovery produced by the injection of CO2 into coal seams.

Estimation The process of calculating emissions and/or removals.

Evaporative emissions Evaporative emissions fall within the class of fugitive emissions and are released from area (rather than point) sources. These are often emissions of Non-Methane Volatile Organic Compounds (NMVOCs), and are produced when the product is exposed to the air – for example in the use of paints or solvents.

Excluded carbon Carbon in non-energy uses of fossil fuels (feed stocks, reductant and non-energy products) excluded from fuel combustion.

Expert judgement A carefully considered, well-documented qualitative or quantitative judgement made in the absence of unequivocal observational evidence by a person or persons who have a demonstrable expertise in the given field.

Feedstock Fossil fuels used as raw materials in chemical conversion processes to produce primarily organic chemicals and, to a lesser extent, inorganic chemicals.

First use Distinguishes first uses (and related emissions) from later non-energy uses of fossil fuels. For example, first-use emissions from lubricants are those which take place as a result of oxidation during use as a lubricant. Used lubricants may be used subsequently for heat raising as waste oils.

Flaring Deliberate burning of natural gas and waste gas/vapour streams, without energy recovery.

Fluorocarbons Halocarbons containing fluorine atoms, including chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), and perfluorocarbons (PFCs).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

G.5

Glossary

Flux (1) Raw materials, such as limestone, dolomite, lime, and silica sand, which are used to reduce the heat or other energy requirements of thermal processing of minerals (such as the smelting of metals). Fluxes also may serve a dual function as a slagging agent. (2) The rate of flow of any liquid or gas, across a given area; the amount of this crossing a given area in a given time. E.g., "Flux of CO2 absorbed by forests".

Fossil carbon Carbon derived from fossil fuel or other fossil source.

Fuel Any substance burned as a source of energy such as heat or electricity. See also Primary Fuels and Secondary Fuels.

Fuel combustion Within the Guidelines fuel combustion is the intentional oxidation of materials within an apparatus that is designed to provide heat or mechanical work to a process, or for use away from the apparatus.

Fuel wood Wood used directly as fuel.

Fugitive Emissions Emissions that are not emitted through an intentional release through stack or vent. This can include leaks from industrial plant and pipelines.

Global warming potential Global Warming Potentials (GWP) are calculated as the ratio of the radiative forcing of one kilogramme greenhouse gas emitted to the atmosphere to that from one kilogramme CO2 over a period of time (e.g., 100 years).

Good Practice Good Practice is a set of procedures intended to ensure that greenhouse gas inventories are accurate in the sense that they are systematically neither over- nor underestimates so far as can be judged, and that uncertainties are reduced so far as possible. Good Practice covers choice of estimation methods appropriate to national circumstances, quality assurance and quality control at the national level, quantification of uncertainties and data archiving and reporting to promote transparency.

Ground truth A term used for data obtained by measurements on the ground, usually as validation for remote sensing, e.g., satellite data.

Hydrocarbon Strictly defined as molecules containing only hydrogen and carbon. The term is often used more broadly to include any molecules in petroleum which also contains molecules with S, N, or O An unsaturated hydrocarbon is any hydrocarbon containing olefinic or aromatic structures.

Hydrochlorofluorocarbons (HCFCs) Halocarbons containing only hydrogen, chlorine, fluorine and carbon atoms. Because HCFCs contain chlorine, they contribute to ozone depletion. They are also greenhouse gases.

Hydrofluorocarbons (HFCs) Halocarbons containing only hydrogen, fluorine and carbon atoms. Because HFCs contain no chlorine, bromine, or iodine, they do not deplete the ozone layer. Like other halocarbons, they are potent greenhouse gases.

Hydrofluoroethers (HFEs) Chemicals composed of hydrogen, fluorine and carbon atoms, with ether structure. Because HFES contain no chlorine, bromine, or iodine, they do not deplete the ozone layer. Like other halocarbons, they are potent greenhouse gases.

G.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Glossary

Independence Two random variables are independent if there is a complete absence of association between how their sample values vary. The most commonly used measure of the lack of independence between two random variables is the correlation coefficient.

Key category A key category is one that is prioritised within the national inventory system because its estimate has a significant influence on a country's total inventory of greenhouse gases in terms of the absolute level of emissions and removals, the trend in emissions and removals, or uncertainty in emissions or removals. Whenever the term key category is used, it includes both source and sink categories.

Key source See key category.

Kilns A tubular heating apparatus used in the manufacture of cement, lime and other materials. The calcination reaction may take place in the kiln itself, or, where so-equipped, it may partly or completely take place in a preheater and/or precalciner apparatus ahead of the kiln.

Land cover The type of vegetation , rock, water etc. covering the earth’s surface.

Land use The type of activity being carried out on a unit of land. Note: in Volume 4 (AFOLU), broad land-use categories are defined in Chapter 2. It is recognized that these categories are a mixture of land cover (e.g., Forest, Grassland, Wetlands) and land use (e.g., Cropland, Settlements) classes.

Landfill gas Municipal solid waste contains significant portions of organic materials that produce a variety of gaseous products when deposited, compacted, and covered in landfills. Anaerobic bacteria thrive in the oxygen-free environment, resulting in the decomposition of the organic materials and the production of primarily carbon dioxide and methane. Carbon dioxide is likely to leach out of the landfill because it is soluble in water. Methane, on the other hand, which is less soluble in water and lighter than air, is likely to migrate directly to the atmosphere.

LTO (landing and take-off) cycle All aircraft activities that occur under 914 metres (3 000 feet) including idling aircraft engines, taxi-out, take-off, climb up to 914 metres, descend, approach and taxi-in. Note: some gatherers of statistics count either single takeoff or landing as one cycle; however, it is both one take-off and one landing that together define the LTO cycle.

Lubricants Lubricants are hydrocarbons produced from distillate or residue, and they are mainly used to reduce friction between bearing surfaces. This category includes all finished grades of lubricating oil, from spindle oil to cylinder oil, and those used in greases, including motor oils and all grades of lubricating oil base stocks.

Manure Waste materials produced by domestic livestock which can be managed for agricultural purposes. When manure is managed in a way that involves anaerobic decomposition, significant emissions of methane can result.

Mean The mean is a value around which values sampled from a probability distribution tend to lie. The sample mean or arithmetic average is an estimator for the mean. It is an unbiased and consistent estimator of the population mean (expected value) and is itself a random variable with its own variance value. The sample mean is the sum of values divided by the number of values: x =

1 n ∑ x i (xi, where i = 1,…., n are items of a sample). n i

2006 IPCC Guidelines for National Greenhouse Gas Inventories

G.7

Glossary

Median The median or population median is a value which divides the integral of a probability density function (PDF) into two halves. For symmetric PDFs, it equals the mean. The median is the 50th population percentile. The sample median is an estimator of the population median. It is the value that divides an ordered sample into two equal halves. If there are 2n + 1 observations, the median is taken as the (n + 1)th member of the ordered sample. If there are 2n, it is taken as being halfway between the nth and (n + 1)th .

Mode The mode of a distribution is the value which has the highest probability of occurrence. Distributions can have one or more modes. In practice, we usually encounter distributions with only one mode. In this case, the mode or population mode of a PDF is the measure of a value around which values sampled from a probability distribution tend to lie. The sample mode is an estimator for the population mode calculated by subdividing the sample range into equal subclasses, counting how many observations fall into each class and selecting the centre point of the class (or classes) with the greatest number of observations.

Model A model is a quantitatively-based abstraction of a real-world situation which may simplify or neglect certain features to better focus on its more important elements. Example: the relationship that emissions equal an emission factor times an activity level is a simple model. The term ‘model’ is also often used in the sense of a computer software realisation of a model abstraction.

Monte Carlo method In these guidelines a Monte Carlo method is recommended to analyse the uncertainty of the inventory. The principle of Monte Carlo analysis is to perform the inventory calculation many times by computer, each time with the uncertain emission factors or model parameters and activity data chosen randomly (by the computer) within the distribution on uncertainties specified initially by the user. Uncertainties in emission factors and/or activity data are often large and may not have normal distributions. In this case the conventional statistical rules for combining uncertainties become very approximate. Monte Carlo analysis can deal with this situation by generating an uncertainty distribution for the inventory estimate that is consistent with the input uncertainty distributions on the emission factors, model parameters and activity data.

Non-energy products Primary or secondary fossil fuels which are used directly for their physical or diluent properties. Examples are: lubricants, paraffin waxes, bitumen, and white spirits and mineral turpentine (as solvent).

Non-energy use Within the Guidelines this term refers to the use of fossil fuels as Feedstock, Reductant or Non-energy products. However, the use of this term differs between countries and sources of energy statistics. In most energy statistics, e.g., of the International Energy Agency (IEA), fuel inputs of reductants to blast furnaces are not included but accounted for as inputs to a fuel conversion activity transforming coke and other inputs to blast furnace gas.

Non-marketed lime production Lime production occurring at facilities where the primary purpose is the production of lime as an intermediate input: such as plants that produce steel, synthetic soda ash, calcium carbide, magnesia and magnesium metal, as well as copper smelter and sugar mills. The lime produced by these facilities is often used on site and thus is often not reported in national statistics. Also referred to as in-house lime production.

Non-Methane Volatile Organic Compounds (NMVOCs) A class of emissions which includes a wide range of specific organic chemical substances. Non-Methane Volatile Organic Compounds (NMVOCs) play a major role in the formation of ozone in the troposphere (lower atmosphere). Ozone in the troposphere is a greenhouse gas. It is also a major local and regional air pollutant, causing significant health and environmental damage. Because they contribute to ozone formation, NMVOCs are considered "precursor" greenhouse gases. NMVOCs, once oxidized in the atmosphere, produce carbon dioxide.

Normal distribution

The normal (or Gaussian) distribution has the PDF given in the following equation and is defined by two parameters (the mean μ and the standard σ deviation). f (x) =

G.8

1

σ 2π

−

e

(x − μ)2 2σ 2

, for - ∞ ≤ x ≤ ∞ .

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Glossary

Observational data Observational data is empirical data from instrumental (usually monitoring equipment) or manual methods (through counts in a survey or census).

Off-gas The exhaust gas from a chemical process (combustion or non-combustion). The off gas may be vented to the atmosphere, burned for energy recovery or flared (without energy recovery), or used as a feedstock for another chemical process. Secondary products may also be recovered from the off gas.

Open burning of waste The combustion of unwanted combustible materials such as paper, wood, plastics, textiles, rubber, and other debris in the open or at an open dump site, where smoke and other emissions are released directly into the air without passing through a chimney or stack. Open burning can also include incineration devices that do not control the combustion air to maintain an adequate temperature and do not provide sufficient residence time for complete combustion.

Oxidation Chemically transform of a substance by combining it with oxygen.

Ozone-depleting substances (ODS) A compound that contributes to stratospheric ozone depletion. Ozone-depleting substances (ODS) include CFCs, HCFCs, halons, methyl bromide, carbon tetrachloride, and methyl chloroform. ODS are generally very stable in the troposphere and only degrade under intense ultraviolet light in the stratosphere. When they break down, they release chlorine or bromine atoms, which then deplete ozone.

PDF See Probability density function.

Percentile The kth percentile or population percentile is a value which separates the lowest kth part of the integral of the probability density function (PDF) – i.e., an integral of a PDF tail from the kth percentile towards lower probability densities.

The kth population percentile (0 ≤ k ≤ 100) of a population with a distribution function F(x) equals to z where z satisfies F(z) = k/100

Sample kth percentile is an approximation for the population percentile which is derived from a sample. It is the value below which k percent of the observations lie.

Perfluorocarbons (PFCs) Synthetically produced halocarbons containing only carbon and fluorine atoms. They are characterized by extreme stability, non-flammability, low toxicity, zero ozone depleting potential, and high global warming potential.

Polar/boreal Regions where mean annual temperature (MAT) is less than 0 oC.

Pool/carbon pool A reservoir. A component or components of the climate system where a greenhouse gas or a precursor of a greenhouse gas is stored. Examples of carbon pools are forest biomass, wood products, soils and the atmosphere. The units are mass.

Population The population is the totality of items under consideration. In the case of a random variable, the probability distribution is considered to define the population of that variable.

Primary fuels Fuels which are extracted directly from natural resources. Examples are: crude oil, natural gas, coals, etc.

Precision Precision is the inverse of uncertainty in the sense that the more precise something is, the less uncertain it is. Closeness of agreement between independent results of measurements obtained under stipulated conditions (see also accuracy).

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G.9

Glossary

Probability A probability is a real number in the scale 0 to 1 attached to a random event. There are different ways in which probability can be interpreted. One interpretation considers a probability as having the nature of a relative frequency (i.e., the proportion of all outcomes corresponding to an event), whilst another interpretation regards a probability as being a measure of degree of belief.

Probability density function The Probability Density Function (PDF) describes the range and relative likelihood of possible values. The PDF can be used to describe uncertainty in the estimate of a quantity that is a fixed constant whose value is not exactly known, or it can be used to describe inherent variability. The purpose of the uncertainty analysis for the emission inventory is to quantify uncertainty in the unknown fixed value of total emissions as well as emissions and activity pertaining to specific categories. Thus, throughout these guidelines it is presumed that the PDF is used to estimate uncertainty, and not variability, unless otherwise stated.

Probability distribution Statistical definition: A function giving the probability that a random variable takes any given value or belongs to a given set of values. The probability on the whole set of values of the random variable equals 1.

Process emissions Emissions from industrial processes involving chemical transformations other than combustion.

Quality Assurance Quality Assurance (QA) activities include a planned system of review procedures conducted by personnel not directly involved in the inventory compilation/development process to verify that data quality objectives were met, ensure that the inventory represents the best possible estimate of emissions and sinks given the current state of scientific knowledge and data available, and support the effectiveness of the quality control (QC) programme.

Quality Control Quality Control (QC) is a system of routine technical activities, to measure and control the quality of the inventory as it is being developed. The QC system is designed to: (i)

Provide routine and consistent checks to ensure data integrity, correctness, and completeness;

(ii)

Identify and address errors and omissions;

(iii)

Document and archive inventory material and record all QC activities.

QC activities include general methods such as accuracy checks on data acquisition and calculations and the use of approved standardised procedures for emission calculations, measurements, estimating uncertainties, archiving information and reporting. More detailed QC activities include technical reviews of source categories, activity and emission factor data, and methods.

Removals Removal of greenhouse gases and/or their precursors from the atmosphere by a sink.

Reporting The process of providing results of the inventory as described in volume 1 chapter 8.

Reservoir (1) A component or components of the climate system where a greenhouse gas or a precursor of a greenhouse gas is stored. (UNFCCC Article 1.7) (2) Water bodies regulated for human activities (energy production, irrigation, navigation, recreation etc.) where substantial changes in water area due to water level regulation may occur.

Secondary fuels Fuels manufactured from primary fuels. Examples are: cokes, motor gasoline and coke oven gas, blast furnace gas.

Sequestration The process of storing carbon in a carbon pool.

Sink Any process, activity or mechanism which removes a greenhouse gas, an aerosol, or a precursor of a greenhouse gas from the atmosphere. (UNFCCC Article 1.8) Notation in the final stages of reporting is the negative (-) sign.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Glossary

Source Any process or activity which releases a greenhouse gas, an aerosol or a precursor of a greenhouse gas into the atmosphere. (UNFCCC Article 1.9) Notation in the final stages of reporting is the positive (+) sign.

Standard deviation The population standard deviation is the positive square root of the variance. It is estimated by the sample standard deviation that is the positive square root of the sample variance.

Surrogate data Surrogate data is data that is used in place of the actual data, where the specific data needed is unobtainable. Often surrogate data is needed to describe changes in an emission source over time, for example population change may be used to approximate change in waste arisings.

Survey data Survey data is derived from random sampling of a population and does not include real data for the whole population, e.g., the number of animals in a country or region by surveying a discrete selection of farms and groups of farms in a country or region, or using more general surrogate data and assumptions.

Systematic and random errors Systematic error (i.e., bias) is the difference between the true, but usually unknown, value of a quantity being estimated, and the mean observed value as would be estimated by the sample mean of an infinite set of observations. The random error of an individual measurement is the difference between an individual measurement and the above limiting value of the sample mean.

Systematic error See systematic and random errors.

Temperate, cold Areas where mean annual temperature (MAT) is between 0 – 10 oC.

Temperate, warm Areas where mean annual temperature (MAT) is between 10 – 20 oC.

Time series A time series is series of values which are affected by random processes and which are observed at successive (usually equidistant) points in time.

Transparency Transparency means that the assumptions and methodologies used for an inventory should be clearly explained to facilitate replication and assessment of the inventory by users of the reported information. The transparency of inventories is fundamental to the success of the process for the communication and consideration of information.

Trend The trend of a quantity measures its change over a time period, with a positive trend value indicating growth in the quantity, and a negative value indicating a decrease. It is defined as the ratio of the change in the quantity over the time period, divided by the initial value of the quantity, and is usually expressed either as a percentage or a fraction.

Tropical Areas where mean annual temperature (MAT) is more than 20 oC.

Unbiased estimator An unbiased estimator is a statistic whose expected value equals the value of the parameter being estimated. Note that this term has a specific statistical meaning and that an estimate of a quantity calculated from an unbiased estimator may lack bias in the statistical sense, but may be biased in the more general sense of the word if the sample has been affected by unknown systematic error. Thus, in statistical usage, a biased estimator can be understood as a deficiency in the statistical evaluation of the collected data, and not in the data themselves or in the method of their measurement or collection. For example, the arithmetic mean (average) x is an unbiased estimator of the expected value (mean).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

G.11

Glossary

Uncertainty Lack of knowledge of the true value of a variable that can be described as a probability density function characterizing the range and likelihood of possible values. Uncertainty depends on the analyst’s state of knowledge, which in turn depends on the quality and quantity of applicable data as well as knowledge of underlying processes and inference methods. (See Volume 1 Chapter 3.)

Uncertainty analysis An uncertainty analysis of a model aims to provide quantitative measures of the uncertainty of output values caused by uncertainties in the model itself and in its input values, and to examine the relative importance of these factors.

Validation Validation is the establishment of sound approach and foundation. In the context of emission inventories, validation involves checking to ensure that the inventory has been compiled correctly in line with reporting instructions and guidelines. It checks the internal consistency of the inventory. The legal use of validation is to give an official confirmation or approval of an act or product.

Variability This refers to observed differences attributable to true heterogeneity or diversity in a population. Variability derives from processes which are either inherently random or whose nature and effects are influential but unknown. Variability is not usually reducible by further measurement or study, but can be characterised by quantities such as the sample variance.

Verification Verification refers to the collection of activities and procedures that can be followed during the planning and development, or after completion of an inventory that can help to establish its reliability for the intended applications of that inventory. Typically, methods external to the inventory are used to check the truth of the inventory, including comparisons with estimates made by other bodies or with emission and uptake measurements determined from atmospheric concentrations or concentration gradients of these gases.

G.12

2006 IPCC Guidelines for National Greenhouse Gas Inventories

LIST OF CONTRIBUTORS

AUTHORS, REVIEW EDITORS AND REVIEWERS

Authors and Review Editors

Authors and Review Editors Overview Coordinating Lead Authors Michael Gytarsky Institute of Global Climate and Ecology Taka Hiraishi c/o Institute for Global Environmental Strategies William Irving U.S. Environmental Protection Agency Thelma Krug Inter-American Institute for Global Change Research Jim Penman Department of Environment, Food and Rural Affairs

Russian Federation Japan USA Brazil UK

Review Editors Bubu Jallow Dina Kruger

Gambia USA

Department of State for Fisheries and Water Resources U.S. Environmental Protection Agency

Volume 1 : General Guidance and Reporting Coordinating Lead Authors Newton Paciornik Ministry of Science and Technology of Brazil Kristin Rypdal Centre for Environmental and Climate Research (CICERO)

Brazil Norway

Lead Authors Ayite-Lo N. Ajavon Sumana Bhattacharya

Togo India

Atmospheric Chemistry Laboratory, FDS/Universite de Lome NATCOM Project Management Cell Ministry of Environment & Forests Simon Eggleston IPCC NGGIP TSU Christopher Frey North Carolina State University Michael Gillenwater Environmental Resources Trust Justin Goodwin AEA Technology plc Lisa Hanle U.S. Environmental Protection Agency Anke Herold European Topic Centre on Air and Climate Change (ETC/ACC) Mirghani Ibnoaf Ministry of Science and Technology William Irving U.S. Environmental Protection Agency Matthias Koch BET GmbH Erda Lin Agro-Environment and Sustainable Development Institute Chinese Academy of Agricultural Sciences Joe Mangino Eastern Research Group, Inc. Katarina Mareckova Consultant Archie McCulloch University of Bristol C.P. (Mick) Meyer CSIRO Marine and Atmospheric Research Suvi Monni VTT Technical Research Centre of Finland Hideaki Nakane National Institute for Environmental Studies Stephen Ogle Colorado State University Jim Penman Department of Environment, Food and Rural Affairs Kristina Saarinen Finnish Environment Institute (SYKE) María José Sanz Sánchez Fundación CEAM Jose Ramon T. Villarin Manila Observatory Wilfried Winiwarter ARC systems research Mike Woodfield AEA Technology plc Hong Yan Chinese Academy of Forestry Contributing Authors Ruta Bubniene Ketil Flugsrud Christopher Frey Rosemary Montgomery Tinus Pulles Deborah Ottinger Schaefer Keith A. Smith Karen Treanton

ARER.2

Center for Environmental Policy Statistics Norway North Carolina State University United Nations Statistical Division The Netherlands Organisation for Applied Scientific Research (TNO) U.S. Environmental Protection Agency University of Edinburgh International Energy Agency (IEA)

IPCC NGGIP TSU USA USA UK USA Germany Sudan USA Germany China USA Slovakia UK Australia Finland Japan USA UK Finland Spain Philippines Austria UK China Lithuania Norway USA UN Statistical Division Netherlands USA UK IEA

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Authors and Review Editors

Mike Woodfield

AEA Technology plc

UK

Review Editors Sadedin Kherfan Klaus Radunsky

Tishreen University / Ministry of Environment Umweltbundesamt GmbH

Syrian Arab Republic Austria

Volume 2 : Energy Coordinating Lead Authors Amit Garg Ministry of Railways, Government of India India (on temporary assignment to UNEP Risoe Center, Denmark) Tinus Pulles The Netherlands Organisation for Applied Scientific Research (TNO) Netherlands Lead Authors Azhari F.M. Ahmed Makoto Akai Branca B. Americano John N. Carras Christina Davies Waldron Simon Eggleston Pamela M. Franklin Eilev Gjerald Darío R. Gómez Chia Ha Jochen Harnisch Leif Hockstad Niklas Höhne Sam Holloway Yuhong Hu Jane Hupe Francis Ibitoye Kazunari Kainou

Qatar Petroleum Qatar National Institute of Advanced Industrial Science and Technology Japan Ministry of Science and Technology of Brazil Brazil CSIRO Energy Technology Australia Science Applications International Corporation (SAIC) USA IPCC NGGIP TSU IPCC NGGIP TSU U.S. Environmental Protection Agency USA Norwegian Pollution Control Authority (SFT) Norway Comisión Nacional de Energía Atómica Argentina Environment Canada Canada ECOFYS GmbH Germany U.S. Environmental Protection Agency USA Ecofys Germany Germany British Geological Survey UK State Administration of Work Safety China International Civil Aviation Organization (ICAO) ICAO Centre for Energy Research and Development Nigeria Research Institute of Economy, Trade and Industry, Japan Government of Japan Anhar Karimjee U.S. Environmental Protection Agency USA David S. Lee Manchester Metropolitan University UK Oswaldo Lucon SMA - Sao Paulo State Environmental Secretariat Brazil Gregg Marland Oak Ridge National Laboratory USA Emmanuel Matsika University of Zambia Zambia Lourdes Q. Maurice U.S. Federal Aviation Administration USA R. Scott McKibbon Environment Canada Canada Lemmy Nenge Namayanga Environmental Council of Zambia (ECZ) Zambia Susann Nordrum Chevron Energy Technology Company USA Jos G.J. Olivier The Netherlands Environmental Assessment Agency (MNP) Netherlands Balgis Osman-Elasha Higher Council for Environment and Natural Resources (HCENR) Sudan David Picard Clearstone Engineering Ltd. Canada Riitta Pipatti Statistics Finland Finland Jan Pretel Czech Hydrometeorological Institute Czech Republic Kristin Rypdal Centre for Environmental and Climate Research (CICERO) Norway Sharon B. Saile U.S. Environmental Protection Agency USA John D. Kalenga Saka Chemistry Department, Chancellor College, University of Malawi Malawi Timothy Simmons Avonlog Ltd UK A.K. Singh Central Mining Research Institute India Oleg V. Tailakov Uglemetan Russian Federation Karen Treanton International Energy Agency (IEA) IEA Fabian Wagner International Institute for Applied Systems Analysis (IIASA) Germany Michael P. Walsh International Consultant USA John D. Watterson AEA Technology plc UK Hongwei Yang Energy Research Institute China National Development and Reform Commission Irina Yesserkepova RSE "KazNIIEK" of the Ministry of Environment Protection Kazakhstan of the Republic of Kazakhstan

2006 IPCC Guidelines for National Greenhouse Gas Inventories

ARER.3

Authors and Review Editors

Contributing Authors Daniel M. Allyn Manmohan Kapshe Maryalice Locke Stephen Lukachko Stylianos Pesmajoglou Roberta Quadrelli

The Boeing Company Maulana Azad National Institute of Technology, Bhopal U.S. Federal Aviation Administration Massachusetts Institute of Technology UNFCCC International Energy Agency (IEA)

USA India USA USA UNFCCC IEA

Review Editors Ian Carruthers Art Jaques Freddy Tejada

Australian Greenhouse Office Environment Canada Ministry of Sustainable Development

Australia Canada Bolivia

Volume 3 : Industrial Processes and Product Use Coordinating Lead Authors William Kojo Agyemang-Bonsu Environmental Protection Agency Jochen Harnisch ECOFYS GmbH

Ghana Germany

Lead Authors Ayite-Lo N. Ajavon Atmospheric Chemistry Laboratory, FDS/Universite de Lome Togo Paul Ashford Caleb UK James A. Baker Delphi Corporation USA Scott Bartos U.S. Environmental Protection Agency USA Laurie S. Beu Laurie S. Beu Consulting USA Mauricio Firmento Born Brazilian Aluminum Association (ABAL) Brazil C. Shepherd Burton Independent Consultant USA Denis Clodic Ecole des Mines de Paris France Roberto De Aguiar Peixoto Maua Institute of Technology (IMT) Brazil Sukumar Devotta National Environmental Engineering Research Institute (NEERI) India Tor Faerden Norwegian Pollution Control Authority (SFT) Norway Charles L. Fraust Semiconductor Industry Association USA Domenico Gaudioso Italian Environment Protection Agency (APAT) Italy Michael Gillenwater Environmental Resources Trust USA David Godwin U.S. Environmental Protection Agency USA Laurel Green Comalco Aluminium Australia Chia Ha Environment Canada Canada Lisa Hanle U.S. Environmental Protection Agency USA Nigel Harper Manchester Royal Infirmary UK Leif Hockstad U.S. Environmental Protection Agency USA Francesca Illuzzi ST Microelectronics Italy William Irving U.S. Environmental Protection Agency USA Mike Jeffs European Diisocyanate and Polyol Producers Association (ISOPA) Belgium Charles Jubb Burnbank Consulting Pty. Ltd. Australia Lambert Kuijpers Technical University Eindhoven Netherlands Halvor Kvande Hydro Aluminium Norway Robert Lanza ICF Consulting, Inc USA Tor Lindstad The Norwegian University of Science and Technology Norway Jonathan S. Lubetsky U.S. Environmental Protection Agency USA Brian T. Mader 3M Company Environmental Laboratory USA Pedro Maldonado Instituto de Asuntos Públicos, Universidad de Chile Chile Jerry Marks International Aluminium Institute USA Kenneth Martchek Alcoa Inc. USA Thomas Martinsen Institute for Energy Technology Norway Archie McCulloch University of Bristol UK Michael T. Mocella DuPont Electronic Technologies USA Abdul Karim W. Mohammad Ministry of Environment Iraq Alexander Nakhutin Institute of Global Climate and Ecology Russian Federation Maarten Neelis Jos G.J. Olivier

ARER.4

Utrecht University, Copernicus Institute Netherlands Unit of Science, Technology and Society The Netherlands Environmental Assessment Agency (MNP) Netherlands

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Authors and Review Editors

Sverre E. Olsen Eiichi Onuma Hi-chun Park Friedrich Plöger Ewald Preisegger Sally Rand Sebastien Raoux Mauro M.O. Santos Deborah Ottinger Schaefer Winfried Schwarz Virginia Carla Sena Cianci Timothy Simmons Bruce A. Steiner Sven Thesen Milos Tichy Gabriella Tranell Tom Tripp Shigehiro Uemura Hendrik G. Van Oss Daniel P. Verdonik Dadi Zhou Contributing Authors Guido Agostinelli Pablo Alonso Erik Alsema

The Norwegian University of Science and Technology Japan Cement Association Inha University Siemens AG, PTD M IR Solvay Fluor GmbH U.S. Environmental Protection Agency Metron / Ecosys Ministry of Science and Technology U.S. Environmental Protection Agency Öko-Recherche Ministry of Environment, Land Planning and Environment Avonlog Ltd American Coke and Coal Chemicals Institute Pacific Gas and Electric Company State Office for Nuclear Safety SINTEF Materials and Chemistry US Magnesium Japan Industrial Conference for Ozone Layer and Climate Protection (JICOP) U.S. Geological Survey Hughes Associates, Inc. Energy Research Institute, NDRC

Norway Japan Korea, Republic of Germany Germany USA USA / France Brazil USA Germany Uruguay UK USA USA Czech Republic Norway USA Japan USA USA China

Sally Rand Timothy Simmons Joseph Van Gompel Vince Van Son Kurt T. Werner Ashley Woodcock

Italy / Belgium France Copernicus Institute of Sustainable Development and Innovation Netherlands Utrecht University G.H. Edwards & Associates, Inc USA International Aluminium Institute UK Samsung Electronics Co, LTD Korea, Republic of Alcoa Alumínio S/A Brazil G.H. Edwards & Associates, Inc USA National Photovoltaic EH&S Research Center USA Brookhaven National Laboratory Alcan Primary Metal Group Canada Global Centre Consulting USA Australian Aluminium Council Australia Canada ICF Consulting, Inc USA U.S. Geological Survey USA Utrecht University, Copernicus Institute Netherlands Unit of Science, Technology and Society Hitachi Displays, Ltd. Japan The Netherlands Environmental Assessment Agency (MNP) Netherlands Japan Electronics and Information Technology Industries Japan Association (JEITA J-SIA) / NEC Electronics Utrecht University, Copernicus Institute Netherlands Unit of Science, Technology and Society Catalan Institution For Research And Advanced Studies (ICREA) Spain And Institute Of Chemical Research Of Catalonia (ICIQ) U.S. Environmental Protection Agency USA Avonlog Ltd UK BOC Edwards USA Alcoa Primary Metals USA 3M USA UK

Review Editors Jamidu H.Y. Katima Audun Rosland

University of Dar es Salaam Norwegian Pollution Control Authority (SFT)

Victor O. Aume Chris Bayliss Seung-Ki Chae Hézio Ávila de Oliveira George H. Edwards Vasilis M. Fthenakis Stéphane Gauthier William G. Kenyon Ron Knapp Michel Lalonde Robert Lanza M. Michael Miller Maarten Neelis Hideki Nishida Jos G.J. Olivier Takayuki Oogoshi Martin Patel Javier Pérez-Ramírez

IMEC vzw

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Tanzania, United Republic of Norway

ARER.5

Authors and Review Editors

Volume 4 : Agriculture, Forestry and Other Land Use Coordinating Lead Authors Keith Paustian Colorado State University USA N.H. Ravindranath Centre for Sustainable Technologies (CST) & Associate Faculty India Centre for Ecological Sciences (CES), Indian Institute of Science Andre van Amstel Wageningen University Netherlands Lead Authors Harald Aalde Jukka Alm Sumana Bhattacharya

Ministry of Agriculture and Food Norway Finnish Forest Research Institute Finland NATCOM Project Management Cell India Ministry of Environment & Forests Kathryn Bickel U.S. Environmental Protection Agency USA Dominique Blain Environment Canada Canada John S. Brenner U.S. Department of Agriculture USA Natural Resources Conservation Service Kenneth Byrne University College Cork Ireland Julius Partson Daka Environmental Council of Zambia Zambia Cecile de Klein AgResearch Limited New Zealand Robert Delmas Toulouse University France Hongmin Dong Institute of Agricultural Environment and Sustainable Development China Chinese Academy of Agricultural Sciences Éric Duchemin DREXenvironnement Canada Nagmeldin G. Elhassan Higher Council for Environment and Natural Resources (HCENR) Sudan Carlos Frederico Silveira Menezes Environmental Department of Centrais Elétricas Brasileiras S.A. Brazil Héctor D. Ginzo Ministerio de Relaciones Exteriores, Comercio Internacional y Culto Argentina Patrick Gonzalez The Nature Conservancy USA Sergio P. González Instituto de Investigaciones Agropecuarias (INIA) - La Platina Chile Michael Gytarsky Institute of Global Climate and Ecology Russian Federation Mariko Handa Research Institute for Landscape and Urban Greenery Technology Japan Organization for Landscape and Urban Greenery Technology Development Jerry L. Hatfield U.S. Department of Agriculture Agricultural Research Service USA National Soil Tilth Laboratory Linda S. Heath U.S. Department of Agriculture (USDA) Forest Service USA Niro Higuchi National Institute for Research in the Amazon - INPA Brazil Jari T. Huttunen Department of Environmental Sciences, University of Kuopio Finland Jennifer C. Jenkins University of Vermont USA Donald E. Johnson Colorado State University USA Samuel Kainja Malawi Water Partnership Malawi Michael Köhl University of Hamburg Germany Thelma Krug Inter-American Institute for Global Change Research Brazil Werner A. Kurz Natural Resources Canada, Canadian Forest Service Canada Rodel D. Lasco World Agroforestry Centre, ICRAF Philippines Philippines Keith R. Lassey National Institute of Water and Atmospheric Research New Zealand Yue Li Chinese Academy of Agricultural Sciences China Magda Aparecida de Lima Brazilian Agricultural Research Corporation (Embrapa) Brazil Joe Mangino Eastern Research Group, Inc. USA Daniel L. Martino Carbosur Uruguay Mitsuo Matsumoto Forestry and Forest Products Research Institute (FFPRI) Japan Tim A. McAllister Agriculture and Agri-Food Canada Canada Brian G. McConkey Agriculture and Agri-Food Canada Canada Arvin Mosier U.S. Department of Agriculture, Agricultural Research Service (Retired) USA Rafael S.A. Novoa Consultant, Instituto de Investigaciones Agropecuarias (INIA) Chile Stephen Ogle Colorado State University USA Faizal Parish Global Environment Center (GEC) GEC Kim Pingoud Finnish Forest Research institute Finland John Raison Ensis Environment Australia Gary Richards Australian Greenhouse Office Australia Philippe Rochette Agriculture and Agri-Food Canada Canada Ricardo L.V. Rodrigues The Nature Conservancy - TNC Brazil Brazil Anna Romanovskaya Institute of Global Climate and Ecology Russian Federation Clark Row Row Associates USA

ARER.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Authors and Review Editors

Kristin Rypdal María José Sanz Sánchez Dieter Schoene Kenneth E. Skog Keith A. Smith Pete Smith Zoltan Somogyi Mario Tonosaki Alain Tremblay Atsushi Tsunekawa Stanley C. Tyler Louis Verchot Reiner Wassmann Thomas C. Wirth Kazuyuki Yagi Washington Zhakata Xiaoquan Zhang Contributing Authors Deborah M. Bartram Jim B. Carle Justin Ford-Robertson Darryl Gibb Mercy Wanja Karunditu John H. Martin, Jr. Tatiana Minayeva Indu K. Murthy Luis Pinguelli Rosa Ronald L. Sass Andrey Sirin Göran Ståhl Margaret Walsh Stephen A. Williams Xiaoyuan Yan

CICERO Centre for Environmental and Climate Research Norway Fundación CEAM Spain Food and Agriculture Organization (FAO) FAO U.S. Department of Agriculture Forest Service USA University of Edinburgh UK University of Aberdeen UK European Commission DG Joint Research Centre EC/Hungary (seconded from Hungarian Forest Research Institute, Budapest, Hungary) Forestry and Forest Products Research Institute Japan Hydro-Quebec Production Canada Arid Land Research Center, Tottori University Japan University of California at Irvine USA International Centre for Research in Agroforestry (ICRAF) ICRAF/USA Institute for Meteorology and Climate Research (IMK/IFU) Germany Forschungszentrum Karlsruhe U.S. Environmental Protection Agency USA National Institute for Agro-Environmental Sciences Japan Climate Change Office, Ministry of environment and Tourism Zimbabwe Chinese Academy of Forestry China Eastern Research Group, Inc. Food and Agriculture Organization (FAO) Ford-Robertson Initiatives Limited Agriculture and Agri-Food Canada World Agroforestry Centre (ICRAF) Hall Associates Wetlands International Russia Programme Centre for Ecological Sciences, Indian Institute of Science Graduate School of Engineering of the Federal University of Rio de Janeiro (COPPE/UFRJ) Rice University Institute of Forest Sciences RAS Swedish University of Agricultural Sciences (SLU) U.S. Department of Agriculture Natural Resource Ecology Laboratory, Colorado State University Institute of Soil Science, Chinese Academy of Sciences

Review Editors Michael Apps Natural Resources Canada, Canadian Forest Service Helen Plume New Zealand Climate Change Office Bernhard Schlamadinger Joanneum Research Soobaraj Nayroo Sok Appadu Meteorological Services

USA FAO New Zealand Canada ICRAF USA Russian Federation India Brazil USA Russian Federation Sweden USA USA China Canada New Zealand Austria Mauritius

Volume 5 : Waste Coordinating Lead Authors Riitta Pipatti Statistics Finland Sonia Maria Manso Vieira Environmental Sanitation Technology Agency (CETESB) (Retired) Lead Authors Joao Wagner Silva Alves Environmental Sanitation Technology Agency (CETESB) of Sao Paulo State Michiel R.J. Doorn ARCADIS Qingxian Gao Chinese Research Academy of Environmental Science G.H. Sabin Guendehou Benin Centre of Scientific and Technical Research Leif Hockstad U.S. Environmental Protection Agency William Irving U.S. Environmental Protection Agency Matthias Koch BET GmbH Carlos López Cabrera Instituto de Meteorologia Katarina Mareckova Consultant Hans Oonk The Netherlands Organisation for Applied Scientific Research (TNO)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Finland Brazil

Brazil Netherlands China Benin USA USA Germany Cuba Slovakia Netherlands

ARER.7

Authors and Review Editors

Craig Palmer Elizabeth Scheehle Chhemendra Sharma

Environment Canada Canada U.S. Environmental Protection Agency USA NATCOM Project Management Cell India Ministry of Environment & Forests India, Government of India Alison Smith AEA Technology UK Per Svardal Norwegian Pollution Control Authority (SFT) Norway Sirintornthep Towprayoon The Joint Graduate School of Energy and Environment Thailand King Mongkut's University of Technology Thonburi Can Wang Department of Environmental Science and Engineering China Tsinghua University Masato Yamada Center for Material Cycles and Waste Management Japan National Institute for Environmental Studies Contributing Authors Jeffrey B. Coburn Kim Pingoud Gunnar Thorsen Fabian Wagner

RTI International Finnish Forest Research Institute (Metla) Norwegian University of Science and Technology International Institute for Applied Systems Analysis (IIASA)

USA Finland Norway Germany

Review Editors Dina Kruger Kirit Parikh

U.S. Environmental Protection Agency Indira Gandhi Institute of Development Research

USA India

ARER.8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Reviewers

Reviewers Argentina Nicolas Di Sbroiavacca Héctor D. Ginzo Ernesto F. Viglizzo

Fundacion Bariloche Ministerio de Relaciones Exteriores, Comercio Internacional y Culto National Institute for Agricultural Technology (INTA)

Australia Government of Australia Mike Atkinson Ram C. Dalal Fabiano de Aquino Ximenes David Gardner Beverley Henry Mark Howden Charles Jubb Hugh Saddler Shi Su

Energy International Australia Department of Natural Resources and Mines, Indooroopilly, Queensland NSW Department of Primary Industries, Forest Resources Research NSW Department of Primary Industries, Science and Research Cooperative Research Centre for Greenhouse Accounting CSIRO Sustainable Ecosystems Burnbank Consulting Pty. Ltd. Energy Strategies Pty Ltd CSIRO

Austria Barbara Amon Michael Anderl Klaus Bernhardt Wojtek Galinski Doris Halper Agnes Kurzweil Tomas Mueller Barbara Muik Stephan Poupa Klaus Radunsky Manfred Ritter Stefan Unterberger Gerhard Zethner

University of Natural Resources and Applied Life Sciences Umweltbundesamt GmbH Association of the Austrian Electrical and Electronics Industries (FEEI) Joanneum Research Umweltbundesamt GmbH Umweltbundesamt GmbH Verband der Elektrizitätsunternehmen Österreichs Umweltbundesamt GmbH Umweltbundesamt GmbH Umweltbundesamt GmbH Umweltbundesamt GmbH dieEnergieSparer Tanzer KEG Environment Agency Vienna

Belarus Pavel Shermanau

Ministry of Natural Resources and Environmental Protection

Belgium Kristien Aernouts Marc Aubinet Lorea Claude Jean Marie Demoulin Vasco de Oliveira Janeiro Arjen Sevenster Nobuhiko Takamatsu J.A.M. van Balken Bas van Wesemael

Flemish Institute of technological Research (Vito) Faculté Universitaire des Sciences Agronomiques de Gembloux The European Cement Association (CEMBUREAU) European Chemical Industry Council Union of the Electricity Industry (EURELECTRIC) European Council of Vinyl Manufacturers International Iron and Steel Institute (IISI) European Fertilizer Manufacturers Association Université catholique de Louvain

Benin G.H. Sabin Guendehou

Benin Centre for Scientific and Technical Research

Brazil Government of Brazil Marco Aurélio Dos Santos

Graduate School of Engineering of the Federal University of Rio de Janeiro (COPPE/UFRJ) Roberto De Aguiar Peixoto Maua Institute of Technology (IMT) Magda Aparecida de Lima Brazilian Agricultural Research Corporation (Embrapa) Oswaldo Lucon São Paulo Environment Secretariat -SMA Odo Primavesi Embrapa - Southeast Cattle Ricardo Leonardo Vianna Rodrigues The Nature Conservancy – TNC Brazil Luiz Pinguelli Rosa COPPE/UFRJ Sonia Maria Manso Vieira Environmental Sanitation Technology Agency (CETESB) (Retired) Canada Alice Au Stefan Bachu Pierre Bernier Dominique Blain

Environment Canada Alberta Energy and Utilities Board Canadian Forest Service, Natural Resources Canada Environment Canada

2006 IPCC Guidelines for National Greenhouse Gas Inventories

ARER.9

Reviewers

Canada (continued) Marie Boehm Pascale Collas Darryl Gibb David Goodenough Chia Ha Neeta Hooda Ted Huffman Henry Janzen Art Jaques Don Leckie Tony Lempriere Chang Liang Steen Magnussen Afshin Matin R. Scott McKibbon Frank Neitzert Craig Palmer Kevin Telmer Alain Tremblay J. A. Trofymow Louis Varfalvy Mike Wulder

Agriculture and Agri-Food Canada Environment Canada Agriculture and Agri-Food Canada Canadian Forest Service, Natural Resources Canada Environment Canada Indian Council of Forestry Research and Education Agriculture and Agri-food Canada Agriculture and Agri-Food Canada Environment Canada Canadian Forest Service, Natural Resources Canada Canadian Forest Service Environment Canada Canadian Forest Service, Natural Resources Canada Environment Canada Environment Canada Environment Canada Environment Canada University of Victoria & University of Campinas, Brazil Hydro-Québec Production Canadian Forest Service, Natural Resources Canada Hydro-Québec Canadian Forest Service, Natural Resources Canada

Chile Sergio P. González Rafael S.A. Novoa

Instituto de Investigaciones Agropecuarias (INIA) - La Platina Consultant, INIA

China Government of China Zucong Cai Qingxian Gao Yao Huang Yue Li

Xiaoquan Zhang Shuang Zheng Songli Zhu

Institute of Soil Science, Chinese Academy of Sciences Chinese Research Academy of Environmental Science Institute of Atmospheric Physics, Chinese Academy of Sciences Institute of Environment and Sustainable Development for Agriculture, Chinese Academy of Agricultural Sciences Agro-Environment and Sustainable Development Institute, Chinese Academy of Agricultural Sciences Chinese Academy of Environmental Science Energy Research Institute, National Development and Reform Commission (ERI, NDRC) Chinese Academy of Forestry NDRC NDRC

Croatia Zeljko Juric

EKONERG

Czech Republic Pavel Fott

Czech Hydrometeorological Institute

Erda Lin Jianguo Wu Huaqing Xu

Denmark Jesper Gundermann Steen Gyldenkaerne Erik Lyck Marianne Thomsen Alejandro Villanueva

Danish Environmental Protection Agency National Environmental Research Institute National Environmental Research Institute National Environmental Research Institute European Topic Centre on Resources and Waste Management European Environment Agency

Egypt Amr Osama Abdel-Azia Mohamed El-Shahawy Rabie Sayed Fouli

Integral Consult - American University in Cairo Egyptian Environmental Affairs Agency (EEAA) Egyptian Met. Authority

Finland Heikki Granholm Kari Grönfors Veijo Klemetti Pertti Laine Tuija Lapveteläinen Aleksi Lehtonen Raisa Mäkipää

Ministry of Agriculture and Forestry Statistics Finland Vapo Oy Energy/Raw materials Finnish Forest Industries Federation Statistics Finland Finnish Forest Research Institute Finnish Forest Research Institute

ARER.10

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Reviewers

Finland (continued) Teemu Oinonen Mikko Peltoniemi Paula Perälä Jouko Petäjä Kim Pingoud Riitta Pipatti Leena Raittinen Kristiina Regina Kristina Saarinen Pirkko Selin Risto Sievänen Saku Slioor Erkki Tomppo Eemeli Tsupari France Nadi Assaf

Statistics Finland Finnish Forest Research Institute MTT Agrifood Research Finland Finnish Environment Institute Finnish Forest Research Institute Statistics Finland Statistics Finland Agrifood Research Finland Finnish Environment Institute (SYKE) Vapo Company Finnish Forest Research Institute Statistics Finland Finnish Forest Research Institute Technical Research Centre of Finland

Jean-Pierre Chang Guillaume Gaborit Denis Loustau Arthur Riedacker

Coordinating Committee for the Associations of Manufacturers of Industrial Electrical Switchgear and Control gear in the European Union (CAPIEL) Centre Interprofessionnel Technique d'Etudes de la Pollution Atmospherique (CITEPA) CITEPA CITEPA Institut National de la Recherche Agronomique (INRA) INRA

Germany Clemens Backhaus Rainer Baritz Rolf Beckers Anja Behnke Rosemarie Benndorf Michael Blohm Volker Brenk Ulrich Dämmgen Dirk Drechsel Karsten Dunger Annette Freibauer Werner Fuchs Jakob Graichen Jochen Harnisch Ralf Harthan Anke Herold Michael Hüllenkrämer Jürgen Ilse Bernt Johnke Dierk Juch Hans-Jürgen Kaltwang Karsten Karschunke David Kuntze Sandra Leithold Heribert Meiners Sebastian Plickert Joachim Rock J. Rothermel Roland Schmidt Lambert Schneider Winfried Schwarz Johannes Stein Michael Strogies Gabriela von Goerne Ernst - Günther Wiess

Fraunhofer Institut UMSICHT Federal Institute for Geosciences and Natural Resources (BGR) Federal Environmental Agency Federal Environmental Agency Federal Environmental Agency Federal Environmental Agency Federal Environmental Agency Federal Agricultural Research Centre, Institut of Agroecology BASF AG Federal Research Centre for Forestry and Forest Products Max-Planck-Institute for Biogeochemistry Bundesverband der Deutschen Kalkindustrie e.V. Öko-Institut ECOFYS GmbH Öko-Institut European Topic Centre on Air and Climate Change (ETC/ACC) Federal Environmental Agency Gesamtverband des deutschen Steinkohlenbergbaus (GVSt) Federal Environmental Agency Geologischer Dienst NRW STEAG Saar Energie AG Federal Environmental Agency Federal Environmental Agency Federal Environmental Agency Deutsche Montan Technologie – DMT Federal Environmental Agency Potsdam Institute for Climate Impact Research Verband der Chemischen Industrie (VCI) Siemens Medical Solutions Öko-Institut Öko-Recherche German Electrical and Electronic Manufacturers' Association (ZVEI) Federal Environmental Agency Greenpeace Bezirksregierung Arnsberg, Abteilung Bergbau und Energie in NRW

Greece Leonidas Ntziachristosis Zissis Samaras Yannis Sarafidis

Aristotle University Thessaloniki Aristotle University Thessaloniki National Observatory of Athens

Hungary László Gáspár

National Directorate for Environment, Nature and Water

Sebastien Beguier

2006 IPCC Guidelines for National Greenhouse Gas Inventories

ARER.11

Reviewers

Hungary (continued) Jozsef Kutas

National Directorate for Environment, Nature and Water

India Tapan K. Adhya Sukumar Devotta V. Jeeva Sunil Kumar R. K. Pachauri

Central Rice Research Institute National Environmental Engineering Research Institute (NEERI) Indian Council of Forestry Research and Education NEERI IPCC / Tata Energy Research Institute (TERI)

Indonesia/CIFOR Markku Kanninen

Center for International Forestry Research (CIFOR)

Italy Lorenzo Ciccarese Rocio Condor G. Mario Contaldi Riccardo De Lauretis Barbara Gonella Daniela Romano Marina Vitullo

Agency for the Protection of the Environment and for Technical Services (APAT) APAT APAT APAT APAT APAT APAT

Ivory Coast Lucien Manan Dja Japan Tomoyuki Aizawa

Capacity Building for Improving the Quality of Greenhouse Gas Inventories in West and Central Africa (Ministry of State, Ministry of Environment)

Chisato Yoshigahara

Greenhouse Gas Inventory Office of Japan, National Institute for Environmental Studies Dupont- Mitsui Fluorochemicals Co.,Ltd. Hokkaido University Tokyo University of Science Ryukoku University Forestry and Forest Products Research Institute Mizuho Information & Research Institute Taiheiyo Cement Corporation Research Institute of Innovative Technology for the Earth (RITE) Graduate School of Applied Informatics, University of Hyogo Forestry and Forest Products Research Institute (FFPRI) National Institute for Environmental Studies Hitachi Displays, Ltd. Japan Cement Association Japan Electronics and Information Technology Industries Association (JEITA J-SIA) / NEC Electronics Corporation Kyoto University Environment Preservation Center Forestry and Forest Products Research Institute Saitama University Forestry and Forest Products Research Institute Japan Industrial Conference for Ozone Layer and Climate Protection (JICOP) National Institute of Public Health National Institute for Agro-Environmental Science Center for Material Cycles and Waste Management National Institute for Environmental Studies Mizuho Information & Research Institute

Korea, Repulic of Chan-Gyu Kim Dong-Hyun Kim Seungdo Kim Seung-Hwan Oh Soon-Chul Park

Korea Energy Management Corporation (KEMCO) Samsung Electronics Hallym University Environmental Management Corporation KEMCO

Malawi John D. Kalenga Saka

Chemistry Department, Chancellor College, University of Malawi

Mauritius Poorundeo Ramgolam

Ministry of Environment & National Development Unit

Mexico Tomas Hernandez-Tejeda Jorge Gasca Ramirez

Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP) Mexican Petroleum Institute

Shoji Ando Ryusuke Hatano Takashi Inoue Tomonori Ishigaki Shigehiro Ishizuka Kenshi Itaoka Yoshito Izumi Yoichi Kaya Nophea Kim-Phat Mitsuo Matsumoto Hideaki Nakane Hideki Nishida Eiichi Onuma Takayuki Oogoshi Shinichi Sakai Masamichi Takahashi Yutaka Tonooka Mario Tonosaki Shigehiro Uemura Ikuo Watanabe Kazuyuki Yagi Masato Yamada

ARER.12

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Reviewers

Morocco Faouzi Senhaji

Groupe d'Etudes et de Recherche sur les Energies Renouvelables et l'Environnement (GERERE)

Netherlands Andre Bannink Dick Both Michiel R.J. Doorn Carolien Kroeze Maarten Neelis Jos G.J. Olivier Hans Oonk Martin Patel Kees J. Peek Hans W. Pulles Cor van Bruggen Guus C.W.M. van den Berghe Hugo A.C. Denier van der Gon Marian W. van Schijndel Tjerk Veenstra Harry H.J. Vreuls Ton F.B. Wildenborg

Wageningen UR SenterNovem ARCADIS Wageningen University Utrecht University, Unit of Science, Technology and Society The Netherlands Environmental Assessment Agency (MNP) The Netherlands Organisation for Applied Scientific Research (TNO) Utrecht University, Unit of Science, Technology and Society MNP Ministry of Transport, Public Works and Water Management Statistics Netherlands (CBS) SenterNovem TNO MNP International Gas Union (IGU) SenterNovem TNO

New Zealand James Barton Peter N. Beets Harry Clark Paul Cruse Cecile de Klein Darren Evans Justin Ford-Robertson Martin Fryer Frank Kelliher Paul Lane Keith R. Lassey Roger Lincoln Kathy Perreau Helen Plume Kimberly Robertson Michael Rynne Gerald Rys Surinder Saggar Peter Stephens Craig M. Trotter Steve Wakelin

Ministry for the Environment New Zealand Forest Research Institute Ltd AgResearch Limited Meridian Energy AgResearch Limited Ministry of Economic Development Ford-Robertson Initiatives Limited Air New Zealand Landcare Research Ministry of Agriculture and Forestry National Institute of Water and Atmospheric Research Ministry for the Environment Ministry for the Environment New Zealand Climate Change Office Force Consulting Limited Holcim Ministry of Agriculture and Forestry Landcare Research Ministry for the Environment Landcare Research ATLAS Technology

Niger Mamadou Diarra

Ecole Professionnelle d’Electricité, Société Nigérienne d’Electricité (Nigelec)

Nigeria Francis Ibitoye

Centre for Energy Research and Development

Norway Øyvind Christophersen Svein Staal Eggen Tor Faerden Todd Flach Eilev Gjerald Terje Gobakken Susanne Haefeli Atle Harby Tore K. Jenssen Karl Erik Johansen Tor Lindstad Marit Viktoria Pettersen Audun Rosland Kristin Rypdal Tormod A. Schei Stein M. Tomter

Norwegian Pollution Control Authority (SFT) GASSNOVA Norwegian Pollution Control Authority (SFT) Det Norske Veritas Norwegian Pollution Control Authority (SFT) Norwegian Institute of Land Inventory Det Norske Veritas SINTEF Yara International ENVIROCON The Norwegian University of Science and Technology Ministry of Environment Norwegian Pollution Control Authority (SFT) CICERO Centre for Environmental and Climate Research Statkraft AS Norwegian Institute of Land Inventory

2006 IPCC Guidelines for National Greenhouse Gas Inventories

ARER.13

Reviewers

Pakistan Shaher Bano Walajahi

Ministry of the Environment

Peru Eduardo Calvo

Universidad Nacional Mayor de San Marcos

Poland Wanda Pazdan

"EMI" Sp. z o.o.

Portugal Vitor Gois

Institute for the Environment

Russian Federation Government of Russia Michael Gytarsky Tatiana Minayeva Anna Romanovskaya Andrey Sirin

Institute of Global Climate and Ecology Wetlands International Russia Programme Institute of Global Climate and Ecology Institute of Forest Sciences RAS

Saudi Arabia Faisal A. Al-Hothali

Environmental Protection Department

South Africa Gerrit Kornelius

Airshed Planning Professionals (Pty) Ltd

Spain Government of Spain Gustavo Eisenberg Ignacio Sanchez Garcia María José Sanz Sánchez

The Spanish National Association of Manufacturers of Capital Goods (SERCOBE) Oficina Española de Cambio Climático (Ministerio de Medio Ambiente) Fundación CEAM

Sri Lanka B.V.R. Punyawardena

Department of Agriculture

Sudan Ismail Elgizouli Sumaia Mohamed Elsayed Ismail Fadl El Moula Mohamed Hassan B. Nimir

Higher Council for Environment and Natural Resources (HCENR) Ahfad University for Women Sudan Meteorological Authority University of Khartoum

Sweden Karin Kindbom Leif Klemedtsson Marianne Lilliesköld Mats Olsson Klas Österberg Göran Ståhl

IVL Swedish Environmental Research Institute Botanical Institute, Göteborg University Swedish Environmental Protection Agency Swedish University of Agricultural Sciences Swedish Environmental Protection Agency Swedish University of Agricultural Sciences (SLU)

Switzerland Christian Bach Jens Leifeld

Swiss Federal Laboratories for Materials Testing and Research (Empa) Agroscope FAL Reckenholz, Swiss Federal Research Station for Agroecology and Agriculture

Thailand Bundit Limmeechokchai

Thammasat University

Togo Ayite-Lo N. Ajavon

Atmospheric Chemistry Laboratory, FDS/Universite de Lome

Tuvalu Ian Fry

Environment Division, Office of the Prime Minister

United Kingdom Government of United Kingdom Lorna Brown Robert Chase Cameron Davies Paul Freund Nigel Grant Steven Kershaw Jim Penman Peter Quinn Bill Senior Timothy Simmons

Institute of Grassland and Environmental Research International Aluminium Institute Alkane Energy plc Private consultant BEAMA Power Ltd White Young Green Environmental Department of Environment, Food and Rural Affairs Corus Group Department for Environment, Food and Rural Affairs Avonlog Ltd

ARER.14

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Reviewers

United Kingdom (continued) Keith A. Smith Robert Walker Malcolm Watson Jason Yapp Ukraine Tetyana Gordiyenko Oleh Velychko USA Susan Asam Scott Bartos Deborah M. Bartram Steven L. Baughcum Steven H. Bernhardt Kathryn Bickel Terence Jack Blasing Barbara Braatz Marvin Branscome Marilyn Buford Melissa Chan Jeffery B. Coburn Michael M. Cote James G. Crawford Steven Crookshank Stephen Del Grosso Jim Dooley Sarah Forbes Pamela M. Franklin Randall Freed S. Julio Friedmann Vasilis M. Fthenakis Debyani Ghosh David Godwin Peter M. Groffman Lisa Hanle Garth Hawkins Leif Hockstad Bill Hohenstein Michael Hoppus Ray Huitric William Irving Cortney Itle Kamala R. Jayaraman Donald E. Johnson Kristen A. Johnson Ravi Kantamaneni Anhar Karimjee Haroon Kheshgi Robert Lanza Miriam Lev-On Jan Lewandrowski Mark Liebig Perry M. Lindstrom Jonathan S. Lubetsky H. Gyde Lund Brian T. Mader Joe Mangino Kenneth Martchek John H. Martin, Jr. Lourdes Q. Maurice Reid Miner Susann Nordrum

University of Edinburgh Society of Motor Manufacturers & Traders Ltd (SMMT) UK Petroleum Industry Association Caleb Management Services Ltd.

Ukrainian Scientific-Research and Educational Centre of Standardization, Certification and Problems of Quality All-Ukrainian State Scientific and Production Centre for Standardization, Metrology, Certification and Protection of Consumer (Ukrmetrteststandard) ICF Consulting U.S. Environmental Protection Agency Eastern Research Group, Inc. Boeing Company Honeywell International U.S. Environmental Protection Agency Oak Ridge National Laboratory ICF Consulting Research Triangle Institute U.S. Department of Agriculture U.S. Department of Energy, National Energy Technology Laboratory Research Triangle Institute Raven Ridge Resources, Incorporated Trane/American Standard American Petroleum Institute U.S. Department of Agriculture, Agricultural Research Service, Northern Plains Area Office, Soil Plant Nutrient Research (USDA-ARS-NPA-SPNR) Joint Global Change Research Institute, Battelle U.S. Department of Energy, National Energy Technology Laboratory U.S. Environmental Protection Agency ICF Consulting Lawrence Livermore National Laboratory National Photovoltaic EH&S Research Center, Brookhaven National Laboratory Belfer Centre for Science and International Affairs, Kennedy School of Government, Harvard University U.S. Environmental Protection Agency Institute of Ecosystem Studies U.S. Environmental Protection Agency Portland Cement Association U.S. Environmental Protection Agency U.S. Department of Agriculture U.S. Department of Agriculture Forest Service, Northeastern Research Station, Forest Inventory and Analysis County Sanitation Districts of Los Angeles County U.S. Environmental Protection Agency Eastern Research Group, Inc. ICF Consulting Colorado State University Washington State University ICF Consulting U.S. Environmental Protection Agency ExxonMobil Research and Engineering Company ICF Consulting, Inc. The LEVON Group, LLC U.S. Department of Agriculture U.S. Department of Agriculture, Agricultural Research Service (USDA-ARS) U.S. Department of Energy U.S. Environmental Protection Agency Forest Information Services 3M Company Environmental Laboratory Eastern Research Group, Inc. Alcoa Inc. Hall Associates U.S. Federal Aviation Administration National Council for Air and Stream Improvement (NCASI) Chevron Energy Technology Company

2006 IPCC Guidelines for National Greenhouse Gas Inventories

ARER.15

Reviewers

USA (continued) John G. Owens Diana Pape Sally Rand Veronica Brieno Rankin Karin Ritter Donald Robinson Clark Row Arthur Rypinski Sharon B. Saile Deborah Ottinger Schaefer Elizabeth Scheehle Margaret Sheppard Mark Sperow Michael J. Stenhouse Amanda Vemuri Michael P. Walsh Melissa Weitz Kurt T. Werner Tristram O. West Thomas C. Wirth Walter Worth

3M ICF Consulting U.S. Environmental Protection Agency Michigan Technological University The American Petroleum Institute (API) ICF Consulting Row Associates U.S. Department of Transportation, Office of the Secretary U.S. Environmental Protection Agency U.S. Environmental Protection Agency U.S. Environmental Protection Agency U.S. Environmental Protection Agency West Virginia University Monitor Scientific LLC ICF Consulting International Consultant U.S. Environmental Protection Agency 3M Oak Ridge National Laboratory U.S. Environmental Protection Agency SEMATECH

Zimbabwe Dominick Kwesha Wilfred Mhanda Washington Zhakata

Forestry Commission Envirotech Climate Change Office, Ministry of Environment and Tourism

IGO European Commission EU Commission Sandro Federici Adrian Leip Zoltan Somogyi

Joint Research Centre Joint Research Centre Joint Research Centre (seconded from Hungarian Forest Research Institute, Budapest, Hungary)

Food and Agriculture Organization (FAO) Gustavo Best Theodor Friedrich Dieter Schoene International Civil Aviation Organization (ICAO) Jane Hupe International Energy Agency (IEA) Roberta Quadrelli Karen Treanton International Maritime Organization (IMO) John Ostergaard United Nations Framework Convention on Climate Change (UNFCCC) Roberto Acosta Moreno Clare Breidenich Harald Diaz-Bone Matthew Dudley Claudio Forner James Grabert Javier Hanna Figueroa Rocio Lichte Astrid Olsson Stylianos Pesmajoglou Jenny Wong

ARER.16

2006 IPCC Guidelines for National Greenhouse Gas Inventories

A report prepared by the Task Force on National Greenhouse Gas Inventories (TFI) of the IPCC and accepted by the Panel but not approved in detail

Whilst the information in this IPCC Report is believed to be true and accurate at the date of going to press, neither the authors nor the publishers can accept any legal responsibility or liability for any errors or omissions. Neither the authors nor the publishers have any responsibility for the persistence of any URLs referred to in this report and cannot guarantee that any content of such web sites is or will remain accurate or appropriate.

Published by the Institute for Global Environmental Strategies (IGES), Hayama, Japan on behalf of the IPCC © The Intergovernmental Panel on Climate Change (IPCC), 2006. When using the guidelines please cite as: IPCC 2006, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). Published: IGES, Japan.

IPCC National Greenhouse Gas Inventories Programme Technical Support Unit ℅ Institute for Global Environmental Strategies 2108 -11, Kamiyamaguchi Hayama, Kanagawa JAPAN, 240-0115 Fax: (81 46) 855 3808 http://www.ipcc-nggip.iges.or.jp

Printed in Japan ISBN 4-88788-032-4

VOLUME 1

GENERAL GUIDANCE AND REPORTING

Coordinating Lead Authors Newton Paciornik (Brazil) and Kristin Rypdal (Norway)

Review Editors Sadedin Kherfan (Syrian Arab Republic) and Klaus Radunsky (Austria)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Table of Contents

Contents Volume 1

General Guidance and Reporting

Chapter 1

Introduction to the 2006 Guidelines

Chapter 2

Approaches to Data Collection

Chapter 3

Uncertainties

Chapter 4

Methodological Choice and Identification of Key Categories

Chapter 5

Time Series Consistency

Chapter 6

Quality Assurance / Quality Control and Verification

Chapter 7

Precursors and Indirect Emissions

Chapter 8

Reporting Guidance and Tables

Annex 8A.1

Prefixes, units and abbreviations, standard equivalents

Annex 8A.2

Reporting Tables

2006 IPCC Guidelines for National Greenhouse Gas Inventories

GGR.v

Chapter 1: Introduction to the 2006 Guidelines

CHAPTER 1

INTRODUCTION TO THE 2006 GUIDELINES

2006 IPCC Guidelines for National Greenhouse Gas Inventories

1.1

Volume 1: General Guidance and Reporting

Authors Kristin Rypdal (Norway), Newton Paciornik (Brazil) Simon Eggleston (TSU), Justin Goodwin (UK), William Irving (USA), Jim Penman (UK), and Mike Woodfield (UK)

1.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction to the 2006 Guidelines

Contents 1

Introduction to the 2006 Guidelines 1.1

Concepts .............................................................................................................................................. 1.4

1.2

Estimation methods ............................................................................................................................. 1.6

1.3

Structure of the Guidelines .................................................................................................................. 1.7

1.4

Inventory quality ................................................................................................................................. 1.7

1.5

Compiling an inventory ....................................................................................................................... 1.8

References ......................................................................................................................................................... 1.12

Figures Figure 1.1

Inventory development cycle ............................................................................................. 1.10

Boxes Box 1.1

Using the flow diagram (Figure 1.1) and the 2006 Guidelines – Livestock example ........ 1.11

2006 IPCC Guidelines for National Greenhouse Gas Inventories

1.3

Volume 1: General Guidance and Reporting

1 INTRODUCTION TO THE 2006 GUIDELINES The 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006 Guidelines) were produced at the invitation of the United Nations Framework Convention on Climate Change (UNFCCC) to update the Revised 1996 Guidelines and associated good practice guidance1 which provide internationally agreed2 methodologies intended for use by countries to estimate greenhouse gas inventories to report to the UNFCCC. This chapter provides an introduction to the 2006 Guidelines for a broad range of users, including countries and inventory compilers setting out to prepare inventory estimates for the first time. Sections 1.1 to 1.3 describe the overarching framework of these Guidelines, focusing on scope, approach, and structure. Sections 1.4 through 1.5 present step-by-step guidance on how to use the 2006 Guidelines for compiling a greenhouse gas inventory.

1.1

CONCEPTS

Inventories rely on a few key concepts for which there is a common understanding. This helps ensure that inventories are comparable between countries, do not contain double counting or omissions, and that the time series reflect actual changes in emissions.

Anthropogenic emissions and removals Anthropogenic emissions and removals means that greenhouse gas emissions and removals included in national inventories are a result of human activities. The distinction between natural and anthropogenic emissions and removals follows straightforwardly from the data used to quantify human activity. In the Agriculture, Forestry and Other Land Use (AFOLU) Sector, emissions and removals on managed land are taken as a proxy for anthropogenic emissions and removals, and interannual variations in natural background emissions and removals, though these can be significant, are assumed to average out over time.

National territory National inventories include greenhouse gas emissions and removals taking place within national territory and offshore areas over which the country has jurisdiction. There are some special issues that are described in Section 8.2.1 of Volume 1. For example, emissions from fuel use in road transport is included in the emissions of the country where the fuel is sold and not where the vehicle is driven, as fuel sale statistics are widely available and usually much more accurate.

Inventory year and time series National inventories contain estimates for the calendar year during which the emissions to (or removals from) the atmosphere occur. Where suitable data to follow this principle are missing, emissions/removals may be estimated using data from other years applying appropriate methods such as averaging, interpolation and extrapolation. A sequence of annual greenhouse gas inventory estimates (e.g., each year from 1990 to 2000) is called a time series. Because of the importance of tracking emissions trends over time, countries should ensure that a time series of estimates is as consistent as possible.

Inventory reporting A greenhouse gas inventory report includes a set of standard reporting tables covering all relevant gases, categories and years, and a written report that documents the methodologies and data used to prepare the estimates. The 2006 Guidelines provide standardised reporting tables, but the actual nature and content of the tables and written report may vary according to, for example, a country’s obligations as a Party to the UNFCCC. The 2006 Guidelines provide worksheets to assist with the transparent application of the most basic (or Tier 1) estimation methodology. 1

The Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 Guidelines, IPCC, 1997), The Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (GPG2000, IPCC, 2000), and The Good Practice Guidance for Land Use, Land-use Change and Forestry (GPG-LULUCF, IPCC, 2003).

2

See the Report of the Fourth Session of the Subsidiary Body for Scientific and Technological Advice (FCCC/SBSTA/1996/20), paragraph 30; decisions 2/CP.3 and 3/CP.5 (UNFCCC reporting guidelines for preparation of national communications by Parties included in Annex I to the Convention, part I: UNFCCC reporting guidelines on annual inventories), decision 18/CP.8, revising the guidelines adopted under decisions 3/CP.5, and 17/CP.8 adopting improved guidelines for the preparation of national communications from Parties not included in Annex I to the Convention, and subsequent decisions 13/CP.9 and decision 15/CP.10.

1.4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction to the 2006 Guidelines

Greenhouse gases The following greenhouse gases are covered in the 2006 Guidelines3: •

•

carbon dioxide (CO2)

•

nitrous oxide (N2O)

•

perfluorocarbons (PFCs)

•

nitrogen trifluoride (NF3)

•

halogenated ethers (e.g., C4F9OC2H5, CHF2OCF2OC2F4OCHF2, CHF2OCF2OCHF2 )

•

methane (CH4)

•

hydrofluorocarbons (HFCs)

•

sulphur hexafluoride (SF6)

•

trifluoromethyl sulphur pentafluoride (SF5CF3) and other halocarbons not covered by the Montreal Protocol including CF3I, CH2Br2 CHCl3, CH3Cl, CH2Cl2 4

The gases listed above have global warming potentials (GWPs) identified by the IPCC prior to finalisation of the 2006 Guidelines. A GWP compares the radiative forcing of a tonne of a greenhouse gas over a given time period (e.g., 100 years) to a tonne of CO2. The 2006 Guidelines also provide methods for gases for which GWP values were not available prior to finalisation, i.e., C3F7C(O)C2F5, C7F16, C4F6, C5F8 and c-C4F8O. These gases are sometimes used as substitutes for gases that are included in the inventory and countries are encouraged to provide estimates for them.

Other gases The 2006 Guidelines also provide information for the reporting of the following precursors: nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (NMVOC), carbon monoxide (CO) and sulphur dioxide (SO2) although methods for estimating emissions of these gases are not given here.

Sectors and Categories Greenhouse gas emission and removal estimates are divided into main sectors, which are groupings of related processes, sources and sinks: •

•

Energy

•

Agriculture, Forestry and Other Land Use (AFOLU)

•

Industrial Processes and Product Use (IPPU)

•

Waste Other (e.g., indirect emissions from nitrogen deposition from non-agriculture sources5)

Each sector comprises individual categories (e.g., transport) and sub-categories (e.g., cars). Ultimately, countries will construct an inventory from the sub-category level because this is how IPCC methodologies are set out, and total emissions calculated by summation. A national total is calculated by summing up emissions and removals for each gas. An exception is emissions from fuel use in ships and aircraft engaged in international transport which is not included in national totals, but is reported separately. In order to calculate a national total it is necessary to choose an approach to include harvested wood products (HWP). Countries can select any of the approaches reflected in Chapter 12 of Volume 4 for the AFOLU Sector to do this. 3

The halogenated gases are typically emitted in smaller amounts than CO2, CH4 and N2O, but may have long atmospheric lifetimes and strong radiative forcing effects.

4

For these gases, emissions could be estimated following the methods described in Section 3.10.2 of Volume 3 if necessary data are available, and then could be reported under sub-category 2B10 ‘Other’.

5

Estimates include N2O emissions from deposition of anthropogenic nitrogen (N) from NOx/NH3 wherever deposited and from whatever source (but not allocated to specific sectors). The reason for this is that emission factors for nitrogen deposited are of the same magnitude for agricultural sources as for other nitrogen sources, even when the N is deposited in the ocean.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

1.5

Volume 1: General Guidance and Reporting

Reporting is generally organised according to the sector actually generating emissions or removals. There are some exceptions to this practice, such as CO2 emissions from biomass combustion for energy, which are reported in AFOLU Sector as part of net changes in carbon stocks. Where CO2 emissions are captured from industrial processes or large combustion sources, emissions should be allocated to the sector generating the CO2 unless it can be shown that the CO2 is stored in properly monitored geological storage sites as set out in Chapter 5 of Volume 2.

1.2

ESTIMATION METHODS

As with the 1996 Guidelines and IPCC Good Practice Guidance the most common simple methodological approach is to combine information on the extent to which a human activity takes place (called activity data or AD) with coefficients which quantify the emissions or removals per unit activity. These are called emission factors (EF). The basic equation is therefore: Emissions = AD • EF

For example, in the energy sector fuel consumption would constitute activity data, and mass of carbon dioxide emitted per unit of fuel consumed would be an emission factor. The basic equation can in some circumstances be modified to include other estimation parameters than emission factors. Where time lags are involved, due for example to the time it takes for material to decompose in a landfill or leakage of refrigerants from cooling devices, other methods are provided, for example first order decay methods. The 2006 Guidelines also allow for more complex modelling approaches, particularly at higher tiers. Though this simple equation is widely used, the 2006 Guidelines also contain mass balance methods, for example the stock change methods used in the AFOLU sector which estimates CO2 emissions from changes over time in carbon content of living biomass and dead organic matter pools. Carbon dioxide from the combustion or decay of short-lived biogenic material removed from where it was grown is reported as zero in the Energy, IPPU and Waste Sectors (for example CO2 emissions from biofuels6,7, and CO2 emissions from biogenic material in Solid Waste Disposal Sites (SWDS)). In the AFOLU Sector, when using Tier 1 methods for short lived products, it is assumed that the emission is balanced by carbon uptake prior to harvest, within the uncertainties of the estimates, so the net emission is zero. Where higher Tier estimation shows that this emission is not balanced by a carbon removal from the atmosphere, this net emission or removal should be included in the emission and removal estimates for AFOLU Sector through carbon stock change estimates. Material with long lifetime is dealt with in the HWP section. IPCC methods use the following concepts: Good Practice: In order to promote the development of high quality national greenhouse gas inventories a collection of methodological principals, actions and procedures were defined in the previous guidelines and collectively referred to as good practice. The 2006 Guidelines retain the concept of good practice including the definition introduced with GPG2000. This has achieved general acceptance amongst countries as the basis for inventory development and says that inventories consistent with good practice are those which contain neither over- nor under-estimates so far as can be judged, and in which uncertainties are reduced as far as practicable. Tiers: A tier represents a level of methodological complexity. Usually three tiers are provided. Tier 1 is the basic method, Tier 2 intermediate and Tier 3 most demanding in terms of complexity and data requirements. Tiers 2 and 3 are sometimes referred to as higher tier methods and are generally considered to be more accurate. Default data: Tier 1 methods for all categories are designed to use readily available national or international statistics in combination with the provided default emission factors and additional parameters that are provided, and therefore should be feasible for all countries. Key Categories: The concept of key category8 is used to identify the categories that have a significant influence on a country’s total inventory of greenhouse gases in terms of the absolute level of emissions and removals, the trend in emissions and removals, or uncertainty in emissions and removals. Key Categories should be the priority for countries during inventory resource allocation for data collection, compilation, quality assurance/quality control and reporting.

6

CO2 emissions from the use of biofuels should be reported as an information item for QA/QC purposes.

7

In these guidelines peat is assumed not to be a biofuel.

8

Chapter 4 of Volume 1 provides more details of key categories and approaches to identifying key categories for national inventories.

1.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction to the 2006 Guidelines

Decision Trees: Decision trees for each category help the inventory compiler navigate through the guidance and select the appropriate tiered methodology for their circumstances based on their assessment of key categories. In general, it is good practice to use higher tier methods for key categories, unless the resource requirements to do so are prohibitive.

1.3

STRUCTURE OF THE GUIDELINES

Volumes: The 2006 IPCC Guidelines contain 5 volumes, one for each sector (Volumes 2-5) and one for general guidance applicable to all sectors (Volume 1). •

•

Volume 1: General Guidance and Reporting

•

Volume 3: Industrial Processes and Product Use (IPPU)

•

Volume 2: Energy

•

Volume 4: Agriculture, Forestry and Other Land Use (AFOLU) Volume 5: Waste

This five-volume structure means the cross referencing will be required between two volumes at most: Volume 1 (General Guidance and Reporting), and the relevant sectoral volume. Chapters: Volume 1 contains chapters that provide detailed cross-cutting guidance by topic as described in more detail in Section 1.5. Volumes 2-5 contain chapters that provide methodological guidance for specific emission and removal categories, along with specific recommendations for uncertainty, QA/QC, time series consistency, and reporting. The volume and chapter structure is presented in Table 1 in the Overview of the 2006 Guidelines. Annexes: Annexes are intended to include additional often detailed information beyond what is necessary for a Tier 1 estimate, for example extended data tables. Appendices: The 2006 IPCC Guidelines present some technical material in appendices, where emissions or removals are poorly understood and where there is insufficient information available to develop reliable, globally applicable, default methods for a particular source or sink. Countries may use appendices as a basis for further methodological development, but a national inventory can be considered complete without the inclusion of estimates for these sources. Worksheets: Worksheets are tools designed to provide easy calculation of Tier 1 methodologies. Worksheets are not provided for higher tiers, although they can also be used where the higher tier method is similar to Tier 1 (e.g., where national data is used instead of default data). Some more complex approaches are provided in spreadsheets in the attached CD. Reporting Tables: The reporting tables are intended to give sufficient detail required for transparent reporting of national greenhouse gas inventories and follow a disaggregated category list. They include summary tables, sectoral tables, background tables and trend tables. The background tables include summary activity data for increased transparency and to facilitate comparison of data across countries. Reporting tables also include results of a key category analysis and uncertainty assessment. Reporting also includes memo items (emissions to be reported but not included in national totals) and information items for increased transparency.

1.4

INVENTORY QUALITY

These guidelines provide guidance on ensuring quality on all steps of the inventory compilation – from data collection to reporting. They also provide tools to focus resources on the areas where they will most benefit the overall inventory and encourage continuous improvement. Experience has demonstrated that using a good practice approach is a pragmatic means of building inventories that are consistent, comparable, complete, accurate and transparent – and maintaining them in a manner that improves inventory quality over time. Indicators of inventory quality are: Transparency: There is sufficient and clear documentation such that individuals or groups other than the inventory compilers can understand how the inventory was compiled and can assure themselves it meets the good practice requirements for national greenhouse gas emissions inventories. Documentation and reporting guidance is provided in Chapter 8, Reporting Guidance and Tables, of Volume 1 and in the respective chapters of Volume 2-6 (see also Volume 1, Chapter 6, QA/QC and Verification).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

1.7

Volume 1: General Guidance and Reporting

Completeness: Estimates are reported for all relevant categories of sources and sinks, and gases. Geographic areas within the scope of the national greenhouse gas inventory are recommended in these Guidelines. Where elements are missing their absence should be clearly documented together with a justification for exclusion (see Volumes 2-5). Consistency: Estimates for different inventory years, gases and categories are made in such a way that differences in the results between years and categories reflect real differences in emissions. Inventory annual trends, as far as possible, should be calculated using the same method and data sources in all years and should aim to reflect the real annual fluctuations in emissions or removals and not be subject to changes resulting from methodological differences. (See Chapter 2: Approaches to Data Collection, Chapter 4: Methodological Choice and Identification of Key Categories, and Chapter5: Time Series Consistency in Volume 1.) Comparability: The national greenhouse gas inventory is reported in a way that allows it to be compared with national greenhouse gas inventories for other countries. This comparability should be reflected in appropriate choice of key categories (see Volume 1, Chapter 4), and in the use of the reporting guidance and tables and use of the classification and definition of categories of emissions and removals presented in Table 8.2 of Chapter 8, and Volumes 2-5. Accuracy: The national greenhouse gas inventory contains neither over- nor under-estimates so far as can be judged. This means making all endeavours to remove bias from the inventory estimates (see especially Chapter 2, Approaches to Data Collection, and Chapter 3, Uncertainties, in Volume 1 and Volumes 2-5). Uncertainty assessment (details provided in Chapter 3 of Volume 1) is an important component of good practice in national greenhouse gas inventory development. The uncertainty analysis characterises the range and likelihood of possible values for the national inventory as a whole as well as for its components. Awareness of the uncertainty of parameters and results provides inventory compilers with insight when evaluating suitable data for the inventory during the data collection and compilation phases. Uncertainty assessment also helps identify the categories that contribute most to the overall uncertainty, which helps the inventory compiler prioritise future inventory improvements. The 2006 Guidelines encourage continuous improvement and rigor through QA/QC and verification activities. A number of concepts and tools in Chapter 6 in Volume 1 are provided to support efficient inventory management, checking and continuous improvement. These activities will ensure that the best use of limited resources can be made and a quality consistent with good practice is achieved for each inventory. Regular communication and consultation with providers of data is recommended throughout the inventory activities (from data collection to final reporting). This communication will build working relationships between data supplier and inventory compilers that will benefit the inventory both in terms of efficiency and quality. This activity will also help to keep the inventory compilers informed of the development of new datasets and even provide opportunities to influence the planning and specifications of data provider’s data collection activities.

1.5

COMPILING AN INVENTORY

Compiling a greenhouse gas inventory is a step-by-step process. This section provides guidance on these steps for the inventory compiler, i.e., the person, persons or institutions who put together or compose the inventory from materials gathered from several sources. Compilation includes the collection of data, estimation of emissions and removals, checking and verification, uncertainty assessment and reporting. Before undertaking estimates of emissions and removals from specific categories an inventory compiler should become familiar with the material in Volume 1 General Guidance and Reporting. This Volume provides good practice guidance on issues that are common to all the estimation methods covered by the sector-specific guidance provided in Volumes 2 to 5 and reporting instructions. Summary of Volume 1: •

•

1.8

Data collection: Collection of data is a fundamental part of inventory preparation. Chapter 2 of Volume 1 provides guidance on initiating and maintaining a data collection program. It covers evaluating existing sources of data, and planning new emission measurements and surveys, extensive reference is made to guidance provided by other organisations. The chapter links the data collection process to the other general issues. Uncertainty assessment: Estimates of uncertainty are needed for all relevant source and sink categories, greenhouse gases, inventory totals as a whole, and their trends. Chapter 3, Uncertainties, provides practical guidance for estimating and combining uncertainties, along with a discussion of the conceptual underpinnings of inventory uncertainty. Uncertainty issues related to specific category of emissions and removals are addressed in Volumes 2-5.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction to the 2006 Guidelines •

•

•

• •

Key category analysis: Good practice guidance on how to identify key categories of emissions and removals is provided in Chapter 4, Methodological Choice and Identification of Key Categories. The key category concept is used, together with the decision trees in Volumes 2-5, to guide users in their methodological choice for each category. These decision trees are the critical link between methodological choice in the sector-specific volumes and the identification of key categories in Volume 1. Time series consistency: Ensuring the time series consistency of inventory estimates is essential for establishing confidence in reported inventory trends. Chapter 5, Time Series Consistency, provides methods for ensuring time-series consistency in cases where it is not possible to use the same method and/or data over the entire period. This chapter also provides good practice guidance on when to recalculate estimates for previous years and methods for accounting for changes in emissions and removals over time. Quality Assurance (QA) and Quality Control (QC): A QA/QC system is an important part of inventory development. Chapter 6, QA/QC and Verification, describes the general QA/QC aspects to consider when compiling an inventory of emissions and removals. Good practice guidance on sector specific quality control checks are addressed in Volumes 2-5. Chapter 6 also describes techniques for verifying inventories using external data. Precursors and indirect N2O emissions: Volume 1 also includes cross-sectoral guidance on dealing with precursors and indirect emissions of N2O from deposition of nitrogen compounds (resulting from NOx and NH3 emissions) in Chapter 7, Precursors and Indirect Emissions. Reporting: Chapter 8, Reporting Guidance and Tables, specifically addresses issues related to reporting, including definitions of national territory, gases and reporting categories. Notation keys are introduced to account for completeness and transparency in reporting. The definitions of categories of sources and sinks take into account the structure of the sector guidance in Volume 2-5. The sectoral and summary reporting tables to be applied for reporting emissions and removals of each category are included in Chapter 8. Reporting tables on uncertainties, key category analysis, and emission trends have also been developed and are included in Chapter 8.

Volumes 1 and Volumes 2 to 5 are complementary. After the compilers tasked with preparing estimates for specific emission and removal categories have familiarised themselves with the general guidance in Volume 1 they should use the specific sectoral volume(s) appropriate to their categories so that they can apply the requirements in a manner appropriate to their national circumstances. Figure 1.1 illustrates the steps of a typical inventory cycle. Quality control measures should be implemented at each step and should be documented according to the requirements of QA/QC and documentation given in Chapter 6 of Volume 1. 1.

The first step for a revised or new greenhouse gas inventory is to identify the key categories for the inventory so that resources can be prioritised. Where an inventory already exists, the key categories can be identified quantitatively from the previous estimates (see Volume 1 Chapter 4). For a new inventory the compiler will have to make a preliminary assessment based on local knowledge and expertise about large emission sources and inventories in countries with similar national circumstances or, if possible, make preliminary Tier 1 estimates to assist in identifying key categories. Assessing the key categories helps the inventory compiler to focus effort and resources on the sectors that contribute most to the overall inventory or inventory uncertainty and so helps to ensure that the best possible inventory is compiled for the available resources.

2.

Once the key categories have been identified, the inventory compiler should identify the appropriate method for estimation for each category in the particular country circumstances. The sector-specific decision trees in Volumes 2-5 and the generalised decision tree in Chapter 4 of Volume 1 provide guidance on selecting appropriate methods. The selection of methods will be determined by the classification of a category as key or not key, and by both the data and the resources available. Guidance on data collection is provided in Chapter 2 of Volume 1.

3.

Data collection should follow the selection of the appropriate methods. (See Chapter 2, 5 and 7 in Volume 1). Data collection activities should consider time series consistency and establish and maintain good verification, documentation and checking procedures (QA/QC) to minimise errors and inconsistencies in the inventory estimates. Data on uncertainties should if possible be collected at the same time. Guidance on the collection of new data in a cost effective way and on uncertainties is provided in Chapter 2 and Chapter 3 of Volume 1 respectively. QA/QC activities should continue throughout this process to minimise errors and document data sources, methods and assumptions. The results of the data collection may lead to refinement of the methods chosen.

4.

Emissions and removals are estimated following the methodological choice and data collection. Care should be taken to follow the general guidance in Chapter 5, Time Series Consistency in Volume 1 especially if the data are incomplete for some years.

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5.

Once the inventory estimates are complete, the next step is to perform an uncertainty analysis and key category analysis (see Chapters 3 and 4 in Volume 1). These analyses may identify categories for which a higher tier should be used and additional data collected.

6.

Following the completion of the final quality assurance (QA) checks, the final step in the inventory process is to report the inventory (See Chapter 8 in Volume 1). The aim here is to present the inventory in an as concise and clear way as possible to enable users to understand the data, methods and assumptions used in the inventory. Provision of concise relevant background information and explanations in the reports helps to ensure the inventory (including the report) is transparent.

The inventory compiler should base future inventory revisions on previous inventories. Thus an iterative process builds on and improves the inventory each time a new inventory is compiled as illustrated in Figure 1.1. When a revised inventory is compiled, all years estimates should be reviewed for consistency and updated integrating any feasible improvements where necessary. Chapter 5 in Volume 1 gives advice on compiling consistent time series and provides good practice approaches for achieving time series consistency. Figure 1.1 Inventory development cycle Start new estimate, building on experience of previous inventories. (if available)

Report inventory. (Chapter 8 and Volumes 2-5)

Identify key categories. (Chapter 4)

Check/Review inventory through QA. (Chapter 6)

Select methods (Volume 2-5) while considering data collection, uncertainty and time series consistency good practice. (Volume 1 Chapters 2, 3 and 5 respectively)

Make necessary revisions.(if any) (Chapter 4)

QC Checking & Documentation

QC Checking & Documentation

Conduct key category analysis. (Chapter 4)

Collect data (Volume 1 Chapter 2) and estimate emissions/removals (Volume 2-5) ensuring adequate QA/QC and time series consistency. (Volume 1 Chapters 5 and 6)

QC Checking & Documentation

QC Checking & Documentation

Conduct uncertainty analysis: Evaluate input data and assess overall inventory. (Chapter 3)

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QC Checking & Documentation

Compile inventory: (Worksheets in Volume 2-5 or own system) considering time series consistency and QA/QC in Volume 1 Chapters 5 and 6.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction to the 2006 Guidelines

Box 1.1 provides an example on using the 2006 Guidelines throughout the inventory cycle when estimating emissions from enteric fermentation.

BOX 1.1 USING THE FLOW DIAGRAM (FIGURE 1.1) AND THE 2006 GUIDELINES – LIVESTOCK EXAMPLE

Inventory compilers tasked with preparing estimates for specific emission and removal categories need to familiarise themselves with guidance in two Volumes: the relevant guidance in a sectoral volume (e.g., Volume 4, Agriculture, Forestry and Other Land Use), and the general guidance in Volume 1. Along with the diagram (see Figure 1.1) this box describes how the guidance in the two Volumes is used for estimating methane emissions from Enteric Fermentation: Start with your previous inventory where available and prioritise categories for estimation. • The inventory compiler can begin with the overall results of the previous national inventory, particularly the key category assessment, as a preliminary step to selecting methods and data (Chapter 4 of Volume 1). Familiarise yourself with general and sector specific QA/QC requirements. • Prior to collecting all the data and estimating emissions, the inventory compiler should consult the general guidance in implementing Quality Control (QC) procedures in Chapter 6 of Volume 1 (QA/QC and Verification) along with the specific QC procedures for enteric fermentation described in Chapter 10 of Volume 4. QC procedures should be implemented at every step of the inventory cycle. This will include regular checking and clear documentation of data sources methods and assumptions. Choose appropriate methods based on category importance and data availability. • The inventory compiler should consult the decision tree and methodological guidance in Chapter 10 of Volume 4 to select an appropriate method. In this example, enteric fermentation is a key category, which indicates that normally Tier 2 or 3 should be selected. •

The general guidance in Chapter 2 (Approaches to Data Collection) of Volume 1 and Chapter 10 of Volume 4 will guide the inventory compiler in choosing appropriate emission factor, activity data and other estimation parameters. This may include identifying or choosing from existing data or collection and classification of new data.

Collect the data necessary for the latest year and a consistent time series and uncertainty estimation. • The next step involves collection of the needed data for all years. The availability of data may sometimes restrict use of higher tier methods for key categories. •

•

Chapter 5 (Time Series Consistency) of Volume 1 should be used if preparing estimates for more than one year. This guidance is particularly relevant if the selected method is different from the one used in previous inventories or the sources of data or their classification have changed. This can imply the need for recalculations of previous estimates or splicing of data series. Chapter 10 of Volume 4 should be consulted for source-specific guidance on timeseries consistency. In estimating uncertainties, inventory compilers should also refer to the general guidance on uncertainty in Chapter 3 of Volume 1 - paying particular attention to guidance on concepts and methods – and the uncertainty section of the enteric fermentation livestock chapter for source-specific information (for example default uncertainties). Ideally, the inventory compiler should collect activity data, emission factors, and uncertainty information at the same time because this is the most efficient strategy.

Estimate emissions/removals consistent with the guidance. • The next step is to estimate methane emissions from enteric fermentation for all relevant years. Relevant guidance for this step includes the specific guidance for enteric fermentation in Volume 4, Chapter 10 relating to completeness, reporting and documentation, and time series consistency sections. •

The enteric fermentation emissions and uncertainty data are used subsequently as input into the compilation of the overall inventory, the estimation of category-specific and overall uncertainty, and the key category assessment. The results of these steps may require changes or revisions to the original estimate of emissions of enteric fermentation.

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BOX 1.1 (CONTINUED) Check and review the estimates. • Following the Quality Assurance (QA) guidance in Volume 1, the inventory compiler should arrange for review of the estimate and documentation by technical experts not involved in the preparation of the inventory. External reviewers may suggest improvements or identify errors that would require a recalculation of the enteric fermentation estimate. Report the estimates. •

The IPCC Guidelines provide guidance on reporting information on enteric fermentation in two places: the enteric fermentation chapter of Volume 4, and the reporting tables in Chapter 8 of Volume 1. The inventory compiler should consult both chapters for a complete description of reporting guidance. Note: In the case of an initial inventory effort, with no previous key category analysis, a qualitative assessment of enteric fermentation could be used. See Chapter 2 and Chapter 4 of Volume 1. In this example, it can be concluded that methane from enteric fermentation is key in most inventories and should therefore be considered initially key.

References IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Volumes 1, 2 and 3. Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2000). Good Practice Guidance and Uncertianty Management in National Greenhouse Gas Inventories. Penman, J., Kruger, D., Galbally, I., Hiraishi, T., Nyenzi, B., Enmanuel, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K. and Tanabe, K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. IPCC (2003). Good Practice Guidance for Land Use, land-Use Change and Forestry. Penman, J., Gytarsky, M., Hiraishi, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. and Wagner, F. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/IGES, Hayama, Japan.

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Chapter 2: Approaches to Data Collection

CHAPTER 2

APPROACHES TO DATA COLLECTION

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Volume 1: General Guidance and Reporting

Authors Justin Goodwin (UK), Mike Woodfield (UK) Mirghani Ibnoaf (Sudan), Matthias Koch (Germany), and Hong Yan (China)

Contributing Authors Christopher Frey (USA), Rosemary Montgomery (United Nation Statistical Division), Tinus Pulles (Netherlands), Deborah Ottinger Schaeffer (USA), and Karen Treanton (IEA)

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Chapter 2: Approaches to Data Collection

Contents 2

Approaches to Data Collection 2.1

Introduction ......................................................................................................................................... 2.4

2.2

Collecting data .................................................................................................................................... 2.4

2.2.1

Gathering existing data ................................................................................................................ 2.6

2.2.2

Generating new data .................................................................................................................... 2.8

2.2.3

Adapting data for inventory use ................................................................................................ 2.10

2.2.4

Emission factors and direct measurement of emissions ............................................................ 2.12

2.2.5

Activity data .............................................................................................................................. 2.17

References ..................................................................................................................................................... 2.19 Annex 2A.1

A protocol for expert elicitation ................................................................................................ 2.20

Annex 2A.2

General guidance on performing surveys .................................................................................. 2.22

Figures Figure 2.1

Process for including data in the EFDB ............................................................................. 2.14

Tables Table 2.1

Generic elements of a measurement programme ................................................................. 2.9

Table 2.2

Potential sources of literature data ..................................................................................... 2.13

Table 2.3

Standard measurement methods for exhaust gas ............................................................... 2.16

Table 2A.1

Example of documentation of expert judgement ............................................................... 2.21

Boxes Box 2.1

Example of using alternative data to approximate activity data .......................................... 2.8

Box 2.2

The difference between census and survey data ................................................................ 2.17

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2 APPROACHES TO DATA COLLECTION 2.1

INTRODUCTION

Data1 collection is an integral part of developing and updating a greenhouse gas inventory. Formalised data collection activities should be established, adapted to countries’ national circumstances, and reviewed periodically as a part of implementing good practice. In most cases generating new source data will be limited by the resources available and prioritisation will be needed, taking account the results of key category analysis set out in Chapter 4, Methodological Choice and Identification of Key Categories. Data collection procedures are necessary for finding and processing existing data, (i.e., data that are compiled and stored for other statistical uses than the inventory), as well as for generating new data by surveys or measurement campaigns. Other activities include maintaining data flows, improving estimates, generating estimates for new categories and/or replacing existing data sources when those currently used are no longer available. The methodological principles of data collection that underpin good practice are the following: •

Focus on the collection of data needed to improve estimates of key categories which are the largest, have the greatest potential to change, or have the greatest uncertainty.

•

Choose data collection procedures that iteratively improve the quality of the inventory in line with the data quality objectives.

•

Put in place data collection activities (resource prioritisation, planning, implementation, documentation etc.) that lead to continuous improvement of the data sets used in the inventory.

•

Collect data/information at a level of detail appropriate to the method used.

•

Review data collection activities and methodological needs on a regular basis, to guide progressive, and efficient, inventory improvement.

•

Introduce agreements with data suppliers to support consistent and continuing information flows.

This chapter provides general guidance for collecting existing national/international data and new data. The material is intended both for countries establishing a data collection strategy for the first time and for countries with established data collection procedures. It is applicable to emission factor, activity, and uncertainty data collection. It covers: •

Developing a data collection strategy to meet data quality objectives regarding timeliness, and also consistency, completeness, comparability, accuracy, and transparency using guidance provided in Chapter 6, QA/QC and Verification, of this volume,

•

Data acquisition activities including generating new source data, dealing with restricted data and confidentiality, and using expert judgement,

•

Turning the raw data into a form that is useful for the inventory.

Advice related to selecting emission factors focuses on understanding and generating measured data as well as addressing where to find and when to use default factors. Guidance on activity data focuses on generating and using new census & survey data as well as providing guidance on the use of existing international data sets. The chapter draws on information from a range of institutions and where possible additional documents have been identified and referenced so that users can find more detailed information. Sector specific data collection issues - like selecting the appropriate activity data for a particular category of emissions by sources and removals by sinks - are described in the sector specific Volumes 2-5.

2.2

COLLECTING DATA

This section provides general guidance for collecting existing data, generating new data, and adapting data for inventory use. The guidance is applicable to emission factors, activity and uncertainty data collection. It

1

Data can be defined as factual information (e.g., measurements or statistics) used as a basis for reasoning, discussion, or calculation. Data collection is the activity of acquiring and compiling information from different sources.

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Chapter 2: Approaches to Data Collection

discusses separately specific issues relating to new data and existing data. Specific guidance for the collection/calculation of emission factors and the collection of activity and uncertainty data is provided subsequently. Throughout the data collection activities the inventory compiler should maintain QA/QC records about the data collected according to the guidance provided in Chapter 6 of Volume 1. While collecting data it is good practice to be aware of future data collection needs.

Maintaining supply of inventory data It is good practice to engage data suppliers in the process of inventory compilation and improvement by involving them in activities such as: •

•

Offering an initial estimate for the category, pointing out the potentially high uncertainties and inviting potential data suppliers to collaborate in improving estimates,

•

Scientific or statistical workshops on the inventory inputs and outputs,

•

Regular/annual informal updates on the methods that use their data,

•

Specific contracts or agreements for regular data supply,

Establishment of terms of reference or memoranda of understanding for government and/or trade organisations providing data to clarify what is needed for the inventory, how it is derived and provided to the inventory compiler and when.

These activities will help to ensure that the most appropriate data are available for the inventory and that the data are properly understood by the inventory compiler. It will also help to establish links to data providing organisations. Where appropriate, it may be useful to explore existing or new legal arrangements as means of guaranteeing the delivery of data to the inventory.

Restricted data and confidentiality Data providers might restrict access to information because it is confidential, unpublished, or not yet finalised. Typically, this is a mechanism to prevent inappropriate use of the data, unauthorised commercial exploitation, or sensitivity to possible imperfections in the data. Sometimes, however, the organisation simply does not have the resources required to compile and check the data. It is advisable, where possible, to cooperate with data providers to find solutions to overcome their concerns by: •

•

explaining the intended use of the data,

•

identifying the increased accuracy that can be gained through its use in inventories,

•

agreeing, in writing, to the level at which it will be made public,

•

offering cooperation to derive a mutually acceptable data sets, and/or giving credit/acknowledgement in the inventory to the data provided.

The protection of confidentiality is one of the fundamental principles of a national statistical agency (NSA2 - see: http://unstats.un.org/unsd/methods/statorg/). NSAs are committed to safeguarding information that plainly reveals the operations, belongings, attitudes or any other characteristics of individual respondents. If respondents are not convinced that the information they provide to the NSA is absolutely confidential, the quality of the information collected may suffer. Detailed individual data must therefore be treated and aggregated so as to draw out the information that is important to the user, without disclosing individual data. This is more likely to be an issue for business statistics, especially where a few companies dominate the sector, than for other data. Sometimes, depending on the size and structure of the original sample, raw data can be aggregated in a way that protects confidentiality and yet produces useful information for emission inventory purposes. If, however, there is a need to preserve confidentiality the NSA, or the body that originally collected the data, are normally the only ones that can carry out this additional treatment of the raw data. Some countries have special arrangements to mask data (i.e., make data anonymous with respect to companies or facilities) to allow researchers access. Inventory compilers may investigate the possibility of making such arrangements. However, as this reprocessing will be required regularly (annually if possible), a better solution would probably be for NSAs to incorporate this into their own work programmes. While this will require an initial investment in data processing, it will probably be quicker and less expensive in the long run. Once the 2

Any main national official data collection organisation is referred to here as national statistical agency.

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reprocessing system is set up it can be reused every time the survey is repeated, with low marginal costs. An added advantage is that the information will then be in the public domain so that others can validate the figures reported in the inventories. Many agencies collect ancillary data during operations for other purposes, such as registration of businesses or vehicles, collection of taxes, granting of licences, allocation of grants and subsidies. Such information is usually also covered by confidentiality clauses. In general, such clauses foresee the use of the data for statistical purposes, and NSAs have the right of access to such data. Often these administrative data form the basis for sample stratification and selection and NSAs will have experience in handling them, perhaps even developing specialist software that allows the required information to be drawn out without breaching the confidentiality rules. For all these reasons, when existing data need to be reprocessed, it is strongly recommended to work together with NSAs or the statistical service of the relevant ministry, not only to protect confidentiality, but also for cost savings.

Expert judgement Expert judgement on methodological choice and choice of input data to use is ultimately the basis of all inventory development and sector specialists can be of particular use to fill gaps in the available data, to select data from a range of possible values or make judgements about uncertainty ranges as described in Section 3.2.2.3. Experts with suitable backgrounds can be found in government, industrial trade associations, technical institutes, industry and universities. The goal of expert judgement may be choosing the proper methodology; the parameter value from ranges provided; the most appropriate activity data to use; the most appropriate way to apply a methodology; or determining the appropriate mix of technologies in use. A degree of expert judgement is required even when applying classical statistical techniques to data sets, since one must judge whether the data are a representative random sample and, if so, what methods to use to analyze the data. This requires both technical and statistical judgement. Interpretation is especially needed for data sets that are small, highly skewed or incomplete3. In all cases the aim is to be as representative as possible in order to reduce possible bias and increase accuracy. Formal methods for obtaining (or eliciting) data from experts are known as expert elicitation, see Annex 2A.1 for details.

2.2.1

Gathering existing data

Although the list below is not exhaustive, it provides a starting point for possible sources of country specific data: •

•

National Statistics Agencies

•

Other national experts

•

Other international experts

•

Sectoral experts, stakeholder organisations

•

IPCC Emission Factor Database

International organisations publishing statistics e.g., United Nations, Eurostat or the International Energy Agency, OECD and the IMF (which maintains international activity as well as economic data)

•

•

Reference libraries (National Libraries)

•

Universities

•

Scientific and technical articles in environmental books, journals and reports.

•

Web search for organisations & specialists National Inventory Reports from Parties to the United Nations Framework Convention on Climate Change

Screening of available data It is best to start data collection activities with an initial screening of available data sources. This will be an iterative process where details of data that are available are built up. This screening process may be slow and require questioning until a final judgement can be made about the usefulness of a data set for the inventory.

3

Methods for characterising sampling distributions for the mean are described by Cullen and Frey (1999), Frey and Rhodes (1996), and Frey and Burmaster (1999).

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Chapter 2: Approaches to Data Collection

The purpose for which data were originally collected may be an important indicator of reliability. Regulatory authorities and official statistical bodies have a responsibility to take representative samples and accurate measurements, and so they often adopt agreed standards. Often official statistics (because they have a more elaborate review process) take a long time to become available but preliminary data may be available at an earlier stage. These preliminary data can be used provided that their validity is documented and can be checked against the data quality objectives set by the quality management system described in Chapter 6.

Refining Data Requirements Once the inventory compiler has selected a data set, unless published data simply can be used in their original form, the next step will be to develop a more formal specification and data request. This formalisation enables efficient annual updating (through knowing what to ask for, from whom, and when) while complying with QA/QC requirements for documentation (see Chapter 6, QA/QC and Verification). A clear definition of data requirements will ensure that when data are delivered they are as expected. The specification should include details such as: •

• • • •

•

•

Definition of the data set (e.g., time series, sectors and sub-sector detail, national coverage, requirements for uncertainty data, emission factors and/or activity data units), Definition of the format (e.g., spreadsheet) and structure (e.g., what different tables are needed and their structure) of the data set, Description of any assumptions made regarding national coverage, the sectors included, representative year, technology/management level, and emission factors or uncertainty parameters, Identification of the routines and timescales for data collection activities (e.g., how often is the data set updated and what elements are updated), Reference to documentation and QA/QC procedures, Contact name and organisation, Date of availability.

It can be useful to seek commitment to these specifications from the organisation providing the data. Maintaining and updating these specifications on a regular basis, in case data requirements change, can also help to document the data sources and provide up-to-date guidance for routine data collection activities. It is not unusual for the delivery of data sets to be delayed so incorporating early warning routines to detect and manage delays can be useful.

Choosing between published national and international data In most cases it is preferable to use national data since national data sources are typically more up to date and provide better links to the originators of the data. Most international datasets rely on nationally-derived data, and in some cases data from reputable international bodies may be more accessible and more applicable to the inventory. In some cases, groups such as international trade associations or international statistical bodies will have country specific datasets for industries or other economic sectors that are not held by national organisations. Often international data have undergone additional checking and verification and may have been adjusted with the aim of increasing consistency, though this will not necessarily lead to improved estimates if the adjusted data are recombined with national information. Countries are encouraged to develop and improve national sources of data to avoid being reliant on international data. Cross-checking national data sets with any available international data can help to assess completeness and identify possible problems with either data set.

Surrogate data It is preferable to use data that are directly related to the item being quantified rather than to use surrogate data (i.e., alternative data that have a correlation with the data that they are replacing). In some cases, however, directly applicable data may be unavailable or have gaps (e.g., if survey and sampling programmes may be infrequent). In these cases surrogate data can help fill gaps and generate a consistent time series or a country average. For example, where a country has information to apply a higher tier method for some but not all of its facilities, then surrogate data can be used to fill the gaps. The surrogate data should be physically and statistically related to the emissions from the set of facilities for which information is not available. These alternative data should be selected based on country-specific circumstances and information, and a relationship between the data and emissions (i.e., an emission factor) developed using information from a representative subset of facilities whose emissions are known. The use of surrogate data to obtain an initial estimate of an emission or removal can help prioritise resources.

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In selecting and using surrogate data to estimate emissions or removals, it is good practice for countries to perform the following steps: (i)

Confirm and document the physical relationship between emissions/removals and the surrogate activity data.

(ii)

Confirm and document a statistically significant correlation between emissions/removals and the surrogate activity data.

(iii)

Using regression analysis, develop a country-specific factor relating emissions/removals to the surrogate data.

An example of this approach is given in Box 2.1 and further explanation and equation (Equation 5.2) given in Section 5.3 of Chapter 5, Time Series Consistency.

BOX 2.1 EXAMPLE OF USING ALTERNATIVE DATA TO APPROXIMATE ACTIVITY DATA

The U.S. receives emission estimates for SF6 associated with electrical equipment based on a mass-balance approach from electric power systems representing about 35 percent of the total length of U.S. transmission lines. (In the U.S., transmission lines are defined as lines carrying electricity at or above 34.5 kV.) To estimate emissions from the remaining systems, the U.S. uses kilometres of transmission lines as alternative activity data. In the U.S., SF6 is primarily used in equipment rated at or above 34.5 kV, and kilometres of transmission lines are therefore expected to be a good predictor of emissions. In addition, statistical analysis has demonstrated a high correlation between emissions and kilometres of transmission lines. Given these relationships, the U.S. uses regression factors relating transmission kilometres to emissions. These factors are then applied to the total transmission kilometres of the systems whose emissions are being estimated. Germany has also used the length of transmission lines to estimate emissions from closed pressure systems for a set of utilities that did not respond to an industry survey. Estimates are based on the electric power systems from utilities for which both transmission kilometres and emissions data were available. The resulting estimates were later confirmed by more comprehensive surveys in subsequent years. Information on equipment banks, available nationally from equipment manufacturers and distributors were used to estimate emissions from sealed-pressure systems.) Transmission kilometres are likely to be a good predictor of emissions where most SF6 is used in high voltage transmission equipment, as in the U.S. Where a high percentage of SF6 is used in medium voltage distribution equipment or in gas-insulated substations, another type of data may be appropriate, such as the combined length of transmission and distribution lines or the number of substations. Combinations of these or other types of data may also be used although this increases the probability that one or more of the types of data will not be available for all the systems whose emissions are to be estimated.

2.2.2

Generating new data

It may be necessary to generate new data if representative emission factors, activity data or other estimation parameters do not exist, or cannot be estimated from existing sources. Generation of new data may entail measurement programmes for industrial process or energy related emissions, sampling of fuels for carbon content, land-use change and forestry sampling activities, or new census or surveys for activity data. Generation of new data is best undertaken by those with appropriate expertise (e.g., measurements carried out by competent organisations using appropriately calibrated equipment or surveys and censuses by any national statistical authority). These activities are often resource intensive and are most appropriately considered when the category is key and there are no other options. To optimise resource use it is recommended as far as possible to generate the required data from an extension of existing programmes rather than the initiation of totally new ones. More specific details for emission factor and activity data are outlined in the respective sections of this chapter. Where guidelines exist for activities that are defined in detail by other official bodies, such as statistical offices and measurement standards committees, these are also referenced in these sections.

Generating data by measurement Measurements should be used in the context of advice in the sectoral Volumes 2-5, for example to determine or revise emission factors, destruction/abatement efficiency factors and activity rates. Measurements can also be used to quantify greenhouse gas emissions directly or to calibrate and verify models that are used to generate data.

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Chapter 2: Approaches to Data Collection

When considering using measurement data it is good practice to check whether it covers a representative sample, i.e., that is typical of a reasonable proportion of the whole category – and also whether a suitable measurement method has been used. The best measurement methods are those that have been developed by official standards organisations and field-tested to determine their operational characteristics.4 Using standardised measurement methods improves the consistency of measured data and provides the inventory compiler with additional information about the method such as statistical uncertainty levels, lower detection limits, sensitivity, and upper limits of measurement etc. The International Standards Organisation (ISO) standards, European Standards (EN) or suitable validated national standards of, e.g., U.S. Environmental Protection Agency (USEPA), or the Association of German Engineers (Verein Deutscher Ingenieure, VDI), may meet these criteria. It is good practice for the inventory compiler to document any measurement or quality management standards that have been used, and to bear in mind the data requirements of the uncertainty analysis in Chapter 3, Uncertainties, of Volume 1. Reliable and comparable results can be achieved using a well-designed measurement programme with defined objectives; suitable methods; clear instructions to the measurement personnel; defined data processing and reporting procedures, and adequate documentation. Table 2.1 sets out the elements of such an approach.

TABLE 2.1 GENERIC ELEMENTS OF A MEASUREMENT PROGRAMME Measurement objective

Clear statement of the parameter(s) to be determined, e.g., HFC-23 emissions from HCFC-22 production.

Methodology protocol

Description of the measurement methodology to be used. This should include: •

•

•

•

•

Any source/sink or installation access requirements;

•

Data capture requirements to be met;

•

•

• •

•

• •

a

4

The analytical equipment needed and its operational requirements; Any accuracy, precision or uncertainty requirements; QA/QC regimes to be followed.

Measurement plan specifies for those carrying out the measurements that includes:

•

Data processing and reporting procedures, and documentation

Methods to ensure that representative samples are taken that reflect the nature of the source category and the measurement objective a; The identification of any standard techniques to be used;

•

Measurement plan with clear instructions to the measurement personnel

The components to be measured and any associated reference conditions;

Number of sampling points for each parameter to be measured and how these are to be selected; Number of individual measurements to be made for each sampling point and set of conditions; Measurement dates and periods of the measurement campaign; Reporting arrangements; Additional source or process related information to be collected to enable data processing or interpretation of the results; Conditions (or range of conditions) of the source (or for industrial plant the capacity, load, fuel or feedstock) to be met during the measurements; Personnel responsible for the measurements, who else is involved and the resources to be used.

Data processing requirements, including; •

•

Reporting procedures that will form an account of the measurements, the description of the measurement objectives, and the measurement plan; Documentation requirements to enable the results to be traced back through the calculations to the collected basic data and process operating conditions.

When making eco-system measurements particular care is required in defining the sampling requirements – see Volume 4.

For example, repeatability, reproducibility detection limit, tolerance to interference, etc.

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Volume 1: General Guidance and Reporting

General guidance to ensure the quality of measured data to determine better emission factors and other parameters are provided in Section 2.2.2.

Relationship of data to models Although models are frequently used to assess complex systems and can be used to generate data, models are a means of data transformation and do not remove the need for data to drive them.

2.2.3

Adapting data for inventory use

Whether using existing data, making new measurements or combining the two it is important to ensure that the level of detail and coverage of the data match, including sectors/process/abatement, location, land type, compound and years included.

Gaps in data sets Greenhouse gas inventories require consistent estimates across time series and between categories. This section introduces approaches to fill gaps if data are missing for one or more years or the data do not represent the year or national coverage required. Examples of data gaps or inconsistencies and guidance for addressing them are presented below. •

•

•

•

•

2.10

Filling gaps in periodic data: Gaps in the time series will exist when data are available at less than annual frequency. For example, time consuming and expensive surveys relating to natural resources - such as national forest inventories - are compiled at intervals of every fifth or tenth year. Time series data may need to be inferred to compile a complete annual estimate for the years between surveys, and for fore- and backcasts (e.g., where estimates are needed for 1990 – 2004 and survey data are only available for 1995 and 2000). Chapter 5, Time Series Consistency, provides details on splicing and extrapolation methods to fill these gaps. Time series revision: In order to meet deadlines, statistical organisations may use modelling and assumptions to complete the most recent year of their estimates. These estimates are then refined the following year when all the data have been processed. Data may have been subject to further revision of historic data to correct errors or to update new methodologies. It is important that the inventory compiler look for these changes in the source data time series and integrate them into the inventory. Chapter 5 of this Volume contains more guidance on this issue. Incorporating improved data: While the ability of countries to collect data generally improves over time so they can implement higher tier methods, the data may not necessarily be suitable for earlier years for the higher tiers. For example when direct sampling and measurement programs are introduced there may be inconsistencies in the time series as the new program cannot measure past conditions. Sometimes this can be addressed if the new data are sufficiently detailed (e.g., if emission factors for modern abated plant can be distinguished from those of older unabated plant) and the historic activity data can be stratified using expert judgement or surrogate data. Chapter 5 provides more details on methods of incorporating improved data consistently across a time series. Compensating for deteriorating data: Splicing techniques, as described in Chapter 5 on Time Series Consistency, can be used to manage data sets that have deteriorated over time. Deterioration can occur as the result of changing priorities within governments, economic restructuring, or diminishing resources. For example, some countries with economies in transition no longer collect certain data sets that were available in the base year, or these data sets may contain different definitions, classifications and levels of aggregation. The international data sources discussed in the activity data section (see Section 2.2.5) may provide another source of relevant activity data. Incomplete coverage: When data do not fully represent the whole country, e.g., measurements for 3 of 10 plants or survey data of the agricultural activity for 80 percent of the country, then the data can still be used but needs to be combined with other data to calculate a national estimate. In these cases expert judgement (see Section 2.2 above for details) or the combination of these data with other data sets (surrogate or exact data) can be used to calculate a national total. In some cases survey or census data are collected in a rolling national programme that samples different provinces or sub-sectors yearly with a repeat cycle that builds a complete data set after a period of years. It is recommended that, bearing in mind that time series consistency, assumptions made in one year must also apply to the other years, and that data providers be requested to compute representative yearly data with a complete coverage.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Approaches to Data Collection

Combining data sets numerically Sometimes an inventory compiler will be presented with several potential datasets to use for the same estimate e.g., a series of independent measurements for the carbon content of a fuel. If the data refer to the same quantity and were collected in a reasonably uniform manner then combining them will increase accuracy and precision. Combination can be achieved by pooling the raw data and re-estimating the mean and 95 percent confidence limits, or by combining summary statistics using the relationships set out in statistical textbooks. It also is possible to combine measurements of a single quantity made using different methods that produce results with different underlying probability distributions. However, the methods for doing this are more complex, and in most cases, it will probably be sufficient to use expert judgement to decide whether to average the results, or to use the more reliable estimate and discard the other. When using data that are not homogeneous (e.g., because of the presence of abatement technology at some plant but not others) the inventory estimate should be stratified (subdivided) so that each stratum is homogeneous and the national total for the source category will then be the sum of the strata. The uncertainty estimates can then be obtained using the methods set out in Chapter 3 by treating each stratum in the same way as an individual category. Inhomogeniety may be identified by specific knowledge of the circumstances of individual plants or technology types, or by a detailed data analysis, e.g., scatter plots of estimated emissions/removals against activity data. Empirical data sets may contain outliers – data points that lie outside the main probability distribution and are regarded as unrepresentative. These may be identified by some rule, for example lying more than three standard deviations from the mean. Before taking this path the inventory compiler should consider whether the apparently anomalous data do in fact indicate some other set of circumstances (e.g., plant in start-up conditions) that should really be represented separately in the inventory estimate. Multi-year averaging: Countries should report annual inventory estimates that are based on best estimates for actual emissions and removals in that year. Generally, single year estimates provide the best approximation of real emissions/removals and a time series of single year estimates prepared according to good practice can be considered consistent. Countries should, where possible, avoid using multi-year averaging of data that would result in over- or under-estimates of emissions over time, increased uncertainty, or reduced transparency, comparability or time-series consistency of the estimates. However, in some specific cases that are described for specific sectors in Volume 2-5, multi-year averaging may be the best or even the only way to estimate data for a single year. In the case of high or uncertain annual variability – as in the growth of various tree species in a year – and where there is higher confidence in the average annual growth rate over a period of years then multi-year averaging can improve the quality of the overall estimate. Non-calendar year data: It is good practice to use calendar year data whenever the data are available. If calendar year data are unavailable, then other types of annual year data (e.g., non-calendar fiscal year data e.g., April – March) can be used provided that it is used consistently over the time series and the collection period for the data is documented. Similarly, different collection periods can be used for different emission and removal categories, again provided that the collection periods are used consistently over time and documented this is acceptable. It is good practice to use the same collection periods consistently over the time series to avoid bias in the trend. Animal population data may, for example, have been collected in the summer and so may not correspond with the annual average. The data should be corrected where possible to represent the calendar year. If uncorrected data are used, it is good practice for the inventory compiler to make consistent use of either calendar year data or fiscal year data for all years in the time series.

Regional inventory data In some circumstances regional activity statistics and emission datasets are more detailed, up-to-date, accurate and/or complete than national datasets. In these cases a regionally compiled and then aggregated inventory can result in a better quality inventory for a country than one compiled using averaged national statistics and datasets. In such cases, and in order to fulfil the requirements of good practice, inventories can be compiled entirely or in part on a regional basis provided that: • • •

Each regional component is compiled in a way that is consistent with good practice QA/QC, choice of tiers, time series consistency and completeness. The approach used to aggregate the regional inventories and fill any gaps at a national level is transparent and in line with the good practice methods provided in the Guidelines. The final country inventory complies with the good practice quality requirements of completeness, consistency, comparability, timeliness, accuracy and transparency. In particular the sector estimates calculated at different regions, and then aggregated in the final inventory, should be self-consistent. There should be no emissions or removals omitted or double counted in the aggregated inventory and the different parts of the inventory should use assumptions and data consistently as far as practical and appropriate.

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Volume 1: General Guidance and Reporting

2.2.4

Emission factors and direct measurement of emissions

This section provides generic advice for the derivation or review of emission factors or other estimation parameters; this includes specialised literature sources, using measured data, and further remarks on combining data sets. It is good practice when developing emission factors or other estimation parameters to follow the stepwise approach to data collection described above: •

•

•

Setting priorities, Developing a strategy for accessing the data, Collecting and processing the data.

Volumes 2-5 provide advice on the selection and use of emission factors or other estimation parameters for specific categories.

Literature sources Inventory compilers commonly rely on the available literature to find emission factors or other estimation parameters. Table 2.2 lists a variety of potential literature sources in order of descending likelihood of the data being representative and appropriate for national circumstances. It is good practice, for countries to use their own, peer-reviewed, published literature because this should provide the most accurate representation of their country’s practices and activities. If there are no country-specific peer-reviewed studies available, then the inventory compiler can use IPCC default factors and Tier 1 methods as indicated by the decision trees in Volumes 2 to 5, or Tier 2 methods with data from Emission Factor Database (EFDB), or other literature values e.g., modelled/estimated energy data from international bodies that reflect national circumstances. The order of presentation in Table 2.2 is indicative only, and inventory compiler should assess each data source individually to make a determination of suitability. A literature review is a useful approach for gathering and selecting from among a variety of possible data sources. Literature reviews can be time-consuming because many lead to old data and in addition the use of conversion units may generate artificial differences. Journal papers can sometimes be accessible through web without a subscription and libraries may facilitate search and access. Specialised literature sources relevant to emission factors are: •

• •

National and international testing facilities (e.g., road traffic testing facilities), Industrial trade associations (technical papers such as reports, guidelines, standards, sectoral surveys or similar technical material), National authorities with responsibility for regulating emissions from industrial processes.

Literature reviews should be fully documented so that the data used for the inventory is transparent (see Chapter 6, QA/QC and Verification). It is also helpful to record the sources not used, providing an explanation of why, to save time in later literature review activities.

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Chapter 2: Approaches to Data Collection

TABLE 2.2 POTENTIAL SOURCES OF LITERATURE DATA Literature Type

Where to find it

Comments

IPCC Guidelines

IPCC website

Provide agreed default factors for Tier 1 methods but may not be representative of national circumstances.

IPCC Emission Factor Database (EFDB)

IPCC website

Described in more detail below. May not be representative of processes in your country or appropriate for key category estimates.

EMEP/CORINAIR Emission Inventory Guidebook

EEA (European Environment Agency website)

Useful defaults or for cross-checking. May not be representative of processes in your country or appropriate for key category estimates.

International Emission Factor Databases: USEPA

USEPA website

Useful defaults or for cross-checking. May not be representative of processes in your country or appropriate for key category estimates.

Country-specific data from international or national peer reviewed journals

National reference libraries, environmental press, environmental news journals

Reliable if representative. Can take time to be published.

National testing facilities (e.g., road traffic testing facilities)

National laboratories

Reliable. Need to make sure the factors are representative and that standard methods are used.

Emission regulating authority records and papers, or pollution release and transfer registries

Industrial process regulating authority

Regularly updated and plant-specific. Quality is dependent on the regulatory requirements, which may not extend to the methods used for estimating/measuring.

Industry, technical and trade papers

Specific trade association

Sector-specific and up-to-date. QA/QC is needed to check for bias in data and to ensure the test conditions and measurement standards are understood.

Other specific studies, census, survey, measurement and monitoring data

Universities (environmental, measurement and monitoring departments)

Need to make sure the factors are representative and that standard methods are used.

International Emission Factor Databases: OECD

OECD website

Useful defaults or for cross-checking. May not be representative of processes in your country or appropriate for key category estimates.

Emission factors or other estimation parameters for other countries

National Inventory Reports from Parties to UNFCCC, other inventory documentation, web search, national library

Appropriate for inventory use. Useful defaults or for cross-checking. May not be representative of processes in your country or appropriate for key category estimates.

Publications, libraries, and Web search

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IPCC Emission Factor Database The Emission Factor Database (EFDB) is a continuously revised web-based information exchange forum for emission factors and other parameters relevant for the estimation of emissions or removals of greenhouse gases at national level. The database can be queried over the internet via the home pages of the IPCC, IPCC-NGGIP or directly at http://www.ipcc-nggip.iges.or.jp/EFDB/main.php.5 The IPCC distributes a CD-ROM with a copy of the database and a query tool at regular intervals.6 It is designed as a platform for experts and researchers to communicate new emission factors or other parameters to a worldwide audience of potential end users. The EFDB is intended to become a recognised library where users can find emission factors and other parameters with background documentation or technical references. The criteria for inclusion of data in the database (see Figure 2.2) are: •

Robustness: The value would be unlikely to change, within the accepted uncertainty of the methodology, if there were to be a repetition of the original measurement programme or modelling activity.

•

Applicability: An emission factor can only be applicable if the source and its mix of technology, operating and environmental conditions and abatement and control technologies under which the emission factor was measured or modelled are clear, and allow the user to see how it can be applied.

•

Documentation: Access information to the original technical reference is provided to evaluate the robustness and applicability as described above.

Figure 2.1

Process for including data in the EFDB

Data Providers (single input or mini-batch import)

EF edit and administrative interface

Output from web-based user searches (web presentations and spreadsheet export)

EFDB WEB

CD Distributable EFDB file

The EFDB invites experts and researchers all over the world to populate the EFDB with their data. The proposal of new emission factors (and other parameters) from data providers will be assessed by the Editorial Board of the EFDB for inclusion into the database. When the proposed new data comply with well-defined quality criteria of robustness, applicability and documentation they are included in the database. These procedures enable the user to judge the applicability of the emission factor or other parameter for use in their inventory and the responsibility of using this information appropriately however will always remain with the users.

5

Information, including manuals, on how to retrieve data from or contribute new data to the EFDB can also be found at this web site.

6

To receive a copy of the EFDB CD-ROM, please contact IPCC NGGIP Technical Support Unit.

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Chapter 2: Approaches to Data Collection

Data obtained by measurements This section applies the guidance in Section 2.2.2 to assessing the quality of measurement data for determination of emissions, emission factors and abatement or destruction efficiencies. Volume 4 provides specific guidance on the use of samples and surveys in Agriculture, Forestry, and Other Land Use (AFOLU) Sector. In this approach the emissions can be determined directly (i.e., using continuous emission monitoring systems) or calculated. Where emissions depend on variable combustion, process and operating conditions, and technologies (e.g., methane and nitrous oxide from combustion), direct monitoring is likely to be the most accurate way to determine emissions. When reviewing energy or industrial plant data, it is important to ensure that the measurements are representative of the specific activity and do not include extraneous components. For example stack measurements may exclude losses to the atmosphere through evaporation or poorly burned fuel (that is emitted as volatile organic compounds (VOC); these should be included in the reported emissions totals. More details of measurement issues are included in the Industrial Processes and Product Use (IPPU) Volume.

In implementing the elements of measurement programme identified in Section 2.2.2 is good practice to:

•

• •

•

distinguish between different components in a mixed fuel/raw material feed e.g., coal and wood in a mixed fuel boiler; specify how the chemical composition of fuels and raw materials should be determined from the analyses of samples taken from delivery trucks/tankers, pipelines, or stockpiles; ensure representative sampling of exhaust gases; use instruments with known performance characteristics or perform relative accuracy audits against established standard reference methods.

Most gas analysers determine the volume concentration of gaseous components (volume/volume) and so unless conditions can be shown to be stable it will be necessary to measure the exhaust gas flow rate, pressure, temperature, and water vapour content, so that the greenhouse gas emission can be converted to reference conditions for temperature and pressure (e.g., 273 K and 101.3 kPa, dry) or quoted on a mass emission basis. Other measurements are usually needed to calculate process specific conversion and oxidation efficiency factors and, if the fuel/raw materials used are not dry, a moisture analysis will be required. Related measurements should be made simultaneously, or in such a way that ensures the correct functional relationship between the variables being sampled, otherwise integrated flows or emissions derived from the measurements are likely to be incorrect. It is good practice to use scales, and flow meters, that are of a known quality, calibrated, maintained, and regularly inspected, when using measurements to calculate activity rates e.g., from measured fuel or raw material feed rates (or sometimes from production data). Measurement equipment can be of variable quality and it is important that there is regular maintenance and calibration procedures in place and that these are subject to regular QA/QC review. When recording is carried out on a continuous basis it is good practice to monitor and record any time when meters are not working and the data capture rate is reduced – the advice on gap filling (in Section 2.2.3, Adapting data for inventory use) can, however, enable imperfect data sets to be repaired sufficiently for some purposes – such as the generation of emission factors. It is also good practice, as part of the measurement programme to include in the scope of a monitoring protocol how and other measurements are to be carried out, if the fuel/raw materials are not dry or there are contaminants that could adversely affect the measurement process, moisture. Quality management is an important factor to take into account. ISO 17025:2005 ‘General requirements for the competence of testing and calibration laboratories’ describes a useful QA/QC regime for testing and measurement. It encourages the use of standard methods by qualified personnel using suitability tested equipment. It also encourages a quality management system which should cover traceable calibration artefacts; taking and storing samples; any subsequent analysis; and the reporting of results. The standards listed in Table 2.3 are relevant to greenhouse gas emissions measurement and should be used where applicable.

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Volume 1: General Guidance and Reporting

TABLE 2.3 STANDARD MEASUREMENT METHODS FOR EXHAUST GAS

CO2

Existing international standard methods

Other widely used standard methods4

ISO 12039:2001 Stationary source emissions Determination of carbon monoxide, carbon dioxide and oxygen - Performance characteristics and calibration of an automated measuring method 1

US EPA Method 3 - Gas analysis for the determination of dry molecular weight US EPA Method 3A - Determination of oxygen and carbon dioxide concentrations in emissions from stationary sources (instrumental analyser procedure)

ISO 10396:2006 Stationary source emissions Sampling for the automated determination of gas concentrations

N2O

Gas velocity

ISO 11564:1998 Stationary source emissions Determination of the mass concentration of nitrogen oxides - Naphthylethylenediamine photometric method ISO 10780:1994 Air Quality - Stationary source emissions - Measurement of velocity and volume flow rate of gas streams in ducts. S-Type pitot tube ISO 3966:1977 Measurement of fluid flow in closed conduits - velocity area method using Pitot static tubes 2. L-Type Pitot tube ISO 14164:1999 Stationary source emissions. Determination of the volume flow rate of gas streams in ducts -automated method. Dynamic pressure method for continuous, in situ/crossduct, measurements ISO/IEC 17025:2005 General requirements for the competence of testing and calibration laboratories

General 3

ISO 10012:2003 Measurement management systems - Requirements for measurement processes and measuring equipment

Standard being developed by ISO TC 264 – Air Quality US EPA method 1 - Sample and velocity traverses for stationary sources US EPA Method 1A - Sample and velocity traverses for stationary sources with small stacks or ducts US EPA Method 2 - Determination of stack gas velocity and volumetric flow rate (Type S pitot tube) (or alternatively Methods 2F, 2G, 2H and CTM-041)5

PrEN 15259:2005 Air Quality – Measurement of stationary source emissions - measurement strategy, measurement planning and reporting, and design of measurement sites EN61207-1:1994 Expression of performance of gas analyzers - Part 1 General

Standards under development CH4

None

US EPA Method 3C - Determination of carbon dioxide, methane, nitrogen and oxygen from stationary sources (i.e., landfills) Standard being developed by ISO TC 264 - Air Quality EN 14790 6

H2O PFC, SF6, HFC, FCs

US EPA Method 4 - Determination of moisture content in stack gases None

(N.B. Where available sector specific methodologies are referenced in the sector specific volumes)

1

This standard describes the performance characteristics, detection principles and the calibration procedures for automated measuring systems for the determination of carbon dioxide and other substances in the flue gases emissions from stationary sources. The reported concentration range of this standard is 6 - 62500 mg m-3 with a measurement uncertainty of 100% and if the model contains multiplicative or quotient terms

Not necessarily reliable for U > 230% Not necessary for models that are purely additive. Where: U

3.60

=

½-range for uncertainty estimated from error propagation, in units of percent

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Uncertainties

Fc

=

Correction factor for analytical estimate of the variance, dimensionless ratio of corrected to uncorrected uncertainty

The empirically-based correction factor produces values from 1.06 to 1.69 as U varies from 100% to 230%. The correction factor is used to develop a new, corrected, estimate of the total inventory uncertainty half-range, Ucorrected, which in turn is used to develop confidence intervals. EQUATION 3.4 CORRECTED UNCERTAINTY HALF-RANGE U corrected = U • FC

Where: Ucorrected

=

Corrected ½-range for uncertainty estimated from error propagation, in units of %

The errors in the analytical estimate of the variance are generally small for uncertainty half-ranges (U) of less than approximately 100 percent. If the correction factor is applied for U > 100% for values of U up to 230%, the typical error in the estimate of U is expected to be within plus or minus 10 percent in most cases. The correction factor will not necessarily be reliable for larger uncertainties because it was calibrated over the range of 10% to 230%. Calculation of asymmetric confidence intervals for large uncertainties: In order to calculate confidence intervals for the model output based upon only the mean and half-range for uncertainty, a distribution must be assumed. For models that are purely additive, and for which the half range of uncertainty is less than approximately 50 percent, a normal distribution is often an accurate assumption for the form of the model output. In this case, a symmetric uncertainty range with respect to the mean can be assumed. For multiplicative models, or when the uncertainty is large for a variable that must be non-negative, a lognormal distribution is typically an accurate assumption for the form of the model output. In such cases, the uncertainty range is not symmetric with respect to the mean, even though the variance for the total inventory may be correctly estimated from Approach 1. Here, we provide a practical methodology for calculating approximate asymmetric uncertainty ranges based upon the results of error propagation, based upon a methodology developed by Frey (2003). A key characteristic of the 95 percent confidence intervals is that they are approximately symmetric for small ranges of uncertainty and they are positively skewed for large ranges of uncertainty. The latter result is necessary for a non-negative variable.

The parameters of the lognormal distribution can be defined in several ways, such as in terms of the geometric mean and geometric standard deviation. The geometric mean can be estimated based upon the arithmetic mean and the arithmetic standard deviation: EQUATION 3.5 ASYMMETRIC CONFIDENCE INTERVALS – GEOMETRIC MEAN ⎧⎪

⎛

⎡ U ⎤ ⎥ ⎣ 200 ⎦

μ g = exp⎨ln(μ ) − ln⎜1 + ⎢ ⎪⎩

1 2

⎜ ⎝

2

⎞⎫⎪ ⎟⎬ ⎟⎪ ⎠⎭

Where:

μg μ

=

geometric mean

=

arithmetic mean

The geometric standard deviation is given by: EQUATION 3.6 ASYMMETRIC CONFIDENCE INTERVALS – GEOMETRIC STANDARD DEVIATION ⎧ ⎛ 2 ⎫ ⎪ ⎜ ⎡ U ⎤ ⎞⎟ ⎪ σ g = exp⎨ ln 1 + ⎢ ⎥ ⎬ ⎪ ⎜⎝ ⎣ 200 ⎦ ⎟⎠ ⎪ ⎭ ⎩

Where:

σg

=

geometric standard deviation

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A confidence interval can be estimated based upon the geometric mean, geometric standard deviation, and the inverse cumulative probability distribution of a standard normal distribution (with a logarithmic transformation): EQUATION 3.7 LOWER/UPPER UNCERTAINTY HALF-RANGE FROM ERROR PROPAGATION

{( )

( )}

⎛ exp ln μ g − 1.96 ln σ g − μ ⎞ ⎟ × 100 U low = ⎜⎜ ⎟ μ ⎠ ⎝ ⎛ exp ln μ g + 1.96 ln σ g − μ ⎞ ⎟ × 100 U high = ⎜⎜ ⎟ μ ⎠ ⎝

{( )

( )}

Where: Ulow =

Lower ½-range for uncertainty estimated from error propagation, in units of %.

Uhigh =

Upper ½-range for uncertainty estimated from error propagation, in units of %.

To illustrate the use of these equations, consider an example. Suppose the mean is 1.0 and the ½-range of uncertainty estimated from error propagation is 100 percent. In this case, the geometric mean is 0.89 and the geometric standard deviation is 1.60. The 95 percent probability range as a percentage relative to the mean is given by the interval from Ulow to Uhigh of Equations 3.7. In the example, the result is -65% to +126%. In contrast, if a normal distribution had been used as the basis for uncertainty estimation, the range would have been estimated as approximately ±100% and there would be a probability of approximately two percent of obtaining negative values. Figure 3.9 illustrates the sensitivity of the lower and upper bounds of the 95 percent probability range, which are the 2.5th and 97.5th percentiles, respectively, calculated assuming a lognormal distribution based upon an estimated uncertainty half-range from an error propagation approach. The uncertainty range is approximately symmetric relative to the mean up to an uncertainty half-range of approximately 10 to 20 percent. As the uncertainty half-range, U, becomes large, the 95 percent uncertainty range shown in Figure 3.9 becomes large and asymmetric. For example, if U is 73 percent, then the estimated probability range is approximately 50% to +100%, or a factor of two. Estimates of asymmetric ranges of uncertainty with respect to the arithmetic mean assuming a lognormal distribution based upon uncertainty half-range calculated from a propagation of error approach Uncertainty Relative to Mean (%)

Figure 3.9

350 300 250

97.5th Percentile, Uhigh

200 150

95 Percent Range

100 50

2.5th Percentile, Ulow

0 -50 -100 0

3.7.4

50

100 150 Uncertainty Half-Range (%)

200

250

Methodology for calculation of the contribution to uncertainty

The methodology for calculation of contribution to uncertainty is based upon apportioning the variance of the inventory to the variance of each category. If the uncertainty is symmetric, then the variance is estimated, on a category basis, as:

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Chapter 3: Uncertainties

EQUATION 3.8 CONTRIBUTION OF CATEGORY X – VARIANCE FOR SYMMETRIC UNCERTAINTY ⎛

U ⎞

σ x2 = ⎜⎜ D x x ⎟⎟ 200 ⎠ ⎝

2

Where: Ux

=

uncertainty half-range for category x, in units of percent;

Dx

=

the total emissions or removals for category x, corresponding to the entries in Column D of Table 3.5.

σx2

=

the variance of emissions or removals for category x.

Even if the uncertainty is asymmetric, the variance can be estimated based on the arithmetic standard deviation or the coefficient of variation. The variance is simply the square of the arithmetic deviation. The variance for the category can be estimated from the coefficient of variation, νx, as: EQUATION 3.9 CONTRIBUTION OF CATEGORY X – VARIANCE FOR ASYMMETRIC UNCERTAINTY

σ x 2 = (Dxυ x )2

Once the variance is known for a category, the variances should be summed over all categories. The result is the approximate total variance in the inventory. However, this result is not likely to agree exactly with a Monte Carlo simulation result for the inventory for at least one and possibly more reasons: (1) because of sample fluctuations in the Monte Carlo simulation, the Monte Carlo estimate of the variance may differ somewhat from the true value; (2) the analytical calculation is based upon assumptions of normality or lognormality of the distributions for combined uncertainty for individual categories, whereas Monte Carlo simulation can accommodate a wide variety of distribution assumptions; and (3) the Monte Carlo simulation may account for nonlinearities and dependencies that are not accounted for in the analytical calculation for contribution to variance. If the emission inventory calculations are linear or approximately linear, without any substantial correlations, then the results should agree fairly well. Furthermore, methods for estimating ‘contribution to variance’ for Monte Carlo methods are approximate. For those methods that potentially can account for all contributions to variance (e.g., Sobol’s method, Fourier Amplitude Sensitivity Test), the measures of sensitivity are more complex (e.g., Mokhtari et al., 2006). Thus, the methodology described here is a practical compromise.

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References Abdel-Aziz, A., and Frey, H.C. (2003). ‘Development of Hourly Probabilistic Utility NOx Emission Inventories Using Time Series Techniques: Part I-Univariate Approach’, Atmospheric Environment, 37:5379-5389 (2003). Ang, A. H-S., and Tang, W.H., (1984). Probability Concepts in Engineering Planning and Design, Volume 2: Decision, Risk, and Reliability. John Wiley and Sons, New York . Ang, A. H-S., and Tang, W.H., (1975). Probability Concepts in Engineering Planning and Design, Volume 1. John Wiley and Sons, New York. Baggott, S.L., Brown, L., Milne, R., Murrells, TP., Passant, N., Thistlethwaite, G., Watterson, J.D. (2005) “UK Greenhouse Gas Inventory, 1990 to 2003: Annual Report for submission under the Framework Convention on Climate Change”, April 2005. pub AEA Technology, UK ref AEAT/ENV/R/1971, ISBN 0-9547136-5-6. Barry, T.M. (1996), Recommendations on the testing and use of pseudo-random number generators used in Monte Carlo analysis for risk assessment, Risk Assessment, 16(1):93-105. Bevington, P.R. and Robinson, D.K. (1992). Data Reduction and Error Analysis for the Physical Sciences. McGraw-Hill: New York. Cohen A.C. and Whitten B. (1998). Parameter Estimation in Reliability and Life Span Models, M. Dekker: New York. Cullen, A.C. and Frey, H.C. (1999), Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs, Plenum: New York. D’Agostino, R.B. and Stephens, M.A. (eds.) (1986). Goodness-of-Fit Techniques, Marcel Dekker, New York. Efron, B. and Tibshirani, R.J. (1993). An Introduction to the Bootstrap, Chapman and Hall, New York. Eggleston, S., et al. (1998). Treatment of Uncertainties for National Greenhouse Gas Emissions, Report AEAT 2688-1 for DETR Global Atmosphere Division, AEA Technology, Culham, UK. Evans, J.S., Graham J.D., Gray, G.M., and Sielken Jr, R.L. (1994). ”A Distributional Approach to Characterizing Low-Dose Cancer Risk,” Risk Analysis, 14(1):25-34 (February 1994). Falloon, P. and Smith, P. (2003). Accounting for changes in soil carbon under the Kyoto Protocol: need for improved long-term data sets to reduce uncertainty in model projections. Soil Use and Management, 19, 265-269. Frey, H.C. and Rubin, E.S. (1991). Development and Application of a Probabilistic Evaluation Method for Advanced Process Technologies, Final Report, DOE/MC/24248-3015, NTIS DE91002095, Prepared by Carnegie-Mellon University for the U.S. Department of Energy, Morgantown, West Virginia, April 1991, 364p. Frey, H.C. and Rhodes, D.S. (1996). “Characterizing, Simulating, and Analyzing Variability and Uncertainty: An Illustration of Methods Using an Air Toxics Emissions Example,” Human and Ecological Risk Assessment: an International Journal, 2(4):762-797 (December 1996). Frey, H.C. and Bammi, S. (2002). Quantification of Variability and Uncertainty in Lawn and Garden Equipment NOx and Total Hydrocarbon Emission Factors, J. Air & Waste Manage. Assoc., 52(4), 435-448. Frey, H.C., Zheng, J., Zhao, Y., Li, S., and Zhu, Y. (2002). Technical Documentation of the AuvTool Software for Analysis of Variability and Uncertainty, Prepared by North Carolina State University for the Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC. February 2002. Frey, H.C. and Zheng, J. (2002). "Probabilistic Analysis of Driving Cycle-Based Highway Vehicle Emission Factors," Environmental Science and Technology, 36(23):5184-5191 (December 2002). Frey, H.C. (2003), “Evaluation of an Approximate Analytical Procedure for Calculating Uncertainty in the Greenhouse Gas Version of the Multi-Scale Motor Vehicle and Equipment Emissions System,” Prepared for Office of Transportation and Air Quality, U.S. Environmental Protection Agency, Ann Arbor, MI, May 30, 2003. Frey, H.C. (2005), “Comparison of Approach 1 and Approach 2,” January 2005, unpublished analysis done for this Chapter.

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Chapter 3: Uncertainties

Gelfand, A. E. (1996). Gibbs Sampling, The Encyclopedia of Statistical Sciences (editors: Kotz J., Reed C. and Banks D.), John Wiley and Sons, New York, 283-292. Hahn, G.J., and Shapiro, S.S. (1967) Statistical Models in Engineering, Wiley Classics Library, John Wiley and Sons, New York. Holland, D.M and Fitz-Simons, T. (1982) “Fitting statistical distributions to air quality data by the maximum likelihood method,” Atmospheric Environment, 16(5):1071-1076. Hora, S.C. and Iman, R.L. (1989). Expert opinion in risk analysis: The NUREG-1150 methodology, Nuclear Science and Engineering, 102:323-331. IPCC (1997). Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2000). Penman, J., Kruger, D., Galbally, I., Hiraishi, T., Nyenzi, B., Emmanuel, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K., and Tanabe, K. (Eds). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. ISO (1993). “Guide to the Expression of Uncertainty in Measurement (GUM)” prepared by ISO, IEC, BIPM, IFCC, OIML, IUPAC, IUPAP and published by ISO, Switzerland in 1993. Kirchner, T.B. (1990). Establishing modeling credibility involves more than validation, Proceedings, On the Validity of Environmental Transfer Models, Biospheric Model Validation Study, Stockholm, Sweden, October 8-10. Manly, B.F.J. (1997). Randomization, Bootstrap, and Monte Carlo Methods in Biology, Second Edition, Chapman and Hall. McCann, T.J. and Associates, and Nosal, M. (1994). Report to Environmental Canada Regarding Uncertainties in Greenhouse Gas Emission Estimates, Calgary, Canada. Merkhofer, M.W. (1987). Quantifying judgmental uncertainty: Methodology, experiences, and insights, IEEE Transactions on Systems, Man, and Cybernetics. 17(5):741-752. Mokhtari, A., Frey H.C. and Zheng J. (2006). “Evaluation and recommendation of sensitivity analysis methods for application to Stochastic Human Exposure and Dose Simulation (SHEDS) models,” Journal of Exposure Assessment and Environmental Epidemiology, Accepted December 2, 2005, In press. Monni, S., Syri, S. and Savolainen I. (2004). ‘Uncertainties in the Finnish greenhouse gas emission inventory’.Environmental Science and Policy 7, pp.87-98. Monte, L, Hakanson, L., Bergstrom, U., Brittain, J. and Heling, R. (1996). Uncertainty analysis and validation of environmental models: the empirically based uncertainty analysis. Ecological Modelling, 91, 139-152. Morgan, M.G., and Henrion, M. (1990). Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis, Cambridge University Press, New York. NARSTO (2005). Improving Emission Inventories for Effective Air Quality Management Across North America, NARSTO, June 2005. NCRP (National Council on Radiation Protection and Measurements). (1996). A Guide for Uncertainty Analysis in Dose and Risk Assessments Related to Environmental Contamination, NCRP Commentary No. 14, Bethesda, MD. Ogle, S.M., Breidt, F.J., Eve, M.D. and Paustian, K. (2003). Uncertainty in estimating land use and management impacts on soil organic carbon storage for U.S. agricultural lands between 1982 and 1997. Global Change Biology 9:1521-1542. Smith, A.E, Ryan, P.B. and Evans J.S. (1992). The effect of neglecting correlations when propagating uncertainty and estimating the population distribution of risk, Risk Analysis, 12:467-474. Spetzler, C.S., and von Holstein, S. (1975). Probability Encoding in Decision Analysis, Management Science, 22(3). Statistics Finland. (2005). Greenhouse gas emissions in Finland 1990-2003. National Inventory Report to the UNFCCC, 27 May 2005. USEPA (1996). Summary Report for the Workshop on Monte Carlo Analysis, EPA/630/R-96/010, Risk Assessment Forum, U.S. Environmental Protection Agency, Washington, DC.

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USEPA (1997). Guiding Principles for Monte Carlo Analysis, EPA/630/R-97/001, Risk Assessment Forum. U.S. Environmental Protection Agency, Washington, DC. USEPA (1999). Report of the Workshop on Selecting Input Distributions for Probabilistic Assessments, EPA/630/R-98/004, U.S. Environmental Protection Agency, Washington, DC, January 1999. http://www.epa.gov/ncea/input.htm Wackerly, D.D., Mendenhall III, W. and Scheaffer, R.L. (1996). Mathematical Statistics with Applications, Duxbury Press: USA. Winiwarter, W. and Rypdal K. (2001). “Assessing the uncertainty associated with national greenhouse gas emission inventories: a case study for Austria,” Atmospheric Environment, 35(22):5425-5440. Zhao, Y. and Frey, H.C. (2004a). “Development of Probabilistic Emission Inventory for Air Toxic Emissions for Jacksonville, Florida,” Journal of the Air & Waste Management Association, 54(11):1405-1421. Zhao, Y., and Frey, H.C. (2004b). “Quantification of Variability and Uncertainty for Censored Data Sets and Application to Air Toxic Emission Factors,” Risk Analysis, 24(3):1019-1034 (2004). Zheng, J. and Frey H.C. (2004). “Quantification of Variability and Uncertainty Using Mixture Distributions: Evaluation of Sample Size, Mixing Weights and Separation between Components,” Risk Analysis, 24(3):553-571 (June 2004).

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Chapter 4: Methodological Choice and Identification of Key Categories

CHAPTER 4

METHODOLOGICAL CHOICE AND IDENTIFICATION OF KEY CATEGORIES

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.1

Volume 1: General Guidance and Reporting

Authors Anke Herold (Germany), Suvi Monni (Finland) Erda Lin (China), and C. P. (Mick) Meyer (Australia)

Contributing Authors Ketil Flugsrud (Norway)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Methodological Choice and Identification of Key Categories

Contents 4

Methodological Choice and Identification of Key Categories 4.1

Introduction ......................................................................................................................................... 4.5

4.1.1

Definition .................................................................................................................................... 4.5

4.1.2

Purpose of the key category analysis ........................................................................................... 4.5

4.1.3

General approach to identify key categories ............................................................................... 4.6

4.2

General rules for identification of key categories ............................................................................... 4.7

4.3

Methodological approaches to identify key categories ..................................................................... 4.12

4.3.1

Approach 1 to identify key categories ....................................................................................... 4.13

4.3.2

Approach 2 to identify key categories ....................................................................................... 4.17

4.3.3

Qualitative criteria to identify key categories ............................................................................ 4.19

4.4

Reporting and Documentation ........................................................................................................... 4.19

4.5

Examples of key category analysis .................................................................................................... 4.20

References ........................................................................................................................................................ 4.30

Equations Equation 4.1

Level Assessment (Approach 1) ........................................................................................ 4.14

Equation 4.2

Trend Assessment (Approach 1) ....................................................................................... 4.15

Equation 4.3

Trend Assessment with zero base year emissions ............................................................. 4.16

Equation 4.4

Level Assessment (Approach 2) ........................................................................................ 4.18

Equation 4.5

Trend Assessment (Approach 2) ....................................................................................... 4.18

Figures Figure 4.1

Decision Tree to choose a Good Practice method ............................................................... 4.6

Figure 4.2

Decision Tree to identify key categories ........................................................................... 4.13

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

Tables

4.4

Table 4.1

Suggested aggregation level of analysis for Approach 1 ..................................................... 4.8

Table 4.2

Spreadsheet for the Approach 1 analysis – Level Assessment .......................................... 4.14

Table 4.3

Spreadsheet for the Approach 1 analysis – Trend Assessment .......................................... 4.16

Table 4.4

Summary of key category analysis .................................................................................... 4.20

Table 4.5

Example of Approach 1 Level Assessment for the Finnish GHG inventory for 2003 ...... 4.20

Table 4.6

Example of Approach 1 Trend Assessment for the Finnish GHG inventory for 2003 ...... 4.23

Table 4.7

Example of Approach 1 Level Assessment for the Finnish GHG inventory for 2003 using a subset .................................................... 4.25

Table 4.8

Example of Approach 1 Trend Assessment for the Finnish GHG inventory for 2003 using a subset .................................................... 4.26

Table 4.9

Example of Approach 2 Level Assessment for the Finnish GHG inventory for 2003 ...... 4.27

Table 4.10

Example of Approach 2 Trend Assessment for the Finnish GHG inventory for 2003 ...... 4.28

Table 4.11

Summary of key category analysis for Finland ................................................................. 4.29

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Methodological Choice and Identification of Key Categories

4 METHODOLOGICAL CHOICE AND IDENTIFICATION OF KEY CATEGORIES 4.1

INTRODUCTION

This chapter addresses how to identify key categories 1 in a national inventory. Methodological choice for individual source and sink categories is important in managing overall inventory uncertainty. Generally, inventory uncertainty is lower when emissions and removals are estimated using the most rigorous methods provided for each category or subcategory in the sectoral volumes of these Guidelines. However, these methods generally require more extensive resources for data collection, so it may not be feasible to use more rigorous method for every category of emissions and removals. It is therefore good practice to identify those categories that have the greatest contribution to overall inventory uncertainty in order to make the most efficient use of available resources. By identifying these key categories in the national inventory, inventory compilers can prioritise their efforts and improve their overall estimates. It is good practice for each country to identify its national key categories in a systematic and objective manner as presented in this chapter. Consequently, it is good practice to use results of key category analysis as a basis for methodological choice. Such a process will lead to improved inventory quality, as well as greater confidence in the estimates that are developed.

4.1.1

Definition

A key category is one that is prioritised within the national inventory system because its estimate has a significant influence on a country’s total inventory of greenhouse gases in terms of the absolute level, the trend, or the uncertainty in emissions and removals. Whenever the term key category is used, it includes both source and sink categories.

4.1.2

Purpose of the key category analysis

As far as possible, key categories should receive special consideration in terms of three important inventory aspects. Firstly, identification of key categories in national inventories enables limited resources available for preparing inventories to be prioritised. It is good practice to focus the available resources for the improvement in data and methods onto categories identified as key. Secondly, in general, more detailed higher tier methods should be selected for key categories. Inventory compilers should use the category-specific methods presented in sectoral decision trees in Volumes 2-5 (see Figure 4.1). For most sources/sinks, higher tier (Tier 2 and 3) methods are suggested for key categories, although this is not always the case. For guidance on the specific application of this principle to key categories, it is good practice to refer to the decision trees and sector-specific guidance for the respective category and additional good practice guidance in chapters in sectoral volumes. In some cases, inventory compilers may be unable to adopt a higher tier method due to lack of resources. This may mean that they are unable to collect the required data for a higher tier or are unable to determine country specific emission factors and other data needed for Tier 2 and 3 methods. In these cases, although this is not accommodated in the category-specific decision trees, a Tier 1 approach can be used, and this possibility is identified in Figure 4.1. It should in these cases be clearly documented why the methodological choice was not in line with the sectoral decision tree. Any key categories where the good practice method cannot be used should have priority for future improvements. Thirdly, it is good practice to give additional attention to key categories with respect to quality assurance and quality control (QA/QC) as described in Chapter 6, Quality Assurance/Quality Control and Verification, and in the sectoral volumes.

1

In Good Practice Guidance for National Greenhouse Gas Inventories (GPG2000, IPCC, 2000), the concept was named ‘key source categories’ and dealt with the inventory excluding the LULUCF Sector.

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Volume 1: General Guidance and Reporting

Figure 4.1

Decision Tree to choose a Good Practice method

Start

Is the source or sink category considered as key category?

No

Choose a method presented in Volumes 2-5 appropriate to available data. Box 1

Yes

Are the data available to follow category-specific good practice guidance for the key categories?

Yes

Estimate emissions or removals following guidance for key categories presented in the decision trees in the sectoral Volumes 2-5. Box 2

No

Can data be collected without significantly jeopardizing the resources for other key categories?

Yes

No

Make arrangements to collect data.

Choose a method presented in Volumes 2-5 appropriate to available data, and document why category-specific guidance cannot be followed. Box 3

4.1.3

General approach to identify key categories

Any inventory compiler who has prepared a national greenhouse gas inventory will be able to identify key categories in terms of their contribution to the absolute level of national emissions and removals. For those inventory compilers who have prepared a time series, the quantitative determination of key categories should include an evaluation of both the absolute level and the trend of emissions and removals. Some key categories may be identified only when their influence on the trend of the national inventory is taken into account. Section 4.2 sets out general rules for identification of key categories, whereas the methodological approaches for determination of key categories are provided in Section 4.3. Both basic Approach 1 and Approach 2 which takes uncertainties into account are described. In addition to making a quantitative determination of key categories, it is good practice to consider qualitative criteria which is described in more detail in Section 4.3.3. Guidance on reporting and documentation of the key category analysis is provided in Section 4.4. Section 4.5 gives examples for key category identification.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Methodological Choice and Identification of Key Categories

4.2

GENERAL RULES FOR IDENTIFICATION OF KEY CATEGORIES

The results of the key category identification will be most useful if the analysis is done at the appropriate disaggregation level of categories. Table 4.1, Suggested aggregation level of analysis for Approach 1, lists the source and sink categories that are recommended and identifies special considerations related to the disaggregation of the analysis, where relevant. For example, the combustion of fossil fuels is a large emission source category that can be broken down into subcategories of 1st, 2nd or 3rd order, and even to the level of individual plants or boilers. Countries may adapt the recommended level of analysis in Table 4.1 to their national circumstances. In particular countries using Approach 2 will probably choose the same level of aggregation that was used for the uncertainty analysis. In some cases, disaggregation to very low levels should be avoided since it may split an important aggregated category into many small subcategories that are no longer key. The following guidance describes good practice in determining the appropriate level of disaggregation of categories to identify key categories: •

•

•

•

•

•

The analysis should be performed at the level of IPCC categories or subcategories at which the IPCC methods and decision trees are generally provided in the sectoral volumes. Each greenhouse gas emitted from each category should be considered separately, unless there are specific methodological reasons for treating gases collectively. For example, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are emitted from road transportation. The key category analysis for this source should be performed for each of these gases separately because the methods, emission factors and related uncertainties differ for each gas. In contrast, a collective analysis of all chemical species of hydrofluorocarbons (HFCs) is appropriate for the category ‘Product Uses as Substitutes for Ozone Depleting Substances’. If data are available, the analysis should be performed for emissions and removals separately within a given category. For example, the land use categories and the pool estimates can include emissions and removals that may cancel or almost cancel at the aggregated level for the categories presented in Table 4.1. In cases where emissions and removals cancel out and where methods do not allow to estimate emissions and removals separately, the inventory compiler should include further disaggregated subcategories in the key category analysis (e.g., including two different areas, one area where carbon stock decreases occur and another area where carbon stock increases take place), in particular when the data for reported subcategories clearly show significant carbon stock changes at more disaggregate level. Similar considerations may apply in the Energy and IPPU (Industrial Processes and Product Use) Sectors, for example, in a situation where CO2 is being captured for storage. Table 4.1 shows the recommended level of analysis.2 Countries may choose to perform the quantitative analysis at a more disaggregated level than suggested in this table. In this case, possible cross-correlations between categories and/or subcategories should be taken into account when performing the key category analysis. When using Approach 2, the assumptions about such correlations should be the same when assessing uncertainties and identifying key categories (see Chapter 3, Uncertainties). The categories and gases included in Table 4.1 are those for which estimation methods are provided in the sectoral volumes. If countries develop estimates for new categories or gases for which GWPs become available, these should be added to the analysis under Miscellaneous for the appropriate sector. It is not possible to include gases for which no GWP is available since the analysis is performed using CO2equivalent emissions3. Indirect N2O emissions from deposition of NOx and other nitrogen compounds from categories other than AFOLU (Agriculture, Forestry and Other Land Use) Sector’s are included in the key category analysis in category 5A, Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3. However, the 2006 Guidelines do not provide decision trees or methodological guidance for estimating emissions from NOx and NH3, and therefore identification of indirect N2O as key does not have an effect on the methodological choice.

2

Most correlations between categories can be avoided by using the aggregation level of this table. Some correlations remain, e.g., in fuel use between stationary combustion and transportation and for HFCs. In practice, the effect of correlations for key category analysis should be taken into account in the disaggregation level used for the Approach 2 assessment (for more advice on correlations in uncertainty analysis, see Chapter 3.)

3

The methodology is also applicable for other weighting scheme, but for the derivation of threshold for Approach 1 and 2 and for the examples in Section 4.5 CO2-equivalent values were calculated using the global warming potentials (GWP) over a 100 year horizon of the different greenhouse gases, provided by the IPCC in its Second Assessment Report.

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Volume 1: General Guidance and Reporting

For each key category where relevant (see Table 4.1 below), the inventory compiler should determine if certain subcategories are particularly significant. Usually, for this purpose, the subcategories should be ranked according to their contribution to the aggregate key category. Those subcategories that contribute together more than 60 percent to the key category should be treated as particularly significant. It may be appropriate to focus efforts towards methodological improvements of these most significant subcategories. For those categories where subcategories need to be identified it is clearly mentioned in the appropriate decision trees in Volumes 2-5. In some cases and alternative method to identify these subcategories is used.

TABLE 4.1 SUGGESTED AGGREGATION LEVEL OF ANALYSIS FOR APPROACH 1 a Source and Sink Categories to be Assessed in Key Category Analysis Category Code

b

Category Title

b

Gases to be Assessed c

Special Considerations

Energy 1A1

Fuel Combustion Activities - Energy Industries

CO2, N2O, CH4

Disaggregate to main fuel types.

1A2

Fuel Combustion Activities Manufacturing Industries and Construction

CO2, N2O, CH4

Disaggregate to main fuel types.

1A3a

Fuel Combustion Activities Transport - Civil Aviation

CO2, N2O, CH4

Domestic aviation only.

1A3b

Fuel Combustion Activities Transport - Road transportation

CO2, N2O, CH4

1A3c

Fuel Combustion Activities Transport - Railways

CO2, N2O, CH4

1A3d

Fuel Combustion Activities Transport - Water-borne Navigation

CO2, N2O, CH4

Disaggregate to main fuel types. Domestic Water-borne navigation only.

1A3e

Fuel Combustion Activities Transport - Other Transportation

CO2, N2O, CH4

If this category is key, the inventory compiler should determine which subcategories are significant.

1A4

Fuel Combustion Activities - Other Sectors

CO2, N2O, CH4

Disaggregate to main fuel types.

1A5

Fuel Combustion Activities - NonSpecified

CO2, N2O, CH4

Disaggregate to main fuel types.

1B1

Fugitive emissions from fuels - Solid Fuels

CO2, CH4

1B2a

Fugitive Emissions from Fuels - Oil and Natural Gas - Oil

CO2, CH4

If this category is key, the inventory compiler should determine which subcategories are significant.

1B2b

Fugitive Emissions from Fuels - Oil and Natural Gas - Natural gas

CO2, CH4

If this category is key, the inventory compiler should determine which subcategories are significant.

1C

Carbon Dioxide Transport and Storage

CO2

If this category is key, the inventory compiler should determine which subcategories are significant.

CO2, CH4, N2O

Assess whether other sources in the Energy Sector not listed above should be included. Key category analysis has to cover all emission sources in the inventory. Therefore all categories not presented above should be either aggregated with some other category, where relevant, or assessed separately.

1

Miscellaneous

Industrial Processes and Product Use 2A1

Mineral industry - Cement Production

CO2

2A2

Mineral Industry - Lime Production

CO2

2A3

Mineral Industry - Glass Production

CO2

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Methodological Choice and Identification of Key Categories

TABLE 4.1 (CONTINUED) SUGGESTED AGGREGATION LEVEL OF ANALYSIS FOR APPROACH 1 a Source and Sink Categories to be Assessed in Key Category Analysis Category Code

b

Category Title

b

Gases to be Assessed c

Special Considerations If this category is key, the inventory compiler should determine which subcategories are significant.

2A4

Mineral Industry - Other Process Uses of Carbonates

CO2

2B1

Chemical Industry - Ammonia Production

CO2

2B2

Chemical industry - Nitric Acid Production

N2O

2B3

Chemical industry - Adipic Acid Production

N2O

2B4

Chemical industry - Caprolactam, Glyoxal and Glyoxylic Acid Production

N2O

2B5

Chemical industry - Carbide Production

CO2, CH4,

2B6

Chemical industry - Titanium Dioxide Production

CO2

2B7

Chemical Industry - Soda Ash Production

CO2

2B8

Chemical Industry - Petrochemical and Carbon Black Production

CO2, CH4

2B9

Chemical Industry - Fluorochemical Production

All gases should be assessed jointly. If HFCs, PFCs, SF6, this category is key, the inventory and other compiler should determine which halogenated subcategories/gases (e.g., HFC-23 from gases HCFC-22 production) are significant.

2C1

Metal Industry - Iron and Steel Production

CO2, CH4

2C2

Metal Industry - Ferroalloys Production

CO2, CH4

2C3

Metal Industry - Aluminium Production

PFCs, CO2

PFCs should be assessed jointly. CO2 should be assessed separately.

2C4

Metal Industry - Magnesium Production

CO2, SF6, PFCs, HFCs, other halogenated gases

Methods for HFCs, PFCs and other halogenated gases are only provided at Tier 3 level. If they are not included in the inventory it is good practice to use qualitative considerations. (See Section 4.3.3.)

2C5

Metal Industry - Lead Production

CO2

2C6

Metal Industry - Zinc Production

CO2

2D

Non-Energy Products from Fuels and Solvent Use

CO2

2E

Electronics Industry

All gases should be assessed jointly. If SF6, PFCs, HCFs, this category is key, the inventory other halogenated compiler should determine which gases subcategories are significant.

2F1

Product Uses as Substitutes for Ozone Depleting Substances Refrigeration and Air Conditioning

HFCs, PFCs

All HFC and PFC gases should be assessed jointly.

2F2

Product Uses as Substitutes for Ozone Depleting Substances - Foam Blowing Agents

HFCs

All HFC gases should be assessed jointly.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

If this category is key, the inventory compiler should determine which subcategories (caprolactam, glyoxal and glyoxylic acid) are significant.

If this category is key, the inventory compiler should determine which subcategories are significant.

If this category is key, the inventory compiler should determine which subcategories are significant.

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Volume 1: General Guidance and Reporting

TABLE 4.1 (CONTINUED) SUGGESTED AGGREGATION LEVEL OF ANALYSIS FOR APPROACH 1 a Source and Sink Categories to be Assessed in Key Category Analysis Category Code 2F3

b

b

Category Title Product Uses as Substitutes for Ozone Depleting Substances - Fire Protection

Gases to be Assessed c

Special Considerations

HFCs, PFCs

All HFC and PFC gases should be assessed jointly.

2F4

Product Uses as Substitutes for Ozone Depleting Substances Aerosolls

HFCs, PFCs

All HFC and PFC gases should be assessed jointly.

2F5

Product Uses as Substitutes for Ozone Depleting Substances Solvents

HFCs, PFCs

All HFC and PFC gases should be assessed jointly.

2F6

Product Uses as Substitutes for Ozone Depleting Substances - Other Applications

HFCs, PFCs

All HFC and PFC gases should be assessed jointly.

Other Product Manufacture and Use

SF6, PFCs, N2O

All PFC gases and SF6 should be assessed jointly. If this category is key, the inventory compiler should determine which subcategories are significant. N2O should be assessed separately.

Miscellaneous

Assess whether other sources in the Industrial Processes and Product Use Sector not listed above should be CO2 , CH4, N2O, included. Key category analysis should HFCs, PFCs, SF6, cover all emission sources in the other halogenated inventory. Therefore all categories not gases presented above should be either aggregated with some other category, where relevant, or assessed separately.

2G

2

Agriculture, Forestry and Other Land Use

3A1

3A2

Enteric Fermentation

Manure Management

CH4

If this category is key, the inventory compiler should determine which animal categories are significant. For key categories, decision trees for livestock population characterisation as well as for CH4 emissions estimation should be followed.

CH4, N2O

If this category is key, the inventory compiler should determine which animal categories and waste management systems are significant. For key categories, decision trees for livestock population characterisation as well as for CH4 or N2O emissions estimation should be followed.

3B1a

Forest Land Remaining Forest Land

CO2

If this category is key, the inventory compiler should determine which pools (biomass, DOM, mineral soils, organic soils) are significant and should then follow the guidance for key categories in decision trees for carbon stock changes for the significant pools.

3B1b

Land Converted to Forest Land

CO2

If this category is key, the inventory compiler should determine which pools and subcategories are significant.

3B2a

Cropland Remaining Cropland

CO2

If this category is key, the inventory compiler should determine which pools are significant.

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TABLE 4.1 (CONTINUED) SUGGESTED AGGREGATION LEVEL OF ANALYSIS FOR APPROACH 1 a Source and Sink Categories to be Assessed in Key Category Analysis Category Code

b

Category Title

b

Gases to be Assessed c

Special Considerations Assess the impact of forest land converted to cropland in a separate category. d If this category is key, the inventory compiler should determine which pools and subcategories are significant

3B2b

Land Converted to Cropland

CO2

3B3a

Grassland Remaining Grassland

CO2

If this category is key, the inventory compiler should determine which pools are significant.

CO2

Assess the impact of forest land converted to grassland in a separate category. d If this category is key, the inventory compiler should determine which pools and subcategories are significant.

3B3b

Land Converted to Grassland

3B4ai

Peatlands Remaining Peatlands

CO2, N2O

3B4aii

Flooded land remaining Flooded land

CO2

3B4b

Land Converted to Wetlands

CO2

Assess the impact of forest land converted to wetland in a separate category (see below). d If this category is key, the inventory compiler should determine which pools and subcategories are significant.

3B5a

Settlements Remaining Settlements

CO2

If this category is key, the inventory compiler should determine which pools are significant. Assess the impact of forest land converted to settlements in a separate category. d If this category is key, the inventory compiler should determine which pools and subcategories are significant.

3B5b

Land Converted to Settlements

CO2

3C1

Biomass Burning

CH4, N2O

3C2

Liming

CO2

3C3

Urea Application

CO2

3C4

Direct N2O Emissions from Managed soils

N2O

If this category is key, the inventory compiler should determine which subcategories are significant.

3C5

Indirect N2O Emissions from Managed soils

indirect N2O

If this category is key, the inventory compiler should determine which subcategories are significant.

3C6

Indirect N2O Emissions from Manure Management

indirect N2O

3C7

Rice Cultivations

CH4

3D1

Harvested Wood Products

CO2

Use of key category analysis is optional.

CO2, CH4, N2O

Assess whether other sources or sinks in the AFOLU Sector not listed above should be included. Key category analysis has to cover all emission sources and sinks in the inventory. Therefore all categories not presented above should be either aggregated with some other category, where relevant, or assessed separately.

3

Miscellaneous

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TABLE 4.1 (CONTINUED) SUGGESTED AGGREGATION LEVEL OF ANALYSIS FOR APPROACH 1 a Source and Sink Categories to be Assessed in Key Category Analysis

Gases to be Assessed c

Special Considerations

Solid Waste Disposal

CH4

If this category is key, the inventory compiler should determine which subcategories are significant.

4B

Biological Treatment of Solid Waste

CH4, N2O

4C

Incineration and Open Burning of Waste

CO2, N2O, CH4

4D

Wastewater Treatment and Discharge

CH4, N2O

Assess whether domestic or industrial wastewater treatment is a significant subcategory. Assess whether other sources in the Waste Sector not listed above should be included. Key category analysis has to cover all emission sources in the inventory. Therefore all categories not presented above should be either aggregated with some other category, where relevant, or assessed separately.

Category Code Waste 4A

b

Category Title

b

4

Miscellaneous

CO2, CH4, N2O

5A

Indirect N2O Emissions from the atmospheric deposition of nitrogen in NOx and NH3

indirect N2O

5B

Other

CO2, N2O, CH4, SF6, PFCs, HCFs

Include sources and sinks reported under 5B. Key category assessment has to cover all emission sources in the inventory. Therefore all categories not presented above should be either aggregated with some other category, where relevant, or assessed separately.

a

In some cases, inventory compilers may modify this list of IPCC categories to reflect particular national circumstances. The categories should include the respective codes and be consistent with the IPCC terminology. c All the gases in this column are to be assessed separately, except ‘Miscellaneous’ category, where gases can be assessed jointly. There may also be some new gases other than those listed here, and those should also be assessed separately. d In the quantitative key category analysis, conversion of forest land is spread out under the different land-use change categories. Countries should identify and sum up the emission estimates associated with forest conversion to any other land category and compare the magnitude to the smallest category identified as key. If its size is larger than the smallest category identified as key it should be considered key.

b

4.3

METHODOLOGICAL APPROACHES TO IDENTIFY KEY CATEGORIES

It is good practice for each country to identify its national key categories in a systematic and objective manner, by performing a quantitative analysis of the relationships between the level and the trend of each category’s emissions and removals and total national emissions and removals. Two Approaches for performing the key category analysis have been developed. Both Approaches identify key categories in terms of their contribution to the absolute level of national emissions and removals and to the trend of emissions and removals. In Approach 1, key categories are identified using a pre-determined cumulative emissions threshold. Key categories are those that, when summed together in descending order of magnitude, add up to 95 percent of the total level4. The method is described in more detail in Section 4.3.1, Approach 1 to identify key categories.

4

The pre-determined threshold has been determined based on an evaluation of several inventories, and is aimed at establishing a general level where 90% of inventory uncertainty will be covered by key categories.

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Approach 2 to identify key categories can be used by inventory compilers, if category uncertainties or parameter uncertainties are available. Under Approach 2, categories are sorted according to their contribution to uncertainty. This approach is described in more detail in Section 4.3.2, Approach 2 to identify key categories. Results of Approach 2 are additional to Approach 1. If both the Approach 1 and the Approach 2 assessment have been performed, it is good practice to report the results of the Approach 2 analysis in addition to the results of Approach 1. Results of both Approach 1 and 2 should be used when setting priorities to inventory preparation. Figure 4.2, Decision Tree to identify key categories, illustrates how inventory compilers can determine which Approach to be used for the identification of key categories. Figure 4.2

Decision Tree to identify key categories Start

Are country-specific uncertainty estimates available for each category estimate?

Yes

Determine key categories using the Approach 1 Level and Trend Assessment, Approach 2 Level and Trend Assessment, and qualitative criteria. Box 1: Approach 1 and 2 Level and Trend Assessment

No

Are inventory data available for more than one year?

Yes

Determine key categories using Approach 1 Level and Trend Assessment and qualitative criteria. Box 2: Approach 1 Level and Trend Assessment

No

Are inventory data available for one year?

Yes

Determine key categories using the Approach 1 Level Assessment and qualitative criteria. Box 3: Approach 1 Level Assessment

No

Determine key categories using qualitative criteria. Box 4: Qualitative criteria

Any country that has developed a greenhouse gas inventory can perform Approach 1 Level Assessment to identify the categories whose level has a significant effect on total national emissions and removals. Those inventory compilers that have developed inventories for more than one year will also be able to perform Approach 1 Trend Assessment and identify categories that are key because of their contribution to the total trend of national emissions and removals.

4.3.1

Approach 1 to identify key categories

Approach 1 to identify key categories assesses the influence of various categories of sources and sinks on the level, and possibly the trend, of the national greenhouse gas inventory. When the inventory estimates are available for

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several years, it is good practice to assess the contribution of each category to both the level and trend of the national inventory. If only a single year’s inventory is available, a level assessment should be performed. Approach 1 can readily be accomplished using a spreadsheet analysis. Tables 4.2 and 4.3 in the following sections illustrate the format of the analysis. Separate spreadsheets are suggested for the level and trend assessments because it is necessary to sort the results of the analysis according to two different columns. It is more difficult to track the process if the analyses are combined in the same table. In both tables, columns A through D are inputs of the national inventory data. Section 4.5 illustrates the application of the Approach 1 to the Finnish inventory.

LEVEL ASSESSMENT The contribution of each source or sink category to the total national inventory level is calculated according to Equation 4.1: EQUATION 4.1 LEVEL ASSESSMENT (APPROACH 1)

Key category level assessment = ⎢source or sink category estimate⎪/ total contribution Lx , t = E x , t /

∑ E y,t y

Where: Lx,t = ⎢Ex,t ⎢

∑ E y, t

level assessment for source or sink x in latest inventory year (year t). =

absolute value of emission or removal estimate of source or sink category x in year t

=

total contribution, which is the sum of the absolute values of emissions and removals in year t calculated using the aggregation level chosen by the country for key category analysis. Because both emissions and removals are entered with positive sign5, the total contribution/level can be larger than a country’s total emissions less removals.6

y

Key categories according to Equation 4.1 are those that, when summed together in descending order of magnitude, add up to 95 percent of the sum of all Lx,t. Table 4.2 presents a spreadsheet that can be used for the level assessment. An example of the use of the spreadsheet is given in Section 4.5. TABLE 4.2 SPREADSHEET FOR THE APPROACH 1 ANALYSIS – LEVEL ASSESSMENT A IPCC Category Code

B IPCC Category

C Greenhouse Gas

D E Latest Year Estimate Absolute Value of Ex,t Latest Year Estimate [in CO2-equivalent units] ⎢Ex,t ⎢

∑ E y,t

Total

y

F Level Assessment Lx,t

G Cumulative Total of Column F

1

Where: Column A :

code of IPCC categories (See Table 8.2 in Chapter 8, Reporting Guidance and Tables.)

Column B :

description of IPCC categories (See Table 8.2 in Chapter 8.)

Column C :

greenhouse gas from the category

5

Removals are entered as absolute values to avoid an oscillating cumulative value Lx,t as could be the case if removals were entered with negative signs, and thus to facilitate straightforward interpretation of the quantitative analysis.

6

This equation can be used in any situation, regardless of whether the national greenhouse gas inventory is a net source (as is most common) or a net sink.

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Column D :

value of emission or removal estimate of category x in latest inventory year (year t) in CO2-equivalent units

Column E :

absolute value of emission or removal estimate of category x in year t

Column F :

level assessment following Equation 4.1

Column G :

cumulative total of Column F

Inputs to Columns A-D will be available from the inventory. The total of Column D presents the net emissions and removals. In Column E, absolute values are taken from each value in Column D. The sum of all entries in Column E is entered in the total line of Column E (note that this total may not be the same as the total net emissions and removals). In Column F, the level assessment is computed according to Equation 4.1. Once the entries in Column F are computed, the categories in the table should be sorted in descending order of magnitude according to Column F. After this step, the cumulative total summed in Column F can be calculated into Column G. Key categories are those that, when summed together in descending order of magnitude, add up to 95 percent of the total in Column G. Where the method is applied correctly, the sum of entries in Column F must be 1. The rationale for the choice of the 95 percent threshold for the Approach 1 builds on Rypdal and Flugsrud (2001) and is also presented in GPG2000, Section 7.2.1.1 in Chapter 7. It is also good practice to examine categories identified between threshold of 95 percent and 97 percent carefully with respect to the qualitative criteria (see Section 4.3.3). The level assessment should be performed for the base year of the inventory and for the latest inventory year (year t). If estimates for the base year have changed or been recalculated, the base year analysis should be updated. Key category analysis can also be updated for other recalculated years. In many cases, however, it is sufficient to derive conclusions regarding methodological choice, resource prioritisation or QA/QC procedures without an updated key category analysis for the entire inventory time series. Any category that meets the threshold for the base year or the most recent year should be identified as key. However, the interpretation of the results of the key category analysis should take longer time series than the most recent year into account if key category analyses are available. Because some categories having emissions/removals that fluctuate from year to year may be identified as key categories in one year but not in the next year. Therefore, for categories between thresholds of 95 and 97 percent it is suggested to compare the most recent key category analysis with the assessments for three or more previous years. If a category has been key for all or most previous years according to the either level or trend assessments or both (the two assessments should be considered separately), they should be identified as key in the latest year estimate except in cases where a clear explanation can be provided why a category may no longer be key in any future years. These additional categories should be addressed in the reporting table for key categories by using a column for comments (see Table 4.4 and reporting table for key categories in Section 4.4 for more information). The qualitative criteria presented in Section 4.3.3 may also help to identify which categories with fluctuating emissions or removals should be considered as key categories.

TREND ASSESSMENT The purpose of the trend assessment is to identify categories that may not be large enough to be identified by the level assessment, but whose trend is significantly different from the trend of the overall inventory, and should therefore receive particular attention. The Trend Assessment can be calculated according to Equation 4.2 if more than one year of inventory data are available. EQUATION 4.2 TREND ASSESSMENT (APPROACH 1) Tx , t =

E x ,0

∑ E y ,0 y

⎛ ⎞ ⎜ E y , t − ∑ E y ,0 ⎟ ⎟ ⎡ (E x ,t − E x,0 )⎤ ⎜ ∑ y y ⎠ ⎥−⎝ • ⎢ E x,0 ⎢⎣ ⎥⎦ ∑ E y ,0 y

Where: Tx,t = trend assessment of source or sink category x in year t as compared to the base year (year 0)

⎢Ex,0⎢ = absolute value of emission or removal estimate of source or sink category x in year 0 Ex,t and Ex,0

=

real values of estimates of source or sink category x in years t and 0, respectively

∑ E y, t and ∑ E y,0 = total inventory estimates in years t and 0, respectively y

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The trend of category refers to the change in the source or sink category emissions or removals over time, computed by subtracting the base year (year 0) estimate for source or sink category x from the latest inventory year (year t) estimate and dividing by the absolute value of the base year estimate. The total trend refers to the change in the total inventory emissions (or removals) over time, computed by subtracting the base year (year 0) estimate for the total inventory from the latest year (year t) estimate and dividing by the absolute value of the base year estimate. In circumstances where the base year emissions for a given category are zero, the expression may be reformulated to avoid zero in the denominator (see Equation 4.3). EQUATION 4.3 TREND ASSESSMENT WITH ZERO BASE YEAR EMISSIONS Tx , t =

E x , t / ∑ E y ,0 y

The trend assessment identifies categories whose trend is different from the trend of the total inventory, regardless whether category trend is increasing or decreasing, or is a sink or source. Categories whose trend diverges most from the total trend should be identified as key, when this difference is weighted by the level of emissions or removals of the category in the base year.

Table 4.3 outlines a spreadsheet that can be used for the Approach 1 Trend Assessment. TABLE 4.3 SPREADSHEET FOR THE APPROACH 1 ANALYSIS – TREND ASSESSMENT A IPCC Category Code

B IPCC Category

C Greenhouse Gas

D Base Year Estimate Ex,0

E Latest Year Estimate Ex,t

Total

F Trend Assessment Tx,t

G % Contribution to Trend

∑ Ty, t

1

y

H Cumulative Total of Column G

Where: Column A :

code of IPCC categories (See Table 8.2 in Chapter 8.)

Column B :

description of IPCC categories (See Table 8.2 in Chapter 8.)

Column C :

greenhouse gas from the category

Column D :

base year estimate of emissions or removals from the national inventory data, in CO2-equivalent units. Sources and sinks are entered as real values (positive or negative values, respectively).

Column E :

latest year estimate of emissions or removals from the most recent national inventory data, in CO2-equivalent units. Sources and sinks are entered as real values (positive or negative values, respectively).

Column F :

trend assessment from Equation 4.2 (from Equation 4.3 for zero base year emissions)

Column G :

percentage contribution of the category to the total of trend assessments in last row of Column F, i.e., Tx, t / ∑ Ty, t . y

Column H :

cumulative total of Column G, calculated after sorting the entries in descending order of magnitude according to Column G.

The entries in Columns A, B, C and E should be identical to those used in the Table 4.2, Spreadsheet for the Approach 1 analysis - Level Assessment. The base year estimate in Column D is always entered in the spreadsheet, while the latest year estimate in Column E will depend on the year of analysis. The value of Tx,t

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Chapter 4: Methodological Choice and Identification of Key Categories

(which is always positive) should be entered in Column F for each category of sources and sinks, following Equation 4.2, and the sum of all the entries entered in the total line of the table. The percentage contribution of each category to the total of Column F should be computed and entered in Column G. The categories (i.e., the rows of the table) should be sorted in descending order of magnitude, based on Column G. The cumulative total of Column G should then be computed in Column H. Key categories are those that, when summed together in descending order of magnitude, add up to more than 95 percent of the total of Column F. An example of Approach 1 analysis for the level and trend is given in Section 4.5. The trend assessment treats increasing and decreasing trends similarly. However, for the prioritisation of resources, there may be specific circumstances where countries may not want to invest additional resources in the estimation of key categories with decreasing trends. Underlying reasons why a category showing strong decreasing trend could be key include activity decrease, mitigation measures leading to reduced emission factors or abatement measures (e.g., F-gases, chemical production) changing the production processes. In particular for a long-term decline of activities (not volatile economic trends) and when the category is not key from the level assessment, it is not always necessary to implement higher tier methods or to collect additional country-specific data if appropriate explanations can be provided why a category may not become more relevant again in the future. This could be the case e.g., for emissions from coal mining in some countries where considerable number of mines are closed or where certain production facilities are shut down. Regardless of the method chosen, countries should endeavour to use the same method for all years in a time series, and therefore it may be more appropriate to continue using a higher tier method if it had been used for previous years. For other reasons of declining trends such as the introduction of abatement measures or other emission reduction measures, it is important to prioritise resources for the estimation of such categories that were identified as key in the trend assessment. Irrespective of the methodological choice, inventory compilers should clearly and precisely explain and document categories with strongly decreasing trends and should apply appropriate QA/QC procedures.

KEY CATEGORY ANALYSIS FOR A SUBSET OF INVENTORY ESTIMATES The IPCC Good Practice Guidance for Land Use, Land-Use Change and Forestry (GPG-LULUCF, IPCC, 2003) provided guidance on how to conduct a key category analysis using a stepwise approach, identifying first the key (source) categories for the inventory excluding LULUCF (Land Use, Land-Use Change and Forestry), and secondly repeating the key category analysis for the full inventory including the LULUCF categories to identify additional key categories. This two step approach is now integrated into one general approach. However, inventory compilers may still want to conduct a key category analysis using a subset of inventory estimates. For example inventory compilers may choose to include only emission sources in order to exclude the effects of removals from the level assessment or in order to exclude the influence of different trends for carbon fluxes from the other emission trends (see examples in Tables 4.7 and 4.8). It is good practice to document on what subsets the analysis was performed and the differences in results comparing with an integrated analysis.

4.3.2

Approach 2 to identify key categories

The Approach 2 to identify key categories of sources and sinks is based on the results of the uncertainty analysis described in Chapter 3 Uncertainties, in this Volume. Inventory compilers are encouraged to use Approach 2 in addition to Approach 1 if possible, because it will provide additional insight into the reasons why particular categories are key and will assist in prioritising activities to improve inventory quality and reduce overall uncertainty. For example, the order of categories resulting from Approach 2 can provide useful information for prioritisation of improvement activities.

APPLICATION OF UNCERTAINTY ESTIMATES TO IDENTIFY KEY CATEGORIES The key category analysis may be enhanced by incorporating the national category uncertainty estimates developed in accordance with methods provided in Chapter 3. Uncertainty estimates based on the Approach 1 described in Chapter 3 are sufficient for this purpose, however, estimates based on the Approach 2 for Uncertainty Assessment should be used when available. The category uncertainties are incorporated by weighting the Approach 1 Level and Trend Assessment results according to the category percentage uncertainty. The key category equations are presented below.

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LEVEL ASSESSMENT Equation 4.4 describes the Approach 2 Level Assessment including uncertainty. EQUATION 4.4 LEVEL ASSESSMENT (APPROACH 2) LU x,t =

( L x , t • U x ,t ) /

∑ [ (L y ,t • U y ,t ) ] y

Where: LUx,t

=

level assessment for category x in latest inventory year (year t) with uncertainty

Lx,t

=

computed as in Equation 4.1

Ux,t

=

category percentage uncertainty in year t calculated as described in Chapter 3 and reported in Column G in Table 3.3. If the uncertainty reported in Table 3.3 is asymmetrical, the larger uncertainty should be used. The relative uncertainty will always have a positive sign.

After computing level assessment with uncertainty, results should be sorted according to decreasing order of magnitude, similarly as in Approach 1. The key categories are those that add up to 90 percent of the sum of all LUx,t. This 90 percent was the basis for the derivation of the threshold used in the Approach 1 analysis (Rypdal and Flugsrud, 2001). The categories identified by the level assessment with Uncertainty that are different from categories identified by Approach 1 should also be treated as key categories. In addition, the order of key categories identified by Approach 2 may be of use for those who are planning to improve inventories.

TREND ASSESSMENT Equation 4.5 shows how the Approach 2 Trend Assessment can be expanded to include uncertainty. EQUATION 4.5 TREND ASSESSMENT (APPROACH 2) TU x ,t = ( T x,t • U x ,t

)

Where: TUx,t

=

trend assessment for category x in latest inventory year (year t) with uncertainty

Tx,t

=

trend assessment computed as in Equation 4.2

Ux,t

=

category percentage uncertainty in year t calculated as described in Chapter 3. Note that this is the same uncertainty as in the total of Column G of Table 3.3 in Chapter 3, not the uncertainty assessment for trend. The relative uncertainty will always have a positive sign.

After computing trend assessment with uncertainty, results should be sorted according to decreasing order of magnitude. The key categories are those that add up to 90 percent of the total value of the total TUx,t. This 90 percent was the basis for the derivation of the threshold used in the Approach 1 analysis (Rypdal and Flugsrud, 2001). The key categories according to trend assessment with Uncertainty should be treated as key categories and should be added to the list of key categories from Approach 1, if they are different from categories identified by Approach 1. In addition, the order of key Categories identified by Approach 2 may be of use for those who are planning to improve inventories.

INCORPORATING MONTE CARLO ANALYSIS In Chapter 3, Monte Carlo analysis is presented as the Approach 2 for quantitative uncertainty assessment. Whereas the Approach 1 uncertainty analysis is based on simplified assumptions to develop uncertainties for each category, Monte Carlo types of analysis can handle large uncertainties, complex probability density functions, correlations or complex emission estimation equations. The output of the Approach 2 Uncertainty Analysis can be used directly in Equations 4.4 and 4.5. If uncertainties are asymmetrical, the larger percentage difference between the mean and the confidence limit should be used. Monte Carlo analysis or other statistical tools can also be used to perform a sensitivity analysis to directly identify the principal factors contributing to the overall uncertainty. Thus, a Monte Carlo or similar analysis can be a

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valuable tool for a key category analysis. Inventory compilers are encouraged to use the method, for example, to analyze more disaggregated subcategories (by modelling correlations), emission factors and activity data separately (to identify key parameters rather than key categories). The use of these methods should be properly documented.

4.3.3

Qualitative criteria to identify key categories

In some cases, the results of the Approach 1 or Approach 2 analysis of key categories may not identify all categories that should be prioritised in the inventory system. If quantitative key category analysis has not been carried out due to lack of completeness in the inventory, it is good practice to use qualitative criteria to identify key categories. The criteria below address specific circumstances that may not be readily reflected in the quantitative assessment. These criteria should be applied to categories not identified in the quantitative analysis, and if additional categories are identified they should be added to the list of key categories. It is particularly important to consider these criteria if the trend assessment has not been compiled. Although it is important to implement a trend assessment as part of good practice if data are available, early identification using qualitative criteria could be used until such assessment is available. Followings are the examples of points in qualitative criteria. •

• •

•

Mitigation techniques and technologies: If emissions from a category have decreased or removals have increased through the use of climate change mitigation techniques, it is good practice to identify such categories as key. This will ensure that such categories are prioritised within the inventory and that better quality estimates are prepared to reflect the mitigation effects as closely as possible. It will also ensure that the methods used are transparent with respect to mitigation which is important for assessing inventory quality. Expected growth: The inventory compiler should assess which categories are likely to show increase of emissions or decrease of removals in the future. The inventory compiler may use expert judgement to make this determination. It is encouraged to identify such categories as key. No quantitative assessment of Uncertainties performed: Where Approach 2 including uncertainties in the key category analysis is not used, inventory compilers are still encouraged to identify categories that are assumed to contribute most to the overall uncertainty as key, because the largest reductions in overall inventory uncertainty can be achieved by improving estimates of categories having higher uncertainties. The qualitative consideration should take into account whether any methodological improvements could reduce uncertainties significantly. This could, for example, be applied to a small net flux results from the subtraction of large emissions and removals, which can imply a very high uncertainty. Completeness: Neither the Approach 1 nor the Approach 2 gives correct results if the inventory is not complete. The analysis can still be performed, but there may be key categories among those are not estimated. In these cases it is good practice to examine qualitatively potential key categories that are not yet estimated quantitatively by applying the qualitative considerations above. The inventory of a country with similar national circumstances can also often give good indications on potential key categories. Chapter 2, Approaches to Data Collection, gives suggestions for methods to approximate activity data that can be used to compile preliminary estimates of emissions/removals from a category. This preliminary analysis can be used to conclude whether a category potentially can be key and prioritise data collection of this category.

4.4

REPORTING AND DOCUMENTATION

It is good practice to clearly document the results of the key category analysis in the inventory report. This information is essential for explaining the choice of method for each category. In addition, inventory compilers should list the criteria by which each category was identified as key (e.g., level, trend, or qualitative), and the method used to conduct the quantitative key category analysis (e.g., Approach 1 or Approach 2). Tables 4.2 and 4.3 should be used to record the results of the key category analysis. Table 4.4 should be used to present a summary of the key category analysis. The notation keys: L = key category according to level assessment; T = key category according to trend assessment; and Q = key category according to qualitative criteria; should be used to describe the assessment method used. The Approach used to identify the key category should be included as L1, L2, T1 or T2. In the column for comments, reasons for a qualitative assessment can be provided.

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TABLE 4.4 SUMMARY OF KEY CATEGORY ANALYSIS Quantitative method used: Approach 1/Approach 1 and Approach 2 A IPCC Category Code

4.5

B IPCC Category

C Greenhouse Gas

D Identification criteria

E Comments

EXAMPLES OF KEY CATEGORY ANALYSIS

The application of the Approach 1 and 2 to the Finnish greenhouse gas inventory for the reporting year 2003 is shown in Tables 4.5 to 4.11. Both the level and the trend assessment were conducted using estimates of emissions, removals and uncertainties from the national inventory of Finland (Statistics Finland, 2005). Although a qualitative assessment was not conducted in this example, it was not anticipated that additional categories would have been identified. The results of the Approach 1 Level Assessment are shown in Table 4.5 with key categories in bold. The results of the Approach 1 Trend Assessment are shown in Table 4.6, with key categories in bold. Tables 4.7 and 4.8 present an Approach 1 Level and Trend key category analysis using a subset of emissions and removals. In this example, it was decided to include other categories (reported in Tables 4.5 and 4.6) than CO2 from category 3B (Land). The results of Approach 2 Level and Trend Assessments are provided in Tables 4.9 and 4.10. Table 4.11 finally summarises the results of the key category analysis.

TABLE 4.5 EXAMPLE OF APPROACH 1 LEVEL ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 (with key categories in bold) A IPCC Category IPCC Category Code

B

C

D

Ex,t Greenhouse Gas (Gg CO2 eq)

E

⎢Ex,t ⎢ (Gg CO2 eq)

F Lx,t

G Cumulative Total of Column F

3B1a

Forest land remaining Forest land

CO2

-21 354

21 354

0.193

0.193

1A1

Energy Industries: Solid

CO2

17 311

17 311

0.157

0.350

1A3b

Road Transportation

CO2

11 447

11 447

0.104

0.454

1A1

Energy Industries: Peat

CO2

9 047

9 047

0.082

0.536

1A1

Energy Industries: Gas

CO2

6 580

6 580

0.060

0.595

1A4

CO2

5 651

5 651

0.051

0.646

CO2

5 416

5 416

0.049

0.695

CO2

4 736

4 736

0.043

0.738

1A1

Other Sectors: Liquid Manufacturing Industries and Construction: Solid Manufacturing Industries and Construction: Liquid Energy Industries: Liquid

CO2

3 110

3 110

0.028

0.767

3B3a

Grassland Remaining Grassland

CO2

2 974

2 974

0.027

0.793

3C4

Direct N2O Emissions from managed soils

N2O

2 619

2 619

0.024

0.817

4A

Solid Waste Disposal Manufacturing Industries and Construction: Gas Enteric Fermentation Manufacturing Industries and Construction: Peat Nitric Acid Production

CH4

2 497

2 497

0.023

0.840

CO2

2 174

2 174

0.020

0.859

CH4

1 537

1 537

0.014

0.873

CO2

1 498

1 498

0.014

0.887

N2O

1 396

1 396

0.013

0.900

CO2

1 083

1 083

0.010

0.909

CO2

830

830

0.008

0.917

1A3e

Non-Specified: Liquid Non-Energy Products from Fuels and Solvent Use Other Transportation

CO2

651

651

0.006

0.923

3C5

Indirect N2O Emissions from managed soils

N2 O

592

592

0.005

0.928

1A2 1A2

1A2 3A1 1A2 2B2 1A5 2D

4.20

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Methodological Choice and Identification of Key Categories

TABLE 4.5 (CONTINUED) EXAMPLE OF APPROACH 1 LEVEL ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 (with key categories in bold) A IPCC Category IPCC Category Code 2F1

B

D

Ex,t Greenhouse (Gg CO2 eq) Gas

E

F

(Gg CO2 eq)

Lx,t

⎢Ex,t ⎢

G Cumulative Total of Column F

HFCs, PFCs

578

578

0.005

0.933

3B4ai

Peatlands remaining Peatlands

CO2

547

547

0.005

0.938

1A3d

Water-borne Navigation

CO2

519

519

0.005

0.943

1A3b

Road Transportation

N2 O

516

516

0.005

0.948

2A2

Lime Production

CO2

513

513

0.005

0.952

2A1

Cement Production

CO2

500

500

0.005

0.957

3A2

Manure Management

N2O

461

461

0.004

0.961

1A5

Non-Specified: Gas

CO2

363

363

0.003

0.964

1A3a

Civil Aviation

CO2

316

316

0.003

0.967

1A4

Other Sectors: Biomass

CH4

307

307

0.003

0.970

3C2

Liming

CO2

277

277

0.003

0.972

1A1

Energy Industries: Peat

N2O

226

226

0.002

0.975

1A4

Other Sectors: Gas

CO2

225

225

0.002

0.977

3A2

Manure Management

CH4

222

222

0.002

0.979

3B2a

Cropland Remaining Cropland

CO2

211

211

0.002

0.980

CO2, HFCs, PFCs, SF6

168

168

0.002

0.982

Energy Industries: Solid

N2O

162

162

0.001

0.983

Limestone and Dolomite Usea

CO2

148

148

0.001

0.985

Railways

CO2

134

134

0.001

0.986

2 1A1 2A3 and 2A4 1A3c

Refrigeration and Air Conditioning

C

Miscellaneous

1A4

Other Sectors: Peat

CO2

131

131

0.001

0.987

4D

Wastewater Treatment and Discharge

CH4

128

128

0.001

0.988

4D

Wastewater Treatment and Discharge

N2O

102

102

0.001

0.989

3C1

Biomass Burning Manufacturing Industries and Construction: Solid Manufacturing Industries and Construction: Biomass Energy Industries: Biomass

CO2

91

91

0.001

0.990

N2O

90

90

0.001

0.991

N2O

81

81

0.001

0.992

N2O

80

80

0.001

0.992 0.993

1A2 1A2 1A1 1B2aii

CO2

63

63

0.001

2F4

Aerosols

HFCs

63

63

0.001

0.994

1A4

Other Sectors: Biomass

N2O

61

61

0.001

0.994

1B2b

Fugitive Emissions from Fuels - Natural gas

CH4

52

52

0.000

0.995

1A1

Energy Industries: Gas

N2O

51

51

0.000

0.995

1A3b

Road Transportation

CH4

47

47

0.000

0.995

1A4

N2O

47

47

0.000

0.996

N2O

41

41

0.000

0.996

2G

Other Sectors: Liquid Manufacturing Industries and Construction: Liquid Other Product Manufacture and Use

N2 O

40

40

0.000

0.997

1A1

Energy Industries: Biomass

CH4

31

31

0.000

0.997

1A1 1A2 1A4

Energy Industries: Liquid Manufacturing Industries and Construction: Peat Other Sectors: Solid

N2O N2O CO2

30 29 25

30 29 25

0.000 0.000 0.000

0.997 0.997 0.998

Foam Blowing Agents

1A2

2F2 2G 2A3 and 2A4 1A2 1A2 1A1

Oil - Flaring

b

HFCs

25

25

0.000

0.998

Other Product Manufacture and Use

SF6

22

22

0.000

0.998

Soda Ash Usea

CO2

20

20

0.000

0.998

Manufacturing Industries and Construction: Gas Manufacturing Industries and Construction: Biomass Energy Industries: Solid

N2O

19

19

0.000

0.998

CH4

19

19

0.000

0.999

CH4

16

16

0.000

0.999

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.21

Volume 1: General Guidance and Reporting

TABLE 4.5 (CONTINUED) EXAMPLE OF APPROACH 1 LEVEL ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 (with key categories in bold) A IPCC Category IPCC Category Code

B

C

D Ex,t

Greenhouse (Gg CO2 eq) Gas

E

F

(Gg CO2 eq)

Lx,t

⎢Ex,t ⎢

G Cumulative Total of Column F

1A4

Other Sectors: Liquid

CH4

15

15

0.000

0.999

1B2a

Fugitive Emissions from Fuels – Oil

CH4

10

10

0.000

0.999

2C1

Iron and Steel Production

CH4

9

9

0.000

0.999

1A5

Non-Specified: Liquid

N2O

9

9

0.000

0.999

1A1

Energy Industries: Gas

CH4

9

9

0.000

0.999

3C1

Biomass Burning

CH4

8

8

0.000

0.999

1A1

CH4

7

7

0.000

0.999

CH4

7

7

0.000

0.999

1A1

Energy Industries: Peat Manufacturing Industries and Construction: Liquid Energy Industries: Liquid

CH4

7

7

0.000

0.999

1A3e

Other Transportation

CH4

6

6

0.000

1.000

1A2

1A2 3

Manufacturing Industries and Construction: Gas

CH4

6

6

0.000

1.000

Miscellaneous

CH4

6

6

0.000

1.000 1.000

2B8

Petrochemical and Carbon Black Production

CH4

5

5

0.000

1A3e

Other Transportation

N2O

5

5

0.000

1.000

1A3d

Water-Borne Navigation

CH4

5

5

0.000

1.000

1A3a

Civil Aviation

N2O

4

4

0.000

1.000 1.000

1A3d

Water-Borne Navigation

N2O

4

4

0.000

Miscellaneous

N2O

3

3

0.000

1.000

CH4

3

3

0.000

1.000

CH4

2

2

0.000

1.000

1A5

Manufacturing Industries and Construction: Peat Manufacturing Industries and Construction: Solid Non-Specified: Liquid

CH4

2

2

0.000

1.000

1A5

Non-Specified: Gas

N2O

2

2

0.000

1.000

1A4

Other Sectors: Peat

N2O

2

2

0.000

1.000

1A4

Other Sectors: Gas

N2O

1

1

0.000

1.000

4 1A2 1A2

1A4

Other Sectors: Peat

CH4

1

1

0.000

1.000

1A3c

Railways

N2O

1

1

0.000

1.000

3C1

Biomass Burning

N2O

1

1

0.000

1.000

1A4

Other Sectors: Solid

CH4

1

1

0.000

1.000

1A5

Non-Specified: Gas

CH4

0.4

0.4

0.000

1.000

1A4

Other Sectors: Solid

N2O

0.3

0.3

0.000

1.000

1A3a

Civil Aviation

CH4

0.3

0.3

0.000

1.000

1A4

Other Sectors: Gas

CH4

0.3

0.3

0.000

1.000

1A3c

Railways

CH4

0.2

0.2

0.000

1.000

67 729

110 438

Total

1

a

Example was based on 2003 inventory of Finland, and therefore glass production could not be separated as recommended in these Guidelines. This does not affect categories identified as key.

b

Example was based on 2003 inventory of Finland, and therefore flaring was separated from other fugitive emissions from oil (1B2a). According to these Guidelines, all emissions under 1B2a should be treated together in key category analysis. This would not affect categories identified as key in this example.

4.22

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Methodological Choice and Identification of Key Categories

TABLE 4.6 EXAMPLE OF APPROACH 1 TREND ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 (with key categories in bold) A

B

IPCC Category IPCC Category Code

C

D

E

F G H % Trend Cumulative Ex,0 Ex,t Greenhouse Assessment ContribuTotal of Gas tion to Column G (Gg CO2 eq) (Gg CO2 eq) Tx,t Trend

3B1a 1A1 1A3b 1A4

Forest Land remaining Forest Land Energy Industries: Solid Road Transportation Other Sectors: Liquid

CO2 CO2 CO2 CO2

-23 798 9 279 10 800 6 714

-21 354 17 311 11 447 5 651

0.078 0.042 0.040 0.040

0.147 0.079 0.076 0.075

0.147 0.227 0.302 0.378

1A2

Manufacturing Industries and Construction: Solid

CO2

6 410

5 416

0.038

0.072

0.450

Grassland Remaining Grassland Energy Industries: Peat Energy Industries: Gas Solid Waste Disposal Direct N2O Emissions from managed soils Manufacturing Industries and Construction: Liquid

CO2 CO2 CO2 CH4

-1 071 3 972 2 659 3 678

2 974 9 047 6 580 2 497

0.037 0.035 0.029 0.028

0.069 0.066 0.054 0.053

0.519 0.585 0.639 0.692

N2O

3 513

2 619

0.024

0.046

0.738

CO2

4 861

4 736

0.022

0.042

0.780

3B2a 3A1 2B2

Cropland Remaining Cropland Enteric Fermentation Nitric Acid Production

CO2 CH4 N2O

1 277 1 868 1 595

211 1 537 1 396

0.017 0.012 0.009

0.031 0.022 0.017

0.811 0.833 0.849

1A2

Manufacturing Industries and Construction: Gas

CO2

2 094

2 174

0.008

0.016

0.865

1A2

Manufacturing Industries and Construction: Peat

CO2

1 561

1 498

0.007

0.014

0.879

2A1 3C2 1A1

Cement Production Liming Energy Industries: Liquid

CO2 CO2 CO2

786 618 2 607

500 277 3 110

0.006 0.006 0.006

0.012 0.012 0.012

0.891 0.903 0.914

2F1

Refrigeration and Air Conditioning

0

578

0.006

0.011

0.925

N2O

735

592

0.005

0.009

0.934

N2O N2 O CO2 CO2 CO2 CO2 CO2 CO2 SF6 CH4 CH4 N2O CO2 CH4

623 160 644 503 180 320 191 123 87 282 153 133 98 215

461 516 651 547 91 316 134 63 22 307 128 102 225 222

0.004 0.003 0.003 0.002 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

0.008 0.006 0.005 0.003 0.003 0.003 0.003 0.002 0.002 0.002 0.002 0.002 0.002 0.002

0.942 0.948 0.953 0.956 0.959 0.962 0.965 0.967 0.969 0.971 0.973 0.974 0.976 0.977

CO2

640

830

0.001

0.002

0.979

CH4

90

47

0.001

0.002

0.981

N2O

111

81

0.001

0.002

0.982

CO2, HFCs, PFCs, SF6

68

168

0.001

0.001

0.983

3B3a 1A1 1A1 4A 3C4 1A2

3C5 3A2 1A3b 1A3e 3B4ai 3C1 1A3a 1A3c 1B2aii 2G 1A4 4D 4D 1A4 3A2 2D 1A3b 1A2 2

Indirect N2O Emissions from managed soils Manure Management Road Transportation Other Transportation Peatlands Remaining Peatlands Biomass Burning Civil Aviation Railways Flaringb Other Product Manufacture and Use Other Sectors: Biomass Wastewater Treatment and Discharge Wastewater Treatment and Discharge Other Sectors: Gas Manure Management Non-Energy Products from Fuels and Solvent Use Road Transportation Manufacturing Industries and Construction: Biomass Miscellaneous

HFCs, PFCs

1A1

Energy Industries: Biomass

N2O

10

80

0.001

0.001

0.985

1A2

Manufacturing Industries and Construction: Solid

N2O

108

90

0.001

0.001

0.986

2F4

Aerosols

HFCs

0

63

0.001

0.001

0.987

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.23

Volume 1: General Guidance and Reporting

TABLE 4.6 (CONTINUED) EXAMPLE OF APPROACH 1 TREND ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 (with key categories in bold) A

B

C

IPCC Categor IPCC Category y Code 1A2

Greenhouse Gas

Manufacturing Industries and Construction: Peat

D

E

Ex,0 (Gg CO2 eq)

F G H Trend % Cumulative Ex,t Assessment Contribu- Total of tion to Column G (Gg CO2 eq) Tx,t Trend

N2O

56

29

0.001

0.001

0.988

N2 O CO2

62 222

40 363

0.000 0.000

0.001 0.001

0.989 0.990

CH4

4

52

0.000

0.001

0.991

CO2 N2O CO2 CO2 N2O CH4 N2O HFCs N2O CO2 N2O CH4

123 85 734 383 56 2 18 0 141 33 56 16

131 162 1083 513 47 31 51 25 226 25 61 8

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.001 0.001 0.001 0.001 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000

0.992 0.993 0.993 0.994 0.995 0.995 0.996 0.996 0.997 0.997 0.997 0.998

N2O

39

41

0.000

0.000

0.998

CH4

19

15

0.000

0.000

0.998

CH4

20

19

0.000

0.000

0.998

N2O

8

3

0.000

0.000

0.998

Limestone and Dolomite Use

CO2

99

148

0.000

0.000

0.999

Energy Industries: Liquid Water-borne Navigation

N2O CH4

26 8

30 5

0.000 0.000

0.000 0.000

0.999 0.999

Soda Ash Use

CO2

18

20

0.000

0.000

0.999

Water-borne Navigation

CO2

361

519

0.000

0.000

0.999

1A2

Manufacturing Industries and Construction: Liquid

CH4

9

7

0.000

0.000

0.999

1A2

Manufacturing Industries and Construction: Gas

N2O

17

19

0.000

0.000

0.999

1A1

Energy Industries: Solid

CH4

9

16

0.000

0.000

0.999

1A2

Manufacturing Industries and Construction: Solid

CH4

4

2

0.000

0.000

0.999

1A1 1A4

Energy Industries: Gas Other Sectors: Solid

CH4 CH4

4 2

9 1

0.000 0.000

0.000 0.000

1.000 1.000

1A2

Manufacturing Industries and Construction: Peat

CH4

4

3

0.000

0.000

1.000

N2O CH4 CH4 N2O N2O CH4 CH4 CH4 N2O CH4 N2O N2O

5 5 5 4 2 5 6 8 2 1 1 1

5 9 6 4 1 6 7 10 1 1 1 2

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

CH4

4

5

0.000

0.000

1.000

2G 1A5 1B2b 1A4 1A1 1A5 2A2 1A4 1A1 1A1 2F2 1A1 1A4 1A4 3C1 1A2 1A4 1A2 4 2A3 and 2A4 1A1 1A3d 2A3 and 2A4 1A3d

1A3e 2C1 3 1A3a 3C1 1A3e 1A1 1B2a 1A3c 1A4 1A4 1A4 2B8

4.24

Other Product Manufacture and Use Non-Specified: Gas Fugitive Emissions from Fuels Natural Gas Other Sectors: Peat Energy Industries: Solid Non-Specified: Liquid Lime Production Other Sectors: Liquid Energy Industries: Biomass Energy Industries: Gas Foam Blowing Agents Energy Industries: Peat Other Sectors: Solid Other Sectors: Biomass Biomass Burning Manufacturing Industries and Construction: Liquid Other Sectors: Liquid Manufacturing Industries and Construction: Biomass Miscellaneous a

a

Other Transportation Iron and Steel Production Miscellaneous Civil Aviation Biomass Burning Other Transportation Energy Industries: Liquid Fugitive Emissions from Fuels - Oil Railways Other Sectors: Peat Other Sectors: Gas Other Sectors: Peat Petrochemical and Carbon Black Production

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Methodological Choice and Identification of Key Categories

TABLE 4.6 (CONTINUED) EXAMPLE OF APPROACH 1 TREND ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 (with key categories in bold) A

B

C

IPCC Categor IPCC Category y Code 1A2 1A4 1A1 1A5 1A3a 1A3c 1A5 1A4 1A3d 1A5 1A5 Total a

b

D

E

Ex,0

Greenhouse Gas

(Gg CO2 eq)

F G H Trend % Cumulative Ex,t Assessment Contribu- Total of tion to Column G (Gg CO2 eq) Tx,t Trend

Manufacturing Industries and Construction: Gas

CH4

5

6

0.000

0.000

1.000

Other Sectors: Solid Energy Industries: Peat Non-Specified: Gas Civil Aviation Railways Non-Specified: Liquid Other Sectors: Gas Water-borne Navigation Non-Specified: Gas Non-Specified: Liquid

N2O CH4 N2O CH4 CH4 N2O CH4 N2O CH4 CH4

0.5 5 1 0.4 0.2 6 0.1 3 0.3 2 47 604

0.3 7 2 0.3 0.2 9 0.3 4 0.4 2 67 729

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.531

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Example was based on 2003 inventory of Finland, and therefore glass production could not be separated as recommended in these Guidelines. This does not affect categories identified as key. Example was based on 2003 inventory of Finland, and therefore flaring was separated from other fugitive emissions from oil (1B2a). According to these Guidelines, all emissions under 1B2a should be treated together in key category analysis. This would not affect categories identified as key in this example.

TABLE 4.7 EXAMPLE OF APPROACH 1 LEVEL ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 USING A SUBSET (CO2 from category 3B was excluded from the analysis). Only key categories are presented. A

B

IPCC Category IPCC Category Code 1A1

C

D

Ex,t Greenhouse Gas (Gg CO2 eq)

E

⎢Ex,t ⎢

F

G

Lx,t

Cumulative Total of Column F

(Gg CO2 eq)

Energy Industries: Solid

CO2

17 311

17 311

0.203

0.203

1A3b

Road Transportation

CO2

11 447

11 447

0.134

0.337

1A1

Energy Industries: Peat

CO2

9 047

9 047

0.106

0.443

1A1

Energy Industries: Gas

CO2

6 580

6 580

0.077

0.520

1A4

CO2

5 651

5 651

0.066

0.586

CO2

5 416

5 416

0.063

0.650

CO2

4 736

4 736

0.055

0.705

1A1

Other Sectors: Liquid Manufacturing Industries and Construction: Solid Manufacturing Industries and Construction: Liquid Energy Industries: Liquid

CO2

3 110

3 110

0.036

0.742

3C4

Direct N2O Emissions from managed soils

N2O

2 619

2 619

0.031

0.772

1A2 1A2

4A

Solid Waste Disposal

CH4

2 497

2 497

0.029

0.802

1A2

Manufacturing Industries and Construction: Gas

CO2

2 174

2 174

0.025

0.827

3A1

Enteric Fermentation Manufacturing Industries and Construction: Peat Nitric Acid Production

CH4

1 537

1 537

0.018

0.845

CO2

1 498

1 498

0.018

0.863

1A2 2B2 1A5

N2O

1 396

1 396

0.016

0.879

CO2

1 083

1 083

0.013

0.892

CO2

830

830

0.010

0.901

CO2

651

651

0.008

0.909

N2O

592

592

0.007

0.916

578

578

0.007

0.923

1A3e

Non-Specified: Liquid Non-Energy Products from Fuels and Solvent Use Other Transportation

3C5

Indirect N2O Emissions from Managed Soils

2F1

Refrigeration and Air Conditioning

HFCs, PFCs

2D

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.25

Volume 1: General Guidance and Reporting

TABLE 4.7 (CONTINUED) EXAMPLE OF APPROACH 1 LEVEL ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 USING A SUBSET (CO2 from category 3B was excluded from the analysis). Only key categories are presented. A

B

IPCC Category IPCC Category Code

C

D

E

Greenhouse Gas

Ex,t

⎢Ex,t ⎢

(Gg CO2 eq)

F

G

Lx,t

Cumulative Total of Column F

(Gg CO2 eq)

1A3d

Water-borne Navigation

CO2

519

519

0.006

0.929

1A3b

Road Transportation

N2O

516

516

0.006

0.935

2A2

Lime Production

CO2

513

513

0.006

0.941

2A1

Cement Production

CO2

500

500

0.006

0.947

3A2

Manure Management

N2O

461

461

0.005

0.952

........................................................................................................................................................................................................................... Total

85 352

85 352

1

TABLE 4.8 EXAMPLE OF APPROACH 1 TREND ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 USING A SUBSET (CO2 from category 3B was excluded from the analysis). Only key categories are presented. A B C D E F G H % Trend IPCC Cumulative Ex,0 Ex,t Greenhouse assessment ContribuCategory IPCC Category Total of Gas tion to Code Column G (Gg CO2 eq) (Gg CO2 eq) Tx,t Trend 1A1 1A1 1A1 1A4

Energy Industries: Solid Energy Industries: Peat Energy Industries: Gas Other Sectors: Liquid

CO2 CO2 CO2 CO2

9 279 3 972 2 659 6 714

17 311 9 047 6 580 5 651

0.086 0.060 0.048 0.035

0.194 0.135 0.107 0.078

0.194 0.329 0.436 0.514

1A2

Manufacturing Industries and Construction: Solid

CO2

6 410

5 416

0.033

0.074

0.588

4A

Solid Waste Disposal

CH4

3 678

2 497

0.028

0.062

0.650

3C4

Direct N2O Emissions from Managed Soils

N2O

3 513

2 619

0.023

0.052

0.702

CO2

10 800

11 447

0.023

0.051

0.752

CO2

4 861

4 736

0.016

0.036

0.788

CH4 HFCs, PFCs N2O CO2 CO2

1 868 0 1 595 618 786

1 537 578 1 396 277 500

0.010 0.008 0.008 0.007 0.006

0.023 0.018 0.017 0.015 0.014

0.811 0.830 0.846 0.861 0.876

1A3b 1A2 3A1 2F1 2B2 3C2 2A1

Road Transportation Manufacturing Industries and Construction: Liquid Enteric Fermentation Refrigeration and Air Conditioning Nitric Acid Production Liming Cement Production

1A2

Manufacturing Industries and Construction: Peat

CO2

1 561

1 498

0.005

0.012

0.888

1A2

Manufacturing Industries and Construction: Gas

CO2

2 094

2 174

0.005

0.011

0.899

1A3b

Road Transportation

N2O

160

516

0.005

0.010

0.909

3C5

Indirect N2O Emissions from Managed Soils

N2O

735

592

0.004

0.009

0.919

3A2 1A5 3C1 1A3e 1A4 1A3c 1A5

Manure Management Non-Specified: Liquid Biomass Burning Other Transportation Other Sectors: Gas Railways Non-Specified: Gas

N2O CO2 CO2 CO2 CO2 CO2 CO2

623 734 180 644 98 191 222

461 1 083 91 651 225 134 363

0.004 0.003 0.002 0.002 0.001 0.001 0.001

0.009 0.006 0.004 0.004 0.003 0.003 0.003

0.928 0.934 0.938 0.942 0.946 0.949 0.952

................... ........................................................................................................................................................................................................ Total

4.26

70 692

85 352

0.445

1

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Methodological Choice and Identification of Key Categories

TABLE 4.9 EXAMPLE OF APPROACH 2 LEVEL ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 The aggregation level used is country-specific, and does not represent recommended aggregation level. Only key categories are presented. A

B

IPCC Category IPCC Category Code

C

D

Ex,t Greenhouse Gas (Gg CO2 eq)

E

⎢Ex,t⎢

F

G

LUx,t

Cumulative Total of Column F

(Gg CO2 eq)

3B1a

Forest Land Remaining Forest Land: carbon stock change in biomass

CO2

-21 354

21 354

0.23

0.23

3C4

Direct N2O Emissions from Managed Soils: Agricultural Soils

N2O

2 608

2 608

0.18

0.41

3B3a

Grassland Remaining Grassland: net carbon stock change in mineral soils

CO2

2 907

2 907

0.09

0.50

3C5

Indirect N2O Emissions from Managed Soils

N2O

592

592

0.06

0.56

1A3b

Road Transportation: Cars with Catalytic Converters

N2O

410

410

0.05

0.61

2B2

Nitric Acid Production

N2O

1 396

1 396

0.04

0.66

3B2a

Cropland Remaining Cropland: net carbon stock change in organic soils

CO2

1 324

1 324

0.04

0.70

3B4ai

Peatlands Remaining Peatlands

CO2

547

547

0.04

0.73

3B2a

Cropland Remaining Cropland: net carbon stock change in mineral soils

CO2

-1 113

1 113

0.03

0.77

4A

Solid Waste Disposal

CH4

2 497

2 497

0.03

0.80

1A

Fuel Combustion Activities: Liquid

CO2

27 640

27 640

0.02

0.82

1A

Fuel Combustion Activities: Solid

CO2

22 753

22 753

0.02

0.85

1A

Fuel Combustion Activities: Peat

CO2

10 676

10 676

0.02

0.87

3A1

Enteric Fermentation

CH4

1 537

1 537

0.01

0.88

1A4

Other Sectors: Biomass Non-Energy Products from Fuels and Solvent Use

CH4

307

307

0.01

0.90

CO2

830

830

0.01

0.91

2D

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Volume 1: General Guidance and Reporting

TABLE 4.10 EXAMPLE OF APPROACH 2 TREND ASSESSMENT FOR THE FINNISH GHG INVENTORY FOR 2003 The aggregation level used is country-specific, and does not represent recommended aggregation level. Only key categories are presented. A B C D E F G H Trend % Assessment Cumulative IPCC Ex,0 Ex,t ContriGreenhouse with Total of Category IPCC Category bution to Gas Uncertainty Column G Code Trend (Gg CO2 eq) (Gg CO2 eq)

3C4 3B3a

Direct N2O Emissions from Managed Soils: Agricultural Soils Grassland Remaining Grassland: net carbon stock change in mineral soils

TUx,t

N2O

3 486

2 608

5.42

0.24

0.24

CO2

-1 181

2 907

3.62

0.16

0.40

3B1a

Forest Land Remaining Forest Land: carbon stock change in biomass

CO2

-23 798

-21 354

2.71

0.12

0.52

3C5

Indirect N2O Emissions from Managed Soils

N2O

735

592

1.54

0.07

0.58

1A3b

Road Transportation: Cars with Catalytic Converters

N2O

32

410

1.45

0.06

0.65

3B2a

Cropland Remaining Cropland: net carbon stock change in organic soils

CO2

1 813

1 324

1.21

0.05

0.70

4A

Solid Waste Disposal

CH4

3 678

2 497

1.20

0.05

0.75

2B2

Nitric Acid Production

N2O

1 595

1 396

0.89

0.04

0.79

3B2a

Cropland Remaining Cropland: net carbon stock change in mineral soils

CO2

-535

-1 113

0.82

0.04

0.83

3B4ai

Peatlands Remaining Peatlands

CO2

503

547

0.36

0.02

0.85

3A2

Manure Management

N2O

623

461

0.36

0.02

0.86

3A1

Enteric Fermentation

CH4

1 868

1 537

0.35

0.02

0.88

1A

Fuel Combustion Activities: Liquid

CO2

27 232

27 640

0.32

0.01

0.89

4D1

Domestic Wastewater Treatment and Discharge: densely populated areas

N2O

84

66

0.20

0.01

0.90

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Methodological Choice and Identification of Key Categories

TABLE 4.11 SUMMARY OF KEY CATEGORY ANALYSIS FOR FINLAND Quantitative method used: Approach 1 and Approach 2 A

IPCC Category Code 1A 1A 1A 1A1 1A1 1A1 1A1

IPCC Category

C

Greenhouse gas

D

Identification criteria

Fuel Combustion Activities: Liquid Fuel Combustion Activities: Solid Fuel Combustion Activities: Peat Energy Industries: Solid Energy Industries: Peat Energy Industries: Gas Energy Industries: Liquid

CO2 CO2 CO2 CO2 CO2 CO2 CO2

L2, T2 L2 L2 L1, T1 L1, T1 L1, T1 L1, T1

1A2

Manufacturing Industries and Construction: Solid

CO2

L1, T1

1A2 1A2 1A2 1A3b 1A3b 1A3b 1A3c 1A3d 1A3e 1A4 1A4 1A4 1A5 1A5 2A1 2A2 2B2

Manufacturing Industries and Construction: Liquid Manufacturing Industries and Construction: Gas Manufacturing Industries and Construction: Peat Road Transportation Road Transportation Road Transportation: Cars with Catalytic Converters Railways Water-borne Navigation Other transportation Other Sectors: Liquid Other Sectors: Gas Other Sectors: Biomass Non-Specified: Liquid Non-Specified: Gas Cement Production Lime Production Nitric Acid Production

CO2 CO2 CO2 CO2 N2O N2O CO2 CO2 CO2 CO2 CO2 CH4 CO2 CO2 CO2 CO2 N2O

L1, T1 L1, T1 L1, T1 L1, T1 L1, T1 L2, T2

T1 L1 L1, L2, T1, T2

2D

Non-Energy Products from Fuels and Solvent Use

CO2

L1, L2

2F1

Refrigeration and Air Conditioning

HFCs, PFCs

L1, T1

3A1 3A2 3B1a 3B2a 3B3a

Enteric Fermentation Manure Management Forest Land Remaining Forest Land Cropland Remaining Cropland Grassland Remaining Grassland

CH4 N2O CO2 CO2 CO2

L1, L2, T1, T2 T1, T2 L1, L2, T1, T2 L2, T1, T2 L1, T1

3B3a

Grassland Remaining Grassland: net carbon stock change in mineral soils

CO2

L2, T2

CO2 CO2 N2O

L1, L2, T2 T1 L1, T1

N2O

L2, T2

N2O CO2 CH4

L1, L2, T1, T2 L1, L2, T1, T2

N2O

T2

3B4ai 3C2 3C4 3C4 3C5 3C1 4A 4D1 a

B

Peatlands Remaining Peatlands Liming Direct N2O Emissions from Managed Soils Direct N2O Emissions from Managed Soils: Agricultural Soils Indirect N2O Emissions from Managed Soils Biomass Burning Solid Waste Disposal Domestic Waste Waster Treatment and Discharge: densely populated areas

E

Commentsa Aggr Aggr Aggr

Aggr Tsub

L1 L1, T1 L1, T1 Tsub L2 L1 Tsub

Aggr

Aggr Tsub

Aggr

Tsub denotes a category that was only identified by trend assessment for a subset without category 3B. Level assessment of the subset did not identify additional categories when compared with Approach 1 analysis of the total inventory. Aggr denotes a category identified by Approach 2, where aggregation level has been different than in Approach 1.

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References IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Volumes 1, 2 and 3. Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds), Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2000). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Penman, J., Kruger, D., Galbally, I., Hiraishi, T., Nyenzi, B., Emmanuel, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K., and Tanabe, K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. IPCC (2001). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K. and Johnson, C.A. (eds.), Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 881pp. IPCC (2003). Good Practice Guidance for Land Use, land-Use Change and Forestry, Penman, J., Gytarsky, M., Hiraishi, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T. and Tanabe, K., Wagner, F. (Eds), Intergovernmental Panel on Climate Change (IPCC), IPCC/IGES, Hayama, Japan. Morgan, M.G., and Henrion, M. (1990). Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis, Cambridge University Press, New York. Rypdal, K., and Flugsrud, K. (2001). Sensititivity Analysis as a Tool for Systematic Reductions in GHG Inventory Uncertainties. Environmental Science and Policy. Vol 4 (2-3): pp. 117-135. Statistics Finland. (2005). Greenhouse gas emissions in Finland 1990-2003. National Inventory Report to the UNFCCC, 27 May 2005.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Time Series Consistency

CHAPTER 5

TIME SERIES CONSISTENCY

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.1

Volume 1: General Guidance and Reporting

Authors William Irving (USA) Hideaki Nakane (Japan), and Jose Ramon T. Villarin (Philippines)

Contributing Authors Ruta Bubniene (Lithuania)

5.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Time Series Consistency

Contents 5

Time Series Consistency 5.1

Introduction ......................................................................................................................................... 5.5

5.2

Ensuring a consistent time series ......................................................................................................... 5.5

5.2.1

Recalculations due to methodological changes and refinements ................................................. 5.5

5.2.2

Adding new categories ................................................................................................................ 5.6

5.2.3

Tracking increases and decreases due to technological change and other factors ....................... 5.7

5.3

Resolving data gaps ............................................................................................................................. 5.8

5.3.1

Issues with data availability ......................................................................................................... 5.8

5.3.2

Non-calendar year data ................................................................................................................ 5.8

5.3.3

Splicing techniques ...................................................................................................................... 5.8

5.4

Reporting and Documentation of trend information ......................................................................... 5.14

5.5

Time series consistency QA/QC ....................................................................................................... 5.15

References ........................................................................................................................................................ 5.16

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.3

Volume 1: General Guidance and Reporting

Equations Equation 5.1

Recalculated emission or removal estimate computed using the overlap method ............... 5.9

Equation 5.2

Emission/removals trend estimates using surrogate parameters ........................................ 5.10

Figures Figure 5.1

Consistent overlap ............................................................................................................... 5.9

Figure 5.2

Inconsistent overlap ........................................................................................................... 5.10

Figure 5.3

Linear interpolation ........................................................................................................... 5.12

Tables Table 5.1

Summary of splicing techniques ........................................................................................ 5.14

Table 5.2

Category-specific documentation of recalculations ........................................................... 5.15

Boxes

5.4

Box 5.1

Recalculation in the Agriculture Forestry and Other Land Use (AFOLU) Sector ............... 5.6

Box 5.2

Case study of surrogate data – Methane emissions from underground coal mining in the United States ............................................................................................................ 5.11

Box 5.3

Case study on periodic data, using extrapolation .............................................................. 5.13

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Time Series Consistency

5 TIME SERIES CONSISTENCY 5.1

INTRODUCTION

The time series is a central component of the greenhouse gas inventory because it provides information on historical emissions trends and tracks the effects of strategies to reduce emissions at the national level. As is the case with estimates for individual years, emission trends should be neither over nor underestimated as far as can be judged. All emissions estimates in a time series should be estimated consistently, which means that as far as possible, the time series should be calculated using the same method and data sources in all years. Using different methods and data in a time series could introduce bias because the estimated emission trend will reflect not only real changes in emissions or removals but also the pattern of methodological refinements. This chapter describes good practice in ensuring time series consistency. Section 5.2 provides guidance on common situations in which time series consistency could be difficult to achieve: carrying out recalculations, on adding new categories, and on accounting for technological change. Section 5.3 describes techniques for combining or “splicing” different methods or data sets to compensate for incomplete or missing data. Additional guidance on reporting and documentation and QA/QC of time series consistency is given in Sections 5.4 and 5.5.

5.2

ENSURING A CONSISTENT TIME SERIES

5.2.1

Recalculations due to methodological changes and refinements

A methodological change in a category is a switch to a different tier from the one previously used. Methodological changes are often driven by the development of new and different data sets. An example of a methodological change is the new use of a higher tier method instead of a Tier 1 default method for an industrial category because a country has obtained site-specific emission measurement data that can be used directly or for development of national emission factors. A methodological refinement occurs when an inventory compiler uses the same tier to estimate emissions but applies it using a different data source or a different level of aggregation. An example of a refinement would be if new data permit further disaggregation of a livestock enteric fermentation model, so that resulting animal categories are more homogenous or applies a more accurate emission factor. In this case, the estimate is still being developed using a Tier 2 method, but it is applied at a more detailed level of disaggregation. Another possibility is that data of a similar level of aggregation but higher quality data could be introduced, due to improved data collection methods. Both methodological changes and refinements over time are an essential part of improving inventory quality. It is good practice to change or refine methods when: •

• •

1

Available data have changed: The availability of data is a critical determinant of the appropriate method, and thus changes in available data may lead to changes or refinements in methods. As countries gain experience and devote additional resources to preparing greenhouse gas inventories, it is expected that data availability will improve.1 The previously used method is not consistent with the IPCC guidelines for that category: Inventory compilers should review the guidance for each category in Volumes 2-5. A category has become key: A category might not be considered key in a previous inventory year, depending on the criteria used, but could become key in a future year. For example, many countries are only beginning to substitute HFCs and PFCs for ozone depleting substances being phased out under the Montreal Protocol. Although current emissions from this category are low, they could become key in the future based on trend or level. Countries anticipating significant growth in a category may want to consider this possibility before it becomes key.

Sometimes collection of data may be reduced which can result in a less rigorous methodological outcome.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.5

Volume 1: General Guidance and Reporting •

•

•

•

The previously used method is insufficient to reflect mitigation activities in a transparent manner: As techniques and technologies for reducing emissions are introduced, inventory compilers should use methods that can account for the resulting change in emissions or removals in a transparent manner. Where the previously used methods are insufficiently transparent, it is good practice to change or refine them. See Section 5.2.3 for further guidance. The capacity for inventory preparation has increased: Over time, the human or financial capacity (or both) to prepare inventories may increase. If inventory compilers increase inventory capacity, it is good practice to change or refine methods so as to produce more accurate, complete and transparent estimates, particularly for key categories. New inventory methods become available: In the future, new inventory methods may be developed that take advantage of new technologies or improved scientific understanding. For example, remote-sensing technology improvements in emission monitoring technology may make it possible to monitor directly more types of emission sources. Correction of errors: It is possible that the implementation of the QA/QC procedures described in Chapter 6, Quality Assurance and Quality Control and Verification, will lead to the identification of errors or mistakes in the inventory. As noted in that chapter, it is good practice to correct errors in previously submitted estimates. In a strict sense, the correction of errors should not be considered a methodological change or refinement. This situation is noted here, however, because the general guidance on time series consistency should be taken into consideration when making necessary corrections.

BOX 5.1 RECALCULATION IN THE AGRICULTURE FORESTRY AND OTHER LAND USE (AFOLU) SECTOR

It is anticipated that the use of recalculation techniques in the AFOLU Sector will be particularly important. The development of inventory methods and interpolation/extrapolation tools (models) for this sector is ongoing and it is anticipated that changes to the methods of many countries will occur over time due to the complexity of the processes involved. In simple cases, sampling or experimentation may provide country-specific emission factors, which might require a time series recalculation. More complicated situations can also arise. For example: •

•

• •

5.2.2

The instruments used to collect activity data may change through time, and it is impossible to go back in time to apply the new instrument. For example, land clearing events can be estimated by the use of satellite imagery, but the satellites available for this work change or degrade through time. In this case, the overlap method described in Section 5.3.3.1 is most applicable. Some data sources such as forest inventories required for AFOLU categories may not be available annually because of resource constraints. In this case, interpolation between years or extrapolation for years after the last year with measured data available may be most appropriate. Extrapolated data may be recalculated when final data become available (see Sections 5.3.3.3 and 5.3.3.4 on interpolation and extrapolation). Emissions and removals from AFOLU typically depend on past land use activity. Thus, data must cover a large historical period (20-100 years), and the quality of such data will often vary through time. Overlap, interpolation or extrapolation techniques may be necessary in these cases. The calculation of emission factors and other parameters in AFOLU may require a combination of sampling and modelling work. Time series consistency must apply to the modelling work as well. Models can be viewed as a way of transforming input data to produce output results. In most cases where changes are made to the data inputs or mathematical relationships in a model, the entire time series of estimates should be recalculated. In circumstances where this is not feasible due to available data, variations of the overlap method could be applied.

Adding new categories

The addition to the inventory of a new category or subcategory requires the calculation of an entire time series, and estimates should be included in the inventory from the year emissions or removals start to occur in the country. A country should make every effort to use the same method and data sets for each year. It may be

5.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Time Series Consistency

difficult to collect data for previous years, however, in which case countries should use the guidance on splicing in Section 5.3.3 to construct a consistent time series. A country may add new categories or new gases to the inventory for a variety of reasons: •

• •

•

A new emission or removal activity is occurring: Some emission processes, particularly in the Industrial Processes and Product Use (IPPU) Sector, only occur as a result of specific technological processes. For example, the use of substitutes for ozone-depleting substances (ODS substitutes) has been phased in at very different rates in different parts of the world. Some applications may only now be starting to occur in some countries. Rapid growth in a very small category: A category that previously was too small to justify resources for inclusion in the national inventory, could experience sudden growth and should be included in future inventories. New IPCC categories: The 2006 IPCC Guidelines contain some categories and subcategories which were not covered in the 1996 IPCC Guidelines (IPCC, 1997). As a result, countries may include new estimates in future national inventories. Countries should include estimates for new categories and subcategories for the entire time series. Additional inventory capacity: A country may be able to use more resources or employ additional experts over time, and thus include new categories and subcategories in the inventory.

If a new emission-causing activity began after the base year, or if a category previously regarded as insignificant (see Section 4.1.2 in Chapter 4, Methodological Choice and Identification of Key Categories, for reasons for not estimating emissions/removals from an existing source/sink) has grown to the point where it should be included in the inventory, it is good practice to document the reason for not estimating the entire time series.

5.2.3

Tracking increases and decreases due to technological change and other factors

Emission inventories can track changes in emissions and removals through changing activity levels or changing emission rates, or both. The way in which such changes are included in methodologies can have a significant impact on time series consistency.

Changes in activity levels National statistics typically will account for significant changes in activity levels. For example, fuel switching from coal to natural gas in electricity generation will be reflected in the national fuel consumption statistics. Further disaggregation of activity data can provide more transparency to indicate specifically where the change in activity is occurring. This approach is relevant when changes are taking place in one or more subcategories, but not throughout the entire category. To maintain time series consistency, the same level of disaggregation into subcategories should so far as possible be used for the entire time series, even if the change began recently.

Changes in emission rates Research may indicate that the average rate of emissions/removals per unit of activity has changed over the time series. In some cases, the factors leading to a technological change may also make it possible to use a higher tier method. For example, an aluminium plant manager who introduces measures to reduce the frequency and intensity of anode effects may also collect plant-specific parameters that can be used to estimate a new emission factor, This new factor might not be appropriate for estimating emissions for earlier years in the time series, before the technological change occurred. In these cases it is good practice to use the updated emission factor or other estimation parameters or data to reflect these changes. Since a general assumption is that emission factors or other estimation parameters do not change over time unless otherwise indicated, countries should clearly document the reason for using different factors or parameters in the time series. This is particularly important if sampling or surveying occurs periodically and emission factors or estimation parameters for years in between are interpolated rather than measured.

Capture, destruction, or combustion of emissions Larger point sources such as chemical manufacturing facilities or power plants might generate emissions but prevent them from being released to the atmosphere through capture and storage (e.g., CO2), destruction (e.g., HFC-23) or combustion (e.g., CH4). These activities do not necessarily change the average emissions generated per unit of activity, and therefore it is not good practice to use different emission factors for different years. Instead, the inventory compiler should estimate total emissions generated and emissions reduced separately, and then subtract reductions from the total generation to arrive at an estimate for total emissions to the atmosphere.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

5.3

RESOLVING DATA GAPS

5.3.1

Issues with data availability

For a complete and consistent time series, it is necessary to determine the availability of data for each year. Recalculating previous estimates using a higher tier method, or developing estimates for new categories will be difficult if data are missing for one or more years. Examples of data gaps are presented below: •

•

Periodic data: Natural resource or environmental statistics, such as national forest inventories and waste statistics, may not cover the entire country on an annual basis. Instead, they may be carried out at intervals such as every fifth or tenth year, or region-by-region, implying that national level estimates can only be directly obtained once the inventory in every region has been completed. When data are available less frequently than annual, several issues arise. First, the estimates need to be updated each time new data become available, and the years between the available data need to be recalculated. The second issue is producing inventories for years after the last available data point and before new data are available. In this case, new estimates should be extrapolated based on available data, and then recalculated when new data become available. Changes and gaps in data availability: A change in data availability or a gap in data is different from periodically available data because there is unlikely to be an opportunity to recalculate the estimate at a later date using better data. In some cases, countries will improve their ability to collect data over time, so that higher tier methods can be applied for recent years, but not for earlier years. This is particularly relevant to categories in which it is possible to implement direct sampling and measurement programs because these new data may not be indicative of conditions in past years. Some countries may find that the availability of certain data sets decreases over time as a result of changing priorities within governments, economic restructuring, or limited resources. Some countries with economies in transition no longer collect certain data sets that were available in the base year, or if available these data sets may contain different definitions, classifications and levels of aggregation.

5.3.2

Non-calendar year data

When using non-calendar year data, it is good practice to use the same collection period consistently over the time series as described in Section 2.2.3 in Chapter 2, Approaches to Data Collection. Countries should not use different collection periods within the same time series because this could lead to a bias in the trend.

5.3.3

Splicing techniques

Splicing in this context refers to the combining or joining of more than one method to form a complete time series. Several splicing techniques are available if it is not possible to use the same method or data source in all years. This section describes techniques that can be used to combine methods to minimise the potential inconsistencies in the time series. Each technique can be appropriate in certain situations, as determined by considerations such as data availability and the nature of the methodological modification. Selecting a technique requires an evaluation of the specific circumstances, and a determination of the best option for the particular case. It is good practice to perform the splicing using more than one technique before making a final decision and to document why a particular method was chosen. The principal approaches for inventory recalculations are summarised in Table 5.1.

5.3.3.1

O VERLAP

The overlap technique is often used when a new method is introduced but data are not available to apply the new method to the early years in the time series, for example when implementing a higher tier methodology. If the new method cannot be used for all years, it may be possible to develop a time series based on the relationship (or overlap) observed between the two methods during the years when both can be used. Essentially, the time series is constructed by assuming that there is a consistent relationship between the results of the previously used and new method. The emission or removal estimates for those years when the new method cannot be used directly are developed by proportionally adjusting the previously developed estimates, based on the relationship observed

5.8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Time Series Consistency

during the period of overlap. In this case, the emissions or removals associated with the new method are estimated according to Equation 5.1:2 EQUATION 5.1 RECALCULATED EMISSION OR REMOVAL ESTIMATE COMPUTED USING THE OVERLAP METHOD ⎛ 1 • y0 = x0 • ⎜⎜ ( − + 1) n m ⎝

∑ n

i=m

yi xi

⎞ ⎟⎟ ⎠

Where: y0

= the recalculated emission or removal estimate computed using the overlap method

x0

= the estimate developed using the previously used method

yi and xi are the estimates prepared using the new and previously used methods during the period of overlap, as denoted by years m through n A relationship between the previously used and new methods can be evaluated by comparing the overlap between only one set of annual estimates, but it is preferable to compare multiple years. This is because comparing only one year may lead to bias and it is not possible to evaluate trends. Figure 5.1 shows a hypothetical example of a consistent overlap between two methods for the years in which both can be applied. In Figure 5.2 there is no consistent overlap between methods and it is not good practice to use the overlap technique in such a case. Other relationships between the old and new estimates may also be observed through an assessment of overlap. For example, a constant difference may be observed. In this case, the emissions or removals associated with the new method are estimated by adjusting the previous estimate by the constant amount equal to the average difference in the years of overlap. Figure 5.1

Consistent overlap Overlap - Consistent Relationship 20 18 16 Emissions

14 12

Tier 1

10

Splice

8

Tier 2

6 4 2 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year

2

Overlap Equation 5.1 is preferred to the equation described in Good Practice Guidance for National Greenhouse Gas Inventories (GPG2000, IPCC, 2000): ⎛ n y 0 = x0 • ⎜ ∑ yi ⎝ i=m

∑ x ⎟⎠ ⎞

n

i=m

i

because the latter gives more weight to overlapping years with the highest emissions. However in practical cases the results will often be very similar and continued use of the previous equation is consistent with good practice where its use gives satisfactory results.

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Figure 5.2

Inconsistent overlap Figure 5.2: Inconsistent Overlap 20 18 16

Emissions

14 12 Tier 1 Tier 2

10 8 6 4 2 0 1990 1991 1992

1993 1994 1995 1996 1997

1998 1999 2000

Year

5.3.3.2

S URROGATE

DATA

The surrogate method relates emissions or removals to underlying activity or other indicative data. Changes in these data are used to simulate the trend in emissions or removals. The estimate should be related to the statistical data source that best explains the time variations of the category. For example, mobile source emissions may be related to trends in vehicle distances travelled, emissions from domestic wastewater may be related to population, and industrial emissions may be related to production levels in the relevant industry. See Chapter 2, Approaches to Data Collection. In its simplest form, the estimate will be related to a single type of data as shown in Equation 5.2:

EQUATION 5.2

( s 0 / st )

EMISSION/REMOVALS TREND ESTIMATES USING SURROGATE PARAMETERS y 0 = yt •

Where: y = the emission/removal estimate in years 0 and t s

= the surrogate statistical parameter in years 0 and t

Although the relationship between emissions/removals and surrogate can be developed on the basis of data for a single year, the use of multiple years might provide a better estimate. Box 5.2 provides an example of the use of surrogate data for estimating methane emissions from underground coal mining in the United States. In some cases, more accurate relationships may be developed by relating emissions to more than one statistical parameter. Regression analysis may be useful in selecting the appropriate surrogate data parameters. Using surrogate methods to estimate otherwise unavailable data can improve the accuracy of estimates developed by the interpolation and trend extrapolation approaches discussed below.

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Chapter 5: Time Series Consistency

BOX 5.2 STUDY OF SURROGATE DATA – METHANE EMISSIONS FROM UNDERGROUND COAL MINING IN THE UNITED STATES CASE

On a quarterly basis, the U.S. Mine Safety and Health Administration (MSHA) measures methane emissions levels at underground mines with detectable levels of methane in their ventilation air. USEPA uses these measurements as a basis for calculating national emissions from underground coal mining. These data were not available for the years 1991-1992, however, because of restructuring within the Department of Labor. To estimate emissions for these years, USEPA used total underground coal production as a surrogate data set. The graph below shows the relationship between underground coal production and measured emissions, which are closely but not perfectly correlated. Differences reflect the fact that individual mines vary greatly in their emission rates, and as production levels at mines change over time, the weighted average emission rate also changes. USEPA applied Equation 5.2 to estimate emissions for 1991 and 1992 using Tier 3 emissions data and coal production for 1990. These data points are crossed by the dashed line in the graph. Note that this procedure is very similar to an overlap with the Tier 1 method because coal production is the recommended activity data for Tier 1. Comparison of implied emission factors from estimates using surrogate data with Tier 1 default factors would be a useful QA/QC check. Surrogate Data for Coal Mining in the United States 120

450,000 400,000

100

80

300,000 250,000

60 200,000 40

150,000

Billion Cubic Feet

Thousand Metric Tons

350,000

Coal Production Measured Emissions

100,000 20 50,000 0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year

5.3.3.3

I NTERPOLATION

In some cases it may be possible to apply a method intermittently throughout the time series. For example, necessary detailed statistics may only be collected every few years, or it may be impractical to conduct detailed surveys on an annual basis. In this case, estimates for the intermediate years in the time series can be developed by interpolating between the detailed estimates. If information on the general trends or underlying parameters is available, then the surrogate method is preferable. Figure 5.3 shows an example of linear interpolation. In this example, data for 1994 and 1995 are not available. Emissions were estimated by assuming a constant annual growth in emissions from 1993-1996. This technique is appropriate in this example because the overall trend appears stable, and it is unlikely that actual emissions for 1994 and 1995 are substantially different from the values predicted through interpolation. For categories that have volatile emission trends (i.e., they fluctuate significantly from year to year), interpolation will not be according to good practice and surrogate data will be a better option. It is good practice to compare interpolated estimates with surrogate data as a QA/QC check.

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Figure 5.3

Linear interpolation

Linear Interpolation 20 18

Emissions

16 14 12 Method

10

Interpolation

8 6 4 2 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year

5.3.3.4

T REND

EXTRAPOLATION

When detailed estimates have not been prepared for the base year or the most recent year in the inventory, it may be necessary to extrapolate from the closest detailed estimates. Trend extrapolation is conceptually similar to interpolation, but less is known about the actual trend. Extrapolation can be conducted either forward (to estimate more recent emissions or removals) or backward (to estimate a base year). Trend extrapolation simply assumes that the observed trend in emissions/removals during the period when detailed estimates are available remains constant over the period of extrapolation. Given this assumption, it is clear that trend extrapolation should not be used if the change in trend is not constant over time. In this situation it will be more appropriate to consider using extrapolations based on surrogate data. Extrapolation should also not be used over long periods of time without detailed checks at intervals to confirm the continued validity of the trend. In the case of periodic data, however, extrapolations will be preliminary and the data point will be recalculated at a later stage. Box 5.3 in this Section shows an example in which activity data for forests are available only at periodic intervals, and data for the most recent years are not yet available. Data for recent years can be extrapolated on the basis of a consistent trend, or on the basis of appropriate data. It should be noted, however, that the uncertainty of the extrapolated estimates increases in proportion to the length of time over which the extrapolation is made. Once the latest set of periodic data becomes available, it will be necessary to recalculate the part of the time series that had been estimated using trend extrapolation. The example in Box 5.3 assumes a linear extrapolation, which is likely to be appropriate for the forest land category. Non-linear extrapolations are possible, and may be more appropriate given an observed trend, (e.g., exponential growth in the use of ODS Substitutes). Countries using non-linear extrapolation should provide clear documentation for the choice and explain why it is more appropriate than linear extrapolation.

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Chapter 5: Time Series Consistency

BOX 5.3 CASE STUDY ON PERIODIC DATA, USING EXTRAPOLATION

Consider a case where a national forest inventory is conducted every 5 years. Estimates of several types of required data (e.g., tree growth) will therefore only be obtained at certain intervals. On the assumption that growth is on average reasonably stable between years, inventory estimates for the years after the last available data should be made using extrapolations of past estimates (i.e., tree growth trends). As shown in the figure below, a biomass estimate for 2005 for a plot is obtained in this way, although the latest measurement was made in 2000. The trend between 1995 and 2000 is simply extrapolated linearly. In practice, a log scale might be used to accommodate exponential behaviour but this is not considered for this simple example. Also, extrapolation can be improved using surrogate data or more sophisticated modelling taking into account parameters influencing the parameter we want to extrapolate.

Linear Extrapolation in AFOLU Actual (Periodic) Data)

Original Extrapolation

65

Tree Growth

60 55 50 45 40 1985

1990

1995

2000

2005

Year

Unlike periodically available data, when data are not available for the first years in the time series (e.g., base year and pre base year data on for example waste disposal and land use) there is no possibility of filling in gaps with future surveys. Trend extrapolation back in time is possible but should be done in combination with other splicing techniques such as surrogate data and overlap. Some countries that have undergone significant administrative and economic transitions since 1990 do not have consistent activity data sets for the entire time series, particularly if national data sets covered different geographic areas in previous years. To extrapolate backwards in these cases, it is necessary to analyze the relationship between different activity data sets for different periods, possibly using multiple surrogate data sets.

5.3.3.5

O THER

TECHNIQUES

In some cases, it may be necessary to develop a customised approach to best estimate the emissions over time. For example, the standard alternatives may not be valid when technical conditions are changing throughout the time series (e.g., due to the introduction of mitigation technology). In this case, it will be necessary to carefully consider the trends in all factors known to influence emissions or removals over the period. Where customised approaches are used, it is good practice to document them thoroughly, and in particular to give special consideration to how the resultant emissions estimates compare to those that would be developed using the more standard alternatives.

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5.3.3.6

S ELECTING

THE MOST APPROPRIATE TECHNIQUE

The choice of splicing technique involves expert judgement, and depends on an expert assessment of the volatility of emissions trend, the availability of data for two overlapping methods, the adequacy and availability of surrogate data sets, and the number of years of missing data. Table 5.1 summarises the requirements for each technique and suggests situations in which they may or may not be appropriate. Countries should use Table 5.1 as a guide rather than a prescription.

TABLE 5.1 SUMMARY OF SPLICING TECHNIQUES Approach

Applicability

Overlap

Data necessary to apply both the previously used and the new method must be available for at least one year, preferably more.

Surrogate Data

Interpolation

Emission factors, activity data or other estimation parameters used in the new method are strongly correlated with other well-known and more readily available indicative data. Data needed for recalculation using the new method are available for intermittent years during the time series.

Trend Extrapolation

Other Techniques

5.4

Comments

Data for the new method are not collected annually and are not available at the beginning or the end of the time series.

The standard alternatives are not valid when technical conditions are changing throughout the time series (e.g., due to the introduction of mitigation technology).

•

• • •

•

• •

•

•

•

•

Most reliable when the overlap between two or more sets of annual estimates can be assessed. If the trends observed using the previously used and new methods are inconsistent, this approach is not good practice. Multiple indicative data sets (singly or in combination) should be tested in order to determine the most strongly correlated. Should not be done for long periods. Estimates can be linearly interpolated for the periods when the new method cannot be applied. The method is not applicable in the case of large annual fluctuations. Most reliable if the trend over time is constant. Should not be used if the trend is changing (in this case, the surrogate method may be more appropriate). Should not be done for long periods. Document customised approaches thoroughly. Compare results with standard techniques.

REPORTING AND DOCUMENTATION OF TREND INFORMATION

If the same method and data sources are used throughout the time series, and there have been no recalculations, then following the reporting guidance for each category should be sufficient to ensure transparency. Generally, countries should explain inventory trends for each category, giving particular attention to outliers, trend changes, and extreme trends. Countries should provide additional documentation if they have recalculated previous estimates and if they have used the techniques in this chapter to splice methodologies. Recalculations: In addition to following the category-specific guidance on each category provided in Volumes 2-5, countries should clearly document any recalculations. The documentation should explain the reason for the recalculation and the effect of the recalculation on the time series. Countries can also include a graph that shows the relationship between the previous data trend and the new data trend. Table 5.2 provides an example of how recalculations can be documented either for reporting purposes or for internal tracking.

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Chapter 5: Time Series Consistency

TABLE 5.2 CATEGORY-SPECIFIC DOCUMENTATION OF RECALCULATIONS Category/Gas

Emissions and Removals (Gg) 1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Previous Data (PD) Latest Data (LD) Difference in percent =100●[(LD–PD)/PD] Documentation (reason for recalculation):

Splicing techniques: Countries should provide documentation of any splicing techniques used to complete a time series. The documentation should identify the years in which data for the method were not available, the splicing technique used, and any surrogate or overlap data used. Graphical plots, such as those shown in Section 5.3 can be useful tools for documenting and explaining the application of splicing techniques. Mitigation: The category-specific guidance in Volumes 2-5 provide targeted guidance on specific information that should be reported for each category, including mitigation and reductions. Generally, countries should document the approach used to track mitigation activities and provide all relevant parameters such as abatement utilisation, destruction efficiency, updated emission factors etc.

5.5

TIME SERIES CONSISTENCY QA/QC

The most effective way to ensure the quality of a time series is to apply both general and category-specific checks to the entire time series (see Chapter 6). For example, the outlier and implied emission factor checks in Chapter 6 will help to identify possible inconsistencies in the time series. Category-specific checks are particularly important because they are targeted to unique features of each category. As described above, plotting and comparing the results of splicing techniques on a graph is a useful QA/QC strategy. If alternative splicing methods produce different results, countries should consider which result is most realistic. In some cases, additional surrogate data can be used to check the spliced time series. A side by side comparison of recalculated estimates with previous estimates can be a useful check on the quality of a recalculation. This can be done through a tabular comparison as shown in Table 5.2, or as a graphical plot. It is important to note, however, that higher tier methods may produce different trends than lower tier methods because they more accurately reflect actual conditions. Differences in trends do not necessarily suggest a problem with the recalculated estimate. Where it is possible to use more than one approach to tracking the effects of mitigation activities, countries should compare the results of multiple approaches. If the results differ by more than would be expected, it is good practice to explain the reason for the differences and evaluate whether or not a different approach should be used. For disaggregated higher tier estimates, implied emission/removal factors can be a useful tool for checking the consistency of the trend and the plausibility of mitigation estimates. In some cases activity data collection may have been interrupted or drastically changed. This situation causes challenges for time series consistency. In this situation it is good practice to examine closely documentation of the previous data collection system to get a good understanding of how changes in data collection, including definitions and delimitations, have affected the data used in the inventory and any implications for inconsistencies in time-series. If appropriate documentation is not available, an alternative is to compile indicators (e.g., emissions per unit production or emissions per car) and compare these between countries with a similar economic structure, across time-series and in the overlap of the two data collection methods. In some cases a country may have undergone changes in geographical coverage, e.g., a country may have divided into two or more new countries. In this situation it is good practice to compare the inventory data with estimates from regional statistics for the years prior to the split. It can also be recommended to collaborate with other countries that were once part of the same country to ensure completeness and avoid-double counting. If regional statistics are not available and such collaboration is not possible, it is good practice, to compare appropriate indicators as described above for the country prior to a split with the data used in the inventory. If inconsistencies are identified, it is good practice to correct them and, if necessary, apply appropriate splicing techniques as described in this chapter.

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References IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories, Houghton, J.T., Meira Filho, L.G., Lim B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2000). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, Penman, J., Kruger, D., Galbally, I., Hiraishi, T., Nyenzi, B., Emmanuel, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K. and Tanabe, K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan.

Other references IPCC (2003). Good Practice Guidance for Land Use, land-Use Change and Forestry, Intergovernmental Panel on Climate Change, Penman, J., Gytarsky, M., Hiraishi, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. and Wagner, F. (Eds), IPCC/IGES, Hayama, Japan USEPA (2004). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2003, United States Environmental Protection Agency (USEPA), National Service Center for Environmental Publications (NSCEP) http://www.epa.gov/globalwarming/publications/emissions

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Chapter 6: QA/QC and Verification

CHAPTER 6

QUALITY ASSURANCE / QUALITY CONTROL AND VERIFICATION

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

Authors Wilfried Winiwarter (Austria), Joe Mangino (USA) Ayite-Lo N. Ajavon (Togo), and Archie McCulloch (UK)

Contributing Author Mike Woodfield (UK)

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Chapter 6: QA/QC and Verification

Contents 6

Quality Assurance / Quality Control and Verification 6.1

Introduction ......................................................................................................................................... 6.5

6.2

Practical considerations in developing QA/QC and verification systems ........................................... 6.6

6.3

Elements of a QA/QC and verification system ................................................................................... 6.7

6.4

Roles and responsibilities .................................................................................................................... 6.7

6.5

QA/QC plan ........................................................................................................................................ 6.8

6.6

General QC procedures ....................................................................................................................... 6.9

6.7

Category-specific QC procedures ...................................................................................................... 6.12

6.7.1

Emissions factor QC .................................................................................................................. 6.12

6.7.2

Activity data QC ........................................................................................................................ 6.14

6.7.3

Calculation-related QC .............................................................................................................. 6.16

6.8

QA procedures .................................................................................................................................. 6.17

6.9

QA/QC and uncertainty estimates ..................................................................................................... 6.18

6.10

Verification ....................................................................................................................................... 6.19

6.10.1

Comparisons of national estimates ............................................................................................ 6.19

6.10.2

Comparisons with atmospheric measurements .......................................................................... 6.21

6.11

Documentation, Archiving and Reporting ........................................................................................ 6.22

6.11.1

Internal documentation and archiving ....................................................................................... 6.22

6.11.2

Reporting ................................................................................................................................... 6.23

References ......................................................................................................................................................... 6.23 Annex 6A.1

QC checklists ............................................................................................................................. 6.25

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Tables Table 6.1

General inventory QC procedures ..................................................................................... 6.10

Boxes

6.4

Box 6.1

Definitions of QA/QC and verification ............................................................................... 6.5

Box 6.2

ISO standards related to quality management systems ........................................................ 6.9

Box 6.3

Evaluation of data quality on external data in the transportation sector ............................ 6.15

Box 6.4

Documentation of calculations .......................................................................................... 6.17

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Chapter 6: QA/QC and Verification

6 QUALITY ASSURANCE/QUALITY CONTROL AND VERIFICATION 6.1

INTRODUCTION

An important goal of IPCC inventory guidance is to support the development of national greenhouse gas inventories that can be readily assessed in terms of quality. It is good practice to implement quality assurance/quality control (QA/QC) and verification procedures in the development of national greenhouse gas inventories to accomplish this goal. The procedures as described in this chapter also serve to drive inventory improvement. The guidance is designed to achieve practicality, acceptability, cost-effectiveness, incorporation of existing experience, and the potential for application on a world-wide basis. A QA/QC and verification system contributes to the objectives of good practice in inventory development, namely to improve transparency, consistency, comparability, completeness, and accuracy of national greenhouse gas inventories. QA/QC and verification activities should be integral parts of the inventory process. The outcomes of QA/QC and verification may result in a reassessment of inventory or category uncertainty estimates and to subsequent improvements in the estimates of emissions or removals. For example, the results of the QA/QC process may point to particular variables within the estimation methodology for a certain category that should be the focus of improvement efforts. The terms ‘quality control’, ‘quality assurance’, and ‘verification’ are often used in different ways. The definitions of QC, QA, and verification in Box 6.1 will be used for the purposes of this guidance.

BOX 6.1 DEFINITIONS OF QA/QC AND VERIFICATION

Quality Control (QC) is a system of routine technical activities to assess and maintain the quality of the inventory as it is being compiled. It is performed by personnel compiling the inventory. The QC system is designed to: (i)

Provide routine and consistent checks to ensure data integrity, correctness, and completeness;

(ii) Identify and address errors and omissions; (iii) Document and archive inventory material and record all QC activities. QC activities include general methods such as accuracy checks on data acquisition and calculations, and the use of approved standardised procedures for emission and removal calculations, measurements, estimating uncertainties, archiving information and reporting. QC activities also include technical reviews of categories, activity data, emission factors, other estimation parameters, and methods. Quality Assurance (QA) is a planned system of review procedures conducted by personnel not directly involved in the inventory compilation/development process. Reviews, preferably by independent third parties, are performed upon a completed inventory following the implementation of QC procedures. Reviews verify that measurable objectives (data quality objectives, see Section 6.5, QA/QC Plan.) were met, ensure that the inventory represents the best possible estimates of emissions and removals given the current state of scientific knowledge and data availability, and support the effectiveness of the QC programme. Verification refers to the collection of activities and procedures conducted during the planning and development, or after completion of an inventory that can help to establish its reliability for the intended applications of the inventory. For the purposes of this guidance, verification refers specifically to those methods that are external to the inventory and apply independent data, including comparisons with inventory estimates made by other bodies or through alternative methods. Verification activities may be constituents of both QA and QC, depending on the methods used and the stage at which independent information is used.

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Volume 1: General Guidance and Reporting

Before implementing QA/QC and verification activities, it is necessary to determine which techniques should be used, and where and when they will be applied. QC procedures may be general with a possible extension to category specific procedures. There are technical and practical considerations in making these decisions. The technical considerations related to the various QA/QC and verification techniques are discussed in general in this chapter, and specific applications to categories are described in the category-specific guidance in Volumes 2 to 5. The practical considerations involve assessing national circumstances such as available resources and expertise, and the particular characteristics of the inventory (e.g., whether or not a category is key).

6.2

PRACTICAL CONSIDERATIONS IN DEVELOPING QA/QC AND VERIFICATION SYSTEMS

In practice inventory compilers do not have unlimited resources. Quality control requirements, improved accuracy and reduced uncertainty need to be balanced against requirements for timeliness and cost effectiveness. A good practice system for QA/QC and verification seeks to achieve that balance, and also to enable continuous improvement of inventory estimates. Judgements to select the respective parameters will need to be made on the following: •

•

Resources allocated to QA/QC for different categories and the compilation process;

•

Frequency of QA/QC checks and reviews on different parts of the inventory;

•

Time allocated to conduct the checks and reviews of emissions and removal estimates;

•

The level of QA/QC appropriate for each category;

•

Availability and access to information on activity data, emission factors and other estimation parameters, including uncertainties and documentation;

•

Acquisition of additional data specifically required, e.g., alternative data sets for comparisons and checks;

•

Requirements for documenting and archiving information;

•

Procedures to ensure confidentiality of inventory and category information, when required;

•

Whether increased effort on QA/QC will result in improved estimates and reduced uncertainties; Whether sufficient independent data and expertise are available to conduct verification activities.

In order to prioritise QA/QC and verification efforts for certain categories, particularly in terms of activities requiring more intensive analysis and review, the following questions should be asked to identify where to focus such activities in a given inventory development cycle: •

•

•

• •

6.6

Is this source/sink a key category according to the definition and methodologies presented in Chapter 4, Methodological Choice and Identification of Key Categories? Has the category been designated as key for qualitative reasons? For example: -

Is there considerable uncertainty associated with the estimates for this category?

-

Have there been significant changes in the characteristics of this category, such as technology changes or management practices?

-

Have significant changes occurred recently in the estimation methodology used for this category?

-

Are there significant changes in the trends of emissions or removals for this category?

Does the methodology use complex modelling steps or large inputs from outside databases? Are emission factors or other parameters associated with the estimation methodology significantly different to recognized IPCC defaults or data used in other inventories? Has a significant amount of time passed since emission factors or other parameters have been updated for this category? Has a significant amount of time passed since this category last underwent thorough QA/QC and verification procedures?

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Chapter 6: QA/QC and Verification • •

Has there been a significant change in how data are processed or managed for this category, such as a database platform change or change in modelling software? Is there a potential overlap with estimates reported under other categories (for example because of common activity data) that can generate double counting or incomplete estimates?

Answering yes to the above questions should help identify those sources/sinks where category specific QA/QC and verification activity should be prioritised. Also, the timing of the QA/QC activity should coincide with changes in the category. One time changes in methodologies or data processing, for example, may only require intensified QA/QC within the inventory cycle where those changes occurred. In terms of the implementation of QA/QC procedures, there should be no difference between confidential and publicly available data; both should carry descriptions of the measurement and calculation procedures and the steps taken to check and verify the values reported. These procedures may be carried out on the confidential data by either the provider of the information or by the inventory compiler and, in either case, confidential source data should be protected and archived accordingly. However, the QA/QC procedures that are implemented need to remain transparent and their description available for review. For example, when data are aggregated across categories at a national level to protect confidentiality, the report should contain a description of the relevant QA/QC procedures.

6.3

ELEMENTS OF A QA/QC AND VERIFICATION SYSTEM

The following are the major elements of a QA/QC and verification system to be implemented in tracking inventory compilation, which are covered in detail in the following sections: •

•

Participation of an inventory compiler who is also responsible for coordinating QA/QC and verification activities and definition of roles/responsibilities within the inventory;

•

A QA/QC plan;

•

Category-specific QC procedures;

•

QA/QC system interaction with uncertainty analyses;

•

General QC procedures that apply to all inventory categories ;

•

QA and review procedures;

•

Verification activities; Reporting, documentation, and archiving procedures.

A complete QA/QC and verification system will typically consist of the elements mentioned above. General QC procedures should be applied routinely to all categories and to the inventory compilation as a whole. In addition, category-specific procedures based on the prioritisation considerations discussed in Section 6.2 should be used. Verification activities may be directed at specific categories or the inventory as a whole, and their application will depend on the availability of independent estimation methodologies that can be used for comparison.

6.4

ROLES AND RESPONSIBILITIES

The inventory compiler should be responsible for coordinating the institutional and procedural arrangements for inventory activities. It is good practice for the inventory compiler to define specific responsibilities and procedures for the planning, preparation, and management of inventory activities, including: •

•

•

•

•

•

Data collection; Selection of methods, emission factors, activity data and other estimation parameters; Estimation of emissions or removals; Uncertainty assessment; QA/QC and verification activities; Documentation and archiving.

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The inventory compiler may designate responsibilities for implementing and documenting QA/QC procedures to other agencies or organisations, such as in cases where national activity data are provided by a central statistical agency. The inventory compiler should ensure that other organisations involved in the preparation of the inventory are following applicable QA/QC procedures and that appropriate documentation of these activities is available. The inventory compiler is also responsible for ensuring that the QA/QC plan is developed and implemented. It is good practice for the inventory compiler to designate a QA/QC coordinator as the person responsible for ensuring that the objectives of the QA/QC process as set out in the QA/QC plan (see Section 6.5) are met.

6.5

QA/QC PLAN

A QA/QC plan is a fundamental element of a QA/QC and verification system. The plan should, in general, outline the QA/QC and verification activities that will be implemented and the institutional arrangements and responsibilities for implementing those activities. The plan should include a scheduled time frame for the QA/QC activities that follows inventory preparation from its initial development through to final reporting in any year. The QA/QC plan is an internal document to organise and implement QA/QC and verification activities that ensure the inventory is fit for purpose and allow for improvement. Once developed, it can be referenced and used in subsequent inventory preparation, or modified as appropriate (notably, when changes in processes occur or on advice of independent reviewers). A key component of a QA/QC plan is the list of data quality objectives, against which an inventory can be measured in a review. Data quality objectives are concrete targets to be achieved in the inventory preparation. They should be appropriate, realistic (taking national circumstances into account) and allow for an improvement of the inventory. Where possible, data quality objectives should be measurable. Such data quality objectives may be based upon and refined from the following inventory principles: •

•

Timeliness

•

Consistency (internal consistency as well as time series consistency)

•

Accuracy

•

Completeness

•

Comparability

•

Transparency Improvement

As part of the QA/QC plan, it is good practice to accommodate procedural changes and a feedback of experience. Conclusions from previous reviews need to be used to improve the procedures. Such changes can also concern data quality objectives and the QA/QC plan itself. The periodic review and revision of the QA/QC plan is an important element to drive the continued inventory improvement. In developing and implementing the QA/QC plan, it may be useful to refer to relevant standards and guidelines published by outside groups involved in inventory development. For example, the International Organization for Standardization (ISO) introduced specifications for quantification, monitoring, and reporting of greenhouse gas emissions and removals (ISO 14064) in organisations. These and other relevant ISO standards are listed in Box 6.2. Also, there are guidelines for corporate and entity level QA/QC and verification techniques, which may be reflected in the overall inventory QA/QC process for categories whose estimates rely on data prepared under those guidelines. Examples of such guidelines include the Greenhouse Gas Protocol developed by the World Business Council for Sustainable Development and the World Resources Institute (The greenhouse gas protocol – A corporate accounting and reporting standard. ISBN 156973-568-9), the Guidelines for the monitoring and reporting of greenhouse gas emissions pursuant to Directive 2003/87/EC, as well as a variety of other regional and national guidelines for emissions trading and reporting systems. Any specific details of a QA/QC and verification system should be defined in the QA/QC plan so that national circumstances can be taken into account.

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BOX 6.2 ISO STANDARDS RELATED TO QUALITY MANAGEMENT SYSTEMS

The International Organization for Standardization (ISO) series programme provides standards for data documentation and audits as part of a quality management system. Within the ISO series, there are several standards that relate to the compilation of greenhouse gas inventories, independent validation and verification, and the accreditation and the requirements for validation and verification bodies. ISO 14064-1:2006 Greenhouse gases – Part 1: Specification with guidance at the organisation level for quantification and reporting of greenhouse gas emissions and removals ISO 14064-2:2006 Greenhouse gases – Part 2: Specification with guidance at the project level for quantification, monitoring and reporting of greenhouse gas emission reductions or removal enhancements ISO 14064-3:2006 Greenhouse gases – Part 3: Specification with guidance for the validation and verification of greenhouse gas assertions Many of the good practice principles of quality management derive from a series of generic quality related standards and their subsidiary parts. Inventory compilers may find these documents useful as source material for developing QA/QC plans for greenhouse gas inventories. ISO 9000:2000 Quality management systems – Fundamentals and vocabulary ISO 9001:2000 Quality management systems – Requirements ISO 9004:2000 Quality management systems – Guidelines for performance improvements ISO 10005:1995 Quality management – Guidelines for quality plans ISO 10012:2003 Measurement management systems – Requirements for measurement processes and measuring equipment ISO/TR 10013:2001 Guidelines for quality management system documentation ISO 19011:2002 Guidelines for quality and/or environmental management systems auditing ISO 17020:1998 General criteria for the operation of various types of bodies performing inspection Source: http://www.iso.org/

6.6

GENERAL QC PROCEDURES

General QC procedures include generic quality checks related to calculations, data processing, completeness, and documentation that are applicable to all inventory source and sink categories. Table 6.1, General inventory level QC procedures, lists the general QC checks that the inventory compiler should use routinely throughout the preparation of the inventory. The checks in Table 6.1 should be applied irrespective of the type of data used to develop the inventory estimates. They are equally applicable to categories where default values or national data are used as the basis for the estimates. The results of these QC activities and procedures should be documented as set out in Section 6.11.1, Internal Documentation and archiving, below. Although general QC procedures are designed to be implemented for all categories and on a routine basis, it may not be necessary or possible to check all aspects of inventory input data, parameters and calculations every year. Checks may be performed on selected sets of data and processes. A representative sample of data and calculations from every category may be subjected to general QC procedures each year. In establishing criteria and processes for selecting sample data sets and processes, it is good practice for the inventory compiler to plan to undertake QC checks on all parts of the inventory over an appropriate period of time as determined in the QA/QC plan.

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TABLE 6.1 GENERAL INVENTORY QC PROCEDURES QC Activity Check that assumptions and criteria for the selection of activity data, emission factors, and other estimation parameters are documented.

Check for transcription errors in data input and references.

Procedures •

• • •

Check that emissions and removals are calculated correctly.

•

•

Check that parameters and units are correctly recorded and that appropriate conversion factors are used.

• •

•

•

Check the integrity of database files.

Check for consistency in data between categories.

• •

Check that the movement of inventory data among processing steps is correct.

• •

Check that uncertainties in emissions and removals are estimated and calculated correctly.

•

•

•

•

• Check time series consistency.

•

•

6.10

Cross-check descriptions of activity data, emission factors and other estimation parameters with information on categories and ensure that these are properly recorded and archived.

Confirm that bibliographical data references are properly cited in the internal documentation. Cross-check a sample of input data from each category (either measurements or parameters used in calculations) for transcription errors. Reproduce a set of emissions and removals calculations. Use a simple approximation method that gives similar results to the original and more complex calculation to ensure that there is no data input error or calculation error. Check that units are properly labelled in calculation sheets. Check that units are correctly carried through from beginning to end of calculations. Check that conversion factors are correct. Check that temporal and spatial adjustment factors are used correctly. Examine the included intrinsic documentation (see also Box 6.4) to: -

confirm that the appropriate data processing steps are correctly represented in the database.

-

confirm that data relationships are correctly represented in the database.

-

ensure that data fields are properly labelled and have the correct design specifications.

-

ensure that adequate documentation of database and model structure and operation are archived.

Identify parameters (e.g., activity data, constants) that are common to multiple categories and confirm that there is consistency in the values used for these parameters in the emission/removal calculations. Check that emissions and removals data are correctly aggregated from lower reporting levels to higher reporting levels when preparing summaries. Check that emissions and removals data are correctly transcribed between different intermediate products. Check that qualifications of individuals providing expert judgement for uncertainty estimates are appropriate. Check that qualifications, assumptions and expert judgements are recorded. Check that calculated uncertainties are complete and calculated correctly. If necessary, duplicate uncertainty calculations on a small sample of the probability distributions used by Monte Carlo analyses (for example, using uncertainty calculations according to Approach 1). Check for temporal consistency in time series input data for each category. Check for consistency in the algorithm/method used for calculations throughout the time series. Check methodological and data changes resulting in recalculations. Check that the effects of mitigation activities have been appropriately reflected in time series calculations.

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TABLE 6.1 (CONTINUED) GENERAL INVENTORY QC PROCEDURES QC Activity

Procedures •

• Check completeness.

•

•

•

Trend checks.

•

• •

Review of internal documentation and archiving.

• • •

Confirm that estimates are reported for all categories and for all years from the appropriate base year to the period of the current inventory. For subcategories, confirm that entire category is being covered. Provide clear definition of ‘Other’ type categories. Check that known data gaps that result in incomplete estimates are documented, including a qualitative evaluation of the importance of the estimate in relation to total emissions (e.g., subcategories classified as ‘not estimated’, see Chapter 8, Reporting Guidance and Tables). For each category, current inventory estimates should be compared to previous estimates, if available. If there are significant changes or departures from expected trends, re-check estimates and explain any differences. Significant changes in emissions or removals from previous years may indicate possible input or calculation errors. Check value of implied emission factors (aggregate emissions divided by activity data) across time series. -

Do any years show outliers that are not explained?

-

If they remain static across time series, are changes in emissions or removals being captured?

Check if there are any unusual and unexplained trends noticed for activity data or other parameters across the time series. Check that there is detailed internal documentation to support the estimates and enable reproduction of the emission, removal and uncertainty estimates. Check that inventory data, supporting data, and inventory records are archived and stored to facilitate detailed review. Check that the archive is closed and retained in secure place following completion of the inventory. Check integrity of any data archiving arrangements of outside organisations involved in inventory preparation.

In some cases, estimates are prepared for the inventory compiler by outside consultants or agencies. The inventory compiler should ensure that the consultants/agencies are aware of the QC procedures listed in Table 6.1 and that these procedures are performed and recorded. In cases where the inventory relies upon official national statistics – as is often the case for activity data – QC procedures may already have been implemented on these national data. However, it is good practice for the inventory compiler to confirm that national statistical agencies have implemented QC procedures equivalent to those in Table 6.1. Because activity data may have been collected for other purposes using standards and data quality objectives different from the inventory, additional QC checks may be necessary. In applying general QC procedures, particular attention should also be given to parts of the inventory development that rely on external, and shared databases. Note that this requirement also includes the case of confidential data. An example of this situation is where a national database may be used for compiling information for a large number of point emission sources. The inventory compiler needs to confirm that quality control of data coming from integrated databases has taken place, or QC should be conducted by the inventory compiler if existing protocols from the data provider are not adequate. Due to the quantity of data that needs to be checked for some categories, automated checks are encouraged where possible. For example, one of the most common QC activities involves checking that data typed into a computer database are correct. A QC procedure could be set up to use an automated range check (based on the range of expected values of the input data from the original reference) for the input values as recorded in the database (see e.g., Winiwarter and Schimak, 2005). A combination of manual and automated checks may constitute the most effective procedures in checking large quantities of input data.

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6.7

CATEGORY-SPECIFIC QC PROCEDURES

Category-specific QC complements general inventory QC procedures and is directed at specific types of data used in the methods for individual source or sink categories. These procedures require knowledge of the specific category, the types of data available and the parameters associated with emissions or removals, and are performed in addition to the general QC checks listed in Table 6.1. Category-specific procedures are applied on a case-by-case basis focusing on key categories (see Chapter 4, Methodological Choice and Identification of Key Categories) and on categories where significant methodological and data revisions have taken place. In particular, inventory compilers applying higher tier methods in compiling national inventories should utilise categoryspecific QC procedures to help evaluate the quality of national approaches. Specific applications of categoryspecific QC procedures are provided in the Energy, Industrial Processes and Product Use (IPPU), Agriculture, Forestry and Other Land Use (AFOLU), and Waste Volumes of this report (Volumes 2 to 5). Category-specific QC activities include both emissions (or removals) data QC and activity data QC. The relevant QC procedures will depend on the method used to estimate the emissions or removals for a given category. If outside agencies develop estimates, the inventory compiler may, upon review, reference the QC activities of the outside agency as part of the QA/QC plan. There is no need to duplicate QC activities if the inventory compiler is satisfied that the QC activities performed by the outside agency meet the requirements of the QA/QC plan. Several of the checking procedures mentioned in this section draw on comparisons with independent datasets. It is important to understand that discrepancies will not always indicate a problem – especially if alternate datasets are a priori expected to be less relevant and for this reason are not used for calculations directly. It should be an aim of inventory compilation to address and if possible explain such discrepancies.

6.7.1

Emissions factor QC

The following sections describe QC checks on IPCC default emission factors, country-specific emission factors, and direct emission measurements from individual sites (used either as the basis for a site-specific emission factor or directly for an emissions estimate). While the term ‘emissions’ is used in this section, the same types of activities are applicable to calculation parameters for ‘removals’ as well. Inventory compilers should take into account the practical considerations discussed in Section 6.2, Practical Considerations in Developing QA/QC and Verification Systems, when determining what level of QC activities to undertake.

6.7.1.1

IPCC

DEFAULT EMISSION FACTORS

When using IPCC default emission factors, it is good practice for the inventory compiler to assess the applicability of these factors to national circumstances. This assessment may include an evaluation of national conditions compared to the context of the studies upon which the IPCC default emission factors were based. If there is insufficient information on the context of the IPCC default emission factors, the inventory compiler should take account of this in assessing the uncertainty of the national emissions estimates based on the IPCC default emission factors. If possible, a supplemental activity is to compare IPCC default emission factors with site or plant-level factors to determine their representativeness relative to actual sources in the country. This supplementary check is good practice even if data are only available for a small percentage of sites or plants.

6.7.1.2

C OUNTRY - SPECIFIC

EMISSION FACTORS

Country-specific emission factors may be developed at a national or other aggregated level within the country based on prevailing technology, science, local characteristics and other criteria. These factors are not necessarily site-specific, but are used to represent a source/sink category or subcategory of the country. The following types of QC checks should be used to evaluate the quality of country-specific factors.

QC checks on the background data used to develop emission factors: It is important to assess the adequacy of the emission factors and the QA/QC performed during their development. If emission factors are based on site-specific or source-level testing, then the inventory compiler should check if the measurement programme included appropriate QC procedures (see Section 6.7.1.3 on QC for direct emission measurements).

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Frequently, country-specific emission factors will be based on secondary data sources, such as published studies or other literature.1 In these cases, the inventory compiler could attempt to determine whether the QC activities conducted during the original preparation of the data are consistent with the applicable QC procedures outlined in Table 6.1 and whether any limitations of the secondary data have been identified and documented. The inventory compiler could also attempt to establish whether the secondary data have undergone peer review and record the scope of such a review. Specifically, it is important to investigate any potential conflicts-of-interest, when the interests of a data provider, e.g., financial interests, might influence results. If the QA/QC associated with the secondary data is inadequate, the inventory compiler should attempt to establish QA/QC checks on the secondary data. The inventory compiler should also reassess the uncertainty of any emissions estimates derived from the secondary data. The inventory compiler may also want to consider if any alternative data, including IPCC default values, may provide a better estimate of emissions from this category.

QC checks on Models: Because models are means of extrapolating and/or interpolating from a limited set of known data, they often require assumptions and procedural steps to represent the entire inventory area. If QA/QC associated with models is inadequate or not transparent, the inventory compiler should attempt to establish checks on the models and data. In particular, the inventory compiler should check the following: (i)

Appropriateness of model assumptions, extrapolations, interpolations, calibration-based modifications, data characteristics, and their applicability to the greenhouse gas inventory methods and national circumstances;

(ii)

Availability of model documentation, including descriptions, assumptions, rationale, and scientific evidence and references supporting the approach and parameters used for modelling;

(iii)

Types and results of QA/QC procedures, including model validation steps, performed by model developers and data suppliers. Responses to these results should be documented;

(iv)

Plans to periodically evaluate and update or replace assumptions with appropriate new measurements. Key assumptions may be identified by performing sensitivity analyses;

(v)

Completeness in relation to the IPCC source/sink categories.

Comparison with IPCC default factors: Inventory compilers should compare country-specific factors with relevant IPCC default emission factors, taking into consideration the characteristics and properties on which the default factors are based. The intent of this comparison is to determine whether country-specific factors are reasonable, given similarities or differences between the national source/sink category and the ‘average’ category represented by the defaults. Large differences between country-specific factors and default factors do not necessarily indicate problems, but nevertheless may point to quality issues if the differences can not be explained.

Comparisons of emission factors between countries: Between-country emission factor comparisons can be combined with historic trends by plotting, for different countries, the reference year value (e.g., 1990), the most recent year value, and the minimum and maximum values. This analysis could be made for each source/sink category and possible aggregations. Comparisons between countries can also be made using aggregate emissions divided by activity data (implied emission factors). This type of comparison may enable outlier detection based on the statistical distribution of values from the sample of countries considered. When using between-country emission factor comparisons as a QC check, it is important to investigate similarities and differences in national circumstances for the relevant category. If source/sink category characteristics are dissimilar between countries, this diminishes the effectiveness of this check.

Comparison to plant-level emission factors: A supplementary step is to compare the countryspecific factors with site-specific or plant-level factors if these are available. For example, if there are emission factors available for a few plants (but not enough to support a bottom-up approach) these plant-specific factors could be compared with the aggregated factor used in the inventory. This type of comparison provides an indication of both the reasonableness of the country-specific factor and its representativeness.

1

Secondary data sources refer to reference sources for inventory data that are not designed for the express purpose of inventory development. Secondary data sources typically include national statistical databases, scientific literature, and other studies produced by agencies or organisations not associated with the inventory development.

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6.7.1.3

D IRECT

EMISSION MEASUREMENTS

Emissions from a category may be estimated using direct measurements in the following ways:

•

•

Sample emissions measurements from a facility may be used to develop a representative emission factor for that individual site, or for the entire category (i.e., for development of a national level emission factor); Continuous emissions monitoring (CEM) data may be used to compile an annual estimate of emissions for a particular process. Properly implemented, CEM can provide a complete set of quantified emissions data across the inventory period for an individual facility process, and does not have to be correlated back to a process parameter or input variable like an emission factor.

The data provider should check all measurements as part of the QC activities . The use of standard measurement methods improves the consistency of resulting data and knowledge of the statistical properties of the data. If standard reference methods for measuring specific greenhouse gas emissions (and removals) are available, inventory compilers should encourage plants to use these. Plants and facilities that implement direct measurements as part of official regulatory requirements may have mandated measurement QC standards already in place. If specific standard methods are not available, the inventory compiler should confirm whether nationally or internationally recognised standard procedures to quantify performance characteristics of air quality measurement (such as ISO 10012) are used to characterize the measurements, and whether the measurement equipment is calibrated, maintained, and situated such that it gives a representative result. Additional details on using direct measurements are provided in Chapter 2, Approaches to Data Collection, specifically in Table 2.2. Where direct measurement data from individual sites are in question, discussions with site managers can be useful to encourage improvement of the QA/QC practices at the sites. Also, supplementary QC activities are encouraged for bottom-up methods based on site-specific emission factors where significant uncertainties remain in the estimates. Site-specific factors can be compared between sites and also to IPCC or national level defaults. Distinct differences between sites or between a particular site and the IPCC defaults should elicit further review and checks on calculations. Large differences should be explained and documented.

6.7.2

Activity data QC

The estimation methods for many categories rely on the use of activity data and associated input variables that are not directly prepared by the inventory compiler. Activity data at a national level are normally drawn from secondary data sources or site-specific data prepared by site or plant personnel from their own measurements. Inventory compilers should take into account the practical considerations discussed in Section 6.2 when determining the level of QC activities to undertake.

6.7.2.1

N ATIONAL

LEVEL ACTIVITY DATA

Following are fundamental QC checks that should be considered for assessing the quality of national level activity data. In all cases, it is important to have a well-defined and documented data set from which appropriate checks can be developed.

QC checks of reference source for national activity data: When using national activity data from secondary data, it is good practice for the inventory compiler to evaluate and document the associated QA/QC activities. This is particularly important with regard to activity data, since most activity data are originally prepared for purposes other than as input to estimates of greenhouse gas emissions. Many statistical organisations, for example, have their own procedures for assessing the quality of the data independently of what the end use of the data may be. The inventory compiler should determine if the level of QC associated with secondary activity data includes, at a minimum, those QC procedures listed in Table 6.1. In addition, the inventory compiler may check for any peer review of the secondary data and document the scope of this review. If the QA/QC associated with the secondary data is adequate, then the inventory compiler can simply reference the data source and document the applicability of the data for use in its estimates (see Box 6.3 for an example of this procedure). If the QC associated with the secondary data is inadequate or if the data have been collected using standards/definitions that deviate from this guidance, then the inventory compiler should establish QA/QC checks on the secondary data. The uncertainty of estimates should be reassessed in the light of the findings. The inventory compiler should also reconsider how the data are used and whether any alternative data and international data sets may provide a better estimate of emissions or removals. If no alternative data sources are

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available, the inventory compiler should document the inadequacies associated with the secondary data QC as part of its summary report on QA/QC.

BOX 6.3 EVALUATION OF DATA QUALITY ON EXTERNAL DATA IN THE TRANSPORTATION SECTOR

Countries typically use either fuel usage or kilometer (km) statistics to develop emissions estimates. The national statistics on fuel usage and km travelled by vehicles are usually prepared by a specialised agency. However, it is the responsibility of the inventory compiler to determine which QA/QC activities were implemented by the agency that prepared the original fuel usage and km statistics for vehicles. Questions that may be asked in this context are: • •

Does the statistical agency have a QA/QC plan that covers the collection and handling of the data?

•

Was an adequate sampling protocol used to collect data on fuel usage or km travelled?

•

Has any potential bias in the data been identified by the statistical agency?

•

How recently was the sampling protocol reviewed?

•

Has the statistical agency identified and documented uncertainties in the data? Has the statistical agency identified and documented errors in the data?

Comparisons with independently compiled data sets: Where possible, a comparison check of the national activity data with independently compiled activity data sources should be undertaken. For example, many of the agricultural source-categories rely on government statistics for activity data such as livestock populations and production by crop type. Comparisons can be made to similar statistics prepared by the United Nations Food and Agriculture Organization (FAO). Similarly, the International Energy Agency (IEA) maintains a database on national energy production and usage that can be used for checks in the energy. Industry trade associations, university research, and scientific literature are also possible sources of independently derived activity data to use in comparison checks. Activity data may also derive from balancing approaches – see Section 6.7.2.2 for a description and an example. As part of the QC check, the inventory compiler should ascertain whether alternative activity data sets are really based on independent data. International information is often based on national reporting which is not independent from the data used in the inventory. Available scientific or technical literature may also be used for a national inventory. In some cases, the same data are treated differently by different agencies to meet varying needs. Comparisons may need to be made at a regional level or with a subset of the national data since many alternative references for such activity data have limited scope and do not cover the entire nation. Comparisons with samples: The availability of partial data sets at sub-national levels may provide opportunities to check the reasonableness of national activity data. For example, if national production data are being used to calculate the inventory for an industrial category, it may also be possible to obtain plant-specific production or capacity data for a subset of the total population of plants. Extrapolation of the sample production data to a national level can then be done using a simple approximation method. The effectiveness of this check depends on how representative the sub-sample is of the national population, and how well the extrapolation technique captures the national population. Trend checks of activity data: National activity data should be compared with previous year’s data for the category being evaluated. Activity data for most categories tend to exhibit relatively consistent changes from year to year without sharp increases or decreases. If the national activity data for any year diverge greatly from the historical trend, they should be checked for errors. If a calculation error is not detected, the reason for the sharp change in activity should be confirmed and documented. A more thorough approach to take advantage of similarities between years has been described in Chapter 5, Time Series Consistency.

6.7.2.2

S ITE - SPECIFIC

ACTIVITY DATA

Some estimation methods rely on the site-specific activity data used in conjunction with IPCC default or country-specific emission factors. Site or plant personnel typically prepare these estimates of activity, often for purposes not related to greenhouse gas inventories. QC checks should focus on any inconsistencies between sites

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to check whether these reflect errors, different measurement techniques, or real differences in emissions, operating conditions or technology. A variety of QC checks can identify errors in site-level activity data.

QC checks of measurement protocol: The inventory compiler should establish whether individual sites carried out measurements using recognised national or international standards. If the measurements conform to recognised national or international standards and a QA/QC process is in place, then no further QA/QC will be necessary. Acceptable QC procedures in use at the site may be directly referenced. If the measurements do not conform to standard methods and QA/QC is not acceptable, then the inventory compiler should carefully evaluate use of these activity data. Comparisons between sites and with national data: Comparisons of activity data from different reference sources and geographic scales can play a role in confirming activity data. For example, in estimating PFC emissions from primary aluminium smelting, many inventory compilers use smelter-specific activity data to prepare the inventory estimates. A QC check of the aggregated activity data from all aluminium smelters against national production statistics for the industry can identify major omissions or over-counting. Also, a comparison of production data across different sites, possibly with adjustments made for plant capacities, can indicate the reasonableness of the production data. Similar comparisons of activity data can be made for other manufacturing-based source categories where there are published data on national production. Any identified outliers should be investigated to determine if the difference can be explained by the unique characteristics of the site or there is an error in the reported activity data. Production and consumption balances: Site-specific activity data checks may also be applied to methods based on product usage. For example, one method for estimating SF6 emissions from the use in electrical equipment relies on an account balance of gas purchases, gas sales for recycling, the amount of gas stored on site (outside of equipment), handling losses, refills for maintenance, and the total holding capacity of the equipment system. This account balance system should be used at each facility where the equipment is in place. A QC check of overall national activity could be made by performing the same kind of account balancing procedure on a national basis. This national account balancing would consider national sales of SF6 for the use in electrical equipment, the nation-wide increase in the total handling capacity of the equipment that may be obtained from equipment manufacturers, and the quantity of SF6 destroyed in the country. The results of the bottom-up and top-down account balancing analyses should agree, or large differences should be explained. Similar accounting techniques can be used as QC checks on other categories based on gas usage, e.g., substitutes for ozone-depleting substances, to check consumption and emissions.

6.7.3

Calculation-related QC

The principles described above for the input data are similarly applicable to all calculation procedures used to prepare a national greenhouse gas inventory. Checks of the calculation algorithm will safeguard against duplication of inputs, unit conversion errors, or similar calculation errors. These checks can be independent ‘back-of-the-envelope’ calculations, which simplify the algorithms to arrive at an approximate method. If the original calculation and the simple approximate method disagree, it is good practice to examine both approaches to find the reason for discrepancy. Further checks on the calculation procedure will require external data (see Section 6.10, Verification). It is a prerequisite that all calculations leading to emission or removal estimates should be fully reproducible. It is good practice to discriminate between input data, the conversion algorithm of a calculation and the output. Not only does the output need to be recorded, but also the input, the conversion algorithm, and how this algorithm accesses the input. Box 6.4 provides practical hints how to record a calculation procedure in standard spreadsheet or database calculations. Such an approach allows for intrinsic documentation of the work, and for easy understanding of the calculation procedure. The documentation should be retained with the material archived in support of the completed inventory.

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BOX 6.4 DOCUMENTATION OF CALCULATIONS

When using spreadsheets: • • • •

Clearly reference to the data source of any numbers typed into the spreadsheet (see above documentation criteria for data sources). Provide subsequent calculations, in the form of formulas, so that auditing tools can be used to track back from a result to the source data, and calculations can be evaluated by analysing the formulae. Clearly mark cells in the spreadsheet containing derived data as ‘results’ and annotate them as to how and where they are then used. Document the spreadsheet itself specifying its name, version, authors, updates, intended use and checking procedures so that it can be used as a data source of the derived results and referenced further on in the inventory process.

When using databases: • • • •

6.8

Clearly reference the source data tables using a referencing column that links to the data source. Use queries when processing the data, where practical, as these provide the means to track back to the source data tables. Where queries are not practical and new tables of data need to be generated, make sure that scripts or macros of the commands used to derive the new data set are recorded and referenced in a referencing column of the dataset. Document the database itself specifying its name, version, authors, intended use and checking procedures so that it can be used as a data source of the derived results and referenced further on in the inventory process.

QA PROCEDURES

Quality assurance comprises activities outside the actual inventory compilation. Good practice for QA procedures includes reviews and audits to assess the quality of the inventory, to determine the conformity of the procedures taken and to identify areas where improvements could be made. QA procedures may be taken at different levels (internal/external), and they are used in addition to the general and category-specific QC procedures described in Section 6.7. The inventory may be reviewed as a whole or in parts. The objective of QA implementation is to involve reviewers that can conduct an unbiased review of the inventory and who may have a different technical perspective. It is important to use QA reviewers that have not been involved in preparing the inventory. Preferably these reviewers would be independent experts from other agencies or national or international experts or groups not closely connected with the national inventory compilation, e.g., inventory experts of other countries. Where third party reviewers who are independent from the inventory compiler are not available, persons who are at least not involved in the portion being reviewed can also perform QA. It is good practice for inventory compilers to conduct a basic expert peer review of all categories before completing the inventory in order to identify potential problems and make corrections where possible. However, this will not always be practical due to timing and resource constraints. Key categories should be given priority as well as categories where significant changes in methods or data have been made. Inventory compilers may also choose to perform more extensive peer reviews or audits as QA procedures within the available resources. In smaller countries, where there may not be external expertise in all technical areas, the inventory compiler should consider contacting inventory compilers from other countries as part of an external review. More specific information on QA procedures related to individual categories is provided in the category-specific QA/QC sections in Volumes 2-5.

EXPERT PEER REVIEW Expert peer review consists of a review of calculations and assumptions by experts in relevant technical fields. This procedure is generally accomplished by reviewing the documentation associated with the methods and

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results, but usually does not include rigorous certification of data or references such as might be undertaken in an audit.2 The objective of the expert peer review is to ensure that the inventory’s results, assumptions, and methods are reasonable as judged by those knowledgeable in the specific field. Also, where a country has formal stakeholder and public review mechanisms in place, these reviews can supplement expert peer reviews although they should not replace them. There are no standard tools or mechanisms for expert peer review of greenhouse gas inventories, and its use should be considered on a case-by-case basis. If there is a high level of uncertainty associated with an estimate for a category, expert peer review may provide information to improve the estimate, or at least to better quantify the uncertainty. Effective peer reviews often involve identifying and contacting key independent organisations or research institutions to identify the most appropriate individuals to conduct the review. It is preferable for this expert input to be sought early in the inventory development process so that the experts can provide review of methods and data acquisition that could affect final calculations. The results of expert analyses from the UNFCCC processes3 should also be considered as part of the overall QA improvement process. Results and suggestions from these processes can provide valuable feedback on areas where the inventories can be improved. However, these processes should only be considered as supplements to a nationally organised QA and review procedures. The results of expert peer review, and the response of the inventory compiler to those findings, may be important to general acceptance of the final inventory. All expert peer reviews should be well documented, preferably in a report or checklist format that shows the findings and recommendations for improvement.

AUDITS For the purpose of good practice in inventory preparation, audits may be used to evaluate how effectively the inventory compiler complies with the minimum QC specifications outlined in the QC plan. It is important that the auditor be independent of the inventory compiler as much as possible so as to be able to provide an objective assessment of the processes and data evaluated. Audits may be conducted during the preparation of an inventory, following inventory preparation, or on a previous inventory. Audits are especially useful when new estimation methods are adopted, or when there are substantial changes in existing methods. In contrast to an expert peer review, audits do not focus on the result of calculation. Instead, they provide an in-depth analysis of the respective procedures taken to develop an inventory, and on the documentation available. It is good practice for the inventory compiler to develop a schedule of audits at strategic points in the inventory development. For example, audits related to initial data collection, measurement work, transcription, calculation and documentation may be conducted. Audits can be used to verify that the QC steps identified in Table 6.1 have been implemented, that category-specific QC procedures have been implemented according to the QC plan, and that the data quality objectives have been met.

6.9

QA/QC AND UNCERTAINTY ESTIMATES

The QA/QC process and uncertainty analyses provide valuable feedback to one another. Staff involved in the QA/QC and uncertainty analyses can identify critical components of the inventory estimates and data sources that contribute to both the uncertainty level and inventory quality and which should therefore be a primary focus of inventory improvement efforts. This information should ultimately be useful in improving the methods and data sources used for the estimates. For example, the uncertainty analysis can provide insights into weaknesses in the estimate, the sensitivity of the estimate to different variables, and the greatest contributors to uncertainty, all of which can assist in setting priorities for improving data sources or methodologies. Some of the uncertainty estimation methods rely on the use of measured data associated with the emission factors or activity data to develop probability density functions from which uncertainty estimates can be made. In the absence of measured data, many uncertainty estimates will rely on expert judgement. It is good practice to apply QC procedures to uncertainty estimation to confirm that calculations are correct and data and calculations well documented. The assumptions on which uncertainty estimation has been based should be documented for each category. Calculations of category-specific and aggregated uncertainty estimates should be checked and any errors addressed. For uncertainty estimates involving expert judgement, the qualifications of experts should also 2

Formal expert review as defined by government agencies in some countries may include standardised procedures and other elements of a thorough audit, as described in this Chapter.

3

Examples of relevant processes include inventory reviews of Annex I Parties, reviews of National Communications and feedback from the Consultative Group of Experts on National Communications from Parties not included in Annex I to the Convention (CGE).

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be checked and documented, as should the process of eliciting expert judgement, including information on the data considered, literature references, assumptions made and scenarios considered. Chapter 2, Approaches to Data Collection, contains advice on how to document expert judgements on uncertainties.

6.10

VERIFICATION

For the purposes of this guidance, verification activities include comparisons with emission or removal estimates prepared by other bodies and comparisons with estimates derived from fully independent assessments, e.g., atmospheric concentration measurements. Verification activities provide information for countries to improve their inventories and are part of the overall QA/QC and verification system. Correspondence between the national inventory and independent estimates increases the confidence and reliability of the inventory estimates by confirming the results. Significant differences may indicate weaknesses in either or both of the datasets. Without knowing which dataset is better, it may be worthwhile to re-evaluate the inventory. This section describes approaches that can be used to verify inventory estimates at both the source/sink category and inventory wide levels. The considerations for selecting verification approaches include: scale of interest, costs, desired level of accuracy and precision, complexity of design and implementation of the verification approaches, availability of data, and the required level of expertise needed for implementation. Not all approaches will be available to every inventory compiler due to some of these criteria, particularly the techniques included in ‘comparisons with atmospheric measurements’ described in Section 6.10.2, which can be resource and data intensive. However, there are a number of relatively simple, comparison techniques that should be available to most inventory compilers, and that can be valuable tools in the overall QA/QC and verification system. As much information required may be available on a national level, we will refer to these as national activities. The same concept can easily be transferred to other spatial units, if data are available. Where verification techniques are used, they should be reflected in the QA/QC plan. The limitations and uncertainties associated with the verification technique itself should be thoroughly investigated prior to its implementation so that the results can be properly interpreted.

6.10.1

Comparisons of national estimates

There are a number of practical verification techniques that do not require specialised modelling expertise or extended analyses. Most of these can be considered as method-based comparisons that consider the differences in national estimates based on using alternative estimation methodologies for the same category or set of categories. These comparisons look for major calculation errors and exclusion of major source categories or subsource categories. Method-based comparisons can be designed around the multi-tier level of methods outlined for each category in the sector guidance, through comparisons to independent estimates developed by other institutions, and, to a limited extent, through cross-country comparisons. The choice of method will depend on the method used in the inventory, a clear definition and correlation of categories between methods, and the availability of alternative data. These checks can be extremely useful in confirming the reasonableness of national inventory estimates and may help identify any gross calculation errors. Some of these techniques, such as the compilation of the reference approach for Energy Sector estimates, should be considered as part of the inventory development process. Discrepancies between inventory data and data compiled using alternative methods do not necessarily imply that the inventory data are in error. When analysing discrepancies, it is important to consider that there may be large uncertainties associated with the alternative calculations themselves.

Applying lower tier methods: Lower tier IPCC methods typically are based on ‘top-down’ approaches that rely on highly aggregated data at a summary category level. Inventory compilers using higher tier, ‘bottomup’ approaches may consider using comparisons to lower-tier methods as a simple verification tool. As an example, for carbon dioxide (CO2) from fossil fuel combustion, a reference calculation based on apparent fuel consumption per fuel type is specified as a verification check in the Energy Sector procedures (see Volume 2: Energy). This reference approach estimate can be compared to the sum of sectoral-based estimates from a Tier 1, 2, or 3 approach. While the quality of the reference approach is typically lower than that of the sectoral approach, it remains useful as a simple approximation method. It is less sensitive to errors due to its simplicity and can be used as a top-down completeness check. Another example, where emissions are calculated as the sum of sectoral activities based on the consumption of a specific commodity, e.g., fuels or products like hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) or sulphur hexafluoride (SF6), the emissions could be estimated using apparent

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consumption figures, e.g., national total production + import – export ± stock changes, taking into consideration any possible time lags in actual emissions. Similar checks can be performed for industrial type sources, e.g., nitrous oxide (N2O) estimates for nitric acid production, where inventory estimates were determined for each individual production plant based on plantspecific data. The check of emission estimates would consist of the comparison between the sum of the individual plant-level emission estimates and a top-down emission estimate based on national nitric acid production figures and IPCC default Tier 1 factors. Large differences do not necessarily indicate that there are problems with the inventory estimate. As lower tier methods typically rely on more highly aggregated data, there may be relatively large uncertainties with the Tier 1 approach compared to an inventory estimated using a bottom up approach based on good practice. If differences cannot easily be explained, the inventory compiler may consider the following questions in any further QA/QC checks: • •

•

•

Are there inaccuracies associated with any of the individual plant estimates (e.g., an extreme outlier may be accounting for an unreasonable quantity of emissions)? Are the plant-specific emission factors significantly different from each other? Are the plant-specific production rates consistent with published national level production rates? Is there any other explanation for a significant difference, such as the effect of controls, the manner in which production is reported or possibly undocumented assumptions?

This is an example of how the results of a relatively simple emission check can lead to a more intensive investigation of the representativeness of the emissions data. Knowledge of the category is required to isolate the parameter that is causing the difference in estimates and to understand the reasons for the difference.

Applying higher tier methods: Higher tier IPCC methods typically are based on detailed ‘bottom-up’ approaches that rely on highly disaggregated data and a well-defined subcategorisation of sources and sinks. Inventory compilers may find that they can not fully implement a higher tier approach because they are lacking sufficient data or resources. However, the availability of even partial estimates for a subcategory of sources may provide a valuable verification tool for the inventory. An estimate based on higher tier data derived from a proportion of the total sources in a country can be extrapolated to the national level, provided that the sample is representative. Such an extrapolation can be used to corroborate the national estimate.

Comparisons with independently compiled estimates: Comparisons with other independently compiled inventory data on national level (if available) are a quick option to evaluate completeness, approximate emission (removal) levels and correct category allocations. Although the inventory compiler is ultimately responsible for preparing the national greenhouse gas inventory, other independent publications on this subject may be available e.g., from scientific literature or publication by other institutes or agencies. For example, national level CO2 emissions estimates associated with the combustion of fossil fuel are compiled by the International Energy Agency (IEA) and the Carbon Dioxide Information and Analysis Centre (CDIAC). Estimates of emissions of other pollutants are available from the Emission Database for Global Atmospheric Research (EDGAR) (http://www.mnp.nl/edgar/). If independently compiled datasets use IPCC Tier 1 methodologies, the same considerations discussed above will apply. While national data are normally considered more reliable as they are able to accommodate more detailed country-specific information, and international data are normally compiled at a lower tier, these international data sets provide a good basis for comparison as they are consistent between countries. The comparisons can be made for different greenhouse gases at national, sectoral, category, and subcategory levels, as far as the differences in definitions enable them. Before conducting these types of comparisons, it is important to check the following items. •

• •

Confirm that the underlying data for the independent estimate are not the same as that used for the inventory; a comparison is only meaningful if data being compared are different. Determine if the relationships between the sectors and categories in the different inventories can be defined and matched appropriately. Account for the data quality (e.g., QA/QC system or review) and for any known uncertainties in the estimate used for the comparison to help interpret results.

Comparisons of intensity indicators between countries: Emission (removal) intensity indicators, e.g., those commonly referred to as ‘implied emission (removal) factors', may be compared between countries (e.g., emissions per capita, industrial emissions per unit of value added, transport emissions per car, emissions from power generation per kWh of electricity produced, emissions from dairy ruminants per tonne of milk produced). These indicators provide a preliminary check and verification of the order of magnitude of the emissions or removals. Different practices and technological developments as well as the varying nature of the

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source categories will be reflected in the emission intensity indicators. Thus differences between countries need to be expected. However, these checks may flag potential anomalies at the country or sector level.

6.10.2

Comparisons with atmospheric measurements

An ideal condition for verification is the use of fully independent data as a basis for comparison. Measurements of atmospheric concentrations potentially provide such datasets, and recent scientific advances allow using such data as a basis for emission modelling. The approach is particularly valuable as it is independent of standard estimation method drivers, such as sector activity data and implied emission factors. The scale of such models can be designed around local, regional, or global boundaries and can provide information on either level or trends in emissions. Some brief examples of these techniques are provided in this section; however, further discussion and elaboration can be found in more comprehensive summaries on the use of these methods for inventory verification (Rypdal et al., 2005; Bergamaschi et al., 2004; Benkovitz, 2001; Benjey and Middleton, 2002; NACP, 2002). It should be recognized that the complexity as well as the limited application potential of atmospheric models to inventory verification, particularly at a national level, can restrict their utility to many inventory compilers. In addition, many of the techniques will require specialised modelling skills and resources in order to appropriately correlate the atmospheric data back to the inventory for comparison, and be cost- and labour intensive. Depending on specific conditions, results may be only applicable to parts of a country, to groups of countries, or to specific categories or gases. The required analysis time will also typically extend beyond an inventory cycle, thus making these types of comparisons more applicable for long term verification programs. In many cases, the uncertainties associated with the atmospheric models themselves may not be sufficiently quantified or may be too large for the model to be used effectively as a verification tool. In contrast to the other methods described in this chapter, comparisons with atmospheric measurements cannot therefore be a standard tool for verification to be applied by an inventory compiler. Still a considerable scientific progress in this area needs to be noted and inventory compilers may wish to take advantage of the potential of this approach, as it gives independent data for verification. If applicable, national inventory compilers may also consider joining forces with neighbouring countries, in cases when emission modelling from atmospheric measurement is more reliable for larger entities than countries. Despite the limitations given, there are a number of evolving techniques that deserve to be mentioned here:

Inverse Modelling: The concentrations of greenhouse gases in air samples are measured at monitoring sites and can be used to provide emission estimates by a technique known as inverse modelling. Inverse models calculate emission fluxes from concentration measurements and atmospheric transport models. For local and regional estimation, complex mathematical and statistical models are required together with continuous, or quasi-continuous, measurements that capture all pollution incidents. The source discrimination of air samplingderived emissions requires highly precise and labour-intensive analysis, which may prevent the applicability of inverse modelling approaches to source-specific emissions verification. In contrast to national inventories, flux assessments from inverse modelling include the effect of natural sources/sinks as well as international transport. Considering the limited monitoring network currently available for many of the greenhouse gases and the resulting uncertainties in the model results, inverse modelling is not likely to be frequently applied as a verification tool of national inventories in the near future. Even the availability of satellite-borne sensors for greenhouse gas concentration measurements (see Bergamaschi et al., 2004) will not fully resolve this problem, due to limitations in spatial, vertical and temporal resolution. However, there is increasing scientific recognition for the potential of these techniques for both level and trend verification of national inventories. Inverse modelling techniques are undergoing rapid development and are being applied now in national inventories estimates (O'Doherty et al., 2003), European emission estimates (Manning et al., 2003) and to provide geographical distributions of emissions within the European Union (Ryall et al., 2001). Ultimately, the application of these techniques relies on a comparison of the uncertainty between the calculated inventory estimates and the inverse model-derived estimates (Rypdal et al., 2005, Bergamaschi et al., 2004). Where the uncertainty of the model results is less than the calculated inventory uncertainty, the model can be used to improve the inventory. Also, where the model results are significantly different from the inventory, this can point to missing sources or possibly large calculation errors. Fluorinated gases and methane (CH4) are considered the most suitable greenhouse gases for which inverse modelling could provide verification of emission estimates (Rypdal et al., 2005, Bergamaschi et al., 2004). The fluorinated compounds are considered good candidates for inverse modelling verification because: they have virtually no natural source interference in the atmospheric measurements, there can be considerable uncertainties in inventory methods, they are long-lived, and the loss mechanisms are well known. Methane is considered a favourable candidate because of the generally high uncertainty in emission estimates resulting from inventory

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methodologies, and the strong atmospheric signal to noise ratio of measurements. Modelling of CO2 emissions for national inventory verification is probably not a priority since the inventory methods already have low uncertainties, except where agriculture, forestry and other land-use is dominant. The impacts of large natural sources and sinks on atmospheric measurements make a correlation to strictly anthropogenic sources difficult. However, it may improve understanding of contributions from forests and natural sources and sinks. Due to the large uncertainties associated with some of the N2O inventory methodologies verification through atmospheric measurements would be desirable. However, the influence of natural sources and sinks on measurements, as well as the long atmospheric lifetime lead to a poor signal to noise ratio in measured concentrations. Thus further investigations are required before inverse modelling can successfully be applied to the verification of inventories of N2O.

Continental Plumes: A strong difference between source and non-source regions may generally be found between a continent and an ocean where routine measurements of the difference between background air concentrations and the offshore plume concentrations, coupled with wind vector analysis or trajectory analysis, may provide an indication of emissions on a broad scale (Cape et al., 2001; Derwent et al., 2001). For example, a number of greenhouse gases, including chlorofluorocarbons (CFCs), N2O and CH4 from the European continental plume have been detected at Mace Head, Ireland. These results have then been used for subsequent quantification of the European emission source strength by inverse modelling (Derwent et al., 1998a, 1998b; Vermeulen et al., 1999). Use of Proxy Emission Databases: In the cases where one of the components measured in the air samples has a well characterised emission inventory (a ‘marker’ or ‘tracer’ compound), the emissions of greenhouse gases may be estimated from atmospheric measurements of their concentration ratio to this marker compound. The technique is appropriate if sources of the compounds are co-located, and it has been used in the U.S.A., for example with carbon monoxide (CO ) as the marker (Barnes et al., 2003a, 2003b), and in the EU employing radon (222Rn: Biraud et al., 2000).

Global Dynamic Approaches: Trends over time in the atmospheric concentration of particular compounds may also indicate a change in the global balance between sources and sinks and give an estimate of the globally aggregated emissions, constraining the total of national emissions from an aggregate perspective and possibly indicating areas of weakness in the inventories. Such approaches have been taken for CH4 (Dlugokencky et al., 1994), sulphur hexafluoride (SF6) (Maiss and Brenninkmeijer, 1998), PFC-14 and carbon tetrafluoride (CF4) ( Harnisch and Eisenhauer, 1998). These methods can be applicable to cover a large proportion of global emissions, and monitoring is possible on a routine basis.

6.11

DOCUMENTATION, ARCHIVING AND REPORTING

6.11.1

Internal documentation and archiving

It is good practice to document and archive all information relating to the planning, preparation, and management of inventory activities. This includes: •

•

• •

Responsibilities, institutional arrangements, and procedures for the planning, preparation, and management of the inventory process; Assumptions and criteria for the selection of activity data and emission factors; Emission factors and other estimation parameters used, including references to the IPCC document for default factors or to published references or other documentation for emission factors used in higher tier methods;

•

Activity data or sufficient information to enable activity data to be traced to the referenced source;

•

Rationale for choice of methods;

•

Changes in data inputs or methods from previous inventories (recalculations);

•

Information on the uncertainty associated with activity data and emission factors;

•

Methods used, including those used to estimate uncertainty and those used for recalculations;

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Chapter 6: QA/QC and Verification • • •

•

•

Details of electronic databases or software used in the production of the inventory, including versions, operating manuals, hardware requirements and any other information required to enable their later use; Worksheets and interim calculations for category estimates, and aggregated estimates and any recalculations of previous estimates; Final inventory report and any analysis of trends from previous years; QA/QC plans and outcomes of QA/QC procedures; Secure archiving of complete datasets, to include shared databases that are used in inventory development. This is particularly important for categories that rely on the multi-step development of emissions from a large set of primary data from outside sources.

It is good practice for inventory compilers to maintain this documentation for every inventory produced and to provide it for review. It is good practice to maintain and archive this documentation in such a way that every inventory estimate can be fully documented and reproduced if necessary. Records of QA/QC procedures are important information to enable continuous improvement to inventory estimates. It is good practice for records of QA/QC activities to include the checks/audits/reviews that were performed, when they were performed, who performed them, and corrections and modifications to the inventory resulting from the QA/QC activity. An example checklist to use for recording QC activities at both the general and category-level is provided in Annex 6A.1.

6.11.2

Reporting

It is good practice to report a summary of implemented QA/QC activities and key findings as a supplement to each country’s national inventory, which itself is described in Volumes 2-5 and by the tables in this volume. However, it is not practical or necessary to report all the internal documentation that is retained by the inventory compiler. In this summary, the inventory compiler should focus on the following activities. • • • •

Reference to a QA/QC plan, its implementation schedule, and the responsibilities for its implementation should be discussed. Describe which activities were performed internally and what external reviews were conducted for each source/sink category and on the entire inventory. Present the key findings, describing major issues regarding quality of input data, methods, processing, or estimates for each category and show how they were addressed or plan to be addressed in the future. Explain significant trends in the time series, particularly where trend checks point to substantial divergences. Any effect of recalculations or mitigation strategies should be included in this discussion.

References Barnes, D.H., Wofsy, S.C., Fehlau, B.P., Gottlieb, E.W., Elkins, J.W., Dutton, G.S. and Montzka S.A. (2003a) Urban/industrial pollution for the New York City-Washington, D. C., corridor, 1996-1998:1. Providing independent verification of CO and PCE emissions inventories, Geophys J. Res., 108(D6), 4185, 10.1029/2001JD001116, 2003a. Barnes, D.H., Wofsy, S.C., Fehlau, B.P., Gottlieb, E.W., Elkins, J.W., Dutton, G.S., and Montzka, S.A. (2003b). Urban/industrial pollution for the New York City-Washington, D. C., corridor, 1996-1998: 2. A study of the efficacy of the Montreal Protocol and other regulatory measures, Geophys J. Res., 108(D6), 4186, 10.1029/2001JD001117, 2003b. Benjey, W. and Middleton, P. (2002). ‘The Climate-Air Quality Scale Continuum and the Global Emission Inventory Activity.’ Presented at the EPA Emissions Conference, April 15-18. Benkovitz C. (2001). ‘Compilation of Regional to Global Inventories of Anthropogenic Emissions’. Submitted for publication in “Emissions of Chemical Species and Aerosols into the Atmosphere”, Precursors of Ozone and their Effects in the Troposphere (POET), Kluwer Academic Publishers, Dordrecht, Netherlands.

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Bergamaschi, P., Behrend, H. and Andre, J., eds.(2004). Inverse Modeling of National and EU Greenhouse Gas Emission Inventories. Report of the October 23-24 workshop “Inverse Modeling for Potential Verification of National and EU Bottom-up GHG Inventories”, held by the European Commission, Joint Research Centre. Report published. Biraud, S., Ciais, P., Ramonet, M., Simmonds, P., Kazan, V., Monfray, P., O'Doherty S., Spain T.G. and Jennings, S.G. (2000). European greenhouse gas emissions estimated from continuous atmospheric measurements and radon 222 at Mace Head, Ireland, J. Geophys. Res., 105(D1), 1351-1366. Cape, J.N., Methven, J. and Hudson L.E. (2000). The use of trajectory cluster analysis to interpret trace gas measurements at Mace Head, Ireland, Atmospheric Environment, 34 (22), 3651-3663. Derwent, R.G., Simmonds, P.G., O'Doherty, S. and Ryall, D.B. (1998a). The impact of the Montreal Protocol on halocarbon concentrations in northern hemisphere baseline and European air masses at Mace Head Ireland over a ten year period from 1987-1996, Atmospheric Environment 32(21), 3689-3702 Derwent, R.G., Simmonds, P.G., O'Doherty, S., Ciais P., and Ryall, D.B. (1998b). European source strengths and northern hemisphere baseline concentrations of radiatively active trace gases at Mace Head Ireland, Atmospheric Environment 32(21), 3703-3715. Derwent, R.G., Manning, A.J. and Ryall D.B. (2001). Interpretation of Long-Term Measurements of OzoneDepleting Substances and Radiatively Active Trace Gases: Phase III, Final Report: DETR Contract No: EPG 1/1/103, Dec 2001. Dlugokencky, E.J., Steele, L.P., Lang, P.M. and Mesarie, K.A., (1994). The growth rate and distribution of atmospheric CH4. J. Geophys. Res. 99, 17021-17043. EDGAR. Emission Database for Global Atmospheric Research (EDGAR). RIVM-MNP, Bilthoven, TNO-MEP, Apeldoorn, JRC-IES, Ispra and MPIC-AC, URL: http://www.mnp.nl/edgar/ Harnisch, J. and Eisenhauer, A. (1998). Natural CF4 and SF6 on Earth, Geophys. Res. Lett., 25(13), 2401-2404. IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Volumes 1, 2 and 3. Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds), Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. Levin I., Glatzel-Mattheier H., Marik T., Cuntz M., Schmidt M., Worthy D.E. (1999) Verification of German methane emission inventories and their recent changes based on atmospheric observations, J. Geophys. Res., 104, 3447-3456. Maiss, M. and Brenninkmeijer, C.A.M. (1998) Atmospheric SF6: trends, sources and prospects. Environ. Sci. Techn. 32, 3077-3086. Manning, A.J., Ryall, D.B., Derwent, R.G., Simmonds, P.G. and O'Doherty S. (2003). Estimating European emissions of ozone-depleting and greenhouse gases using observations and a modelling back-attribution technique, J. Geophys. Res. Vol. 108, No. D14, 4405, 10.1029/2002JD002312, 17 July 2003. NACP. (2002). The North American Carbon Programme. NACP Committee of the U.S. Carbon Cycle Science Steering Group (Steven C. Wofsy, Robert C. Harris, co-chairs), Chapter 2, Major Elements of the North American Carbon Program Plan. U.S. Global Change Research Program, Wachington, D.C., 2002. http://www.esig.ucar.edu/nacp O' Doherty, S., McCulloch, A., O' Leary, E., Finn, J. and Cunningham, D. (2003). Climate Change: Emissions of Industrial Greenhouse Gases (HFCs, PFCs and Sulphur Hexafluoride), Final Report, Environmental Protection Agency ERDTI Report Series No. 10, EPA, Johnstown Castle, C. Wexford, Ireland, 2003. Ryall, D.B., Derwent, R.G., Manning, A.J., Simmonds, P.G. and O'Doherty S. (2001). Estimating source regions of European emissions of trace gases from observations at Mace Head, Atmospheric Environment, 35, 2507-2523. Rypdal, K., Stordal, F., Fuglestvedt, J.S. and Berntsen, T. (2005). Bottom-up vs. top-down methods in assessing compliance with the Kyoto Protocol, Climate Policy 5, 393-405. Vermeulen, A.T., Eisma, R., Hensen, A. and Slanina J. (1999). Transport model calculations of NW-European methane emissions, Environmental Science & Policy, 2, 315-324. Winiwarter, W. and Schimak G. (2005). Environmental Software Systems for Emission Inventories, Environmental Modelling & Software 20, 1469-1477.

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Annex 6A.1 QC checklists FORMS AND CHECKLISTS FOR QUALITY CONTROL FOR SPECIFIC SOURCE CATEGORIES This annex contains a number of example forms that provide means to record both general and category-specific QC activities. These forms are only examples, and inventory compilers may find other means to effectively record their QA/QC activities (to be defined in the QA/QC plan). Refer to the IPCC Guidelines chapters on QA/QC and Verification, Data Collection, and for each category as described in Volume 2-5 for more detailed guidance on developing QC checks.

A1. GEN ERA L QC CHECKLIST (to be completed for each catego ry and for each inv entory)

A2. CATEGORY-SPECIFIC QC CHECKLIST (CHECKS TO BE DESIGNED FOR EACH CATEGORY) Part A: Data Gathering and Selection Part B: Secondary Data and Direct Emission Measurement

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A1. GENERAL QC CHECKLIST Inventory Report: ___________ Source/Sink Category4:____________________________________________

Title(s) and Date(s) of Inventory Spreadsheet(s): __________________________________________________

Source (sink) category estimates prepared by (name/affiliation):______________________________________

INSTRUCTIONS FOR COMPLETING THIS FORM: This form is to be completed for each source/sink category, and provides a record of the checks performed and any corrective actions taken. The form may be completed by hand or electronically. The form should be distributed and filed according as specified in the QA/QC plan. If appropriate actions to correct any errors that are found are not immediately apparent, the QC staff performing the check should discuss the results according to the procedures predefined in the QA/QC plan. The first page of this form summarises the results of the checks (once completed) and highlights any significant findings or actions. The remaining pages in this form list categories of checks to be performed. The analyst has discretion over how the checks are implemented. Not all checks will be applicable to every category. Checks/rows that are not relevant or not available should indicate ‘n/r’ (not relevant) or ‘n/a’ (not available) so that no check and no row is left blank or deleted. Rows for additional checks that are relevant to the source/sink category should be added to the form. The column for supporting documentation should be used to reference any relevant Supplemental Reports or Contact Reports providing additional information.

Summary of general QC checks and corrective action Summary of results of checks and corrective actions taken:

Suggested checks to be performed in the future:

4

Any residual problems after corrective actions have been taken:

Use IPCC recognized source/sink category names. See Table 8.2 of Chapter 8.

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Checklist for general QC checks (complete table for each category): Check completed Date Individual Errors (first initial, (Y/N) last name)

Item

Corrective action Date Individual (first initial, last name)

Supporting documents (provide reference)

DATA GATHERING, INPUT, AND HANDLING ACTIVITIES: QUALITY CHECKS 1. Check a sample of input data for transcription errors 2.

Review spreadsheets with computerised checks and/or quality check reports

3.

Identify spreadsheet modifications that could provide additional controls or checks on quality

4.

Other (specify):

DATA DOCUMENTATION: QUALITY CHECKS 5. Check project file for completeness 6.

Confirm that bibliographical data references are included (in spreadsheet) for every primary data element

7.

Check that all appropriate citations from the spreadsheets appear in the inventory document

8.

Check that all citations in spreadsheets and inventory are complete (i.e., include all relevant information)

9.

Randomly check bibliographical citations for transcription errors

10.

Check that originals of new citations are in current docket submittal

11.

Randomly check that the originals of citations (including Contact Reports) contain the material & content referenced

12.

Check that assumptions and criteria for selection of activity data, emission factors and other estimation parameters are documented

13.

Check that changes in data or methodology are documented

14.

Check that citations in spreadsheets and inventory document conform to acceptable style guidelines

15.

Other (specify):

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

Checklist for general QC checks (complete table for each category) (Continued): Check completed Item

Date

Individual (first initial, last name)

Corrective action Errors (Y/N)

Date

Individual (first initial, last name)

Supporting documents (provide reference)

CALCULATING EMISSIONS AND CHECKING CALCULATIONS 16. Check that all calculations are included (instead of presenting results only) 17.

Check whether units, parameters, and conversion factors are presented appropriately

18.

Check if units are properly labelled and correctly carried through from beginning to end of calculation

19.

Check that conversion factors are correct

20.

Check that temporal and spatial adjustment factors are used correctly

21.

Check the data relationships (comparability) and data processing steps (e.g., equations) in the spreadsheets

22.

Check that spreadsheet input data and calculated data are clearly differentiated

23.

Check a representative sample of calculations, by hand or electronically

24.

Check some calculations with abbreviated calculations

25.

Check the aggregation of data within a category

26.

When methods or data have changed, check consistency of time series inputs and calculations

27.

Check current year estimates against previous years (if available) and investigate unexplained departures from trend

28.

Check value of implied emission/removal factors across time series and investigate unexplained outliers

29.

Check for any unexplained or unusual trends for activity data or other calculation parameters in time series

27.

Check for consistency with IPCC inventory guidelines and good practices, particularly if changes occur

28.

Other (specify):

6.28

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: QA/QC and Verification

A2. CATEGORY-SPECIFIC QC CHECKLIST Inventory Report: ___________ Source/sink Category5: ____________________________________________

Key category (or includes a key subcategory): ( Y / N ): ____________________________________________

Title(s) and Date(s) of Inventory Spreadsheet(s): __________________________________________________

Category estimates prepared by (name/affiliation): ________________________________________________

GENERAL INSTRUCTIONS FOR COMPLETING THIS FORM: Category-specific checks focus on the particular data and methodology used for an individual source or sink category. The specificity and frequency of these checks will vary across source categories. The form may be completed by hand or electronically. Once completed, the form should be saved and included as part of the inventory archive, as defined in the QA/QC plan. The first table on this form summarises generally the results of the category-specific checks and highlights any significant findings or corrective actions. The remaining pages in this form list categories of checks to be performed or types of questions to be asked. Part A checks are designed to identify potential problems in the estimates, factors, and activity data. Part B checks focus on the quality of secondary data and direct emission measurement. The analyst has discretion over how the checks are implemented. Checks/rows that are not relevant or not available should indicate ‘n/r’ (not relevant) or ‘n/a’ (not available) so that no check and no row is left blank or deleted. Rows for additional checks that are relevant to the category should be added to the form. The column for supporting documentation should be used to reference any relevant Supplemental Reports or Contact Reports that provide additional information. Other sources may be included here, if they can be clearly referenced. Any documents associated with the category specific plan should be clearly referenced in the column for supporting documentation. Summary of category-specific QC activities Summary of results of checks and corrective actions taken:

Suggested checks to be performed in the future:

Any residual problems after corrective actions have been taken:

5 Use IPCC recognized source/sink category names.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

ADDITIONAL INSTRUCTIONS FOR PART A: The checklist below indicates the types of checks and comparisons that can be performed and is not intended to be exhaustive. Supplemental Reports, Contact Reports, or other documents may be used to report detailed information on the checks conducted. For example, a Supplemental Report could provide information on the variables or sub-variables checked, comparisons made, conclusions that were drawn and rationale for conclusions, sources of information (published, unpublished, meetings, etc.) consulted, and corrective actions required. Category-specific checklist - Part A: Data gathering and selection Check completed Date Individual Errors (first initial, (Y/N) last name)

Item

Corrective action Date Individual (first initial, last name)

Supporting documents (provide reference)

EMISSION DATA QUALITY CHECKS 1. Emission comparisons: historical data for source, significant sub-source categories 2.

Checks against independent estimates or estimates based on alternative methods

3.

Reference calculations

4.

Completeness

5.

Other (detailed checks)

EMISSION FACTOR QUALITY CHECK 6. Assess representativeness of emission factors, given national circumstances and analogous emissions data 7.

Compare to alternative factors (e.g., IPCC default, crosscountry, literature)

8.

Search for options for more representative data

9.

Other (detailed checks)

ACTIVITY DATA QUALITY CHECK: NATIONAL LEVEL ACTIVITY DATA 10. Check historical trends 11.

Compare multiple reference sources

12.

Check applicability of data

13.

Check methodology for filling in time series for data that are not available annually

14.

Other (detailed checks)

ACTIVITY DATA QUALITY CHECK: SITE-SPECIFIC ACTIVITY DATA 15. Check for inconsistencies across sites 16.

Compare aggregated and national data

17.

Other (detailed checks)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: QA/QC and Verification

ADDITIONAL INSTRUCTIONS FOR PART B: Completing the QC checks on secondary data and direct emission measurement may require consulting the primary data sources or authors. The checklist below is intended to be indicative, not exhaustive. Additional information on appropriate checks can be found in the QA/QC, Data Collection, and sectoral chapters of the IPCC Guidelines. Additional documentation is likely to be necessary to record the specific actions taken to check the data underlying the category estimates. For example, Supplemental Reports may be needed to record the data or variables that were checked, and the published references and individuals or organisations consulted as part of the investigation. Contact Reports should be used to report the details of personal communications. Supplemental Reports may also be used to explain the rationale for a finding reported in the summary, the results of research into the QC procedures associated with a survey, or checks of site measurement procedures. Be sure to provide references to all supporting documentation. Category-specific checklist - Part B: Secondary data and direct emission measurement Item

Check completed Date

Individual (first initial, last name)

Errors (Y/N)

Corrective action Date

Supporting documents Individual (provide (first initial, reference) last name)

SECONDARY DATA: SAMPLE QUESTIONS REGARDING THE QUALITY OF INPUT DATA 1. Are QC activities conducted during the original preparation of the data (either as reported in published literature or as indicated by personal communications) consistent with and adequate when compared against (as a minimum), general QC activities? 2.

Does the statistical agency have a QA/QC plan that covers the preparation of the data?

3.

For surveys, what sampling protocols were used and how recently were they reviewed?

4.

For site-specific activity data, are any national or international standards applicable to the measurement of the data? If so, have they been employed?

5.

Have uncertainties in the data been estimated and documented?

6.

Have any limitations of the secondary data been identified and documented, such as biases or incomplete estimates? Have errors been found?

7.

Have the secondary data undergone peer review and, if so, of what nature?

8.

Other (detailed checks)

DIRECT EMISSION MEASUREMENT: CHECKS ON PROCEDURES TO MEASURE EMISSIONS 9. Identify which variables rely on direct emission measurement 10.

Check procedures used to measure emissions, including sampling procedures, equipment calibration and maintenance.

11.

Identify whether standard procedures have been used, where they exist (such as IPCC methods or ISO standards).

12.

Other (detailed checks)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Chapter 7: Precursors and Indirect Emissions

CHAPTER 7

PRECURSORS AND INDIRECT EMISSIONS

2006 IPCC Guidelines for National Greenhouse Gas Inventories

7.1

Volume 1: General Guidance and Reporting

Authors Michael Gillenwater (USA), Kristina Saarinen (Finland), and Ayite-Lo N. Ajavon (Togo)

Contributing Author Keith A. Smith (UK)

7.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Precursors and Indirect Emissions

Contents 7

Precursors and Indirect Emissions 7.1

Introduction ......................................................................................................................................... 7.4

7.2

Precursor emissions ............................................................................................................................. 7.4

7.2.1

Inventory of precursors ............................................................................................................... 7.5

7.2.2

Link to relevant methodology chapters in the EMEP/CORINAIR Emission Inventory Guidebook ......................................................... 7.7

7.3

Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3 .................... 7.15

7.3.1

Methodology ............................................................................................................................. 7.15

7.3.2

Quality Assurance/Quality Control, Reporting and Documentation ......................................... 7.16

References ........................................................................................................................................................ 7.16

Equations Equation 7.1

N2O emissions from atmospheric deposition of NOx and NH3 .......................................... 7.15

Tables Table 7.1

Link between the IPCC categories and the corresponding methodology chapters in EMEP/CORINAIR Guidebook ........................................................................................... 7.7

Boxes Box 7.1

CLRTAP and Emission Inventory Guidebook .................................................................... 7.5

Box 7.2

Calculating CO2 inputs to the atmosphere from emissions of carbon-containing compounds ............................................................... 7.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

7 PRECURSORS AND INDIRECT EMISSIONS 7.1

INTRODUCTION

Although they are not included in global warming potential-weighted greenhouse gas emission totals, emissions of carbon monoxide (CO), oxides of nitrogen (NOx), non-methane volatile organic compounds (NMVOCs), and sulphur dioxide (SO2) are reported in greenhouse gas inventories. Carbon monoxide (CO), Nitrogen oxides (NOx) and NMVOC in the presence of sunlight contribute to the formation of the greenhouse gas ozone (O3) in the troposphere and are therefore often called ‘ozone precursors’. Furthermore, NOx emission plays an important role in the earth’s nitrogen cycle. Sulphur Dioxide emissions lead to formation of sulphate particles, which also play a role in climate change. Ammonia (NH3) is an aerosol precursor, but is less important for aerosol formation than SO2. Section 7.2 addresses the estimation and reporting of the precursors for national inventories. The methodologies for ambient air quality emission inventories have been elaborated in detail in the EMEP1/CORINAIR Emission Inventory Guidebook (Guidebook), and these methodologies for CO, NOx, NMVOCs, and SO2 emissions are referenced in this chapter rather than to be included in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006 Guidelines). Exceptions are for sources not well-covered by the Guidebook. Section 7.3 addresses nitrous oxide (N2O) emissions that result from the deposition of the nitrogen emitted as NOx and NH3. Nitrous oxide is produced in soils through the biological processes of nitrification and denitrification. Simply defined, nitrification is the aerobic microbial oxidation of ammonium to nitrate and denitrification is the anaerobic microbial reduction of nitrate to nitrogen gas (N2). Nitrous oxide is a gaseous intermediate in the reaction sequence of denitrification and a by-product of nitrification that leaks from microbial cells into the soil atmosphere. One of the main controlling factors in this reaction is the availability of inorganic nitrogen in the soil and therefore deposition of nitrogen resulting from NOx and ammonia (NH3) will enhance emissions. N2O emissions will also be enhanced if nitrogen is deposited in the ocean or in lakes. For this reason the 2006 Guidelines include guidance for estimating N2O emissions resulting from nitrogen deposition of all anthropogenic sources of NOx and NH3. Only agricultural sources of nitrogen were considered in the Revised 1996 Guidelines (IPCC, 1997). Guidance is provided in Section 7.3 on estimating N2O emissions from atmospheric deposition resulting from all categories except agricultural soil management and manure management. Section 7.3 provides information on NOx emissions. Countries may use national methodologies to estimate emissions of NH3 not originating from agriculture. NH3 emissions are also covered in the EMEP/CORINAIR Emission Inventory Guidebook.

7.2

PRECURSOR EMISSIONS

Where the country already has inventories for precursors, the results should be reported in the inventory. In some countries, air pollutant emission inventories are collected via separate procedures than the inventory of direct greenhouse gases, and the methods to produce these inventories can differ from those for greenhouse gases. Also, while the greenhouse gas emissions and sinks inventories are often based on national statistics, air pollutant emission inventories are often developed using plant specific data. Countries should consider whether there is any scope for improving consistency between inventories or cross-checking estimates. Detailed methodologies for estimating the emissions of precursors are provided in the EMEP/CORINAIR Emission Inventory Guidebook (http://reports.eea.eu.int/EMEPCORINAIR4/en). This guidebook has been developed for emission inventories of substances regulated under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP) (see Box 7.1) and covers all source sectors and should therefore be considered as primary source of information for estimation of these emissions. Table 7.1 provides a linkage between the IPCC categories and the corresponding methodology chapters in the EMEP/CORINAIR Guidebook. This table provides information on the specific EMEP/CORINAIR chapters that list 2 methodologies for preparing NOx, CO, NMVOCs, NH3 and SO2 inventories. It also includes information on the availability of methods and the significant precursor emissions from particular categories. Some of the methodologies and emission factors in the EMEP/CORINAIR Guidebook are technology-specific and are relevant to conditions and categories in both developed and developing countries. However, for some 1

Cooperative programme for the monitoring and evaluation of the long-range transmission of air pollutants in Europe (EMEP).

2

The EMEP/CORINAIR Nomenclature for Reporting (NFR) source categories have been developed to be compatible to the IPCC reporting categories.

7.4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Precursors and Indirect Emissions

sectors, like solvents, small combustion sources (biomass in particular) and open burning, differences between the developed and developing countries may be larger, and the EMEP/CORINAIR Guidebook should be used with great care. BOX 7.1 CLRTAP AND EMISSION INVENTORY GUIDEBOOK

The Convention on Long-Range Transboundary Air Pollution has been in force since 1979 and includes eight protocols with requirements to reduce emissions and technical annexes on abatement techniques. More detailed information on the Convention is available at http://www.unece.org/env/lrtap/welcome.html. As emissions of oxides of nitrogen (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), and sulphur dioxide (SO2) are reported both to the UNFCCC and UNECE CLRTAP it is important to ensure consistent methodologies and reporting between these two Conventions. (UNECE, 2003) The EMEP/CORINAIR Guidebook has been prepared by the LRTAP Task Force on Emission Inventories and Projections (TFEIP) and is updated regularly by the Expert Panels under the TFEIP (http://tfeip-secretariat.org/unece.htm) to provide comprehensive information and methodologies for estimating emissions. The EMEP/CORINAIR Emission Inventory Guidebook is published by the European Environment Agency (EEA).

7.2.1

Inventory of precursors

An inventory of precursors typically includes oxides of nitrogen, carbon monoxide, non-methane volatile organic compounds, and emissions of sulphur compounds. When estimating emissions of these air pollutants, the use of detailed process or facility-specific data (bottom-up data) gives more accurate estimates than the use of general aggregated emission factors. For all pollutants and source categories it is critical to apply methodologies and emission factors that account for the presence of any emission controls or abatement measures. For large point sources many countries have a registry of individual air quality pollutant emissions reported by the plants. When using data reported by the plants it is good practice to ensure that emissions are not double counted with the topdown inventory data. Data reported by the plants can also be used to check completeness of the inventory.

7.2.1.1

E NERGY

For most countries, road transportation will be a major source of NOx, CO, and NMVOC emissions. Public electricity and heat production will likely be the major source of SO2 emissions in countries where coal is used extensively, and also an important source of NOx emissions. Industrial combustion will also be a source of SO2, NOx and CO emissions and residential combustion a source of CO emissions. Oil production will likely be a source of NMVOC, NOx, and, CO emissions in countries that produce oil and gas. Most NOx emissions resulting from fuel combustion are typically ‘fuel-NO’ that is formed from the conversion of chemically bound nitrogen in the fuel. The content of nitrogen in different fuel varies. Depending on the combustion temperature, thermal-NOx can also be formed from nitrogen contained in the combustion intake air. Carbon monoxide and NMVOCs are generated during under-stoichiometric combustion conditions and are dependent on a variety of factors, including fuel type and combustion conditions. Emissions of sulphur oxides (SOx) are primarily related to the sulphur content of the fuel, although some sulphur can be retained in the ash. Abatement in stationary combustion can reduce the amount emitted.

7.2.1.2

I NDUSTRIAL

PROCESSES AND PRODUCT USE

Industrial processes can generate NOx, CO, NMVOC and SO2 emissions. Emissions of these gases depend on the type of process, abatement techniques, and other conditions. Industrial process and product use emissions include both channelled emissions (e.g., point sources emissions from a stack) and diffuse emission sources. For example, diffuse emissions from the evaporation of solvents and storage and handling of products are typical primary sources of NMVOC emissions. In some cases, exceptional emissions (e.g., accidental releases) can constitute major emissions from source. Further guidance on estimating total emissions from an industrial site are provided in the EU IPPC (European Union Integrated Pollution Prevention and Control) Reference Document on Monitoring of

2006 IPCC Guidelines for National Greenhouse Gas Inventories

7.5

Volume 1: General Guidance and Reporting

Emissions (EC, 2002) 3.

7.2.1.3

A GRICULTURE ,

FORESTRY AND OTHER LAND USE

The burning of crop residues emits NOx as does the addition of nitrogen to the soils from nitrogen fertilizers and other nutrients. CO and SO2 are emitted when biomass is burned. The primary sources of the NMVOC emissions are burning of crop residues and other plant wastes, and the anaerobic degradation of livestock feed and animal excreta. Plants, mainly trees and cereals, also contribute to NMVOC concentrations in the atmosphere. The EMEP/CORINAIR Guidebook does not fully cover emissions from burning of biomass, therefore additional guidance is given in AFOLU Volume, Chapter 4.2.4 for Non-CO2 emissions from biomass burning from forest, Chapter 5.2.4 and 5.3.4 for Non-CO2 emissions from biomass burning in Cropland, and Chapter 6.2.4 and 6.3.4 for Non-CO2 emissions from biomass burning in Grassland (CO, CH4, N2O, NOx). Biomass burning when forest and grasslands are converted to other uses, forest fires, and biomass burning due to forest management practices are discussed in these chapters of Volume 4 for AFOLU sector.

7.2.1.4

W ASTE

Emissions of NOx, CO, and SO2 are produced by domestic and municipal waste incineration processes as well as the incineration of sledges from wastewater treatment. NMVOC emissions can originate from wastewater treatment plants and solid waste disposal on land.

7.2.1.5

C ARBON

EMITTTED IN GASES OTHER THAN

CO 2

The 2006 Guidelines estimate carbon emissions in terms of the species which are emitted. Most of the carbon emitted in the form of non-CO2 species eventually oxidises to CO2 in the atmosphere and this amount can be estimated from the emissions estimates of the non-CO2 gases. Box 7.2 provides an approach for making this calculation. In some cases the emissions of these non-CO2 gases contain very small amounts of carbon compared to the CO2 estimate and it may be more accurate to base the CO2 estimate on the total carbon. Examples are fossil fuel combustion (where the emission factor is derived from the carbon content of the fuel) and a few IPPU categories where the carbon mass balance can be estimated much better than individual gases. BOX 7.2 CALCULATING CO2 INPUTS TO THE ATMOSPHERE FROM EMISSIONS OF CARBON-CONTAINING COMPOUNDS

Methane, carbon monoxide (CO) or NMVOC emissions will eventually be oxidised to CO2 in the atmosphere. These CO2 inputs could be included in national inventories. They can be calculated from emissions of methane, CO and NMVOCs. The basic calculation principles are: From CH4: From CO: From NMVOC:

InputsCO2 = EmissionsCH4 • 44/16

InputsCO2 = EmissionsCO • 44/28

InputsCO2 = EmissionsNMVOC • C • 44/12

Where C is the fraction carbon in NMVOC by mass (default = 0.6) The carbon content in NMVOCs will vary depending on the source. Therefore, an inventory based on the speciation of the NMVOC compounds gives more accurate results. In making these estimates inventory compilers should assess each category to ensure that this carbon is not already covered by the assumptions and approximations made in estimating CO2 emissions. Relevant examples include carbon from; • • •

3

Fugitive emissions from energy use, Carbon from Non-CO2 gases from IPPU, AFOLU emissions where non-CO2 gases have been explicitly deducted.

Chapter 3.1 in EU IPPC Reference Document on Monitoring of Emissions, which is available from website http://eippcb.jrc.es/pages/ FActivities.htm.

7.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Precursors and Indirect Emissions

7.2.2

Link to relevant methodology chapters in the EMEP/CORINAIR Emission Inventory Guidebook

Table 7.1 provides specific information on methodologies for preparing national emission inventories of NOx, CO, NMVOCs, and SO2. The table includes information on the availability of methodologies in the EMEP/CORINAIR Emission Inventory Guidebook and the expected significance of the emissions for each IPCC category under the 2006 Guidelines (see Table 8.2 of Chapter 8 of this Volume) and gas. The Guidebook’s codes are equivalents in function to the IPCC reporting categories under the 1996 Guidelines. A mapping between the EMEP/CORINAIR Nomenclature for Reporting (NFR) and the IPCC common reporting framework (CRF) of the 1996 Guidelines with categories under the 2006 Guidelines is also provided in the table. In case the inventory compiler does not find a corresponding category to a specific IPCC 2006 category in Table 7.1, it is advisable to attempt to find a similar category (e.g., a corresponding boiler size for another industrial branch) in Table 7.1 and apply the corresponding methodology in the EMEP/CORINAIR Emission Inventory Guidebook for this category or to search for other sources of information (see also Chapter 2 of this Volume). The following codes are used to indicate whether the emissions from the specific source are relevant and covered by the Guidebook: A

=

Emissions of this gas from this category are likely to be emitted and a methodology is provided in the EMEP/CORINAIR Guidebook.

NI

=

Emissions of this gas from this category are likely to be emitted, but a methodology is not currently included in the EMEP/CORINAIR Guidebook.

B

=

Emissions of this air pollutant from this category are likely to be emitted and the methodology may be included in the EMEP/CORINAIR Guidebook in the future.

NS =

Emissions of this gas from this category not expected to be significant.

NO =

Emissions of this gas from this category do not occur.

TABLE 7.1 LINK BETWEEN THE IPCC CATEGORIES AND THE CORRESPONDING METHODOLOGY CHAPTERS IN EMEP/CORINAIR GUIDEBOOK Reporting category IPCC category

Source Sector

EMEP/CORINAIR Inventory Guidebook Chapter

1

NMSOx VOC Relevance of emissions from the category (see codes above the table)

NOx

CO

CRF

NFR

1A1a

1A1a

1A1a

Main Activity Electricity and Heat Production

B111 and B112

A

A

A

A

1A1b

1A1b

1A1b

Petroleum Refining

B132 and B136

A

A

A

A

B142, B146 and B152

A

A

A

A

1A2 Manufacturing Industries and Construction

1A1 Energy Industries

1 ENERGY

1A1c

1A1c

1A1c

Manufacture of Solid Fuels and Other Energy Industries

1A2a

1A2a

1A2a

Iron and Steel

B111, B112, B323, B324, B325, B331, B332, B333

A

A

A

A

1A2b

1A2b

1A2b

Non-ferrous Metals

B336, B337, B338, B339, B3310, B3322, B3323

A

A

A

A

1A2c

1A2c

1A2c

Chemicals

1A2d

1A2d

1A2d

Pulp, Paper and Print

1A2e

1A2e

1A2e

Food Processing, Beverages and Tobacco

1A2f

1A2f

1A2f

Non-Metallic Minerals

B111 and B112

A

A

A

A

B3321

A

A

A

A

B111 and B112

A

A

A

A

B3311, B3312, B3313, B3314, B3318, B3319, B3320, B3323

A

A

A

A

1A2g

Transport Equipment

B111 and B112

A

A

A

A

1A2h

Machinery

B111 and B112

A

A

A

A

1A2i

Mining and Quarrying

B111 and B112

A

A

A

A

1A2j

Wood and Wood Products

B111 and B112

A

A

A

A

1A2k

Construction

B111 and B112

A

A

A

A

1A2l

Textile and Leather

B111 and B112

A

A

A

A

1A2m

Non-specified Industry

B111 and B112

A

A

A

A

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

TABLE 7.1 (CONTINUED) LINK BETWEEN THE IPCC CATEGORIES AND THE CORRESPONDING METHODOLOGY CHAPTERS IN EMEP/CORINAIR GUIDEBOOK Reporting category IPCC category 1A3a

Source Sector CRF

NFR

1A3a

1A3ai(ii) 1A3ai(ii) International Aviation (Cruise)

1A4 Other Sectors

1A3 Transport

Domestic 1A3aii(i) 1A3aii(i) Civil Aviation (Domestic, LTO) Aviation 1A3aii(ii) 1A3aii(ii) Civil Aviation (Domestic, Cruise)

1A3b

1A3b

1A3b

Road Transportation

1A3bi

1A3bi

1A3bi

R.T., Passenger cars

1A5 NonSpecified 1B Fugitive Emissions from Fuels

CO

B851

A

A

A

A

B851

A

A

A

A

B851

A

A

A

A

B851

A

A

A

A

B710

A

A

A

A

1A3bii

1A3bii

1A3bii R.T., Light-duty vehicles

A

B710

A

A

A

1A3biii 1A3biii 1A3biii R.T., Heavy duty vehicles

B710

A

A

A

A

1A3biv 1A3biv 1A3biv R.T., Mopeds & Motorcycles

B710

A

A

A

A

1A3bv

1A3bv

1A3bv R.T., Evaporative Emissions

B760

NO

NO

A

NO

1A3c

1A3c

1A3c

Railways

B810

A

A

A

A

1A3d

1A3d

1A3d

Water-borne Navigation

1A3di

1A3di

1A3di

International Water-borne Navigation (International bunkers)/International maritime navigation

B842

A

A

A

A

1A3dii

1A3dii

1A3dii

Domestic Water-borne Navigation//National Navigation

B810 and B842

A

A

A

A

B561 and B152

B

B

A

B

B810

A

A

A

A

B111, B112, B216 and Small Combustion Installations *)

A

A

A

A

B111, B112 and Small Combustion Installations *)

A

A

A

A

B111, B112 and Small Combustion Installations *)

A

A

A

A

A

1A3e

1A3e

1A3e

Other Transportation

1A3ei

1A3ei

1A3ei

Pipeline Transport/Compressors

1A3eii

1A3eii

Off-road/Other mobile sources and 1A3eii machinery

1A4a

1A4a

1A4a

Commercial/Institutional

1A4b

1A4b

1A4b

Residential

1A4b

1A4bi

1A4bi

Residential plants

1A4b

1A4bii

1A4bii Household and gardening (mobile)

1A4c

1A4c

1A4c

Agriculture/Forestry/Fishing/Fish farms Stationary

1A4ci

1A4ci

1A4ci

1A4cii

1A4cii

1A4cii Off-road Vehicles and Other Machinery

1A4ciii 1A4ciii 1A4ciii National Fishing (mobile combustion)

7.8

NMSOx VOC Relevance of emissions from the category (see codes above the table)

NOx

EMEP/CORINAIR Inventory Guidebook Chapter

Civil Aviation

1A3ai Inter1A3ai (i) 1A3ai (i) International Aviation (LTO) national Aviation 1A3aii

1

1A5a

1A5a

1A5a

Other, Stationary (including military)

1A5b

1A5b

1A5b

Other, Mobile (including military)

1B1

1B1

1B1

Solid Fuel

1B1a

1B1a

1B1a

Coal Mining and Handling, including Post-mining activities/Solid Fuel Transformation

1B1b

1B1c

1B1c

Uncontrolled Combustion and Burning Coal Dumps /Other

1B1c

1B1b

1B1b

Solid Fuel Transformation

1B2

1B2

1B2

Oil and Natural Gas

1B2a

1B2a

1B2a

Oil

B111, B112 and B235

A

A

A

B111, B112, B235 and B810

A

A

A

A

B111, B112, B235 and B842

A

A

A

A

B111, B112, B216 and Small Combustion Installations *)

A

A

A

A

B810

A

A

A

A

A

NO

B511

NO/A NO NI

NI

NI

NI

B142 and B424

NI

NI

A

NI

1B2ai

1B2c

1B2c

Venting

B521, B923 and B926

NI

NI

NI

NI

1B2aii

1B2d

1B2d

Flaring

B521, B923 and B926

NI

NI

NI

NI

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Precursors and Indirect Emissions

TABLE 7.1 (CONTINUED) LINK BETWEEN THE IPCC CATEGORIES AND THE CORRESPONDING METHODOLOGY CHAPTERS IN EMEP/CORINAIR GUIDEBOOK

1B2aiii1

1B2ai

1B2ai

Exploration

B521 and B541

NMSOx VOC Relevance of emissions from the category (see codes above the table) A A A A

1B2aiii2

1B2aii

1B2aii

Production and Upgrading

B521 and B541

A

1B2aiii3

1B2aiii

1B2aiii

Transport

B521 and B541

A

A

A

A

1B2aiii4

1B2aiv

1B2aiv

Refining

B521 and B541

A

A

A

A

Distribution of Oil Products

B551

NO

NO

A/B

NO

Reporting category Source Sector CRF

NFR

EMEP/CORINAIR Inventory Guidebook Chapter

NOx

CO

A

A

A

1B2aiii5

1B2av

1B2av

1B2aiii6

1B2avi

1B2avi

Other

B521 and B541

NO

NO

NO

NO

1B2b

1B2b

1B2b

Natural Gas

B521 and B561

NO

NO

A

NO

NI

NI

NI

NI

1B2bi

1B2c

1B2c

Venting

B521, B923 and B926

1B2bii

1B2d

1B2d

Flaring

B521, B923 and B926

NI

NI

NI

NI

1B2biii

1B2e

1B2e

Other

B521 and B561

NO

NO

NO

NO

1B3

1B3

1B3

Other Emissions from Energy Production

B570

NI

NI

NI

NI

B3311

(A = fuel rated)

(A = fuel rated)

(A = fuel rated)

(A = fuel rated, process rated depends on the process)

1B Fugitive Emissions from Fuels

IPCC category

1C CO2 Transport, and Storage

1

B3312 (fuel rated and diffuse) and B461

(A = fuel rated)

(A = fuel rated)

(A = fuel rated)

(A = fuel rated)

Emissions from CO2 transport injection and storage

1C

2A1

2A1

2A1

Cement (decarbonizing)

2A2

2A2

2A2

Lime (decarbonizing)

2A4

2A3

2A3

Other uses of carbonites/Limestone and Dolomite Use

B4618

B

B

B

B

2A4b

2A4

2A4

Other uses of Soda Ash/Soda Ash Production and use

B4619

B

B

B

B

2A3

2A7

2A7

(A) depending on the process

(NS ) depending on the process

( NS ) depending on the process

(A) depending on the process

2A7

2A7

2A7

2A7

2A5 Other

2 B CHEMICAL INDUSTRY

2A7

2A7

Other including Non Fuel Mining & Construction

2A Mineral Industry

2 INDUSTRIAL PROCESSES AND PRODUCT USE

Glass (decarbonizing)

B3314

Batteries Manufacturing

B461

NS/B NS /B NS/B NS/B

Extraction of Mineral Ores

B461

NS/B NS/B NS/B NS/B

Other (including asbestos products manufacturing)

B461

NS

NS

NS

NS

2B1

2B1

2B1

Ammonia Production

B443

2B2

2B2

2B2

Nitric Acid Production

B442

A

NS

NS

NO

2B3

2B3

2B3

Adipic Acid Production

B4521

NS/B

NO

NO

NO

B443

NS/B NS/B NS/B NS/B

-

NS/B NS/B NS/B NS/B

2B5

2B4

2B4

Carbide Production/Calcium Carbide Production

2B4

2B5

2B5

Caprolactam Production

2B4

2B5

2B5

Glyoxylic Acid Production

B453

2B6

2B5

2B5

Titanium Dioxide Production

B443

2B7

2A4

2A4

Soda Ash Production

B4619

2B8 2B8a

NS/B NS/B NS/B NS/B

NS

NS

B

NS

NS/B NS/B NS/B NS/B B

B

B

B

NS

NS

A

NS

Petrochemical and Carbon Black Production 2B5

2B5

Methanol Production

2006 IPCC Guidelines for National Greenhouse Gas Inventories

7.9

Volume 1: General Guidance and Reporting

TABLE 7.1 (CONTINUED) LINK BETWEEN THE IPCC CATEGORIES AND THE CORRESPONDING METHODOLOGY CHAPTERS IN EMEP/CORINAIR GUIDEBOOK

CRF

NFR

2B5

2B5

Ethylene Production

B451

NMSOx VOC Relevance of emissions from the category (see codes above the table) NS NS A NS

B454

NO

Reporting category Source Sector

IPCC category 2B8b

EMEP/CORINAIR Inventory Guidebook Chapter

NOx

CO

NS

NS

2B8c

2B5

2B5

Vinylchloride (except 1,2 dichloroethane+vinylchloride) Production

2B8d

2B5

2B5

Ethylene Oxide Production

B453

NS

NS

NS

NS

2B8e

2B5

2B5

Acrylonitrile Producton

B4520

NO

NO

A

NO

2B8f

2B5

2B5

Carbon Black Producton

B443

NS

NS

NS

NS

2B5

2B5

Sulphuric Acid Production

B441

NS

NS

NS

A

2B5

2B5

Ammonium Sulphate Manufacturing

B443

NS

NS

NS

NS

2B10 Other

2B9

7.10

1

NO

Fluorochemical Production

2B5

2B5

Ammonium Nitrate Production

B443

NS

NS

NS

NS

2B5

2B5

Ammonium Phosphate Production

B443

NS

NS

NS

NS

2B5

2B5

NPK fertilizers

B443

NS

NS

NS

NS

2B5

2B5

Urea

B443

NS

NS

NS

NS

2B5

2B5

Graphite

B443

NS

NS

NS

NS

2B5

2B5

Chlorine Production

B443

NS

NS

NS

NS

2B5

2B5

Phosphate Fertilisers Production

B443

NS

NS

NS

NS

2B5

2B5

Storage and Handling of Inorganic Chemical Products

B443

NS

NS

B

NS

2B5

2B5

Other

B443

NS

NS

NS

NS

2B5

2B5

Propylene Production

B452

NO

NO

A

NO

2B5

2B5

1,2 dichoroethane (except 1,2 dichloroethane+vinylchloride) Production

B453

NS

NS

NS

NS

2B5

2B5

1,2 dichloroethane + vinylchloride (balanced process)

B455

NO

NO

A

NO

2B5

2B5

Polyethylene (low density) Production

B456

NO

NO

A

NO

2B5

2B5

Polyethylene (high density) Production

B456

NO

NO

A

NO

2B5

2B5

Polyvinylchloride Production

B458

NO

NO

A

NO

2B5

2B5

Polypropylene Production

B459

NO

NO

A

NO

2B5

2B5

Styrene Production

B4510

NO

NO

A

NO

2B5

2B5

Polystyrene Production

B4511

NO

NO

A

NO

2B5

2B5

Styrene Butadiene Production

B4512

NO

NO

A

NO

2B5

2B5

B4512

NO

NO

A

NO

2B5

2B5

B4512

NO

NO

A

NO

2B5

2B5

Styrene-butadiene Latex Production Styrene-butadiene Rubber (SBR) Production Acrylonitrile Butadiene Styrene (ABS) Resins Production

B4512

NO

NO

A

NO

2B5

2B5

Formaldehyde Production

B453

NS

NS

NS

NS

2B5

2B5

Ethylbenzene Production

B4518

NO

NO

NS

NO

2B5

2B5

Phtalic Anhydride Production

B4519

NO

NS

A

NS

2B5

2B5

Storage & Handling of Organic Chemical Products

B453

NS

NS

B

NS

2B5

2B5

Halogenated Hydrocarbons Production

B453

NS

NS

B

NS

2B5

2B5

Pesticide Production

B453

NS

NS

B

NS

2B5

2B5

Production of Persistent Organic Compounds

B453

NS

NS

B

NS

2B5

2B5

Other (phytosanitary )

B453

NS

NS

B

NS

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Precursors and Indirect Emissions

TABLE 7.1 (CONTINUED) LINK BETWEEN THE IPCC CATEGORIES AND THE CORRESPONDING METHODOLOGY CHAPTERS IN EMEP/CORINAIR GUIDEBOOK

Blast Furnace Charging

B422

NMSOx VOC Relevance of emissions from the category (see codes above the table) NS A NS NS

Pig Iron Tapping

B423

NS

NS

Reporting category Source Sector

IPCC category

CRF

NFR

Open Hearth Furnace Steel Plant

EMEP/CORINAIR Inventory Guidebook Chapter

NOx

CO

NO

NS NS

B425

A

NS

NS

B426

NS

A

NS

A

B427

A

A

NS

NS

B428

NS

NS

NS

NS

Sinter and Pelletizing Plants (except combustion)

B331

A

A

A

A

Other

B4210

NS

NS

NS

NS

NS

NS

NS

NS

NS

B431

NS

A

NS

A

2C2

2C2

2C2

Ferroalloys Production

2C3

2C3

2C3

Aluminium Production (electrolysis)

2C6

2C5

2C5

Zinc Production

NO

NO

NO

NI

2C5

2C5

2C5

Lead Production

NO

NO

NO

NI

2C5

2C5

Magnesium Production (except combustion)

B432

NS

NS

NS

NS

2C5

2C5

Silicium Production

B432

NS

NS

NS

NS NS

2C4

2C7 Other

2 C METAL INDUSTRY

Basic Oxygen Furnace Steel Plant 2C1 Processes in Iron and Steel Industries Electric Furnace Steel Plant and Colliers Rolling Mills

2D NON-ENERGY PRODUCTS FROM FUELS AND SOLVENT USE

1

2C5

2C5

Nickel Production (except combustion)

B432

NS

NS

NS

2C5

2C5

Allied Metal Manufacturing

B432

NS

NS

NS

NS

2C5

2C5

Galvanising

B432

NS

NS

NS

NS

2C5

2C5

Electroplating

B432

NS

NS

NS

NS

2C5

2C5

Other

B432

NS

NS

NS

NS

2D1

3D

3D

Lubricant Use

NO

NO

NI

NO

2D2

3D

3D

Paraffin Waxes Use

NO

NO

NI

NO

2D4

2D3

2A5

2A5

Asphalt Roofing

B4610

NS

A

A

NS

2A6

2A6

Road Paving with Asphalt

B4611

A

A

A

A

See "SOLVENT USE" below

Solvent Use

CRF/NFR 3A PAINT APPLICATION 3A

3A

Manufacture of Automobiles

B610

NO

NS

A/B

NO

3A

3A

Car Repairing

B610

NO

NO

A/B

NO

3A

Construction and Buildings (except wood painting)

B610

NO

NO

A/B

NO

2D3 SOLVENT USE

3A 3A

3A

Domestic Use (except wood painting)

B610

NO

NO

A/B

NO

3A

3A

Coil Coating

B610

NO

NO

A/B

NO

3A

3A

Boat Building

B610

NO

NO

A/B

NO

3A

3A

Wood Painting/Coating

B610

NO

NO

A/B

NO

3A

3A

Other Industrial Paint Application

B610

NO

NO

A/B

NO

3A

3A

Other Non-industrial Paint Application

B610

NO

NO

A/B

NO

CRF/NFR 3B DEGREASING AND DRY CLEANING 3B

3B

Metal Degreasing

B621

NS

NS

A

NS

3B

3B

Dry Cleaning

B622

NO

NO

A

NO

3B

3B

Electronic Components Manufacturing

B623

NS

NS

NS

NS

3B

3B

Other Industrial Cleaning

B623

NS

NS

NS

NS

2006 IPCC Guidelines for National Greenhouse Gas Inventories

7.11

Volume 1: General Guidance and Reporting

TABLE 7.1 (CONTINUED) LINK BETWEEN THE IPCC CATEGORIES AND THE CORRESPONDING METHODOLOGY CHAPTERS IN EMEP/CORINAIR GUIDEBOOK Reporting category IPCC category

Source Sector CRF

NFR

EMEP/CORINAIR Inventory Guidebook Chapter

1

NMSOx VOC Relevance of emissions from the category (see codes above the table)

NOx

CO

2D4 OTHER

CRF/NFR 3 C CHEMICAL PRODUCTS, MANUFACTURE AND PROCESSING 3C

3C

Polyester Processing

B631

NS

NS

A/B

NS

3C

3C

Polyvinylchloride Processing

B631

NS

NS

A/B

NS

3C

3C

Polyurethane Foam Processing

B633

NS

NS

A

NS

3C

3C

Polystyrene Foam Processing

B633

NS

NS

A

NS

3C

3C

Rubber Processing

B631

NS

NS

A/B

NS

3C

3C

Pharmaceutical Products Manufacturing

B631

NS

NS

A/B

NS

3C

3C

Paints Manufacturing

B631

NS

NS

A/B

NS NS

3C

3C

Inks Manufacturing

B631

NS

NS

A/B

3C

3C

Glues Manufacturing

B631

NS

NS

A/B

NS

3C

3C

Asphalt Blowing

B6310

NS

A

A

NS

3C

3C

Adhesive, Magnetic Tapes, Films & Photographs Manufacturing

B631

NS

NS

A/B

NS

3C

3C

Textile Finishing

B631

NS

NS

A/B

NS

3C

3C

Leather Tanning

B631

NS

NS

A/B

NS

3C

3C

Other

B631

NS

NS

A/B

NS

3D

3D

Glass Wool Enduction

B641

NS

NS

B

NS

3D

3D

Mineral Wool Enduction

B641

NS

NS

B

NS

3D

3D

Printing Industry

B643

NO

NO

A/B

NO

3D

3D

Fat, Edible and Not Edible Oil Extraction

B644

NS

NS

A

NS

3D

3D

Application of Glues and Adhesives

B641

NS

NS

B

NS

3D

3D

Preservation of Wood

B646

NO

NO

A

NO

3D

3D

Underseal Treatment and Conservation of Vehicles

B647

NO

NO

IE 3A (car manufacturing & repairing)

2D4 OTHER

CRF/NFR 3 D OTHER including products containing HMs and POPs

NO

3D

3D

Domestic Solvent Use (other than paint application)

B648

NO

NO

A/B

NO

3D

3D

Vehicles Dewaxing

B647

NO

NO

A

NO

3D

3D

Domestic Use of Pharmaceutical Products

B641

NS

NS

B

NS

3D

3D

Other (preservation of seeds, etc.)

B641

NS

NS

B

NS

3D

Other (anaesthesia, refrigeration and air conditioning, electrical equipment, etc.)

B651

NS

NS

B

NS

3D

See for relevant subcategories under NFR 3D

-

NS

NS

NS

NS

3D 2E ELECTRONICS INDUSTRY

2F

2F PRODUCT USES AS SUBSTITUTES FOR OZONE DEPLETING SUBSTANCES

2G OTHER PRODUCT USES

7.12

2F

2F, 3D

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Precursors and Indirect Emissions

TABLE 7.1 (CONTINUED) LINK BETWEEN THE IPCC CATEGORIES AND THE CORRESPONDING METHODOLOGY CHAPTERS IN EMEP/CORINAIR GUIDEBOOK Reporting category Source Sector

2D1

2D1

2D1

2D1

2D1

2D1

2D1

2D1

2D1

2D1

2D2

2D2

2D2

2D2

2D2

2D2

2D2

2D2

2D2

2D2

3D

3D

Mineral Wool Enduction

B641

NS

NS

B

NS

3D

3D

Printing Industry

B643

NO

NO

A/B

NO

3D

3D

Fat, Edible and Not Edible Oil Extraction

B644

NS

NS

A

NS

Processes in wood, paper pulp, food, drink and other industries

Pulp and Paper Pulp and Paper - Chipboard

B461

NS

NS

NS

NS

Pulp and Paper - Paper pulp (kraft process)

B462

A

NS

A

A

Pulp and Paper - Paper pulp (acid sulphite process)

B463

A

NO

A

A

Pulp and Paper - Paper pulp (neutral sulphite semichemical process.)

B464

A

NO

A

A

Food and Drink - Bread

B465

NS

NS

A

NS

Food and Drink - Wine

B466

NS

NS

A

NS

Food and Drink - Beer

B466

NS

NS

A

NS

Food and Drink - Spirits

B466

NS

NS

A

NS

Food and Drink

2H3

Other

3D

3D

Application of Glues and Adhesives

B641

NS

NS

B

NS

3D

3D

Preservation of Wood

B646

NO

NO

A

NO

3D

3D

Underseal Treatment and Conservation of Vehicles

B647

NO

NO

IE 3A (car manufacturing & repairing)

2 H OTHER

CO

NFR

2H1

2H2

NMSOx VOC Relevance of emissions from the category (see codes above the table)

NOx

CRF

Processes in wood, paper pulp, food, drink and other industries

IPCC category

EMEP/CORINAIR Inventory Guidebook Chapter

1

NO

3D

3D

Domestic Solvent Use (other than paint application)

B648

NO

NO

A/B

NO

3D

3D

Vehicles Dewaxing

B647

NO

NO

A

NO

3D

3D

Domestic Use of Pharmaceutical Products

B641

NS

NS

B

NS

3D

3D

Other (preservation of seeds,....)

B641

NS

NS

B

NS

3D

3D

Other (anaesthesia, refrigeration and air conditioning, electrical equipment, etc.)

B651

NS

NS

B

NS

3B Land

3A Livestock

3 AGRICULTURE, FORESTRY, AND OTHER LAND USE (AFOLU) 3A1

4A

4A

Enteric Fermentation

B1040

NO

NO

NO

NO

3A2

4B

4B

Manure Management

B1050, B100511, N1090

NO

NO

B

NO

5A

5A

B112100

B

B

A

B

5B

5B

B112200

A

B

NS

B

5C

5C

B112300

A

B

NS

B

Managed Forests (broadleaf and coniferous)

B1101, B110117

NI

NI

A

NI

Non-managed Forests (broadleaf and coniferous)

B1101, B110117

NI

NI

A

NI

B112500

NS

NS

NS

NS

NS

NS

NS

NS

3B1 Forest Land

5E

5E

Changes in Forest and Other Woody Biomass Stocks Forest and Grassland Conversion (tropical, temperate, boreal forests, grassland, other) Abandonment of Managed Land (tropical, temperate, boreal forests, grassland, other)

Other 3B2 Cropland

2006 IPCC Guidelines for National Greenhouse Gas Inventories

7.13

Volume 1: General Guidance and Reporting

TABLE 7.1 (CONTINUED) LINK BETWEEN THE IPCC CATEGORIES AND THE CORRESPONDING METHODOLOGY CHAPTERS IN EMEP/CORINAIR GUIDEBOOK Reporting category CRF

CO

NMSOx VOC

Source Sector

EMEP/CORINAIR Inventory Guidebook Chapter

B1104 B110117

A

NI

A

NI

NFR

Relevance of emissions from the category (see codes above the table)

3B3 Grassland

4D

4D

Natural Grassland and Other Vegetation (grassland, tundra, other low vegetation, other vegetation (Mediterranean, scrub…)), Soils

3B4 Wetland

4D

4D

Wetlands (marshes - swamps)

B1105

NI

NI

NI

A

3B5 Settlem ents

4G

4G

Other

B1060

NO

NO

NO

NO

3C1a

5B

5B

Forest and vegetation fires (man-induced, other)

B1103

A

A

A

A

3C1b

4F

4F

Field burning of agricultural wastes

B1030

A

A

A

A

3C1c

4D

4D

Prescribed burning of savannas

B

B

B

B

3C4

4D

4D1

Agricultural soils, direct soil emissions

B1010, B1020 and B1105

A

NO

A

NO

3C7

4C

4C

Rice Cultivation

B1010, B1020

A

NO

A

NO

3D1

NA

NA

Harvested Wood Products

NO

NO

B

NO

NA

NA

Volcanoes

B1108

NO

NO

NO

A

NA

NA

Gas Seeps

B110900

NO

NO

NO

NO

NA

NA

Lightning

B111000

A

NO

NO

NO

NA

NA

Wildlife animals

B1107

NO

NO

NS

NO

4D

4D

Waters

B1106

NO

NO

B

B

4A and 4B

6A

6A

Solid Waste Treatment and Disposal and Biological treatment of solid waste

B940

NO

NO

A/B

NO

4C

6C

6C

Incineration and Open Burning of Waste/Waste Incineration

B921, B922, B924, B925, B927, B970, B991, B992

A

A

NI/B

A

4D

6B

6B

Wastewater Treatment and Discharge/Wastewater Handling

B9101 and B9107

NO

NO

A

NO

4E

6D

6D

Other waste

B9101, B9203, B9105, B9106

A

A

A

NO

3D OTHER

3C AGGREGATED SOURCES AND NONCO2 EMISSION SOURCES ON LAND

3B6 Other land

3B Land

IPCC category

NOx

1

3D2

5A Indirect N2O emissions

5 OTHER

4D WASTE

4 WASTE

5B 7 7 Geothermal energy extraction B570 NO NO NI NO/B Other *) Chapter Small Combustion Installations is available from website http://tfeip-secretariat.org/unece.htm > Expert Panels > Expert Panel on Combustion and Industry 1

Current references are to the version of the EMEP/CORINAIR Guidebook available when these guidelines are published.

7.14

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Precursors and Indirect Emissions

7.3

INDIRECT N 2 O EMISSIONS FROM THE ATMOSPHERIC DEPOSITION OF NITROGEN IN NO X AND NH 3

In this Guidance, direct nitrous oxide emissions are estimated on the basis of human-induced net nitrogen input to managed soils (e.g., synthetic or organic fertilizers, deposited manure, crop residues, sewage sludge), or of other changes in inorganic nitrogen in the soil as a result of interventions by management practices in nitrogen cycling, e.g., mineralization of nitrogen in soil organic matter, following drainage/management of organic soils, or cultivation/land use change on mineral soils. In addition to these direct emissions of N2O, indirect emissions also take place as a result of two different nitrogen loss pathways. These pathways are (1) the volatilization/emission of nitrogen as NH3 and NOx and the subsequent deposition of these forms of nitrogen as ammonium (NH4+) and oxidised nitrogen (NOx) on soils and waters, and (2) the leaching and runoff of nitrogen from synthetic and organic nitrogen fertilizer inputs, crop residues, mineralization of nitrogen through land use change or management practices, and urine and dung deposition from grazing animals, into groundwater, riparian areas and wetlands, rivers and eventually the coastal ocean. The volatilization of nitrogen as NH3 and NOx results both from agricultural fertilizer applied to land and from manure management, as well as from fossil fuel and biomass combustion, and industrial processes. Before being redeposited, NOx and NH3 are typically transformed to other nitrogen containing compounds. Oxides of nitrogen are commonly hydrolysed in the atmosphere or upon deposition to form nitric acid (HNO3), while NH3 gas generally combines with atmospheric nitric acid or sulphuric acid (H2SO4) to form ammonium nitrate and ammonium sulphate aerosols, which are then transformed to a particulate ammonium (NH4+) form. The deposition of these reactive nitrogen compounds from non-agricultural sources onto soils and waters causes N2O emissions in an exactly analogous way to those resulting from their deposition from agricultural sources. Therefore the indirect N2O emissions resulting from these various sources are included in these Guidelines using the assumption that same emission factor applies to soil and water deposition.

7.3.1

Methodology

All anthropogenic NH3 or NOx emissions are potential sources of N2O emissions 4 . Specific guidance on estimating N2O emissions from that portion of nitrogen compounds associated with the volatilisation of NOx and NH3 from (1) manure management systems and applied sewage sludge and (2) synthetic and organic nitrogen input to managed soils, and urine and dung nitrogen deposited by grazing animals, are provided in Section 10.5 of Chapter 10, Emissions from livestock and manure management, and Section 11.2.2 of Chapter 11, N2O and CO2 emissions from soil amendment, of Volume 4 of AFOLU. This section provides guidance on estimating N2O emissions from the atmospheric deposition of nitrogen compounds from all other sources of NOx and NH3 emissions, such as fuel combustion, industrial processes, and burning of crop residues and agricultural wastes. The method needs only to be applied where data on NOx and NH3 emissions from these sources are available, e.g., from the inventories identified Section 7.2. Equation 7.1 and EF4 from Equation 11.9 in Section 11.2.2.1 of Volume 4 can be used to estimate N2O emissions from the atmospheric deposition of nitrogen resulting from NOx and NH3.

EQUATION 7.1 N2O EMISSIONS FROM ATMOSPHERIC DEPOSITION OF NOX AND NH3 ⎡⎛ ⎞⎤ ⎞ ⎛ N 2 O(i ) = ⎢⎜⎜ NO x N (i ) ⎟⎟ + ⎜⎜ NH 3 N (i ) ⎟⎟⎥ • EF4 • 44 / 28 ⎠⎦⎥ ⎠ ⎝ ⎣⎢⎝

4

In addition to being redeposited on soils and surface waters, NH3 can also lead to the formation of N2O from atmospheric chemical reactions. However, there is currently no method available for estimating conversion of NH3 to N2O in the atmosphere.

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Where: N2O(i)

=

N2O produced from atmospheric deposition of N from NOx and NH3 emissions from source i, in Gg

NOx-N(i) =

Nitrogen content of NOx emissions from source i assuming that NOx is reported in NO2 equivalents (Gg NOx-N or Gg NO2 • 14/46)

NH3-N(i) =

Nitrogen content of NH3 emissions from source i (Gg NH3-N or Gg NH3 • 14/17)

EF4

Emission factor for N2O emissions from atmospheric deposition of N on soils and water surfaces (kg N2O-N/kg NH3-N or NOx-N emitted).

=

The activity data NOx-N(i) and NH3 –N(i) are taken from the inventories as identified in Section 7.2, if available. This method assumes that N2O emissions from atmospheric deposition are reported by the country that produced the original NOx and NH3 emissions. In reality the ultimate formation of N2O may occur in another country due to atmospheric transport of emissions. The method also does not account for the probable lag time between NOx and NH3 emissions and subsequent production of N2O in soils and surface waters. This time lag is expected to be small relative to an annual reporting cycle.

7.3.2

Quality Assurance/Quality Control, Reporting and Documentation

It is good practice to estimate and report N2O emissions from atmospheric deposition of NOx and NH3 where a country already has an inventory of these gases. For the purposes of calculation, it is assumed that N2O is emitted in the same year that the original NOx and NH3 were emitted. It is good practice to estimate emissions ensuring consistency with the emissions estimated for agriculture sources and avoiding double-counting. Because N2O emissions may occur outside the country emitting NH3 or NOx, use of country- or region-specific emission factors should be thoroughly documented. N2O emissions from atmospheric deposition of NH3 and NOx are reported in Table 5A of reporting tables in Annex 8A.2 for all sectors, and the AFOLU Sector is also reported in Table 3.8 in Annex 8A.2.

References EC (2003). Reference document on the general principles of monitoring, July 2003, 111 pp. European Commission (EC) Directorate-General for Environment, Integrated Pollution Prevention and Control (IPPC). http://eippcb.jrc.es/pages/ FActivities.htm EEA (2001). EMEP/CORINAIR Emission Inventory Guidebook, third ed. Technical report No. 30, European Environmental Agency (EEA). http://reports.eea.eu.int/technical_report_2001_3/en EEA (2005). “EMEP/CORINAIR. Emission Inventory Guidebook – 2005”, Technical report No 30. European Environmental Agency (EEA). Copenhagen, Denmark, (December 2005). http://reports.eea.eu.int/EMEPCORINAIR4/en IPCC (1997a). Revised 1996 IPCC Guidelines for National Greenhouse Inventories, Volume 1-3.. Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. and Callander B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. UNECE (1979). Convention on Long-Range Transboundary Air Pollution, United Nations Economic Commission for Europe (UNECE). http://www.unece.org/env/lrtap/welcome.html UNECE. (2003). Guidelines for Estimating and Reporting Emission Data under the Convention on Long-range Transboundary Air Pollution. ECE/EB.AIR/80. ISSN 1014-4625. ISBN 92-1-116861-9. Air Pollution Studies No. 15. United Nations Economic Commission for Europe (UNECE), United Nations, New York and Geneva.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

CHAPTER 8

REPORTING GUIDANCE AND TABLES

2006 IPCC Guidelines for National Greenhouse Gas Inventories

8.1

Volume 1: General Guidance and Reporting

Authors María José Sanz Sánchez (Spain), Sumana Bhattacharya (India), and Katarina Mareckova (Slovakia)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

Contents 8

Reporting Guidance and Tables 8.1

Introduction ......................................................................................................................................... 8.4

8.2

Reporting guidance ............................................................................................................................. 8.4

8.2.1

Coverage ...................................................................................................................................... 8.4

8.2.2

Gases included ............................................................................................................................. 8.5

8.2.3

Time frame of reporting .............................................................................................................. 8.6

8.2.4

Sectors and categories ................................................................................................................. 8.6

8.2.5

Notation keys and completeness information .............................................................................. 8.7

8.2.6

Units and digits ............................................................................................................................ 8.7

8.2.7

Time series .................................................................................................................................. 8.7

8.2.8

Indirect N2O ................................................................................................................................ 8.8

8.3

Introduction to reporting tables ........................................................................................................... 8.8

8.4

Other reporting .................................................................................................................................... 8.9

8.5

Classification and definition of categories .......................................................................................... 8.9

References ......................................................................................................................................................... 8.34 Annex 8A.1

Prefixes, units and abbreviations, standard equivalents ......................................................... 8A1.1

Annex 8A.2

Reporting Tables ........................................................................................................................ T.1

Tables Table 8.1

Notation Keys ...................................................................................................................... 8.7

Table 8.2

Classification and definition of categories of emissions and removals ............................. 8.10

Boxes Box 8.1

Reporting emissions of precursors ....................................................................................... 8.6

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8 REPORTING GUIDANCE AND TABLES 8.1

INTRODUCTION

This chapter provides guidance for reporting complete, consistent and transparent national greenhouse gas inventories, regardless the method used to produce the data. The framework for reporting emissions and removals provided in the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 Guidelines, IPCC, 1997)has been further elaborated for the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006 Guidelines) without introducing substantial changes. Most of the changes from the 1996 Guidelines are motivated by the need to report emissions and removals from additional categories of sources and sinks in a transparent way. Other changes are introduced to increase the consistency in reporting, or as a result of methodology development over the last 10 years. The categories of agriculture and land-use change and forestry have been restructured resulting in increased completeness and consistency. Since many countries will have prepared inventories for more than one year, tables to report trends in emissions and removals have been included as reporting tables. Reporting tables for general inventory issues, such as uncertainties, key category identification are also provided.

8.2

REPORTING GUIDANCE

8.2.1

Coverage

Anthropogenic emissions and removals The 2006 Guidelines are designed to estimate and report on national inventories of anthropogenic greenhouse gas emissions and removals. Anthropogenic emissions and removals means that greenhouse gas emissions and removals included in national inventories are a result of human activities.

National inventory National inventories should include greenhouse gas emissions and removals taking place within national territory and offshore areas over which the country has jurisdiction. There are, however, some specific issues to be taken into account: • • • •

• •

8.4

Emissions from fuel for use on ships or aircraft engaged in international transport should not be included in national totals. To ensure global completeness, these emissions should be reported separately. CO2 emissions from road vehicles should be attributed to the country where the fuel is sold to the end user. The same allocation principle can be applied to other gases depending on the tier used to estimate emissions. Fishing includes emissions from fuel used in inland, coastal and deep sea fishing. Emissions resulting from fuel used in coastal and deep sea fishing should be allocated to the country delivering the fuel. Military fuel use is reported under “1A5 Non-specified”, and this category includes fuel deliveries for all mobile and stationary consumption (e.g., ships, aircraft, road and energy used in living quarters) of the country. Emissions from multilateral operations pursuant to the Charter of the United Nations are not included in national totals. It is good practice to document clearly which activities have been included under the category multilateral operations and report as memo item in the reporting tables. Fugitive emissions from pipelines transporting, e.g., oil, gas, or CO2, should be allocated according to the national territory of the pipeline, including offshore areas. This implies that emissions from one pipeline may be distributed between two or more countries. Emissions associated with the injection and possible subsequent leakage of CO2 stored in geological formations should be linked to the country in whose national jurisdiction or by whose international right the point of injection is located. This includes any emissions arising from leakage of CO2 from a geological formation that crosses a national boundary.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables • •

• • •

The IPCC methodology for carbon stored in non fuel products manufactured from fossil fuels or other nonbiogenic sources of carbon takes into account emissions released from their production, use and destruction. Emissions are estimated at each stage when and where they occur, for example in waste incineration. Where CO2 emissions are captured from industrial processes or large combustion sources, emissions should be allocated to the sector generating the CO2 unless it can be shown that the CO2 is stored in properly monitored geological storage sites as set out in Chapter 5 of Volume 2. Emissions from CO2 captured for use, for example in greenhouses and soft drinks, and transported offsite should be allocated to the sector where the CO2 was captured. CO2 emissions from biomass combustion for energy are estimated and reported in AFOLU Sector as part of net changes in carbon stocks. When reporting harvested wood products (HWP), countries can select any of the approaches reflected in Chapter 12 of Volume 4 for the AFOLU Sector when estimating their emissions/removals from HWP. N2O resulting from atmospheric nitrogen deposition is allocated to the country emitting nitrogen oxides and ammonia and it is assumed that N2O is emitted in the same year.

8.2.2

Gases included

The 2006 Guidelines can be applied for the following two groups of greenhouse gases1:

Greenhouse gases with a GWP in the TAR and not covered by the Montreal Protocol In addition to the greenhouse gases included in the 1996 Guidelines, gases for which global warming potential (GWP) values are given in the IPCC Third Assessment Report (TAR) are included in the 2006 Guidelines2 unless they are covered by the Montreal Protocol. The greenhouse gases included are: •

•

carbon dioxide (CO2)

•

nitrous oxide (N2O)

•

perfluorocarbons (PFCs: CF4, C2F6, C3F8, C4F10, c-C4F8, C5F12, C6F14)

• •

methane (CH4)

hydroflurocarbons (HFCs: e.g., HFC-23 (CHF3), HFC-134a (CH2FCF3), HFC-152a (CH3CHF2))

•

sulphur hexafluoride (SF6)

•

trifluoromethyl sulphur pentafluoride (SF5CF3)

•

nitrogen trifluoride (NF3)

•

halogenated ethers (e.g., C4F9OC2H5, CHF2OCF2OC2F4OCHF2, CHF2OCF2OCHF2 ) other halocarbons not covered by the Montreal Protocol including CF3I, CH2Br2, CHCl3, CH3Cl, CH2Cl2.

Other halogenated greenhouse gases not covered by the Montreal Protocol The 2006 Guidelines also provide estimation methods for halogenated greenhouse gases which are not covered by the Montreal Protocol and for which a GWP values are not available from the TAR, inter alia:

1

In a few cases, although methods are available, the 2006 Guidelines do not provide default emission factors for all category-gas combinations due to limited research or literature. If a country expects that emissions of these gases occur in a category for which no default emission factors are provided, it is good practice to explore the feasibility of developing country-specific data in order to include these emissions in the inventory. If it is not possible to develop country-specific data, countries should provide documentation that these emissions occur but were not estimated.

2

See the IPCC Third Assessment Report “Climate Change 2001: The Scientific Basis” by Working Group I: Table 6.7 (http://www.grida.no/climate/ipcc_tar/wg1/248.htm#tab67), and Table 6.8 (http://www.grida.no/climate/ipcc_tar/wg1/249.htm#tab68).

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C3F7C(O)C2F5 3

•

•

C7F16

•

C5F8

C4F6

•

c- C4F8O

Some of the methods can be used for other halocarbons not controlled by the Montreal Protocol (including e.g., several fluids and blends e.g., traded under the commercial labels of the Fluorinert™, and Galden® product families)4. These and other greenhouse gases can only be considered in key category analysis or included in national total emissions using GWP values from sub-sequent Assessment Reports of the IPCC. If these GWP values are not yet available countries are encouraged to provide estimates for them in mass units using the methods provided in the 2006 Guidelines. Reporting tables are provided for this purpose.

Other gases Emissions of the ozone precursors nitrogen oxide (NOx) non-methane volatile organic compounds (NMVOC) and carbon monoxide (CO) and the aerosol precursors sulphur dioxide (SO2) and ammonia (NH3) should be reported in the appropriate tables if the country has prepared an inventory of these gases. Box 8.1 gives brief explanation of these gases. BOX 8.1 REPORTING EMISSIONS OF PRECURSORS5

NOx includes NO and NO2 reported in NO2 mass equivalents. SO2 includes all sulphur compounds expressed in SO2 mass equivalents. NMVOC means any non-methane organic compound having at 293.15 K a vapour pressure of 0.01 kP or more, or having a corresponding volatility under the particular conditions of use. NH3 is reported in NH3 mass units.

8.2.3

Time frame of reporting

It is good practice to use a calendar year for reporting emissions and removals. Chapter 2, Approaches to Data Collection, provide guidance how to proceed when data for the calendar year reporting are not available or not considered suitable.

8.2.4

Sectors and categories

The 2006 Guidelines group emissions and removals categories into five main sectors.

•

•

Energy

•

Agriculture, Forestry and Other Land Use (AFOLU)

•

Industrial Processes and Product Use (IPPU)

•

Waste Other

3

This gas is traded as Novec™612 which is a fluorinated ketone produced by 3M (Milbrath, 2002).

4

The Fluorinert™ materials are selected from fully fluorinated alkanes, ethers, tertiary amines and aminoethers and mixtures thereof to obtain the desired properties. The Galden® fluids span a range of fully fluorinated polyethers, called perfluoropolyethers (PFPEs).

5

Guidance on reporting and definitions are consistent with the 2002 reporting guidelines of the Convention on Long-Range Transboundary Air Pollution, available in Air Pollution Studies series, No.15, 2003. (http://www.emep.int/index.html)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

Compared to the 1996 Guidelines grouping the sector “Solvent and Other product Use” has been combined with Industrial Processes, and Agriculture has been combined with Land-Use Change and Forestry. Additional subcategories or further disaggregation have been added to increase the completeness and transparency. Table 8.2 in Section 8.5 shows the classification and definition of categories and subcategories of emissions and removals for all 5 sectors.

8.2.5

Notation keys and completeness information

In all tables used by countries to summarise their inventory data, it is good practice to fill in information for all entries. If actual emission and removal quantities have not been estimated or can not otherwise be reported in the tables, the inventory compiler should use qualitative notation keys in Table 8.1 and provide supporting documentation. Notation keys are appropriate if emission estimates or removal are incomplete, or representative of only a part of the total activity, or require clarification when specific greenhouse gas emissions were not reported, for any particular source or sink category. In this way it is good practice to report on the completeness of each individual emission estimate. Completeness means that inventory estimates have been prepared for all categories and gases. A country may consider that a disproportionate amount of effort would be required to collect data for a category or a gas from a specific category that would be insignificant in terms of the overall level and trend in national emissions. In these circumstances a country should list all categories and gases from categories excluded on these grounds, together with a justification for exclusion in terms of the likely level of emissions or removals and identify the category as 'Not Estimated' using the notation key 'NE' in the reporting tables.

TABLE 8.1 NOTATION KEYS Notation Key

Definition

Explanation

NE

Not estimated

Emissions and/or removals occur but have not been estimated or reported.

IE

Included elsewhere

Emissions and/or removals for this activity or category are estimated and included in the inventory but not presented separately for this category. The category where these emissions and removals are included should be indicated (for example in the documentation box in the correspondent table).

C

Confidential information

Emissions and/or removals are aggregated and included elsewhere in the inventory because reporting at a disaggregated level could lead to the disclosure of confidential information.

NA

Not applicable

The activity or category exists but relevant emissions and removals are considered never to occur. Such cells are normally shaded in the reporting tables.

NO

Not occurring

An activity or process does not exist within a country.

8.2.6

Units and digits

SI units (International System of Units) should be used in the worksheets, sectoral and summary tables and other documentation. Emissions and removals should be expressed in mass units and units have to be used consistently within the sector. Emissions in summary and sectoral tables are generally expressed in gigagram (Gg). Other SI mass units may be used to increase the transparency. The number of significant digits of values reported should be appropriate to their magnitude (precision 0.1 percent of national total is adequate for each gas). For some gases, as specified in individual sector tables, emissions and removals should be reported as CO2 equivalents. All conversion factors used to convert from original units should be reported in a transparent way.

8.2.7

Time series

It is good practice to complete all the reporting tables (summary, sectoral, cross-sectoral) for each year in which an inventory is available.

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Volume 1: General Guidance and Reporting

It is good practice to summarise the aggregated inventory data from different years in the trend tables (Table 6A to 6G).

8.2.8

Indirect N 2 O

N2O emissions from atmospheric deposition of NH3 and NOx are reported in Table 5.2 for all sectors. An overview and general description of methodologies to estimate indirect emissions of N2O are given in Chapter 7 of Volume 1.

8.3

INTRODUCTION TO REPORTING TABLES

The reporting tables in Annex 8A.2 are designed to ensure that inventory compilers can report quantitative data in a standard format and to facilitate consistency between countries, categories, gases and years. The set of inventory reporting tables consist of:

Summary and short summary tables Summary and short summary tables allow the inventory compiler to report all emissions and removals at aggregated level for an overview of national totals for the actual year. The summary tables also allow reporting of memo items including international bunkers and multilateral operations. These emissions are not included in national total emissions of greenhouse gases. Two tables are included: Table A

Summary table

Table B

Short summary table

Sectoral and background tables Sectoral tables enable reporting of emissions and removals, for all relevant categories and subcategories listed in Table 8.2. Background tables allow reporting of activity data and related emissions at the subcategory level to facilitate transparency and consistency of information. Information items that are usually not themselves emissions, for example carbon dioxide stored long-term in the storage sites, are reported separately as additional information under respective sectors for increased transparency. The following tables are included. Table 1

Energy Sectoral Table

Table 1.1 – 1.5

Energy Background Tables

Table 2

IPPU Sectoral Table

Table 2.1 – 2.12 IPPU Background Tables Table 3

AFOLU Sectoral Table

Table 3.1 – 3.10 AFOLU Background Tables Table 4

Waste Sectoral Table

Table 4.1 – 4.3

Waste Background Tables

Cross-sectoral table Cross-sectoral tables enable inventory compilers to report indirect emissions of N2O. Indirect missions are reported in separate columns of Cross-sectoral Table 5A. Table 5A

8.8

Cross-sectoral Table: Indirect emissions of N2O

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

Emission trend tables by gas Trend tables enable inventory compilers to report all greenhouse gas emissions and removals at an aggregated level for entire inventory period. It is good practice to complete trend tables if an inventory is available, even if the information is not complete. Reporting of emission trends can help inventory compilers to track time series consistency of the estimates. Table 6A – 6C

Trends of CO2, CH4 and N2O

Emissions of fluorinated gases are aggregated in three groups and expressed in Gg of CO2 equivalent. Table 6D – 6F

Trends of HFC, PFC and SF6

Emissions of other greenhouse gases are aggregated and expressed in Gg of CO2 equivalent, if they are reported and included in national totals. Table 6G

Trends of Other Gases

Uncertainty and key categories tables

8.4

Table 7A

Uncertainties

Table 7B

Summary of key category analysis

OTHER REPORTING

In addition to reporting tables listed in Section 8.3, it is good practice to report tabular information on recalculations (see Table 5.2 in Chapter 5, Time Series Consistency, of this Volume). Additional documentation is needed to ensure the transparency of inventories as part of an inventory report document. An inventory report should clearly explain the assumptions and methodologies used to facilitate replication and assessment of the inventory by users and third parties. Transparency can be ensured through following the guidance on documentation of each category described in the sectoral Volumes 2-5, and for Tier 1 methods by completing the worksheets. Countries using higher tier methods should provide additional documentation in addition to, or instead of the worksheets. Such explanatory information should include crossreferences to the tables. The documentation should include a description of the basis for methodological choice, emission factors, activity data and other estimation parameters, including appropriate references and documentation of expert judgements. The inventory report should also include information on the implementation of a QA/QC plan, verification, splicing of methodologies, recalculations and uncertainty assessment as well as other qualitative information relative to data collection, uncertainty, identification of key categories and recalculation mentioned in the correspondent documentation section of the sectoral volumes.

8.5

CLASSIFICATION AND DEFINITION OF CATEGORIES

Table 8.2 introduces the classification and definition of categories and subcategories6 of emissions and removals (consistent with the sectoral, sectoral background and cross-sectoral tables provided in Annex 8A.2). The correspondence with the reporting categories of the 1996 Guidelines is also provided in the third column of Table 8.2. A fourth column identifies gases that may be relevant to each category. Additional guidance on gases is provided in Volumes 2-5 and in Table 7.1 of Chapter 7 of this Volume for indirect gases. 7

6

The nomenclature for the levels within the category list is: category, subcategory - 1st order, subcategory - 2nd order, subcategory – 3rd order, etc.

7

In order to facilitate transparent reporting of emissions of non-CO2 gases and CO2 emissions from liming in the AFOLU Sector, reporting is based on aggregated categories (3C) taking into account that data may not be available to report those emissions by land.

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TABLE 8.2

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS Category Code and Name 1 ENRGY

Definition

96 GLs Category Code

Gases CO2, CH4, N2O, NOx, CO, NMVOC, SO2

This category includes all GHG emissions arising from combustion and fugitive releases of fuels. Emissions from the non-energy uses of fuels are generally not included here, but reported under Industrial Processes and Product Use Sector.

1 A

Fuel Combustion Activities

Emissions from the intentional oxidation of materials within an apparatus that is designed to raise heat and provide it either as heat or as mechanical work to a process or for use away from the apparatus.

1A

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 1

Energy Industries

Comprises emissions from fuels combusted by the fuel extraction or energy-producing industries.

1A1

CO2, CH4, N2O, NOx, CO, NMVOC, SO2 CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 1

a

Main Activity Electricity and Heat Sum of emissions from main activity producers of electricity Production generation, combined heat and power generation, and heat plants. Main activity producers (formerly known as public utilities) are defined as those undertakings whose primary activity is to supply the public. They may be in public or private ownership. Emissions from own on-site use of fuel should be included. Emissions from autoproducers (undertakings which generate electricity/heat wholly or partly for their own use, as an activity that supports their primary activity) should be assigned to the sector where they were generated and not under 1 A 1 a. Autoproducers may be in public or private ownership.

1 A 1

a i

Electricity Generation

Comprises emissions from all fuel use for electricity generation from main activity producers except those from combined heat and power plants.

1A1a i

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 1

a ii

Combined Heat and Power Generation (CHP)

Emissions from production of both heat and electrical power from main activity producers for sale to the public, at a single CHP facility.

1A1a ii

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 1

a iii

Heat Plants

Production of heat from main activity producers for sale by pipe network.

1A1a iii

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 1

b

Petroleum Refining

All combustion activities supporting the refining of petroleum products including on-site combustion for the generation of electricity and heat for own use. Does not include evaporative emissions occurring at the refinery. These emissions should be reported separately under 1 B 2 a.

1A1b

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 1

c

Manufacture of Solid Fuels and Combustion emissions from fuel use during the manufacture Other Energy Industries of secondary and tertiary products from solid fuels including production of charcoal. Emissions from own on-site fuel use should be included. Also includes combustion for the generation of electricity and heat for own use in these industries.

1A1c

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 1

c i

Manufacture of Solid Fuels

1A1c i

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

8.10

Emissions arising from fuel combustion for the production of coke, brown coal briquettes and patent fuel.

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Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

1 A 1

Other Energy Industries

Combustion emissions arising from the energy-producing industries own (on-site) energy use not mentioned above or for which separate data are not available. This includes the emissions from own-energy use for the production of charcoal, bagasse, saw dust, cotton stalks and carbonizing of biofuels as well as fuel used for coal mining, oil and gas extraction and the processing and upgrading of natural gas. This category also includes emissions from pre-combustion processing for CO2 capture and storage. Combustion emissions from pipeline transport should be reported under 1 A 3 e.

1A1c ii

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

Manufacturing Industries and Construction

Emissions from combustion of fuels in industry. Also includes combustion for the generation of electricity and heat for own use in these industries. Emissions from fuel combustion in coke ovens within the iron and steel industry should be reported under 1 A 1 c and not within manufacturing industry. Emissions from the industry sector should be specified by sub-categories that correspond to the International Standard Industrial Classification of all Economic Activities (ISIC). Energy used for transport by industry should not be reported here but under Transport (1 A 3). Emissions arising from off-road and other mobile machinery in industry should, if possible, be broken out as a separate subcategory. For each country, the emissions from the largest fuel-consuming industrial categories ISIC should be reported, as well as those from significant emitters of pollutants. A suggested list of categories is outlined below.

1A2

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

c ii

1 A 2

Gases

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2

a

Iron and Steel

ISIC Group 271 and Class 2731.

1 A 2

b

Non-Ferrous Metals

ISIC Group 272 and Class 2732.

1A2b

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2

c

Chemicals

ISIC Division 24.

1A2c

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2

d

Pulp, Paper and Print

ISIC Divisions 21 and 22.

1A2d

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2

e

Food Processing, Beverages and Tobacco

ISIC Divisions 15 and 16.

1A2e

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2

f

Non-Metallic Minerals

Includes products such as glass ceramic, cement, etc. ISIC Division 26.

1A2f

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2

g

Transport Equipment

ISIC Divisions 34 and 35.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

8.11

Volume 1: General Guidance and Reporting

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

1 A 2

h

Machinery

Includes fabricated metal products, machinery and equipment other than transport equipment. ISIC Divisions 28, 29, 30, 31 and 32.

1A2f

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2

i

Mining (excluding fuels) and Quarrying

ISIC Divisions 13 and 14.

NA

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2

j

Wood and Wood Products

ISIC Division 20.

NA

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2

k

Construction

ISIC Division 45.

1A2f

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2

l

Textile and Leather

ISIC Divisions 17, 18 and 19.

NA

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

Non-specified Industry:

Any manufacturing industry/construction not included above or for which separate data are not available. Includes ISIC Divisions 25, 33, 36 and 37.

NA

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

Emissions from the combustion and evaporation of fuel for all transport activity (excluding military transport), regardless of the sector, specified by sub-categories below. Emissions from fuel sold to any air or marine vessel engaged in international transport (1 A 3 a i and 1 A 3 d i) should as far as possible be excluded from the totals and subtotals in this category and should be reported separately.

1A3

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 2 m

1 A 3

Transport

Gases

1 A 3

a

Civil Aviation

Emissions from international and domestic civil aviation, including take-offs and landings. Comprises civil commercial use of airplanes, including: scheduled and charter traffic for passengers and freight, air taxiing, and general aviation. The international/domestic split should be determined on the basis of departure and landing locations for each flight stage and not by the nationality of the airline. Exclude use of fuel at airports for ground transport which is reported under 1 A 3 e Other Transportation. Also exclude fuel for stationary combustion at airports; report this information under the appropriate stationary combustion category.

1A3a

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

a i

International Aviation (International Bunkers)

Emissions from flights that depart in one country and arrive in a different country. Include take-offs and landings for these flight stages. Emissions from international military aviation can be included as a separate sub-category of international aviation provided that the same definitional distinction is applied and data are available to support the definition.

1A3a i

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

a ii

Domestic Aviation

Emissions from civil domestic passenger and freight traffic that departs and arrives in the same country (commercial, private, agriculture, etc.), including take-offs and landings for these flight stages. Note that this may include journeys of considerable length between two airports in a country (e.g. San Francisco to Honolulu). Exclude military, which should be reported under 1 A 5 b.

1A3a ii

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

8.12

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

1 A 3

b

Road Transportation

All combustion and evaporative emissions arising from fuel use in road vehicles, including the use of agricultural vehicles on paved roads.

1A3b

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

b i

Cars

Emissions from automobiles so designated in the vehicle registering country primarily for transport of persons and normally having a capacity of 12 persons or fewer.

1A3b i

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

b i 1 Passenger Cars With 3-way Catalysts

Emissions from passenger car vehicles with 3-way catalysts.

1A3b i

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

b i 2 Passenger Cars Without 3-way Catalysts

Passenger car emissions from vehicles without 3-way catalysts.

1A3b i

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

b ii

Emissions from vehicles so designated in the vehicle registering country primarily for transportation of lightweight cargo or which are equipped with special features such as four-wheel drive for off-road operation. The gross vehicle weight normally ranges up to 3500-3900 kg or less.

1A3b ii, 1A3b i

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

b ii 1 Light-duty Trucks With 3-way Catalysts

Emissions from light duty trucks with 3-way catalysts.

1A3b ii

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

b ii 2 Light-duty Trucks Without 3-way Emissions from light duty trucks without 3-way catalysts. Catalysts

1A3b ii

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

b iii

Heavy-duty Trucks and Buses

Emissions from any vehicles so designated in the vehicle registering country. Normally the gross vehicle weight ranges from 3500-3900 kg or more for heavy duty trucks and the buses are rated to carry more than 12 persons.

1A3b iii

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

b iv

Motorcycles

Emissions from any motor vehicle designed to travel with not more than three wheels in contact with the ground and weighing less than 680 kg.

1A3b iv

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

b v

Evaporative Emissions from Vehicles

Evaporative emissions from vehicles (e.g. hot soak, running losses) are included here. Emissions from loading fuel into vehicles are excluded.

1A3b v

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

b vi

Urea-based Catalysts

CO2 emissions from use of urea-based additives in catalytic converters (non-combustive emissions).

1 A 3

c

Railways

Emissions from railway transport for both freight and passenger traffic routes.

Light-duty Trucks

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Gases

CO2, CH4, N2O, NOx, CO, NMVOC, SO2 1A3c

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

8.13

Volume 1: General Guidance and Reporting

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

1 A 3

d

Water-borne Navigation

Emissions from fuels used to propel water-borne vessels, including hovercraft and hydrofoils, but excluding fishing vessels. The international/domestic split should be determined on the basis of port of departure and port of arrival, and not by the flag or nationality of the ship.

1A3d

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

d i

International Water-borne Navigation (International Bunkers)

Emissions from fuels used by vessels of all flags that are engaged in international water-borne navigation. The international navigation may take place at sea, on inland lakes and waterways and in coastal waters. Includes emissions from journeys that depart in one country and arrive in a different country. Exclude consumption by fishing vessels (see Other Sector - Fishing). Emissions from international military water-borne navigation can be included as a separate sub-category of international waterborne navigation provided that the same definitional distinction is applied and data are available to support the definition.

1A3d i

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

d ii

Domestic Water-borne Navigation

Emissions from fuels used by vessels of all flags that depart and arrive in the same country (exclude fishing, which should be reported under 1 A 4 c iii, and military, which should be reported under 1 A 5 b). Note that this may include journeys of considerable length between two ports in a country (e.g. San Francisco to Honolulu).

1A3d ii

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

e

Other Transportation

Combustion emissions from all remaining transport activities including pipeline transportation, ground activities in airports and harbours, and off-road activities not otherwise reported under 1 A 4 c Agriculture or 1 A 2. Manufacturing Industries and Construction. Military transport should be reported under 1 A 5 (see 1 A 5 Nonspecified).

1A3de

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

e i

Pipeline Transport

Combustion related emissions from the operation of pump stations and maintenance of pipelines. Transport via pipelines includes transport of gases, liquids, slurry and other commodities via pipelines. Distribution of natural or manufactured gas, water or steam from the distributor to final users is excluded and should be reported in 1 A 1 c ii or 1 A 4 a.

1A3e

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 3

e ii

Off-road

Combustion emissions from Other Transportation excluding Pipeline Transport.

1A3e

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

Emissions from combustion activities as described below, including combustion for the generation of electricity and heat for own use in these sectors.

1A4

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 4

Other Sectors

Gases

1 A 4

a

Commercial/Institutional

Emissions from fuel combustion in commercial and institutional buildings; all activities included in ISIC Divisions 41,50, 51, 52, 55, 63-67, 70-75, 80, 85, 90-93 and 99.

1A 4 a

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 4

b

Residential

All emissions from fuel combustion in households.

1A4b

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 4

c

Agriculture/Forestry/Fishing/Fish Emissions from fuel combustion in agriculture, forestry, Farms fishing and fishing industries such as fish farms. Activities included in ISIC Divisions 01, 02 and 05. Highway agricultural transportation is excluded.

1A4c

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

8.14

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

1 A 4

c i

Stationary

Emissions from fuels combusted in pumps, grain drying, horticultural greenhouses and other agriculture, forestry or stationary combustion in the fishing industry.

1A4ci

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 4

c ii

Off-road Vehicles and Other Machinery

Emissions from fuels combusted in traction vehicles on farm land and in forests.

1A3e

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 4

c iii

Fishing (mobile combustion)

Emissions from fuels combusted for inland, coastal and deep-sea fishing. Fishing should cover vessels of all flags that have refuelled in the country (include international fishing).

1A4ciii

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

All remaining emissions from fuel combustion that are not specified elsewhere. Include emissions from fuel delivered to the military in the country and delivered to the military of other countries that are not engaged in multilateral operations Emissions from fuel sold to any air or marine vessel engaged in multilateral operation pursuant to the Charter of the United Nations should be excluded from the totals and subtotals of the military transport, and should be reported separately.

1A5

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 5

Non-Specified

Gases

1 A 5

a

Stationary

Emissions from fuel combustion in stationary sources that are not specified elsewhere.

1A5a

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 5

b

Mobile

Emissions from vehicles and other machinery, marine and aviation (not included in 1 A 4 c ii or elsewhere).

1A5b

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 5

b i

Mobile (Aviation Component)

All remaining aviation emissions from fuel combustion that are not specified elsewhere. Include emissions from fuel delivered to the country’s military not otherwise included separately in 1 A3 a i as well as fuel delivered within that country but used by militaries of other countries that are not engaged in multilateral operation pursuant to the Charter of the United Nations.

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 5

b ii

Mobile (Water-borne Component)

All remaining water-borne emissions from fuel combustion that are not specified elsewhere. Include emissions from fuel delivered to the country’s military not otherwise included separately in 1 A3 d i as well as fuel delivered within that country but used by militaries of other countries that are not engaged in multilateral operation pursuant to the Charter of the United Nations.

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 5

b iii

Mobile (Other)

All remaining emissions from mobile sources not included elsewhere.

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 A 5

c

Multilateral Operations

Emissions from fuel sold to any air or marine vessel engaged in multilateral operations pursuant to the Charter of the United Nations should be excluded from the totals and subtotals of the military transport, and should be reported separately.

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

8.15

Volume 1: General Guidance and Reporting

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

1 B

Fugitive Emissions from Fuels

Includes all intentional and unintentional emissions from the extraction, processing, storage and transport of fuel to the point of final use.

1B

CO2, CH4, N2O, NOx, CO, NMVOC,

1 B 1

Solid Fuels

Includes all intentional and unintentional emissions from the extraction, processing, storage and transport of fuel to the point of final use.

1B1

CO2, CH4,

Gases

1 B 1

a

Coal Mining and Handling

Includes all fugitive emissions from coal.

1B1a

CO2, CH4,

1 B 1

a i

Underground Mines

Includes all emissions arising from mining, post-mining, abandoned mines and flaring of drained methane.

1B1a i

CO2, CH4,

1 B 1

a i 1 Mining

Includes all seam gas emissions vented to atmosphere from coal mine ventilation air and degasification systems.

1B1a i

CO2, CH4,

1 B 1

a i 2 Post-mining Seam Gas Emissions

Includes methane and CO2 emitted after coal has been mined, brought to the surface and subsequently processed, stored and transported.

1B1a i

CO2, CH4,

1 B 1

a i 3 Abandoned Underground Mines Includes methane emissions from abandoned underground mines.

1B1a i

CO2, CH4,

1 B 1

a i 4 Flaring of Drained Methane or Methane drained and flared, or ventilation gas converted to Conversion of Methane to CO2 CO2 by an oxidation process should be included here. Methane used for energy production should be included in Volume 2, Energy, Chapter 2 ‘Stationary Combustion’.

1B1a i

CO2, CH4,

1 B 1

a ii

1B1a ii

CO2, CH4,

1 B 1

a ii 1 Mining

Includes methane and CO2 emitted during mining from breakage of coal and associated strata and leakage from the pit floor and high wall.

1 B 1

a ii 2 Post-mining Seam Gas Emissions

Includes methane and CO2 emitted after coal has been mined, subsequently processed, stored and transported.

1B1a ii

CO2, CH4,

1 B 1

b

Uncontrolled Combustion, and Burning Coal Dumps

Includes fugitive emissions of CO2 from uncontrolled combustion in coal.

1B1c

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

1 B 1

c

Solid Fuel Transformation

Fugitive emissions arising during the manufacture of secondary and tertiary products from solid fuels.

1B1b

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

Comprises fugitive emissions from all oil and natural gas activities. The primary sources of these emissions may include fugitive equipment leaks, evaporation losses, venting, flaring and accidental releases.

1B2

CO2, CH4, N2O, NOx, CO, NMVOC,

1B2a

CO2, CH4, NMVOC,

1 B 2

Surface Mines

Oil and Natural Gas

Includes all seam gas emissions arising from surface coal mining.

CO2, CH4,

1 B 2

a

Oil

Comprises emissions from venting, flaring and all other fugitive sources associated with the exploration, production, transmission, upgrading, and refining of crude oil and distribution of crude oil products.

1 B 2

a i

Venting

Emissions from venting of associated gas and waste gas/vapour streams at oil facilities.

CO2, CH4, NMVOC,

1 B 2

a ii

Flaring

Emissions from flaring of natural gas and waste gas/vapour streams at oil facilities.

CO2, CH4, N2O, NOx, CO, NMVOC,

1 B 2

a iii

All Other

Fugitive emissions at oil facilities from equipment leaks, storage losses, pipeline breaks, well blowouts, land farms, gas migration to the surface around the outside of wellhead casing, surface casing vent bows, biogenic gas formation from tailings ponds and any other gas or vapour releases not specifically accounted for as venting or flaring.

CO2, CH4, N2O, NOx, CO, NMVOC,

8.16

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

1 B 2

a iii I

Fugitive emissions (excluding venting and flaring) from oil well drilling, drill stem testing, and well completions.

1B2a i

CO2, CH4, NMVOC,

1 B 2

a iii 2 Production and Upgrading

Fugitive emissions from oil production (excluding venting and flaring) occur at the oil wellhead or at the oil sands or shale oil mine through to the start of the oil transmission system. This includes fugitive emissions related to well servicing, oil sands or shale oil mining, transport of untreated production (i.e , well effluent, emulsion, oil shale and oilsands) to treating or extraction facilities, activities at extraction and upgrading facilities, associated gas reinjection systems and produced water disposal systems. Fugitive emission from upgraders are grouped with those from production rather than those from refining since the upgraders are often integrated with extraction facilities and their relative emission contributions are difficult to establish. However, upgraders may also be integrated with refineries, co-generation plants or other industrial facilities and their relative emission contributions can be difficult to establish in these cases.

1B2a ii

CO2, CH4, N2O, NOx, CO, NMVOC,

1 B 2

a iii 3 Transport

Fugitive emissions (excluding venting and flaring) related to the transport of marketable crude oil (including conventional, heavy and synthetic crude oil and bitumen) to upgraders and refineries. The transportation systems may comprise pipelines, marine tankers, tank trucks and rail cars. Evaporation losses from storage, filling and unloading activities and fugitive equipment leaks are the primary sources of these emissions.

1B2a iii

CO2, CH4, NMVOC,

1 B 2

a iii 4 Refining

Fugitive emissions (excluding venting and flaring) at petroleum refineries. Refineries process crude oils, natural gas liquids and synthetic crude oils to produce final refined products (e.g., primarily fuels and lubricants). Where refineries are integrated with other facilities (for example, upgraders or co-generation plants) their relative emission contributions can be difficult to establish.

1B2a iv

CO2, CH4, NMVOC,

1 B 2

a iii 5 Distribution of Oil Products

This comprises fugitive emissions (excluding venting and flaring) from the transport and distribution of refined products, including those at bulk terminals and retail facilities. Evaporation losses from storage, filling and unloading activities and fugitive equipment leaks are the primary sources of these emissions.

1B2a v

CO2, CH4, NMVOC,

1 B 2

a iii 6 Other

Fugitive emissions from oil systems (excluding venting and flaring) not otherwise accounted for in the above categories. This includes fugitive emissions from spills and other accidental releases, waste oil treatment facilities and oilfield waste disposal facilities.

1B2a vi

CO2, CH4, NMVOC,

1 B 2

b

Natural Gas

Comprises emissions from venting, flaring and all other fugitive sources associated with the exploration, production, processing, transmission, storage and distribution of natural gas (including both associated and non-associated gas).

1B2b

CO2, CH4, NMVOC,

1 B 2

b i

Venting

Emissions from venting of natural gas and waste gas/vapour streams at gas facilities.

CO2, CH4, NMVOC,

1 B 2

b ii

Flaring

Emissions from flaring of natural gas and waste gas/vapour streams at gas facilities.

CO2, CH4, N2O, NOx, CO, NMVOC,

1 B 2

b iii

All Other

Fugitive emissions at natural gas facilities from equipment leaks, storage losses, pipeline breaks, well blowouts, gas migration to the surface around the outside of wellhead casing, surface casing vent bows and any other gas or vapour releases not specifically accounted for as venting or flaring.

CO2, CH4, N2O, NOx, CO, NMVOC,

1 B 2

b iii 1 Exploration

Exploration

Fugitive emissions (excluding venting and flaring) from gas well drilling, drill stem testing and well completions.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

1Bb i

Gases

CO2, CH4, NMVOC,

8.17

Volume 1: General Guidance and Reporting

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

1 B 2

b iii 2 Production

Fugitive emissions (excluding venting and flaring) from the gas wellhead through to the inlet of gas processing plants, or, where processing is not required, to the tie-in points on gas transmission systems. This includes fugitive emissions related to well servicing, gas gathering, processing and associated waste water and acid gas disposal activities.

1Bb ii

CO2, CH4, NMVOC,

1 B 2

b iii 3 Processing

Fugitive emissions (excluding venting and flaring) from gas processing facilities.

1Bb iii

CO2, CH4, NMVOC,

1 B 2

b iii 4 Transmission and Storage

Fugitive emissions from systems used to transport processed natural gas to market (i.e., to industrial consumers and natural gas distribution systems). Fugitive emissions from natural gas storage systems should also be included in this category. Emissions from natural gas liquids extraction plants on gas transmission systems should be reported as part of natural gas processing (Sector 1.B.2.b.iii.3). Fugitive emissions related to the transmission of natural gas liquids should be reported under Category 1.B.2.a.iii.3.

1B2b ii

CO2, CH4, NMVOC,

1 B 2

b iii 5 Distribution

Fugitive emissions (excluding venting and flaring) from the distribution of natural gas to end users.

NA

CO2, CH4, NMVOC,

1 B 2

b iii 6 Other

Fugitive emissions from natural gas systems (excluding venting and flaring) not otherwise accounted for in the above categories. This may include emissions from well blowouts and pipeline ruptures or dig-ins.

1B2 c

CO2, CH4, NMVOC,

Gases

1 B 3

Other Emissions from Energy Production

Other fugitive emissions for example, from geo thermal energy production, peat and other energy production not included in 1.B.2.

CO2, CH4, N2O, NOx, CO, NMVOC,

1 C

Carbon Dioxide Transport and Storage

Carbon dioxide (CO2) capture and storage (CCS) involves the capture of CO2 from anthropogenic sources, its transport to a storage location and its long-term isolation from the atmosphere. Emissions associated with CO2 transport, injection and storage are covered under category 1C. Emissions (and reductions) associated with CO2 capture should be reported under the IPCC Sector in which capture takes place (e.g. Fuel Combustion or Industrial Activities).

CO2,

Transport of CO2

This comprises fugitive emissions from the systems used to transport captured CO2 from the source to the injection site. These emissions may comprise losses due to fugitive equipment leaks, venting and releases due to pipeline ruptures or other accidental releases (e.g., temporary storage).

CO2,

1 C 1

1 C 1

a

Pipelines

Fugitive emissions from the pipeline system used to transport CO2 to the injection site.

CO2,

1 C 1

b

Ships

Fugitive emissions from the ships used to transport CO2 to the injection site.

CO2,

1 C 1

c

Other (please specify)

Fugitive emissions from other systems used to transport CO2 to the injection site and temporary storage

CO2,

Injection and Storage

Fugitive emissions from activities and equipment at the injection site and those from the end containment once the CO2 is placed in storage.

CO2,

1 C 2

1 C 2

a

Injection

Fugitive emissions from activities and equipment at the injection site.

CO2,

1 C 2

b

Storage

Fugitive emissions from the end equipment once the CO2 is placed in storage.

CO2,

Other

Any other emissions from CCS not reported elsewhere.

CO2,

1 C 3

8.18

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS Category Code and Name

Definition

96 GLs Category Code

Gases CO2, CH4, N2O, HFCs, PFCs, SF6, other halogen ated gases, NOx, CO, NMVOC, SO2

2 INDUSTRIAL PROCESSES AND PRODUCT Emissions from industrial processes and product use, USE excluding those related to energy combustion (reported under 1A), extraction, processing and transport of fuels (reported under 1B) and CO2 transport, injection and storage (reported under 1C).

2A

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

Process-related emissions from the production of various types of cement (ISIC: D2694).

2A1

CO2, CH4,

Lime Production

Process-related emissions from the production of various types of lime (ISIC: D2694).

2A2

CO2, CH4

2 A 3

Glass Production

Process-related emissions from the production of various types of glass (ISIC: D2610).

2A3, 2A4 CO2, CH4

2 A 4

Other Process Uses of Carbonates

Includes limestone, dolomite and other carbonates etc. Emissions from the use of limestone, dolomite and other carbonates should be included in the industrial source category where they are emitted. Therefore, for example, where a carbonate is used as a flux for iron and steel production, resultant emissions should be reported under 2C1 “Iron and Steel Production” rather than this subcategory.

2A3, 2A4 CO2, CH4, NOx, CO, NMVOC, SO2

2 A

Mineral Industry

2 A 1

Cement Production

2 A 2

2 A 4

a

Ceramics

Process-related emissions from the production of bricks and roof tiles, vitrified clay pipes, refractory products, expanded clay products, wall and floor tiles, table and ornamental ware (household ceramics), sanitary ware, technical ceramics, and inorganic bonded abrasives (ISIC: D2691, D2692 and D2693).

2A3

CO2, CH4

2 A 4

b

Other Uses of Soda Ash

This should include emissions from soda ash use that are not included elsewhere. For example, soda ash used for glass should be reported in 2A3.

2A4

CO2, CH4, NOx, CO, NMVOC, SO2

2 A 4

c

Non Metallurgical Magnesia Production

This source category should include emissions from magnesia production that are not included elsewhere. For example, where magnesia production is used for primary and secondary magnesium production, emissions should be reported in relevant source category in Metals.

2A3

CO2, CH4

2 A 4

d

Other (please specify)

Process-related emissions reported under this sub-category should include all other miscellaneous uses of limestone, dolomite and other carbonates, except from uses already listed in the sub-categories above, and uses as fluxes or slagging agents in the Metals and Chemicals industries, or for the liming of soils and wetlands in Agriculture, Forestry and Other Land Uses (ISIC D269).

2A3

CO2, CH4, NOx, CO, NMVOC, SO2

2A7

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2 A 5

Other (please specify)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS Category Code and Name

Definition

96 GLs Category Code

Gases

2B, 2A4, CO2, 3C CH4, N2O, HFCs, PFCs, SF6, other halogen ated gases, NOx, CO, NMVOC, SO2

2 B

Chemical Industry

2 B 1

Ammonia Production

Ammonia (NH3) is a major industrial chemical and the most important nitrogenous material produced. Ammonia gas is used directly as a fertilizer, in heat treating, paper pulping, nitric acid and nitrates manufacture, nitric acid ester and nitro compound manufacture, explosives of various types, and as a refrigerant. Amines, amides, and miscellaneous other organic compounds, such as urea, are made from ammonia. The main greenhouse gas emitted from NH3 production is CO2. CO2 used in the production of urea, a downstream process, should be subtracted from the CO2 generated and accounted for in the AFOLU Sector.

2B1

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2 B 2

Nitric Acid Production

Nitric acid is used as a raw material mainly in the manufacture of nitrogenous-based fertiliser. Nitric acid may also be used in the production of adipic acid and explosives (e.g., dynamite), for metal etching and in the processing of ferrous metals. The main greenhouse gas emitted from HNO3 production is nitrous oxide.

2B2

CO2, CH4, N2O, NOx, CO, NMVOC,

2 B 3

Adipic Acid Production

Adipic acid is used in the manufacture of a large number of products including synthetic fibres, coatings, plastics, urethane foams, elastomers and synthetic lubricants. The production of Nylon 6.6 accounts for the bulk of adipic acid use. The main greenhouse gas emitted from adipic acid production is nitrous oxide.

2B3

N2O, CO2, CH4 NOx,

,

2 B 4

Caprolactam, Glyoxal and Glyoxylic Acid Production

Most of the annual production of caprolactam (NH(CH2)5CO) is consumed as the monomer for nylon-6 fibres and plastics, with a substantial proportion of the fibre used in carpet manufacturing. All commercial processes for the manufacture of caprolactam are based on either toluene or benzene. This subcategory also covers production of glyoxal (ethanedial) and glyoxylic acid production. The main greenhouse gas emitted from this subcategory is nitrous oxide.

2B5

CO2, CH4, N2O, NOx, CO, NMVOC,

2 B 5

Carbide Production

The production of carbide can result in emissions of CO2, CH4, CO and SO2. Silicon carbide is a significant artificial abrasive. It is produced from silica sand or quartz and petroleum coke. Calcium carbide is used in the production of acetylene, in the manufacture of cyanamide (a minor historical use), and as a reductant in electric arc steel furnaces. It is made from calcium carbonate (limestone) and carbon-containing reductant (petroleum coke).

2B4

CO2, CH4, N2O, NOx, CO, NMVOC,

2 B 6

Titanium Dioxide Production

Titanium dioxide (TiO2) is the most important white pigment. The main use is in paint manufacture followed by paper, plastics, rubber, ceramics, fabrics, floor covering, printing ink, and other miscellaneous uses. The main production process is the chloride route, giving rise to CO2 emissions that are likely to be significant. This category also includes synthetic rutile production using the Becher process, and titanium slag production, both of which are reduction processes using fossil fuels and resulting in CO2 emissions. Synthetic rutile is the major input to TiO2 production using the chloride route.

2B5

CO2, CH4, N2O, NOx, CO, NMVOC,

8.20

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS Category Code and Name

Definition

2 B 7

Soda Ash Production

Soda ash (sodium carbonate, Na2CO3) is a white crystalline solid that is used as a raw material in a large number of industries including glass manufacture, soap and detergents, pulp and paper production and water treatment. Emissions of CO2 from the production of soda ash vary dependent on the manufacturing process. Four different processes may be used to produce soda ash. Three of these processes, monohydrate, sodium sesquicarbonate (trona) and direct carbonation, are referred to as natural processes. The fourth, the Solvay process, is classified as a synthetic process.

2 B 8

Petrochemical and Carbon Black Production

96 GLs Category Code

Gases

2A4

CO2, CH4, N2O, NOx, CO, NMVOC,

2B5

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2 B 8 a

Methanol

Methanol production covers production of methanol from fossil fuel feedstocks [natural gas, petroleum, coal] using steam reforming or partial oxidation processes. Production of methanol from biogenic feedstocks (e.g., by fermentation) is not included in this source category.

2B5

CO2, CH4, N2O, NMVOC

2 B 8 b

Ethylene

Ethylene production covers production of ethylene from fossil fuel-derived feedstocks at petrochemical plants by the steam cracking process. Production of ethylene from processes situation within the boundaries of petroleum refineries is not included in this source category. The greenhouse gases produced from ethylene production are carbon dioxide and methane.

2B5

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2 B 8 c

Ethylene Dichloride and Vinyl Chloride Monomer

Ethylene dichloride and vinyl chloride monomer production covers production of ethylene dichloride by direct oxidation or oxychloination of ethylene, and the production of vinyl chloride monomer from ethylene dichloride. The greenhouse gases produced from production of ethylene dichloride production and vinyl chloride monomer production are carbon dioxide and methane.

2B5

CO2, CH4, N2O, CO, NMVOC

2 B 8 d

Ethylene Oxide

Ethylene oxide production covers production of ethylene oxide by reaction of ethylene and oxygen by catalytic oxidation. The greenhouse gases produced from ethylene oxide production are carbon dioxide and methane.

2B5

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2 B 8 e

Acrylonitrile

Acrylonitrile production covers production of acrylonitrile from ammoxidation of propylene, and associated production of acetonitrile and hydrogen cyanide from the ammoxidation process. The greenhouse gases produced from production of acrylonitrile are carbon dioxide and methane.

2B5

CO2, CH4, N2O, NMVOC

2 B 8 f

Carbon Black

Carbon black production covers production of carbon black from fossil fuel-derived feedstocks (petroleum or coalderived carbon black feedstock, natural gas, acetylene). Production of carbon black from biogenic feedstocks is not included in this source category.

2B5, 3C

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2E

HFCs, PFCs, SF6, other halogen ated gases,

2E1

HFCs, PFCs, SF6, other halogen ated gases

2 B 9

2 B 9 a

Fluorochemical Production

By-product Emissions

Fluorochemical Production covers the complete range of fluorochemicals, whether or not the principal products are greenhouse gases. Emissions encompass HFCs, PFCs, SF6 and all other halogenated gases with global warming potential listed in IPCC assessment reports. The most significant by-product emission is that of HFC-23 from the manufacture of HCFC-22 and this is described separately.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

2 B 9 b

These are emissions of the principal product from the process to manufacture it and so fluorochemical production in this context is limited to HFCs, PFCs, SF6 and other halogenated gases with global warming potential listed in IPCC assessment reports.

2E2

HFCs, PFCs, SF6, other halogen ated gases

For example, gases with global warming potential listed in IPCC assessment reports that do not fall within any categories above could be reported here, if they are estimated.

2B5

CO2, CH4, N2O, HFCs, PFCs, SF6, other halogen ated gases, NOx, CO, NMVOC, SO2

2C

CO2, CH4, N2O, HFCs, PFCs, SF6, other halogen ated gases, NOx, CO, NMVOC, SO2

Fugitive Emissions

Gases

2 B 10

Other (Please specify)

2 C

Metal Industry

2 C 1

Iron and Steel Production

Carbon dioxide is the predominant gas emitted from the production of iron and steel. The sources of the carbon dioxide emissions include that from carbon-containing reducing agents such as coke and pulverized coal, and, from minerals such as limestone and dolomite added.

2C1

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2 C 2

Ferroalloys Production

Ferroalloys production covers emissions from primary metallurgical reduction production of the most common ferroalloys, i.e. ferro-silicon, silicon metal, ferro-manganese, silicon manganese, and ferro-chromium, excluding those emissions relating to fuel use. From the production of these alloys, carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) originating from ore- and reductant raw materials, is emitted.

2C2

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2 C 3

Aluminium Production

Aluminium Production covers primary production of aluminium, except the emissions related to the use of fuel. Carbon dioxide emissions result from the electrochemical reduction reaction of alumina with a carbon-based anode. Tetrafluoromethane (CF4) and hexafluoroethane (C2F6) are also produced intermittently. No greenhouse gases are produced in recycling of aluminium other than from the fuels uses for metal remelting. Sulphur hexafluoride (SF6) emissions are not associated with primary aluminium production; however, casting of some high magnesium containing alloys does result in SF6 emissions and these emissions are accounted for in Section 2C4, Magnesium Production.

2C3

CO2, CH4, PFCs, NOx, CO, NMVOC, SO2

2 C 4

Magnesium Production

Magnesium production covers GHG emissions related to both primary magnesium production as well as oxidation protection of magnesium metal during processing (recycling and casting), excluding those emissions relating to fuel use. In the primary production of magnesium, carbon dioxide (CO2) is emitted during calcination of dolomite and magnesite raw materials. Primary production of magnesium from non-carbonate raw materials does not emit carbon dioxide. In the processing of liquid magnesium, cover gases containing carbon dioxide (CO2), sulphur hexafluoride (SF6), the hydrofluorocarbon HFC 134a or the fluorinated ketone FK 5-1-12 ( C3F7C(O)C2F5) may be used. Partial thermal decomposition and/or reaction between these compounds and liquid magnesium generates secondary compounds such as perfluorocarbons (PFCs), which are emitted in addition to unreacted cover gas constituents.

2C4

CO2, HFCs, PFCs, SF6, other halogen ated gases, NOx, CO, NMVOC, SO2

8.22

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Gases

Lead production covers production by the sintering/smelting process as well as direct smelting. Carbon dioxide emissions result as a product of the use of a variety of carbon-based reducing agents in both production processes.

2C5

CO2

Zinc production covers emissions from both primary production of zinc from ore as well as recovery of zinc from scrap metals, excluding emissions related to fuel use. Following calcination, zinc metal is produced through one of three methods; 1-electro-thermic distillation, 2-pyrometallurgical smelting or 3-electrolysis. If method 1 or 2 is used, carbon dioxide (CO2) is emitted. Method 3 does not result in carbon dioxide emissions. Recovery of zinc from metal scrap often uses the same methods as primary production and may thus produce carbon dioxide emissions, which is included in this section.

2C5

CO2

Other (please specify)

2C5

CO2, CH4, N2O, HFCs, PFCs, SF6, other halogen ated gases, NOx, CO, NMVOC, SO2

2 D

Non-Energy Products from Fuels The use of oil products and coal-derived oils primarily and Solvent Use intended for purposes other than combustion.

1, 2A5, 2A6, 3

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2 D 1

Lubricant Use

Category Code and Name

Definition

2 C 5

Lead Production

2 C 6

Zinc Production

2 C 7

Lubricating oils, heat transfer oils, cutting oils and greases.

1, 3

CO2 CO2, CH4, N2O NMVOC

2 D 2

Paraffin Wax Use

Oil-derived waxes such as petroleum jelly, paraffin waxes and other waxes.

1, 3

2 D 3

Solvent Use

NMVOC emissions from solvent use e.g. in paint application, degreasing and dry cleaning should be contained here. Emissions from the use of HFCs and PFCs as solvents should be reported under 2F5.

3A, 3B

2 D 4

Other (please specify)

For example, CH4, CO and NMVOC emissions from asphalt production and use (including asphalt blowing), as well as NMVOC emissions from the use of other chemical products than solvents should be contained here, if relevant.

2A5, 2A6, CO2, 3D CH4, N2O, NOx, CO, NMVOC, SO2

2 E

Electronics Industry

2 E 1

Integrated Circuit or Semiconductor

2 E 2

TFT Flat Panel Display

2F6

CO2, CH4, N2O, PFCs, HFCs, SF6, other halogen ated gases

Emissions of CF4, C2F6, C3F8, c-C4F8, C4F6, C4F8O, C5F8, CHF3, CH2F2, NF3 and SF6 from uses of these gases in Integrated Circuit (IC) manufacturing in rapidly evolving ways and in varying amounts, which depend on product (e.g., memory or logic devices) and equipment manufacturer.

2F6

CO2, N2O, PFCs, HFCs, SF6, other halogen ated gases

Uses and emissions of predominantly CF4, CHF3, NF3 and SF6 during the fabrication of thin-film transistors (TFTs) on glass substrates for flat panel display manufacture. In addition to these gases, C2F6, C3F8 and c-C4F8 may also be used and emitted during the manufacture of thin and smart displays.

2F6

PFCs, HFCs, SF6, other halogen ated gases

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

2 E 3

Photovoltaics

Photovoltaic cell manufacture may use and emit CF4 and C2F6 among others.

2F6

PFCs, HFCs, SF6, other halogen ated gases

2 E 4

Heat Transfer Fluid

Heat transfer fluids, which include several fully fluorinated carbon compounds (either in pure form or in mixtures) with six or more carbon atoms, used and emitted during IC manufacture, testing and assembly. They are used in chillers, temperature shock testers and vapour phase reflow soldering.

2F6

other halogen ated gases

2 E 5

Other (please specify)

2F6

CO2, CH4, N2O, HFCs, PFCs, SF6, other halogen ated gases

2 F

Product Uses as Substitutes for Ozone Depleting Substances

2F

CO2, HFCs, PFCs, other halogen ated gases

2 F 1

Refrigeration and Air Conditioning

2F1

CO2, HFCs, PFCs, other halogen ated gases

Refrigeration and air-conditioning systems are usually classified in six application domains or categories. These categories utilise different technologies such as heat exchangers, expansion devices, pipings and compressors. The six application domains are domestic refrigeration, commercial refrigeration, industrial processes, transport refrigeration, stationary air conditioning, mobile airconditioning systems. For all these applications, various HFCs are selectively replacing CFCs and HCFCs. For example, in developed countries, HFC-134a has replaced CFC-12 in domestic refrigeration and mobile air conditioning systems, and blends of HFCs such as R-407C (HFC-32/HFC-125/HFC-134a) and R-410A (HFC-32/HFC125) are replacing HCFC-22 mainly in stationary air conditioning. Other, non HFC substances are used to replace CFCs and HCFCs such as iso-butane in domestic refrigeration or ammonia in industrial refrigeration. HFC152a is also being considered for mobile air conditioning in several regions.

Gases

2 F 1 a

Refrigeration and Stationary Air The application domains are domestic refrigeration, Conditioning commercial refrigeration, industrial processes, stationary air conditioning.

2F1

CO2, HFCs, PFCs, other halogen ated gases

2 F 1 b

Mobile Air Conditioning

2F1

CO2, HFCs, PFCs, other halogen ated gases

8.24

The application domains are transport refrigeration, mobile air-conditioning systems.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

2 F 2

Foam Blowing Agents

HFCs are being used as replacements for CFCs and HCFCs in foams, particularly in closed-cell insulation applications. Compounds that are being used include HFC245fa, HFC-365mfc, HFC-227ea, HFC-134a, and HFC152a. The processes and applications for which these various HFCs are being used include insulation boards and panels, pipe sections, sprayed systems and onecomponent gap filling foams. For open-cell foams, such as integral skin products for automotive steering wheels and facias, emissions of HFCs used as blowing agents are likely to occur during the manufacturing process. In closedcell foam, emissions not only occur during the manufacturing phase, but usually extend into the in-use phase and often the majority of emission occurs at the endof-life (de-commissioning losses). Accordingly, emissions can occur over a period of up to 50 years or even longer.

2F2

CO2, HFCs, PFCs, other halogen ated gases

2 F 3

Fire Protection

There are two general types of fire protection (fire suppression) equipment that use greenhouse gases as partial replacements for halons: portable (streaming) equipment, and fixed (flooding) equipment. The non-ozone depleting, industrial gases HFCs, PFCs and more recently a fluoroketone are mainly used as substitutes for halons, typically halon 1301, in flooding equipment. PFCs played an early role in halon 1301 replacement but current use is limited to replenishment of previously installed systems. HFCs in portable equipment, typically replacing halon 1211, are available but have achieved very limited market acceptance due primarily to their high cost. PFC use in new portable extinguishers is currently limited to a small amount (few percent) in an HCFC blend.

2F3

CO2, HFCs, PFCs, other halogen ated gases

2 F 4

Aerosols

Most aerosol packages now contain hydrocarbon (HC) as propellants but, in a small fraction of the total, HFCs and PFCs may be used as propellants or solvents. Emissions from aerosols usually occur shortly after production, on average six months after sale. During the use of aerosols, 100% of the chemical is emitted. The 5 main sources are metered dose inhalers (MDIs), personal care products (e.g. hair care, deodorant, shaving cream), household products (e.g. air-fresheners, oven and fabric cleaners), industrial products (e.g. special cleaning sprays such as those for operating electrical contact, lubricants, pipe-freezers) and other general products (e.g. silly string, tire inflators, claxons), although in some regions the use of such general products is restricted. The HFCs currently used as propellants are HFC 134a, HFC 227ea, and HFC 152a. The substance HFC 43 10mee and a PFC, perfluorohexane, are used as solvents in industrial aerosol products.

2F4

HFCs, PFCs, other halogen ated gases

2 F 5

Solvents

HFCs and, to a much lesser extent PFCs, are being used as substitutes for ozone depleting substances (most notably CFC-113). Typical HFCs used are HFC-365mfc and HFC-43-10mee. Use of these fluorinated replacements is much less widespread than the ozone depleting substances they replace. Re-capture and re-use is also much more widely practiced The primary areas of use are precision cleaning, electronics cleaning, metal cleaning and deposition applications. Emissions from aerosols containing solvents should be reported undercategory 2F4 "Aerosols" rather than under this category.

2F5

HFCs, PFCs, other halogen ated gases

2 F 6

Other Applications (please specify)

The properties of ozone depleting substances have made them attractive for a variety of niche applications not covered in other sub-source categories. These include electronics testing, heat transfer, dielectric fluid and medical applications. The properties of HFCs and PFCs are equally attractive in some of these sectors and they have been adopted as substitutes. There are also some historical uses of PFCs, as well as emerging use of HFCs, in these applications. These applications have leakage rates ranging from 100% emissive in year of application to around 1% per annum.

2F6

CO2, CH4, N2O, HFCs, PFCs, other halogen ated gases

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Gases

8.25

Volume 1: General Guidance and Reporting

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS Category Code and Name 2 G

OTHER PRODUCT MANUFACTURE AND USE

2 G 1

Electrical Equipment

Definition

Electrical equipment is used in the transmission and distribution of electricity above 1 kV. SF6 is used in gasinsulated switchgear (GIS), gas circuit breakers (GCB), gas-insulated transformers (GIT), gas-insulated lines (GIL), outdoor gas-insulated instrument transformers, reclosers, switches, ring main units and other equipment.

96 GLs Category Code

Gases

2F6, 3D

CO2, CH4, N2O, HFCs, PFCs, SF6, other halogen ated gases

2F6

SF6, PFCs, other halogen ated gases

2 G 1 a

Manufacture of Electrical Equipment

2F6

SF6, PFCs, other halogen ated gases

2 G 1 b

Use of Electrical Equipment

2F6

SF6, PFCs, other halogen ated gases

2 G 1 c

Disposal of Electrical Equipment

2F6

SF6, PFCs, other halogen ated gases

2F6

SF6, PFCs, other halogen ated gases

2 G 2

SF6 and PFCs from Other Product Uses

2 G 2 a

Military Applications

Military applications include AWACS, which are military reconnaissance planes of the Boeing E-3A type. In AWACS (and possibly other reconnaissance planes), the SF6 is used as an insulating gas in the radar system.

2F6

SF6, PFCs, other halogen ated gases

2 G 2 b

Accelerators

Particle accelerators are used for research purposes (at universities and research institutions), for industrial applications (in cross-linking polymers for cable insulation and for rubber parts and hoses), and in medical (radiotherapy) applications.

2F6

SF6, PFCs, other halogen ated gases

2 G 2 c

Other (please specify)

This source includes adiabatic uses, sound-proof glazing, PFCs used as heat transfer fluids in consumer and commercial applications, PFCs used in cosmetic and medical applications, and PFCs and SF6 used as tracers.

2F6

SF6, PFCs, other halogen ated gases

2 G 3 2 G 3 a

N2O from Product Uses Medical Applications

2 G 3 b

Propellant for Pressure and Aerosol Products

2 G 3 c

Other (Please specify)

2 G 4

8.26

Other (Please specify)

This source covers evaporative emissions of nitrous oxide (N2O) that arise from medical applications (anaesthetic use, analgesic use and veterinary use). N2O is used during anaesthesia for two reasons: a) as an anaesthetic and analgesic and as b) a carrier gas for volatile fluorinated hydrocarbon anaesthetics such as isoflurane, sevoflurane and desflurane. This source covers evaporative emissions of nitrous oxide (N2O) that arise from use as a propellant in aerosol products primarily in food industry. Typical usage is to make whipped cream, where cartridges filled with N2O are used to blow the cream into foam.

3D

N2O

3D

N2O

3D

N2O

3D

N2O

2F6, 3D

CO2, CH4, HFCs, other halogen ated gases

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS Category Code and Name

Definition

96 GLs Category Code

Gases

2 H

Other

2D1, 2D2, 2G

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

2 H 1

Pulp and Paper Industry

2D1

CO2, CH4, NOx, CO, NMVOC, SO2

2 H 2

Food and Beverages Industry

2D2

CO2, CH4, NOx, CO, NMVOC, SO2

2 H 3

Other (please specify)

2G

CO2, CH4, N2O, NOx, CO, NMVOC, SO2

3 AGRICULTURE, FORESTRY, AND OTHER LAND USE

Emissions and removals from forest land, cropland, grassland, wetlands, settlements, and other land. Also includes emissions from livestock and manure management, emissions from managed soils, and emissions from liming and urea application. Methods to estimate annual harvested wood product (HWP) variables are also covered in this category.

4,5

CH4, N2O, CO2

3 A

Livestock

Methane emissions from enteric fermentation, and methane and nitrous oxide emissions from manure management.

4

CH4

3 A 1

Enteric Fermentation

Methane emissions from herbivores as a by-product of enteric fermentation (a digestive process by which carbohydrates are broken down by micro-organisms into simple molecules for absorption into the bloodstream). Ruminant animals (e.g., cattle, sheep) are major sources with moderate amounts produced from non-ruminant animals (e.g., pigs, horses).

4A

CH4

3 A 1 a

Cattle

Methane emissions from dairy cows and other cattle.

4A1

CH4

3 A 1 a i

Dairy Cows

Methane emissions from cattle producing milk for commercial exchange and from calves and heifers being grown for dairy purposes.

4A1a

CH4

3 A 1 a ii

Other Cattle

Methane emissions from all non-dairy cattle including: cattle kept or grown for meat production, draft animals, and breeding animals.

4A1b

CH4

3 A 1 b

Buffalo

Methane emissions from buffalo.

4A2

CH4

3 A 1 c

Sheep

Methane emissions from sheep.

4A3

CH4

3 A 1 d

Goats

Methane emissions from goats.

4A4

CH4

3 A 1 e

Camels

Methane emissions from camels.

4A5

CH4

3 A 1 f

Horses

Methane emissions from horses.

4A6

CH4 CH4

3 A 1 g

Mules and Asses

Methane emissions from mules and asses.

4A7

3 A 1 h

Swine

Methane emissions from swine.

4A8

CH4

3 A 1 j

Other (please specify)

Methane emissions from other livestock (e.g. alpacas, llamas, deer, reindeer, etc.).

4A10

CH4

Methane and nitrous oxide emissions from the decomposition of manure under low oxygen or anaerobic conditions. These conditions often occur when large numbers of animals are managed in a confined area (e.g. dairy farms, beef feedlots, and swine and poultry farms), where manure is typically stored in large piles or disposed of in lagoons and other types of manure management systems.

4B

CH4, N2O

Methane and nitrous oxide emissions from the decomposition of manure from cattle.

4B1

CH4, N2O

3 A 2

3 A 2 a

Manure Management

Cattle

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 1: General Guidance and Reporting

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

3 A 2 a i

Dairy Cows

Methane and nitrous oxide emissions from the decomposition of manure from dairy cows.

3 A 2 a ii

Other Cattle

Methane and nitrous oxide emissions from the decomposition of manure from other cattle.

3 A 2 b

Buffalo

Methane and nitrous oxide emissions from the decomposition of manure from buffalo.

4B2

CH4, N2O

3 A 2 c

Sheep

Methane and nitrous oxide emissions from the decomposition of manure from sheep.

4B3

CH4, N2O

3 A 2 d

Goats

Methane and nitrous oxide emissions from the decomposition of manure from goats.

4B4

CH4, N2O

3 A 2 e

Camels

Methane and nitrous oxide emissions from the decomposition of manure from camels.

4B5

CH4, N2O

3 A 2 f

Horses

Methane and nitrous oxide emissions from the decomposition of manure from horses.

4B6

CH4, N2O

3 A 2 g

Mules and Asses

Methane and nitrous oxide emissions from the decomposition of manure from mules and assess.

4B7

CH4, N2O

3 A 2 h

Swine

Methane and nitrous oxide emissions from the decomposition of manure from swine.

4B8

CH4, N2O

3 A 2 i

Poultry

Methane and nitrous oxide emissions from the decomposition of manure from poultry including chicken, broilers, turkeys, and ducks.

4B9

CH4, N2O

3 A 2 j

Other (please specify)

Methane and nitrous oxide emissions from the decomposition of manure from other livestock (e.g. alpacas, llamas, deer, reindeer, fur-bearing animals, ostriches, etc.)

4B13

CH4, N2O

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

4B1a

Gases CH4, N2O CH4, N2O

3 B

Land

Emissions and removals from five land use categories (Forest land, Cropland, Grasslands, Settlements, and Other land) except for sources listed under 3C (Aggregate sources and non-CO2 emissions sources on land) . Except for Wetlands, the greenhouse gas inventory involves estimation of changes in carbon stock from five carbon pools (i.e. aboveground biomass, belowground biomass, dead wood, litter, and soil organic matter), as appropriate.

5

3 B 1

Forest Land

Emissions and removals from lands with woody vegetation consistent with thresholds used to define forest land in the national GHG inventory, sub-divided into managed and unmanaged, and possibly also by climatic region, soil type and vegetation type as appropriate. It also includes systems with vegetation that currently fall below, but are expected to later exceed, the threshold values used by a country to define the forest land category.

5A,5B,5D CO2, CH4 N2O, NOx, CO, NMVOC, SO2

Emissions and removals from managed forests and plantations which have always been under forest land use or other land categories converted to forest over 20 years ago (default assumption).

5A

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 1 a

Forest land Remaining Forest Land

3 B 1 b

Land Converted to Forest Land Emissions and removals from lands converted to forest land. Includes conversion of cropland, grassland, wetlands, settlements, and other land to forest land. Even abandoned lands which are regenerating to forest due to human activities are also included.

3 B 1 b i

Cropland Converted to Forest Land

Emissions and removals from cropland converted to forest land.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 1 b ii

Grassland Converted to Forest Land

Emissions and removals from grassland converted to forest land.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

8.28

5A,5C,5D CO2, CH4 N2O, NOx, CO, NMVOC, SO2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

3 B 1 b iii

Wetlands Converted to Forest Land

Emissions and removals from wetlands converted to forest land.

3 B 1 b iv

Settlements Converted to Forest Emissions and removals from settlements converted to forest land. Land

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 1 b v

Other Land Converted to Forest Emissions and removals from other land converted to forest land. Land

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 2

Cropland

Gases CO2, CH4 N2O, NOx, CO, NMVOC, SO2

Emissions and removals from arable and tillage land, rice fields, and agro-forestry systems where vegetation falls below the thresholds used for the forest land category.

4C, 4D, 4F, 5A, 5B, 5D

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 2 a

Cropland Remaining Cropland

Emissions and removals from cropland that has not undergone any land use change during the inventory period.

4C, 4D, 4F, 5A, 5D

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 2 b

Land Converted to Cropland

Emissions and removals from lands converted to cropland. Includes conversion of forest land, grassland, wetlands, settlements, and other land to cropland.

5B, 5D

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 2 b i

Forest Land Converted to Cropland

Emissions and removals from forest land converted to cropland.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 2 b ii

Grassland Converted to Cropland

Emissions and removals from grassland converted to cropland.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 2 b iii

Wetlands Converted to Cropland

Emissions and removals from wetlands converted to cropland.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 2 b iv

Settlements Converted to Cropland

Emissions and removals from settlements converted to cropland.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 2 b v

Other Land Converted to Cropland

Emissions and removals from other land converted to cropland.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

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TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS Category Code and Name

Definition

3 B 3

Emissions and removals from rangelands and pasture land that is not considered cropland. It also includes systems with woody vegetation that fall below the threshold values used in the forest land category and are not expected to exceed them, without human intervention. The category also includes all grassland from wild lands to recreational areas as well as agricultural and silvi-pastural systems, subdivided into managed and unmanaged, consistent with national definitions.

Grassland

96 GLs Category Code

Gases

4D, 4E, CO2, 5A,5B,5C CH4 5D N2O, NOx, CO, NMVOC, SO2

3 B 3 a

Grassland Remaining Grassland Emissions and removals from grassland remaining grassland.

4D, 4E, 5A,5D

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 3 b

Land Converted to Grassland

Emissions and removals from land converted to grassland.

5B, 5C, 5D

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 3 b i

Forest Land Converted to Grassland

Emissions and removals from forest land converted to grassland.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 3 b ii

Cropland Converted to Grassland

Emissions and removals from cropland converted to grassland.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 3 b iii

Wetlands Converted to Grassland

Emissions and removals from wetlands converted to grassland.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 3 b iv

Settlements Converted to Grassland

Emissions and removals from settlements converted to grassland.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 3 b v

Other Land Converted to Grassland

Emissions and removals from other land converted to grassland.

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 4

Wetlands

Emissions from land that is covered or saturated by water for all or part of the year (e.g., peatland) and that does not fall into the forest land, cropland, grassland or settlements categories. The category can be subdivided into managed and unmanaged according to national definitions. It includes reservoirs as a managed sub-division and natural rivers and lakes as unmanaged sub-divisions.

5A, 5B, 5E, 4D

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

Emissions from peatland undergoing peat extraction and from flooded land remaining flooded land.

5A, 5D, 5E, 4D

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

5A, 5E, 4D

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 4 a

Wetlands Remaining Wetlands

3 B 4 a i

Peatlands Remaining peatlands Includes (1) on-site emissions from peat deposits during the extraction phase and (2) off-site emissions from horticultural use of peat. The off-site emissions from the energy use of peat are reported in the Energy Sector and are therefore not included in this category.

8.30

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

3 B 4 a ii

Flooded Land Remaining Flooded Land

Emissions from flooded land remaining flooded land. Flooded lands are defined as water bodies where human activities have caused changes in the amount of surface area covered by water, typically through water level regulation. Examples of flooded lands include reservoirs for the production of hydroelectricity, irrigation, navigation, etc. Regulated lakes and rivers that have not experienced substantial changes in water area in comparison with the pre-flooded ecosystem are not considered as flooded lands. Some rice paddies are cultivated through flooding of land, but because of the unique characteristics of rice cultivation, rice paddies are addressed in 3C7.

5A, 5E

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 4 b

Land Converted to Wetlands

Emissions from land being converted for peat extraction from land converted to wetland.

5B, 5E

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 4 b i

Land Converted for Peat Extraction

Emissions from land being converted for peat extraction

5B, 5E

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 4 b ii

Land Converted to Flooded Land

Emissions from land converted to flooded land

5B, 5E

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

3 B 4 b iii

Land Converted to Other Wetlands

Emissions from land converted to other wetlands than flooded land and land for peat extraction.

5E

CO2, CH4 N2O, NOx, CO, NMVOC, SO2

Emissions and removals from all developed land, including transportation infrastructure and human settlements of any size, unless they are already included under other categories. This should be consistent with national definitions.

5A, 5D, 5E, 5B

CO2

3 B 5

Settlements

Gases

3 B 5 a

Settlements Remaining Settlements

Emissions and removals from settlements that have not undergone any land use change during the inventory period.

CO2

3 B 5 b

Land Converted to Settlements

Emissions and removals from lands converted to settlements. Includes conversion of forest land, cropland, grassland, wetlands, and other land to settlements.

CO2

3 B 5 b i

Forest Land Converted to Settlements

Emissions and removals from forest land converted to settlements.

CO2

3 B 5 b ii

Cropland Converted to Settlements

Emissions and removals from cropland converted to settlements.

CO2

3 B 5 b iii

Grassland Converted to Settlements

Emissions and removals from grassland converted to settlements.

CO2

3 B 5 b iv

Wetlands Converted to Settlements

Emissions and removals from wetlands converted to settlements.

CO2

3 B 5 b v

Other Land Converted to Settlements

Emissions and removals from other land converted to settlements.

CO2

Emissions and removals from bare soil, rock, ice, and all unmanaged land areas that do not fall into any of the other five categories. It allows the total of identified land areas to match the national area, where data are available.

CO2

Emissions and removals from other land that has not undergone any land use change during the inventory period.

CO2

3 B 6

3 B 6 a

Other Land

Other Land Remaining Other Land

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TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

3 B 6 b

Land Converted to Other Land

Emissions and removals from lands converted to other land. Includes conversion of forest land, cropland, grassland, wetlands, and settlements to other land.

3 B 6 b i

Forest Land Converted to Other Emissions and removals from forest land converted to other land. Land

3 B 6 b ii

Cropland Converted to Other Land

Emissions and removals from cropland converted to other land.

CO2

3 B 6 b iii

Grassland Converted to Other Land

Emissions and removals from grassland converted to other land.

CO2

3 B 6 b iv

Wetlands Converted to Other Land

Emissions and removals from wetlands converted to other land.

CO2

3 B 6 b v

Settlements Converted to Other Emissions and removals from settlements converted to other land. Land

Gases CO2

CO2

CO2

3 C

Aggregate Sources and Non-CO2 Emissions Sources on Land

Includes emissions from activities that are likely to be reported at very high aggregation land level or even country level.

3 C 1

Emissions from Biomass Burning

Emissions from biomass burning that include N2O and CH4. CO2 emissions are included here only if emissions are not included in 3B categories as carbon stock changes.

N2O, CH4, CO2*

3 C 1 a

Biomass Burning in Forest Lands

Emissions from biomass burning that include N2O and CH4 in forest lands. CO2 emissions are included here only if emissions are not included in 3B1 categories as carbon stock changes.

N2O, CH4, CO2*

3 C 1 b

Biomass Burning in Croplands

Emissions from biomass burning that include N2O and CH4 in croplands. CO2 emissions are included here only if emissions are not included in 3B2 categories as carbon stock changes.

N2O, CH4, CO2*

3 C 1 c

Biomass Burning in Grasslands Emissions from biomass burning that include N2O and CH4 in grasslands. CO2 emissions are included here only if emissions are not included in 3B3 categories as carbon stock changes.

N2O, CH4, CO2*

3 C 1 d

Biomass Burning in All Other Land

Emissions from biomass burning that include N2O and CH4 in settlements, and all other land. CO2 emissions are included here only if emissions are not included in 3B6 categories as carbon stock changes.

N2O, CH4, CO2*

3 C 2

Liming

CO2 emissions from the use of lime in agricultural soils, managed forest soils or lakes.

CO2

3 C 3

Urea Application

CO2 emissions from urea application

CO2

3 C 4

Direct N2O Emissions from Managed Direct N2O emissions from managed soils from the synthetic N fertilizers application; organic N applied as Soils fertilizer (e.g. animal manure, compost, sewage sludge, rendering waste); urine and dung N deposited on pasture, range and paddock by grazing animals; N in crop residues (above and below ground), including from N-fixing crops and from forages during pasture renewal; N mineralization/immobilization associated with loss/gain of soil organic matter resulting from change of land use or management of mineral soils; and drainage/management of organic soils (i.e., histosols).

4D

N2O

3 C 5

Indirect N2O Emissions from Managed Soils

Indirect N2O emissions from: (1) the volatilization of N (as NH3 and NOx) following the application of synthetic and organic N fertilizers and /or urine and dung deposition from grazing animals, and the subsequent deposition of the N as ammonium (NH4+) and oxides of N (NOx) on soils and waters, and (2) the leaching and runoff of N from synthetic and organic N fertilizer additions, crop residues, mineralization /immobilization of N associated with loss/gain of soil C in mineral soils through land use change or management practices, and urine and dung deposition from grazing animals, into groundwater, riparian areas and wetlands, rivers and eventually the coastal ocean.

4D

N2O

8.32

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Reporting Guidance and Tables

TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS 96 GLs Category Code

Category Code and Name

Definition

3 C 6

Indirect N2O Emissions from Manure Management

Indirect N2O emissions from manure management (activity data amount of nitrogen in the manure excreted).

3 C 7

Rice Cultivations

Methane (CH4) emissions from anaerobic decomposition of organic material in flooded rice fields. Any N2O emissions from the use of nitrogen-based fertilizers in rice cultivation should be reported under N2O emissions from managed soils.

3 C 8

Other (please specify)

Other sources of CH4 and N2O emissions on land.

N2O, CH4

3 D

Other

3 D 1

Harvested Wood Products

CO2 net emissions or removals resulting from Harvest Wood Products.

CO2

3 D 2

Other (please specify)

Gases N2O

4C

CH4

CO2, CH4, N2O, NOx, CO, NMVOC SO2

4 WASTE

4 A

Solid Waste Disposal

Methane is produced from anaerobic microbial decomposition of organic matter in solid waste disposal sites. Carbon dioxide (CO2) is also produced but CO2 from biogenic or organic waste sources is covered by the AFOLU Sector. Emissions of halogenated gases should be accounted in IPPU. Long-term storage of carbon in SWDS is reported as an information item.

6A

CH4 N2O, NOx, CO, NMVOC

4 A 1

Managed Waste Disposal Sites

A managed solid waste disposal site must have controlled placement of waste (i.e. waste directed to specific deposition areas, a degree of control of scavenging and fires) and will include at least one of the following: cover material; mechanical compaction; or leveling of the waste. This category can be subdivided into aerobic and anaerobic.

6A 1

CH4 N2O, NOx, CO, NMVOC

4 A 2

Unmanaged Waste Disposal Sites

These are all other solid waste disposal sites that do not fall into the above category. This category can be subdivided into deep and shallow.

6A2

CH4 N2O, NOx, NMVOC

4 A 3

Uncategorised Waste Disposal Sites Mixture of above 4 A1 and 4 A2. Countries that do not have data on division of managed/unmanaged may use this category.

NA

CH4 N2O, NOx, NMVOC

4 B

Biological Treatment of Solid Waste

Solid waste composting and other biological treatment. Emissions from biogas facilities (anaerobic digestion) with energy production are reported in the Energy Sector (1A4).

6A3

CH4, N2O NOx, CO, NMVOC

4 C

Incineration and Open Burning of Waste

Incineration of waste and open burning waste, not including waste-to-energy facilities. Emissions from waste burnt for energy are reported under the Energy Sector, 1A. Emissions from burning of agricultural wastes should be reported under AFOLU (3C1). All non-CO2 greenhouse gases as well as CO2 from fossil waste should be reported here for incineration and open burning.

6C

CO2, CH4, N2O, NOx, CO, NMVOC

4 C 1

Waste Incineration

Combustion of solid wastes in controlled incineration facilities.

6C

CO2, CH4, N2O, NOx, CO, NMVOC

4 C 2

Open Burning of Waste

Combustion of waste in the open-air or in an open dump.

NA

CO2, CH4, N2O, NOx, CO, NMVOC

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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TABLE 8.2 (CONTINUED)

CLASSIFICATION AND DEFINITION OF CATEGORIES OF EMISSIONS AND REMOVALS Category Code and Name

Definition

4 D

Wastewater Treatment and Discharge

Methane is produced from anaerobic decomposition of organic matter by bacteria in sewage facilities and from food processing and other industrial facilities during wastewater treatment. N2O is also produced by bacteria (denitrification and nitrification) in wastewater treatment and discharge.

4 D 1

96 GLs Category Code

Gases

6B

CH4, N2O NOx, CO, NMVOC

Domestic Wastewater Treatment and Treatment and discharge of liquid wastes and sludge from housing and commercial sources (including human waste) Discharge through: wastewater sewage systems collection and treatment systems, open pits / latrines, anaerobic lagoons, anaerobic reactors and discharge into surface waters. Emissions from sludge disposed at SWDS are reported under category 4A.

6B2

CH4, N2O NOx, CO, NMVOC

4 D 2

Industrial Wastewater Treatment and Treatment and discharge of liquid wastes and sludge from industrial processes such as: food processing, textiles, or Discharge pulp and paper production. This includes anaerobic lagoons, anaerobic reactors, and discharge into surface waters. Industrial wastewater released into domestic wastewater sewage should be included under 4D1.

6B1

CH4, N2O NOx, CO, NMVOC

4 E

Other (please specify)

6D

CO2, CH4, N2O, NOx, CO, NMVOC

5

Release of GHGs from other waste handling activities than listed in categories 4A to 4D.

7

Other

5 A

Indirect N2O Emissions from the Excluding indirect emissions from NOx and NH3 in Atmospheric Deposition of Nitrogen in agriculture which are reported in 3C5. NOx and NH3

NA

5 B

Other (please specify)

7

Only use this category exceptionally, for any categories than cannot be accommodated in the categories described above. Include a reference to where a detailed explanation of the category can be found.

N2O

(1) Under the 2006 IPCC Guidelines, emissions from the use of carbonates should be reported in the subcategories (industries) where they occur. Therefore, part of emissions that were reported in 2A3 or 2A4 under the 1996 Guidelines should be reported in various relevant subcategories (for example 2C1) under the 2006 Guidelines. In this column of this table, however, the 96GLs Category Code 2A3 and 2A4 are entered not everywhere possibly relevant, for the sake of simplicity. Note: NA or blank cells under the column ‘96 GLs category code’: categories that are not defined in 1996 Guidelines.

References IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2000). Good Practice Guidance and Uncertianty Management in National Greenhouse Gas Inventories. Penman, J., Kruger, D., Galbally, I., Hiraishi, T., Nyenzi, B., Enmanuel, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K. and Tanabe, K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. IPCC (2001). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Houghton, J.T., Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.). Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 881pp. IPCC (2003). Good Practice Guidance for Land Use, land-Use Change and Forestry. Penman, J., Gytarsky, M., Hiraishi, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. and Wagner, F. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/IGES, Hayama, Japan.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Annex 8A.1: Prefixes, units and abbreviations, standard equivalents

ANNEX 8A.1

PREFIXES, UNITS AND ABBREVIATIONS, STANDARD EQUIVALENTS

2006 IPCC Guidelines for National Greenhouse Gas Inventories

8A1.1

Volume 1: General Guidance and Reporting

Annex 8A.1 Prefixes, units and abbreviations, standard equivalents Prefixes and multiplication factors Multiplication Factor

Abbreviation

Prefix

Symbol

1 000 000 000 000 000

1015

peta

P

1 000 000 000 000

12

tera

T

giga

G

mega

M

kilo

k

hecto

h

10

1 000 000 000

109

1 000 000

6

10

1 000

103

100

10

2

10

101

deca

da

0.1

10

-1

deci

d

0.01

10-2

centi

c

0.001

-3

milli

m

0.000 001

10

10-6

micro

μ

Units and abbreviations

8A1.2

cubic metre

m3

hectare

ha

gram

g

tonne

t

Joule

J

degree Celsius

℃

calorie

cal

year

yr

capita

cap

gallon

gal

dry matter

d.m.

kilogram

kg

pound

lb

atmosphere

atm

Pascal

Pa

hour

h

Watt

W

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Annex 8A.1: Prefixes, units and abbreviations, standard equivalents

Units and abbreviations, and standard equivalents 1 tonne of oil equivalent (toe)

1 toe

1 ktoe

1 x 1010 calories

1 x 1010 cal

41.868 terajoules

41.868 TJ

1 short ton

1 sh t

0.9072 tonne

0.9072 t

1 tonne

1t

1.1023 short tons

1.1023 sh t

1 tonne

1t

1 megagram

1 Mg

1 kilotonne

1 kt

1 gigagram

1 Gg

1 megatonne

1 Mt

1 teragram

1 Tg

1 gigatonne

1 Gt

1 petagram

1 Pg

1 kilogram

1 kg

2.2046 pounds

2.2046 lb

1 hectare

1 ha

104 squire meters

104 m2

1 calorieIT

1 calIT

4.1868 Joules

4.1868 J

1 atmosphere

1 atm

101.325 kilopascal

101.325 kPa

1 gram

1g

0.002205 pounds

0.00205 lb

1 pound

1 lb

453.6 gram

453.6 g

5

1 terajoule

1 TJ

2.78 x 10 kiloWatt hour

2.78 x 105 kWh

1 kilowatt hour

1 kWh

3.6 x 106 Joules

3.6 x 106 J

Formulae for chemical compounds Chemical formula

CO2 CH4 N2O HFCs PFCs SF6 NF3 SF5CF3 CFCs CHF3 CH2F2 CH3F CHF2CF3 CHF2CHF2 CH2FCF3 CHF2CH2F CF3CH3 CH2FCH2F CH3CHF2 CH3CH2F CF3CHFCF3 CH2FCF2CF3 CHF2CHFCF3

Gas

Carbon dioxide Methane Nitrous oxide Hydrofluorocarbons Perfluorocarbons Sulphur hexafluoride Nitrogen trifluoride Trifluoromethyl sulphur pentafluoride Chlorofluorocarbons HFC-23 HFC-32 HFC-41 HFC-125 HFC-134 HFC-134a HFC-143 HFC-143a HFC-152 HFC-152a HFC-161 HFC-227ea HFC-236cb HFC-236ea

2006 IPCC Guidelines for National Greenhouse Gas Inventories

8A1.3

Volume 1: General Guidance and Reporting

Formulae for chemical compounds (Continued) Chemical formula

CF3CH2CF3 CH2FCF2CHF2 CHF2CH2CF3 CF3CH2CF2CH3 CF3CHFCHFCF2CF3 CF3OCHF2 CHF2OCHF2 CH3OCF3 CF3CHClOCHF2 CF3CF2OCH3 CF3CH2OCHF2 CHF2CF2OCH3 CF3CF2CF2OCH3 CHF2CF2CH2OCHF2 CHF2CF2OCH2CH3 C4F9OCH3 C4F9OC2H5 CHF2OCF2OC2F4OCHF2 CHF2OCF2OCHF2 CHF2OCF2CF2OCHF2 CF4 C2F6 C3F8 C4F10 c-C4F8 C5F12 C6F14 c-C3F6 CF3CHFOCF3 CF3CHFOCHF2 CF3CH2OCF3 CHF2CH2OCF3 CF3CH2OCH3 CF3CF2OCF2CHF2 CF3CF2OCH2CF3 CF3CF2OCH2CHF2 CF3CHFCF2OCH3 CHF2CF2CF2OCH3 CHF2CF2OCH2CHF2 CF3CF2CH2OCH3 CO NOX NMVOC SO2 NH3

8A1.4

Gas

HFC-236fa HFC-245ca HFC-245fa HFC-365mfc HFC-43-10mee HFE-125 HFE-134 HFE-143a HCFE-235da2 HFE-245cb2 HFE-245fa2 HFE-254cb2 HFE-347mcc3 HFE-356pcf3 HFE-374pc2 HFE-7100 HFE-7200 H-Galden 1040x HG-10 HG-01 Perfluoromethane Perfluoroethane Perfluoropropane Perfluorobutane Perfluorocyclobutane Perfluourpentane Perfluorohexane Perfluorocyclopropane HFE-227ea HFE-236ea2 HFE-236fa HFE-245fa1 HFE-263fb2 HFE-329mcc2 HFE-338mcf2 HFE-347mcf2 HFE-356mec3 HFE-356pcc3 HFE-356pcf2 HFE-365mcf3 Carbon monoxide Nitrogen oxides Non-methane volatile organic compound Sulphur dioxide Ammonia

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Annex 8A.2: Reporting Tables

ANNEX 8A.2

REPORTING TABLES

Year of the Inventory Contact Name Country Organisation Address

Phone Fax e-mail

2006 IPCC Guidelines for National Greenhouse Gas Inventories

T.1

Volume 1: General Guidance and Reporting

Contents S u m ma r y a nd sh ort s u m mar y t a b le s Table A

Summary Table ........................................................................................................................ T.4

Table B

Short Summary Table ............................................................................................................. T.10

S e cto ra l an d b ack gro un d t a b les Energy Sector Tables Table 1

Energy Sectoral Table ............................................................................................................ T.12

Table 1.1 Energy Background Table: 1A1-1A2 ...................................................................................... T.15 Table 1.2 Energy Background Table: 1A3-1A5 ...................................................................................... T.17 Table 1.3 Energy Background Table: 1B ................................................................................................ T.19 Table 1.4a Energy Background Table: 1C CO2 Transport, Injection and Storage ................................... T.20 Table 1.4b Energy Background Table: 1C CO2 Transport, Injection and Storage - Overview ................. T.21 Table 1.5 Energy Background Table: Reference Approach ................................................................... T.22 IPPU Sector Tables Table 2

IPPU Sectoral Table ............................................................................................................... T.24

Table 2.1 IPPU Background Table: 2A Mineral Industry, 2B (2B1-2B8, 2B10) Chemical Industry CO2, CH4 and N2O .................................................................................................................. T.26 Table 2.2 IPPU Background Table: 2B (2B9 - 2B10) Chemical Industry HFCs, PFCs, SF6 and other halogenated gases ..................................................................... T.27 Table 2.3 IPPU Background Table: 2C Metal Industry CO2, CH4 and N2O ........................................... T.28 Table 2.4 IPPU Background Table: 2C (2C3, 2C4, 2C7) Metal Industry HFCs, PFC, SF6 and other halogenated gases ...................................................................... T.29 Table 2.5 IPPU Background Table: 2D Non-Energy Products from Fuels and Solvent Use CO2, CH4 and N2O .................................................................................................................. T.30 Table 2.6 IPPU Background Table: 2E Electronics Industry HFCs, PFCs, SF6 NF3 and other halogenated gases ............................................................. T.31 Table 2.7 IPPU Background Table: 2F Product Uses as Substitutes for Ozone Depleting Substances HFCs, PFCs and other halogenated gases ............................................................................ T.32 Table 2.8 IPPU Background Table: 2G (2G1, 2G2, 2G4) Other Product Manufacture and Use PFCs, SF6 and other halogenated gases ............................................................................... T.33 Table 2.9 IPPU Background Table: 2G (2G3, 2G4) Other Product Manufacture and Use N2O, CO2, CH4 ........................................................................................................................ T.34 Table 2.10 IPPU Background Table: 2H Other ......................................................................................... T.35 Table 2.11 IPPU Background Table: Greenhouse gases without CO2 equivalent conversion factors ...... T.36 Table 2.12 IPPU Background Table: Allocation of CO2 emissions from Non-Energy Use of fossil fuels: IPPU and other sectors ........................................................................................................... T.37 AFOLU Sector Tables Table 3

AFOLU Sectoral Table ............................................................................................................ T.38

Table 3.1 AFOLU Background Table: 3A1 - 3A2 Agriculture/livestock ................................................... T.40 Table 3.2 AFOLU Background Table: 3B Carbon stock changes in FOLU ............................................ T.41 Table 3.3 AFOLU Background Table: Emissions in Wetlands (3B4) ....................................................... T.43

T.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Annex 8A.2: Reporting Tables

Table 3.4 AFOLU Background Table: Biomass Burning (3C1) ............................................................... T.44 Table 3.5 AFOLU Background Table: CO2 emissions from Liming (3C2) ............................................... T.46 Table 3.6 AFOLU Background Table: CO2 emissions from Urea Fertilization (3C3) .............................. T.47 Table 3.7 AFOLU Background Table: Direct N2O emissions from Managed Soils (3C4) ...................... T.48 Table 3.8 AFOLU Background Table: Indirect N2O emissions from Managed Soils and Manure Management (3C5 and 3C6) ..................................................................................... T.49 Table 3.9 AFOLU Background Table: Non-CO2 GHG emissions not included elsewhere (3C7 and 3C8) ........................................................................................................................ T.50 Table 3.10 AFOLU Background Table: Harvested Wood Products (3D1) - Annual carbon HWP contribution to total AFOLU CO2 removals and emissions and background information ........ T.51 Waste Sector Tables Table 4

Waste Sectoral Table ............................................................................................................. T.52

Table 4.1 Waste Background Table: CO2, CH4, N2O emissions ............................................................. T.53 Table 4.2 Waste Background Table: CH4 recovery ................................................................................ T.54 Table 4.3 Waste Background Table: Long-term storage of carbon ........................................................ T.55

C ros s-s ect or al tab le Table 5A

Cross-sectoral Table: Indirect emissions of N2O ................................................................... T.56

E m i ss i on t r en d t a b les b y g a s Table 6A

Trends of CO2 .......................................................................................................................... T.57

Table 6B

Trends of CH4 ...........................................................................................................................T.60

Table 6C Trends of N2O .......................................................................................................................... T.63 Table 6D Trends of HFCs ....................................................................................................................... T.66 Table 6E

Trends of PFCs ....................................................................................................................... T.67

Table 6F

Trends of SF6 ........................................................................................................................... T.68

Table 6G Trends of other gases .............................................................................................................. T.69

Uncertainty and Key Categories Table 7A

Uncertainties ........................................................................................................................... T.70

Table 7B

Summary of Key Category analysis ........................................................................................ T.71

2006 IPCC Guidelines for National Greenhouse Gas Inventories

T.3

Volume 1: General Guidance and Reporting

Table A Summary Table (1 of 6)

Categories

Net CO2

CH4

(1) (2)

(Gg)

N2O

HFCs

PFCs

SF6

Other Other halogenated halogenated gases with CO2 gases without equivalent CO2 equivalent conversion conversion (3) (4) factors factors

CO2 equivalents (Gg)

(Gg)

NOx

CO

NMVOCs

SO2

(Gg)

Total National Emissions and Removals 1 ENERGY 1A Fuel Combustion Activities 1A1 Energy Industries 1A2 Manufacturing Industries and Construction 1A3 Transport 1A4 Other Sectors 1A5 Non-Specified 1B Fugitive Emissions from Fuels 1B1 Solid Fuels 1B2 Oil and Natural Gas 1B3 Other Emissions from Energy Production 1C Carbon Dioxide Transport and Storage 1C1 Transport of CO2 1C2 Injection and Storage

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T.4

Annex 8A.2: Reporting Tables

Table A Summary Table (2 of 6)

Categories

Net CO2

(1) (2)

CH4

(Gg)

N2O

HFCs

PFCs

SF6

Other Other halogenated halogenated gases with CO2 gases without equivalent CO2 equivalent conversion conversion (3) (4) factors factors

CO2 equivalents (Gg)

(Gg)

NOx

CO

NMVOCs

SO2

(Gg)

2 INDUSTRIAL PROCESSES AND PRODUCT USE 2A Mineral Industry 2A1 Cement Production 2A2 Lime Production 2A3 Glass Production 2A4 Other Process Uses of Carbonates 2A5 Other (please specify) 2B Chemical Industry 2B1 Ammonia Production 2B2 Nitric Acid Production 2B3 Adipic Acid Production 2B4 Caprolactam, Glyoxal and Glyoxylic Acid Production 2B5 Carbide Production 2B6 Titanium Dioxide Production 2B7 Soda Ash Production Petrochemical and Carbon Black 2B8 Production 2B9 Fluorochemical Production 2B10 Other (please specify)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

T.5

Volume 1: General Guidance and Reporting

Table A Summary Table (3 of 6)

Categories

Net CO2

CH4

(1) (2)

(Gg) 2C 2C1 2C2 2C3 2C4 2C5 2C6 2C7 2D 2D1 2D2 2D3 2D4 2E 2E1 2E2 2E3 2E4 2E5

N2O

HFCs

PFCs

SF6

Other Other halogenated halogenated gases with CO2 gases without equivalent CO2 equivalent conversion conversion (3) (4) factors factors

CO2 equivalents (Gg)

(Gg)

NOx

CO

NMVOCs

SO2

(Gg)

Metal Industry Iron and Steel Production Ferroalloys Production Aluminium Production Magnesium Production Lead Production Zinc Production Other (please specify) Non-Energy Products from Fuels and Solvent Use Lubricant Use Paraffin Wax Use Solvent Use Other (please specify) Electronics Industry Integrated Circuit or Semiconductor TFT Flat Panel Display Photovoltaics Heat Transfer Fluid Other (please specify)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

T.6

Annex 8A.2: Reporting Tables

Table A Summary Table (4 of 6)

Categories

Net CO2

CH4

(1) (2)

(Gg) 2F 2F1 2F2 2F3 2F4 2F5 2F6 2G 2G1 2G2 2G3 2G4 2H 2H1 2H2 2H3

N2O

HFCs

PFCs

SF6

Other Other halogenated halogenated gases with CO2 gases without equivalent CO2 equivalent conversion conversion (3) (4) factors factors

CO2 equivalents (Gg)

(Gg)

NOx

CO

NMVOCs

SO2

(Gg)

Product Uses as Substitutes for Ozone Depleting Substances Refrigeration and Air Conditioning Foam Blowing Agents Fire Protection Aerosols Solvents Other Applications Other Product Manufacture and Use Electrical Equipment SF6 and PFCs from Other Product Uses N2O from Product Uses Other (please specify) Other (please specify) Pulp and Paper Industry Food and Beverages Industry Other (please specify)

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T.7

Volume 1: General Guidance and Reporting

Table A Summary Table (5 of 6)

Categories

Net CO2

CH4

(1) (2)

(Gg)

N2O

HFCs

PFCs

SF6

Other Other halogenated halogenated gases with CO2 gases without equivalent CO2 equivalent conversion conversion (3) (4) factors factors

CO2 equivalents (Gg)

(Gg)

NOx

CO

NMVOCs

SO2

(Gg)

3 AGRICULTURE, FORESTRY AND OTHER LAND USE 3A Livestock 3A1 Enteric Fermentation 3A2 Manure Management 3B Land 3B1 Forest Land 3B2 Cropland 3B3 Grassland 3B4 Wetlands 3B5 Settlements 3B6 Other Land 3C Aggregate Sources and Non-CO2 Emissions Sources on Land 3C1 Biomass Burning 3C2 Liming 3C3 Urea Application 3C4 Direct N2O Emissions from Managed Soils 3C5 Indirect N2O Emissions from Managed Soils 3C6 Indirect N2O Emissions from Manure Management 3C7 Rice Cultivations 3C8 Other (please specify) 3D Other 3D1 Harvested Wood Products 3D2 Other (please specify)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

T.8

Annex 8A.2: Reporting Tables

Table A Summary Table (6 of 6)

Categories

Net CO2 (1) (2)

CH4

N2O

(Gg)

HFCs

PFCs

SF6

Other Other halogenated halogenated gases with CO2 gases without equivalent CO2 equivalent conversion conversion (3) (4) factors factors

CO2 equivalents (Gg)

(Gg)

NOx

CO

NMVOCs

SO2

(Gg)

4 WASTE 4A Solid Waste Disposal 4B Biological Treatment of Solid Waste 4C Incineration and Open Burning of Waste 4D Wastewater Treatment and Discharge 4E Other (please specify) 5 OTHER 5A Indirect N2O Emissions from the Atmospheric Deposition of Nitrogen in NOx and NH3 5B Other (please specify) Memo items (5) International Bunkers International Aviation (International Bunkers) International Water-borne Transport (International Bunkers) Multilateral Operations (1) CO2 net emissions (emissions minus removals) (2) Total amount of CO2 captured for long-term storage is to be reported separately for domestic storage and for export in the documentation box. (3) The other halogenated gases for which the CO2 equivalent conversion factor is not available should not be included in this column. Such gases should be reported in the column ‘Other halogenated gases without CO2 equivalent conversion factors’. (4) When this column is used, gases should be listed separately (in IPPU Background Tables and Table 2.11) and the name of the gas should be given in the documentation box. (5) Emissions that are not included in the national total should be reported as memo items. * Cells to report emissions of NOx, CO, NMVOC and SO2 have not been shaded although the physical potential for emissions is lacking for some categories.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

T.9

Volume 1: General Guidance and Reporting

Table B Short Summary Table (1 of 2)

Categories

Net CO2

CH4

(1) (2)

(Gg)

N2O

HFCs

PFCs

SF6

Other Other halogenated halogenated gases with CO2 gases without equivalent CO2 equivalent conversion conversion (3) (4) factors factors

CO2 equivalents (Gg)

(Gg)

NOx

CO

NMVOCs

SO2

(Gg)

Total National Emissions and Removals 1 ENERGY 1A Fuel Combustion Activities 1B Fugitive Emissions from Fuels 1C Carbon Dioxide Transport and Storage 2 INDUSTRIAL PROCESSES AND PRODUCT USE 2A Mineral Industry 2B Chemical Industry 2C Metal Industry 2D Non-Energy Products from Fuels and Solvent Use 2E Electronics Industry Product Uses as Substitutes for Ozone 2F Depleting Substances 2G Other Product Manufacture and Use 2H Other 3 AGRICULTURE, FORESTRY AND OTHER LAND USE 3A Livestock 3B Land 3C Aggregate Sources and Non-CO2 Emissions Sources on Land 3D Other 4 WASTE 4A Solid Waste Disposal 4B Biological Treatment of Solid Waste

2006 IPCC Guidelines for National Greenhouse Gas Inventories

T.10

Annex 8A.2: Reporting Tables

Table B Short Summary Table (2 of 2)

Categories

Net CO2 (1)(2)

CH4

N2O

HFCs

(Gg)

PFCs

SF6

Other Other halogenated halogenated gases with CO2 gases without equivalent CO2 equivalent conversion conversion (3) (4) factors factors

CO2 equivalents (Gg)

NOx

CO

(Gg)

NMVOCs

SO2

(Gg)

4C Incineration and Open Burning of Waste 4D Wastewater Treatment and Discharge 4E Other (please specify) 5 OTHER Indirect N2O emissions from the Atmospheric Deposition of Nitrogen in NOx 5A and NH3 5B Other (please specify) Memo items (5) International Bunkers International Aviation (International Bunkers) International Water-borne Transport (International Bunkers) Multilateral Operations (1) CO2 net emissions (emissions minus removals) (2) Total amount of CO2 captured for long-term storage is to be reported separately for domestic storage and for export in the documentation box. (3) The other halogenated gases for which the CO2 equivalent conversion factor is not available should not be included in this column. Such gases should be reported in the column ‘Other halogenated gases without CO2 equivalent conversion factors’. (4) When this column is used, gases should be listed separately in IPPU Background Tables and Table 2.11 and the name of the gas should be given in the documentation box. (5) Emissions that are not included in the national total should be reported as memo items. * Cells to report emissions of NOx, CO, NMVOC and SO2 have not been shaded although the physical potential for emissions is lacking for some categories.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

T.11

Volume 1: General Guidance and Reporting

Table 1 Energy Sectoral Table (1 of 3) CO2

Categories

CH4

N2O

NOx (Gg)

CO

NMVOCs

SO2

1 ENERGY 1A Fuel Combustion Activities 1A1 Energy Industries 1A1 a Main Activity Electricity and Heat Production 1A1 ai Electricity Generation Combined Heat and Power Generation 1A1 aii (CHP) 1A1 aiii Heat Plants 1A1 b Petroleum Refining 1A1 c Manufacture of Solid Fuels and Other Energy Industries 1A1 ci Manufacture of Solid Fuels 1A1 cii Other Energy Industries 1A2 Manufacturing Industries and Construction 1A2 a Iron and Steel 1A2 b Non-Ferrous Metals 1A2 c Chemicals 1A2 d Pulp, Paper and Print 1A2 e Food Processing, Beverages and Tobacco 1A2 f Non-Metallic Minerals 1A2 g Transport Equipment 1A2 h Machinery 1A2 i Mining (excluding fuels) and Quarrying 1A2 j Wood and Wood Products 1A2 k Construction 1A2 l Textile and Leather 1A2 m Non-specified Industry 1A3 Transport 1A3 a Civil Aviation 1A3 ai International Aviation (International Bunkers) (1) 1A3 aii Domestic Aviation 1A3 b Road Transportation 1A3 bi Cars 1A3 bi Passenger Cars with 3-way Catalysts 1A3 bi2 Passenger Cars without 3-way Catalysts 1A3 bii Light-duty Trucks 1A3 bii1 Light-duty Trucks with 3-way Catalysts 1A3 bii2 Light-duty Trucks without 3-way Catalysts 1A3 biii Heavy-duty Trucks and Buses 1A3 biv Motorcycles 1A3 bv Evaporative Emissions from Vehicles 1A3 bvi Urea-based Catalysts 1A3 c Railways 1A3 d Water-borne Navigation 1A3 di International Water-borne Navigation (International Bunkers) (1) 1A3 dii Domestic Water-borne Navigation 1A3 e Other Transportation 1A3 ei Pipeline Transport 1A3 eii Off-road 1A4 Other Sectors 1A4 a Commercial/Institutional 1A4 b Residential

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Annex 8A.2: Reporting Tables

Table 1 Energy Sectoral Table (2 of 3) Categories 1A4 c 1A4 ci 1A4 cii 1A4 ciii 1A5 1A5 a 1A5 b 1A5 bi 1A5 bii 1A5 biii 1A5 c 1B 1B1 1B1 a 1B1 ai 1B1 ai1 1B1 ai2 1B1 ai3 1B1 ai4 1B1 aii 1B1 aii1 1B1 aii2 1B1 b 1B1 c 1B2 1B2 a 1B2 ai 1B2 aii 1B2 aiii 1B2 aiii1 1B2 aiii2 1B2 aiii3 1B2 aiii4 1B2 aiii5 1B2 aiii6 1B2 b 1B2 bi 1B2 bii 1B2 biii 1B2 biii1 1B2 biii2 1B2 biii3 1B2 biii4 1B2 biii5 1B2 biii6 1B3 1C 1C1 1C1 a 1C1 b 1C1 c 1C2 1C2 a 1C2 b

CO2

CH4

N2O

NOx (Gg)

CO

NMVOCs

SO2

Agriculture/Forestry/Fishing/Fish Farms Stationary Off-road Vehicles and Other Machinery Fishing (mobile combustion) Non-Specified Stationary Mobile Mobile (aviation component) Mobile (water-borne component) Mobile (other) Multilateral Operations (1) (2) Fugitive Emissions from Fuels Solid Fuel Coal Mining and Handling Underground Mines Mining Post-mining Seam Gas Emissions Abandoned Underground Mines Flaring of Drained Methane or Conversion of Methane to CO2 Surface Mines Mining Post-mining Seam Gas Emissions Uncontrolled Combustion, and Burning Coal Dumps Solid Fuel Transformation Oil and Natural Gas Oil Venting Flaring All Other Exploration Production and Upgrading Transport Refining Distribution of Oil Products Others Natural Gas Venting Flaring All Other Exploration Production Processing Transmission and Storage Distribution Others Other Emissions from Energy Production Carbon Dioxide Transport and Storage Transport of CO2 Pipelines Ships Other (Please specify) Injection and Storage Injection Storage

2006 IPCC Guidelines for National Greenhouse Gas Inventories

T.13

Volume 1: General Guidance and Reporting

Table1 Energy Sectoral Table (3 of 3) Categories

CO2

CH4

N2O

NOx (Gg)

CO

NMVOCs

SO2

Memo items (3) International Bunkers International Aviation (International Bunkers) International Water-borne Transport (International Bunkers) Multilateral Operations Information items CO2 from Biomass Combustion for Energy Production (1) To be reported as a memo item, and not part of the national inventory. (2) Multilateral operations pursuant to the Charter of the United Nations: including emissions from fuel delivered to the military in the country and delivered to the military of other countries. (3) Emissions that are not included in the national total should be reported as memos. * Cells to report emissions of NOx, CO, NMVOC and SO2 have not been shaded although the physical potential for emissions is lacking for some categories

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T.14

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Annex 8A.2: Reporting Tables

Table 1.1 Energy Background Table: 1A1-1A2 (1 of 2)

Solid

Solid Liquid Gas

Other fossil fuel

Peat

BioCO2 mass

CH4

Liquid

N2O

CO2

CH4

Other fossil fuel

Gas

N2O

CO2

CH4

N2O

CO2

CH4

N2O

(1)

Peat

CO2

CH4

Biomass

N2O

CH4

N2O

Total

CO2

CH4

N2O

Biomass

Activity (TJ) Categories

CO2 amount (3) captured

Information (2) item (Gg)

Emissions (Gg)

CO2

CO2 emitted

1A Fuel Combustion Activities 1A1 Energy Industries 1A1a

Main Activity Electricity and Heat Production 1A1ai Electricity Generation 1A1aii Combined Heat and Power Generation (CHP) 1A1aiii Heat Plants 1A1b Petroleum Refining 1A1c

Manufacture of Solid Fuels and Other Energy Industries

1A1ci Manufacture of Solid Fuels 1A1cii Other Energy Industries 1A2 Manufacturing Industries and Construction 1A2a Iron and Steel 1A2b Non-Ferrous Metals 1A2c Chemicals 1A2d Pulp, Paper and Print 1A2e Food Processing, Beverages and Tobacco 1A2f Non-Metallic Minerals 1A2g Transport Equipment

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Volume 1: General Guidance and Reporting

Table 1.1 Energy Background Table: 1A1-1A2 (2 of 2)

Solid

Solid Liquid Gas

1A2h 1A2i 1A2j 1A2k 1A2l 1A2m

Other fossil fuel

Peat

BioCO2 mass

CH4

Liquid

N2O

CO2

CH4

Other fossil fuel

Gas

N2O

CO2

CH4

N2O

CO2

CH4

N2O

(1)

Peat

CO2

CH4

Biomass

N2O

CH4

N2O

Total

CO2

CH4

N2O

Biomass

Activity (TJ) Categories

CO2 Amount (3) captured

Information (2) item (Gg)

Emissions (Gg)

CO2

CO2 emitted

Machinery Mining and Quarrying Wood and Wood Products Construction Textile and Leather Non-specified Industry

(1) Although peat is not strictly speaking a fossil fuel, the CO2 emissions from combustion of peat are included in the national emissions as for fossil fuels. See Chapter 1 of Energy Volume, page 1.15. (2) Information items that are not themselves emissions, therefore not included in the national total. The carbon should be converted to carbon dioxide. It is subtracted in the CO2 emission columns (net emissions). Only CO2 captured for permanent storage in geological reservoirs should be subtracted. (3) Enter the amount of CO2 captured as a negative number since this amount is subtracted from total CO2 produced.

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T.16

Annex 8A.2: Reporting Tables

Table 1.2 Energy Background Table: 1A3-1A5 (1 of 2) Activity (TJ)

Solid

Category Solid Liquid Gas

Other fossil fuel

BioPeat mass

CO2

CH4

Emissions (Gg) Other fossil Gas fuel

Liquid N2O

CO2

CH4

N2O

CO2

CH4

N2O

CO2

CH4

N2O

(1)

Peat CO2

CH4

Biomass N2O

CH4

N2O

Total emissions (Gg) CO2

CH4

N2O

1A3 Transport 1A3a Civil Aviation (2) 1A3ai International Aviation (International Bunkers) 1A3aii Domestic Aviation 1A3b Road Transportation 1A3bi Cars 1A3bi1 Passenger Cars with 3-way catalysts 1A3bi2 Passenger Cars without 3-way Catalysts 1A3bii Light-duty Trucks 1A3bii1 Light-duty Trucks with 3-way Catalysts 1A3bii2 Light-duty Trucks without 3-way Catalysts 1A3biii Heavy-duty Trucks and Buses 1A3biv Motorcycles 1A3bv Evaporative Emissions from Vehicles (3) 1A3bvi Urea based Catalyst 1A3c Railways 1A3d Water-borne Navigation 1A3di International Water-borne Navigation (2) (International Bunkers) 1A3dii Domestic Water-borne Transport 1A3e Other Transportation 1A3ei Pipeline Transport 1A3eii Off-road 1A4 Other Sectors 1A4a Commercial/Institutional 1A4b Residential 14Ac Agriculture/Forestry/Fishing/Fish Farms 1A4ci Stationary 1A4cii Off-road Vehicles and Other Machinery 1A4ciii Fishing (mobile combustion)

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Volume 1: General Guidance and Reporting

Table 1.2 Energy Background Table: 1A3-1A5 (2 of 2) Activity (TJ)

Solid

Category Solid Liquid Gas

Other fossil fuel

BioPeat mass

CO2

CH4

Emissions (Gg) Other fossil Gas fuel

Liquid N2O

CO2

CH4

N2O

CO2

CH4

N2O

CO2

CH4

N2O

(1)

Peat CO2

CH4

Biomass N2O

CH4

N2O

Total emissions (Gg) CO2

CH4

N2O

1A5 Non-Specified 1A5a Stationary 1A5b Mobile 1A5bi Mobile (aviation component) 1A5bii Mobile (water-borne component) 1A5biii Mobile (other) 1A5c Multilateral Operation

Memo items (4) International Bunkers International Aviation (International Bunkers) International Water-borne Transport (International Bunkers) Multilateral Operations

(5)

(1) Although peat is not strictly speaking a fossil fuel, the CO2 emissions from combustion of peat are included in the national emissions as for fossil fuels. See Chapter 1 of Energy Volume, page 1.15. (2) To be reported as a memo item, and not part of the national inventory. (3) Report the amount of urea-based additive used and its purity in the documentation box. (4) Emissions that are not included in the national total should be reported as memo items. (5) Multilateral operations pursuant to the Charter of the United Nations: including emissions from fuel delivered to the military in the country and delivered to the military of other countries.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Annex 8A.2: Reporting Tables

Table 1.3 Energy Background Table: 1B Emissions (Gg)

Activity Data

Category

Description 1B Fugitive Emissions from Fuels 1B1 Solid Fuel 1B1a Coal Mining and Handling 1B1ai Underground Mines 1B1ai1 Mining 1B1ai2 Post mining Seam Gas Emissions 1B1ai3 Abandoned Underground Mines 1B1ai4 Flaring of Drained Methane or Conversion of CH4 to CO2 1B1aii Surface Mines 1B1aii1 Mining 1B1aii2 Post-mining Seam Gas Emissions Uncontrolled Combustion, and 1B1b Burning Coal Dumps 1B1c Solid fuel Transformation 1B2 Oil and Natural Gas 1B2a Oil 1B2ai

Venting

1B2aii 1B2aiii 1B2aiii1 1B2aiii2 1B2aiii3 1B2aiii4 1B2aiii5 1B2biii6 1B2b 1B2bi

Flaring All other Exploration Production and Upgrading Transport Refining Distribution of Oil Products Others Natural Gas Venting

1B2bii

Flaring

1B2biii 1B2biii1 1B2biii2 1B2biii3

All Other Exploration Production Processing

1B2biii4 Transmission and Storage 1B2biii5 Distribution 1B2biii6 Others 1B3 Other Emissions from Energy Production

coal produced coal produced coal produced number of mines gas flared

Unit

(1)

6

10 Sm

3

ktonnes ktonnes

solid fuel combusted

ktonnes

solid fuel transformed

ktonnes

total gas vented from oil production gas flared from oil production

10 Sm

wells drilled oil produced crude oil transported refinery crude oil throughput amount distributed

number 3 3 10 m 3 3 10 m 3 3 10 m 3 3 10 m

number wells drilled Gas produced Amount of gas processed at facilities Amount transported and stored Amount of gas distributed

CO2

ktonnes ktonnes ktonnes number

coal produced coal produced

Total gas vented from natural gas production gas flared from natural gas production

Value CO2 CH4 N2O

Information item: Amount captured (2) (Gg)

6

3

6

3

10 Sm

6

3

6

3

10 Sm 10 Sm

number 6 3 10 Sm 6

3

6

3

10 Sm 10 Sm 3 3 10 m

(1) The units given here are the most commonly used for respective activity data. For convenience and/or consistency, they can be converted into appropriate energy units. (2) The amount of CO2 captured is given for information purposes; it is subtracted in the CO2 emission columns (net emissions).

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Table 1.4a Energy Background Table: 1C CO2 Transport, Injection and Storage Activity (Gg) Category

Annual mass of CO2 transported

Annual mass of CO2 (1) injected

Annual mass of fugitive CO2 emissions to the atmosphere or sea bed (2) (Gg)

1C1 Transport of CO2 1C1a Pipelines 1C1b Ships 1C1c Other (please specify) (3) 1C2 Injection and Storage 1C2a Injection 1C2b Storage 1C3 Other (1) Excluding recycled CO2 for enhanced recovery. (2) Corrected for baseline background fluxes. (3) Fugitive emissions during above ground operations such as processing and CO2 recycling during enhanced oil and gas recovery operations should be reported as fugitive emissions from oil and natural gas and reported under the appropriate categories for that sector.

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Table 1.4b Energy Background Table: 1C CO2 Transport, Injection and Storage - Overview Category

(1)

CO2 (Gg)

Total amount captured for storage (A) Total amount of import for storage (B) Total amount of export for storage (C) Total amount of CO2 injected at storage sites (D) Total amount of leakage during transport (E1) category 1C1 Total amount of leakage during injection (E2) category 1C2a Total amount of leakage from storage sites (E3) category 1C2b Total leakage (E4 = E1 + E2 + E3)) Capture + imports (F = A + B) Injection + leakage + exports (G = D + E4 + C) Discrepancy (F – G)

(1) Once captured, there is no differentiated treatment between biogenic carbon and fossil carbon. Emissions and storage of both biogenic and fossil carbons will be estimated and reported.

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Table 1.5 Energy Background Table: Reference Approach (1 of 1) Fuel Types Liquid Fossil

Primary Fuels

Production

Import

Export

(Unit)

(Unit)

(Unit)

International bunkers (Unit)

Stock change (Unit)

Apparent Apparent Carbon Conversion consumconsum- emission factor ption ption factor (Unit) (TJ/Unit) (TJ) (tC/TJ)

Carbon content

Carbon content

Excluded carbon

(t C)

(Gg C)

(Gg C)

Net Fraction Actual CO2 carbon of carbon carbon emission emission oxidised emission (Gg C) (Gg C) (Gg CO2)

Crude Oil Orimulsion Natural Gas Liquids

Secondary Fuels Gasoline Jet Kerosene Other Kerosene Shale Oil Gas / Diesel Oil Residual Fuel Oil LPG Ethane Naphtha Bitumen Lubricants Petroleum Coke Refinery Feedstocks Other Oil Liquid Fossil Totals Solid Fossil Primary Fuels

(1)

Anthracite Coking Coal Other Bit. Coal Sub-bit. Coal

Lignite Oil Shale and Tar Sands Secondary Fuels BKB & Patent Fuel Coke Oven/Gas Coke Coal Tar Solid Fossil Totals Gaseous Fossil Other Fossil Fuels (2) Peat Total

Natural Gas (Dry)

(1) If anthracite is not separately available, include with Other Bituminous Coal. (2) Although peat is not strictly speaking a fossil fuel, the CO2 emissions from combustion of peat are included in the national emissions as for fossil fuels. See Chapter 1 of Energy Volume, page 1.15.

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Table 2 IPPU Sectoral Table (1 of 2) (See Volume 3, Chapter 1, Table 1.1.) CO2 CH4 N2O HFCs PFCs

Category

(Gg)

Other Other halogenated halogenated gases with gases without CO2 SF6 CO2 equivalent equivalent conversion conversion (2) factors (1) factors

CO2 equivalents (Gg)

NOx CO NMVOCs SO2

(Gg)

2 INDUSTRIAL PROCESSES AND PRODUCT USE 2A Mineral Industry 2A1 Cement Production 2A2 Lime Production 2A3 Glass Production 2A4 Other Process Uses of Carbonates 2A4a Ceramics 2A4b Other Uses of Soda Ash 2A4c Non Metallurgical Magnesia Production 2A4d Other (please specify) (3) 2A5 Other (please specify) (3) 2B Chemical Industry 2B1 Ammonia Production 2B2 Nitric Acid Production 2B3 Adipic Acid Production 2B4 Caprolactam, Glyoxal and Glyoxylic Acid Production 2B5 Carbide Production 2B6 Titanium Dioxide Production 2B7 Soda Ash Production 2B8 Petrochemical and Carbon Black Production 2B8a Methanol 2B8b Ethylene 2B8c Ethylene Dichloride and Vinyl Chloride Monomer 2B8d Ethylene Oxide 2B8e Acrylonitrile 2B8f Carbon Black 2B9 Fluorochemical Production 2B9a By-product Emissions (4) 2B9b Fugitive Emissions (4) 2B10 Other (please specify) (3) 2C Metal Industry 2C1 Iron and Steel Production 2C2 Ferroalloys Production 2C3 Aluminium Production 2C4 Magnesium Production (5) 2C5 Lead Production 2C6 Zinc Production 2C7 Other (please specify) (3) 2D Non-Energy Products from Fuels and Solvent Use(6) 2D1 Lubricant Use 2D2 Paraffin Wax Use 2D3 Solvent Use(7) 2D4 Other (please specify) (3), (8) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor (9) 2E2 TFT Flat Panel Display (9) 2E3 Photovoltaics(9) 2E4 Heat Transfer Fluid (10) 2E5 Other (please specify) (3) 2F Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F1a Refrigeration and Stationary Air Conditioning

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Table 2 IPPU Sectoral Table (2 of 2) Category

CO2 CH4 N2O HFCs PFCs

(Gg)

Other Other halogenated halogenated gases with gases without CO2 SF6 CO2 equivalent equivalent conversion conversion (2) factors (1) factors

CO2 equivalents (Gg)

NOx CO NMVOCs SO2

(Gg)

2F1b Mobile Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosols 2F5 Solvents 2F6 Other Applications (3) 2G Other Product Manufacture and Use 2G1 Electrical Equipment 2G1a Manufacture of Electrical Equipment 2G1b Use of Electrical Equipment 2G1c Disposal of Electrical Equipment 2G2 SF6 and PFCs from Other Product Uses 2G2a Military Applications 2G2b Accelerators 2G2c Other (please specify)(3) 2G3 N2O from Product Uses 2G3a Medical Applications 2G3b Propellant for Pressure and Aerosol Products 2G3c Other (please specify)(3) 2G4 Other (please specify)(3) 2H Other 2H1 Pulp and Paper Industry 2H2 Food and Beverages Industry 2H3 Other (please specify) (3)

(1) The other halogenated gases for which the CO2 equivalent conversion factor is not available should not be included in this column. Such gases should be reported in the column “Other halogenated gases without CO2 equivalent conversion factors”. (2) When this column is used, gases should be listed separately (in IPPU background tables and Table 2.11) and the name of the gas should be given in the documentation box. Insert additional columns if necessary. (3) Insert additional rows if needed (4) The "Other halogenated gases" are fluorinated alcohols, fluorinated ethers, NF3, SF5CF3. (5) Small amounts of CO2 used as a diluent for SF6 and emitted during magnesium processing is considered insignificant and is usually counted elsewhere. The "Other halogenated gases" here mainly comprise fluorinated ketones. (6) Emissions from feedstock uses in petrochemical industry should be addressed in 2B8 (Petrochemical and Carbon Black Production). Emissions from some product uses should be allocated to each industry source category (e.g., CO2 from carbon anodes and electrodes Æ 2C (Metal Industry)). (7) Only NMVOC emissions and no direct GHGs are relevant to this category. (8) Emissions from asphalt production, and paving of roads and roofing are included here. (9) "Other halogenated gases" are NF3, c-C4F8O, etc. (10) The "Other halogenated gases" here include C4F9OC2H5 (HFE-7200), CHF2OCF2OC2F4OCHF2 (H-Galden 1040x), CHF2OCF2OCHF2 (HG-10), etc. * Cells to report emissions of NOx, CO, NMVOC and SO2 have not been shaded although the physical potential for emissions is lacking for some categories.

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Table 2.1 IPPU Background Table: 2A Mineral Industry, 2B (2B1-2B8, 2B10) Chemical Industry - CO2, CH4 and N2O Activity data Categories

Production/Consumption quantity

Description (1)

Emissions CO2 (Gg)

CH4 (Gg)

Information (memo) Information item Other Emissions item (2) (3) Captured Quantity Unit (3) Emissions Reduction Reduction and Stored (5) (6)

N2O (Gg) Emissions (3)

Information item Reduction

(4)

(6)

2A Mineral Industry 2A1 Cement production 2A2 Lime production 2A3 Glass Production 2A4 Other Process Uses of (7) Carbonates 2A4a Ceramics 2A4b Other Uses of Soda Ash 2A4c Non Metallurgical Magnesia Production 2A4d Other (8) 2A5 Other (please specify) 2B Chemical Industry 2B1 Ammonia Production 2B2 Nitric Acid Production 2B3 Adipic Acid Production 2B4 Caprolactam, Glyoxal and Glyoxylic Acid Production 2B5 Carbide Production 2B6 Titanium Dioxide Production 2B7 Soda Ash Production 2B8 Petrochemical and Carbon Black Production 2B8a Methanol 2B8b Ethylene 2B8c Ethylene Dichloride and Vinyl Chloride Monomer 2B8d Ethylene Oxide 2B8e Acrylonitrile 2B8f Carbon Black (8) 2B10 Other (please specify) (1) Where the options for activity data, e.g., cement or clinker or carbonates for estimating the emissions from Cement Production, specify the activity data used in order to make the choice of emission factor more transparent. (2) Unit of activity data should be specified. (3) Enter the reported emissions (adjusted with captured and/or reduced amount). (4) Where generated CO2 is captured for injection into a storage, the captured amount should be reported here. These data are provided as the additional information. They are not emissions, therefore should not be included in the national total. (5) Where reduction of generated CO2 except for capture and storage occurs (e.g., re-conversion to carbonates) and its amount is available, it should be reported here. (6) Enter the quantities of reduction of generated gas (emission recovery, destruction, etc.) (7) Report here only the emissions from carbonate uses not covered in other categories. (8) Insert additional rows if necessary. Note: Where information is confidential the entries should provide notation key “C” but there should be a note indicating this in the documentation box below. Also, More specific information could be provided in the documentation box.

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Other halogenated (2) gases (please specify)

SF6

Total PFCs

C6F14

C5F12

c-C4F8

C4F10

C 3F8

C2F6

CF4

Total HFCs

(2)

Other PFCs (please specify)

(2)

Other HFCs (please specify)

HFC-43-10mee

HFC-365mfc

HFC-245fa

HFC-245ca

HFC-236fa

HFC-236ea

HFC-236cb

HFC-227ea

HFC-161

HFC-152a

HFC-152

HFC-143a

HFC-143

HFC-134a

HFC-134

HFC-41

HFC-32

HFC-23

Categories

HFC-125

Table 2.2 IPPU Background Table: 2B (2B9 - 2B10) Chemical Industry HFCs, PFCs, SF6 and other halogenated gases

(1)

CO2 equivalent conversion factors [Source of the factor: ]

Emissions in original mass unit (tonne) 2B9 2B9a

2B10

Fluorochemical Production (3) By-product Emissions (4) (information) Reduced amount (3) Fugitive Emissions (4) (information) Reduced amount (5) Other (please specify)

2B9 2B9a 2B9b 2B10

Fluorochemical Production By-product Emissions Fugitive Emissions (5) Other (please specify)

2B9b

Emissions in CO2 equivalent unit (Gg-CO2)

(1) Typically, global warming potential (100 year time horizon) identified in the IPCC Assessment Report can be used. The source of the factors must be specified in the bracket. (2) Insert additional columns if necessary. The other halogenated gases for which the CO2 equivalent conversion factor is not available should not be included in this table. Such gases should be reported in Table 2.11 IPPU background table: Greenhouse Gases without CO2 equivalent conversion factors. (3) Enter the reported emissions (adjusted with captured and/or reduced amount). (4) Enter the quantities of reduction of generated gas (emission recovery, destruction, etc.). (5) Insert additional rows if necessary. Note: Where information is confidential the entries should provide aggregate figures but there should be a note indicating this in the documentation box below.

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Table 2.3 IPPU Background Table: 2C Metal Industry CO2, CH4 and N2O Activity Data Production/Consumption quantity Categories Description

(1)

Quantity

Unit

(2)

Emissions

(3)

CO2 (Gg) (information) Captured and (4) Stored

Emissions CH4 (Gg) (information) Other (5) Reduction

Emissions

(3)

(information) (6) Reduction

N2O (Gg) Emissions

(3)

(information) (6) Reduction

2C Metal Industry 2C1 Iron and Steel Production 2C2 Ferroalloys Production 2C3 Aluminium Production 2C4 Magnesium Production 2C5 Lead Production 2C6 Zinc Production (7) 2C7 Other (please specify) (1) Where the options for activity data, e.g. steel production or process materials consumption for estimating the emissions from Iron and Steel Production, specify the activity data used in order to make the choice of emission factor more transparent. (2) Unit of activity data should be specified. (3) Enter the reported emissions (adjusted with captured and/or reduced amount). (4) Where generated CO2 is captured for injection into a storage, the captured amount should be reported here. These data are provided as the additional information. They are not emissions, therefore should not be included in the national total. (5) Where reduction of generated CO2 except for capture and storage occurs and its amount is available, it should be reported here. (6) Enter the quantities of reduction of generated gas (emission recovery, destruction, etc.). (7) Insert additional rows if necessary. Note: Where information is confidential the entries should provide notation key “C” but there should be a note indicating this in the documentation box below. Also, More specific information (e.g. data on virgin and recycled steel production) could be provided in the documentation box.

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Other halogenated (2) gases (please specify)

SF6

Total PFCs

(2)

Other PFCs (please specify)

C6F14

C5F12

c-C4F8

C4F10

C 3F8

C2F6

CF4

Categories

Total HFCs

HFC-134a

(2)

Other HFCs (please specify)

Table 2.4 IPPU Background Table: 2C (2C3, 2C4, 2C7) Metal Industry HFCs, PFCs, SF6 and other halogenated gases

(1)

CO2 equivalent conversion factors [Source of the factor: ] Emissions in original mass unit (tonne) (3) 2C3 Aluminium Production (4) (information) Reduced amount (3) 2C4 Magnesium Production (4) (information) Reduced amount (5) 2C7 Other Metals (please specify) (4) (information) Reduced amount Emissions in CO2 equivalent unit (Gg-CO2) 2C3 Aluminium Production 2C4 Magnesium Production (5) 2C7 Other (please specify) (1) Typically, global warming potential (100 year time horizon) identified in the IPCC Assessment Report can be used. The source of the factors must be specified in the bracket. (2) Insert additional columns if necessary. The other halogenated gases for which the CO2 equivalent conversion factor is not available should not be included in this table. Such gases should be reported in Table 2.11 IPPU background table: Greenhouse Gases without CO2 equivalent conversion factors. (3) Enter the reported emissions (adjusted with captured and/or reduced amount). (4) Enter the quantities of reduction of generated gas (emission recovery, destruction, etc.). (5) Insert additional rows if necessary. Note: Where information is confidential the entries should provide aggregate figures but there should be a note indicating this in the documentation box below.

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Table 2.5 IPPU Background Table: 2D Non-Energy Products from Fuels and Solvent Use CO2, CH4 and N2O Activity Data Categories

Production/Consumption quantity Description

2D Non-Energy Products from Fuels and Solvent Use 2D1 Lubricant Use 2D2 Paraffin Wax Use 2D3 Solvent Use 2D4 Other Product (please specify) Product (please specify) (1) Product (please specify) (1)

Emissions

Lubricant consumption Wax consumption

Quantity

Unit

CO2

CH4

N2O

(Gg)

(Gg)

(Gg)

tonne tonne

Insert additional rows if necessary.

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Other halogenated gases (please specify)

NF3

SF6

Total PFCs

(3)

Other PFCs (please specify)

c-C4F8

C 3F8

C2F6

CF4

Total HFCs

HFC-32

HFC-23

(2)

N2O

CO2

(2)

(3)

Categories

Other HFCs (please specify)

(3)

Table 2.6 IPPU Background Table: 2E Electronics Industry HFCs, PFCs, SF6 NF3 and other halogenated gases

(1)

CO2 equivalent conversion factors [Source of the factor: ]

Emissions in original mass unit (tonne) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor 2E2 TFT Flat Panel Display 2E3 Photovoltaics 2E4 Heat Transfer Fluid (4) 2E5 Other (please specify) Emissions in CO2 equivalent unit (Gg-CO2) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor 2E2 TFT Flat Panel Display 2E3 Photovoltaics 2E4 Heat Transfer Fluid (3) 2E5 Other (please specify) (1) Typically, global warming potential (100 year time horizon) identified in the IPCC Assessment Report can be used. The source of the factors must be specified in the bracket. (2) Emissions may occur but no methodological guidance is provided in these Guidelines. (3) Insert additional columns if necessary. The other halogenated gases for which the CO2 equivalent conversion factor is not available should not be included in this table. Such gases should be reported in Table 2.11 IPPU background table: Greenhouse gases without CO2 equivalent conversion factors. (4) Insert additional rows if necessary. Note: Where information is confidential the entries should provide aggregate figures but there should be a note indicating this in the documentation box below.

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Other halogenated (3) gases (please specify)

Total PFCs

C4F10

C 3F8

C2F6

CF4

Total HFCs

(3)

Other PFCs (please specify)

(3)

Other HFCs (please specify)

HFC-43-10mee

HFC-365mfc

HFC-245fa

HFC-236fa

HFC-227ea

HFC-152a

HFC-143a

HFC-125

HFC-32

HFC-23

CO2

(2)

Categories

HFC-134a

Table 2.7 IPPU Background Table: 2F Product Uses as Substitutes for Ozone Depleting Substances HFCs, PFCs and other halogenated gases

CO2 equivalent (1) conversion factors [Source of the factor: ] Emissions in original mass unit (tonne) 2F Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F1a Refrigeration and Stationary Air Conditioning 2F1b Mobile Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosols 2F5 Solvents 2F6 Other (4) Applications Emissions in CO2 equivalent unit (Gg-CO2) 2F Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F1a Refrigeration and Stationary Air Conditioning 2F1b Mobile Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosol 2F5 Solvents 2F6 Other (4) Applications (1) Typically, global warming potential (100 year time horizon) identified in the IPCC Assessment Report can be used. The source of the factors must be specified in the bracket. (2) Emissions may occur but no methodological guidance is provided in these Guidelines. (3) Insert additional columns if necessary. The other halogenated gases for which the CO2 equivalent conversion factor is not available should not be included in this table. Such gases should be reported in Table 2.11 IPPU background table: Greenhouse gases without CO2 equivalent conversion factors. (4) Insert additional rows if necessary. Note: Where information is confidential the entries should provide aggregate figures but there should be a note indicating this in the documentation box below.

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Annex 8A.2: Reporting Tables

Other halogenated (2) gases (please specify)

SF6

C6F14

C5F12

c-C4F8

C4F10

C 3F8

C2F6

CF4

Categories

Total PFCs

(2)

Other PFCs (please specify)

Table 2.8 IPPU Background Table: 2G (2G1, 2G2, 2G4) Other Product Manufacture and Use – PFCs, SF6 and other halogenated gases

(1)

CO2 equivalent conversion factors [Source of the factor: ]

Emissions in original mass unit (tonne) 2G Other Product Manufacture and Use 2G1 Electrical Equipment (3) 2G1a Manufacture of Electrical Equipment (4) (information) Reduced amount (3) 2G1b Use of Electrical Equipment (4) (information) Reduced amount (3) 2G1c. Disposal of Electrical Equipment (4) (information) Reduced amount 2G2 SF6 and PFCs from Other Product Uses (3) 2G2a Military Applications (4) (information) Reduced amount (3) 2G2b Accelerators (3) University and Research Particle Accelerators (4) (information) Reduced amount (3) Industrial and Medical Particle Accelerators (4) (information) Reduced amount (3), (5) 2G2c Other (please specify) (4), (5) (information) Reduced amount (3), (5), (6) 2G4 Other (please specify) (4) , (5), (6) (information) Reduced amount Emissions in CO2 equivalent unit (Gg-CO2) 2G Other Product Manufacture and Use 2G1 Electrical Equipment 2G1a Manufacture of Electrical Equipment 2G1b Use of Electrical Equipment 2G1c Disposal of Electrical Equipment 2G2 SF6 and PFCs from Other Product Uses 2G2a Military Applications (AWACS) 2G2b Accelerators University and Research Particle Accelerators Industrial and Medical Particle Accelerators (5) 2G2c Other (please specify) (5), (6) 2G4 Other (please specify) (1) Typically, global warming potential (100 year time horizon) identified in the IPCC Assessment Report can be used. The source of the factors must be specified in the bracket. (2) Insert additional columns if necessary. The other halogenated gases for which the CO2 equivalent conversion factor is not available should not be included in this table. Such gases should be reported in Table 2.11 IPPU background table: Greenhouse gases without CO2 equivalent conversion factors. (3) Enter the reported emissions (adjusted with captured and/or reduced amount). (4) Enter the quantities of reduction of generated gas (emission recovery, destruction, etc.) (5) Insert additional rows if necessary. (6) If HFCs with CO2 equivalent conversion factor are estimated, include them in the column for “Other halogenated gases”. Note: Where information is confidential the entries should provide aggregate figures but there should be a note indicating this in the documentation box below.

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Table 2.9 IPPU Background Table: 2G (2G3, 2G4) Other Product Manufacture and Use - N2O, CO2, CH4, Emissions

Activity Data Categories

Description 2G3

Medical Applications

2G3b

Propellant for Pressure and Aerosol Products (3) Other (please specify)

2G4

Unit

CO2 (Gg) (information) (1) Emissions (2) Reduction

CH4 (Gg) (information) (1) Emissions (2) Reduction

N2O from Product Uses

2G3a

2G3c

Quantity

N2O (Gg) (information) (1) Emissions (2) Reduction

Other (please specify)

N2O supplied

tonne

N2O supplied

tonne

N2O supplied

tonne

(3)

(1) Enter the reported emissions (adjusted with captured and/or reduced amount). (2) Enter the quantities of reduction of generated gas (emission recovery, destruction, etc.) (3) Insert additional rows if necessary.

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Table 2.10 IPPU Background Table: 2H Other Emissions

Activity Data

CO2 (Gg)

Categories Quantity 2H

Unit

Emissions

(1)

(information) (2) Reduction

CH4 (Gg) Emissions

(1)

(information) (2) Reduction

N2O (Gg) Emissions

(1)

(information) (2) Reduction

Other

2H1

Pulp and Paper Industry

2H2

Food and Beverages Industry

2H3

Other (please specify)

(3)

(1) Enter the reported emissions (adjusted with captured and/or reduced amount). (2) Enter the quantities of reduction of generated gas (emission recovery, destruction, etc.). (3) Insert additional rows if necessary.

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(please specify)

(please specify)

(please specify)

Categories

(please specify)

(please specify)

(1)

Table 2.11 IPPU Background Table: Greenhouse gases without CO2 equivalent conversion factors

Emissions in original mass unit (tonne) Total 2B Chemical Industry 2B9 Fluorochemical Production 2B9a By-product Emissions 2B9b Fugitive Emissions (2) 2B10 Other (please specify) 2C Metal Industry 2C4 Magnesium Production (2) 2C7 Other (please specify) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor 2E2 TFT Flat Panel Display 2E3 Photovoltaics 2E4 Heat Transfer Fluid (2) 2E5 Other (please specify) 2F Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F1a Refrigeration and Stationary Air Conditioning 2F1b Mobile Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosols 2F5 Solvents (2) 2F6 Other Applications (please specify) 2G. Other Product Uses 2G1 Electrical Equipment 2G1a Manufacture of Electrical Equipment 2G1b Use of Electrical Equipment 2G1c Disposal of Electrical Equipment 2G2 SF6 and PFCs from Other Product Uses 2G2a Military Applications (AWACS) 2G2b Accelerators (2) 2G2c Other (please specify) (2) 2G4 Other (please specify) (1) Insert additional columns if necessary. The gases for which the CO2 equivalent conversion factor is available should not be included in this table. Such gases should be reported in the respective sectoral background tables and included in national totals. (2) Insert additional rows if necessary. Note: Where information is confidential the entries should provide aggregate figures but there should be a note indicating this in the documentation box below.

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Table 2.12 IPPU Background Table: Allocation of CO2 emissions from NonEnergy Use of fossil fuels: IPPU and other sectors [See also section 1.4 of Volume 3.] Reported in year: …….. Primary NEU fuel 2 Industrial Processes and Product Use 2A Mineral Industry (Please specify the sub-category) (coal, ..) 2B Chemical Industry 2B1 Ammonia Production natural gas 2B5 Carbide Production pet coke 2B6 Titanium Dioxide Production coal 2B8 Petrochemical and Carbon Black Production 2B8a Methanol natural gas 2B8b Ethylene naphtha 2B8f Carbon Black 2B10 Other 2C Metal Industry 2C1 Iron and Steel Production

natural gas

coke

(1)

Other NEU fuel(s)

(1)

Emissions Amount Reported in IPPU Sector CO2 (2) (Gg)

In case reported elsewhere: Sub-category in 1A where these emissions are (partly) reported

Notes

Category

4 oil, coal oil

coal, oil gas oil; butane, ethane, propane, LPG oil, coke oven gas

5 5

coal, pet coke (carbon electrode) coke, coal coke, coal

6

2C2 Ferroalloys Production (carbon electrode) 2C3 Aluminium Production (carbon electrode) 2C5 Lead Production coke 2C6 Zinc Production coke 2C7 Other (carbon electrode) coke, coal 2D Non-Energy Products from Fuels and Solvent Use 2D1 Lubricant Use lubricants greases 2D2 Paraffin Wax Use waxes 2D3 Solvent Use (mineral turpentine) coal tars and oils 2D4 Other 2H Other 2H1 Pulp and Paper Industry 2H2 Food and Beverages Industry coke 2H3 Other 1 ENERGY

8 9

Reported in (3) Sector 1A

1A Fuel Combustion Activities 1A1a Main Activity Electricity and Heat Production 1A1b Petroleum Refining 1A1c Manufacture of Solid Fuels and Other Energy Industries 1A2 Manufacturing Industries and Construction

7 7

(BF gas)

(chemical off-gases)

10

BF gas (BF gas)

(lubricants, chemical offgases))

(1)

The columns ‘Primary NEU fuel’ and ‘Other NEU fuel’ should be completed with the actual fuel types used.

(2)

These are the same emissions reported in the sectoral background table (also the same emissions notation keys NE, NO, IE, where applicable). If (partly) reported elsewhere, a reference to that other source category should be added in the next column.

(3)

Report here only the CO2 emissions from combustion of waste gases produced from industrial processes but used for fuel combustion in other economic sectors and reported in the Energy sector.(e.g. from combustion of blast furnace gas or chemical off-gases transferred offsite to another source category).

(4)

For example powdered anthracite coal may be used in Glass Production (2A3).

(5)

In cases where the production of off-gases (i.e. byproduct gases) is fully accounted for in the energy statistics, the combustion of these gases may be used to calculate and report CO2 emissions from the feedstock losses. Part of these off-gases may be combusted off-site (i.e. in a sector other than the petrochemical industry) and should thus be accounted for separately as fuel combustion in the Energy Sector.

(6)

Part of the blast furnace gas produced from coke used in blast furnaces may be combusted off-site (i.e. in a sector other than the iron and steel industry) and should thus be accounted for separately as fuel combustion in the Energy Sector.

(7)

Carbon electrodes are generally manufactured from coke, coal or tar either on-site by the users themselves or separately by anode production plants and then sold to users domestically and/or exported. If anodes are also imported and/or exported, there is no direct correspondence between fuels used for anode production and the amounts of anodes used in the country.

(8)

Mineral turpentines are often used as solvent, possibly blended with other liquids. Aromatics derived from coal oils may also be used as solvents.

(9)

Emissions from asphalt production, paving of roads and roofing should be reported under 2D4. However, bitumen - and other oil as diluent or 'road oil' - used for this activity does not result in CO2 emissions.

(10)

CO2 from blast furnace gas and chemical off-gases should be reported here only when utilised in public power or heat production.

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Table 3 AFOLU Sectoral Table (1 of 2) Categories

Net CO2 emissions/ removals

Emissions CH4

N2O

NOx

CO

NMVOCs

(Gg) 3 AFOLU 3A Livestock 3A1 Enteric Fermentation 3A1a Cattle 3A1ai Dairy Cows 3A1aii Other Cattle 3A1b Buffalo 3A1c Sheep 3A1d Goats 3A1e Camels 3A1f Horses 3A1g Mules and Asses 3A1h Swine 3A1j Other (please specify) (1) 3A2 Manure Management 3A2a Cattle 3A2ai Dairy Cows 3A2aii Other Cattle 3A2b Buffalo 3A2c Sheep 3A2d Goats 3A2e Camels 3A2f Horses 3A2g Mules and Asses 3A2h Swine 3A2i Poultry 3A2j Other (please specify) 3B Land 3B1 Forest Land 3B1a Forest Land Remaining Forest Land 3B1b Land Converted to Forest Land 3B1bi Cropland Converted to Forest Land 3B1bii Grassland Converted to Forest Land 3B1biii Wetlands Converted to Forest Land 3B1biv Settlements Converted to Forest Land 3B1bv Other Land Converted to Forest Land 3B2 Cropland 3B2a Cropland Remaining Cropland 3B2b Land Converted to Cropland 3B2bi Forest Land Converted to Cropland 3B2bii Grassland Converted to Cropland 3B2biii Wetlands Converted to Cropland 3B2biv Settlements Converted to Cropland 3B2bv Other Land Converted to Cropland 3B3 Grassland 3B3a Grassland Remaining Grassland 3B3b Land Converted to Grassland 3B3bi Forest Land Converted to Grassland 3B3bii Cropland Converted to Grassland 3B3biii Wetlands Converted to Grassland 3B3biv Settlements Converted to Grassland 3B3bv Other Land Converted to Grassland

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Table 3 AFOLU Sectoral Table (2 of 2) Categories

Net CO2 emissions/ removals

Emissions CH4

N2O

NOx

CO

NMVOCs

(Gg) 3B4 Wetlands 3B4a Wetlands Remaining Wetlands 3B4ai Peatlands Remaining Peatlands 3B4aii Flooded Land Remaining Flooded Land 3B4b Land Converted to Wetlands 3B4bi Land Converted for Peat Extraction 3B4bii Land Converted to Flooded Land 3B4biii Land Converted to Other Wetlands 3B5 3B5 Settlements 3B5a Settlements Remaining Settlements 3B5b Land Converted to Settlements 3B5bi Forest Land Converted to Settlements 3B5bii Cropland Converted to Settlements 3B5biii Grassland Converted to Settlements 3B5biv Wetlands Converted to Settlements 3B5bv Other Land Converted to Settlements 3B6 3B6 Other Land 3B6a Other Land Remaining Other Land 3B6b Land Converted to Other Land 3B6bi Forest Land Converted to Other Land 3B6bii Cropland Converted to Other Land 3B6biii Grassland Converted to Other Land 3B6biv Wetlands Converted to Other Land 3B6bv Settlements Converted to Other Land 3C Aggregate Sources and Non-CO2 Emissions (2) Sources on Land 3C1 Biomass Burning 3C1a Biomass Burning in Forest Land 3C1b Biomass Burning in Cropland 3C1c Biomass Burnings in Grassland 3C1d Biomass Burnings in All Other Land 3C2 Liming 3C3 Urea Fertilization (3) 3C4 Direct N2O Emissions from Managed Soils 3C5 Indirect N2O Emissions from Managed Soils Indirect N2O Emissions from Manure 3C6 Management 3C7 Rice Cultivations 3C8 Other (please specify) 3D Other 3D1 Harvested Wood Products 3D2 Other (please specify) (1) Indirect N2O emissions are not included here (see category 3C6). (2) If CO2 emissions from Biomass Burning are not already included in Table 3.2 (Carbon stock changes background table), they should be reported here. (3) Countries may report by land categories if they have the information. * Cells to report emissions of NOx, CO, and NMVOC have not been shaded although the physical potential for emissions is lacking for some categories.

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Table 3.1 AFOLU Background Table: 3A1 - 3A2 Agriculture/Livestock Categories

Activity data (number of animals)

Emissions CH4

N2O (Gg)

3A Livestock 3A1 Enteric Fermentation 3A1a Cattle 3A1ai Dairy Cows 3A1aii Other Cattle 3A1b Buffalo 3A1c Sheep 3A1d Goats 3A1e Camels 3A1f Horses 3A1g Mules and Asses 3A1h Swine 3A1j Other (please specify) 3A2 Manure (1) Management 3A2a Cattle 3A2ai Dairy Cows 3A2aii Other Cattle 3A2b Buffalo 3A2c Sheep 3A2d Goats 3A2e Camels 3A2f Horses 3A2g Mules and Asses 3A2h Swine 3A2i Poultry 3A2j Other (please specify)

(1) Indirect N2O emissions are not included here.

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Table 3.2 AFOLU Background Table: 3B Carbon stock changes in FOLU (1 of 2) Activity data

Net carbon stock change and CO2 emissions Biomass

Categories

Dead organic matter Soils Thereof: Carbon Net CO2 Carbon Carbon Net carbon Total Area of Net carbon Net carbon Net carbon loss from emissions emitted as stock change area organic Increase Decrease emitted as stock stock stock drained CH4 and CO CH4 and CO in mineral soils change change change organic (1) (1) (2) from fires from fires soils soils (ha)

3B Land 3B1 3B1a 3B1b 3B1bi 3B1bii 3B1biii 3B1biv 3B1bv 3B2 3B2a 3B2b 3B2bi 3B2bii 3B2biii 3B2biv 3B2bv 3B3 3B3a 3B3b 3B3bi 3B3bii 3B3biii 3B3biv 3B3bv 3B4 3B5

(Gg C)

(Gg CO2)

Forest Land Forest Land Remaining Forest Land Land Converted to Forest Land Cropland Converted to Forest Land Grassland Converted to Forest Land Wetlands Converted to Forest Land Settlements Converted to Forest Land Other Land Converted to Forest Land Cropland Cropland Remaining Cropland Land Converted to Cropland Forest Land Converted to Cropland Grassland Converted to Cropland Wetlands Converted to Cropland Settlements Converted to Cropland Other Land Converted to Cropland Grassland Grassland Remaining Grassland Land Converted to Grassland Forest Land Converted to Grassland Cropland Converted to Grassland Wetlands Converted to Grassland Settlements Converted to Grassland Other Land Converted to Grassland (3) Wetlands Settlements

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Table 3.2 AFOLU Background Table: 3B Carbon stock changes in FOLU (2of 2) Activity data

Net carbon stock change and CO2 emissions Biomass

Categories

Dead organic matter Soils Thereof: Carbon Net CO2 Carbon Carbon Net carbon Total Area of Net carbon Net carbon Net carbon loss from emissions emitted as stock change area organic Increase Decrease emitted as stock stock stock drained CH4 and CO CH4 and CO in mineral soils change change change organic (1) (1) (2) from fires from fires soils soils (ha)

3B5a 3B5b 3B5bi 3B5bii 3B5biii 3B5biv 3B5bv 3B6 3B6a 3B6b 3B6bi 3B6bii 3B6biii 3B6biv 3B6bv

(Gg C)

(Gg CO2)

Settlements Remaining Settlements Land Converted to Settlements Forest Land Converted to Settlements Cropland Converted to Settlements Grassland Converted to Settlements Wetlands Converted to Settlements Other Land Converted to Settlements Other Land Other Land Remaining Other Land Land Converted to Other Land Forest Land Converted to Other Land Cropland Converted to Other Land Grassland Converted to Other Land Wetlands Converted to Other Land Settlements Converted to Other Land

(1) Where the carbon contained in the emissions of CH4 and CO is significant part of the sectoral emissions, this should be copied from the corresponding columns in the Sectoral Background Table 3.4. This amount of carbon emitted as CH4 and CO is then subtracted from carbon stock change to avoid double counting (see Volume 4, Section 2.2.3). (2) The activity data used for this column correspond to the difference between the column Area and the Area of organic soils. (3) CO2 Emissions from Wetlands are reported in a separate background table (Table 3.3) that includes all gases emitted from Wetlands.

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Table 3.3 AFOLU Background Table: Emissions in Wetlands (3B4) Categories

Activity data Area (ha)

CO2

Emissions CH4 (Gg)

N2O

3B4 Wetlands 3B4a Wetlands Remaining Wetlands 3B4ai Peatlands Remaining Peatlands 3B4aii Flooded Land Remaining Flooded Land 3B4b Land Converted to Wetlands 3B4bi Land Converted for Peat Extraction 3B4bii Land Converted to Flooded Land 3B4biii Land Converted to Other Wetlands

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Table 3.4 AFOLU Background Table: Biomass Burning (3C1) (1 of 2) Activity data Categories

(1)

(4)

(4)

Description

(2)

Unit (ha or kg dm)

Information item: Carbon emitted as (5) CH4 and CO

Emissions Values

(3)

CO2

CH4 Biomass DOM

N2O (Gg)

CO Biomass DOM

NOx

Biomass

DOM

(C Gg)

3C1 Biomass Burning 3C1a Biomass Burning in Forest Land Controlled Burning Wildfires 3C1b Biomass Burning in Cropland Biomass Burning in Cropland Remaining Cropland Controlled Burning Wildfires Biomass burning in Forest Land Converted to Cropland Controlled Burning Wildfires Biomass Burning in Non Forest Land Converted to Cropland Controlled Burning Wildfires 3C1c Biomass Burning in Grassland Burning in Grassland Remaining Grassland Controlled Burning Wildfires Burning in Forest Land Converted to Grassland Controlled Burning Wildfires Burning in Non Forest Land Converted to Grassland Controlled Burning Wildfires 3C1d Biomass Burning in All Other Land Biomass Burning in Other Land Remaining All Other Land Controlled Burning Wildfires

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Table 3.4 AFOLU Background Table: Biomass Burning (3C1) (2 of 2) Activity data Categories

(1)

(4)

(4)

Description

Unit

(2)

Information item: Carbon emitted as (5) CH4 and CO

Emissions Values

CO2

(3)

CH4 Biomass DOM

(ha or kg dm)

N2O

CO Biomass DOM

NOx

Biomass

(Gg)

DOM

(C Gg)

Biomass Burning in Forest Land Converted to All Other Land Controlled Burning Wildfires Biomass Burning in Non Forest Land Converted to All Other Land Controlled Burning Wildfires

(1) Parties should report both Controlled/Prescribed Burning and Wildfires emissions, where appropriate, in a separate manner. (2) For each land type data should be selected between area burned or biomass burned. Units for area will be in hectare (ha) and for biomass burned in kilogram dry matter (kg dm). (3) If CO2 emissions from biomass burning are not already included in Table 3.2 (Carbon stock changes background table), they should be reported here. Carbon stock changes associated with biomass burning should not also be reported in Table 3.2 to avoid double counting. (4) CH4 and CO emissions from biomass burning and DOM are reported separately. (5) Where the carbon contained in the emissions of CH4 and CO is a significant part of the sectoral emissions this should be transferred to the corresponding columns in the Sectoral Background Table 3.2. This amount of carbon emitted as CH4 and CO is then subtracted from carbon stock change to avoid double counting. The conversion factors to convert CH4 and CO to C (as input to Table 3.2) are 12/16 for CH4 and 12/28 for CO. (see Volume 4, Section 2.2.3).

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Table 3.5 AFOLU Background Table: CO2 emissions from Liming (3C2) Activity data Categories

Limestone CaCO3

Dolomite CaMg(CO3)2 (Mg/yr)

3C2 Liming

Emissions Total amount of lime (2) applied (Mg/yr)

CO2 (Gg)

(1)

Forest Land Cropland Grassland Wetland Other Land Other

(1) If countries are not able to separate liming application for different land use categories, they should use the main category “Liming”. Also, if a country has data broken down to limestone and dolomite at national level, it can be reported under this category. (2) A country may report aggregate estimates for total lime applications when data are not available for limestone and dolomite.

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Table 3.6 AFOLU Background Table: CO2 emissions from Urea Fertilization (3C3) Categories

Activity data

Emissions

Total amount of urea applied

CO2

(Mg/yr)

(Gg)

(1)

3C3 Urea applied Forest Land Cropland Grassland Settlements Other Land

(1) If countries are not able to separate urea application for different land use categories, they should use the main category “Urea applied”.

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Table 3.7 AFOLU Background Table: Direct N2O emissions from Managed Soils (3C4) Activity data Categories

(1)

Emissions

Total amount of nitrogen applied

N2O

(Gg N/yr)

(Gg)

3C4 Direct N2O Emissions from Managed Soils Inorganic N fertilizer application Forest Land Cropland Grassland Settlements Other Land Organic N applied as fertilizer (manure and sewage sludge) Forest Land Cropland Grassland Settlements Other Land Urine and dung N deposited on pasture, range and paddock by grazing (2) animals N in crop residues Area (ha) N mineralization/immobilization associated with loss/gain of soil organic matter resulting from change of land use or management of mineral soils Drainage/management of organic soils (i.e., Histosols) (1) Countries will report at the aggregation level if their activity data allows them within each category. If country has disaggregated data by land use, reporting is also possible using this table. (2) Only for Grassland. (3) Only for Cropland.

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Table 3.8 AFOLU Background Table: Indirect N2O emissions from Managed Soils and Manure Management (3C5 and 3C6) Categories

(1)

Activity data Total amount of nitrogen applied / excreted (Gg N/yr)

Emissions N2O (Gg)

3C5 Indirect N2O emissions from Managed Soils From atmospheric deposition of N volatilized from managed soils from agricultural inputs of N (synthetic N fertilizers; (2) organic N applied as fertilizer; urine and dung N deposited on pasture, range and paddock by grazing animals ; N in (3) crop residues ; and N mineralization/immobilization associated with loss/gain of soil organic matter resulting from (3) change of land use or management of mineral soils ) Forest Land Cropland Grasslands Settlements Other Land From N leaching/runoff from managed soils (i.e. from synthetic N fertilizers; organic N applied as fertilizer; urine and dung (2) (3) N deposited on pasture, range and paddock by grazing animals ; N in crop residues ; and N mineralization/immobilization associated with loss/gain of soil organic matter resulting from change of land use or (3) management of mineral soils ) Forest Land Cropland Grasslands Settlements Other Land 3C6 Indirect N2O emissions from Manure Management

(1)

Countries will report at the aggregation level if their activity data allows them within each category. If country has disaggregated data by land use, reporting is also possible using this table.

(2)

Only for Grassland.

(3)

Only for Cropland.

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Table 3.9 AFOLU Background Table: Non-CO2 GHG emissions not included elsewhere (3C7 and 3C8) Activity data

Categories

(ha)

Emissions CH4

N2O (Gg)

(1)

3C7 Rice Cultivations 3C8 Other (please specify)

(1) If a country wishes to report direct N2O emissions from N fertilizer application to rice field, it should be reported here. Otherwise, in Table 3.7.

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Table 3.10 AFOLU Background Table: Harvested Wood Products (3D1) - Annual carbon HWP contribution to total AFOLU CO2 removals and emissions and background information Variable number 1A Inventory year

1B

Annual Change Annual Change in stock of HWP in stock of HWP in use from in SWDS from consumption consumption

∆CHWP IU DC

∆CHWP SWDS DC

2A

2B

3

4

Annual Change Annual Change Annual Imports Annual Exports in stock of HWP in stock of HWP of wood, and of wood, and in use produced in SWDS paper products paper products from domestic produced from + wood fuel, + wood fuel, harvest domestic pulp, recovered pulp, recovered harvest paper, paper, roundwood/ roundwood/ chips chips

∆C HWP IU DH

∆CHWP SWDS DH

PIM

PEX

Gg C /yr

5 Annual Domestic Harvest

H

6

7

8

Annual release Annual release HWP of carbon to the of carbon to the Contribution to atmosphere atmosphere AFOLU CO2 from HWP from HWP emissions/ consumption (including removals (from fuelwood fuelwoood) & products in where wood use and came from products in domestic SWDS) harvest (from products in use and products in SWDS ) ↑CHWP DC

9 Approach used to estimate HWP Contribution

↑CHWP DH Gg CO2 /yr

1990 ….. Report Col 6 or 7 as needed for the approach used. Col 6 or 7 may be computed using Cols 1 through 5 or by a Tier 3 method. Always report Cols 3, 4, and 5. Report Cols 1A, 1B, 2A, 2B if they are used. The HWP contribution and approach should be reported in Columns 8 and 9 together with a description of the approach chosen and main assumptions in the Documentation Box Additional Variables calculated and used should be reported to enhance the transparency of the results. (e.g., CH4 from SWDS if this was used) Add additional columns if needed. Note: ↑C HWP DC = H + PIM – PEX - ∆C HWP IU DC - ∆C HWP SWDS DC AND ↑C HWP DH = H - ∆C HWP IU DH - ∆C HWP SWDS DH

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Table 4 Waste Sectoral Table Categories

CO2

CH4

N2O

NOx

CO

NMVOC (1)

SO2

(Gg)

4 WASTE 4A Solid Waste Disposal 4A1 Managed Waste Disposal Sites 4A2 Unmanaged Waste Disposal Sites 4A3 Uncategorised Waste Disposal Sites 4B Biological Treatment of Solid Waste 4C Incineration and Open Burning of Waste 4C1 Waste Incineration 4C2 Open Burning of Waste 4D Wastewater Treatment and Discharge 4D1 Domestic Wastewater Treatment and Discharge 4D2 Industrial Wastewater Treatment and Discharge 4E Other (please specify) (2) (1) Countries may wish to report emissions of NMVOCs from waste disposal sites and waste water treatment. (2) Insert additional rows if necessary. * Cells to report emissions of NOx, CO, NMVOC and SO2 have not been shaded although the physical potential for emissions is lacking for some categories.

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Table 4.1 Waste Background Table: CO2, CH4, N2O emissions Type of activity data

Categories

unit

CO2

Emission factor CH4 (Gg/unit activity data)

N2O

CO2

Emissions CH4 (Gg)

N2O

(1)

4A Solid Waste Disposal 4A1 Managed Waste Disposal Sites 4A2 Unmanaged Waste Disposal Sites 4A3 Uncategorised Waste Disposal Sites 4B Biological Treatment of Solid Waste (2) 4C Incineration and Open Burning of Waste 4C1 Waste Incineration 4C2 Open Burning of Waste 4D Wastewater Treatment and Discharge 4D1 Domestic Wastewater Treatment and Discharge (3) CH4 emissions (4) N2O emissions 4D2 Industrial Wastewater Treatment and Discharge (3) CH4 emissions (4) N2O emissions (5) 4E Other (please specify) (1) Amount of waste deposited in the SWDS in the inventory year. [mil. tonnes of wet waste/yr] Specification by waste type is encouraged. Emission factor data (parameters used in the calculations) should be reported in FOD parameter sheet or reported separately, when other methods are used. (2) Waste burned for energy is reported in the Energy Sector under 1A. Information on reporting of waste combustion in the Energy Sector should be given in the documentation box. (3) Activity data for estimation of CH4 emissions is total amount of organically degradable material in the wastewater (TOW) [Gg BOD/yr or Gg COD/yr]. (4) Activity data for estimation of N2O emissions is total amount of nitrogen in effluent [Gg N/yr]. (5) Insert additional rows if necessary.

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Table 4.2 Waste Background Table: CH4 recovery (1) (2) Unit

Categories

Gg CH4

CH4 Flared

(3)

Energy recovery

(4)

4A Solid Waste Disposal 4B Biological Treatment of Solid Waste 4D Wastewater Treatment and Discharge 4D1

Domestic Wastewater Treatment and Discharge

4D2

Industrial Wastewater Treatment and Discharge

4E Other (please specify)

(5)

(1) The amount of CH4 recovery should be reported in this table even if the gas is used for energy. (2) Flaring and energy recovery should be reported separately, if possible. (3) Default EF for CH4 and N2O from flaring is zero. The CO2 emissions are not reported as the gas is of biogenic origin. (4) When CH4 recovered is used for energy, the emissions from the combustion of the gas should be reported in the Energy sector (under 1A). Default EF for CH4 and N2O from the combustion of the gas is zero. (5) Insert additional rows if necessary.

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Table 4.3 Waste Background Table: Long-term storage of carbon Information items C

Categories

(1)

(Gg) (2)

Information items

Long-term storage of carbon in waste disposal sites Annual change in total long-term storage of carbon stored Annual change in long-term storage of carbon in HWP waste

(3)

(1) Report in mass carbon. (2) These items are listed for information only and will not be added to the totals. The carbon should be converted to carbon dioxide. (3) Carbon stored in wood, paper, cardboard, garden (yard) and park (equal to the annual change in stock of HWP in SWDS from consumption, reported in Table 3.10, Column 1B).

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Table 5A Cross-sectoral Table: Indirect emissions of N2O

(1)(2)

Activity data / source emissions Categories

Emissions

Emissions NH3

Emissions NOx

N2O

(Gg NH3)

(Gg NO2-equivalents)

(Gg N2O)

1 Energy 2 Industrial Processes and Product Use 3 Agriculture, Forestry and Other Land Use 3C5 Indirect N2O Emissions from managed soils 3C6 Indirect N2O Emissions from manure management Other (3) (Please specify) 4 Waste 5 Other (Please specify)

(4)

(1) 90 to 99 percent of ammonia emissions originate in the Agriculture Sector. Other emission sources for ammonia are in the Energy Sector (such as combustion, petroleum refining, catalyst cars in the transport sector), in the Industrial processes sector in particular from production of ammonia, nitric acid, ammonium nitrate and phosphate, urea, and fertilizers), and from metal industry (coke ovens battery operations), and also in the Waste Sector (solid waste disposal and waste incineration). (2) Indirect N2O emissions from nitrogen leaching /runoff from managed soils in AFOLU categories are included in Table 3.8. (3) Any other sources not included in 3C5 and 3C6. (4) Insert additional rows if necessary.

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Table 6A Trends of CO2 (1 of 3) (Gg)

Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

Total National Emissions and Removals 1 ENERGY 1A Fuel Combustion Activities 1A1 Energy Industries 1A2 Manufacturing Industries and Construction 1A3 Transport 1A4 Other Sectors 1A5 Non-Specified 1B Fugitive Emissions from Fuels 1B1 Solid Fuels 1B2 Oil and Natural Gas 1B3 Other Emissions from Energy Production 1C Carbon Dioxide Transport and Storage 2 INDUSTRIAL PROCESSES AND PRODUCT USE 2A Mineral Industry 2A1 Cement Production 2A2 Lime Production 2A3 Glass Production 2A4 Other Process Uses of Carbonates 2A5 Other (please specify) 2B Chemical Industry 2B1 Ammonia Production 2B2 Nitric Acid Production 2B3 Adipic Acid Production 2B4 Caprolactam, Glyoxal and Glyoxylic Acid Production 2B5 Carbide Production 2B6 Titanium Dioxide Production 2B7 Soda Ash Production 2B8 Petrochemical and Carbon Black Production 2B9 Fluorochemical Production 2B10 Other (please specify) 2C Metal Industry 2C1 Iron and Steel Production 2C2 Ferroalloys Production 2C3 Aluminium Production 2C4 Magnesium Production 2C5 Lead Production 2C6 Zinc Production 2C7 Other (please specify) 2D Non-Energy Products from Fuels and Solvent Use 2D1 Lubricant Use 2D2 Paraffin Wax Use 2D3 Solvent Use 2D4 Other (please specify) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor 2E2 TFT Flat Panel Display 2E3 Photovoltaics 2E4 Heat Transfer Fluid 2E5 Other (please specify)

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Table 6A Trends of CO2 (2of 3) (Gg)

Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

2F

Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosols 2F5 Solvents 2F6 Other Applications 2G Other Product Manufacture and Use 2G1 Electrical Equipment 2G2 SF6 and PFCs from Other Product Uses 2G3 N2O from Product Uses 2G4 Other (please specify) 2H Other 2H1 Pulp and Paper Industry 2H2 Food and Beverages Industry 2H3 Other (please specify) 3 AGROCULTURE, FORESTRY AND OTHER LAND USE 3A Livestock 3A1 Enteric Fermentation 3A2 Manure Management 3B Land 3B1 Forest Land 3B2 Cropland 3B3 Grassland 3B4 Wetlands 3B5 Settlements 3B6 Other Land 3C Aggregate Sources and Non-CO2 Emissions Sources on Land 3C1 Biomass Burning 3C2 Liming 3C3 Urea Application 3C4 Direct N2O Emissions from Managed Soils 3C5 Indirect N2O Emissions from Managed Soils 3C6 Indirect N2O Emissions from Manure Management 3C7 Rice Cultivations 3C8 Other (please specify) 3D Other 3D1 Harvested Wood Products 3D2 Other (please specify) 4 WASTE 4A Solid Waste Disposal 4A1 Managed Waste Disposal Sites 4A2 Unmanaged Waste Disposal Sites 4A3 4A3 Uncategorised Waste Disposal Sites 4B Biological Treatment of Solid Waste 4C Incineration and Open Burning of Waste 4C1 Waste Incineration 4C2 Open Burning of Waste

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Table 6A Trends of CO2 (3 of 3) (Gg)

Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

4D

Wastewater Treatment and Discharge 4D1 Domestic Wastewater Treatment and Discharge 4D2 Industrial Wastewater Treatment and Discharge 4E Other (please specify) 5 OTHER 5A

Indirect N2O emissions from the Atmospheric Deposition of Nitrogen in NOx and NH3

5B

Other (please specify)

Memo items International Bunkers International Aviation (International Bunkers) International Water-borne Transport (International Bunkers) Multilateral Operations (1) Information items CO2 from Biomass Burning for Energy Production CO2 captured For domestic storage For storage in other countries Long-term storage of carbon in waste disposal sites Annual change in total long-term storage of carbon stored Annual change in long-term storage of carbon in HWP waste Other (please specify)

(1) Here, both emissions and removals can be listed.

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Table 6B Trends of CH4 (1 of 3) (Gg)

Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

Total National Emissions and Removals 1 ENERGY 1A Fuel Combustion Activities 1A1 Energy Industries 1A2 Manufacturing Industries and Construction 1A3 Transport 1A4 Other Sectors 1A5 Non-Specified 1B Fugitive Emissions from Fuels 1B1 Solid Fuels 1B2 Oil and Natural Gas Other Emissions from Energy 1B3 Production 1C Carbon Dioxide Transport and Storage 2 INDUSTRIAL PROCESSES AND PRODUCT USE 2A Mineral Industry 2A1 Cement Production 2A2 Lime Production 2A3 Glass Production 2A4 Other Process Uses of Carbonates 2A5 Other (please specify) 2B Chemical Industry 2B1 Ammonia Production 2B2 Nitric Acid Production 2B3 Adipic Acid Production 2B4 Caprolactam, Glyoxal and Glyoxylic Acid Production 2B5 Carbide Production 2B6 Titanium Dioxide Production 2B7 Soda Ash Production 2B8 Petrochemical and Carbon Black Production 2B9 Fluorochemical Production 2B10 Other (please specify) 2C Metal Industry 2C1 Iron and Steel Production 2C2 Ferroalloys Production 2C3 Aluminium Production 2C4 Magnesium Production 2C5 Lead Production 2C6 Zinc Production 2C7 Other (please specify) 2D Non-Energy Products from Fuels and Solvent Use 2D1 Lubricant Use 2D2 Paraffin Wax Use 2D3 Solvent Use 2D4 Other (please specify) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor 2E2 TFT Flat Panel Display 2E3 Photovoltaics 2E4 Heat Transfer Fluid 2E5 Other (please specify)

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Table 6B Trends of CH4 (2 of 3) (Gg)

Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

2F

Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosols 2F5 Solvents 2F6 Other Applications 2G Other Product Manufacture and Use 2G1 Electrical Equipment 2G2 SF6 and PFCs from Other Product Uses 2G3 N2O from Other Product Uses 2G4 Other (please specify) 2H Other 2H1 Pulp and Paper Industry 2H2 Food and Beverages Industry 2H3 Other (please specify) 3 AGROCULTURE, FORESTRY AND OTHER LAND USE 3A Livestock 3A1 Enteric Fermentation 3A2 Manure Management 3B Land 3B1 Forest Land 3B2 Cropland 3B3 Grassland 3B4 Wetlands 3B5 Settlements 3B6 Other Land 3C Aggregate Sources and Non-CO2 Emissions Sources on Land 3C1 Biomass Burning 3C2 Liming 3C3 Urea Application 3C4 Direct N2O Emissions from Managed Soils 3C5 Indirect N2O Emissions from Managed Soils 3C6 Indirect N2O Emissions from Manure Management 3C7 Rice Cultivations 3C8 Other (please specify) 3D Other 3D1 Harvested Wood Products 3D2 Other (please specify) 4 WASTE 4A Solid Waste Disposal 4A1 Managed Waste Disposal Sites 4A2 Unmanaged Waste Disposal Sites 4A3 Uncategorised Waste Disposal Sites 4B Biological Treatment of Solid Waste 4C Incineration and Open Burning of Waste 4C1 Waste Incineration 4C2 Open Burning of Waste

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Table 6BTrends of CH4 (3 of 3) (Gg) Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

4D

Wastewater Treatment and Discharge 4D1 Domestic Wastewater Treatment and Discharge 4D2 Industrial Wastewater Treatment and Discharge 4E Other (please specify) 5 OTHER 5A

Indirect N2O emissions from the Atmospheric Deposition of Nitrogen in NOx and NH3

5B

Other (please specify)

Memo items International Bunkers International Aviation (International Bunkers) International Water-borne Transport (International Bunkers) Multilateral Operations (1) Information items CO2 from Biomass Burning for Energy Production CO2 captured For domestic storage For storage in other countries Long-term storage carbon in waste disposal sites Annual change in total long-term storage of carbon stored Annual change in long-term storage of carbon in HWP waste Other (please specify)

(1) Here, both emissions and removals can be listed.

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Table 6C Trends of N2O (1 of 3) (Gg)

Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

Total National Emissions and Removals 1 ENERGY 1A Fuel Combustion Activities 1A1 Energy Industries 1A2 Manufacturing Industries and Construction 1A3 Transport 1A4 Other Sectors 1A5 Non-Specified 1B Fugitive Emissions from Fuels 1B1 Solid Fuels 1B2 Oil and Natural Gas 1B3 Other Emissions from Energy Production 1C Carbon Dioxide Transport and Storage 2 INDUSTRIAL PROCESSES AND PRODUCT USE 2A Mineral Industry 2A1 Cement Production 2A2 Lime Production 2A3 Glass Production 2A4 Other Process Uses of Carbonates 2A5 Other (please specify) 2B Chemical Industry 2B1 Ammonia Production 2B2 Nitric Acid Production 2B3 Adipic Acid Production 2B4 Caprolactam, Glyoxal and Glyoxylic Acid Production 2B5 Carbide Production 2B6 Titanium Dioxide Production 2B7 Soda Ash Production 2B8 Petrochemical and Carbon Black Production 2B9 Fluorochemical Production 2B10 Other (please specify) 2C Metal Industry 2C1 Iron and Steel Production 2C2 Ferroalloys Production 2C3 Aluminium Production 2C4 Magnesium Production 2C5 Lead Production 2C6 Zinc Production 2C7 Other (please specify) 2D Non-Energy Products from Fuels and Solvent Use 2D1 Lubricant Use 2D2 Paraffin Wax Use 2D3 Solvent Use 2D4 Other (please specify) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor 2E2 TFT Flat Panel Display 2E3 Photovoltaics 2E4 Heat Transfer Fluid 2E5 Other (please specify)

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Table 6C Trends of N2O (2of 3) (Gg)

Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

2F

Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosols 2F5 Solvents 2F6 Other Applications 2G Other Product Manufacture and Use 2G1 Electrical Equipment 2G2 SF6 and PFCs from Other Product Uses 2G3 N2O from Other Product Uses 2G4 Other (please specify) 2H Other 2H1 Pulp and Paper Industry 2H2 Food and Beverage Industry 2H3 Other (please specify) 3 AGROCULTURE, FORESTRY AND OTHER LAND USE 3A Livestock 3A1 Enteric Fermentation 3A2 Manure Management 3B Land 3B1 Forest land 3B2 Cropland 3B3 Grassland 3B4 Wetlands 3B5 Settlements 3B6 Other land 3C Aggregate Sources and non-CO2 Emissions Sources on Land 3C1 Biomass Burning 3C2 Liming 3C3 Urea Application 3C4 Direct N2O Emissions from Managed Soils 3C5 Indirect N2O Emissions from Managed Soils 3C6 Indirect N2O Emissions from Manure Management 3C7 Rice Cultivations 3C8 Other (please specify) 3D Other 3D1 Harvested Wood Products 3D2 Other (please specify) 4 WASTE 4A Solid Waste Disposal 4A1 Managed Waste Disposal Sites 4A2 Unmanaged Waste Disposal Sites 4A3 Uncategorised Waste Disposal Sites Biological Treatment of Solid 4B Waste 4C Incineration and Open Burning of Waste 4C1 Waste Incineration 4C2 Open Burning of Waste

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Table 6C Trends of N2O (3 of 3) (Gg)

Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

4D

Wastewater Treatment and Discharge 4D1 Domestic Wastewater Treatment and Discharge 4D2 Industrial Wastewater Treatment and Discharge 4E Other (please specify) 5 OTHER 5A

Indirect N2O emissions from the Atmospheric Deposition of Nitrogen in NOx and NH3

5B

Other (please specify)

Memo items International Bunkers International Aviation (International Bunkers) International Water-borne Transport (International Bunkers) Multilateral Operations (1) Information items CO2 from Biomass Burning for Energy Production CO2 captured For domestic storage For storage in other countries Long-term storage of carbon in waste disposal sites Annual change in total long-term storage of carbon stored Annual change in long-term storage of carbon in HWP waste Other (please specify)

(1) Here, both emissions and removals can be listed.

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Table 6D Trends of HFCs

(CO2 equivalents (Gg))

Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

Total National Emissions and Removals 2 INDUSTRIAL PROCESSES AND PRODUCT USE 2A Mineral Industry 2A1 Cement Production 2A2 Lime Production 2A3 Glass Production 2A4 Other Process Uses of Carbonates 2A5 Other (please specify) 2B Chemical Industry 2B1 Ammonia Production 2B2 Nitric Acid Production 2B3 Adipic Acid Production 2B4 Caprolactam, Glyoxal and Glyoxylic Acid Production 2B5 Carbide Production 2B6 Titanium Dioxide Production 2B7 Soda Ash Production 2B8 Petrochemical and Carbon Black Production 2B9 Fluorochemical Production 2B10 Other (please specify) 2C Metal Industry 2C1 Iron and Steel Production 2C2 Ferroalloys Production 2C3 Aluminium Production 2C4 Magnesium Production 2C5 Lead Production 2C6 Zinc Production 2C7 Other (please specify) 2D Non-Energy Products from Fuels and Solvent Use 2D1 Lubricant Use 2D2 Paraffin Wax Use 2D3 Solvents Use 2D4 Other (please specify) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor 2E2 TFT Flat Panel Display 2E3 Photovoltaics 2E4 Heat Transfer Fluid 2E5 Other (please specify) 2F Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosols 2F5 Solvents 2F6 Other Applications 2G Other Product Manufacture and Use 2G1 Electrical Equipment 2G2 SF6 and PFCs from Other Product Uses 2G3 N2O from Other Product Uses 2G4 Other (please specify) 2H Other 2H1 Pulp and Paper Industry 2H2 Food and Beverages Industry 2H3 Other (please specify)

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Table 6E Trends of PFCs (CO2 equivalents (Gg)) Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

Total National Emissions and Removals 2 INDUSTRIAL PROCESSES and PRODUCT USE 2A Mineral Industry 2A1 Cement Production 2A2 Lime Production 2A3 Glass Production 2A4 Other Process Uses of Carbonates 2A5 Other (please specify) 2B Chemical Industry 2B1 Ammonia Production 2B2 Nitric Acid Production 2B3 Adipic Acid Production 2B4 Caprolactam, Glyoxal and Glyoxylic Acid Production 2B5 Carbide Production 2B6 Titanium Dioxide Production 2B7 Soda Ash Production 2B8 Petrochemical and Carbon Black Production 2B9 Fluorochemical Production 2B10 Other (please specify) 2C Metal Industry 2C1 Iron and Steel Production 2C2 Ferroalloys Production 2C3 Aluminium Production 2C4 Magnesium Production 2C5 Lead Production 2C6 Zinc Production 2C7 Other (please specify) 2D Non-Energy Products from Fuels and Solvent Use 2D1 Lubricant Use 2D2 Paraffin Wax Use 2D3 Solvent Use 2D4 Other (please specify) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor 2E2 TFT Flat Panel Display 2E3 Photovoltaics 2E4 Heat Transfer Fluid 2E5 Other (please specify) 2F Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosols 2F5 Solvents 2F6 Other Applications 2G Other Product Manufacture and Use 2G1 Electrical Equipment 2G2 SF6 and PFCs from Other Product Uses 2G3 N2O from Other Product Uses 2G4 Other (please specify) 2H Other 2H1 Pulp and Paper Industry 2H2 Food and Beverages Industry 2H3 Other (please specify)

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Table 6F Trends of SF6

(CO2 equivalents (Gg))

Categories

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

Total National Emissions and Removals 2 INDUSTRIAL PROCESSES AND PRODUCT USE 2A Mineral Industry 2A1 Cement Production 2A2 Lime Production 2A3 Glass Production 2A4 Other Process Uses of Carbonates 2A5 Other (please specify) 2B Chemical Industry 2B1 Ammonia Production 2B2 Nitric Acid Production 2B3 Adipic Acid Production 2B4 Caprolactam, Glyoxal and Glyoxylic Acid Production 2B5 Carbide Production 2B6 Titanium Dioxide Production 2B7 Soda Ash Production 2B8 Petrochemical and Carbon Black Production 2B9 Fluorochemical Production 2B10 Other (please specify) 2C Metal Industry 2C1 Iron and Steel Production 2C2 Ferroalloys Production 2C3 Aluminium Production 2C4 Magnesium Production 2C5 Lead Production 2C6 Zinc Production 2C7 Other (please specify) 2D Non-Energy Products from Fuels and Solvent Use 2D1 Lubricant Use 2D2 Paraffin Wax Use 2D3 Solvent Use 2D4 Other (please specify) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor 2E2 TFT Flat Panel Display 2E3 Photovoltaics 2E4 Heat Transfer Fluid 2E5 Other (please specify) 2F Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosols 2F5 Solvents 2F6 Other Applications 2G Other Product Manufacture and Use 2G1 Electrical Equipment 2G2 SF6 and PFCs from Other Product Uses 2G3 N2O from Other Product Uses 2G4 Other (please specify) 2H Other 2H1 Pulp and Paper Industry 2H2 Food and Beverages Industry 2H3 2H3 Other (please specify)

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Table 6G Trends of other gases Categories

(1)

(Gg)

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

…

Total National Emissions and Removals 2 INDUSTRIAL PROCESSES AND PRODUCT USE 2A Mineral Industry 2A1 Cement Production 2A2 Lime Production 2A3 Glass Production 2A4 Other Process Uses of Carbonates 2A5 Other (please specify) 2B Chemical Industry 2B1 Ammonia Production 2B2 Nitric Acid Production 2B3 Adipic Acid Production 2B4 Caprolactam, Glyoxal and Glyoxylic Acid Production 2B5 Carbide Production 2B6 Titanium Dioxide Production 2B7 Soda Ash Production 2B8 Petrochemical and Carbon Black Production 2B9 Fluorochemical Production 2B10 Other (please specify) 2C Metal Industry 2C1 Iron and Steel Production 2C2 Ferroalloys Production 2C3 Aluminium Production 2C4 Magnesium Production 2C5 Lead Production 2C6 Zinc Production 2C7 Other (please specify) 2D Non-Energy Products from Fuels and Solvent Use 2D1 Lubricant Use 2D2 Paraffin Wax Use 2D3 Solvent Use 2D4 Other (please specify) 2E Electronics Industry 2E1 Integrated Circuit or Semiconductor 2E2 TFT Flat Panel Display 2E3 Photovoltaics 2E4 Heat Transfer Fluid 2E5 Other (please specify) 2F Product Uses as Substitutes for Ozone Depleting Substances 2F1 Refrigeration and Air Conditioning 2F2 Foam Blowing Agents 2F3 Fire Protection 2F4 Aerosols 2F5 Solvents 2F6 Other Applications (please specify) 2G Other Product Manufacture and Use 2G1 Electrical Equipment 2G2 SF6 and PFCs from Other Product Uses 2G3 N2O from Other Product Uses 2G4 Other (please specify) 2H Other 2H1 Pulp and Paper Industry 2H2 Food and Beverages Industry 2H3 Other (please specify) (1) This includes all other GHGs including fluorinated gases.

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Table 7A Uncertainties IPCC category

Gas

E.g. 1.A.1. Energy Industrie s Fuel 1

CO2

E.g. 1.A.1. Energy Industrie s Fuel 2

CO2

Etc...

…

Base year emissions /removals

Year t emissions /removals

Activity data uncertainty

Gg CO2 equivalent

Gg CO2 equivalent

(-) %

(+) %

Emission factor /estimation parameter uncertainty (combined if more than one estimation parameter is used) (-) %

(+) %

Combined uncertainty

(-) %

(+) %

Contribution to variance in Year t

Inventory trend in national emissions for year t increase with respect to base year

(fraction)

(% of base year)

Uncertainty introduced into the trend in total national emissions with respect to Base Year (-) %

Approach and Comments

(+) %

Total

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Table 7B Summary of Key Category analysis Quantitative method used: Approach 1/Approach 1 and Approach 2 IPCC Category Code

IPCC Category

Greenhouse Gas

Identification criteria (1)

Comments (2)

(1) The notation keys to be used for this column: L1 = key category according to Approach 1 Level Assessment L2 = key category according to Approach 2 Level Assessment T1 = key category according to Approach 1 Trend Assessment T2 = key category according to Approach 2 Trend Assessment Q = key category according to qualitative criteria (2) In the column for comments, reasons for a qualitative assessment can be provided.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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A report prepared by the Task Force on National Greenhouse Gas Inventories (TFI) of the IPCC and accepted by the Panel but not approved in detail

Whilst the information in this IPCC Report is believed to be true and accurate at the date of going to press, neither the authors nor the publishers can accept any legal responsibility or liability for any errors or omissions. Neither the authors nor the publishers have any responsibility for the persistence of any URLs referred to in this report and cannot guarantee that any content of such web sites is or will remain accurate or appropriate.

Published by the Institute for Global Environmental Strategies (IGES), Hayama, Japan on behalf of the IPCC © The Intergovernmental Panel on Climate Change (IPCC), 2006. When using the guidelines please cite as: IPCC 2006, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T., and Tanabe K. (eds). Published: IGES, Japan.

IPCC National Greenhouse Gas Inventories Programme Technical Support Unit ℅ Institute for Global Environmental Strategies 2108 -11, Kamiyamaguchi Hayama, Kanagawa JAPAN, 240-0115 Fax: (81 46) 855 3808 http://www.ipcc-nggip.iges.or.jp

Printed in Japan ISBN 4-88788-032-4

VOLUME 2

ENERGY

Coordinating Lead Authors Amit Garg (India) and Tinus Pulles (Netherlands)

Review Editors Ian Carruthers (Australia), Art Jaques (Canada), and Freddy Tejada(Bolivia)

Table of Contents

Contents Volume 2

Energy

Chapter 1

Introduction

Chapter 2

Stationary Combustion

Chapter 3

Mobile Combustion

Chapter 4

Fugitive Emissions

Chapter 5

Carbon Dioxide Transport, Injection and Geological Storage

Chapter 6

Reference Approach

Annex 1

Worksheets

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Energy.v

Chapter 1: Introduction

CHAPTER 1

INTRODUCTION

2006 IPCC Guidelines for National Greenhouse Gas Inventories

1.1

Volume 2: Energy

Authors Amit Garg (India), Kainou Kazunari (Japan), and Tinus Pulles (Netherlands),

1.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction

Contents 1

Introduction 1.1

Introduction ...........................................................................................................................................1.5

1.2

Source categories...................................................................................................................................1.5

1.3

Methodological approaches...................................................................................................................1.6

1.3.1

Emissions from fossil fuel combustion .........................................................................................1.6

1.3.1.1

Tiers .........................................................................................................................................1.6

1.3.1.2

Selecting tiers: a general decision tree.....................................................................................1.8

1.3.1.3

Relation to other inventory approaches .................................................................................1.10

1.3.2

Fugitive emissions.......................................................................................................................1.10

1.3.3

CO2 capture and storage ..............................................................................................................1.11

1.4

Data collection issues ..........................................................................................................................1.11

1.4.1 1.4.1.1

Fuel definitions ......................................................................................................................1.11

1.4.1.2

Conversion of energy units ....................................................................................................1.16

1.4.1.3

Activity data sources..............................................................................................................1.17

1.4.1.4

Time series consistency .........................................................................................................1.20

1.4.2

1.5

Activity data ................................................................................................................................1.11

Emission factors ..........................................................................................................................1.20

1.4.2.1

CO2 emission factors .............................................................................................................1.20

1.4.2.2

Other greenhouse gases .........................................................................................................1.24

1.4.2.3

Indirect greenhouse gases ......................................................................................................1.24

Uncertainty in inventory estimates......................................................................................................1.25

1.5.1

General ........................................................................................................................................1.25

1.5.2

Activity data uncertainties...........................................................................................................1.25

1.5.3

Emission factor uncertainties ......................................................................................................1.25

1.6

QA/QC and completeness ...................................................................................................................1.27

1.6.1

Reference Approach ....................................................................................................................1.27

1.6.2

Potential double counting between sectors..................................................................................1.28

1.6.2.1

Non-energy use of fuels.........................................................................................................1.28

1.6.2.2

Waste as a fuel .......................................................................................................................1.28

1.6.3

Mobile versus stationary combustion ..........................................................................................1.28

1.6.4

National boundaries.....................................................................................................................1.28

1.6.5

New sources ................................................................................................................................1.29

References

.....................................................................................................................................................1.29

2006 IPCC Guidelines for National Greenhouse Gas Inventories

1.3

Volume 2: Energy

Figures Figure 1.1

Activity and source structure in the energy sector.................................................................1.7

Figure 1.2

Generalised decision tree for estimating emissions from fuel combustion............................1.9

Figure 1.3

Some typical examples of probability distribution functions (PDFs) for the effective CO2 emission factors for the combustion of fuels.................................................1.26

Tables Table 1.1

Definitions of fuel types used in the 2006 IPCC Guidelines...............................................1.12

Table 1.2

Default net calorific values (NCVs) and lower and upper limits of the 95 percent confidence intervals ............................................................................................................1.18

Table 1.3

Default values of carbon content ........................................................................................1.21

Table 1.4

Default CO2 emission factors for combustion ....................................................................1.23

Box Box 1.1

1.4

Conversion between gross and net calorific values .............................................................1.17

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction

1 INTRODUCTION 1.1

INTRODUCTION

Energy systems are for most economies largely driven by the combustion of fossil fuels. During combustion the carbon and hydrogen of the fossil fuels are converted mainly into carbon dioxide (CO2) and water (H2O), releasing the chemical energy in the fuel as heat. This heat is generally either used directly or used (with some conversion losses) to produce mechanical energy, often to generate electricity or for transportation. The energy sector is usually the most important sector in greenhouse gas emission inventories, and typically contributes over 90 percent of the CO2 emissions and 75 percent of the total greenhouse gas emissions in developed countries. CO2 accounts typically for 95 percent of energy sector emissions with methane and nitrous oxide responsible for the balance. Stationary combustion is usually responsible for about 70 percent of the greenhouse gas emissions from the energy sector. About half of these emissions are associated with combustion in energy industries mainly power plants and refineries. Mobile combustion (road and other traffic) causes about one quarter of the emissions in the energy sector.

1.2

SOURCE CATEGORIES

The energy sector mainly comprises: •

•

exploration and exploitation of primary energy sources,

•

transmission and distribution of fuels

•

conversion of primary energy sources into more useable energy forms in refineries and power plants

use of fuels in stationary and mobile applications.

Emissions arise from these activities by combustion and as fugitive emissions, or escape without combustion. For inventory purposes, fuel combustion may be defined as the intentional oxidation of materials within an apparatus that is designed to provide heat or mechanical work to a process, or for use away from the apparatus. This definition aims to separate the combustion of fuels for distinct and productive energy use from the heat released from the use of hydrocarbons in chemical reactions in industrial processes, or from the use of hydrocarbons as industrial products. It is good practice to apply this definition as fully as possible but there are cases where demarcation with the industrial processes and product use (IPPU) sector is needed. The following principle has been adopted for this: Combustion emissions from fuels obtained directly or indirectly from the feedstock for an IPPU process will normally be allocated to the part of the source category in which the process occurs. These source categories are normally 2B and 2C. However, if the derived fuels are transferred for combustion in another source category, the emissions should be reported in the appropriate part of Energy Sector source categories (normally 1A1 or 1A2). Please refer to Box 1.1 and section 1.3.2 in chapter 1 of the IPPU Volume for examples and further details. When the total emissions from the gases are calculated, the quantity transferred to the energy sector should be noted as an information item under IPPU source category and reported in the relevant energy sector source category to avoid double counting. Typically, only a few percent of the emissions in the energy sector arise as fugitive emissions from extraction, transformation and transportation of primary energy carriers. Examples are leakage of natural gas and the emissions of methane during coal mining and flaring during oil/gas extraction and refining1. In some cases where countries produce or transport significant quantities of fossil fuels, fugitive emissions can make a much larger contribution to the national total. Combustion and fugitive emissions from production, processing and handling of oil and gas should be allocated according to the national territory of the facilities including offshore areas (see Chapter 8 - section 8.2.1 in Vol. 1). These offshore areas may be an economic zone agreed upon with other countries.

1

Note that the combustion emissions due to transport of energy carriers by ship, rail and road are included in the mobile combustion processes.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

Figure 1.1 shows the structure of activities and source categories within the energy sector. This structure is based on the coding and naming as defined in the 1996 IPCC Guidelines and the Common Reporting Format (CRF) used by the UNFCCC. The technical chapters of this Volume follow this source category structure.

1.3

METHODOLOGICAL APPROACHES

1.3.1

Emissions from fossil fuel combustion

There are three Tiers presented in the 2006 IPCC Guidelines for estimating emissions from fossil fuel combustion. In addition a Reference Approach is presented. It can be used as an independent check of the sectoral approach and to produce a first-order estimate of national greenhouse gas emissions if only very limited resources and data structures are available to the inventory compiler. The 2006 IPCC Guidelines estimate carbon emissions in terms of the species which are emitted. During the combustion process, most carbon is immediately emitted as CO2. However, some carbon is released as carbon monoxide (CO), methane (CH4) or non-methane volatile organic compounds (NMVOCs). Most of the carbon emitted as these non-CO2 species eventually oxidises to CO2 in the atmosphere. This amount can be estimated from the emissions estimates of the non-CO2 gases (See Volume 1, Chapter 7). In the case of fuel combustion, the emissions of these non-CO2 gases contain very small amounts of carbon compared to the CO2 estimate and, at Tier 1, it is more accurate to base the CO2 estimate on the total carbon in the fuel. This is because the total carbon in the fuel depends on the fuel alone, while the emissions of the nonCO2 gases depend on many factors such as technologies, maintenance etc which, in general, are not well known. At higher tiers, the amount of carbon in these non-CO2 gases can be accounted for. Since CO2 emissions are independent of combustion technology whilst CH4 and N2O emissions are strongly dependent on the technology, this chapter only provides default emission factors for CO2 that are applicable to all combustion processes, both stationary and mobile. Default emission factors for the other gases are provided in subsequent chapters of this volume, since combustion technologies differ widely between source categories within the source sector “Combustion” and hence will vary between these subsectors.

1.3.1.1

T IERS

TIER 1 The Tier 1 method is fuel-based, since emissions from all sources of combustion can be estimated on the basis of the quantities of fuel combusted (usually from national energy statistics) and average emission factors. Tier 1 emission factors are available for all relevant direct greenhouse gases. The quality of these emission factors differs between gases. For CO2, emission factors mainly depend upon the carbon content of the fuel. Combustion conditions (combustion efficiency, carbon retained in slag and ashes etc.) are relatively unimportant. Therefore, CO2 emissions can be estimated fairly accurately based on the total amount of fuels combusted and the averaged carbon content of the fuels. However, emission factors for methane and nitrous oxide depend on the combustion technology and operating conditions and vary significantly, both between individual combustion installations and over time. Due to this variability, use of averaged emission factors for these gases, that must account for a large variability in technological conditions, will introduce relatively large uncertainties.

TIER 2 In the Tier 2 method for energy, emissions from combustion are estimated from similar fuel statistics, as used in the Tier 1 method, but country-specific emission factors are used in place of the Tier 1 defaults. Since available country-specific emission factors might differ for different specific fuels, combustion technologies or even individual plants, activity data could be further disaggregated to properly reflect such disaggregated sources. If these country-specific emission factors indeed are derived from detailed data on carbon contents in different batches of fuels used or from more detailed information on the combustion technologies applied in the country, the uncertainties of the estimate should decrease, and the trends over time can be better estimated. If an inventory compiler has well documented measurements of the amount of carbon emitted in non-CO2 gases or otherwise not oxidised, it can be taken into account in this tier in the country-specific emission factors. It is good practice to document how this has been done.

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Chapter 1: Introduction

Figure 1.1

Activity and source structure in the Energy Sector

.

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TIER 3 In the Tier 3 methods for energy, either detailed emission models or measurements and data at individual plant level are used where appropriate. Properly applied, these models and measurements should provide better estimates primarily for non-CO2 greenhouse gases, though at the cost of more detailed information and effort. Continuous emissions monitoring (CEM) of flue gases is generally not justified for accurate measurement of CO2 emissions only (because of the comparatively high cost) but could be undertaken particularly when monitors are installed for measurement of other pollutants such as SO2 or NOx. Continuous emissions monitoring is particularly useful for combustion of solid fuels where it is more difficult to measure fuel flow rates, or when fuels are highly variable, or fuel analysis is otherwise expensive. Direct measurement of fuel flow, especially for gaseous or liquid fuels, using quality assured fuel flow meters may improve the accuracy of CO2 emission calculations for sectors using these fuel flow meters. When considering using measurement data, it is good practice to assess the representativeness of the sample and suitability of measurement method. The best measurement methods are those that have been developed by official standards organisations and field-tested to determine their operational characteristics. For further information on the usage of measured data, check Chapter 2, Approaches to Data Collection in Volume 1. It should be noted that additional types of uncertainties are introduced through the use of such models and measurements should therefore be well validated, including a comparison of calculated fuel consumption with energy statistics, thorough assessments of their uncertainties and systematic errors, as described in Volume 1, Chapter 6. If an inventory compiler has well documented measurements of the amount of carbon emitted in non-CO2 gases or otherwise not oxidised, it can be taken into account in this tier in the country-specific emission factors. It is good practice to document how this has been done. If emission estimates are based on measurements then they will already include the direct emissions of CO2 only.

1.3.1.2

S ELECTING

TIERS : A GENERAL DECISION TREE

For each source category and greenhouse gas, the inventory compiler has a choice of applying different methods, as described in the Tiers for the source category and gas. The inventory compiler could use different tiers for different source categories, depending on the importance of the source category within the national total (cf. key categories Chapter 4 of Volume 1) and the availability of resources in terms of time, work force, sophisticated models, and budget. To perform a key category analysis, data on the relative importance of each source category already calculated is required. This knowledge could be derived from an earlier inventory, and updated if necessary. Figure 1.2 presents a generalized decision tree for selecting Tiers for fuel combustion. This decision tree applies in general for each of the fuel combustion activities and for each of the gases. The measurements referred to in this decision tree should be considered as continuous measurements. Continuous measurements are becoming more widely available and this increase in availability is in part driven by regulatory pressure and emissions trading. The decision tree allows available emission measurements to be used (Tier 3) in combination with a Tier 2 or Tier 1 estimate within the same activity. Measurements will typically be available only for larger industrial sources and hence only occur in stationary combustion. For CO2, particularly for gaseous and liquid fuels, such measurements should in most cases preferably be used to determine the carbon content of the fuel before combustion, whereas for other gases stack measurements could be applied. For some inhomogeneous solid fuels, stack measurements might provide more precise emission data. Particularly for road transport, using a Tier 2 or Tier 3 technology-specific method for estimating N2O and CH4 emissions will usually bring large benefits. However, for CO2 in general, a Tier 1 method based on fuel carbon and fuel amount used will often suffice. This means that the generalized decision tree might result in different approaches for different gases for the same source category. Since emission models and technology-specific methods for road transport might be based on vehicle kilometres travelled rather than on fuel used, it is good practice to show that the activity data applied in such models and higher tier methods are consistent with the fuel sales data. These fuel sales data are likely to be used to estimate CO2 emissions from road transport. The decision tree allows the inventory compiler to use sophisticated models in combination with any other Tier methodology, including measurements, provided that the model is consistent with the fuel combustion statistics. In cases where a discrepancy between fuel sales and vehicle kilometres travelled is detected, the activity data, used in the technology-specific method should be adjusted to match fuel sales statistics, unless it can be shown that the fuel sales statistics are inaccurate.

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Chapter 1: Introduction

Figure 1.2

Generalised decision tree for estimating emissions from fuel combustion Start

Are emissions measurements available with satisfactory QC?

Are all single sources in the source category measured?

Yes

Use measurements Tier 3 approach.

Yes

No Is specific fuel use available for the category?

Yes

Are countryspecific EFs available for the unmeasured part of the key category?

No

No Does the unmeasured part belong to a key category?

No

Is a detailed estimation model available?

Can the fuel consumption estimated by the model be reconciled with national fuel statistics or be verified by independent sources?

Yes

No

No No

Yes

Are country-specific EFs available? Yes

No Is this a key category?

Yes

Use measurements Tier 3 approach and combine with AD and countryspecific EFs Tier 2 approach.

Yes

Yes

Get CountrySpecific Data

Use measurements Tier 3 approach and combine with AD and default EFs Tier 1 approach.

Use model Tier 3 approach.

Use countryspecific EFs and suitable AD Tier 2 approach.

Get countryspecific data. No

Use default EFs and suitable AD Tier 1 approach.

Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees.

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1.3.1.3

R ELATION

TO OTHER INVENTORY APPROACHES

The IPCC Guidelines for National Greenhouse Gas Inventories are specifically designed for countries to prepare and report inventories of greenhouse gases. Some countries may also be required to submit emission inventories of various gases from the Energy Sector to United Nations Economic Commission for Europe (UNECE) Long Range Transboundary Air Pollution (LRTAP) Convention 2 . The UNECE has adopted the joint European Monitoring Evaluation Programme (EMEP)/CORINAIR Emission Inventory Guidebook 3 for inventory reporting. Countries which are Parties to different Conventions have to use the appropriate reporting procedures when reporting to a specific Convention. The IPCC approach meets UNFCCC needs for calculating national totals (without further spatial resolution) and identifying sectors within which emissions occur, whereas the EMEP/CORINAIR approach is technology based and includes spatial allocation of emissions (point and area sources). Both systems follow the same basic principles: •

•

complete coverage of anthropogenic emissions (CORINAIR also considers natural emissions);

•

clear distinction between energy and non-energy related emissions;

annual source category totals of national emissions;

•

transparency and full documentation permitting detailed verification of activity data and emission factors.

Considerable progress has been made in the harmonisation of the IPCC and EMEP/CORINAIR approaches. UNECE LRTAP reporting now has accepted a source category split that is fully compatible with the UNFCCC split as defined in the Common Reporting Framework (CRF). Differences only occur in the level of aggregation for some specific sources. Such differences only occur in the energy sector in the transport source categories, where UNECE LRTAP requires further detail in the emissions from road transport. The CORINAIR programme has developed its approach further to include additional sectors and sub-divisions so that a complete CORINAIR inventory, including emission estimates, can be used to produce reports in both the UNFCCC/IPCC or EMEP/CORINAIR reporting formats for submission to their respective Conventions. Minor adjustments based on additional local knowledge may be necessary to complete such reports for submission. One significant difference between the approaches that remain is the spatial allocation of road transport emissions: while CORINAIR, with a view to the input requirements of atmospheric dispersion models, applies the principle of territoriality (emission allocation according to fuel consumption), the 2006 IPCC Guidelines follow what is usually the most accurate data: fuel sales (usually fuel sales are more accurate than vehicle kilometres). In the context of these IPCC Guidelines, countries with a substantial disparity between emissions as calculated from fuel sales and from fuel consumption have the option of estimating true consumption and reporting the emissions from consumption and trade separately using appropriate higher tier methods. National totals must be consistent with fuel sales. Since both approaches are now generally well harmonised, the 2006 IPCC Guidelines will concentrate on emissions of direct greenhouse gases, CO2, CH4 and N2O with some advice on NMVOCs where these are closely linked to emissions of direct greenhouse gases (non-energy use of fuels, CO2 inputs to the atmosphere from oxidation of NMVOCs). Users are referred to the EMEP/CORINAIR Emission Inventory Guidebook for emission estimation methods for indirect greenhouse gases and other air pollutants.

1.3.2

Fugitive emissions

This volume provides methodologies for the estimation of fugitive emissions of CO2, CH4 and N2O. Methodologies for estimating fugitive emissions from the Energy Sector are very different from those used for fossil fuel combustion. Fugitive emissions tend to be diffuse and may be difficult to monitor directly. In addition, the methods are quite specific to the type of emission release. For example, methods for coal mining are linked

2

There are 49 parties to the UNECE Convention on Long-range Transboundary Air Pollution including USA, Canada, most of Europe including Russia, Armenia and Georgia and some central Asian countries such as Kazakhstan and Kyrgyzstan.

3

See EEA 2005.

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Chapter 1: Introduction

to the geological characteristics of the coal seams, whereas methods for fugitive leaks from oil and gas facilities are linked to common types of equipment. There can be anthropogenic emissions associated with the use of geothermal power. At this stage no methodology to estimate these emissions is available. However if these emissions can be measured, they should be reported in source category 1.B.3 “Other emissions from energy production”.

1.3.3

CO 2 capture and storage

According to the IPCC Third Assessment Report, over the 21st century substantial amounts of CO2 emissions need to be avoided to achieve stabilization of atmospheric greenhouse gas concentrations. CO2 capture and storage (CCS) will be one of the options in the portfolio of measures for stabilization of greenhouse gas concentrations while the use of fossil fuels continues. Chapter 5 of this volume presents an overview of the CCS system and provides emission estimation methods for CO2 capture, CO2 transport, CO2 injection and underground CO2 storage. It is good practice for inventory compilers to ensure that the CCS system is handled in a complete and consistent manner across the entire Energy Sector.

1.4

DATA COLLECTION ISSUES

1.4.1

Activity data

In the Energy sector, the activity data are typically the amounts of fuels combusted. Such data are sufficient to perform a Tier 1 analysis. In higher Tier approaches additional data are required on fuel characteristics and the combustion technologies applied. In order to ensure transparency and comparability, a consistent classification scheme for fuel types need to be used. This section provides: 1.

definitions of the different fuels

2.

the units in which to express the activity data

3.

guidance on possible sources of activity data and

4.

guidance on time series consistency

A clear explanation of energy statistics and energy balances is provided in the “Energy Statistics Manual” of the International Energy Agency (IEA)4.

1.4.1.1

F UEL

DEFINITIONS

Common terms and definitions of fuels are necessary for countries to describe emissions from fuel combustion activities, consistently. A list of fuel types based primarily on the definitions of the International Energy Agency (IEA) is provided below. These definitions are used in the 2006 IPCC Guidelines.

4

OECD/IEA Energy Statistics Manual (2004), OECD/IEA, Paris. This publication can be downloaded for free at www.iea.org .

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TABLE 1.1 DEFINITIONS OF FUEL TYPES USED IN THE 2006 IPCC GUIDELINES English Description

Comments

LIQUID (Crude oil and petroleum products)

Orimulsion

A tar-like substance that occurs naturally in Venezuela. It can be burned directly or refined into light petroleum products.

Natural Gas Liquids (NGLs)

NGLs are the liquid or liquefied hydrocarbons produced in the manufacture, purification and stabilisation of natural gas. These are those portions of natural gas which are recovered as liquids in separators, field facilities, or gas processing plants. NGLs include but are not limited to ethane, propane, butane, pentane, natural gasoline and condensate. They may also include small quantities of non-hydrocarbons.

Gasoline

Crude Oil

Crude oil is a mineral oil consisting of a mixture of hydrocarbons of natural origin, being yellow to black in colour, of variable density and viscosity. It also includes lease condensate (separator liquids) which are recovered from gaseous hydrocarbons in lease separation facilities.

Motor Gasoline

This is light hydrocarbon oil for use in internal combustion engines such as motor vehicles, excluding aircraft. Motor gasoline is distilled between 35ºC and 215ºC and is used as a fuel for land based spark ignition engines. Motor gasoline may include additives, oxygenates and octane enhancers, including lead compounds such as TEL (Tetraethyl lead) and TML (Tetramethyl lead).

Aviation Gasoline

Aviation gasoline is motor spirit prepared especially for aviation piston engines, with an octane number suited to the engine, a freezing point of -60ºC, and a distillation range usually within the limits of 30ºC and 180ºC.

Jet Gasoline

This includes all light hydrocarbon oils for use in aviation turbine power units. They distil between 100ºC and 250ºC. It is obtained by blending kerosenes and gasoline or naphthas in such a way that the aromatic content does not exceed 25 percent in volume, and the vapour pressure is between 13.7 kPa and 20.6 kPa. Additives can be included to improve fuel stability and combustibility.

Jet Kerosene

This is medium distillate used for aviation turbine power units. It has the same distillation characteristics and flash point as kerosene (between 150ºC and 300ºC but not generally above 250ºC). In addition, it has particular specifications (such as freezing point) which are established by the International Air Transport Association (IATA).

Other Kerosene

Kerosene comprises refined petroleum distillate intermediate in volatility between gasoline and gas/diesel oil. It is a medium oil distilling between 150ºC and 300ºC.

Shale Oil

A mineral oil extracted from oil shale.

Gas/Diesel Oil

Gas/diesel oil includes heavy gas oils. Gas oils are obtained from the lowest fraction from atmospheric distillation of crude oil, while heavy gas oils are obtained by vacuum redistillation of the residual from atmospheric distillation. Gas/diesel oil distils between 180ºC and 380ºC. Several grades are available depending on uses: diesel oil for diesel compression ignition (cars, trucks, marine, etc.), light heating oil for industrial and commercial uses, and other gas oil including heavy gas oils which distil between 380ºC and 540ºC and are used as petrochemical feedstocks.

Residual Fuel Oil

This heading defines oils that make up the distillation residue. It comprises all residual fuel oils, including those obtained by blending. Its kinematic viscosity is above 0.1cm2 (10 cSt) at 80ºC. The flash point is always above 50ºC and the density is always more than 0.90 kg/l.

Liquefied Petroleum Gases

These are the light hydrocarbons fraction of the paraffin series, derived from refinery processes, crude oil stabilisation plants and natural gas processing plants comprising propane (C3H8) and butane (C4H10) or a combination of the two. They are normally liquefied under pressure for transportation and storage.

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TABLE 1.1 (CONTINUED) DEFINITIONS OF FUEL TYPES USED IN THE 2006 IPCC GUIDELINES English Description

Comments

LIQUID (Crude oil and petroleum products) Ethane

Ethane is a naturally gaseous straight-chain hydrocarbon (C2H6). It is a colourless paraffinic gas which is extracted from natural gas and refinery gas streams.

Naphtha

Naphtha is a feedstock destined either for the petrochemical industry (e.g. ethylene manufacture or aromatics production) or for gasoline production by reforming or isomerisation within the refinery. Naphtha comprises material in the 30ºC and 210ºC distillation range or part of this range.

Bitumen

Solid, semi-solid or viscous hydrocarbon with a colloidal structure, being brown to black in colour, obtained as a residue in the distillation of crude oil, vacuum distillation of oil residues from atmospheric distillation. Bitumen is often referred to as asphalt and is primarily used for surfacing of roads and for roofing material. This category includes fluidised and cut back bitumen.

Lubricants

Lubricants are hydrocarbons produced from distillate or residue; they are mainly used to reduce friction between bearing surfaces. This category includes all finished grades of lubricating oil, from spindle oil to cylinder oil, and those used in greases, including motor oils and all grades of lubricating oil base stocks.

Petroleum Coke

Petroleum coke is defined as a black solid residue, obtained mainly by cracking and carbonising of petroleum derived feedstocks, vacuum bottoms, tar and pitches in processes such as delayed coking or fluid coking. It consists mainly of carbon (90 to 95 percent) and has a low ash content. It is used as a feedstock in coke ovens for the steel industry, for heating purposes, for electrode manufacture and for production of chemicals. The two most important qualities are "green coke" and "calcinated coke". This category also includes "catalyst coke" deposited on the catalyst during refining processes: this coke is not recoverable and is usually burned as refinery fuel.

Refinery Feedstocks

A refinery feedstock is a product or a combination of products derived from crude oil and destined for further processing other than blending in the refining industry. It is transformed into one or more components and/or finished products. This definition covers those finished products imported for refinery intake and those returned from the petrochemical industry to the refining industry. Refinery gas is defined as non-condensable gas obtained during distillation of crude oil or treatment of oil products (e.g. cracking) in refineries. It consists mainly of hydrogen, methane, ethane and olefins. It also includes gases which are returned from the petrochemical industry.

Waxes

Saturated aliphatic hydrocarbons (with the general formula CnH2n+2). These waxes are residues extracted when dewaxing lubricant oils, and they have a crystalline structure with carbon number greater than 12. Their main characteristics are that they are colourless, odourless and translucent, with a melting point above 45ºC.

White Spirit & SBP

White spirit and SBP are refined distillate intermediates with a distillation in the naphtha/kerosene range. They are sub-divided as: i) Industrial Spirit (SBP): Light oils distilling between 30ºC and 200ºC, with a temperature difference between 5 percent volume and 90 percent volume distillation points, including losses, of not more than 60ºC. In other words, SBP is a light oil of narrower cut than motor spirit. There are 7 or 8 grades of industrial spirit, depending on the position of the cut in the distillation range defined above. ii) White Spirit: Industrial spirit with a flash point above 30ºC. The distillation range of white spirit is 135ºC to 200ºC.

Other Petroleum Products

Includes the petroleum products not classified above, for example: tar, sulphur, and grease. This category also includes aromatics (e.g. BTX or benzene, toluene and xylene) and olefins (e.g. propylene) produced within refineries.

Other Oil

Refinery Gas

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TABLE 1.1 (CONTINUED) DEFINITIONS OF FUEL TYPES USED IN THE 2006 IPCC GUIDELINES English Description

Comments

SOLID (Coal and coal products)

Coking Coal

Coking coal refers to bituminous coal with a quality that allows the production of a coke suitable to support a blast furnace charge. Its gross calorific value is greater than 23 865 kJ/kg (5 700 kcal/kg) on an ash-free but moist basis.

Other Bituminous Coal

Other bituminous coal is used for steam raising purposes and includes all bituminous coal that is not included under coking coal. It is characterized by higher volatile matter than anthracite (more than 10 percent) and lower carbon content (less than 90 percent fixed carbon). Its gross calorific value is greater than 23 865 kJ/kg (5 700 kcal/kg) on an ash-free but moist basis.

Sub-Bituminous Coal

Non-agglomerating coals with a gross calorific value between 17 435 kJ/kg (4 165 kcal/kg) and 23 865 kJ/kg (5 700 kcal/kg) containing more than 31 percent volatile matter on a dry mineral matter free basis.

Lignite

Lignite/brown coal is a non-agglomerating coal with a gross calorific value of less than 17 435 kJ/kg (4 165 kcal/kg), and greater than 31 percent volatile matter on a dry mineral matter free basis.

Oil Shale and Tar Sands

Oil shale is an inorganic, non-porous rock containing various amounts of solid organic material that yields hydrocarbons, along with a variety of solid products, when subjected to pyrolysis (a treatment that consists of heating the rock at high temperature). Tar sands refers to sand (or porous carbonate rocks) that are naturally mixed with a viscous form of heavy crude oil sometimes referred to as bitumen. Due to its high viscosity this oil cannot be recovered through conventional recovery methods.

Brown Coal Briquettes

Brown coal briquettes (BKB) are composition fuels manufactured from lignite/brown coal, produced by briquetting under high pressure. These figures include dried lignite fines and dust.

Patent Fuel

Patent fuel is a composition fuel manufactured from hard coal fines with the addition of a binding agent. The amount of patent fuel produced may, therefore, be slightly higher than the actual amount of coal consumed in the transformation process.

Coke

Anthracite

Anthracite is a high rank coal used for industrial and residential applications. It has generally less than 10 percent volatile matter and a high carbon content (about 90 percent fixed carbon). Its gross calorific value is greater than 23 865 kJ/kg (5 700 kcal/kg) on an ash-free but moist basis.

Coke Oven Coke and Lignite Coke Gas Coke

Derived Gases

Gas coke is a by-product of hard coal used for the production of town gas in gas works. Gas coke is used for heating purposes. The result of the destructive distillation of bituminous coal. Coal tar is the liquid by-product of the distillation of coal to make coke in the coke oven process. Coal tar can be further distilled into different organic products (e.g. benzene, toluene, naphthalene) which normally would be reported as a feedstock to the petrochemical industry.

Coal Tar

1.14

Coke oven coke is the solid product obtained from the carbonisation of coal, principally coking coal, at high temperature. It is low in moisture content and volatile matter. Also included are semi-coke, a solid product obtained from the carbonisation of coal at a low temperature, lignite coke, semi-coke made from lignite/brown coal, coke breeze and foundry coke. Coke oven coke is also known as metallurgical coke.

Gas Works Gas

Gas works gas covers all types of gases produced in public utility or private plants, whose main purpose is manufacture, transport and distribution of gas. It includes gas produced by carbonization (including gas produced by coke ovens and transferred to gas works gas), by total gasification with or without enrichment with oil products (LPG, residual fuel oil, etc.), and by reforming and simple mixing of gases and/or air. It excludes blended natural gas, which is usually distributed through the natural gas grid.

Coke Oven Gas

Coke oven gas is obtained as a by-product of the manufacture of coke oven coke for the production of iron and steel.

Blast Furnace Gas

Blast furnace gas is produced during the combustion of coke in blast furnaces in the iron and steel industry. It is recovered and used as a fuel partly within the plant and partly in other steel industry processes or in power stations equipped to burn it.

Oxygen Steel Furnace Gas

Oxygen steel furnace gas is obtained as a by-product of the production of steel in an oxygen furnace and is recovered on leaving the furnace. The gas is also known as converter gas, LD gas or BOS gas.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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TABLE 1.1 (CONTINUED) DEFINITIONS OF FUEL TYPES USED IN THE 2006 IPCC GUIDELINES English Description

Comments

GAS (Natural Gas)

Natural Gas

Natural gas should include blended natural gas (sometimes also referred to as Town Gas or City Gas), a high calorific value gas obtained as a blend of natural gas with other gases derived from other primary products, and usually distributed through the natural gas grid (eg coal seam methane). Blended natural gas should include substitute natural gas, a high calorific value gas, manufactured by chemical conversion of a hydrocarbon fossil fuel, where the main raw materials are: natural gas, coal, oil and oil shale.

OTHER FOSSIL FUELS Municipal Wastes (non-biomass fraction)

Non-biomass fraction of municipal waste includes waste produced by households, industry, hospitals and the tertiary sector which are incinerated at specific installations and used for energy purposes. Only the fraction of the fuel that is non-biodegradable should be included here.

Industrial Wastes

Industrial waste consists of solid and liquid products (e.g. tyres) combusted directly, usually in specialised plants, to produce heat and/or power and that are not reported as biomass.

Waste Oils

Waste oils are used oils (e.g. waste lubricants) that are combusted for heat production.

PEAT Combustible soft, porous or compressed, sedimentary deposit of plant origin including woody material with high water content (up to 90 percent in the raw state), easily cut, can contain harder pieces of light to dark brown colour. Peat used for non-energy purposes is not included.

Peat 5

Solid Biofuels

BIOMASS

5

Wood/Wood Waste

Wood and wood waste combusted directly for energy. This category also includes wood for charcoal production but not the actual production of charcoal (this would be double counting since charcoal is a secondary product).

Sulphite Lyes (Black Liquor)

Sulphite lyes is an alkaline spent liquor from the digesters in the production of sulphate or soda pulp during the manufacture of paper where the energy content derives from the lignin removed from the wood pulp. This fuel in its concentrated form is usually 65-70 percent solid.

Other Primary Solid Biomass

Other primary solid biomass includes plant matter used directly as fuel that is not already included in wood/wood waste or in sulphite lyes. Included are vegetal waste, animal materials/wastes and other solid biomass. This category includes non-wood inputs to charcoal production (e.g. coconut shells) but all other feedstocks for production of biofuels should be excluded.

Charcoal

Charcoal combusted as energy covers the solid residue of the destructive distillation and pyrolysis of wood and other vegetal material.

Although peat is not strictly speaking a fossil fuel, its greenhouse gas emission characteristics have been shown in life cycle studies to be comparable to that of fossil fuels (Nilsson and Nilsson, 2004; Uppenberg et al., 2001; Savolainen et al., 1994). Therefore, the CO2 emissions from combustion of peat are included in the national emissions as for fossil fuels.

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TABLE 1.1 (CONTINUED) DEFINITIONS OF FUEL TYPES USED IN THE 2006 IPCC GUIDELINES

Biogasoline should only contain that part of the fuel that relates to the quantities of biofuel and not to the total volume of liquids into which the biofuels are blended. This category includes bioethanol (ethanol produced from biomass and/or the biodegradable fraction of waste), biomethanol (methanol produced from biomass and/or the biodegradable fraction of waste), bioETBE (ethyl-tertio-butyl-ether produced on the basis of bioethanol: the percentage by volume of bioETBE that is calculated as biofuel is 47 percent) and bioMTBE (methyl-tertio-butyl-ether produced on the basis of biomethanol: the percentage by volume of bioMTBE that is calculated as biofuel is 36 percent).

Biodiesels

Biodiesels should only contain that part of the fuel that relates to the quantities of biofuel and not to the total volume of liquids into which the biofuels are blended. This category includes biodiesel (a methyl-ester produced from vegetable or animal oil, of diesel quality), biodimethylether (dimethylether produced from biomass), fischer tropsh (fischer tropsh produced from biomass), cold pressed bio oil (oil produced from oil seed through mechanical processing only) and all other liquid biofuels which are added to, blended with or used straight as transport diesel.

Other Liquid Biofuels

Other liquid biofuels not included in biogasoline or biodiesels.

Landfill Gas

Landfill gas is derived from the anaerobic fermentation of biomass and solid wastes in landfills and combusted to produce heat and/or power.

Sludge Gas

Sludge gas is derived from the anaerobic fermentation of biomass and solid wastes from sewage and animal slurries and combusted to produce heat and/or power.

Other Biogas

Other biogas not included in landfill gas or sludge gas.

Municipal Wastes (biomass fraction)

Biomass fraction of municipal waste includes waste produced by households, industry, hospitals and the tertiary sector which are incinerated at specific installations and used for energy purposes. Only the fraction of the fuel that is biodegradable should be included here.

Liquid Biofuels

Biogasoline

Gas Biomass

Comments

Other non-fossil fuels

English Description

1.4.1.2

C ONVERSION

OF ENERGY UNITS

In energy statistics and other energy data compilations, production and consumption of solid, liquid and gaseous fuels are specified in physical units, e.g. in tonnes or cubic metres. To convert these data to common energy units, eg joules, requires calorific values. To convert tonnes to energy units, in this case terajoules, requires calorific values. These Guidelines use net calorific values (NCVs), expressed in SI units or multiples of SI units (for example TJ/Mg). Some statistical offices use gross calorific values (GCV). The difference between NCV and GCV is the latent heat of vaporisation of the water produced during combustion of the fuel. As a consequence for coal and oil, the NCV is about 5 percent less than the GCV For most forms of natural and manufactured gas, the NCV is about 10 percent less. The Box 1.1 below provides an algorithm for the conversion if fuel characteristics (moisture, hydrogen and oxygen contents) are known. For common biomass fuels default conversion from NCV to GCV especially bark, wood and wood waste are derived in the Pulp and Paper Greenhouse Gas Calculation Tools available via the WRI/WBCSD Greenhouse Gas Protocol web site6. If countries use GCV, they should identify them as such. For further explanations of this issue and how to convert from the one into the other, please consult the IEA’s Energy Statistics Manual (OECD/IEA, 2004).

6

See page 9 of "Calculation Tools for Estimating Greenhouse Gas Emissions from Pulp and Paper Mills, Version 1.1, July 8, 2005" page 9 available at http://www.ghgprotocol.org/includes/getTarget.asp?type=d&id=MTYwNjQ

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BOX 1.1 CONVERSION BETWEEN GROSS AND NET CALORIFIC VALUES

Units: MJ/kg - Megajoules per kilogram; 1 MJ/kg = 1 Gigajoule/tonne (GJ/tonne) Gross CV (GCV) or ‘higher heating value’ (HHV) is the calorific value under laboratory conditions. Net CV (NCV) or 'lower heating value' (LHV) is the useful calorific value in boiler plant. The difference is essentially the latent heat of the water vapour produced. Conversions - Gross/Net (per ISO, for As Received* figures) in MJ/kg:

Net CV = Gross CV − 0.212 H − 0.0245M − 0.008Y

where M is percent Moisture, H is percent Hydrogen, Y is percent Oxygen (from ultimate analysis which determines the amount of carbon, hydrogen, oxygen, nitrogen and sulphur) As Received (i.e. includes Total Moisture (TM)). Source: World Coal Institute (http://www.worldcoal.org/pages/content/index.asp?PageID=190), which provides more details.

Default NCV values to convert from units of 103 tonnes to units of terajoules are in Table 1.2. These values are based on a statistical analysis of three data sources: 1.

Annual greenhouse gas inventory submissions of Annex I Parties: UNFCCC Annex-1 countries’ national submissions in 2004 on 2002 emissions (Table-1A(b) of the CRF). This dataset contains Net Calorific Values (NCVs), Carbon Emission Factor (CEF) and Carbon Oxidation Factor (COF) for individual fuels for more than 33 Annex 1 countries.

2.

Emission Factor Database: The IPCC Emission Factor Database (EFDB), version-1, as of December 2003 contains all default values included in the 1996 IPCC Guidelines and additional data accepted by the EFDB editorial board. The EFDB contains country-specific data for NCV and CEF including developing countries.

3.

IEA Database: International Energy Agency NCV database for all fuels, as of November 2004. The IEA database contains country-specific NCV data for many countries, including developing countries.

The statistical analysis performed on these datasets has been described in detail in a separate document (Kainou, 2005). The same data set was used to compile a table of default values and uncertainty ranges.

1.4.1.3

A CTIVITY

DATA SOURCES

Fuel statistics collected by an officially recognised national body are usually the most appropriate and accessible activity data. In some countries, however, those charged with the task of compiling inventory information may not have ready access to the entire range of data available within their country and may wish to use data specially provided by their country to the international organisations. There are currently two main sources of international energy statistics: the International Energy Agency (IEA), and the United Nations (UN). Both international organisations collect energy data from the national administrations of their member countries through systems of questionnaires. The data gathered are therefore “official” data. To avoid duplication of reporting, where countries are members of both organisations, the UN receives copies of the IEA questionnaires for the OECD member countries rather than requiring these countries to complete the UN questionnaires. When compiling its statistics of non-OECD member countries, the IEA, for certain countries, uses UN data to which it may add additional information obtained from the national administration, consultants or energy companies operating within the countries. Statistics for other countries are obtained directly from national sources. The number of countries covered by the IEA publications is fewer than that of the UN. 7

7

Approximately 130 countries (of about 170 UN Member countries) are included in the IEA data, and represent about 98 per cent of worldwide energy consumption and nearly all energy production.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

TABLE 1.2 DEFAULT NET CALORIFIC VALUES (NCVs) AND LOWER AND UPPER LIMITS OF THE 95% CONFIDENCE INTERVALS 1 Net calorific value (TJ/Gg)

Fuel type English description

Lower

Upper

42.3

40.1

44.8

Orimulsion

27.5

27.5

28.3

Natural Gas Liquids

44.2

40.9

46.9

Gasoline

Crude Oil

Motor Gasoline

44.3

42.5

44.8

Aviation Gasoline

44.3

42.5

44.8

Jet Gasoline

44.3

42.5

44.8

Jet Kerosene

44.1

42.0

45.0

Other Kerosene

43.8

42.4

45.2

Shale Oil

38.1

32.1

45.2

Gas/Diesel Oil

43.0

41.4

43.3

Residual Fuel Oil

40.4

39.8

41.7

Liquefied Petroleum Gases

47.3

44.8

52.2

Ethane

46.4

44.9

48.8

Naphtha

44.5

41.8

46.5

Bitumen

40.2

33.5

41.2

Lubricants

40.2

33.5

42.3

Petroleum Coke

32.5

29.7

41.9

Refinery Feedstocks

43.0

36.3

46.4

49.5

47.5

50.6

Paraffin Waxes

40.2

33.7

48.2

White Spirit and SBP

40.2

33.7

48.2

Other Petroleum Products

40.2

33.7

48.2

Anthracite

26.7

21.6

32.2

Coking Coal

28.2

24.0

31.0

Other Bituminous Coal

25.8

19.9

30.5

Other Oil

Refinery Gas

2

Sub-Bituminous Coal

18.9

11.5

26.0

Lignite

11.9

5.50

21.6

Oil Shale and Tar Sands

8.9

7.1

11.1

Brown Coal Briquettes

20.7

15.1

32.0

Patent Fuel

20.7

15.1

32.0

28.2

25.1

30.2

Coke

Coke Oven Coke and Lignite Coke Gas Coke

Coal Tar 3

Derived Gases

Gas Works Gas

4

Coke Oven Gas

5

28.2

25.1

30.2

28.0

14.1

55.0

38.7

19.6

77.0

38.7

19.6

77.0

Blast Furnace Gas 6

2.47

1.20

5.00

Oxygen Steel Furnace Gas 7

7.06

3.80

15.0

Natural Gas

48.0

46.5

50.4

Municipal Wastes (non-biomass fraction)

10

7

18

Industrial Wastes

NA

NA

NA

Waste Oil Peat

1.18

８

40.2

20.3

80.0

9.76

7.80

12.5

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction

TABLE 1.2 (CONTINUED) DEFAULT NET CALORIFIC VALUES (NCVs) AND LOWER AND UPPER LIMITS OF THE 95% CONFIDENCE INTERVALS 1 Net calorific value (TJ/Gg)

Lower

Upper

Wood/Wood Waste 9

15.6

7.90

31.0

Sulphite lyes (black liquor) 10

11.8

5.90

23.0

11.6

5.90

23.0

Solid Biofuels

Fuel type English description

Gas Biomass

Liquid Biofuels

Other nonfossil fuels

Other Primary Solid Biomass Charcoal

11

12

29.5

14.9

58.0

Biogasoline 13

27.0

13.6

54.0

Biodiesels 14 Other Liquid Biofuels 15

27.0

13.6

54.0

27.4

13.8

54.0

50.4

25.4

100

Landfill Gas Sludge Gas

16

17

50.4

25.4

100

Other Biogas 18

50.4

25.4

100

Municipal Wastes (biomass fraction)

11.6

6.80

18.0

Notes: 1

The lower and upper limits of the 95 percent confidence intervals, assuming lognormal distributions, fitted to a dataset, based on national inventory reports, IEA data and available national data. A more detailed description is given in section 1.5.

2

Japanese data; uncertainty range: expert judgement

3

EFDB; uncertainty range: expert judgement

4

Coke Oven Gas; uncertainty range: expert judgement

5-7 8

Japan and UK small number data; uncertainty range: expert judgement

For waste oils the values of "Lubricants" are taken

9

EFDB; uncertainty range: expert judgement

10

Japanese data ; uncertainty range: expert judgement

11

Solid Biomass; uncertainty range: expert judgement

12

EFDB; uncertainty range: expert judgement

13-14 15

Ethanol theoretical number; uncertainty range: expert judgement;

Liquid Biomass; uncertainty range: expert judgement

16 -18

Methane theoretical number uncertainty range: expert judgement;

In general, the IEA and UN data for a country can be obtained free of charge by that country’s national inventory agencies by contacting [email protected] or [email protected]. Two types of fuels deserve special attention: Biomass: Biomass data are generally more uncertain than other data in national energy statistics. A large fraction of the biomass, used for energy, may be part of the informal economy, and the trade in these type of fuels (fuel wood, agricultural residues, dung cakes, etc.) is frequently not registered in the national energy statistics and balances. The AFOLU Volume 4 Chapter 4 (Forest Land) provides an alternative method to estimate activity data for fuel wood use. Where data from energy statistics and AFOLU statistics are both available, the inventory compiler should take care to avoid any double counting, and should indicate how data from both sources have been integrated to obtain the best possible estimate of fuel wood use in the country. CO2 emissions from biomass combustion are not included in national totals, but are recorded as an information item for cross-checking purposes as well as avoiding double counting. Note that peat is not treated as biomass in these guidelines, therefore CO2 emissions from peat are estimated. Waste: Waste incineration may occur in installations where the combustion heat is used as energy in other processes. In such cases, this waste must be treated as a fuel and the emissions should be reported in the energy sector. When waste is incinerated without using the combustion heat as energy, emissions should be reported under waste incineration. Methodologies in both cases are provided in Volume 5 Chapter 5. CO2 emissions from combustion

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

of biomass in waste used for energy are not included in national totals, but are recorded as an information item for cross-checking purposes.

1.4.1.4

T IME

SERIES CONSISTENCY

Many countries have long time series of energy statistics that can be used to derive time series of energy sector greenhouse gas emissions. However, in many cases statistical practices (including definitions of fuels, of fuel use by sectors) will have changed over time and recalculations of the energy data in the latest set of definitions is not always feasible. In compiling time series of emissions from fuel combustion, these changes might give rise to time series inconsistencies, which should be dealt with using the methods provided in Time Series Consistency Chapter 5 of Volume 1 of the 2006 IPCC Guidelines.

1.4.2 1.4.2.1

Emission factors CO 2

EMISSION FACTORS

Combustion processes are optimized to derive the maximum amount of energy per unit of fuel consumed, hence delivering the maximum amount of CO2. Efficient fuel combustion ensures oxidation of the maximum amount of carbon available in the fuel. CO2 emission factors for fuel combustion are therefore relatively insensitive to the combustion process itself and hence are primarily dependent only on the carbon content of the fuel. The carbon content may vary considerably both among and within primary fuel types on a per mass or per volume basis: •

• •

For natural gas, the carbon content depends on the composition of the gas which, in its delivered state, is primarily methane, but can include small quantities of ethane, propane, butane, and heavier hydrocarbons. Natural gas flared at the production site will usually contain far larger amounts of non-methane hydrocarbons. The carbon content will be correspondingly different. Carbon content per unit of energy is usually less for light refined products such as gasoline than for heavier products such as residual fuel oil. For coal, carbon emissions per tonne vary considerably depending on the coal's composition of carbon, hydrogen, sulphur, ash, oxygen, and nitrogen.

By converting to energy units this variability is reduced. A small part of the fuel carbon entering the combustion process escapes oxidation. This fraction is usually small (99 to 100 percent of the carbon is oxidized) and so the default emission factors in Table 1.4 are derived on the assumption of 100 percent oxidation. For some fuels, this fraction may in practice not be negligible and where representative country-specific values, based on measurements are available, they should be used. In other words: the fraction of carbon oxidised is assumed to be 1 in deriving default CO2 emission factors. Table 1.3 gives carbon contents of fuels from which emission factors on a full molecular weight basis can be calculated (Table 1.4). These emission factors are default values that are suggested only if country-specific factors are not available. More detailed and up-to-date emission factors may be available at the IPCC EFDB. Note that CO2 emissions from biomass fuels are not included in the national total but are reported as an information item. Net emissions or removals of CO2 are estimated in the AFOLU sector and take account of these emissions. Note that peat is treated as a fossil fuel and not a biofuel and emissions from its combustion are therefore included in the national total. The data presented in Table 1.3 is used to calculate default emission factors for each fuel on a per energy basis. If activity data are available on a per mass basis, a similar approach can be applied to these activity data directly. Obviously the carbon content then should be known on a per mass basis.

1.20

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction

TABLE 1.3 DEFAULT VALUES OF CARBON CONTENT Default carbon content 1 (kg/GJ)

Lower

Upper

Crude Oil

20.0

19.4

20.6

Orimulsion

21.0

18.9

23.3

Natural Gas Liquids

17.5

15.9

19.2

Motor Gasoline

18.9

18.4

19.9

Aviation Gasoline

19.1

18.4

19.9

Jet Gasoline

19.1

18.4

19.9

Jet Kerosene

19.5

19

20.3

Other Kerosene

19.6

19.3

20.1

Shale Oil

20.0

18.5

21.6

Gas/Diesel Oil

20.2

19.8

20.4

Residual Fuel Oil

21.1

20.6

21.5

Liquefied Petroleum Gases

17.2

16.8

17.9

Ethane

16.8

15.4

18.7

Naphtha

20.0

18.9

20.8

Bitumen

22.0

19.9

24.5

Lubricants

20.0

19.6

20.5

Petroleum Coke

26.6

22.6

31.3

Refinery Feedstocks

20.0

18.8

20.9

15.7

13.3

19.0

Paraffin Waxes

20.0

19.7

20.3

White Spirit & SBP

20.0

19.7

20.3

Other Petroleum Products

20.0

19.7

20.3

Anthracite

26.8

25.8

27.5

Coking Coal

25.8

23.8

27.6

Other Bituminous Coal

25.8

24.4

27.2

Sub-Bituminous Coal

26.2

25.3

27.3

Lignite

27.6

24.8

31.3

Oil Shale and Tar Sands

29.1

24.6

34

Brown Coal Briquettes

26.6

23.8

29.6

Patent Fuel

26.6

23.8

29.6

Coke Oven Coke and Lignite Coke

29.2

26.1

32.4

Gas Coke

29.2

26.1

32.4

22.0

18.6

26.0

12.1

10.3

15.0

12.1

10.3

15.0

70.8

59.7

84.0

49.6

39.5

55.0

15.3

14.8

15.9

Fuel type English description

Refinery Gas

Coal Tar

2

3

Gas Works Gas

4

Coke Oven Gas 5 Blast Furnace Gas

6

Oxygen Steel Furnace Gas

7

Natural Gas

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

TABLE 1.3 (CONTINUED) DEFAULT VALUES OF CARBON CONTENT Fuel type English description Municipal Wastes (non-biomass fraction)8 Industrial Wastes Waste Oils 9 Peat Wood/Wood Waste 10 Sulphite lyes (black liquor) 11 Other Primary Solid Biomass 12 Charcoal 13 Biogasoline 14 Biodiesels 15 Other Liquid Biofuels 16 Landfill Gas 17 Sludge Gas 18 Other Biogas 19 Municipal Wastes (biomass fraction) 20

Default carbon content 1 (kg/GJ)

Lower

Upper

25.0 39.0 20.0 28.9 30.5 26.0 27.3 30.5 19.3 19.3 21.7 14.9 14.9 14.9 27.3

20.0 30.0 19.7 28.4 25.9 22.0 23.1 25.9 16.3 16.3 18.3 12.6 12.6 12.6 23.1

33.0 50.0 20.3 29.5 36.0 30.0 32.0 36.0 23.0 23.0 26.0 18.0 18.0 18.0 32.0

Notes: 1

The lower and upper limits of the 95 percent confidence intervals, assuming lognormal distributions, fitted to a dataset, based on national inventory reports, IEA data and available national data. A more detailed description is given in section 1.5

2

Japanese data; uncertainty range: expert judgement;

3

EFDB; uncertainty range: expert judgement

4

Coke Oven Gas; uncertainty range: expert judgement

5

Japan & UK small number data; uncertainty range: expert judgement

6

7. Japan & UK small number data; uncertainty range: expert judgement

8

Solid Biomass; uncertainty range: expert judgement

9

Lubricants ; uncertainty range: expert judgement

10

EFDB; uncertainty range: expert judgement

11

Japanese data; uncertainty range: expert judgement

12

Solid Biomass; uncertainty range: expert judgement

13

EFDB; uncertainty range: expert judgement

14

Ethanol theoretical number; uncertainty range: expert judgement

15

Ethanol theoretical number; uncertainty range: expert judgement

16

Liquid Biomass; uncertainty range: expert judgement

17-19 20

Methane theoretical number; uncertainty range: expert judgement

Solid Biomass; uncertainty range: expert judgement

1.22

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction

TABLE 1.4 DEFAULT CO2 EMISSION FACTORS FOR COMBUSTION 1 Effective CO2 emission factor (kg/TJ) 2

Default carbon content (kg/GJ)

Default carbon oxidation factor

A

B

C=A*B*44/ 12*1000

Lower

Upper

Crude Oil

20.0

1

73 300

71 100

75 500

Orimulsion

21.0

1

77 000

69 300

85 400

Natural Gas Liquids

17.5

1

64 200

58 300

70 400

Motor Gasoline

18.9

1

69 300

67 500

73 000

Aviation Gasoline

19.1

1

70 000

67 500

73 000

Jet Gasoline

19.1

1

70 000

67 500

73 000

Jet Kerosene

19.5

1

71 500

69 700

74 400

Other Kerosene

19.6

1

71 900

70 800

73 700

Shale Oil

20.0

1

73 300

67 800

79 200

Gas/Diesel Oil

20.2

1

74 100

72 600

74 800

Residual Fuel Oil

21.1

1

77 400

75 500

78 800

Liquefied Petroleum Gases

17.2

1

63 100

61 600

65 600

Ethane

16.8

1

61 600

56 500

68 600

Naphtha

20.0

1

73 300

69 300

76 300

Bitumen

22.0

1

80 700

73 000

89 900

Lubricants

20.0

1

73 300

71 900

75 200

Petroleum Coke

26.6

1

97 500

82 900

115 000

Refinery Feedstocks

20.0

1

73 300

68 900

76 600

Refinery Gas

15.7

1

57 600

48 200

69 000

Paraffin Waxes

20.0

1

73 300

72 200

74 400

White Spirit & SBP

20.0

1

73 300

72 200

74 400

Other Petroleum Products

20.0

1

73 300

72 200

74 400

Anthracite

26.8

1

98 300

94 600

101 000

Coking Coal

25.8

1

94 600

87 300

101 000

Other Bituminous Coal

25.8

1

94 600

89 500

99 700

Sub-Bituminous Coal

26.2

1

96 100

92 800

100 000

Lignite

27.6

1

101 000

90 900

115 000

Oil Shale and Tar Sands

29.1

1

107 000

90 200

125 000

Brown Coal Briquettes

26.6

1

97 500

87 300

109 000

Patent Fuel

26.6

1

97 500

87 300

109 000

Coke oven coke and lignite Coke

29.2

1

107 000

95 700

119 000

Gas Coke

29.2

1

107 000

95 700

119 000

22.0

1

80 700

68 200

95 300

12.1

1

44 400

37 300

54 100

12.1

1

44 400

37 300

54 100

70.8

1

260 000

219 000

308 000

49.6

1

182 000

145 000

202 000

Coke

Other Oil

Gasoline

Fuel type English description

Derived Gases

Coal Tar Gas Works Gas Coke Oven Gas Blast Furnace Gas

4

Oxygen Steel Furnace Gas

5

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Default value 3

95% confidence interval

1.23

Volume 2: Energy

TABLE 1.4 (CONTINUED) DEFAULT CO2 EMISSION FACTORS FOR COMBUSTION 1

Default carbon oxidation Factor

A

B

C=A*B*44/ 12*1000

Lower

Upper

Natural Gas

15.3

1

56 100

54 300

58 300

Municipal Wastes (non-biomass fraction)

25.0

1

91 700

73 300

121 000

Industrial Wastes

39.0

1

143 000

110 000

183 000

Waste Oil

20.0

1

73 300

72 200

74 400

Peat

28.9

1

106 000

100 000

108 000

30.5

1

112 000

95 000

132 000

Sulphite lyes (black liquor)

26.0

1

95 300

80 700

110 000

Other Primary Solid Biomass

27.3

1

100 000

84 700

117 000

Charcoal

30.5

1

112 000

95 000

132 000

Biogasoline

19.3

1

70 800

59 800

84 300

Biodiesels

19.3

1

70 800

59 800

84 300

Other Liquid Biofuels

21.7

1

79 600

67 100

95 300

Gas biomass

Landfill Gas

14.9

1

54 600

46 200

66 000

Sludge Gas

14.9

1

54 600

46 200

66 000

Other Biogas

14.9

1

54 600

46 200

66 000

Other nonfossil fuels

Effective CO2 emission factor (kg/TJ) 2

Default carbon content (kg/GJ)

Municipal Wastes (biomass fraction)

27.3

1

100 000

84 700

117 000

Liquid Biofuels

Solid Biofuels

Fuel type English description

Wood/Wood Waste 5

Default value

95% confidence interval

Notes: 1

The lower and upper limits of the 95 percent confidence intervals, assuming lognormal distributions, fitted to a dataset, based on national inventory reports, IEA data and available national data. A more detailed description is given in section 1.5

2

TJ = 1000GJ

3

The emission factor values for BFG includes carbon dioxide originally contained in this gas as well as that formed due to combustion of this gas.

4

The emission factor values for OSF includes carbon dioxide originally contained in this gas as well as that formed due to combustion of this gas

5

Includes the biomass-derived CO2 emitted from the black liquor combustion unit and the biomass-derived CO2 emitted from the kraft mill lime kiln.

1.4.2.2

O THER

GREENHOUSE GASES

Emission factors for non-CO2 gases from fuel combustion are strongly dependent on the technology used. Since the set of technologies, applied in each sector varies considerably, so do the emission factors. Therefore it is not useful to provide default emission factors for these gases on the basis of fuels only. Tier 1 default emission factors are therefore provided in the subsequent chapters for each subsector separately.

1.4.2.3

I NDIRECT

GREENHOUSE GASES

This volume will not present guidance on the estimation of emissions of indirect greenhouse gases. For information on these gases, the user is referred to guidance provided under other conventions (see also section 1.3.1.3 Relation to other inventory approaches). Default methods for estimating these emissions are provided in the EMEP/CORINAIR Guidebook. Chapter 7 of Volume 1 provides full details on how to link to this information.

1.24

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction

1.5

UNCERTAINTY IN INVENTORY ESTIMATES

1.5.1

General

A general treatment of uncertainties in emission inventories is provided in Chapter 3 of Volume 1 of the 2006 IPCC Guidelines. A quantitative analysis of the uncertainties in the inventory need quantitative input values for both activity data and emission factors. This chapter will provide recommended default uncertainty ranges (95 percent confidence interval limits) to be used if further information is not available. The lower limit (marked as “lower” in the tables) is set at the 2.5 percent percentile of the probability distribution function and the upper limit (marked “upper” in the tables) at the 97.5 percentile. All default values in this chapter are rounded to three significant digits, both for the default emission factor itself and for the lower and upper limits of the 95 percent confidence intervals. Although applying exact arithmetic could provide more digits, these are not considered as significant.

1.5.2

Activity data uncertainties

Activity data needed for emission estimates in the Energy Sector are largely derived from national and international energy balances and energy statistics. Such data are generally seen as quite accurate. Uncertainty information on the fuel combustion statistics or the energy balances could be obtained from the national or international institutions responsible. If no further data are available, the recommended default uncertainty range for fossil fuel combustion data should be assumed to be plus or minus 5 percent. In other words: •

•

•

The value in the energy statistics or energy balance is interpreted as the point estimate for the activity data The lower limit value of the 95 percent confidence interval is 0.95 times the point estimate; The upper limit value of the 95 percent confidence interval is 1.05 times this value.

The "statistical difference", frequently given in energy balances, could also be used to obtain a feeling for the uncertainty in the data. The “statistical difference” is calculated from the difference between data derived from the supply of fuels and data derived from the demand of fuels. The year-to-year variation in its value reflects the aggregated uncertainty in all underlying fuel data including their inter relationships. Hence, the variation of the “statistical difference” will be an indication of the combined uncertainty of all supply and demand data for a specific fuel. Recalling that the uncertainties are expressed in percentage terms, the uncertainties in the fuel combustion data for specific sectors or applications will usually be higher than the uncertainty suggested by the “statistical difference”. The recommended default uncertainty range is based on this line of thought. However, if a “statistical difference” is zero, the balance is immediately suspect and should be treated as though a “statistical difference” had not been given. In these instances, the data quality should be examined for QA/QC purposes and subsequent improvements made if appropriate. Since data on biomass as fuel are not as well developed as for fossil fuels, the uncertainty range for biomass fuels will be significantly higher. A value of plus or minus 50 percent is recommended.

1.5.3

Emission factor uncertainties

The default emission factors, derived in this chapter are based on a statistical analysis of available data on fuel characteristics. The analysis provides lower and upper limits of the 95 percent confidence intervals as provided in Table 1.2 for net calorific values and Table 1.3 for carbon contents of fuels. The uncertainty ranges, provided in Table 1.4 are calculated from this information, using a Monte Carlo analysis (5 000 iterations). In this analysis, lognormal distributions, fitted to the provided lower and upper limits of the 95 percent confidence intervals were applied for the probability distribution functions. For a few typical examples, the resulting probability distribution functions for the default final effective CO2 emission factors are given below in Figure 1.3.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

1.25

Volume 2: Energy

Figure 1.3

Some typical examples of probability distribution functions (PDFs) for the effective CO 2 emission factors for the combustion of fuels. Natural Gas

Landfill Gas

40%

9%

35%

8% 7% Frequency (%)

Frequency (%)

Gaseous

30% 25% 20% 15%

6% 5% 4% 3%

10%

2%

5%

1%

0%

0% 70 000

60 000

50 000

40 000

30 000

75 000

65 000

55 000

45 000

35 000

Emission factor (kg/TJ)

Emission factor (kg/TJ)

Motor Gasoline

Gas/Diesel Oil

35%

60%

30%

50%

Frequency (%)

Frequency (%)

Liquid

25% 20% 15%

40%

30%

20% 10% 5%

10%

0%

0% 85 000

75 000

65 000

55 000

45 000

85 000

75 000

65 000

55 000

45 000

Emission factor (kg/TJ)

Emission factor (kg/TJ)

Jet Kerosene

Residual Fuel Oil

30%

35%

25%

30%

Frequency (%)

Frequency (%)

25% 20%

15%

20% 15%

10% 10% 5%

5%

0%

0% 95 000

85 000

75 000

65 000

55 000

1.26

85 000

75 000

65 000

55 000

45 000

Emission factor (kg/TJ)

Emission factor (kg/TJ)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction

Other Bituminous Coal 12%

25%

10%

20%

8%

Frequency (%)

Frequency (%)

Solid

Anthracite 30%

15%

10%

6%

4%

5%

2%

0%

0% 105 000

95 000

85 000

75 000

65 000

115 000

105 000

95 000

85 000

75 000

Emission factor (kg/TJ)

Emission factor (kg/TJ)

Coke Oven Coke and Lignite Coke

Wood/Wood Waste 5%

6%

5% 4% 4%

4%

Frequency (%)

Frequency (%)

5%

3%

2%

3% 3% 2% 2% 1%

1%

1% 0%

0% 120 000

110 000

100 000

90 000

80 000

120 000

110 000

100 000

90 000

80 000

Emission factor (kg/TJ)

Emission factor (kg/TJ)

The uncertainty information as presented in Table 1.4 can also be used when comparing country-specific emission factors with the default ones. Whenever a national specific emission factor falls within the 95 percent confidence interval, it could be regarded as consistent with the default value. In addition, one would expect the uncertainty range of country-specific values for application in that country to be smaller than the range provided in Figure 1.3. Uncertainties in emission factors for non-CO2 emission factors are treated in the subsequent chapters for the different source categories separately.

1.6

QA/QC AND COMPLETENESS

1.6.1

Reference Approach

As carbon dioxide emissions from fuel combustion dominate greenhouse gas emissions in many countries, it is worthwhile to use an independent check providing a quick and easy alternative estimate of these emissions. The Reference Approach provides a methodology for producing a first-order estimate of national greenhouse gas emissions based on the energy supplied to a country, even if only very limited resources and data structures are available to the inventory compiler. Since the Reference Approach is a top-down approach and in that respect is relatively independent of the bottom-up approach as described in the Tier 1, 2 and 3 methods of this chapter, the Reference Approach can be seen as a verification cross-check. As such it is part of the required QA/QC for the energy sector. The Reference Approach is described in full detail in Chapter 6 of this Volume. The Reference Approach requires statistics on the production of fuels, on their external trade, as well as on changes in their stocks. It also requires a limited amount of data on the consumption of fuels used for non-energy purposes where carbon may need to be excluded. The Reference Approach is based on the assumption that, once carbon is brought into a national economy in the form of a fuel, it is either released into the atmosphere in the form of a greenhouse gas, or it is diverted (e.g., in increases of fuel stocks, stored in products, left unutilised in ash) and does not enter the atmosphere as a

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

greenhouse gas. In order to calculate the amount of carbon released into the atmosphere, it is not necessary to know exactly how the fuel was used or what intermediate transformations it underwent. In view of this, the methodology may be described as top-down in contrast to the bottom-up methodologies applied in a sectoral approach.

1.6.2 1.6.2.1

Potential double counting between sectors N ON - ENERGY

USE OF FUELS

For a number of applications, mainly in larger industrial processes, fossil hydrocarbons are not only used as energy sources, but also have other uses e.g. feedstocks, lubricants, solvents, etc. The sectoral approaches (Tier 1, 2 and 3) are therefore based on fuel combustion statistics. Hence, the use of fuel combustion statistics rather than fuel delivery statistics is key to avoid double counting in emission estimates. When activity data are not quantities of fuel combusted but are instead deliveries to enterprises or main subcategories, there is a risk of double counting emissions from the IPPU (Chapter 5) or Waste Sectors. In some types of non-energy use of fossil hydrocarbons, emissions of fossil carbon containing substances might occur. Such emissions should be reported under the IPPU sector where they occur. Methods to estimate these emissions are provided in Volume 3, Industrial Processes and Product Use.

1.6.2.2

W ASTE

AS A FUEL

Some waste incinerators also produce heat or power. In such cases the waste stream will show up in national energy statistics and it is good practice to report these emissions under the energy sector. This could lead to double counting when in the waste sector the total volume of waste is used to estimate emissions. Only the fossil fuel derived fraction of CO2 from waste is included in national total emissions. For details please see Volume 5 (Waste)-Chapter 5 (Incineration and Open Burning of Waste) where methodological issues to estimate emissions are discussed.

1.6.3

Mobile versus stationary combustion

For most sources the distinction between mobile and stationary combustion is quite clear. In energy statistics, this however is not always the case. In some industries it might occur that fuels are in part used for stationary equipment and in part for mobile equipment. This could for example occur in agriculture, forestry, construction industry etc. When this occurs and a split between mobile and stationary is not feasible, the emissions could be reported in the source category that is expected to have the largest part of the emissions. In such cases, care must be taken to properly document the method and choices.

1.6.4

National boundaries

Mobile sources, while moving across national borders, might carry part of the fuel sold in one country for use in a second country. To estimate these emissions, however, the principle of using fuel sold to estimate the emissions should prevail over a strict application of the national territory for several reasons: •

• •

8

data on fuels moving across borders in vehicle fuel tanks is unlikely to be available at all, and if it were it is likely to be much less accurate than national fuel sales data it is important that emissions from fuel sold appear in only one county’s inventory. It would be nearly impossible to ensure consistency between neighbouring countries in most cases the net effect of trans-boundary traffic will be small since most vehicles will in the end return to their own country with fuel in their tanks. Only in cases of “fuel tourism8” this might not be the case.

People living near national borders might have an incentive to buy gasoline in one country for use in the other country if gasoline prices differ between these countries. In some regions this effect is substantial. See: Fuel tourism in border regions, Silvia Banfi, Massimo Filippini, Lester C. Hunt, CEPE, Centre for Energy Policy and Economics, Swiss Federal Institutes of Technology, 2003, http://e-collection.ethbib.ethz.ch/show?type=incoll&nr=888

1.28

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction

Other advice on boundary issues associated with bunker fuels and carbon capture and storage is provided in subsequent chapters, consistent with the principles set out in Volume 1, Chapter 8.

1.6.5

New sources

The 2006 IPCC Guidelines include, for the first time, methods for estimating emissions from carbon dioxide capture and storage (Chapter 5) so that the effect of these technologies on reducing emissions overall can be properly reflected in national inventories. The Guidelines also include new methods for estimation emissions from abandoned coal mines (Section 4.1), to complement the methods for working mines which were already included in the 1996 IPCC Guidelines and GPG2000.

References Kainou, K (2005). ‘Revision of default net calorific values, carbon content factors, carbon oxidization factors and carbon dioxide emission factors for various fuels in 2006 IPCC GHG Inventory Guidelines’. RIETI, IAI, Govt of Japan. Nilsson, K and. Nilsson, M (2004). ‘The climate impact of energy peat utilization in Sweden - the effect of former land use and after-treatment’. Report IVL B1606. OECD/IEA, (2004). Energy Statistics Manual Savolainen, I., Hillebrand, K., Nousiainen, I. and Sinisalo, J. (1994). ‘Greenhouse gas impacts of the use of peat and wood for energy.’ Espoo, Finland’. VTT Research Notes 1559. 65p.+app. Uppenberg, S. Zetterberg, L. and Åhman, M. (2001). ‘Climate impact from peat utilisation in Sweden’. (2001). Report IVL B1423.

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Chapter 2: Stationary Combustion

CHAPTER 2

STATIONARY COMBUSTION

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.1

Volume 2: Energy

Authors Darío R. Gómez (Argentina) and John D. Watterson (UK) Branca B. Americano (Brazil), Chia Ha (Canada), Gregg Marland (USA), Emmanuel Matsika (Zambia), Lemmy Nenge Namayanga (Zambia), Balgis Osman-Elasha (Sudan), John D. Kalenga Saka (Malawi), and Karen Treanton (IEA)

Contributing Author Roberta Quadrelli (IEA)

2.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

Contents 2

Stationary Combustion 2.1

Overview ...............................................................................................................................................2.6

2.2

Description of sources...........................................................................................................................2.6

2.3

Methodological issues .........................................................................................................................2.11

2.3.1

Choice of method ........................................................................................................................2.11

2.3.1.1

Tier 1 approach ......................................................................................................................2.11

2.3.1.2

Tier 2 approach ......................................................................................................................2.12

2.3.1.3

Tier 3 approach ......................................................................................................................2.12

2.3.1.4

Decision trees.........................................................................................................................2.14

2.3.2 Choice of emission factors ..................................................................................................................2.14 2.3.2.1

Tier 1......................................................................................................................................2.14

2.3.2.2

Tier 2 country-specific emission factors................................................................................2.24

2.3.2.3

Tier 3 technology-specific emission factors ..........................................................................2.24

2.3.3

Choice of activity data.................................................................................................................2.24

2.3.3.1

Tier 1 and tier 2......................................................................................................................2.29

2.3.3.2

Tier 3......................................................................................................................................2.32

2.3.3.3

Avoiding double counting activity data with other sectors....................................................2.32

2.3.3.4

Treatment of biomass.............................................................................................................2.33

2.3.4

Carbon dioxide capture ...............................................................................................................2.34

2.3.5

Completeness ..............................................................................................................................2.37

2.3.6

Developing a consistent time series and recalculation ................................................................2.37

2.4

Uncertainty assessment .......................................................................................................................2.38

2.4.1

Emission factor uncertainties ......................................................................................................2.38

2.4.2

Activity data uncertainties...........................................................................................................2.40

2.5

Inventory Quality Assurance/Quality Control QA/QC .......................................................................2.41

2.5.1 2.6

Reporting and Documentation.....................................................................................................2.41

Worksheets..........................................................................................................................................2.42

References

.....................................................................................................................................................2.45

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.3

Volume 2: Energy

Equations Equation 2.1

Greenhouse gas emissions from stationary combustion ......................................................2.11

Equation 2.2

Total emissions by greenhouse gas......................................................................................2.12

Equation 2.3

Greenhouse gas emissions by technology ...........................................................................2.12

Equation 2.4

Fuel consumption estimates based on technology penetration ............................................2.13

Equation 2.5

Technology-based emission estimation ...............................................................................2.13

Equation 2.6

CO2 capture efficiency.........................................................................................................2.35

Equation 2.7

Treatment of CO2 capture....................................................................................................2.36

Figures Figure 2.1

Generalised decision tree for estimating emissions from stationary combustion ................2.15

Figure 2.2

Power and heat plants use fuels to produce electric power and/or useful heat. ...................2.30

Figure 2.3

A refinery uses energy to transform crude oil into petroleum products. .............................2.31

Figure 2.4

Fuels are used as an energy source in manufacturing industries to convert raw materials into products.........................................................................................................2.31

Figure 2.5

CO2 capture systems from stationary combustion sources ..................................................2.34

Figure 2.6

Carbon flows in and out of the system boundary for a CO2 capture system associated with stationary combustion processes ................................................................2.35

Tables

2.4

Table 2.1

Detailed sector split for stationary combustion .....................................................................2.7

Table 2.2

Default emission factors for stationary combustion in the energy industries (kg of greenhouse gas per TJ on a net calorific basis).........................................................2.16

Table 2.3

Default emission factors for stationary combustion in manufacturing industries and construction (kg of greenhouse gas per TJ on a net calorific basis) ...................................2.18

Table 2.4

Default emission factors for stationary combustion in the commercial/institutional category (kg of greenhouse gas per TJ on a net calorific basis) ........................................................2.20

Table 2.5

Default emission factors for stationary combustion in the residential and agriculture/forestry/fishing/fishing farms categories (kg of greenhouse gas per TJ on a net calorific basis) ........................................................2.22

Table 2.6

Utility source emission factors ............................................................................................2.25

Table 2.7

Industrial source emission factors .......................................................................................2.26

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

Table 2.8

Kilns, ovens, and dryers source emission factors ................................................................2.27

Table 2.9

Residential source emission factors.....................................................................................2.28

Table 2.10

Commercial/institutional source emission factors ...............................................................2.29

Table 2.11

Typical CO2 capture efficiencies for post and pre-combustion systems..............................2.36

Table 2.12

Default uncertainty estimates for stationary combustion emission factors..........................2.38

Table 2.13

Summary of uncertainty assessment of CO2 emission factors for stationary combustion sources of selected countries ...............................................................................................2.39

Table 2.14

Summary of uncertainty assessment of CH4 and N2O emission factors for stationary combustion sources of selected countries............................................................................2.40

Table 2.15

Level of uncertainty associated with stationary combustion activity data...........................2.41

Table 2.16

List of source categories for stationary combustion ............................................................2.42

Table 2.17

QA/QC procedures for stationary sources ...........................................................................2.43

Box Box 2.1

Autoproducers .....................................................................................................................2.11

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

2 STATIONARY COMBUSTION 2.1

OVERVIEW

This chapter describes the methods and data necessary to estimate emissions from Stationary Combustion, and the categories in which these emissions should be reported. Methods are provided for the sectoral approach in three tiers based on: •

Tier 1: fuel combustion from national energy statistics and default emission factors;

•

Tier 2: fuel combustion from national energy statistics, together with country-specific emission factors, where possible, derived from national fuel characteristics;

•

Tier 3: fuel statistics and data on combustion technologies applied together with technology-specific emission factors; this includes the use of models and facility level emission data where available.

The chapter provides default Tier 1 emission factors for all source categories and fuels. The IPCC Emission Factor Database1 may be consulted for information appropriate to national circumstances, though the correct use of information from the database is the responsibility of greenhouse gas inventory compilers. This chapter covers elements formerly presented in the ‘Energy’ chapter of the GPG2000. The organisation of the IPCC 2006 Guidelines is different from both the IPCC 1996 Guidelines and the GPG2000. The changes to the stationary combustion information are summarised below. Content: •

A table detailing which sectors this chapter covers, and which IPCC source codes the emissions are to be reported under is included.

•

Some of the emission factors have been revised, and some new factors have also been included. The tables containing the emission factors indicate which factors are new, and which have been revised from the IPCC 1996 Guidelines and GPG2000.

•

The default oxidation factor is assumed to be 1, unless better information is available.

•

In the Tier 1 sectoral approach, the oxidation factor is included with the emission factor, which simplifies the worksheet.

•

Building on the GPG2000, this chapter includes extended information about uncertainty assessment of both the activity data and the emission factors.

•

Some definitions have changed or been refined.

•

A new section on carbon dioxide capture and storage has been added.

Structure: •

The methodology for estimating emissions is now subdivided into smaller sections for each Tier approach.

•

The tables have been designed to present emission factors for CO2, CH4, and N2O together, where possible.

2.2

DESCRIPTION OF SOURCES

In the Sectoral Approach, emissions from stationary combustion are specified for a number of societal and economic activities, defined within the IPCC sector 1A, Fuel Combustion Activities (see Table 2.1). A distinction is made between stationary combustion in energy industries (1.A.1), manufacturing industries and construction (1.A.2) and other sectors (1.A.4). Although these distinct subsectors are intended to include all stationary combustion, an additional category is available in sector 1.A.5 for any emissions that cannot be allocated to one of the other subcategories. Table 2.1 also indicates the mobile source categories in 1.A.4 and 1.A.5 that are treated in Chapter 3 of this Volume.

1

Available at http://www.ipcc-nggip.iges.or.jp/efdb/main.php

2.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

TABLE 2.1 DETAILED SECTOR SPLIT FOR STATIONARY COMBUSTION2 Code number and name

1 ENERGY

All GHG emissions arising from combustion and fugitive releases of fuels. Emissions from the non-energy uses of fuels are generally not included here, but reported under Industrial Processes and Product Use.

1 A Fuel Combustion Activities

Emissions from the intentional oxidation of materials within an apparatus that is designed to raise heat and provide it either as heat or as mechanical work to a process or for use away from the apparatus.

1A1

Comprises emissions from fuels combusted by the fuel extraction or energy-producing industries.

Energy Industries

Sum of emissions from main activity producers of electricity generation, combined heat and power generation, and heat plants. Main activity producers (formerly known as public utilities) are defined as those undertakings whose primary activity is to supply the public. They may be in public or private ownership. Emissions from own on-site use of fuel should be included. Emissions from autoproducers (undertakings which generate electricity/heat wholly or partly for their own use, as an activity that supports their primary activity) should be assigned to the sector where they were generated and not under 1 A 1 a. Autoproducers may be in public or private ownership.

1A1

a

Main Activity Electricity and Heat Production

1A1

a

i

Electricity Generation

Comprises emissions from all fuel use for electricity generation from main activity producers except those from combined heat and power plants.

ii

Combined Heat and Power Generation (CHP)

Emissions from production of both heat and electrical power from main activity producers for sale to the public, at a single CHP facility.

iii

Heat Plants

Production of heat from main activity producers for sale by pipe network.

1A1

1A1

2

Definitions

a

b

Petroleum Refining

All combustion activities supporting the refining of petroleum products including on-site combustion for the generation of electricity and heat for own use. Does not include evaporative emissions occurring at the refinery. These emissions should be reported separately under 1 B 2 a.

Methods for mobile sources occurring in sub-categories 1 A 4 and 1 A 5 are dealt with in Chapter 3 and the emissions are reported under Stationary Combustion.

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Volume 2: Energy

TABLE 2.1 (CONTINUED) DETAILED SECTOR SPLIT FOR STATIONARY COMBUSTION3 Code number and name

1A1

c

Manufacture of Solid Fuels and Other Energy Industries

1A1

c

i

1A1

3

Definitions

c

ii

Combustion emissions from fuel use during the manufacture of secondary and tertiary products from solid fuels including production of charcoal. Emissions from own on-site fuel use should be included. Also includes combustion for the generation of electricity and heat for own use in these industries.

Manufacture of Solid Fuels

Emissions arising from fuel combustion for the production of coke, brown coal briquettes and patent fuel.

Other Energy Industries

Combustion emissions arising from the energy-producing industries own (on-site) energy use not mentioned above or for which separate data are not available. This includes the emissions from own-energy use for the production of charcoal, bagasse, saw dust, cotton stalks and carbonizing of biofuels as well as fuel used for coal mining, oil and gas extraction and the processing and upgrading of natural gas. This category also includes emissions from pre-combustion processing for CO2 capture and storage. Combustion emissions from pipeline transport should be reported under 1 A 3 e.

1A2

Manufacturing Industries and Construction

Emissions from combustion of fuels in industry. Also includes combustion for the generation of electricity and heat for own use in these industries. Emissions from fuel combustion in coke ovens within the iron and steel industry should be reported under 1 A 1 c and not within manufacturing industry. Emissions from the industry sector should be specified by sub-categories that correspond to the International Standard Industrial Classification of all Economic Activities (ISIC). Energy used for transport by industry should not be reported here but under Transport (1 A 3). Emissions arising from offroad and other mobile machinery in industry should, if possible, be broken out as a separate subcategory. For each country, the emissions from the largest fuel-consuming industrial categories ISIC should be reported, as well as those from significant emitters of pollutants. A suggested list of categories is outlined below.

1A2

a

Iron and Steel

ISIC Group 271 and Class 2731

1A2

b

Non-Ferrous Metals

ISIC Group 272 and Class 2732

1A2

c

Chemicals

ISIC Division 24

1A2

d

Pulp, Paper and Print

ISIC Divisions 21 and 22

1A2

e

Food Processing, Beverages and Tobacco

ISIC Divisions 15 and 16

1A2

f

Non-Metallic Minerals

Includes products such as glass, ceramic, cement, etc.; ISIC Division 26

1A2

g

Transport Equipment

ISIC Divisions 34 and 35

1A2

h

Machinery

Includes fabricated metal products, machinery and equipment other than transport equipment; ISIC Divisions 28, 29, 30, 31 and 32.

Methods for mobile sources occurring in sub-categories 1 A 4 and 1 A 5 are dealt with in Chapter 3 and the emissions are reported under Stationary Combustion.

2.8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

TABLE 2.1 (CONTINUED) DETAILED SECTOR SPLIT FOR STATIONARY COMBUSTION4 Code number and name

4

Definitions

1A2

i

Mining (excluding fuels) and Quarrying

ISIC Divisions 13 and 14

1A2

j

Wood and Wood Products

ISIC Division 20

1A2

k

Construction

ISIC Division 45

1A2

l

Textile and Leather

ISIC Divisions 17, 18 and 19

1A2

m

Non-specified Industry

Any manufacturing industry/construction not included above or for which separate data are not available. Includes ISIC Divisions 25, 33, 36 and 37.

1A4

Other Sectors

1A4

a

Commercial / Institutional

Emissions from fuel combustion in commercial and institutional buildings; all activities included in ISIC Divisions 41, 50, 51, 52, 55, 63-67, 70-75, 80, 85, 90-93 and 99.

1A4

b

Residential

All emissions from fuel combustion in households.

1A4

c

Agriculture / Forestry / Fishing / Fish farms

Emissions from fuel combustion in agriculture, forestry, fishing and fishing industries such as fish farms. Activities included in ISIC Divisions 01, 02 and 05. Highway agricultural transportation is excluded.

1A4

c

i

Stationary

Emissions from fuels combusted in pumps, grain drying, horticultural greenhouses and other agriculture, forestry or stationary combustion in the fishing industry. Emissions from fuels combusted in traction vehicles on farm land and in forests. Emissions from fuels combusted for inland, coastal and deepsea fishing. Fishing should cover vessels of all flags that have refuelled in the country (include international fishing).

Emissions from combustion activities as described below, including combustion for the generation of electricity and heat for own use in these sectors.

1A4

c

ii

Off-road Vehicles and Other Machinery

1A4

c

iii

Fishing (mobile combustion)

Methods for mobile sources occurring in sub-categories 1 A 4 and 1 A 5 are dealt with in Chapter 3 and the emissions are reported under Stationary Combustion.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

TABLE 2.1 (CONTINUED) DETAILED SECTOR SPLIT FOR STATIONARY COMBUSTION5 Code number and name

Definitions

1A5

Non-Specified

All remaining emissions from fuel combustion that are not specified elsewhere. Include emissions from fuel delivered to the military in the country and delivered to the military of other countries that are not engaged in multilateral operations.

1A5

a

Stationary

Emissions from fuel combustion in stationary sources that are not specified elsewhere.

1A5

b

Mobile

Emissions from vehicles and other machinery, marine and aviation (not included in 1 A 4 c ii or elsewhere).

1A5

b

i

Mobile (aviation component)

All remaining aviation emissions from fuel combustion that are not specified elsewhere. Include emissions from fuel delivered to the country’s military as well as fuel delivered within that country but used by the militaries of other countries that are not engaged in multilateral operations. All remaining water-borne emissions from fuel combustion that are not specified elsewhere. Include emissions from fuel delivered to the country’s military as well as fuel delivered within that country but used by the militaries of other countries that are not engaged in multilateral operations. All remaining emissions from mobile sources not included elsewhere.

1A5

b

ii

Mobile (waterborne component)

1A5

b

iii

Mobile (other)

Multilateral operations (Information item)

Emissions from fuels used in multilateral operations pursuant to the Charter of the United Nations. Include emissions from fuel delivered to the military in the country and delivered to the military of other countries.

The category “Manufacturing industries and Construction” has been subdivided using the International Standard Industrial Classification6. This industrial classification is widely used in energy statistics. Note that this table adds a number of industrial sectors in the category “Manufacturing Industries and Construction” to better align to the ISIC definitions and common practice in energy statistics. Emissions from autoproducers (public or private undertakings that generate electricity/heat wholly or partly for their own use, as an activity that supports their primary activity, see Box 2.1) should be assigned to the sector where they were generated and not under 1 A 1 a.

5

6

Methods for mobile sources occurring in sub-categories 1 A 4 and 1 A 5 are dealt with in Chapter 3 and the emissions are reported under Stationary Combustion. International Standard Industrial Classification of all Economic Activities, United Nations, New York. The publication can be downloaded from http://unstats.un.org/unsd/cr/.

2.10

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

BOX 2.1 AUTOPRODUCERS

An autoproducer of electricity and/or heat is an enterprise that, in support of its primary activity, generates electricity and/or heat for its own use or for sale, but not as its main business. This should be contrasted with main activity producers who generate and sell electricity and/or heat as their primary activity. Main activity producers were previously referred to as “Public” electricity and heat suppliers, although, as with autoproducers, they might be publicly or privately owned. Note that the ownership does not determine the allocation of emissions. The IPCC 2006 Guidelines follow the IPCC 1996 Guidelines in attributing emissions from autoproduction to the industrial or commercial branches in which the generation activity occurred, rather than to 1 A 1 a. Category 1 A 1a is for main activity producers only. With the complexity of plant activities and inter-relationships, there may not always be a clear separation between autoproducers and main activity producers. The most important issue is that all facilities be accounted under the most appropriate category and in a complete and consistent manner.

2.3

METHODOLOGICAL ISSUES

This section explains how to choose an approach, and summarises the necessary activity data and emission factors the inventory compiler will need. These sections are subdivided into Tiers as set out in Volume 1 General Guidance. The Tier 1 sections set out the steps needed for the simplest calculation methods, or the methods that require the least data. These are likely to provide the least accurate estimates of emissions. The Tier 2 and Tier 3 approaches require more detailed data and resources (time, expertise and country-specific data) to produce an estimate of emissions. Properly applied, the higher tiers should be more accurate.

2.3.1

Choice of method

In general, emissions of each greenhouse gas from stationary sources are calculated by multiplying fuel consumption by the corresponding emission factor. In the Sectoral Approach, “Fuel Consumption” is estimated from energy use statistics and is measured in terajoules. Fuel consumption data in mass or volume units must first be converted into the energy content of these fuels. All tiers described below use the amount of fuel combusted as the activity data. Section 1.4.1.2 of the Introduction chapter contains information on how to find and apply energy statistics data. Different tiers can be applied for different fuels and gases, consistent with the requirements of key category analysis and avoidance of double counting (see also the General Decision Tree in section 1.3.1.2).

2.3.1.1

T IER 1

APPROACH

Applying a Tier 1 emission estimate requires the following for each source category and fuel: •

•

Data on the amount of fuel combusted in the source category A default emission factor

Emission factors come from the default values provided together with associated uncertainty range in Section 2.3.2.1. The following equation is used: EQUATION 2.1 GREENHOUSE GAS EMISSIONS FROM STATIONARY COMBUSTION

EmissionsGHG, fuel = Fuel Consumption fuel • Emission FactorGHG, fuel

Where: EmissionsGHG ,fuel

= emissions of a given GHG by type of fuel (kg GHG)

Fuel Consumptionfuel

= amount of fuel combusted (TJ)

Emission FactorGHG,fuel

= default emission factor of a given GHG by type of fuel (kg gas/TJ). For CO2, it includes the carbon oxidation factor, assumed to be 1.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

To calculate the total emissions by gas from the source category, the emissions as calculated in Equation 2.1 are summed over all fuels: EQUATION 2.2 TOTAL EMISSIONS BY GREENHOUSE GAS Emissions GHG = ∑ Emissions GHG , fuel fuels

2.3.1.2

T IER 2

APPROACH

Applying a Tier 2 approach requires: •

•

Data on the amount of fuel combusted in the source category; A country-specific emission factor for the source category and fuel for each gas.

Under Tier 2, the Tier 1 default emission factors in Equation 2.1 are replaced by country-specific emission factors. Country-specific emission factors can be developed by taking into account country-specific data, for example carbon contents of the fuels used, carbon oxidation factors, fuel quality and (for non-CO2 gases in particular) the state of technological development. The emission factors may vary over time and, for solid fuels, should take into account the amount of carbon retained in the ash, which may also vary with time. It is good practice to compare any country-specific emission factor with the default ones given in Tables 2.2 to 2.5. If such country-specific emission factors are outside the 95 percent confidence intervals, given for the default values, an explanation should be sought and provided on why the value is significantly different from the default value. A country-specific emission factor can be identical to the default one, or it may differ. Since the country-specific value should be more applicable to a given country’s situation, it is expected that the uncertainty range associated with a country-specific value will be smaller than the uncertainty range of the default emission factor. This expectation should mean that a Tier 2 estimate provides an emission estimate with lower uncertainty than a Tier 1 estimate. Emissions can be also estimated as the product of fuel consumption on a mass or volume basis, and an emission factor expressed on a compatible basis. For example, the use of activity data expressed in mass unit is relevant when the Tier 2 approach described in Chapter 5 of Volume 5 is used alternatively to estimate emissions that arise when waste is incinerated for energy purposes.

2.3.1.3

T IER 3

APPROACH

The Tier 1 and Tier 2 approaches of estimating emissions described in the previous sections necessitate using an average emission factor for a source category and fuel combination throughout the source category. In reality, emissions depend on the: •

•

fuel type used,

•

operating conditions,

•

quality of maintenance,

•

combustion technology,

•

control technology,

age of the equipment used to burn the fuel.

In a Tier 3 approach this is taken into account by splitting the fuel combustion statistics over the different possibilities and using emission factors that are dependent upon these differences. In Equation 2.3, this is indicated by making the variables and parameters technology dependent. Technology here stands for any device, combustion process or fuel property that might influence the emissions. EQUATION 2.3 GREENHOUSE GAS EMISSIONS BY TECHNOLOGY EmissionsGHG, fuel ,technology = Fuel Consumption fuel,technology • Emission FactorGHG, fuel,technology Where:

2.12

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

EmissionsGHG gas,fuel, technology GHG)

= emissions of a given GHG by type of fuel and technology (kg

Fuel Consumptionfuel, technology

= amount7 of fuel combusted per type of technology (TJ)

Emission FactorGHG gas,fuel,technology

= emission factor of a given GHG by fuel and technology type (kg GHG/TJ)

When the amount of fuel combusted for a certain technology is not directly known, it can be estimated by means of models. For example, a simple model for this is based on the penetration of the technology into the source category. EQUATION 2.4 FUEL CONSUMPTION ESTIMATES BASED ON TECHNOLOGY PENETRATION Fuel Consumption fuel ,technology = Fuel Consumption fuel • Penetrationtechnology Where: Penetrationtechnology =

the fraction of the full source category occupied by a given technology. This fraction can be determined on the basis of output data such as electricity generated which would ensure that appropriate allowance was made for differences in utilisation between technologies.

To calculate the emissions of a gas for a source category, the result of Equation 2.3 must be summed over all technologies applied in the source category.

Emissions GHG , fuel

EQUATION 2.5 TECHNOLOGY-BASED EMISSION ESTIMATION = ∑ Fuel Consumption fuel ,technology • Emission FactorGHG, fuel ,technology technologies

Total emissions are again calculated by summing over all fuels (Equation 2.2). Application of a Tier 3 emission estimation approach requires: • • •

Data on the amount of fuel combusted in the source category for each relevant technology (fuel type used, combustion technology, operating conditions, control technology, and maintenance and age of the equipment). A specific emission factor for each technology (fuel type used, combustion technology, operating conditions, control technology, oxidation factor, and maintenance and age of the equipment). Facility level measurements can also be used when available.

Using a Tier 3 approach to estimate emissions of CO2 is often unnecessary because emissions of CO2 do not depend on the combustion technology. However, plant-specific data on CO2 emissions are increasingly available and they are of increasing interest because of the possibilities for emissions trading. Plant-specific data can be based on fuel flow measurements and fuel chemistry or on flue gas flow measurements and flue gas chemistry data. Continuous emissions monitoring (CEM) of flue gases is generally not justified for accurate measurement of CO2 emissions alone (because of the comparatively high cost) but could be undertaken particularly when monitors are installed for measurement of other pollutants such as SO2 or NOx. Continuous emissions monitoring is also particularly useful for combustion of solid fuels where it is more difficult to measure fuel flow rates, or when fuels are highly variable, or fuel analysis is otherwise expensive. Rigorous, continuous monitoring is required to provide a comprehensive accounting of emissions. Care is required when continuous emissions monitoring of some facilities is used but monitoring data are not available for a full reporting category. Continuous emissions monitoring requires attention to quality assurance and quality control. This includes certification of the monitoring system, re-certification after any changes in the system, and assurance of continuous operation 8 . For CO2 measurements, data from CEM systems can be compared with emissions estimates based on fuel flows. 7

Fuel consumption could be expressed on a mass or volume basis, and emissions can be estimated as the product of fuel consumption and an emission factor expressed on a compatible basis.

8

See for example: U.S. EPA (2005a).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.13

Volume 2: Energy

If detailed monitoring shows that the concentration of a greenhouse gas in the discharge from a combustion process is equal to or less than the concentration of the same gas in the ambient intake air to the combustion process, then emissions may be reported as zero. Reporting these emissions as “negative emissions” would require continuous high quality monitoring of both the air intake and the atmospheric emissions.

2.3.1.4

D ECISION

TREES

The tier used to estimate emissions will depend on the quantity and quality of data that are available. If a category is key, it is good practice to estimate emissions using a Tier 2 or Tier 3 approach. The decision tree (Figure 2.1) below will help in selecting which tier should be used to estimate emissions from sources of stationary combustion. To use a decision tree correctly, the inventory compiler needs to undertake a thorough survey of available national activity data and national or regional emission factor data, by relevant source category. This survey needs to be completed before the first inventory is compiled, and the results of the survey should be reviewed regularly. It is good practice to improve the data quality if an initial calculation with a Tier 1 approach indicates a key source, or if an estimate is associated with a high level of uncertainty. The decision tree and key source category determination should be applied to CO2, CH4 and N2O emissions separately.

2.3.2

Choice of emission factors

This section provides default emission factors for CO2, CH4 and N2O, and discusses provision of emission factors at higher Tiers. CO2 emission factors for all Tiers reflect the full carbon content of the fuel less any nonoxidised fraction of carbon retained in the ash, particulates or soot. Since this fraction is usually small, the Tier 1 default emission factors derived in Chapter 1 of this Volume neglect this effect by assuming a complete oxidation of the carbon contained in the fuel (carbon oxidation factor equal to 1). For some solid fuels, this fraction will not necessarily be negligible, and higher Tier estimates can be applied. Where this is known to be the case it is good practice to use country-specific values, based on measurements or other well documented data. The Emission Factor Database (EFDB) provides a variety of well-documented emission factors and other parameters that may be better suited to national circumstances than the default values, although the responsibility to ensure appropriate application of material from the database remains with the inventory compiler.

2.3.2.1

T IER 1

This section presents for each of the fuels used in stationary sources a set of default emission factors for use in Tier 1 emission estimates for the source categories. In a number of source categories, the same fuels are used. These will have the same emission factors for CO2. The derivation of the CO2 emission factors is presented in the Introduction chapter of this Volume. Emission factors for CO2 are in units of kg CO2/TJ on a net calorific value basis and reflect the carbon content of the fuel and the assumption that the carbon oxidation factor is 1. Emission factors for CH4 and N2O for different source categories differ due to differences in combustion technologies applied in the different source categories. The default factors presented for Tier 1 apply to technologies without emission controls. The default emission factors, particularly those in Tables 2.2 and 2.3, assume effective combustion in high temperature. They are applicable for steady and optimal conditions and do not take into account the impact of start-ups, shut downs or combustion with partial loads. Default emission factors for stationary combustion are given in Tables 2.2 to 2.5. The CO2 emission factors are the same ones as presented in Table 1.4 of the Introduction chapter. The emission factors for CH4 and N2O are based on the IPCC 1996 Guidelines. These emission factors were established using the expert judgement of a large group of inventory experts and are still considered valid. Since not many measurements of these types of emission factors are available, the uncertainty ranges are set at plus or minus a factor of three. Tables 2.2 to 2.5 do not provide default emission factors for CH4 and N2O emissions from combustion by off-road machinery that are reported in the 1A category. These emission factors are provided in Section 3.3 of this Volume.

2.14

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

Figure 2.1

Generalised decision tree for estimating emissions from stationary combustion Start

Are emissions measurements available with satisfactory QC?

Are all single sources in the source category measured?

Yes

Use measurements Tier 3 approach.

Yes

No Is specific fuel use available for the category?

Yes

Are countryspecific EFs available for the unmeasured part of the key category?

No

No Does the unmeasured part belong to a key category?

No

Is a detailed estimation model available?

Can the fuel consumption estimated by the model be reconciled with national fuel statistics or be verified by independent sources?

Yes

No

No No

Yes

Are country-specific EFs available? Yes

No Is this a key category?

Yes

Use measurements Tier 3 approach and combine with AD and countryspecific EFs Tier 2 approach.

Yes

Yes

Get CountrySpecific Data

Use measurements Tier 3 approach and combine with AD and default EFs Tier 1 approach.

Use model Tier 3 approach.

Use countryspecific EFs and suitable AD Tier 2 approach.

Get countryspecific data. No

Use default EFs and suitable AD Tier 1 approach.

Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.15

Volume 2: Energy

TABLE 2.2 DEFAULT EMISSION FACTORS FOR STATIONARY COMBUSTION IN THE ENERGY INDUSTRIES (kg of greenhouse gas per TJ on a Net Calorific Basis) CO2 Fuel

CH4

Default Emission Factor

Lower

Upper

73 300

71 100

75 500

r

Crude Oil

Default Emission Factor

N2O

Lower

Upper

Default Emission Factor

Lower

Upper

3

1

10

0.6

0.2

2

r

77 000

69 300

85 400

r

3

1

10

0.6

0.2

2

Natural Gas Liquids

r

64 200

58 300

70 400

r

3

1

10

0.6

0.2

2

Motor Gasoline

r

69 300

67 500

73 000

r

3

1

10

0.6

0.2

2

Aviation Gasoline

r

70 000

67 500

73 000

r

3

1

10

0.6

0.2

2

Gasoline

Orimulsion

Jet Gasoline

r

70 000

67 500

73 000

r

3

1

10

0.6

0.2

2

r

71 500

69 700

74 400

r

3

1

10

0.6

0.2

2

Other Kerosene

71 900

70 800

73 700

r

3

1

10

0.6

0.2

2

Shale Oil

73 300

67 800

79 200

r

3

1

10

0.6

0.2

2

Gas/Diesel Oil

74 100

72 600

74 800

r

3

1

10

0.6

0.2

2

Residual Fuel Oil

77 400

75 500

78 800

r

3

1

10

0.6

0.2

2

Jet Kerosene

Liquefied Petroleum Gases

63 100

61 600

65 600

r

1

0.3

3

0.1

0.03

0.3

Ethane

61 600

56 500

68 600

r

1

0.3

3

0.1

0.03

0.3

Naphtha

73 300

69 300

76 300

r

3

1

10

0.6

0.2

2

Bitumen

80 700

73 000

89 900

r

3

1

10

0.6

0.2

2

Lubricants

73 300

71 900

75 200

r

3

1

10

0.6

0.2

2

97 500

82 900

115 000

r

3

1

10

0.6

0.2

2

Refinery Feedstocks

73 300

68 900

76 600

r

3

1

10

0.6

0.2

2

Refinery Gas

Petroleum Coke

r

48 200

69 000

r

1

0.3

3

0.1

0.03

0.3

73 300

72 200

74 400

r

3

1

10

0.6

0.2

2

White Spirit and SBP

73 300

72 200

74 400

r

3

1

10

0.6

0.2

2

Other Petroleum Products

73 300

72 200

74 400

r

3

1

10

0.6

0.2

2

Anthracite

98 300

94 600

101 000

1

0.3

3

r

1.5

0.5

5

Coking Coal

94 600

87 300

101 000

1

0.3

3

r

1. 5

0.5

5

Other Bituminous Coal

94 600

89 500

99 700

1

0.3

3

r

1. 5

0.5

5

Other Oil

n 57 600

Paraffin Waxes

96 100

92 800

100 000

1

0.3

3

r

1.5

0.5

5

Lignite

101 000

90 900

115 000

1

0.3

3

r

1. 5

0.5

5

Oil Shale and Tar Sands

107 000

90 200

125 000

1

0.3

3

r

1. 5

0.5

5

Brown Coal Briquettes

97 500

87 300

109 000

1

0.3

3

r

1. 5

0.5

5

Patent Fuel

97 500

87 300

109 000

1

0.3

3

n

1. 5

0.5

5

Coke Oven Coke and Lignite Coke

r 107 000

95 700

119 000

1

0.3

3

r

1. 5

0.5

5

Gas Coke

r 107 000

95 700

119 000

r

1

0.3

3

0.03

0.3

n 80 700

68 200

95 300

n

1

0.3

3

0.5

5

Gas Works Gas

n 44 400

37 300

54 100

n

1

0.3

3

0.1

0.03

0.3

Coke Oven Gas

n 44 400

37 300

54 100

r

1

0.3

3

0.1

0.03

0.3

Blast Furnace Gas

n 260 000

219 000

308 000

r

1

0.3

3

0.1

0.03

0.3

Oxygen Steel Furnace Gas

n 182 000

145 000

202 000

r

1

0.3

3

0.1

0.03

0.3

56 100

54 300

58 300

1

0.3

3

0.1

0.03

0.3

Coke

Sub-Bituminous Coal

Derived Gases

Coal Tar

Natural Gas

2.16

n

0.1 r

1. 5

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

TABLE 2.2 (CONTINUED) DEFAULT EMISSION FACTORS FOR STATIONARY COMBUSTION IN THE ENERGY INDUSTRIES (kg of greenhouse gas per TJ on a Net Calorific Basis) CO2 Fuel

Lower

Upper

Default Emission Factor

Lower

Upper

Default Emission Factor

Lower

Upper

91 700

73 300

121 000

30

10

100

4

1.5

15

110 000

183 000

30

10

100

4

1.5

15

73 300

72 200

74 400

30

10

100

4

1.5

15

n

Industrial Wastes

n 143 000

Waste Oils

n

Liquid Biofuels

Solid Biofuels

Peat

Gas Biomass

N2O

Default Emission Factor

Municipal Wastes (non-biomass fraction)

Other nonfossil fuels

CH4

106 000

100 000

108 000

Wood / Wood Waste

n 112 000

95 000

132 000

Sulphite lyes (Black Liquor)a

n 95 300

80 700

110 000

Other Primary Solid Biomass

n

n

1

0.3

3

30

10

100

3

1

18

1.5

n 4

2

n

0.5

5

1.5

15

1

21

n 100 000

84 700

117 000

30

10

100

4

1.5

15

Charcoal

n 112 000

95 000

132 000

200

70

600

4

1.5

15

Biogasoline

n 70 800

59 800

84 300

r

3

1

10

0.6

0.2

2

Biodiesels

n 70 800

59 800

84 300

r

3

1

10

0.6

0.2

2

Other Liquid Biofuels

n 79 600

67 100

95 300

r

3

1

10

0.6

0.2

2

Landfill Gas

n 54 600

46 200

66 000

r

1

0.3

3

0.1

0.03

0.3

Sludge Gas

n 54 600

46 200

66 000

r

1

0.3

3

0.1

0.03

0.3

Other Biogas

n

54 600

46 200

66 000

r

1

0.3

3

0.1

0.03

0.3

Municipal Wastes (biomass fraction)

n 100 000

84 700

117 000

30

10

100

4

1.5

15

(a) Includes the biomass-derived CO2 emitted from the black liquor combustion unit and the biomass-derived CO2 emitted from the kraft mill lime kiln. n indicates a new emission factor which was not present in the 1996 Guidelines r indicates an emission factor that has been revised since the 1996 Guidelines

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.17

Volume 2: Energy

TABLE 2.3 DEFAULT EMISSION FACTORS FOR STATIONARY COMBUSTION IN MANUFACTURING INDUSTRIES AND CONSTRUCTION (kg of greenhouse gas per TJ on a Net Calorific Basis) CO2 Fuel

CH4

Default Emission Factor

Lower

Upper

73 300

71 100

75 500

r

Orimulsion

r 77 000

69 300

85 400

Natural Gas Liquids

r 64 200

58 300

Motor Gasoline

r 69 300

67 500

Aviation Gasoline

r 70 000

67 500

73 000

r

3

1

10

0.6

0.2

2

Jet Gasoline

r 70 000

67 500

73 000

r

3

1

10

0.6

0.2

2

Jet Kerosene

71 500

69 700

74 400

r

3

1

10

0.6

0.2

2

Other Kerosene

71 900

70 800

73 700

r

3

1

10

0.6

0.2

2

Shale Oil

73 300

67 800

79 200

r

3

1

10

0.6

0.2

2

Gas/Diesel Oil

74 100

72 600

74 800

r

3

1

10

0.6

0.2

2

Residual Fuel Oil

77 400

75 500

78 800

r

3

1

10

0.6

0.2

2

Gasoline

Crude Oil

Default Emission Factor

N2O

Lower

Upper

Default Emission Factor

Lower

Upper

3

1

10

0.6

0.2

2

r

3

1

10

0.6

0.2

2

70 400

r

3

1

10

0.6

0.2

2

73 000

r

3

1

10

0.6

0.2

2

Liquefied Petroleum Gases

63 100

61 600

65 600

r

1

0.3

3

0.1

0.03

0.3

Ethane

61 600

56 500

68 600

r

1

0.3

3

0.1

0.03

0.3

Naphtha

73 300

69 300

76 300

r

3

1

10

0.6

0.2

2

Bitumen

80 700

73 000

89 900

r

3

1

10

0.6

0.2

2

Lubricants

73 300

71 900

75 200

r

3

1

10

0.6

0.2

2

r 97 500

82 900

115 000

r

3

1

10

0.6

0.2

2

Petroleum Coke Refinery Feedstocks

73 300

68 900

76 600

r

3

1

10

0.6

0.2

2

n 57 600

48 200

69 000

r

1

0.3

3

0.1

0.03

0.3

Paraffin Waxes

73 300

72 200

74 400

r

3

1

10

0.6

0.2

2

White Spirit and SBP

73 300

72 200

74 400

r

3

1

10

0.6

0.2

2

Other Petroleum Products

73 300

72 200

74 400

r

3

1

10

0.6

0.2

2

Other Oil

Refinery Gas

Anthracite

98 300

94 600

101 000

10

3

30

r

1.5

0.5

5

Coking Coal

94 600

87 300

101 000

10

3

30

r

1.5

0.5

5

Other Bituminous Coal

94 600

89 500

99 700

10

3

30

r

1.5

0.5

5

Sub-Bituminous Coal

96 100

92 800

100 000

10

3

30

r

1.5

0.5

5

Lignite

101 000

90 900

115 000

10

3

30

r

1.5

0.5

5

Oil Shale and Tar Sands

107 000

90 200

125 000

10

3

30

r

1.5

0.5

5

Brown Coal Briquettes

n 97 500

87 300

109 000

n 10

3

30

n

1.5

0.5

5

97 500

87 300

109 000

10

3

30

r

1.5

0.5

5

10

3

30

r

1.5

0.5

5

0.03

0.3

0.5

5

Coke

Patent Fuel Coke Oven Coke and Lignite Coke

r 107 000

95 700

119 000

Gas Coke

r 107 000

95 700

119 000

r

1

0.3

3

n 80 700

68 200

95 300

n

10

3

30

Gas Works Gas

n 44 400

37 300

54 100

r

1

0.3

3

0.1

0.03

0.3

Coke Oven Gas

n 44 400

37 300

54 100

r

1

0.3

3

0.1

0.03

0.3

Blast Furnace Gas

n260 000

219 000

308 000

r

1

0.3

3

0.1

0.03

0.3

Oxygen Steel Furnace Gas

n 182 000

145 000

202 000

r

1

0.3

3

0.1

0.03

0.3

56 100

54 300

58 300

r

1

0.3

3

0.1

0.03

0.3

Derived Gases

Coal Tar

Natural Gas

2.18

0.1 n

1.5

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

TABLE 2.3 (CONTINUED) DEFAULT EMISSION FACTORS FOR STATIONARY COMBUSTION IN MANUFACTURING INDUSTRIES AND CONSTRUCTION (kg of greenhouse gas per TJ on a Net Calorific Basis) CO2 Fuel

CH4

N2O

Default Emission Factor

Lower

Upper

Default Emission Factor

Lower

Upper

Default Emission Factor

Lower

Upper

Municipal Wastes (non-biomass fraction)

n 91 700

73 300

121 000

30

10

100

4

1.5

15

Industrial Wastes

n143 000

110 000

183 000

30

10

100

4

1.5

15

Waste Oils

n 73 300

72 200

74 400

30

10

100

4

1.5

15

106 000

100 000

108 000

2

0.6

6

0.5

5

30

10

100

1.5

15

3

1

18

1

21

30

10

100

1.5

15

Peat

Other Gas non-fossil Biomass fuels

Liquid Biofuels

Solid Biofuels

Wood / Wood Waste

n

1.5

n

n 112 000

95 000

132 000

a

Sulphite lyes (Black Liquor)

n 95 300

80 700

110 000

Other Primary Solid Biomass

n 100 000

84 700

117 000

Charcoal

n 112 000

95 000

132 000

70

600

4

1.5

15

Biogasoline

n 70 800

59 800

84 300

r

3

1

10

0.6

0.2

2

Biodiesels

n 70 800

59 800

84 300

r

3

1

10

0.6

0.2

2

Other Liquid Biofuels

n 79 600

67 100

95 300

r

3

1

10

0.6

0.2

2

Landfill Gas

n 54 600

46 200

66 000

r

1

0.3

3

0.1

0.03

0.3

Sludge Gas

n 54 600

46 200

66 000

r

1

0.3

3

0.1

0.03

0.3

Other Biogas

n 54 600

46 200

66 000

r

1

0.3

3

0.1

0.03

0.3

Municipal Wastes (biomass fraction)

n100 000

84 700

117 000

10

100

4

1.5

15

n

200

30

4 2

n 4

(a) Includes the biomass-derived CO2 emitted from the black liquor combustion unit and the biomass-derived CO2 emitted from the kraft mill lime kiln. n indicates a new emission factor which was not present in the 1996 Guidelines r indicates an emission factor that has been revised since the 1996 Guidelines

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.19

Volume 2: Energy

TABLE 2.4 DEFAULT EMISSION FACTORS FOR STATIONARY COMBUSTION IN THE COMMERCIAL/INSTITUTIONAL CATEGORY (kg of greenhouse gas per TJ on a Net Calorific Basis) CO2 Fuel

CH4

N2O

Default Emission Factor

Lower

Upper

Default Emission Factor

Lower

Upper

Default Emission Factor

Lower

Upper

73 300

71 100

75 500

10

3

30

0.6

0.2

2

Crude Oil Orimulsion

r

77 000

69 300

85 400

10

3

30

0.6

0.2

2

Natural Gas Liquids

r

64 200

58 300

70 400

10

3

30

0.6

0.2

2

r

69 300

67 500

73 000

10

3

30

0.6

0.2

2

Gasoline

Motor Gasoline Aviation Gasoline

r

70 000

67 500

73 000

10

3

30

0.6

0.2

2

Jet Gasoline

r

70 000

67 500

73 000

10

3

30

0.6

0.2

2

Jet Kerosene

71 500

69 700

74 400

10

3

30

0.6

0.2

2

Other Kerosene

71 900

70 800

73 700

10

3

30

0.6

0.2

2

Shale Oil

73 300

67 800

79 200

10

3

30

0.6

0.2

2

Gas/Diesel Oil

74 100

72 600

74 800

10

3

30

0.6

0.2

2

Residual Fuel Oil

77 400

75 500

78 800

10

3

30

0.6

0.2

2

Liquefied Petroleum Gases

63 100

61 600

65 600

5

1.5

15

0.1

0.03

0.3

Ethane

61 600

56 500

68 600

5

1.5

15

0.1

0.03

0.3

Naphtha

73 300

69 300

76 300

10

3

30

0.6

0.2

2

Bitumen

80 700

73 000

89 900

10

3

30

0.6

0.2

2

Lubricants

73 300

71 900

75 200

10

3

30

0.6

0.2

2

97 500

82 900

115 000

10

3

30

0.6

0.2

2

73 300

68 900

76 600

10

3

30

0.6

0.2

2

n 57 600

48 200

69 000

5

1.5

15

0.1

0.03

0.3

Paraffin Waxes

73 300

72 200

74 400

10

3

30

0.6

0.2

2

White Spirit and SBP

73 300

72 200

74 400

10

3

30

0.6

0.2

2

Other Petroleum Products

73 300

72 200

74 400

10

3

30

0.6

0.2

2

r 98 300

94 600

101 000

10

3

30

1.5

0.5

5

Coking Coal

94 600

87 300

101 000

10

3

30

1.5

0.5

5

Other Bituminous Coal

94 600

89 500

99 700

10

3

30

1.5

0.5

5

Sub-Bituminous Coal

96 100

92 800

100 000

10

3

30

1.5

0.5

5

Lignite

101 000

90 900

115 000

10

3

30

1.5

0.5

5

Oil Shale and Tar Sands

107 000

90 200

125 000

10

3

30

1.5

0.5

5

Petroleum Coke

r

r

Refinery Feedstocks

Other Oil

Refinery Gas

Anthracite

Brown Coal Briquettes

Coke

Patent Fuel Coke Oven Coke and Lignite Coke Gas Coke

2.20

87 300

109 000

n 10

3

30

r

1.5

0.5

5

97 500

87 300

109 000

10

3

30

n

1.5

0.5

5

n 107 000

95 700

119 000

10

3

30

1.5

0.5

4

n 107 000

95 700

119 000

5

1.5

15

0.1

0.03

0.3

n

97 500

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

TABLE 2.4 (CONTINUED) DEFAULT EMISSION FACTORS FOR STATIONARY COMBUSTION IN THE COMMERCIAL/INSTITUTIONAL CATEGORY (kg of greenhouse gas per TJ on a Net Calorific Basis) CO2 Fuel

Derived Gases

Upper

n 10

30

30

54 100

5

1.5

54 100

5

1.5

219 000

308 000

5

n 182 000

145 000

202 000

56 100

54 300

91 700

Upper

1.5

0.5

5

15

0.1

0.03

0.3

15

0.1

0.03

0.3

1.5

15

0.1

0.03

0.3

5

1.5

15

0.1

0.03

0.3

58 300

5

1.5

15

0.1

0.03

0.3

73 300

121 000

300

100

900

4

1.5

15

110 000

183 000

300

100

900

4

1.5

15

73 300

72 200

74 400

300

100

900

4

1.5

15

106 000

100 000

108 000

n 10

3

30

1.4

0.5

5

Wood / Wood Waste

r 112 000

95 000

132 000

300

4

1.5

15

Sulphite lyes (Black Liquor)a

n

95 300

80 700

110 000

2

1

21

Other Primary Solid Biomass

n 100 000

84 700

117 000

Charcoal

n 112 000

95 000

Biogasoline

n 70 800

Biodiesels Other Liquid Biofuels Landfill Gas

n

Upper

Default Emission Factor

n 80 700

68 200

95 300

Gas Works Gas

n 44 400

37 300

Coke Oven Gas

n 44 400

37 300

Blast Furnace Gas

n 260 000

Oxygen Steel Furnace Gas

Municipal Wastes (nonbiomass fraction)

n

Industrial Wastes

n 143 000

Waste Oils

n

Peat

Solid Biofuels

Default Emission Factor

Lower

Lower

Natural Gas

Liquid Biofuels

N2O

Lower

Default Emission Factor

Coal Tar

n

n

100

900

1

18

300

100

900

4

1.5

15

132 000

200

70

600

1

0.3

3

59 800

84 300

10

3

30

0.6

0.2

2

n 70 800

59 800

84 300

10

3

30

0.6

0.2

2

n 79 600

67 100

95 300

10

3

30

0.6

0.2

2

54 600

46 200

66 000

5

1.5

15

0.1

0.03

0.3

Sludge Gas

n 54 600

46 200

66 000

5

1.5

15

0.1

0.03

0.3

Other Biogas

n

54 600

46 200

66 000

5

1.5

15

0.1

0.03

0.3

unicipal Wastes (biomass fraction)

n 100 000

84 700

117 000

4

1..5

15

Other nonf ilM

Gas Biomass

CH4

3

n

300

100

900

n

(a) Includes the biomass-derived CO2 emitted from the black liquor combustion unit and the biomass-derived CO2 emitted from the kraft mill lime kiln. n indicates a new emission factor which was not present in the 1996 Guidelines r indicates an emission factor that has been revised since the 1996 Guidelines

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.21

Volume 2: Energy

TABLE 2.5 DEFAULT EMISSION FACTORS FOR STATIONARY COMBUSTION IN THE RESIDENTIAL AND AGRICULTURE/FORESTRY/FISHING/FISHING FARMS CATEGORIES (kg of greenhouse gas per TJ on a Net Calorific Basis) CO2 Fuel

CH4

Default Emission Factor

Lower

Upper

73 300

71 100

75 500

Crude Oil

Default Emission Factor

N2O

Lower

Upper

Default Emission Factor

Lower

Upper

10

3

30

0.6

0.2

2

Orimulsion

r

77 000

69 300

85 400

10

3

30

0.6

0.2

2

Natural Gas Liquids

r 64 200

58 300

70 400

10

3

30

0.6

0.2

2

69 300

67 500

73 000

10

3

30

0.6

0.2

2

Gasoline

Motor Gasoline

r

Aviation Gasoline

r

70 000

67 500

73 000

10

3

30

0.6

0.2

2

Jet Gasoline

r

70 000

67 500

73 000

10

3

30

0.6

0.2

2

r 71 500

69 700

74 400

10

3

30

0.6

0.2

2

Other Kerosene

71 900

70 800

73 700

10

3

30

0.6

0.2

2

Shale Oil

73 300

67 800

79 200

10

3

30

0.6

0.2

2

Gas/Diesel Oil

74 100

72 600

74 800

10

3

30

0.6

0.2

2

Residual Fuel Oil

77 400

75 500

78 800

10

3

30

0.6

0.2

2

Jet Kerosene

Liquefied Petroleum Gases

63 100

61 600

65 600

5

1.5

15

0.1

0.03

0.3

Ethane

61 600

56 500

68 600

5

1.5

15

0.1

0.03

0.3

Naphtha

73 300

69 300

76 300

10

3

30

0.6

0.2

2

Bitumen

80 700

73 000

89 900

10

3

30

0.6

0.2

2

Lubricants

73 300

71 900

75 200

10

3

30

0.6

0.2

2

97 500

82 900

115 000

10

3

30

0.6

0.2

2

Petroleum Coke

r

Refinery Feedstocks

73 300

68 900

76 600

10

3

30

0.6

0.2

2

n 57 600

48 200

69 000

5

1.5

15

0.1

0.03

0.3

Paraffin Waxes

73 300

72 200

74 400

10

3

30

0.6

0.2

2

White Spirit and SBP

73 300

72 200

74 400

10

3

30

0.6

0.2

3

Other Petroleum Products

73 300

72 200

74 400

10

3

30

0.6

0.2

2

94 600

101 000

300

100

900

1.5

0.5

5

Other Oil

Refinery Gas

Anthracite

98 300

Coking Coal

94 600

87 300

101 000

300

100

900

1.5

0.5

5

Other Bituminous Coal

94 600

89 500

99 700

300

100

900

1.5

0.5

5

Sub-Bituminous Coal

96 100

92 800

100 000

300

100

900

1.5

0.5

5

Lignite

101 000

90 900

115 000

300

100

900

1.5

0.5

5

Oil Shale and Tar Sands

107 000

90 200

125 000

300

100

900

1.5

0.5

5

97 500

87 300

109 000

n 300

100

900

1.5

0.5

5

97 500

87 300

109 000

300

100

900

1.5

0.5

5

Coke Oven Coke and Lignite Coke

r 107 000

95 700

119 000

300

100

900

1.5

0.5

5

Gas Coke

r 107 000

95 700

119 000

5

1.5

15

0.03

0.3

n 80 700

68 200

95 300

n 300

100

900

0.5

5

Gas Works Gas

n 44 400

37 300

54 100

5

1.5

15

0.1

0.03

0.3

Coke Oven Gas

n 44 400

37 300

54 100

5

1.5

15

0.1

0.03

0.3

Blast Furnace Gas

n 260 000

219 000

308 000

5

1.5

15

0.1

0.03

0.3

Oxygen Steel Furnace Gas

n 182 000

145 000

202 000

5

1.5

15

0.1

0.03

0.3

Brown Coal Briquettes

Coke

Patent Fuel

Derived Gases

Coal Tar

2.22

n

r

n

n

0.1 n

1.5

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

TABLE 2.5 (CONTINUED) DEFAULT EMISSION FACTORS FOR STATIONARY COMBUSTION IN THE RESIDENTIAL AND AGRICULTURE/FORESTRY/FISHING/FISHING FARMS CATEGORIES (kg of greenhouse gas per TJ on a Net Calorific Basis) CO2 Fuel

Default Emission Factor

Lower

Upper

56 100

54 300

58 300

91 700

73 300 110 000

Natural Gas Municipal Wastes (nonbiomass fraction)

n

Industrial Wastes

n 143 000

Waste Oils

n

Solid Biofuels Liquid Biofuels Gas Biomass

Default Emission Factor

N2O

Lower

Upper

5

1.5

15

121 000

300

100

183 000

300

100

Default Emission Factor

Lower

Upper

0.1

0.03

0.3

900

4

1.5

15

900

4

1.5

15

73 300

72 200

74 400

300

100

900

106 000

100 000

108 000

n 300

100

900

4

1.5

15

1.4

0.5

5

Wood / Wood Waste

n 112 000

95 000

132 000

300

100

900

4

1.5

15

Sulphite lyes (Black Liquor)a

n

95 300

80 700

110 000

1

18

2

1

21

Other Primary Solid Biomass

n 100 000

84 700

117 000

300

100

4

1.5

15

Charcoal

n 112 000

95 000

132 000

200

70

600

1

0.3

3

Biogasoline

n

70 800

59 800

84 300

10

3

30

0.6

0.2

2

Biodiesels

n

70 800

59 800

84 300

10

3

30

0.6

0.2

2

Other Liquid Biofuels

r

79 600

67 100

95 300

10

3

30

0.6

0.2

2

Landfill Gas

n

54 600

46 200

66 000

5

1.5

15

0.1

0.03

0.3

Sludge Gas

n

54 600

46 200

66 000

5

1.5

15

0.1

0.03

0.3

Other Biogas

n

54 600

46 200

66 000

5

1.5

15

0.1

0.03

0.3

Municipal Wastes (biomass fraction)

n 100 000

84 700

300

100

900

4

1.5

15

Peat

Other nonfossil fuels

CH4

117 000

n

3

n

n

900

(a) Includes the biomass-derived CO2 emitted from the black liquor combustion unit and the biomass-derived CO2 emitted from the kraft mill lime kiln. n indicates a new emission factor which was not present in the 1996 IPCC Guidelines. r indicates an emission factor that has been revised since the 1996I PCC Guidelines.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.23

Volume 2: Energy

2.3.2.2

T IER 2

COUNTRY - SPECIFIC EMISSION FACTORS

Good practice is to use the most disaggregated, technology-specific and country-specific emission factors available, particularly those derived from direct measurements at the different stationary combustion sources. When using the Tier 2 approach, two possible types of emission factors exist: •

•

National emission factors: These emission factors may be developed by national programmes already measuring emissions of indirect greenhouse gases such as NOx, CO and NMVOCs for local air quality; Regional emission factors.

Chapter 2 of Volume 1 provides general guidance for acquiring and compiling information from different sources, specific guidance for generating new data (Section 2.2.3) and generic guidance on emission factors (Section 2.2.4). When measurements are used to obtain emission factors, it is good practice to test a reasonable number of sources representing the average conditions in the country including fuel type and composition, type and size of the combustion unit, firing conditions, load, type of control technologies and maintenance level.

2.3.2.3

T IER 3

TECHNOLOGY - SPECIFIC EMISSION FACTORS

Due to the nature of the emissions of non-CO2 greenhouse gases, technology-specific emission factors are needed for Tier 3. Tables 2.6 to 2.10 give, for example purposes, give representative emission factors for CH4 and N2O by main technology and fuel type. National experts working on detailed bottom-up inventories may use these factors as a starting point or for comparison. They show uncontrolled emission factors for each of the technologies indicated. These emission factor data, therefore, do not include the level of control technology that might be in place in some countries. For instance, for use in countries where control policies have significantly influenced the emission profile, either the individual factors or the final estimate will need to be adjusted.

2.3.3

Choice of activity data

For Stationary Combustion, the activity data for all tiers are the amounts and types of fuel combusted. Most fuels consumers (enterprises, small commercial consumers, or households) normally pay for the solid, liquid and gaseous fuels they consume. Therefore, the masses or volumes of fuels they consume are measured or metered. Quantities of carbon dioxide can normally be easily calculated from fuel consumption data and the carbon contents of the fuels, taking into account the fraction of carbon unoxidised. The quantities of non-CO2 greenhouse gases formed during combustion depend on the combustion technology used, and therefore detailed statistics on fuel combustion technology are needed to rigorously estimate emissions of non-CO2 greenhouse gases. The amount and types of fuel combusted are obtained from one, or a combination, of the sources in the list below: • •

national energy statistics agencies (national energy statistics agencies may collect data on the amount and types of fuel combusted from individual enterprises that consume fuels) reports provided by enterprises to national energy statistics agencies (these reports are most likely to be produced by the operators or owners of large combustion plants)

•

reports provided by enterprises to regulatory agencies (for example, reports produced to demonstrate how enterprises are complying with emission control regulations)

•

individuals within the enterprise responsible for the combustion equipment

• •

periodic surveys, by statistical agencies, of the types and quantities of fuels consumed by a sample of enterprises suppliers of fuels (who may record the quantities of fuels delivered to their customers, and may also record the identity of their customers usually as an economic activity code).

2.24

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

TABLE 2.6 UTILITY SOURCE EMISSION FACTORS Emission factors1 (kg/TJ energy input) Basic technology

Configuration

CH4

N2O

Liquid Fuels Residual Boilers

Fuel

Oil/Shale

Oil

Gas/Diesel Oil Boilers

Normal Firing

r

0.8

0.3

Tangential Firing

r

0.8

0.3

Normal Firing

0.9

0.4

Tangential Firing

0.9

0.4

4

NA

Large Diesel Oil Engines >600hp (447kW) Solid Fuels Pulverised Bituminous Combustion Boilers

Bituminous Boilers Bituminous Combustor

Spreader

Stoker

Fluidised

Bed

Dry Bottom, wall fired

0.7

r

0.5

Dry Bottom, tangentially fired

0.7

r

1.4

Wet Bottom

0.9

r

1.4

With and without re-injection

1

r

0.7

Circulating Bed

1

r

61

Bubbling Bed

1

r

61

Bituminous Cyclone Furnace

0.2

Lignite Atmospheric Fluidised Bed

NA

1.6 r

71

Natural Gas Boilers

r

1

n

1

Gas-Fired Gas Turbines >3MW

r

4

n

1

Large Dual-Fuel Engines

r 258

Combined Cycle

n

1

Circulating Bed

n

3

7

Bubbling Bed

n

3

3

Wood/Wood Waste Boilers3

n

11

n

7

Wood Recovery Boilers

n

1

n

1

NA 3

n

Peat Peat Fluidised Bed Combustor2

Biomass

Source: US EPA, 2005b except otherwise indicated. Values were originally based on gross calorific value; they were converted to net calorific value by assuming that net calorific values were 5 per cent lower than gross calorific values for coal and oil, and 10 per cent lower for natural gas. These percentage adjustments are the OECD/IEA assumptions on how to convert from gross to net calorific values. 1

Source: Tsupari et al, 2006.

2

Values were originally based on gross calorific value; they were converted to net calorific value by assuming that net calorific value for dry wood was 20 per cent lower than the gross calorific value (Forest Product Laboratory, 2004).

NA, data not available. n indicates a new emission factor which was not present in the IPCC 1996 Guidelines r indicates an emission factor that has been revised since the IPCC 1996 Guidelines

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.25

Volume 2: Energy

TABLE 2.7 INDUSTRIAL SOURCE EMISSION FACTORS Emission factors1 (kg/TJ energy input) Basic technology

Configuration

CH4

N2O

3

0.3

0.2

0.4

Liquid Fuels Residual Fuel Oil Boilers Gas/Diesel Oil Boilers Large Stationary Diesel Oil Engines >600hp (447 kW)

4

r

Liquefied Petroleum Gases Boilers

NA

0.9

n

4

n

Solid Fuels Other Bituminous/Sub-bit. Overfeed Stoker Boilers

1

r

0.7

Other Bituminous/Sub-bit. Underfeed Stoker Boilers

14

r

0.7

0.7

r

0.5

Dry Bottom, tangentially fired

0.7

r

1.4

Wet Bottom

0.9

r

1.4

1

r

0.7

Dry Bottom, wall fired Other Bituminous/Sub-bituminous Pulverised Other Bituminous Spreader Stokers Other Bituminous/Sub-bit. Fluidised Bed Combustor

Circulating Bed

1

r

61

Bubbling Bed

1

r

61

n

1

Natural Gas Boilers

1

r 2

Gas-Fired Gas Turbines >3MW Natural Gas-fired Reciprocating Engines3

4

1

2-Stroke Lean Burn

r 693

NA

4-Stroke Lean Burn

r 597

NA

4-Stroke Rich Burn

r 110

NA

Biomass Wood/Wood Waste Boilers4 1

n 11

n

7

Source: US EPA, 2005b except otherwise indicated. Values were originally based on gross calorific value; they were converted to net calorific value by assuming that net calorific values were 5 per cent lower than gross calorific values for coal and oil, and 10 per cent lower for natural gas. These percentage adjustments are the OECD/IEA assumptions on how to convert from gross to net calorific values.

2

Factor was derived from units operating at high loads (80 percent load) only.

3

Most natural gas-fired reciprocating engines are used in the natural gas industry at pipeline compressor and storage stations and at gas processing plants.

4

Values were originally based on gross calorific value; they were converted to net calorific value by assuming that net calorific value for dry wood was 20 per cent lower than the gross calorific value (Forest Product Laboratory, 2004).

NA, data not available n indicates a new emission factor which was not present in the IPCC 1996 Guidelines. r indicates an emission factor that has been revised since the IPCC 1996 Guidelines .

2.26

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

TABLE 2.8 KILNS, OVENS, AND DRYERS SOURCE EMISSION FACTORS

Industry

1

Source

Emission factors1 (kg/TJ energy input CH4

N2O

Cement, Lime

Kilns - Natural Gas

1.1

NA

Cement, Lime

Kilns - Oil

1.0

NA

Cement, Lime

Kilns - Coal

1.0

NA

Coking, Steel

Coke Oven

1.0

NA

Chemical Processes, Wood, Asphalt, Copper, Phosphate

Dryer - Natural Gas

1.1

NA

Chemical Processes, Wood, Asphalt, Copper, Phosphate

Dryer – Oil

1.0

NA

Chemical Processes, Wood, Asphalt, Copper, Phosphate

Dryer – Coal

1.0

NA

Source: Radian, 1990. Values were originally based on gross calorific value; they were converted to net calorific value by assuming that net calorific values were 5 per cent lower than gross calorific values for coal and oil, and 10 per cent lower for natural gas. These percentage adjustments are the OECD/IEA assumptions on how to convert from gross to net calorific values.

NA, data not available.

There are a number of points of good practice that inventory compilers should follow when they collect and use fuel consumption data. It is good practice to use, where possible, the quantities of fuel combusted rather than the quantities of fuel delivered. 9 Agencies collecting emission data from companies under an environmental reporting regulation may request fuel combustion data on this basis. For further information on the general framework for the derivation or review of activity data, check Chapter 2, Approaches to Data Collection, in Volume 1. Due to the technology-specific nature of emissions of non-CO2 greenhouse gases, detailed fuel combustion technology statistics are needed in order to provide rigorous emission estimates. It is good practice to collect activity data in units of fuel used, and to disaggregate as far as possible into the share of fuel used by major technology types. Disaggregation can be achieved through a bottom-up survey of fuel consumption and combustion technology, or through top-down allocations based on expert judgement and statistical sampling. Specialised statistical offices or ministerial departments are generally in charge of regular data collection and handling. Including representatives from these departments in the inventory process is likely to facilitate the acquisition of appropriate activity data. For some source categories (e.g. combustion in the Agriculture Sector), there may be some difficulty in separating fuel used in stationary equipment from fuel used in mobile machinery. Given the different emission factors for non-CO2 gases of these two sources, good practice is to derive shares of energy use of each of these sources by using indirect data (e.g. number of pumps, average consumption, needs for water pumping etc.). Expert judgement and information available from other countries may also be relevant. Good practice for electricity autoproduction (self-generation) is to assign emissions to the source categories (or sub-source categories) where they were generated and to identify them separately from those associated with other end-uses such as process heat. In many countries, the statistics related to autoproduction are available and regularly updated, so activity data should not represent a serious obstacle to estimating non-CO2 emissions. Where confidentiality is an issue, direct discussion with the company affected often allows the data to be used. Otherwise aggregation of the fuel consumption or emissions with those from other companies is usually sufficient. For further information on dealing with restricted data sources or confidentiality issues, check Chapter 2, Approaches to Data Collection, in Volume 1.

9

Quantities of solid and liquid fuels delivered to enterprises will, in general, differ from quantities combusted. This difference is normally the amount put into or taken from stocks held by the enterprise. Stock figures shown in national fuel balances may not include stocks held by final consumers, or may include only stocks held by a particular source category (for example electricity producers). Delivery figures may also include quantities used for mobile sources or as feedstock.

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TABLE 2.9 RESIDENTIAL SOURCE EMISSION FACTORS

Basic technology

Emission factors1 (kg/TJ energy input)

Configuration

CH4

N2O

Liquid Fuels Residual Fuel Oil Combustors

1.4

NA

Gas/Diesel Oil Combustors

0.7

NA

Furnaces Liquefied Petroleum Gas Furnaces

5.8

0.2

1.1

Other Kerosene Stoves2 2

Liquified Petroleum Gas Stoves

NA

Wick

n

2.2 – 23

1.2 – 1.9

Standard

n

0.9 – 23

0.7 – 3.5

Solid Fuels Anthracite Space Heaters 3

Other Bituminous Coal Stoves

Brick or Metal

r 147

NA

n 267 – 2650

NA

Natural Gas Boilers and Furnaces

1

n

1

n

Biomass Wood Pits4 Wood Stoves5, 6

200

NA

Conventional

r 932

NA

Non-catalytic

n 497

NA

r 360 n 258 – 2190

NA 4 – 18.5

NA

n

Catalytic Wood Stoves7 6

Wood Fireplaces

9

8

Charcoal Stoves n 275 – 386 n 1.6 – 9.3 Other Primary Solid Biomass n 230 – 4190 n 9.7 (Agriculture Wastes) Stoves9 Other Primary Solid Biomass n 281 n 27 (Dung) Stoves10 1 Source: US EPA, 2005b except otherwise indicated. Values were originally based on gross calorific value; they were converted to net calorific value by assuming that net calorific values were 5 per cent lower than gross calorific values for coal and oil, and 10 per cent lower for natural gas. These percentage adjustments are the OECD/IEA assumptions on how to convert from gross to net calorific values. 2

Sources: Smith et al., 1992, 1993; Smith et al., 2000; Zhang et al., 2000. Results of experimental studies conducted on a number of household stoves from China (CH4), India and Philippines (CH4 and N2O).

3

Source: Zhang et al., 2000. Results of experimental studies conducted on a number of household stoves from China.

4

Source: Adapted from Radian, 1990; Revised IPCC 1996 Guidelines.

5

U.S. Stoves. Conventional stoves do not have any emission reduction technology or design features and, in most cases, were manufactured before July 1, 1986.

6

Values were originally based on gross calorific value; they were converted to net calorific value by assuming that net calorific value for dry wood was 20 per cent lower than the gross calorific value (Forest Product Laboratory, 2004).

7

Sources: Bhattacharya et al., 2002; Smith et al., 1992, 1993; Smith et al., 2000; Zhang et al., 2000. Results of experimental studies conducted on a number of traditional and improved stoves collected from: Cambodia, China, India, Lao PDR, Malaysia, Nepal, Philippines and Thailand. N2O was measured only in the stoves from India and Philippines. The values represent ultimate emission factors that take into account the combustion, at later stages, of charcoal produced during earlier combustion stages.

8

Sources: Bhattacharya et al., 2002; Smith et al., 1992, 1993; Smith et al., 2000. Results of experimental studies conducted on a number of traditional and improved stoves collected from: Cambodia, India, Lao PDR, Malaysia, Nepal, Philippines and Thailand. N2O was measured only in the stoves from India and Philippines.

9

Sources: Smith et al, 2000; Zhang et al., 2000. Results of experimental studies conducted on a number of household stoves from China (CH4) and India (CH4 and N2O).

10

Source: Smith et al., 2000. Results of experimental studies conducted on a number of household stoves from India.

NA, data not available. n indicates a new emission factor which was not present in the IPCC 1996 Guidelines r indicates an emission factor that has been revised since the IPCC 1996 Guidelines s

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Chapter 2: Stationary Combustion

TABLE 2.10 COMMERCIAL/INSTITUTIONAL SOURCE EMISSION FACTORS

Basic technology

Emission factors1 (kg/TJ energy input)

Configuration

CH4

N2O

Liquid Fuels Residual Fuel Oil Boilers

1.4

0.3

Gas/Diesel Oil Boilers

0.7

0.4

Liquefied Petroleum Gases Boilers

n

0.9

n

4

Other Bituminous/Sub-bit. Overfeed Stoker Boilers

n

1

n

0.7

Other Bituminous/Sub-bit. Underfeed Stoker Boilers

n 14

n

0.7

Other Bituminous/Sub-bit. Hand-fed Units

n 87

n

0.7

Solid Fuels

Dry Bottom, wall fired

n

0.7

n

0.5

Dry Bottom, tangentially fired

n

0.7

n

1.4

Wet Bottom

n

0.9

n

1.4

n

1

n

0.7

Circulating Bed

n

1

n 61

Bubbling Bed

n

1

n 61

Boilers

r

1

r

1

Gas-Fired Gas Turbines >3MWa

n

4

n

1.4

n 11

n

7

Other Bituminous/Sub-bituminous Pulverised Boilers

Other Bituminous Spreader Stokers Other Bituminous/Sub-bit. Fluidised Bed Combustor Natural Gas

Biomass Wood/Wood Waste Boilers2 1

Source: US EPA, 2005b Values were originally based on gross calorific value; they were converted to net calorific value by assuming that net calorific values were 5 per cent lower than gross calorific values for coal and oil, and 10 per cent lower for natural gas. These percentage adjustments are the OECD/IEA assumptions on how to convert from gross to net calorific values.

2

Values were originally based on gross calorific value; they were converted to net calorific value by assuming that net calorific value for dry wood was 20 per cent lower than the gross calorific value (Forest Product Laboratory, 2004).

n indicates a new emission factor which was not present in the IPCC 1996 Guidelines r indicates an emission factor that has been revised since the IPCC 1996 Guidelines

2.3.3.1

T IER 1

AND TIER

2

The activity data used in a Tier 1 approach for combustion in the energy sector are derived from energy statistics, compiled by the national statistical agency. Comparable statistics are published by the International Energy Agency (IEA), based on national returns. If national data are not directly available to the national inventory compiler, a request could be sent to the IEA at [email protected] to receive the country’s data free of charge. Primary data on fuel consumption are normally collected in mass or in volume units. Because the carbon content of fuels is generally correlated with the energy content, and because the energy content of fuels is generally measured, it is recommended to convert values for fuel consumption into energy units. Default values for the conversion of fuel consumption numbers into conventional energy units are given in section 1.4.1.2. Information on energy statistics and balances methodology is available in the "Energy Statistics Manual" published by the IEA. This manual can be downloaded free of charge from www.iea.org. Key issues about more important source categories are given below.

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EN ERGY INDUSTR IES In energy industries, fossil fuels are both raw materials for the conversion processes, and sources of energy to run these processes. The energy industry comprises three kinds of activities: 1

Primary fuel production (e.g. coal mining and oil and gas extraction);

2

Conversion to secondary or tertiary fossil fuels (e.g. crude oil to petroleum products in refineries, coal to coke and coke oven gas in coke ovens);

3

Conversion to non-fossil energy vectors (e.g. from fossil fuel into electricity and/or heat).

Emissions from combustion during production and conversion processes are counted under energy industries. Emissions from the secondary fuels produced by the energy industries are counted in the sector where they are used. When collecting activity data, it is essential to distinguish between the fuel that is combusted and the fuel that is converted into a secondary or tertiary fuel in Energy Industries. MA IN AC TIVITY ELEC TRIC I TY AND HEAT PRODUCTION The main activity electricity and heat production (formerly known as public electricity and heat production) converts the chemical energy stored in the fuels to either electrical power (counted under electricity generation) or heat (counted under heat production) or both (counted under combined heat and power, CHP); see Table 2.1. Figure 2.2 shows the energy flows. In conventional power plants, the total energy losses to the environment might be as high as 70 percent of the chemical energy in the fuels, depending on the fuel and the specific technology. In a modern high efficiency power plant, losses are down to about half of the chemical energy contained in the fuels. In a combined heat and power plant most of the energy in the fuel is delivered to final users, either as electricity or as heat (for industrial processes or residential heating or similar uses). The width of the arrows roughly represents the relative magnitude of the energy flows involved. Figure 2.2

Power and heat plants use fuels to produce electric power and/or useful heat.

Heat plant

Power plant

Energy losses

Energy losses

Combustion

Combustion Heat out

Generator

Fuel In

Fuel In

Power out

Power & Heat Energy losses plant Energy losses Combustion Fuel In

Heat out Generator Power Out

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Chapter 2: Stationary Combustion

P ETRO LEUM R EFINI NG In a petroleum refinery, crude oil is converted to a broad range of products (Figure 2.3). For this transformation to occur, part of the energy content of the products obtained from crude oil is used in the refinery (See Table 2.1.). This complicates the derivation of activity data from energy statistics. Figure 2.3

A refinery uses energy to transform crude oil into petroleum products.

Refinery Petroleum products out

Crude Oil In

Combustion

In principle all petroleum products are combustible as fuel to provide the process heat and steam needed for the refining processes. The petroleum products include a broad range from the heavy products like tar, bitumen, heavy fuel oils via the middle distillates like gas oils, naphtha, diesel oils, kerosenes to light products like motor gasoline, LPG and refinery gas. In many cases, the exact products and fuels used in refineries to produce the heat and steam needed to run the refinery processes are not easily derived from the energy statistics. The fuel combusted within petroleum refineries typically amounts to 6 to 10 percent of the total fuel input to the refinery, depending on the complexity and vintage of the technology. It is good practice to ask the refinery industry for fuel consumption in order to select or verify the appropriate values reported by energy statistics. MANU FACTURIN G INDUSTR IES AND CONSTRUC TION In manufacturing industries, raw materials are converted into products as is schematically presented in Figure 2.4. For construction, the same principle holds: the inputs include the building materials and the outputs are the buildings. Manufacturing industries are generally classified according to the nature of their products. This is done via the International Standard Industrial Classification of economic activities that is used in Table 2.1 for convenient cross-referencing. Figure 2.4

Fuels are used as an energy source in manufacturing industries to convert raw materials into products. 10

Manufacturing Industry Raw materials in

Fuels in

10

Products out

Combustion

For some industries raw materials might include fossil fuel. Some fuel might be derived from by-products or waste streams generated in the production process.

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Volume 2: Energy

Raw materials used in manufacturing industries can also include fossil fuels. Examples include production of petrochemicals (eg methanol), other bulk chemicals (eg ammonia) and primary iron where coke is an input. In some cases, the situation is more complicated, because the energy to drive the process might be directly delivered from the chemical reactions of the manufacturing processes. An example of this is the manufacture of primary iron and steel, where the chemical reaction between the coke and the iron ore produces gas and heat that are sufficient to run the process11. The reporting of emissions from gases obtained from processing feedstock and process fuels obtained directly from the feedstock (e.g. ammonia production) follows the principle stated in Section 1.2 of this Volume and detailed guidance given in the IPPU Volume. In summary, if the emissions occur in the IPPU source category which produced the gases emitted they remain as industrial processes emissions in that source category. If the gases are exported to another source category in the IPPU sector, or to the energy sector, then the fugitive, combustion or other emissions associated with them should be reported in the sector where they occur. Inventory compilers are reminded to discriminate between emissions from processes where the same fossil fuel is used both for energy and for feedstock purposes (e.g. synthesis gas production, carbon black production), and to report these emissions in the correct sectors. Some countries may face some difficulties in obtaining disaggregated activity data or may have different definitions for industrial source categories. For example, some countries may include residential energy consumption of the workers in industry consumption. In this case, any deviations from the definitions should be documented.

2.3.3.2

T IER 3

Tier 3 estimates incorporate data at the level of individual facilities, and this type of information is increasingly available, because of the requirements of emissions trading schemes. It is often the case, that coverage of facility level data does not correspond exactly to coverage of classifications within the national energy statistics, and this can give rise to difficulties in combining the various sources of information. Methods for combining data are discussed in Chapter 2 of Volume 1 on General Guidance and Reporting.

2.3.3.3

A VOIDING

DOUBLE COUNTING ACTIVITY DATA WITH OTHER SECTORS

The use of fuel combustion statistics rather than fuel delivery statistics is key to avoid double counting in emission estimates. Fuel combustion data, however, are very seldom complete, since it is not practical to measure the fuel consumption or emissions of every residential or commercial source. Hence, national inventories using this approach will generally contain a mixture of combustion data for larger sources and delivery data for other sources. The inventory compiler must take care to avoid both double counting and omission of emissions when combining data from multiple sources. When activity data are not quantities of fuel combusted but are instead deliveries to enterprises or main subcategories, there is a risk of double counting emissions from the IPPU or Waste Sectors. Identifying double counting is not always easy. Fuels delivered and used in certain processes may give rise to by-products used as fuels elsewhere in the plant or sold for fuel use to third parties (e.g. blast furnace gas that is derived from coke and other carbon inputs to blast furnaces). It is good practice to coordinate estimates between the stationary source category and relevant industrial categories to avoid double counting or omissions. Some of the categories and subcategories where fossil fuel carbon is reported and between which double counting of fossil fuel carbon could, in principle, occur are summarized below. •

IPPU – Production of non-fuel products from energy feedstocks such as coke, ethane, gas/diesel oil, LPG, naphtha and natural gas. The production of synthesis gas (syngas), namely the mixture of carbon monoxide and hydrogen, through steam reforming or partial oxidation of energy feedstocks deserves particular attention since these processes produce CO2 emissions. Synthesis gas is an intermediate in the production of chemicals such as ammonia, formaldehyde, methanol, pure carbon monoxide and pure hydrogen. Emissions from these processes should be accounted for in the IPPU sector. Note that CO2 emissions should be counted at the point of emission if the gas is stored for only a short time (e.g. CO2 used in the food and drink industry generated as a by product of ammonia production).

11

The best available techniques reference documents (BREFs) of the European Integrated Pollution Prevention and Control Bureau (IPPC) for Iron and Steel (http://eippcb.jrc.es/) show that about one third of the heat requirement for the process comes from the blast furnace gas produced and combusted in the blast air heaters. Also the heat produced by the production of CO as the blast air passes over the coke is not strictly part of the reduction of the ore.

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Chapter 2: Stationary Combustion

Synthesis gas is also produced by partial oxidation/gasification of solid and liquid fuel feedstocks in the relatively newer Integrated Gasification Combined Cycle (IGCC) technology for power generation. When synthesis gas is produced in IGCC for the purpose of generating power, associated emissions should be accounted for in 1A, fuel combustion. In the production of carbides, CO2 is released when carbon-rich fuels, particularly petroleum coke, are used as a carbon source. These emissions should be accounted for in the IPPU sector. •

•

•

•

For further information, refer to Volume 3, which gives details of completeness check of carbon emissions from feedstock and other non-energy use. IPPU, AFOLU – Use of carbon as reducing agent in metal production The greenhouse gas emissions originating from the use of coal, coke, natural gas, prebaked anodes and coal electrodes as reducing agents in the commercial production of metals from ores should be accounted for in the IPPU sector. Wood chips and charcoal may also be used in some of the processes. In this case, the resulting emissions are counted in the AFOLU sector. By-product fuels (coke oven gas and blast furnace gas) are produced in some of these processes. These fuels may be sold or used within the plant. They may or may not be included in the national energy balance. Care should consequently be taken not to double count emissions. ENERGY, WASTE – methane from coal mine waste, landfill gas and sewage gas In these cases, it is important to ensure that the amounts of fuel accounted for in stationary combustion are the same as the quantities netted out from “Fugitive emissions from coal mining and handling”, “Waste Incineration” and “Wastewater Treatment and Discharge” respectively. WASTE – Incineration of waste When energy is recovered from waste combustion, the associated greenhouse gas emissions are accounted for in the Energy sector under stationary combustion. Waste incineration with no associated energy purposes should be reported in the Waste source category; see Chapter 5 (Incineration and Open Burning of Waste) of Volume 5. It is good practice to assess the content of waste and differentiate between the part containing plastics and other fossil carbon materials from the biogenic part and estimate the associated emissions accordingly. The CO2 emission from the fossil-carbon part can be included in the fuel category Other fuels, while the CO2 emissions from the biomass part should be reported as an information item. For higher tier estimations, inventory compiler may refer to Chapter 5 of the Waste Volume. It is good practice to contact those responsible for recovering used oils in order to assess the extent to which used oils are burned in the country and estimate and report these emissions in the Energy sector if they are used as fuel. ENERGY – Mobile combustion

The main issue is to ensure that double counting of agricultural and off-road vehicles is avoided.

2.3.3.4

T REATMENT

OF BIOMASS

Biomass is a special case: •

• • •

•

Emissions of CO2 from biomass fuels are estimated and reported in the AFOLU sector as part of the AFOLU methodology. In the reporting tables, emissions from combustion of biofuels are reported as information items but not included in the sectoral or national totals to avoid double counting. In the emission factor tables presented in this chapter, default CO2 emission factors are presented to enable the user to estimate these information items. For biomass, only that part of the biomass that is combusted for energy purposes should be estimated for inclusion as an information item in the Energy sector. The emissions of CH4 and N2O, however, are estimated and included in the sector and national totals because their effect is in addition to the stock changes estimated in the AFOLU sector. For fuel wood, activity data are available from the IEA or the FAO (Food and Agriculture Organisation of the United Nations). These data originate from national sources and inventory compilers can obtain a better understanding of national circumstances by contacting national statistical agencies to find the organisations involved. For agricultural crop residues (part of other primary solid biomass) and also for fuel wood, estimation methods for activity data are available in Chapter 5 of the AFOLU volume.

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Volume 2: Energy •

In some instances, biofuels will be combusted jointly with fossil fuels. In this case, the split between the fossil and non-fossil fraction of the fuel should be established and the emission factors applied to the appropriate fractions.

2.3.4

Carbon dioxide capture

Capture and storage removes carbon dioxide from the gas streams that would otherwise be emitted to the atmosphere, and transfers it for indefinite long term storage in geological reservoirs, such as depleted oil and gas fields or deep saline aquifers. In the energy sector, candidates for carbon dioxide capture and storage undertakings include large stationary sources such as power stations and natural gas sweetening units. This chapter deals only with CO2 capture associated with combustion activities, particularly those relative to power plants. Fugitive emissions arising from the transfer of carbon dioxide from the point of capture to the geological storage, and emissions from the storage site itself, are covered in Chapter 5 of this Volume. Other possibilities also exist in industry to capture CO2 from process streams. These are covered in Volume 3. There are three main approaches for capturing CO2 arising from the combustion of fossil fuels and/or biomass (Figure 2.5). Post-combustion capture refers to the removal of CO2 from flue gases produced by combustion of a fuel (oil, coal, natural gas or biomass) in air. Pre-combustion capture involves the production of synthesis gas (syngas), namely the mixture of carbon monoxide and hydrogen, by reacting energy feedstocks with steam and/or oxygen or air. The resulting carbon monoxide is reacted with steam by the shift reaction to produce CO2 and more hydrogen. The stream leaving the shift reactor is separated into a high purity CO2 stream and H2-rich fuel that can be used in many applications, such as boilers, gas turbines and fuel cells. Oxy-fuel combustion uses either almost pure oxygen or a mixture of almost pure oxygen and a CO2-rich recycled flue gas instead of air for fuel combustion. The flue gas contains mainly H2O and CO2 with excess oxygen required to ensure complete combustion of the fuel. It will also contain any other components in the fuel, any diluents in the oxygen stream supplied, any inert matter in the fuel and from air leakage into the system from the atmosphere. The net flue gas, after cooling to condense water vapour, contains from about 80 to 98 percent CO2 depending on the fuel used and the particular oxy-fuel combustion process. Figure 2.5

Oil Coal Gas Biomass

CO 2 capture systems from stationary combustion sources

Post-combustion

N2, O2 (CO2)

Air

CO2 Power & Heat

Compression

CO2 Separation

Dehydration

Pre-combustion

Steam Gas - Light hydrocarbons

H2-rich fuel

Reforming

Oil Coal Gas Biomass

Syngas

Air/O2 Steam

Syngas

reactor

Partial Oxidation / Gasification

CO2 Separation

Power & Heat

N2, O2 (CO2)

Air CO2

Compression Dehydration

N2, O2 (CO2)

Oil Coal Gas Biomass

Shift

Power & Heat

Compression

Oxyfuel combustion

Dehydration

O2 Air

N2 Air Separation

Carbon dioxide capture has some energy requirements with a corresponding increase in fossil fuel consumption. Also the capture process is less than 100 percent efficient, so a fraction of CO2 will still be emitted from the gas stream. Chapter 3 of the IPCC Special Report on CO2 Capture and Storage (Thambimuthu et al., 2005) provides a thorough overview of the current and emerging technologies for capturing CO2 from different streams arising in the energy and the industrial processes sectors.

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Chapter 2: Stationary Combustion

The general scheme concerning the carbon flows in the three approaches for capturing CO2 from streams arising in combustion processes is depicted in Figure 2.6. The system boundary considered in this chapter includes the power plant or other process of interest, the CO2 removal unit and compression/dehydration of the captured CO2 but does not include CO2 transport and storage systems. This general scheme also contemplates the possibility that pre-combustion capture systems can also be applied to multi-product plants (also known as polygeneration plants). The type of polygeneration plant considered in this chapter employs fossil fuel feedstocks to produce electricity and/or heat plus a variety of co-products such as hydrogen, chemicals and liquid fuels. In those processes associated with post-combustion and oxyfuel combustion capture systems, no carbonaceous coproducts are typically produced. Figure 2.6

Carbon flows in and out of the system boundary for a CO 2 capture system associated with stationary combustion processes

Non-captured CO2 (emitted)

Oil Coal Gas Biomass

Power & Heat + CO2 separation

Captured CO2 (to transport and storage)

+ Compression & dehydration

Carbonaceous products (chemicals or liquid fuels)

The CO2 capture efficiency of any system represented in Figure 2.6 is given in Equation 2.6. Table 2.11 summarises estimates of CO2 capture efficiencies for post and pre-combustion systems of interest that have been recently reported in several studies. This information is provided for illustrative purposes only as it is good practice to use measured data on volume captured rather than efficiency factors to estimate emissions from a CO2 capture installation.

EQUATION 2.6 CO2 CAPTURE EFFICIENCY Ccaptured CO2 EfficiencyCO2 capture technology = • 100 C fuel − C products Where: Efficiency CO 2 capture

C captured

CO 2

technology

= CO2 capture system efficiency (percent) = amount of carbon in the captured CO2 stream (kg)

C fuel

= amount of carbon in fossil fuel or biomass input to the plant (kg)

C products

= amount of carbon in carbonaceous chemical or fuel products of the plant (kg).

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TABLE 2.11 TYPICAL CO2 CAPTURE EFFICIENCIES FOR POST AND PRE-COMBUSTION SYSTEMS Technologies

Efficiency (%)

References

Power plant / Capture system

Average

Minimum

Maximum

Pulverised subbituminous/bituminous coal (250-760 MWe, 41-45% net plant efficiency)1,2 / Aminebased post-combustion capture.

90

85

96

Alstom, 2001; Chen et al., 2003; Gibbins et al., 2005; IEA GHG, 2004; Parsons, 2002; Rao and Rubin, 2002; Rubin et al., 2005; Simbeck, 2002; Singh et al., 2003.

Natural gas combined cycle (380-780 MWe, 55-58% net plant efficiency, LHV)1 / Amine-based post-combustion capture.

88

85

90

CCP, 2005; EPRI, 2002; IEA GHG, 2004; NETL, 2002; Rubin et al., 2005.

Integrated gasification combined cycle (400-830 MWe, 31-40% net plant efficiency)1 / Physical solvent-based precombustion capture (Selexol)

88

85

91

IEA GHG, 2003; NETL, 2002; Nsakala et al., 2003; Parsons, 2002; Rubin et al., 2005; Simbeck, 2002.

Electricity + H2 plant (coal, 2600-9900 GJ/hr input capacity)1 / Physical solventbased pre-combustion capture (mostly Selexol)

83

80

90

Kreutz et al., 2005, Mitretek, 2003; NRC, 2004; Parsons, 2002.

Electricity + dimethyl ether (coal, 7900-8700 GJ/hr input capacity)1 / Physical solventbased pre-combustion capture (Selexol or Rectisol)

64

32

97

Celik et al., 2005; Larson, 2003

Electricity + methanol (coal, 9900 GJ/hr input capacity)1 / Physical solvent-based precombustion capture (Selexol)

60

58

63

Larson, 2003

Electricity + Fischer-Tropsch liquids (coal, 16000 GJ/hr input capacity)1 / Physical solventbased pre-combustion capture (Selexol)

91

-

-

Mitretek, 2001

1

Reference plant without CO2 capture system

2

These options include existing plants with retrofitting post-combustion capture system as well as new designs integrating power generation and capture systems.

TIER 3 CO 2 EMISSION ESTIMATES Because this is an emerging technology, it requires plant-specific reporting at Tier 3. Plants, with capture and storage will most probably meter the amount of gas removed by the gas stream and transferred to geological storage. Capture efficiencies derived from the measured data can be compared with the values in Table 2.11 as a verification cross-check. Under Tier 3, the CO2 emissions are therefore estimated from the fuel consumption estimated as described in earlier sections of this chapter minus the metered amount removed. EQUATION 2.7 TREATMENT OF CO2 CAPTURE Emissions s = Pr oduction s − Capture s Where: s

2.36

= source category or subcategory where capture takes place

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Stationary Combustion

Captures

= Amount captured.

Productions

= Estimated emissions, using these guidelines assuming no capture

Emissionss

= Reported emission for the source category or sub-category

This method automatically takes into account any increase in energy consumption at the plant because of the capture process (since this will be reflected in the fuel statistics), and it does not require independent estimation of the capture efficiency, since the residual emissions are estimated more accurately by the subtraction. If the plant is supplied with biofuels, the corresponding CO2 emissions will be zero (these are already included in national totals due to their treatment in the AFOLU sector), so the subtraction of the amount of gas transferred to long-term storage may give negative emissions. This is correct since if the biomass carbon is permanently stored, it is being removed from the atmosphere. The corollary of this is that any subsequent emissions from CO2 transport, CO2 injection and the storage reservoir itself should be counted in national total emissions, irrespective of whether the carbon originates from fossil sources or recent biomass production. This is why in sections 5.3 (CO2 transport), 5.4 (Injection) and 5.5 (Geological Storage) no reference is made to the origin of the CO2 stored in underground reservoirs. The metering for the amount removed should be installed in line with industrial practice and will normally be accurate to about 1 percent. Quantities of CO2 for later use and short-term storage should not be deducted from CO2 emissions except when the CO2 emissions are accounted for elsewhere in the inventory12.

2.3.5

Completeness

A complete estimate of emissions from fuel combustion should include emissions from all fuels and all source categories identified within the IPCC 2006 Guidelines. Completeness should be established by using the same underlying activity data to estimate emissions of CO2, CH4 and N2O from the same source categories. All fuels delivered by fuel producers must be accounted for. Misclassification of enterprises and the use of distributors to supply small commercial customers and households increase the chance of systematic errors in the allocation of fuel delivery statistics. Where sample survey data that provide figures for fuel consumption by specific economic sectors exist, the figures may be compared with the corresponding delivery data. Any systematic difference should be identified and the adjustment to the allocation of delivery data may then be made accordingly. Systematic under-reporting of solid and liquid fuels may also occur if final consumers import fuels directly. Direct imports will be included in customs data and therefore in fuel supply statistics, but not in the statistics of fuel deliveries provided by national suppliers. If direct importing by consumers is significant, then the statistical difference between supplies and deliveries will reveal the magnitude. Own use of fuels supplied by dedicated mines may occur in such sectors of manufacturing as iron and steel and cement, and is also a potential source of under-reporting. Once again, a comparison with consumption survey results will reveal which main source categories are involved in direct importing. Concerning biomass fuels, the national energy statistics agencies should be consulted about their use, including possible use of non-commercially traded biomass fuels. Experience has shown that some activities such as change in producer stocks of fossil fuels and own fuel combustion by energy industries may be poorly covered in existing inventories. This also applies to statistics on biomass fuels and from waste combustion. Their presence should be specifically checked with statistical agencies, sectoral experts and organisations as well as supplementary sources of data included if necessary. Chapter 2 of Volume 1 covers data collection in general.

2.3.6

Developing a consistent time series and recalculation

Using a consistent method to estimate emissions is the main mechanism for ensuring time series consistency. However, the variability in fuel quality over time is also important to consider within the limits of the national fuel characterisation or the fuel types listed in Tables 2.2 to 2.5. This includes variation in carbon content, typically reflected in variation in the calorific values used to convert the fuels from mass or volume units to the energy units used in the estimation. It is good practice for inventory compilers to check that variations of calorific values over time are in fact reflected in the information used to construct the national energy statistics.

12

Examples include urea production (Volume 3, section 3.2) and the use of CO2 in methanol production (Volume 3, section 3.9) where the CO2 due to the final products is accounted for.

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Application of these IPCC 2006 Guidelines may result in revisions in some components of the emissions inventory, such as emissions factors or the sectoral classification of some emissions. For example, the component of emissions of CO2 from non-fuel use of fossil fuels will move from the Energy Sector under the IPCC 1996 Guidelines to the IPPU sector under the IPCC 2006 Guidelines. Whereas the IPCC 1996 Guidelines for the energy sector estimated total potential emissions from fossil-fuel use and then subtracted the portion of the carbon that ended up stored in long-lived products, the IPCC 2006 Guidelines include all non-fuel uses in the IPPU sector. This should result in slightly decreased CO2 emissions reported from the Energy sector and increased emissions reported in the IPPU sector. For further information on ensuring a consistent time series, check Chapter 5, Time Series Consistency, in Volume 1.

2.4

UNCERTAINTY ASSESSMENT

2.4.1

Emission factor uncertainties

For fossil fuel combustion, uncertainties in CO2 emission factors are relatively low. These emission factors are determined by the carbon content of the fuel and thus there are physical constraints on the magnitude of their uncertainty. However, it is important to note there are likely to be intrinsic differences in the uncertainties of CO2 emission factors of petroleum products, coal and natural gas. Petroleum products typically conform to fairly tight specifications which limit the possible range of carbon content and calorific value, and are also sourced from a relatively small number of refineries and/or import terminals. Coal by contrast may be sourced from mines producing coals with a very wide range of carbon contents and calorific values and is mostly supplied under contract to users who adapt their equipment to match the characteristics of the particular coal. Hence at the national level, the single energy commodity "black coal" can have a range of CO2 emission factors. Emission factors for CH4 and especially N2O are highly uncertain. High uncertainties in emission factors may be ascribed to lack of relevant measurements and subsequent generalisations, uncertainties in measurements, or an insufficient understanding of the emission generating process. Furthermore, due to stochastic variations in process conditions, a high variability of the real time emission factors for these gases might also occur (Pulles and Heslinga, 2004). Such variability obviously will also contribute to the uncertainty in the emission estimates. The uncertainties of emission factors are seldom known or accessible from empirical data. Consequently, uncertainties are customarily derived from indirect sources or by means of expert judgements. The IPCC 1996 Guidelines (Table A1-1, Vol. I, p. A1.4) suggest an overall uncertainty value of 7 per cent for the CO2 emission factors of Energy. The default uncertainties shown in Table 2.12 derived from the EMEP/CORINAIR Guidebook ratings (EMEP/CORINAIR, 1999) may be used in the absence of country-specific estimates. TABLE 2.12 DEFAULT UNCERTAINTY ESTIMATES FOR STATIONARY COMBUSTION EMISSION FACTORS Sector

CH4

Public Power, co-generation and district heating Commercial, Institutional and Residential combustion Industrial combustion

50-150% 50-150% 50-150%

N2O Order of magnitude* Order of magnitude Order of magnitude

* i.e. having an uncertainty range from one-tenth of the mean value to ten times the mean value. Source: IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (2000)

While these default uncertainties can be used for the existing emission factors (whether country-specific or taken from the IPCC Guidelines), there may be additional uncertainties associated with applying emission factors that are not representative of the combustion conditions in the country. Uncertainties can be lower than the values in Table 2.12 if country-specific emission factors are used. It is good practice to obtain estimates of these uncertainties from national experts taking into account the guidance concerning expert judgements provided in Volume 1. There is currently relatively little experience in assessing and compiling inventory uncertainties and more experience is needed to assess whether the few available results are typical and comparable, and what the main weaknesses in such analyses are. Some articles addressing uncertainty assessment of greenhouse inventories have recently appeared in the peer-reviewed literature. Rypdal and Winiwater (2001) evaluated the uncertainties in greenhouse gas inventories and compared the results reported by five countries namely Austria (Winiwarter and Rypdal, 2001), the Netherlands (van Amstel et al., 2000), Norway (Rypdal, 1999), UK (Baggott et al., 2005)

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Chapter 2: Stationary Combustion

and USA (EIA, 1999). More recently, Monni et al. (2004) evaluated the uncertainties in the Finnish greenhouse gas emission inventory. Tables 2.13 and 2.14 summarise the uncertainty assessments of emission factors for stationary combustion reported in the studies noted above. To complement this information, the approaches and emission factors used by each country as (reported in the corresponding 2003 National Greenhouse Gas Inventory submission to the UNFCCC) have been added to Tables 2.13 and 2.14. It can be seen that higher tier approaches and a higher number of country-specific (CS) emission factors were used for CO2 as compared to CH4 and N2O. Conversely, lower tier approaches and greater reliance on default emission factors were used for N2O. This information is provided primarily for illustrative purposes. These uncertainty ranges could be used as a starting point or for comparison by national experts working on uncertainty assessment. TABLE 2.13 SUMMARY OF UNCERTAINTY ASSESSMENT OF CO2 EMISSION FACTORS FOR STATIONARY COMBUSTION SOURCES OF SELECTED COUNTRIES

2003 GHG inventory submission2

95% confidence interval1

Distribution

Austria

± 0.5

Norway

Country

References

Approach3

Emission factor4

Normal

C

CS

Winiwarter and Rypdal, 2001

±3

Normal

C

CS

Rypdal, 1999

The Netherlands

±2

-

T2, CS

CS, PS

UK

±2

Normal

T2

CS

Baggott et al., 2005

USA

±2

-

T1

CS

EIA, 1999

Austria

± 0.5

Normal

C

CS

Winiwarter and Rypdal, 2001

Norway

±7

Normal

C

CS

Rypdal, 1999

The Netherlands

± 1-10

-

T2, CS

CS, PS

UK

± 1-6

Normal

T2

CS

Baggott et al., (2005)

USA

± 0-1

-

T1

CS

EIA, 1999

Normal

T2, CS

D, CS, PS

Oil

Van Amstel et al., 2000

Coal, coke, gas

Van Amstel et al., 2000

Other fuels (mainly peat) Finland

±5

Monni et al., 2004

1

Data are given as upper and lower bounds of the 95 percent confidence interval, and expressed as percent relative to the mean value.

2

The information in the columns is based on the 2003 National Greenhouse Gas Inventory submissions from Annex I Parties to the UNFCCC.

3

Notation keys that specify the approach applied: T1 (IPCC Tier 1), T2 (IPCC Tier 2), T3 (IPCC Tier 3), C (CORINAIR), CS (Country-specific).

4

Notation keys that specify the emission factor used: D (IPCC default), C (CORINAIR), CS (Country-specific), PS (Plant Specific).

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TABLE 2.14 SUMMARY OF UNCERTAINTY ASSESSMENT OF CH4 AND N2O EMISSION FACTORS FOR STATIONARY COMBUSTION SOURCES OF SELECTED COUNTRIES

Country

95% confidence interval1

Distribution

2003 GHG inventory submission2 3

References 4

Approach

Emission factor

Normal

C, CS

CS

β

T1, T2, CS

CS, PS

Lognormal

T2, CS

D, CS, PS

CH4 Austria

± 50

Finland

-75 to +10

Norway

-50 to + 100

The Netherlands

± 25

-

T2, CS

CS, PS

UK

± 50

Truncated normal

T2

D, C, CS

USA

Order of magnitude

-

T1

D, CS

Normal

C, CS

CS

Winiwarter and Rypdal, 2001 Monni et al., 2004 Rypdal, 1999 Van Amstel et al., 2000 Baggott et al., 2005 EIA, 1999

N2O Winiwarter and Rypdal, 2001

Austria

± 20

Finland

-75 to +10

Beta

T1, T2, CS

CS, PS

Monni et al., 2004

Norway

-66 to + 200

Beta

T1, T2

D, CS

Rypdal, 1999

The Netherlands

± 75

-

T1, CS

D, PS

Van Amstel et al., 2000

UK

± 100 to 200

-

T2

D, C, CS

USA

-55 to + 200

-

T1

D, CS

Baggott et al., 2005 EIA, 1999

1

Data are given as upper and lower bounds of the 95 percent confidence interval, and expressed as percent relative to the mean value.

2

The information in the columns is based on the 2003 National Greenhouse Gas Inventory submissions from Annex I Parties to the UNFCCC.

3

Notation keys that specify the approach applied: T1 (IPCC Tier 1), T2 (IPCC Tier 2), T3 (IPCC Tier 3), C (CORINAIR), CS (Country-specific).

4

Notation keys that specify the emission factor used: D (IPCC default), C (CORINAIR), CS (Country-specific), PS (PlantSpecific).

2.4.2

Activity data uncertainties

Statistics of fuel combusted at large sources obtained from direct measurement or obligatory reporting are likely to be within 3 percent of the central estimate. For some energy intensive industries, combustion data are likely to be more accurate. It is good practice to estimate the uncertainties in fuel consumption for the main subcategories in consultation with the sample survey designers, because the uncertainties depend on the quality of the survey design and the size of sample used. In addition to any systematic bias in the activity data as a result of incomplete coverage of consumption of fuels, the activity data will be subject to random errors in the data collection that will vary from year to year. Countries with good data collection systems, including data quality control, may be expected to keep the random error in total recorded energy use to about 2-3 percent of the annual figure. This range reflects the implicit confidence limits on total energy demand seen in models using historical energy data and relating energy demand to economic factors. Percentage errors for individual energy use activities can be much larger. Overall uncertainty in activity data is a combination of both systematic and random errors. Most developed countries prepare balances of fuel supply and deliveries and this provides a check on systematic errors. In these circumstances, overall systematic errors are likely to be small. Experts believe that the uncertainty resulting from the two errors combined is probably in the range of ±5 percent for most developed countries. For countries with

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Chapter 2: Stationary Combustion less well-developed energy data systems, this could be considerably larger, probably about ±10 percent. Informal activities may increase the uncertainty up to as much as 50 percent in some sectors for some countries. Uncertainty ranges for stationary combustion activity data are shown in Table 2.15. This information may be used when reporting uncertainties. It is good practice for inventory compilers to develop, if possible, countryspecific uncertainties using expert judgement and/or statistical analysis. TABLE 2.15 LEVEL OF UNCERTAINTY ASSOCIATED WITH STATIONARY COMBUSTION ACTIVITY DATA Well developed statistical systems

Less developed statistical systems

Sector Surveys

Extrapolation

Surveys

Extrapolation

Less than 1%

3-5%

1-2%

5-10%

Commercial, institutional, residential combustion

3-5%

5-10%

10-15%

15-25%

Industrial combustion (Energy intensive industries)

2-3%

3-5%

2-3%

5-10%

Industrial combustion (others)

3-5%

5-10%

10-15%

15-20%

10-30%

20-40%

30-60%

60-100%

Main activity electricity and heat production

Biomass in small sources

The inventory compiler should judge which type of statistical system best describes their national circumstances. Source: IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (2000)

2.5

INVENTORY QUALITY ASSURANCE/QUALITY CONTROL QA/QC

Specific QA/QC procedures to optimise the quality of estimates of emissions from stationary combustion are given in Table 2.16.

2.5.1

Reporting and Documentation

It is good practice to document and archive all information required to produce the national emissions inventory estimates, as outlined in Chapter 8 of Volume 1. It is not practical to include all documentation in the inventory report. However, the inventory should include summaries of methods used and references to data sources such that the reported emissions estimates are transparent and steps in their calculation can be retraced. Some examples of specific documentation and reporting that are relevant to stationary combustion sources are discussed below. For all tiers, it is good practice to provide the sources of the energy data used and observations on the completeness of the data set. Most energy statistics are not considered confidential. If inventory compilers do not report disaggregated data due to confidentiality concerns, it is good practice to explain the reasons for these concerns, and to report the data in a more aggregated form. The current IPCC reporting format (spreadsheet tables, aggregate tables) tries to provide a balance between the requirement of transparency and the level of effort that is realistically achievable by most inventory compilers. Good practice involves some additional effort to fulfil the transparency requirements completely. In particular, if Tier 3 is used, additional tables showing the activity data that are directly associated with the emission factors should be prepared. For country-specific CO2 emission factors, it is good practice to provide the sources of the calorific values, carbon content and oxidation factors (whether the default factor of 100 percent is used or a different value depending on circumstances). For country- and technology-specific non-CO2 greenhouse gas estimates, it may be necessary to cite different references or documents. It is good practice to provide citations for these references, particularly if they describe new methodological developments or emission factors for particular technologies or national circumstances. For all country- and technology-specific emission factors, it is good practice to provide the date of the last revision and any verification of the accuracy.

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In those circumstances where double counting could occur, it is good practice to state clearly whether emission estimates have been allocated to the Energy or to other sectors such as AFOLU, IPPU or Waste, to show that no double counting has occurred.

2.6

WORKSHEETS

The four pages of the worksheets (Annex 1 of this Volume) for the Tier I Sectoral Approach should be filled in for each of the source categories indicated in Table 2.16. Only the amount of fuel combusted for energy purposes should be included in column A of the worksheets. When filling in column A of the worksheets, the following issues should be taken into account: 1) some fuels are used for purposes other than for combustion, 2) wastederived fuels are sometimes burned for energy purposes, and 3) some of the fuel combustion emissions should be included in Industrial Processes. Table 1 in the Annex lists the main considerations that should be taken into consideration in deciding what fraction of consumption should be included in the activity data for each fuel. TABLE 2.16 LIST OF SOURCE CATEGORIES FOR STATIONARY COMBUSTION Code

2.42

Name

1A1a

Main Activity Electricity and Heat Production

1A1b

Petroleum Refining

1A1c

Manufacture of Solid Fuels and Other Energy Industries

1A2a

Iron and Steel

1A2b

Non-Ferrous Metals

1A2c

Chemicals

1A2d

Pulp, Paper and Print

1A2e

Food Processing, Beverages and Tobacco

1A2f

Non-Metallic Minerals

1A2g

Transport Equipment

1A2h

Machinery

1A2i

Mining (excluding fuels) and Quarrying

1A2j

Wood and Wood Products

1A2k

Construction

1A2l

Textile and Leather

1A2m

Non-specified Industry

1A4a

Commercial / Institutional

1A4b

Residential

1A4c

Agriculture / Forestry / Fishing / Fish Farms (Stationary combustion)

1A5a

Non-Specified Stationary

2006 IPCC Guidelines for National Greenhouse Gas Inventories

•

• •

•

•

•

•

•

•

If a Tier 2 approach with country-specific factors is used, the inventory compiler should compare the result to emissions calculated using the Tier 1 approach with default IPCC factors. This type of comparison may require aggregating Tier 2 emissions to the same sector and fuel groupings as the Tier 1 approach. The approach should be documented and any discrepancies investigated. If possible, the inventory compiler should compare the consistency of the calculations in relation to the maximum carbon content of fuels that are combusted by stationary sources. Anticipated carbon balances should be maintained throughout the combustion sectors.

Calculations of non- CO2 emissions from stationary combustion

The national agency in charge of energy statistics should construct, if resources permit, national commodity balances expressed in mass units, and construct mass balances of fuel conversion industries. The time series of statistical differences should be checked for systematic effects (indicated by the differences persistently having the same sign) and these effects eliminated where possible. The national agency in charge of energy statistics should also construct, if resources permit, national energy balances expressed in energy units and energy balances of fuel conversion industries. The time series of statistical differences should be checked, and the calorific values cross-checked with the default values given in the Introduction chapter. This step will only be of value where different calorific values for a particular fuel (for example, coal) are applied to different headings in the balance (such as production, imports, coke ovens and households). Statistical differences that change in magnitude or sign significantly from the corresponding mass values provide evidence of incorrect calorific values. The inventory compiler should confirm that gross carbon supply in the Reference Approach has been adjusted for fossil fuel carbon from imported or exported non-fuel materials in countries where this is expected to be significant. Energy statistics should be compared with those provided to international organisations to identify inconsistencies. There may be routine collections of emissions and fuel combustion statistics at large combustion plants for pollution legislation purposes. If possible, the inventory compiler can use these plant-level data to cross-check national energy statistics for representativeness. If secondary data from national organisations are used, the inventory compiler should ensure that these organisations have appropriate QA/QC programmes in place.

The inventory compiler should compare estimates of CO2 emissions from fuel combustion prepared using the Sectoral Approach with the Reference Approach, and account for any difference greater than or equal to 5 percent. In this comparative analysis, emissions from fuels other than by combustion, that are accounted for in other sections of a GHG inventory, should be subtracted from the Reference Approach.

Calculations of CO2 emissions from stationary combustion

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Activity data check

Comparison of emission estimates using different approaches

Activity

TABLE 2.17 QA/QC PROCEDURES FOR STATIONARY SOURCES

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Chapter 2: Stationary Combustion

•

•

•

•

•

•

If direct measurements are used, the inventory compiler should ensure that they are made according to good measurement practices including appropriate QA/QC procedures. Direct measurements should be compared to the results derived from using IPCC default factors.

The inventory compiler should compare the emission factors used with site or plant level factors, if these are available. This type of comparison provides an indication of how reasonable and representative the national factor is.

If country-specific emission factors are used, the inventory compiler should compare them to the IPCC defaults, and explain and document differences.

Not applicable

•

•

•

The inventory compiler should carry out a review involving national experts and stakeholders in the different fields related to emissions from stationary sources, such as: energy statistics, combustion efficiencies for different sectors and equipment types, fuel use and pollution controls. In developing countries, expert review of emissions from biomass combustion is particularly important.

CO2 capture should be reported only when linked with long-term storage. The captured amounts should be checked with amount of CO2 stored. The reported CO2 captured should not exceed the amount of stored CO2 plus reported fugitive emissions from the measure. The amount of stored CO2 should be based on measurements of the amount injected to storage.

The inventory compiler should evaluate the quality control associated with facility-level fuel measurements that have been used to calculate site-specific emission and oxidation factors. If it is established that there is insufficient quality control associated with the measurements and analysis used to derive the factor, continued use of the factor may be questioned.

The inventory compiler should construct national energy balances expressed in carbon units and carbon balances of fuel conversion industries. The time series of statistical differences should be checked. Statistical differences that change in magnitude or sign significantly from the corresponding mass values provide evidence of incorrect carbon content. Monitoring systems at large combustion plants may be used to check the emission and oxidation factors in use at the plant. Some countries estimate emissions from fuel consumed and the carbon contents of those fuels. In this case, the carbon contents of the fuels should be regularly reviewed.

Calculations of non- CO2 emissions from stationary combustion

TABLE 2.17(CONTINUED) QA/QC PROCEDURES FOR STATIONARY SOURCES

Calculations of CO2 emissions from stationary combustion

2006 IPCC Guidelines for National Greenhouse Gas Inventories

External review

CO2 capture

Evaluation of direct measurements

Emission factors check and review

Activity

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Chapter 2: Stationary Combustion

References Alstom Power Inc. (2001). ‘Engineering feasibility and economics of CO2 capture on an existing coal-fired power plant’. Report No. PPL-01-CT-09 to Ohio Dept. of Development, Columbus and US Dept. of Energy/NETL, Pittsburgh. Baggott, S.L., Brown, L., Milne, R., Murrells, T.P., Passant, N., Thistlethwaite, G. and Watterson, J.D. (2005). ‘UK Greenhouse Gas Inventory, 1990 to 2003 - Annual report for submission under the Framework Convention on Climate Change’. National Environmental Technology Centre (Netcen), AEA Technology plc, Building 551, Harwell, Didcot, Oxon., OX11 0QJ, UK. AEAT report AEAT/ENV/R/1971. ISBN 09547136-5-6. The work formed part of the Global Atmosphere Research Programme of the Department for Environment, Food and Rural Affairs. Battacharya, S.C., Albina, D.O. and Salam, P. Abdul (2002). ‘Emission factors of wood and charcoal-fired cookstoves’. Biomass and Bioenergy, 23: 453-469 Celik, F., Larson, E.D. and. Williams R.H. (2005). ‘Transportation fuel from coal with low CO2 emissions.’ Wilson, M., T. Morris, J. Gale and K. Thambimuthu (eds.), Proceedings of 7th International Conference on Greenhouse Gas Control Technologies. Volume II: Papers, Posters and Panel Discussion, Elsevier Science, Oxford UK (in press). CCP (2005). ‘Economic and cost analysis for CO2 capture costs in the CO2 capture project, Scenarios’. In D.C. Thomas (Ed.), Volume 1 - Capture and separation of carbon dioxide from combustion Sources, Elsevier Science, Oxford, UK. Chen, C., Rao, A.B. and Rubin, E.S. (2003). ‘Comparative assessment of CO2 capture options for existing coalfired power plants.’ presented at the Second National Conference on Carbon Sequestration, Alexandria, VA, USA, 5-8 May. EPRI (1993). Technical Assessment Guide, Volume 1: Electricity Supply-1993 (Revision 7), Electric Power Research Institute, Palo Alto, CA, June. EIA (1999). ‘Emissions of greenhouse gases in the United States of America’. (available at http://www.eia.doe.gov/oiaf/1605/ggrpt). Forest Products Laboratory (2004). Fuel value calculator, USDA Forest Service, Forest Products Laboratory, Pellet Fuels Institute, Madison. (Available at http://www.fpl.fs.fed.us) Gibbins, J., Crane, R.I., Lambropoulos, D., Booth, C., Roberts, C.A. and Lord (2005). ‘Maximising the effectiveness of post-combustion CO2 capture systems’. Proceedings of the 7 th International Conference on Greenhouse Gas Control Technologies. Volume I: Peer Reviewed Papers and Overviews, E.S. Rubin, D.W. Keith, and C.F.Gilboy (eds.), Elsevier Science, Oxford, UK (in press). IEA GHG (2003). ‘Potential for improvements in gasification combined cycle power generation with CO2 capture’, Report PH4/19, IEA Greenhouse Gas R&D Programme, Cheltenham, UK. IEA GHG (2004). ‘Improvements in power generation with post-combustion capture of CO2.’ Report PH4/33, Nov. 2004, IEA Greenhouse Gas R&D Programme, Cheltenham, UK. Korhonen, S., Fabritius, M. and. Hoffren, H. (2001), ‘Methane and nitrous oxide emissions in the Finnish energy production.’ Fortum publication Tech-4615. 36 pages. (Available at http://www.energia.fi/attachment.asp?Section=1354&Item=1691) Kreutz, T., Williams, R., Chiesa, P. and Consonni, S. (2005). ‘Co-production of hydrogen, electricity and CO2 from coal with commercially ready technology’. Part B: Economic analysis, International Journal of Hydrogen Energy, 30 (7): 769-784. Larson, E.D. and Ren, T. (2003). ‘Synthetic fuels production by indirect coal liquefaction’. Energy for Sustainable Development, VII(4), 79-102. Mitretek (2003). ‘Hydrogen from coal.’ Technical Paper MTR-2003-13, Prepared by D. Gray and G. Tomlinson for the National Energy Technology Laboratory, US DOE, April. Monni, S., Syri, S. and Savolainen, I. (2004). ‘Uncertainties in the Finnish greenhouse gas emission inventory.’ Environmental Science & Policy, 7: 87-98. NETL (2002). ‘Advanced fossil power systems comparison study.’ Final report prepared for NETL by E.L. Parsons (NETL, Morgantown, WV), W.W. Shelton and J.L. Lyons (EG&G Technical Services, Inc., Morgantown, WV), December.

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NRC (2004). ‘The hydrogen economy: opportunities, costs, barriers, and R&D needs’. Prepared by the Committee on Alternatives and Strategies for Future Hydrogen Production and Use, Board on Energy and Environmental Systems of the National Research Council, The National Academies Press, Washington, DC. Nsakala, N., Liljedahl, G., Marion, J., Bozzuto, C., Andrus H. and Chamberland R. (2003). ‘Greenhouse gas emissions control by oxygen firing in circulating fluidised bed boilers.’ Presented at the Second Annual National Conference on Carbon Sequestration. Alexandria, VA, May 5-8. Parsons Infrastructure & Technology Group, Inc. (2002). ‘Updated cost and performance estimates for fossil fuel power plants with CO2 removal.’ Report under Contract No. DE-AM26-99FT40465 to U.S.DOE/NETL, Pittsburgh, PA, and EPRI, Palo Alto, CA., December. Pulles, T., and Heslinga, D. (2004). ‘On the variability of air pollutant emissions from gas-fired industrial combustion plants.’ Atmospheric Environment, 38(23): 3829 - 3840. Rao, A.B. and Rubin, E.S. (2002). ‘A technical, economic, and environmental assessment of amine-based CO2 capture technology for power plant greenhouse gas control’. Environmental Science and Technology, 36: 4467-4475. Radian Corporation (1990). ‘Emissions and cost estimates for globally significant anthropogenic combustion sources of NOx , N2O, CH4, CO, and CO2.’ Prepared for the Office of Research and Development, US Environmental Protection Agency, Washington, D.C., USA. Rubin, E.S., Rao, A.B. and Chen, C. (2005). ‘Comparative assessments of fossil fuel power plants with CO2 capture and storage.’ Proceedings of 7th International Conference on Greenhouse Gas Control Technologies. Volume 1: Peer-Reviewed Papers and Overviews, E.S. Rubin, D.W. Keith and C.F. Gilboy (eds.), Elsevier Science, Oxford, UK (in press). Rypdal, K. (1999). ‘An evaluation of the uncertainties in the national greenhouse gas inventory.’ SFT Report 99:01. Norwegian Pollution Control Authority, Oslo, Norway Rypdal, K. and Winiwarter, W. (2001). ’Uncertainties in greenhouse gas emission inventories - evaluation, comparability and implications.’ Environmental Science & Policy, 4: 107–116. Simbeck, D. (2002). ‘New power plant CO2 mitigation costs.’ SFA Pacific, Inc., Mountain View, CA. Singh, D., Croiset, E. Douglas, P.L. and Douglas, M.A. (2003). ‘Techno-economic study of CO2 capture from an existing coal-fired power plant: MEA scrubbing vs. O2/CO2 recycle combustion.’ Energy Conversion and Management, 44: 3073-3091. Smith K.R., Rasmussen, R.A., Manegdeg, F. and Apte, M. (1992). ‘Greenhouse gases from small-scale combustion in developing countries: A Pilot Study in Manila.’ EPA/600/R-92-005, U.S. Environmental Protection Agency, Research Triangle Park. Smith K.R., M.A.K. Khalil, R.A. Rasmussen, M. Apte and F. Manegdeg (1993). ‘Greenhouse gases from biomass fossil Fuels stoves in developing countries: a Manila Pilot Study.’ Chemosphere, 26(1-4): 479505. Smith, K.R., Uma, R., Kishore, V.V.N, Lata, K., Joshi, V., Zhang, J., Rasmussen, R.A. and Khalil, M.A.K. (2000). ‘Greenhouse gases from small-scale combustion devices in developing countries, Phase IIa: Household Stoves in India.’ U.S. EPA/600/R-00-052, U.S. Environmental Protection Agency, Research Triangle Park. Thambimuthu, K., Soltanieh, M.,. Abanades, J.C., Allam, R., Bolland, O., Davison, J., Feron, P., Goede, F., Herrera, A., Iijima, M., Jansen, D., Leites, I., Mathieu, P., Rubin, E., Simbeck, D., Warmuzinski, K., Wilkinson, M., and Williams, R. (2005). Capture. In: IPCC Special Report on Carbon Dioxide Capture and Storage. Prepared by Working Group III of the Intergovernmental Panel on Climate Change [Metz, B., O. Davidson, H. C. de Coninck, M. Loos, and L. A. Meyer (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Tsupari, E., Tormonen, K., Monni, S., Vahlman, T., Kolsi, A. and Linna, V. (2006). Emission factors for nitrous oxide (N2O) and methane (CH4) from Finnish power and heating plants and small-scale combustion. VTT, Espoo, Finland. VTT Working Papers 43. (In Finnish with Engllish summary). See website: http://www.vtt.fi/inf/pdf/workingpapers/2006/W43.pdf U.S. EPA (2005a), Plain English Guide to the Part 75 Rule, U.S. Environmental Protection Agency, Clear Air Markets Division, Washington, DC. Available at: http://www.epa.gov/airmarkets/monitoring/ plain_english_guide_part75_rule.pdf

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Chapter 2: Stationary Combustion

U.S. EPA (2005b). Air CHIEF, Version 12, EPA 454/C-05-001, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Washington, DC. Available at: http:// http://www.epa.gov/ttn/chief/ap42/index.html van Amstel, A., Olivier, J.G.J., Ruyssenaars, P. (Eds.) (2000). ‘Monitoring of greenhouse gases in the Netherlands: Uncertainty and Priorities for improvement’ Proceedings of a National Workshop, Bilthoven, The Netherlands, 1 September 1999. WIMEK:RIVM report 773201 003, July Winiwarter, W. and Rypdal, K. (2001). ‘Assessing the uncertainty associated with a national greenhouse gas emission inventory: a case study for Austria.’ Atmospheric Environment, 35: 5425-5440 Zhang, J., Smith, K.R., Ma, Y., Ye, S., Jiang, F., Qi, W., Liu, P., Khalil, M.A.K., Rasmussen, R.A. and Thorneloe, S.A. (2000). ‘Greenhouse gases and other airborne pollutants from household stoves in China: A database for emission factors.’ Atmospheric Environment, 34: 4537-4549.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.47

Chapter 3: Mobile Combustion

CHAPTER 3

MOBILE COMBUSTION

2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.1

Volume 2: Energy

Authors O ve rv i e w Christina Davies Waldron (USA) Jochen Harnisch (Germany), Oswaldo Lucon (Brazil), R. Scott Mckibbon (Canada), Sharon B. Saile (USA), Fabian Wagner (Germany), and Michael P. Walsh (USA)

Off -road transporta tion Christina Davies Waldron (USA) Jochen Harnisch (Germany), Oswaldo Lucon (Brazil), R. Scott McKibbon (Canada), Sharon Saile (USA), Fabian Wagner (Germany), and Michael Walsh (USA) R a i l wa y s Christina Davies Waldron (USA) Jochen Harnisch (Germany), Oswaldo Lucon (Brazil), R. Scott McKibbon (Canada), Sharon B. Saile (USA), Fabian Wagner (Germany), and Michael P. Walsh (USA) Wa te r-bo rne na viga tion Lourdes Q. Maurice (USA) Leif Hockstad (USA), Niklas Höhne (Germany), Jane Hupe (ICAO), David S. Lee (UK), and Kristin Rypdal (Norway) Ci v i l av ia t ion Lourdes Q. Maurice (USA) Leif Hockstad (USA), Niklas Höhne (Germany), Jane Hupe (ICAO), David S. Lee (UK), and Kristin Rypdal (Norway)

Contributing Authors Road transpo rta tio n, O ff- road transpo rta tio n and Railwa ys Manmohan Kapshe (India) Wa te r-bo rne na viga tion and Civil Avia tio n Daniel M. Allyn (USA), Maryalice Locke (USA, Stephen Lukachko (USA), and Stylianos Pesmajoglou (UNFCCC)

3.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

Contents 3

Mobile Combustion 3.1

Overview ...............................................................................................................................................3.8

3.2

Road Transportation............................................................................................................................3.10

3.2.1

Methodological issues .................................................................................................................3.10

3.2.1.1

Choice of method...................................................................................................................3.10

3.2.1.2

Choice of emission factors.....................................................................................................3.16

3.2.1.3

Choice of activity data ...........................................................................................................3.25

3.2.1.4

Completeness.........................................................................................................................3.28

3.2.1.5

Developing a consistent time series .......................................................................................3.29

3.2.2

Uncertainty assessment ...............................................................................................................3.29

3.2.3

Inventory Quality Assurance/Quality Control (QA/QC).............................................................3.31

3.2.4

Reporting and Documentation.....................................................................................................3.32

3.2.5

Reporting tables and worksheets .................................................................................................3.32

3.3

Off-road Transportation ......................................................................................................................3.32

3.3.1

Methodological issues .................................................................................................................3.32

3.3.1.1

Choice of method...................................................................................................................3.32

3.3.1.2

Choice of emission factors.....................................................................................................3.35

3.3.1.3

Choice of activity data ...........................................................................................................3.36

3.3.1.4

Completeness.........................................................................................................................3.37

3.3.1.5

Developing a consistent time series .......................................................................................3.37

3.3.2 3.3.2.1

Uncertainty assessment ...............................................................................................................3.38 Activity data uncertainty........................................................................................................3.38

3.3.3

Inventory Quality Assurance/Quality Control (QA/QC).............................................................3.38

3.3.4

Reporting and Documentation.....................................................................................................3.39

3.3.5

Reporting tables and worksheets .................................................................................................3.39

3.4

Railways..............................................................................................................................................3.39

3.4.1

Methodological issues .................................................................................................................3.40

3.4.1.1

Choice of method...................................................................................................................3.40

3.4.1.2

Choice of emission factors.....................................................................................................3.42

3.4.1.3

Choice of activity data ...........................................................................................................3.44

3.4.1.4

Completeness.........................................................................................................................3.45

3.4.1.5

Developing a consistent time series .......................................................................................3.45

3.4.1.6

Uncertainty assessment..........................................................................................................3.45

3.4.2

Inventory Quality Assurance/Quality Control (QA/QC).............................................................3.46

3.4.3

Reporting and Documentation.....................................................................................................3.46

3.4.4

Reporting tables and worksheets .................................................................................................3.47

3.5

Water-borne Navigation ......................................................................................................................3.47

2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.3

Volume 2: Energy

3.5.1 3.5.1.1

Choice of method...................................................................................................................3.47

3.5.1.2

Choice of emission factors.....................................................................................................3.50

3.5.1.3

Choice of activity data ...........................................................................................................3.51

3.5.1.4

Military ..................................................................................................................................3.53

3.5.1.5

Completeness.........................................................................................................................3.53

3.5.1.6

Developing a consistent time series .......................................................................................3.53

3.5.1.7

Uncertainty assessment..........................................................................................................3.54

3.5.2

Inventory Quality Assurance/Quality Control (QA/QC).............................................................3.54

3.5.3

Reporting and Documentation.....................................................................................................3.55

3.5.4

Reporting tables and worksheets .................................................................................................3.55

3.5.5

Definitions of specialist terms .....................................................................................................3.56

3.6

Civil Aviation......................................................................................................................................3.56

3.6.1

Methodological issues .................................................................................................................3.57

3.6.1.1

Choice of method...................................................................................................................3.57

3.6.1.2

Choice of emission factors.....................................................................................................3.64

3.6.1.3

Choice of activity data ...........................................................................................................3.65

3.6.1.4

Military aviation ....................................................................................................................3.66

3.6.1.5

Completeness.........................................................................................................................3.68

3.6.1.6

Developing a consistent time series .......................................................................................3.68

3.6.1.7

Uncertainty assessment..........................................................................................................3.69

3.6.2

Inventory Quality Assurance/Quality Control (QA/QC).............................................................3.69

3.6.3

Reporting and Documentation.....................................................................................................3.73

3.6.4

Reporting tables and worksheets .................................................................................................3.73

3.6.5

Definitions of specialist terms .....................................................................................................3.74

References

3.4

Methodological issues .................................................................................................................3.47

.....................................................................................................................................................3.74

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

Equations Equation 3.2.1 CO2 from road transport ...................................................................................................3.12 Equation 3.2.2 CO2 from urea-based catalytic converters ...........................................................................3.12 Equation 3.2.3 Tier 1 emissions of CH4 and N2O........................................................................................3.13 Equation 3.2.4 Tier 2 emissions of CH4 and N2O........................................................................................3.13 Equation 3.2.5 Tier 3 emissions of CH4 and N2O........................................................................................3.15 Equation 3.2.6 Validating fuel consumption ...............................................................................................3.26 Equation 3.3.1 Tier 1 emissions estimate ....................................................................................................3.33 Equation 3.3.2 Tier 2 emissions estimate ....................................................................................................3.33 Equation 3.3.3 Tier 3 emissions estimate ....................................................................................................3.34 Equation 3.3.4 Emissions from urea-based catalytic converters..................................................................3.35 Equation 3.4.1 General method for emissions from locomotives ................................................................3.41 Equation 3.4.2 Tier 2 method for CH4 and N2O from locomotives .............................................................3.42 Equation 3.4.3 Tier 3 example of a method for CH4 and N2O from locomotives........................................3.42 Equation 3.4.4 Weighting of CH4 and N2O emission factors for specific technologies ..............................3.43 Equation 3.4.5 Estimating yard locomotive fuel consumption ....................................................................3.45 Equation 3.5.1 Water-borne navigation equation ........................................................................................3.47 Equation 3.6.1 (Aviation equation 1)...........................................................................................................3.59 Equation 3.6.2 (Aviation equation 2)...........................................................................................................3.59 Equation 3.6.3 (Aviation equation 3)...........................................................................................................3.59 Equation 3.6.4 (Aviation equation 4)...........................................................................................................3.59 Equation 3.6.5 (Aviation equation 5)...........................................................................................................3.59

Figures Figure 3.2.1

Steps in estimating emissions from road transport ..............................................................3.11

Figure 3.2.2

Decision tree for CO2 emissions from fuel combustion in road vehicles ............................3.11

Figure 3.2.3

Decision tree for CH4 and N2O emissions from road vehicles ............................................3.14

Figure 3.3.1

Decision tree for estimating emissions from off-road vehicles ...........................................3.34

Figure 3.4.1

Decision tree for estimating CO2 emissions from railways .................................................3.40

Figure 3.4.2

Decision tree for estimating CH4 and N2O emissions from railways ..................................3.41

Figure 3.5.1

Decision tree for emissions from water-borne navigation ...................................................3.49

Figure 3.6.1

Decision tree for estimating aircraft emissions (applied to each greenhouse gas)...............3.60

Figure 3.6.2

Estimating aircraft emissions with Tier 2 method ...............................................................3.62

2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.5

Volume 2: Energy

Tables

3.6

Table 3.1.1

Detailed sector split for the transport sector ..........................................................................3.8

Table 3.2.1

Road transport default CO2 emission factors and uncertainty ranges..................................3.16

Table 3.2.2

Road transport N2O and CH4 default emission factors and uncertainty ranges ...................3.21

Table 3.2.3

N2O and CH4 emission factors for USA gasoline and diesel vehicles.................................3.22

Table 3.2.4

Emission factors for alternative fuel vehicles......................................................................3.23

Table 3.2.5

Emission factors for European gasoline and diesel vehicles, COPERT IV model ..............3.24

Table 3.3.1

Default emission factors for off-road mobile sources and machinery .................................3.36

Table 3.4.1

Default emission factors for the most common fuels used for rail transport .......................3.43

Table 3.4.2

Pollutant weghting factors as functions of engine design parameters for uncontrolled engines(dimensionless)...................................................................................3.43

Table 3.5.1

Source category structure ....................................................................................................3.48

Table 3.5.2

CO2 emission factors ...........................................................................................................3.50

Table 3.5.3

Default water-borne navigation CH4 and N2O emission factors..........................................3.50

Table 3.5.4

Criteria for defining international or domestic water-borne navigation (applies to each segment of a voyage calling at more than two ports) ................................3.51

Table 3.5.5

Average fuel consumption per engine type (ships >500 GRT) ...........................................3.52

Table 3.5.6

Fuel consumption factors, full power ..................................................................................3.52

Table 3.6.1

Source categories.................................................................................................................3.58

Table 3.6.2

Data requirements for different tiers....................................................................................3.58

Table 3.6.3

Correspondence between representative aircraft and other aircraft types............................3.63

Table 3.6.4

CO2 emission factors ...........................................................................................................3.64

Table 3.6.5

Non-CO2 emission factors ...................................................................................................3.64

Table 3.6.6

Criteria for defining international or domestic aviation (applies to individual legs of journeys with more than one take-off and landing) ................................................3.65

Table 3.6.7

Fuel consumption factors for military aircraft.....................................................................3.67

Table 3.6.8

Fuel consumption per flight hour for military aircraft.........................................................3.67

Table 3.6.9

LTO emission factors for typical aircraft ............................................................................3.70

Table 3.6.10

NOx emission factors for various aircraft at cruise levels....................................................3.72

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

Boxes Box 3.2.1

Examples of biofuel use in road transportation ...................................................................3.18

Box 3.2.2

Refining emission factors for mobile sources in developing countries ...............................3.20

Box 3.2.3

Vehicle deterioration (scrappage) curves ............................................................................3.28

Box 3.2.4

Lubricants in mobile combustion ........................................................................................3.29

Box 3.3.1

Nonroad emission model (USEPA).....................................................................................3.37

Box 3.3.2

Canadian experience with nonroad model...........................................................................3.38

Box 3.4.1

Example of Tier 3 approach ................................................................................................3.44

2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.7

Volume 2: Energy

3

MOBILE COMBUSTION

3.1

OVERVIEW

Mobile sources produce direct greenhouse gas emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) from the combustion of various fuel types, as well as several other pollutants such as carbon monoxide (CO), Non-methane Volatile Organic Compounds (NMVOCs), sulphur dioxide (SO2), particulate matter (PM) and oxides of nitrate (NOx), which cause or contribute to local or regional air pollution. This chapter covers good practice in the development of estimates for the direct greenhouse gases CO2, CH4, and N2O. For indirect greenhouse gases and precursor substances CO, NMVOCs, SO2, PM, and NOx, please refer to Volume 1 Chapter 7. This chapter does not address non-energy emissions from mobile air conditioning, which is covered by the IPPU Volume (Volume 3, Chapter 7). Greenhouse gas emissions from mobile combustion are most easily estimated by major transport activity, i.e., road, off-road, air, railways, and water-borne navigation. The source description (Table 3.1.1) shows the diversity of mobile sources and the range of characteristics that affect emission factors. Recent work has updated and strengthened the data. Despite these advances more work is needed to fill in many gaps in knowledge of emissions from certain vehicle types and on the effects of ageing on catalytic control of road vehicle emissions. Equally, the information on the appropriate emission factors for road transport in developing countries may need further strengthening, where age of fleet, maintenance, fuel sulphur content, and patterns of use are different from those in industrialised countries. TABLE 3.1.1 DETAILED SECTOR SPLIT FOR THE TRANSPORT SECTOR Code and Name 1A3

Explanation Emissions from the combustion and evaporation of fuel for all transport activity (excluding military transport), regardless of the sector, specified by sub-categories below.

TRANSPORT

Emissions from fuel sold to any air or marine vessel engaged in international transport (1 A 3 a i and 1 A 3 d i) should as far as possible be excluded from the totals and subtotals in this category and should be reported separately.

3.8

1A3

a

Civil Aviation

1A3

a

i

International Aviation Emissions from flights that depart in one country and arrive in a different (International Bunkers) country. Include take-offs and landings for these flight stages. Emissions from international military aviation can be included as a separate subcategory of international aviation provided that the same definitional distinction is applied and data are available to support the definition.

1A3

a

ii

Domestic Aviation

1A3

b

Road Transportation

All combustion and evaporative emissions arising from fuel use in road vehicles, including the use of agricultural vehicles on paved roads.

1A3

b

i

Cars

Emissions from automobiles so designated in the vehicle registering country primarily for transport of persons and normally having a capacity of 12 persons or fewer.

1A3

b

i

1

Emissions from passenger car vehicles with 3-way catalysts.

1A3

b

i

2

Passenger cars with 3way catalysts Passenger cars without 3-way catalysts

Emissions from international and domestic civil aviation, including takeoffs and landings. Comprises civil commercial use of airplanes, including: scheduled and charter traffic for passengers and freight, air taxiing, and general aviation. The international/domestic split should be determined on the basis of departure and landing locations for each flight stage and not by the nationality of the airline. Exclude use of fuel at airports for ground transport which is reported under 1 A 3 e Other Transportation. Also exclude fuel for stationary combustion at airports; report this information under the appropriate stationary combustion category.

Emissions from civil domestic passenger and freight traffic that departs and arrives in the same country (commercial, private, agriculture, etc.), including take-offs and landings for these flight stages. Note that this may include journeys of considerable length between two airports in a country (e.g. San Francisco to Honolulu). Exclude military, which should be reported under 1 A 5 b.

Emissions from passenger car vehicles without 3-way catalysts.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

TABLE 3.1.1(CONTINUED) DETAILED SECTOR SPLIT FOR THE TRANSPORT SECTOR Code and Name

Explanation

1A3

b

ii

1A3

b

ii

1

1A3

b

ii

2

1A3

b

iii

1A3

b

1A3

Light duty trucks

Emissions from vehicles so designated in the vehicle registering country primarily for transportation of light-weight cargo or which are equipped with special features such as four-wheel drive for off-road operation. The gross vehicle weight normally ranges up to 3500-3900 kg or less.

Light duty trucks with 3-way catalysts Light duty trucks without 3-way catalysts Heavy duty trucks and buses

Emissions from light duty trucks with 3-way catalysts.

iv

Motorcycles

Emissions from any motor vehicle designed to travel with not more than three wheels in contact with the ground and weighing less than 680 kg.

b

v

Evaporative emissions from vehicles

Evaporative emissions from vehicles (e.g. hot soak, running losses) are included here. Emissions from loading fuel into vehicles are excluded.

1A3

b

vi

Urea-based catalysts

CO2 emissions from use of urea-based additives in catalytic converters (non-combustive emissions)

1A3

c

Railways

Emissions from railway transport for both freight and passenger traffic routes.

1A3

d

Water-borne Navigation

Emissions from fuels used to propel water-borne vessels, including hovercraft and hydrofoils, but excluding fishing vessels. The international/domestic split should be determined on the basis of port of departure and port of arrival, and not by the flag or nationality of the ship.

1A3

d

i

International waterEmissions from fuels used by vessels of all flags that are engaged in borne navigation international water-borne navigation. The international navigation may (International bunkers) take place at sea, on inland lakes and waterways and in coastal waters. Includes emissions from journeys that depart in one country and arrive in a different country. Exclude consumption by fishing vessels (see Other Sector - Fishing). Emissions from international military water-borne navigation can be included as a separate sub-category of international water-borne navigation provided that the same definitional distinction is applied and data are available to support the definition.

1A3

d

ii

Domestic water-borne Emissions from fuels used by vessels of all flags that depart and arrive in Navigation the same country (exclude fishing, which should be reported under 1 A 4 c iii, and military, which should be reported under 1 A 5 b). Note that this may include journeys of considerable length between two ports in a country (e.g. San Francisco to Honolulu).

1A3

e

Other Transportation

Combustion emissions from all remaining transport activities including pipeline transportation, ground activities in airports and harbours, and offroad activities not otherwise reported under 1 A 4 c Agriculture or 1 A 2. Manufacturing Industries and Construction. Military transport should be reported under 1 A 5 (see 1 A 5 Non-specified).

1A3

e

i

Pipeline Transport

Combustion related emissions from the operation of pump stations and maintenance of pipelines. Transport via pipelines includes transport of gases, liquids, slurry and other commodities via pipelines. Distribution of natural or manufactured gas, water or steam from the distributor to final users is excluded and should be reported in 1 A 1 c ii or 1 A 4 a.

1A3

e

ii

Off-road

Combustion emissions from Other Transportation excluding Pipeline Transport.

1A4

c

iii

Fishing (mobile combustion)

Emissions from fuels combusted for inland, coastal and deep-sea fishing. Fishing should cover vessels of all flags that have refuelled in the country (include international fishing).

Emissions from light duty trucks without 3-way catalysts. Emissions from any vehicles so designated in the vehicle registering country. Normally the gross vehicle weight ranges from 3500-3900 kg or more for heavy duty trucks and the buses are rated to carry more than 12 persons.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

TABLE 3.1.1(CONTINUED) DETAILED SECTOR SPLIT FOR THE TRANSPORT SECTOR Code and Name

Explanation

1A5

a

Non specified stationary

Emissions from fuel combustion in stationary sources that are not specified elsewhere.

1A5

b

Non specified mobile

Mobile Emissions from vehicles and other machinery, marine and aviation (not included in 1 A 4 c ii or elsewhere). Includes emissions from fuel delivered for aviation and water-borne navigation to the country's military as well as fuel delivered within that country but used by the militaries of other countries that are not engaged in.

Multilateral Operations Multilateral operations. Emissions from fuels used for aviation and waterborne navigation in multilateral operations pursuant to the Charter of the (Memo item) United Nations. Include emissions from fuel delivered to the military in the country and delivered to the military of other countries.

3.2

ROAD TRANSPORTATION

The mobile source category Road Transportation includes all types of light-duty vehicles such as automobiles and light trucks, and heavy-duty vehicles such as tractor trailers and buses, and on-road motorcycles (including mopeds, scooters, and three-wheelers). These vehicles operate on many types of gaseous and liquid fuels. In addition to emissions from fuel combustion, emissions associated with catalytic converter use in road vehicles (e.g., CO2 emissions from catalytic converters using urea) 1 are also addressed in this section.

3.2.1

Methodological Issues

The fundamental methodologies for estimating greenhouse gas emissions from road vehicles, which are presented in Section 3.2.1.1, have not changed since the publication of the 1996 IPCC Guidelines and the GPG2000, except that, as discussed in Section 3.2.1.2, the emission factors now assume full oxidation of the fuel. This is for consistency with the Stationary Combustion Chapter in this Volume. The method for estimating CO2 emissions from catalytic converters using urea, a source of emissions, was not addressed previously. Estimated emissions from road transport can be based on two independent sets of data: fuel sold (see section 3.2.1.3) and vehicle kilometres. If these are both available it is important to check that they are comparable, otherwise estimates of different gases may be inconsistent. This validation step (Figure 3.2.1) is described in sections 3.2.1.3 and 3.2.3. It is good practice to perform this validation step if vehicle kilometre data are available.

3.2.1.1

C HOICE

OF

M ETHOD

Emissions can be estimated from either the fuel consumed (represented by fuel sold) or the distance travelled by the vehicles. In general, the first approach (fuel sold) is appropriate for CO2 and the second (distance travelled by vehicle type and road type) is appropriate for CH4 and N2O. CO 2 EM IS SI ONS Emissions of CO2 are best calculated on the basis of the amount and type of fuel combusted (taken to be equal to the fuel sold, see section 3.2.1.3) and its carbon content. Figure 3.2.2 shows the decision tree for CO2 that guides the choice of either the Tier 1 or Tier 2 method. Each tier is defined below.

1

Urea consumption for catalytic converters in vehicles is directly related to the vehicle fuel consumption and technology.

3.10

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

Figure 3.2.1

Steps in estimating emissions from road transport Start

Validate fuel statistics and vehicle kilometre data and correct if necessary.

Estimate CO2. (see decision tree)

Estimate CH4 and N2O. (see decision tree)

Figure 3.2.2

Decision tree for CO 2 emissions from fuel combustion in road vehicles Start

Are country-specific fuel carbon contents available?

Yes

Use country-specific carbon contents. Box 1: Tier 2

No

Is this a key category?

Yes

Collect countryspecific carbon.

No

Use default carbon contents. Box 2: Tier 1 Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

The Tier 1 approach calculates CO2 emissions by multiplying estimated fuel sold with a default CO2 emission factor. The approach is represented in Equation 3.2.1. EQUATION 3.2.1 CO2 FROM ROAD TRANSPORT

Emission = ∑ [Fuela • E Fa ] a

Where: Emission = Emissions of CO2 (kg) Fuela

= fuel sold (TJ)

EFa

= emission factor (kg/TJ). This is equal to the carbon content of the fuel multiplied by 44/12.

a

= type of fuel (e.g. petrol, diesel, natural gas, LPG etc)

The CO2 emission factor takes account of all the carbon in the fuel including that emitted as CO2, CH4, CO, NMVOC and particulate matter 2 . Any carbon in the fuel derived from biomass should be reported as an information item and not included in the sectoral or national totals to avoid double counting as the net emissions from biomass are already accounted for in the AFOLU sector (see section 3.2.1.4 Completeness). The Tier 2 approach is the same as Tier 1 except that country-specific carbon contents of the fuel sold in road transport are used. Equation 3.2.1 still applies but the emission factor is based on the actual carbon content of fuels consumed (as represented by fuel sold) in the country during the inventory year. At Tier 2, the CO2 emission factors may be adjusted to take account of un-oxidised carbon or carbon emitted as a non-CO2 gas. There is no Tier 3 as it is not possible to produce significantly better results for CO2 than by using the existing Tier 2. In order to reduce the uncertainties, efforts should concentrate on the carbon content and on improving the data on fuel sold. Another major uncertainty component is the use of transport fuel for non-road purposes. CO 2 EM IS SI ONS FROM UR EA- BASED CATA LYSTS For estimating CO2 emissions from use of urea-based additives in catalytic converters (non-combustive emissions), it is good practice to use Equation 3.2.2: EQUATION 3.2.2 CO2 FROM UREA-BASED CATALYTIC CONVERTERS 12 44 Emission = Activity • • Purity • 60 12 Where: Emissions Activity Purity

= CO2 Emissions from urea-based additive in catalytic converters (Gg CO2) = amount of urea-based additive consumed for use in catalytic converters (Gg) = the mass fraction (= percentage divided by 100) of urea in the urea-based additive

The factor (12/60) captures the stochiometric conversion from urea (CO(NH2)2) to carbon, while factor (44/12) converts carbon to CO2. On the average, the activity level is 1 to 3 percent of diesel consumption by the vehicle. Thirty two and half percent can be taken as default purity in case country-specific values are not available (Peckham, 2003). As this is based on the properties of the materials used, there are no tiers for this source. CH 4 AND N 2 O EM ISS ION S Emissions of CH4 and N2O are more difficult to estimate accurately than those for CO2 because emission factors depend on vehicle technology, fuel and operating characteristics. Both distance-based activity data (e.g. vehiclekilometres travelled) and disaggregated fuel consumption may be considerably less certain than overall fuel sold. CH4 and N2O emissions are significantly affected by the distribution of emission controls in the fleet. Thus higher tiers use an approach taking into account populations of different vehicle types and their different pollution control technologies. 2

Research on carbon mass balances for U.S. light-duty gasoline cars and trucks indicates that “the fraction of solid (unoxidized) carbon is negligible” USEPA (2004a). This did not address two-stroke engines or fuel types other than gasoline. Additional discussion of the 100 percent oxidation assumption is included in Section 1.4.2.1 of the Energy Volume Introduction chapter.

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Chapter 3: Mobile Combustion

Although CO2 emissions from biogenic carbon are not included in national totals, the combustion of biofuels in mobile sources generates anthropogenic CH4 and N2O that should be calculated and reported in emissions estimates. The decision tree in Figure 3.2.3 outlines choice of method for calculating emissions of CH4 and N2O. The inventory compiler should choose the method on the basis of the existence and quality of data. The tiers are defined in the corresponding equations 3.2.3 to 3.2.5, below. Three alternative approaches can be used to estimate CH4 and N2O emissions from road vehicles: one is based on vehicle kilometres travelled (VKT) and two are based on fuel sold. The Tier 3 approach requires detailed, country-specific data to generate activity-based emission factors for vehicle subcategories and may involve national models. Tier 3 calculates emissions by multiplying emission factors by vehicle activity levels (e.g., VKT) for each vehicle subcategory and possible road type. Vehicle subcategories are based on vehicle type, age, and emissions control technology. The Tier 2 approach uses fuel-based emission factors specific to vehicle subcategories. Tier 1, which uses fuel-based emission factors, may be used if it is not possible to estimate fuel consumption by vehicle type. The equation for the Tier 1 method for estimating CH4 and N2O from road vehicles may be expressed as: EQUATION 3.2.3 TIER 1 EMISSIONS OF CH4 AND N2O Emission = ∑ [ Fuela • EFa ] a

Where: Emissions = emission in kg EFa

= emission factor (kg/TJ)

Fuela

= fuel consumed, (TJ) (as represented by fuel sold)

a

= fuel type a (e.g., diesel, gasoline, natural gas, LPG)

Equation 3.2.3 for the Tier 1 method implies the following steps: •

• •

Step 1: Determine the amount of fuel consumed by fuel type for road transportation using national data or, as an alternative, IEA or UN international data sources (all values should be reported in terajoules). Step 2: For each fuel type, multiply the amount of fuel consumed by the appropriate CH4 and N2O default emission factors. Default emission factors may be found in the next Section 3.2.1.2 (Emission Factors). Step 3: Emissions of each pollutant are summed across all fuel types.

The emission equation for Tier 2 is: EQUATION 3.2.4 TIER 2 EMISSIONS OF CH4 AND N2O Emission = ∑ [ Fuela ,b,c • EFa ,b,c ] a ,b ,c

Where: Emission = emission in kg. EFa,b,c

= emission factor (kg/TJ)

Fuela,b,c

= fuel consumed (TJ) (as represented by fuel sold) for a given mobile source activity

a

= fuel type (e.g., diesel, gasoline, natural gas, LPG)

b

= vehicle type

c

= emission control technology (such as uncontrolled, catalytic converter, etc)

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Volume 2: Energy

Figure 3.2.3

Decision tree for CH 4 and N 2 O emissions from road vehicles Start

VKT by fuel and technology type available?

Yes

Are Country-specific technology based emission factors available?

Yes

Use vehicle activity based model and country-specific factors e.g. COPERT. Box 1: Tier 3

No

No

Can you allocate fuel data to vehicle technology types?

Yes

Use default factors and disaggregation by technology. Box 2: Tier 2

No

Is this a key category?

Yes

Collect data to allocate fuel to technology types.

No

Use fuel-based emission factors. Box 3: Tier 1 Notes: 1. See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees. 2.The decision tree and key category determination should be applied to methane and nitrous oxide emissions separately.

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Chapter 3: Mobile Combustion

Vehicle type should follow the reporting classification 1.A.3.b (i to iv) (i.e., passenger, light-duty or heavy-duty for road vehicles, motorcycles) and preferably be further split by vehicle age (e.g., up to 3 years old, 3-8 years, older than 8 years) to enable categorization of vehicles by control technology (e.g., by inferring technology adoption as a function of policy implementation year). Where possible, fuel type should be split by sulphur content to allow for delineation of vehicle categories according to emission control system, because the emission control system operation is dependent upon the use of low sulphur fuel during the whole system lifespan3. Without considering this aspect, CH4 may be underestimated. This applies to Tiers 2 and 3. The emission equation for Tier 3 is: EQUATION 3.2.5 TIER 3 EMISSIONS OF CH4 AND N2O

Emission = ∑ [ Distancea ,b,c ,d • EFa ,b,c,d ] + ∑ C a ,b ,c ,d

a ,b ,c ,d

a ,b ,c ,d

Where: Emission

= emission or CH4 or N2O (kg)

EFa,b,c,d

= emission factor (kg/km)

Distancea,b,c,d = distance travelled (VKT) during thermally stabilized engine operation phase for a given mobile source activity (km) Ca,b,c,d

= emissions during warm-up phase (cold start) (kg)

a

= fuel type (e.g., diesel, gasoline, natural gas, LPG)

b

= vehicle type

c

= emission control technology (such as uncontrolled, catalytic converter, etc.)

d

= operating conditions (e.g., urban or rural road type, climate, or other environmental factors)

It may not be possible to split by road type in which case this can be ignored. Often emission models such as the USEPA MOVES or MOBILE models, or the EEA’s COPERT model will be used (USEPA 2005a, USEPA 2005b, EEA 2005, respectively). These include detailed fleet models that enable a range of vehicle types and control technologies to be considered as well as fleet models to estimate VKT driven by these vehicle types. Emission models can help to ensure consistency and transparency because the calculation procedures may be fixed in software packages that may be used. It is good practice to clearly document any modifications to standardised models. Additional emissions occur when the engines are cold, and this can be a significant contribution to total emissions from road vehicles. These should be included in Tier 3 models. Total emissions are calculated by summing emissions from the different phases, namely the thermally stabilized engine operation (hot) and the warming-up phase (cold start) – Eq 3.2.5 above. Cold starts are engine starts that occur when the engine temperature is below that at which the catalyst starts to operate (light-off threshold, roughly 300oC) or before the engine reaches its normal operation temperature for non-catalyst equipped vehicles. These have higher CH4 (and CO and HC) emissions. Research has shown that 180-240 seconds is the approximate average cold start mode duration. The cold start emission factors should therefore be applied only for this initial fraction of a vehicle’s journey (up to around 3 km) and then the running emission factors should be applied. Please refer to USEPA (2004b) and EEA (2005a) for further details. The cold start emissions can be quantified in different ways. Table 3.2.3 (USEPA 2004b) gives additional emissions per start. This is added to the running emission and so requires knowledge of the number of starts per vehicle per year4. This can be derived through knowledge of the average trip length. The European model COPERT has more complex temperature dependant corrections for the cold start (EEA 2000) for methane.

3

4

This especially applies to countries where fuels with different sulphur contents are sold (e.g. “metropolitan” diesel). Some control systems (for example, diesel exhaust catalyst converters) require ultra low sulphur fuels (e.g. diesel with 50 ppm S or less) to be operational. Higher sulphur levels deteriorate such systems, increasing emissions of CH4 as well as nitrogen oxides, particulates and hydrocarbons. Deteriorated catalysts do not effectively convert nitrogen oxides to N2, which could result in changes in emission rates of N2O. This could also result from irregular misfuelling with high sulphur fuel. This simple method of adding to the running emission the cold start (= number of starts • cold start factor) assumes individual trips are longer than 4 km.

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Both Equation 3.2.4 and 3.2.5 for Tier 2 and 3 methods involves the following steps: •

•

•

•

•

Step 1: Obtain or estimate the amount of fuel consumed by fuel type for road transportation using national data (all values should be reported in terajoules; please also refer to Section 3.2.1.3.) Step 2: Ensure that fuel data or VKT is split into the vehicle and fuel categories required. It should be taken into consideration that, typically, emissions and distance travelled each year vary according to the age of the vehicle; the older vehicles tend to travel less but may emit more CH4 per unit of activity. Some vehicles may have been converted to operate on a different type of fuel than their original design. Step 3: Multiply the amount of fuel consumed (Tier 2), or the distance travelled (Tier 3) by each type of vehicle or vehicle/control technology, by the appropriate emission factor for that type. The emission factors presented in the EFDB or Tables 3.2.3 to 3.2.5 may be used as a starting point. However, the inventory compiler is encouraged to consult other data sources referenced in this chapter or locally available data before determining appropriate national emission factors for a particular subcategory. Established inspection and maintenance programmes may be a good local data source. Step 4: For Tier 3 approaches estimate cold start emissions. Step 5: Sum the emissions across all fuel and vehicle types, including for all levels of emission control, to determine total emissions from road transportation.

3.2.1.2

C HOICE

OF EMISSION FACTORS

Inventory compilers should choose default (Tier 1) or country-specific (Tier 2 and Tier 3) emission factors based on the application of the decision trees which consider the type and level of disaggregation of activity data available for their country. CO 2 EM IS SI ONS

CO2 emission factors are based on the carbon content of the fuel and should represent 100 percent oxidation of the fuel carbon. It is good practice to follow this approach using country-specific net-calorific values (NCV) and CO2 emission factor data if possible. Default NCV of fuels and CO2 emission factors (in Table 3.2.1 below) are presented in Tables 1.2 and 1.4, respectively, of the Introduction Chapter of this Volume and may be used when country-specific data are unavailable. Inventory compilers are encouraged to consult the IPCC Emission Factor Database (EFDB, see Volume 1) for applicable emission factors. It is good practice to ensure that default emission factors, if selected, are appropriate to local fuel quality and composition.

TABLE 3.2.1 ROAD TRANSPORT DEFAULT CO2 EMISSION FACTORS AND UNCERTAINTY RANGES

a

Fuel Type

Default (kg/TJ)

Lower

Upper

Motor Gasoline

69 300

67 500

73 000

Gas/ Diesel Oil

74 100

72 600

74 800

Liquefied Petroleum Gases

63 100

61 600

65 600

Kerosene

71 900

70 800

73 700

73 300

71 900

75 200

Compressed Natural Gas

56 100

54 300

58 300

Liquefied Natural Gas

56 100

54 300

58 300

Lubricants

b

Source: Table 1.4 in the Introduction chapter of the Energy Volume. Notes: a Values represent 100 percent oxidation of fuel carbon content. b See Box 3.2.4 Lubricants in Mobile Combustion for guidance for uses of lubricants.

At Tier 1, the emission factors should assume that 100 percent of the carbon present in fuel is oxidized during or immediately following the combustion process (for all fuel types in all vehicles) irrespective of whether the CO2

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

has been emitted as CO2, CH4, CO or NMVOC or as particulate matter. At higher tiers the CO2 emission factors may be adjusted to take account of un-oxidised carbon or carbon emitted as a non-CO2 gas. CO 2 EM IS SI ONS FROM BIOFU ELS

The use of liquid and gaseous biofuels has been observed in mobile combustion applications (see Box 3.2.1). To properly address the related emissions from biofuel combusted in road transportation, biofuel-specific emission factors should be used, when activity data on biofuel use are available. CO2 emissions from the combustion of the biogenic carbon of these fuels are treated in the AFOLU sector and should be reported separately as an information item. To avoid double counting, the inventory compiler should determine the proportions of fossil versus biogenic carbon in any fuel-mix which is deemed commercially relevant and therefore to be included in the inventory. There are a number of different options for the use of liquid and gaseous biofuels in mobile combustion (see Table 1.1 of the Introduction chapter of this Volume for biofuel definitions). Some biofuels have found widespread commercial use in some countries driven by specific policies. Biofuels can either be used as pure fuel or as additives to regular commercial fossil fuels. The latter approach usually avoids the need for engine modifications or re-certification of existing engines for new fuels. To avoid double counting, over or under-reporting of CO2 emissions, it is important to assess the biofuel origin so as to identify and separate fossil from biogenic feedstocks5. This is because CO2 emissions from biofuels will be reported separately as an information item to avoid double counting, since it is already treated in the AFOLU Volume. The share of biogenic carbon in the fuel can be acknowledged by either refining activity data (e.g. subtracting the amount of non-fossil inputs to the combusted biofuel or biofuel blend) or emission factors (e.g. multiplying the fossil emission factor by its fraction in the combusted biofuel or biofuel blend, to obtain a new emission factor), but not both simultaneously. If national consumption of these fuels is commercially significant, the biogenic and fossil carbon streams need to be accurately accounted for thus avoiding double counting with refinery and petrochemical processes or the waste sector (recognising the possibility of double counting or omission of, for example, landfill gas or waste cooking oil as biofuel). Double counting or omission of landfill gas or waste cooking oil as biofuel should be avoided. CH 4 AND N 2 O

CH4 and N2O emission rates depend largely upon the combustion and emission control technology present in the vehicles; therefore default fuel-based emission factors that do not specify vehicle technology are highly uncertain. Even if national data are unavailable on vehicle distances travelled by vehicle type, inventory compilers are encouraged to use higher tiered emission factors and calculate vehicle distance travelled data based on national road transportation fuel use data and an assumed fuel economy value (see 3.2.1.3 Choice of Activity Data) for related guidance. If CH4 and N2O emissions from mobile sources are not a key category, default CH4 and N2O emission factors presented in Table 3.2.2 may be used when national data are unavailable. When using these default values, inventory compilers should note the assumed fuel economy values that were used for unit conversions and the representative vehicle categories that were used as the basis of the default factors (see table notes for specific assumptions). It is good practice to ensure that default emission factors, if selected, best represent local fuel quality/composition and combustion or emission control technology. If biofuels are included in national road transportation fuel use estimates, biofuel-specific emission factors should be used and associated CH4 and N2O emissions should be included in national totals. Because CH4 and N2O emission rates are largely dependent upon the combustion and emission control technology present, technology-specific emission factors should be used, if CH4 and N2O emissions from mobile sources are a key category. Tables 3.2.3 and 3.2.5 give potentially applicable Tier 2 and Tier 3 emission factors from US and European data respectively. In addition, the U.S. has developed emission factors for some alternative fuel vehicles (Table 3.2.4). The IPCC EFDB and scientific literature may also provide emission factors (or standard emission estimation models) which inventory compilers may use, if appropriate to national circumstances. 5

For example, biodiesel made from coal methanol with animal feedstocks has a non-zero fossil fuel fraction and is therefore not fully carbon neutral. Ethanol from the fermentation of agricultural products will generally be purely biogenic (carbon neutral), except in some cases, such as fossil-fuel derived methanol. Products which have undergone further chemical transformation may contain substantial amounts of fossil carbon ranging from about 5-10 percent in the fossil methanol used for biodiesel production upwards to 46 percent in ethyl-tertiary-butyl-ether (ETBE) from fossil isobutene (ADEME/DIREM, 2002). Some processes may generate biogenic by-products such as glycol or glycerine, which may then be used elsewhere.

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BOX 3.2.1 EXAMPLES OF BIOFUEL USE IN ROAD TRANSPORTATION

Examples of biofuel use in road transportation include: • Ethanol is typically produced through the fermentation of sugar cane, sugar beets, grain, corn or potatoes. It may be used neat (100 percent, Brazil) or blended with gasoline in varying volumes (512 percent in Europe and North America, 10 percent in India, while 25 percent is common in Brazil). The biogenic portion of pure ethanol is 100 percent. • Biodiesel is a fuel made from the trans-esterification of vegetable oils (e.g., rape, soy, mustard, sun-flower), animal fats or recycled cooking oils. It is non-toxic, biodegradable and essentially sulphur-free and can be used in any diesel engine either in its pure form (B100 or neat Biodiesel) or in a blend with petroleum diesel (B2 and B20, which contain 2 and 20 per cent biodiesel by volume). B100 may contain 10 percent fossil carbon from the methanol (made from natural gas) used in the esterification process. • Ethyl-tertiary-butyl-ether (ETBE) is used as a high octane blending component in gasoline (e.g., in France and Spain in blends of up to 15 percent content). The most common source is the etherification of ethanol from the fermentation of sugar beets, grain and potatoes with fossil isobutene. • Gaseous Biomass (landfill gas, sludge gas, and other biogas) produced by the anaerobic digestion of organic matter is occasionally used in some European countries (e.g. Sweden and Switzerland). Landfill and sewage gas are common sources of gaseous biomass currently. Other potential future commercial biofuels for use in mobile combustion include those derived from lignocellulosic biomass. Lignocellulosic feedstock materials include cereal straw, woody biomass, corn stover (dried leaves and stems), or similar energy crops. A range of varying extraction and transformation processes permit the production of additional biogenic fuels (e.g., methanol,dimethyl-ether (DME), and methyl-tetrahydrofuran (MTHF)).

It is good practice to select or develop an emission factor based on all the following criteria:

• •

•

• •

Fuel type (gasoline, diesel, natural gas) considering, if possible, fuel composition (studies have shown that decreasing fuel sulphur level may lead to significant reductions in N2O emissions6) Vehicle type (i.e. passenger cars, light trucks, heavy trucks, motorcycles) Emission control technology considering the presence and performance (e.g., as function of age) of catalytic converters (e.g., typical catalysts convert nitrogen oxides to N2, and CH4 into CO2). Díaz et al (2001) reports catalyst conversion efficiency for total hydrocarbons (THCs), of which CH4 is a component, of 92 (+/- 6) percent in a 1993-1995 fleet. Considerable deterioration of catalysts with relatively high mileage accumulation; specifically, THC levels remained steady until approximately 60 000 kilometers, then increased by 33 percent to between 60 000 to 100 000 kilometres. The impact of operating conditions (e.g., speed, road conditions, and driving patterns, which all affect fuel economy and vehicle systems’ performance)7. Consideration that any alternative fuel emission factor estimates tend to have a high degree of uncertainty, given the wide range of engine technologies and the small sample sizes associated with existing studies8.

The following section provides a method for developing CH4 emission factors from THC values. Well conducted and documented inspection and maintenance (I/M) programmes may provide a source of national data for emission factors by fuel, model, and year as well as annual mileage accumulation rates. Although some I/M programmes may only have available emission factors for new vehicles and local air pollutants, (sometimes called regulated pollutants, e.g. NOx, PM, NMVOCs, THCs), it may be possible to derive CH4 or N2O emission factors from these data. A CH4 emission factor may be calculated as the difference between emission factors for THCs and NMVOCs. In many countries, CH4 emissions from vehicles are not directly measured. They are a 6 7

8

UNFCCC (2004) Lipman and Delucchi (2002) provide data and explanation of the impact of operating conditions on CH4 and N2O emissions. Some useful references on bio fuels are available in Beer et al (2000), CONCAWE (2002).

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Chapter 3: Mobile Combustion

fraction of THCs, which is more commonly obtained through laboratory measurements. USEPA (1997) and Borsari (2005) and CETESB (2004 & 2005) provide conversion factors for reporting hydrocarbon emissions in different forms. Based on these sources, the following ratios of CH4 to THC may be used to develop CH4 emission factors from country-specific THC data9: •

•

2-stroke gasoline: 0.9 percent,

•

diesel: 1.6 percent,

•

natural gas vehicles: 88.0-95.2 percent,

•

4-stroke gasoline: 10-25 percent,

•

LPG: 29.6 percent,

•

gasohol E22: 24.3-25.5 percent, and ethanol hydrated E100: 26.0-27.2 percent.

Some I/M programmes may collect data on evaporatives, which may be assumed to be equal to NMVOCs.10 Recent and ongoing research has investigated the relationship between N2O and NOx emissions. Useful data may become available from this work11. Further refinements in the factors can be made if additional local data (e.g. on average driving speeds, climate, altitude, pollution control devices, or road conditions) are available, for example, by scaling emission factors to reflect the national circumstances by multiplying by an adjustment factor (e.g., traffic congestion or severe loading). Emission factors for both CH4 and N2O are established not just during a representative compliance driving test, but also specifically tested during running conditions and cold start conditions. Thus, data collected on the driving patterns in a country (based on the relationship of starts to running distances) can be used to adjust the emission factors for CH4 and N2O. Although ambient temperature has been shown to have impacts on local air pollutants, there is limited research on the effects of temperature on CH4 and N2O (USEPA 2004b). Please see Box 3.2.2 for information on refining emission factors for mobile sources in developing countries.

9

Gamas et. al. (1999) and Díaz, et.al (2001) report measured THC data for a range of vehicle vintage and fuel types.

10

IPCC (1997).

11

For light motor vehicles and passenger cars, ratios N2O/NOx obtained in literature range around 0.10-0.25 (Lipmann and Delucchi, 2002 and Behrentz, 2003).

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Volume 2: Energy

BOX 3.2.2 REFINING EMISSION FACTORS FOR MOBILE SOURCES IN DEVELOPING COUNTRIES

In some developing countries, the estimated emission rates per kilometre travelled may need to be altered to accommodate national circumstances, which could include: •Technology variations - In many cases due to tampering of emission control systems, fuel adulteration, or simply vehicle age, some vehicles may be operating without a functioning catalytic converter. Consequently, N2O emissions may be low and CH4 may be high when catalytic converters are not present or operating improperly. Díaz et al (2001) provides information on THC values for Mexico City and catalytic converter efficiency as a function of age and mileage, and this also chapter provides guidance on developing CH4 factors from THC data. ▪ Engine loading - Due to traffic density or challenging topography, the number of accelerations and decelerations that a local vehicle encounters may be significantly greater than that for corresponding travel in countries where emission factors were developed. This happens when these countries have well established road and traffic control networks. Increased engine loading may correlate with higher CH4 and N2O emissions. ▪ Fuel Composition - Poor fuel quality and high or varying sulphur content may adversely affect the performance of engines and conversion efficiency of post-combustion emission control devices such as catalytic converters. For example, N2O emission rates have been shown to increase with the sulphur content in fuels (UNFCCC, 2004). The effects of sulphur content on CH4 emissions are not known. Refinery data may indicate production quantities on a national scale. Section 3.2.2 Uncertainty Assessment provides information on how to develop uncertainty estimates for emission factors for road transportation. Further information on emission factors for developing countries is available from Mitra et al. (2004).

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Chapter 3: Mobile Combustion

TABLE 3.2.2 ROAD TRANSPORT N2O AND CH4 DEFAULT EMISSION FACTORS AND UNCERTAINTY RANGES (a) CH4 ( kg /TJ)

Fuel Type/Representative Vehicle Category

N2O (kg /TJ)

Default

Lower

Upper

Default

Lower

Upper

33

9.6

110

3.2

0.96

11

Motor Gasoline –Oxidation Catalyst (c)

25

7.5

86

8.0

2.6

24

Motor Gasoline –Low Mileage Light Duty Vehicle Vintage 1995 or Later (d)

3.8

1.1

13

5.7

1.9

17

Gas / Diesel Oil (e)

3.9

1.6

9.5

3.9

1.3

12

92

50

1 540

3

1

77

62

na

na

0.2

na

na

260

77

880

41

13

123

18

13

84

na

na

na

Motor Gasoline -Uncontrolled

Natural Gas

(f)

Liquified petroleum gas Ethanol, trucks, US (h) Ethanol, cars, Brazil

(i)

(g)

(b)

Sources: USEPA (2004b), EEA (2005a), TNO (2003) and Borsari (2005) CETESB (2004 & 2005) with assumptions given below. Uncertainty ranges were derived from data in Lipman and Delucchi (2002), except for ethanol in cars. (a) Except for LPG and ethanol cars, default values are derived from the sources indicated using the NCV values reported in the Energy Volume Introduction chapter; density values reported by the U.S. Energy Information Administration; and the following assumed representative fuel consumption values: 10 km/l for motor gasoline vehicles; 5 km/l for diesel vehicles; 9 km/l for natural gas vehicles (assumed equivalent to gasoline vehicles); 9 km/l for ethanol vehicles. If actual representative fuel economy values are available, it is recommended that they be used with total fuel use data to estimate total distance travelled data, which should then be multiplied by Tier 2 emission factors for N2O and CH4. (b) Motor gasoline uncontrolled default value is based on USEPA (2004b) value for a USA light duty gasoline vehicle (car) – uncontrolled, converted using values and assumptions described in table note (a). If motorcycles account for a significant share of the national vehicle population, inventory compilers should adjust the given default emission factor downwards. (c) Motor gasoline – light duty vehicle oxidation catalyst default value is based on the USEPA (2004b) value for a USA Light Duty Gasoline Vehicle (Car) – Oxidation Catalyst, converted using values and assumptions described in table note (a). If motorcycles account for a significant share of the national vehicle population, inventory compilers should adjust the given default emission factor downwards. (d) Motor gasoline – light duty vehicle vintage 1995 or later default value is based on the USEPA (2004b) value for a USA Light Duty Gasoline Vehicle (Car) – Tier 1, converted using values and assumptions described in table note (a). If motorcycles account for a significant share of the national vehicle population, inventory compilers should adjust the given default emission factor downwards. (e) Diesel default value is based on the EEA (2005a) value for a European heavy duty diesel truck, converted using values and assumptions described in table note (a). (f) Natural gas default and lower values were based on a study by TNO (2003), conducted using European vehicles and test cycles in the Netherlands. There is a lot of uncertainties for N2O. The USEPA (2004b) has a default value of 350 kg CH4/TJ and 28 kg N2O/TJ for a USA CNG car, converted using values and assumptions described in table note (a). Upper and lower limits are also taken from USEPA (2004b) (g) The default value for methane emissions from LPG, considering for 50 MJ/kg low heating value and 3.1 g CH4/kg LPG was obtained from TNO (2003). Uncertainty ranges have not been provided. (h) Ethanol default value is based on the USEPA (2004b) value for a USA ethanol heavy duty truck, converted using values and assumptions described in table note (a). (i) Data obtained in Brazilian vehicles by Borsari (2005) and CETESB (2004 & 2005). For new 2003 models, best case: 51.3 kg THC/TJ fuel and 26.0 percent CH4 in THC. For 5 years old vehicles: 67 kg THC/TJ fuel and 27.2 percent CH4 in THC. For 10 years old: 308 kg THC/TJ fuel and 27.2 percent CH4 in THC.

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TABLE 3.2.3 N2O AND CH4 EMISSION FACTORS FOR USA GASOLINE AND DIESEL VEHICLES N2O Vehicle Type

Light Duty Gasoline Vehicle (Car)

Light Duty Diesel Vehicle (Car)

Light Duty Gasoline Truck

Light Duty Diesel Truck

Heavy Duty Gasoline Vehicle

Heavy Duty Diesel Vehicle Motorcycles

Emission Control Technology

CH4

Running (hot)

Cold Start

Running (hot)

Cold Start

mg/km

mg/start

mg/km

mg/start

Low Emission Vehicle (LEV)

0

90

6

32

Advanced Three-Way Catalyst

9

113

7

55

Early Three-Way Catalyst

26

92

39

34

Oxidation Catalyst

20

72

82

9

Non-oxidation Catalyst

8

28

96

59

Uncontrolled

8

28

101

62

Advanced

1

0

1

-3

Moderate

1

0

1

-3

Uncontrolled

1

-1

1

-3

Low Emission Vehicle (LEV)

1

59

7

46

Advanced Three-Way Catalyst

25

200

14

82

Early Three-Way Catalyst

43

153

39

72

Oxidation Catalyst

26

93

81

99

Non-oxidation catalyst

9

32

109

67

Uncontrolled

9

32

116

71

Advanced and moderate

1

-1

1

-4

Uncontrolled

1

-1

1

-4

Low Emission Vehicle (LEV)

1

120

14

94

Advanced Three-Way Catalyst

52

409

15

163

Early Three-Way Catalyst

88

313

121

183

Oxidation catalyst

55

194

111

215

Non-oxidation catalyst

20

70

239

147

Heavy Duty Gasoline Vehicle Uncontrolled

21

74

263

162

All -advanced, moderate, or uncontrolled

3

-2

4

-11

Non-oxidation catalyst

3

12

40

24

Uncontrolled

4

15

53

33

Source: USEPA (2004b). Notes:

3.22

a

These data have been rounded to whole numbers.

b

Negative emission factors indicate that a vehicle starting cold produces fewer emissions than a vehicle starting warm or running warming.

c

A database of technology dependent emission factors based on European data is available in the COPERT tool at http://vergina.eng.auth.gr/mech0/lat/copert/copert.htm.

d

Because of the total-hydrocarbon limits in Europe, the CH4-emissions of European vehicles may be lower than the indicated values from USA (Heeb, et. al., 2003)

e

These “cold starts” were measured at an ambient temperature of 68ºF to 86ºF (20°C to 30°C).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

TABLE 3.2.4 EMISSION FACTORS FOR ALTERNATIVE FUEL VEHICLES (mg/km) Vehicle Type Vehicle Control Technology

N2O Emission Factor

CH4 Emission Factor

39

9

CNG

27 - 70

215 - 725

LPG

5

24

12 - 47

27 - 45

Methanol

135

401

CNG

185

5 983

LNG

274

4 261

LPG

93

67

Ethanol

191

1227

Methanol

135

401

CNG

101

7 715

Ethanol

226

1 292

Light Duty Vehicles Methanol

Ethanol Heavy Duty Vehicles

Buses

Sources: USEPA 2004c, and Borsari (2005) CETESB (2004 & 2005).

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Volume 2: Energy

TABLE 3.2.5 EMISSION FACTORS FOR EUROPEAN GASOLINE AND DIESEL VEHICLES (mg/km), COPERT IV MODEL

Power Two Wheeler

CNG

Gasoline

>50 cm3 4stroke

30

Highway

30

Rural

30

10 22 11 3 2 0 2 4 9 9 0 21 13 5 10 52 22 5 2 0 2 4 9 9

Hot

6.5 8.0 2.5 1.5 0.7 0 4 6 4 4 0 8 2 1 6.5 52 22 5 2 0 4 6 4 4 6 30 30

10 38 24 12 6 0 0 3 15 15 0 38 23 9 10 122 62 36 16 0 0 3 15 15

Cold

6 30 30

6.5 17 4.5 2.0 0.8 0 4 6 4 4 0 13 3 2 6.5 52 22 5 2 0 4 6 4 4 6 30 30

Hot

Highway

Diesel

Urban

Cold

Fuel Diesel Gasoline Diesel Gasoline

Urban

Vehicle Technology/ Class

pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 pre-ECE Euro 1 Euro 2 Euro 3 and later pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 All Technologies GVW16t Urban Busses & Coaches pre-Euro 4 Euro 4 and later (incl. EEV) 50 cm3 2-stroke

CH4 Emission Factors (mg/km)

Rural

Heavy Duty Truck & Bus

Light Duty Vehicles

LPG

Passenger Car

Gasoline

Vehicle Type

N2O Emission Factors (mg/km)

201 45 94 83 57 22 18 6 7 0

131 26 17 3 2 28 11 7 3 0

86 16 13 2 2 12 9 3 0 0

41 14 11 4 0 8 3 2 0 0

80 201 45 94 83 57 22 18 6 7 0 140 85 175

131 26 17 3 2 28 11 7 3 0

35

25

86 16 13 2 2 12 9 3 0 0 110 23 80

41 14 11 4 0 8 3 2 0 0 70 20 70

80

70

175 5400

n.a.

900

1 2

1 2

1 2

219 150

219 150

219 150

2

2

2

200

200

200

Notes:

3.24

1

Personal Communication: Ntziachristos, L., and Samaras, Z., (2005), LAT (2005) and TNO (2002).

2

The urban emission factor is distinguished into cold and hot for passenger cars and light duty trucks. The cold emission factor is relevant for trips which start with the engine at ambient temperature. A typical allocation of the annual mileage of a passenger car into the different driving conditions could be: 0.3/0.1/0.3/0.3 for urban cold, urban hot, rural and highway respectively.

3

Passenger car emission factors are also proposed for light duty vehicles when no more detailed information exists.

4

The sulphur content of gasoline has both a cumulative and an immediate effect on N2O emissions. The emission factors for gasoline passenger cars correspond to fuels at the period of registration of the different technologies and a vehicle fleet of ~50 000 km average mileage.

5

N2O and CH4 emission factors from heavy duty vehicles and power two wheelers are also expected to depend on vehicle technology. There is no adequate experimental information though to quantify this effect.

6

N2O emission factors from diesel and LPG passenger cars vehicles are proposed by TNO (2002). Increase in diesel N2O emissions as technology improves may be quite uncertain but is also consistent with the developments in the after treatment systems used in diesel engines (new catalysts, SCR-DeNOx).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

3.2.1.3

C HOICE

OF ACTIVITY DATA

Activity data may be provided either by fuel consumption or by vehicle kilometres travelled VKT. Use of adequate VKT data can be used to check top-down inventories. FU EL C ONSUM PTION

Emissions from road vehicles should be attributed to the country where the fuel is sold; therefore fuel consumption data should reflect fuel that is sold within the country’s territories. Such energy data are typically available from the national statistical agency. In addition to fuel sold data collected nationally, inventory compilers should collect activity data on other fuels used in that country with minor distributions that are not part of the national statistics (i.e., fuels that are not widely consumed, including those in niche markets such as compressed natural gas or biofuels). These data are often also available from the national statistical agency or they may be accounted for under separate tax collection processes. For Tier 3 methods, the MOBILE or COPERT models may help develop activity data. It is good practice to check the following factors (as a minimum) before using the fuel sold data: •

•

• • •

•

•

•

Does the fuel data relate to on-road only or include off-road vehicles as well? National statistics may report total transportation fuel without specifying fuel consumed by on-road and off-road activities. It is important to ensure that fuel use data for road vehicles excludes that used for off-road vehicles or machinery (see OffRoad Transportation Section 3.3). Fuels may be taxed differently based on their intended use. A Road-Taxed fuel survey may provide an indication of the quantity of fuel sold for on-road use. Typically, the on-road vehicle fleet and associated fuel sales are better documented than the off-road vehicle population and activity. This fact should be considered when developing emission estimates. Is agricultural fuel use included? Some of this may be stationary use while some will be for mobile sources. However, much of this will not be on-road use and should not be included here. Is fuel sold for transportation uses used for other purposes (e.g., as fuel for a stationary boiler), or vice versa? For example, in countries where kerosene is subsidized to lower its price for residential heating and cooking, the national statistics may allocate the associated kerosene consumption to the residential sector even though substantial amounts of kerosene may have been blended into and consumed with transportation fuels. How are biofuels accounted for? How are blended fuels reported and accounted for? Accounting for official blends (e.g. addition of 25 percent of ethanol in gasoline) in activity data is straightforward, but if fuel adulteration or tampering (e.g. spent solvents in gasoline, kerosene in diesel fuel) is prevalent in a country, appropriate adjustments should be applied to fuel data, taking care to avoid double counting. Are the statistics affected by fuel tourism? Is there significant fuel smuggling? How is the use of lubricants as an additive in 2-stroke fuels reported? It may be included in the road transport fuel use or may be reported separately as a lubricant (see Box 3.2.4.).

Two alternative approaches are suggested to separate non-road and on-road fuel use: (1) For each major fuel type, estimate the fuel used by each road vehicle type from vehicle kilometres travelled data. The difference between this road vehicle total and the apparent consumption is attributed to the off-road sector; or (2) The same fuel-specific estimate in (1) is supplemented by a similarly structured bottom-up estimate of offroad fuel use from a knowledge of the off-road equipment types and their usage. The apparent consumption in the transportation sector is then disaggregated according to each vehicle type and the off-road sector in proportion to the bottom-up estimates. Depending on national circumstances, inventory compilers may need to adjust national statistics on road transportation fuel use to prevent under- or over-reporting emissions from road vehicles. It is good practice to adjust national fuel sales statistics to ensure that the data used just reflects on-road use. Where this adjustment is necessary it is good practice to cross-check with the other appropriate sectors to ensure that any fuel removed from on-road statistics is added to the appropriate sector, or vice versa. As validation, and if distance travelled data are available (see below vehicle kilometres travelled), it is good practice to estimate fuel use from the distance travelled data. The first step (Equation 3.2.6) is to estimate fuel consumed by vehicle type i and fuel type j.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

EQUATION 3.2.6 VALIDATING FUEL CONSUMPTION Estimated Fuel = ∑ [Vehiclesi, j ,t •Distancei, j ,t • Consumptioni, j ,t ] i, j ,t

Where: Estimated Fuel

=total estimated fuel use estimated from distance travelled (VKT) data (l)

Vehiclesi,j,t

= number of vehicles of type i and using fuel j on road type t

Distancei,j,t

= annual kilometres travelled per vehicle of type i and using fuel j on road type t (km)

Consumptioni,j,t = average fuel consumption (l/km) by vehicles of type i and using fuel j on road type t i

= vehicle type (e.g., car, bus)

j

= fuel type (e.g. motor gasoline, diesel, natural gas, LPG)

t

= type of road (e.g., urban, rural)

If data are not available on the distance travelled on different road types, this equation should be simplified by removing the “t” the type of road. More detailed estimates are also possible including the additional fuel used during the cold start phase. It is good practice to compare the fuel sold statistics used in the Tier 1 approach with the result of equation 3.2.6. It is good practice to consider any differences and determine which data is of higher quality. Except in rare cases (e.g. large quantities of fuel sold for off-road uses, extensive fuel smuggling), fuel sold statistics are likely to be more reliable. This provides an important quality check. Significant differences between the results of two approaches may indicate that one or both sets of statistics may have errors, and that there is need for further analysis. Areas of investigation to pursue when reconciling fuel sold statistics and vehicle kilometre travelled data are listed in Section 3 2.3, Inventory quality assurance/quality control (QA/QC). Distance travelled data for vehicles by type and fuel are important underpinnings for the higher tier calculations of CH4 and N2O emissions from road transport. So it may be necessary to adjust the distance travelled data to be consistent with the fuel sold data before proceeding to estimating emissions of CH4 and N2O. This is especially important in cases where the discrepancy between the estimated fuel use (Eq 3.2.6) and the statistical fuel sold is significant compared to the uncertainties in fuel sold statistics. Inventory compilers will have to use their judgement on the best way of adjusting distance travelled data. This could be done pro rata with the same adjustment factor applied to all vehicle type and road type classes or, where some data are judged to be more accurate, different adjustments could be applied to different vehicle types and road types. An example of the latter could be where the data on vehicle travelled on major highways is believed to be reasonably well known and on the other hand rural traffic is poorly measured. In any case, the adjustments made for reasons of the choice of adjustment factor and background data as well as any other checks should be well documented and reviewed. V EH I C LE K I LOM E TR ES TR A V E L L ED ( V K T)

While fuel data can be used at Tier 1 for CH4 and N2O, higher tiers also need vehicle kilometres travelled (VKT) by vehicle type, fuel type and possibly road type as well. Many countries collect, measure, or otherwise estimate VKT. Often this is done by sample surveys counting vehicle numbers passing fixed points. These surveys can be automatic or manual and count vehicle numbers by type of vehicle. There may be differences between the vehicle classification used in the counts and other data (e.g. tax classes) that also give data on vehicle numbers. In addition they are unlikely to differentiate between similar vehicle using different fuels (e.g. motor gasoline and diesel cars). Sometimes more detailed information is also collected (e.g. vehicle speeds as well as numbers) especially where more detailed traffic planning has been performed. This may only be available for a municipality rather than the whole country. From these traffic counts, transport authorities can make estimates of the total VKT travelled in a country. Alternatives ways to determine the mileage are direct surveys of vehicle owners (private and commercial) and use of administrative records for commercial vehicles, taking care to account for outdated registration records for scrapped vehicles (Box 3.2.3 provides an approach to estimate the remaining fleets). Where VKT is estimated in a country it is good practice to use this data, especially to validate the fuel sold data (see section 3.2.1.4).

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

OTHER PARAM ETERS.

If CH4 or N2O emissions from road transportation are a key category, it is good practice to obtain more information on parameters that influence emission factors to ensure the activity data is compatible with the applicable Tier 2 or Tier 3 emission factor. This will require more dissagregated activity data in order to implement Equation 3.2.3 or 3.2.5: •

• •

•

the amount of fuel consumed (in terajoules) by fuel type (all tiers); for each fuel type, the amount of fuel (or VKT driven) that is consumed by each representative vehicle type (e.g., passenger, light-duty or heavy-duty for road vehicles) preferably with age categories (Tiers 2 and 3); and the emission control technology (e.g., three-way catalysts) (Tiers 2 and 3). It may also be possible to collect VKT data by type of road (e.g. urban, rural, highway)

If the distribution of fuel use by vehicle and fuel type is unknown, it may be estimated from the number of vehicles by type. If the number of vehicles by vehicle and fuel type is not known, it may be estimated from national statistics (see below). Vehicle technology, which is usually directly linked to the model and year of vehicle, affects CH4 and N2O emissions. Therefore, for Tier 2 and Tier 3 methods, activity data should be grouped based on Original Equipment Manufacturer (OEM) emission control technologies fitted to vehicle types in the fleet. The fleet age distribution helps stratify the fleet into age and subsequently technology classes. If the distribution is not available, vehicle deterioration curves may be used to estimate vehicle lifespan and therefore the number of vehicles remaining in service based on the number introduced annually (see Box 3.2.3). In addition, if possible, determine (through estimates or from national statistics) the total distance travelled (i.e., VKT) by each vehicle technology type (Tier 3). If VKT data are not available, they can be estimated based on fuel consumption and assumed national fuel economy values. To estimate VKT using road transport fuel use data, convert fuel data to volume units (litres) and then multiply the fuel-type total by an assumed fuel economy value representative of the national vehicle population for that fuel type (km/l). If using the Tier 3 method and national VKT statistics are available, the fuel consumption associated with these distance-travelled figures should be calculated and aggregated by fuel for comparison with national energy balance figures. Like the Tier 2 method, for Tier 3 it is suggested to further subdivide each vehicle type into uncontrolled and key classes of emission control technology. It should be taken into consideration that typically, emissions and distance travelled each year vary according to the age of the vehicle; the older vehicles tend to travel less but may emit more CH4 and N2O per unit of activity. Some vehicles, especially in developing countries, may have been converted to operate on a different type of fuel than their original design. To implement the Tier 2 or 3 method, activity data may be derived from a number of possible sources. Vehicle inspection and maintenance (I/M) programmes, where operating, may provide insight into annual mileage accumulation rates. National vehicle licensing records may provide fleet information (counts of vehicles per model-year per region) and may even record mileage between license renewals. Other sources for developing activity data include vehicle sales, import, and export records. Alternatively, vehicle stocks may be estimated from the number of new vehicle imports and sales by type, fuel and model year. The populations of vehicles remaining in service may be estimated by applying scrappage or attrition curves. Higher tier methods involving an estimate of cold start emissions require knowledge of the number of starts. This can be derived from the total distance travelled and the average trip length. Typically, this can be obtained from traffic surveys. This data is often collected for local or traffic studies for transport planning.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

BOX 3.2.3 VEHICLE DETERIORATION (SCRAPPAGE) CURVES

Deterioration (scrappage) curves can be used to adjust data obtained from fleet statistics based on vehicle licensing plates, where older vehicles are out of service but still registered in official records, leading to overestimation of emissions. They are approximated by Gompertz functions limiting maximum vehicle age. In the case of Brazil, the maximum vehicle age of 40 years was used for the National Communication of Greenhouse Gases (MCT,2002 and http://www.mct.gov.br/clima/comunic_old/veicul03.htm ) utilizing the S-shaped Gompertz scrapping curve illustrated in this box, Vehicle Scrappage Function. This curve was provided by Petrobras, and is currently utilized by environmental agencies for emission inventories. The share of scrapped vehicles aged t is defined by the equation S(t) = exp [ - exp (a + b(t)) ]; where (t) is the age of the vehicle (in years) and S(t) is the fraction of scrapped vehicles aged t. In the year 1994, national values were provided for automobiles (a = 1.798 and b= -0.137) and light commercial vehicles (a= 1.618 and b= -0.141). (Ministério da Ciencia e Tecnologia (2002), Primeiro Inventário Brasileiro De Emissões Antrópicas De Gases De Efeito Estufa Relatórios De Refencia Emissões De Gasses De Efeito Por Fontes Móveis, No Setor Eergético. Brasília, Bazil 2002)

100%

Scrapping curves in Brazil.

100% 99% 99%

97% 98%

Scrappage rates are assumed to follow the following equation:

93%

94%

90%

For more details see: Ministério da Ciência e Tecnologia (2002)

80%

78%

80% Percentage of vehicles remaining

S(t) = exp(-exp(a+b·t))

87%

89%

70%

69%

71% 60%

59%

60% 50%

49% 50% 40%

40% 41%

32%

30%

33% 26% 26%

light commercial vehicles remaining

20%

20% 20%

automobiles remaining

16% 12%

16%

10%

9%

12% 9%

7%

0% 0

3.2.1.4

2

4

6

8

10

12

14

16

18 20 22 vehicle age

24

26

28

30

7% 5% 32

6%

4%

3%

2%

4% 34

3% 36

2% 38

2% 40

C OMPLETENESS

In establishing completeness, it is recommended that: •

• •

•

Where cross-border transfers take place in vehicle tanks, emissions from road vehicles should be attributed to the country where the fuel is loaded into the vehicle. Carbon emitted from oxygenates and other blending agents which are derived from biomass should be estimated and reported as an information item to avoid double counting, as required by Volume 1. For more information on biofuels, see section 3.2.1.2. Ensure the reliability of the fuel sold data by following the recommendations listed in Section 3.2.1.3. Emissions from lubricants that are intentionally mixed with fuel and combusted in road vehicles should be captured as mobile source emissions. For more information on combustion of lubricants, please refer to Box 3.2.4

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

BOX 3.2.4 LUBRICANTS IN MOBILE COMBUSTION

Lubrication of a two-stroke petrol engine is conceptually quite different from that of a four-stroke engine, as it is not possible to have a separate lubricating oil sump. A two-stroke petrol engine should be lubricated by a mixture of lubricating oil and petrol in suitable proportion according to the manufacturer's recommendations. Depending on the engine type, mixtures of 1:25, 1:33 and 1:50 are common. In the latest generation two-stroke engines, the lubricating oil is directly injected by an accurate metering device from a separate tank into the petrol in quantities that depend on the speed and load of the engine. Older or inexpensive two-stroke engines will receive the lubricant as part of the fuel mixture. Often these mixtures are prepared by the fuel supplier and delivered to the gas station but sometimes the vehicle owner will add oil at the service station. In some countries two stroke engines have been historically very significant as recent as the 1990’s (e.g. Eastern Europe) or are still very significant (e.g. India and parts of South-East Asia). The classification of these lubricants in energy statistics as lubricant or fuel may vary. Inventory compilers need to make sure that these lubricants are allocated to end use appropriately, accounted for properly, and that double counting or omission is avoided (compare treatment of lubricants in Volume 3 Chapter 5: Non-energy product and feedstock use of fuels). Lubricants intentionally mixed with fuel and combusted in road vehicles should be reported as energy and the associated emissions calculated using mobile source guidelines. When the chosen activity data for 2-stroke engines are based on kilometres travelled, the added lubricants should be considered in the fuel economy, as a part of the fuel blend.

3.2.1.5

D EVELOPING

A CONSISTENT TIME SERIES

When data collection and accounting procedures, emission estimation methodologies, or models are revised, it is good practice to recalculate the complete time series. A consistent time series with regard to initial collection of fleet technology data may require extrapolation, possibly supported by the use of proxy data. This is likely to be needed for early years. Inventory compilers should refer to the discussion in Volume 1 Chapter 5: Time Series Consistency for general guidance. Since this chapter contains many updated emission factors, for CO2 (accounting for 100 percent fuel oxidation), CH4, and N2O, inventory compilers should ensure time series consistency. A consistent time series should consider the technological change in vehicles and their catalysts control systems. The time series should take into account the gradual phase-in among fleets, which is driven by legislation and market forces. Consistency can be maintained with accurate data on fleet distribution according to engine and control system technology, maintenance, control technology obsolescence, and fuel type. If VKT are not available for the whole time series but for a recent year, guidelines in Volume 1 Chapter 5: Time Series Consistency should be used to select a splicing method.

3.2.2

Uncertainty Assessment

CO2, N2O, and CH4 contribute typically around 97, 2-3 and 1 percent of CO2-equivalent emissions from the road transportation sector, respectively. Therefore, although uncertainties in N2O and CH4 estimates are much higher, CO2 dominates the emissions from road transport. Use of locally estimated data will reduce uncertainties, particularly with bottom-up estimates. Emission factor uncertainty For CO2, the uncertainty in the emission factor is typically less than 2 percent when national values are used (see Table 1.4 of the Introduction Chapter of this Volume). Default CO2 emission factors given in Table 3.2.1. Road Transport Default Carbon Dioxide Emission Factors have an uncertainty of 2-5 percent), due to uncertainty in the fuel composition. Use of fuel blends, e.g. involving biofuels, or adulterated fuels may increase the uncertainty in emission factors if the composition of the blend is uncertain.

The uncertainties in emission factors for CH4 and N2O are typically relatively high (especially for N2O) and are likely to be a factor of 2-3. They depend on: •

Uncertainties in fuel composition (including the possibility of fuel adulteration) and sulphur content;

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy • •

•

• •

Uncertainties in fleet age distribution and other characterisation of the vehicle stock, including cross-border effects - the technical characteristics of vehicles from another country that take on fuel may be covered by technology models; Uncertainties in maintenance patterns of the vehicle stock; Uncertainties in combustion conditions (climate, altitude) and driving practices, such as speed, proportion of running distance to cold starts, or load factors (CH4 and N2O); Uncertainties in application rates of post-combustion emission control technologies (e.g. three-way catalyst); Uncertainties in the use of additives to minimize the aging effect of catalysts;

•

Uncertainties in operating temperatures (N2O); and

•

Uncertainties of test equipment and emission measurement equipment.

It is good practice to estimate uncertainty based on published studies from which the emission factors were obtained. At least the following types of uncertainties may be discussed in published sources and need to be considered in the development of national emission factors from empirical data: •

•

A range in the emission factor of an individual vehicle, represented as a variance of measurements, due to variable emissions in different operating conditions (e.g. speed, temperature); and Uncertainty in the mean of emission factors of vehicles within the same vehicle class.

In addition, the vehicle sample that was measured may have been quite limited, or even a more robust sample of measurements may not be representative of the national fleet. Test driving cycles cannot fully reflect real driving behaviour, so at least some emission factor studies now test cold start emissions separately from running emissions, so that countries may be able to create country-specific adjustments, though those adjustments will themselves require more data collection with its own uncertainties. Another source of uncertainty may be the conversion of the emission factor into units in which the activity data are given (e.g. from kg/GJ to g/km) because this requires additional assumptions about other parameters, such as fuel economy, which have an associated uncertainty as well. The uncertainty in the emission factor can be reduced by stratifying vehicle fleets further by technology, age and driving conditions. Activity data uncertainty Activity data are the primary source of uncertainty in the emission estimate. Activity data are either given in energy units (e.g. TJ) or other units for different purposes such as person-/ton-kilometres, vehicle stocks, trip length distributions, fuel efficiencies, etc. Possible sources of uncertainty, which will typically be about +/-5 percent, include: •

•

Uncertainties in national energy surveys and data returns;

•

Misclassification of fuels;

Unrecorded cross-border transfers;

•

•

•

Misclassification in vehicle stock; Lack of completeness (fuel not recorded in other source categories may be used for transportation purposes); and Uncertainty in the conversion factor from one set of activity data to another (e.g. from fuel consumption data to person-/ton-kilometres, or vice versa, see above).

Stratification of activity data may reduce uncertainty, if they can be connected to results from a top-down fuel use approach. For estimating CH4 and N2O emissions, a different tier and hence different sets of activity data may be used. It is good practice to ensure that top-down and bottom-up approaches match, and to document and explain deviations if they do not match (see also Section 3.2.1.4 Completeness). For these gases, the emission factor uncertainty will dominate and the activity data uncertainty may be taken to be the same as for CO2. Further guidance on uncertainty estimates for activity data can be found in Volume 1 Chapter 3: Uncertainties.

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Chapter 3: Mobile Combustion

3.2.3

Inventory Quality Assurance/Quality Control (QA/QC)

It is good practice to conduct quality control checks as outlined in Volume 1 Chapter 6: Quality Assurance/Quality Control and Verification and expert review of the emission estimates. Additional quality control checks as outlined in Tier 2 procedures in the same chapter and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventory compilers are encouraged to use higher tier QA/QC for source categories as identified in Volume 1 Chapter 4: Methodological Choice and Identification of Key Categories. In addition to the guidance in the referenced chapters, specific procedures of relevance to this source category are outlined below. Comparison of emissions using alternative approaches

For CO2 emissions, the inventory compiler should compare estimates using both the fuel statistics and vehicle kilometre travelled data. Any anomalies between the emission estimates should be investigated and explained. The results of such comparisons should be recorded for internal documentation. Revising the following assumptions could narrow a detected gap between the approaches: Off-road/non transportation fuel uses; Annual average vehicle mileage; Vehicle fuel efficiency; Vehicle breakdowns by type, technology, age, etc.; Use of oxygenates/biofuels/other additives; Fuel use statistics; and Fuel sold/used. Review of emission factors

If default emission factors are used, the inventory compiler should ensure that they are applicable and relevant to the categories. If possible, the default factors should be compared to local data to provide further indication that the factors are applicable. For CH4 and N2O emissions, the inventory compiler should ensure that the original data source for the local factors is applicable to the category and that accuracy checks on data acquisition and calculations have been performed. Where possible, the default factors and the local factors should be compared. If the default factors were used to estimate N2O emissions, the inventory compiler should ensure that the revised emission factors in Table 3.2.3 were used in the calculation. Activity data check

The inventory compiler should review the source of the activity data to ensure applicability and relevance to the category. Section 3.2.1.3 provides good practice for checking activity data. Where possible, the inventory compiler should compare the data to historical activity data or model outputs to detect possible anomalies. The inventory compiler should ensure the reliability of activity data regarding fuels with minor distribution; fuel used for other purposes, on- and off-road traffic, and illegal transport of fuel in or out of the country. The inventory compiler should also avoid double counting of agricultural and off-road vehicles. External review

The inventory compiler should perform an independent, objective review of the calculations, assumptions, and documentation of the emissions inventory to assess the effectiveness of the QC programme. The peer review should be performed by expert(s) who are familiar with the source category and who understand the inventory requirements. The development of CH4 and N2O emission factors is particularly important due to the large uncertainties in the default factors.

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Volume 2: Energy

3.2.4

Reporting and Documentation

It is good practice to document and archive all information required to produce the national emissions inventory estimates. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced. This applies particularly to national models used to estimate emissions from road transport, and to work done to improve knowledge of technology-specific emission factors for nitrous oxide and methane, where the uncertainties are particularly great. This type of information, provided the documentation is clear, should be submitted for inclusion in the EFDB. Confidentiality is not likely to be a major issue with regard to road emissions, although it is noted that in some countries the military use of fuel may be kept confidential. The composition of some additives is confidential, but this is only important if it influences greenhouse gas emissions. Where a model such as the USEPA MOVES or MOBILE models or the EEA COPERT model is used (EPA 2005a, EPA 2005b, EEA 2005, respectively), a complete record of all input data should be kept. Also any specific assumptions that were made and modifications to the model should be documented.

3.2.5

Reporting tables and worksheets

See the four pages of the worksheets (Annex 1) for the Tier I Sectoral Approach which are to be filled in for each of the source categories. The reporting tables are available in Volume 1, Chapter 8.

3.3

OFF-ROAD TRANSPORTATION

The off-road category (1 A 3 e ii) in Table 3.1.1 includes vehicles and mobile machinery used within the agriculture, forestry, industry (including construction and maintenance), residential, and sectors, such as airport ground support equipment, agricultural tractors, chain saws, forklifts, snowmobiles. For a brief description of common types of off-road vehicles and equipment, and the typical engine type and power output of each, please refer to EEA 2005. Sectoral desegregations are also available at USEPA, 2005b12. Engine types typically used in these off-road equipment include compression-ignition (diesel) engines, sparkignition (motor gasoline), 2-stroke engines, and motor gasoline 4-stroke engines.

3.3.1

Methodological issues

Emissions from off-road vehicles are estimated using the same methodologies used for mobile sources, as presented in Section 3.2. These have not changed since the publication of the 1996 IPCC Guidelines and the GPG2000, except that, as discussed in Section 3.2.1.2, the emission factors now assume full oxidation of the fuel. This is for consistency with the Stationary Combustion Chapter. Also these guidelines contain a method for estimating CO2 emissions from catalytic converters using urea, a source of emissions that was not addressed previously.

3.3.1.1

C HOICE

OF METHOD

There are three methodological options for estimating CO2, CH4, and N2O emissions from combustion in offroad mobile sources: Tier 1, Tier 2, and Tier 3. Figure 3.3.1: Decision tree for estimating emissions from offroad vehicles provides the criteria for choosing the appropriate method. The preferred method of determining CO2 emissions is to use fuel consumption for each fuel type on a country-specific basis. However, there may be difficulties with activity data because of the number and diversity of equipment types, locations, and usage

12

Appendix B of this reference provides Source Classification Codes (SCC) and definitions for: (a) Recreational vehicles; (b) Construction equipment; (c) Industrial equipment; (d) Lawn and garden equipment; (e) Agricultural equipment; (f) Commercial equipment; (g) Logging; (h) GSE/underground mining/oil field equipment; (i) Recreational marine and; (j) Railway maintenance are provided in Appendix B.

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patterns associated with off-road vehicles and machinery. Furthermore, statistical data on fuel consumption by off-road vehicles are not often collected and published. In this case higher tier methods will be needed for CO2 and they are necessary for non-CO2 gases because these are much more dependent on technology and operating conditions. A single method is provided for estimating CO2 emissions from catalytic converters using urea. Many types of off-road vehicles will not have catalytic converters installed, but emission controls will probably increasingly be used for some categories of off-road vehicles, especially those operated in urban areas (e.g., airport or harbour ground support equipment) in developed countries. If catalytic converters using urea are used in off-road vehicles, the associated CO2 emissions should be estimated. The general method for estimating greenhouse gas emissions from energy sources can be described as:

(

EQUATION 3.3.1 TIER 1 EMISSIONS ESTIMATE Emissions = ∑ Fuel j • EF j j

)

Where: Emissions = Emissions (kg) Fuelj

= fuel consumed (as represented by fuel sold) (TJ)

EFj

= emission factor (kg/TJ)

j

= fuel type

For Tier 1, emissions are estimated using fuel-specific default emission factors as listed in Table 3.3.1, assuming that for each fuel type, the total fuel is consumed by a single off-road source category. For Tier 2, emissions are estimated using country-specific and fuel-specific emission factors which, if available, are specific to broad type of vehicle or machinery. There is little or no advantage in going beyond Tier 2 for CO2 emissions estimates, provided reliable fuel consumption data are available.

(

)

EQUATION 3.3.2 TIER 2 EMISSIONS ESTIMATE Emissions = ∑ Fuelij • EFij

Where: Emissions =emissions (kg) Fueli,j

= fuel consumed (as represented by fuel sold) (TJ)

EFi,j

= emission factor (kg/TJ)

i

= vehicle/equipment type

j

= fuel type

For Tier 3, if data are available, the emissions can be estimated from annual hours of use and equipment-specific parameters, such as rated power, load factor, and emission factors based on power usage. For off-road vehicles, these data may not be systematically collected, published, or available in sufficient detail, and may have to be estimated using a combination of data and assumptions.

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Figure 3.3.1

Decision tree for estimating emissions from off-road vehicles Start

Is equipment level data available?

Estimate emissions using country-specific emission factors and detailed activity data. (e.g., using models)

Yes

Box 3: Tier 3 No

Is broad technology level fuel data available?

Collect data for higher Tiers.

Estimate emissions.

Yes

Box 2: Tier 2

No

Are country-specific emission factors available?

Yes

No Yes Is off-road transportation a key category?

No

Estimate emissions using fuel data and default emission factors. Box 1: Tier 1 Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees.

Equation 3.3.3 represents the Tier 3 methodology, where the following basic equation is applied to calculate emissions (in Gg):

(

EQUATION 3.3.3 TIER 3 EMISSIONS ESTIMATE Emission = ∑ N ij • H ij • Pij • LFij • EFij ij

)

Where: Emission = emission in kg. Nij

3.34

= source population

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

Hij

= annual hours of use of vehicle i (h)

Pij

= average rated power of vehicle i (kW)

LFij

= typical load factor of vehicle i (fraction between 0 and 1)

EFij

= average emission factor for use of fuel j in vehicle i (kg/kWh)

i

= off-road vehicle type

j

= fuel type

Equation 3.3.3 may be stratified by factors such as age, technological vintage or usage pattern, and this will increase the accuracy of the estimates provided self-consistent sets of parameters H, P, LF and EF are available to support the stratification, (EEA 2005). Other detailed modelling tools are available for estimating off-road emissions using Tier 3 methodology (e.g., NONROAD (USEPA 2005a) and COPERT (Ntziachristos 2000)). For estimating CO2 emissions from use of urea-based additives in catalytic converters (non-combustive emissions), Equation 3.3.4 is used: EQUATION 3.3.4 EMISSIONS FROM UREA-BASED CATALYTIC CONVERTERS ⎛ 44 ⎞ ⎛ 12 ⎞ Emissions = Activity • ⎜ ⎟ • Purity Factor • ⎜ ⎟ ⎝ 12 ⎠ ⎝ 60 ⎠

Where: Emission

=

Emission of CO2 (kg)

Activity

=

Mass (kg) of urea-based additive consumed for use in catalytic converters

Purity factor

=

Fraction of urea in the urea-based additive (if percent, divide by 100)

The factor (12/60) captures the stochiometric conversion from urea ((CO(NH2)2)) to carbon, while factor (44/12) converts carbon to CO2.

3.3.1.2

C HOICE

OF

E MISSION F ACTORS

Default CO2 emission factors assume that 100% of the fuel carbon is oxidised to CO2. This is irrespective of whether the carbon is emitted initially as CO2, CO, NMVOC or as particulate matter. Country-specific NCV and CEF data should be used for Tiers 2 and 3. Inventory compilers may wish to consult CORINAIR 2004 or the EFDB for emission factors, noting that responsibility remains with the inventory compilers to ensure that emission factors taken from the EFDB are applicable to national circumstances. For a Tier 3 approach example, please see Box 3.3.1 where more information on tailoring the NONROAD emissions model using country-specific data as well as the model to enhance national emission factors are given. The default emission factors for CO2 and their uncertainty ranges, and the default emission factors for CH4 and N2O for Tier 1 are provided in Table 3.3.1. To estimate CO2 emissions, inventory compilers also have the option of using emission factors based on country-specific fuel consumption by off-road vehicles.

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TABLE 3.3.1 DEFAULT EMISSION FACTORS FOR OFF-ROAD MOBILE SOURCES AND MACHINERY (a)

OffRoad Source

Default (kg/TJ)

Lower

N2O (c)

CH4(b)

CO2 Upper

Default (kg/TJ)

Lower

Upper

Default (kg/TJ)

Lower

Upper

Diesel Agriculture

74 100

72 600

74 800

4.15

1.67

10.4

28.6

14.3

85.8

Forestry

74 100

72 600

74 800

4.15

1.67

10.4

28.6

14.3

85.8

Industry

74 100

72 600

74 800

4.15

1.67

10.4

28.6

14.3

85.8

Household

74 100

72 600

74 800

4.15

1.67

10.4

28.6

14.3

85.8

Motor Gasoline 4-stroke Agriculture

69 300

67 500

73 000

Forestry

69 300

67 500

73 000

80

32

200

2

1

6

Industry

69 300

67 500

73 000

50

20

125

2

1

6

Household

69 300

67 500

73 000

120

48

300

2

1

6

Motor Gasoline 2-Stroke Agriculture

69 300

67 500

73 000

140

56

350

0.4

0.2

1.2

Forestry

69 300

67 500

73 000

170

68

425

0.4

0.2

1.2

Industry

69 300

67 500

73 000

130

52

325

0.4

0.2

1.2

Household

69 300

67 500

73 000

180

72

450

0.4

0.2

1.2

Source: EEA 2005. Note: CO2 emission factor values represent full carbon content. a

Data provided in Table 3.3.1 are based on European off-road mobile sources and machinery. For gasoline, in case fuel consumption by sector is not discriminated, default values may be obtained according to national circumstances, e.g. prevalence of a given sector or weighting by activity

b

Including diurnal, soak and running losses.

c

In general, off-road vehicles do not have emission control catalysts installed (there may be exceptions among off-road vehicles in urban areas, such as ground support equipment used in urban airports and harbours). Properly operating catalysts convert nitrogen oxides to N2O and CH4 to CO2. However, exposure of catalysts to high-sulphur or leaded fuels, even once, causes permanent deterioration (Walsh, 2003). This effect, if applicable, should be considered when adjusting emission factors.

3.3.1.3

C HOICE

OF

A CTIVITY D ATA

Comprehensive top-down activity data on off-road vehicles are often unavailable, and where this is the case statistical surveys will be necessary to estimate the share of transport fuel used by off-road vehicles. Survey design is discussed in Chapter 2 of Vol.1 (Approaches to Data Collection). The surveys should be at the level of disaggregation indicated in Table 3.3.1 to make use of the default emission factor data, and be more detailed for the higher tiers. For the Tier 3 approach, modelling tools are available to estimate the amount of fuel consumed by each subcategory of equipment. Box 3.3.1 provides further information on using the NONROAD emissions model. This model may also be developed to incorporate country-specific modifications (see Box 3.3.2 for the Canadian experience).

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Chapter 3: Mobile Combustion

BOX 3.3.1 NONROAD EMISSION MODEL (USEPA)

NONROAD 2005 is a mathematical model developed by the USEPA and may be used to estimate and forecast emissions from the non-road (off-road) transportation sectors. The model itself and all available supporting documentation are accessible on the EPA’s website (http://www.epa.gov/otaq/nonrdmdl.htm). This model estimates emissions for six exhaust gases: hydrocarbons (HC), NOX, carbon monoxide (CO), carbon dioxide (CO2), sulphur oxides (SOX), and particulate matter (PM). The user selects among five different types for reporting HC — as total hydrocarbons (THC), total organic gases (TOG), non-methane organic gases (NMOG), nonmethane hydrocarbons (NMHC), and volatile organic compounds (VOC). Generally, this model can perform a bottom-up estimation of emissions from the defined sources using equipment specific parameters such as: (i) engine populations; (ii) annual hours of use; (iii) power rating (horsepower); (iv) load factor (percent load or duty cycle), and (v) brake-specific fuel consumption (fuel consumed per horsepower-hour). The function will calculate the amount of fuel consumed by each subcategory of equipment. Subsequently, sub-sector (technology/fuel)-specific emission factors may then be applied to develop the emission estimate. The model is sensitive to the chosen parameters but may be used to apportion emissions estimates developed using a topdown approach. It is not uncommon for the bottom-up approach using this model to deviate from a similar topdown result by a factor of 2 (100%) and therefore users are cautioned to review documentation for areas where this gap may be reduced through careful adjustment of their own inputs. Consequently, users must have some understanding of the population and fuel/technology make-up of the region being evaluated. However, reasonable adjustments can be established based upon: national manufacturing levels; importation/export records; estimated lifespan and scrappage functions. Scrappage functions attempt to define the attrition rate of equipment and may help illustrate present populations based upon historic equipment inventories (see Box 3.2.3 of Section 3.2 of this volume).

3.3.1.4

C OMPLETENESS

Duplication of off-road and road transport activity data should be avoided. Validation of fuel consumption should follow the principles outlined in Section 3.2.1.3. Lubricants should be accounted for based on their use in off-road vehicles. Lubricants that are mixed with motor gasoline and combusted should be included with fuel consumption data. Other uses of lubricants are covered in the Volume 3: IPPU Chapter 5). Amounts of carbon from biomass, eg. biodiesel, oxygenates and some other blending agents should be estimated separately, and reported as an information item to avoid double counting as these emissions are already treated in the AFOLU sector.

3.3.1.5

D EVELOPING

A CONSISTENT TIME SERIES

It is good practice to determine activity data (e.g., fuel use) using the same method for all years. If this is not possible, data collection should overlap sufficiently in order to check for consistency in the methods employed. If it is not possible to collect activity data for the base year (e.g. 1990), it may be appropriate to extrapolate data backwards using trends in other activity data records. Emissions of CH4 and N2O will depend on engine type and technology. Unless technology-specific emission factors have been developed, it is good practice to use the same fuel-specific set of emission factors for all years. Mitigation activities resulting in changes in overall fuel consumption will be readily reflected in emission estimates if actual fuel activity data are collected. Mitigation options that affect emission factors, however, can only be captured by using engine-specific emission factors, or by developing control technology assumptions. Changes in emission factors over time should be well documented. For more information on determining base year emissions and ensuring consistency in the time series, see Volume 1, Chapter 5 (Time Series Consistency).

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BOX 3.3.2 CANADIAN EXPERIENCE WITH NONROAD MODEL

Using the model to enhance national emission factors:

NONROAD is initially populated with data native to the United States but may be customised for a given region or Party by simply adjusting the assumed input parameters to accommodate local situations. Parties may wish to designate their region as similar to one of those present in the USA to better emulate the seasonal climate. However, a designated temperature regime may also be input elsewhere. The NONROAD model is, thus, pre-loaded with local USA defaults thereby allowing their constituents to query it immediately. Canada has begun to adjust this model by commencing national studies to better evaluate countryspecific engine populations, available technologies, load factors and brake-specific fuel consumption values (BSFC) unique to the Canadian region. This new information will facilitate creation of Canada-specific input files and therefore not alter the core EPA programme algorithm but allow complete exploitation of the programmes strengths by providing more representative population and operating definitions. Through the introduction of lower uncertainty input data, the model may be used in conjunction with national fuel consumption statistics to arrive at a reasonable, disaggregated emission estimate. When operated with a similarly constructed On-Road model, for which operating parameters are better understood, a complete bottom-up, “apparent” fuel consumption estimate may be scaled to total national fuel sales. The country has used this modelling concept to help improve country-specific emission factors for the off-road consumption of fuel. The total fuel consumed is estimated by fuel type for each of the highly aggregated equipment sectors: (i) 2 cycle versus 4 cycle engines; (ii) Agriculture, Forestry, Industrial, Household and Recreational sub-sectors; (iii) gasoline versus diesel (spark vs. compression ignition). Once the model reports the total amount of fuels consumed according to this matrix, a composite emission factor is built based on the weighted averages of the contributing sub-sectors and their unique emission factors. The 2 cycle versus 4 cycle proportions will contribute to an average Off-Road gasoline EF while the Diesel EF is directly determined. Emission factors representing most GWP gases are not well researched and documented currently in North America and therefore, Canada has historically utilized applicable CORINAIR emission factors for these aggregated equipment sectors. The similarities between earlier technologies present in Europe and North America allow this utilization without introducing unreasonable uncertainty.

3.3.2

Uncertainty assessment

Greenhouse gas emissions from off-road sources are typically much smaller than those from road transportation, but activities in this category are diverse and are thus typically associated with higher uncertainties because of the additional uncertainty in activity data. The types of equipment and their operating conditions are typically more diverse than that for road transportation, and this may give rise to a larger variation in emission factors and thus to larger uncertainties. However, the uncertainty estimate is likely to be dominated by the activity data, and so it is reasonable to assume as a default that the values in section 3.2.1.2 apply. Also, emission controls, if installed, are likely to be inoperable due to catalyst failure (e.g., from exposure to high-sulphur fuel). Thus, N2O and CH4 emissions are more closely related to combustion-related factors such as fuel and engine technology than to emission control systems.

3.3.2.1

A CTIVITY

DATA UNCERTAINTY

Uncertainty in activity data is determined by the accuracy of the surveys or bottom-up models on which the estimates of fuel usage by off-road source and fuel type (see Table 3.3.1 for default classification) are based. This will be very case-specific, but factor of 2 uncertainties are certainly possible, unless if there is evidence to the contrary from the survey design.

3.3.3

Inventory Quality Assurance/Quality Control (QA/QC)

It is good practice to conduct quality control checks as outlined in Chapter 6 of Volume 1, and expert review of the emission estimates, plus additional checks if higher tier methods are used.

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Chapter 3: Mobile Combustion

In addition to the guidance above, specific procedures of relevance to this source category are outlined below. R ev ie w of em is sio n fa cto rs

The inventory compiler should ensure that the original data source for national factors is applicable to each category and that accuracy checks on data acquisition and calculations have been performed. For default factors, the inventory compiler should ensure that the factors are applicable and relevant to the category. If possible, the default factors should be compared to national factors to provide further indication that the factors are applicable and reasonable. Check of act iv ity data

The source of the activity data should be reviewed to ensure applicability and relevance to the category. Where possible, the data should be compared to historical activity data or model outputs to look for anomalies. Where surveys data have been used, the sum of on-road and off-road fuel usage should be consistent with total fuel used in the country. In addition, a completeness assessment should be conducted, as described in Section 3.3.1.4. Ext er na l rev ie w

The inventory compiler should carry out an independent, objective review of calculations, assumptions or documentation or both of the emissions inventory to assess the effectiveness of the QC programme. The peer review should be performed by expert(s) who are familiar with the source category and who understand national greenhouse gas inventory requirements.

3.3.4

Reporting and Documentation

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Chapter 8 of Volume 1. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced. Some examples of specific documentation and reporting issues relevant to this source category are provided below. In addition to reporting emissions, it is good practice to provide:

•

•

Source of fuel and other data;

•

Analysis of uncertainty or sensitivity of results or both to changes in input data and assumptions.

•

Emission factors used and their associated references;

•

Basis for survey design, where used to determine activity data References to models used in making the estimates

3.3.5

Reporting tables and worksheets

See the four pages of the worksheets (Annex 1) for the Tier I Sectoral Approach which are to be filled in for each of the source categories. The reporting tables are available in Volume 1, Chapter 8.

3.4

RAILWAYS

Railway locomotives generally are one of three types: diesel, electric, or steam. Diesel locomotives generally use diesel engines in combination with an alternator or generator to produce the electricity required to power their traction motors. Diesel locomotives are in three broad categories – shunting or yard locomotives, railcars, and line haul locomotives. Shunting locomotives are equipped with diesel engines having a power output of about 200 to 2000 kW. Railcars are mainly used for short distance rail traction, e.g., urban/suburban traffic. They are equipped with a diesel engine having a power output of about 150 to 1000 kW. Line haul locomotives are used for long distance rail traction – both for freight and passenger. They are equipped with a diesel engine having a power output of about 400 to 4000 kW (EEA, 2005).

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Electric locomotives are powered by electricity generated at stationary power plants as well as other sources. The corresponding emissions are covered under the Stationary Combustion Chapter of this Volume. Steam locomotives are now generally used for very localized operations, primarily as tourist attractions and their contribution to greenhouse gas emissions is correspondingly small. However for a few countries, up to the 1990s, coal was used in a significant fraction of locomotives. For completeness, their emissions should be estimated using an approach similar to conventional steam boilers, which are covered in the Stationary Combustion Chapter.

3.4.1

Methodological issues

Methodologies for estimating greenhouse gas emissions from railway vehicles (Section 3.4.1.1), have not changed fundamentally since the publication of the 1996 IPCC Guidelines and the GPG2000. However, for consistency with the Stationary Combustion Chapter, CO2 emissions are now estimated on the basis of the full carbon content of the fuel. This chapter covers good practice in the development of estimates for the direct greenhouse gases CO2, CH4 and N2O. For the precursor gases, or indirect greenhouse gases of CO, NMVOCs, SO2, PM, and NOx, please refer to the EMEP/Corinair Guidebook (EEA, 2005) for other mobile sources).

3.4.1.1

C HOICE

OF METHOD

There are three methodological options for estimating CO2, CH4, and N2O emissions from railways. The decision trees in Figures 3.4.1 and 3.4.2 give the criteria for choosing methodologies. Figure 3.4.1

Decision tree for estimating CO 2 emissions from railways Start

Are Country-specific data on fuel carbon contents available?

Yes

Calculate emissions using Eq. 3.4.1. Box 1: Tier 2

No

Is this a key category?

Yes

Collect countryspecific data on fuel carbon contents.

No

Calculate emissions using Eq. 3.4.1 and default emission factors. Box 2: Tier 1 Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees.

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Chapter 3: Mobile Combustion

Figure 3.4.2

Decision tree for estimating CH 4 and N 2 O emissions from railways Start

Is locomotive-specific activity data and emission factor available?

Calculate emissions using detailed model and emission factors.

Yes

Box 3: Tier 3 No

Are fuel statistics by locomotive type available?

Calculate emissions using Eq. 3.4.2.

Yes

Box 2: Tier 2 No

Is this a key category?

Estimate fuel consumption by locomotive type, and/or country- specific emission factors.

Yes

No

Calculate emissions using Eq. 3.4.1. Box 1: Tier 1 Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees.

The three tiers of estimation methodologies are variations of the same fundamental equation:

(

)

EQUATION 3.4.1 GENERAL METHOD FOR EMISSIONS FROM LOCOMOTIVES Emissions = ∑ Fuel j • EF j j

Where: Emissions

= emissions (kg)

Fuel j

= fuel type j consumed (as represented by fuel sold) in (TJ)

EF j

= emission factor for fuel type j, (kg/TJ)

j

= fuel type

For Tier 1, emissions are estimated using fuel-specific default emission factors as listed in Table 3.4.1, assuming that for each fuel type the total fuel is consumed by a single locomotive type. For CO2, Tier 2 uses equation 3.4.1 again with country-specific data on the carbon content of the fuel. There is little or no advantage in going beyond Tier 2 for estimating CO2 emissions.

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With respect to Tier 2 for CH4 and N2O, emissions are estimated using country-specific and fuel-specific emission factors in equation 3.4.2. The emission factors, if available, should be specific to broad locomotive technology type. EQUATION 3.4.2 TIER 2 METHOD FOR CH4 AND N2O FROM LOCOMOTIVES Emissions = ∑ (Fueli • EFi ) i

Where: Emissions = emissions (kg) Fueli

= fuel consumed (as represented by fuel sold) by locomotive type i, (TJ)

EFi

= emission factor for locomotive type i, (kg/TJ)

i

= locomotive type

Tier 3 methods , if data are available, use more detailed modelling of the usage of each type of engine and train, which will affect emissions through dependence of emission factors on load. Data needed includes the fuel consumption which can be further stratified according to typical journey (e.g. freight, intercity, regional) and kilometres travelled by type of train. This type of data may be collected for other purposes (e.g. emissions of air pollutants depending on speed and geography, or from the management of the railway). Equation 3.4.3 is an example of a more detailed methodology (Tier 3), which is mainly based on the USEPA method for estimating off-road emissions (USEPA 2005 a & b). This uses the following basic formula to calculate emissions (in Gg): EQUATION 3.4.3 TIER 3 EXAMPLE OF A METHOD FOR CH4 AND N2O FROM LOCOMOTIVES Emission = ∑ (N i • H i • Pi • LFi • EFi ) i

Where: Emission = emissions of CH4 or N2O (kg) Ni

= number of locomotives of type i

Hi

= annual hours of use of locomotive i [h]

Pi

= average rated power of locomotive i [kW]

LFi

= typical load factor of locomotive i (fraction between 0 and 1)

EFi

= average emission factor for use in locomotive i [kg/kWh]

i

= locomotive type and journey type

In this methodology, the parameters H, P, LF and EF may be subdivided, such as H into age dependent usage pattern (EEA, 2005). A number of detailed modelling tools are available for estimating locomotive emissions using Tier 3 methodologies (e.g., RAILI (VTT 2003); NONROAD (USEPA 2005a and b); COST 319 (Jorgensen & Sorenson, 1997)). Please refer to Box 3.4.1 for an example of a Tier 3 approach.

3.4.1.2

C HOICE

OF EMISSION FACTORS

The default emission factors for CO2, CH4 and N2O and their uncertainty ranges for Tier 1 are provided in Table 3.4.1. To estimate CH4 and N2O emissions, inventory compilers are encouraged to use country-specific emission factors for locomotives if available.

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Chapter 3: Mobile Combustion

TABLE 3.4.1 DEFAULT EMISSION FACTORS FOR THE MOST COMMON FUELS USED FOR RAIL TRANSPORT Gas

Diesel (kg/TJ)

CO2

Sub-bituminous Coal (kg/TJ)

Default

Lower

Upper

Default

Lower

Upper

74 100

72 600

74 800

96 100

72 800

100 000

1

4.15

1.67

10.4

2

0.6

6

N2O 1

28.6

14.3

85.8

1.5

0.5

5

CH4

Notes: 1

2

For an average fuel consumption of 0.35 litres per bhp-hr (break horse power-hour) for a 4000 HP locomotive, (0.47 litres per kWh for a 2983 kW locomotive).(Dunn, 2001). The emission factors for diesel are derived from (EEA, 2005) (Table 8-1), while for coal from Table 2.2 of the Stationary Combustion chapter.

These default emission factors may, for non-CO2 gases, be modified depending on the engine design parameters in accordance with Equation 3.4.4, using pollutant weighing factors in Table 3.4.2 EQUATION 3.4.4 WEIGHTING OF CH4 AND N2O EMISSION FACTORS FOR SPECIFIC TECHNOLOGIES EFi ,diesel = PWFi • EFdefault , diesel

Where: EFi,diesel

= engine specific emission factor for locomotive of type i (kg/TJ)

PWFi

= pollutant weighing factor for locomotive of type i [dimensionless]

EFdefault,diesel

= default emission factor for diesel (applies to CH4, N2O) (kg/TJ)

TABLE 3.4.2 POLLUTANT WEGHTING FACTORS AS FUNCTIONS OF ENGINE DESIGN PARAMETERS FOR UNCONTROLLED ENGINES(DIMENSIONLESS) Engine type

CH4

N2O

Naturally Aspirated Direct Injection

0.8

1.0

Turbo-Charged Direct Injection / Inter-cooled Turbo-Charged Direct Injection

0.8

1.0

Naturally Aspirated Pre-chamber Injection

1.0

1.0

Turbo-Charged Pre-chamber Injection

0.95

1.0

Inter-cooled Turbo-Charged Pre-chamber Injection

0.9

1.0

Source: EEA 2005 (Table 8-9);

To take into account the increase in CH4 and N2O emissions with the age, the default emission factors for CH4 may be increased by 1.5 percent per year while deterioration for N2O is negligible (EEA, 2005).

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BOX 3.4.1 EXAMPLE OF TIER 3 APPROACH

The 1998 EPA non-road diesel engine regulations are structured as a 3-tiered progression (USEPA, 1998). Each USEPA-tier involves a phase in (by horse power rating) over several years. USEPA-Tier 0 standards were in phase until 2001. The more stringent USEPA-Tier 1 standards took effect from 2002 to 2004, and yet more stringent USEPA-Tier 2 standards phase-in from 2005 and beyond. The main improvements are in the NOx and PM emissions over the USEPA-tiers. Use of improved diesel with lower sulphur content contributes to reduced SO2 emissions. The table below provides broad technology level emission factors for these and other locomotives above 3000 HP. Emission factors may also be provided in g/passenger-kilometer for passenger trains and g/ton-kilometer for freight trains for higher tiers if country-specific information is available (e.g., Hahn, 1989; UNECE 2002). BROAD TECHNOLOGY LEVEL EMISSION FACTORS

Model

Power

Engine HP

kW

EMD SD-40 645E3B

3000

2237

EMD SD-60 710G3

3800

2834

EMD SD-70 710G3C

4000

EMD SD-75 710G3EC

Brake specific diesel fuel consumption (kg/kWh)

Reported emission levels (g/kWh) NOx

CO

HC

CO2

0.246

15.82

2.01

0.36

440

0.219

13.81

2.68

0.35

391

2983

0.213

17.43

0.80

0.38

380

4300

3207

0.206

17.84

1.34

0.40

367

GE Dash 8

7FDL

3800

2834

0.219

16.63

6.44

0.64

391

GE Dash 9

7FDL

4400

3281

0.215

15.15

1.88

0.28

383

GE Dash 9

7FDL (Tier 0) 4400

3281

0.215

12.74

1.88

0.28

383

Evolution

GEVO 12

4400

3281

NA

10.86

1.21

0.40

NA

2

116

1А-5 49

6035 2●2250

0.214

16.05

10.70

4.07

382

2

10

10 100

5900 2●2200

0.226

15.82

10.62

4.07

403

П60

11 45

2950

2200

0.236

16.05

10.62

3.84

421

П70

2А-5 49

3420

2550

0.211

15.83

10.55

4.01

377

14 40

3943 2●1470

0.231

13.40

9.01

3.23

412

2 62 Sources: 1

EMD and GE locomotive information based on Dunn, 2001. Lower tier CO and HC estimates for line-haul locomotives are 6.7 g/kWh and 1.3 g/kWh respectively.

2

For the TE models and 2M62, estimations are based on GSTU 1994.

3.4.1.3

C HOICE

OF ACTIVITY DATA

National level fuel consumption data are needed for estimating CO2 emissions for Tier 1 and Tier 2 approaches. For estimating CH4 and N2O emissions using Tier 2, locomotive category level data is needed. Tier 3 approaches require activity data for operations (for example gross tonne kilometre (GTK) and duty cycles) at specific line haul locomotive level. These methods also require other locomotive-specific information, such as source population (with age and power ranges), mileage per train tonnage, annual hours of use and age-dependent usage patterns, average rated horse power (with individual power distribution within given power ranges), load factor, section information (such as terrain topography and train speeds). There are alternate modelling approaches for Tier 3 estimation (VTT 2003; EEA 2005). The railway or locomotive companies, or the relevant transport authorities may be able to provide fuel consumption data for the line haul and yard locomotives. The contribution from yard locomotives is likely to be very small for almost all countries. If the annual fuel consumption is not provided separately for yard locomotives, it may be possible to estimate fuel use if typical data on their use and daily fuel use is available according to the following equation:

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Chapter 3: Mobile Combustion

EQUATION 3.4.5 ESTIMATING YARD LOCOMOTIVE FUEL CONSUMPTION Inventory fuel consumption = Number of yard locomotives • Average fuel consumption per locomotive and day • Average number of days of operation per locomotive in the year

The number of yard locomotives can be obtained from railway companies or transport authorities. If average fuel consumption per day is unknown, a value of 863 litres per day can be used (USEPA, 2005a). The number of days of operation is usually 365. If data for the number of yard locomotives cannot be obtained, the emissions inventory can be approximated by assuming that all fuel is consumed by line haul locomotives. If fuel consumption data are available for the jurisdiction (State or Territory) as a whole, double counting may occur when locomotives of one company fill-in the jurisdiction of another company. This can be resolved at higher Tiers by the use of operating data. Where higher tier approaches are used, care should be taken that the fuel consumption data used for CO2 is consistent with the activity data used for CH4 and N2O.

3.4.1.4

C OMPLETENESS

Diesel fuel is the most common fuel type used in railways, but inventory compilers should be careful not to omit or double count the other fuels that may be used in diesel locomotives for traction purposes. These may be mixed with diesel and may include petroleum fuels (such as residual fuel, fuel oils, or other distillates), bio-diesel (e.g. oil esters from rape seed, soy bean, sunflower, Jatropha, or Karanjia oil, or recovered vegetable and animal fats), and synthetic fuels. Bio-diesel can be used in all diesel engines with slight or no modification. Blending with conventional diesel is possible. Synthetic fuels include synthetic middle distillates (SMD) and Dimethyl Ether (DME) to be produced from various carbonaceous feedstocks, including natural gas, residual fuel oil, heavy crude oils, and coal via the production of synthesis gas. The mix varies and presently it is between 2 to 5 percent bio-diesel and the remaining petroleum diesel. The emission properties of these fuels are considered to be similar to those used for the road transport sector. CO2 emissions from fuels derived from biomass should be reported as information items, and not included in the national total to avoid double counting. Diesel locomotives may combust natural gas or coal for heating cars. Although these energy sources may be “mobile,” the methods for estimating emissions from combustion of fuels for heat are covered under the Stationary Combustion section of this Energy Volume. Inventory compilers should be careful not to omit or double count the emissions from energy used for carriage heating in railways. Diesel locomotives also consume significant amounts of lubricant oils. The related emissions are dealt with in Chapter 5 of the IPPU volume. There are potential overlaps with other source sectors. A lot of statistical data will not include fuel used in other activities such as stationary railway sources; off-road machinery, vehicles and track machines in railway fuel use. Their emissions should not be included here but in the relevant non-railway categories as stationary sources, offroad etc. If this is not the case and it is impossible to separate these other uses from the locomotives, then it is good practice to note this in any inventory report or emission reporting tables.

3.4.1.5

D EVELOPING

A CONSISTENT TIME SERIES

Emissions of CH4 and N2O will depend on engine type and technology. Unless technology-specific emission factors have been developed, it is good practice to use the same fuel-specific set of emission factors for all years. Mitigation options that affect emission factors can only be captured by using engine-specific emission factors, or by developing control technology assumptions. These changes should be adequately documented. For more information on determining base year emissions and ensuring consistency in the time series, see Chapter 5 of Volume 1: Time Series Consistency.

3.4.1.6

U NCERTAINTY

ASSESSMENT

Greenhouse gas emissions from railways are typically much smaller than those from road transportation because the amounts of fuel consumed are less, and also because operations often occur on electrified lines, in which case the emissions associated with railway energy use will be reported under power generation and will depend on the characteristics of that sector.

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To reduce uncertainty, a comprehensive approach is needed for both emission factors and activity data, especially where bottom-up activity data are used. The use of representative locally estimated data is likely to improve accuracy although uncertainties may remain large. It is good practice to document the uncertainties both in the emission factors as well as in the activity data. Further guidance on uncertainty estimates for emission factors can be found in Chapter 3 of Volume 1: Uncertainties. Emission factor uncertainty

Table 3.4.1 provides ranges indicating the uncertainties associated with diesel fuel. In the absence of specific information, the percentage relationship between the upper and lower limiting values and the central estimate may be used to derive default uncertainty ranges associated with emission factors for additives. Activity data uncertainty

The uncertainty in top-down activity data (fuel use) is likely to be of the order 5 percent. The uncertainty in disaggregated data for bottom-up estimates (usage or fuel use by type of train) is unlikely to be less than 10 percent and could be several times higher, depending on the quality of the underlying statistical surveys. Bottomup estimates are however necessary for estimating non-CO2 gases at higher tiers. These higher tier calculations could also yield CO2 estimates, but these will probably be more uncertain than Tier 1 or 2. Thus the way forward where railways are a key category is to use the top-down estimate for CO2 with country-specific fuel carbon contents, and higher tier estimates for the other gases. A bottom-up CO2 estimate can then be used for QA/QC cross-checks. Further guidance on uncertainty estimates for activity data can be found in Chapter 3 of Volume 1: Uncertainties.

3.4.2

Inventory Quality Assurance/Quality Control (QA/QC)

It is good practice to conduct quality control checks as outlined in Chapter 6 of Volume 1: Quality Assurance/ Quality Control and Verification. Additional quality control checks as outlined in Tier 2 procedures in Chapter 6 of Volume 1 may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Chapter 4 of Volume 1: Methodological Choice and Identification of Key Categories. In addition to the above guidance, specific procedures of relevance to this source category are outlined below. Review of emission factors

The inventory compiler should ensure that the original data source for national factors is applicable to each category and that accuracy checks on data acquisition and calculations have been performed. For the IPCC default factors, the inventory compiler should ensure that the factors are applicable and relevant to the category. If possible, the IPCC default factors should be compared to national factors to provide further indication that the factors are applicable and reasonable. Check of activity data

The source of the activity data should be reviewed to ensure applicability and relevance to the category. Where possible, the data should be compared to historical activity data or model outputs to look for anomalies. Data could be checked with productivity indicators such as fuel per unit of distance railway performance (freight and passenger kilometres) compared with other countries and compared across different years.

3.4.3

Reporting and Documentation

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Chapter 8 of Volume 1: Reporting Guidance and Tables. In addition to reporting emissions, it is good practice to provide:

•

•

•

•

3.46

the way in which detailed information needed for bottom-up estimates has been obtained, and what uncertainties are to be estimated; how any bottom-up method of fuel use has been reconciled with top-down fuel use statistics. emission factors used and their associated references, especially for additives the way in which any biofuel components have been identified.

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Chapter 3: Mobile Combustion

The possible inclusion of fuels used for non-locomotive uses (see section 3.4.1.2 above).

3.4.4

Reporting tables and worksheets

See the four pages of the worksheets (Annex 1) for the Tier I Sectoral Approach which are to be filled in for each of the source categories. The reporting tables are available in Volume 1, Section 8.

3.5

WATER-BORNE NAVIGATION

This source category covers all water-borne transport from recreational craft to large ocean-going cargo ships that are driven primarily by large, slow and medium speed diesel engines and occasionally by steam or gas turbines. It includes hovercraft and hydrofoils. Water-borne navigation causes emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), sulphur dioxide (SO2), particulate matter (PM) and oxides of nitrogen (NOx). Section 3.5.5 contains definitions of specialist terms that may be useful to an inventory compiler.

3.5.1

Methodological issues

This section deals with the direct greenhouse gases CO2, CH4, and N2O. The source category is set out in detail in Table 3.5.1. The methods discussed can be used also to estimate emissions from military water-borne navigation (see section 3.5.1.4). For the purpose of the emissions inventory, a distinction is made between domestic and international water-borne navigation. Any fugitive emissions from the transport of fossil fuels (e.g., by tanker) should be estimated and reported under the category “Fugitive emissions” as set out in Chapter 4 of this Volume.

3.5.1.1

C HOICE

OF METHOD

Two methodological tiers for estimating emissions of CO2, CH4, and N2O from water-borne navigation are presented. Both tiers apply emission factors to fuel consumption activity data. The decision tree shown in Figure 3.5.1 helps in making a choice between the two tiers. Emissions are estimated separately for domestic and international water-borne navigation. Tier 1

The Tier 1 method is the simplest and can be applied with either default values or country-specific information. The fuel consumption data and emission factors in the Tier 1 method are fuel-type-specific and should be applied to the corresponding activity data (e.g. gas/diesel oil used for navigation). The calculation is based on the amount of fuel combusted and on emission factors for CO2, CH4, and N2O. The calculation is shown in Equation 3.5.1 and emission factors are provided in Table 3.5.2 and Table 3.5.3 EQUATION 3.5.1 WATER-BORNE NAVIGATION EQUATION Emissions = ∑ ( Fuel Consumed ab • Emission Factorab )

Where: a = fuel type (diesel, gasoline, LPG, bunker, etc.) b = water-borne navigation type (i.e., ship or boat, and possibly engine type.) (Only at Tier 2 is the fuel used differentiated by type of vessel so b can be ignored at Tier 1) Tier 2

The Tier 2 method also uses fuel consumption by fuel type, but requires country-specific emission factors with greater specificity in the classification of modes (e.g. ocean-going ships and boats), fuel type (e.g. fuel oil), and even engine type (e.g. diesel) (Equation 3.5.1). In applying Tier 2, the inventory compilers should note that the EMEP/Corinair emission inventory guidebook (EEA, 2005) offers a detailed methodology for estimating ship emissions based on engine and ship type and ship movement data. The ship movement methodology can be used when detailed ship movement data and technical information on the ships are both available and can be used to differentiate emissions between domestic and international water-borne navigation.

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TABLE 3.5.1 SOURCE CATEGORY STRUCTURE Source category

3.48

Coverage

1 A 3 d Water-borne Navigation

Emissions from fuels used to propel water-borne vessels, including hovercraft and hydrofoils, but excluding fishing vessels. The international/domestic split should be determined on the basis of port of departure and port of arrival, and not by the flag or nationality of the ship.

1 A 3 d i International Water-borne Navigation (International bunkers)

Emissions from fuels used by vessels of all flags that are engaged in international water-borne navigation. The international navigation may take place at sea, on inland lakes and waterways and in coastal waters. Includes emissions from journeys that depart in one country and arrive in a different country. Exclude consumption by fishing vessels (see Other Sector - Fishing). Emissions from international military water-borne navigation can be included as a separate sub-category of international water-borne navigation provided that the same definitional distinction is applied and data are available to support the definition.

1 A 3 d ii Domestic Water-borne Navigation

Emissions from fuels used by vessels of all flags that depart and arrive in the same country (exclude fishing, which should be reported under 1 A 4 c iii, and military, which should be reported under 1 A 5 b). Note that this may include journeys of considerable length between two ports in a country (e.g. San Francisco to Honolulu).

1 A 4 c iii Fishing (mobile combustion)

Emissions from fuels combusted for inland, coastal and deep-sea fishing. Fishing should cover vessels of all flags that have refuelled in the country (include international fishing).

1 A 5 b Mobile (water-borne navigation component)

All remaining water-borne mobile emissions from fuel combustion that are not specified elsewhere. Includes military water-borne navigation military emissions from fuel delivered to the country’s military not otherwise included separately in 1 A3 d i as well as fuel delivered within that country but used by the militaries of external countries that are not engaged in multilateral operations.

Multilateral operations (waterborne navigation component)

Emissions from fuels used for water-borne navigation in multilateral operations pursuant to the Charter of the United Nations. Include emissions from fuel delivered to the military in the country and delivered to the military of other countries.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Mobile Combustion

Figure 3.5.1

Decision tree for emissions from water-borne navigation Start

Are fuel consumption data available by fuel type for water-borne navigation?

Collect data or estimate using proxy data.

No

Yes

Have the data been differentiated between international and domestic?

Develop statistics based on other information or proxy data.

No

Yes

Are national carbon content data available? Initiate data collection.

Yes

Yes

Are fuel-use data and CH4 and N2O emission factors by engine type available?

No

No

Is this a key source category?

Use Tier 2 for CO2 with country-specific carbon contents and a Tier 1 for CH4 and N2O with IPCC default emission factors.

Yes

Estimate emissions using Tier 2 with country-specific carbon content factors and engine-specific CH4 and N2O emission factors. Box 1

Box 2 No

Estimate CO2 emissions using IPCC default carbon contents; estimate CH4 and N2O emissions using IPCC default emission factors. Box 3

Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees.

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3.5.1.2

C HOICE

OF EMISSION FACTORS

TIER 1

Default carbon dioxide emission factors (Table 3.5.2) are based on the fuel type and carbon content and take account of the fraction of carbon oxidised (100 percent), as described in Chapter 1, Introduction, of this Volume and Table 1.4). TABLE 3.5.2 CO2 EMISSION FACTORS kg/TJ

Default

Lower

Upper

Gasoline

69 300

67 500

73 000

Other Kerosene

71 900

70 800

73 600

Gas/Diesel Oil

74 100

72 600

74 800

Residual Fuel Oil

77 400

75 500

78 800

Liquefied Petroleum Gases

63 100

61 600

65 600

Refinery Gas

57 600

48 200

69 000

Paraffin Waxes

73 300

72 200

74 400

White Spirit & SBP

73 300

72 200

74 400

Other Petroleum Products

73 300

72 200

74 400

56 100

54 300

58 300

Other Oil

Fuel

Natural Gas

For non-CO2 gases, Tier 1 default emissions factors on a very general level are provided in Table 3.5.3. TABLE 3.5.3 DEFAULT WATER-BORNE NAVIGATION CH4 AND N2O EMISSION FACTORS CH4 (kg/TJ)

Ocean-going Ships *

7 + 50%

N2O (kg/TJ) 2 +140% -40%

*Default values derived for diesel engines using heavy fuel oil. Source: Lloyd’s Register (1995) and EC (2002)

TIER 2

Tier 2 emission factors should be country-specific and, if possible, derived by in-country testing of fuels and combustion engines used in water-borne navigation. Sources of emission factors should be documented in accordance with the provisions of these Guidelines. The EMEP/Corinair Emission inventory guidebook (EEA 2005) guidebook can be a source for NOx, CO and NMVOC emission factors for both Tier 1 and Tier 2 calculations.

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3.5.1.3

C HOICE

OF ACTIVITY DATA

Data on fuel consumption by fuel type and engine type (for N2O and CH4) are required to estimate emissions from water-borne navigation. In addition, in the current reporting procedures, emissions from domestic waterborne navigation are reported separately from international water-borne navigation which requires disaggregating the activity data to this level. For consistency, it is good practice to use similar definitions of domestic and international activities for aviation and water-borne navigation. These definitions are presented in Table 3.5.4 and are independent of the nationality or flag of the carrier. In some cases, the national energy statistics may not provide data consistent with this definition. It is good practice that countries separate the activity data consistent with this definition. In most countries, tax and custom dues are levied on bunkers for domestic consumption, and bunkers for international consumption are free of such dues. In the absence of more direct sources of data, information about domestic taxes may be used to distinguish between domestic and international fuel consumption. In any case, a country must clearly define the methodologies and assumptions used13. TABLE 3.5.4 CRITERIA FOR DEFINING INTERNATIONAL OR DOMESTIC WATER-BORNE NAVIGATION (APPLIES TO EACH * SEGMENT OF A VOYAGE CALLING AT MORE THAN TWO PORTS) Journey type between two ports

Domestic

International

Departs and arrives in same country

Yes

No

Departs from one country and arrives in another

No

Yes

* Most shipping movement data are collected on the basis of individual trip segments (from one departure to

the next arrival) and do not distinguish between different types of intermediate stop (as called for in GPG 2000). Basing the distinction on individual segment data is therefore simpler and is likely to reduce uncertainties. It is very unlikely that this change would make a significant change to the emission estimates. This does not change the way in which emissions from international journeys are reported as an information item and not included in national totals.

Fuel use data may be obtained using several approaches. The most feasible approach will depend on the national circumstances, but some of the options provide more accurate results than others. Several likely sources of actual fuel or proxy data are listed below, in order of typically decreasing reliability:

•

•

National energy statistics from energy or statistical agencies;

•

Surveys of shipping companies (including ferry and freight);

•

Surveys of individual port and marine authorities;

•

Equipment counts, especially for small gasoline powered fishing and pleasure craft;

•

Ship movement data and standard passenger and freight ferry schedules;

•

International Maritime Organisation (IMO), engine manufacturers, or Jane's Military Ships Database;

•

International Energy Agency (IEA) statistical information;

•

Surveys of fuel suppliers (e.g. quantity of fuels delivered to port facilities);

•

Surveys of fishing companies;

•

Import/export records;

•

Passenger counts and cargo tonnage data;

Ship movement data derived from Lloyds Register data

It may be necessary to combine and compare these data sources to get full coverage of shipping activities. Marine diesel engines are the main power unit used within the marine industry for both propulsion and auxiliary power generation. Some vessels are powered by steam plants (EEA 2005). Water-borne navigation should also account for the fuel that may be used in auxiliary engines powering for example refrigeration plants and cargo 13

It is good practice to clearly state the reasoning and justification if any country opts to use the GPG2000 definitions.

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pumps, and in boilers aboard vessels. Many steam powered oil tankers are still in operation, which consume more fuel per day when discharging their cargo in a port to operate the pumps than they do in deep sea steaming. Table 3.5.5 presents the average percentage of fuel consumed by both the main engines and auxiliary engines of the total fuel consumed by water-borne navigation vessel types. This allows the inventory compiler to apply the appropriate emissions factors, if available, as these factors may differ between main engines and auxiliary engines. Table 3.5.6 provides fuel consumption factors for various water-borne navigation vessel types, if the ship fleet by tonnage and category is collected. TABLE 3.5.5 AVERAGE FUEL CONSUMPTION PER ENGINE TYPE (SHIPS >500 GRT) Main Engine Consumption (%)

Avg. Number of Aux. Engines Per Vessel

Aux. Engine Consumption (%)

Bulk Carriers

98%

1.5

2%

Combination Carriers

99%

1.5

1%

Container Vessels

99%

2

1%

Dry Cargo Vessels

95%

1.5

5%

Offshore Vessels

98%

1

2%

Ferries/Passenger Vessels

98%

2

2%

Reefer Vessels

97%

2

3%

RoRo Vessels

99%

1.5

1%

Tankers

99%

1.5

1%

Miscellaneous Vessels

98%

1

2%

Totals

98%

Ship Type

2%

Source: Fairplay Database of Ships, 2004. GRT = Gross Registered Tonnage

TABLE 3.5.6 FUEL CONSUMPTION FACTORS, FULL POWER Average Consumption (tonne/day)

Consumption at full power(tonne/day) as a function of gross tonnage(GRT)

Solid Bulk

33.8

20.186 + 0.00049*GRT

Liquid Bulk

41.8

14.685 + 0.00079*GRT

General Cargo

21.3

9.8197 + 0.00143*GRT

Container

65.9

8.0552 + 0.00235*GRT

Passenger/Ro-Ro/Cargo

32.3

12.834 + 0.00156*GRT

Passenger

70.2

16.904 + 0.00198*GRT

High Speed Ferry

80.4

39.483 + 0.00972*GRT

Inland Cargo

21.3

9.8197 + 0.00143*GRT

Sail Ships

3.4

0.4268 +0.00100*GRT

Tugs

14.4

5.6511 +0.01048*GRT

Fishing

5.5

1.9387 +0.00448*GRT

Other Ships

26.4

9.7126 +0.00091*GRT

All Ships

32.8

16.263 + 0. 001*GRT

Ship type Bulk Carriers

Source: Techne (1997)

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In addition, although gases from cargo boil-off (primarily LNG or VOC recovery) may be used as fuels on ships, the amounts are usually not large in comparison to the total fuel consumed. Due to the small contribution, it is not required to account for this in the inventory.

3.5.1.4

M ILITARY

The 2006 IPCC Guidelines do not provide a distinct method for calculating military water-borne emissions. Emissions from military water-borne fuel use can be estimated using the equation 3.5.1 and the same calculation approach is recommended for non-military shipping. Due to the special characteristics of the operations, situations, and technologies (e.g., .aircraft carriers, very large auxiliary power plants, and unusual engine types) associated with military water-borne navigation, a more detailed method of data analysis is encouraged when data are available. Inventory compilers should therefore consult military experts to determine the most appropriate emission factors for the country’s military water-borne navigation. Due to confidentiality issues (see completeness and reporting), many inventory compilers may have difficulty obtaining data for the quantity of military fuel use. Military activity is defined here as those activities using fuel purchased by or supplied to military authorities in the country. It is good practice to apply the rules defining civilian domestic and international operations in water-borne navigation to military operations when the data necessary to apply those rules are comparable and available. Data on military fuel use should be obtained from government military institutions or fuel suppliers. If data on fuel split are unavailable, all the fuel sold for military activities should be treated as domestic. Emissions resulting from multilateral operations pursuant to the Charter of the United Nations should not be included in national totals, but reported separately; other emissions related to operations shall be included in the national emissions totals of one or more Parties involved. The national calculations should take into account fuel delivered to the country’s military, as well as fuel delivered within that country but used by the military of other countries. Other emissions related to operations (e.g., off-road ground support equipment) should be included in the national emissions totals in the appropriate source category.

3.5.1.5

C OMPLETENESS

For water-borne navigation emissions, the methods are based on total fuel use. Since countries generally have effective accounting systems to measure total fuel consumption. The largest area of possible incomplete coverage of this source category is likely to be associated with misallocation of navigation emissions in another source category. For instance, for small watercraft powered by gasoline engines, it may be difficult to obtain complete fuel use records and some of the emissions may be reported as industrial (when industrial companies use small watercraft), other off-road mobile or stationary power production. Estimates of water-borne emissions should include not only fuel for marine shipping, but also for passenger vessels, ferries, recreational watercraft, other inland watercraft, and other gasoline-fuelled watercraft. Misallocation will not affect completeness of the total national CO2 emissions inventory. It will affect completeness of the total non-CO2 emissions inventory, because non-CO2 emission factors differ between source categories. Fugitive emissions from transport of fossil fuels should be estimated and reported under the category “Fugitive emissions”. Most fugitive emissions occur during loading and unloading and are therefore accounted under that category. Emissions during travel are considered insignificant. Completeness may also be an issue where military data are confidential, unless military fuel use is aggregated with another source category. There are additional challenges in distinguishing between domestic and international emissions. As each country's data sources are unique for this category, it is not possible to formulate a general rule regarding how to make an assignment in the absence of clear data. It is good practice to specify clearly the assumptions made so that the issue of completeness can be evaluated.

3.5.1.6

D EVELOPING

A CONSISTENT TIME SERIES

It is good practice to determine fuel use using the same method for all years. If this is not possible, data collection should overlap sufficiently in order to check for consistency in the methods employed. Emissions of CH4 and N2O will depend on engine type and technology. Unless technology-specific emission factors have been developed, it is good practice to use the same fuel-specific set of emission factors for all years. Mitigation activities resulting in changes in overall fuel consumption will be readily reflected in emission estimates if actual fuel activity data are collected. Mitigation options that affect emission factors, however, can

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only be captured by using engine-specific emission factors, or by developing control technology assumptions. Changes in emission factors over time should be well documented. Marine diesel oil and heavy fuel oil are the fuels used primarily for large sources within water-borne navigation. As the carbon contents of these fuels may vary over the time series, the source of CO2 emission factors should be explicitly stated, as well as the dates the fuels were tested.

3.5.1.7

U NCERTAINTY

ASSESSMENT

Emission factors According to expert judgment, CO2 emission factors for fuels are generally well determined as they are primarily dependent on the carbon content of the fuel (EPA, 2004). For example, the default uncertainty value for diesel fuel is about ± -1.5 percent and for residual fuel oil ± -3 percent. The uncertainty for non-CO2 emissions, however, is much greater. The uncertainty of the CH4 emission factor may range as high as 50 percent. The uncertainty of the N2O emission factor may range from about 40 percent below to about 140 percent above the default value (Watterson, 2004).

Activity data Much of the uncertainty in water-borne navigation emission estimates is related to the difficulty of distinguishing between domestic and international fuel consumption. With complete survey data, the uncertainty may be low (say ± -5 percent), while for estimations or incomplete surveys the uncertainties may be considerable (say ± -50 percent). The uncertainty will vary widely from country to country and is difficult to generalise. Global data sets may be helpful in this area, and it is expected that reporting will improve for this category in the future.

3.5.2

Inventory Quality Assurance/Quality Control (QA/QC)

It is good practice to conduct quality control checks. Specific procedures of relevance to this source category are outlined below. Comparison of emissions using alternative approaches

If possible, the inventory compiler should compare estimates determined for water-borne navigation using both Tier 1 and Tier 2 approaches. The inventory compiler should investigate and explain any anomaly between the emission estimates. The results of such comparisons should be recorded. Review of emission factors

The inventory compiler should ensure that the original data source for national factors is applicable to each category and that accuracy checks on data acquisition and calculations have been performed. If national emission factors are available, they should be used, provided that they are well documented. For the default factors, the inventory compiler should ensure that the factors are applicable and relevant to the category. If emissions from military use were developed using data other than default factors, the inventory compiler should check the accuracy of the calculations and the applicability and relevance of the data. Check of activity data

The source of the activity data should be reviewed to ensure applicability and relevance to the category. Where possible, the data should be compared to historical activity data or model outputs to look for anomalies. Data could be checked with productivity indicators such as fuel per unit of water-borne navigation traffic performance compared with other countries. The European Environmental Agency provides a useful dataset, http://airclimate.eionet.eu.int/databases/TRENDS/TRENDS_EU15_data_Sep03.xls, which presents emissions and passenger/freight volume for each transportation mode for Europe. The information for shipping is very detailed. Examples of such indicators include: for ships with less than 3000 GT are from 0.09 to 0.16 kg CO2/tonne-km; for larger ships between 0.04 and 0.14; and for passenger ferries, the factors range from 0.1-0.5 kg/passenger-km. External review

The inventory compiler should perform an independent, objective review of calculations, assumptions or documentation of the emissions inventory to assess the effectiveness of the QC programme. The peer review should be performed by expert(s) (e.g. transport authorities, shipping companies, and military staff) who are familiar with the source category and who understand inventory requirements.

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3.5.3

Reporting and Documentation

Emissions related to water-borne navigation are reported in different categories depending on their nature. For good practice, the categories to use are:

•

•

Domestic water-borne navigation;

•

Fishing (mobile combustion);

•

International water-borne navigation (international bunkers);

•

Mobile (Military [water-borne navigation]) Non-specified Mobile (Vehicles and Other Machinery)

Emissions from international water-borne navigation are reported separately from domestic, and not included in the national total. Emissions related to commercial fishing are not reported under water-borne navigation. These emissions are to be reported under the Agriculture/Forestry/Fishing category in the Energy Sector. By definition, all fuel supplied to commercial fishing activities in the reporting country is considered domestic, and there is no international bunker fuel category for commercial fishing, regardless of where the fishing occurs. Military water-borne emissions should be clearly specified to improve the transparency of national greenhouse gas inventories. (see section 3.5.1.4). In addition to reporting emissions, it is good practice to provide:

•

•

Source of fuel and other data;

•

Emission factors used and their associated references;

•

Method used to separate domestic and international navigation;

Analysis of uncertainty or sensitivity of results or both to changes in input data and assumptions.

3.5.4

Reporting tables and worksheets

The four pages of the worksheets (Annex 1) for the Tier I Sectoral Approach should be filled in for each of the source categories in Table 3.5.1. The reporting tables are available in Volume 1, Chapter 8.

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3.5.5

Definitions of specialist terms DEFINITIONS

Bulk Carriers – Ships used to transport large amounts of non-containerized cargoes such as oil, lumber, grain, ore, chemicals, etc. Identifiable by the hatches raised above deck level, which cover the large cargo holds. Combination Carriers – Ships used to transport, in bulk, oil or, alternatively, solid cargoes. Container Vessels – Ships used to transport large, rectangular metal boxes, usually containing manufactured goods. Dry Cargo Vessels – Ships used to transport cargo that is not liquid and normally does not require temperature control. Ferries/Passenger Vessels – Ships used to perform short journeys for a mix of passengers, cars and commercial vehicles. Most of these ships are Ro-Ro (roll on - roll off) ferries, where vehicles can drive straight on and off. Passenger vessels can also include vacation cruise ships. Offshore Vessels – Term for ships engaging in a variety of support operations to larger ships. Can include offshore supply vessels, anchor handling vessels, tugboats, liftboats (i.e., deck barges), crew boats, dive support vessels, and seismic vessels. Reefer Vessels – Ships with refrigerated cargo holds in which perishables and other temperature-controlled cargoes are bulk loaded. Ro-Ro Vessels – Ships with roll-on/roll-off cargo spaces or special category spaces, which allows wheeled vehicles to be loaded and discharged without cranes. Tankers – Ships used to transport crude oil, chemicals and petroleum products. Tankers can appear similar to bulk carriers, but the deck is flush and covered by oil pipelines and vents.

3.6

CIVIL AVIATION

Emissions from aviation come from the combustion of jet fuel (jet kerosene and jet gasoline) and aviation gasoline14. Aircraft engine emissions are roughly composed of about 70 percent CO2, a little less than 30 percent H2O, and less than 1 percent each of NOx, CO, SOx, NMVOC, particulates, and other trace components including hazardous air pollutants. Little or no N2O emissions occur from modern gas turbines (IPCC, 1999). Methane (CH4) may be emitted by gas turbines during idle and by older technology engines, but recent data suggest that little or no CH4 is emitted by modern engines. Emissions depend on the number and type of aircraft operations; the types and efficiency of the aircraft engines; the fuel used; the length of flight; the power setting; the time spent at each stage of flight; and, to a lesser degree, the altitude at which exhaust gases are emitted. For the purpose of these guidelines, operations of aircraft are divided into (1) Landing/Take-Off (LTO) cycle and (2) Cruise. Generally, about 10 percent of aircraft emissions of all types, except hydrocarbons and CO, are produced during airport ground level operations and during the LTO cycle15. The bulk of aircraft emissions (90 percent) occur at higher altitudes. For hydrocarbons and CO, the split is closer to 30 percent local emissions and 70 percent at higher altitudes, (FAA, 2004a). Section 3.6.5 contains definitions of specialist terms that may be useful to an inventory compiler.

14

A fuel used only in small piston engine aircraft, and which generally represents less than 1 percent of fuel used in aviation.

15

LTO cycle is defined in ICAO, 1993. If countries have more specific data on times in mode these can be used to refine computations in higher tier methods.

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3.6.1

Methodological issues

This source category includes emissions from all civil commercial use of airplanes, including civil and general aviation (e.g. agricultural airplanes, private jets or helicopters). Methods discussed in this section can also be used to estimate emissions from military aviation, but emissions should be reported under category 1A 5 ’Other‘ or the Memo Item “Multilateral Operations.” For the purpose of the emissions inventory, a distinction is made between domestic and international aviation, and it is good practice to report under the source categories listed in Table 3.6.1. All emissions from fuels used for international aviation (bunkers) and multilateral operations pursuant to the Charter of UN are to be excluded from national totals, and reported separately as memo items.

3.6.1.1

C HOICE

OF METHOD

Three methodological tiers for estimating emissions of CO2, CH4 and N2O from aviation are presented. Tier 1 and Tier 2 methods use fuel consumption data. Tier 1 is purely fuel based, while Tier 2 method is based on the number of landing/take-off cycles (LTOs) and fuel use. Tier 3 uses movement16 data for individual flights. All tiers distinguish between domestic and international flights. However, energy statistics used in Tier 1 often do not accurately distinguish between domestic and international fuel use or between individual source categories, as defined in Table 3.6.1. Tiers 2 and 3 provide more accurate methodologies to make these distinctions. The choice of methodology depends on the type of fuel, the data available, and the relative importance of aircraft emissions. For aviation gasoline, though country-specific emission factors may be available, the numbers of LTOs are generally not available. Therefore, Tier 1 and its default emission factors would probably be used for aviation gasoline. All tiers can be used for operations using jet fuel, as relevant emission factors are available for jet fuel. Table 3.6.2 summarizes the data requirements for the different tiers: The decision tree shown in Figure 3.6.1 should help to select the appropriate method. The resource demand for the various tiers depends in part on the number of air traffic movements. Tier 1 should not be resource intensive. Tier 2, based on individual aircraft, and Tier 3A, based on Origin and Destination (OD) pairs, would use incrementally more resources. Tier 3B, which requires sophisticated modelling, requires the most resources. Given the current limited knowledge of CH4 and N2O emission factors, more detailed methods will not significantly reduce uncertainties for CH4 and N2O emissions. However, if aviation is a key category, then it is recommended that Tier 2 or Tier 3 approaches are used, because higher tiers give better differentiation between domestic and international aviation, and will facilitate estimating the effects of changes in technologies (and therefore emission factors) in the future. The estimates for the cruise phase become more accurate when using Tier 3A methodology or Tier 3B models. Moreover because Tier 3 methods use flight movement data instead of fuel use, they provide a more accurate separation between domestic and international flights. Data may be available from the operators of Tier 3 models (such as SAGE, (Kim, 2005a and b; Malwitz, 2005) and AERO2K (Eyers, 2004). Other methods for differentiating national and international fuel use such as considering LTOs, passenger-kilometer data, a percentage split based on flight timetables (e.g., OAG data, ICAO statistics for tonne-kilometres performed by countries) are shortcuts. The methods may be used if no other methods or data are available.

16

Movement data refers to, at a minimum, information on the origin and destination, aircraft type, and date of individual flights.

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TABLE 3.6.1 SOURCE CATEGORIES Source category

Coverage

1 A 3 a Civil Aviation

Emissions from international and domestic civil aviation, including take-offs and landings. Comprises civil commercial use of airplanes, including: scheduled and charter traffic for passengers and freight, air taxiing, and general aviation. The international/domestic split should be determined on the basis of departure and landing locations for each flight stage and not by the nationality of the airline. Exclude use of fuel at airports for ground transport which is reported under 1 A 3 e Other Transportation. Also exclude fuel for stationary combustion at airports; report this information under the appropriate stationary combustion category.

1 A 3 a i International aviation (International Bunkers)

Emissions from flights that depart in one country and arrive in a different country. Include take-offs and landings for these flight stages. Emissions from international military aviation can be included as a separate sub-category of international aviation provided that the same definitional distinction is applied and data are available to support the definition.

1 A 3 a ii Domestic Aviation

Emissions from civil domestic passenger and freight traffic that departs and arrives in the same country (commercial, private, agriculture, etc.), including take-offs and landings for these flight stages. Note that this may include journeys of considerable length between two airports in a country (e.g. San Francisco to Honolulu). Exclude military, which should be reported under 1 A 5 b.

1 A 5 b Mobile (aviation component)

All remaining aviation mobile emissions from fuel combustion that are not specified elsewhere. Include emissions from fuel delivered to the country’s military not otherwise included separately in 1 A3 a i as well as fuel delivered within that country but used by the militaries of other countries that are not engaged in multilateral operations.

1.A.5 c Multilateral Operations (aviation component)

Emissions from fuels used for aviation in multilateral operations pursuant to the Charter of the United Nations. Include emissions from fuel delivered to the military in the country and delivered to the military of other countries.

TABLE 3.6.2 DATA REQUIREMENTS FOR DIFFERENT TIERS Data, both Domestic and International

Tier 1

Aviation gasoline consumption

X

Jet Fuel consumption

X

Tier 2

Tier 3A

Tier 3B

X

Total LTO LTO by aircraft type

X

Origin and Destination (OD) by aircraft type

X

Full flight movements with aircraft and engine data

X

Other reasons for choosing to use a higher tier include estimation of emissions jointly with other pollutants (e.g. NOx) and harmonisation of methods with other inventories. In Tier 2 (and higher) the emissions for the LTO and cruise phases are estimated separately, in order to harmonise with methods that were developed for air pollution programmes that cover only emissions below 914 meters (3000 feet). There may be significant discrepancies between the results of a bottom-up approach and a top-down fuel-based approach for aircraft. An example is presented in Daggett et al. (1999). TIER 1 METHOD

The Tier 1 method is based on an aggregate quantity of fuel consumption data for aviation (LTO and cruise) multiplied by average emission factors. The methane emission factors have been averaged over all flying phases based on the assumption that 10 percent of the fuel is used in the LTO phase of the flight. Emissions are calculated according to Equation 3.6.1:

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EQUATION 3.6.1 (AVIATION EQUATION 1) Emissions = Fuel Consumption • Emission Factor

Tier 1 method should be used to estimate emissions from aircraft that use aviation gasoline which is only used in small aircraft and generally represents less than 1 percent of fuel consumption from aviation. Tier 1 method is also used for jet-fuelled aviation activities when aircraft operational use data are not available. Domestic and international emissions are to be estimated separately using the above equation, using one of the methods discussed in section 3.6.1.3 to allocate fuel between the two. TIER 2 METHOD

Tier 2 method is only applicable for jet fuel use in jet aircraft engines. Operations of aircraft are divided into LTO and cruise phases. To use Tier 2 method, the number of LTO operations must be known for both domestic and international aviation, preferably by aircraft type. In Tier 2 method a distinction is made between emissions below and above 914 m (3000 feet); that is emissions generated during the LTO and cruise phases of flight. Tier 2 method breaks the calculation of emissions from aviation into the following steps: 1.

Estimate the domestic and international fuel consumption totals for aviation.

2.

Estimate LTO fuel consumption for domestic and international operations.

3.

Estimate the cruise fuel consumption for domestic and international aviation.

4.

Estimate emissions from LTO and cruise phases for domestic and international aviation.

Tier 2 approach uses Equations 3.6.2 to 3.6.5 to estimate emissions: EQUATION 3.6.2 (AVIATION EQUATION 2) Total Emissions = LTO Emissions + Cruise Emissions

Where: EQUATION 3.6.3 (AVIATION EQUATION 3) LTO Emissions = Number of LTOs • Emission Factor LTO

EQUATION 3.6.4 (AVIATION EQUATION 4) LTO Fuel Consumption = Number of LTOs • Fuel Consumption per LTO

EQUATION 3.6.5 (AVIATION EQUATION 5) Cruise Emissions = (Total Fuel Consumption – LTO Fuel Consumption) • Emission Factor Cruise

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Figure 3.6.1

Decision tree for estimating aircraft emissions (applied to each greenhouse gas) Start Yes

Are data available on the origin and destination of flights and on air traffic movements?

Yes

Estimate emissions using Tier 3.

No

Develop method for collecting data for a Tier 2 or 3 method.

Are LTO data available for individual aircraft?

Yes

Estimate emissions using Tier 2. (See figure 3.6.2)

No

Yes

Is this a key category, or will data for a higher Tier method improve the international/domestic split?

No

Estimate emissions using Tier 1 using fuel consumption data split by domestic and international. Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees.

The basis of the recommended Tier 2 methodology is presented schematically in Figure 3.6.2. In Tier 2 method, the fuel used in the cruise phase is estimated as a residual: total fuel use minus fuel used in the LTO phase of the flight (Equation 3.6.5). Fuel use is estimated for domestic and international aviation separately. The estimated fuel use for cruise is multiplied by aggregate emission factors (average or per aircraft type) in order to estimate the CO2 and NOx cruise emissions.17 Emissions and fuel used in the LTO phase are estimated from statistics on the number of LTOs (aggregate or per aircraft type) and default emission factors or fuel use factors per LTO cycle (average or per aircraft type).

17

Current scientific understanding does not allow other gases (e.g., N2O and CH4) to be included in calculation of cruise emissions. (IPCC,1999).

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Tier 2 method considers activity data at the level of individual aircraft types and therefore needs data on the number of domestic LTOs by aircraft type and international LTOs by aircraft type. The estimate should include all aircraft types frequently used for domestic and international aviation. Table 3.6.3 provides a way of mapping actual aircraft to representative aircraft types in the database. Cruise emission factors for emissions other than NOx are not provided in Tier 2 method; either national emission factors or the tier default emission factors must be used to estimate these cruise emissions. TIER 3 METHODS

Tier 3 methods are based on actual flight movement data, either: for Tier 3A origin and destination (OD) data or for Tier 3B full flight trajectory information. National Tier 3 approaches can be used if they are well documented and have been reviewed following the guidance provided in Volume 1, Chapter 6 (QA/QC). To facilitate data review, countries that use Tier 3 methodology could separately report emissions for Commercial Scheduled Aviation and Other Jet Fuelled Activities. Tier 3A takes into account cruise emissions for different flight distances. Details on the origin (departure) and destination (arrival) airports and aircraft type are needed to use Tier 3A, for both domestic and international flights. In Tier 3A, inventories are modelled using average fuel consumption and emissions data for the LTO phase and various cruise phase lengths, for an array of representative aircraft categories. The data used in Tier 3A methodology takes into account that the amount of emissions generated varies between phases of flight. The methodology also takes into account that fuel burn is related to flight distance, while recognizing that fuel burn can be comparably higher on relatively short distances than on longer routes. This is because aircraft use a higher amount of fuel per distance for the LTO cycle compared to the cruise phase. The EMEP/CORINAIR Emission inventory guidebook (EEA 2002) provides an example of Tier 3A method for calculating emissions from aircraft. The EMEP/CORINAIR Emission inventory guidebook is continually being refined and is published electronically via the European Environment Agency Internet web site. EMEP/CORINAIR provides tables with emissions per flight distance. (Note that there are three EMEP/CORINAIR methods for calculating aircraft emissions; but, only the Detailed CORINAIR Methodology equates to Tier 3A.) Tier 3B methodology is distinguished from Tier 3A by the calculation of fuel burnt and emissions throughout the full trajectory of each flight segment using aircraft and engine-specific aerodynamic performance information. To use Tier 3B, sophisticated computer models are required to address all the equipment, performance and trajectory variables and calculations for all flights in a given year. Models used for Tier 3B level can generally specify output in terms of aircraft, engine, airport, region, and global totals, as well as by latitude, longitude, altitude and time, for fuel burn and emissions of CO, hydrocarbons (HC), CO2, H2O, NOx, and SOx. To be used in preparing annual inventory submissions, Tier 3B model must calculate aircraft emissions from input data that take into account air-traffic changes, aircraft equipment changes, or any input-variable scenario. The components of Tier 3B models ideally are incorporated so that they can be readily updated, so that the models are dynamic and can remain current with evolving data and methodologies. Examples of models include the System for assessing Aviation’s Global Emissions (SAGE), by the United States Federal Aviation Administration (Kim, 2005 a and b; Malwitz, 2005), and AERO2k, (Eyers, 2004), by the European Commission.

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Figure 3.6.2

Estimating aircraft emissions with Tier 2 method Start

Collect fuel consumption data for international and domestic aviation separately.

Separate domestic and international LTO data and conduct the following process for each data set. DOMESTIC

INTERNATIONAL For each aircraft type apply the LTO emissions factor from Table 3.6.9.

Sum individual aircraft emissions for total LTO emissions.

For each aircraft type apply the LTO fuel consumption factor from Table 3.6.9.

Sum individual aircraft LTO fuel consumption for total domestic LTO fuel consumption.

From total fuel consumption subtract LTO domestic fuel consumption to calculate total domestic Cruise fuel consumption.

Apply Tier 1 fuel consumption emissions factor to calculate total domestic Cruise emissions.

Sum total LTO emissions and total Cruise emissions for total aviation emissions.

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TABLE 3.6.3 CORRESPONDENCE BETWEEN REPRESENTATIVE AIRCRAFT AND OTHER AIRCRAFT TYPES Generic aircraft type

ICAO

IATA aircraft in group

Generic aircraft type

A30B

Boeing 737-700 Boeing 737-800 Boeing 737-900

B738

Boeing 747-100

B741 N74S B74R B74R

Boeing 747-200

B742

Boeing 747-300

B743

Airbus A320

A320

Airbus A321 Airbus A330-200 Airbus A330-300

A321 A330 A332 A330 A333

AB3 AB4 AB6 ABF ABX ABY 310 312 313 31F 31X 31Y 319 318 320 32S 321 330 332 330 333

Airbus A340-200

A342

342

Airbus A300

Airbus A310

Airbus A319

Airbus A340-300 Airbus A340-500 Airbus A340-600 Boeing 707 Boeing 717 Boeing 727-100

Boeing 727-200

Boeing 737-100

A306

A310

A319 A318

A340

340

A343

343

A345

345

A346

346

B703 B712 B721

B722

B731

Boeing 737-200

B732

Boeing 737-300

B733

Boeing 737-400 Boeing 737-500 Boeing 737-600

B734 B735 B736

703 707 70F 70M 717 721 72M 722 727 72C 72B 72F 72S 731 732 73M 73X 737 73F 733 73Y 737 734 737 735

Boeing 747-400

ICAO

B737

B739

B744

IATA aircraft in group 73G 73W 738 73H 739 74T 74L 74R 74V 742 74C 74X 743 74D 747 744 74E 74F 74J 74M 74Y 757

Boeing 757-200

B752

75F 75M

Boeing 757-300 Boeing 767-200 Boeing 767-300 Boeing 767-400 Boeing 777-200 Boeing 777-300

Douglas DC-10

B753 B762

B763

762 76X 767 76F 763 76Y

B764

764

B772

777 772

B773

773

DC10

DC85 DC86 Douglas DC-8

753

DC87

D10 D11 D1C D1F D1M D1X D1Y D8F D8L D8M D8Q D8T D8X D8Y

Generic aircraft type

ICAO

IATA aircraft in group

DC9 DC91 DC92 DC93 DC94

Lockheed L-1011

L101

McDonnell Douglas MD11

MD11

McDonnell Douglas MD80

MD80 MD81 MD82 MD83 MD87 MD88

DC9 D91 D92 D93 D94 D95 D9C D9F D9X L10 L11 L15 L1F M11 M1F M1M M80 M81 M82 M83 M87 MD88

MD90

M90

T134

TU3

Douglas DC-9

DC95

McDonnell Douglas MD90 Tupolev Tu134 Tupolev Tu154 Avro RJ85

T154 RJ85 B461 B462

BAe 146 B463

Embraer ERJ145

E145 F100 F70

Fokker 100/70/28

BAC 111

Donier Do 328 Gulfstream IV/V Yakovlev Yak 42

F28

BA11

D328

TU5 AR8 ARJ 141 142 143 146 14F 14X 14Y 14Z ER4 ERJ 100 F70 F21 F22 F23 F24 F28 B11 B12 B13 B14 B15 D38 GRJ

YK42

YK2

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3.6.1.2

C HOICE

OF EMISSION FACTORS

TIER 1

Carbon dioxide emission factors are based on the fuel type and carbon content. National emission factors for CO2 should not deviate much from the default values because the quality of jet fuel is well defined. It is good practice to use the default CO2 emission factors in Table 3.6.4 for Tier 1 (see Chapter 1, Introduction, of this Volume and Table 1.4). National carbon content could be used if available. CO2 should be estimated on the basis of the full carbon content of the fuel. TABLE 3.6.4 CO2 EMISSION FACTORS Fuel

Default (kg/TJ)

Lower

Upper

Aviation Gasoline

69 300

67 500

73 000

Jet Kerosene

71 500

69 800

74 400

Default values for CH4 and N2O from aircraft are given in Table 3.6.5. Different types of aircraft/engine combinations have specific emission factors and these factors may also vary according to distance flown. Tier 1 assumes that all aircraft have the same emission factors for CH4 and N2O based on the rate of fuel consumption. This assumption has been made because more disaggregated emission factors are not available at this level of aggregation. TABLE 3.6.5 NON-CO2 EMISSION FACTORS

Fuel

All fuels a

CH4 Default (Uncontrolled) Factors (in kg/TJ)

N2O Default (Uncontrolled) Factors (in kg/TJ)

2 0.5a (-57%/+100%)b (-70%/+150%) b

NOx Default (Uncontrolled) Factors (in kg/TJ) 250 +25% c

In the cruise mode CH4 emissions are assumed to be negligible (Wiesen et al., 1994). For LTO cycles only (i.e., below an altitude of 914 metres (3000 ft.)) the emission factor is 5 kg/TJ (10% of total VOC factor) (Olivier, 1991). Since globally about 10% of the total fuel is consumed in LTO cycles (Olivier, 1995), the resulting fleet averaged factor is 0.5 kg/TJ.

b

IPCC, 1999.

c

Expert Judgement. Emission factors for other gases (CO and NMVOC) and sulphur content which were included in the 1996 IPCC Guidelines can be found in the EFDB.

TIER 2

For Tier 2 method, it is good practice to use emission factors from Table 3.6.9 (or updates reflected in the EFDB) for the LTO emissions. For cruise calculations only NOx emissions can be computed directly based on specific emission factors (Table 3.6.10) and N2O can be computed indirectly from NOx emissions 18 . CO2 cruise emissions are calculated using Tier 1 CO2 emission factors (Table 3.6.4). The CH4 emissions are negligible and are assumed to be zero unless new information becomes available. Note that there is limited information on the emission factors for CH4 and N2O from aircraft, and the default values provided in Table 3.6.5 are similar to values found in the literature. TIER 3

Tier 3A emission factors may be found in the EMEP/CORINAIR emission inventory guidebook, while Tier 3B uses emissions factors contained within the models necessary to employ this methodology. Inventory compilers should check that these emission factors are in fact appropriate.

18

Countries vary on the method to be used to convert NOx emissions to N2O

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3.6.1.3

C HOICE

OF ACTIVITY DATA

Since emissions from domestic aviation are reported separately from international aviation, it is necessary to disaggregate activity data between domestic and international components. For this purpose, the following definitions should be applied irrespective of the nationality of the carrier (Table 3.6.6). For consistency, it is good practice to use similar definitions of domestic and international activities for aviation and water-borne navigation. In some cases, the national energy statistics may not provide data consistent with this definition. It is good practice that countries separate the activity data consistent with this definition. In any case, a country must clearly define the methodologies and assumptions used. TABLE 3.6.6 CRITERIA FOR DEFINING INTERNATIONAL OR DOMESTIC AVIATION (APPLIES TO INDIVIDUAL LEGS OF JOURNEYS WITH MORE THAN ONE TAKE-OFF AND LANDING) Journey type between two airports

Domestic

International

Departs and arrives in same country

Yes

No

Departs from one country and arrives in another

No

Yes

Based on past experience compiling aviation emissions inventories, difficulties have been identified regarding the international/domestic split, in particular obtaining the information on passenger and freight drop-off and pick up at stops in the same country that was required by the 1996 IPCC Guidelines/GPG2000 (Summary report of ICAO/UNFCCC Expert Meeting April 2004). Most flight data are collected on the basis of individual flight segments (from one take-off to the next landing) and do not distinguish between different types of intermediate stops (as called for in GPG2000). Basing the distinction on flight segment data (origin/destination) is therefore simpler and is likely to reduce uncertainties. It is very unlikely that this change would make a significant change to the emission estimates.19 This does not change the way in which emissions from international flights are reported as a memo item and not included in national totals. Improvements in technology and optimization of airline operating practices have significantly reduced the need for intermediate technical stops. An intermediate technical stop would also not change the definition of a flight as being domestic or international. For example if explicit data is available, countries may define as international flight segments that depart one country with a destination in another country and make an intermediate technical stop. A technical stop is solely for the purpose of refuelling or solving a technical difficulty and not for the purpose of passenger or cargo exchange. If national energy statistics do not already provide data consistent with this definition, countries should then estimate the split between domestic and international fuel consumption according to the definition, using the approaches set out below.

Top-down data can be obtained from taxation authorities in cases where fuel sold for domestic use is subject to taxation, but that for international use is not taxed. Airports or fuel suppliers may have data on delivery of aviation kerosene and aviation gasoline to domestic and to international flights. In most countries tax and custom dues are levied on fuels for domestic consumption, and fuels for international consumption (bunkers) are free of such dues. In the absence of more direct sources of data, information about domestic taxes may be used to distinguish between domestic and international fuel consumption. Bottom-up data can be obtained from surveys of airline companies for fuel used on domestic and international flights, or estimates from aircraft movement data and standard tables of fuel consumed or both. Fuel consumption factors for aircraft (fuel used per LTO and per nautical mile cruised) can be used for estimates and may be obtained from the airline companies. Examples of sources for bottom-up data, including aircraft movement, are:

•

•

•

19

Statistical offices or transport ministries as a part of national statistics; Airport records; ATC (Air Traffic Control) records, for example EUROCONTROL statistics;

It is good practice to clearly state the reasoning and justification if any country opts to use the GPG2000 definitions.

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•

Air carrier schedules published monthly by OAG which contains worldwide timetable passenger and freight aircraft movements as well as regular scheduled departures of charter operators. It does not contain ad-hoc charter aircraft movements;

Some of these sources do not cover all flights (e.g. charter flights may be excluded). On the other hand, airline timetable data may include duplicate flights due to codeshares between airlines or duplicate flight numbers. Methods have been developed to detect and remove these duplicates. (Baughcum et al., 1996; Sutkus et al., 2001). The aircraft types listed in Table 3.6.9, LTO Emission Factors were defined based on the assumptions listed below. Aircraft were divided into four major groups to reflect and note the distinct data source for each group: Large Commercial Aircraft: This includes aircraft that reflect the 2004 operating fleet and some aircraft types for back compatibility, identified by minor model. It was felt that this method would most accurately reflect operational fleet emissions. To minimize table size, some aircraft minor models were grouped when LTO emissions factors were similar. The Large Commercial Aircraft group LTO emissions factors data source is the ICAO Engine Exhaust Emissions Data Bank (ICAO, 2004a). Regional Jets: This group includes aircraft that are representative of the 2004 operating Regional Jet (RJ) fleet. Representative RJ aircraft were selected based on providing an appropriate range of RJ aircraft with LTO emissions factors available. The RJ group LTO emissions factors data source is the ICAO Engine Exhaust Emissions Data Bank (ICAO, 2004a). Low Thrust Jets: In some countries, aircraft in the low thrust category (engines with thrust below 26.7 kN) make up a non-trivial number of movements and therefore should be included in inventories. However, aircraft engines in this group are not required to satisfy ICAO engine emissions standards, thus LTO emissions factors data are not included in the ICAO Engine Exhaust Emissions Data Bank and difficult to provide. Therefore, there is one representative aircraft with typical emissions for aircraft in this group. The Low Thrust Jets group LTO emissions factors data source is the FAA’s Emissions and Dispersion Modelling System (EDMS) (FAA 2004b). Turboprops: This group includes aircraft that are representative of the 2004 Turboprop fleet, which can be represented by three typical aircraft size based on engine shaft horsepower. The Turboprop group LTO emissions factors data source is the Swedish Aeronautical Institute (FOI) LTO Emissions Database.

Similar data could be obtained from other sources (e.g. EEA, 2002). The equivalent data for turboprop and piston engine aircraft need to be obtained from other sources. The relationship between actual aircraft and representative aircraft are provided in the Table 3.6.3. Aircraft Fleet data may be obtained from various sources. ICAO collects fleet data through two of its statistics sub-programmes: the fleet of commercial air carriers, reported by States for their commercial air carriers, and civil aircraft on register, reported by States for the civil aircraft on their register at 31 December (ICAO 2004b). Some ICAO States do not participate in this data collection, in part because of the difficulty to split the fleet into commercial and non-commercial entities. Because of this, ICAO also makes use of other external sources. One of these sources is the International Register of Civil Aircraft, 2004, published by the Bureau Veritas (France), the CAA (UK) and ENAC (Italy) in cooperation with ICAO. This database contains the information from the civil aircraft registers of some 45 States (including the United States) covering over 450 000 aircraft. In addition to the above, there are commercial databases of which ICAO also makes use. None of them cover the whole fleet as they have limitations in scope and aircraft size. Among these one can find the BACK Aviation Solutions Fleet Data (fixed wing aircraft over 30 seats), AirClaims CASE database (fixed wing jet and turboprop commercial aircraft), BUCHAir, publishers of the JP Airline Fleet (covers both fixed and rotary wing aircraft). Other companies such as AvSoft may also have relevant information. Further information may be obtained from these companies’ websites.

3.6.1.4

M ILITARY

AVIATION

Military activity is defined here as those activities using fuel purchased by or supplied to the military authorities of the country. Emissions from aviation fuel use can be estimated using equation 3.6.1 and the same calculations approach recommended for civilian aviation. Some types of military transport aircraft and helicopters have fuel and emissions characteristics similar to civil types. Therefore default emission factors for civil aircraft should be used for military aviation unless better data are available. Alternatively, fuel use may be estimated from the hours in operation. Default fuel consumption factors for military aircraft are given in Tables 3.6.7 and 3.6.8. For fuel use factors see Section 3.6.1.3 ‘Choice of activity data’.

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TABLE 3.6.7 FUEL CONSUMPTION FACTORS FOR MILITARY AIRCRAFT Group

Sub- group

Combat

Fuel flow(kg/hour)

F16 Tiger F-5E

3 283

Jet trainers Turboprop trainers

Hawk PC-7

720

Large tanker/ transport

2 225

Small Transport

C-130 ATP

MPAs Maritime Patrol

C-130

2 225

Fast Jet – High Thrust Fast Jet – Low Thrust

Trainer

Tanker/transport

Other

Representative type

2 100

120

499

Sources: Tables 3.1 and 3.2 of Gardner et. al 1998 USEPA, 2005)

TABLE 3.6.8 FUEL CONSUMPTION PER FLIGHT HOUR FOR MILITARY AIRCRAFT AIRCRAFT TYPE

Aircraft Description

FUEL USE (LITRES PER HOUR)

A-10A

Twin engine light bomber

2 331

B-1B

Four engine long-range strategic bomber. Used by USA only

13 959

B-52H

Eight engine long-range strategic bomber. Used by USA only.

12 833

C-12J

Twin turboprop light transport. Beech King Air variant.

C-130E

Four turboprop transport. Used by many countries.

2 956

C-141B

Four engine long-range transport. Used by USA only

7 849

C-5B

Four engine long-range heavy transport. Used by USA only

13 473

C-9C

Twin engine transport. Military variant of DC-9.

3 745

E-4B

Four engine transport. Military variant of Boeing 747.

17 339

F-15D

Twin engine fighter.

5 825

F-15E

Twin engine fighter-bomber

6 951

F-16C

Single engine fighter. Used by many countries.

3 252

KC-10A

Three engine tanker. Military variant of DC-10

10 002

KC-135E

Four engine tanker. Military variant of Boeing 707.

7 134

KC-135R

Four engine tanker with newer engines. Boeing 707 variant.

6 064

T-37B

Twin engine jet trainer.

694

T-38A

Twin engine jet trainer. Similar to F-5.

262

398

Military aircraft (transport planes, helicopters and fighters) may not have a civilian analogue, so a more detailed method of data analysis is encouraged where data are available. Inventory compilers should consult military experts to determine the most appropriate emission factors for the country’s military aviation. Due to confidentiality issues (see completeness and reporting), many inventory compilers may have difficulty obtaining data for the quantity of fuel used by the military. Military activity is defined here as those activities using fuel purchased by or supplied to the military authorities in the country. Countries can apply the rules defining civilian, national and international aviation operations to military operations when the data necessary to apply those rules are comparable and available. In this case, the international military emissions may be reported under International Aviation (International Bunkers), but must then be shown separately. Data on military fuel

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use should be obtained from government military institutions or fuel suppliers. If data on fuel split are unavailable, all the fuel sold for military activities should be treated as domestic. Emissions resulting from multilateral operations pursuant to the Charter of the United Nations should not be included in national totals; other emissions related to operations shall be included in the national emissions totals of one or more Parties involved. The national calculations should take into account fuel delivered to the country’s military, as well as fuel delivered within that country but used by the military of other countries. Other emissions related to operations (e.g., off-road ground support equipment) shall be included in the national emissions totals in the appropriate source category. These data should be used with care as national circumstances may vary from those assumed in this table. In particular, distances travelled and fuel consumption may be affected by national route structures, airport congestion and air traffic control practices.

3.6.1.5

C OMPLETENESS

Regardless of method, it is important to account for all fuel used for aviation in the country. The methods are based on total fuel use, and should completely cover CO2 emissions. However, the allocation between LTO and cruise will not be complete for Tier 2 method if the LTO statistics are not complete. Also, Tier 2 method focuses on passenger and freight carrying scheduled and charter flights, and thus not all aviation. In addition, Tier 2 method does not automatically include non-scheduled flights and general aviation such as agricultural airplanes, private jets or helicopters, which should be added if the quantity of fuel is significant. Completeness may also be an issue where military data are confidential; in this situation it is good practice to aggregate military fuel use with another source category. Other aviation-related activities that generate emissions include: fuelling and fuel handling in general, maintenance of aircraft engines and fuel jettisoning to avoid accidents. Also, in the wintertime, anti-ice and deice treatment of wings and aircraft is a source of emissions at airport complexes. Many of the materials used in these treatments flow off the wings when planes are idling, taxiing, and taking off, and then evaporate. These emissions are, however, very minor and specific methods to estimate them are not included. There are additional challenges in distinguishing between domestic and international emissions. As each country’s data sources are unique for this category, it is not possible to formulate a general rule regarding how to make an assignment in the absence of clear data. It is good practice to specify clearly the assumptions made so that the issue of completeness can be evaluated.

3.6.1.6

D EVELOPING

A CONSISTENT TIME SERIES

Volume 1 Chapter 5: Time Series Consistency and Recalculation of the 2006 IPCC Guidelines provides more information on how to develop emission estimates in cases where the same data sets or methods cannot be used during every year of the time series. If activity data are unavailable for the base year (e.g. 1990) an option may be to extrapolate data to this year by using changes in freight and passenger kilometres, total fuel used or supplied, or the number of LTOs (aircraft movements). Emissions trends of CH4 and NOx (and by inference N2O) will depend on aircraft engine technology and the change in composition of a country's fleet. This change in fleet composition may have to be accounted for in the future, and this is best accomplished using Tier 2 and Tier 3B methods based on individual aircraft types for 1990 and subsequent years. If fleet composition is not changing, the same set of emission factors should be used for all years. Every method should be able to reflect accurately the results of mitigation options that lead to changes in fuel use. However, only Tier 2 and 3B methods, based on individual aircraft, can capture the effect of mitigation options that result in lower emission factors. Tier 2 has been revised to account for NOx emissions in the climb phase, which are substantially different from those in cruise, and the differences in the amount of NOx calculated during that phase could be in the range of approximately 15 to 20 percent, due to the thrust/power required in that phase, and its relation with the higher production of NOx. Special care should be taken to develop a consistent time series if Tier 2 is used.

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3.6.1.7

U NCERTAINTY

ASSESSMENT

EMI SS ION FAC TORS

The CO2 emission factors should be within a range of ±5 percent, as they are dependent only on the carbon content of the fuel and fraction oxidised. However, considerable uncertainty is inherent in the computation of CO2 based on the uncertainties in activity data discussed below. For Tier 1, the uncertainty of the CH4 emission factor may range between -57 and +100 percent. The uncertainty of the N2O emission factor may range between -70 and +150 percent Moreover, CH4 and N2O emission factors vary with technology and using a single emission factor for aviation in general is a considerable simplification. Information to assist in computing uncertainties associated with LTO emission factors found in Table 3.6.9 can be found in Lister and Norman, 2003; and ICAO, 1993. Information to assist in computing the uncertainties associated with cruise emission factors found in Table 3.6.10 data can be found in: Baughcum et al, 1996. Sutkus, et al, 2001; Eyers et al, 2004; Kim, 2005 a and b; Malwitz, 2005. If resources are not available to compute uncertainties, uncertainty bands can be used as defined as default factors in Section 3.6.1.2. Special attention should be taken with the cruise NOx emission factors for Tier 2 found in Table 3.6.10. These emission factors, have been updated from the 1996 Guidelines to reflect the fact that climb phase emissions are substantially different from those in cruise. The calculation of the NOx emission factors is based on two sets of data, one from 1 km to 9 km, and the second from 9 km to 13 km., and the differences in the amount of NOx calculated during that phase could be in the range of approximately 15 to 20 percent, due to the thrust/power required in that phase, and its relation with the higher production of NOx. If Tier 2 is used, care should be taken to report a consistent time series (see Section 3.6.1.6 and Volume 1, Chapter 5). AC TIV I TY DA TA

The uncertainty in the reporting will be strongly influenced by the accuracy of the data collected on domestic aviation separately from international aviation. With complete survey data, the uncertainty may be very low (less than 5 percent) while for estimates or incomplete surveys the uncertainties may become large, perhaps a factor of two for the domestic share. The uncertainty ranges cited represent an informal polling of experts aiming to approximate the 95 percent confidence interval around the central estimate. The uncertainty will vary widely from country to country and is difficult to generalise. The use of global data sets, supported by radar, may be helpful in this area, and it is expected that reporting will improve for this category in the future.

3.6.2

Inventory Quality Assurance/Quality Control (QA/QC)

It is good practice to conduct quality control checks as outlined in Chapter 6 of Volume 1 (Quality Assurance/ Quality Control and Verification), Tier 1 General Inventory Level QC Procedures. It is good practice to conduct expert review of the emission estimates when using Tier 2 or 3 methods. Additional quality control checks as outlined in Tier 2 procedures in the same chapter and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Chapter 4 of Volume I. Specific procedures relevant to this source category are outlined below. Comparison of emissions using alternative approaches

If higher tier approaches are used, the inventory compiler should compare inventories to estimates with lower tiers. Any anomaly between the emission estimates should be investigated and explained. The results of such comparisons should be recorded for internal documentation. Review of Emission factors

If national factors are used rather than the default values, directly reference the QC review associated with the publication of the emission factors, and include this review in the QA/QC documentation to ensure that the procedures are consistent with good practice. If possible, the inventory compiler should compare the IPCC default values to national factors to provide further indication that the factors are applicable. If emissions from military use were developed using data other than the default factors, the accuracy of the calculations and the applicability and relevance of the data should be checked.

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TABLE 3.6.9 LTO EMISSION FACTORS FOR TYPICAL AIRCRAFT LTO emissions factors (kg/LTO/) (12) AIRCRAFT

Large Commercial Aircraft (1) (2)

CO2(11

3.70

)

CH4(7

)

N2O(9)

NOX

CO

LTO FUEL CONSUMPTION

NMVOC(8)

SO2(10)

(Kg/LTO)

A300

5450

0.12

0.2

25.86

14.80

1.12

1.72

1720

A310

4760

0.63

0.2

19.46

28.30

5.67

1.51

1510

A319

2310

0.06

0.1

8.73

6.35

0.54

0.73

730

A320

2440

0.06

0.1

9.01

6.19

0.51

0.77

770

A321

3020

0.14

0.1

16.72

7.55

1.27

0.96

960

A330-200/300

7050

0.13

0.2

35.57

16.20

1.15

2.23

2230

A340-200

5890

0.42

0.2

28.31

26.19

3.78

1.86

1860

A340-300

6380

0.39

0.2

34.81

25.23

3.51

2.02

2020

A340-500/600

10660

0.01

0.3

64.45

15.31

0.13

3.37

3370

707

5890

9.75

0.2

10.96

92.37

87.71

1.86

1860

717

2140

0.01

0.1

6.68

6.78

0.05

0.68

680

727-100

3970

0.69

0.1

9.23

24.44

6.25

1.26

1260

727-200

4610

0.81

0.1

11.97

27.16

7.32

1.46

1460

737-100/200

2740

0.45

0.1

6.74

16.04

4.06

0.87

870

737300/400/500

2480

0.08

0.1

7.19

13.03

0.75

0.78

780

737-600

2280

0.10

0.1

7.66

8.65

0.91

0.72

720

737-700

2460

0.09

0.1

9.12

8.00

0.78

0.78

780

737-800/900

2780

0.07

0.1

12.30

7.07

0.65

0.88

880

747-100

10140

4.84

0.3

49.17

114.59

43.59

3.21

3210

747-200

11370

1.82

0.4

49.52

79.78

16.41

3.60

3600

747-300

11080

0.27

0.4

65.00

17.84

2.46

3.51

3510

747-400

10240

0.22

0.3

42.88

26.72

2.02

3.24

3240

757-200

4320

0.02

0.1

23.43

8.08

0.20

1.37

1370

757-300

4630

0.01

0.1

17.85

11.62

0.10

1.46

1460

767-200

4620

0.33

0.1

23.76

14.80

2.99

1.46

1460

767-300

5610

0.12

0.2

28.19

14.47

1.07

1.77

1780

767-400

5520

0.10

0.2

24.80

12.37

0.88

1.75

1750

777-200/300

8100

0.07

0.3

52.81

12.76

0.59

2.56

2560

DC-10

7290

0.24

0.2

35.65

20.59

2.13

2.31

2310

DC-8-50/60/70

5360

0.15

0.2

15.62

26.31

1.36

1.70

1700

DC-9

2650

0.46

0.1

6.16

16.29

4.17

0.84

840

L-1011

7300

7.40

0.2

31.64

103.33

66.56

2.31

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TABLE 3.6.9 (CONTINUED) LTO EMISSION FACTORS FOR TYPICAL AIRCRAFT LTO emissions factors (kg/LTO/) (12) AIRCRAFT

(Fn

(3)

Turboprops4)

Jets

Regional Jets

CO2(11

)

CH4(7

)

N2O(9)

NOX

CO

LTO FUEL CONSUMPTION

NMVOC(8)

SO2(10)

(KG/LTO)

MD-11

7290

0.24

0.2

35.65

20.59

2.13

2.31

2310

MD-80

3180

0.19

0.1

11.97

6.46

1.69

1.01

1010

MD-90

2760

0.01

0.1

10.76

5.53

0.06

0.87

870

TU-134

2930

1.80

0.1

8.68

27.98

16.19

0.93

930

TU-154-M

5960

1.32

0.2

12.00

82.88

11.85

1.89

1890

TU-154-B

7030

11.90

0.2

14.33

143.05

107.13

2.22

2230

RJ-RJ85

1910

0.13

0.1

4.34

11.21

1.21

0.60

600

BAE 146

1800

0.14

0.1

4.07

11.18

1.27

0.57

570

CRJ-100ER

1060

0.06

0.03

2.27

6.70

0.56

0.33

330

ERJ-145

990

0.06

0.03

2.69

6.18

0.50

0.31

310

Fokker 100/70/28

2390

0.14

0.1

5.75

13.84

1.29

0.76

760

BAC111

2520

0.15

0.1

7.40

13.07

1.36

0.80

800

Dornier 328 Jet

870

0.06

0.03

2.99

5.35

0.52

0.27

280

Gulfstream IV

2160

0.14

0.1

5.63

8.88

1.23

0.68

680

Gulfstream V

1890

0.03

0.1

5.58

8.42

0.28

0.60

600

Yak-42M

2880

0.25

0.1

10.66

10.22

2.27

0.91

910

Cessna 525/560

1070

0.33

0.03

0.74

34.07

3.01

0.34

340

Beech King Air (5)

230

0.06

0.01

0.30

2.97

0.58

0.07

70

640

0.00

0.02

1.51

2.24

0.00

0.20

200

620

0.03

0.02

1.82

2.33

0.26

0.20

200

DHC8-100 (6) ATR72-500

(7)

Notes: (1) ICAO Engine Exhaust Emissions Data Bank (ICAO, 2004) based on average measured data. Emissions factors apply to LTO (Landing and Take off) only. (2) Engine types for each aircraft were selected on a consistent basis of the engine with the most LTOs. This approach, for some engine types, may underestimate (or overestimate) fleet emissions which are not directly related to fuel consumption (eg NOx, CO, HC). (2) Emissions and Dispersion Modelling System (EDMS) (FAA 2004b) (4) FOI (The Swedish Defence Research Agency) Turboprop LTO Emissions database (5) Representative of Turboprop aircraft with shaft horsepower of up to 1000 shp/engine (6) Representative of Turboprop aircraft with shaft horsepower of 1000 to 2000 shp/engine (7) Representative of Turboprop aircraft with shaft horsepower of more than 2000 shp/engine (8) Assuming 10% of total VOC emissions in LTO cycles are methane emissions (Olivier, 1991) (as in the 1996 IPCC Guidelines). (9) Estimates based on Tier I default values (EF ID 11053) (as in the 1996 IPCC Guidelines). (10) The sulphur content of the fuel is assumed to be 0.05% (as in the 1996 IPCC Guidelines). (11) CO2 for each aircraft based on 3.16 kg CO2 produced for each kg fuel used, then rounded to the nearest 10 kg. (12) Information regarding the uncertainties associated with this data can be found in: Lister and Norman, 2003; ICAO, 1993. Table prepared in 2005 updates will be available in the Emission Factor Data Base.

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Turboprops

Low Thrust Jets (Fn < 26.7 kN)

Regional Jets

Large Commercial Aircraft

TABLE 3.6.10 NOX EMISSION FACTORS FOR VARIOUS AIRCRAFT AT CRUISE LEVELS Aircraft

NOx Emission Factor (g/kg) (1) (5)

A300 A310 A319 A320 A321 A330-200/300 A340-200 A340-300 A340-500/600 707 717 727-100 727-200 737-100/200 737-300/400/500 737-600 737-700 737-800/900 747-100 747-200 747-300 747-400 757-200 757-300 767-200 767-300 767-400 777-200/300 DC-10 DC-8-50/60/70 DC-9 L-1011 MD-11 MD-80 MD-90 TU-134 TU-154-M TU-154-B RJ-RJ85 BAE 146 CRJ-100ER ERJ-145 Fokker 100/70/28 BAC111 Dornier 328 Jet Gulfstream IV Gulfstream V Yak-42M

14.8 12.2 11.6 12.9 16.1 13.8 14.5 14.6 13.0 (2) 5.9 11.5 (3) 8.7 9.5 8.7 11.0 12.8 12.4 14.0 15.5 12.8 15.2 12.4 11.8 9.8 (3) 13.3 14.3 13.7 (3) 14.1 13.9 10.8 9.1 15.7 13.2 12.4 14.2 8.5 9.1 9.1 15.6 8.4 8.0 7.9 8.4 12.0 14.8 (2) 8.0 (2) 9.5 (2) 15.6 (4)

Cessna 525/560

7.2 (4)

Beech King Air DHC8-100

8.5 12.8

ATR72-500

14.2

Notes: (1) Sutkus et al 2001, Unless otherwise noted. (2) Data from SAGE model Kim, 2005 a and b; Malwitz, 2005 (3) Sutkus, Baughcum, DuBois, 2003 (4) Average of the data from SAGE (Kim, 2005 a and b; Malwitz, 2005) and AERO2k (Eyers et al, 2004) (5) Information to assist in computing uncertainties can be found in: Baughcum et al, 1996; Sutkus, et al, 2001; Eyers et al, 2004; Kim, 2005 a and b; Malwitz, 2005.

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Activity data check

The source of the activity data should be reviewed to ensure applicability and relevance to the source category. Where possible, the inventory compiler should compare current data to historical activity data or model outputs to look for anomalies. In preparing the inventory estimates, the inventory compiler should ensure the reliability of the activity data used to differentiate emissions between domestic and international aviation. Data can be checked with productivity indicators such as fuel per unit of traffic performance (per passenger km or ton km). Where data from different countries are being compared, the band of data should be small. The European Environmental Agency provides a useful dataset20 which presents emissions and passenger/freight volume for each transportation mode for Europe. For example, Norway estimates that for domestic aviation, emissions are 0.22 kg CO2/passenger-km. However, note that the global fleet includes many small aircraft with relatively low energy efficiency. The U.S. Department of Transportation estimates an average energy intensity for the U.S. fleet of 3666 Btu/passenger mile (2403 kJ/passenger km). The International Air Transport Association estimates that the average aircraft consumes 3.5 litres of jet fuel per 100 passenger-km (67 passenger miles per U.S. gallon). Reliance on scheduled operations for activity data may introduce higher uncertainties than simple reliance on fuel use for CO2. However, fuel loss and use of jet fuel for other activities will result in over estimates of aviation’s contributions. External review

The inventory compiler should perform an independent, objective review of calculations, assumptions or documentation of the emissions inventory to assess the effectiveness of the QC programme. The review should be performed by expert(s) (e.g. aviation authorities, airline companies, and military staff) who are familiar with the source category and who understand inventory requirements.

3.6.3

Reporting and Documentation

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Chapter 8 of Volume 1 of the 2006 IPCC Guidelines. Some examples of specific documentation and reporting relevant to this source category are provided below. Inventory compilers are required to report emissions from international aviation separately from domestic aviation, and exclude international aviation from national totals. It is expected that all countries have aviation activity and should therefore report emissions from this category. Though countries covering small areas might not have domestic aviation, emissions from international aviation should be reported. Inventory compilers should explain how the definition for international and domestic in the guidelines has been applied. Transparency would be improved if inventory compilers provide data on emissions from LTO separately from cruise operations. Emissions from military aviation should be clearly specified, so as to improve the transparency on national greenhouse gas inventories. In addition to the numerical information reported in the standard tables, provision of the following data would increase transparency:

•

•

Sources of fuel data and other essential data (e.g. fuel consumption factors) depending on the method used;

•

Emission factors used, if different from default values. Data sources should be referenced.

•

The number of flight movements split between domestic and international;

If Tier 3 method is used, emissions data could be provided separately for Commercial Scheduled Aviation and Other Jet Fuelled Activities.

Confidentiality may be a problem if only one or two airline companies operate domestic transport in a given country. Confidentiality may also be a problem for reporting military aviation in a transparent manner.

3.6.4

Reporting tables and worksheets

The four pages of the worksheets (Annex 1) for the Tier 1 Sectoral Approach are to be filled in for each of the source categories in Table 3.6.1. The reporting tables are available in Volume 1, Chapter 8.

20

See http://air-climate.eionet.eu.int/databases/TRENDS/TRENDS_EU15_data_Sep03.xls

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3.6.5

Definitions of specialist terms

Aviation Gasoline - A fuel used only in small piston engine aircraft, and which generally represents less than 1% of fuel used in aviation Climb – The part of a flight of an aircraft, after take off and above 914 meters (3000 feet) above ground level, consisting of getting an aircraft to the desired cruising altitude. Commercial scheduled – All commercial aircraft operations that have publicly available schedules (e.g., the Official Airline Guide, OAG 2006), which would primarily include passenger services. Activities that do not operate with publicly available schedules are not included in this definition, such as non-scheduled cargo, charter, air-taxi and emergency response operations. Note: Commercial Scheduled Aviation is used as a subset of jet fuel powered aviation operations. Cruise – All aircraft activities that take place at altitudes above 914 meters (3000 feet), including any additional climb or descent operations above this altitude. No upper limit is given. Gas Turbine Engines – Rotary engines that extract energy from a flow of combustion gas. Energy is added to the gas stream in the combustor, where air is mixed with fuel and ignited. Combustion increases the temperature and volume of the gas flow. This is directed through a nozzle over a turbine's blades, spinning the turbine and powering a compressor. For an aircraft, energy is extracted either in the form of thrust or through a turbine driving a fan or propeller.

References ROAD TRANSPORTATION ADEME/DIREM (2002). Agence de l’Environnement et de la Maltrlse de l’Energle, La direction des ressources énergétiques et minérales, Ecobilan, PricewaterhouseCoopers, ‘Energy and greenhouse gas balances of biofuels’ production chains in France.’ December, www.ademe.fr/partenaires/agrice/publications/ ocuments_anglais/synthesis_energy_and_greenhouse_english.pdf ARB (2004). ‘Technical Support Document for Staff Proposal Regarding Reduction of greenhouse gas emissions from motor vehicles, climate change emissions inventory’. California Air Resources Board (August 6 2004) Ballantyne, V. F., Howes, P., and Stephanson, L. (1994). ‘Nitrous oxide emissions from light duty vehicles.’ SAE Tech. Paper Series (#940304), 67–75. Beer, T., Grant, T., Brown, R., Edwards, J., Nelson, P., Watson, H., Williams, D., (2000). ‘Life-cycle emissions analysis of alternative fuels for heavy vehicles’. CSIRO Atmospheric Research Report C/0411/1.1/F2 to the Australian Greenhouse Office, Australia. (March 2000) Behrentz, E. (2003). ‘Measurements of nitrous oxide emissions from light-duty motor vehicles: analysis of important variables and implications for California´s greenhouse gas emission Inventory.’ Dissertation Prospectus University of California, USA, (2003). See http://ebehrent.bol.ucla.edu/N2O.pdf Borsari, V. (2005). ‘As emissoes veiculares e os gases de efeito estufa.’ SAE - Brazilian Society of Automotive Engineers CETESB (2004). Air Quality Report (Relatório de Qualidade do Ar 2003, in Portuguese, (Air Quality Report 2003), available at http://www.cetesb.sp.gov.br/Ar/Relatorios/RelatorioAr2003.zip and CETESB (2005). Personal communication with Oswaldo Lucon, São Paulo State Environment Agency, Mobile Sources Division. Information based on measurements conducted by Renato Linke, Vanderlei Borsari and Marcelo Bales, (Vehicle Inspection Division, ph. +5511 3030 6000). Partially published. CONCAWE Report 2/02 Brussels, Belgium, (April 2002). ‘Energy and greenhouse gas balance of biofuels for Europe - an update.’ Díaz, L. et.al (2001). ‘Long-term efficiency of catalytic converters operating in Mexico City.’ Air & Waste Management Association, ISSN 1047-3289, Vol 51, pp.725-732,

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EEA (2000). European Environment Agency (EEA). ‘COPERT III computer programe to calculate emissions from road transport, methodology and emission factors report.’ (Version 2.1), Copenhagen, Denmark November 2000. (For more details see http://vergina.eng.auth.gr/mech/lat/copert/copert.htm) EEA (2005a). EMEP/CORINAIR. Emission Inventory Guidebook – 2005 European Environment Agency, Technical report No 30. Copenhagen, Denmark, (December 2005). Available from web site: http://reports.eea.eu.int/EMEPCORINAIR4/en EEA (2005b). European Environment Agency (EEA), Computer programme to calculate emissions from road transport (COPERT), http:/vergina.eng.auth.gr/mech/lat/copert/copert.htm Gamas, D.J., Diaz, L., Rodriguez, R., López-Salinas, E., Schifter, I.,. (1999). ‘Exhaust emissions from gasoline and LPG-powered vehicles operating at the altitude of Mexico City.’ in Journal of the Air & Waste Management Association, October 1999. Heeb, Norbert., et al (2003). ‘Methane, benzene and alkyl benzene cold start emission data of gasoline-driven passenger cars representing the vehicle technology of the last two decades.’ Atmospheric Environment 37 (2003) 5185-5195. IEA (2004). ‘Bioenergy; biofuels for transport: an overview.’ IEA Bioenergy.’ T39:2004:01 (Task 39); March 2004, Intergovernmental Panel on Climate Change (IPCC) (1997). Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, J.T. Houghton et al., IPCC/OECD/IEA, Paris, France. LAT (2005). ‘Emission factors of N2O and NH3 from road vehicles.’ LAT Report 0507 (in Greek), Laboratory of Applied Thermodynamics, Aristotle University of Thessaloniki, Greece Lipman, T. and Delucchi, M (2002). Lipman, Timothy, University of California-Berkeley; and Mark Delucchi, University of California-Davis (2002). ‘Emissions of nitrous oxide and methane from conventional and alternative fuel motor vehicles.’ Climate Change, 53(4), 477-516, Kluwer Academic Publishers, Netherlands. MCT (2002). ‘Greenhouse gas emissions inventory from mobile sources in the energy sector.’ (in Portuguese: Emissões de gases de efeito estufa por fontes móveis, no setor energético). Brazilian Ministry of Science and Technology, Brasília, 2002, pp. 25-26. Mitra,A. P., Sharma, Subodh K., Bhattacharya, S., Garg, A., Devotta, S. and Sen, Kalyan (Eds.), (2004). ‘Climate Change and India: Uncertainty reduction in GHG inventories.’ Universities Press (India) Pvt Ltd, Hyderabad. Ntziachristos, L and Samaras, Z (2005). Personal Communication Leonidas Ntziachristos and Zissis Samaras based on draft COPERT IV. Laboratory of Applied Thermodynamics, Aristotle University Thessaloniki, PO Box 458, GR 54124, Thessaloniki, GREECE, Peckham, J. (2003). ‘Europe's 'AdBlue' urea-SCR project starts to recruit major refiners - selective catalytic reduction’. Diesel Fuel News, July 7, 2003. TNO (2002). ‘N2O formation in vehicles catalysts.’ Report # 02.OR.VM.017.1/NG. Nederlandse Organisatie voor toegepastnatuurwetenschappelijk onderzoek (Netherlands Organisation for Applied Scientific Research), Delft, Netherlands. TNO (2003). ‘Evaluation of the environmental impact of modern passenger cars on petrol, diesel and automotive LPG, and CNG.’ Report. 03.OR.VM.055.1/PHE. Nederlandse Organisatie voor toegepastnatuurwetenschappelijk onderzoek (Netherlands Organisation for Applied Scientific Research) December 24 2003. UNFCCC (2004). ‘Estimation of emissions from road transport.’ United Nations Framework Convention on Climate Change, FCCC/SBSTA/2004/INF.3, June 2004 USEPA (1997). ‘Conversion factors for hydrocarbon emission components.’ prepared by Christian E Lindhjem, USEPA Office of Mobile Sources, Report Number NR-002, November 24. USEPA (2004a). ‘Update of carbon oxidation fraction for GHG calculations.’ prepared by ICF Consulting for US Environmental Protection Agency, Washington DC, USA. USEPA (2004b). ‘Update of methane and nitrous oxide emission factors for on-highway vehicles.’ Report Number EPA420-P-04-016, US Environmental Protection Agency, Washington DC, USA .November 2004

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USEPA (2004c). ‘Inventory of greenhouse gas emissions and sinks: 1990-2002’. (April 2004) USEPA #430-R04-003. Table 3-19 , US Environmental Protection Agency, Washington DC, USA. USEPA (2005a). U.S. Environmental Protection Agency, ‘Motor Vehicle Emission Simulator (MOVES).’ See website: http://www.epa.gov/otaq/ngm.htm. USEPA (2005b). U.S. Environmental Protection Agency: ‘MOBILE Model (on-road vehicles).’ See website: http://www.epa.gov/otaq/mobile.htm. Wenzel, T., Singer, B., Slott, R., (2000). ‘Some issues in the statistical analysis of vehicle emissions’. Journal of Transportation and Statistics. pages 1-14, Volume 3, Number 2, September 2000, ISSN 1094-8848

OFF-ROAD TRANSPORTATION EEA (2005). EMEP/CORINAIR. Emission Inventory Guidebook – 2005, European Environment Agency, Technical report No 30. Copenhagen, Denmark, (December 2005). Available from web site: http://reports.eea.eu.int/EMEPCORINAIR4/en Ntziachristos, L., Samaras. Z., Eggleston, S., Gorißen, N., Hassel, D., Hickman, A.J., Joumard, R., Rijkeboer, R., White, L., and Zierock, K. H. (2000). ‘COPERT III computer programme to calculate emissions from road transport methodology and emission factors.’ (Version 2.1) European Environment Agency, Technical report No 49. Copenhagen, Denmark, (November 2000). Software available from web site: http:/vergina.eng.auth.gr/mech/lat/copert/copert.htm USEPA (2005a). NONROAD 2005 Model, For software, data and information, see website: http://www.epa.gov/otaq/nonrdmdl.htm. USEPA (2005b). User’s Guide for the Final NONROAD2005 Model. Environment Protection Agency, Report EPA420-R-05-0, 13 December 2005, Washington, DC, USA. (December 2005) Walsh, M. (2003).’Vehicle emissions trends and forecasts: The lessons of the past 50 years, blue sky in the 21st century conference, Seoul, Korea.’ May 2003, see the website: http://www.walshcarlines.com/pdf/vehicle_trends_lesson.cf9.pdf

RAILWAYS Dunn, R. (2001). ‘Diesel fuel quality and locomotive emissions in Canada’. Transport Canada Publication Number Tp 13783e (Table 8). EEA (2005). EMEP/CORINAIR. ‘Emission Inventory Guidebook – 2005 European Environment Agency.’ Technical report No 30. Copenhagen, Denmark, (December 2005). Available from web site: http://reports.eea.eu.int/EMEPCORINAIR4/en GSTU (1994). 32.001-94. ‘Emissions of pollution gases with exhaust gases from diesel locomotive.’ Rates and definition methods (GSTU, 32.001-94) – in Russian ( 32.001-94. ы ы х ых . ы ы ). Hahn, J. (1989). Eisenbahntechnishne Rundschau, № 6, S. 377 - 384. ISO 8178-4 (1996). ‘Reciprocating internal combustion engines – Exhaust emission measurement – Part 4: Test cycles for different engine applications.’ Jorgensen, M.W. and. Sorenson, S.C (1997). ‘Estimating emission from railway traffic.’ DTU report, N°ET-EO97-03, Dept of Energy Eng.’ Lyngby, Denmark, 135 p. VTT (2003). RAILI (2003). ‘Calculation system for Finnish railway traffic emissions VTT building and transport, Finland.’ For information see web site http://lipasto.vtt.fi/lipastoe/railie/ TRANS/SC.2/2002/14/Add.1 13 AUGUST (2002). Economic Commission for Europe. inland Transport Committee. Working Party on rail transport. – Productivity in rail transport. Transmitted by the International Union of Railways (UIC). UNECE (2002). ‘Productivity in rail transport UN Economic Commission For Europe, Inland Transport Committee Working Party on Rail Transport.’ (Fifty-sixth session, 16-18 October 2002, agenda item 15) Transmitted by the International Union of Railways (UIC) TRANS/SC.2/2002/14/Add.1 USEPA (1998) http://www.epa.gov/fedrgstr/EPA-AIR/1998/October/Day-23/a24836.htm

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USEPA (2005a). NONROAD 2005 Model, http://www.epa.gov/otaq/nonrdmdl.htm.

For

software,

data

and

information.

see

website:

USEPA (2005b). User’s Guide for the Final NONROAD2005 Model. Environment Protection Agency, Report EPA420-R-05-0, 13 December 2005, Washington DC, USA.

WATER-BORNE NAVIGATION Baggott, S.L., Brown, L., Cardenas, L., Downes, M.K., Garnett, E., Hobson, M., Jackson, J., Milne. R., Mobbs, D.C., Passant, N., Thistlethwaite, G., Thomson, A. and Watterson, J.D. (2004). ‘UK Greenhouse gas inventory 1990 to 2002: Annual report for submission under the Framework Convention on Climate Change.’ United Kingdom Department for Environment, Food and Rural Affairs. EC (2002). ‘Quantification of emissions from ships associated with ship movements between ports in the European Community.’ Final Report Entec UK Limited (July 2002), page 12. Available from EU web site http://europa.eu.int/comm/environment/air/pdf/chapter2_ship_emissions.pdf EEA (2005). EMEP/CORINAIR. Emission Inventory Guidebook – 2005 European Environment Agency, Technical report No 30. Copenhagen, Denmark, (December 2005). Available from web site See: http://reports.eea.eu.int/EMEPCORINAIR4/en Gunner, T.,( 2004). E-mail Correspondence containing estimates of total fuel consumption of the world fleet of ships of 500 gross tons and over, as found in the Fairplay Database of Ships, November 2004. See http://www.fairplay.co.uk Lloyd’s Register (1995). ‘Marine exhaust emissions research programme.’ Lloyd’s Register House, Croydon, England. Trozzi, C., Vaccaro, R., (1997): ‘Methodologies for estimating air pollutant emissions from ships’. MEET Deliverable No. 19. European Commission DG VII, June 1997. Techne (1997). U.S. EPA, (2004). ‘Inventory of U.S. greenhouse gas emissions and sinks: 1990-2002.’ United States Environmental Protection Agency, Washington, DC.

CIVIL AVIATION Baughcum, S.L., Tritz, T.G., Henderson, S.C. and Pickett, D.C. (1996). ‘Scheduled civil aircraft emission inventories for 1992: database development and analysis.’ NASA/CR-4700, National Aeronautics and Space Administration, NASA Center for AeroSpace Information, 7121 Standard Drive, Hanover, USA. Daggett, D.L., Sutkus, D.J., Dubois, D.P. and. Baughcum, S.L. (1999). ‘An evaluation of aircraft emissions inventory methodology by comparisons with reported airline data.’ NASA/CR-1999-209480, National Aeronautics and Space Administration, NASA Center for AeroSpace Information, 7121 Standard Drive, Hanover, USA, September 1999. EEA (2002).EMEP/CORINAIR Emission Inventory Guidebook, 3rd edition (October 2002 Update) EEA Technical Report No 30, Copenhagen, Denmark, 2002. Eyers, C.J., Norman, P., Plohr, M., Michot, S., Atkinson, K., and Christou, R.A., (2004). ‘AERO2k Global aviation emissions inventories for 2002 and 2025.’ QINEYIQ/04/01113 UK, December 2004. FAA (2004a). ‘Aviation emissions: a primer.’ Federal Aviation Administration, USA, 2004. FAA (2004b) ‘Emissions and dispersion modelling system’. (EDMS) User’s Manual FAA-AEE-04-02 (Rev. 1 – 10/28/04) Federal Aviation Administration Office of Environment and Energy, Washington, DC September 2004. Additional information is available from the FAA web site: www.faa.gov. Kim, B., Fleming, G., Balasubramanian, S., Malwitz, A., Lee, J., Ruggiero, J., Waitz, I., Klima, K., Stouffer, V., Long, D., Kostiuk, P., Locke, M., Holsclaw, C., Morales, A., McQueen, E., Gillett, W., (2005a). ‘SAGE: The system for assessing aviation’s global emissions’. FAA-EE-2005-01, (September 2005). Kim, B., Fleming, G., Balasubramanian, S., Malwitz, A., Lee, J., Waitz, I., Klima, K., Locke, M., Holsclaw, C., Morales, A., McQueen, E., Gillette, W., (2005b), ‘SAGE: Global aviation emissions inventories for 2000 through 2004’. FAA-EE-2005-02 (September 2005). Malwitz, A., Kim, B., Fleming, G., Lee, J., Balasubramanian, S., Waitz, I., Klima, K., Locke, M., Holsclaw, C., Morales, A., McQueen, E., Gillette, W., (2005), ‘SAGE: Validation assessment, model assumptions and uncertainties FAA-EE-2005-03, (September 2005)’.

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Gardner, R. M., Adams, J. K., Cook, T., Larson, L. G., Falk, R., Fleuit, S. E., Förtsch, W., Lecht, M., Lee, D. S., Leech, M. V., Lister, D. H. Massé, B., Morris, K., Newton, P. J., Owen, A., Parker, E., Schmitt, A., ten Have, H.,. Vandenberghe, C. (1998). ‘ANCAT/EC2 aircraft emissions inventories for 1991/1992 and 2015’. Final Report., Report by the ECAC/ANCAT and EC working group. EUR No: 18179, ISBN No: 92-828-2914-6. ICAO (1993). ‘International Standards and Recommended Practices Environmental Protection - Annex 16 to the Convention on International Civil Aviation.’ - Volume II Aircraft Engine Emissions, 2nd edition (1993) International Civil Aviation Organisation, Montreal. ICAO (2004a). ‘Engine exhaust emissions data bank.’ Issue 13 (Doc 9646), ICAO, Montreal, Canada. 1995. Subsequent updates are available from the ICAO web site www.icao.int ICAO (2004b). ‘Statistics data series collection - Montreal, Canada’. For details and access see ICAO web site at http://www.icao.int/icao/en/atb/sea/DataDescription.pdf. International Register of Civil Aircraft, (2004). For information and acesss see http://www.aviationregister.com/english/. IPCC (1999). ‘Aviation and the global atmosphere.’ Eds: Penner, J.E., Lister, D.H., Griggs, D.J., Dokken, D.J., MsFarland, M., Intergovernmental Panel on Climate Change, Cambridge University Press 1999. Lister, D.H., Norman, P.D. (2003). EC-NEPAir: Work Package 1 ‘Aircraft engine emissions certification – a review of the development of ICAO Annex 16.’ Volume II, QinetiQ/FST/CR030440, UK (September 2003) OAG (2006). OAG Flight Guide – ‘Worldwide airline flights schedules’. See web site www.oag.com Olivier, J.G.J. (1991). ‘Inventory of aircraft emissions: a review of recent literature’. RIVM Rapport 736301008, Bilthoven, The Netherlands, 1991. Olivier, J.G.J. (1995). ‘Scenarios for global emissions from air traffic’. Report No. 773 002 003, RIVM, Bilthoven, The Netherlands, 1995 Penman, J., Kruger, D., Galbally, I., Hiraishi, T., Nyenzi, B., Emmanuel, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K. & Tanabe, K. (2000). ‘Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Hayama: Intergovernmental Panel on Climate Change.’ (IPCC). ISBN 4-88788-000-6. Sutkus, D.J., Baughcum, S.L., DuBois, D.P.,(2001) ‘Scheduled civil aircraft emission inventories for 1999: database development and Analysis.’ NASA/CR—2001-211216, National Aeronautics and Space Administration, Glenn Research Center, USA, October 2001. Sutkus, D.J., Baughcum, S.L., DuBois, D.P., (2003). ‘Commercial aircraft emission scenario for 2020: Database Development and Analysis.’ NASA/CR—2003-212331, National Aeronautics and Space Administration, Glenn Research Center, USA May 2003 US Department of Transportation, Bureau of Transportation Statistics, National Transportation Statistics (2002). (BTS 02-08), Table 4-20: Energy Intensity of Passenger Modes (Btu per passenger-mile), page 281, http://www.bts.gov/publications/national_transportation_statistics/2002/pdf/entire.pdf. USEPA (2005). ‘Inventory of U.S. greenhouse gas emissions and sinks: 1990-2003 U.S’. Environmental Protection Agency, Washington, U.S.A. Wiesen, P., Kleffmann, J., Kortenbach, R. and Becker, K.H (1994). ‘Nitrous oxide and methane emissions from aero engines.’ Geophys. Res. Lett. 21:18 2027-2030.

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Chapter 4: Fugitive Emissions

CHAPTER 4

FUGITIVE EMISSIONS

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.1

Volume 2: Energy

Authors Coal Mining John N. Carras (Australia) Pamela M. Franklin (USA), Yuhong Hu (China), A. K. Singh (India), and Oleg V. Tailakov (Russian Federation)

Oil and natural gas David Picard (Canada) Azhari F. M. Ahmed (Qatar), Eilev Gjerald (Norway), Susann Nordrum (USA), and Irina Yesserkepova (Kazakhstan)

4.2

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Chapter 4: Fugitive Emissions

Contents 4

Fugitive Emissions 4.1

Fugitive emissions from mining, processing, storage and transportation of coal..................................4.6

4.1.1

Overview and description of sources.............................................................................................4.6

4.1.1.1

Coal mining and handling........................................................................................................4.6

4.1.1.2

Summary of sources .................................................................................................................4.8

4.1.2

Methodological issues ...................................................................................................................4.9

4.1.3

Underground coal mines................................................................................................................4.9

4.1.3.1

Choice of method...................................................................................................................4.10

4.1.3.2

Choice of emission factors for underground mines ...............................................................4.11

4.1.3.3

Choice of activity data ...........................................................................................................4.14

4.1.3.4

Completeness for underground coal mines ............................................................................4.14

4.1.3.5

Developing a consistent time series .......................................................................................4.14

4.1.3.6

Uncertainty assessment..........................................................................................................4.15

4.1.4

Surface coal mining.....................................................................................................................4.17

4.1.4.1

Choice of method...................................................................................................................4.17

4.1.4.2

Emission factors for surface mining ......................................................................................4.18

4.1.4.3

Activity data...........................................................................................................................4.19

4.1.4.4

Completeness for surface mining...........................................................................................4.19

4.1.4.5

Developing a consistent time series .......................................................................................4.19

4.1.4.6

Uncertainty assessment in emissions .....................................................................................4.20

4.1.5

Abandoned underground coal mines ...........................................................................................4.20

4.1.5.1

Choice of method...................................................................................................................4.20

4.1.5.2

Choice of emission factors.....................................................................................................4.23

4.1.5.3

Choice of activity data ...........................................................................................................4.28

4.1.5.4

Completeness.........................................................................................................................4.28

4.1.5.5

Developing a consistent time series .......................................................................................4.28

4.1.5.6

Uncertainty assessment..........................................................................................................4.29

4.1.6

Completeness for coal mining .....................................................................................................4.30

4.1.7

Inventory Quality Assurance/Quality Control (QA/QC).............................................................4.30

4.2

4.1.7.1

Quality control and documentation........................................................................................4.30

4.1.7.2

Reporting and Documentation ...............................................................................................4.31

Fugitive Emissions from Oil and Natural Gas Systems ......................................................................4.32

4.2.1

Overview, description of sources ................................................................................................4.32

4.2.2

Methodological issues .................................................................................................................4.35

4.2.2.1

Choice of method, decision trees, tiers...................................................................................4.36

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

4.2.2.2

Choice of method ...................................................................................................................4.41

4.2.2.3

Choice of emission factor.......................................................................................................4.46

4.2.2.4

Choice of activity data............................................................................................................4.65

4.2.2.5

Completeness..........................................................................................................................4.70

4.2.2.6

Developing consistent time series ..........................................................................................4.71

4.2.2.7

Uncertainty assessment ..........................................................................................................4.72

4.2.3

Inventory Quality Assurance/Quality Control (QA/QC)............................................................ 4.73

4.2.4

Reporting and Documentation.................................................................................................... 4.74

References

.....................................................................................................................................................4.77

Equations Equation 4.1.1 Estimating emissions from underground coal mines for Tier 1 and Tier 2 without adjustment for methane utilisation or flaring ..........................................4.9 Equation 4.1.2 Estimating emissions from underground coal mines for Tier 1 and Tier 2 with adjustment for methane utilisation or flaring ............................4.10 Equation 4.1.3 Tier 1: global average method – underground mining – before adjustment for any methane utilisation or flaring ...............................................................4.11 Equation 4.1.4 Tier 1: global average method – post-mining emissions – underground mines...................4.12 Equation 4.1.5 Emissions of CO2 and CH4 from drained methane flared or catalytically oxidised.............4.13 Equation 4.1.6 General equation for estimating fugitive emissions from surface coal mining....................4.17 Equation 4.1.7 Tier 1: global average method – surface mines ...................................................................4.18 Equation 4.1.8 Tier 1: global average method – post-mining emissions – surface mines ...........................4.19 Equation 4.1.9 General equation for estimating fugitive emissions from abandoned underground coal mines.......................................................................................................4.20 Equation 4.1.10 Tier 1 approach for abandoned underground mines ............................................................4.21 Equation 4.1.11 Tier 2 approach for abandoned underground mines without methane recovery and utilization .......................................................................................................4.26 Equation 4.1.12 Tier 2 – abandoned underground coal mines emission factor .............................................4.27 Equation 4.1.13 Example of Tier 3 emissions calculation – abandoned underground mines ........................4.27 Equation 4.2.1 Tier 1: estimating fugitive emissions from an industry segment .........................................4.41 Equation 4.2.2 Tier 1: total fugitive emissions from industry segments......................................................4.41 Equation 4.2.3 Alternative Tier 2 approach (emissions due to venting) ......................................................4.44 Equation 4.2.4 Alternative Tier 2 approach (CH4 emissions due to flaring) ...............................................4.44 Equation 4.2.5 Alternative Tier 2 approach(CO2 emissions due to flaring) ................................................4.45 Equation 4.2.6 CH4 emissions from flaring and venting..............................................................................4.45 Equation 4.2.7 CO2 emissions from venting and flaring..............................................................................4.45 Equation 4.2.8 N2O emissions from flaring.................................................................................................4.45

4.4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions

Figures Figure 4.1.1

Decision tree for underground coal mines...........................................................................4.11

Figure 4.1.2

Decision tree for surface coal mining..................................................................................4.18

Figure 4.1.3

Decision tree for abandoned underground coal mines.........................................................4.22

Figure 4.2.1

Decision tree for natural gas systems ..................................................................................4.38

Figure 4.2.2

Decision tree for crude oil production .................................................................................4.39

Figure 4.2.3

Decision tree for crude oil transport, refining and upgrading..............................................4.40

Tables Table 4.1.1

Detailed sector split for emissions from mining, processing, storage and transport of coal..................................................................................................4.8

Table 4.1.2

Estimates of uncertainty for underground mining for Tier 1 and Tier 2 approaches..........................................................................................4.15

Table 4.1.3

Estimates of uncertainty for underground coal mining for a Tier 3 approach .....................4.16

Table 4.1.4

Estimates of uncertainty for surface mining for Tier 1 and Tier 2 approaches....................4.20

Table 4.1.5

Tier 1 – abandoned underground mines - default values - percentage of coal mines that are gassy.................................................................................................4.24

Table 4.1.6

Tier 1 – abandoned underground mines - emission factor, million m3 methane / mine ......4.25

Table 4.1.7

Tier 1 – abandoned underground mines ..............................................................................4.25

Table 4.1.8

Tier 2 – abandoned underground coal mines - default values for active mine emissions prior to abandonment .................................................................4.27

Table 4.1.9

Coefficients for Tier 2 – abandoned underground coal mines.............................................4.27

Table 4.2.1

Detailed sector split for emissions from production and transport of oil and natural gas....4.33

Table 4.2.2

Major categories and subcategories in the oil and gas industry...........................................4.42

Table 4.2.3

Typical ranges of gas-to-oil ratios for different types of production ...................................4.44

Table 4.2.4

Tier 1 emission factors for fugitive emissions (including venting and flaring) from oil and gas operations-in developed countries ...........................................................4.48

Table 4.2.5

Tier 1 emission factors for fugitive emissions (including venting and flaring) from oil and gas operations in developing countries and countries with economies in transition..............................................................................................................................4.55

Table 4.2.6

Typical activity data requirements for each assessment approach for fugitive emissions from oil and gas operations by type of primary source category...........4.66

Table 4.2.7

Guidance on obtaining the activity data values required for use in the Tier 1 approach to estimate fugitive emissions from oil and gas operations .......................4.67

Table 4.2.8

Classification of gas losses as low, medium or high at selected types of natural gas facilities .............................................................................................................4.71

Table 4.2.9

Format for summarizing the applied methodology and basis for estimated emissions from oil and natural gas systems showing sample entries...................................4.75

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

4 FUGITIVE EMISSIONS 4.1

FUGITIVE EMISSIONS FROM MINING, PROCESSING, STORAGE AND TRANSPORTATION OF COAL

Intentional or unintentional release of greenhouse gases may occur during the extraction, processing and delivery of fossil fuels to the point of final use. These are known as fugitive emissions.

4.1.1

Overview and description of sources

Fugitive emissions associated with coal can be considered in terms of the following broad categories.1

4.1.1.1

C OAL

MINING AND HANDLING

The geological processes of coal formation also produce methane (CH4), and carbon dioxide (CO2) may also be present in some coal seams. These are known collectively as seam gas, and remain trapped in the coal seam until the coal is exposed and broken during mining. CH4 is the major greenhouse gas emitted from coal mining and handling. The major stages for the emission of greenhouse gases for both underground and surface coal mines are: •

Mining emissions – These emissions result from the liberation of stored gas during the breakage of coal, and the surrounding strata, during mining operations.

•

Post-mining emissions – Not all gas is released from coal during the process of coal breakage during mining. Emissions, during subsequent handling, processing and transportation of coal are termed postmining emissions. Therefore coal normally continues to emit gas even after it has been mined, although more slowly than during the coal breakage stage.

•

Low temperature oxidation - These emissions arise because once coal is exposed to oxygen in air, the coal oxidizes to produce CO2. However, the rate of formation of CO2 by this process is low.

•

Uncontrolled combustion – On occasions, when the heat produced by low temperature oxidation is trapped, the temperature rises and an active fire may result. This is commonly known as uncontrolled combustion and is the most extreme manifestation of oxidation. Uncontrolled combustion is characterised by rapid reactions, sometimes visible flames and rapid CO2 formation, and may be natural or anthropogenic. It is noted that uncontrolled combustion only due to coal exploitation activities is considered here.

After mining has ceased, abandoned coal mines may also continue to emit methane. A brief description of some of the major processes that need to be accounted for in estimating emissions for the different types of coal mines follows: UND ERGROUND MINES Active Underground Coal Mines The following potential source categories for fugitive emissions for active underground coal mines are considered in this document: Seam gas emissions vented to the atmosphere from coal mine ventilation air and degasification systems

•

Post-mining emissions

•

1

Low temperature oxidation

Methods for determining emissions from peat extraction are described in Volume 4 AFOLU Chapter 7 ‘Wetlands’.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions •

Uncontrolled combustion

Coal mine ventilation air and degasification systems arise as follows: Coal Mine Ventilation Air Underground coal mines are normally ventilated by flushing air from the surface, through the underground tunnels in order to maintain a safe atmosphere. Ventilation air picks up the CH4 and CO2 released from the coal formations and transports these to the surface where they are emitted to atmosphere. The concentration of methane in the ventilation air is normally low, but the volume flow rate of ventilation air is normally large and therefore the methane emissions from this source can be very significant. Coal Mine Degasification Systems Degasification systems comprise wells drilled before, during, and after mining to drain gas (mainly CH4) from the coal seams that release gas into the mine workings. During active mining the major purpose of degasification is to maintain a safe working atmosphere for the coal miners, although the recovered gas may also be utilised as an energy source. Degasification systems can also be used at abandoned underground coal mines to recover methane. The amount of methane recovered from coal mine degasification systems can be very significant and is accounted for, depending on its final use, as described in Section 4.1.3.2 of this chapter. Abandoned Underground Mines After closure, coal mines that were significant methane emitters during mining operations continue to emit methane unless there is flooding that cuts off the emissions. Even if the mines have been sealed, methane may still be emitted to the atmosphere as a result of gas migrating through natural or manmade conduits such as old portals, vent pipes, or cracks and fissures in the overlying strata. Emissions quickly decline until they reach a near-steady rate that may persist for an extended period of time. Abandoned mines may flood as a result of intrusion of groundwater or surface water into the mine void. These mines typically continue to emit gas for a few years before the mine becomes completely flooded and the water prevents further methane release to the atmosphere. Emissions from completely flooded abandoned mines can be treated as negligible. Mines that remain partially flooded can continue to produce methane emissions over a long period of time, as with mines that do not flood. A further potential source of emissions occurs when some of the coal from abandoned mines ignites through the mechanism of uncontrolled combustion. However, there are currently no methodologies for estimating potential emissions from uncontrolled combustion at abandoned underground mines. SUR FAC E COA L MINES Active Surface Mines The potential source categories for surface mining considered in this chapter are: •

•

•

•

Methane and CO2 emitted during mining from breakage of coal and associated strata and leakage from the pit floor and highwall Post-mining emissions Low temperature oxidation Uncontrolled combustion in waste dumps

Emissions from surface coal mining occur because the mined and surrounding seams may also contain methane and CO2. Although the gas contents are generally less than for deeper underground coal seams, the emission of seam gas from surface mines needs to be taken into account, particularly for countries where this mining method is widely practised. In addition to seam gas emissions, the waste coal that is dumped into overburden or reject dumps may generate CO2, either by low temperature oxidation or by uncontrolled combustion. Abandoned Surface Mines After closure, abandoned or decommissioned surface mines may continue to emit methane as the gas leaks from the coal seams that were broken or damaged during mining. There are at present no methods for estimating emissions from this source.

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Volume 2: Energy

4.1.1.2

S UMMARY

OF SOURCES

The major sources are summarised in Table 4.1.1 below. TABLE 4.1.1 DETAILED SECTOR SPLIT FOR EMISSIONS FROM MINING, PROCESSING, STORAGE IPCC code

Sector name

1B

Fugitive emissions from fuels

1B1

1B a

Includes all intentional and unintentional emissions from the extraction, processing, storage and transport of solid fuel to the point of final use.

Coal mining and handling Underground mines

Includes all fugitive emissions from coal Includes all emissions arising from mining, post-mining, abandoned mines and flaring of drained methane.

1B1ai1

Mining

Includes all seam gas emissions vented to atmosphere from coal mine ventilation air and degasification systems.

1B1ai2

Post-mining seam gas emissions

Includes methane and CO2 emitted after coal has been mined, brought to the surface and subsequently processed, stored and transported.

1 B 1 a i.3

Abandoned underground mines

Includes methane emissions from abandoned underground mines

1 B 1 a i. 4

Flaring of drained methane or conversion of methane to CO2

Methane drained and flared, or ventilation gas converted to CO2 by an oxidation process should be included here. Methane used for energy production should be included in Volume 2, Energy, Chapter 2 ‘Stationary Combustion’.

1 B 1 a ii 1 B 1 a.ii 1

4.8

Includes all intentional and unintentional emissions from the extraction, processing, storage and transport of fuel to the point of final use.

Solid Fuels

1B 1 a i

AND TRANSPORT OF COAL

Includes all seam gas emissions arising from surface coal mining

Surface mines

Includes methane and CO2 emitted during mining from breakage of coal and associated strata and leakage from the pit floor and highwall

Mining

1 B 1 a.ii. 2

Post-mining seam gas emissions

Includes methane and CO2 emitted after coal has been mined, subsequently processed, stored and transported.

1B1b

Uncontrolled combustion and burning coal dumps

Includes emissions of CO2 from uncontrolled combustion due to coal exploitation activities.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions

4.1.2

Methodological issues

The following sections focus on methane emissions, as this gas is the most important fugitive emission for coal mining. CO2 emissions should also be included in the inventory where data are available. UND ERGROUND MINING Fugitive emissions from underground mining arise from both ventilation and degasification systems. These emissions are normally emitted at a small number of centralised locations and can be considered as point sources. They are amenable to standard measurement methods. SUR FAC E MINING For surface mining the emissions of greenhouse gases are generally dispersed over sections of the mine and are best considered area sources. These emissions may be the result of seam gases emitted through the processes of breakage of the coal and overburden, low temperature oxidation of waste coal or low quality coal in dumps, and uncontrolled combustion. Measurement methods for low temperature oxidation and uncontrolled combustion are still being developed and therefore estimation methods are not included in this chapter. A BANDONED M IN ES Abandoned underground mines present difficulties in estimating emissions, although a methodology for abandoned underground mines is included in this chapter. Methodologies do not yet exist for abandoned or decommissioned surface mines, and therefore they are not included in this chapter. M ETHAN E R EC OVERY AND U TILISA TION Methane recovered from drainage, ventilation air, or abandoned mines may be mitigated in two ways: (1) direct utilization as a natural gas resource or (2) by flaring to produce CO2, which has a lower greenhouse warming potential than methane. T I ER S Use of appropriate tiers to develop emissions estimates for coal mining in accordance with good practice depends on the quality of data available. For instance, if limited data are available and the category is not key, then Tier 1 is good practice. The Tier 1 approach requires that countries choose from a global average range of emission factors and use country-specific activity data to calculate total emissions. Tier 1 is associated with the highest level of uncertainty. The Tier 2 approach uses country- or basin-specific emission factors that represent the average values for the coals being mined. These values are normally developed by each country, where appropriate. The Tier 3 approach uses direct measurements on a mine-specific basis and, properly applied, has the lowest level of uncertainty.

4.1.3

Underground coal mines

The general form of the equation for estimating emissions for Tier 1 and 2 approaches, based on coal production activity data from underground coal mining and post-mining emissions is given by Equation 4.1.1 below. Methods to estimate emissions from abandoned underground mines, included in the guidelines for the first time, are described in detail in Section 4.1.5. Equation 4.1.1 represents emissions before adjustment for any utilisation or flaring of recovered gas: EQUATION 4.1.1 ESTIMATING EMISSIONS FROM UNDERGROUND COAL MINES FOR TIER 1 AND TIER 2 WITHOUT ADJUSTMENT FOR METHANE UTILISATION OR FLARING

Greenhouse gas emissions = Raw coal production●Emission Factor● Units conversion factor

The definition of the Emission Factor used in this equation depends on the activity data used. For Tier 1 and Tier 2, the Emission Factor for underground, surface and post-mining emissions has units of m3tonne-1, the same units as in situ gas content. This is because these Emission Factors are used with activity data on raw coal production which has mass units (i.e. tonnes). However, the Emission Factor and the in situ gas content are not

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy

the same and should not be confused. The Emission Factor is always larger than the in situ gas content, because the gas released during mining draws from a larger volume of coal and adjacent gas-bearing strata than simply the volume of coal produced. For abandoned underground mines, the Emission Factor has different units, because of the different methodologies employed, see section 4.1.5 for greater detail. The equation to be used along with Equation 4.1.1 in order to adjust for methane utilisation and flaring for Tier 1 and Tier 2 approaches is shown in Equation 4.1.2. EQUATION 4.1.2 ESTIMATING EMISSIONS FROM UNDERGROUND COAL MINES

FOR TIER 1 AND TIER 2 WITH ADJUSTMENT FOR METHANE UTILISATION OR FLARING

CH4 emissions from underground mining activities = Emissions from underground mining CH4 + Post-mining emission of CH4 – CH4 recovered and utilized for energy production or flared

Emissions from underground mines in equations 4.1.1 and 4.1.2 include abandoned mines (see section 4.1.5) and both go into the total for 1.B. 1.a.i (Underground mines). Equation 4.1.2 is used for Tiers 1 and 2 because they use Emission Factors to account for emissions from coal mines on a national or coal-basin level. The emission factors already include all the methane likely to be released from mining activities. Thus, any methane recovery and utilization must be explicitly accounted for by the subtraction term in Equation 4.1.2. Tier 3 methods involve mine-specific calculations which take into account the methane drained and recovered from individual mines rather than emission factors, and therefore Equation 4.1.2 is not appropriate for Tier 3 methods.

4.1.3.1

C HOICE

OF METHOD

UND ERGROUND MINING Figure 4.1.1 shows the decision tree for underground coal mining activities. For countries with underground mining, and where mine-specific measurement data are available it is good practice to use a Tier 3 method. Mine-specific data, based on ventilation air measurements and degasification system measurements, reflect actual emissions on a mine-by-mine basis, and therefore produce a more accurate estimate than using Emission Factors. Hybrid Tier 3 - Tier 2 approaches are appropriate in situations when mine-specific measurement data are available only for a subset of underground mines. For example, if only mines that are considered gassy report data, emissions from the remaining mines can be calculated with Tier 2 emission factors. The definition of what constitutes a gassy mine will be determined by each country. For instance, in the United States, gassy mines refers to coal mines with average annual ventilation emissions exceeding the range of 2 800 to 14 000 cubic meters per day. Emission factors can be based on specific emission rates derived from Tier 3 data if the mines are operating within the same basin as the Tier 3 mines, or on the basis of mine-specific properties, such as the average depth of the coal mines. When no mine-by-mine data are available, but country- or basin-specific data are, it is good practice to employ the Tier 2 method. Where no data (or very limited data) are available, it is good practice to use a Tier 1 approach, provided underground coal mining is not a key sub source category. If it is, then it is good practice to obtain emissions data to increase the accuracy of these emissions estimates (see Figure 4.1.1). P OS T- M INI N G Direct measurement (Tier 3) of all post-mining emissions is not feasible, so an emission factor approach must be used. The Tier 2 and Tier 1 methods described below represent good practice for this source, given the difficulty of obtaining better data. LOW TEMPERA TURE OX IDA TION Oxidation of coal when it is exposed to the atmosphere by coal mining releases CO2. This source will usually be insignificant when compared with the total emissions from gassy underground coal mines. Consequently, no methods are provided to estimate it. Where there are significant emissions of CO2 in addition to methane in the seam gas, these should be reported on a mine-specific basis.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions

A BANDONED UND ERGR OUND MINES Fugitive methane emissions from abandoned underground mines should be reported in Underground Mines in IPCC Category 1.B.1.a.i.3, using the methodology presented in Section in 4.1.5. Figure 4.1.1

Decision tree for underground coal mines

Start

Are mine-specific measurements available from all mines?

Estimate emissions using a Tier 3 method.

Yes

Box 1: Tier 3

No

Is underground mining a key category?

Are mine-specific data available for gassy mines?

Yes

Are basin-specific emission factors available?

No

Yes

No

Estimate emissions using Tier 1 methods. Box 2: Tier 1

Estimate emissions using a Tier 3 method for gassy mines with direct measurements and Tier 2 for mines without direct measurements.

Yes

No

Collect measurement data.

Estimate emissions using a Tier 2 method. Box 3: Tier 2

Box 4: Hybrid Tier 2/Tier 3

Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees

4.1.3.2

C HOICE

OF EMISSION FACTORS FOR UNDERGROUND MINES

MINING Tier 1 Emission Factors for underground mining are shown below. The emission factors are the same as those described in the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (BCTSRE, 1992; Bibler et al, 1991; Lama, 1992; Pilcher et al, 1991;USEPA, 1993a,b and Zimmermeyer, 1989). EQUATION 4.1.3 TIER 1: GLOBAL AVERAGE METHOD – UNDERGROUND MINING – BEFORE ADJUSTMENT FOR ANY METHANE UTILISATION OR FLARING

Ch4 emissions = CH4 Emission Factor ● Underground Coal Production ● Conversion Factor Where units are: Methane Emissions (Gg year-1) CH4 Emission Factor (m3 tonne-1) Underground Coal Production (tonne year-1)

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Emission Factor: Low CH4 Emission Factor

= 10 m3 tonne-1

Average CH4 Emission Factor

=18 m3 tonne-1

High CH4 Emission Factor

= 25 m3 tonne-1

Conversion Factor: This is the density of CH4 and converts volume of CH4 to mass of CH4. The density is taken at 20˚C and 1 atmosphere pressure and has a value of 0.67 ● 10-6 Gg m-3. Countries using the Tier 1 approach should consider country-specific variables such as the depth of major coal seams to determine the emission factor to be used. As gas content of coal usually increases with depth, the low end of the range should be chosen for average mining depths of 400 m the high value is appropriate. For intermediate depths, average values can be used. For countries using a Tier 2 approach, basin-specific emission factors may be obtained from sample ventilation air data or from a quantitative relationship that accounts for the gas content of the coal seam and the surrounding strata affected by the mining process, along with raw coal production. For a typical longwall operation, the amount of gas released comes from the coal being extracted and from any other gas-bearing strata that are located within 150 m above and 50 m below the mined seam (Good Practice Guidance, 2000). P OS T- M INI N G EMI SS ION S For a Tier 1 approach the post-mining emissions factors are shown below together with the estimation method: EQUATION 4.1.4 TIER 1: GLOBAL AVERAGE METHOD – POST-MINING EMISSIONS – UNDERGROUND MINES Methane emissions = CH4 Emission Factor ● Underground Coal Production ● Conversion Factor Where units are: Methane Emissions (Gg year-1) CH4 Emission Factor (m3 tonne-1) Underground Coal Production (tonne year-1) Emission Factor: Low CH4 Emission Factor

= 0.9 m3 tonne-1

Average CH4 Emission Factor

=2.5 m3 tonne-1

High CH4 Emission Factor

= 4.0 m3 tonne-1

Conversion Factor: This is the density of CH4 and converts volume of CH4 to mass of CH4. The density is taken at 20˚C and 1 atmosphere pressure and has a value of 0.67●10-6 Gg m-3. Tier 2 methods to estimate post-mining emissions take into account the in situ gas content of the coal. Measurements on coal as it emerges on a conveyor from an underground mine without degasification prior to mining indicate that 25-40 percent of the in situ gas remains in the coal (Williams and Saghafi, 1993). For mines that practice pre-drainage, the amount of gas in coal will be less than the in situ value by some unknown amount. For mines with no pre-drainage, but with knowledge of the in situ gas content, the post-mining emission factor can be set at 30 percent of the in situ gas content. For mines with pre-drainage, an emission factor of 10 percent of the in situ gas content is suggested. Tier 3 methods are not regarded as feasible for post-mining operations. EMISSIONS FROM DRAINED METHANE Methane drained from working (or abandoned) underground (or surface) coal mines can be vented directly to the atmosphere, recovered and utilised, or converted to CO2 through combustion (flaring or catalytic oxidation) without any utilisation. The manner of accounting for drained methane varies, depending on the final use of the methane.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions

In general: •

•

• •

•

Tier 1 represents an aggregate emissions estimate using emission factors. In general, it is not expected that emissions associated with drained methane would be applicable for Tier 1. Presumably, if methane were being drained, there would be better data to enable use of Tier 2 or even Tier 3 methods to make emissions estimates. However, Tier 1 has been included in the discussion below, in case Tier 1 methods are being used to estimate national emissions where there are methane drainage operations. When methane is drained from coal seams as part of coal mining and subsequently flared or used as a fuel, it is good practice to subtract this amount from the total estimate of methane emissions for Tier 1 and Tier 2 (Equation 4.1.2). Data on the amount of methane that is flared or otherwise utilised should be obtained from mine operators with the same frequency of measurement as pertains to underground mine emissions generally. For Tiers 1 and 2, if methane is drained and vented to the atmosphere rather than utilized, it should not be re-counted as it already forms part of the emissions estimates for these approaches. For Tier 3, methane recovered from degasification systems and vented to the atmosphere prior to mining should be added to the amount of methane released through ventilation systems so that the total estimate is complete. In some cases, because degasification system data are considered confidential, it may be necessary to estimate degasification system collection efficiency, and then subtract known reductions to arrive at the net degasification system emissions. All methane emissions associated with coal seam degasification related to coal mining activities should be accounted for in the inventory year in which the emissions and recovery operations occur. Thus, the total emissions from all ventilation shafts and from all degasification operations that emit methane to the atmosphere are reported for each year, regardless of when the coal seam is mined through, as long as the emissions are associated with mining activities. This represents a departure from the previous guidelines where the drained methane was accounted for in the year in which the coal seam was mined through.

When recovered methane is utilized as an energy source: • •

Any emissions resulting from use of recovered coal mine methane as an energy source should be accounted for based on its final end-use, for example in the Energy Volume, Chapter 2, ‘Stationary Combustion’ when used for stationary energy production. Where recovered methane from coal seams is fed into a gas distribution system and used as natural gas, the fugitive emissions are dealt with in the oil and natural gas source category (Section 4.2).

When recovered methane is flared: •

When the methane is simply combusted with no useful energy, as in flaring or catalytic oxidation to CO2, the corresponding CO2 production should be added to the total greenhouse gas emissions (expressed as CO2 equivalents) from coal mining activities. Such emissions should be accounted for as shown by Equation 4.1.5, below. Amounts of nitrous oxide and non-methane volatile organic compounds emitted during flaring will be small relative to the overall fugitive emissions and need not be estimated. EQUATION 4.1.5 EMISSIONS OF CO2 AND CH4 FROM DRAINED METHANE FLARED OR CATALYTICALLY OXIDISED (a)Emissions of CO2 from CH4 combustion = 0.98●Volume of methane flared ●Conversion Factor ● Stoichiometric Mass Factor (a)Emissions of unburnt methane = 0.02 ● Volume of methane flared ● Conversion Factor

Where units are: Emissions of CO2 from methane combustion (Gg year-1) Volume of methane oxidised (m3 year-1) Stoichiometric Mass Factor is the mass ratio of CO2 produced from full combustion of unit mass of methane and is equal to 2.75 Note: 0.98 represents the combustion efficiency of natural gas that is flared (Compendium of Greenhouse gas Emission Methodologies for the Oil and gas Industry, American Petroleum Institute, 2004) Conversion Factor: This is the density of CH4 and converts volume of CH4 to mass of CH4. The density is taken at 20˚C and 1 atmosphere pressure and has a value of 0.67●10-6 Gg m-3.

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4.1.3.3

C HOICE

OF ACTIVITY DATA

The activity data required for Tiers 1 and 2 are raw coal production. If the data on raw coal production are available these should be used directly. If coal is not sent to a coal preparation plant or washery for upgrading by removal of some of the mineral matter, then raw coal production equals the amount of saleable coal. Where coal is upgraded, some coal is rejected in the form of coarse discards containing high mineral matter and also in the form of unrecoverable fines. The amount of waste is typically around 20 percent of the weight of raw coal feed, but may vary considerably by country. Where activity data are in the form of saleable coal, an estimate should be made of the amount of production that is washed. Raw coal production is then estimated by increasing the amount of ‘saleable coal’ by the fraction lost through washing. An alternative approach that may be more suitable for mines whose raw coal output contains rock from the roof or floor as a deliberate part of the extraction process, is to use saleable coal data in conjunction with emission factors referenced to the clean fraction of the coal, not raw coal. This should be noted in the inventory. For the Tier 3 methods, coal production data are unnecessary because actual emissions measurements are available. However, it is good practice to collect and report these data to illustrate the relationship, if any, between underground coal production and actual emissions on an annual basis. High quality measurements of methane drained by degasification systems should also be available from mine operators for mines where drainage is practised. If detailed data on drainage rates are absent, good practice is to obtain data on the efficiency of the systems (i.e. the fraction of gas drained) or to make an estimate using a range (e.g. 30-50 percent, typical of many degasification systems). If associated mines have data available these may also be used to provide guidance. Annual total gas production records for previous years should be maintained; these records may be available from appropriate agencies or from individual mines. Where data on methane recovery from coal mines and utilisation are not directly available from mine operators, gas sales could be used as a proxy. If gas sales are unavailable, the alternative is to estimate the amount of utilised methane from the known efficiency specifications of the drainage system. Only methane that would have been emitted from coal mining activities should be considered as recovered and utilized. These emissions should be accounted for in Volume 2, Chapter 4, Section 4.2, ‘Fugitive emissions from oil and natural gas’, or if the emissions are combusted for energy, in Volume 2, Chapter 2 ‘Stationary Combustion’.

4.1.3.4

C OMPLETENESS

FOR UNDERGROUND COAL MINES

The estimate of emissions from underground mining should include: •

•

Drained gas produced from degasification systems

•

Post-mining emissions

•

Ventilation emissions

•

Estimates of volume of methane recovered and utilized or flared Abandoned underground coal mines (see Section 4.1.5 for methodological guidance)

These sub sources categories are included in the current Guidelines.

4.1.3.5

D EVELOPING

A CONSISTENT TIME SERIES

Comprehensive mine-by-mine (i.e. Tier 3) data may be available for some but not all years. If there have been no major changes in the number of active mines, emissions can be scaled to production for missing years, if any. If there were changes in the mine number, the mines involved can be removed from the scaling extrapolation and handled separately. However, care must be taken in scaling because the coal being mined, the virgin exposed coal and the disturbed mining zone each have different emission rates. Furthermore, mines may have a high background emission level that is independent of production. The inventory guidelines recommend that methane emissions associated with coal seam degasification related to mining should be accounted for in the inventory year in which the emissions and recovery operations occur. This is a departure from previous guidelines which suggested that the methane emissions or reductions only be accounted for during the year in which the coal was produced (e.g. the degasification wells were “mined through.”) Thus, if feasible, re-calculation of previous inventory years is desirable to make a consistent time series.

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Chapter 4: Fugitive Emissions

In cases where an inventory compiler moves from a Tier 1 or Tier 2 to a Tier 3 method, it may be necessary to calculate implied emissions factors for years with measurement data, and apply these emission factors to coal production for years in which these data do not exist. It is important to consider if the composition of the mine population has changed dramatically during the interim period, because this could introduce uncertainty. For mines that have been abandoned since 1990, data may not be archived if the company disappears. These mines should be treated separately when adjusting the time series for consistency. For situations where the emissions of greenhouse gases from active underground mines have been well characterized and the mines have passed from being considered 'active' to 'abandoned', care should be taken so as not to introduce major discontinuities in the total emissions record from coal mining.

4.1.3.6

U NCERTAINTY

ASSESSMENT

EMI SS ION FAC TOR UNC ER TAIN TI ES Em iss io n Fa ctor s fo r Tie r s 1 a nd 2 The major sources of uncertainty for a Tier 1 approach arise from two sources. These are: •

•

The applicability of global emission factors to individual countries Inherent uncertainties in the emission factors themselves

The uncertainty due to the first point above is difficult to quantify, but could be significant. The inherent uncertainty in the emission factor is also difficult to quantify because of natural variability within the same coal region is known to occur. For a Tier 2 approach, the same broad comments apply, although basin-specific data will reduce the inherent uncertainty in the Emission Factor compared with a Tier 1 approach. With regard to the inherent variability in the Emission Factor, ‘Expert Judgement’ in the Good Practice Guidance (2000) suggested that this was likely to be at least ±50 percent. Table 4.1.2 shows the Tier 1 and Tier 2 uncertainties associated with emissions from underground coal mining. The uncertainties for these Tiers are based on expert judgement. TABLE 4.1.2 ESTIMATES OF UNCERTAINTY FOR UNDERGROUND MINING FOR TIER 1 AND TIER 2 APPROACHES Likely uncertainties of coal mine methane Emission factors ( Expert judgment - GPG, 2000* ) Method

Mining

Post-Mining

Tier 2

± 50-75%

± 50%

Tier 1

Factor of 2 greater or smaller

Factor of 3 greater or smaller

*

GPG, 2000 IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (2000)

Tier 3 Methane emissions from underground mines have a significant natural variability due to variations in the rate of mining and drainage of gas. For instance, the gas liberated by longwall mining can vary by a factor of up to two during the life of a longwall panel. Frequent measurements of underground mine emissions can account for such variability and also reduce the intrinsic errors in the measurement techniques. As emissions vary over the course of a year due to variations in coal production rate and associated drainage, good practice is to collect measurement data as frequently as practical, preferably biweekly or monthly to smooth out variations. Daily measurements would ensure a higher quality estimate. Continuous monitoring of emissions represents the highest stage of emission monitoring, and is implemented in some modern longwall mines. Spot measurements of methane concentration in ventilation air are probably accurate to ±20 percent depending on the equipment used. Time series data or repeat measurements will significantly reduce the uncertainty of annual emissions to ±5 percent for continuous monitoring, and 10-15 percent for monitoring conducted every two weeks. Ventilation airflows are usually fairly accurately known (±2 percent). When combining the inaccuracies in emissions concentration measurements with the imprecision due to measurement and calculation of instantaneous measurements, overall emissions for an individual mine may be under-represented by as much as 10 percent or over-represented by as much as 30 percent (Mutmansky and Wang, 2000).

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Spot measurement of methane concentration in drained gas (from degasification systems) is likely to be accurate to ±2 percent because of its higher concentration. Measurements should be made with a frequency comparable to those for ventilation air to obtain representative sampling. Measured degasification flowrates are probably known to be ±5 percent. Degasification flowrates that are estimated based on gas sales are also likely to have an uncertainty of at least ±5 percent due to the tolerances in pipeline gas quality. For a single longwall operation, with continuous or daily emission measurements, the accuracy of monthly or annual average emissions data is probably ±5 percent. The accuracy of spot measurements performed every two weeks is ±10 percent, at 3-monthly intervals: ±30 percent. Aggregating emissions from mines based on the less frequent type of measurement procedures will reduce the uncertainty caused by fluctuations in gas production. However, as fugitive emissions are often dominated by contributions from only a small number of mines, it is difficult to estimate the extent of this improvement. The uncertainty estimates for underground mines are shown in Table 4.1.3. TABLE 4.1.3 ESTIMATES OF UNCERTAINTY FOR UNDERGROUND COAL MINING FOR A TIER 3 APPROACH Source Drainage gas

Ventilation gas

Details

Uncertainty

Reference

Spot measurements of CH4 for drainage gas

± 2%

Expert judgment (GPG, 2000* )

Degasification flows

± 5%

Expert judgment (GPG, 2000)

Continuous or daily measurements

± 5%

Expert judgment (GPG, 2000)

Spot measurements every 2 weeks

± 10%

Mutmansky and Wang, 2000

Spot measurements every 3 months

± 30%

Mutmansky and Wang, 2000

*

GPG, 2000 - IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (2000)

AC TIV I TY DA TA UNC ER TAIN TI ES Coal production: Country-specific tonnages are likely to be known to 1-2 percent, but if raw coal data are not available, then the uncertainty will increase to about ±5 percent, when converting from saleable coal production data. The data are also influenced by moisture content, which is usually present at levels between 5-10 percent, and may not be determined with great accuracy. Apart from measurement uncertainty, there can be further uncertainties introduced by the nature of the statistical databases that are not considered here. In countries with a mix of regulated and unregulated mines, activity data may have an uncertainty of ±10 percent

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions

4.1.4

Surface coal mining

The fundamental equation to be used in estimating emissions from surface mining is as shown in Equation 4.1.6. EQUATION 4.1.6 GENERAL EQUATION FOR ESTIMATING FUGITIVE EMISSIONS FROM SURFACE COAL MINING CH4 emissions = Surface mining emissions of CH4 + Post-mining emission of CH4

4.1.4.1

C HOICE

OF METHOD

It is not yet feasible to collect mine-specific Tier 3 measurement data for surface mines. The alternative is to collect data on surface mine coal production and use emission factors. For countries with significant coal production and multiple coal basins, disaggregation of data and emission factors to the coal basin level will improve accuracy. Given the uncertainty of production-based emission factors, choosing emission factors from the range specified within these guidelines can provide reasonable estimates for a Tier 1 approach. As with underground mining, direct measurement of post-mining emissions is infeasible so an emission factor approach is recommended. Tier 2 and Tier 1 methods should be reasonable for this source, given the difficulty of obtaining better data. Oxidation of coal in the atmosphere to produce CO2 is known to occur at surface mines, but emissions from this are not expected to be significant, especially taking into account the effects of rehabilitation of the waste dumps. Rehabilitation practices, which involve covering the dumps with topsoil and re-vegetation, act to reduce oxygen fluxes into the dump and hence reduce the rate of CO2 production. Uncontrolled combustion in waste piles is a feature for some surface mines. However, these emissions, where they occur, are extremely difficult to quantify and it is infeasible to include a methodology.

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Figure 4.1.2

Decision tree for surface coal mining

Start

Are country or coal basin specific emission factors available?

Yes

Use a Tier 2 method. Box 2: Tier 2

No

Is surface coal mining a key category?

No

Use a Tier 1 method. Box 1: Tier 1

Yes

Collect data to provide Tier 2 method. Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees

4.1.4.2

E MISSION

FACTORS FOR SURFACE MINING

Although measurements of methane emissions from surface mining are increasingly available, they are difficult to make and at present no routine widely applicable methods exist. Data on in situ gas contents before overburden removal are also scarce for many surface mining operations. The Tier 1 emission factors are shown together with the estimation method in Equation 4.1.7. EQUATION 4.1.7 TIER 1: GLOBAL AVERAGE METHOD – SURFACE MINES Methane emissions = CH4 Emission Factor ●Surface Coal Production ● Conversion Factor Where units are: Methane Emissions (Gg year-1) CH4 Emission Factor (m3 tonne-1) Surface Coal Production (tonne year-1) Emissions Factor: Low CH4Emission Factor

= 0.3 m3 tonne-1

Average CH4 Emission Factor

= 1.2 m3 tonne-1

High CH4 Emission Factor

= 2.0 m3 tonne-1

Conversion Factor:

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions

This is the density of CH4 and converts volume of CH4 to mass of CH4. The density is taken at 20˚C and 1 atmosphere pressure and has a value of 0.67 ● 10-6 Gg m-3. For the Tier 1 approach, it is good practice to use the low end of the specific emission range for those mines with average overburden depths of less than 25 meters and the high end for overburden depths over 50 meters. For intermediate depths, average values for the emission factors may be used. In the absence of data on overburden thickness, it is good practice to use the average emission factor, namely 1.2 m3/tonne. The Tier 2 method uses the same equation as for Tier 1, but with data disaggregated to the coal basin level. POST-M INING EMISSION S – SUR FACE MINING For a Tier 1 approach the post-mining emissions can be estimated using the emission factors shown in Equation 4.1.8. EQUATION 4.1.8 TIER 1: GLOBAL AVERAGE METHOD – POST-MINING EMISSIONS – SURFACE MINES Methane emissions = CH4 Emission Factor ● Surface Coal Production ● Conversion Factor Where units are: Methane Emissions (Gg year-1) CH4 Emission Factor (m3 tonne-1) Surface Coal Production (tonne year-1) Emission Factor: Low CH4 Emission Factor

= 0 m3 tonne-1

Average CH4 Emission Factor

= 0.1 m3 tonne-1

High CH4 Emission Factor

= 0.2 m3 tonne-1

Conversion Factor: This is the density of CH4 and converts volume of CH4 to mass of CH4. The density is taken at 20˚C and 1 atmosphere pressure and has a value of 0.67 ● 10-6 Gg m-3. The average emission factor should be used unless there is country-specific evidence to support use of the low or high emission factor.

4.1.4.3

A CTIVITY

DATA

As with underground coal mines, the activity data required for Tiers 1 and 2 are raw coal production. The comments relating to coal production data, made for Tier 1 and Tier 2 for underground mining in Section 4.1.3.3 also apply to surface mining.

4.1.4.4

C OMPLETENESS

FOR SURFACE MINING

The estimate of emissions from surface mining should include: •

•

•

Emissions during mining through the breaking of coal and from surrounding strata Post-mining emissions Waste pile/ overburden dump fires

At present only the first two sources above are taken into account. While there will be some emissions from low temperature oxidation, these are expected to be insignificant for this source.

4.1.4.5

D EVELOPING

A CONSISTENT TIME SERIES

There may be missing inventory data for surface mines for certain inventory years. If there have been no major changes in the number of active surface mines, emissions can be scaled to production for the missing years. If there were changes in the number of mines, the mines involved can be removed from the scaling extrapolation

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and handled separately. Where new mines have started production in new coalfields, it is important that the emissions applicable to these mines be assessed as each coal basin will have different characteristic in situ gas contents and emission rates. If coal seam degasification is practiced at surface mines, the methane should be estimated and reported in the inventory year in which the emissions and recovery operations occur.

4.1.4.6

U NCERTAINTY A SSESSMENT

IN EMISSIONS

EMI SS ION FAC TOR UNC ER TAIN TY Uncertainties in the emissions from surface mines are less well quantified than for underground mining. Briefly, the sources of the uncertainty are the same as described in Section 4.1.3.6 for underground coal mines. However, the variability in the emission factors for large surface mines may be expected to be greater than for underground coal mines, because surface mines can show significant variability across the extent of the mine as a result of local geological features. Table 4.1.4 shows the Tier 1 and Tier 2 uncertainties associated with surface mining emissions. ESTIMATES OF UNCERTAINTY FOR

TABLE 4.1.4 SURFACE MINING FOR TIER 1 AND TIER 2 APPROACHES

Likely Uncertainties of Coal Mine Methane Emission Factors for Surface Mining (Expert Judgement*) Method

Surface

Post-Mining

Tier 2

Factor of 2 greater or lower

± 50%

Tier 1

Factor of 3 greater or lower

Factor of 3 greater or lower

GPG, 2000 - IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (2000)

AC TIV I TY DA TA UNC ER TAIN TY The comments made for underground mining in Section 4.1.3.6 also apply to surface mining.

4.1.5

Abandoned underground coal mines

Closed, or abandoned, underground coal mines may continue to be a source of greenhouse gas emissions for some time after the mines have been closed or decommissioned. For the purpose of the emissions inventory, it is critical that each mine is classified in one and only one inventory database (e.g., active or abandoned). As abandoned mines appear in these guidelines for the first time, the Tier 1 and Tier 2 approaches are described in some detail. The Tier 1 and Tier 2 approaches presented below are largely based on an approach originally developed by the USEPA (Franklin et al, 2004) and have been adapted to be more globally applicable. It is anticipated that, where country-specific data exists for abandoned mines, the country-specific data will be used. The Tier 3 approach provides flexibility for use of mine-specific data. The Tier 3 methodology outlined below has been adapted from the USA methodology (Franklin et al 2004; US EPA 2004). Other relevant work has been sponsored by the UK (Kershaw, 2005), which provides another example of a Tier 3 approach.

4.1.5.1

C HOICE

OF METHOD

The fundamental equation for estimating emissions from abandoned underground coal mines is shown in Equation 4.1.9. EQUATION 4.1.9 GENERAL EQUATION FOR ESTIMATING FUGITIVE EMISSIONS FROM ABANDONED UNDERGROUND COAL MINES

CH4 emissions = Emissions from abandoned mines – CH4 emissions recovered

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Chapter 4: Fugitive Emissions

Developing emissions estimates from abandoned underground coal mines requires historical records. Figure 4.1.3 is a decision tree that shows how to determine which Tier to use. Tier 1 and 2 The two key parameters used to estimate abandoned mine emissions for each mine (or group of mines) are the time (in years) elapsed since the mine was abandoned, relative to the year of the emissions inventory, and emission factors that take into account the mine’s gassiness. If applicable and appropriate, methane recovery at specific mines can be incorporated for specific mines in a hybrid Tier 2 – Tier 3 approach (see below). • •

Tier 2 incorporates coal-type-specific information and narrower time intervals for abandonment of coal mines. Tier 1 includes default values and broader time intervals.

For a Tier 1 approach, the emissions for a given inventory year can be calculated from Equation 4.1.10. EQUATION 4.1.10 TIER 1 APPROACH FOR ABANDONED UNDERGROUND MINES Methane Emissions = Number of Abandoned Coal Mines remaining unflooded ● Fraction of gassy Coal Mines ● Emission Factor ● Conversion Factor Where units are: Methane Emissions (Gg year-1) Emission Factor (m3 year-1 ) Note: the Emission Factor has different units here compared with the definitions for underground, surface and post-mining emissions. This is because of the different method for estimating emissions from abandoned mines compared with underground or surface mining. This equation is applied for each time interval, and emissions from each time interval are added to calculate the total emissions. Conversion Factor: This is the density of CH4 and converts volume of CH4 to mass of CH4. The density is taken at 20˚C and 1 atmosphere pressure and has a value of 0.67●10-6 Gg m-3.

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Figure 4.1.3

Decision tree for abandoned underground coal mines Start

Are historical mine-specific emissions and/or physical characteristics available for gassy abandoned mines?

Yes

Estimate emissions using a Tier 3 method. Box 1: Tier 3

No

Are abandoned mines a key category?

Yes

Are emissions data available for at least some of the abandoned mines?

Yes

Estimate emissions using a Tier 3 method for mines with direct measurements and Tier 2 for those without. Box 2: Tier 2/3

No

No

Estimate emissions using a Tier 1 method.

Estimate emissions using a Tier 2 method.

Box 4: Tier 1

Box 3: Tier 2

Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees

Tier 3 The Tier 3 approaches (Franklin et al, 2004 and Kershaw, 2005) require mine-specific information such as ventilation emissions from the mine when active, characteristics of the mined coal seam, mine size and depth, and the condition of the abandoned mine (e.g., hydrologic status, flooding or flooded, and whether sealed or vented). Each country may generate its own profiles of abandoned mine emissions as a function of time (also known as emission decline curves) based on known national- or basin-specific coal properties, or it may use more generic curves based on coal rank, or measurements possibly in combination with mathematical modelling methods. If there are any methane recovery projects occurring at abandoned mines, data on these projects are expected to be available. A mine-specific Tier 3 methodology would be appropriate for calculating emissions from a mine that has associated methane recovery projects and could be incorporated as part of a hybrid approach with a national level Tier 2 emissions inventory. In general, the Tier 3 process for developing a national inventory of abandoned mine methane (AMM) emissions consists of the following steps: 1.

Creating a database of gassy abandoned coal mines.

2.

Identifying key factors affecting methane emissions: hydrologic (flooding) status, permeability mine condition (whether sealed or vented) and time elapsed since abandonment.

3.

Developing mine- or coal basin-specific emission rate decline curves, or equivalent models.

4.

Validating mathematical models through a field measurement programme.

5.

Calculating a national emissions inventory for each year.

6.

Adjusting for emissions reductions due to methane recovery and utilization.

7.

Determining the net total emissions.

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Chapter 4: Fugitive Emissions

Hybrid Approaches A combination of different Tier methodologies may be used to reflect the best data availability for different historical periods. For example, for a given country, emissions from mines abandoned in the distant past may need to be determined using a Tier 1 method. For that same country, it may be possible to determine emissions from mines abandoned more recently using a Tier 2 or 3 method if more accurate data are available. Fully Flooded Mines It is good practice to include mines that are known to be fully flooded in databases and other records used for inventory development, but they should be assigned an emission of zero as the emissions from such mines are negligible. Emissions Reductions through Recovery and Utilization In some cases, methane from closed or abandoned mines may be recovered and utilised or flared. Methane recovery from abandoned mines generally entails pumping which increases, or “accelerates”, the amount of methane recovered above the amount that would have been emitted had pumping not taken place. Under a mine-specific (Tier 3) approach in which emissions decline curves or models are used to estimate emissions, if emissions reductions are less than the projected emissions that would have occurred at the mine had recovery not taken place for a given year, then the emissions reductions from the recovery and utilization should be subtracted from the projected emissions to provide the net emissions. If the methane recovered and utilized in a given year exceeds the emission that would have occurred had recovery not taken place, then the net emissions from that mine for that year are considered to be zero. If a Tier 3 method is not used (singly or in combination with Tier 2), the total amount of methane recovered and utilized from abandoned mines should be subtracted from the total emissions inventory for abandoned mines, per Equation 4.1.9, subject to the reported emissions being no less than zero. The Tier 3 method should be used where suitable data are available.

4.1.5.2

C HOICE

OF EMISSION FACTORS

Tier 1 : G loba l Average Ap proa ch – Aban doned U nd erg rou nd M ines A Tier 1 approach for determining emissions from abandoned underground mines is described below and is largely based on methods developed by the USEPA (Franklin et al , 2004). It incorporates a factor to account for the fraction of those mines that, when they were actively producing coal, were considered gassy. Thus, this methodology is based on the total number of coal mines abandoned, adjusted for the fraction considered gassy, as described below. Abandoned mines that were considered non-gassy when they were actively mined are presumed to have negligible emissions. In the US methodology, the term gassy mines refers to coal mines that, when they were active, had average annual ventilation emissions that exceeded the range of 2 800 to 14 000 cubic meters per day (m3/d), or 0.7 to 3.4 Gg per year. The Tier 1 – approach for abandoned underground coal mines is as follows:

1.

Determine the approximate time (year interval) from the following time intervals when gassy coal mines were abandoned: a. b. c. d. e.

1901 – 1925 1926 – 1950 1951 – 1975 1976 – 2000 2001 - present

2.

Multiple intervals may be used where appropriate. It is recommended that the number of gassy coal mines abandoned during each time interval be estimated using the smallest time intervals possible based on available data. Ideally, for more recent periods, time intervals will decrease (e.g., intervals of ten years prior to 1990; annual intervals since 1990). Information for different coal mine-clusters abandoned during different time periods should be considered, since multiple time periods may be combined in the Tier 1 approach

3.

Estimate the total number of abandoned mines in each time band since 1901 remaining unflooded. If there is no knowledge on the extent of flooding it is good practice to assume that 100 percent of mines remain unflooded. For the purposes of estimating the number of abandoned mines, prospect excavations and hand cart mines of only a few acres in size should be disregarded.

4.

Determine the percentage of coal mines that would be considered gassy at the time of mine closure. Based on the time intervals selected above, choose an estimated percentage of gassy coal mines from

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the high and low default values listed in Table 4.1.5. Actual estimates can range anywhere from 0 to 100 percent. When choosing within the high and low default values listed in Table 4.1.5, a country should consider all available historical information that may contribute to the percentage of gassy mines, such as coal rank, gas content, and depth of mining. Countries with recorded instances of gassy mines (e.g., methane explosions or outbursts) should choose the high default values in the early part of the century. From 1926 to 1975, countries where mines were relatively deep and hydraulic equipment was used should choose the high default value. Countries with deep longwall mines or with evidence of gassiness should choose the high values for the time periods after 1975. The low range of the default values may be appropriate for a given time interval for specific regions, coal basins, or nations, based on geologic conditions or known mining practices. 5.

For the inventory year of interest (between 1990 and the present), select the appropriate emissions factor from Table 4.1.6. For example, for mines abandoned in the interval 1901 to 1925 and for the inventory reporting year 2005, the Emission Factor for these mines would have a value of 0.256 million m3 of methane per mine.

6.

Calculate for each time band the total methane emissions from Equation 4.1.10 to the inventory year of interest.

7.

Sum the emissions for each time interval to derive the total abandoned mine emissions for each inventory year. TABLE 4.1.5 TIER 1 – ABANDONED UNDERGROUND MINES DEFAULT VALUES - PERCENTAGE OF COAL MINES THAT ARE GASSY

4.24

Time Interval

Low

High

1900-1925

0%

10%

1926-1950

3%

50%

1950-1976

5%

75%

1976-2000

8%

100%

2001-Present

9%

100%

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Chapter 4: Fugitive Emissions

TABLE 4.1.6 TIER 1 – ABANDONED UNDERGROUND MINES EMISSION FACTOR, MILLION M3 METHANE / MINE Interval of mine closure Inventory Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

1901 – 1925

1926 – 1950

0.281 0.279 0.277 0.275 0.273 0.272 0.270 0.268 0.267 0.265 0.264 0.262 0.261 0.259 0.258 0.256 0.255 0.253 0.252 0.251 0.249 0.248 0.247 0.246 0.244 0.243 0.242

1951 - 1975

0.343 0.340 0.336 0.333 0.330 0.327 0.324 0.322 0.319 0.316 0.314 0.311 0.308 0.306 0.304 0.301 0.299 0.297 0.295 0.293 0.290 0.288 0.286 0.284 0.283 0.281 0.279

1976 – 2000

0.478 0.469 0.461 0.453 0.446 0.439 0.432 0.425 0.419 0.413 0.408 0.402 0.397 0.392 0.387 0.382 0.378 0.373 0.369 0.365 0.361 0.357 0.353 0.350 0.346 0.343 0.340

2001 – Present

1.561 1.334 1.183 1.072 0.988 0.921 0.865 0.818 0.778 0.743 0.713 0.686 0.661 0.639 0.620 0.601 0.585 0.569 0.555 0.542 0.529 0.518 0.507 0.496 0.487 0.478 0.469

NA NA NA NA NA NA NA NA NA NA NA 5.735 2.397 1.762 1.454 1.265 1.133 1.035 0.959 0.896 0.845 0.801 0.763 0.730 0.701 0.675 0.652

As abandoned underground mines are included for the first time an example calculation has been included in Table 4.1.7. TABLE 4.1.7 TIER 1 – ABANDONED UNDERGROUND MINES Example Calculation Interval of mine closure 1901 – 1925

1926 – 1950

1951 1975

1976 – 2000

2001 – Present

Number of mines closed per time band

20

15

10

5

1

Fraction of gassy mines

0.1

0.5

0.75

1.0

1.0

Emission factor for Inventory year, 2005 (from Table 4.1.6)

0.256

0.301

0.382

0.601

1.265

Total emissions (Gg CH4 per year from Eqn 4.1.10)

0.34

1.51

1.92

2.07

0.85

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Total for inventory year 2005

6.64

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T i e r 2 – C o u n t ry - o r Ba sin - S p e c if i c A p p ro a ch The Tier 2 approach for developing an abandoned mine methane emission inventory follows a similar approach to Tier 1, but it incorporates country- or basin-specific data. The methodology presented below is intended to utilize coal basin-specific or country-specific data wherever possible (for example, for active mine emissions prior to abandonment, for basin-specific parameters for emissions factors, etc.). In some cases, default parameters have been provided for these values but these should be used only if countryspecific or basin-specific data are not available. Calculate emissions for a given inventory year using Equation 4.1.11: EQUATION 4.1.11 TIER 2 APPROACH FOR ABANDONED UNDERGROUND MINES WITHOUT METHANE RECOVERY AND UTILIZATION

Methane Emissions = Number of Coal Mines Abandoned Remaining Unflooded ● Fraction of Gassy Mines ● Average Emissions Rate ● Emission Factor ● Conversion Factor Where units are: Emissions of methane (Gg year-1) Emission Rate (m3 year-1 ) Emission Factor (dimensionless, see Equation 4.1.11) Conversion Factor: This is the density of CH4 and converts volume of CH4 to mass of CH4. The density is taken at 20˚C and 1 atmosphere pressure and has a value of 0.67●10-6 Gg m-3 If individual mines are known to be completely flooded, they may be assigned an emissions value of zero. Methane emissions reductions due to recovery projects that utilize or flare methane at abandoned mines should be subtracted from the emissions estimate. For either of these cases, it is recommended that a hybrid Tier 2 – Tier 3 approach be used to incorporate such mine-specific information (see the discussion of methane recovery and utilization projects from abandoned mines, Sections 4.1.5.1 and 4.1.5.3). The basic steps in the Tier 2 approach for abandoned underground coal mines are as follows:

•

• • • •

•

•

Determine the approximate time interval(s) when significant numbers of gassy coal mines were closed. Multiple intervals may be used where appropriate. It is recommended that the number of gassy coal mines abandoned during each time interval be estimated using the smallest time intervals possible based on available data. Ideally, for more recent periods, time intervals will decrease (e.g., intervals of ten years prior to 1990; annual intervals since 1990). Estimate the total number of abandoned mines in each time interval selected remaining unflooded. If there is no available information on the flooded status of the abandoned mines, assume 100 percent remain unflooded. Determine the number (or percentage) of coal mines that would be considered gassy at the time of mine closure. For each time interval, determine the average emissions rate. If country or basin-specific data do not exist, low and high estimates for active mine emissions prior to abandonment can be selected from Table 4.1.8. For each time interval, calculate an appropriate emissions factor using Equation 4.1.12, based on the difference in years between the estimated data of abandonment and the year of the emissions inventory. Note that default values for this emissions factor equation are provided in Table 4.1.9, but these default values should be used only where country- or basin-specific information are not available. Calculate the emissions for each time interval using Equation 4.1.11. Sum the emissions for each time interval to derive the total abandoned mine emissions for each inventory year.

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Chapter 4: Fugitive Emissions

TABLE 4.1.8 TIER 2 – ABANDONED UNDERGROUND COAL MINES DEFAULT VALUES FOR ACTIVE MINE EMISSIONS PRIOR TO ABANDONMENT Parameter

Emissions, million m3/yr

Low

1.3

High

38.8

EQUATION 4.1.12 TIER 2 – ABANDONED UNDERGROUND COAL MINES EMISSION FACTOR Emission Factor = (1 + aT)b Where: a and b are constants determining the decline curve. Country or basin-specific values should be used wherever possible. Default values are provided in Table 4.1.9, below. T = years elapsed since abandonment (difference of the mid point of the time interval selected and the inventory year) and inventory year. A separate emission factor must be calculated for each time interval selected. This emission factor is dimensionless. TABLE 4.1.9 COEFFICIENTS FOR TIER 2 – ABANDONED UNDERGROUND COAL MINES

Coal Rank

A

b

Anthracite

1.72

-0.58

Bituminous

3.72

-0.42

Sub-bituminous

0.27

-1.00

T i e r 3 - M in e - S p e c if i c A p p ro a ch Tier 3 provides a great deal of flexibility. Directly measured emissions, where available, can be used in place of estimates and calculations. Models may be used in conjunction with measured data to estimate time series emissions. Each country may generate their own decline curves or other characterizations based on measurements, known basin-specific coal properties, and/or hydrological models. Equation 4.1.13 describes one possible, approach. EQUATION 4.1.13 EXAMPLE OF TIER 3 EMISSIONS CALCULATION – ABANDONED UNDERGROUND MINES Methane Emissions = (Emission rate at closure ● Emission Factor ● Conversion Factor) – Methane Emissions Reductions from Recovery and Utilisation Where units are: Methane Emissions (Gg year-1) Emission rate at Closure (m3 year-1) Emission Factor (dimensionless, see Franklin et al., 2004) Conversion Factor: This is the density of CH4 and converts volume of CH4 to mass of CH4. The density is taken at 20˚C and 1 atmosphere pressure and has a value of 0.67 ● 10-6 Gg m-3. •

The basic steps in the Tier 3 methodology involve the following: Determine a database of mine closures with relevant geological and hydrological information and the approximate abandonment dates (when all active mine ventilation ceased) consistently for all mines in the country’s inventory.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2: Energy •

•

•

Estimate emissions based on measured emissions and/or an emissions model. This may be based on the average emission rate at time of mine closure, determined by the last measured emission rate (or preferably, an average of several measurements taken the year prior to abandonment), or estimated methane reserves susceptible to release. If actual measurements have not been taken at a given mine, emissions may be calculated using an appropriate decline curve or modelling approach for openly vented mines, sealed mines, or flooded mines. Use the selected decline equation or modelling approach for the mine and the number of years between abandonment and the inventory year to calculate emissions or an appropriate emission factor for each mine. Sum abandoned mine emissions to develop an annual inventory.

4.1.5.3

C HOICE

OF ACTIVITY DATA

Estimating emissions from abandoned mines requires historical data, rather than current activity data. For Tier 1, country experts should estimate the number of mines abandoned by time interval in Table 4.1.5, on the basis of historical data available from appropriate national international agencies or regional experts. For Tier 2, the total number of abandoned mines and the time period of their abandonment are required. These data may be obtained from appropriate national, state, or provincial agencies, or companies active in the coal industry. If a country consists of more than one coal region or basin, production and emissions data may be disaggregated by region. Expert judgment and statistical analysis may be used to estimate ventilation emissions or specific emissions based on measurements from a limited number of mines (see Franklin et al (2004)). For Tier 3, abandoned coal mine emissions estimates should be based on detailed data about the characteristics, data of abandonment and geographical location of individual mines. In the absence of direct measurements of the abandoned mine, Tier 3 emissions factors may be based on mine-specific emissions data, including historical emissions data from degasification and ventilation systems when the mine(s) were active (see Franklin et al, 2004). EMISSIONS R EDUCTIONS FROM M ETHAN E R ECOVERY A T ABANDON ED M IN ES Abandoned mines where recovery and utilisation or flaring of abandoned mine methane is taking place should be accounted for by comparing the amount of methane recovered and utilized with the amount expected to have been emitted naturally. The method for accounting for methane recovered from abandoned coal mines is described in Section 4.1.5.1. The CO2 emissions produced from combustion of methane from abandoned mine recovery and utilization projects should be included in the energy sector estimates where there is utilisation, or under fugitive abandoned mine emissions where there is flaring. To make this estimate, abandoned mine methane project recovery or production data may be publicly available through appropriate government agencies depending on the end use. This information may be in the form of metered gas sales and is often publicly available in oil and gas industry or governmental databases. An additional 3 to 8 percent of undocumented abandoned mine methane is typically recovered and used as fuel for compression of the gas. The actual percentage of methane used will depend on the efficiency of the compression equipment. The emissions from this energy use should be reported under Volume 2, Chapter 2 ‘Stationary Combustion’. For projects that use recovered methane from abandoned mines for electricity generation, metered flow rates and compression factors, if available, can be used. If public data accurately reflect electricity produced, then the heat rate or efficiency of the electricity generator can be used to determine its fuel consumption rate.

4.1.5.4

C OMPLETENESS

The emissions estimates from abandoned underground mines should include all emissions leaking from the abandoned mines. Until recently, there were no methods by which these emissions could be estimated. Good practice is to record the date of mine closure and the method of sealing. Data on the size and depth of such mines would be useful for any subsequent estimation.

4.1.5.5

D EVELOPING

A CONSISTENT TIME SERIES

It is unlikely that comprehensive mine-by-mine (Tier 3) data will be available for all years. Therefore, in order to prepare hybrid Tier 2 – Tier 3 inventories, as well as Tier 1 or Tier 2 inventories, the number of abandoned mines may need to be estimated for years for which there are sparse data.

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These inventory guidelines recommend that methane emissions associated with abandoned mines should be accounted for in the inventory year in which the emissions and recovery operations occur. For situations where the emissions of greenhouse gases from active underground mines have been well characterized and the mines have passed from being considered ‘active’ to ‘abandoned’, data from the active mine emissions (during the year in which the mine was closed) should be collected. Great care should be taken in transferring mines from the active to the abandoned inventory so that no double-counting or omissions occur.

4.1.5.6

U NCERTAINTY

ASSESSMENT

TIER 1 The primary causes of the uncertainty related to the Tier 1 methodology include the following: • • •

The global nature of the emission factors. The range of uncertainty of these emission factors is intentionally large to account for the uncertainty in the determining parameters such as mine size, mine depth, and coal rank. Time of abandonment. Because emissions from abandoned mines are strongly time dependent, selecting a single interval that best represents the dates of closure for all mines is critical in establishing an emissions rate. The activity data. Both the number of gassy abandoned mines and the amount of coal that has been produced from gassy mines are strongly country-dependent. The uncertainty will be defined by the availability of historic mining and production records.

The total estimated range of uncertainty associated with Tier 1 estimations will depend on each of the factors discussed above. Actual emissions are likely to be in the range of one-third to three times the estimated emissions value. TIER 2 The primary causes of uncertainty related to the Tier 2 approaches include the following: •

•

•

•

The country- or basin-specific emission factors. Uncertainty is associated with the emission factor decline equations for each coal rank. This uncertainty is a function of the inherent variability of gas content, adsorption characteristics, and permeability within a given coal rank. The number of mines producing a given coal rank. The number of mines abandoned through time. The percentage of gassy mines as a function of time.

The total estimated uncertainty associated with Tier 2 estimations depends on the range of uncertainty associated with each of these factors. These parameters should be more narrowly defined than for Tier 1. Thus, total actual emissions are likely to be in the range of one-half to twice the estimated value. TIER 3 The primary uncertainties associated with emissions inventories generated using the Tier 3 methodology include the following: •

•

•

Active mine emission rate Decline curve equation or modelling approach that describes the function relating adsorption characteristics and gas content of the coal, mine size, and coal permeability Hydrological status of the abandoned mine (flooded or flooding) and condition (sealed or vented).

The Tier 3 methodology has lower associated uncertainty than Tiers 1 and 2 because the emissions inventory is based either on direct measurements or on mine-specific information including active emission rates and mine closure dates. Although the range of uncertainty associated with estimated emissions from an individual mine may be large (in the ±50 percent range), summing the uncertainty range of a sufficient number of individual mine emissions actually reduces the range of uncertainty of the final inventory, per the central limits theorem (Murtha, 2002), provided the uncertainties are independent. Given the expected range of the number of abandoned coal mines across different countries, the overall uncertainty associated with Tier 3 methodology for

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abandoned mines may vary from ±20 percent for countries with a large number of abandoned mines to ±30 percent for a country with a fewer number of abandoned mines whose emissions are included in the inventory. A combination of different Tiers may be used. For example, the emissions from mines abandoned during the first half of the twentieth century may be determined using a Tier 1 method, while emissions from mines abandoned after 1950 may be determined using a Tier 2 method. The Tier 1 and Tier 2 methods will each have their own uncertainty distribution. It is important to properly sum these distributions in order to arrive at the appropriate range of uncertainty for the final emissions inventory.

4.1.6

Completeness for coal mining

There are three remaining gaps in developing a complete inventory for fugitive emissions from coal mining. These are abandoned surface mines, uncontrolled combustion and CO2 in coal seam gas. A BANDONED SURFAC E M IN ES After closure, emissions from abandoned surface mines may include the following: •

•

•

•

The standing highwall Leakage from the pit floor Low temperature oxidation Uncontrolled combustion

At present, no comprehensive methods to quantify these emissions have been developed and therefore they have not been included in these guidelines. They remain subjects for further research. EMISSIONS FROM UNCON TR OLLED COM BUSTION AND BURN ING C OA L D EP OSI T S While emissions from this source may be significant for an individual coal mine, it is unclear as to how significant these emissions may be for an individual country. In some countries where such fires are widespread, the emissions may be very significant. There are no clear methods available at present to systematically measure or precisely estimate the activity data, though where countries have data on amounts of coal burned, the CO2 should be estimated on the basis of the carbon content of the coal and reported in the relevant subcategory of 1.B.1.b. It is noted that uncontrolled combustion only due to coal exploration activities is considered here. Care should be taken to avoid double counting with fugitive CH4 and low oxidation CO2 emissions. CO 2 IN C OA L M IN E GA S Countries with data available on CO2 in their coal mine gas should include it with the sub-category used for the corresponding methane emissions.

4.1.7

Inventory Quality Assurance/Quality Control (QA/QC)

4.1.7.1

Q UALITY C ONTROL

AND

D OCUMENTATION

EMI SS ION FAC TORS •

Quality control a)

Tier 1: reviewing the national circumstances and documenting the rationale for selecting specific values.

b) Tier 2: checking the equations and calculations used to determine the emissions factor, and ensuring that sampling follows consistent protocols so that conditions are representative and uniform c)

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Tier 3: working with mine operators to ensure the quality of data from degasification systems. Individual operating mines should already have in place QA/QC procedures for monitoring ventilation emissions.

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Documentation Provide transparent information on the steps to calculate emissions factors or measure emissions, including the numbers and the sources of any data collected.

AC TIV I TY DA TA •

•

Quality control Describe activity data collection methods, including an assessment of areas requiring improvement. Documentation a)

Comprehensive description of the methods used to collect the activity data

b)

Discussion of potential areas of bias in the data, including a discussion of whether the characteristics are representative of the country

INVEN TORY C OMPILER R EVIEW (QA) The inventory compiler should ensure that suitable methodologies are used to calculate emissions from coal mining, including use of the highest applicable Tier for a given country, taking into account what are considered key category for that country as well as the availability of data. The inventory compiler should ensure that appropriate emission factors are used. For active underground and surface mines, the best available activity data should be used in accordance with the appropriate Tiers, especially the amount of methane recovered and utilized wherever possible. For abandoned mines, the compiler should ensure the most accurate available historical information is used. I NVEN TORY C OMP ILER QC ON C OMP ILI NG NA TIONA L EM IS SI ONS Methods the inventory compiler can employ to provide quality control for the national inventory may include, for example: •

•

Back-calculating national and regional emission factors from Tier 3 measurement data, where applicable

•

Ensuring that all mines are included

•

Ensuring that emission factors are representative of the country (for Tier 1 and Tier 2)

Comparing with national trends to look for anomalies

EX TERNAL INV ENTORY QUALITY ASSURANC E (QA /QC) SYSTEMS The inventory compiler should arrange for an independent, objective review of calculations, assumptions, and/or documentation of the emissions inventory to be performed to assess the effectiveness of the QC programme. The peer review should be performed by expert(s) who are familiar with the source category and who understand inventory requirements.

4.1.7.2

R EPORTING

AND DOCUMENTATION

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, chapter 8 of the 2006 IPCC Guidelines. The national inventory report should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced. However, to ensure transparency, the following information should be supplied: •

• •

Emissions by underground, surface, and post-mining components of CH4 and CO2 (where appropriate), the method used for each of the sub-source categories, the number of active mines in each sub-source category and the reasons for the chosen emission factors (e.g. depth of mining, data on in situ gas contents etc.). The amount of drained gas and the degree of any mitigation or utilisation should be presented with a description of the technology used, where appropriate. Activity data: Specify the amount and type of production, underground and surface coal, listing raw and saleable amounts where available. Where issues of confidentiality arise, the name of the mine need not be disclosed. Most countries will have more than three mines, so mine-specific production cannot be back calculated from the emission estimates.

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It is important to ensure that in the transition of mines from ‘active’ to ‘abandoned’ each mine is included once and only once in the national inventory.

4.2

FUGITIVE EMISSIONS FROM OIL AND NATURAL GAS SYSTEMS

Fugitive emissions from oil and natural gas systems are accounted for in IPCC subcategory 1.B.2 of the energy sector. For reporting purposes, this subcategory is subdivided as shown in Table 4.2.1. The main distinction is made between oil and natural gas systems, with each being subdivided according to the primary type of emissions source, namely: venting, flaring and all other types of fugitive emissions. The latter category is further subdivided into the different parts (or segments) of the oil or gas system according to the type of activity. The term fugitive emissions is broadly applied here to mean all greenhouse gas emissions from oil and gas systems except contributions from fuel combustion. Oil and natural gas systems comprise all infrastructure required to produce, collect, process or refine and deliver natural gas and petroleum products to market. The system begins at the well head, or oil and gas source, and ends at the final sales point to the consumer. Emissions excluded from this category are as follows: • •

• •

Fuel combustion for the production of useful heat or energy by stationary or mobile sources (see Chapters 2 and 3 of the Energy Volume). Fugitive emissions from carbon capture and storage projects, the transport and disposal of acid gas from oil and gas facilities by injection into secure underground formations, or the transport, injection and sequestering of CO2 as part of enhanced oil recovery (EOR), enhanced gas recovery (EGR) or enhanced coal bed methane (ECBM) projects (see Chapter 5 of the Energy Volume on carbon dioxide capture and storage systems). Fugitive emissions that occur at industrial facilities other than oil and gas facilities, or that are associated with the end use of oil and gas products at anything other than oil and gas facilities (see the Industrial Processes and Product Use Volume). Fugitive emissions from waste disposal activities that occur outside the oil and gas industry (see the Waste Volume).

Fugitive emissions from the oil and gas production portions of EOR, EGR and ECBM projects are part of Category 1.B.2. When determining fugitive emissions from oil and natural gas systems it may, primarily in the areas of production and processing, be necessary to apply greater disaggregation than is shown in Table 4.2.1 to account better for local factors affecting the amount of emissions (i.e., reservoir conditions, processing/treatment requirements, design and operating practices, age of the industry, market access, regulatory requirements and the level of regulatory enforcement), and to account for changes in activity levels in progressing through the different parts of the system. The percentage contribution by each category in Table 4.2.1 to total fugitive emissions by the oil and gas sector will vary according to a country’s circumstances and the amount of oil and gas imported and exported. Typically, production and processing activities tend to have greater amounts of fugitive emissions as a percentage of throughput than downstream activities. Some examples of the potential distribution of fugitive emissions by subcategory are provided in the API (2004) Compendium.

4.2.1

Overview, description of sources

The sources of fugitive emissions on oil and gas systems include, but are not limited to, equipment leaks, evaporation and flashing losses, venting, flaring, incineration and accidental releases (e.g., pipeline dig-ins, well blow-outs and spills). While some of these emission sources are engineered or intentional (e.g., tank, seal and process vents and flare systems), and therefore relatively well characterised, the quantity and composition of the emissions is generally subject to significant uncertainty. This is due, in part, to the limited use of measurement systems in these cases, and where measurement systems are used, the typical inability of these to cover the wide range of flows and variations in composition that may occur. Even where some of these losses or flows are tracked as part of routine production accounting procedures, there are often inconsistencies in the activities which get accounted for and whether the amounts are based on engineering estimates or measurements. Throughout this chapter, an effort is made to state the precise type of fugitive emission source being discussed,

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Chapter 4: Fugitive Emissions

and to only use the term fugitive emissions or fugitive emission sources when discussing these emissions or sources at a higher, more aggregated, level. Streams containing pure or high concentrations of CO2 may occur at oil production facilities where CO2 is being injected into an oil reservoir for EOR, ECBM or EGR. They may also occur at gas processing, oil refining and heavy oil upgrading facilities as a by-product of gas treating to meet sales or fuel gas specifications, and at refineries and heavy oil upgraders as a by-product of hydrogen production. Where CO2 occurs as a process byproduct it is usually vented to the atmosphere, injected into a suitable underground formation for disposal or supplied for use in EOR projects. Fugitive CO2 emissions from these streams should be accounted for under the appropriate subcategories of 1.B.2. Fugitive CO2 emissions from CO2 capture should be accounted for in the industry where capture occurs, while the fugitive CO2 emissions from transport, injection and storage activities should be accounted for separately in category 1.C (refer to Chapter 5). EOR is the recovery of oil from a reservoir by means other than using the natural reservoir pressure. It can begin after a secondary recovery process or at any time during the productive life of an oil reservoir. EOR generally results in increased amounts of oil being removed from a reservoir in comparison to methods using natural pressure or pumping alone. The three major types of enhanced oil recovery operations are chemical flooding (alkaline flooding or micellar-polymer flooding), miscible displacement (CO2 injection or hydrocarbon injection), and thermal recovery (steamflood or in-situ combustion). TABLE 4.2.1 DETAILED SECTOR SPLIT FOR EMISSIONS FROM PRODUCTION AND TRANSPORT OF OIL AND NATURAL GAS IPCC code 1B2

Sector name

Explanation

Oil and Natural Gas

Comprises fugitive emissions from all oil and natural gas activities. The primary sources of these emissions may include fugitive equipment leaks, evaporation losses, venting, flaring and accidental releases.

Oil

Comprises emissions from venting , flaring and all other fugitive sources associated with the exploration, production, transmission, upgrading, and refining of crude oil and distribution of crude oil products.

1B2ai

Venting

Emissions from venting of associated gas and waste gas/vapour streams at oil facilities

1 B 2 a ii

Flaring

Emissions from flaring of natural gas and waste gas/vapour streams at oil facilities

1 B 2 a iii

All Other

Fugitive emissions at oil facilities from equipment leaks, storage losses, pipeline breaks, well blowouts, land farms, gas migration to the surface around the outside of wellhead casing, surface casing vent bows, biogenic gas formation from tailings ponds and any other gas or vapour releases not specifically accounted for as venting or flaring

Exploration

Fugitive emissions (excluding venting and flaring) from oil well drilling, drill stem testing, and well completions

1B2a

1 B 2 a iii 1

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TABLE 4.2.1(CONTINUED) DETAILED SECTOR SPLIT FOR EMISSIONS FROM PRODUCTION AND TRANSPORT OF OIL AND NATURAL GAS IPCC code

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Sector name

Explanation

1 B 2 a iii 2

Production and Upgrading

Fugitive emissions from oil production (excluding venting and flaring) occur at the oil wellhead or at the oil sands or shale oil mine through to the start of the oil transmission system. This includes fugitive emissions related to well servicing, oil sands or shale oil mining, transport of untreated production (i.e , well effluent, emulsion, oil shale and oilsands) to treating or extraction facilities, activities at extraction and upgrading facilities, associated gas re-injection systems and produced water disposal systems. Fugitive emissions from upgraders are grouped with those from production rather than those from refining since the upgraders are often integrated with extraction facilities and their relative emission contributions are difficult to establish. However, upgraders may also be integrated with refineries, cogeneration plants or other industrial facilities and their relative emission contributions can be difficult to establish in these cases

1 B 2 a iii 3

Transport

Fugitive emissions (excluding venting and flaring) related to the transport of marketable crude oil (including conventional, heavy and synthetic crude oil and bitumen) to upgraders and refineries. The transportation systems may comprise pipelines, marine tankers, tank trucks and rail cars. Evaporation losses from storage, filling and unloading activities and fugitive equipment leaks are the primary sources of these emissions

1 B 2 a.iii 4

Refining

Fugitive emissions (excluding venting and flaring) at petroleum refineries. Refineries process crude oils, natural gas liquids and synthetic crude oils to produce final refined products (e.g., primarily fuels and lubricants). Where refineries are integrated with other facilities (for example, upgraders or co-generation plants) their relative emission contributions can be difficult to establish.

1 B 2 a iii 5

Distribution of Oil Products

This comprises fugitive emissions (excluding venting and flaring) from the transport and distribution of refined products, including those at bulk terminals and retail facilities. Evaporation losses from storage, filling and unloading activities and fugitive equipment leaks are the primary sources of these emissions

1 B 2 a iii 6

Other

Fugitive emissions from oil systems (excluding venting and flaring) not otherwise accounted for in the above categories. This includes fugitive emissions from spills and other accidental releases, waste oil treatment facilities and oilfield waste disposal facilities

1B2b

Natural Gas

Comprises emissions from venting, flaring and all other fugitive sources associated with the exploration, production, processing, transmission, storage and distribution of natural gas (including both associated and non-associated gas).

1B2bi

Venting

Emissions from venting of natural gas and waste gas/vapour streams at gas facilities

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TABLE 4.2.1(CONTINUED) DETAILED SECTOR SPLIT FOR EMISSIONS FROM PRODUCTION AND TRANSPORT OF OIL AND NATURAL GAS IPCC code

Sector name

Explanation

1 B 2 b ii

Flaring

Emissions from flaring of natural gas and waste gas/vapour streams at gas facilities.

1 B 2 b iii

All Other

Fugitive emissions at natural gas facilities from equipment leaks, storage losses, pipeline breaks, well blowouts, gas migration to the surface around the outside of wellhead casing, surface casing vent bows and any other gas or vapour releases not specifically accounted for as venting or flaring.

1B 2 b iii 1

Exploration

Fugitive emissions (excluding venting and flaring) from gas well drilling, drill stem testing and well completions

1B 2 b iii 2

Production

Fugitive emissions (excluding venting and flaring) from the gas wellhead through to the inlet of gas processing plants, or, where processing is not required, to the tie-in points on gas transmission systems. This includes fugitive emissions related to well servicing, gas gathering, processing and associated waste water and acid gas disposal activities

1 B 2 b iii 3

Processing

Fugitive emissions (excluding venting and flaring) from gas processing facilities

1 B 2 b iii 4

Transmission and Storage

Fugitive emissions from systems used to transport processed natural gas to market (i.e., to industrial consumers and natural gas distribution systems). Fugitive emissions from natural gas storage systems should also be included in this category. Emissions from natural gas liquids extraction plants on gas transmission systems should be reported as part of natural gas processing (Sector 1.B.2.b.iii.3). Fugitive emissions related to the transmission of natural gas liquids should be reported under Category 1.B.2.a.iii.3

1 B 2 b iii 5

Distribution

Fugitive emissions (excluding venting and flaring) from the distribution of natural gas to end users

1 B 2 b iii 6

Other

Fugitive emissions from natural gas systems (excluding venting and flaring) not otherwise accounted for in the above categories. This may include emissions from well blowouts and pipeline ruptures or dig-ins

1B3

Other emissions from Energy Production

Emissions from geo thermal energy production and other energy production not included in 1.B.1 or 1.B.2

4.2.2

Methodological issues

Fugitive emissions are a direct source of greenhouse gases due to the release of methane (CH4) and formation carbon dioxide (CO2) (i.e., CO2 present in the produced oil and gas when it leaves the reservoir), plus some CO2 and nitrous oxide (N2O) from non-productive combustion activities (primarily waste gas flaring). As is done for fuel combustion (see Chapter 1 of this Volume), CO2 emissions are calculated in Tier 1 assuming that all hydrocarbons are fully oxidized. If information is available on partial oxidation, this can be taken into account in higher Tiers. Venting comprises all engineered or intentional discharges of waste gas streams and process by-products to the atmosphere, including emergency discharges. These releases may occur on either a continuous or intermittent basis, and may include the following: •

•

•

Use of pressurized natural gas instead of compressed air as the supply medium for pneumatic devices (e.g., chemical injection pumps, starter motors on compressor engines and instrument control loops). Pressure relief and disposal of off-specification product during process upsets. Purging and blowdown events related to maintenance and tie-in activities.

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• • •

Disposal of off-gas streams from oil and gas treatment units (e.g., still-column off-gas from glycol dehydrators, emulsion treater overheads and stabilizer overheads). Gas releases from drilling, well-testing and pipeline pigging activities. Disposal of waste associated gas at oil production facilities and casing-head gas at heavy oil wells where there is no gas conservation or re-injection. Solution gas emissions from storage tanks, evaporation losses from process sewers, API separators, dissolved air flotation units, tailings ponds and storage tanks, and biogenic gas formation from tailings ponds. Discharge of CO2 extracted from the produced natural gas or produced as a process byproduct.

Some or all of the vented gas may be captured for storage or utilization. In this instance, the inventory of vented emissions should include only the net emissions to the atmosphere. Flaring means broadly all burning of waste natural gas and hydrocarbon liquids by flares or incinerators as a disposal option rather than for the production of useful heat or energy. The decision on whether to vent or flare depends largely on the amount of gas to be disposed of and the specific circumstances (e.g., public, environmental and safety issues as well as local regulatory requirements). Normally, waste gas is only vented if it is non-odourous and non-toxic, and even then may often be flared. Flaring is most common at production, processing, upgrading and refining facilities. Waste gas volumes are usually vented on gas transmission systems and may be either vented or flared on gas distribution systems, depending on the circumstances and the company’s policies. Sometimes fuel gas may be used to enrich a waste gas stream; so it will support stable combustion during flaring. Fuel gas may also be used for other purposes where it may ultimately be vented or flared, such as purge or blanket gas and supply gas for gas-operated devices (e.g., for instrument controllers). The emissions from these types of fuel uses should be reported under the appropriate venting and flaring subcategories rather than under Category 1.A (Fuel Combustion Activities). Formation CO2 removed from natural gas by the sweetening units at gas processing plants and released to the atmosphere is a fugitive emission and should be reported under subcategory 1.B.2.b.i. The CO2 resulting from the production of hydrogen at refineries and heavy oil/bitumen upgraders should be reported under subcategory 1.B.2.a.i. Care should be taken to ensure that the feedstock for the hydrogen plant is not also reported as fuel in these cases. Fugitive emissions from oil and natural gas systems are often difficult to quantify accurately. This is largely due to the diversity of the industry, the large number and variety of potential emission sources, the wide variations in emission-control levels and the limited availability of emission-source data. The main emission assessment issues are: •

•

•

The use of simple production-based emission factors introduces large uncertainty; The application of rigorous bottom-up approaches requires expert knowledge and detailed data that may be difficult and costly to obtain; Measurement programmes are time consuming and very costly to perform.

If a rigorous bottom-up approach is chosen, then it is good practice to involve technical representatives from the industry in the development of the inventory.

4.2.2.1

C HOICE

OF METHOD , DECISION TREES , TIERS

There are three methodological tiers for determining fugitive emissions from oil and natural gas systems, as set out in Section 4.2.2.2. It is good practice to disaggregate the activities into Major Categories and Subcategories in the Oil and Gas Industry (see Table 4.2.2 in Section 4.2.2.2), and then evaluate the emissions separately for each of these. The methodological tier applied to each segment should be commensurate with the amount of emissions and the available resources. Consequently, it may be appropriate to apply different methodological tiers to different categories and subcategories, and possibly even include actual emission measurement or monitoring results for some larger sources. The overall approach, over time, should be one of progressive refinement to address the areas of greatest uncertainty and consequence, and to capture the impact of control measures. Figure 4.2.1 provides a general decision tree for selecting an appropriate approach for a given segment of the natural gas industry. The decision tree is intended to be applied successively to each subcategory within the

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natural gas system (e.g., gas production, then gas processing, then gas transmission, then gas distribution). The basic decision process is as follows: •

• • •

check if the detailed data needed to apply a Tier 3 approach are readily available, and if so, then apply a Tier 3 approach (i.e., regardless of whether the category is key and the subcategory is significant), otherwise, if these data are not readily available: check if the detailed data needed to apply a Tier 2 approach are readily available, and if so, then apply a Tier 2 approach, otherwise, if these data are not readily available: check to see if the category is key and the specific subcategory being considered is significant based on the IPCC definitions of key and significant, and if so, go back and gather the data needed to apply a Tier 3 or Tier 2 approach, otherwise, if the subcategory is not significant: apply a Tier 1 approach.

The ability to use a Tier 3 approach will depend on the availability of detailed production statistics and infrastructure data (e.g., information regarding the numbers and types of facilities and the amount and type of equipment used at each site), and it may not be possible to apply it under all circumstances. A Tier 1 approach is the simplest method to apply but is susceptible to substantial uncertainties and may easily be in error by an order-of-magnitude or more. For this reason, it should only be used as a last resort option. Where a Tier 3 approach is used in one year and the results are used to develop Tier 2 emission factors for use in other years, the applied methodology should be reported as Tier 2 in those other years. Similarly, Figures 4.2.2 and 4.2.3 apply to crude oil production and transport systems, and to oil upgraders and refineries, respectively. Where a country has estimated fugitive emissions from oil and gas systems based on a compilation of estimates reported by individual oil and gas companies, this may either be a Tier 2 or Tier 3 approach, depending on the actual approaches applied by individual companies and facilities. In both cases, care needs to be taken to ensure there is no omitting or double counting of emissions.

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Figure 4.2.1

Decision tree for natural gas systems Start

Are actual measurements or sufficient data available to estimate emissions using rigorous source emissions models?

Yes

Report measurement results or estimate emissions using rigorous emission source models. (Tier 3) Box 3

No

Are national Tier 2 emission factors available?

Estimate emissions using a Tier 2 approach.

Yes

Box 2 No

If emissions from oil and gas operations are a key category, are contributions by the natural gas system significant?

No

Estimate emissions using a Tier 1 approach. Box 1

Yes Collect activity and infrastructure data to apply either a Tier 2 or Tier 3 approach, depending on the effort required. Note: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees

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Figure 4.2.2

Decision tree for crude oil production Start

Are actual measurements or sufficient data available to estimate emissions using rigorous emission source models?

Yes

Report measurement results or estimate emissions using rigorous emission source models. Box 4: Tier 3

No Are national Tier 2 emissions factors available?

Estimate emissions using a Tier 2 approach.

Yes

Box 3: Tier 2 No

Is it possible to estimate total associated and solution gas volumes (e.g. based on GOR data2, and is more than 20% vented or flared?

Yes

Estimate emissions using the alternative GOR-based Tier 2 approach. Box 2: Tier 2

No

If emissions from oil and gas operations are a key category, are contributions by the oil system significant?

No

Estimate emissions using a Tier 1 approach. Box 1: Tier 1

Yes Collect detailed activity and infrastructure data to apply either a Tier 2 or Tier 3 approach, depending on the effort required.

Note 1: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees Note 2: GOR stands for gas/Oil Ratio (see Section 4.2.2.2).

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Figure 4.2.3

Decision tree for crude oil transport, refining and upgrading Start

Is there oil transport, upgrading, refining or product distribution in the country?

No

Report 'Not Occurring'

Yes

Report measurement results or estimate emissions using rigorous emission source models. (Tier 3)

Yes

Are actual measurements or sufficient data available to estimate emissions using rigorous emission source models?

Box 3 No Are national Tier 2 emissions factors available?

Yes

Estimate emissions using a Tier 2 approach. Box 2

No

If emissions from oil and gas operations are a key category, are contributions from the oil system significant?

No

Estimate emissions using a Tier 1 approach. Box 1

Yes Collect detailed activity and infrastructure data to apply either a Tier 2 or Tier 3 approach, depending on the effort required. Note 1: See Volume 1 Chapter 4, “Methodological Choice and Key Categories” (noting section 4.1.2 on limited resources) for discussion of key categories and use of decision trees

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions

4.2.2.2

C HOICE

OF METHOD

The three methodological tiers for estimating fugitive emissions from oil and natural gas systems are described below. TIER 1 Tier 1 comprises the application of appropriate default emission factors to a representative activity parameter (usually throughput) for each applicable segment or subcategory of a country’s oil and natural gas industry and should only be used for non-key sources. The application of a Tier1 approach is done using Equations 4.2.1 and 4.2.2 presented below: EQUATION 4.2.1 TIER 1: ESTIMATING FUGITIVE EMISSIONS FROM AN INDUSTRY SEGMENT

E gas , industry segment = Aindustry segment • EFgas , industry segment

EQUATION 4.2.2 TIER 1: TOTAL FUGITIVE EMISSIONS FROM INDUSTRY SEGMENTS E gas =

∑

E gas ,industry segment industry segments

Where: Egas,industry segement

= Annual emissions (Gg)

EFgas,industry segement = emission factor (Gg/unit of activity), A industry segement

= activity value (units of activity),

The industry segments to be considered are listed in Table 4.2.2. Not all segments will necessarily apply to all countries. For example, a country that only imports natural gas and does not produce any will probably only have gas transmission and distribution. The available Tier 1 default emission factors are presented in Tables 4.2.4 and 4.2.5 in Section 4.2.2.3. These factors have been related to throughput, because production, imports and exports are the only national oil and gas statistics that are consistently available. On a small scale, fugitive emissions are completely independent of throughput. The best relation for estimating emissions from fugitive equipment leaks is based on the number and type of equipment components and the type of service, which is a Tier-3 approach. On a larger scale, there is a reasonable relationship between the amount of production and the amount of infrastructure that exists. Consequently, the reliability of the presented Tier 1 factors for oil and gas systems will depend on the size of a country's oil and gas industry. The larger the industry, the more important its fugitive emissions contribution will be and the more reliable the presented Tier 1 emission factors will be. Besides having a high degree of uncertainty, the Tier 1 approach for oil and natural gas systems does not allow countries to show any real changes in emission intensities over time (e.g., due to the implementation of control measures or changing source characteristics). Rather, emissions become fixed in proportion to the activity levels, and the changes in reported emissions over time simply reflect the changes in activity levels. Tier 2 and 3 approaches are needed to capture real changes in emission intensities. However, going to these higher tier approaches requires considerably more effort and, for Tier 3 approaches, more detailed activity data. The completeness and accuracy of the input information used for higher tier approaches will generally need to be comparable to, or better than, the values of the input information used for the lower methodological tiers in order to achieve more accurate results. Fugitive greenhouse gas emissions from oil and gas related CO2 capture and injection activities (e.g., acid gas injection and EOR projects involving CO2 floods) will normally be small compared to the amount of CO2 being injected (e.g., less than 1 percent of the injection volumes). At the Tier 1 or 2 methodology levels they are indistinguishable from fugitive greenhouse gas emissions by the associated oil and gas activities. The emission contributions from CO2 capture and injection were included in the original data upon which the presented Tier 1 factors were developed (i.e., through the inclusion of acid gas injection and EOR activities, along with conventional oil and gas activities, with consideration of CO2 concentrations in the leaked, vented and flared natural gases, vapours and acid gases). Losses from CO2 capture should be accounted for in the industry where capture occurs, while losses from, transport, injection and storage activities are assessed separately in Chapter 5.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.41

Volume 2: Energy

TABLE 4.2.2 MAJOR CATEGORIES AND SUBCATEGORIES IN THE OIL AND GAS INDUSTRY Industry Segment

Sub-Categories

Well Drilling

All

Well Testing

All

Well Servicing

All

Gas Production

Dry Gasa Coal Bed Methane (Primary and Enhanced Production) Other enhanced gas recovery Sweet Gasb Sour Gasc

Gas Processing

Sweet Gas Plants Sour Gas Plants Deep-cut Extraction Plantsd

Gas Transmission & Storage

Pipeline Systems Storage Facilities

Gas Distribution

Rural Distribution Urban Distribution

Liquefied Gases Transport

Condensate Liquefied Petroleum Gas (LPG) Liquefied Natural Gas (LNG) (including associated liquefaction and gasification facilities)

Oil Production

Light and Medium Density Crude Oil (Primary, Secondary and Tertiary Production) Heavy Oil (Primary and Enhanced Production) Crude Bitumen (Primary and Enhanced Production) Synthetic Crude Oil (From Oil Sands) Synthetic Crude Oil (From Oil Shales)

Oil Upgrading

Crude Bitumen Heavy Oil

Waste Oil Reclaiming

All

Oil Transport

Marine Pipelines Tanker Trucks and Rail Cars

Oil Refining

Heavy Oil Conventional and Synthetic Crude Oil

Refined Product Distribution

Gasoline Diesel Aviation Fuel Jet Kerosene Gas Oil (Intermediate Refined Products)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions

a

b

c d

Dry gas is natural gas that does not require any hydrocarbon dew-point control to meet sales gas specifications. However, it may still require treating to meet sales specifications for water and acid gas (i.e. H2S and CO2) content. Dry gas is usually produced from shallow (less than 1000 m deep) gas wells. Sweet gas is natural gas that does not contain any appreciable amount of H2S (i.e. does not require any treatment to meet sales gas requirements for H2S). Sour gas is natural gas that must be treated to satisfy sales gas restrictions on H2S content. Deep-cut extraction plants are gas processing plants located on gas transmission systems which are used to recover residual ethane and heavier hydrocarbons present in the natural gas.

TIER 2 Tier 2 consists of using Tier 1 equations (4.2.1 and 4.2.2) with country-specific, instead of default, emission factors. It should be applied to key categories where the use of a Tier 3 approach is not practicable. The countryspecific values may be developed from studies and measurement programmes, or be derived by initially applying a Tier 3 approach and then back-calculating Tier 2 emission factors using Equations 4.2.1 and 4.2.2. For example, some countries have been applying Tier 3 approaches for particular years and have then used these results to develop Tier 2 factors for use in subsequent years until the next Tier 3 assessment is performed. In general, all emission factors (including Tier 1 and Tier 2 values) should be periodically re-affirmed or updated. The frequency at which such updates are performed should be commensurate with the rates at which new technologies, practices, standards and other relevant factors (e.g., changes in the types of oil and gas activities, aging of the fields and facilities, etc.) are penetrating the industry. Since new emission factors developed in this manner account for real changes within the industry, they should not be applied backwards through the time series. An alternative Tier 2 approach that may be applied to estimate the amount of venting and flaring emissions from the production segment of oil systems consists of performing a mass balance using country-specific production volumes, gas-to-oil ratios (GORs), gas compositions and information regarding the level of gas conservation. This approach may be applied using equations 4.2.3 to 4.2.8 below and is appropriate where reliable venting and flaring values are unavailable but representative GOR data can be obtained and venting and flaring emissions are expected to be the dominant sources of fugitive emissions (i.e., most of the associated gas production is not being captured/conserved or utilized). Under these circumstances, the alternative Tier 2 approach may also be used to estimate fugitive greenhouse gas emissions from EOR activities provided representative associated gas and vapour analyses are available and contributions due to fugitive emissions from the CO2 transport and injection systems are small in comparison (as would normally be expected). Where the alternative Tier 2 approach is applied, any reported venting or flaring data that may be available for the target sources should not also be accounted for as this would result in double counting. However, it is good practice to compare the estimated gas vented and flared volumes determined using the GOR data to the available reported vented and flared data to identify and resolve any potential anomalies (i.e., the calculated volumes should be comparable to the available reported data, or greater if these latter data are believed to be incomplete). Table 4.2.3 shows examples of typical GOR values for oil wells from selected locations. Actual GOR values may vary from 0 to very high values depending on the local geology, state of the producing reservoir and the rate of production. Notwithstanding this, average GOR values for large numbers of oil wells tend to be more predictable. A review of limited data for a number of countries and regions indicates that average GOR values for conventional oil production would usually be in the range of about 100 to 350 m3/m3, depending on the location.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.43

Volume 2: Energy

TABLE 4.2.3 TYPICAL RANGES OF GAS-TO-OIL RATIOS FOR DIFFERENT TYPES OF PRODUCTION Type of Crude Oil

Typical GOR Values (m3/m3)

Location

Production

Range6

Conventional Oil

Average 2, 3

NA

Alaska (Prudhoe Bay)

142 to 6234

Canada

0 to 2,000+ 1,2

Not Available (NA)

167 to 1844

173

316 to 3864

333

Qatar (Onshore, 1 Oil Field) Qatar (Offshore, 3 Oil Fields) Primary Heavy Oil

Canada

0 to 325+ 1,5

NA

Thermal Heavy Oil

Canada

0 to 901

NA

Crude Bitumen

Canada

0 to 201

NA

1

Source: Based on unpublished data for a selection of wells in Canada.

2

Appreciably higher GOR values may occur, but these wells are normally either classified as gas wells or there is a significant gas cap present and the gas would normally be reinjected until all the recoverable oil had been produced.

3

Source: Mohaghegh, S.D., L.A. Hutchins and C.D. Sisk. 2002. Prudhoe Bay Oil Production Optimization: Using Virtual intelligence Techniques, Stage One: Neural Model Building. Presented at the SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, 29 September–2 October 2002.

4

Source: Corporate HSE, Qatar Petroleum, Qatar-Doha 2004.

5

Values as high as 7,160 m3/m3 have been observed for some wells where there is a significant gas cap present. Gas reinjection is not done in these applications. The gas is conserved, vented or flared.

6

Referenced at standard conditions of 15°C and 101.325 kPa.

To apply a mass balance method in the alternative Tier 2 approach, it is necessary to consider the fate of all of the produced gas and vapour. This is done, in part, through the application of a conservation efficiency (CE) factor which expresses the amount of the produced gas and vapour that is captured and used for fuel, produced into gas gathering systems or re-injected. A CE value of 1.0 means all gas is conserved, utilized or re-injected and a value of 0 means all of the gas is either vented or flared. Values may be expected to range from about 0.1 to 0.95. The lower limit applies where only process fuel is drawn from the produced gas and the rest is vented or flared. A value of 0.95 reflects circumstances where there is, generally, good access to gas gathering systems and local regulations emphasize vent and flare gas reduction.

EQUATION 4.2.3 ALTERNATIVE TIER 2 APPROACH (EMISSIONS DUE TO VENTING) E gas ,oil

prod , venting

= GOR • QOIL • (1 − CE ) • (1 − X Flared ) • M gas • y gas • 42.3 × 10 −6

EQUATION 4.2.4 ALTERNATIVE TIER 2 APPROACH (CH4 EMISSIONS DUE TO FLARING) ECH 4,oil

4.44

prod , flaring

= GOR • QOIL • (1 − CE ) • X Flared • (1 − FE ) • M CH 4 • yCH 4 • 42.3 × 10−6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions

EQUATION 4.2.5 ALTERNATIVE TIER 2 APPROACH (CO2 EMISSIONS DUE TO FLARING)

ECO 2, oilprod , flaring = GOR • QOIL • (1 − CE ) • X Flared • M CO 2

[

]

• yCO 2 + ( NcCH 4 • yCH 4 + Nc NMVOC • y NMVOC )(1 − X Soot ) • 4.23 x10 −6

EQUATION 4.2.6 CH4 EMISSIONS FROM FLARING AND VENTING

E CH 4 , oil

prod

= E CH 4 , oil

prod , venting

+ E CH 4 , oil

prod , flaring

EQUATION 4.2.7 CO2 EMISSIONS FROM VENTING AND FLARING

E CO 2 , oil

prod

= E CO 2 , oil

prod , venting

+ E CO 2 , oil

prod , flaring

EQUATION 4.2.8 N2O EMISSIONS FROM FLARING

E N 2 O ,oil

prod , flaring

= GOR • Q OIL (1 − CE ) X Flared EF N 2 O

Where: Ei, oil prod, venting

= Direct amount (Gg/y) of GHG gas i emitted due to venting at oil production facilities.

Ei, oil prod, flaring

= Direct amount (Gg/y) of GHG gas i emitted due to flaring at oil production facilities.

GOR

= Average gas-to-oil ratio (m3/m3) referenced at 15ºC and 101.325 kPa.

QOIL

= Total annual oil production (103 m3/y).

Mgas

= Molecular weight of the gas of interest (e.g., 16.043 for CH4 and 44.011 for CO2).

NC,i

= Number of moles of carbon per mole of compound i (i.e., 1 for CH4, 2 for C2H6, 3 for C3H8, 1 for CO2, 2.1 to 2.7 for the NMVOC fraction in natural gas and 4.6 for the NMVOC fraction of crude oil vapours)

yi

= Mol or volume fraction of the associated gas that is composed of substance i (i.e., CH4, CO2 or NMVOC).

CE

= Gas conservation efficiency factor.

XFlared

= Fraction of the waste gas that is flared rather than vented. With the exception of primary heavy oil wells, usually most of the waste gas is flared.

FE

= flaring destruction efficiency (i.e., fraction of the gas that leaves the flare partially or fully burned). Typically, a value of 0.995 is assumed for flares at refineries and a value 0.98 is assumed for those used at production and processing facilities.

Xsoot

= fraction of the non-CO2 carbon in the input waste gas stream that is converted to soot or particulate matter during flaring. In the absence of any applicable data this value may be assumed to be 0 as a conservative approximation.

EFN2O

= emission factor for N2O from flaring (Gg/103 m3 of associated gas flared). Refer to the IPCC emission factor database (EFDB), manufacturer’s data or other appropriate sources for the value of this factor.

42.3x10-6

= is the number of kmol per m3 of gas referenced at 101.325 kPa and 15ºC (i.e. 42.3x10-3 kmol/m3) times a unit conversion factor of 10-3 Gg/Mg which brings the results of each applicable equation to units of Gg/y.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.45

Volume 2: Energy

The values of ECH4, oil prod, venting and ECO2, oil prod, venting in Equations 4.2.6 and 4.2.7 are estimated using Equation 4.2.3. It should be noted that Equation 4.2.5 accounts for emissions of CO2 using a similar approach to what is done for fuel combustion in Section 1.3 of the Introduction chapter of the Energy Volume. The term yCO2 in this equation effectively accounts for the amount of raw (or formation CO2) present in the waste gas being flared. The terms NcCH4 ● yCH4 and NcNMVOC ● yNMVOC in Equation 4.2.5 account for the amount of CO2 produced per unit of CH4 and NMVOC oxidized. TIER 3 Tier 3 comprises the application of a rigorous bottom-up assessment by primary type of source (e.g., venting, flaring, fugitive equipment leaks, evaporation losses and accidental releases) at the individual facility level with appropriate accounting of contributions from temporary and minor field or well-site installations. It should be used for key categories where the necessary activity and infrastructure data are readily available or are reasonable to obtain. Tier 3 should also be used to estimate emissions from surface facilities where EOR, EGR and ECBM are being used in association with CCS. Approaches that estimate emissions at a less disaggregated level than this (e.g., relate emissions to the number of facilities or the amount of throughput) are deemed to be equivalent to a Tier 1 approach if the applied factors are taken from the general literature, or a Tier 2 approach if they are country-specific values. The key types of data that would be utilized in a Tier 3 assessment would include the following: •

•

Facility inventory, including an assessment of the type and amount of equipment or process units at each facility, and major emission controls (e.g., vapour recovery, waste gas incineration, etc.).

•

Inventory of wells and minor field installations (e.g., field dehydrators, line heaters, well site metering, etc.).

•

Facility-level acid gas production, analyses and disposition data.

•

Country-specific flare, vent and process gas analyses for each subcategory.

•

Reported atmospheric releases due to well blow-outs and pipeline ruptures.

•

Country-specific emission factors for fugitive equipment leaks, unaccounted/unreported venting and flaring, flashing losses at production facilities, evaporation losses, etc. The amount and composition of acid gas that is injected into secure underground formations for disposal.

Oil and gas projects that involve CO2 injection as a means of enhancing production (e.g., EOR, EGR and ECBM projects) or as a disposal option (e.g., acid gas injection at sour gas processing plants) should distinguish between the CO2 capture, transport, injection and sequestering part of the project, and the oil and gas production portion of the project. The net amount of CO2 sequestered and the fugitive emissions from the CO2 systems should be determined based on the criteria specified in Chapter 5 for CO2 capture and storage. Any fugitive emissions from the oil and gas systems in these projects should be assessed based on the guidance provided here in Chapter 4 and will exhibit increasing concentrations of CO2 over time in the emitted natural gas and hydrocarbon vapours. Accordingly, the applied emission factors may need to be periodically updated to account for this fact. Also, care should be taken to ensure that proper total accounting of all CO2 between the two portions of the project occurs.

4.2.2.3

C HOICE

OF EMISSION FACTOR

TIER 1 The available Tier 1 default emission factors are presented in Tables 4.2.4 and 4.2.5. All of the presented emission factors are expressed in units of mass emissions per unit volume of oil or gas throughput. While some types of fugitive emissions correlate poorly with, or are unrelated to, throughput on an individual source basis (e.g., fugitive equipment leaks), the correlations with throughput become more reasonable when large populations of sources are considered. Furthermore, throughput statistics are the most consistently available activity data for use in Tier 1 calculations. Table 4.2.4 should only be applied to systems designed, operated and maintained to North American and Western European standards. Table 4.2.5 generally applies to systems in developing countries and countries with economies in transition where there are much greater amounts of fugitive emissions per unit of activity (often by an order of magnitude or more). The reasons for the greater emissions in these cases may include less stringent design standards, use of lower quality components, restricted access to natural gas markets, and, in some cases,

4.46

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Fugitive Emissions

artificially low energy pricing resulting in reduced energy conservation. Reference should also be made to the IPCC emission factor database (EFDB) since it would contain the values for higher tier emission factors.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.47

All

All

All

Well Testing

Well Servicing

Gas Production

Sour Gas Plants

Raw CO2 Venting

Flaring

Fugitives

Flaring

Fugitives

Flaringe

Fugitivesd

Flaring and Venting

Flaring and Venting

Flaring and Venting

Emission source

±100%

3.8E-04 to 2.3E-03

1.B.2.b.i

1.B.2.b.ii

1.B.2.b.iii.3

1.B.2.b.ii

1.B.2.b.iii.3

1.B.2.b.ii

1.B.2.b.iii.2

±50%

1.1E-04

1.B.2.a.ii or 1.B.2.b.ii

NA

2.4E-06

9.7E-05

NA

±25%

±100%

±25%

±100%

4.8E-04 to 10.3E-04 1.2E-06

±25%

7.6E-07

±50%

5.1E-05

1.B.2.a.ii or 1.B.2.b.ii

±100%

3.3E-05

Value

Uncertainty (% of value)

1.B.2.a.ii or 1.B.2.b.ii

Code

IPCC

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Gas Processing

All

Well Drilling

Sweet Gas Plants

Subcategoryc

Category

CH4

6.3E-02

3.6E-03

7.9E-06

1.8E-03

1.5E-04 to 3.2E-04

1.2E-03

1.4E-05 to 8.2E-05

1.9E-06

9.0E-03

1.0E-04

Value

CO2l

a,b

1.9E-06

NA

-10 to +1000%

6.8E-05

9.6E-07

2.2E-04 to 4.7E-04

6.2E-07

9.1E-05 to 5.5E-04

1.7E-05

1.2E-05

8.7E-07

Value

NMVOC

±25%

±100%

±25%

±100%

±25%

±100%

±50%

±50%

±50%

Uncertainty (% of Value)

IN DEVELOPED COUNTRIES

NA

±25%

±100%

±25%

±100%

±25%

±100%

±50%

±50%

±100%

NA

5.4E-08

NA

NA

-10 to +1000%

NA

Gg per 106 m3 raw gas feed

Gg per 106 m3 raw gas feed

Gg per 106 m3 raw gas feed

Gg per 106 m3 raw gas feed -10 to +1000% 2.5E-08

Gg per 106 m3 raw gas feed

NA

NA

4.48

Gg per 106 m3 gas production

Gg per 106 m3 gas production

Gg per 103 m3 total oil production

Gg per 103 m3 total oil production

Gg per 103 m3 total oil production

-10 to +1000%

NA

ND

-10 to +1000%

ND

Units of measure

2.1E-08

NA

ND

6.8E-08

ND

Value

N2O

TABLE 4.2.4 TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

Uncertainty (% of value)

Volume 2 : Energy

Uncertainty (% of value)

1.B.2.b.ii

1.B.2.b.iii.3

Flaring

Fugitives

Storage

Transmission

Default Weighted Total

1.B.2.b.iii.3

Fugitives

1.B.2.b.iii.4

Allk

1.B.2.b.iii.4

Fugitivesf,k 1.B.2.b.i

1.B.2.b.i

Raw CO2 Venting

Ventingg,k

1.B.2.b.ii

Flaring

Code

Deep-cut Extraction Plants (Straddle Plants)

IPCC

Emission source

Subcategoryc

2.5E-05

3.1E-06 1.1E-07

-20 to +500%

8.8E-07

4.0E-02

3.0E-03

1.2E-05 to 3.2E-04

1.1E-04

1.6E-06

Value

CO2l

±75%

±100%

6.6E-05 to 4.8E-04 4.4E-05 to 3.2E-04

N/A

NA

±25%

±100%

1.5E-04 to 10.3E-04 2.0E-06

±25%

±100%

Uncertainty (% of value)

7.2E-08

1.1E-05

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Gas Transmission & Storage

Category

CH4

a,b

4.6E-06 3.6E-07

-20 to +500%

7.0E-06

±75%

±100%

NA

-10 to +1000%

-20 to +500%

±75%

±100%

N/A

±25%

±100%

1.4E-04 to 4.7E04 1.6E-06

±25%

±100%

5.9E-08

2.7E-05

Value

NMVOC

±50%

±100%

±50%

±100%

Uncertainty (% of Value)

IN DEVELOPED COUNTRIES

Uncertainty (% of value)

ND

NA

NA

NA

3.3E-08

NA

1.2E-08

NA

Value

N2O

TABLE 4.2.4(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

ND

NA

NA

N/A

-10 to +1000%

NA

-10 to +1000%

NA

4.49

Gg per 106 m3 of marketable gas

Gg per 106 m3 of marketable gas

Gg per 106 m3 of marketable gas

Gg per 106 m3 gas production

Gg per 106 m3 gas production

Gg per 106 m3 gas production

Gg per 106 m3 raw gas feed

Gg per 106 m3 raw gas feed

Units of measure

Chapter 4: Fugitive Emissions

Uncertainty (% of value)

1.B.2.a.iii.3

Allk

Condensate

1.B.2.a.i

1.B.2.a.ii

Flaring

1.B.2.a.iii.2

Fugitives (Offshore)

Venting

1.B.2.a.iii.2

Fugitives (Onshore)

Conventional Oil

1.B.2.a.iii.3

All

Liquefied Natural Gas

1.B.2.a.iii.3

All

Liquefied Petroleum Gas

2.5E-05

7.2E-04

5.9E-07

1.5E-06 to 3.6E-03

ND

NA

1.1E-04

1.1E-03

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Oil Production

Natural Gas Liquids Transport

1.B.2.b.iii.5

Allk

Code

All

IPCC

Gas Distribution

Emission source

Subcategoryc

Category

CH4

±50%

±50%

±100%

±100%

ND

NA

4.1E-02

9.5E-05

4.3E-08

1.1E-07 to 2.6E-04

ND

4.3E-04

7.2E-06

5.1E-05

-20 to +500% ±100%

Value

CO2l Uncertainty (% of value)

a,b

±50%

±50%

±100%

±100%

ND

±50%

±100%

-20 to +500%

Uncertainty (% of Value)

IN DEVELOPED COUNTRIES

2.1E-05

4.3E-04

±50%

±50%

±100%

±100%

1.8E-06 to 4.5E03 7.4E-07

ND

ND

±100%

-20 to +500%

ND

ND

1.1E-03

1.6E-05

Value

NMVOC

6.4E-07

NA

NA

NA

ND

2.2E-09

ND

ND

Value

N2O

TABLE 4.2.4(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

Uncertainty (% of value)

Volume 2 : Energy

Gg per 103 m3 conventional oil production -10 to +1000%

4.50

Gg per 103 m3 conventional oil production

Gg per 103 m3 conventional oil production

Gg per 103 m3 conventional oil production

Gg per 106 m3 of marketable gas

Gg per 103 m3 LPG

Gg per 103 m3 Condensate and Pentanes Plus

Gg per 106 m3 of utility sales

Units of measure

NA

NA

NA

ND

-10 to +1000%

ND

ND

Uncertainty (% of value)

Synthetic Crude (from Oilsands)

Thermal Oil Production

Heavy Oil/Cold Bitumen

Subcategoryc

All

1.B.2.a.iii.2

1.B.2.a.ii

Flaring

1.B.2.a.iii.2

Fugitives

1.B.2.a.i

1.B.2.a.ii

Flaring

Venting

1.B.2.a.i

1.B.2.a.iii.2

Code

IPCC

Venting

Fugitives

Emission source

2.3E-03

1.6E-05

3.5E-03

1.8E-04

1.4E-04

1.7E-02

7.9E-03

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Category

CH4 Uncertainty (% of value) ±75%

±75%

±50%

±100%

±75%

±75%

±100%

ND

2.7E-02

2.2E-04

2.9E-05

2.2E-02

5.3E-03

5.4E-04

Value

CO2l

a,b

ND

±75%

±50%

±100%

±75%

±75%

±100%

Uncertainty (% of Value)

IN DEVELOPED COUNTRIES

9.0E-04

1.3E-05

8.7E-04

2.3E-04

1.1E-05

2.7E-03

2.9E-03

Value

NMVOC

±75%

±75%

±50%

±100%

±75

±75%

±100%

Uncertainty (% of value)

ND

2.4E-07

NA

NA

4.6E-07

NA

NA

Value

N2O

TABLE 4.2.4(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

ND

Gg per 103 m3 thermal bitumen production -10 to +1000%

4.51

Gg per 103 m3 synthetic crude production from oilsands

Gg per 103 m3 thermal bitumen production NA

Gg per 103 m3 thermal bitumen production

Gg per 103 m3 heavy oil production -10 to +1000% NA

Gg per 103 m3 heavy oil production NA

NA

Gg per 103 m3 heavy oil production

Units of measure

Chapter 4: Fugitive Emissions

Uncertainty (% of value)

Pipelines

Oil Transport

Allk 1.B.2.a.iii.3

1.B.2.a.iii.2

1.B.2.a.ii

Flaring

All

1.B.2.a.i

Venting

5.4E-06

ND

2.1E-05

8.7E-03

2.2E-03

ND

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

All

1.B.2.a.iii.2

1.B.2.a.iii.2

Code

IPCC

Fugitives

All

Synthetic Crude (from Oil Shale)

Default Weighted Total

Emission source

Subcategoryc

Oil Upgrading

Category

CH4 Uncertainty (% of value) ±100%

ND

±75%

±75%

±100%

ND

4.9E-07

ND

3.4E-02

1.8E-03

2.8E-04

ND

Value

CO2l

a,b

±100%

ND

±75%

±75%

±100%

ND

Uncertainty (% of Value)

IN DEVELOPED COUNTRIES

5.4E-05

ND

1.7E-05

1.6E-03

3.1E-03

ND

Value

NMVOC

ND

ND

±75

±75%

±100%

ND

NA

ND

5.4E-07

NA

NA

ND

Value

N2O

TABLE 4.2.4(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

Uncertainty (% of value)

Volume 2 : Energy

NA

ND

-10 to +1000%

NA

NA

ND

Uncertainty (% of value)

4.52

Gg per 103 m3 oil transported by pipeline

Gg per 103 m3 oil upgraded

Gg per 103 m3 total oil production

Gg per 103 m3 total oil production

Gg per 103 m3 total oil production

Gg per 103 m3 synthetic crude production from oil shale

Units of measure

All

All

All

All

Gasoline

Diesel

Aviation Fuel

Jet Kerosene

All

Ventingk

Loading of Off-shore Production on Tanker Ships

All

Venting

k

Emission source

Tanker Trucks and Rail Cars

Subcategoryc

1.B.2.a.iii.5

1.B.2.a.iii.5

1.B.2.a.iii.5

1.B.2.a.iii.5

1.B.2.a.iii.4

1.B.2.a.i

1.B.2.a.i

Code

IPCC

NA

NA

NA

NA

NA

NA

NA

±100%

2.6x10-6 to 41.0x10-6 NA

ND

±50%

Uncertainty (% of value)

NDh

2.5E-05

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Refined Product Distribution

Oil Refining

Category

CH4

NA

NA

NA

NA

ND

NDh

2.3E-06

Value

CO2l

a,b

NA

NA

NA

NA

ND

ND

±50%

Uncertainty (% of Value)

IN DEVELOPED COUNTRIES

ND

ND

ND

0.0022j

0.0013i

NDh

2.5E-04

Value

NMVOC

ND

ND

ND

±100%

±100%

ND

ND

Uncertainty (% of value)

NA

NA

NA

NA

ND

NA

NA

Value

N2O

TABLE 4.2.4(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

NA

NA

NA

NA

ND

NA

NA

4.53

Gg per 103 m3 product transported.

Gg per 103 m3 product transported.

Gg per 103 m3 product transported.

Gg per 103 m3 product distributed.

Gg per 103 m3 oil refined.

Gg per 103 m3 oil transported by Tanker Ships

Gg per 103 m3 oil transported by Tanker Truck

Units of measure

Chapter 4: Fugitive Emissions

Uncertainty (% of value)

NMVOC values are derived from methane values based on the ratio of the mass fractions of NMVOC to CH4. Values of 0.0144 kg/kg for gas transmission and distribution, 9.951 kg/kg for oil and condensate transportation and 0.3911 kg/kg for synthetic crude oil production are used.

The presented CO2 emissions factors account for direct CO2 emissions only, except for flaring, in which case the presented values account for the sum of direct CO2 emissions and indirect contributions due to the atmospheric oxidation of gaseous non-CO2 carbon emissions.

k

l

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.54

Estimated based on assumed average evaporation losses of 0.15 percent of throughput at the distribution terminal and additional losses of 0.15 percent of throughput at the retail outlet. These values will be much lower where Stage 1 and Stage 2 vapour recovery occurs and may be much greater in warm climates.

j

Sources: Canadian Association of Petroleum Producers (1999, 2004); API (2004); GRI/US EPA (1996); US EPA (1999).

Estimated based on an aggregated emission factors for fugitive equipment leaks, fluid catalytic cracking and storage and handling of 0.53 kg/m3 (CPPI and Environment Canada, 1991), 0.6 kg/m3 ( US EPA, 1995) and 0.2 g/kg (assuming the majority of the volatile products are stored in floating roof tanks with secondary seals) (EMEP/CORINAIR, 1996).

While the presented emission factors may all vary appreciably between countries, the greatest differences are expected to occur with respect to venting and flaring, particularly for oil production due to the potential for significant differences in the amount of gas conservation and utilisation practised. The range in values for fugitive emissions is attributed primarily to differences in the amount of process infrastructure (e.g. average number and sizes of facilities) per unit of gas throughput. ‘All’ denotes all fugitive emissions as well as venting and flaring emissions. ‘Fugitives’ denotes all fugitive emissions including those from fugitive equipment leaks, storage losses, use of natural gas as the supply medium for gas-operated devices (e.g. instrument control loops, chemical injection pumps, compressor starters, etc.), and venting of still-column off-gas from glycol dehydrators. The presented range in values reflects the difference between fugitive emissions at offshore (the smaller value) and onshore (the larger value) emissions. ‘Flaring’ denotes emissions from all continuous and emergency flare systems. The specific flaring rates may vary significantly between countries. Where actual flared volumes are known, these should be used to determine flaring emissions rather than applying the presented emission factors to production rates. The emission factors for direct estimation of CH4, CO2 and N2O emissions from reported flared volumes are 0.012, 2.0 and 0.000023 Gg, respectively, per 106 m3 of gas flared based on a flaring efficiency of 98% and a typical gas analysis at a gas processing plant (i.e. 91.9% CH4, 0.58% CO2, 0.68% N2 and 6.84% non-methane hydrocarbons by volume). The larger factor reflects the use of mostly reciprocating compressors on the system while the smaller factor reflects mostly centrifugal compressors. ‘Venting’ denotes reported venting of waste associated and solution gas at oil production facilities and waste gas volumes from blowdown, purging and emergency relief events at gas facilities. Where actual vented volumes are known, these should be used to determine venting emissions rather than applying the presented emission factors to production rates. The emission factors for direct estimation of CH4 and CO2 emissions from reported vented volumes are 0.66 and 0.0049 Gg, respectively, per 106 m3 of gas vented based on a typical gas analysis for gas transmission and distribution systems (i.e. 97.3% CH4, 0.26% CO2, 1.7% N2 and 0.74% non-methane hydrocarbons by volume). While no factors are available for marine loading of offshore production for North America, Norwegian data indicate a CH4 emission factor of 1.0 to 3.6 Gg/103 m3 of oil transferred (derived from data provided by Norwegian Pollution Control Authority, 2000).

a,b

I

h

g

f

e

d

c

b

a

IN DEVELOPED COUNTRIES

TABLE 4.2.4(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

NA - Not Applicable ND - Not Determined

Volume 2 : Energy

All

All

All

Well Testing

Well Servicing

Gas Production

Sour Gas Plants

Fugitives

Flaring 1.B.2.b.iii.3

1.B.2.b.ii

1.B.2.b.iii.3

1.B.2.b.iii.2

Fugitivesd

Fugitives

1.B.2.a.ii or 1.B.2.b.ii

Flaring and Venting

1.B.2.b.ii

1.B.2.a.ii or 1.B.2.b.ii

Flaring and Venting

Flaringe

1.B.2.a.ii or 1.B.2.b.ii

Code

IPCC

Flaring and Venting

Emission source

±75% -40 to +250%

9.7E-05 to 2.2E-04

-40 to +250%

4.8E-04 to 1.1E-03 1.2E-06 to 1.6E-06

±75%

-40 to +250%

-12.5 to + 800%

-12.5 to +800%

-12.5 to +800%

Uncertainty (% of value)

7.6E-07 to 1.0E-06

3.8E-04 to 2.4E-02

1.1E-04 to 1.8E-03

5.1E-05 8.5E-04

3.3E-05 to 5.6E-04

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Gas Processing

All

Well Drilling

Sweet Gas Plants

Sub-categoryc

Category

CH4

7.9E-06 to 1.8E-05

1.8E-03 to 2.5E-03

1.5E-04 to 3.5E-04

1.2E-03 to 1.6E-03

1.4E-05 to 1.8E-04

1.9E-06 to 3.2E-05

9.0E-03 to 1.5E-01

1.0E-04 to 1.7E-03

Value

CO2i

-40 to +250%

±75%

-40 to +250%

±75%

-40 to +250%

-12.5 to +800%

-12.5 to +800%

-12.5 to +800%

Uncertainty (% of value)

a,b

6.8E-05 to 1.6E-04

9.6E-07 to 1.3E-06

2.2E-04 to 5.1E-04

6.2E-07 to 8.5E-07

9.1E-05 to 1.2E-03

1.7E-05 to 2.8E-04

1.2E-05 to 2.0E-04

8.7E-07 to 1.5E-05

Value

NMVOC

IN DEVELOPING COUNTRIES AND COUNTRIES WITH ECONOMIES IN TRANSITION

-40 to +250%

±75%

-40 to +250%

±75%

-40 to +250%

-12.5 to +800%

-12.5 to +800%

-12.5 to +800%

Uncertainty (% of Value)

NA

2.5E-08 to 3.4E-08

NA

2.1E-08 to 2.9E-08

NA

ND

6.8E-08 to 1.1E-06

ND

Value

N2O

TABLE 4.2.5 TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

NA

-10 to +1000%

NA

-10 to +1000%

NA

ND

-10 to +1000%

ND

4.55

Gg per 106 m3 raw gas feed

Gg per 106 m3 raw gas feed

Gg per 106 m3 raw gas feed

Gg per 106 m3 gas production

Gg per 106 m3 gas production

Gg per 103 m3 total oil production

Gg per 103 m3 total oil production

Gg per 103 m3 total oil production

Units of measure

Chapter 4: Fugitive Emissions

Uncertainty (% of value)

Default Weighted Total

Deep-cut Extraction Plants (Straddle Plants)

Sub-categoryc

1.B.2.b.iii.3

1.B.2.b.ii

1.B.2.b.i

Flaring

Raw CO2 Venting

1.B.2.b.ii

Flaring

Fugitives

1.B.2.b.iii.3

1.B.2.b.i

Raw CO2 Venting

Fugitives

1.B.2.b.ii

Code

IPCC

Flaring

Emission source

N/A

±75%

2.0E-06 to 2.8E-06 NA

-40 to +250%

±75%

7.2E-08 to 9.9E-08 1.5E-04 to 3.5E-04

-40 to +250%

1.1E-05 to 2.5E-05

2.4E-06 to 3.3E-06 NA

±75%

Value

NA

Uncertainty (% of value)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Category

CH4

4.0E-02 to 9.5E-02

3.0E-03 to 4.1E-03

1.2E-05 to 2.8E-05

1.1E-04 to 1.5E-04

-10 to +1000%

±75%

-40 to +250%

±75%

-40 to +250%

-10 to +1000%

6.3E-02 to 1.5E-01 1.6E-06 to 3.7E-06

±75%

3.6E-03 to 4.9E-03

Value

CO2i Uncertainty (% of value)

NA

1.6E-06 to 2.2E-06

1.4E-04 to 3.2E-04

5.9E-08 to 8.1E-08

2.7E-05 to 6.2E-05

NA

1.9E-06 to 2.6E-06

Value

NMVOC

IN DEVELOPING COUNTRIES AND COUNTRIES WITH ECONOMIES IN TRANSITION

a,b

N/A

±75%

-40 to +250%

±75%

-40 to +250%

NA

±75%

NA

3.3E-08 to 4.5E-08

NA

1.2E-08 to 8.1E-08

NA

NA

5.4E-08 to 7.4E-08

Value

N2O

TABLE 4.2.5(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

Uncertainty (% of Value)

Volume 2 : Energy

N/A

-10 to +1000%

NA

-10 to +1000%

NA

NA

-10 to +1000%

Uncertainty (% of value)

4.56

Gg per 106 m3 gas production

Gg per 106 m3 gas production

Gg per 106 m3 gas production

Gg per 106 m3 raw gas feed

Gg per 106 m3 raw gas feed

Gg per 106 m3 raw gas feed

Gg per 106 m3 raw gas feed

Units of measure

All

All

All

Liquefied Petroleum Gas

Liquefied Natural Gas

All

All

Ventingg

Fugitivesf

Emission source

Condensate

All

Storage

Transmission

Sub-categoryc

1.B.2.a.iii.3

1.B.2.a.iii.3

1.B.2.a.iii.3

1.B.2.b.iii.5

1.B.2.b.iii.4

1.B.2.b.i

1.B.2.b.iii.4

Code

IPCC

ND

NA

1.1E-04

ND

NA

-50 to +200%

-20 to +500%

-20 to +500%

2.5E-05 to 5.8E-05 1.1E-03 to 2.5E-03

-40 to +250%

-40 to +250%

Uncertainty (% of value)

4.4E-05 to 7.4E-04

16.6E-05 to 1.1E-03

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Natural Gas Liquids Transport

Gas Distribution

Gas Transmission & Storage

Category

CH4

ND

4.3E-04

7.2E-06

5.1E-05 to 1.4E-04

1.1E-07 to 2.6E-07

3.1E-06 to 7.3E-06

8.8E-07 to 2.0E-06

Value

CO2i

ND

±100%

-50 to +200%

-20 to +500%

-20 to +500%

-40 to +250%

-40 to +250%

Uncertainty (% of value)

a,b

ND

ND

1.1E-03

1.6E-05 to 3.6E-5

3.6E-07 to 8.3E-07

4.6E-06 to 1.1E-05

7.0E-06 to 1.6E-05

Value

NMVOC

IN DEVELOPING COUNTRIES AND COUNTRIES WITH ECONOMIES IN TRANSITION

ND

ND

-50 to +200%

-20 to +500%

-20 to +500%

-40 to +250%

-40 to +250%

Uncertainty (% of Value)

ND

2.2E-09

ND

ND

ND

NA

NA

Value

N2O

TABLE 4.2.5(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

ND

-10 to +1000%

ND

ND

ND

NA

NA

4.57

Gg per 106 m3 of marketable gas

Gg per 103 m3 LPG

Gg per 103 m3 Condensate and Pentanes Plus

Gg per 106 m3 of utility sales

Gg per 106 m3 of marketable gas

Gg per 106 m3 of marketable gas

Gg per 106 m3 of marketable gas

Units of measure

Chapter 4: Fugitive Emissions

Uncertainty (% of value)

Conventional Oil

Oil Production 1.B.2.a.iii.2

1.B.2.a.iii.2

1.B.2.a.i

1.B.2.a.ii

Fugitives (Offshore)

Venting

Flaring

Code

IPCC

Fugitives (Onshore)

Emission source

-12.5 to +800% ±75%

±75%

7.2E-04 to 9.9E-04 2.5E-05 to 3.4E-05

-12.5 to +800%

Uncertainty (% of value)

5.9E-07

1.5E-06 to 6.0E-02

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Sub-categoryc

Category

CH4

4.1E-02 to 5.6E-02

9.5E-05 to 1.3E-04

4.3E-08

1.1E-07 to 4.3E-03

Value

CO2i

±75%

±75%

-12.5 to +800%

-12.5 to +800%

Uncertainty (% of value)

2.1E-05 to 2.9E-05

4.3E-04 to 5.9E-04

7.4E-07

1.8E-06 to 7.5E-02

Value

NMVOC

IN DEVELOPING COUNTRIES AND COUNTRIES WITH ECONOMIES IN TRANSITION

a,b

±75%

±75%

-12.5 to +800%

-12.5 to +800%

6.4E-07 to 8.8E-07

-10 to +1000%

NA

NA

NA

NA

NA

NA

Value

N2O

TABLE 4.2.5(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

Uncertainty (% of Value)

Volume 2 : Energy

Uncertainty (% of value)

4.58

Gg per 103 m3 conventional oil production

Gg per 103 m3 conventional oil production

Gg per 103 m3 conventional oil production

Gg per 103 m3 conventional oil production

Units of measure

Thermal Oil Production

Heavy Oil/Cold Bitumen

Sub-categoryc

1.B.2.a.i

1.B.2.a.ii

Venting

Flaring

1.B.2.a.ii

Flaring

1.B.2.a.iii.2

1.B.2.a.i

Venting

Fugitives

1.B.2.a.iii.2

Code

IPCC

Fugitives

Emission source

-67 to +150%

-67 to +150%

3.5E-03 to 4.8E-03

1.6E-05 to 2.2E-05

-67 to +150%

1.4E-04 to 1.9E-04 -12.5 to +800%

-67 to +150%

1.7E-02 to 2.3E-02

1.8E-04 to 3.0E-03

-12.5 to +800%

Uncertainty (% of value)

7.9E-03 to 1.3E-01

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Category

CH4

2.7E-02 to 3.7E-02

2.2E-04 to 3.0E-04

2.9E-05 to 4.8E-04

2.2E-02 to 3.0E-02

5.3E-03 to 7.3E-03

5.4E-04 to 9.0E-03

Value

CO2i

-67 to +150%

-67 to +150%

-12.5 to +800%

-67 to +150%

-67 to +150%

-12.5 to +800%

Uncertainty (% of value)

a,b

1.3E-05 to 1.8E-05

8.7E-04 to 1.2E-03

2.3E-04 to 3.8E-03

1.1E-05 to 1.5E-05

2.7E-03 to 3.7E-03

2.9E-03 to 4.8E-02

Value

NMVOC

IN DEVELOPING COUNTRIES AND COUNTRIES WITH ECONOMIES IN TRANSITION

-67 to +150%

-67 to +150%

-12.5 to +800%

-67 to +150%

-67 to +150%

-12.5 to +800%

Uncertainty (% of Value)

2.4E-07 to 3.3E-07

NA

NA

4.6E-07 to 6.3E-07

NA

NA

Value

N2O

TABLE 4.2.5(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

-10 to +1000%

NA

NA

-10 to +1000%

NA

NA

4.59

Gg per 103 m3 thermal bitumen production

Gg per 103 m3 thermal bitumen production

Gg per 103 m3 thermal bitumen production

Gg per 103 m3 heavy oil production

Gg per 103 m3 heavy oil production

Gg per 103 m3 heavy oil production

Units of measure

Chapter 4: Fugitive Emissions

Uncertainty (% of value)

1.B.2.a.iii.2

All

Fugitives

Synthetic Crude (from Oil Shale)

Default Weighted Total

1.B.2.a.iii.2

All

1.B.2.a.i

1.B.2.a.ii

Venting

Flaring

1.B.2.a.iii.2

Code

Synthetic Crude (from Oilsands)

IPCC

Emission source

Sub-categoryc

±75%

±75%

2.1E-05 to 2.9E-05

-12.5 to +800%

2.2E-03 to 3.7E-02 8.7E-03 to 1.2E-02

ND

-67 to +150%

Uncertainty (% of value)

ND

2.3E-03 to 3.8E-02

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Category

CH4

3.4E-02 to 4.7E-02

1.8E-03 to 2.5E-03

2.8E-04 to 4.7E-03

ND

ND

Value

CO2i

±75%

±75%

-12.5 to +800%

ND

ND

Uncertainty (% of value)

1.7E-05 to 2.3

1.6E-03 to 2.2E-03

3.1E-03 to 5.2E-02

ND

9.0E-04 to 1.5E-02

Value

NMVOC

IN DEVELOPING COUNTRIES AND COUNTRIES WITH ECONOMIES IN TRANSITION

a,b

±75

±75%

-12.5 to +800%

ND

-67 to +150%

5.4E-07 to 7.4E-07

NA

NA

ND

ND

Value

N2O

TABLE 4.2.5(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

Uncertainty (% of Value)

Volume 2 : Energy

-10 to +1000%

NA

NA

ND

ND

Uncertainty (% of value)

4.60

Gg per 103 m3 total oil production

Gg per 103 m3 total oil production

Gg per 103 m3 total oil production

Gg per 103 m3 synthetic crude production from oil shale

Gg per 103 m3 synthetic crude production from oilsands

Units of measure

Venting

Loading of Off-shore Production on Tanker Ships

All

1.B.2.a.iii.4

1.B.2.a.i

1.B.2.a.i

1.B.2.a.iii.3

1.B.2.a.iii.2

Code

IPCC

ND

NDh

2.5E-05

5.4E-06

ND

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

All

Venting

Tanker Trucks and Rail Cars

Oil Refining

All

Pipelines

Oil Transport

All

All

Oil Upgrading

Emission source

Sub-categoryc

Category

CH4 Uncertainty (% of value) ND

ND

-50 to +200%

-50 to +200%

ND

ND

NDh

2.3E-06

4.9E-07

ND

Value

CO2i

ND

ND

-50 to +200%

-50 to +200%

ND

Uncertainty (% of value)

a,b

ND

ND

2.5E-04

5.4E-05

ND

Value

NMVOC

IN DEVELOPING COUNTRIES AND COUNTRIES WITH ECONOMIES IN TRANSITION

ND

ND

-50 to +200%

-50 to +200%

ND

Uncertainty (% of Value)

ND

NA

NA

NA

ND

Value

N2O

TABLE 4.2.5(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

ND

NA

NA

NA

ND

4.61

Gg per 103 m3 oil refined.

Gg per 103 m3 oil transported by Tanker Truck

Gg per 103 m3 oil transported by Tanker Truck

Gg per 103 m3 oil transported by pipeline

Gg per 103 m3 oil upgraded

Units of measure

Chapter 4: Fugitive Emissions

Uncertainty (% of value)

All

All

All

All

Diesel

Aviation Fuel

Jet Kerosene

Emission source

Gasoline

Sub-categoryc

1.B.2.a.iii.5

1.B.2.a.iii.5

1.B.2.a.iii.5

1.B.2.a.iii.5

Code

IPCC

NA

NA

NA

NA

Value

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Refined Product Distribution

Category

CH4 Uncertainty (% of value) NA

NA

NA

NA

NA

NA

NA

NA

Value

CO2i

NA

NA

NA

NA

Uncertainty (% of value)

ND

ND

ND

ND

Value

NMVOC

IN DEVELOPING COUNTRIES AND COUNTRIES WITH ECONOMIES IN TRANSITION

a,b

ND

ND

ND

ND

NA

NA

NA

NA

Value

N2O

TABLE 4.2.5(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

Uncertainty (% of Value)

Volume 2 : Energy

NA

NA

NA

NA

Uncertainty (% of value)

4.62

Gg per 103 m3 product transported.

Gg per 103 m3 product transported.

Gg per 103 m3 product transported.

Gg per 103 m3 product transported.

Units of measure

‘fugitives’ denotes all fugitive emissions including those from fugitive equipment leaks, storage losses, use of natural gas as the supply medium for gas-operated devices (e.g. instrument control loops, chemical injection pumps, compressor starters, etc.), and venting of still-column off-gas from glycol dehydrators.

‘Flaring’ denotes emissions from all continuous and emergency flare systems. The specific flaring rates may vary significantly between countries. Where actual flared volumes are known, these should be used to determine flaring emissions rather than applying the presented emission factors to production rates. The emission factors for direct estimation of CH4, CO2 and N2O emissions from reported flared volumes are 0.012, 2.0 and 0.000023 Gg, respectively, per 106 m3 of gas flared based on a flaring efficiency of 98% and a typical gas analysis at a gas processing plant (i.e. 91.9% CH4, 0.58% CO2, 0.68% N2 and 6.84% nonmethane hydrocarbons by volume).

The larger factor reflects the use of mostly reciprocating compressors on the system while the smaller factor reflects mostly centrifugal compressors.

‘Venting’ denotes reported venting of waste associated and solution gas at oil production facilities and waste gas volumes from blowdown, purging and emergency relief events at gas facilities. Where actual vented volumes are known, these should be used to determine venting emissions rather than applying the presented emission factors to production rates. The emission factors for direct estimation of CH4 and CO2 emissions from reported vented volumes are 0.66 and 0.0049 Gg, respectively, per 106 m3 of gas vented based on a typical gas analysis for gas transmission and distribution systems (i.e. 97.3% CH4, 0.26% CO2, 1.7% N2 and 0.74% non-methane hydrocarbons by volume).

While no factors are available for marine loading of offshore production for North America, Norwegian data indicate a CH4 emission factor of 1.0 to 3.6 Gg/103 m3 of oil transferred (derived from data provided by Norwegian Pollution Control Authority, 2000).

The presented CO2 emissions factors account for direct CO2 emissions only, except for flaring, in which case the presented values account for the sum of direct CO2 emissions and indirect contributions due to the atmospheric oxidation of gaseous non-CO2 carbon emissions.

d

e

f

g

h

I

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Sources: The factors presented in this table have been determined by setting the lower limit of the range for each category equal to at least the values published in Table 4.2.4 for North America. Otherwise, all presented values have been adapted from applicable data provided in the 1996 IPCC Guidelines and from limited measurement data available from more recent unpublished studies of natural gas systems in China, Romania and Uzbekistan.

The range in values for fugitive emissions is attributed primarily to differences in the amount of process infrastructure (e.g. average number and sizes of facilities) per unit of gas throughput.

‘All’ denotes all fugitive emissions as well as venting and flaring emissions.

b

c

While the presented emission factors may all vary appreciably between countries, the greatest differences are expected to occur with respect to venting and flaring, particularly for oil production due to the potential for significant differences in the amount of gas conservation and utilisation practised.

a

NA - Not Applicable ND – Not Determined

IN DEVELOPING COUNTRIES AND COUNTRIES WITH ECONOMIES IN TRANSITION

a,b

TABLE 4.2.5(CONTINUED) TIER 1 EMISSION FACTORS FOR FUGITIVE EMISSIONS (INCLUDING VENTING AND FLARING) FROM OIL AND GAS OPERATIONS

Chapter 4: Fugitive Emissions

Volume 2 : Energy

The factors in Table 4.2.4 for North America are derived from detailed emission inventory results for Canada and the United States and, where possible, have been updated from the values previously presented in the IPCC Good Practice Guidance (2000) document to reflect the results of more current and refined emissions inventories. Where applicable, factors from the API Compendium of Emissions Estimating Methodologies for the Petroleum Industry have been indicated. The factors in Table 4.2.4 are presented as examples and reflect the following practices and state of the oil and gas industry: •

•

Most associated gas is conserved;

•

Sour waste gas is flared;

•

•

Sweet waste gas is flared or vented;

Many gas transmission companies are voluntarily implementing programmes to reduce methane losses due to fugitive equipment leaks;

•

The oil and gas industry is mature and actually in decline in many areas;

•

Equipment is generally well maintained and high-quality components are used;

•

System reliability is high;

•

Line breaks and well blowouts are rare; The industry is highly regulated and these regulations are generally well enforced.

The emission factors presented in Table 4.2.5 have been set so that the lower limit of each range is at least equal to the corresponding value from Table 4.2.4. Otherwise, all values have been adapted from the factors presented in the 1996 Revised IPCC Guidelines and from limited measurement data available for several recent unpublished studies of natural gas systems in developing countries or countries with economies in transition. Where ranges in values are presented, these are either based on the relative ranges given in the 1996 Revised IPCC Guidelines or are estimated based on expert judgement and data from unpublished reports. A similar approach has also been used to estimate the uncertainty values given for the presented emission factors. The large uncertainties given for some of the emission factors reflect the corresponding high variability between individual sources, the types and extent of applied controls and, in some cases, the limited amount of data available. For many source categories (e.g., equipment leaks), the fugitive emissions have a skewed distribution where most of the emissions are emitted by only a small percentage of the population. Where uncertainties are less than or equal to ±100 percent, a normal distribution has been assumed, resulting in a symmetric distribution about the mean. Wherever the reported uncertainty U percent for a quantity Q is greater than 100 percent, the upper limit is Q(100+U)/100 and the lower limit is 100Q/(100+U). TIER 3 AND 2 Emission factors for conducting Tier 3 and Tier 2 assessments are not provided in the IPCC Guidelines due to the large amount of such information and the fact these data are continually being updated to include additional measurement results and to reflect development and penetration of new control technologies and requirements. Rather, the IPCC has developed an Emission Factor Database (EFDB) which will be periodically updated and is available through the Internet at www.ipcc-nggip.iges.or.jp/EFDB/main.php. In addition regular reviews of the literature should still be conducted to ensure that the best available factors are being used. The references for the chosen values should be clearly documented. Typically, emission factors are developed and published by environmental agencies and industry associations. It may be necessary to develop inventory estimates in consultation with these organisations. For example, the American Petroleum Institute(API) maintains a Compendium of Emissions Estimating Methodologies for the Oil and Gas Industry, most recently updated in 2004. The API Compendium is available at: http://api-ec.api.org/policy/index.cfm. A software tool for estimating greenhouse gas emissions using equations from the API Compendium is available at: http://ghg.api.org Guidance for estimating greenhouse gas emissions has also been developed by a number of national oil and gas industry associations. Such documents may be useful supplemental references and often provide tiered sourcespecific calculation procedures. Guidance on inventory accounting principles as they apply to the oil and gas industry, and boundary definitions is available in the Petroleum Industry Guidelines for Reporting Greenhouse Gas Emissions (International Petroleum Industry Environmental Conservation Association, 2003):

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www.ipieca.org/downloads/climate_change/GHG_Reporting_Guidelines.pdf. When selecting emission factors, the chosen values must be valid for the given application and be expressed on the same basis as the activity data. It also may be necessary to apply other types of factors to correct for site and regional differences in operating conditions and design and maintenance practices, for example: •

•

•

Composition profiles of gases from particular oil and gas fields to correct for the amount of CH4, formation CO2 and other target emissions; Annual operating hours to correct for the amount of time a source is in active service; Efficiencies of the specific control measures used.

The following are additional matters to consider in choosing emission factors: • • •

It is important to assess the applicability of the selected factors for the target application to ensure similar or comparable source behaviour and characteristics; In the absence of better data, it may sometimes be necessary to apply factors reported for other regions that practice similar levels of emission control and feature comparable types of equipment; Where measurements are performed to develop new emission factors, only recognised or defensible test procedures should be applied. The method and quality assurance (QA)/quality control (QC) procedures should be documented, the sampled sources should be representative of typical variations in the overall source population and a statistical analysis should be conducted to establish the 95 percent confidence interval on the average results.

4.2.2.4

C HOICE

OF ACTIVITY DATA

The activity data required to estimate fugitive emissions from oil and gas activities includes production statistics, infrastructure data (e.g., inventories of facilities/installations, process units, pipelines, and equipment components), and reported emissions from spills, accidental releases, and third-party damages. The basic activity data required for each tier and each type of primary source are summarised in Table 4.2.6, Typical Activity Data Requirements for each Assessment Approach by Type of Primary Source Category. TIER 1 The activity data required at the Tier 1 level has been limited to information that may either be obtained directly from typical national oil and gas statistics or easily estimated from this information. Table 4.2.7 below lists the activity data required by each of the Tier 1 emission factors presented in Tables 4.2.4 and 4.2.5, and gives appropriate guidance for obtaining or estimating each of the required activity values. TIER 2 The activity data required for the standard Tier 2 methodological approach is the same as that required for the Tier 1 approach. If the alternative Tier 2 approach described in Section 4.2.2.2 for crude oil systems is used, then additional, more detailed, information is required including average GOR values, information on the extent of gas conservation and factors for apportioning waste associated gas volumes between venting and flaring. This additional information should be developed based on input from the industry.

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TABLE 4.2.6 TYPICAL ACTIVITY DATA REQUIREMENTS FOR EACH ASSESSMENT APPROACH FOR FUGITIVE EMISSIONS FROM OIL AND GAS OPERATIONS BY TYPE OF PRIMARY SOURCE CATEGORY

Assessment Tier 3

Primary Source Category Process Venting/Flaring

Minimum Required Activity Data Reported Volumes Gas Compositions Proration Factors for Splitting Venting from Flaring

Storage Losses

Solution Gas Factors Liquid Throughputs Tank Sizes Vapour Compositions

Equipment Leaks

Facility/Installation Counts by Type Processes Used at Each Facility Equipment Component Schedules by Type of Process Unit Gas/Vapour Compositions

Gas-Operated Devices

Schedule of Gas-operated Devices by Type of Process Unit Gas Consumption Factors Type of Supply Medium Gas Composition

Accidental Releases & Third-Party Damages

Incident Reports/Summaries

Gas Migration to the Surface & Surface Casing Vent Blows

Average Emission Factors & Numbers of Wells

Drilling

Number of Wells Drilled Reported Vented/Flared Volumes from Drill Stem Tests Typical Emissions from Mud Tanks

Well Servicing

Tally of Servicing Events by Types

Pipeline Leaks

Type of Piping Material Length of Pipeline

Exposed Oils ands/Oil Shale

Exposed Surface Area Average Emission Factors

2

Venting and Flaring from Oil Production

Gas to Oil Ratios Flared and Vented Volumes Conserved Gas Volumes Re-injected Gas Volumes Utilised Gas Volumes Gas Compositions

1

4.66

All Others

Oil and Gas Throughputs

All

Oil and Gas Throughputs

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TABLE 4.2.7 GUIDANCE ON OBTAINING THE ACTIVITY DATA VALUES REQUIRED FOR USE IN THE TIER 1 APPROACH TO ESTIMATE FUGITIVE EMISSIONS FROM OIL AND GAS OPERATIONS Category Well Drilling Well Testing Well Servicing Gas Production

Gas Processing

Gas Transmission & Storage

Gas Distribution

Natural Gas Liquids Transport

Sub-Category

Required Activity Data Value

Guidance

All

103 m3 total oil production

Reference directly from national statistics.

All

103 m3 total oil production

Reference directly from national statistics.

All

103 m3 total oil production

Reference directly from national statistics.

106 m3 gas production

Reference directly from national statistics.

106 m3 gas production

Reference directly from national statistics.

Sweet Gas Plants

106 m3 raw gas feed

Sour Gas Plants

106 m3 raw gas feed

Reference directly from national statistics if total gas receipts by gas plants is reported, otherwise, assume this value is equal to total gas production. Apportion this value accordingly between sweet and sour plants. In the absence of any information to allow such apportioning assume all plants are sweet.

All

Deep-cut Extraction Plants (Straddle Plants)

106 m3 raw gas feed

Reference directly from national statistics if total gas receipts by straddle plants located on gas transmission systems is reported, otherwise, assume this value is equal to an appropriate portion of total marketable natural gas. In the absence of any information to make this apportionment, assume there are no straddle plants.

Default Weighted Total

106 m3 gas production

Reference directly from national statistics.

Transmission

106 m3 of marketable gas

Storage

106 m3 of marketable gas

Reference directly from national statistics using the value reported for total net supply. This is the sum of imports plus total net gas receipts from gas fields and processing or reprocessing plants after all upstream uses, losses and re-injection volumes have been deducted.

All

106 m3 of utility sales

Reference directly from national statistics if reported if available; otherwise, set equal to the amount of gas handled by gas transmission and storage systems minus exports.

Condensate

103 m3 Condensate and Pentanes Plus

Reference directly from national statistics.

Liquefied Petroleum Gas

103 m3 LPG

Reference directly from national statistics.

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TABLE 4.2.7(CONTNUED) GUIDANCE ON OBTAINING THE ACTIVITY DATA VALUES REQUIRED FOR USE IN THE TIER 1 APPROACH TO ESTIMATE FUGITIVE EMISSIONS FROM OIL AND GAS OPERATIONS Oil Production

Conventional Oil

103 m3 conventional oil production

Heavy Oil/Cold Bitumen

103 m3 heavy oil production

statistics.

103 m3 thermal bitumen production

Reference directly from national statistics.

Synthetic Crude (from Oilsands)

103 m3 synthetic crude production from oilsands

Reference directly from national statistics.

Synthetic Crude (from Oil Shale)

103 m3 synthetic crude production from oil shale

Reference directly from national statistics.

Default Weighted Total

103 m3 total oil production

Reference directly from national statistics.

All

103 m3 oil upgraded

Reference directly from national statistics if available; otherwise, set equal to total heavy oil and bitumen production minus any exports of these crude oils.

Pipelines

103 m3 oil transported by pipeline

Reference directly from national statistics if available; otherwise set equal to total crude oil production plus imports.

Tanker Trucks and Rail Cars

103 m3 oil transported by Tanker Truck

Reference directly from national statistics if available; otherwise, assume (as a first approximation) that 50 percent of the total crude.

103 m3 oil transported by Tanker Ship

Reference directly from national statistics using the value reported for crude oil exports, and apportion this amount to account for only the fraction exported by tanker ships. While exports may occur by pipeline, tanker ship, or tanker trucks, they will usually be almost exclusively by one of these methods. Tanker ships are assumed to be used almost exclusively for exports.

Oil Transport

Loading of Off-shore Production on Tanker Ships

Oil Refining All

4.68

Reference directly from national

Thermal Oil Production

Oil Upgrading

Refined Product Distribution

Reference directly from national statistics.

Gasoline

3

3

3

3

10 m oil refined.

10 m product distributed.

Reference directly from national statistics if available; otherwise set this value equal to total production plus imports minus exports.. Reference directly from national statistics if available; otherwise, set it equal to total gasoline production by refineries plus imports minus exports.

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TABLE 4.2.7(CONTINUED) GUIDANCE ON OBTAINING THE ACTIVITY DATA VALUES REQUIRED FOR USE IN THE TIER 1 APPROACH TO ESTIMATE FUGITIVE EMISSIONS FROM OIL AND GAS OPERATIONS

Diesel

Aviation Fuel

Jet Kerosene

3

3

3

3

3

3

10 m product transported.

10 m product transported.

10 m product transported.

Reference directly from national statistics if available; otherwise, set it equal to total gasoline production by refineries plus imports minus exports. Reference directly from national statistics if available; otherwise, set it equal to total gasoline production by refineries plus imports minus exports. Reference directly from national statistics if available; otherwise, set it equal to total gasoline production by refineries plus imports minus exports.

TIER 3 Specific matters to consider in compiling the detailed activity data required for use in a Tier 3 approach include the following: •

• • •

•

• •

•

Production statistics should be disaggregated to capture changes in throughputs (e.g., due to imports, exports, reprocessing, withdrawals, etc.) in progressing through oil and gas systems. Production statistics provided by national bureaux should be used in favour of those available from international bodies, such as the IEA or the UN, due to their generally better reliability and disaggregation. Regional, provincial/state and industry reporting groups may offer even more disaggregation. Production data used in estimating fugitive emissions should be corrected, where applicable, to account for any net imports or exports. It is possible that import and export data may be available for a country while production data are not; however, it is unlikely that the opposite would be true. Where coalbed methane is produced into a natural gas gathering system, any associated fugitive emissions should be reported under the appropriate natural gas exploration and production categories. This will occur by default since the produced gas becomes a commodity once it enters the gas gathering system and automatically gets accounted for the same way gas from any other well does when it enters the gathering system. The fact that gas is coming from a coal formation would only be discernable at a very disaggregated level. Where a coal formation is degassed, regardless of the reason, and the gas is not produced into a gathering system, the associated emissions should be allocated to the coal sector under the appropriate section of IPCC category 1.B.1. Vented and flared volumes from oil and gas statistics may be highly suspect since these values are usually estimates and not based on actual measurements. Additionally, the values are often aggregated and simply reported as flared volumes. Operating practices of each segment of the industry should be reviewed with industry representatives to determine if the reported volumes are actually vented or flared, or to develop appropriate apportioning of venting relative to flaring. Audits or reviews of each industry segment should also be conducted to determine if all vented and flared volumes are actually reported (for example, solution gas emissions from storage tanks and treaters, emergency flaring/venting, leakage into vent/flare systems, and blowdown and purging volumes may not necessarily be accounted for). Infrastructure data are more difficult to obtain than production statistics. Information concerning the numbers and types of major facilities and the types of processes used at these facilities may often be available from regulatory agencies and industry groups, or directly from the actual companies. Information on minor facilities (e.g., numbers of field dehydrators and field compressors) usually is not available, even from oil and gas companies. Consequently, assumptions must be made, based on local design practices, to estimate the numbers of these facilities. This may require some fieldwork to develop appropriate estimation factors or correlations. Many companies use computerised inspection-and-maintenance information management systems. These systems can be a very reliable means of counting major equipment units (e.g., compressor units, process heaters and boilers, etc.) at selected facilities. Also, some departments within a company may maintain databases of certain types of equipment or facilities for various internal reasons (e.g., tax accounting,

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• • •

• •

production accounting, insurance records, quality control programmes, safety auditing, license renewals, etc.). Efforts should be made to identify these potentially useful sources of information. Component counts by type of process unit may vary dramatically between facilities and countries due to differences in design and operating practices. Thus, while initially it may be appropriate to use values reported in the general literature, countries should strive to develop their own values. Use of consistent terminology and clear definitions is critical in developing counts of facilities and equipment components, and to allow any meaningful comparisons of the results with others. Some production statistics may be reported in units of energy (based on their heating value) and will need to be converted to a volume basis, or vice versa, for application of the available emission factors. Typically, where production values are expressed in units of energy, it is in terms of the gross (or higher) heating value of the product. However, where emission factors are expressed on an energy basis it is normally in terms of the net (or lower) heating value of the product. To convert from energy data on a GCV basis to a NCV basis, the International Energy Agency assumes a difference of 5 percent for oil and 10 percent for natural gas. Individual natural gas streams that are either very rich or high in impurities may differ from these average values. Emission factors and activity data must be consistent with each other. Oil and gas imports and exports will change the activity levels in corresponding downstream portions of these systems. Production activities will tend to be the major contributor to fugitive emissions from oil and gas activities in countries with low import volumes relative to consumption and export volumes. Gas transmission and distribution and petroleum refining will tend to be the major contributors to these emissions in countries with high relative import volumes. Overall, net importers will tend to have lower specific emissions than net exporters.

4.2.2.5

C OMPLETENESS

Completeness is a significant issue in developing an inventory of fugitive emissions for the oil and gas industry. It can be addressed through direct comparisons with other countries and, for refined inventories, through comparisons between individual companies in the same industry segment and subcategory. This requires the use of consistent definitions and classification schemes. For example, in Canada, the upstream petroleum industry has adopted a benchmarking scheme that compares the emission inventory results of individual companies in terms of production-energy intensity and production-carbon intensity. Such benchmarking allows companies to assess their relative environmental performance. It also flags, at a high level, anomalies or possible errors that should be investigated and resolved. The indicative factors presented in Table 4.2.8 may be used to qualify specific methane losses as being low, medium or high and help assess their reasonableness. If specific methane losses are appreciably less than the low benchmark or greater than the high benchmark, this should be explained; otherwise, it may be an indication of possible missed or double counted contributions, respectively. The ranking of specific methane losses relative to the presented indicative factors should not be used as a basis for choosing the most appropriate assessment approach; rather, total emissions (i.e. the product of activity data and emission factors), the complexity of the industry and available assessment resources should all be considered. Where emission inventories are developed based on a compilation of individual company-level inventories, care should be taken to ensure that all companies are included. Appropriate extrapolations may be needed to account for any non-reporting companies.

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TABLE 4.2.8 CLASSIFICATION OF GAS LOSSES AS LOW, MEDIUM OR HIGH AT SELECTED TYPES OF NATURAL GAS FACILITIES Yearly emission factors Facilities

Activity data

Low

Medium

High

Production and Processing

Net gas production (i.e. marketed production)

Transmission Pipeline Systems

Length of transmission pipelines

200

2 000

20 000

Compressor Stations

Installed compressor capacity

6 000

20 000

100 000

Underground Storage

Working capacity of underground storage stations

0.05

0.2

0.7

m3/MW/yr

% of working gas capacity 0.05

0.1

0.7

Gas throughput

Meter and Regulator Stations

Number of stations

1 000

5 000

50 000

Length of distribution network

100

1 000

10 000

Gas Use

Number of gas appliances

% of net production

m3/km/yr

LNG Plant (liquefaction or regasification)

Distribution

Units of Measure

0.005

0.05

0.1

% of throughput

m3/station/yr

m3/km/yr

m3/appliance/yr 2

5

20

Source: Adapted by the authors from currently unpublished work by the International Gas Union, and based on data for a dozen countries including Russia and Algeria.

Smaller individual sources, when aggregated nationally over the course of a year, may often be significant total contributors. Therefore, good practice is not to disregard them. Once a thorough assessment has been done, a basis exists for simplifying the approach and better allocating resources in the future to best reduce uncertainties in the results. Where a country has estimated its fugitive emissions from part or all of its oil and natural gas system based on a roll-up of estimates reported by individual oil and gas companies, it is good practice to document the steps taken to ensure that these results are complete, transparent and consistent across the time series. Corrections made to account for companies or facilities that did not report, and measures taken to avoid missed or double counting (particularly where ownership changes have occurred) and to assess uncertainties should be highlighted.

4.2.2.6

D EVELOPING

CONSISTENT TIME SERIES

Ideally, emission estimates will be prepared for the base year and subsequent years using the same method. The aim is to have emission estimates across the time series reflect true trends in greenhouse gas emissions. Emission or control factors that change over time (e.g., due to changes in source demographics or the penetration of control technologies) should be regularly updated and, each time, only applied to the period for which they are valid. For, example, if an emission control device is retrofit to a source then a new emission factor will apply to that source from then onwards; however, the previously applied emission factor reflecting conditions before the retrofit should still be applied for all previous years in the time series. If an emission factor has been refined

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through further testing and now reflects a better understanding of the source or source category, then all previous estimates should be updated to reflect the use of the improved factor and be reported in a transparent manner. Where some historical data are missing, it should still be possible to use source-specific measurement results combined with back-casting techniques to establish an acceptable relationship between emissions and activity data in the base year. Approaches for doing this will depend on the specific situation, and are discussed in general terms in Volume 1 Chapter 5 of the 2006 Guidelines. If emission estimates are developed based on a roll-up of individual company estimates, greater effort will be required to maintain time series consistency, particularly where frequent facility ownership changes occur and different methodologies and emission factors are applied by each new owner without also carrying these changes back through the time series.

4.2.2.7

U NCERTAINTY

ASSESSMENT

Sources of error that may occur include the following: •

•

Measurement errors;

•

Inherent uncertainties of the selected estimation techniques;

•

Poor understanding of temporal and seasonal variations in the sources;

•

Misapplication of activity data or emission factors;

•

Extrapolation errors;

•

Missing or incomplete information regarding the source population and activity data;

•

Over or under accounting due to confusion or inconsistencies in category divisions and source definitions;

•

Errors in reported activity data;

•

Missed accounting of intermediate transfer operations and reprocessing activities (for example, re-treating of slop oil, treating of foreign oil receipts and repeated dehydration of gas streams: in the field, at the plant, and then following storage); Differences in the effectiveness of control devices, potential deterioration of their performance over time and missed accounting of control measures.

Guidance regarding the assessment of uncertainties in emission factors and activity data are presented in the subsections below.

4.2.2.7.1

EMISSION FACTOR UNCERTAINTIES

The uncertainty in an emission factor will depend both on the accuracy of the measurements upon which it is based and the degree to which these results reflect the average behaviour of the target source population. Accordingly, emission factors developed based on data measured in one country may have one set of uncertainties when the factors are applied in that country and another set of uncertainties when they are applied similarly in a different country. Thus, while it is difficult to establish one set of uncertainties that will always apply, a set of default values has been provided for the default factors provided in Tables 4.2.4 and 4.2.5. These uncertainties are estimated based on expert judgement and reflect the level of uncertainty that may be expected when the corresponding emission factors are used to develop emission estimates at the national level. Use of the presented factors to estimate emissions from individual facilities or sources would be expected to result in much greater uncertainties.

4.2.2.7.2

ACTIVITY DATA UNCERTAINTIES

The percentages cited in this section are based on expert judgement and aim to approximate the 95 percent confidence interval around the central estimate. Gas compositions are usually accurate to within ±5 percent on individual components. Flow rates typically have errors of ±3 percent or less for sales volumes and ±15 percent or more for other volumes. Production statistics or disposition analyses 2 may not agree between different

2 A disposition analysis provides a reconciled accounting of produced hydrocarbons from the wellhead, or point of receipt, through to the final sales point or point of export. Typical disposition categories include flared/vented volumes, fuel usage, system losses, volumes added to/removed from inventory/storage, imports, exports, etc.

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reporting agencies even though they are based on the same original measurement results (e.g. due to possible differences in terminology and potential errors in summarising these data). These discrepancies may be used as an indication of the uncertainty in the data. Additional uncertainty will exist if there is any inherent bias in the original measurement results (for example, sales meters are often designed to err in favour of the customer, and liquid handling systems will have a negative bias due to evaporation losses). Random metering and accounting errors may be assumed to be negligible when aggregated over the industry. Counts of major facilities (e.g., gas plants, refineries and transmission compressor stations) will usually be known with little if any error (e.g., less than 5 percent). Where errors in these counts occur it is usually due to some uncertainties regarding the number of new facilities built and old facilities decommissioned during the time period. Counts of well site facilities, minor field installations and gas gathering compressor stations, as well as the type and amount of equipment at each site, will be much less accurately known, if known at all (e.g., at least ±25 percent uncertainty or more).

Estimates of emission reductions from individual control actions may be accurate to within a few percent to ±25 percent depending on the number of subsystems or sources considered.

4.2.3

Inventory Quality Assurance/Quality Control (QA/QC)

It is good practice to conduct quality control checks as outlined in Volume 1 Chapter 6 of the 2006 IPCC Guidelines, Tier 1 General Inventory Level QC Procedures, and expert review of the emission estimates. Additional quality control checks, as outlined in Volume 1 Chapter 5 of the 2006 IPCC Guidelines, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1 Chapter 4 of the 2006 IPCC Guidelines. In addition to the guidance in Volume 1 Chapter 6 of the 2006 IPCC Guidelines, specific procedures of relevance to this source category are outlined below. INDUSTRY INVOLV EM EN T Emission inventories for large, complex oil and gas industries will be susceptible to significant errors due to missed or unaccounted for sources. To minimise such errors, it is important to obtain active industry involvement in the preparation and refinement of these inventories. R EVI E W OF DIR EC T E MI SS ION M EA SUREM EN TS If direct measurements are used to develop country-specific emission factors, the inventory compiler should establish whether measurements at the sites were made according to recognised standard methods. If the measurement practices fail this criterion, then the use of these emissions data should be carefully evaluated, estimates reconsidered and qualifications documented. EMI SS ION FAC TORS C HECK The inventory compiler should compare measurement-based factors to IPCC default factors and factors developed by other countries with similar industry characteristics. If IPCC default factors are used, the inventory compiler should ensure that they are applicable and relevant to the category. If possible, the IPCC default factors should be compared to national or local data to provide further indication that the factors are applicable. AC TIV I TY DA TA CHECK Several different types of activity data may be required for this source category, depending on which methodological tier is used to estimate the emissions. Where activity data are available from multiple sources (i.e. from national statistics and industry organisations) these data sets should be checked against each other to assess reasonableness. Significant differences in data should be explained and documented. Trends in the main emission drivers and activity data over time should be checked and any anomalies investigated. EX TERNAL R EV IEW Emission inventories for large, complex oil and gas industries will be susceptible to significant errors due to missed or unaccounted for sources, or due to customization of average emission factors taken from a data source

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 2 : Energy

that represents estimates from another country or region with operating characteristics different from those in the country where the emission factor is being applied. To minimise such errors, it is important to obtain active industry involvement in the preparation and refinement of these inventories.

4.2.4

Reporting and Documentation

It is good practice to document and archive all information required to produce the national emissions inventory estimates, as outlined in Volume 1 Chapter 8 of the 2006 Guidelines. It may not be practical to include all supporting documentation in the inventory report. However, at a minimum, the inventory report should include summaries of the methods used and references to source data such that the reported emissions estimates are transparent and the steps in their calculation may be retraced. It is expected that many countries will use a combination of methodological tiers to evaluate the amount of fugitive greenhouse gas emissions from the different parts of their oil and natural gas systems. The specific choices should reflect the relative importance of the different subcategories and the availability of the data and resources needed to support the corresponding calculations. Table 4.2.9 is a sample template, with some example data entries, that may be used to conveniently summarize the applied methodologies and sources of emission factors and activity data. Since emission factors and estimation procedures are continually being improved and refined, it is possible for changes in reported emissions to occur without any real changes in actual emissions. Accordingly, the basis for any changes in results between inventory recalculations should be clearly discussed and those due strictly to changes in methods and factors should be highlighted. The issue of confidential business information will vary from region to region depending on the number of firms in the market and the nature of the business. The significance of this issue tends to increase in progressing downstream through the oil and gas industry. A common means to address such issues where they do arise is to aggregate the data using a reputable independent third party. The above reporting and documentation guidance is applicable to all methodological choices. Where Tier 3 approaches are employed, it is important to ensure that either the applied procedures are detailed in the inventory report or that available references for these procedures are cited since the IPCC Guidelines do not describe a standard Tier 3 approach for the oil and gas sector. There is a wide range in what potentially may be classified as a Tier 3 approach, and correspondingly, in the amount of uncertainty in the results. If available, summary performance and activity indicators should be reported to help put the results in perspective (e.g. total production levels and transportation distances, net imports and exports, and specific energy, carbon and emission intensities). Reported emission results should also include a trend analysis to show changes in emissions, activity data and emission intensities (i.e., average emissions per unit of activity indicator) over time. The expected accuracy of the results should be stated and the areas of greatest uncertainty clearly noted. This is critical for proper interpretation of the results and any claims of net reductions. The current trend by some government agencies and industry associations is to develop detailed methodology manuals and reporting formats for specific segments and subcategories of the industry. This is perhaps the most practical means of maintaining, documenting and disseminating the subject information. However, all such initiatives must conform to the common framework established in the IPCC Guidelines so that the emission results can be compared across countries.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Distribution of oil products

Other

1.B.2.a.iii.5

1.B.2.a.iii.6

Subcategory

2006 IPCC Guidelines for National Greenhouse Gas Inventories

1.B.2.b.i

Venting

Refining

1.B.2.a.iii.4

Natural Gas

Transport

1.B.2.a.iii.3

1.B.2.b

Production and Upgrading

1.B.2.a.iii.2

All Other

1.B.2.a.iii

Exploration

Flaring

1.B.2.a.ii

1.B.2.a.iii.1

Venting

Oil

1.B.2.a

1.B.2.a.i

Oil and Natural Gas

Name

Code

1.B.2

Sector

IPCC Source Category Method Type

Activity Data

Basis

Year

CH4

CO2

Basis/Reference

Emission Factors

N 2O

TABLE 4.2.9 FORMAT FOR SUMMARIZING THE APPLIED METHODOLOGY AND BASIS FOR ESTIMATED EMISSIONS FROM OIL AND NATURAL GAS SYSTEMS SHOWING SAMPLE ENTRIES

Country Specific Values Updated

Date

4.75

Chapter 4: Fugitive Emissions

Processing

Transmission and Storage

Distribution

Other

1.B.2.b.iii.3

1.B.2.b.iii.4

1.B.2.b.iii.5

1.B.2.b.iii.6

– IPCC Emission Factor Database

CS

EFDB

Gas Transmission

Equipment Leaks

Equipment Leaks

Equipment Leaks

Gas Production

All

All

Source Category

Well Servicing

Subcategory

2006 IPCC Guidelines for National Greenhouse Gas Inventories

– IPCC Default Emission Factors

– Country-Specific Emission Factors

D

– API Compendium

AP

Other emissions from Energy Production

Production

1.B.2.b.iii.2

1.B.3

Exploration

All Other

1.B.2.b.iii

1.B.2.b.iii.1

Flaring

Name

Code

1.B.2.b.ii

Sector

IPCC

Tier 2

Tier 1

Tier 1

Tier 1

Method

Number of facilities

Throughput

Throughput

Number of Active Wells

Type

Activity Data

Industry Survey

National Statistics

National Statistics

National Statistics

Basis

2005

2005

2005

2005

Year

CS

D

EFDB

D

CH4

CS

EFDB

EFDB

D

CO2

Basis/Reference

Emission Factors

-----

EFDB

EFDB

D

N 2O

TABLE 4.2.9 (CONTINUED) FORMAT FOR SUMMARIZING THE APPLIED METHODOLOGY AND BASIS FOR ESTIMATED EMISSIONS FROM OIL AND NATURAL GAS SYSTEMS SHOWING SAMPLE ENTRIES

Volume 2: Energy

2005

---

---

---

Country Specific Values Updated

Date

4.76

Chapter 4: Fugitive Emissions

References: Coal Mining BCTRSE (1992). Quantification of methane emissions from British coal mine sources’, prepared by British Coal Technical Services and Research Executive for the Working Group on Methane Emissions, The Watt Committee on Energy, UK. Bibler C.J. et al (1992). Assessment of the potential for economic development and utilisation of coalbed methane in Czechoslovakia’. EPA/430/R-92/1008. US Environmental Protection Agency, Office of Air and Radiation, Washington, DC, USA. Franklin, P., Scheehle, E., Collings R.C., Cote M.M. and Pilcher R.C. (2004). White Paper: ‘Proposed methodology for estimating emission inventories from abandoned coal mines’. USEPA, Prepared for 2006 IPCC Greenhouse Gas Inventories Guidelines Fourth Authors Experts Meeting. Energy : Methane Emissions for Coal Mining and Handling, Arusha, Tanzania IPCC/UNEP/OECD/IEA, (1997). Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Paris: Intergovernmental Panel on Climate Change; J. T. Houghton, L.G. Meiro Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell, eds.; Cambridge University Press, Cambridge, U.K. IPCC/UNEP/OECD/IEA, (2000). ‘IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories’ UNDP & WMO. Kershaw S, (2005). Development of a methodology for estimating emissions of methane from abandoned coal mines in the UK, White Young Green for the Department for the Environment, Food and Rural Affairs. Lama RD (1992). Methane gas emissions from coal mining in Australia: estimates and control Strategies’ in Proceedings of the IEA/OECD Conference on Coal, the Environment and Development: Technologies to Reduce Greenhouse Gas Emissions, IEA/OECD, Paris, France, pp. 255-266. Murtha, James A., (2002). Sums and products of distributions: Rules of thumb and applications’, Society of Petroleum Engineers, Paper 77422. Mutmansky, J.M., and Y. Wang, (2000). Analysis of potential errors in determination of coal mine annual methane emissions, Mineral Resources Engineering, 9, 2, pp. 465-474. Pilcher R.C. et al (1991). Assessment of the potential for economic development and utilisation of coalbed methane in Poland’. EPA/400/1-91/032, US Environmental Protection Agency, Washington, DC, USA. US EPA (1993a). Anthropogenic methane emissions in the United States: estimates for the 1990 Report to the US Congress, US Environmental Protection Agency, Office of Air and Radiation, Washington DC, USA. US EPA (1993b). Global anthropogenic methane emissions; estimates for the 1990 Report to the US Congress, US Environmental Protection Agency, Office of Policy, Planning and Evaluation. Washington, DC, USA. Williams, D.J. and Saghafi, A. (1993). Methane emission from coal mining – a perspective’. Coal J., 41, 37-42. Zimmermeyer G. (1989). ‘Methane emissions and hard coal mining’, gluckaufhaus, Essen, Germany, Gesamtverband des deutschen Steinkohlenbergbaus, personal communication.

References: Oil and Gas American Petroleum Institute. 2004. Compendium of Greenhouse Gas Emissions Estimation Methodologies for the Oil and Gas Industry. Washington, DC. Canadian Association of Petroleum Producers (1999). CH4 and VOC Emissions From The Canadian Upstream Oil and Gas Industry. Volumes 1 to 4. CValgary, AB. Canadian Association of Petroleum Producers (2004). A National Inventory of Greenhouse Gas (GHG), Criteria Air Contaminant (CAC) and Hydrogen Sulphide (H2S) Emissions by the Upstream Oil and Gas Industry. Volumes 1 to 5. Calgary, AB. Canadian Petroleum Products Institute (CPPI) and Environment Canada (1991), Atmospheric Emissions from Canadian Petroleum Refineries and the Associated Gasoline Distribution System for 1988. CPPI Report No. 91-7. Prepared by B.H Levelton and Associates Ltd. and RTM Engineering Ltd. Gas Research Institute and US Environmental Protection Agency (1996). Methane Emissions from the Natural gas Industry. Volumes 1 to 15. Chicago, IL. IPIECA (2003). “Petroleum Industry Guidelines for Reporting Greenhouse Gas Emissions.” International Petroleum Industry Environmental Conservation Association, London, UK.(December 2003) Joint EMEP/CORINAIR (1996), Atmospheric Emission Inventory Guidebook. Volume 1, 2.

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Mohaghegh, S.D., L.A. Hutchins and C.D. Sisk. 2002. Prudhoe Bay Oil Production Optimization: Using Virtual intelligence Techniques, Stage One: Neural Model Building. Presented at the SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, 29 September–2 October 2002. SFT/SN 2000b: The Norwegian Emission Inventory. Documentation of methodology and data for estimating emissions of greenhouse gases and lomg-range transboundary air pollutants. Statistics Norway/ Norwegian Pollution Control Authority. SN report 2000/1 US EPA (1995), Compilation of Air Pollutant Emission Factors. Vol. I: Stationary Point and Area Sources, 5th Edition, AP-42; US Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina, USA. US EPA (1999). Methane Emissions from the U.S. Petroleum Industry. EPA Report No. EPA-600/R-99-010, p. 158, prepared by Radian International LLC for United States Environmental Protection Agency, Office of Research and Development.

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Chapter 5: Carbon Dioxide Transport, Injection and Geological Storage

CHAPTER 5

CARBON DIOXIDE TRANSPORT, INJECTION AND GEOLOGICAL STORAGE

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.1

Volume 2: Energy

Authors Sam Holloway (UK), Anhar Karimjee (USA), Makoto Akai (Japan), Riitta Pipatti (Finland), and Kristin Rypdal (Norway)

5.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Carbon Dioxide Transport, Injection and Geological Storage

Contents 5

Carbon Dioxide Transport, Injection and Geological Storage 5.1

Introduction ...........................................................................................................................................5.5

5.2

Overview ...............................................................................................................................................5.5

5.3

CO2 capture ...........................................................................................................................................5.7

5.4

CO2 transport.........................................................................................................................................5.8

5.4.1

CO2 transport by pipeline ..............................................................................................................5.8

5.4.2

CO2 transport by ship ..................................................................................................................5.10

5.4.3

Intermediate storage facilities on CO2 transport routes ...............................................................5.10

5.5

CO2 injection .......................................................................................................................................5.10

5.6

Geological storage of CO2...................................................................................................................5.11

5.6.1 5.7

Description of emissions pathways/sources ................................................................................5.11

Methodological issues .........................................................................................................................5.13

5.7.1

Choice of method ........................................................................................................................5.14

5.7.2

Choice of emission factors and activity data ...............................................................................5.16

5.7.3

Completeness ..............................................................................................................................5.17

5.7.4

Developing a consistent time series.............................................................................................5.17

5.8

Uncertainty assessment .......................................................................................................................5.18

5.9

Inventory Quality Assurance/Quality Control (QA/QC).....................................................................5.18

5.10

Reporting and Documentation.............................................................................................................5.20

Annex 5.1

Summary description of potential monitoring technologies for geological CO2 storage sites ....5.22

References

.....................................................................................................................................................5.31

Equation Equation 5.1

Total national emissions ......................................................................................................5.16

Figures Figure 5.1

Schematic representation of the carbon capture and storage process with numbering linked to systems discussion above. ............................................................5.6

Figure 5.2

CO2 capture systems (After the SRCCS):..............................................................................5.7

Figure 5.3

Procedures for estimating emissions from CO2 storage sites ..............................................5.13

Figure A1

An illustration of the potential for leakage of CO2 from a geological storage reservoir to occur outside the storage site...............................................................................................5.22

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.3

Volume 2: Energy

Table

Table 5.1

Source categories for CCS.....................................................................................................5.7

Table 5.2

Default Tier 1 emission factors for pipeline transport of CO2 from a CO2 capture site to the final storage site...................................................................5.10

Table 5.3

Potential emission pathways from geological reservoirs.....................................................5.12

Table 5.4

Overview table: overview of CO2 capture, transport, injection and CO2 for long-term storage.............................................................................................5.19

Table A 5.1

Potential deep subsurface monitoring technologies and their likely application .................5.24

Table A 5.2

Potential shallow subsurface monitoring technologies and their likely application ............5.26

Table A 5.3

Technologies for determining fluxes from ground or water to atmosphere, and their likely application ..............................................................................5.27

Table A 5.4

Technologies for detection of raised CO2 levels in air and soil (leakage detection)............5.28

Table A 5.5

Proxy measurements to detect leakage from geological CO2 storage sites..........................5.29

Table A 5.6

Technologies for monitoring CO2 levels in sea water and their likely application..............5.30

Box Box 5.1

5.4

Derivation of default emission factors for CO2 pipeline transport ........................................5.9

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Carbon Dioxide Transport, Injection and Geological Storage

5 CARBON DIOXIDE TRANSPORT, INJECTION AND GEOLOGICAL STORAGE 5.1

INTRODUCTION

Carbon dioxide (CO2) capture and storage (CCS) is an option in the portfolio of actions that could be used to reduce greenhouse gas emissions from the continued use of fossil fuels. At its simplest, the CCS process is a chain consisting of three major steps: the capture and compression of CO2 (usually at a large industrial installation1 ), its transport to a storage location and its long-term isolation from the atmosphere. IPCC (2005) has produced a Special Report on Carbon Dioxide Capture and Storage (SRCCS), from which additional information on CCS can be obtained. The material in these Guidelines has been produced in consultation with the authors of the SRCCS. Geological storage can take place in natural underground reservoirs such as oil and gas fields, coal seams and saline water-bearing formations utilizing natural geological barriers to isolate the CO2 from the atmosphere. A description of the storage processes involved is given in Chapter 5 of the SRCCS. Geological CO2 storage may take place either at sites where the sole purpose is CO2 storage, or in tandem with enhanced oil recovery, enhanced gas recovery or enhanced coalbed methane recovery operations (EOR, EGR and ECBM respectively). These Guidelines provide emission estimation guidance for carbon dioxide transport, injection and geological storage (CCGS) only. No emissions estimation methods are provided for any other type of storage option such as ocean storage or conversion of CO2 into inert inorganic carbonates. With the exception of the mineral carbonation of certain waste materials, these technologies are at the research stage rather than the demonstration or later stages of technological development IPCC (2005). If and when they reach later stages of development, guidance for compiling inventories of emissions from these technologies may be given in future revisions of the Guidelines. Emissions resulting from fossil fuels used for capture, compression, transport, and injection of CO2, are not addressed in this chapter. Those emissions are included and reported in the national inventory as energy use in the appropriate stationary or mobile energy use categories. Fuel use by ships engaged in international transport will be excluded where necessary by the bunker rules, whatever the cargo, and it is undesirable to extend the bunker provisions to emissions from any energy used in operating pipelines.

5.2

OVERVIEW

In these Guidelines, the CO2 capture and geological storage chain is subdivided into four systems (Figure 5.1) 1.

Capture and compression system. The systems boundary includes capture, compression and, where necessary, conditioning, for transport.

2.

Transport system. Pipelines and ships are considered the most likely means of large-scale CO2 transport. The upstream systems boundary is the outlet of the compression / conditioning plant in the capture and compression system. The downstream systems boundary is the downstream end of a transport pipeline, or a ship offloading facility. It should be noted that there may be compressor stations located along the pipeline system, which would be additional to any compression in System 1 or System 3.

3.

Injection system. The injection system comprises surface facilities at the injection site, e.g. storage facilities, distribution manifold at end of transport pipeline, distribution pipelines to wells, additional compression facilities, measurement and control systems, wellhead(s) and the injection wells. The upstream systems boundary is the downstream end of transport pipeline, or ship offloading facility. The downstream systems boundary is the geological storage reservoir.

4.

Storage system. The storage system comprises the geological storage reservoir.

1

Examples of large point sources of CO2 where capture is possible include power generation, iron and steel manufacturing, natural gas processing, cement manufacture, ammonia production, hydrogen production and ethanol manufacturing plants.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.5

Volume 2: Energy

Figure 5.1

Schematic representation of the carbon capture and storage process with numbering linked to systems discussion above.

Plant power plant, industrial process 1

2

3

1

1

1

CO2 Capture

Liquefaction

Intermediate Storage

Compression

Ship Transport

Pipeline Transport

Intermediate Storage

Injection

Injection

2

2

3

4 Geological Storage Site

Possible Emissions (emission values linked to Table 5.1)

This chapter does not include guidance for CO2 capture and compression. A brief summary and information on where to find emissions estimation guidelines for CO2 capture and compression can be found in Section 5.3. Guidelines for compiling inventories of emissions from the CO2 transport, injection and storage systems of the CCGS chain are given in Sections 5.4, 5.5 and 5.6 of this Chapter, respectively. Fugitive emissions from surface facilities at EOR, EGR and ECBM site (with or without CO2 storage) are classified as oil and gas operations and Volume 2, Chapter 4 provides guidance on estimating these emissions. Emissions from underground storage reservoirs at EOR, EGR and ECBM sites are classified as emissions from geological storage sites and Section 5.7 of this Chapter provides guidance on estimating these emissions. Table 5.1 shows the categories in which the emissions from the CO2 transport, injection and storage systems are reported.

5.3

CO 2 CAPTURE

Anthropogenic carbon dioxide emissions arise mainly from combustion of fossil fuels (and biomass) in the power generation, industrial, buildings and transport sectors. CO2 is also emitted from non-combustion sources in certain industrial processes such as cement manufacture, natural gas processing and hydrogen production. CO2 capture produces a concentrated stream of CO2 at high pressure that can be transported to a storage site and stored. In these Guidelines, the systems boundary for capture includes compression and any dehydration or other conditioning of the CO2 that takes place before transportation. Electric power plants and other large industrial facilities are the primary candidates for CO2 capture, although it is the high purity streams of CO2 separated from natural gas in the gas processing industry that have been captured and stored to date. Available technology is generally deployed in a way that captures around 85-95 percent of the CO2 processed in a capture plant IPCC (2005). Figure 5.2, taken from the SRCCS provides an overview of the relevant processes. The main techniques are briefly described below. Further detail is available in Chapter 3 of the SRCCS: •

•

5.6

Post-combustion capture: CO2 can be separated from the flue gases of the combustion plant or from natural gas streams and fed into a compression and dehydration unit to deliver a relatively clean and dry CO2 stream to a transportation system. These systems normally use a liquid solvent to capture the CO2. Pre-combustion capture: This involves reacting a fuel with oxygen or air, and/or steam to produce a ‘synthesis gas’ or ‘fuel gas’ composed mainly of carbon monoxide and hydrogen. The carbon monoxide is reacted with steam in a catalytic reactor, called a shift converter, to give CO2 and more hydrogen. CO2 is then separated from the gas mixture, usually by a physical or chemical absorption process, resulting in a hydrogen-rich fuel which can be used in many applications, such as boilers, furnaces, gas turbines and fuel cells. This technology is widely used in hydrogen production, which is used mainly for ammonia and fertilizer manufacture, and in petroleum refining operations. Guidance on how to estimate and report emissions from this process is provided in Chapter 2, section 2.3.4 of this Volume.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Carbon Dioxide Transport, Injection and Geological Storage

•

Oxy-fuel capture: In oxy-fuel combustion, nearly pure oxygen is used for combustion instead of air, resulting in a flue gas that is mainly CO2 and H2O. This flue gas stream can directly be fed into a CO2 compression and dehydration unit. This technology is at the demonstration stage. Guidance on how to estimate and report emissions from this process is provided in Chapter 2, section 2.3.4 of this volume. TABLE 5.1 SOURCE CATEGORIES FOR CCS Carbon dioxide Transport and Storage

1

C

1

C

1

1

C

1

1

C

1

Carbon dioxide (CO2) capture and storage (CCS) involves the capture of CO2, its transport to a storage location and its long-term isolation from the atmosphere. Emissions associated with CO2 transport, injection and storage are covered under category 1C. Emissions (and reductions) associated with CO2 capture should be reported under the IPCC sector in which capture takes place (e.g. Stationary Combustion or Industrial Activities).

Transport of CO2

Fugitive emissions from the systems used to transport captured CO2 from the source to the injection site. These emissions may comprise fugitive losses due to equipment leaks, venting and releases due to pipeline ruptures or other accidental releases (e.g. temporary storage).

a

Pipelines

Fugitive emissions from the pipeline system used to transport CO2 to the injection site.

1

b

Ships

Fugitive emissions from the ships used to transport CO2 to the injection site.

C

1

c

Other (please specify)

Fugitive emissions from other systems used to transport CO2 to the injection site and temporary storage.

1

C

2

Injection and Storage

Fugitive emissions from activities and equipment at the injection site and those from the end containment once the CO2 is placed in storage.

1

C

2

a

Injection

Fugitive emissions from activities and equipment at the injection site.

1

C

2

b

Storage

Fugitive emissions from the end containment once the CO2 is placed in storage.

1

C

3

Other

Any other emissions from CCS not reported elsewhere.

Figure 5.2

CO 2 capture systems (After the SRCCS): N2 O2

Post combustion

Coal Gas Biomass

CO2 Separation

Power & Heat Air

Coal Gas Biomass

Pre combustion

CO2

Air/O2 Steam

Gasification Gas, Oil

CO2 H2

Reformer +CO2 Sep

Power & Heat

N2 O 2

Air

Oxyfuel

Coal Gas Biomass

Power & Heat O2 Air

Air Separation

CO2 Compression & Dehydration

CO2

N2

Air/O2

Industrial

Coal Gas Processes Biomass

Process +CO2 Sep.

Raw material

CO2

Gas, Ammonia, Steel

As already mentioned in a number of industrial processes, chemical reactions lead to the formation of CO2 in quantities and concentrations that allow for direct capture or separation of the CO2 from their off gases, for example: ammonia production, cement manufacture, ethanol manufacture, hydrogen manufacture, iron and steel manufacture, and natural gas processing plant.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.7

Volume 2: Energy

The location of guidelines for compiling inventories of emissions from the CO2 capture and compression system depends on the nature of the CO2 source:

• •

•

•

Stationary combustion systems (mainly electric power and heat production plants): Volume 2, Chapter 2, Section 2.3.4. Natural gas processing plants: Volume 2, Section 4.2.1. Hydrogen production plants: Volume 2, Section 4.2.1. Capture from other industrial processes: Volume 3 (IPPU) Chapter 1, Section 1.2.2, and specifically for (i)

Cement manufacture: IPPU Volume, Section 2.2

(ii)

Methanol manufacture: IPPU Volume, Section 3.9

(iii)

Ammonia production: IPPU Volume, Section 3.2

(iv)

Iron and steel manufacture: IPPU Volume section 4.2

Negative emissions may arise from the capture and compression system if CO2 generated by biomass combustion is captured. This is a correct procedure and negative emissions should be reported as such. Although many of the potential emissions pathways are common to all types of geological storage, some of the emission pathways in enhanced hydrocarbon recovery operations differ from those for geological CO2 storage without enhanced hydrocarbon recovery. In EOR operations, CO2 is injected into the oil reservoir, but a proportion of the amount injected is commonly produced along with oil, hydrocarbon gas and water at the production wells. The CO2-hydrocarbon gas mixture is separated from the crude oil and may be reinjected into the oil reservoir, used as fuel gas on site or sent to a gas processing plant for separation into CO2 and hydrocarbon gas, depending upon its hydrocarbon content. EGR and ECBM processes attempt to avoid CO2 production because it is costly to separate the CO2 from a produced gas mixture. CO2 separated from the hydrocarbon gas may be recycled and re-injected in the EOR operation, or vented; depending on the economics of recycling versus injecting imported CO2. CO2-rich gas is also released from the crude oil storage tanks at the EOR operation. This vapour may be vented, flared or used as fuel gas depending upon its hydrocarbon content. Thus there are possibilities for additional sources of fugitive emissions from the venting of CO2 and the flaring or combustion of CO2-rich hydrocarbon gas, and also from any injected CO2 exported with the incremental hydrocarbons. These emissions along with fugitive emissions from surface operations at EOR, and EGR and ECBM sites (from the injection of CO2, and/or the production, recycling, venting, flaring or combustion of CO2rich hydrocarbon gas), and including any injected CO2 exported with the incremental hydrocarbons, can be estimated and reported using the higher methods described guidance given in Volume 2 Chapter 4.

5.4

CO 2 TRANSPORT

Fugitive emissions may arise e.g. from pipeline breaks, seals and valves, intermediate compressor stations on pipelines, intermediate storage facilities, ships transporting low temperature liquefied CO2, and ship loading and offloading facilities. Emissions from transport of captured CO2 are reported under category 1C (see Table 5.1). CO2 pipelines are the most prevalent means of bulk CO2 transport and are a mature market technology in operation today. Bulk transport of CO2 by ship also already takes place, though on a relatively minor scale. This occurs in insulated containers at temperatures well below ambient, and much lower pressures than pipeline transport. Transport by truck and rail is possible for small quantities of CO2, but unlikely to be significant in CCS because of the very large masses likely to be captured. Therefore no methods of calculating emissions from truck and rail transport are given here. Further information on CO2 transport is available in Chapter 4 of the SRCCS (IPCC 2005).

5.4.1

CO 2 transport by pipeline

To estimate emissions from pipeline transport of CO2, default emission factors can be derived from the emission factors for transmission (pipeline transport) of natural gas as provided in section 4.2 of this volume. The Tier 1 emission factors for natural gas pipeline transport, presented in, Tables 4.2.4 and 4.2.5 are provided on the basis of gas throughput primarily because pipeline length is not a national statistic that is commonly available. However, fugitive emissions from pipeline transport are largely independent of the throughput, but depend on the size of and the equipment installed in the pipeline systems. Since it is assumed that there exists a relationship between the size of the systems and natural gas used, such an approach is acceptable as Tier 1 method for natural gas transport.

5.8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Carbon Dioxide Transport, Injection and Geological Storage

The above might not be true for the transport of CO2 in CCS applications. Since it is good practice to treat both capture and storage in a per plant or facility basis, the length of the transporting CO2 pipeline system will be known and should be used to estimate emissions from transport. BOX 5.1 DERIVATION OF DEFAULT EMISSION FACTORS FOR CO2 PIPELINE TRANSPORT

The pressure drop of a gas over any geometry is described by: ΔP =

f l ρ ∗v2 2 D

in which • • • •

v is the linear velocity of the gas through the leak and, with the same size of the leak, is proportional to the leaking volume; ρ is the density of the gas; f is the dimensionless friction number l/D (length divided by diameter) is characterizing the physical size of the system.

For leaks, f = 1 and independent on the nature of the gas. So assuming the internal pressure of the pipe-line and the physical dimensions being the same for CO2 and CH4 transport, the leak-velocity is inversely proportional to the root of the density of the gas and hence proportional to the root of the molecular mass. So when ΔP is the same for methane and carbon dioxide v~

1

ρ

The molecule mass of CO2 is 44 and of CH4 is 16. So on a mass-basis the CO2-emission rate is 44 = 1.66 times the CH4-emission rate. 16 From this the default emission factors for CO2 pipeline transport are obtained by multiplying the relevant default emission factorsa in Table 4.2.8 for natural gas (is mainly CH4) by a factor of 1.66. Notes: a

to convert the factors expressed in m3 to mass units, a specific mass of 0.7 kg/m3 for methane is applied. See chapter 5 in: R.H. Perry, D. Green, Perry's chemical engineers handbook, 6th edition, McGraw Hill Book Company - New York, 1984. Table 4.2.8 in section 4.2 of this volume provides indicative leakage factors for natural gas pipeline transport. To obtain Tier 1 default emission factors for CO2 transport by pipeline these values should be converted from cubic metres to mass units and multiplied by 1.66 (see Box 1). The resulting default emission factors are given in Table 5.2.

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TABLE 5.2 DEFAULT TIER 1 EMISSION FACTORS FOR PIPELINE TRANSPORT OF CO2 FROM A CO2 CAPTURE SITE TO THE FINAL STORAGE SITE

Value Emission Source Low Fugitive emissions from CO2 transportation by pipeline

0.00014

Medium

High

0.0014

0.014

Uncertainty

Units of Measure

± a factor of 2

Gg per year and per km of transmission pipeline

Although the leakage emissions from pipeline transport are independent of throughput, the number of leaks is not necessarily correlated to the length of the pipeline. The best correlation will be between the number and type of equipment components and the type of service. Most of the equipment tends to occur at the facilities connected to the pipeline rather than with the pipeline itself. In fact, unless the CO2 is being transported over very large distances and intermediate compressor stations are required, virtually all of the fugitive emissions from a CCS system will be associated with the initial CO2 capture and compression facilities at the start of the pipeline and the injection facilities at the end of the pipeline, with essentially no emissions from the pipeline itself. In Tier 3 approach, the leakage emissions from the transport pipeline could be obtained from data on number and type of equipment and equipment-specific emission factors.

5.4.2

CO 2 transport by ship

Default emission factors for fugitive emissions from CO2 transport by ship are not available. The amounts of gas should be metered during loading and discharge using flow metering and losses reported as fugitive emissions of CO2 resulting from transport by ship under category 1C1 b.

5.4.3

Intermediate storage facilities on CO2 transport routes

If there is a temporal mismatch between supply and transport or storage capacity, a CO2 buffer (above ground or underground) might be needed to temporarily store the CO2. If the buffer is a tank, fugitive emissions should be measured and treated as part of the transport system and reported under category 1C1 c (other). If the intermediate storage facility (or buffer) is a geological storage reservoir, fugitive emissions from it can be treated in the same way as for any other geological storage reservoir (see Section 5.6 of this Chapter) and reported under category 1C3.

5.5

CO 2 INJECTION

The injection system comprises surface facilities at the injection site, e.g. storage facilities, any distribution manifold at the end of the transport pipeline, distribution pipelines to wells, additional compression facilities, measurement and control systems, wellhead(s) and the injection wells. Additional information on the design of injection wells can be found in the SRCCS, Chapter 5, Section 5.5. Meters at the wellhead measure the flow rate, temperature and pressure of the injected fluid. The wellhead also contains safety features to prevent the blowout of the injected fluids. Safety features, such as a downhole safety valve or check valve in the tubing, may also be inserted below ground level, to prevent backflow in the event of the failure of the surface equipment. Valve and other seals may be affected by supercritical CO2, so appropriate materials will need to be selected. Carbon steel and conventional cements may be liable to be attacked by highly saline brines and CO2-rich fluids (Scherer et al. 2005). Moreover the integrity of CO2 injection wells needs to be maintained for very long terms, so appropriate well construction materials and regulations will be needed. Cements used for sealing between the well and the rock formation and, after abandonment, plugging the well, must also be CO2/salt brine resistant over long terms. Such cements have been developed but need further testing. Due to the potential for wells to act as conduits for CO2 leakage back to the atmosphere, they should be monitored as part of a comprehensive monitoring plan as laid out in Section 5.7 of this Chapter. The amount of CO2 injected into a geological formation through a well can be monitored by equipment at the wellhead, just before it enters the injection well. A typical technique is described by Wright and Majek (1998). Meters at the wellhead continuously measure the pressure, temperature and flow rate of the injected gas. The

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composition of the imported CO2 commonly shows little variation and is analyzed periodically using a gas chromatograph. The mass of CO2 passing through the wellhead can then be calculated from the measured quantities. No default method is suggested and the reporting of the mass of CO2 injected as calculated from direct measurements is good practice. If the pressure of the CO2 arriving at the storage site is not as high as the required injection pressure, compression will be necessary. Any emissions from compression of the stored gas at the storage site should be measured and reported.

5.6

GEOLOGICAL STORAGE OF CO 2

Chapter 5 of the SRCCS (IPCC 2005) indicates that geological storage of carbon dioxide may take place onshore or offshore, in: • • • •

Deep saline formations. These are porous and permeable reservoir rocks containing saline water in their pore spaces. Depleted or partially depleted oil fields - either as part of, or without, enhanced oil recovery (EOR) operations. Depleted or partially depleted natural gas fields – either with or without enhanced gas recovery (EGR) operations. Coal seams (= coal beds) – either with or without enhanced coalbed methane recovery (ECBM) operations.

Additionally, niche opportunities for storage may arise from other concepts such as storage in salt caverns, basalt formations and organic-rich shales. Further information on these type of storage sites and the trapping mechanisms that retain CO2 within them can be found in Chapter 5 of the SRCCS (IPCC 2005).

5.6.1

Description of emissions pathways/sources

The Introduction to the SRCCS states that >99% of the CO2 stored in geological reservoirs is likely to remain there for over one thousand years. Therefore potential emissions pathways created or activated by slow or longterm processes need to be considered as well as those that may act in the short to medium term (decades to centuries). In these Guidelines the term migration is defined as the movement of CO2 within and out of a geological storage reservoir whilst remaining below the ground surface or the sea bed, and the term leakage is defined as a transfer of CO2 from beneath the ground surface or sea bed to the atmosphere or ocean. The only emissions pathways that need to be considered in the accounting are CO2 leakage to the ground surface or seabed from the geological storage reservoir2. Potential emission pathways from the storage reservoir are shown in Table 5.3. There is a possibility that methane emissions, as well as CO2 emissions, could arise from geological storage reservoirs that contain hydrocarbons. Although there is insufficient information to provide guidance for estimating methane emissions, it would be good practice to undertake appropriate assessment of the potential for methane emissions from such reservoirs and, if necessary, include any such emissions attributable to the CO2 storage process in the inventory.

2

Emissions of CO2 may occur as free gas or gas dissolved in groundwater that reaches the surface e.g. at springs.

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TABLE 5.3 POTENTIAL EMISSION PATHWAYS FROM GEOLOGICAL RESERVOIRS Type of emission Direct leakage pathways created by wells and mining

•

Potential emissions pathways/ sources

•

• Natural leakage and migration pathways (that may lead to emissions over time)

•

• • •

•

•

Other Fugitive Emissions at the Geological Storage Site

5.12

•

Operational or abandoned wells

Well blow-outs (uncontrolled emissions from injection wells)

Future mining of CO2 reservoir

Through the pore system in low permeability cap rocks if the capillary entry pressure is exceeded or the CO2 is in solution If the cap rock is locally absent Via a spill point if reservoir is overfilled Through a degraded cap rock as a result of CO2/water/rock reactions Via dissolution of CO2 into pore fluid and subsequent transport out of the storage site by natural fluid flow Via natural or induced faults and/or fractures

Fugitive methane emissions could result from the displacement of CH4 by CO2 at geological storage sites. This is particularly the case for ECBM, EOR, and depleted oil and gas reservoirs.

Additional comments •

•

• •

• • •

• •

It is anticipated that every effort will be made to identify abandoned wells in and around the storage site. Inadequately constructed, sealed, and/or plugged wells may present the biggest potential risk for leakage. Techniques for remediating leaking wells have been developed and should be applied if necessary. Possible source of high-flux leakage, usually over a short period of time. Blowouts are subject to remediation and likely to be rare as established drilling practice reduces risk. An issue for coal bed reservoirs

Proper site characterization and selection and controlled injection pressure can reduce risk of leakage.

Proper site characterization and selection can reduce risk of leakage. Proper site characterization and selection, including an evaluation of the hydrogeology, can reduce risk of leakage. Proper site characterization and selection can reduce risk of leakage. Detailed assessment of cap rock and relevant geochemical factors will be useful. Proper site characterization and selection, including an evaluation of the hydrogeology, can determine/reduce risk of leakage. Possible source of high-flux leakage. Proper site characterization and selection and controlled injection pressure can reduce risk of leakage.

Needs appropriate assessment.

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5.7

METHODOLOGICAL ISSUES

Geological conditions vary widely and only a few published studies of monitoring programmes that identify and quantify fugitive anthropogenic carbon dioxide emissions from geological storage operations currently exist (Arts et al. 2003, Wilson and Monea 2005; Klusman 2003a, b, c). Although the Summary for Policymakers of the SRCCS suggests that properly selected geological storage sites are likely to retain greater than 99 percent of the stored CO2 over 1000 years and may retain it for up to millions of years, at the time of writing, the small number of monitored storage sites means that there is insufficient empirical evidence to produce emission factors that could be applied to leakage from geological storage reservoirs. Consequently, this guidance does not include Tier 1 or Tier 2 methodology. However, there is the possibility of developing such methodologies in the future, when more monitored storage sites are in operation and existing sites have been operating for a long time (Yoshigahara et al. 2005). However a site-specific Tier 3 approach can be developed. Monitoring technologies have been developed and refined over the past 30 years in the oil and gas, groundwater and environmental monitoring industries (also see Annex 1). The suitability and efficacy of these technologies can be strongly influenced by the geology and potential emissions pathways at individual storage sites, so the choice of monitoring technologies will need to be made on a site-by-site basis. Monitoring technologies are advancing rapidly and it would be good practice to keep up to date on new technologies. Tier 3 procedures for estimating and reporting emissions from CO2 storage sites are summarised in Figure 5.3 and discussed below. Figure 5.3

Procedures for estimating emissions from CO 2 storage sites

Confirm that geology of storage site has been evaluated and that local and regional hydrogeology and leakage pathways (Table 5.1) have been identified.

Monitoring

Confirm that the potential for leakage has been evaluated through a combination of site characterization and realistic models that predict movement of CO2 over time and locations where emissions might occur.

Ensure that an adequate monitoring plan is in place. The monitoring plan should identify potential leakage pathways, measure leakage and/or validate update models as appropriate.

Reporting

Assessment of Site Risk of Leakage Characterization

Estimating, Verifying & Reporting Emissions from CO2 Storage Sites

Report CO2 injected and emissions from storage site

In order to understand the fate of CO2 injected into geological reservoirs over long timescales, assess its potential to be emitted back to the atmosphere or seabed via the leakage pathways identified in Table 5.3, and measure any fugitive emissions, it is necessary to: (a) Properly and thoroughly characterise the geology of the storage site and surrounding strata; (b) Model the injection of CO2 into the storage reservoir and the future behaviour of the storage system; (c) Monitor the storage system;

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(d) Use the results of the monitoring to validate and/or update the models of the storage system. Proper site selection and characterization can help build confidence that there will be minimal leakage, improve modelling capabilities and results, and ultimately reduce the level of monitoring needed. Further information on site characterisation is available in the SRCCS and from the International Energy Agency Greenhouse Gas R & D Programme (IEAGHG 2005). Monitoring technologies have been developed and refined over the past 30 years in the oil and gas, groundwater and environmental monitoring industries. The most commonly used technologies are described in Tables 5.1-5.6 in Annex I of this chapter. The suitability and efficacy of these technologies can be strongly influenced by the geology and potential emissions pathways at individual storage sites, so the choice of monitoring technologies will need to be made on a site-by-site basis. Monitoring technologies are advancing rapidly and it would be good practice to keep up to date on new technologies. A range of modelling tools is available, some of which have undergone a process of code inter-comparison (Pruess et al. 2004). All models approximate and/or neglect some processes, and make simplifications. Moreover, their results are dependent on their intrinsic qualities and, especially, on the quality of the data put into them. Many of the physico-chemical factors involved (changes in temperature and pressure, mixing of the injected gas with the fluids initially present in the reservoir, the type and rate of carbon dioxide immobilization mechanisms and fluid flow through the geological environment) can be modelled successfully with numerical modelling tools known as reservoir simulators. These are widely used in the oil and gas industry and have proved effective in predicting movement of gases and liquids, including CO2, through geological formations. Reservoir simulation can be used to predict the likely location, timing and flux of any emissions, which, in turn, could be checked using direct monitoring techniques. Thus it can be an extremely useful technique for assessing the risk of leakage from a storage site. However, currently there is no single model that can account for all the processes involved at the scales and resolution required. Thus, sometimes, additional numerical modelling techniques may need to be used to analyze aspects of the geology. Multi-phase reaction transport models, which are normally used for the evaluation of contaminant transport can be used to model transport of CO2 within the reservoir and CO2/water/rock reactions, and potential geomechanical effects may need to be considered using geomechanical models. Such models may be coupled to reservoir simulators or independent of them. Numerical simulations should be validated by direct measurements from the storage site, where possible. These measurements should be derived from a monitoring programme, and comparison between monitoring results and expectations used to improve the geological and numerical models. Expert opinion is needed to assess whether the geological and numerical modelling are valid representations of the storage site and surrounding strata and whether subsequent simulations give an adequate prediction of site performance. Monitoring should be conducted according to a suitable plan, as described below. This should take into account the expectations from the modelling on where leakage might occur, as well as measurements made over the entire zone in which CO2 is likely to be present. Site managers will typically be responsible for installing and operating carbon dioxide storage monitoring technologies (see Annex 1). The inventory compiler will need to ensure that it has sufficient information from each storage site to assess annual emissions in accordance with the guidance provided in this Chapter. To make this assessment, the inventory compiler should establish a formal arrangement with each site operator that will allow for annual reporting, review and verification of site-specific data.

5.7.1

Choice of method

At the time of writing, the few CO2 storage sites that exist are part of petroleum production operations and are regulated as such. For example, acid gas storage operations in western Canada need to conform to requirements that deal with applications to operate conventional oil and gas reservoirs (Bachu and Gunter, 2005). Regulatory development for CCS is in its early stages. There are no national or international standards for performance of geological CO2 storage sites and many countries are currently developing relevant regulations to address the risks of leakage. Demonstration of monitoring technologies is a necessary part of this development (see Annex 1). As these standards and regulatory approaches are developed and implemented, they may be able to provide emissions information with relative certainty. Therefore, as part of the annual inventory process, if one or more appropriate governing bodies that regulate carbon dioxide capture and storage exist, then the inventory compiler may obtain emissions information from those bodies. If the inventory compiler relies on this information, he/she should submit supporting documentation that explains how emissions were estimated or measured and how these methods are consistent with IPCC practice. If no such agency exists, then it would be good practice for the inventory compiler to follow the methodology presented below. In the methodology presented below, site characterization, modelling, assessment of the risk of leakage and monitoring activities are the responsibility of the storage project manager and/or an appropriate governing body that regulates carbon dioxide capture and

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storage. In addition, the storage project manager or regulatory authority will likely develop the emission estimates that will be reported to the national inventory compiler as part of the annual inventory process. The responsibility of the national inventory compiler is to request the emissions data and seek assurance of its validity. In the case of CCS associated with ECBM recovery, the methodology should be applied both to CO2 and CH4 detection. 1. Identify and document all geological storage operations in the jurisdiction. The inventory compiler should keep an updated record of all geological storage operations, including all the information needed to crossreference from this section to other elements of the CO2 capture and storage chain for QA/QC purposes, that is for each operation: •

•

The location of the site;

•

The year in which CO2 storage began;

• •

The type of operation (whether or not associated with EOR, EGR, ECBM);

Source(s), annual mass of CO2 injected attributable to each source and the imputed cumulative amount in storage; and Associated CO2 transport, injection and recycling infrastructure, if appropriate (i.e. on-site generation and capture facilities, pipeline connections, injection technology etc.) and emissions therefrom.

Although the inventory compiler is only responsible for reporting on the effect of operations in its jurisdiction, he/she must record cross-border transfers of CO2 for cross-checking and QA/QC purposes (see Section 5.9). 2. Determine whether an adequate geological site characterization report has been produced for each storage site. The site characterization report should identify and characterize potential leakage pathways such as faults and pre-existing wells, and quantify the hydrogeological properties of the storage system, particularly with respect to CO2 migration. The site characterisation report should include sufficient data to represent such features in a geological model of the site and surrounding area. It should also include all the data necessary to create a corresponding numerical model of the site and surrounding area for input into an appropriate numerical reservoir simulator. 3. Determine whether the operator has assessed the potential for leakage at the storage site. The operator should determine the likely timing, location and flux of any fugitive emissions from the storage reservoir, or demonstrate that leakage is not expected to occur. Short-term simulations of CO2 injection should be made, to predict the performance of the site from the start of injection until significantly after injection ceases (likely to be decades). Long-term simulations should be performed to predict the fate of the CO2 over centuries to millenia. Sensitivity analysis should be conducted to assess the range of possible emissions. The models should be used in the design of a monitoring programme that will verify whether or not the site is performing as expected. The geological model and reservoir model should be updated in future years in the light of any new data and to account for any new facilities or operational changes. 4. Determine whether each site has a suitable monitoring plan. Each site’s monitoring plan should describe monitoring activities that are consistent with the leakage assessment and modelling results. Existing technologies presented in Annex 1 can measure leaks to the ground surface or seabed. The SRCCS includes detailed information on monitoring technologies and approaches (see Annex 1). In summary the monitoring programme should include provisions for: (i)

Measurement of background fluxes of CO2 (and if appropriate CH4) at both the storage site and any likely emission points outside the storage site. Geological storage sites may have a natural, seasonally variable (ecological and/or industrial) background flux of emissions prior to injection. This background flux should not be included in the estimate of annual emissions. See Annex 1 for a discussion of potential methods. Isotopic analysis of any background fluxes of CO2 is recommended, as this is likely to help distinguish between natural and injected CO2.

(ii)

Continuous measurement of the mass of CO2 injected at each well throughout the injection period, see Section 5.5 above.

(iii)

Monitoring to determine any CO2 emissions from the injection system.

(iv)

Monitoring to determine any CO2 (and if appropriate CH4) fluxes through the seabed or ground surface, including where appropriate through wells and water sources such as springs. Periodic investigations of the entire site, and any additional area below which monitoring and modelling suggests CO2 is distributed, should be made to detect any unpredicted leaks.

(v)

Post-injection Monitoring: The plan should provide for monitoring of the site after the injection phase. The post-injection phase of monitoring should take account of the results of

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the forward modelling of CO2 distribution to ensure that monitoring equipment is deployed at appropriate places and appropriate times. Once the CO2 approaches its predicted long-term distribution within the reservoir and there is agreement between the models of CO2 distribution and measurements made in accordance with the monitoring plan, it may be appropriate to decrease the frequency of (or discontinue) monitoring. Monitoring may need to be resumed if the storage site is affected by unexpected events, for example seismic events. (vi)

Incorporating improvements in monitoring techniques/technologies over time.

(vii)

Periodic verification of emissions estimates. The necessary periodicity is a function of project design, implementation and early determination of risk potential. During the injection period, verification at least every five years or after significant change in site operation is suggested.

Continuous monitoring of the injection pressure and periodic monitoring of the distribution of CO2 in the subsurface would be useful as part of the monitoring plan. Monitoring the injection pressure is necessary to control the injection process, e.g. to prevent excess pore fluid pressure building up in the reservoir. It can provide valuable information on the reservoir characteristics and early warning of leakage. This is already common practice and can be a regulatory requirement for current underground injection operations. Periodically monitoring the distribution of CO2 in the subsurface, either directly or remotely would also be useful because it can provide evidence of any migration of CO2 out of the storage reservoir and early warning of potential leaks to the atmosphere or seabed. 5. Collect and verify annual emissions from each site: The operators of each storage site should, on an annual basis, provide the inventory compiler with annual emissions estimates, which will be made publicly available. The emissions recorded from the site and any leaks that may occur inside or outside the site in any year will be the emissions as estimated from the modelling (which may be zero), adjusted to take account of the annual monitoring results. If a sudden release occurs, e.g. from a well blowout, the amount of CO2 emitted should be estimated in the inventory. To simplify accounting for offshore geological storage, leakage to the seabed should be considered as emissions to the atmosphere for the purposes of compiling the inventory. In addition to total annual emissions, background data should include the total amount of CO2 injected, the source of the injected CO2, the cumulative total amount of CO2 stored to date, the technologies used to estimate emissions, and any verification procedures undertaken by the site operators in accordance with the monitoring plan as indicated under 4(iii) and 4(iv) above. To verify emissions, the inventory compiler should request and review documentation of the monitoring data, including the frequency of monitoring, technology detection limits, and the share of emissions coming from the various pathways identified in the emission monitoring plan and any changes introduced as a result of verification. If a model was used to estimate emissions during years in which direct monitoring did not take place, the inventory compiler should compare modelled results against the most recent monitoring data. Steps 2, 3, and 4 above should indicate the potential for, and likely timing of future leaks and the need for direct monitoring. Total national emissions for geological carbon dioxide storage will be the sum of the site-specific emission estimates: EQUATION 5.1 TOTAL NATIONAL EMISSIONS National Emissions from geological carbon dioxide storage = ∑carbon dioxide storage site emissions Further guidance on reporting emissions where more than one country is involved in CO2 capture, storage, and/or emissions is provided in Section 5.10: Reporting and Documentation.

5.7.2

Choice of emission factors and activity data

Tier 1 or 2 emission factors are not currently available for carbon dioxide storage sites, but may be developed in the future (see Section 5.7). However, as part of a Tier 3 emissions estimation process, the inventory compiler should collect activity data from the operator on annual and cumulative CO2 stored. These data can be easily monitored at the injection wellhead or in adjacent pipework. Monitoring in early projects may help obtain useful data that could be used to develop Tier 1 or 2 methodologies in the future. Examples of the application of monitoring technologies are provided by the monitoring programmes at the enhanced oil recovery projects at Rangely in Colorado, USA (Klusman, 2003a, b, c) Weyburn in Saskatchewan, Canada (Wilson and Monea, 2005), and the Sleipner CO2 storage project, North Sea (Arts et al., 2003; also see Annex 5.1). None of the other CO2-injection projects around the world have yet published the results of systematic monitoring for leaking CO2.

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The Rangely enhanced oil recovery project started injecting CO2 into the Weber Sand Unit oil reservoir in the Rangely field in 1986. Cumulative CO2 injection to 2003 was approximately 23 million tonnes. A monitoring programme was undertaken (Klusman 2003a, b, c), based on 41 measurement locations scattered across a 78 km2 site. No pre-injection background measurements were available (which, at a new site, would be determined at step 4 (i) in the monitoring plan outlined above). In lieu of a pre-injection baseline, 16 measurement locations in a control area outside the field were sampled. The results of the monitoring programme indicate an annual deepsourced CO2 emission of less than 3 800 tonnes/yr from the ground surface above the oil field. It is likely that at least part, if not all, of this flux is due to the oxidation of deep-sourced methane derived from the oil reservoir or overlying strata, but it is possible that part of it could be fugitive emissions of CO2 injected into the oil reservoir. The absence of pre-injection baseline measurements prevents definitive identification of its source. CO2 has been injected at the Weyburn oil field (Saskatchewan, Canada) for EOR since September 2000. Soil gas sampling, with the primary aims of determining background concentrations and whether there have been any leaks of CO2 or associated tracer gases from the reservoir, took place in three periods from July 2001 and October 2003. There is no evidence to date for escape of injected CO2. However, further monitoring of soil gases is necessary to verify that this remains the case in the future and more detailed work is necessary to understand the causes of variation in soil gas contents, and to investigate further possible conduits for gas escape (Wilson and Monea 2005). The Sleipner CO2 storage site in the North Sea, offshore Norway (Chadwick et al. 2003) has been injecting approximately 1 million tonnes of CO2 per year into the Utsira Sand, a saline formation, since 1996. Cumulative CO2 injection to 2004 was >7 million tonnes. The distribution of CO2 in the subsurface is being monitored by means of repeated 3-D seismic surveys (pre-injection and two repeat surveys are available publicly to date) and, latterly, by gravity surveys (only one survey has been acquired to date). The results of the 3D seismic surveys indicate no evidence of leakage (Arts et al. 2003). Taken together, these studies show that a Tier 3 methodology can be implemented so as to support not only zero emissions estimates but also to detect leakage, even at low levels, if it occurs. There has been only one large-scale trial of enhanced coalbed methane (ECBM) production using CO2 as an injectant; the Allison project in the San Juan Basin, USA (Reeves, 2005). There was sufficient information derived from the Allison project to indicate that CO2 was sequestered securely in the coal seams. Pressure and compositional data from 4 injection wells and 15 production wells indicated no leakage. Some CO2 was recovered from the production wells after approximately five years. However, this was expected and, for inventory purposes, it would be accounted as an emission (if it was not separated from the produced coalbed methane and recycled). No monitoring of the ground surface for CO2 or methane leakage was undertaken.

5.7.3

Completeness

All emissions (CO2 and if relevant, CH4) from all CO2 storage sites should be included in the inventory. In cases where CO2 capture occurs in a different country from CO2 storage, arrangements to ensure that there is no double accounting of storage should be made between the relevant national inventory compilers. The site characterization and monitoring plans should identify possible sources of emissions outside the site (e.g., lateral migration, groundwater, etc.). Alternatively, a reactive strategy to locations outside the site could be deployed, based on information from inside it. If the emissions are predicted and/or occur outside the country where the storage operation (CO2 injection) takes place, arrangements should be made between the relevant national inventory compilers to monitor and account these emissions.( see Section 5.10 below). Estimates of CO2 dissolved in oil and emitted to the atmosphere as a result of surface processing are covered under the methodologies for oil and gas production. The inventory compiler should ensure that information on these emissions collected from CO2 storage sites is consistent with estimates under those source categories.

5.7.4

Developing a consistent time series

If the detection capabilities of monitoring equipment improve over time, or if previously unrecorded emissions are identified, or if updating of models suggests that unidentified emissions have occurred, and an updated monitoring programme corroborates this, appropriate recalculation of emissions will be necessary. This is particularly important given the generally low precision associated with current monitoring suites, even using the most advanced current technologies. Establishment of the background flux and variability is also critical. For dedicated CO2 storage sites, anthropogenic emissions prior to injection and storage will be zero. For some enhanced oil recovery operations, there may be anthropogenic emissions prior to conversion to a CO2 storage site.

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5.8

UNCERTAINTY ASSESSMENT

It is a part of good practice that an uncertainty assessment is included when using Tier 3 methods. Uncertainty in the emissions estimates will depend on the precision of the monitoring techniques used to verify and measure any emissions and the modelling used to predict leakage from the storage site. The concept of percentage uncertainties may not be applicable for this sector and therefore confidence intervals and/or probability curves could be given. The uncertainty in field measurements is most important and will depend on the sampling density and frequency of measurement and can be determined using standard statistical methods. An effective reservoir simulation should address the issues of variability and uncertainty in the physical characteristics, especially reservoir rock and reservoir fluid properties, because reservoir models are designed to predict fluid movements over a long timescale and because geological reservoirs are inherently heterogeneous and variable. The uncertainty in estimates derived from modelling will therefore depend on: •

•

The completeness of the primary data used during the site assessment; The correspondence between the geological model and critical aspects of the geology of the site and surrounding area, in particular the treatment of possible migration pathways;

The accuracy of critical data that support the model: •

•

Its subsequent numerical representation by grid blocks Adequate representation of the processes in the physico-chemical numerical and analytical models

Uncertainty estimates are typically made by varying the model input parameters and undertaking multiple simulations to determine the impact on short-term model results and long-term predictions. The uncertainty in field measurements will depend on the sampling density and frequency of measurement and can be determined using standard statistical methods. Where model estimates and measurements are both available, the best estimate of emissions will be made by validating the model, and then estimating emissions with the updated model. Multiple realizations using the history-matched model can address uncertainty in these estimates. These data may be used to modify original monitoring requirements (e.g. add new locations or technology, increase or reduce frequency) and ultimately comprise the basis of an informed decision to decommission the facility.

5.9

INVENTORY QUALITY ASSURANCE/QUALITY CONTROL (QA/QC)

QA/QC for the whole CCS system CO2 capture should not be reported without linking it to long-term storage. A check should be made that the mass of CO2 captured does not exceed the mass of CO2 stored plus the reported fugitive emissions in the inventory year (Table 5.4). There has been limited experience with CCS to date, but it is expected that experience will increase over the next few years. Therefore, it would be good practice to compare monitoring methods and possible leakage scenarios between comparable sites internationally. International cooperation will also be advantageous in developing monitoring methodologies and technologies.

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TABLE 5.4 OVERVIEW TABLE: OVERVIEW OF CO2 CAPTURE, TRANSPORT, INJECTION AND CO2 FOR LONG-TERM STORAGE

Category

Activity Data Source

CO2 (Gg)

Total amount captured for storage (A)

Summed from all relevant categories

Gg

Total amount of import for storage (B)

Data from pipeline companies, or statistical agencies

Gg

Total amount of export for storage (C)

Data from pipeline companies, or statistical agencies

Total amount of CO2 injected at storage sites (D)

Data from storage sites provided by operators, as described in Chapter 5

Gg

Total amount of leakage during transport (E1)

Summed from IPCC reporting category 1 C 1

Gg

Total amount of leakage during injection (E2)

Summed from IPCC reporting category 1 C 2 a

Gg

Total amount of leakage from storage sites (E3)

Summed from IPCC reporting category 1 C 2 b

Gg

Total leakage (E4)

E1 + E2 + E3

Gg

Capture + Imports (F)

A+B

Injection + Leakage + Exports (G)

D + E4 + C

Discrepancy

F-G

1

1

Unit

Gg

Gg

Gg Gg

Once captured, there is no differentiated treatment between biogenic carbon and fossil carbon: emissions and storage of both will be estimated and reported.

Ideally, (Capture + Imports) = (Injection + Exports + Leakage) If (Capture + Imports) < (Injection + Exports + Leakage) then there is need to check that Exports are not overestimated Imports are not underestimated Data for CO2 injection does not include EOR operations not associated with storage If (Capture + Imports) > (Injection + Exports + Leakage) then need to check that Exports are not under-estimated Imports are not overestimated CO2 capture designated as ‘for long-term storage’ is actually going to other short-term emissive uses (e.g., products, EOR without storage) Site QA/QC On-site QA/QC will be achieved by regular inspection of monitoring equipment and site infrastructure by the operator. Monitoring equipment and programmes will be subject to independent scrutiny by the inventory compiler and/or regulatory agency. All data including the site characterization reports, geological models, simulations of CO2 injection, predictive modelling of the site, risk assessments, injection plans, licence applications, monitoring strategies and results and verification should be retained by the operator and forwarded to the inventory compiler for QA/QC.

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The inventory compiler should compare (benchmark) the leak rates of a given storage facility against analogous storage sites and explain the reasons for differences in performance. Where applicable, the relevant regulatory body can provide verification of emissions estimates and/or the monitoring plan described above. If no such body exists, the site operator should at the outset provide the inventory compiler with the results of peer review by a competent third party confirming that the geological and numerical models are representative, the reservoir simulator is suitable, the modelling realistic and the monitoring plan suitable. As they become available, the site operator should compare the results of the monitoring programme with the predictive models and adjust models, monitoring programme and/or injection strategy appropriately. The site operator should inform the inventory compiler of changes made.

5.10

REPORTING AND DOCUMENTATION

Guidelines for reporting emissions from geological storage: Prior to the start of the geological storage operation, the national inventory compiler where storage takes place should obtain and archive the following: •

•

Report on the methods and results of the site characterization

•

A description of the proposed monitoring programme including appropriate background measurements

•

Report on the methods and results of modelling

•

The year in which CO2 storage began or will begin The proposed sources of the CO2 and the infrastructure involved in the whole CCGS chain between source and storage reservoir

The same national inventory compiler should receive annually from each site: •

•

The mass of CO2 injected during the reporting year

•

The cumulative mass of CO2 stored at the site

The mass of CO2 stored during the reporting year

•

• • • • •

The source (s) of the CO2 and the infrastructure involved in the whole CCGS chain between source and storage reservoir A report detailing the rationale, methodology, monitoring frequency and results of the monitoring programme - to include the mass of any fugitive emissions of CO2 and any other greenhouse gases to the atmosphere or sea bed from the storage site during the reporting year A report on any adjustment of the modelling and forward modelling of the site that was necessary in the light of the monitoring results The mass of any fugitive emissions of CO2 and any other greenhouse gases to the atmosphere or sea bed from the storage site during the reporting year Descriptions of the monitoring programmes and monitoring methods used, the monitoring frequency and their results Results of third party verification of the monitoring programme and methods

There may be additional reporting requirements at the project level where the site is part of an emissions trading scheme. Reporting of cross-border CCS operations CO2 may be captured in one country, Country A, and exported for storage in a different country, Country B. Under this scenario, Country A should report the amount of CO2 captured, any emissions from transport and/or temporary storage that takes place in Country A, and the amount of CO2 exported to Country B. Country B should report the amount of CO2 imported, any emissions from transport and/or temporary storage (that takes place in Country B), and any emissions from injection and geological storage sites. If CO2 is injected in one country, Country A, and travels from the storage site and leaks in a different country, Country B, Country A is responsible for reporting the emissions from the geological storage site. If such leakage is anticipated based on site characterization and modelling, Country A should make an arrangement with Country B to ensure that appropriate standards for long-term storage and monitoring and/or estimation of

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Chapter 5: Carbon Dioxide Transport, Injection and Geological Storage

emissions are applied (relevant regulatory bodies may have existing arrangements to address cross-border issues with regard to groundwater protection and/or oil and gas recovery). If more than one country utilizes a common storage site, the country where the geological storage takes place is responsible for reporting emissions from that site. If the emissions occur outside of that country, they are still responsible for reporting those emissions as described above. In the case where a storage site occurs in more than one country, the countries concerned should make an arrangement whereby each reports an agreed fraction of the total emissions.

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Volume 2: Energy

Annex 5.1 Summary description of potential monitoring technologies for geological CO2 storage sites Introduction Monitoring of the geological storage of CO2 requires the use of a range of techniques that can define the distribution, phase and mass of the injected CO2 anywhere along any path from the injection point in the geological storage reservoir to the ground surface or seabed. This will commonly require the application of several different techniques concurrently. The geology of the storage site and its surrounding area should be characterized to identify features, events and processes that could lead to an escape of CO2 from the storage reservoir, and also to model potential CO2 transport routes and fluxes in case there should be an escape of CO2 from a storage reservoir, as this will not necessarily be on the injection site (Figure A1). Figure A1

An illustration of the potential for leakage of CO 2 from a geological storage reservoir to occur outside the storage site .

If CO2 migrates from a storage reservoir (a) via an undetected fault into porous and permeable reservoir rock (b), it may be transported by buoyancy towards the ground surface at point (c). This may result in the emission of CO2 at the ground surface several kilometres from the site itself at an unknown time in the future. Characterization of the geology of the storage site and surrounding area and numerical modelling of potential leakage scenarios and processes can provide the information needed to correctly site surface and subsurface monitoring equipment during and after the injection process. Tables A5.1 - A5.6 list the more common monitoring techniques and measurement tools that can be used for monitoring CO2 in the deep subsurface (here considered to be the zone approximately 200 metres to 5 000 metres below the ground surface or sea bed), the shallow subsurface (approximately the top 200 metres below the ground surface or sea bed) and the near surface (regions less than 10 metres above and below the ground surface or sea bed). The techniques that will produce the most accurate results given the circumstances should be used. The appropriate techniques will usually be apparent to specialists, but different techniques can also be assessed for relative suitability. There are no sharply defined detection limits for most techniques. In the field, their ability to measure the distribution, phase and mass of CO2 in a subsurface reservoir will be site-specific. It will be determined as much by the geology of the site and surrounding area, and ambient conditions of temperature, pressure and water saturation underground as by the theoretical sensitivity of the techniques or measurement instruments themselves.

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Chapter 5: Carbon Dioxide Transport, Injection and Geological Storage

Similarly, the detection limits of surface monitoring techniques are determined by environmental parameters as well as the sensitivity of the monitoring instruments themselves. In near-surface systems on land, CO2 fluxes and concentrations are determined by uptake of CO2 by plants during photosynthesis, root respiration, microbial respiration in soil, deep outgassing of CO2 and exchange of CO2 between the soil and atmosphere [Oldenburg and Unger 2003]. Any outgassing of CO2 from a man-made CO2 storage reservoir needs to be distinguished from the variable natural background (Oldenburg and Unger 2003, Klusman 2003a, c). Analysis of stable and radiogenic carbon isotope ratios in detected CO2 can help this process. Most techniques require calibration or comparison with baseline surveys made before injection starts, e.g. to determine background fluxes of CO2. Strategies for monitoring in the deep subsurface have been applied at the Weyburn oil field and Sleipner CO2 storage site (Wilson and and Monea 2005, Arts et al. 2003). Interpretation of 4D seismic surveys has been highly successful in both cases. In the Weyburn field, geochemical information obtained from some of the many wells has also proved extremely useful. Strategies for monitoring the surface and near-surface onshore have been proposed (Oldenburg and Unger 2003) and applied (Klusman 2003a, c; Wilson and Monea 2005). Soil gas surveys and surface gas flux measurements have been used. To date there has been no application of shallow subsurface or seabed monitoring specifically for CO2 offshore. However, monitoring of natural gas seepage and its effects on the shallow subsurface and seabed has been undertaken and considered as an analogue for CO2 seepage [e.g., Schroot and Schüttenhelm 2003a, b].

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Images geological structure of site and surrounding area; structure, distribution and thickness of the reservoir rock and cap rock; distribution (and with timelapse surveys movement) of CO2 in reservoir. May verify (within limits) mass of CO2 in reservoir. Permanent seismic arrays can be installed (but are not necessary) for time-lapse (4D) acquisition.

Images velocity distribution between wells. Provides 2D information about rocks and their contained fluids.

Image velocity distribution around a single well. Map fluid pressure distribution around well. Potential early warning of leakage around well.

2D, 3D and 4D (timelapse) and multicomponent seismic reflection surveys

Crosshole seismic

Vertical seismic profile

Detection limits

Site specific

Site specific. Resolution could be higher than surface seismic reflection surveys but coverage more restricted

Site-specific. Optimum depth of target commonly 500-3000 m. At Sleipner, which is close to optimum for the technique, detection limit in Utsira Sand is c. 2800 tonnes CO2. At Weyburn, detection limit is c. 2500 - 7500 tonnes CO2 (White et al. 2004). Likely that dispersed CO2 in overlying strata could be detected - shallow natural gas pockets imaged as bright spots and dispersed methane in gas chimneys can be well imaged.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Capabilities

Onshore and offshore

Onshore and offshore

Onshore and offshore. Imaging poorer through karst, beneath salt, beneath gas, in general resolution decreases with depth

Where applicable, costs

As above and limited to small area around a single well

As above, and limited to area between wells

Cannot image dissolved CO2 (insufficient impedance contrast between CO2saturated pore fluid and native pore fluid). Cannot image well in cases in which there is little impedance contrast between fluid and CO2-saturated rock. These will be fairly common (Wang, 1997)

Limitations

TABLE A 5.1 POTENTIAL DEEP SUBSURFACE MONITORING TECHNOLOGIES AND THEIR LIKELY APPLICATION

Technique

Volume 2: Energy

Highly developed with full commercial deployment in oil and gas industry

Highly developed with full commercial deployment in oil and gas industry

Highly developed with full commercial deployment in oil and gas industry

Current technology status

5.24

Detects and triangulates location of microfractures in the reservoir rock and surrounding strata. Provides an indication of location of injected fluid fronts. Assesses induced seismic hazard.

Microseismic monitoring

Proven technology for oil and gas field reservoir engineering and reserves estimation. ICP-MS used to detect subtle changes in elemental composition due to CO2 injection.

Minimum amounts detectable in the order of hundreds of thousands to low millions of tonnes (Benson et al. 2004; Chadwick et al 2003). Actual amounts detectable are site specific. The greater the porosity and the density contrast between the native pore fluid and the injected CO2, the better the resolution

Injection pressure can be continuously monitored at the wellhead by meters (Wright & Majek 1998). Downhole pressure can be monitored with gauges. Injection pressure tests and production tests applied in well to determine permeability, presence of barriers in reservoir, ability of cap rocks to retain fluids.

Determine mass and approximate distribution of CO2 injected from minute change in gravity caused by injected CO2 displacing the original pore fluid from the reservoir. Can detect vertical CO2 migration from repeat surveys, especially where phase change from supercritical fluid to gas is involved because of change in density. Detection limit is poor and site-specific.

Wellhead pressure monitoring during injection, formation pressure testing

Gravity surveys

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Downhole geochemical samples can be analyzed by Inductively Coupled Plasma Mass Spectrometer (has resolution of parts per billion). Perflourocarbon tracers can be detected in parts per 1012. Well logs provide accurate measurement of many parameters (porosity, resistivity, density, etc).

Site specific. Depends on background noise amongst other factors. More receivers in more wells provides greater accuracy in location of events

Detection limits

Many potential functions including measurement of CO2 saturation, fluid pressure, temperature. Cement and or casing degradation or failure. Well logging. Tracer detection - fast-moving tracers might provide an opportunity to intervene in the leakage prevention by modifying operating parameters. Detection of geochemical changes in formation fluids. Physical sampling of rocks and fluids. In-well tilt meters for detecting ground movement caused by CO2 injection. Monitoring formations overlying the storage reservoir for signs of leakage from the reservoir.

Monitoring wells

Capabilities

Technique

Onshore and offshore. Cheap onshore.

Onshore and offshore. More expensive offshore

Onshore and offshore. More expensive to access offshore.

Onshore and offshore

Where applicable, costs

Cannot image dissolved CO2 (insufficient density contrast with native pore fluid).

Certain functions can only be performed before the well is cased. Others require the perforation of certain intervals of the casing. Cost is a limitation, especially offshore

Requires wells for deployment

Limitations

TABLE A 5.1(CONTINUED) POTENTIAL DEEP SUBSURFACE MONITORING TECHNOLOGIES AND THEIR LIKELY APPLICATION

5.25

Highly developed with full commercial deployment in oil and gas industry. Widely used in geophysical research

Highly developed with full commercial deployment in oil and gas industry

Monitoring wells deployed e.g. in natural gas storage industry. Many tools highly developed and routinely deployed in oil and gas industry, others under development

Well developed with some commercial deployment

Current technology status

Chapter 5: Carbon Dioxide Transport, Injection and Geological Storage

Onshore and offshore surface EM capability demonstrated. Needs development for application in CO2 storage

Relatively low cost and low resolution

May detect change in resistivity due to replacement of native pore fluid with CO2, especially when the CO2 is supercritical. EM and electrical methods potentially could map the spread of CO2 in a storage reservoir. Surface EM may have potential to map CO2 saturation changes within the reservoir.

Electrical methods

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Offshore

Can identify changes in sea bed morphology of as little as 10 cm.

Image the morphology of the sea bed. Repeat surveys allow quantification of morphological change. Sea bed lithology identified from backscatter.

Offshore

Offshore

Offshore

Where applicable, costs

Multi-beam echo-sounding (Swath bathymetry)

Characterisation of sea bed lithology eg carbonate cementation

Optimum method for detecting gas bubbles.

Image the morphology of the sea bed. Image bubble streams in sea water

Sidescan sonar

Generally free gas concentrations >2% identified by acoustic blanking. Resolution of sea bed morphology typically less than 1 metre. Penetration can be up to about 200 m below sea bed but generally less.

Image (changes in) shallow gas distribution in sediments (typically represented by acoustic blanking, bright spots, etc.). Image the morphology of the sea bed. Image bubble streams in sea water

Deep towed boomer: Seismic source generating a broad band sound pulse with a central frequency around 2.5 kHz is towed at depth.

Generally free gas concentrations >2% identified by acoustic blanking. Vertical resolution >1m

Image (changes in) gas distribution in the shallow subsurface (typically represented by acoustic blanking, bright spots, reflector enhancement).

Sparker: Seismic source with central frequency around 0.1 to 1.2 kHz is towed generally at shallow depth.

Detection limits

Capabilities

Resolution - Needs development and further demonstration

As above. Greater coverage in shorter time

As above. Accurate positioning of side scan sonar fish is critical.

Bubble streams more soluble than methane bubbles therefore may dissolve in relatively shallow water columns (approximately 50 m). Bubble streams may be intermittent and missed by a single survey. Accurate positioning of boomer is critical

Gas quantification can be difficult when concentrations above 5%

Greater penetration but less resolution than deep towed boomer

Limitations

TABLE A 5.2 POTENTIAL SHALLOW SUBSURFACE MONITORING TECHNOLOGIES AND THEIR LIKELY APPLICATION

Technique

Volume 2: Energy

At research stage

Widely deployed in marine research

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Highly developed, widely deployed commercially in sea bed survey industry, also in marine research

Highly developed, widely deployed commercially, in sea bed and shallow seismic survey industry, also in marine research

Highly developed, widely deployed commercially, in sea bed and shallow seismic survey industry, also in marine research

Current technology status

Accumulation chambers of known volume are placed on the ground and loosely connected to the ground surface, e.g. by building up soil around them, or placed on collars inserted into the ground. Gas in chambers is sampled periodically and analysed e.g. by portable IR gas detectors, and then returned to chamber to monitor build-up over time,. Detects any fluxes through the soil.

Samples and measures gas content of groundwater and surface water such as springs. Could:

Accumulation chambers technique, using field IR or lab analysis of sampled gas to measure flux (Klusman 2003).

Groundwater and surface water gas analysis.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

b) For a fresh sample, analyse for bicarbonate content. This is essentially what was done at Weyburn in the field and at the well-head (Shevalier et al. 2004). As dissolved CO2 and bicarbonate contents are linked, then analysis of bicarbonate can be directly related to dissolved CO2 content (assuming equilibrium conditions).

a) Place a partial vacuum over the liquid and extract dissolved gases. Analyse for gases by gas chromatography, mass spectrometry etc.

Measures CO2 fluxes in air from a mathematically-defined footprint upwind of the detection equipment. Equipment is mounted on a platform or tower. Gas analysis data, usually from fixed open- or closed-path infra-red CO2 detectors, is integrated with wind speed and direction to define footprint and calculate flux.

Capabilities

Eddy covariance technique (Miles, Davis and Wyngaard 2005).

Technique

Background levels likely to be in low ppm range. Detection limit for bicarbonate in 90 percent TiO2) and rutile TiO2.

3.7.2

Methodological issues

TiO2 is produced as anatase TiO2 and rutile TiO2. The forms of TiO2 differ in terms of the crystalline structure and purity of the final product. Anatase TiO2 may be produced by digesting ilmenite (essentially ferrous titanate (FeO.TiO2)) with sulphuric acid, the sulphate process, or from titanium slag. Basic reaction equations for the acid digestion route are (Lowenheim and Moran, 1975; p. 814): FeTiO3 + 2H2SO4 → FeSO4 + TiO.SO4 + 2H2O TiO.SO4 + 2H2O → TiO2.H2O + H2SO4 TiO2.H2O + heat → TiO2 + H2O The sulphate route process does not give rise to process greenhouse gas emissions that are of significance. There are three processes that are used in the production of TiO2 that lead to process greenhouse gas emissions: titanium slag production in electric furnaces, synthetic rutile production using the Becher process, and rutile TiO2 production via the chloride route. Titanium slag used for production of anatase TiO2 is produced from electric furnace smelting of ilmenite. Where titanium slag is used the acid reduction step is not required as the electric furnace smelting reduces the ferric iron contained as an impurity in ilmenite. Rutile TiO2 may be produced by further processing of the anatase TiO2. Process emissions arise from the reductant used in the process. Production of synthetic rutile can give rise to CO2 emissions where the Becher process is used. This process reduces the iron oxide in ilmenite to metallic iron and then reoxidises it to iron oxide, and in the process separates out the titanium dioxide as synthetic rutile of about 91 to 93 percent purity (Chemlink, 1997). Black coal is used as the reductant and the CO2 emissions arising should be treated as industrial process emissions. The main route for the production of rutile TiO2 is the chloride route. Rutile TiO2 is produced through the carbothermal chlorination of rutile ore or synthetic rutile to produce titanium tetrachloride (TiCl4) and oxidation of the TiCl4 vapours to TiO2 according to the following reactions (Kirk-Othmer, 1999; p.2017): 2TiO2 + 4Cl2 + 3C → 2TiCl4 + 2CO + CO2 TiCl4 + O2 → TiO2 + 2Cl2 Based on stoichiometry and assuming complete conversion of the input C to CO2 through further conversion of CO in excess air, the CO2 emission factor cannot be less than 0.826 tonnes of CO2 per tonne of TiO2 (based on 1.5 moles of CO2 per mole of TiO2).

3.7.2.1

C HOICE

OF METHOD

The general approach for calculating CO2 emissions from titanium dioxide production is the same irrespective of the product because the emissions are based on the quantity of reducing agent or carbothermal input. The choice of a good practice method depends on national circumstances as shown in the decision tree in Figure 3.6. Process emissions of carbon dioxide in TiO2 production take place primarily as a result of anode carbon oxidisation in the production of titanium slag, coal oxidisation in the process of producing synthetic rutile using the Becher process, and petroleum coke oxidisation in the process of producing rutile TiO2 via the chloride route. Methods are classified according to the extent of plant-level data that are available.

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TIER 1 METHOD The Tier 1 method uses a default emission factor per unit of output multiplied by activity data obtained from national statistics. The basic equation for estimating CO2 emissions is: EQUATION 3.12 CO2 EMISSIONS FROM TITANIUM SLAG, SYNTHETIC RUTILE AND RUTILE TIO2 PRODUCTION – TIER 1 ECO 2 = ∑ ( ADi • EFi ) i

Where: ECO2 = emissions of CO2, tonnes ADi = production of titanium slag, synthetic rutile or rutile TiO2 (product i), tonnes EFi = CO2 emissions per unit of production of titanium slag, synthetic rutile or rutile TiO2 (product i), tonnes CO2/tonne product

TIER 2 METHOD Emissions can be calculated from the consumption of the reducing agent for electrode carbon (titanium slag), and coal (synthetic rutile) in the Becher process, and the carbothermal input (petroleum coke) for rutile TiO2 from the chloride rout process. The Tier 2 method uses plant-level data on the quantities of reducing agent and carbothermal input to derive emissions as follows: EQUATION 3.13 CO2 EMISSIONS FROM TITANIUM SLAG, SYNTHETIC RUTILE AND RUTILE TIO2 PRODUCTION – TIER 2 ECO 2 = ∑ ( ADi • CCFi • COFi • 44 12) i

Where: ECO2 = emissions of CO2, kg ADi = amount of reducing agent or carbothermal input i, GJ CCFi = carbon content factor of reducing agent or carbothermal input i, kg C/GJ COFi = carbon oxidation factor for reducing agent or carbothermal input i, fraction To achieve the highest accuracy, good practice is to apply Equation 3.13 at the plant-level with all data inputs obtained from plant operators. Where plant-level information is not available, good practice provides default CO2 emission factors for synthetic rutile and rutile TiO2 as shown in Table 3.9. A default factor for titanium slag is not available because of the small number of plants.

BOX 3.6 DOUBLE COUNTING

In order to avoid double counting, the quantities of electrode carbon, coal used as a reductant, and petroleum coke used in the chloride route process, must be subtracted from the quantity reported under energy and non-energy use in the Energy Sector.

3.7.2.2

C HOICE

OF EMISSION FACTORS

TIER 1 METHOD If plant-level information is not available, it is good practice to use default factors. These default values often represent midpoint or mean values of data sets (as determined by expert analysis). The extent to which they represent a specific plant’s emission rate is unknown. Default factors by product are provided in Table 3.9, and should be used only in cases where plant-specific data are not available. The default factors are based on

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estimates of reductant or carbothermal input per unit of output assuming complete conversion of the C content to CO2.

TIER 2 METHOD Plant-level data provides the most rigorous data for calculating CO2 emissions from titanium dioxide production. For the Tier 3 method, C content of the reductant and carbothermal inputs along with the proportion of C oxidised are the key emission factor variables for deriving the quantity of CO2 emitted. TABLE 3.9 DEFAULT FACTORS FOR TITANIUM DIOXIDE PRODUCTION (TONNES CO2 PER TONNE PRODUCT) Product

Emission factor and respective uncertainty (tonnes CO2/tonne product)

Titanium slag1

Not available 2

Synthetic rutile

1.43 (± 10%) 3

Rutile titanium dioxide (chloride route)

1.34 (± 15%)

Source: 1

A default emission factor is not available because there are two plants only, Richards Bay in South Africa, and Allard Lake in Canada, and data are confidential. It is good practice for the respective countries to include plant specific estimates of emissions in their national greenhouse gas inventories.

2

Derived from data provided by Iluka Resources.

3

Adapted from EIPPCB (2004a; p.99).

Figure 3.6

Decision tree for estimation of CO 2 emissions from titanium dioxide production Start

Are plant-level data on the quantities of reducing agent and carbothermal input available?

Use the plant-level data on the quantities of reducing agent and carbothermal input, and carbon content and carbon oxidation factors.

Yes

Box 2: Tier 2 No

Is this a key category1?

No

Are national aggregate data on production of titanium slag, synthetic rutile or rutile TiO2 available?

Yes Yes Collect data for the Tier 2 method.

No Gather production data or use production capacity data.

Calculate emissions using national aggregate data on production of titanium slag, synthetic rutile or rutile TiO2 and default emission factors. Box 1: Tier 1

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

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3.7.2.3

C HOICE

OF ACTIVITY DATA

It is good practice to compile activity data at a level of detail that allows the use of the Tier 2 method. When applying the methods it is essential that a clear distinction is made between the products to avoid multiplying the incorrect emission factor by activity data.

TIER 1 METHOD The Tier 1 method requires data on national production of titanium slag, synthetic rutile and rutile TiO2. If national-level activity data are not available, information on production capacity can be used with emissions estimated using a default emission factor. It is good practice to multiply the total national production capacity by a capacity utilisation factor of 80 percent ± 10 percent (i.e., a range of 70-90 percent).

TIER 2 METHOD The plant-level activity data required for the Tier 2 method are total reductant use, total carbon electrode consumption, and total carbothermal input. It is good practice to also collect data on total titanium slag production, total synthetic rutile production, and total rutile TiO2 production. Collection of production data enables comparisons of inputs per unit of outputs over time and provides a sound basis for ensuring time series consistency. Where plant-level emission factors are used, good practice is to collect plant-level production data. Typical plant-level activity data is assumed to be accurate to ±2 percent due to the economic value of having accurate information. If plant-level data are not available, nationally compiled production data may be used.

3.7.2.4

C OMPLETENESS

Complete coverage for titanium dioxide production requires accounting for all emissions from all sources including titanium slag, synthetic rutile and rutile TiO2. CO2 emissions are the main process emissions. In order to include emissions of NOx, CO and SO2 from this source category, see guidance provided in Chapter 7 of Volume 1: General Guidance and Reporting.

3.7.2.5

D EVELOPING

A CONSISTENT TIME SERIES

CO2 emissions should be recalculated for all years whenever emission calculation methods are changed (e.g., if the inventory compiler changes from the use of default values to actual values determined at the plant level). If plant-specific data are not available, including plant-specific production data, for all years in the time series, it will be necessary to consider how current plant data can be used to recalculate emissions for previous years. It may be possible to apply current plant-specific emission factors to production data from previous years, provided that plant operations have not changed substantially. Recalculation is required to ensure that any changes in emissions trends are real and not an artefact of changes in procedure. It is good practice to recalculate the time series according to the guidance provided in Volume 1, Chapter 5.

3.7.3 3.7.3.1

Uncertainty assessment E MISSION

FACTOR UNCERTAINTIES

Uncertainties for the default values are estimates based on expert judgement. It is good practice to obtain uncertainty estimates at the plant level which should be lower than uncertainty values associated with default values.

3.7.3.2

A CTIVITY

DATA UNCERTAINTIES

Where activity data are obtained from plants, uncertainty estimates can be obtained from producers. This will include uncertainty estimates for reductant use, carbothermal inputs, and production data. Data that are obtained from national statistical agencies usually do not include uncertainty estimates. It is good practice to consult with national statistical agencies to obtain information on any sampling errors. Where national statistical agencies collect data from the population of titanium dioxide production facilities, uncertainties in national statistics are not expected to differ from uncertainties established from plant-level consultations. Where uncertainty values are not available from other sources, a default value of ±5 percent can be used.

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3.7.4 3.7.4.1

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6. More extensive quality control checks and quality assurance procedures are applicable, if higher tier methods are used to determine emissions. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4.

Comparison of emission factors Inventory compilers should check if the estimated emission factors are within the range of default emission factors provided for the Tier 1 method, and also ensure that the emission factors are consistent with the values derived from analysis of the process chemistry. For example, the CO2 generation rate for rutile TiO2 from the chloride route process should not be less than 0.826 tonnes of CO2 per tonne of rutile TiO2 produced. If the emission factors are outside of the estimated ranges, it is good practice to assess and document the plant-specific conditions that account for the differences. If emission measurements from individual plants are collected, inventory compilers should ensure that the measurements were made according to recognised national or international standards. QC procedures in use at the site should be directly referenced and included in the QC plan. If the measurement practices were not consistent with QC standards, the inventory compiler should reconsider the use of these data.

3.7.4.2

R EPORTING

AND

D OCUMENTATION

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced.

Plant-specific data check The following plant-specific data is required for adequate auditing of emissions estimates:

•

• •

•

•

Activity data comprising electrode carbon consumption (titanium slag), coal reductant use (synthetic rutile), carbothermal input (rutile TiO2), titanium slag production, synthetic rutile production, and rutile TiO2 production; Emission factor data including the carbon content of the reductant (carbon electrode and coal) and carbothermal input (petroleum coke), and the proportion oxidised in the process; Calculations and estimation method; List of assumptions; Documentation of any plant-specific measurement method, and measurement results.

In general production and process data are considered proprietary by operators, especially where there are only a small number of plants within a country. It is good practice to apply appropriate techniques, including aggregation of data, to ensure protection of confidential data.

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3.8

SODA ASH PRODUCTION

3.8.1

Introduction

Soda ash (sodium carbonate, Na2CO3) is a white crystalline solid that is used as a raw material in a large number of industries including glass manufacture, soap and detergents, pulp and paper production and water treatment. Carbon dioxide (CO2) is emitted from the use of soda ash and these emissions are accounted for as a source under the relevant using industry as discussed in Volume 3, Chapter 2. CO2 is also emitted during production with the quantity emitted dependent on the industrial process used to manufacture soda ash. Emissions of CO2 from the production of soda ash vary substantially with the manufacturing process. Four different processes may be used commercially to produce soda ash. Three of these processes, monohydrate, sodium sesquicarbonate (trona) and direct carbonation, are referred to as natural processes. The fourth, the Solvay process, is classified as a synthetic process. Calcium carbonate (limestone) is used as a source of CO2 in the Solvay process. Other uses of limestone and other carbonates are discussed in Volume 3, Chapter 2.

3.8.2

Natural soda ash production

About 25 percent of the world production is produced from natural sodium carbonate-bearing deposits referred to as natural processes. During the production process, Trona (the principal ore from which natural soda ash is made) is calcined in a rotary kiln and chemically transformed into a crude soda ash. Carbon dioxide and water are generated as by-products of this process. Carbon dioxide emissions can be estimated based on the following chemical reaction: 2Na2CO3.NaHCO3.2H2O (Trona) → 3Na2CO3 (Soda Ash) + 5H2O + CO2

3.8.2.1

M ETHODOLOGICAL

ISSUES

CHOICE OF METHOD The choice of method will depend on national circumstances. Emissions can be estimated using an output-based approach (emissions per unit of output), or an input-based approach (emissions per unit of input). However, it is good practice to use the input-based method where data are available. Methods are classified according to the extent of plant-level data that are available. The Tier 1 method is based on default values and national statistics, and the Tier 2 method is based on complete plant-level input or output data and plant specific emission factors. If there is monitoring and direct measurement of CO2 emissions this would be equivalent to a Tier 3 method.

Tier 1 method Natural soda ash production emits CO2 through the thermal decomposition (calcination) of the Trona (Na2CO3.NaHCO3.2H2O) to produce soda ash. According to the chemical reaction presented above, it takes 10.27 tonnes of Trona to produce 1 tonne of carbon dioxide. Hence, for natural soda ash production using Trona, emissions of carbon dioxide can be calculated from the Trona input or natural soda ash output by the following formula: EQUATION 3.14 CO2 EMISSIONS FROM NATURAL SODA ASH PRODUCTION – TIER 1 E CO 2 = AD • EF

Where: ECO2 = emissions of CO2, tonnes AD = quantity of Trona used or soda ash produced, tonnes of Trona used or tonnes natural soda ash produced EF = emission factor per unit of Trona input or natural soda ash output, tonnes CO2/tonne of Trona or tonnes CO2/tonne natural soda ash produced: EFTrona = 0.097 tonnes CO2/tonne of Trona, EFSoda Ash = 0.138 tonnes CO2/tonnes natural soda ash produced.

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It is good practice to assess the available national statistics for completeness. The choice of good practice methods depends on national circumstances, as shown in Figure 3.7: Decision Tree for Estimation of CO2 Emissions from Natural Soda Ash Production. If no data are available for the purity of the Trona input, it is good practice to assume it is 90 percent and adjust the emission factor shown in Equation 3.14.

Tier 2 method To use the Tier 2 method, it is necessary to gather complete data on Trona consumption or natural soda ash production for each of the plants within the country along with plant-specific emission factors for the Trona input or soda ash output. The CO2 emissions for each plant can be calculated using either variation of Equation 3.14. For plants where plant-specific emission factors are not available, the default emission factors provided in Equation 3.14 can be used. Total CO2 emissions are the sum of the emissions from all plants.

Tier 3 method The Tier 3 method uses plant-level CO2 emissions data obtained from direct measurement. Total emissions are the sum of emissions from all plants.

Figure 3.7

Decision tree for estimation of CO 2 emissions from natural soda ash production Start

Are plant-level CO2 emissions data obtainable from direct measurement?

Yes

Use plant-level CO2 emissions data obtained from direct measurement. Box 4: Tier 3

Collect data for the Tier 3 or 2 method.

No Are plant-level data on Trona consumption available?

Yes

Box 3: Tier 2

No

Yes

Calculate emissions using plant-level Trona consumption data, using plant-specific emission factor if available.

Are plant-level data on natural soda ash production available?

Yes

Calculate emissions using plant-level natural soda ash production data and plant-specific emission factors, if available. Box 2: Tier 2

No

Is this a key category1?

No

Calculate emissions using national aggregate data of Trona consumption or natural soda ash production and default emission factor. In case the national aggregate data are not available, estimate it by using production capacity data. Box 1: Tier 1

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

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CHOICE OF EMISSION FACTORS Tier 1 method The Tier method uses the default emission factors presented in Equation 3.14. The default emission factors are derived from the stoichiometric ratio between soda ash produced and purified sodium sesquicarbonate obtained from Trona. They are based on the main natural production process that is used at present, where soda ash is produced by calcination of purified sodium sesquicarbonate.

Tier 2 method The Tier 2 method requires plant-level emission factors per unit of Trona input or per unit of natural soda ash output. Plant-level emission factors should reflect the fractional purities of the Trona input and natural soda ash output and it is good practice to ensure that these are taken into account in the derivation of plant-level emission factors.

CHOICE OF ACTIVITY DATA It is good practice to compile activity data at a level of detail that allows the use of the Tier 2 method. When applying the methods it is essential that a clear distinction is made between the products to avoid multiplying the incorrect emission factor by activity data.

Tier 1 method The Tier 1 method requires data on national consumption of Trona or national production of natural soda ash. If national-level activity data are not available, information on production capacity can be used with emissions estimated using a default emission factor. It is good practice to multiply the total national production capacity by a capacity utilisation factor of 80 percent ± 10 percent (i.e., a range of 70-90 percent).

Tier 2 method Activity data should be collected at the plant-level to use the Tier 2 method. The most important data are the amount of Trona used for soda ash production and the amount of natural soda ash produced at each plant. Although soda ash production is not used in the calculation if emissions are derived from Trona input, it is good practice to collect and report these data to enable comparisons of inputs per unit of outputs over time and provide a sound basis for ensuring time series consistency.

COMPLETENESS Completeness of the activity data (e.g., Trona utilisation) is a crucial attribute of good practice. Therefore, it is good practice to assess the available national statistics for completeness. If data are available at the plant-level, it is good practice to aggregate these data and check the result with the data available at a national level. This practice enables assessment of whether any significant soda ash producer is omitted, and ensures that all production processes within the country have been considered. If data at the plant-level are not available, it is good practice to use production capacity data along with national statistics to estimate the emissions for completeness purposes.

DEVELOPING A CONSISTENT TIME SERIES It is good practice to calculate emissions from soda ash using the same method for every year in the time series. Where data are unavailable to support a more rigorous method for all years in the time series, good practice is to recalculate these gaps according to the guidance provided in Volume 1, Chapter 5.

3.8.2.2

U NCERTAINTY A SSESSMENT

EMISSION FACTOR UNCERTAINTIES The stoichiometric ratio is an exact number and assuming 100 percent purity of the input or output, the uncertainty of the default emission factor is negligible. However, the default factors do not take into account the fractional purities of either the Trona input or soda ash output and, in both cases, are expected to result in consistent over-estimation of emissions. As noted earlier, if no data are available for the purity of the Trona input, it is good practice to assume it is 90 percent and adjust the emission factor shown in Equation 3.14. It is good practice to develop uncertainty estimates based on plant-level data.

ACTIVITY DATA UNCERTAINTIES Where activity data are obtained from plants, uncertainty estimates can be obtained from producers. This will include uncertainty estimates for Trona used and natural soda ash used. Data that are obtained from national statistical agencies usually do not include uncertainty estimates. It is good practice to consult with national statistical agencies to obtain information on any sampling errors. Where national statistical agencies collect data

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from the population of soda ash production facilities, uncertainties in national statistics are not expected to differ from uncertainties established from plant-level consultations. Where uncertainty values are not available from other sources, a default value of ±5 percent can be used.

3.8.2.3

Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC), R EPORTING AND D OCUMENTATION

QUALITY ASSURANCE/QUALITY CONTROL It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4.

Comparison of the emissions estimates using different approaches If the bottom-up approach is used, then inventory compilers should compare the emissions estimates to the estimate calculated using the top-down approach. The results of such comparisons should be recorded for internal documentation, including explanations for any discrepancies.

REPORTING AND DOCUMENTATION It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to data sources, and all the information needed to reproduce the estimate. Besides the emissions, good practice is to report the activity data used in the calculation (Trona utilisation) and the corresponding emission factors along with all assumptions used in the derivation. To preserve an internally consistent emission time series, whenever national methods change, good practice is to recalculate the entire time series. If confidentiality is an issue for any type of production, estimates may be aggregated to the minimum extent possible to maintain confidentiality. In addition, inventory compilers should document the QA/QC procedures.

3.8.3

Solvay soda ash production

About 75 percent of the world production of soda ash is synthetic ash made from sodium chloride. In the Solvay process, sodium chloride brine, limestone, metallurgical coke and ammonia are the raw materials used in a series of reactions leading to the production of soda ash. Ammonia, however, is recycled and only a small amount is lost. The series of reactions involved in the Solvay process may be described as follows: CaCO3 + heat → CaO + CO2 CaO + H2O → Ca(OH)2

2NaCl + 2H2O + 2NH3 + 2CO2 → 2NaHCO3 + 2NH4Cl 2NaHCO3 + heat → Na2CO3 + CO2 + H2O

Ca(OH)2 + 2NH4Cl → CaCl2 + 2NH3 + 2H2O The net overall reaction may be summarised as: CaCO3 + 2NaCl → Na2CO3 + CaCl2 From the series of reactions presented above, CO2 is generated in two pyrolysis processes. The CO2 generated is captured, compressed and directed to Solvay precipitating towers for consumption in a mixture of brine (aqueous NaCl) and ammonia. Although CO2 is generated as a by-product, the CO2 is recovered and recycled for use in the carbonation stage and in theory the process is neutral, i.e., CO2 generation equals uptake. In practice, some CO2 is emitted to the atmosphere during production by the Solvay process because more CO2 is produced than is stoichiometrically required. The excess CO2 arises from calcining the limestone with metallurgical grade coke. The limestone is combined with the coke at approximately 7 percent of limestone by weight.

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The estimation of the CO2 emissions from a stand alone soda ash plant should be based on an overall balance of CO2 around the whole chemical process. For inventory purposes, a simplified version of the balance may be used assuming that the CO2 emissions result from the stoichiometric oxidation of the coke carbon. The Solvay ammonia soda ash production process is a chemical industry activity and emissions should be reported under the Industrial Processes and Product Use (IPPU) Sector.

BOX 3.7 DOUBLE COUNTING

In order to avoid double counting, CO2 emissions generated in the process of soda ash production should be accounted in the IPPU Sector, and should not be included in the Energy Sector. Coke used in the production process should be deducted from the Energy Sector as a non-energy use of coke.

3.8.3.1

Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC), R EPORTING AND D OCUMENTATION

The allocation of emissions from the use of metallurgical grade coke in the Solvay process to the Energy Sector means that a methodology for estimating these emissions is not provided in the Industrial Processes and Product Use Sector. However, data on soda ash production from the Solvay process should be collected and collated to ensure that all data on soda ash production by process are available for recording, reporting, archiving and reconciliation with national statistics on soda ash use.

QUALITY ASSURANCE/ QUALITY CONTROL It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6. Additional quality control checks as outlined in Volume 1, and quality assurance procedures may also be applicable. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4.

REPORTING AND DOCUMENTATION It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to data sources, and all the information needed to reproduce the estimate.

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3.9

PETROCHEMICAL AND CARBON BLACK PRODUCTION

3.9.1

Introduction

The petrochemical industry uses fossil fuels (e.g., natural gas) or petroleum refinery products (e.g., naphtha) as feedstocks. This section provides guidance for estimating emissions from the production of methanol, ethylene and propylene2, ethylene dichloride, ethylene oxide, and acrylonitrile. These petrochemicals are addressed in detail because their global production volume and associated greenhouse gas emissions are relatively large. However, the chemicals included are not intended to represent the entire petrochemical process industry. There are a number of other petrochemical processes that emit small amounts of greenhouse gases for which specific guidance is not provided (e.g., styrene production). This section also provides guidance for production of carbon black. Carbon black is not considered to be a petrochemical; however, the carbon black production process uses petrochemical feedstocks. Emissions from carbon black production are smaller than for petrochemical processes but may be significant for certain countries. Examples of feedstock to product production chains for methanol, ethylene and propylene, ethylene dichloride, ethylene oxide, acrylonitrile, and carbon black are included in the Annex to Section 3.9.

Allocation and Reporting Within the petrochemical industry and carbon black industry, primary fossil fuels (natural gas, petroleum, coal) are used for non-fuel purposes in the production of petrochemicals and carbon black. The use of these primary fossil fuels may involve combustion of part of the hydrocarbon content for heat raising and the production of secondary fuels (e.g., off gases). Combustion emissions from fuels obtained from the feedstocks should be allocated to the source category in the IPPU Sector. However, where the fuels are not used within the source category but are transferred out of the process for combustion elsewhere (e.g., for district heating purposes) the emissions should be reported in the appropriate Energy Sector source category. The industries are included in the source category Chemical Industry (2B1 – 2B10), see Figure 1.1, Industrial Process and Product Use Source Categories in Chapter 1 of this volume. Further discussion of the non-energy use of fuels is included in Chapters 1 and 5 of this volume. Note that national energy statistics may include total combustion of fossil fuels (including natural gas, oil, and coal,) and also secondary fuels (such as industrial process off gases) for energy production. It is important to investigate if fuels used in petrochemical industries are included in national energy statistics. If this is the case, emissions from petrochemical processes should be subtracted from the calculated energy sector emissions to avoid double counting. This is particularly relevant for ethylene and methanol, where primary fuel (e.g., natural gas, ethane, propane) feedstock consumption may be reported in national energy statistics. Should carbon dioxide (CO2) capture technology be installed and used at a plant, it is good practice to deduct the CO2 captured in a higher tier emissions calculation. The default assumption is that there is no CO2 capture and storage (CCS) taking place. Any methodology taking into account CO2 capture should consider that CO2 emissions captured in the process may be both combustion and process-related. In cases where combustion and process emissions are to be reported separately, inventory compilers should ensure that the same quantities of CO2 are not double counted. In these cases the total amount of CO2 captured should preferably be reported in the corresponding energy combustion and IPPU source categories in proportion to the amounts of CO2 generated in these source categories. For additional information on CO2 capture and storage refer to Volume 3, Section 1.2.2 and for more details on capture and storage to Volume 2, Section 2.3.4. Petrochemical processes may utilise CO2 captured elsewhere as a feedstock, and CO2 may also be captured from petrochemical processes. This may create potential double counting issues. For example, some methanol plants may utilise by-product CO2 captured from other industrial processes as a feedstock for methanol production. To avoid double counting the CO2 captured should not be reported as CO2 emissions from the process from which the CO2 is captured.

2

Note that there is no separate inventory methodology for propylene. Propylene is assumed to be a co-product of ethylene production.

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METHANOL Worldwide almost all methanol is made by way of steam reforming of natural gas. The steam reforming and shift reaction produce ‘synthesis gas’ comprised of CO2, carbon monoxide (CO), and hydrogen (H2). The natural gas to methanol production process produces methanol and by-product CO2, CO, and H2 from the synthesis gas. There are several alternative processes for producing methanol from natural gas or other feedstocks. These include conventional reforming process, combined reforming and partial oxidation process. An example of a feedstock to product process flow diagram for methanol production is provided in an Annex to Section 3.9 (Annex 3.9A). Process descriptions for methanol production are included in Box 3.8 below. BOX 3.8 METHANOL PROCESS DESCRIPTIONS

Conventional Reforming Process The Conventional Reforming Process for methanol production involves steam reforming (which may include either a single reformer unit or both a primary reformer unit and a secondary reformer unit) and methanol synthesis. The overall equations for the Conventional Reforming Process are: Steam Reforming

Shift Reaction

Methanol Production

CH4 + H2O → CO + 3 H2

CO + H2O → CO2 + H2

Reforming/Shift Reaction

CO + CO2 + 7 H2 → 2 CH3OH + 2 H2 + H2O

CnHm + nH2O → nCO + (m/2 + n) H2

2 CH4 + 3 H2O → CO + CO2 + 7 H2

CO + 2 H2 → CH3OH

CO2 + 3 H2 → CH3OH + H2O

Methanol Production

The surplus hydrogen from this process and methanol process purge gas containing methane (CH4) and non-methane volatile organic compounds (NMVOC) are recovered and burned for energy recovery, generally within the methanol production process, to produce process steam and/or electricity for the process. The Conventional Reforming Process may utilise CO2 captured from other industrial processes as a supplemental feedstock to the methanol production process. Combined Reforming Process The Combined Reforming Process combines the Conventional Steam Reforming process with a Catalytic Partial Oxidation process. The Partial Oxidation chemical equations are: Methanol Steam Reforming Reaction

CH4 + ½ O2 → CO + 2 H2 → CH3OH

Feedstock Oxidation Reaction CH4 + O2 → CO2 + 2 H2

The Combined Reforming Process produces a synthesis gas that contains a more balanced ratio of hydrogen to carbon monoxide (CO) and CO2 than does the Conventional Reforming Process, and does not produce a hydrogen gas stream for energy recovery. The Combined Reforming Process produces a purge gas containing CH4 that is burned for energy recovery within the methanol process. Other Production Processes Methanol may also be produced from the partial oxidation of oil, coal, or petrochemical feedstocks, or by gasification of coal to synthesis gas, however; these feedstocks and processes currently represent only a small amount of worldwide methanol production.

ETHYLENE Worldwide almost all ethylene is made by way of steam cracking of petrochemical feedstocks. Ethylene may be produced from steam cracking of petrochemical feedstocks in a petrochemical plant, and may also be produced from cracking and other processes operated at petroleum refineries. Steam cracking for ethylene production also produces secondary products including propylene and butadiene. A process description for steam cracking process for ethylene production is provided in Box 3.9 below.

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BOX 3.9 ETHYLENE PROCESS DESCRIPTION

Steam Cracking The fundamental chemical equation for ethylene production is as follows: Ethane Dehydrogenation to Ethylene

C2H6 → C2H4 + H2

The types and mix of feedstock used in steam cracking for ethylene production varies by region, and include ethane, propane, butane, naphtha, gas oil, and other petrochemical feedstocks. In the United States, most ethylene is produced from steam cracking of ethane, while in Europe, Korea, and Japan most ethylene is produced from steam cracking of naphtha. Steam cracking of petrochemical feedstocks to produce ethylene also produces other high value (saleable) petrochemical products, including propylene, butadiene, and aromatic compounds. Most propylene worldwide is produced as a by-product of ethylene production, recovered either from steam crackers or from fluid catalytic cracking units at petroleum refineries. Steam crackers using naphtha feedstock are the largest source of propylene. There are other process technologies that are used to produce propylene including catalytic dehydrogenation of propane. Note that the emissions estimation methods in this section apply only to production of ethylene and propylene in steam crackers and do not apply to other process technologies used to produce ethylene or propylene. The steam cracking process also produces by-product hydrogen and methane and C4+ hydrocarbons that are generally burned for energy recovery within the process. (Houdek, 2005: Figure 1 on Page 3, Page 4)

ETHYLENE DICHLORIDE AND VINYL CHLORIDE MONOMER Worldwide almost all ethylene dichloride (1, 2 dichloroethane) is made by way of direct chlorination or oxychlorination of ethylene, or by a combination of the two processes (referred to as the ‘balanced process.’) An example of a feedstock to product process flow diagram for ethylene dichloride production is provided in an Annex to Section 3.9 (Annex 3.9A). Process descriptions for ethylene dichloride and vinyl chloride monomer production are provided in Box 3.10 below. Note that the chemical compound ‘ethylene dichloride’ is also referred to as 1,2-dichloroethane. The chemical compound ‘dichloroethylene,’ also referred to as 1, 2dichloroethene, is a different compound. BOX 3.10 ETHYLENE DICHLORIDE AND VINYL CHLORIDE MONOMER PROCESS DESCRIPTIONS

Direct Chlorination and Oxychlorination Processes The direct chlorination process involves gas-phase reaction of ethylene with chlorine to produce ethylene dichloride. The oxychlorination process involves gas-phase reaction of ethylene with hydrochloric acid and oxygen to produce ethylene dichloride and water. The ethylene dichloride is then cracked to produce vinyl chloride monomer and hydrochloric acid. The oxychlorination process produces a process off gas containing by-product CO2 produced from the direct oxidation of the ethylene feedstock. The fundamental chemical equations for the direct chlorination and oxychlorination processes are as follows: Direct chlorination

C2H4 + Cl2 → C2H4Cl2

Oxychlorination reaction C2H4 + ½ O2 + 2 HCl → C2H4Cl2 + H2O

Ethylene dichloride>vinyl chloride

2 C2H4Cl2 → 2 CH2CHCl + 2 HCl

[C2H4 + 3 O2 → 2 CO2 + 2 H2O]

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BOX 3.10 (CONTINUATION) ETHYLENE DICHLORIDE AND VINYL CHLORIDE MONOMER PROCESS DESCRIPTIONS

Balanced Process The combination of the direct chlorination process to produce ethylene dichloride and the ethylene dichloride cracking process to produce vinyl chloride monomer produces a surplus of hydrogen chloride. The oxychlorination process provides a sink for the hydrogen chloride. Therefore, ethylene dichloride/vinyl chloride monomer production facilities may operate a ‘balanced process’ in which both the direct chlorination process and the oxychlorination process are combined. The ‘balanced process’ also produces process vent gas containing by-product CO2 from the direct oxidation of the ethylene feedstock. The fundamental chemical equations for the ‘balanced process’ for producing vinyl chloride monomer from ethylene are as follows: Ethylene Dichloride-Vinyl Chloride Monomer Reaction

2 C2H4 + Cl2 + ½ O2 → 2 CH2CHCl + H2O

Feedstock Oxidation Reaction

[C2H4 + 3 O2 → 2 CO2 + 2 H2O]

The direct chlorination process and the oxychlorination process for ethylene dichloride production are not 100 percent efficient in the utilisation of the ethylene feedstock. On the order of three percent of the ethylene feedstock is not converted to ethylene dichloride but is converted either to CO2 (by direct oxidation in the oxychlorination process) or to other chlorinated hydrocarbons (in either the oxychlorination process or the direct chlorination process.) Process off gas containing other chlorinated hydrocarbons is generally treated prior to discharge to the atmosphere. The chlorinated hydrocarbons are converted to CO2 in a thermal incineration process or a catalytic incineration process. Most ethylene dichloride/vinyl chloride monomer plants recover energy from the incinerator off gases and process off gases.

ETHYLENE OXIDE Ethylene oxide (C2H4O) is manufactured by reacting ethylene with oxygen over a catalyst. The by-product CO2 from the direct oxidation of the ethylene feedstock is removed from the process vent stream using a recycled carbonate solution, and the recovered CO2 may be vented to the atmosphere or recovered for further utilisation (e.g., food production.) The oxygen may be supplied to the process through either air or through pure oxygen separated from air. An example of a feedstock to product process flow diagram for ethylene oxide production is provided in an Annex to Section 3.9 (Annex 3.9A). A process description for ethylene oxide production is provided in Box 3.11 below. BOX 3.11 ETHYLENE OXIDE PROCESS DESCRIPTION

The fundamental chemical equations for the production of ethylene oxide from ethylene and the production of monoethylene glycol are as follows: Ethylene Oxide Reaction

C2H4 + ½ O2 → C2H4O

Feedstock Oxidation Reaction

C2H4 + 3 O2 → 2 CO2 + 2 H2O

Monoethylene Glycol Production

C2H4O + H2O → HO- C2H4 - OH

The ratio of the ethylene oxide reaction and the by-product reaction defines the selectivity of the ethylene oxide process, in terms of tonnes of ethylene consumed per tonne of ethylene oxide produced. The combined ethylene oxide reaction and by-product CO2 reaction is exothermic and generates heat, which is recovered to produce steam for the process. The ethylene oxide process also produces other liquid and off-gas by-products (e.g., ethane) that may be burned for energy recovery within the process. The amount of CO2 and other by-products produced from the process and the amount of steam produced from the process is dependent upon the selectivity of the process. Ethylene oxide is used as a feedstock in the manufacture of glycols, glycol ethers, alcohols, and amines. Worldwide approximately 70 percent of ethylene oxide produced is used in the manufacture of glycols, including monoethylene glycol.

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ACRYLONTRILE Worldwide more than 90 percent of acrylonitrile (vinyl cyanide) is made by way of direct ammoxidation of propylene with ammonia (NH3) and oxygen over a catalyst. This process is referred to as the SOHIO process, after the Standard Oil Company of Ohio (SOHIO). Acrylonitrile can also be manufactured by ammoxidation of propane or directly from reaction of propane with hydrogen peroxide. The propane-peroxide direct process has recently been commercialised by British Petroleum (BP) and other manufacturers. (DOE, 2000) However, process data were not readily available for production of acrylonitrile from propane feedstocks. Therefore no emission estimation methodology is provided for this process. An example of a feedstock to product process flow diagram for acrylonitrile production from propylene is provided in an Annex to Section 3.9 (Annex 3.9A). Process descriptions for acrylonitrile production are provided in Box 3.12 below. BOX 3.12 ACRYLONITRILE PROCESS DESCRIPTION

SOHIO Process The SOHIO process involves a fluidized bed reaction of chemical-grade propylene, ammonia, and oxygen over a catalyst. The catalyst is a mixture of heavy metal oxides (including bismuth and molybdenum). The process produces acrylonitrile as its primary product and acetonitrile (methyl cyanide) and hydrogen cyanide (HCN) as secondary products. The process yield of the primary product acrylonitrile depends in part on the type of catalyst used and the process configuration. The ammoxidation process also produces by-product CO2, CO, and water from the direct oxidation of the propylene feedstock, and produces other hydrocarbons from side reactions in the ammoxidation process. The acetonitrile and hydrogen cyanide are separated from the acrylonitrile by absorption, and the hydrogen cyanide may be used in manufacturing other products on site or sold as product. Hydrogen cyanide that is not used or sold may be burned for energy recovery or flared. The acetonitrile may be also recovered for sale as a product, but more often the acetonitrile is burned for energy recovery or flared. The off gas from the main absorber vent containing CO2, CO, nitrogen, water, unreacted propylene, and other hydrocarbons, may be flared or treated in a thermal or catalytic oxidation unit, with or without energy recovery. Heavy bottoms liquids from the acetonitrile – hydrogen cyanide - acrylonitrile absorption separations process may also be burned for energy recovery or recycled. Acrylonitrile and other non-methane hydrocarbons are also released from miscellaneous process vents, including storage tanks. These miscellaneous process vents may be flared or captured and burned for energy recovery. The fundamental chemical equations for the production of acrylonitrile by ammoxidation are as follows: Acrylonitrile Reaction CH2=CHCH3 + 1.5 O2 + NH3 → CH2=CHCN + 3 H2O

Hydrogen Cyanide Reaction

Acetonitrile Reaction CH2=CHCH3 + 1.5 O2 + 1.5 NH3 → 1.5 CH3CN + 3 H2O

Feedstock Oxidation

CH2=CHCH3 + 3 O2 + 3 NH3 → 3 HCN + 6 H2O

C3H6 + 4.5 O2 → 3 CO2 + 3 H2O

C3H6 + 3 O2 → 3 CO + 3 H2O

The ammoxidation of propylene to acrylonitrile is not 100 percent efficient in utilisation of the propylene feedstock. On the order of 70 percent of the propylene feedstock is converted to acrylonitrile. On the order of 85 percent of the propylene feedstock is converted to either the primary product acrylonitrile or secondary products acetonitrile or hydrogen cyanide. The remainder of the propylene feedstock is either converted directly to CO2 by direct oxidation of the feedstock in the ammoxidation process or converted to other hydrocarbons through side reactions in the ammoxidation process.

CARBON BLACK Worldwide almost all carbon black is produced from petroleum-based or coal-based feedstocks using the ‘furnace black’ process. Process descriptions for carbon black production are provided in Box 3.13 below.

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The furnace black process is a partial combustion process where a portion of the carbon black feedstock is combusted to provide energy to the process. Carbon black may also be produced using other petroleumfeedstock or coal-based feedstock partial oxidation processes, including the ‘channel black’ process and ‘lamp black’ process, or may be produced directly by the partial oxidation of natural gas or aromatic oils (‘channel black process’). Carbon black may also be produced by the thermal cracking of acetylene-containing feedstocks (‘acetylene black process’) or by the thermal cracking of other hydrocarbons (‘thermal black process’.) Approximately 95 percent of worldwide carbon black production is by way of the furnace black process; the remaining 5 percent being produced by way of other processes. Approximately 90 percent of carbon black produced worldwide is used in the tire and rubber industry (referred to as ‘rubber black.’) and the remainder is used in pigment applications (e.g., inks) and other applications (e.g., carbon dry cell batteries.) Carbon black may be produced using a furnace black process, thermal black process, acetylene carbon black process, channel black process and lamp black process. These processes are further described in Box 3.13 below. An example of a feedstock to product process flow diagram for carbon black production using the furnace black process is provided in an Annex to Section 3.9 (Annex 3.9A). BOX 3.13 CARBON BLACK PRODUCTION PROCESS DESCRIPTIONS

Furnace Black Process The furnace black process produces carbon black from ‘carbon black feedstock’ (also referred to as ‘carbon black oil’) which is a heavy aromatic oil that may be derived either as a by-product of the petroleum refining process or the metallurgical (coal) coke production process. For either petroleum-derived or coal-derived feedstock, the carbon black feedstock, the ‘primary feedstock,’ is injected into a furnace heated by a ‘secondary feedstock’ (generally natural gas or oil). Both the natural gas secondary feedstock and a portion of the carbon black feedstock are oxidized to provide heat to the production process that pyrolyzes the remaining carbon black feedstock to carbon black. The vent gas from the furnace black process contains CO2, CO, sulphur compounds, CH4, and NMVOCs. A portion of the tail gas is generally burned for energy recovery to heat the downstream carbon black product dryers. The remaining tail gas may also be burned for energy recovery, flared, or vented uncontrolled to the atmosphere. Thermal Black Process Carbon black is produced in the thermal black process by thermal decomposition of gaseous hydrocarbons or atomized petroleum oils in the absence of air in a pair of production furnaces. The carbon black feedstock is introduced into a preheated furnace that is heated by a secondary feedstock, usually natural gas, and by the off gas from the carbon black production process. One of the pair of furnaces is being preheated by the secondary feedstock while the other furnace is receiving carbon black feedstock. Yield from this process is approximately 45 percent of total carbon input to the process (or 40 percent with respect to the total carbon black feedstock used) and energy utilisation is approximately 280 MJ/kg carbon black produced. Acetylene Black Process Carbon black produced from acetylene or acetylene-containing light hydrocarbons by feeding the feedstock to a preheated reactor where the acetylene decomposes to carbon black in an exothermic process. Total worldwide production of acetylene black is only approximately 40 000 metric tons per year. The carbon black yield from this process is approximately 95-99 percent of theoretical yield. Acetylene black is approximately 99.7 percent carbon. Other Production Processes The channel black process involves partial oxidation of vaporised carbon black feedstock that is burned in a furnace with a carrier gas (which may be coke oven gas, hydrogen, or methane). The carbon black yield for this process may be 60 percent of total carbon input for production of rubber-grade carbon black or 10-30 percent of total carbon input for pigment-grade carbon black. The lamp black process involves open burning of carbon black feedstock in shallow pans. Data are not readily available concerning feedstock yield and energy consumption for the lamp black process. This process represents an insignificant percentage of worldwide carbon black production. (Kirk Othmer, 1992)

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3.9.2 3.9.2.1

Methodological issues C HOICE

OF METHOD

The emissions from petrochemical and carbon black production vary both with the process used and the feedstock used. The choice of method should thus be repeated for each product, process and feedstock used. Three methodological tiers are provided depending on the availability of data. The choice of method depends on national circumstances and is given by the decision trees in Figure 3.8 and Figure 3.9. Figure 3.8

Decision tree for estimation of CO 2 emissions from petrochemical industry and carbon black industry Start

Collect feedstock and the process data1.

Are both the type of feedstock used and process known?

Repeat for each feedstock and petrochemical. Are plant-specific emission factors available?

Yes

Estimate emissions using Tier 3 plant-specific emission factors.

No Are the activity data for all carbon flows available?

No No

Yes

Yes

Is Petrochemical and Carbon Black Production a key category and is this subcategory significant?

Box 4: Tier 3

Yes Are by-products transferred out of the process?

No

No

Is it the process that is unknown?

Is supplementary fossil fuel transferred into the process?

Yes Assume default process.

Yes

Yes

No No Is it the feedstock that is unknown?

No

Estimate emissions using Tier 1 default emission factors using Equation 3.15. If the annual primary product production data are not available, use Equation 3.16.

Yes

Box 2: Tier 1

Assume default feedstock.

Estimate emissions using Tier 1 default emission factors using Equation 3.15.

Subtract carbon from carbon balance.

Add carbon emitted to carbon balance.

Estimate emissions using Tier 2 carbon balance. Box 3: Tier 2

Box 1: Tier 1

Note: 1. See Volume 1 Chapter 4,"Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

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Figure 3.9

Decision tree for estimation of CH 4 emissions from petrochemical industry and carbon black industry Start Repeat for each feedstock and petrochemical. Are both the type of feedstock used and process known?

Collect feedstock and the process data1.

Are plant-specific emission factors available?

Yes

Yes Estimate emissions using Tier 3 plant-specific emission factors.

No

Box 3: Tier 3 No

Yes

Is Petrochemical and Carbon Black Production a key category and is this subcategory significant?

No Is it the process that is unknown?

Yes Assume default process.

No

Is it the feedstock that is unknown?

No

Estimate emissions using Tier 1 default emission factors. If the annual primary product production data are not available, use Equation 3.16. Box 2: Tier 1

Yes

Assume default feedstock.

Estimate emissions using Tier 1 default emission factors. Box 1: Tier 1

Note: 1. See Volume 1 Chapter 4,"Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 2. Note that there is no Tier 2 method for estimating CH4 emissions. The Tier 2 method is a total feedstock carbon mass balance method that is applicable to estimating total carbon (CO2) emissions but not applicable to estimating CH4 emissions.

The Tier 3 methodology can be used to estimate plant-level CO2 emissions and CH4 emissions. The Tier 3 method depends upon the availability of plant-specific data for the petrochemical process. The Tier 2 methodology is a mass balance approach that is applicable to estimating CO2 emissions but is not applicable to estimating CH4 emissions. When using the Tier 2 methodology, both carbon flows of primary and secondary feedstocks to the process are included in the mass balance calculation. Carbon flows of primary fuels to the

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process may involve combustion of part of the hydrocarbon content for heat raising and the production of secondary fuels (e.g., off gases). In order to apply the Tier 2 methodology the flows of primary and secondary feedstocks to the process and the flows of primary and secondary products must be characterised, and the flows of by-products burned for energy recovery within the process and flows of by-products transferred out of the process must be characterised.

CARBON DIOXIDE The decision tree for choice of method for CO2 emissions is shown in Figure 3.8. The Tier 1, Tier 2, and Tier 3 methods are described in this section.

Tier 1 product-based emission factor method The Tier 1 emission factor methodology is applied to estimate CO2 emissions from the petrochemical process in cases where neither plant specific data nor activity data for carbon flows are available for the petrochemical process. The Tier 1 emission factor method does not require activity data for the consumption of each carboncontaining feedstock to the petrochemical production process. It requires only activity data for the amount of product produced. The Tier 1 methodology does not consider the carbon content of emissions of carbon monoxide or NMVOC that may be generated by the petrochemical processes. The equations in this section for petrochemical production processes also apply to carbon black production. The Tier 1 method calculates emissions from petrochemical processes on the basis of activity data for production of each petrochemical and the process-specific emission factor for each petrochemical, as shown in the Equation 3.15 for production of each primary petrochemical product (e.g., methanol, ethylene, ethylene dichloride, ethylene oxide, acrylonitrile) and carbon black. EQUATION 3.15 TIER 1 CO2 EMISSION CALCULATION ECO 2 i = PPi • EFi • GAF / 100 Where: ECO2i = CO2 emissions from production of petrochemical i, tonnes PPi = annual production of petrochemical i, tonnes EFi = CO2 emission factor for petrochemical i, tonnes CO2/tonne product produced GAF = Geographic Adjustment Factor (for Tier 1 CO2 emission factors for ethylene production, See Table 3.15), percent Tier 1 CO2 emission factors for ethylene production (discussed in Section 3.9.2.2) have been developed based on data for ethylene steam crackers operating in Western Europe. Geographic Adjustment Factors are applied to the Tier 1 emission factor to account for regional variability in steam cracker operating efficiency. Geographic Adjustment Factors are only applicable to ethylene production. If activity data for annual primary product production are not available, primary product production may be estimated from feedstock consumption, as shown in the Equation 3.16: EQUATION 3.16 PRIMARY PRODUCT PRODUCTION ESTIMATE CALCULATION PPi = ∑ (FAi , k • SPPi , k ) k

Where: PPi = annual production of petrochemical i, tonnes FAi,k = annual consumption of feedstock k consumed for production of petrochemical (i), tonnes SPPi,k = specific primary product production factor for petrochemical i and feedstock k ,tonnes primary product/tonne feedstock consumed Either Equation 3.15 or both Equation 3.15 and Equation 3.16 would be applied separately to each of the known feedstocks for each petrochemical process. The Tier 1 emissions estimate shown in Box 1 of Figure 3.8 would utilise Equation 3.15, while the Tier 1 emissions estimate shown in Box 2 of Figure 3.8 would use either

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Equation 3.15 or both Equation 3.16 and Equation 3.15. Equation 3.15 would be utilised alone in cases where annual primary product production data are available for the petrochemical process. In cases where annual primary product production data are not available but feedstock consumption data are available for the petrochemical process, Equation 3.16 would be utilised to estimate the annual production of primary products, and the annual primary product production estimated using Equation 3.16 would then be applied in Equation 3.15 to estimate the emissions.

Tier 2 total feedstock carbon balance method The Tier 2 method is a feedstock-specific and process-specific carbon balance approach. This approach is applicable in cases where activity data are available for both feedstock consumption and primary and secondary product production and disposition. Activity data for all carbon flows are required to implement the Tier 2 methodology. Examples of process flow diagrams that illustrate feedstock and product flows for the methanol, ethylene dichloride, ethylene oxide, acrylonitrile, and carbon black production processes are included in an Annex to Section 3.9. The number of potential feedstocks and products for ethylene production from the steam cracking process is such that the process is better illustrated by a feedstock-product matrix rather than by a process flow diagram. The feedstock-product matrix for ethylene production is included in Table 3.25 in Section 3.9.2.3. A flow diagram of the Tier 2 method is shown in Figure 3.10. Figure 3.10

Tier 2 carbon mass balance flow diagram Identify Petrochemical Production Process.

Identify Primary Feedstock to Process.

Identify Secondary Feedstock(s) to Process.

Identify Primary Product of Process.

Identify Secondary Product(s) of Process.

Determine Primary Feedstock Consumption using activity data.

Determine Secondary Feedstock Consumption using activity data.

Determine Primary Product Production using activity data.

Determine Secondary Product Production using activity data or Table 3.25 and Equation 3.18 (ethylene) or Table 3.26 and Equation 3.19 (acrylonitrile).

Determine Primary Feedstock Carbon Content using Table 3.10 or activity data.

Determine Secondary Feedstock Carbon Content using Table 3.10 or activity data.

Determine Primary Product Carbon Content using Table 3.10 or activity data.

Determine Secondary Product Carbon Content using Table 3.10 or activity data.

Calculate carbon input {∑k(FAi,k・FCk) in Equation 3.17}

Calculate carbon output {[PPi・PCi + ∑j(SPi,j・SCj)] in Equation 3.17}

Calculate carbon emissions from process using Equation 3.17.

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The Tier 2 method calculates the difference between the total amount of carbon entering into the production process as primary and secondary feedstock and the amount of carbon leaving the production process as petrochemical products. The difference in carbon content of the primary and secondary feedstocks and the carbon content of the primary and secondary products produced by and recovered from the process is calculated as CO2. The Tier 2 mass balance methodology is based on the assumption that all of the carbon input to the process is converted either into primary and secondary products or into CO2. This means that any of the carbon input to the process that is converted into CO, CH4, or NMVOC are assumed to be CO2 emissions for the purposes of the mass balance calculation. The overall mass balance equation for the Tier 2 methodology is Equation 3.17.

EQUATION 3.17 OVERALL TIER 2 MASS BALANCE EQUATION

(

⎧⎪ ⎡ ECO 2i = ⎨∑ (FAi , k • FCk ) − ⎢ PPi • PCi + ∑ SPi , j • SC j ⎪⎩ k j ⎣⎢

)⎤⎥ ⎪⎬ • 44 12 ⎫

⎦⎥ ⎪⎭

Where: ECO2i = CO2 emissions from production of petrochemical i, tonnes FAi,k = annual consumption of feedstock k for production of petrochemical i, tonnes FCk = carbon content of feedstock k, tonnes C/tonne feedstock PPi = annual production of primary petrochemical product i, tonnes PCi = carbon content of primary petrochemical product i, tonnes C/tonne product SPi,j = annual amount of secondary product j produced from production process for petrochemical i, tonnes [The value of SPi,j is zero for the methanol, ethylene dichloride, ethylene oxide, and carbon black processes because there are no secondary products produced from these processes. For ethylene production and acrylonitrile production, see secondary product production Equations 3.18 and 3.19 below to calculate values for SPi,j.] SCj = carbon content of secondary product j, tonnes C/tonne product For ethylene production and acrylonitrile production there are both primary and secondary products produced by the process. If activity data are not available for the amount of secondary products produced by these processes, the amount of secondary products produced may be estimated by applying default values to the primary feedstock consumption, as shown in Equations 3.18 and 3.19:

EQUATION 3.18 ESTIMATE SECONDARY PRODUCT PRODUCTION FROM PRIMARY PRODUCT [ETHYLENE]

(

SPEthylene, j = ∑ FAEthylene, k • SSPj , k PRODUCTION k

)

Where: SPEthylene ,j = annual production of secondary product j from ethylene production, tonnes FAEthylene k = annual consumption of feedstock k consumed for ethylene production, tonnes SSPj,k = specific secondary product production factor for secondary product j and feedstock k, tonnes secondary product/tonne feedstock consumed EQUATION 3.19 ESTIMATE SECONDARY PRODUCT PRODUCTION FROM PRIMARY PRODUCT [ACRYLONITRILE]

(

SPAcrylonitrile, j = ∑ FPAcrylonitrile, k • SSPj , k PRODUCTION k

)

Where:

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SP Acrylonitrile,j = annual production of secondary product j from acrylonitrile production, tonnes FP Acrylonitrile,k = annual production of acrylonitrile from feedstock k, tonnes SSPj,k = specific secondary product production factor for secondary product j and feedstock k, tonnes secondary product/tonne acrylonitrile produced Note: It is anticipated that in most cases only a single feedstock (propylene) would be used for acrylonitrile production.

Feedstock and product carbon contents Carbon contents of feedstocks and products of petrochemical production processes are listed in Table 3.10, in units of tonnes of carbon per tonne of feedstock or product. Carbon contents of pure substances (e.g., methanol) are calculated from the chemical formula. Carbon contents of other feedstocks and products (e.g., carbon black feedstock, carbon black) are estimated from literature sources. Representative carbon contents of fossil fuels (e.g., natural gas, naphtha) can be found in Table 1.3 in Chapter 1 of Volume 2: Energy; however, carbon contents for fossil fuels will vary by country and region and are best obtained from national energy statistics or fossil fuel product specifications or national standards.

Tier 3 direct estimate of plant-specific emissions The most rigorous good practice method is to use plant specific data to calculate CO2 emissions from the petrochemical production process. In order to apply the Tier 3 method, plant-specific data and/or plant-specific measurements are required. The emissions from the petrochemical production process include CO2 emitted from fuel or process by-products combusted to provide heat or thermal energy to the production process, CO2 emitted from process vents, and CO2 emitted from flared waste gases. These emissions are calculated using Equations 3.20 through 3.22. Overall CO2 emissions from the petrochemical production process are calculated using Equation 3.20 EQUATION 3.20 TIER 3 CO2 EMISSIONS CALCULATION EQUATION ECO 2i = ECombustion , i + E Process Vent , i + E Flare, i Where: ECO2i = CO2 emissions from production of petrochemical i, tonnes E Combustion,i = CO2 emitted from fuel or process by-products combusted to provide heat or thermal energy to the production process for petrochemical i, tonnes E Process Vent,i = CO2 emitted from process vents during production of petrochemical i, tonnes E Flare,i = CO2 emitted from flared waste gases during production of petrochemical i, tonnes

E combustion and E flare are given by Equations 3.21 and 3.22 where plant specific or national net calorific value data should be used. The emission factor is given by the carbon content of the fuel, the combustion oxidation factor and a constant (44/12) converting the result from carbon to CO2. If the emission factor is not known a default value may be found in Table 1.4 in Chapter 1 of Volume 2: Energy. Net calorific values are included in Table 1.2 in Chapter 1 of Volume 2: Energy. Carbon contents are included in Table 1.3 in Chapter 1 of Volume 2: Energy. For the process vents, inventory compilers should measure/estimate emissions of CO2 directly and thus no further equation is provided. EQUATION 3.21 FUEL COMBUSTION TIER 3 CO2 EMISSIONS CALCULATION ECombustion , i = ∑ (FAi , k • NCVk • EFk ) k

Where: FAi,k = amount of fuel k consumed for production of petrochemical i, tonnes NCVk = net calorific value of fuel k, TJ/tonne (Note: In Table 1.2 in Chapter 1 of Volume 2, net calorific values are expressed in TJ/kg)

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EFk = CO2 emission factor of fuel k, tonnes CO2/TJ (Note: In Table 1.4 in Chapter 1 of Volume 2, CO2 emission factors are expressed in kg/TJ)

EQUATION 3.22 FLARE GAS TIER 3 CO2 EMISSIONS CALCULATION E Flare, i = ∑ (FGi , k • NCVk • EFk ) k

Where: FGi,k = amount of gas k flared during production of petrochemical i, tonnes NCVk = net calorific value of flared gas k, TJ/tonne (Note: In Table 1.2 in Chapter 1 of Volume 2, net calorific values are expressed in TJ/kg) EFk = CO2 emission factor of flared gas k, tonnes CO2/TJ (Note: In Table 1.4 in Chapter 1 of Volume 2, CO2 emission factors are expressed in kg/TJ)

TABLE 3.10 SPECIFIC CARBON CONTENT OF PETROCHEMICAL FEEDSTOCKS AND PRODUCTS Substance

Carbon (tonne carbon per tonne feedstock or product)

Acetonitrile

0.5852

Acrylonitrile

0.6664

Butadiene

0.888

Carbon black

0.970

Carbon Black Feedstock

0.900

Ethane

0.856

Ethylene

0.856

Ethylene dichloride

0.245

Ethylene glycol

0.387

Ethylene oxide

0.545

Hydrogen Cyanide

0.4444

Methanol

0.375

Methane

0.749

Propane

0.817

Propylene

0.8563

Vinyl Chloride Monomer

0.384

Note: Carbon content values for natural gas and naphtha vary by country and region. Net calorific values (NCV) for natural gas, naphtha, and other primary fuels that may be used as petrochemical feedstocks are included in Table 1.2 in Chapter 1 of Volume 2: Energy. Feedstock carbon contents are included in Table 1.3 in Chapter 1 of Volume 2: Energy.

METHANE The decision tree for choice of method for CH4 emissions is shown in Figure 3.9. The Tier 1 and Tier 3 methods for CH4 are described in this section. There is no Tier 2 method applicable to CH4 emissions.

Tier 1 product-based emission factor method CH4 emissions from petrochemical processes may be fugitive emissions and/or process vent emissions. Fugitive emissions are emitted from flanges, valves, and other process equipment. Emissions from process vent sources include incomplete combustion of waste gas in flare and energy recovery systems. CH4 emissions using the Tier 1 method may be calculated using Equation 3.23 for fugitive CH4 emissions and Equation 3.24 for process vent

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emissions and Equation 3.25 for total CH4 emissions. If annual primary product production data are not available but feedstock consumption data are available for the petrochemical process, Equation 3.16 would be utilised to estimate the annual production of primary products, and the annual primary product production estimated using Equation 3.16 would then be applied in Equations 3.23 and 3.24 to estimate the emissions. EQUATION 3.23 TIER 1 CH4 FUGITIVE EMISSION CALCULATION ECH 4 Fugitive, i = PPi • EFf i

EQUATION 3.24 TIER 1 CH4 PROCESS VENT EMISSION CALCULATION ECH 4 Process Vent , i = PPi • EFpi

EQUATION 3.25 TIER 1 CH4 TOTAL EMISSIONS CALCULATION ECH 4Total , i = ECH 4 Fugitive,i + ECH 4 Process Vent , i Where: ECH4 Total,i = total emissions of CH4 from production of petrochemical i, kg ECH4 Fugitive,i = fugitive emissions of CH4 from production of petrochemical i, kg ECH4 Process Vent,i = process vent emissions of CH4 from production of petrochemical i, kg PPi = annual production of petrochemical i, tonnes EFfi = CH4 fugitive emission factor for petrochemical i, kg CH4/tonne product EFpi = CH4 process vent emission factor for petrochemical i, kg CH4/tonne product

Tier 2 total feedstock carbon balance method The total feedstock carbon mass balance method is not applicable to estimation of CH4 emissions. The total carbon mass balance method estimates the total carbon emissions from the process but does not directly provide an estimate of the amount of the total carbon emissions that is emitted as CO2, CH4, CO, or NMVOC.

Tier 3 direct estimate of plant-specific emissions The Tier 3 method is based on continuous or periodic plant-specific measurements. The emissions from the petrochemical production process include CH4 emitted from fuel or process by-products combusted to provide heat or thermal energy to the production process, CH4 emitted from process vents, and CH4 emitted from flared waste gases. If methane is vented directly to the atmosphere this will dominate the emissions. CH4 emissions from process vents may also be combusted in a flare or energy recovery device. Measurement of atmospheric concentration of VOCs directly above the plants or in the plume is the preferred activity data for estimating fugitive CH4 emissions; however, such data may not be available. The atmospheric measurements are generally expensive and will most often not be continuous measurements but rather a discrete and periodic measurement program to obtain data to be used as basis for the development of plant specific emission factors. The results of such measurement programs would then be related to other plant process parameters to enable estimation of emissions between measurement periods. Direct measurement of VOC and CH4 concentrations in plant exhaust gas streams and direct measurement of fugitive VOC and CH4 emissions from plant valves, fittings, and related equipment using a comprehensive leak detection programme can also be used to obtain plant-specific activity data for developing Tier 3 estimates of CH4 emissions. However the plant-specific leak detection programme should provide fugitive CH4 emissions data for all of the relevant CH4-emitting plant equipment. Similarly, the plant-specific measurement data for stacks and vents would need to cover the major portion of stack and vent CH4 emissions sources at the plant in order to provide a basis for a Tier 3 emission calculation.

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Emissions of CH4 from process stacks and vents may be estimated by direct measurement of the CH4 concentration of the exhaust gas or estimated as a component of the total VOC concentration measured in the exhaust gas. Fugitive emissions of CH4 from plant equipment (e.g., valves, fittings) may be estimated through application of plant-specific leak detection data and plant equipment inventories, provided that the plant-specific leak detection program and equipment inventory are comprehensive, such that the program provides fugitive CH4 emissions data for all of the relevant CH4-emitting plant equipment. Similarly, the plant-specific measurement data for stacks and vents would need to cover the major portion of stack and vent CH4 emissions sources at the plant in order to provide a basis for a Tier 3 emission calculation. Measurement of fugitive emissions may also be based on the CH4 concentration in the atmosphere immediately above the plant or in a plume downwind. Such atmospheric measurement data would generally measure emissions from the entire plant, and does not separate between the different sources. In addition to CH4 concentration the area of the plume and the wind speed must be measured. The emissions are given by Equation 3.26. EQUATION 3.26 TIER 3 CH4 EMISSION CALCULATION BASED ON ATMOSPHERIC MEASUREMENT DATA

[(

)

]

CH 4 Emissions = ∫t C total VOCs • CH 4 fraction − CH 4 background level • WS • PA

Where: CH4 Emissions = total plant CH4 emissions, µg/s C total VOCs = VOC concentration at the plant, µg/m3 CH4 fraction = fraction of total VOC concentration that is CH4, fraction CH4 background level = ambient CH4 concentration at background location, µg/m3 WS = wind speed at the plant, m/s PA = plume area, m2

Note: ∫t means the quantity should be summed over time. Note that the Tier 3 methodology does not direct inventory compilers to conduct atmospheric measurements or other specific types of direct measurements to estimate site-specific CH4 emissions. It is anticipated that plantspecific leak detection data and plant-specific stack and vent emission data will be more readily available than atmospheric measurement data. However, if atmospheric measurement data are available the data may be used to develop Tier 3 estimates of CH4 emissions, or to verify other estimates. Atmospheric measurement data may provide a more accurate estimate of process CH4 emissions than leak detection data and stack and vent emission data. A plant would use either i) Equation 3.26 or ii) Equations 3.27, 3.28, and 3.29 to estimate CH4 emissions. Process vent emissions are assumed to be monitored either discretely or continuously. The method of calculation will vary depending upon the type of data, and therefore no separate equation is provided for process vent emissions calculation. Overall emissions of CH4 from the petrochemical production process based on plant-specific leak detection data and plant-specific stack and vent emissions data are calculated using Equation 3.27 EQUATION 3.27 TIER 3 CH4 EMISSIONS CALCULATION EQUATION ECH 4i = ECombustion, i + E Process Vent , i + E Flare, i Where: ECH4i = total emissions of CH4 from production of petrochemical i, kg E Combustion,i = emissions of CH4 from fuel or process by-products combusted to provide heat or thermal energy to the production process for petrochemical i, kg E Process Vent,i = emissions of CH4 from process vents during production of petrochemical i, kg E Flare,i = emissions of CH4 from flared waste gases during production of petrochemical i, kg

E combustion and E flare are given by Equations 3.28 and 3.29 where plant specific or national net calorific value data should be used.

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EQUATION 3.28 FUEL COMBUSTION TIER 3 CH4 EMISSIONS CALCULATION ECombustion, i = ∑ (FAi , k • NCVk • EFk ) k

Where: FAi,k = amount of fuel k consumed for production of petrochemical i, tonnes NCVk = net calorific value of fuel k, TJ/tonne (Note: In Table 1.2 in Chapter 1 of Volume 2, net calorific values are expressed in TJ/kg) EFk = CH4 emission factor of fuel k, kg/TJ

EQUATION 3.29 FLARE GAS TIER 3 CH4 EMISSIONS CALCULATION E Flare, i = ∑ (FGi , k • NCVk • EFk ) k

Where: FGi,k = amount of gas k flared during production of petrochemical i, tonnes NCVk = net calorific value of flared gas k, TJ/tonne (Note: In Table 1.2 in Chapter 1 of Volume2, net calorific values are expressed in TJ/kg) EFk = CH4 emission factor of flared gas k, kg/TJ

3.9.2.2

C HOICE

OF EMISSION FACTORS

This section includes a discussion of the choice of emission factors for the Tier 1 method. The Tier 2 method is based on mass balance principles and the Tier 3 method is based on plant-specific data; therefore there are no default emission factors applicable to the Tier 2 and Tier 3 methods. TABLE 3.11 PETROCHEMICAL PRODUCTION TIER 1 DEFAULT FEEDSTOCKS AND PROCESSES Petrochemical Process

Default Feedstock

Default Process

Methanol

Natural Gas

Conventional steam reforming without primary reformer

Ethylene

North America, South America, Australia - Ethane Other Continents - Naphtha

Steam cracking

Ethylene Dichloride / Vinyl Chloride Monomer

Ethylene

Balanced Process for EDC production with integrated VCM production plant

Ethylene Oxide

Ethylene

Catalytic Oxidation, Air Process, with thermal treatment

Acrylonitrile

Propylene

Direct Ammoxidation with secondary products burned for energy recovery or flared

Carbon Black

Carbon black feedstock and natural gas

Furnace black process with thermal treatment

Steam cracking

TIER 1 Tier 1 emission factors for CO2 emissions and CH4 emissions for petrochemical products are provided below. Tier 1 emission factors for CO2 emissions do not include carbon emitted as CO, CH4, or NMVOC. Separate Tier 1 emission factors are provided for CH4 emissions from petrochemical processes. Tier 1 emission factors are not provided for carbon monoxide and NMVOC emissions. The Tier 1 method allows for the selection of a ‘default’ feedstock and ‘default’ process in instances where activity data are not available to identify the feedstock or the process utilised to produce the petrochemical. Table 3.11 provides the default feedstocks and default processes for each petrochemical production process. In

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the event that no activity data are available concerning the specific processes and feedstocks used within a country to produce the petrochemical, the default process and default feedstock identified in Table 3.11 and the associated Tier 1 emission factors identified in the subsequent tables in this section are used to estimate the CO2 emissions from the petrochemical production process. Country-specific emission factors may be used instead of the default emission factors if country-specific factors are available.

Methanol Carbon dioxide emissions Emissions of CO2 from methanol production from the steam reforming and partial oxidation processes may be estimated by applying the default process feedstock emission factors, or the feedstock-specific and processspecific emission factors in Table 3.12, to activity data for methanol production, process configuration and process feedstock. The default emission factors are based on the average of plant-specific CO2 emissions data reported for four methanol plants using the conventional steam reforming process without primary reformer and using natural gas feedstock. Emissions data used in developing the default CO2 emission factor were reported for conventional process methanol plants in New Zealand, Chile, and Canada and in the Netherlands. Emission factors in the table include both the CO2 emissions arising from the process feedstock and the CO2 emissions arising from feedstock combusted within the steam reforming process. Table 3.13 summarises the total feedstock consumption, in units of GJ/tonne methanol produced, for the various methanol production process configurations and feedstocks shown in Table 3.12. The conventional reforming process can include a single reformer unit or both a primary reformer unit and a secondary reformer. The emission factors differ depending upon the number of reformer units. Lurgi is a provider of methanol process technology and has published emission factors for several conventional reforming process technologies, see Table 3.12. The production capacity of Mega Methanol plants is generally greater than 5 000 tonnes per day of methanol. The emission factors for the Lurgi Conventional process technologies should be applied only if the specific process technology is known. Otherwise the emission factor for conventional steam reforming without primary reformer, or the emission factor for conventional steam reforming with primary reformer, should be applied. The conventional steam reforming process for methanol production can be integrated with an ammonia production process. The emission factor for integrated methanol and ammonia production should be used only if the specific process technology is known. TABLE 3.12 METHANOL PRODUCTION CO2 EMISSION FACTORS tonne CO2/tonne methanol produced Process Configuration

Feedstock

Nat. gas Nat. gas + CO2

Conventional Steam Reforming, without primary reformer (a) (Default Process and Natural Gas Default Feedstock)

0.67

Conventional Steam Reforming, with primary reformer (b)

0.497

Conventional Steam Reforming, Lurgi Conventional process (c1)

0.385

Conventional Steam Reforming, Lurgi Low Pressure Process (c2)

0.267

Combined Steam Reforming, Lurgi Combined Process (c3)

0.396

Conventional Steam Reforming, Lurgi Mega Methanol Process (c4)

0.310

Partial oxidation process (d) Conventional Steam Reforming with integrated ammonia production

Oil

Coal

Lignite

1.376

5.285

5.020

0.267

1.02

Nat. gas + CO2 feedstock process based on 0.2-0.3 tonne CO2 feedstock per tonne methanol Emission factors in this table are calculated from the feedstock consumption values in Table 3.13 based on the following feedstock carbon contents and heating values: Natural Gas:

56 kg CO2/GJ

48.0 GJ/tonne

Oil:

74 kg CO2/GJ

42.7 GJ/tonne

Coal:

93 kg CO2/GJ

27.3 GJ/tonne

Lignite:

111 kg CO2/GJ

Uncertainty values for this table are included in Table 3.27 Sources: (a) Struker, A, and Blok, K, 1995; Methanex, 2003: (b) Hinderink, 1996: (c1 – c4) Lurgi, 2004a; Lurgi, 2004b; Lurgi, 2004c: (d) FgH-ISI, 1999

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TABLE 3.13 METHANOL PRODUCTION FEEDSTOCK CONSUMPTION FACTORS GJ feedstock input /tonne methanol produced Process Configuration

Feedstock

Nat. gas Nat. gas + CO2

Conventional Steam Reforming, without primary reforme (a) (Default Process and Natural Gas Default Feedstock)

36.5

Conventional Steam Reforming, with primary reformer (b)

33.4

Conventional Steam Reforming, Lurgi Conventional process (c1)

31.4

Conventional Steam Reforming, Lurgi Low Pressure Process (c2)

29.3

Combined Steam Reforming, Lurgi Combined Process (c3)

31.6

Conventional Steam Reforming, Lurgi Mega Methanol Process (c4)

30.1

Partial oxidation process (d)

Oil

Coal

Lignite

37.15

71.6

57.6

29.3

Nat. gas + CO2 feedstock process based on 0.2-0.3 tonne CO2 feedstock per tonne methanol Sources: (a) Struker, A, and Blok, K, 1995; Methanex, 2003: (b) Hinderink, 1996: (c1 – c4) Lurgi, 2004a; Lurgi, 2004b; Lurgi, 2004c : (d) FgH-ISI, 1999 Uncertainty values for this table are included in Table 3.27

Methane emissions Methanex reported CH4 emissions from two Canadian methanol production plants in their 1996 Climate Change Action Plan (Methanex, 1996). Methanex reported that CH4 emissions from methanol production may arise from reformers, package boilers, methanol distillation units, and crude methanol storage tanks. CH4 emissions from the plants accounted for approximately 0.5 percent to 1.0 percent of the total greenhouse gas emissions from the plants, but were reported to vary depending upon the level of maintenance and operational control of the plant equipment. The average emission factor reported for two reporting years is 2.3 kg CH4 emissions per tonne of methanol produced. CH4 emissions from a second Methanex methanol production plant were reported to be 0.15 kg CH4 per tonne of methanol produced. The higher of the two reported values, 2.3 kg CH4 per tonne of methanol produced, should be applied as the default CH4 emission factor for methanol production. CH4 emissions as low as 0.1 kg/tonne have been estimated for the methanol plant Tjeldbergodden, Norway (SFT, 2003a).

Ethylene Carbon dioxide emissions Emissions of CO2 from steam cracking for ethylene production may be estimated using the feedstock-specific emission factors in Table 3.14 and activity data for the amount of ethylene produced from the steam cracking processes. Separate emission factors are provided in Table 3.14 for the CO2 emissions from feedstock consumption and from supplemental energy consumption in the steam cracking process. However, the CO2 emissions from both feedstock consumption and supplemental energy consumption are to be reported as Industrial Process emissions under the reporting convention discussed above. The default emission factors are derived from plant-specific data for steam crackers operating in Western Europe. The emission factors may be adjusted by applying the default geographic adjustment factors in Table 3.15 to account for differences in the energy efficiency of steam cracking units among various countries and regions. Note that as indicated in Table 3.11, the default feedstock for steam crackers operating in North and South America and Australia is ethane, and the default feedstock for steam crackers operating on other continents is naphtha. These default emission factors do not include CO2 emissions from flaring. Emissions from flaring amount to about 7 percent of total emissions in a well-maintained plant in Norway. Steam cracking processes that utilise naphtha, propane, and butane feedstocks are assumed to be energy neutral, requiring no use of supplemental fuel, therefore there are assumed to be no CO2 emissions associated with supplemental fuel consumption for these feedstocks.

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TABLE 3.14 STEAM CRACKING ETHYLENE PRODUCTION TIER 1 CO2 EMISSION FACTORS tonnes CO2/tonne ethylene produced Feedstock

Naphtha

Gas Oil

Ethane

Propane

Butane

Other

Ethylene (Total Process and Energy Feedstock Use)

1.73

2.29

0.95

1.04

1.07

1.73

- Process Feedstock Use

1.73

2.17

0.76

1.04

1.07

1.73

0

0.12

0.19

0

0

0

- Supplemental Fuel (Energy Feedstock) Use

Source: Neelis, M., Patel, M., and de Feber, M., 2003, Table 2.3, Page 26. Default feedstocks for ethylene production are identified in Table 3.11. The emission factors do not include supplemental fuel use in flares. Other feedstocks are assumed to have the same product yields as naphtha feedstock. Uncertainty values for this table are included in Table 3.27.

The emission factors in Table 3.14 may be used in the event that activity data are available only for the amount of ethylene produced by the steam cracking process. Steam cracking is a multi-product process that leads to ethylene, propylene, butadiene, aromatics, and several other high-value chemicals. There is an inherent assumption of a specific product mix in the default emission factors in Table 3.14. The default product mix for each emission factor in Table 3.14 is identified in the ethylene steam cracking feedstock-product matrix in Section 3.9.2.3. The feedstock/product matrix identifies the default values for production of ethylene, propylene, and other hydrocarbon products from the steam cracking process in units of kilograms of each product produced per tonne of feedstock. In order to develop the emission factors for steam cracking shown in Table 3.14 the total CO2 process emissions of a steam cracker have been divided by the output of ethylene only. In other words ethylene has been chosen as the reference for estimating the total CO2 emissions from the steam cracking process as a whole. Multiplication of the emission factors in Table 3.14 by the ethylene production therefore leads to the total CO2 emissions resulting not only from the production of ethylene but also from the production of propylene, butadiene, aromatics, and all other chemicals produced by the steam cracking process. The default emission factors in Table 3.14 provide the total CO2 emissions from the steam cracking process, not only the CO2 emissions associated with the production of the ethylene from the steam cracking process. TABLE 3.15 DEFAULT GEOGRAPHIC ADJUSTMENT FACTORS FOR TIER 1 CO2 EMISSION FACTORS FOR STEAM CRACKING ETHYLENE PRODUCTION

Geographic Region

Adjustment Factor

Notes

Western Europe

100%

Values in Table 3.14 are based on data from Western European steam crackers

Eastern Europe

110%

Not including Russia

Japan and Korea

90%

Asia, Africa, Russia

130%

North America and South America and Australia

110%

Including Asia other than Japan and Korea

Source: Adjustment factors are based on data provided by Mr. Roger Matthews in personal communication to Mr. Martin Patel, May 2002. Uncertainty values for this table are included in Table 3.27.

Methane emissions Default fugitive CH4 emission factors for steam cracking of ethane and naphtha for ethylene production are estimated from total VOC emissions factors and VOC species profile data from EMEP/CORINAIR Emission Inventory Guidebook (EEA, 2005). Overall volatile organic compound emissions from steam cracking are estimated to be 5 kg/tonne ethylene produced based on a European publication, for which the feedstock is assumed to be naphtha, and estimated to be 10 kg VOC/tonne ethylene produced based on a U.S. publication, for which the feedstock is assumed to be ethane. From the total VOC emission factors the overall CH4 emissions from steam cracking of naphtha are estimated from the VOC species profile to be 3 kg/tonne ethylene produced, primarily from leakage losses, and the overall CH4 emissions from cracking of ethane are estimated from the species profile to be twice those from cracking of naphtha (6 kg/tonne ethylene produced); however these factors are subject to uncertainty as the overall VOC emission factors of 5 kg VOC/tonne ethylene for naphtha feedstock and 10 kg VOC/tonne ethylene for ethane feedstock are each based on a single publication. Emissions of CH4

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from steam cracking of feedstocks other than ethane and naphtha have been assumed to be the same as that estimated from the EMEP/CORINAIR data for steam cracking of naphtha. Published data show a large variability in reported CH4 emission factors for ethylene production. The European Association of Plastics Manufacturers (APME) Eco-Profiles of the European Plastics Industry reports a CH4 emission factor for ethylene production of 2.9 kg CH4/tonne ethylene produced, as referenced in the APME EcoProfiles for Olefins Production (Boustead, 2003a). The CH4 emission factor for ethylene steam cracker process operations is based on life-cycle analysis data for 15 European steam crackers. Emissions as low as 0.14 kg CH4/tonne ethylene are estimated on the basis of direct measurement at a Norwegian ethylene plant (SFT 2003b) and as low as 0.03 kg CH4/tonne ethylene based on company data reported in the Australian Methodology for the Estimation of Greenhouse Gas Emissions and Sinks, 2003 (AGO, 2005). Other European and Australian steam cracker operators reported plant-specific CH4 emissions on the order of 10 percent of the values reported in Table 3.16 (DSM, 2002; Qenos, 2003; Qenos, 2005). Therefore, the emission factors in Table 3.16 should not be used to estimate CH4 emissions from steam cracker ethylene plants for which plant-specific data are available. In this case the plant-specific data and the Tier 3 method should be used. Default CH4 emission factors for various process feedstocks are shown in Table 3.16. Note that the default feedstocks for ethylene production are identified in Table 3.11.

TABLE 3.16 DEFAULT METHANE EMISSION FACTORS FOR ETHYLENE PRODUCTION Feedstock

kg CH4/ tonne ethylene produced

Ethane

6

Naphtha

3

All Other Feedstocks

3

Source: EEA, 2005 (EMEP/CORINAIR Emission Inventory Guidebook) Uncertainty values for this table are included in Table 3.27.

Ethylene dichloride and vinyl chloride monomer Carbon dioxide emissions Emission factors are provided in Table 3.17 for the ethylene dichloride and vinyl chloride monomer production processes, including the direct chlorination process, oxychlorination process, and balanced process. The CO2 emission factors are derived by averaging plant-specific CO2 emissions data for European plants reported in the Integrated Pollution Prevention and Control (IPPC) Reference Document on Best Available Techniques in the Large Volume Organic Chemical Industry (European IPPC Bureau, February 2003; referred to in this section as the IPPC LVOC BAT Document). Note that as indicated in Table 3.11, the default process is the balanced process for EDC production with an integrated VCM production plant. The total CO2 emission factor for each process includes noncombustion CO2 emissions from the ethylene dichloride process vent and combustion CO2 emissions from ethylene dichloride plant combustion sources. Plant combustion source emission factors include combustion of both process waste gas and auxiliary fuel in the process waste gas thermal incinerator. The combustion-related emission factor does not include emissions from flares. Combustion-related emission factors in Table 3.17 are based on data from oxychlorination process plants but the emission factors are assumed also to apply to direct chlorination and balanced process plants. Feedstock consumption factors for ethylene dichloride and vinyl chloride monomer production processes are provided in Table 3.18. The PlasticsEurope EcoProfiles (Boustead, 2005) for EDC production indicates ethylene utilisation of 0.306 tonnes ethylene per tonne EDC produced, based on eight European plants. It should be noted that the CO2 emission factors in Table 3.17 in units of tonnes CO2 per tonne EDC produced and in units of tonnes CO2 per tonne VCM produced are not additive. The two CO2 emission factors both apply to the integrated EDC/VCM production process, however the tonnes CO2 per tonne EDC factor is based on EDC production activity data while the tonnes CO2 per tonne VCM factor is based on VCM production activity data. The CO2 emission factor that will be applied will depend upon whether activity data for EDC production or activity data for VCM production are available. Similarly, the feedstock consumption factors in Table 3.18 in units of tonnes ethylene consumed per tonne EDC produced and in units of tonnes ethylene consumed per tonne VCM produced are not additive. The feedstock consumption factor that will be applied will depend upon whether activity data are available for EDC production or for VCM production.

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TABLE 3.17 ETHYLENE DICHLORIDE/VINYL CHLORIDE PRODUCTION PROCESS TIER 1 CO2 EMISSION FACTORS Process Configuration

tonne CO2/tonne EDC produced

tonne CO2/tonne VCM produced

negligible emissions

negligible emissions

Combustion Emissions

0.191

0.286

Total CO2 Emission Factor

0.191

0286

Noncombustion Process Vent

0.0113

0.0166

Combustion Emissions

0.191

0.286

Total CO2 Emission Factor

0.202

0.302

Noncombustion Process Vent

0.0057

0.0083

Combustion Emissions

0.191

0.286

Total CO2 Emission Factor

0.196

0.294

Direct Chlorination Process Noncombustion Process Vent

Oxychlorination Process

Balanced Process [default process]

Values for CO2 emissions from EDC and VCM production for several European production plants were provided in Tables 12.6 and 12.7 of the IPPC LVOC BAT Document (European IPPC Bureau, 2003). These values were averaged to calculate CO2 emission factors for EDC and VCM production. One EDC plant that is equipped with a CO2 control device and that reported zero CO2 emissions from the process is not included in the average emission factor. Source: European IPPC Bureau, 2003 (IPPC LVOC BAT Document, Tables 12.6 and 12.7 data). Uncertainty values for this table are included in Table 3.27.

TABLE 3.18 ETHYLENE DICHLORIDE/VINYL CHLORIDE MONOMER PROCESS TIER 1 FEEDSTOCK CONSUMPTION FACTORS Process Configuration

tonne ethylene/tonne EDC produced

tonne ethylene/tonne VCM produced

Direct Chlorination Process

0.290

--

Oxychlorination Process

0.302

--

Balanced Process

0.296

0.47

Source: European IPPC Bureau, 2003 (IPPC LVOC BAT Document, Section 12.3.1, Page 299-300, Section 12.1 Table 12.3, Page 293). Uncertainty values for this table are included in Table 3.27.

Methane emissions The EMEP/CORINAIR ‘species profile’ for the ethylene dichloride/vinyl chloride monomer balanced process indicates that there are no CH4 emissions from the process other than CH4 emissions from combustion sources. The EMEP/CORINAIR species profile reports that VOC emissions from leakage losses and storage and handling do not contain CH4. The EMEP/CORINAIR also reports that 2 percent of the total VOC emissions from the balanced process are from combustion sources and that CH4 constitutes 1.2 percent of overall VOC emissions. Therefore it may be assumed that non-combustion CH4 emissions from ethylene dichloride/vinyl chloride monomer production are negligible. CH4 emissions from combustion of natural gas supplemental fuel in the ethylene dichloride/vinyl chloride monomer production process may be estimated from activity data for natural gas supplemental fuel consumption and CH4 emission factor for natural gas combustion. Natural gas consumption for integrated ethylene dichloride/vinyl chloride monomer production is estimated to be 110.1 Nm3 natural gas/tonne VCM produced for an integrated ethylene dichloride/vinyl chloride monomer production plant in the Netherlands and 126.4 Nm3 natural gas/tonne VCM produced for an integrated ethylene dichloride/vinyl chloride monomer production plant in Germany. The average of these two values is 118.3 Nm3 natural gas/tonne VCM. The CH4 emission factor for the integrated EDC/VCM production process is based on a CH4 emission factor of 5 g CH4/GJ natural gas combusted and the average natural gas consumption of the two European plants. The default CH4 emission factor for the integrated ethylene dichloride/vinyl chloride monomer production process is provided in Table 3.19. The

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default emission factor is not applicable to stand-alone EDC production plants. If natural gas consumption activity data are available, the CH4 emission factor of 5 g CH4/GJ may be applied directly to the activity data, rather than using the default emission factor. TABLE 3.19 ETHYLENE DICHLORIDE/VINYL CHLORIDE PROCESS TIER 1 DEFAULT CH4 EMISSION FACTOR Process Configuration

kg CH4/tonne VCM product produced

Integrated EDC/VCM Production Plant

0.0226

Sources: European IPPC Bureau, 2003 (IPPC LVOC BAT Document, Section 12.3.1, Table 12.4, Page 300); EEA, 2005 (EMEP/CORINAIR Emission Inventory Guidebook, Processes in Organic Chemical Industries (Bulk Production) 1, 2-Dichloroethane and Vinyl Chloride (Balanced Process), Activity 040505, February 15, 1996, Section 3.4, Page B455-3, and Table 9.2, B455-5).

Ethylene oxide Carbon dioxide emissions Emissions of CO2 from ethylene oxide production may be estimated using emission factors based on activity data for ethylene oxide production, and activity data for process configuration and catalyst selectivity. Separate CO2 emission factors are provided in Table 3.20 for the CO2 emissions from the air process and for the CO2 emissions from the oxygen process for a range of catalyst selectivity. The default emission factors for the air process and for the oxygen process are estimated from process-specific catalyst selectivity data provided in the IPPC LVOC BAT document. Specific data concerning the type of process and the selectivity of the process catalyst are needed in order to select emission factors from Table 3.20. The emission factors are derived from the catalyst selectivity using stoichiometric principles and are based on the assumption that emissions of CH4 and NMVOC from the process are negligible and that all of the carbon contained in the ethylene feedstock is converted either into ethylene oxide product or to CO2 emissions. The emission factors in Table 3.20 do not include emissions from flares. As shown in Table 3.20, the default emission factor for the air process is based on a default process catalyst selectivity of 70 percent and the default emission factor for the oxygen process is based on a default catalyst selectivity of 75 percent. If activity data are not available for the process configuration or the catalyst selectivity, the default process configuration is the air process and the default catalyst selectivity is 70 percent. If activity data are available that identify the process used as the oxygen process, but activity data are not for the catalyst selectivity for the oxygen process, the emission factor for the default catalyst selectivity of 75 percent for the oxygen process in Table 3.20 should be used. TABLE 3.20 ETHYLENE OXIDE PRODUCTION FEEDSTOCK CONSUMPTION AND CO2 EMISSION FACTORS Process Configuration

Air Process [default process]

Oxygen Process

Catalyst Selectivity Default (70) 75 80 Default (75) 80 85

Feedstock Consumption (tonne ethylene/ tonne ethylene oxide)

Emission Factor (tonne CO2/ tonne ethylene oxide)

0.90 0.85 0.80 0.85 0.80 0.75

0.863 0.663 0.5 0.663 0.5 0.35

Source: European IPPC Bureau, 2003 (IPPC LVOC BAT Document, Section 9.2.1, Page 224, Section 9.3.1.1, Page 231, Figure 9.6)

Methane emissions The IPPC LVOC BAT document for ethylene oxide production reported CH4 emissions factors (in units of kilograms methane per tonne ethylene oxide produced) for the ethylene oxide process vent, ethylene oxide purification process exhaust gas steam, and fugitive emissions sources. CH4 emission factors were reported in the IPPC LVOC BAT document for European ethylene oxide plant carbon dioxide removal vents before and after treatment. CH4 emissions were also reported for two ethylene oxide plants in the Netherlands. CH4 emission factors for ethylene oxide production were developed by averaging these data. Emissions of CH4 may be estimated by applying the emissions factors included in Table 3.21 to activity data for ethylene oxide production. The default CH4 emission factor for ethylene oxide production assumes no thermal treatment process.

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ETHYLENE OXIDE

TABLE 3.21 PRODUCTION TIER 1 CH4 EMISSION FACTORS

Process Configuration

kg CH4/tonne ethylene oxide produced

No Thermal Treatment [default factor]

1.79

Thermal Treatment

0.79

Source: European IPPC Bureau, 2003 (IPPC LVOC BAT Document, Table 9.6, Page 233; Table 9.8, Page 236; Table 9.9, Page 236).

Acrylonitrile Carbon dioxide emissions Process vent CO2 emissions from the acrylonitrile production process by the direct ammoxidation of propylene may be calculated from acrylonitrile production activity data using the emission factors provided in Table 3.22: TABLE 3.22 ACRYLONITRILE PRODUCTION CO2 EMISSION FACTORS Process Configuration Direct Ammoxidation of Propylene

tonnes CO2/tonne acrylonitrile produced

Secondary Products Burned for Energy Recovery/Flared (default)

1.00

Acetonitrile Burned for Energy Recovery/Flared

0.83

Acetonitrile and Hydrogen Cyanide Recovered as Product

0.79

Source: European IPPC Bureau, 2003 (IPPC LVOC BAT Document, Section 11.3.1.1, Table 11.2, Page 274 and Section 11.3.1.2, Page 275)

The emission factors in Table 3.22 are based on an average (default) propylene feedstock consumption factor of 1.09 tonnes propylene feedstock per tonne acrylonitrile produced, corresponding to a primary product yield factor of approximately 70 percent. The default CO2 emission factor is based on conversion of propylene feedstock to secondary product acetonitrile at 18.5 kilograms per tonne acrylonitrile produced, and conversion of propylene to secondary product hydrogen cyanide at 105 kilograms per tonne acrylonitrile produced, and is based on process-specific acrylonitrile yield data and process-specific feedstock consumption data reported in the IPPC LVOC BAT document (European IPPC Bureau, 2003). Note however that the acrylonitrile production process may be configured and operated to produce a greater or lesser amount of secondary products. The default CO2 emission factor is based on the assumption that the secondary products (acetonitrile and hydrogen cyanide) of the acrylonitrile production process and hydrocarbon by products in the main absorber vent gas are either burned for energy recovery or flared to CO2 and are not recovered as products or emitted to the atmosphere without combustion treatment. The CO2 emission factors do not include CO2 emissions from any combustion of auxiliary fuel (e.g., natural gas) for the process waste gas energy recovery or flare systems. If activity data are not available concerning whether secondary products are recovered for sale, the default assumption is that the secondary products are either burned for energy recovery or flared to CO2 and the default primary product process yield factor is 70 percent. For the process configuration where secondary products (acetonitrile and hydrogen cyanide) are recovered for sale and are not either flared to CO2 or burned for energy recovery, the overall process yield factor of primary and secondary products is 85 percent. If activity data for propylene feedstock consumption are not available, the propylene feedstock consumption may be estimated from the acrylonitrile production activity data by applying a default feedstock consumption factor of 1.09 tonnes propylene feedstock consumed per tonne acrylonitrile produced.

Methane emissions The Life-Cycle Analysis Data Summary for Acrylonitrile reports a CH4 emission factor for acrylonitrile production of 0.18 kg CH4/tonne acrylonitrile produced, as referenced in the European Association of Plastics Manufacturers (APME) Life-Cycle Analysis Report (Boustead, 1999). The CH4 emission factor for acrylonitrile process operations is based on life-cycle analysis data for European acrylonitrile plants in Germany, Italy, and the United Kingdom collected between 1990 and 1996. CH4 emissions from acrylonitrile production may be estimated by applying this default emission factor to the acrylonitrile production data.

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Carbon black Carbon dioxide emissions Emissions of CO2 from carbon black production may be estimated by applying the process and feedstockspecific emission factors to the carbon black production activity data. Separate emission factors are provided in Table 3.23 for the furnace black process, thermal black process, and acetylene black process and their associated feedstocks, and separate emission factors are provided for primary feedstock and secondary feedstock. The emission factors are based on the assumption that process emissions are subjected to a thermal treatment process. A range of values for primary and secondary carbon black feedstock is included in Table 4.11 of the draft Integrated Pollution Prevention and Control (IPPC) Reference Document for Best Available Techniques in the Large Volume Inorganic Chemicals (LVIC) Solid and Others Industry (European IPPC Bureau, June 2005; referred to in this chapter as the Draft IPPC LVIC BAT Document.) The CO2 emission factors in Table 3.23 are based on the average of the range of values. Primary and secondary feedstock consumption is converted to carbon consumption using average values for carbon black feedstock carbon content. The CO2 emission factors are calculated from the carbon input to the process (primary and secondary feedstocks) and carbon output (carbon black) from the process, using an average value for carbon black carbon content. TABLE 3.23 CARBON BLACK PRODUCTION TIER 1 CO2 EMISSION FACTORS tonnes CO2/tonne carbon black produced Process Configuration

Primary Feedstock

Secondary Feedstock

Total Feedstock

Furnace Black Process (default process)

1.96

0.66

2.62

Thermal Black Process

4.59

0.66

5.25

Acetylene Black Process

0.12

0.66

0.78

Source: European IPPC Bureau, 2005 (Draft IPPC LVIC BAT Document, Table 4.11 data)

Methane emissions CH4 emissions for the carbon black production process are provided in Table 3.24. The draft IPPC LVIC BAT document for carbon black reported the CH4 content of uncombusted tail gas from the carbon black production process and the estimated rate of generation of tail gas from the carbon black production process. Based on 10,000 Nm3 tail gas per tonne carbon black produced and an average reported CH4 concentration of 0.425 percent by volume, the uncontrolled CH4 emission factors is 28.7 kg CH4/tonne carbon black produced. Combustion flare efficiency for carbon black process flare systems was reported in the Draft IPPC LVIC BAT Document as 99.8 percent for carbon monoxide, and the same efficiency is assumed to apply to CH4. The CH4 emission factor for carbon black production after application of combustion control is 0.06 kg CH4/tonne carbon black produced. An overall CH4 emission factor of 0.11 kg CH4/tonne carbon black, based on company data, was reported in the Australian Methodology for the Estimation of Greenhouse Gas Emissions and Sinks, 2003 (AGO, 2005.) Three carbon black production plants in Germany reported a common CH4 emission factor of 0.03 kg CH4/tonne carbon black produced, based on measurement data after waste gas combustion using BAT (Thermische Nachverbrennung als Stand der Technik.) TABLE 3.24 CARBON BLACK PRODUCTION TIER 1 CH4 EMISSION FACTORS Process Configuration

kilogram CH4/tonne carbon black produced (Carbon Black Process Tail Gas )

No Thermal Treatment

28.7

Thermal Treatment (default process)

0.06

Source: European IPPC Bureau, 2005 (Draft IPPC LVIC BAT Document, Table 4.8, Page 209; Table 4.10, Page 213, Section 4.3.2.3, Page 210).

TIER 2 The Tier 2 methodology is based on mass balance calculations and therefore there are no emission factors associated with the methodology.

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TIER 3 For the Tier 3 method plant specific emissions may be estimated using Equations 3.20 through 3.22 for CO2, and using either Equation 3.26 or Equations 3.27 through 3.29 for CH4. The emission factors may be related to annual production for estimation of emissions between measurements when these are not continuous.

3.9.2.3

C HOICE

OF ACTIVITY DATA

General aspects of data collection for obtaining activity data are discussed in Chapter 2 of Volume 1. When using the Tier 3 method plant-specific activity data should be obtained from the production plants. Direct measurements of the total flow to the steam cracker and flare system together with an analysis of the gas carbon content will provide the most accurate basis for an emissions estimate. Plant specific energy balance and/or carbon balance may also be used to derive plant specific emission factors. The variety of energy and carbon flows across the plant boundary makes this a data intensive but still much less resource intensive approach. While feedstock consumption data may be hard to obtain sales data and national statistics may provide approximate production volumes of the chemicals.

METHANOL Emissions of CO2 from methanol production may be calculated from specific feedstock (e.g., natural gas) consumption and product (methanol) production activity data and carbon mass balance calculations.

ETHYLENE Emissions of CO2 from ethylene production may be calculated from specific feedstock consumption and product production activity data and carbon mass balance calculations. In order to create a complete mass balance for the ethylene production process and implement the Tier 2 methodology for ethylene production, all feedstocks and the production and disposition of all primary and secondary products of the process should be identified using activity data. In cases where activity data are available for ethylene production but not available for production of secondary products from the steam cracking process, the production of secondary products may be estimated using the default factors in Table 3.25 and Equation 3.18. However, use of these default factors is a less accurate method than use of specific activity data for all primary and secondary products, and will increase the uncertainty of the estimate, as performance of steam crackers may vary depending on site-specific conditions. For example, site-specific data reported for steam crackers operating in Germany indicate that hydrocarbon losses under normal operating conditions are on the order of 8.5 kg per tonne of hydrocarbon feedstock (BASF, 2006) whereas the default value for hydrocarbon losses shown in Table 3.25 is 5 kg per tonne of hydrocarbon feedstock. In the event that activity data are not available for all secondary products, the Tier 1 method can be applied instead of the Tier 2 method. Secondary products produced by the steam cracking process may recovered and transferred to a petrochemical plant or petroleum refinery for material reuse, recycled within the steam cracking process as feedstock, or burned for energy recovery. Typically C4+ secondary products are recycled as feedstock or recovered for material reuse (BASF, 2006). Allocation of CO2 emissions from combustion of secondary products for energy recovery is described in Box 1.1 in Chapter 1 of this volume. If activity data are not available for the disposition of C4+ secondary products the default assumption is that the C4+ secondary products are recovered and transferred to another process for material reuse. If data are not available for the disposition of CH4 produced by the steam cracking process, the default assumption is that the CH4 is burned for energy recovery within the steam cracking process and results in CO2 emissions from the process. Steam crackers operated within the petrochemical industry may obtain the petrochemical feedstock for the ethylene production process directly from an adjacent petroleum refinery. Depending upon the feedstock and process operating conditions, steam crackers may also generate ‘backflows’ of hydrocarbon by-products that are returned to the adjacent refinery for further processing. Any CO2 emissions from processing backflows at petroleum refineries are not included in the process CO2 emission factors for the steam cracker ethylene production process, but are considered in the feedstock and carbon flow analysis for the process.

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TABLE 3.25 ETHYLENE STEAM CRACKING FEEDSTOCK-PRODUCT MATRIX kg product/tonne feedstock Product

Feedstock

Naphtha 645

Gas oil 569

Ethane 842

Propane 638

Butane 635

Others 645

Ethylene

324

250

803

465

441

324

Propylene

168

144

16

125

151

168

Butadiene

50

50

23

48

44

50

High Value Chemicals

Aromatics

104

124

0

0

0

104

Fuel grade products and backflows

355

431

157

362

365

355

Hydrogen

11

8

60

15

14

11

Methane

139

114

61

267

204

139

Ethane and propane after recycle

0

0

0

0

0

0

Other C4

62

40

6

12

33

62

C5/C6

40

21

26

63

108

40

C7+ non-aromatics

12

21

0

0

0

12

430C

34

196

0

0

0

34

Losses

5

5

5

5

5

5

1 000

1 000

1 000

1 000

1 000

1 000

Total

Source: Neelis, M; Patel, M; de Feber, M; Copernicus Institute, April 2003, Table 2.2, Page 24

ETHYLENE DICHLORIDE AND VINYL CHLORIDE MONOMER Emissions of CO2 from ethylene dichloride and vinyl chloride monomer production may be calculated from specific feedstock (ethylene) consumption and product (ethylene dichloride) production activity data and carbon mass balance calculations.

ETHYLENE OXIDE Emissions of CO2 from ethylene oxide production may be calculated from specific feedstock (ethylene) consumption and product (ethylene oxide) production activity data and carbon mass balance calculations.

ACRYLONITRILE In the event that activity data are not available for production of secondary products (acetonitrile and hydrogen cyanide), the default values in Table 3.26 and Equation 3.19 may be applied to the activity data for primary product production to estimate secondary product production. TABLE 3.26 SECONDARY PRODUCT PRODUCTION FACTORS FOR ACRYLONITRILE PRODUCTION PROCESS Secondary Product

kg secondary product/tonne acrylonitrile produced

Acetonitrile

18.5

Hydrogen Cyanide

105

Note: The secondary product production factors in this table are based on acrylonitrile production from propylene feedstock. In the event that feedstocks other than propylene are used, the factors in this table would not apply. Process-specific factors would need to be developed in order to apply the Tier 2 mass balance approach to acrylonitrile production from feedstocks other than propylene. Source: European IPPC Bureau, 2005 (IPPC LVOC BAT Document, Section 11.3.4, Page 27)

If no activity data are available concerning acetonitrile product recovery it may be assumed that it is not recovered as product and is burned for energy recovery to CO2. If no activity data are available concerning thermal treatment of the acetonitrile main absorber vent gas it may be assumed that the vent gas is thermally treated and combusted to CO2 and is not vented to the atmosphere uncontrolled.

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CARBON BLACK Emissions of CO2 from carbon black production may be calculated from specific primary feedstock (e.g., carbon black feedstock) and secondary feedstock (e.g., natural gas) consumption and product (carbon black) production activity data and carbon mass balance calculations.

3.9.2.4

C OMPLETENESS

In estimating CO2 emissions from petrochemical and carbon black processes, there is a risk of double-counting or omission in either the IPPU or the Energy Sector. Petrochemical and carbon black plants produce methane and non-methane hydrocarbon by-products that may be burned for energy recovery and such energy recovery may be reported in national energy statistics under ‘other’ fuels or some similar categorisation. If CO2 emissions from ‘other’ fuel combustion include industrial process off gases that are burned for energy recovery some adjustment to the energy statistics or to the CO2 emissions calculation for petrochemical production would be needed to avoid double counting of the CO2 emissions.

METHANOL There may be production of methanol from biogenic (renewable) sources. Such biogenic methanol may be incorporated into methanol national production statistics, which would result in overestimation of CO2 emissions from fossil fuel (e.g., natural gas) derived methanol unless adjustments are made to the methanol production activity data.

ETHYLENE There may be production of ethylene from petroleum refining processes or from petrochemical processes other than steam crackers. Such ethylene may be incorporated into ethylene national production statistics, which would result in overestimation of CO2 emissions from steam cracker derived ethylene unless adjustments are made to the ethylene production activity data.

ETHYLENE DICHLORIDE AND VINYL CHLORIDE MONOMER Ethylene dichloride is an intermediate petrochemical product used to manufacture vinyl chloride monomer and other products. Activity data for production of ethylene dichloride may not be complete because the ethylene dichloride may be converted directly to vinyl chloride monomer in an integrated EDC/VCM plant. Therefore it may be the case that the vinyl chloride monomer production activity data are more complete with respect to industry coverage than the ethylene dichloride production activity data. However, utilisation of vinyl chloride monomer activity data as a surrogate for ethylene dichloride data also has issues related to completeness because not all of the ethylene dichloride is used to manufacture vinyl chloride monomer. Therefore adjustments to the activity data for vinyl chloride monomer may be needed to account for utilisation of ethylene dichloride in the production of other products. Based on data for North America and Europe utilisation of ethylene dichloride for products other than vinyl chloride monomer would amount to the order of 5 percent of total ethylene dichloride production.

ETHYLENE OXIDE Ethylene oxide is an intermediate petrochemical product used to manufacture ethylene glycols and other products. Activity data for production of ethylene oxide may not be complete because the ethylene oxide may be converted directly to ethylene glycol in an integrated EO/EG plant. Ethylene oxide may also be converted into other products (e.g., amines, ethers, etc.) in integrated plants. Since only on the order of 70 percent of ethylene oxide production worldwide is used in the manufacture of ethylene glycols, production activity data for chemical products of ethylene oxide may not be more complete with respect to industry coverage than the ethylene oxide production activity data.

CARBON BLACK There may be small amounts of production of carbon black from biogenic (renewable) sources such as animal black and bone black. Such biogenic carbon black may be incorporated into carbon black national production statistics, which would result in overestimation of CO2 emissions from fossil fuel derived carbon black. There also may be carbon black production within the physical boundaries of petroleum refineries rather than within the chemical industry. Carbon black produced within petroleum refineries is anticipated to be incorporated into national carbon black production statistics, therefore CO2 emissions from carbon black production within petroleum refineries should be reported along with other emissions from carbon black production within the chemical industry as industrial process emissions. There may be gaps in completeness with respect to carbon black feedstock consumption activity data. Activity data for carbon black feedstock derived from coal tar products, waste gases, or acetylene may not be available,

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which would result in underestimating of CO2 emissions from carbon black production if a higher-tier carbon balance approach was used.

3.9.2.5

D EVELOPING

CONSISTENT TIME SERIES

The emissions from petrochemical and carbon black production should be estimated using the same Tier and type of activity data for all years. Constructing a time series for emissions from petrochemical and carbon black production using plant specific measurement activity data will give the most accurate current emissions. However activity data on flaring and fugitive emissions will most likely not be available for previous years. If no technology upgrades have taken place calculating a plant specific emission factor based on recent measurement data related to production of petrochemicals may provide a reasonable result. Petrochemical production is often integrated in an industrial complex producing more than one chemical, or exchanges energy or chemical flows with adjacent industrial plants, and carbon black may be produced within petroleum refineries. When constructing a time series based on feedstock consumption great care should be taken to assure that the activity data includes the same flows every year in the time series. Again a Tier 1 type calculation using emission factors developed from recent plant specific emission estimates based on Tier 2 carbon balance calculation may be used. Investigations to uncover a change in choice of feedstock as well as variations in primary and secondary chemicals produced both within a single year and between years. Reconstruction of gaps in emission estimates and recalculations should follow the guidance in Volume 1, Chapter 5.

3.9.3

Uncertainty assessment

Uncertainty assessments for each emissions factor and activity data applicable to each process are discussed in this section. Uncertainty ranges for the emission factors and activity data included in the Tables in the previous sections are summarised in Table 3.27.

METHANOL Much of the uncertainty in emission estimates for methanol production is related to the difficulty in determining activity data including the quantity of methanol produced and, for higher tier methodologies, the amount of natural gas and other feedstocks consumed on an annual basis. Natural gas and other feedstock consumption may only be reported on an annual basis in national energy statistics, without any breakout of consumption for methanol production. If natural gas consumption activity data are not available then only an emission factor approach rather than a higher tier carbon balance approach is applicable. If activity data are not available for consumption of other feedstocks for methanol production, it may be assumed that all of the national methanol production is from natural gas feedstock. However, this assumption would introduce some uncertainty. Further, activity data may not be available for annual CO2 feedstock consumption in methanol production plants that utilise CO2 as a supplemental feedstock in the production process.

ETHYLENE Uncertainty in activity data for ethylene production is related to the difficulty in determining the types, quantities, and characteristics of feedstocks to the steam cracking process (e.g., ethane, naphtha) and the types, quantities, and characteristics of products from the process (e.g., ethylene, propylene). Feedstock consumption and product production may only be reported on an annual basis in national energy statistics and commodity statistics, without any breakout of feedstock consumption for ethylene production or product production from the steam cracking ethylene production process. The ability to conduct a carbon balance calculation for ethylene production depends upon the availability of both activity data for consumption of specific feedstocks and production of specific products of the steam cracking process. If only activity data for national annual ethylene production are available, the default feedstock for the country/region may be assumed and the default emission factor applied. In this case the feedstocks analysis would be conducted by utilising the default yield table for the default feedstock. However, considering the wide variability in emission factors and yield factors among the feedstocks, the unavailability of specific feedstock consumption data would introduce significant uncertainty into the emissions calculations and feedstocks analysis. If specific feedstock consumption activity data are available then a separate emissions estimate and feedstocks analysis may be conducted for each feedstock, which would reduce the uncertainty. Ideally, however, activity data would be available for both specific feedstock consumption and specific product production, allowing a higher tier carbon balance calculation to be conducted. Another source of uncertainty is related to the difficulty in determining other details of the steam cracking ethylene process configuration, including backflows of products of the steam cracking process from the petrochemical plant to the [potentially adjacent] petroleum refinery and flows of by products to energy recovery or flaring. The unavailability of activity for refinery backflows would introduce uncertainty into the feedstocks analysis.

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ETHYLENE DICHLORIDE AND VINYL CHLORIDE MONOMER Sources of uncertainty for ethylene dichloride include the difficulty in determining the specific process utilised for the ethylene dichloride production and in determining activity data for the consumption of ethylene feedstock in the production process. If only activity data for ethylene dichloride production are available, the emission calculation may be conducted using the default [balanced] process product yield factor and default emission factor for the process. However, considering the variability in emission factors and yield factors for the oxychlorination process, direct oxidation process, and balanced process, the unavailability of specific ethylene feedstock consumption data by process would introduce significant uncertainty into the emissions calculations

ETHYLENE OXIDE A main source of uncertainty for ethylene oxide production is the difficulty in determining activity data for the consumption of ethylene feedstock for ethylene oxide production. If ethylene consumption activity data are not available then only an emission factor approach rather than a higher tier carbon balance approach is applicable. If only activity data for national annual ethylene oxide production are available, the default product yield may be assumed and the default emission factor applied. In this case the feedstocks analysis would be conducted by utilising the default product yield factor. However, considering the range of reported product yield factors and emission factors for the ethylene oxide process, the unavailability of specific ethylene feedstock consumption data would introduce significant uncertainty into the emissions calculations.

ACRYLONITRILE Sources of uncertainty for acrylonitrile production include the difficulty in determining the specific process configuration for acrylonitrile production, in determining activity data for the consumption of propylene feedstock in the production process, and in determining activity data for the production of acrylonitrile and acetonitrile from the process. If only activity data for acrylonitrile production are available, the emission calculation may be conducted using the default process configuration (assuming no acetonitrile recovery) and default emission factor for the process. However, the assumption that acetonitrile is not recovered from the process introduces significant uncertainty in the emission and feedstocks calculations and may result in overestimation of emissions and underestimation of feedstocks flows from the acrylonitrile process. Activity data for national production of both acrylonitrile and acetonitrile from the acrylonitrile production process would allow application of the process-specific emission factor for the percentage of national acrylonitrile production from which acetonitrile is recovered. Ideally, however, activity data for propylene consumption and activity data for acrylonitrile, acetonitrile, and hydrogen cyanide production from the acrylonitrile production process would allow utilisation of a higher-tier method, which would reduce the uncertainty.

CARBON BLACK Uncertainty in activity data for carbon black production is related to the difficulty in determining the types, quantities, and characteristics of primary and secondary feedstocks to the carbon black process, and in determining the type of process used for the carbon black production and the characteristics of the carbon black product from the process. Primary and secondary feedstock consumption and carbon black production may only be reported on an annual basis in national energy statistics and commodity statistics, without any breakout of feedstock consumption for carbon black production for each carbon black production process. Most worldwide production of carbon black is by the furnace black process, therefore if feedstock consumption activity data are not available by process, all of the carbon black production may be assumed to be from the furnace black process without introducing a large amount of uncertainty. Also, if activity data are available for primary carbon black feedstock consumption, the data may be reported in generic terms as ‘carbon black feedstock’ without any indication of whether the feedstock is a petroleum-based feedstock produced at petroleum refineries or a coal tar-based feedstock produced from metallurgical coke production. Activity data may also not be available for other primary carbon black feedstocks (e.g., acetylene). Also, specific activity data may be available for natural gas consumption as secondary carbon black feedstock, however, activity data may not be available for other secondary feedstocks that may be used in carbon black production (e.g., coke oven gas). Unavailability of specific primary and secondary feedstock consumption data would add uncertainty to the feedstocks analysis. The ability to conduct a carbon balance calculation for carbon black production depends upon the availability of activity data for the consumption and the characteristics of primary and secondary feedstocks. If only activity data for national annual carbon black production are available, the default feedstock characteristics and product yield may be assumed and the default emission factor may be applied. However, considering the variability in feedstock characteristics and origin, the unavailability of specific feedstock consumption and composition activity data would introduce significant uncertainty into the emissions and feedstocks calculations. If specific feedstock consumption and characteristics activity data and associated carbon black production activity data are

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available then a separate carbon balance and feedstocks analysis may be conducted for each feedstock and process using a higher tier method, which would reduce the uncertainty.

UNCERTAINTY RANGES Uncertainty ranges for Tier 1 emission factors and Tier 2 activity data and Tier 3 activity data for each process are provided in Table 3.27. The source of the data or expert judgement used in preparing the uncertainty estimate is identified in the table for each factor or activity data. Expert judgement elicitation was conducted by evaluating the range of available data. In many cases process-specific data were available only for several plants; the relatively large uncertainty ranges are the result of the relatively few data available and the expected variability of process configurations and feedstock utilisation efficiency among petrochemical and carbon black plants. TABLE 3.27 UNCERTAINTY RANGES FOR EMISSION FACTORS AND ACTIVITY DATA Method

Reference

Tier 3

Factor

Uncertainty Range

Source

Direct measurement of fuel consumption together with gas composition samples for all substances

- 5 to + 5 %

Expert judgement by Lead Authors of Section 3.9, on the basis of discussions with national industry January 2005.

Tier 1

Table 3.12

Methanol production CO2 emission factors

-30% to +30%

Expert judgement by Lead Authors of Section 3.9.

Tier 1

Table 3.13

Methanol Production Feedstock Consumption Factors

-30% to +30%

Expert judgement by Lead Authors of Section 3.9.

Methane Emission Factor for Methanol Production

-80% to +30%

Expert judgement by Lead Authors of Section 3.9 on the basis of Methanex plant data.

Tier 1

Tier 1

Table 3.14

Ethylene Production CO2 Emission Factors

-30% to +30%

IPPC LVOC BAT Document, Figure 7.10,

Tier 1

Table 3.15

Geographic Adjustment Factors For CO2 Emissions Factors For Ethylene Production

-10% to +10%

Expert judgement by Lead Authors of Section 3.9.

Tier 1

Table 3.16

Methane Emission Factors for Ethylene Production

-10% to +10%

Expert judgement by Lead Authors of Section 3.9.

Tier 1

Table 3.17

Ethylene Dichloride/Vinyl Chloride Production Process Vent CO2 Emission Factors

-20% to +10%

IPPC LVOC BAT Document, Tables 12.6 and 12.7

Tier 1

Table 3.17

Ethylene Dichloride/Vinyl Chloride Production CO2 Emission Factors

-50% to +20%

IPPC LVOC BAT Document, Tables 12.6 and 12.7

Tier 1

Table 3.18

Ethylene Dichloride/Vinyl Chloride Monomer Process Feedstock Consumption Factors

-2% to +2%

IPPC LVOC BAT Document, Section 12.3.1, Page 300

Tier 1

Table 3.19

Ethylene Dichloride/Vinyl Chloride Monomer Process CH4 Emission Factors

-10% to +10%

IPPC LVOC BAT Document, Section 12.3.1, Table 12.4, Page 300

Tier 1

Table 3.20

Ethylene Oxide Production Feedstock Consumption and CO2 Emission Factors

-10% to +10%

Expert judgement by Lead Authors of Section 3.9.

Tier 1

Table 3.21

Ethylene Oxide Production CH4 Emission Factors

-60% to +60%

IPPC LVOC BAT Document, Table 9.6, Page 233; Table 9.8, Page 236; Table 9.9, Page 236

Tier 1

Table 3.22

Acrylonitrile Production CO2 Emission Factors

-60% to +60%

IPPC LVOC BAT Document, Section 11.3.1.1, Table 11.2, Page 274.

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TABLE 3.27 (CONTINUATION) UNCERTAINTY RANGES FOR EMISSION FACTORS AND ACTIVITY DATA Method

Reference

Tier 1

Factor

Uncertainty Range

Source

Acrylonitrile Production CH4 Emission Factors

-10% to +10%

Boustead, 2003b (Eco-Profiles of the European Plastics Industry Methodology I. Boustead, Report prepared for APME, July 2003, Page 40)

Tier 1

Table 3.23

Carbon Black Production CO2 Emission Factors

-15% to +15%

Draft IPPC LVIC BAT Document, Table 4.11, Page 214

Tier 1

Table 3.24

Carbon Black Production CH4 Emission Factors

-85% to +85%

Draft IPPC LVIC BAT Document, Table 4.8, Page 209

Tier 2

Table 3.25

Ethylene Steam Cracking Feedstock-Product Matrix

-10% to + 10%

Expert judgement by Contributing Authors of Section 3.9

Tier 2

Table 3.26

Secondary Product Production Factors for Acrylonitrile Production Process

-20% to +20%

Expert judgement by Lead Authors of Section 3.9.

3.9.4 3.9.4.1

Quality Assessment/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSESSMENT /Q UALITY C ONTROL

Quality Assurance/Quality Control for emissions factors and activity data involves methods to improve the quality or better understand the uncertainty of the emissions estimates. It is good practice to conduct quality control checks for the Tier 1 method as outlined in Volume 1, Chapter 6. More extensive quality control checks and quality assurance procedures are applicable, if Tier 2 or Tier 3 methods are used to determine emissions. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4.

Evaluation of Tier 1 and Tier 2 method activity data The Tier 1 and Tier 2 methods both depend upon the application of activity data for petrochemical and carbon black production and/or activity data for feedstock consumption. These activity data should not be expected to vary by more than about +/- 10 percent year to year, barring significant changes in the overall economic output of the country, the construction of new petrochemical production capacity, or other similar factors. If the activity data vary by more than about +/-10 percent year to year, it is good practice to assess and document the country-specific conditions that account for the differences.

Evaluation of Tier 1 method emission factors Inventory compilers that develop country-specific emission factors for petrochemical and carbon black production and apply the Tier 1 method should assess whether the estimated emission factors are within the range of the default emission factors and process-specific emission factors provided for the Tier 1 method in this guidance. If the emission factors are outside of the range of factors reported in this Guidance, then the reasons why this is the case should be investigated (e.g., the process configuration differs from that for the emission factors reported in this guidance; the feedstock is a unique material not considered in this Guidance.) Inventory compilers should also ensure that the country-specific emission factors are consistent with the values derived from analysis of the process chemistry. For example, for methanol production from natural gas the carbon content of the CO2 generated, as estimated using the emission factor, should equal approximately the difference between the carbon content of the natural gas feedstock and the carbon content of the methanol product. If the emission factors are outside of the estimated ranges, it is good practice to assess and document the plant-specific conditions that account for the differences. It is also good practice or inventory compilers using Tier 1 method emission factors included in this Guidance to conduct quality control checks to assess whether the data characteristics of the emission factor conform to the characteristics of the petrochemical and carbon black production processes in the country in which the emission factor is applied.

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Evaluation of Tier 2 method mass balance calculations Application of the Tier 2 mass balance method depends upon the identification and characterisation of process flows. For the Tier 2 method, failure to identify all carbon-containing process flows or mischaracterisation of the flow rates or carbon contents of such process flows could result in significant deviation of the estimated CO2 emissions from the actual CO2 emissions. The quality of Tier 2 mass balance calculation results are generally more dependent upon the quality of the activity data than are Tier 1 calculation results because in general a greater number of activity data need to be applied to the Tier 2 method than to the Tier 1 method. Therefore, it is good practice to assess and document the quality of each activity data applied to the Tier 2 method and the completeness of the activity data prior to applying the Tier 2 method. If the data quality or completeness are deemed not adequate for application of the Tier 2 method then the Tier 1 method should be applied.

Evaluation of Tier 3 method plant-specific data The Tier 3 method is based on the application of plant-specific emissions data. It is good practice for inventory compilers that conduct audits of plant-specific emissions estimates used in the inventory. This involves evaluating whether the plant-specific data are representative of plant emissions and, if plant-specific data for a specific plant are applied to the national inventory, evaluating whether the plant-specific data representative of petrochemical and carbon black production processes in the country as a whole. Audits of plant-specific data would involve the evaluation of: •

•

Documentation of plant-specific measurement methodology;

•

Emissions estimation method and calculations;

•

Activity data employed in emissions calculations;

•

Documentation of plant-specific measurement results;

•

Process feedstock(s) and product(s)

•

Documentation of process technology and configuration; List of assumptions;

If the specific process for which plant-specific data are obtained is deemed not to be representative of other plants in the country producing the same petrochemical (e.g., if the feedstock differs or the process configuration differs) then the plant-specific data should not be applied to the overall inventory but only to the activity data for the specific plant. If emission measurements from individual plants are collected, inventory compilers should ensure that the measurements were made according to recognised national or international standards and the quality control methods were applied to the emissions measurement. Quality control procedures in use at the plant should be directly referenced and included in the quality control plan. If the measurement practices were not consistent with quality control standards or if the measurement procedures and results cannot be adequately documented, the inventory compiler should reconsider the use of the plant-specific data.

3.9.4.2

R EPORTING

AND

D OCUMENTATION

Combustion emissions from combustion of off gases generated by petrochemical production processes are attributed to the IPPU Sector source category which produces them, and are reported as industrial process emissions. However, if any portion of the off gases generated by an IPPU Sector source category is combusted within a different IPPU Sector source category, or combusted within an Energy Sector source category, the corresponding emissions are reported as fuel combustion emissions rather than as industrial process emissions. This means that if the combustion emissions occur within the IPPU Sector source category which produced the off gases, then the emissions are reported as industrial process emissions attributed to that IPPU Sector source category. However, if the off gases are transferred out of the process to another source category in the IPPU Sector or a source category in the Energy Sector, then the emissions from the combustion of the off gases are reported as fuel combustion emissions within that source category. When the total emissions from the combustion of the off gases are calculated, the quantity transferred to and reported in the Energy Sector and the quantity transferred to and reported in a different IPPU Sector source category should be clearly quantified in the IPPU Sector source category calculations and in the Energy Sector source category calculations. If a countryspecific emission factor was developed, the corresponding data should be provided as how the emission factor was developed and applied in the emission factor calculation, including reporting of the production process configuration upon which the emission factor and calculation are based.

METHANOL The amount of methanol produced, the amount of natural gas feedstock consumed in methanol production, and the amount of supplemental CO2 feedstock consumed in methanol production are to be reported when available.

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If a default emission factor is used, this should be noted in the reporting documentation, and the methanol production process configuration should be reported in the event that the default process configuration is not used.

ETHYLENE The amount of each feedstock consumed in ethylene production and the amounts of ethylene and each other primary product produced and recovered as product are to be reported when available. If a default emission factor is used, this should be noted in the reporting documentation, and the ethylene production process configuration and feedstock(s) should be reported in the event that the default process configuration and default feedstock for the country/region are not used.

ETHYLENE DICHLORIDE The amount of ethylene dichloride produced and ethylene feedstock consumed in ethylene dichloride production are to be reported when available. If a default emission factor is used, this should be noted in the reporting documentation, and the ethylene dichloride production process configuration should be reported in the event that the default process configuration is not used.

ETHYLENE OXIDE The amount of ethylene oxide produced and ethylene feedstock consumed in ethylene oxide production are to be reported when available. If a default emission factor is used, this should be noted in the reporting documentation, and the ethylene oxide production process configuration should be reported in the event that the default process configuration is not used.

ACRYLONITRILE The amount of propylene feedstock consumed in acrylonitrile production and the amounts of acrylonitrile, acetonitrile, and hydrogen cyanide produced and recovered as product are to be reported when available. If a default emission factor is used, this should be noted in the reporting documentation, and the acrylonitrile production process configuration should be reported in the event that the default process configuration is not used.

CARBON BLACK The amount of carbon black produced and the amounts and characteristics (carbon content) of each primary and secondary feedstock consumed in carbon black production are to be reported when available. If a default emission factor is used, this should be noted in the reporting documentation, and the carbon black production process configuration should be reported in the event that the default process configuration is not used.

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Annex 3.9A Figure 3.11

Feedstock-product flow diagrams Methanol production feedstock-product flow diagram Vent Gas CO2

Flare CO2

Energy Recovery Process Steam Secondary Feedstock Carbon Dioxide

Primary Feedstock Natural Gas

Figure 3.12

Process Purge Gas

Steam Reforming Process

Methanol Production Process

Methanol Product

Methanol Product Purification

Process Vent Gas

Ethylene dichloride production feedstock-product flow diagram Vent Gas CO2

Flare CO2

Vent Gas Incinerator

Process Vent Gas Primary Feedstock Ethylene

Chlorinated Hydrocarbons

Oxychlorination Process Reactor Ethylene Dichloride Purification Process

Primary Feedstock Ethylene

Direct Chlorination Process Reactor Ethylene Dichloride Product

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Figure 3.13

Ethylene oxide production feedstock-product flow diagram Flare CO2

Vent Gas CO2

Energy Recovery

Process Vent Gas Primary Feedstock Ethylene

Figure 3.14

Ethylene Oxide Reactor Process

Ethylene Oxide Recovery Process

Ethylene Oxide Product

Acrylonitrile production feedstock-product flow diagram Flare CO2

Vent Gas CO2

Energy Recovery

No

Absorber Vent Gas

Primary Feedstock Propylene

Ammoxidation Reactor Process

Acetonitrile By-product Recovery?

Product Recovery Process

Acrylonitrile Product Purification

Acrylonitrile Product

Acetonitrile By-product

Yes No

Hydrogen Cyanide By-product

Figure 3.15

Yes

Hydrogen Cyanide Recovery?

Carbon black production feedstock-product flow diagram Flare CO2

Vent Gas CO2

Energy Recovery

Process Vent Gas Primary Feedstock Carbon Black Feedstock Furnace Black Process Reactor

Carbon Black Product Dryers

Carbon Black Product

Secondary Feedstock Natural Gas

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3.10

FLUOROCHEMICAL PRODUCTION

3.10.1

HFC-23 emission from HCFC-22 production

3.10.1.1

I NTRODUCTION

Trifluoromethane (HFC-23 or CHF3), is generated as a by-product during the manufacture of chlorodifluoromethane (HCFC-22 or CHClF2).3 Materials such as HFC-23 (and other HFCs, PFCs and SF6) are not significantly removed by aqueous (acidic, neutral or alkaline) scrubbing processes and will be released into the atmosphere. It is estimated that in 1990 the HFC-23 released from HCFC-22 plants was at most 4 percent of the production of HCFC-22 (U.S. EPA, 2001), in the absence of abatement measures. There are a small number of HCFC-22 production plants globally and thus a discrete number of point sources of HFC-23 emissions. While the methodology described here is applicable to by-product emissions of any fluorinated greenhouse gas, it has been written specifically for HFC-23. The methodology for emissions of fluorinated by-products in general and ‘fugitive emissions’ is covered by Section 3.10.2.

3.10.1.2

M ETHODOLOGICAL

ISSUES

CHOICE OF METHOD There are two broad measurement approaches to estimating HFC-23 emissions from HCFC-22 plants. These are described in IPCC (2000), DEFRA (2002a and 2002b), EFCTC (2003) and UN (2004) and have been translated into Tier 2 and 3 methodologies described below. National emissions using either of these methodologies are the sum of those from the individual facilities. Tier 1 (default) methodology can be applied to individual plants or, if there is no abatement by destruction, to the total national output of HCFC-22. Accounting for HFC-23 emissions is not simply mechanistic but requires information on the process operations responsible for producing and emitting HFC-23, so that the most appropriate methodology and factors can be adopted. Therefore, it is good practice, to the extent possible, to establish contacts with plant managers in order to obtain the necessary data. The Tier 1 method is relatively simple, involving the application of a default emission factor to the quantity of HCFC-22 produced. This method can be applied at the plant level or the national level. Tier 2 and Tier 3 methodologies are suitable only for plant level calculations because they rely on data that are only available from plants. In cases where there are Tier 3 data available for some plants, the Tier 1 or Tier 2 methods can be applied to the remainder to ensure complete coverage. It is good practice to estimate national emissions by summing measured parameters from all HCFC-22 plants in a country. Tier 3 plant emission measurements are the most accurate, followed by Tier 2 measurements based on plant efficiencies. Direct measurement is significantly more accurate than Tier 1 because it reflects the conditions specific to each manufacturing facility. In most cases, the data necessary to prepare Tier 3 estimates should be available because facilities operating to good business practice perform regular or periodic sampling of the final process vent or within the process itself as part of routine operations. The Tier 1 (default) method should be used only in cases where plant-specific data are unavailable and this subcategory is not identified as significant subcategory under key category. (See Section 4.2 of Volume 1.) Modern plant using process optimization will need to keep accurate HFC-23 generation data as part of this optimization, so plant-specific data should be available to most countries in most cases. The choice of good practice method will depend on national circumstances. The decision tree in Figure 3.16 describes good practice in adapting the methods in these Guidelines to country-specific circumstances. Procedures to abate emissions include destruction of HFC-23 in a discrete facility and, in this case, emissions occur only when the destruction facility is not in operation. The tiers of methodology provide estimates for the quantity of HFC-23 that is produced and the share of production that is ultimately emitted depends on the length of time that the destruction facility is not operated. For facilities using abatement techniques such as HFC-23 destruction, verification of the abatement efficiency is also done routinely. It is good practice to subtract abated HFC-23 emissions from national estimates where the abatement has been verified by process records on every plant.

3

HCFC-22 is used as a refrigerant in several different applications, as a blend component in foam blowing, and as a chemical feedstock for manufacturing synthetic polymers.

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Tier 1 In the Tier 1 methodology, a default factor is used to estimate production (and potential emissions) of HFC-23 from the total HCFC-22 production from each facility (for both potentially dispersive uses, as reported under the Montreal Protocol, and feedstock uses, which are reported separately to the Ozone Secretariat). See Equation 3.30. EQUATION 3.30 TIER 1 CALCULATION OF HFC-23 FROM HCFC-22 (PRODUCED) USING DEFAULT FACTOR E HFC − 23 = EFdefault • PHCFC − 22 Where: EHFC-23 = by-product HFC-23 emissions from HCFC-22 production, kg EFdefault = HFC-23 default emission factor, kg HFC-23/kg HCFC-22 PHCFC-22 = total HCFC-22 production, kg This methodology is suitable where plant-specific measurements are not available and, in that case, the default condition is that all of the estimated HFC-23 production is released into the atmosphere.

Tier 2 In the Tier 2 methodology, the HFC-23 emission factor is derived from records of process efficiencies and used in the calculation shown as Equation 3.31. This is a material balance approach and relies on calculating the difference between the expected production of HCFC-22 and the actual production and then assigning that difference to loss of raw materials, loss of product (HCFC-22) and conversion to by-products, including HFC-23. These parameters will be different for each plant and so should be assessed separately for each facility reporting into the national data. EQUATION 3.31 TIER 2 CALCULATION OF HFC-23 FROM HCFC-22 (PRODUCED) USING FACTOR(S) CALCULATED E HFC − 23 = EFcalculated • PHCFC − 22 • Freleased FROM PROCESS EFFICIENCIES

Where: EHFC-23 = by-product HFC-23 emissions from HCFC-22 production, kg EFcalculated = HFC-23 calculated emission factor, kg HFC-23/kg HCFC-22 PHCFC-22 = total HCFC-22 production, kg Freleased = Fraction of the year that this stream was released to atmosphere untreated, fraction The emission factor can be calculated from both the carbon efficiency (Equation 3.32) and the fluorine efficiency (Equation 3.33) and the value used in Equation 3.31 should normally be the average of these two values unless there are overriding considerations (such as a much lower uncertainty of one of the efficiency measures) that can be adequately documented. Annual average carbon and fluorine balance efficiencies are features of a wellmanaged HCFC-22 plant and are either normally available to the plant operator or may be obtained by examination of process accounting records. Similarly, if there is a vent treatment system, the length of time that this was in operation, and treating the vent stream from the HCFC-22 plant, should be available from records. Total HCFC-22 production includes material that is used as a chemical feedstock as well as that which is sold for potentially dispersive uses. EQUATION 3.32 CALCULATION OF HFC-23 EMISSION FACTOR FROM CARBON BALANCE EFFICIENCY (100 − CBE ) • F EFcarbon _ balance = efficiency loss • FCC 100

Where: EFcarbon_balance = HFC-23 emission factor calculated from carbon balance efficiency, kg HFC-23/kg HCFC-22 CBE = carbon balance efficiency, percent

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Fefficiency loss = factor to assign efficiency loss to HFC-23, fraction FCC = factor for the carbon content of this component (= 0.81), kg HFC-23/kg HCFC-22 and EQUATION 3.33 CALCULATION OF HFC-23 EMISSION FACTOR FROM FLUORINE BALANCE EFFICIENCY (100 − FBE ) • F EF fluorine _ balance = efficiency loss • FFC 100

Where: EFfluorine_balance = HFC-23 emission factor calculated from fluorine balance efficiency, kg HFC-23/kg HCFC-22 FBE = fluorine balance efficiency, percent Fefficiency loss = factor to assign efficiency loss to HFC-23, fraction FFC = factor for the fluorine content of this component (= 0.54), kg HFC-23/kg HCFC-22 The factor to assign the efficiency loss to HFC-23 is specific to each plant and, if this method of calculation is used, the factor should have been established by the process operator. By default, the value is 1; that is all of the loss in efficiency is due to co-production of HFC-23. In practice, this is commonly the most significant efficiency loss, being much larger than losses of raw materials or products. The factors for carbon and fluorine contents are calculated from the molecular compositions of HFC-23 and HCFC-22 and are common to all HCFC-22 plants at 0.81 for carbon and 0.54 for fluorine.

Tier 3 Tier 3 methodologies are potentially the most accurate. The Tier 3 methodologies provided here give equivalent results and the choice between them will be dictated by the information available in individual facilities. In each case, the national emission is the sum of factory specific emissions, each of which may be determined using a Tier 3 method to estimate the composition and flowrate of gas streams vented to atmosphere (either directly and continuously – as in Tier 3a - or by continuous monitoring of a process parameter related to the emission - Tier 3b - or by monitoring the HFC-23 concentration continuously within the reactor product stream - Tier 3c): EQUATION 3.34 TIER 3a CALCULATION OF HFC-23 EMISSIONS FROM INDIVIDUAL PROCESS STREAMS (DIRECT METHOD) E HFC − 23 = ∑ ∑ ∫t C ij • f ij i

j

[ ∫t means the quantity should be summed over time.]

Where: EHFC-23 = total HFC-23 emissions: the sum over all i plants, over all j streams in each plant of the emitted mass flows f and concentrations C is integrated over time t. (See Equation 3.37 for calculation of ‘instantaneous’ HFC-23 emissions in an individual process stream.) or, where proxy methodology is used: EQUATION 3.35 TIER 3b CALCULATION OF HFC-23 EMISSIONS FROM INDIVIDUAL PROCESS STREAMS (PROXY METHOD) E HFC − 23 = ∑ ∑ ∫t E ij i

j

[ ∫t means the quantity should be summed over time.]

Where: EHFC-23 = total HFC-23 emissions: Ei,j are the emissions from each plant and stream determined by the proxy methods. (See Equation 3.38 for calculation of HFC-23 emissions in an individual process stream.) or, where the HFC-23 concentration within the reactor product stream is used:

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EQUATION 3.36 TIER 3c CALCULATION OF HFC-23 EMISSIONS FROM INDIVIDUAL PROCESS STREAMS (BY MONITORING REACTOR PRODUCT) E HFC − 23 = ∑ ∫t C i • Pi i

[ ∫t means the quantity should be summed over time.]

Where: EHFC-23 = total HFC-23 emissions: Pi is the mass flow of HCFC-22 product from the plant reactor at the plant i, and Ci is the concentration of HFC-23 relative to the HCFC-22 product at the plant i. (See Equation 3.40 for calculation of HFC-23 emissions at an individual facility by in-process measurement.)

Tier 3a The Tier 3a method is based on frequent or continuous measurement of the concentration and flow-rate from the vent at an individual plant. So that the quantity emitted to atmosphere is the mathematical product of the mass concentration of the component in the stream, the flowrate of the total stream (in units compatible with the mass concentration) and the length of time that this flow occurred: EQUATION 3.37 TIER 3a CALCULATION OF ‘INSTANTANEOUS’ HFC-23 EMISSIONS IN AN INDIVIDUAL PROCESS STREAM (DIRECT METHOD) Eij = Cij • f ij • t

Where: Eij = ‘instantaneous’ HFC-23 emissions from process stream j at plant i, kg Cij = the concentration of HFC-23 in the gas stream actually vented from process stream j at plant i, kg HFC-23/kg gas fij = the mass flow of the gas stream from process stream j at plant i (generally measured volumetrically and converted into mass flow using standard process engineering methods), kg gas/hour t = the length of time over which these parameters are measured and remain constant, hours If any HFC-23 is recovered from the vent stream for use as chemical feedstock, and hence destroyed, it should be discounted from this emission; material recovered for uses where it may be emitted may be discounted here, if the emissions are included in the quantity calculated by the methods in Chapter 7. Because emissions are measured directly in this tier, it is not necessary to have a separate term for material recovered, unlike Tiers 3b and 3c. The total quantity of HFC-23 released is then the annual sum of these measured instantaneous releases. Periods when the vent stream is processed in a destruction unit to remove HFC-23 should not be counted in this calculation. If it is necessary to estimate the quantity destroyed at each facility, the operator should calculate this based on the difference between the operating time of the plant and the duration of release (t above).

Tier 3b In many cases, measurements are not continuous but were gained during an intensive process survey or plant trial, and the results of the trial may be used to provide a proxy for calculating emissions during normal plant operation. In this case, the emission rate of the by-product is related to a more easily (or accurately) measurable parameter, such as feedstock flow rate. The trial(s) must meet the following conditions: •

•

There should have been no major process design, construction or operating changes that affect the plant upstream of the measurement point and so could render relationships between emissions and production invalid. (See also Box 3.14) The relationship between emissions and plant operating rate must be established during the trial(s), together with its uncertainty.

For almost all cases the rate of plant operation is a suitable proxy and the quantity of HFC-23 emitted depends on the current plant operating rate and the length of time that the vent flow was released.

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EQUATION 3.38 TIER 3b CALCULATION OF HFC-23 EMISSIONS IN AN INDIVIDUAL PROCESS STREAM (PROXY METHOD) Eij = Sij • Fij • PORij • t − Rij

Where: Eij = the mass emission of HFC-23 in vent stream j at plant i, kg Sij = the standard mass emission of HFC-23 in vent stream j at plant i per ‘unit’ of proxy quantity, such as process operating rate (described in Equation 3.39, below), kg/‘unit’ Fij = a dimensionless factor relating the measured standard mass emission rate to the emission rate at the actual plant operating rate. In many cases, the fraction produced is not sensitive to operating rate and Fi is unity (i.e., the emission rate is proportional to operating rate). In other cases the emission rate is a more complex function of the operating rate. In all cases Fi should be derived during the plant trial by measuring HFC-23 production at different operating rates. For situations where a simple function relating the emissions to the operating rate cannot be determined from testing, the proxy method is not considered appropriate and continuous measurement is desirable. PORij = the current process operating rate applicable to vent stream j at plant i averaged over t in ‘unit/hour’. The units of this parameter must be consistent between the plant trial establishing the standard emission rate and the estimate of ongoing, operational emissions (described in Equation 3.39, below). t = the actual total duration of venting for the year, or the period if the process is not operated continuously in hours. Annual emissions become the sum of all the periods during the year. The periods during which the vent stream is processed in a destruction system should not be counted here. Rij = the quantity of HFC-23 recovered for vent stream j at plant i for use as chemical feedstock, and hence destroyed, kg. Material recovered for uses where it may be emitted potentially may be counted here if the emissions are included in the quantity calculated by the methods for ODS substitutes in Chapter 7 of this volume.

EQUATION 3.39 TIER 3b CALCULATION OF STANDARD EMISSION FOR PROXY METHOD

S T ,ij = CT ,ij • f T ,ij PORT ,ij

Where (for each test T): Sij = the standard mass emission of HFC-23 in vent stream j at plant i, kg/‘unit’ (in units compatible with the factors in Equation 3.38, see PORT,ij below) CT,ij = the average mass fractional concentration of HFC-23 in vent stream j at plant i during the trial, kg/kg f T,ij= the average mass flowrate of vent stream j at plant i during the trial, kg/hour PORT,ij = the proxy quantity (such as process operating rate) at plant i during the trial, ‘unit’/hour. The ‘unit’ depends on the proxy quantity adopted for plant i vent stream j (for example, kg/hour or m3/hour of feedstock)

Tier 3c It is a relatively simple procedure to monitor the concentration of HFC-23 in the product of a reaction system relative to the amount of HCFC-22. This provides a basis for estimation of the quantity of HFC-23 released as the mathematical product of the monitored concentration and the mass flow of HCFC-22 made. If there is no vent treatment to abate emissions, this is a simple procedure. However, where there is abatement then it must be shown that this actually treats all streams that may be released into the atmosphere, including direct gas vents and the outgassing of aqueous streams. The latter, especially, may not be passed to the destruction facility. If all potential vent streams are not treated, the method cannot be used.

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EQUATION 3.40 TIER 3c CALCULATION OF HFC-23 EMISSIONS FROM AN INDIVIDUAL FACILITY BY IN-PROCESS

Ei = Ci • Pj • tF − Ri MEASUREMENT

Where: Ei = HFC-23 emissions from an individual facility i, kg Ci = the concentration of HFC-23 in the reactor product at facility i, kg HFC-23/kg HCFC-22 Pi = the mass of HCFC-22 produced at facility i while this concentration applied, kg tF = the fractional duration during which this HFC-23 is actually vented to the atmosphere, rather than destroyed, fraction Ri = the quantity of HFC-23 recovered from facility i for use as chemical feedstock, and hence destroyed, kg Material recovered for uses where it may be emitted potentially may be counted here if the emissions are included in the quantity calculated by the methods in Chapter 7 of this volume. The total quantity of HFC-23 released into the atmosphere is the sum of the quantities from the individual release periods and individual reaction systems. HFC-23 that is recovered for use as chemical feedstock should be subtracted from the total quantity estimated here. In summary, the Tier 1 method is relatively simple, involving the application of a default emission factor to the quantity of HCFC-22 produced. This method can be applied at the plant level or the national level. Tier 2 and Tier 3 methodologies are suitable only for plant level calculations. In cases where there are Tier 3 data available for some plants, the Tier 1 or Tier 2 methods can be applied to the remainder to ensure complete coverage. Uncertainty in the national emission is then calculated using production weighted uncertainties of the individual sources and standard statistical techniques. Regardless of the method, emissions abated should be subtracted from the gross estimate from each plant to determine net emissions before these are added together in the national estimate.

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Figure 3.16

Decision tree for HFC-23 emissions from HCFC-22 production (or other similar by-product emissions from fluorochemical production) Start

Are plant-level HFC-23 measurement data available?

Yes

Calculate plant-level emissions using plant-level data and Tier 3a, 3b or 3c methodology.

Does any HFC-23 destruction take place?

No Obtain plant-level HFC23 measurement data. Is the Fluorochemical Production a key category1, and is this subcategory significant?

No

Yes

No

Yes

Are plant-level HCFC-22 production data available?

Are plant-level efficiencies available?

Yes

Yes

Estimate emissions by aggregating plant-level measurements, and estimates for plants without measurements2, adjusting for HFC-23 destruction. Box 5: Tier 3 Estimate emissions by aggregating plant-level measurements2, and estimates for plants without measurements.

Calculate plantlevel emissions using the Tier 2 emission factor. Is it possible to document any HFC-23 destruction?

Box 4: Tier 3

Yes

Aggregate plant-level emissions, adjusting for HFC-23 destruction.

No No Collect national HCFC-22 production data. Estimate emissions using the default emission factor. If the emissions are estimated at the plant level, aggregate plant-level emissions.

Box 3: Tier 2

No Aggregate plant-level emissions. Box 2: Tier 2

Box 1: Tier 1 Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 2. If there are Tier 3 data available for some plants, the Tier 1 or Tier 2 methods can be applied to the remainder to ensure complete coverage.

It is good practice to use the Tier 3 method if possible. Direct measurement is significantly more accurate than Tier 1 because it reflects the conditions specific to each manufacturing facility. In most cases, the data necessary to prepare Tier 3 estimates should be available because facilities operating to good business practice perform regular or periodic sampling of the final process vent or within the process itself as part of routine operations. For facilities using abatement techniques such as HFC-23 destruction, verification of the abatement efficiency is also done routinely. The Tier 1 (default) method should be used only in rare cases where plant-specific data are unavailable and this subcategory is not identified as significant subcategory under key category. (See Section 4.2 of Volume 1.)

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CHOICE OF EMISSION FACTORS There are several measurement options within the Tier 3 method relating to the location and frequency of the sampling. In general, direct measurement of the emissions of HFC-23 may provide the highest accuracy but continuous or frequent measurement of parameters within the production process area itself may be more pragmatic and can be equally accurate. In both cases, the frequency of measurement must be high enough to represent the variability in the process (e.g., across the life of the catalyst). Issues related to measurement frequency are summarised in Box 3.14, Plant Measurement Frequency. General advice on sampling and representativeness is provided in Volume 1, Chapter 2. In cases where plant-specific measurements or sampling are not available and Tier 1 methods are used, the default emission factor should be used, assuming no abatement methods. For plants in operation prior to 1995 the default emission factor is 0.04 kg HFC-23/kg HCFC-22 (4 percent) (IPCC, 1996; USEPA, 2001). This is a default to be used when there are no measurements and describes the output of HFC-23 from a typical HCFC-22 plant in the absence of recovery or destruction of HFC-23. The value is consistent with atmospheric observations of HFC-23 concentrations in the 1978-1995 time period (Oram et al.,1998). These showed globally averaged emissions to be equivalent to 2 percent of the total quantity of HCFC-22 produced at a time when significant HFC-23 was being recovered and converted into Halon 1301 (McCulloch, 1992) and abatement was required practice in several countries where there was significant production. It is possible, by process optimisation, to reduce the production to between 0.014 and 0.03 kg HFC-23/kg HCFC-22 (1.4 to 3 percent) but it is not possible to completely eliminate HFC-23 formation this way (IPCC, 2000). Furthermore, the extent of the reduction is highly dependent on the process design and the economic environment (measures to reduce HFC-23 can often reduce the process output). In an optimised process HFC-23 production and emissions will, invariably, have been measured; it is not possible to optimise process operation without such measurements and so default values have no meaning in this context for an individual plant. However, the state of the technological art has been advanced by optimisation of individual plants and that art should have been built into the design of recent plants, suggesting a default emission factor of 0.03 kg HFC23/kg HCFC-22 (3 percent). These default values have a large uncertainty (in the region of 50 percent). For more accurate assessments, the actual emissions should be determined by Tier 2 or Tier 3 methodology and, if necessary, assigned to previous years using the guidance provided in Chapter 7 of this volume.

TABLE 3.28 HFC-23 DEFAULT EMISSION FACTORS Technology Old, unoptimised plants (e.g., 1940s to 1990/1995)

0.04

Plants of recent design, not specifically optimised

0.03

Global average emissions (1978 - 1995)4

0.02

For comparison: Optimised large plant- requiring measurement of HFC-23 (Tier 3) Plant with effective capture and destruction of HFC-23 (Tier 3)

4

Emission Factor (kg HFC-23/kg HCFC-22 produced)

Down to 0.014 Down to zero

The global average is calculated from the change in atmospheric concentration of HFC-23. It does not discriminate between plant emissions, which range from nothing to greater than 4 percent of the HCFC-22 production.

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BOX 3.14 PLANT MEASUREMENT FREQUENCY

The accuracy and precision of the estimates of annual HFC-23 emissions depend on the number of samples (the frequency of sample collection) together with the accuracy of measurement of flowrates and the extent to which discrete flow measurements can represent the total quantity vented. Since production processes are not completely static, the greater the process variability, the more frequently plants need to measure. As a general rule, sampling and analysis should be repeated whenever a plant makes any significant process changes. Before choosing a sampling frequency, the plant should set a goal for accuracy and use statistical tools to determine the sample size necessary to achieve the goal. For example, a study of HCFC-22 producers indicates that sampling once per day is sufficient to achieve an extremely accurate annual estimate. This accuracy goal should then be revised, if necessary, to take into account the available resources. (RTI, Cadmus, 1998)

CHOICE OF ACTIVITY DATA When using the Tier 1 method, production data should be obtained directly from producers. There are several ways producers may determine their production levels, including shipment weights and measuring volumetimes-density, using flow meters. These data should account for all HCFC-22 production for the year, whether for sale or for use internally as feedstock, and the plant should describe how the HCFC-22 production rate is determined. In some circumstances, producers may consider plant production data to be confidential. For national-level activity data, submission of HCFC-22 production data is already required under the Montreal Protocol.

COMPLETENESS It should be possible to obtain complete sampling data because there are only a small number of HCFC-22 plants in each country, and it is standard practice for each plant operator to monitor process efficiencies and hence HFC-23 losses, leading to the adoption of Tier 2 methodology. The destruction efficiencies of thermal oxidisers used to abate HFC-23 are generally high (>99 percent) but it is important to establish the composition of the exit gas in order to ensure that account is taken of emissions of fluorinated greenhouse gases from this point.

DEVELOPING A CONSISTENT TIME SERIES Emission of HFC-23 from HCFC-22 production should be estimated using the same method for the entire time series and appropriate emission factors. If data for any years in the time series are unavailable for the Tier 3 method, these gaps should be filled according to the guidance provided in Volume 1, Chapter 5.

3.10.1.3

U NCERTAINTY

ASSESSMENT

TIER 1 Unlike the other Tiers, where uncertainties are based on measurements and statistics, Tier 1 uncertainties are assessed through expert judgement and an error of approximately 50 percent could be considered for Tier 1 based upon knowledge of the variability in emissions from different manufacturing facilities. An error of this magnitude will completely outweigh the uncertainty in the activity.

TIER 2 Uncertainty of the Tier 2 result is calculated by the root-squared sum of the individual uncertainties in production mass quantity and efficiencies, assuming the carbon and fluorine uncertainties are the same. Where the uncertainties in carbon and fluorine efficiency differ significantly (enough to cause a material difference to the calculated emission), the value with the lower uncertainty should be used throughout the calculation. Uncertainty in the value derived by Tier 2 methods is much larger than that expected from Tier 3 but is, nevertheless, quantifiable. Typically, for a plant producing about 4 percent HFC-23, the carbon efficiency is in the region of 95 percent and the fluorine efficiency 92 percent. If these efficiencies can be measured to within 1 percent, then the error in the Tier 2 HFC-23 estimate would be less than 20 percent. Estimating efficiencies to this degree of accuracy will require rigorous accounting procedures and that all raw materials and product for sale should be weighed in or out of the facility. Such a regime sets the expected accuracy of the overall activity (for both Tiers 1 and 2); with good accounting and measurement of production by weight, it should be possible to reduce the error in the activity to below 1 percent.

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TIER 3 For HFC-23, the Tier 3 method is significantly more accurate than either the Tier 2 measured or Tier 1 default methods. Regular Tier 3 sampling of the vent stream can achieve an accuracy of 1-2 percent at a 95 percent confidence level in HFC-23 emissions and the uncertainty of the Tier 3 (proxy) result may be similar. In both cases, the uncertainty may be calculated statistically from the uncertainties of the input parameters and, because these methods do not rely on emission factors or activities, the concept of subdividing uncertainty has no validity. Uncertainty of the estimate is expressed as a coefficient of variance (percent) and, for each of these streams, there will be an uncertainty as a consequence of uncertainties in measured concentration and flowrate and uncertainty in the duration of the flow. The combined uncertainty can be determined analytically and should be calculated using the standard methodology described in Chapter 3 of Volume 1.

3.10.1.4

Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC), R EPORTING AND D OCUMENTATION

QUALITY ASSURANCE/QUALITY CONTROL It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, Chapter 6, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. In addition to the guidance in Volume 1, specific procedures of relevance to this sub-source category are outlined below:

Comparison of emissions estimates using different approaches Inventory compilers should compare reported plant emissions estimates against those determined using the Tier 1 default factor and production data. If only national production data are available, they should compare aggregated plant emissions to a national default estimate. If significant differences are found in the comparison, they should answer the following questions: 1.

Are there inaccuracies associated with any of the individual plant estimates (e.g., an extreme outlier may be accounting for an unreasonable quantity of emissions)?

2.

Are the plant-specific emission factors significantly different from one another?

3.

Are the plant-specific production rates consistent with published national level production rates?

4.

Is there any other explanation for a significant difference, such as the effect of controls, the manner in which production is reported or possibly undocumented assumptions?

Direct emission measurement check •

• •

•

Inventory compilers should confirm that internationally recognised, standard methods were used for plant measurements. If the measurement practices fail this criterion, then the use of these emissions data should be carefully evaluated. It is also possible that, where a high standard of measurement and QA/QC is in place at sites, the uncertainty of the emissions estimates may be revised downwards. Each plant’s QA/QC process should be evaluated to assess if the number of samples and the frequency of sample collection is appropriate given the variability in the process itself. Where possible, inventory compilers should verify all measured and calculated data through comparison with other systems of measurement or calculation. For example, emissions measurement within the process itself can be verified periodically with measurement of the vent stream. Inventory compilers should verify abatement system utilisation and efficiency. With a periodic external audit of the plant measurement techniques and results, it is also possible to compare implied emission factors across plants and account for major differences.

REPORTING AND DOCUMENTATION It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. Some examples of specific documentation and reporting relevant to this source category are provided below:

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To provide for completely transparent reporting, emissions of HFC-23 from HCFC-22 production should be reported as a separate item, rather than included with other HFC emissions. Documentation should also include: (i) Methodological description; (ii) Number of HCFC-22 plants; (iii) HCFC-22 production (if multiple producers); (iv) Presence of abatement technology; (v) Process descriptions, operating parameters; and (vi) Related emission factors.

Confidentiality •

•

•

Th e us e of th e T ier s 2 and 3 methods would mean that the plant emissions of HFC-23 are reported separately from the production of HCFC-22. By de-coupling the HFC-23 emissions and HCFC-22 production, the emission data on HFC-23 cannot be considered to be of commercial confidence as it does not reveal the levels of production of HCFC-22 without detailed and confidential knowledge of the individual manufacturing facility. Th e app lication of the T ier 1 method to total national production of HCFC-22 would enable this to be calculated from published emissions of HFC-23 and, if there were less than three producers, such production data could be considered confidential business information. In such cases, steps should be taken to protect confidentiality through, for example, the aggregation of all HFC emissions. For transparency reasons, whenever there is aggregation, a qualitative discussion of HCFC-22 production should be included. When national emissions are calculated as the sum from individual facilities and these have been calculated using different methodologies, it is not possible to recalculate the HCFC-22 production from these data alone and there should be no problems concerning confidentiality.

3.10.2 3.10.2.1

Emissions from production of other fluorinated compounds I NTRODUCTION

A large number of fluorine containing greenhouse gases can be produced as by-products of fluorochemical manufacture and emitted into the atmosphere. For example, in a recent national inventory, significant by-product emissions of SF6, CF4, C2F6, C3F8, C4F10, C5F12 and C6F14 were reported for a fluorochemical plant (UNFCCC, 2005). Other examples include the release of by-product CF4 from the production of CFC-11 and 12 or of SF6 from the production of uranium hexafluoride in the nuclear fuel cycle. Emissions of a chemical occur during its production and distribution or as a by-product during the production of a related chemical (HFC-23 from HCFC-22 production is covered specifically in Section 3.10.1 above). There may also be emissions of the material that is being produced; the so-called ‘fugitive emissions’. Both by-product and fugitive emissions are calculated in the same way. In this section, emissions associated with use are not addressed specifically, being counted in the emissions related to consumption (see Chapters 4.5, 6, 7 and 8 in this volume). Typically, fluorochemicals may be released from chemical processes involving a broad range of technologies and processes5: •

•

Telomerization Process used in the production of fluorochemicals fluids and polymers

•

Direct Fluorination often used in SF6 production

•

Photooxidation of tetrafluoroethylene to make fluorochemical fluids

•

Halogen Exchange Processes to make low boiling PFCs like C2F6 and CF4, HFC 134a and 245fa

• 5

NF3 manufacturing by direct fluorination Production of uranium hexafluoride This list is illustrative.

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•

•

Production of fluorinated monomers like tetrafluoroethylene and hexafluoropropylene Production of fluorochemical agrochemcials Production of fluorochemical anesthetics

Halogen exchange processes are extensively used for HFC manufacture, while most PFCs and SF6 require elemental fluorine, generated electrochemically. In ‘electrochemical fluorination’ processes, the fluorine is not separated but makes the desired product in the electrochemical cell. In other processes it is separated and subsequently used, either as the elemental gas or as a component of a carrier system, such as CoF3. Each process will have a different spectrum of emissions, in terms of both chemical nature and quantities, and so a common default emission function is of relatively little value. It is essential that the existence of potentially emissive plants is identified within each country, hence this step is first in the decision tree (Figure 3.17). The common factor for these plants is the use of anhydrous hydrogen fluoride, which is the source of fluorine in halogen exchange processes and in processes that use elemental fluorine. The production and importation of anhydrous hydrogen fluoride can therefore be used as a means of tracing significant producers of fluorochemicals. Further enquiries (see Figure 3.17) can then elucidate whether or not there are significant fluorochemical greenhouse gas emissions.

3.10.2.2

M ETHODOLOGICAL

ISSUES

CHOICE OF METHOD It is good practice to choose the method using the decision tree shown in Figure 3.17. If the Category 2B9 Fluorochemical Production is identified as key and this subcategory is judged to be significant, inventory compilers should consider whether or not emissions are dominated by the production of a sub-set of chemicals, and focus more sophisticated data collection efforts on production of these chemicals. The number of major producers of these fluorinated greenhouse gases is quite small: in the case of SF6, there are globally about 6 companies with about 10 production facilities world-wide (Preisegger, 1999). The number of smaller producers may grow in the near future, particularly in developing economies. However, a survey of national producers should not be difficult to compile.

Tier 1 In the Tier 1 methodology, a default emission factor, or a similar number derived for the particular country's circumstances, can be used to estimate national production-related emissions of individual HFCs, PFCs, SF6 and other fluorinated greenhouse gases. EQUATION 3.41 TIER 1 CALCULATION OF PRODUCTION-RELATED EMISSIONS E k = EFdefault ,k • Pk

Where: Ek = production-related emissions of fluorinated greenhouse gas k, kg EFdefault, k = default emission factor, kg/kg Pk = total production of fluorinated greenhouse gas k, kg Problems of confidentiality arising from reporting specific component data can be circumvented by providing a single number for total national emissions of each HFC, PFC and SF6. This may be facilitated if data are collected by a third party and reported only as this total.

Tier 2 The method based on process efficiencies, which works for HFC-23 emissions from HCFC-22 plants, is of less value for other types of plants. This is due in part to the lower inefficiency expected from these other by-product emissions; the uncertainty in measurement of efficiencies is likely to be much greater than the by-product emission factor. Furthermore, a range of by-products may be responsible for process inefficiency (unlike the case for HCFC-22 where one by-product predominates). However, production efficiency data should exist for each process and, in the absence of a more rigorous estimate, the quantity of emissions estimated from process inefficiencies may be used in a qualitative decision as to whether or not these emissions are a significant subcategory under a key category (in which case, Tier 3 methodology is specified).

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Tier 3 The Tier 3 methodology is potentially the most accurate estimate and is the sum of factory specific emissions of each by-product fluorinated greenhouse gas determined using standard methods to estimate the composition and flowrate of gas streams actually vented to atmosphere after any abatement technology. In this case: EQUATION 3.42 TIER 3 DIRECT CALCULATION OF PRODUCTION-RELATED EMISSIONS

E k = ∑ ∑ ∫t C ijk • f ijk i

j

[ ∫t means the quantity should be summed over time.]

Where: Ek = total production-related emissions of fluorinated greenhouse gas k: the sum over all i plants, over all j streams in each plant of the emitted mass flows f and concentrations C integrated over time t. or, where proxy methodology is used, for example where the emission rate of the by-product is normalised to a more easily (or accurately) measurable parameter, such as feedstock flow rate, as described in Equation 3.35 in Section 3.10.1: EQUATION 3.43 TIER 3 PROXY CALCULATION OF PRODUCTION-RELATED EMISSIONS

Ek = ∑ ∑ ∫t Eijk i

j

[ ∫t means the quantity should be summed over time.]

Where: Ek = total production-related emissions of fluorinated greenhouse gas k: Eijk = the emissions of fluorinated greenhouse gas k from each plant and stream determined by the proxy methods, described in Equations 3.38 and 3.39 in Section 3.10.1 Note that, generally, flows are measured volumetrically and should be converted into mass flow (kg/hour) based on the ideal gas law, temperature, pressure and composition, similarly concentration should be converted into compatible units (e.g., kg/kg). In this case, the flowrates, concentrations and duration should be calculated separately for the periods when the abatement technology is or is not operating and only those that lead to actual emissions should be summed and reported.

CHOICE OF EMISSION FACTORS Tier 3 relies on measurements of the quantities of individual materials that are released into the atmosphere and neither Tier 2 nor Tier 3 relies on emission factors. For Tier 1, in the absence of abatement measures, a default emission factor of 0.5 percent of production, not counting losses in transport and transfer of materials, is suggested for HFCs and PFCs, based on data supplied to AFEAS (2004). There is a wide range of substances that may potentially be released. However, the AFEAS data showed that the components that were lost during production of a particular fluorochemical had, in general, radiative forcing properties similar to those of the desired fluorochemical. Consequently, for sources that are not significant subcategories under key category, fugitive and by-product emissions are the same and are included in the 0.5 percent emission factor. In the case of SF6, based on German experience, a default emission factor of 0.2 percent of the total quantity of SF6 produced is suggested for those countries in which the predominant end use does not require highly purified SF6 gas (e.g., electrical equipment, insulated windows) (Preisegger, 1999). Based on experience in Japan, in countries where the major uses require highly purified SF6 gas (e.g., semiconductor manufacturing), the default value should be 8 percent because of handling losses during disposal of residual gas (i.e., the ‘heel’ that is not used or recycled) in returned cylinders (Suizu, 1999). If national data are available, these should be used, particularly for other materials not specifically listed here. The default emission factors are based on situations where no abatement measures are employed. If the quantity of gas emitted to the atmosphere is reduced by, for example, thermal oxidation of the vent stream, the quantity emitted should be adjusted to account for the destruction efficiency of the oxidiser and the length of time that it is in service. Based on the experience in the destruction of HFC-23, a default destruction efficiency of 100 percent is suggested but the on-line time of the destruction process will have a greater effect on emissions and should be recorded.

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Figure 3.17

Decision tree for emissions of fluorinated greenhouse gases from production processes, applicable to both fugitive and by-product emissions Start

Compile a list of all fluorochemical manufacturers. Box 5: Tier 3 Are detailed data available on plant-specific estimates?

Yes

Sum data for each greenhouse gas from plants, taking account of abatement (Tier 3).

No Are national activity data available?

No

Collect national activity data.

Yes

Is Fluorochemical Production a key category, and is this production process/gas significant1,2?

Yes

Collect emissions data from plants.3

No

Is there abatement of emissions?

Yes

No

Estimate emissions from fluorochemical plants (Tier 1).

Estimate emissions from fluorochemical plants adjusting for abatement (Tier 1). Box 2: Tier 1

Box 1: Tier 1 Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 2. Tier 2 methodology may be used at this point to establish whether or not this is a key category but, as explained in Section 3.10.2.2, this is the only use for Tier 2. 3. Data may be collected as a country study by a third party in order to preserve confidentiality.

CHOICE OF ACTIVITY DATA Again, activity data has no role in the Tiers 3 and 2 estimates, which are based on measurements. For Tier 1, the activity is the annual mass of the desired fluorochemical that is produced.

Recycling Recycling of used gas may be done by the producers of new gas or by other recycling firms. Emissions may occur during handling and purification of old gas and handling of recycled gas. Specific emission factors are not available. Thus, good practice is to use the same default factor as for new production.

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COMPLETENESS For some inventory compilers, identifying smaller producers and, in particular, recycling firms may be a difficult task. However, initial estimates based on the national mass balance of these fluorinated greenhouse gases should identify if production related emissions from such entities provide a sizeable contribution to total national emissions.

DEVELOPING A CONSISTENT TIME SERIES Both by-product and fugitive emissions of fluorocompounds from production processes should be estimated using the same method for the entire time series and appropriate emission factors. If data for any years in the time series are unavailable for the Tier 3 method, these gaps should be filled according to the guidance provided in Volume 1, Chapter 5.

3.10.2.3

U NCERTAINTY

ASSESSMENT

For Tier 1, the uncertainty in activity data needs to be determined for the reporting country and statistically combined with the uncertainty in the default emission factor. Typically, in a well operated facility, the default uncertainty in activity data should be in the region of 1 percent, assuming that rigorous accounting records are maintained and that production is monitored by weight. The actual emission factor may range from well in excess of the default value to zero. The default uncertainty of the default emission factors is therefore set at 100 percent, for example 0.5±0.5 (%). For Tier 3 emissions, the uncertainty of the measurements should be determined individually and combined (using standard statistical methods) to provide a total uncertainty for the estimate. The methodology is identical to that described for HFC-23 from HCFC-22. In the Tier 2 methodology, the uncertainty both of the measurements of efficiencies and the assignment of losses to individual compounds should be assessed. Because these are liable to produce a much larger uncertainty than that from Tier 3, the utility of Tier 2 is likely to be limited to assessing whether or not by-product fluorochemical emissions are a significant subcategory under key category.

3.10.2.4

Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC), R EPORTING AND D OCUMENTATION

QUALITY ASSURANCE/QUALITY CONTROL It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, Chapter 6, quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4.

Comparison of emissions estimates using different approaches Inventory compilers should compare the estimate based on aggregated producer-level data to an estimate based on national production data and the suggested default emission factors. They should investigate significant discrepancies in cooperation with the producers to determine if there are unexplained differences.

REPORTING AND DOCUMENTATION Confidentiality issues may arise where there are limited numbers of manufacturers. In these cases more aggregate reporting of total national emissions may be necessary. If survey responses cannot be released as public information, third-party review of survey data may be necessary to support data verification efforts. It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced.

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References SECTIONS 3.2 - 3.8 Ashford, R.D. (1994). Ashford’s Dictionary of Industrial Chemicals, Wavelength Publications Ltd, London England. Austin, G.T. (1984). Shreve’s Chemical Process Industries, Fifth Edition, McGraw-Hill, Inc., USA. Babusiaux, P. (2005). Note on production of Glyoxal and Glyoxylic acid, Clariant, Lamotte, France. Bockman, O. and Granli, T. (1994). ‘Nitrous oxide from agriculture’. Norwegian Journal of Agricultural Sciences, Supplement No. 12. Norsk Hydro Research Centre, Porsgrunn, Norway. Bouwman, A.F., van der Hoek, K.W. and Olivier, J.G.J. (1995). ‘Uncertainties in the global source distribution of nitrous oxide’. Journal of Geophysical Research, 100:D2, pp. 2785-2800, February 20, 1995. Burtscher, K. (1999). Personal communication between Kurt Burtscher of Federal Environment Agency of Austria and plant operator of chemical industry in Linz, Austria, 1999. Chemlink (1997). Website http://www.chemlink.com.au/titanium.htm. Chemlink Pty Ltd ACN 007 034 022. Publications 1997. Choe J.S., Gook, P.J. and Petrocelli, F.P. (1993). Developing N2O abatement technology for the nitric acid industry. Paper presented at the 1993 ANPSG Conference, Destin, Florida, USA, 6 October, 1993. Cook, P. (1999). Personal communication between Phillip Cook of Air Products and Chemicals, Inc., USA, and Heike Mainhardt of ICF, Inc., USA. March 5, 1999. Cotton, F.A. and Wilkinson, G. (1988). Advanced Inorganic Chemistry, 5th Edition, ISBN 0-471-84997-9. Wiley, New York, USA. de Beer, J., Phylipsen, D. and Bates, J. (2001). Economic Evaluation of Sectoral Emission Reduction Objectives for Climate Change: Economic Evaluation of Carbon Dioxide and Nitrous Oxide Emission Reductions in Industry in the EU – Bottom-up Analysis, Contribution to a Study for DG Environment, European Commission by Ecofys Energy and Environment, AEA Technology Environment and National Technical University of Athens. Environment Canada (1987). Review of the Canadian Fertiliser Industry and Evaluation of Control Technology, Conservation and Protection Report EPS 2/AG/1. EFMA (2000a). European Fertilizer Manufacturers’ Association, Best Available Techniques for Pollution Prevention and Control in the European Fertilizer Industry: Production of Ammonia, Booklet No. 1 of 8, European Fertilizer Manufacturers’ Association, Brussels. EFMA (2000b). European Fertilizer Manufacturers’ Association, Best Available Techniques for Pollution Prevention and Control in the European Fertilizer Industry: Production of Nitric Acid, Booklet No. 2 of 8, European Fertilizer Manufacturers’ Association, Brussels. EFMA (2000c). European Fertilizer Manufacturers’ Association, Best Available Techniques for Pollution Prevention and Control in the European Fertilizer Industry: Production of Urea and Urea Ammonium Nitrate, Booklet No. 5 of 8, European Fertilizer Manufacturers’ Association, Brussels. EIPPCB (2004a). European Integrated Pollution Prevention and Control Bureau, Draft Reference Document on Best Available Techniques in the Large Volume Inorganic Chemicals, Ammonia, Acids and Fertilisers Industries, Draft March 2004, European Commission Directorate General JRC, Joint Research Centre, Institute for Prospective Technological Studies, Spain. EIPPCB (2004b). European Integrated Pollution Prevention and Control Bureau, Draft Reference Document on Best Available Techniques in the Large Volume Inorganic Chemicals-Solid and Others Industry, Draft August 2004, European Commission Directorate General JRC, Joint Research Centre, Institute for Prospective Technological Studies, Spain. Hocking, M. B. (1998). Handbook of Chemical Technology and Pollution Control, Academic Press USA. IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. Callander B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. Japan Environment Agency (1995). Study of Emission Factors for N2O from Stationary Sources.

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Kirk-Othmer (1999). Concise Encyclopedia of Chemical Technology, Fourth Edition, John Wiley & Sons, Inc. USA. Lowenheim, F.A. and Moran, M.K. (1975). Faith, Keyes, and Clark’s Industrial Chemicals, Fourth Edition, John Wiley & Sons, Inc. USA. Olivier, J. (1999). Personal communication between Jos Olivier of National Institute of Public Health and the Environment (RIVM), The Netherlands and Heike Mainhardt of ICF, Inc., USA. February 2, 1999. Olsen, S.E. (1991). Kalsiumkarbid og CO2, STF34 A91142. SINTEF. Perez-Ramirez, J., Kapteijn, F., Shoffel, K. and Moulijn, J. A. (2003). ‘Formation and control of N2O in nitric acid production: Where do we stand today?’, Applied Catalysis B: Environmental 44, pp.117-131, Elsevier Science B.V. Raaness, O. (1991). Silisiumkarbid og CO2, STF34 A91134. SINTEF 1991. Reimer, R.A., Slaten, C.S., Seapan, M., Koch, T.A. and Triner, V.G. (1999). ‘Implementation of Technologies for Abatement of N2O Emissions Associated with Adipic Acid Manufacture. Proceedings of the 2nd Symposium on Non-CO2 Greenhouse Gases (NCGG-2), Noordwijkerhout, The Netherlands, 8-10 Sept. 1999, Ed. J. van Ham et al., Kluwer Academic Publishers, Dordrecht, pp. 347-358. Reimer, R., (1999a). Personal communication between Ron Reimer of DuPont, USA and Heike Mainhardt of ICF, Inc., USA. February 8, 1999. Reimer, R., (1999b). Personal communication between Ron Reimer of DuPont, USA and Heike Mainhardt of ICF, Inc., USA. May 19, 1999. Reimschuessel, H. K. (1977). ‘Nylon 6 Chemistry and Mechanisms’, Journal of Polymer Science: Macromolecular Reviews, Vol. 12, 65-139, John Wiley & Sons, Inc. Scott, A. (1998). ‘The winners and losers of N2O emission control’. Chemical Week, February 18, 1998. Thiemens, M.H. and Trogler, W.C. (1991). ‘Nylon production; an unknown source of atmospheric nitrous oxide’. Science, 251, pp. 932-934. U.S. EPA (1985). Criteria Pollutant Emissions Factors. Volume 1, Stationary Point and Area Sources. AP-42 4th Edition (and Supplements A and B). U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA. van Balken, J.A.M. (2005). Personal communication from J.A.M. van Balken (European Fertilizer Manufacturers Association).

SECTION 3.9 AGO (2005). Australian Methodology for the Estimation of Greenhouse Gas Emissions and Sinks 2003: Industrial Processes, Australian Government, Department of the Environment and Heritage, Australian Greenhouse Gas Office, 2005, Table 4, Page 18. BASF (2006). Personal Communication from Silke Schmidt, BASF Aktiengesellschaft, Ludwidshafen, Germany to Robert Lanza, ICF Consulting, Inc., Washington, DC, USA, January 9, 2006. Boustead, I. (1999). Eco-Profiles of Plastics and Related Intermediates, published by APME, Brussels, 1999. Boustead, I. (2003a). Eco-Profiles of the European Plastics Industry: Olefins. A Report for the European Association of Plastics Manufacturers (APME), Brussels, July 2003, Table 7, Page 9. http://www.apme.org/dashboard/business_layer/template.asp?url=http://www.apme.org/media/public_do cuments/20030820_114355/olefinsreport_july2003.pdf&title=Microsoft+Word+%2D+olefins%2Edoc&k euze1=&keuze2=&keuze3=&invulstrook=olefin+AND+eco%2Dprofile Boustead, I. (2003b). Eco-Profiles of the European Plastics Industry, Methodology: A Report for APME, Brussels, July 2003. http://www.apme.org/media/public_documents/20010817_141031/method.pdf Boustead, I. (2005). ETHYLENE DICHLORIDE: A report by I Boustead for The European Council of Vinyl Manufacturers (ECVM) & PlasticsEurope, March 2005. DOE (2000). Energy and Environmental Profile of the U.S. Chemical Industry, U.S. Department of Energy Office of Industrial Technologies, May 2000, Section 3.1.4, Page 92. DSM (2002). DSM Responsible Care Progress Report 2001; Safety, Health and Environmental Management at DSM, 2002

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EEA (2005). EMEP/CORINAIR. Emission Inventory Guidebook – 2005, European Environment Agency, Technical report No 30. Copenhagen, Denmark, (December 2005). Available from web site see: http://reports.eea.eu.int/EMEPCORINAIR4/en European IPPC Bureau (2005). Integrated Pollution Prevention and Control (IPPC) Draft Reference Document on Best Available Techniques in the Large Volume Inorganic Chemicals (LVIC) – Solid and Others Industry, EK/EIPPCB/LVIC-S_Draft_2, Draft, June 2005. http://eippcb.jrc.es/pages/FActivities.htm European IPPC Bureau (2003). Integrated Pollution Prevention and Control (IPPC) Reference Document on Best Available Techniques in the Large Volume Organic Chemical (LVOC) Industry, February 2003. http://eippcb.jrc.es/pages/FActivities.htm FgH-ISI (1999). Fraunhofer-Institut für Systemtechnik und Innovationsforschung. C-Ströme Abschätzung der Material –Energie und CO2 Ströme für Modellsysteme in Zuzammenhang mit dem nichenergetishen Verbrauch, orientiert am Lebensweg – Stand und Szenarienbetractthung, Karlsrue, 1999. Cited in Neelis, M; Patel, M; de Feber, M; 2003. Improvement of CO2 Emissions Estimates from the Non-energy Use of Fossil Fuels in the Netherlands. Report Number NW&S-E-2003-10, Copernicus Institute, Department of Science, Technology, and Society, Utrecht, The Netherlands, April 2003 Hinderink, et al. (1996). Exergy Analysis with Flowsheeting Simulator – II Application Synthesis Gas Production from Natural Gas, Chemical Engineering Science, Volume 51, No. 20, Page 4701-4715, 1996. Cited in Neelis, M; Patel, M; de Feber, M; 2003. Improvement of CO2 Emissions Estimates from the Non-energy Use of Fossil Fuels in the Netherlands. Report Number NW&S-E-2003-10, Copernicus Institute, Department of Science, Technology, and Society, Utrecht, The Netherlands, April 2003 Houdek, J.M., Andersen, J. (2005). “On Purpose“ Propylene – Technology Developments, UOP LLC. Presented at the ARTC 8th Annual Meeting, Kuala Lumpur, April 29, 2005, Figure 1, Page 3 and Page 4. Kirk Othmer (1992). Encyclopedia of Chemical Technology, 4th Edition, Volume 4, 1992. Carbon Black. Page 1054. Lurgi (2004a). Lurgi Mega Methanol. Lurgi Oel-Gas-Chemie Lurgi (2004b). Integrated Low Pressure Methanol Process: Synthesis Gas Production by Combined Reforming of Natural Gas or Oil Associated Gas. Lurgi Oel-Gas-Chemie. http://www.lurgi-oel.de/lurgi_oel/english/nbsp/main/info/methanol_combined_reforming.pdf Lurgi (2004c). Integrated Low Pressure Methanol Process: Synthesis Gas Production by Conventional Steam Reforming of Natural Gas or Oil Associated Gas. Lurgi Oel-Gas-Chemie. http://www.lurgi-oel.de/lurgi_oel/english/nbsp/main/info/methanol_conventional_reforming.pdf Methanex (1996). Methanex Corporation Climate Change Voluntary Challenge and Registry Program Action Plan, September 1996. http://www.vcr-mvr.ca/registry/out/C969-METHANEX-W52.PDF Methanex (2003). Global Environmental Excellence Report 2002, Methanex Corporation, 2003 Neelis, M., Patel, M. and de Feber, M. (2003). Improvement of CO2 Emissions Estimates from the Non-energy Use of Fossil Fuels in the Netherlands, Report Number NW&S-E-2003-10, Copernicus Institute, Department of Science, Technology, and Society, Utrecht, The Netherlands, April 2003. Qenos (2003). Annual Report on Manufacturing Operations at Qenos Olefins, Plastics, Resins, and Elastomers Sites to Altona Complex Neighborhood Consultative Group, April 2003, Qenos Pty. Ltd. Qenos (2005). 2004 Annual Report on Manufacturing Operations at Qenos Olefins, Plastics, Resins, and Elastomers Sites to Altona Complex Neighborhood Consultative Group, April 2005, Qenos Pty. Ltd. SFT (2003a). Self-reporting of emissions to the Norwegian Pollution Control Authority based on direct measurements at Statoil Tjeldbergodden Methanol Plant. (In Norwegian). SFT (2003b). Self-reporting of emissions to the Norwegian Pollution Control Authority based on direct measurements at Nordetyl ethylene Plant. (In Norwegian). Struker, A. and Blok, K. (1995). Sectorstudie organische chemie, National Energy Efficiency Data Informatie Systeem (NEEDIS), Patten, December 1995. Cited in Neelis, M; Patel, M; de Feber, M; 2003. Improvement of CO2 Emissions Estimates from the Non-energy Use of Fossil Fuels in the Netherlands, Report Number NW&S-E-2003-10, Copernicus Institute, Department of Science, Technology, and Society, Utrecht, The Netherlands, April 2003

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SECTION 3.10.1 Defra (2002a). Protocol C1: Measurement of HFCs and PFCs from the Manufacture of HF, CTF, HCFC-22, HFC-125 and HFC-134a, in Guidelines for the Measurement and Reporting of Emissions by Direct Participants in the UK Emissions Trading Scheme, UK Department for Environment, Food and Rural Affairs, Report No. UKETS(01)05rev1, Defra, London, 2002. Defra (2002b). Protocol C9: Measurement of HFCs and PFCs from Chemical Process Operations, UK Department for Environment, Food and Rural Affairs, as above, London, 2002. EFCTC (2003). Protocol for the Measurement of HFC and PFC Greenhouse Gas Emissions from Chemical Process Operations, Standard Methodology, European Fluorocarbon Technical Committee, Cefic, Brussels, 2003. IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. Callander B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2000). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Penman J., Kruger D., Galbally I., Hiraishi T., Nyenzi B., Emmanuel S., Buendia L., Hoppaus R., Martinsen T., Meijer J., Miwa K., Tanabe K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. McCulloch A. (1992). Global Production and Emissions of Bromochlorodifluoromethane Bromotrifluoromethane (Halons 1211 and 1301), Atmos. Environ., 26A(7), 1325-1329.

and

Oram D.E., Sturges, W.T., Penkett, S.A., McCulloch, A. and Fraser, P.J. (1998). Growth of fluoroform (CHF3, HFC-23) in the background atmosphere, Geophys. Res. Lett., 25(1), 35-38. RTI, Cadmus, (1998). ‘Performance Standards for Determining Emissions of HFC-23 from the Production of HCFC-22’, draft final report prepared for USEPA, February 1998. UN (2004). Approved baseline methodology, ‘Incineration of HFC 23 waste streams’, AM0001/Version 02, CDM – Executive Board, United Nations Framework Convention on Climate Change, 7 April 2004 U.S. EPA (2001). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-1999. United States Environmental Protection Agency, Report No. EPA 236-R-01-001, Washington, U.S.A., 2001.

SECTION 3.10.2 AFEAS (2004). Production, Sales and Estimated Atmospheric Emissions of CFCs, HCFCs and HFCs, Alternative Fluorocarbons Environmental Acceptability Study, Arlington, U.S.A., 2004. Available at www.afeas.org. Preisegger, E. (1999). Statement on experiences of Solvay Fluor und Derivate GmbH, Hannover, Germany regarding an emission factor at the IPCC expert group meeting on Good practice in Inventory Preparation, Washington D.C. Jan, 1999. Suizu, T. (1999). Partnership activities for SF6 gas emission reduction from gas insulated electrical equipment in Japan. Proc. Joint IPCC/TEAP Expert Meeting on Options for the Limitation of Emissions of HFCs and PFCs, Petten, Netherlands, 26-28 May 1999. ECN, Petten. UNFCCC (2005). Belgium’s Greenhouse Gas Inventory (1990-2003), National Inventory Report 2005, submitted under the United Nations Framework Convention on Climate Change, April 2005. http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/2761. php.

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CHAPTER 4

METAL INDUSTRY EMISSIONS

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Volume 3: Industrial Processes and Product Use

Authors Section 4.1 Jerry Marks (USA) Section 4.2 Jonathan Lubetsky (USA) and Bruce A. Steiner (USA) Section 4.3 Tor Faerden (Norway), Jonathan S. Lubetsky (USA), Tor Lindstad (Norway), Sverre E. Olsen (Norway), and Gabriella Tranell (Norway) Section 4.4 Jerry Marks (USA), William Kojo Agyemang-Bonsu (Ghana), Mauricio Firmento Born (Brazil), Laurel Green (Australia), Halvor Kvande(Norway), Kenneth Martchek (USA), and Sally Rand (USA) Section 4.5 Gabriella Tranell (Norway) and Tom Tripp (USA) Section 4.6 Jonathan S. Lubetsky (USA) and Jerry Marks (USA) Section 4.7 Jonathan S. Lubetsky (USA)

Contributing Authors Section 4.2 Robert Lanza (USA) Section 4.4 Vince Van Son (USA), Pablo Alonso (France), Ron Knapp (Australia), Stéphane Gauthier (Canada), Michel Lalonde (Canada), Hézio Ávila de Oliveira (Brazil), and Chris Bayliss (UK)

4.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Metal Industry Emissions

Contents 4

Metal Industry Emissions..............................................................................................................................4.8 4.1

Introduction ...........................................................................................................................................4.8

4.2

Iron & steel and Metallurgical Coke Production...................................................................................4.9

4.2.1

Introduction .................................................................................................................................4.11

4.2.2

Methodological issues .................................................................................................................4.17

4.2.2.1

Choice of method: metallurgical coke production .................................................................4.17

4.2.2.2

Choice of method: iron and steel production .........................................................................4.19

4.2.2.3

Choice of emission factors.....................................................................................................4.24

4.2.2.4

Choice of activity data ...........................................................................................................4.28

4.2.2.5

Completeness.........................................................................................................................4.28

4.2.2.6

Developing a consistent time series .......................................................................................4.29

4.2.3

Uncertainty assessment ...............................................................................................................4.30

4.2.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................4.30

4.3

4.2.4.1

Quality Assurance/Quality Control (QA/QC)........................................................................4.30

4.2.4.2

Reporting and Documentation ...............................................................................................4.31

Ferroalloy production..........................................................................................................................4.32

4.3.1

Introduction .................................................................................................................................4.32

4.3.2

Methodological issues .................................................................................................................4.32

4.3.2.1

Choice of method...................................................................................................................4.32

4.3.2.2

Choice of emission factors.....................................................................................................4.37

4.3.2.3

Choice of activity data ...........................................................................................................4.39

4.3.2.4

Completeness.........................................................................................................................4.40

4.3.2.5

Developing a consistent time series .......................................................................................4.40

4.3.3 4.3.3.1

Emission factor uncertainties.................................................................................................4.40

4.3.3.2

Activity data uncertainties .....................................................................................................4.40

4.3.4

4.4

Uncertainty assessment ...............................................................................................................4.40

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................4.41

4.3.4.1

Quality Assurance/Quality Control (QA/QC)........................................................................4.41

4.3.4.2

Reporting and Documentation ...............................................................................................4.41

Primary aluminium production ...........................................................................................................4.43

4.4.1

Introduction .................................................................................................................................4.43

4.4.2

Methodological issues .................................................................................................................4.43

4.4.2.1

Choice of method for CO2 emissions from primary aluminium production ..........................4.43

4.4.2.2

Choice of emission factors for CO2 emissions from primary aluminium production ............4.47

4.4.2.3

Choice of method for PFCs ...................................................................................................4.49

4.4.2.4

Choice of emission factors for PFCs .....................................................................................4.53

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Volume 3: Industrial Processes and Product Use

4.4.2.5

Choice of activity data ...........................................................................................................4.55

4.4.2.6

Completeness.........................................................................................................................4.55

4.4.2.7

Developing a consistent time series .......................................................................................4.55

4.4.3 4.4.3.1

Emission factor uncertainties.................................................................................................4.56

4.4.3.2

Activity data uncertainties .....................................................................................................4.57

4.4.4

4.5

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................4.57

4.4.4.1

Quality Assurance/Quality Control (QA/QC)........................................................................4.57

4.4.4.2

Reporting and Documentation ...............................................................................................4.57

Magnesium production........................................................................................................................4.59

4.5.1

Introduction .................................................................................................................................4.59

4.5.2

Methodological issues .................................................................................................................4.61

4.5.2.1

Choice of method...................................................................................................................4.61

4.5.2.2

Choice of emission factors.....................................................................................................4.65

4.5.2.3

Choice of activity data ...........................................................................................................4.66

4.5.2.4

Completeness.........................................................................................................................4.67

4.5.2.5

Developing a consistent time series .......................................................................................4.67

4.5.3

Uncertainty assessment ...............................................................................................................4.68

4.5.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................4.69

4.6

4.5.4.1

Quality Assurance/Quality Control (QA/QC)........................................................................4.69

4.5.4.2

Reporting and Documentation ...............................................................................................4.70

Lead production...................................................................................................................................4.71

4.6.1

Introduction .................................................................................................................................4.71

4.6.2

Methodological Issues.................................................................................................................4.71

4.6.2.1

Choice of method...................................................................................................................4.71

4.6.2.2

Choice of emission factors.....................................................................................................4.73

4.6.2.3

Choice of activity data ...........................................................................................................4.74

4.6.2.4

Completeness.........................................................................................................................4.75

4.6.2.5

Developing a consistent time series .......................................................................................4.75

4.6.3

4.7

Uncertainty assessment ...............................................................................................................4.75

4.6.3.1

Emission factor uncertainties.................................................................................................4.75

4.6.3.2

Activity data uncertainties .....................................................................................................4.75

4.6.4

4.4

Uncertainty assessment ...............................................................................................................4.56

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................4.76

4.6.4.1

Quality Assurance/Quality Control (QA/QC)........................................................................4.76

4.6.4.2

Reporting and Documentation ...............................................................................................4.77

Zinc production ...................................................................................................................................4.78

4.7.1

Introduction .................................................................................................................................4.78

4.7.2

Methodological issues .................................................................................................................4.78

4.7.2.1

Choice of method...................................................................................................................4.78

4.7.2.2

Choice of emission factors.....................................................................................................4.80

4.7.2.3

Choice of activity data ...........................................................................................................4.80

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Metal Industry Emissions

4.7.2.4

Completeness.........................................................................................................................4.81

4.7.2.5

Developing a consistent time series .......................................................................................4.82

4.7.3

Uncertainty assessment ...............................................................................................................4.82

4.7.3.1

Emission factor uncertainties.................................................................................................4.82

4.7.3.2

Activity data uncertainties .....................................................................................................4.82

4.7.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................4.82

4.7.4.1

Quality Assurance/Quality Control (QA/QC)........................................................................4.82

4.7.4.2

Reporting and Documentation ...............................................................................................4.83

Reference

.....................................................................................................................................................4.84

Equations Equation 4.1

Emissions from coke production (Tier 1) ............................................................................4.17

Equation 4.2

CO2 emissions from onsite coke production (Tier 2) ..........................................................4.17

Equation 4.3

CO2 emissions from offsite coke production (Tier 2)..........................................................4.18

Equation 4.4

CO2 emissions from iron and steel production (Tier 1).......................................................4.21

Equation 4.5

CO2 emissions from production of pig iron not processed into steel (Tier 1) .....................4.21

Equation 4.6

CO2 emissions from production of direct reduced iron (Tier 1) ..........................................4.21

Equation 4.7

CO2 emissions from sinter production (Tier 1) ...................................................................4.21

Equation 4.8

CO2 emissions from pellet production (Tier 1) ...................................................................4.21

Equation 4.9

CO2 emissions from iron & steel production (Tier 2)..........................................................4.22

Equation 4.10 CO2 emissions from sinter production (Tier 2) ...................................................................4.22 Equation 4.11 CO2 emissions from direct reduced iron production (Tier 2) ..............................................4.23 Equation 4.12 CH4 emissions from sinter production (Tier 1) ...................................................................4.24 Equation 4.13 CH4 emissions from blast furnace production of pig iron (Tier 1) ......................................4.24 Equation 4.14 CH4 emissions from direct reduced iron production (Tier 1) ..............................................4.24 Equation 4.15 CO2 emissions for ferroalloy production by the Tier 1 method...........................................4.33 Equation 4.16 CO2 emissions for ferroalloy production by Tier 2 method.................................................4.33 Equation 4.17 CO2 emissions for ferroalloy productiion by Tier 3 method ...............................................4.34 Equation 4.18 CH4 emissions for ferroalloy production by the Tier 1 method...........................................4.36 Equation 4.19 Carbon contents of ferroalloy reducting agents ...................................................................4.38 Equation 4.20 Process CO2 emissions from anode and/or paste consumption (Tier 1 method) .................4.45 Equation 4.21 CO2 emissions from prebaked anode consumption (Tier 2 and Tier 3 methods) ................4.45 Equation 4.22 CO2 emissions from pitch volatiles combustion (Tier 2 and Tier 3 methods).....................4.46 Equation 4.23 CO2 emissions from bake furnace packing material (Tier 2 and Tier 3 methods)...............4.46 Equation 4.24 CO2 emissions from paste consumption (Tier 2 and Tier 3 methods) .................................4.46 Equation 4.25 PFC emissions (Tier 1 method) ...........................................................................................4.51 Equation 4.26 PFC emissions by Slope method (Tier 2 and Tier 3 methods) ............................................4.51 Equation 4.27 PFC emissions by Overvoltage method (Tier 2 and Tier 3 methods)..................................4.52 Equation 4.28 CO2 emissions from primary magnesium production (Tier 1) ............................................4.61

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 3: Industrial Processes and Product Use

Equation 4.29 CO2 emissions from primary magnesium production (Tier 2) ............................................4.61 Equation 4.30 SF6 emissions from magnesium casting (Tier 1) .................................................................4.62 Equation 4.31 SF6 emissions from magnesium casting (Tier 2) .................................................................4.62 Equation 4.32 CO2 emissions from lead production...................................................................................4.72 Equation 4.33 CO2 emissions from zinc production (Tier 1)......................................................................4.79 Equation 4.34 CO2 emissions from zinc production (Tier 1)......................................................................4.79

Figures Figure 4.1

Illustration of main processes for integrated iron and steel production ...............................4.10

Figure 4.2

Illustration of coke production process (emissions reported in Category 1A of the Energy Sector) .................................................................................................................................4.13

Figure 4.3

Illustration of sinter production process ..............................................................................4.14

Figure 4.4

Illustration of pig iron production processes .......................................................................4.15

Figure 4.5

Illustration of steel production processes.............................................................................4.16

Figure 4.6

Estimation of CO2 emissions from metallurgical coke production......................................4.19

Figure 4.7

Decision tree for estimation of CO2 emissions from iron and steel production...................4.20

Figure 4.8

Decision tree for estimation of CH4 emissions from iron and steel production...................4.20

Figure 4.9

Decision tree for estimation of CO2 emissions from ferroalloy production ........................4.35

Figure 4.10

Decision tree for estimation of CH4 emissions from FeSi and Si alloy production.............4.36

Figure 4.11

Decision tree for calculation of CO2 emissions from primary aluminium production ........4.44

Figure 4.12

Decision tree for calculation of PFC emissions from primary aluminium production ........4.53

Figure 4.13

Decision tree for estimation of CO2 emissions from raw materials calcination in the primary magnesium production process ...........................................................................................4.63

Figure 4.14

Decision tree for estimation of SF6 emissions from magnesium processing .......................4.64

Figure 4.15

Decision tree for estimation of CO2 emissions from lead production .................................4.72

Figure 4.16

Decision tree for estimation of CO2 emissions from zinc production .................................4.81

Tables

4.6

Table 4.1

Tier 1 default CO2 emission factors for coke production and iron & steel production........4.25

Table 4.2

Tier 1 default CH4 emission factors for coke production and iron & steel production........4.26

Table 4.3

Tier 2 material-specific carbon contents for iron & steel and coke production...................4.27

Table 4.4

Uncertainty ranges...............................................................................................................4.30

Table 4.5

Generic CO2 emission factors for ferroalloy production (tonnes CO2/tonne product) ........4.37

Table 4.6

CO2 emission factors for ferroalloy production (tonnes CO2/tonne reducing agent)...........4.38

Table 4.7

Default emission factors for CH4 (kg CH4/tonne product) ..................................................4.39

Table 4.8

Emission factors for CH4 (kg CH4/tonne product) ..............................................................4.39

Table 4.9

Uncertainty ranges...............................................................................................................4.40

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Metal Industry Emissions

Table 4.10

Tier 1 technology specific emission factors for calculating carbon dioxide emissions from anode or paste consumption ................................................................................................4.47

Table 4.11

Data sources and uncertainties for parameters used in Tier 2 or 3 method for CO2 emissions from prebake cells (CWPB and SWPB) ..............................................................................4.48

Table 4.12

Data sources and uncertainties for parameters used in Tier 2 or 3 method for CO2 emissions from pitch volatiles combustion (CWPB and SWPB)........................................................4.48

Table 4.13

Data sources and uncertainties for parameters used in Tier 2 or 3 method for CO2 emissions from bake furnace packing material (CWPB and SWPB)..................................................4.48

Table 4.14

Data sources and uncertainties for parameters used in Tier 2 or 3 method for CO2 emissions from Søderberg cells (VSS and HSS)..................................................................................4.49

Table 4.15

Default emission factors and uncertainty ranges for the calculation of PFC emissions from aluminium production by cell technology type (Tier 1 method) .........................................4.54

Table 4.16

Technology specific slope and overvoltage coefficients for the calculation of PFC emissions from aluminium production (Tier 2 method).......................................................................4.54

Table 4.17

Good practice reporting information for calculating CO2 and PFC emissions from aluminium production by tier ................................................................................................................4.58

Table 4. 18

Possible GHG emissions related to production and processing of magnesium ...................4.59

Table 4.19

Emission factors for ore-specific primary Mg metal production.........................................4.65

Table 4.20

SF6 emission factors for magnesium casting processes (Tier 1).........................................4.66

Table 4.21

Generic CO2 emission factors for lead production by source and furnace type ..................4.73

Table 4.22

Material-specific carbon content for lead production..........................................................4.74

Table 4.23

Uncertainty ranges...............................................................................................................4.76

Table 4.24

Tier 1 CO2 emission factors for zinc production .................................................................4.80

Table 4.25

Uncertainty Ranges .............................................................................................................4.82

Boxes Box 4.1

Definitions for words/symbols used in equations in this section.........................................4.33

Box 4.2

Anode effect description......................................................................................................4.50

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.7

Volume 3: Industrial Processes and Product Use

4 METAL INDUSTRY EMISSIONS 4.1

INTRODUCTION

The following sections 4.2 through 4.7 give guidance for estimating greenhouse gas emissions that result from the production of metals. •

•

Section 4.2 covers emissions from iron and steel, and metallurgical coke production;

•

Section 4.4 covers emissions from aluminium production;

•

Section 4.6 covers emissions from lead production;

•

Section 4.3 covers emissions from ferroalloy production;

•

Section 4.5 covers emissions from magnesium production;

Section 4.7 covers emissions from zinc production.

Care should be exercised to avoid double counting of carbon dioxide (CO2) emissions in both this chapter and in Volume 2 on Energy Sector, or, in omitting CO2 emissions since CO2 emissions resulting from carbon’s role as process reactant and as a heat source to drive the chemical reactions involved in the metallurgical processes are closely related in many cases. Should CO2 capture technology be installed at a metals production facility, the CO2 captured should be deducted in a higher tier emissions calculation. Any methodology taking into account CO2 capture should consider that CO2 emissions captured in the process may be both combustion and processrelated. In cases where combustion and process emissions are to be reported separately, e.g., for iron and steel production, inventory compilers should ensure that the same quantities of CO2 are not double counted. In these cases the total amount of CO2 captured should preferably be reported in the corresponding energy combustion and IPPU source categories in proportion to the amounts of CO2 generated in these source categories. The default assumption is that there is no CO2 capture and storage (CCS) taking place. For additional information on CO2 capture and storage refer to Volume 3, Section 1.2.2 and for more details to Volume 2, Section 2.3.4.

4.8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Metal Industry Emissions

4.2

IRON & STEEL AND METALLURGICAL COKE PRODUCTION

The production of iron and steel leads to emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). This chapter provides guidance for estimating emissions of CO2 and CH4.1 The iron and steel industry broadly consists of: •

•

Primary facilities that produce both iron and steel;

•

Iron production facilities; and

•

Secondary steelmaking facilities;

Offsite production of metallurgical coke.

Figure 4.1 illustrates the main processes for iron and steel production: metallurgical coke production, sinter production, pellet production, iron ore processing, iron making, steelmaking, steel casting and very often combustion of blast furnace and coke oven gases for other purposes. The main processes may occur under what is referred to as an ‘integrated’ facility and typically include blast furnaces, and basic oxygen steelmaking furnaces (BOFs), or in some cases open hearth furnaces (OHFs). It is also common for parts of the production to be offsite under the responsibility of another operator such as an offsite coke production facility. In some countries, there will be coke production facilities that are not integrated with iron and steel production (i.e., ‘offsite’). This chapter provides guidance for estimating emissions of CO2 and CH4 from all coke production to ensure consistency and completeness. Countries should estimate emissions from onsite and offsite coke production separately under higher tiers as the by-products of onsite coke production (e.g., coke oven gas, coke breeze, etc.) are often used during the production of iron and steel. P r imary and s eco nda ry st ee l- making: Steel production can occur at integrated facilities from iron ore, or at secondary facilities, which produce steel mainly from recycled steel scrap. Integrated facilities typically include coke production, blast furnaces, and basic oxygen steelmaking furnaces (BOFs), or in some cases open hearth furnaces (OHFs). Raw steel is produced using a basic oxygen furnace from pig iron produced by the blast furnace and then processed into finished steel products. Pig iron may also be processed directly into iron products. Secondary steelmaking most often occurs in electric arc furnaces (EAFs). In 2003, BOFs accounted for approximately 63 percent of world steel production and EAFs approximately accounted for 33 percent; OHF production accounted for the remaining 4 percent but is today declining. Iro n product ion: Iron production can occur onsite at integrated facilities or at separate offsite facilities containing blast furnaces and BOFs. In addition to iron production via blast furnace, iron can be produced through a direct reduction process. Direct reduction involves the reduction of iron ore to metallic iron in the solid state at process temperatures less than 1000°C. M eta llu rg ica l cok e prod uc tio n: Metallurgical coke production is considered to be an energy use of fossil fuel, and as a result emissions should be reported in Category 1A of the Energy Sector. The methodologies are presented here in Volume 3, however, because the activity data used to estimate emissions from energy and non-energy in integrated iron and steel production have significant overlap. All fuel consumed in this source category not allocated as inputs to the sinter plants, pelletisation plants and blast furnace should be regarded as fuel combustion, which is dealt with and reported in the Energy Sector (see Volume 2: Energy).

1

No methodologies are provided for N2O emissions. These emissions are likely to be small, but countries can calculate estimates provided they develop country-specific methods based on researched data.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.9

Pelletizing Plant

Sinter Plant

Coke Plant

Blast furnace pig iron production

Pig Iron to Iron Processing

Coke Oven Gas

Steelmaking [Basic Oxygen Furnace]

Basic Oxygen Furnace Gas

Steel to Steel Processing

* Modified from: European Conference on "The Sevilla Process: A Driver for Environmental Performance in Industry" Stuttgart, 6 and 7 April 2000, BREF on the Production of Iron and Steel conclusion on BAT, Dr. Harald Schoenberger, Regional State Governmental Office Freiburg, April 2000. (Schoenberger, 2000)

Coal Injection

Iron Ore. Additives

Iron Ore. Additives

Coke Breeze

Coking Coal

Illustration of main processes for integrated iron and steel production*

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Figure 4.1

Volume 3: Industrial Processes and Product Use

4.10

Chapter 4: Metal Industry Emissions

4.2.1

Introduction

METALLURGICAL COKE PRODUCTION Metallurgical coke is primarily used in the blast furnace to make iron. Coke is also used in other metallurgical processes, such as the manufacture of cast iron, ferroalloys, lead, and zinc, and in kilns to make lime and magnesium. Metallurgical coke is the solid product obtained from the carbonisation of coal, principally coking coal, at high temperature. It is low in moisture content and volatile matter. Coking coal refers to bituminous coal with a quality that allows the production of a coke suitable to support a blast furnace charge. Its gross calorific value is greater than 23 865 kJ/kg (5 700 kcal/kg) on an ash-free but moist basis. Coke oven gas is a by-product of the manufacture of metallurgical coke for the production of iron and steel. Figure 4.2 illustrates the coke production process and associated sources of CH4 and CO2 emissions. Note that coke oven gas may be burned to heat coke ovens or transferred onsite in an integrated iron and steel plant and used in sinter production or iron production processes. Coke oven gas may also be transferred off site (e.g., into the natural gas distribution system) and used as an energy source. The combustion of coke in blast furnaces during the iron and steel-making process produces blast furnace gas, which may then be recovered and transferred from the iron and steel mill to the onsite coke plant and burned to heat coke ovens or used in sinter production. The combustion of blast furnace gas and coke oven gas is the main sources of CO2 and CH4 emissions in coke production.

SINTER PRODUCTION Iron ore and other iron-containing materials may be agglomerated in sinter plants at integrated iron and steel plants prior to introduction into the blast furnace. Feedstock to sinter plants may include fine iron ores, additives (e.g., lime, olivine), and iron-bearing recycled materials from downstream iron and steelmaking processes (e.g., dust from blast-furnace gas cleaning). Coke breeze (small-grade coke oven coke with particle sizes of < 5 mm) is the most commonly used process material in sinter plants. The coke breeze may be produced from the onsite coke ovens in integrated iron and steel plants, or may be purchased from offsite coke producers. Blast furnace gas or coke oven gas produced onsite during integrated iron and steel production may be used in sinter plants. Operation of sinter plants produces carbon dioxide emissions from oxidation of the coke breeze and other inputs. Off gas from sinter production also contains methane and other hydrocarbons. Figure 4.3 illustrates the sinter production process.

PELLET PRODUCTION Pellets are formed from iron-containing raw materials (i.e., fine ore and additives) into 9-16 mm spheres in a very high temperature process. The process includes grinding, drying, balling, and thermal treatment of the raw materials. Pelletisation plants are principally located at iron mines or at shipping ports, but can also be located onsite as part of an integrated iron and steel facility. Natural gas or coal may be used as fuel for pelletisation plants; for pelletisation plants located onsite at an integrated iron and steel facility, coke oven gas may be used as a fuel. Energy consumption for the process and the associated CO2 emissions will depend in part on the quality of the iron ore and other raw materials used in the process. The CO2 emissions will also depend upon the carbon contents and heating values of fuels used in the process.

IRONMAKING AND THE ROLE OF COKE Most CO2 emitted by the iron and steel industry is associated with the production of iron, more specifically the use of carbon to convert iron ore to iron. Figure 4.4 describes the iron-making process and associated sources of emissions. Carbon is supplied to the blast furnace mainly in the form of coke produced from metallurgical grade coking coal (but can also be in the form charcoal made from wood or other forms of carbon.). Carbon serves a dual purpose in the iron making process, primarily as a reducing agent to convert iron oxides to iron, but also as an energy source to provide heat when carbon and oxygen react exothermically. Blast furnace gas is produced during the combustion of coke in blast furnaces. It is typically recovered and used as a fuel partly within the plant and partly in other steel industry processes, or in power stations equipped to burn it. Blast furnace gas may also be recovered and transferred from the iron and steel mill to the onsite coke plant and burned for energy within the coke ovens. Blast furnace gas may also be transferred offsite and used as an energy source both within the furnace and when blast furnace gas is combusted to heat blast air. Oxygen steel furnace gas is obtained as a by-product of the production of steel in a basic oxygen furnace (BOF) and is recovered on leaving the furnace. All carbon used in blast furnaces should be considered process-related IPPU emissions. Additionally, iron can be produced through a direct reduction process. Direct reduction involves the reduction of iron ore to metallic iron in the solid state at process temperatures less than 1 000°C. A solid product referred to as direct reduced iron (DRI) is produced by the direct reduction process. DRI has a carbon content of < 2 percent. DRI is normally used as a replacement for scrap metal in the electric arc furnace steelmaking route, but may also

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.11

Volume 3: Industrial Processes and Product Use

be used as a feedstock for blast furnace iron making. DRI may also be melted into briquettes, referred to as hot briquetted iron (HBI), when the product has to be stored or transported. Inventory preparers can estimate the CO2 emissions from these processes from the energy consumption and carbon content of the fuel (e.g., natural gas, coal).

STEELMAKING Steel production in a BOF begins by charging the vessel with 70-90 percent molten iron and 10-30 percent steel scrap. High purity oxygen then combines with the carbon in the iron to create an exothermic reaction that melts the charge while lowering the carbon content. Iron from the blast furnace usually contains 3-4 percent carbon, which must be reduced to less than 1 percent, refined, and alloyed to produce the desired grade of steel. Steel production in an EAF typically occurs by charging 100 percent recycled steel scrap, which is melted using electrical energy imparted to the charge through carbon electrodes and then refined and alloyed to produce the desired grade of steel. Although EAFs may be located in integrated plants, typically they are stand-alone operations because of their fundamental reliance on scrap and not iron as a raw material. Since the EAF process is mainly one of melting scrap and not reducing oxides, carbon’s role is not as dominant as it is in the blast furnace/BOF process. In a majority of scrap-charged EAF, CO2 emissions are mainly associated with consumption of the carbon electrodes. All carbon used in EAFs and other steelmaking processes should be considered process-related IPPU emissions. Figure 4.5 illustrates the steel making process and associated sources of emissions.

4.12

2006 IPCC Guidelines for National Greenhouse Gas Inventories

By-product fuels from Integrated Iron and Steel (e.g., Blast Furnace Gas)

Coke Oven Heating

Coke Oven Process

COG burned on I&S site

Coal Tars and Light Oils

Coke Oven Gas and other by-products

Coke Oven Gas (COG)

Breeze

Metallurgical Coke

Used Onsite in Blast Furnace or other Integrated Iron and Steel Process

Note: Bold lines apply only to Onsite Coke Production at Integrated Iron and Steel Mill. Dashed lines apply to transfers of materials to Off Site processes. Integrated Iron and Steel production processes, which are categorised as Onsite.

Metallurgical Coal and other process carbon

CO2 and CH4 emissions

COG transferred Off Site

Transferred Off Site

Use Onsite (e.g., Sinter Plants) or Off Site

Off Site process does not include

Illustration of coke production process (emissions reported in Category 1A of the Energy Sector)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Figure 4.2

4.13

Chapter 4: Metal Industry Emissions

Iron Ore

Coke Breeze

Natural Gas

Other Waste Gas

Illustration of sinter production process

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Figure 4.3

Volume 3: Industrial Processes and Product Use

Coke Oven Gas

Sintering Process

CH4 Emissions CO2 Emissions

Blast Furnace Gas

Sinter to Blast Furnace Pig Iron Production

4.14

From Integrated Coke Oven

Purchased Coke

Integrated Coke Oven -Coke Oven Gas

Integrated Coke Oven -- Coke

Scrap Iron

*Coal Tar, Light Oil, Breeze

Blast Furnace

Slag

Coal

Transferred Off Site

Pig Iron

Tar, Oil

Transferred to Steel Mill

Integrated Coke Oven -By-products*

Iron Ore, Pellets, Sinter

Illustration of pig iron production processes

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Figure 4.4

Dolomite

Transferred Off Site

Transferred to Integrated Coke Oven

Limestone

Direction of Process Connects to Steel Production Connects to an External Process Process Byproduct Raw Material Product

Burned Onsite within iron and steel process

Blast Furnace Gas

CH4 Emissions CO2 Emissions

Natural Gas

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Chapter 4: Metal Industry Emissions

Iron Ore

Natural Gas

EAF Charge Carbon

EAF Steel

Slag

Electric Arc Furnace (EAF)

Direct Reduced/ Hot Briquetted

Pig Iron

CH4 Emissions CO2 Emissions

Direct Reduced/ Hot Briquetted

EAF Anode

CH4 Emissions CO2 Emissions

Waste Gas

Illustration of steel production processes

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Figure 4.5

Volume 3: Industrial Processes and Product Use

Tar

Slag

Basic Oxygen Furnace (BOF)

CH4 Emissions CO2 Emissions

Limestone

BOF Steel

Fuel Oil

Scrap Steel

Other non-fuel C additions

Slag

Open Hearth Furnace (OHF)

CH4 Emissions CO2 Emissions

OHF Steel

Dolomite

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Chapter 4: Metal Industry Emissions

4.2.2 4.2.2.1

Methodological issues C HOICE

OF METHOD : METALLURGICAL C O K E P R O D U C T I O N

The IPCC Guidelines outline three tiers for calculating CO2 emissions and two tiers for calculating CH4 emissions from coke production. The choice of a good practice method for estimation of CO2 emissions depends on national circumstances as shown in the decision tree in Figure 4.6 Estimation of CO2 Emissions from Metallurgical Coke Production. For CH4 emissions, use the decision tree in Figure 4.8. Metallurgical coke is produced either at the iron and steel facility (‘onsite’) or at separate facilities (‘offsite’).The Tier 1 method calculates emissions from all coke production using default emission factors applied to national coke production. The Tier 2 method for estimating CO2 emissions distinguishes between onsite and offsite coke production. It uses national activity data for the consumption and production of process materials (e.g., coking coal consumed, coke produced, and coal tar products produced). As discussed above, the Tier 2 method is not applicable to estimating CH4 emissions. The Tier 3 method requires plant-specific CO2 emissions data and plant-specific CH4 emissions data, or plant-specific activity data.

TIER 1 METHOD – PRODUCTION-BASED EMISSION FACTORS Equation 4.1 calculates emissions from all coke production. The Tier 1 method assumes that all coke made onsite at iron and steel production facilities is used onsite. The Tier 1 method is to multiply default emission factors by tonnes of coke produced. Emissions should be reported in the Energy Sector.

E CO 2

EQUATION 4.1 EMISSIONS FROM COKE PRODUCTION (TIER 1) = Coke • EFCO 2 and E CH 4 = Coke • EFCH 4 (To be reported in Energy Sector)

Where: ECO2 or ECH4 = emissions of CO2 or CH4 from coke production, tonnes CO2 or tonnes CH4 Coke = quantity of coke produced nationally, tonnes EF= emission factor, tonnes CO2/tonne coke production or tonnes CH4/tonne coke production Note: The Tier 1 method assumes that all of the coke oven by-products are transferred off site and that all of the coke oven gas produced is burned on site for energy recovery.

TIER 2 METHOD The Tier 2 method is appropriate if national statistics on process inputs and outputs from integrated and nonintegrated coke production processes are available. Tier 2 will produce a more accurate estimate than Tier 1 because it takes into account the actual quantity of inputs into and outputs rather than making assumptions. As expressed in Equations 4.2 and 4.3, Tier 2 estimates CO2 emissions from onsite coke production separately from off-site production. This separation is due to overlapping data requirements when estimating emissions from onsite coke production and emissions from iron and steel production. EQUATION 4.2 CO2 EMISSIONS FROM ONSITE COKE PRODUCTION (TIER 2)

⎡ E CO 2, energy = ⎢CC • C CC + ∑ (PM a • C a ) + BG • C BG a ⎣

⎤ 44 − CO • C CO − COG • C COG − ∑ (COB b • C b )⎥ • b ⎦ 12

Where: ECO2, energy = emissions of CO2 from onsite coke production to be reported in Energy Sector, tonnes

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Volume 3: Industrial Processes and Product Use

CC = quantity of coking coal consumed for coke production in onsite integrated iron and steel production facilities, tonnes PMa = quantity of other process material a, other than those listed as separate terms, such as natural gas, and fuel oil, consumed for coke and sinter production in onsite coke production and iron and steel production facilities, tonnes BG = quantityof blast furnace gas consumed in coke ovens, m3 (or other unit such as tonnes or GJ. Conversion of the unit should be consistent with Volume 2: Energy) CO = quantity of coke produced onsite at iron and steel production facilities, tonnes COG = quantity of coke oven gas transferred offsite , m3 (or other unit such as tonnes or GJ. Conversion of the unit should be consistent with Volume 2: Energy) COBb = quantity of coke oven by-product b, transferred offsite either to other facilities, tonnes Cx = carbon content of material input or output x, tonnes C/(unit for material x) [e.g., tonnes C/tonne] For offsite coke production, the inventory compiler should use Equation 4.3. Total emissions are the sum of emissions from all plants using both Equations 4.2 and 4.3. EQUATION 4.3 CO2 EMISSIONS FROM OFFSITE COKE PRODUCTION (TIER 2)

⎤ 44 ⎡ E CO 2,energy = ⎢CC • C CC + ∑ (PM a • C a ) − NIC • C NIC − COG • C COG − ∑ (COBb • C b )⎥ • a b ⎦ 12 ⎣

Where: ECO2, energy = emissions of CO2 from offsite coke production to be reported in Energy Sector, tonnes CC = quantity of coking coal used in non-integrated coke production facilities, tonnes PMa = quantity of other process material a, other than coking coal, such as natural gas, and fuel oil consumed nationally in non-integrated coke production, tonnes NIC = quantity of coke produced offsite in non-integrated coke production facilities nationally, tonnes COG = quantity of coke oven gas produced in offsite non-integrated coke production facilities nationally that is transferred to other facilities, m3 (or other unit such as tonnes or GJ. Conversion of the unit should be consistent with Volume 2: Energy) COBb= quantity of coke oven by-product b, produced nationally in offsite non-integrated facilities and transferred offsite to other facilities, tonnes Cx = carbon content of material input or output x, tonnes C/(unit for material x) [e.g., tonnes C/tonne]

TIER 3 METHOD Unlike the Tier 2 method, the Tier 3 method uses plant specific data because plants can differ substantially in their technology and process conditions. If actual measured CO2/CH4 emissions data are available from onsite and offsite coke production plants, these data can be aggregated and used directly to account for national emissions from metallurgical coke production using the Tier 3 method. Total national emissions will equal the sum of emissions reported from each facility. If facility-specific CO2 emissions data are not available, CO2 emissions can be calculated from plant-specific activity data applying the Tier 2 method, Equations 4.2 and 4.3. Total national emissions will equal the sum of emissions reported from each facility.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Metal Industry Emissions

Figure 4.6

Estimation of CO 2 emissions from metallurgical coke production Start

Are plant-specific emissions or activity data available?

Yes

Use or Calculate emissions using plant-specific emissions data or activity data. Box 3: Tier 3

No Are activity data available for onsite and offsite coke production?

Yes

Calculate emissions from coke production using material specific carbon contents. Box 2: Tier 2

No Assume all coke is produced onsite, using default emission factors and national production data. Box 1: Tier 1

4.2.2.2

C HOICE

OF METHOD : IRON AND STEEL PRODUCTION

These Guidelines outline three tiers for calculating CO2 emissions and two tiers for calculating CH4 emissions from iron and steel production. The choice of a good practice method depends on national circumstances as shown in the decision tree in Figure 4.7 for CO2 emissions and Figure 4.8 for CH4 emissions: Decision Tree for Estimation of CO2 Emissions from Iron & Steel Production and Decision Tree for Estimating of CH4 Emissions from Iron and Steel Production. The Tier 1 method is based on national production data and default emission factors. It may lead to errors due to its reliance on assumptions rather than actual data for the quantity of inputs into the sinter production and iron and steel production sector that contribute to CO2 emissions. Therefore, the Tier 1 is appropriate only if iron and steel production is not a key category. Default emission factors are provided for sinter production, blast furnace iron making, direct reduced iron production, pellet production, and each method of steelmaking. The primary sources of emissions are the blast furnace iron making, and steelmaking. The Tier 2 method for estimating CO2 emissions from iron and steel production is based on data for the known consumption of raw materials, including reducing agents, and industry-wide data. It uses a mass balance approach and material-specific carbon contents. The Tier 2 method is not applicable to estimating CH4 emissions. The Tier 3 method requires plant-specific emissions or activity data aggregated to the national level for estimating CO2 and CH4 emissions.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 3: Industrial Processes and Product Use

Figure 4.7

Decision tree for estimation of CO 2 emissions from iron and steel production Start

Are plant-specific emissions or activity data available?

Yes

Use or calculate emissions using plant specific data. Box 3: Tier 3

No

Are national process materials data available?

Yes

Calculate emissions using material-specific carbon contents. Box 2: Tier 2

No

Is this a key category1?

Calculate emissions using default emission factors and national production data.

No

Box 1: Tier 1

Yes Collect data for the Tier 3 or the Tier 2 method.

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

Figure 4.8

Decision tree for estimation of CH 4 emissions from iron and steel production Start

Are plant-specific emissions or activity data available?

Yes

Use or calculate emissions using plant specific data. Box 2: Tier 3

No

Is this a key category1?

Yes

No

Calculate emissions using default emission factors and national production data. Box 1: Tier 1

Collect data for the Tier 3 method. Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Metal Industry Emissions

METHODOLOGY FOR ESTIMATING CO 2 EMISSIONS Tier 1 method – production-based emission factors The Tier 1 approach for emissions from iron and steel production is to multiply default emission factors by national production data, as shown in Equation 4.4. Because emissions per unit of steel production vary widely depending on the method of steel production, it is good practice to determine the share of steel produced in different types of steelmaking processes, calculate emissions for each process, and then sum the estimates. Equation 4.4 considers steel production from Basic Oxygen Furnaces (BOF), Electric Arc Furnaces (EAF), and Open Hearth Furnaces (OHF). In the event that activity data for steel production for each process is not available, default allocation of total national steel production among these three steelmaking processes is provided in Table 4.1 in Section 4.2.2.3. Equation 4.5 calculates emissions from pig iron production that is not converted into steel. It is preferable to estimate emissions from this production separately because the emission factors for integrated iron and steel production (BOF and OHF processes) take into account emissions from both steps. Equation 4.6 calculates CO2 emissions from production of Direct Reduced Iron (DRI) for the Tier 1 method using a CO2 emission factor. It is also good practice to estimate separately the emissions from sinter production and national pellet production, using Equations 4.7 and 4.8. Equations 4.7 and 4.8 should be used if the inventory compiler does not have detailed information about the process materials used. If the process materials are known, emissions should be calculated using the Tier 2 method. Total emissions are the sum of Equations 4.4 to 4.8. EQUATION 4.4 CO2 EMISSIONS FROM IRON AND STEEL PRODUCTION (TIER 1) Iron & Steel: E CO 2, non − energy = BOF • EFBOF + EAF • EFEAF + OHF • EFOHF

EQUATION 4.5 CO2 EMISSIONS FROM PRODUCTION OF PIG IRON NOT PROCESSED INTO STEEL (TIER 1) Pig Iron Production: E CO 2, non −energy = IP • EFIP

EQUATION 4.6 CO2 EMISSIONS FROM PRODUCTION OF DIRECT REDUCED IRON (TIER 1) Direct Reduced Iron: E CO 2, non − energy = DRI • EFDRI

EQUATION 4.7 CO2 EMISSIONS FROM SINTER PRODUCTION (TIER 1) Sinter Production: E CO 2, non −energy = SI • EFSI

EQUATION 4.8 CO2 EMISSIONS FROM PELLET PRODUCTION (TIER 1) Pellet Production: E CO 2, non − energy = P • EFP Where: ECO2, non-energy = emissions of CO2 to be reported in IPPU Sector, tonnes BOF= quantity of BOF crude steel produced, tonnes EAF = quantity of EAF crude steel produced, tonnes OHF = quantity of OHF crude steel produced, tonnes

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Volume 3: Industrial Processes and Product Use

IP = quantity of pig iron production not converted to steel, tonnes DRI = quantity of Direct Reduced Iron produced nationally, tonnes SI = quantity of sinter produced nationally, tonnes P = quantity of pellet produced nationally, tonnes EFx= emission factor, tonnes CO2/tonne x produced

Tier 2 method The Tier 2 method is appropriate if the inventory compiler has access to national data on the use of process materials for iron and steel production, sinter production, pellet production, and direct reduced iron production. In addition, as discussed in Section 4.2.2.5, there are a number of other process inputs and outputs that could be considered under Tier 2. These data may be available from governmental agencies responsible for manufacturing or energy statistics, business or industry trade associations, or individual iron and steel companies. The Tier 2 method will produce a more accurate estimate than the Tier 1 method because it takes into account the actual quantity of inputs that contribute to CO2 emissions. In calculating pellet production emissions, energy consumption and heating value and carbon content of the fuel can be used similarly to the other methodologies.

EQUATION 4.9 CO2 EMISSIONS FROM IRON & STEEL PRODUCTION (TIER 2)

⎡ E CO 2,non −energy = ⎢ PC • C PC + ∑ (COB a • C a ) + CI • C CI + L • C L + D • C D + CE • C CE a ⎣ ⎤ 44 + ∑ (Ob • C b ) + COG • C COG − S • C S − IP • C IP − BG • C BG ⎥ • b ⎦ 12

EQUATION 4.10 CO2 EMISSIONS FROM SINTER PRODUCTION (TIER 2)

⎤ 44 ⎡ E CO 2,non −energy = ⎢CBR • C CBR + COG • C COG + BG • C BG + ∑ (PM a • C a ) − SOG • C SOG ⎥ • a ⎦ 12 ⎣ Where, for iron and steel production: ECO2, non-energy = emissions of CO2 to be reported in IPPU Sector, tonnes PC = quantity of coke consumed in iron and steel production (not including sinter production), tonnes COBa = quantity of onsite coke oven by-product a, consumed in blast furnace, tonnes CI= quantity of coal directly injected into blast furnace, tonnes L = quantity of limestone consumed in iron and steel production, tonnes D = quantity of dolomite consumed in iron and steel production, tonnes CE = quantity of carbon electrodes consumed in EAFs, tonnes Ob = quantity of other carbonaceous and process material b, consumed in iron and steel production, such as sinter or waste plastic, tonnes COG= quantity of coke oven gas consumed in blast furnace in iron and steel production, m3 (or other unit such as tonnes or GJ. Conversion of the unit should be consistent with Volume 2: Energy) S = quantity of steel produced, tonnes IP = quantity of iron production not converted to steel, tonnes BG = quantity of blast furnace gas transferred offsite, m3 (or other unit such as tonnes or GJ. Conversion of the unit should be consistent with Volume 2: Energy) Cx = carbon content of material input or output x, tonnes C/(unit for material x) [e.g., tonnes C/tonne]

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Chapter 4: Metal Industry Emissions

Where, for sinter production: ECO2, non-energy = emissions of CO2 to be reported in IPPU Sector, tonnes CBR = quantity of purchased and onsite produced coke breeze used for sinter production, tonnes COG= quantity of coke oven gas consumed in blast furnace in sinter production, m3 (or other unit such as tonnes or GJ. Conversion of the unit should be consistent with Volume 2: Energy) BG = quantity of blast furnace gas consumed in sinter production, m3 (or other unit such as tonnes or GJ. Conversion of the unit should be consistent with Volume 2: Energy) PMa = quantity of other process material a, other than those listed as separate terms, such as natural gas, and fuel oil, consumed for coke and sinter production in integrated coke production and iron and steel production facilities, tonnes SOG = quantity of sinter off gas transferred offsite either to iron and steel production facilities or other facilities, m3 (or other unit such as tonnes or GJ. Conversion of the unit should be consistent with Volume 2: Energy) Cx = carbon content of material input or output x, tonnes C/(unit for material x) [e.g., tonnes C/tonne] Equation 4.11 calculates CO2 emissions from production of direct reduced iron for the Tier 2 method based on fuel consumption and fuel carbon content. Emissions from DRI production are derived from combusting fuel, coke breeze, metallurgical coke or other carbonaceous materials, and are to be reported as IPPU emissions. EQUATION 4.11 CO2 EMISSIONS FROM DIRECT REDUCED IRON PRODUCTION (TIER 2) 44 E CO 2,non −energy = (DRI NG • C NG + DRI BZ • C BZ + DRI CK • C CK ) • 12

Where: ECO2, non-energy = emissions of CO2 to be reported in IPPU Sector, tonnes DRING = amount of natural gas used in direct reduced iron production, GJ DRIBZ = amount of coke breeze used in direct reduced iron production, GJ DRICK = amount of metallurgical coke used in direct reduced iron production, GJ CNG = carbon content of natural gas, tonne C/GJ CBZ = carbon content of coke breeze, tonne C/GJ CCK = carbon content of metallurgical coke, tonne C/GJ

Tier 3 method Unlike the Tier 2 method, the Tier 3 method uses plant specific data. The Tier 3 method provides an even more accurate estimate of emission than the Tier 2 method because plants can differ substantially in their technology and process conditions. If actual measured CO2 emissions data are available from iron and steelmaking facilities, these data can be aggregated to account for national CO2 emissions. If facility-specific CO2 emissions data are not available, CO2 emissions can be calculated from plant-specific activity data for individual reducing agents, exhaust gases, and other process materials and products. Total national emissions will equal the sum of emissions reported from each facility. Equations 4.9 through 4.11 describe the parameters that are necessary for an accounting of plant-specific emissions using the Tier 3 method and plant-specific activity data at a facility level. Plant-specific carbon contents for each material are required for the Tier 3 method.

METHODOLOGY FOR CH 4 When carbon-containing materials are heated in the furnace for sinter production or iron production, the volatiles, including methane, are released. With open or semi-covered furnaces, most of the volatiles will burn to CO2 above the charge, in the hood and off-gas channels, but some will remain un-reacted as CH4 and non-methane volatile organic compounds (NMVOC). The amounts depend on the operation of the furnace. Sprinkle-charging will reduce the amounts of CH4 compared to batch-wise charging. Increased temperature in the hood (less false air) will reduce the content of CH4 further.

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Volume 3: Industrial Processes and Product Use

This section describes a Tier 1 default method and a more advanced Tier 3 facility-level method for CH4 from sinter production or iron production, both of which are similar to the approaches described for estimating CO2 emissions. There is no Tier 2 method. CH4 may be emitted from steel–making processes as well, however those emissions are assumed to be negligible. Therefore CH4 emissions from steel-making processes are not discussed here. The Tier 1 methodology for CH4 is based on emission factors and national production statistics.

EQUATION 4.12 CH4 EMISSIONS FROM SINTER PRODUCTION (TIER 1) Sinter Production: E CH 4, non −energy = SI • EFSI

EQUATION 4.13 CH4 EMISSIONS FROM BLAST FURNACE PRODUCTION OF PIG IRON (TIER 1) Pig Iron Production: E CH 4, non − energy = PI • EFPI

EQUATION 4.14 CH4 EMISSIONS FROM DIRECT REDUCED IRON PRODUCTION (TIER 1) Direct Reduced Iron Production: E CH 4, non − energy = DRI • EFDRI

Where: ECH4, non-energy = emissions of CH4 to be reported in IPPU Sector, kg SI = quantity of sinter produced nationally, tonnes PI = quantity of iron produced nationally including iron converted to steel and not converted to steel, tonnes DRI = quantity of direct reduced iron produced nationally, tonnes EFx = emission factor, kg CH4/tonne x produced The Tier 3 method uses plant specific emissions data. If actual measured CH4 emissions data are available for coke production, these data can be aggregated to account for national CH4 emissions. Total national emissions will equal the sum of emissions reported from each facility.

4.2.2.3

C HOICE

OF EMISSION FACTORS

TIER 1 METHOD Carbon dioxide emission factors Table 4.1 provides default emission factors for coke, sinter, pellet, iron, and steel production. The emission factors for the three steelmaking methods are based on expert judgment using typical practice for the different steel production scenarios listed. The default emission factors account for all carbon input into the blast furnace. It is assumed based on the Integrated Pollution Prevention and Control (IPPC) Reference Document on Production of Iron and Steel (European IPPC Bureau, 2001) (referred to in this section as ‘IPPC I&S BAT Document’) that most of the carbon input to the blast furnace is from coke (60 -90 percent). The default CO2 emission factor for coke production is derived by averaging plant-specific CO2 emissions data for 11 European coke plants reported in the IPPC I&S BAT Document. Emissions of CO2 are reported in Table 6.2 of the IPPC I&S BAT Document in units of kilograms of CO2 per tonne of liquid steel produced. The CO2 emissions range from 175 to 200 kg CO2 per tonne liquid steel. The conversion factors provided in Table 6.2 of the IPPC Document are 940 kg pig iron per tonne liquid steel and 358 kg coke per tonne pig iron. Based on these conversion factors the average CO2 emissions from the 11 European coke plants is 0.56 tonne CO2 per tonne coke produced. The CO2 emission factor for sinter plants is derived by averaging plant-specific CO2 emissions data for four European sinter plants reported in the IPPC I&S BAT Document. Emissions of CO2 are reported in Table 4.1 of

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Metal Industry Emissions

the IPPC I&S BAT Document in units of kilograms of CO2 per tonne of liquid steel produced. The CO2 emissions range from 205 to 240 kg CO2 per tonne liquid steel. The conversion factors provided in Table 4.1 of the IPPC I&S BAT Document are 940 kg pig iron per tonne liquid steel and 1160 kg sinter per tonne pig iron. Based on these conversion factors the average CO2 emissions from the four European sinter plants is 0.2 kg CO2 per kg sinter produced. The CO2 emission factor for blast furnace iron making is derived by averaging plant-specific CO2 emissions data for European sinter plants reported in the IPPC I&S BAT Document. The CO2 and CO content of blast furnace gas produced by the iron making process is reported in Tables 7.2 and 7.3 of the IPPC I&S BAT Document in units of kilograms of CO2 per tonne of pig iron produced and kilograms of CO per tonne of pig iron produced. The CO2 content ranges from 400 to 900 kg CO2 per tonne pig iron produced and the CO content ranges from 300 to 700 kg CO per tonne of pig iron produced. Based on the assumption that all of the blast furnace gas burned for energy recovery (and combusted to CO2) within the integrated iron and steel mill and that no blast furnace gas is transferred off site, this corresponds to an emission factor of 1.35 kg CO2 per kg pig iron produced.

TABLE 4.1 TIER 1 DEFAULT CO2 EMISSION FACTORS FOR COKE PRODUCTION AND IRON & STEEL PRODUCTION Process

Emission Factor

Source

Sinter Production (tonne CO2 per tonne sinter produced)

0.20

Sinter Production: European IPPC Bureau (2001), Integrated Pollution Prevention and Control (IPPC) Best Available Techniques Reference Document on the Production of Iron and Steel, December 2001, Table 4.1, Page 29. http://eippcb.jrc.es/pages/FActivities.htm

Coke Oven (tonne CO2 per tonne coke produced)

0.56

Coke Production: European IPPC Bureau (2001), Integrated Pollution Prevention and Control (IPPC) Best Available Techniques Reference Document on the Production of Iron and Steel, December 2001, Table 6.2, Page 122. http://eippcb.jrc.es/pages/FActivities.htm

Iron Production (tonne CO2 per tonne pig iron produced)

1.35

Iron Production: European IPPC Bureau (2001), Integrated Pollution Prevention and Control (IPPC) Best Available Techniques Reference Document on the Production of Iron and Steel, December 2001, Tables 7.2 and 7.3. http://eippcb.jrc.es/pages/FActivities.htm

Direct Reduced Iron production (tonne CO2 per tonne DRI produced)

0.70

Direct Reduced Iron Production: European IPPC Bureau (2001), Integrated Pollution Prevention and Control (IPPC) Best Available Techniques Reference Document on the Production of Iron and Steel, December 2001, Table 10.1 Page 322 and Table 10.4 Page 331. http://eippcb.jrc.es/pages/FActivities.htm

Pellet production (tonne CO2 per tonne pellet produced)

0.03

Pellet Production: European IPPC Bureau (2001), Integrated Pollution Prevention and Control (IPPC) Best Available Techniques Reference Document on the Production of Iron and Steel, December 2001, Table 5.1 Page 95. http://eippcb.jrc.es/pages/FActivities.htm

Steelmaking Method Basic Oxygen Furnace (BOF) (tonne CO2 per tonne of steel produced)

1.46

Electric Arc Furnace (EAF) (tonne CO2 per tonne of steel produced) **

0.08

Open Hearth Furnace (OHF) (tonne CO2 per tonne of steel produced)

1.72

Global Average Factor (65% BOF, 30% EAF, 5% OHF)* (tonne CO2 per tonne of steel produced)

Steel Production: Consensus of experts and IISI Environmental Performance Indicators 2003 STEEL (International Iron and Steel Institute, 2004) Steel Production: Consensus of experts and IISI Environmental Performance Indicators 2003 STEEL (International Iron and Steel Institute, 2004) Steel Production: Consensus of experts and IISI Environmental Performance Indicators 2003 STEEL (International Iron and Steel Institute, 2004)

1.06

Steel Production: Consensus of experts and IISI Environmental Performance Indicators 2003 STEEL (International Iron and Steel Institute, 2004)

* Factor based on 2003 international data where BOFs accounted for approximately 63 percent of world steel production and EAFs approximately 33 percent; OHF production accounted for the remaining 4 percent but is declining. ** The emission factor for EAF steelmaking does not include emissions from iron production. The emission factors for BOF and OHF steelmaking do include emissions from blast furnace iron production. Note that the CO2 emission factor for EAF steelmaking in this table is based on production of steel from scrap metal, and therefore the EAF emission factor does not account for any CO2 emissions from blast furnace iron making. The Tier 1 CO2 emission factor for EAFs in this table is therefore not applicable to EAFs that use pig iron as a raw material.

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Volume 3: Industrial Processes and Product Use

The emission factor for pellet production is based on the IPPC I&S BAT Document which provides an emission factor range of 15.6 to 31.8 kg CO2 per tonne product. However, the CO2 emission factor for a specific process will depend on the characteristic of the raw materials and fuels used in the process. The emission factor would vary depending upon whether coal, natural gas, or coke oven gas was used as the primary fuel. The ‘default’ emission factor provided is at the high end of the range, 30 kg CO2 per tonne product, and should be used if the inventory compiler does not know anything about the fuels or raw materials used. If the inventory compiler knows the inputs used, CO2 emissions should be calculated using the Tier 2 method, accounting for the fuel consumption, heating value and carbon content of the fuel. For the purposes of Tier 1 emission calculations, it is assumed that the default fuel for production of Direct Reduced Iron is natural gas. Natural gas-based processes account for the vast majority of installed direct reduced iron (DRI) production capacity worldwide, with 63 percent of that capacity being the MIDREX process. Fuel consumption for production of direct reduced iron using the MIDREX process is typically 10.5 - 14.5 GJ natural gas/metric tonne solid DRI assuming 100 percent lump iron ore operation. Fuel consumption for production of hot briquetted iron from iron fines was reported to be 12.5 GJ natural gas per tonne of product for the FINMET process and 14 GJ natural gas per tonne of product for the CIRCORED process. The default energy consumption of 12.5 GJ natural gas per tonne of DRI produced and the default carbon content of natural gas of 15.3 kg carbon per GJ natural gas correspond to a CO2 emission factor of 191.3 kg carbon per tonne DRI produced (0.7 tonnes CO2 per tonne DRI produced).

Methane emission factors Default CH4 emission factors are provided in Table 4.2 below. The Tier 1 CH4 emission factor for coke production is derived by averaging plant-specific CH4 emissions data for 11 European coke plants reported in the IPPC I&S BAT Document. Emissions of CH4 are reported in Table 6.2 and Table 6.3 of the IPPC I&S BAT Document in units of grams of CH4 per tonne of liquid steel produced. The CH4 emissions reported range from 27 to 32 grams CH4 per tonne liquid steel. Based on the conversion factors the average CH4 emissions from the 11 European coke plants is 0.1 grams CH4 per tonne coke produced. The Tier 1 CH4 emission factor for sinter production is derived by averaging plant-specific CH4 emissions data for European sinter plants reported in the EMEP/CORINAIR Emissions Inventory Guidebook (EEA, 2005) and in other emission inventory reports. Emissions of CH4 are reported in Table 8.2a of the EMEP/CORINAIR Emission Inventory Guidebook for sinter and palletising plants. For sinter plants using coke breeze an emission factor of 50 mg CH4 per MJ was reported and a range of coke input of 38 to 55 kg coke per tonne sinter was reported. This corresponds to an average emission factor of 0.07 kg CH4 per tonne sinter using the default value of 28.2 TJ/Gg coke. An emission factor of 0.05 kg CH4 per tonne sinter was reported for sinter plants operating in Finland. (Pipatti, 2001) TABLE 4.2 TIER 1 DEFAULT CH4 EMISSION FACTORS FOR COKE PRODUCTION AND IRON & STEEL PRODUCTION Process

Emission Factor

Coke Production

0.1 g per tonne of coke produced

Sinter Production

DRI Production

Source Coke Production: European IPPC Bureau (2001), Integrated Pollution Prevention and Control (IPPC) Best Available Techniques Reference Document on the Production of Iron and Steel, December 2001, Table 6.2-3, Page 122. http://eippcb.jrc.es/pages/FActivities.htm

0.07 kg per tonne of EMEP/CORINAIR Emission Inventory Guidebook (EEA, 2005). sinter produced Processes With Contact: Sinter and Pelletizing Plants: Sinter and Pelletizing Plants (Except Combustion 030301) Table 8.2a Emission factors for gaseous compounds 1 kg /TJ (on a net calorific basis)

Energy Volume default emission factor for CH4 Emissions from natural gas combustion. [See Table 2.3 of Volume 2, Chapter 2.]

TIER 2 METHOD The default carbon contents in Table 4.3 should be used if an inventory compiler does not have information on conditions in iron and steel-making facilities and coke production facilities, but has detailed activity data for the process materials and offsite transfers. The Tier 2 method, as described in Equation 4.2 for integrated coke production, Equations 4.9 to 4.11 for iron and steel production and Equation 4.3 for non-integrated coke production includes the major material flows in iron and steel-making and coke production that lead to emissions. Carbon contents in Table 4.3 are based on those provided in Table 1.2 and 1.3 in Volume 2, Chapter 1.

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TABLE 4.3 TIER 2 MATERIAL-SPECIFIC CARBON CONTENTS FOR IRON & STEEL AND COKE PRODUCTION (kg C/kg ) Process Materials

Carbon Content

Blast Furnace Gas

0.17

Charcoal*

0.91

1

0.67

Coal

Coal Tar

0.62

Coke

0.83

Coke Oven Gas

0.47

Coking Coal

0.73

Direct Reduced Iron (DRI)

0.02

Dolomite

0.13 2

EAF Carbon Electrodes 3

EAF Charge Carbon Fuel Oil

4

0.82 0.83 0.86

Gas Coke

0.83

Hot Briquetted Iron

0.02

Limestone

0.12

Natural Gas

0.73

Oxygen Steel Furnace Gas

0.35

Petroleum Coke

0.87

Purchased Pig Iron

0.04

Scrap Iron

0.04

Steel

0.01

Source: Default values are consistent with the those provided in Vol 2 and have been calculated with the assumptions below. Complete references for carbon content data are included in Table 1.2 and 1.3 in Volume 2, Chapter 1. Notes: 1

Assumed other bituminous coal

2

Assumed 80 percent petroleum coke and 20 percent coal tar

3

Assumed coke oven coke

4

Assumed gas/diesel fuel

* The amount of CO2 emissions from charcoal can be calculated by using this carbon content value, but it should be reported as zero in national greenhouse gas inventories. (See Section 1.2 of Volume 1.)

TIER 3 METHODS The Tier 3 method is based on aggregated plant-specific emission estimates or the application of the Tier 2 equations at a plant specific level. The inventory compiler should ensure that each facility has documented the emission factors and carbon contents used, and that these emission factors are indicative of the processes and materials used at the facility. The Tier 3 method requires carbon contents and production/consumption mass rates for all of the process materials and off-site transfers such as those listed in Table 4.3. While Table 4.3 provides default carbon contents, it is good practice under Tier 3 to adjust these values to reflect variations at the plant level from default values represented in the table. The default factors listed in Table 4.3 are only appropriate for the Tier 3 method if plant-specific information indicates that they correspond to actual conditions. It is anticipated that for the Tier 3 method the plant-specific data would include both carbon content data and production/consumption mass rate data, and that therefore the default values in Table 4.3 would not be applied to the Tier 3 method in most instances.

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4.2.2.4

C HOICE

OF ACTIVITY DATA

TIER 1 METHOD The Tier 1 method requires only the amount of steel produced in the country by process type, the total amount of pig iron produced that is not processed into steel, and the total amount of coke, direct reduced iron, pellets, and sinter produced; in this case the total amount of coke produced is assume to be produced in integrated coke production facilities. These data may be available from governmental agencies responsible for manufacturing statistics, business or industry trade associations, or individual iron and steel companies. If a country only has aggregate data available, a weighted factor should be used. Total crude steel production is defined as the total output of usable ingots, continuously-cast semi-finished products, and liquid steel for castings.

TIER 2 METHOD The Tier 2 method requires the total amount of iron and steel, coke oven gas, blast furnace gas, and process materials such as limestone used for iron and steel production, direct reduced iron production, and sinter production in the country, in addition to onsite and offsite production of coke. These data may be available from governmental agencies responsible for manufacturing or energy statistics, business or industry trade associations, or individual iron and steel companies. These amounts can then be multiplied by the appropriate default carbon contents in Table 4.3 and summed to determine total CO2 emission from the sector. However, activity data collected at the plant-level is preferred (Tier 3). If this is not a key category and data for total industry-wide reducing agents and process materials are not available, emissions can be estimated using the Tier 1 approach.

TIER 3 METHOD The Tier 3 method requires collection, compilation, and aggregation of facility-specific measured emissions data or facility-specific process material production/consumption mass data and carbon content data The Tier 3 method can be based on a plant-specific mass balance approach (for CO2 emissions) or on plant-specific direct emissions monitoring data (for both CO2 and CH4 emissions) . The Tier 3 method also may require activity data to be collected at the plant level and aggregated for the sectors. The plant-specific data should preferably be aggregated from data furnished by individual iron and steel and coke production companies. The amounts of process materials are more accurately determined in this manner. These data may also be available from governmental agencies responsible for manufacturing or energy statistics, or from business or industry trade associations. The appropriate amounts can then be multiplied by facility specific carbon content data and summed to determine total CO2 emissions from the sectors, and the total emissions will be more accurate than when using the Tier 2 method. This approach also allows for additional accuracy by allowing individual companies to provide more accurate plant-specific data and/or to use more relevant carbon contents that may differ from the default factors used in Tier 2 method.

4.2.2.5

C OMPLETENESS

RELATIONSHIP TO THE ENERGY SECTOR In estimating emissions from this source category: coke production (Energy) and iron and steel production (IPPU), there is a risk of double counting or omission in either the Industrial Processes or the Energy Sector. Since the primary use of carbon sources (predominantly coke, but also coal, oil, natural gas, limestone, etc.) is to produce pig iron, the CO2 and CH4 emissions from iron and steel production including sinter production are considered industrial process emissions and should be reported as such. The CO2 and CH4 emissions from coke production (both fuel consumption and conversion losses) are categorised as energy production and should be reported as such. However, for integrated production and iron and steel with onsite coke production, there may be flows of by-products (e.g., coke oven gas, blast furnace gas, coke oven by-products) between the coke production facility and the iron and steel production facility, creating potential double counting issues. Carbon consumed in the from of coke oven gas at an iron and steelmaking facility and the resulting CO2 and CH4 emissions would be categorized as IPPU emissions and reported as such. Carbon consumed in the form of blast furnace gas at an onsite coke production facility and the resulting CO2 and CH4 emissions would be categorized as Energy emissions and should reported as such Tracking of such carbon flows will require good knowledge of the inventory in that source category. Because of the dominant role of coke, it is important to consider the existence of coke making at a facility and define the boundary limits of a carbon balance at an iron and steelmaking facility to assure that CO2 emissions are not double-counted. CO2 and CH4 emissions associated with onsite and offsite coke making are to be reported under Energy Sector (see Volume 2).

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OTHER FORMS OF CARBON Although the dominant means of producing crude iron, or pig iron, is the blast furnace using coke, other forms of carbon (e.g., pulverized coal, coal derivatives, recycled plastics or tires, natural gas, or fuel oil) can also be used to substitute for some portion of the coke in the blast furnace. In these cases, these materials should be accounted for as process sources of carbon in the same manner as coke, and care should be taken to deduct these materials from any general energy statistics if they are included there. Iron can also be produced in other types of iron making vessels besides blast furnaces, often using natural gas or coal instead of coke, and these carbon sources should be accounted for in the same manner as coke because they are serving the same purpose. In most blast furnaces, the iron making process is aided by the use of carbonate fluxes (limestone or dolomite). Because these materials are necessary raw materials for the process, they should be accounted for as part of the iron and steelmaking inventory. Again, however, care should be taken not to double-count emissions associated with limestone and dolomite usage if accounted for separately in the minerals sector. (See Section 2.5, Other Process Uses of Carbonates, in this volume.)

SINTER Some integrated facilities also utilize sinter plants to convert iron-bearing fines into an agglomerate (or sinter) suitable for use as a raw material in the blast furnace. Typically, coke fines (or coke breeze) are used as a fuel in the sintering process and are a source of CO2 and CH4 emissions. If the coke fines are produced at a coke plant within the facility and the CO2 and CH4 emissions are accounted for in the coal entering the facility, or if the coke breeze is otherwise accounted for as purchased coke, the CO2 and CH4 emissions from coke used in sintering should not be double-counted. Emissions from sinter production are categorised as IPPU emissions and should be reported as such.

EXHAUST GASES It is important not to double count the use of blast-furnace-derived by-product gases such as blast furnace gas, or recovered BOF off-gas as energy in the energy sector as sources of CO2, if they have been accounted for as process emissions. Process emissions should include all carbon inputs in the blast furnace, used as the primary reductant. In a typical fully integrated coke and iron and steel plant situation, adjustments may need to be made for coke oven by-products and the carbon content of shipped steel, which should be clearly mentioned in the description of the sources. In some cases, it may also be necessary to make adjustments for blast furnace gas, or iron that may be sold or transferred offsite. The process flow of exhaust gases are clearly illustrated in Figures 4.1-4.5.

ELECTRODE CONSUMPTION Electrode consumption amounts to about 3.5 kg/tonne for EAF furnaces. However, depending upon the characteristics of the charged materials, some carbon may be added to the EAF (typically about 20 kg/tonne) for process control purposes or may be contained in the charged materials themselves as iron substitutes, an increasingly more frequent trend. In these cases, CO2 and CH4 emissions from these additional carbon-bearing materials should be considered process-related and accounted for in the inventory because their carbon content is not as likely to have been accounted for elsewhere in the inventory. In addition, if natural gas is used to enhance reactions in an EAF as reducing agent it should be accounted for as a carbon source as all process materials used in iron and steel manufacturing are reported as IPPU emissions. Some specialty steel production takes place in electric induction furnaces, in which case the charge is 100 percent steel scrap and where there are no carbon electrodes. There are no appreciable CO2 or CH4 emissions from this steelmaking process.

OHF PROCESS Although the OHF is no longer prevalent, it may be necessary to inventory CO2 and CH4 emissions from this steelmaking process in some countries. An open hearth furnace is typically charged with both molten iron and scrap as in the case of a BOF, and oxygen is injected into the furnace, but reduction of carbon in the iron and melting of the charge also takes place by firing fossil fuels (e.g., natural gas, fuel oil, coal or tar) across the surface of the raw material bath. Carbon in the iron may be ignored, as in the case of the BOF, because it has been accounted for as a source of carbon for iron-making. However, carbon in the fuels used in the open hearth process should be accounted for as IPPU emissions.

4.2.2.6

D EVELOPING

A CONSISTENT TIME SERIES

Emissions from coke production, sinter production, and iron and steel and production should be calculated using the same method for every year in the time series. Where data are unavailable to support a more rigorous method

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for all years in the time series, these gaps should be recalculated according to the guidance provided in Volume 1, Chapter 5, Time Series Consistency and Recalculation.

4.2.3

Uncertainty assessment

The default emission factors for coke production and iron and steel production used in Tier 1 may have an uncertainty of ± 25 percent. Tier 2 material-specific carbon contents would be expected to have an uncertainty of 10 percent. Tier 3 emission factors would be expected to be within 5 percent if plant-specific carbon content and mass rate data are available. Table 4.4 provides an overview of the uncertainties for emission factors, carbon contents and activity data.

TABLE 4.4 UNCERTAINTY RANGES Method

Data Source

Uncertainty Range

Tier 1

Default Emission Factors National Production Data

± 25% ± 10%

Tier 2

Material-Specific Default Carbon Contents National Reducing Agent & Process Materials Data

± 10% ± 10%

Tier 3

Company-Derived = Process Materials Data Company-Specific Measured CO2 and CH4 Data Company-Specific Emission Factors

± 5% ± 5% ± 5%

For Tier 1 the most important type of activity data is the amount of steel produced using each method. National statistics should be available and likely have an uncertainty of ± 10 percent. For Tier 2, the total amount of reducing agents and process materials used for iron and steel production would likely be within 10 percent. Tier 3 requires plant-specific information on the amounts of reducing agents and process materials (about 5 percent uncertainty). Also actual emissions data for Tier 3 would be expected to have ± 5 percent uncertainty. Tier 3 uncertainty may be more accurately derived based on an analysis of the actual data received.

4.2.4 4.2.4.1

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC)

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, Chapter 6, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventories agencies are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. In addition to the guidance in Volume 1, specific procedures of relevance to this source category are outlined below.

Review of emission factors and carbon contents Inventory compilers should compare aggregated national emission factors and carbon contents with the IPCC default factors carbon contents in order to determine if the national value is reasonable relative to the IPCC default. Differences between national default values should be explained and documented, particularly if they are representative of different circumstances.

Site-specific activity data check For site-specific data, inventory compilers should review inconsistencies between sites to establish whether they reflect errors, different measurement techniques, or result from real differences in emissions, operational conditions or technology. Inventory compilers should ensure that emission factors and activity data are developed in accordance with internationally recognised and proven measurement methods. If the measurement practices fail this criterion,

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then the use of these emissions or activity data should be carefully evaluated, uncertainty estimates reconsidered and qualifications documented. If there is a high standard of measurement and QA/QC is in place at most sites, then the uncertainty of the emissions estimates may be revised downwards.

Expert review Inventory compilers should include key industrial trade organisations associated with iron and steel production in a review process. This process should begin early in the inventory development process to provide input to the development and review of methods and data acquisition Third party reviews are also useful for this source category, particularly related to initial data collection, measurement work, transcription, calculation and documentation.

Activity data check For all tier levels, inventory compilers should check with Volume 2, Chapter 2 (Stationary Combustion of Energy Sector) to ensure that emissions from reducing agents and process materials (coal, coke, natural gas, etc.) are not double-counted or omitted. Inventory compilers should examine any inconsistency between data from different plants to establish whether these reflect errors, different measurement techniques or result from real differences in emissions, operational conditions or technology. This is particularly relevant to the plant-specific estimates of amounts of reducing agents or reported carbon content of process materials. Inventory compilers should compare aggregated plant-level estimates to industry totals for process materials consumption where such trade data are available.

4.2.4.2

R EPORTING

AND

D OCUMENTATION

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced.

TIER 1 METHOD Besides reporting of estimated emissions, it is good practice to report the total steel production by process and corresponding emission factors used and to report the amount of iron produced that is not processed into steel. In the corresponding table, it should be noted that reported emissions are only part of total emissions from the sector and that coke production emissions are categorized as energy emissions and are reported in Volume 2, Chapter 2, Stationary Combustion of Energy Sector.

TIER 2 METHODS Good practice is to document the estimated or calculated emissions, all activity data, and corresponding emission factors and any assumptions or data justifying alternative emission factors. There should be a clear explanation of the linkage with the source category 1A (Fuel Combustion) estimate for integrated coke production emissions to demonstrate that double counting or missing emissions have not occurred.

TIER 3 METHOD Good practice is to document the calculated emissions and source of all data, taking into account the need to protect the confidentiality of data for specific facilities if the data are business-sensitive or of a proprietary nature. In addition, inventory compilers should for all tiers, document all information needed to reproduce the estimate, as well as the QA/QC procedures.

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4.3

FERROALLOY PRODUCTION

4.3.1

Introduction

Ferroalloy is the term used to describe concentrated alloys of iron and one or more metals such as silicon, manganese, chromium, molybdenum, vanadium and tungsten. Silicon metal production is usually included in the ferroalloy group because silicon metal production process is quite similar to the ferrosilicon process. These alloys are used for deoxidising and altering the material properties of steel. Ferroalloy facilities manufacture concentrated compounds that are delivered to steel production plants to be incorporated in alloy steels. Silicon metal is used in aluminium alloys, for production of silicones and in electronics. Ferroalloy production involves a metallurgical reduction process that results in significant carbon dioxide emissions. In ferroalloy production, raw ore, carbon materials and slag forming materials are mixed and heated to high temperatures for reduction and smelting. The carbonaceous reductants are usually coal and coke, but bio-carbon (charcoal and wood) is also commonly used as a primary or secondary carbon source. Carbon dioxide (CO2) and methane (CH4) emissions from coke production are estimated in Section 4.2 and reported within the Energy Sector. Electric submerged arc furnaces with graphite electrodes or consumable Søderberg electrodes are used. The heat is produced by the electric arcs and by the resistance in the charge materials. The furnaces may be open, semi-covered or covered. A commonly used technology is the submerged-arc open-top electric furnace (EAF). In the EAF, passing current through graphite electrodes suspended in a cup-shaped, refractory-lined steel shell accomplishes heating. Carbon reduction of the metallic oxides occurs as both coke and graphite electrodes are consumed. The carbon in the electrodes captures the oxygen from the metal oxides to form CO, while the ores are reduced to molten base metals. The component metals then combine in the solution. In addition to emissions originating from reducing agents and electrodes, the calcination of carbonate fluxes such as limestone or dolomite, when used, contribute to the emission of greenhouse gases. Primary emissions in covered arc furnaces consist almost entirely of CO as opposed to CO2, due to the strong reducing environment. This CO is either utilised for energy production in boilers, or it is flared. The energy produced is assumed to be used internally at the site and the carbon content of the CO subsequently converted to CO2 in-plant. The CO gas produced in open or semi-closed furnaces is burnt to CO2 above the charge level. Any CO emitted to the atmosphere is assumed to be converted to CO2 within days afterwards. While CO2 is the main greenhouse gas from ferroalloy production, recent research has shown that CH4, and N2O account for an equivalent greenhouse emission of up to 5 percent of the CO2 emissions from ferrosilicon (FeSi) and silicon-metal (Si-metal) production. Methodologies are presented for CO2 and CH4 emissions in this section. These emissions should be better investigated for all ferroalloy production, and more measurements of these emissions should be done from FeSi and Si-metal production.

4.3.2 4.3.2.1

Methodological issues C HOICE

OF METHOD

METHODOLOGY FOR CO 2 The IPCC Guidelines outline several approaches for calculating CO2 emissions from ferroalloy production. For practical purposes, this section adopts a mass balance approach where all CO emitted is reported as emitted CO2. The choice of a good practice method depends on national circumstances as shown in the decision tree in Figure 4.9. The Tier 1 method calculates emissions from general emission factors applied to a country’s total ferroalloy production. The Tier 1 method is very simple, and it may lead to errors due to its reliance on assumptions rather than actual data. Therefore it is appropriate only when ferroalloy production is not a key category. The Tier 2 method calculates emissions from a known consumption of reducing agents, preferably from plant-specific consumption data, but alternatively from industry-wide data using emission factors similar to those used to estimate combustion emissions. The Tier 3 method is based on facility-specific emissions data.

Tier 1 method: production-based emission factors The simplest estimation method is to multiply default emission factors by ferroalloy product type as shown in Equation 4.15.

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EQUATION 4.15 CO2 EMISSIONS FOR FERROALLOY PRODUCTION BY THE TIER 1 METHOD E CO 2 = ∑ (MPi • EFi ) i

Where: ECO2 = CO2 emissions, tonnes MPi = production of ferroalloy type i, tonnes EFi = generic emission factor for ferroalloy type i, tonnes CO2/tonne specific ferroalloy product

Tier 2 method: production-based, raw material specific emission factors An alternate approach is to use emission factors for the reducing agents. For the other raw materials and products carbon contents should be considered. BOX 4.1 DEFINITIONS FOR WORDS/SYMBOLS USED IN EQUATIONS IN THIS SECTION

Content means weight-fraction in all equations ∑ means the sum of all i, h, j, k or l

EQUATION 4.16 CO2 EMISSIONS FOR FERROALLOY PRODUCTION BY TIER 2 METHOD

(

)

ECO 2 = ∑ M reducing agent , i • EFreducing agent , i + ∑ (M ore, h • CContent ore, h ) • i

(

+ ∑ M slag j

(

forming material , j

• CContent slag

)

− ∑ M product , k • CContent product , k • k

(

h

forming material , j

44 12

44 )• 12

)

− ∑ M non − product outgoing stream, l • CContent non − product outgoing stream, l • l

44 12

44 12

Where: ECO2 = CO2 emissions frm ferroalloy production, tonnes Mreducing agent, i = mass of reducing agent i, tonnes EFreducing agent, i = emission factor of reducing agent i, tonnes CO2/tonne reducing agent More, h = mass of ore h, tonnes CContentore, h = carbon content in ore h, tonnes C/tonne ore Mslag forming material, j = mass of slag forming material j, tonnes CContentslag forming material, j = carbon content in slag forming material j, tonnes C/tonne material Mproduct, k = mass of product k, tonnes CContentproduct, k = carbon content in product k, tonnes C/tonne product Mnon-product outgoing stream, l = mass of non-product outgoing stream l, tonnes CContentnon-product outgoing stream, l = carbon content in non-product outgoing stream l, tonnes C/tonne The constant 44/12 is the multiplication factor for the mass of CO2 emitted from each mass unit of total carbon used.

Tier 3 method: calculations based on amounts and analyses of reducing agents The producers use coal and coke with different contents of ash, fixed carbon and volatiles. Further, the amounts of carbon in carbonate ores and slag forming materials will vary. The most accurate method is therefore to

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calculate the CO2 emissions from the total amount of carbon in reducing agents, electrode paste, ores, slag forming materials and products, and this calculation is carried out for each ferroalloy produced.

EQUATION 4.17 CO2 EMISSIONS FOR FERROALLOY PRODUCTIION BY TIER 3 METHOD 44 ECO 2 = ∑ M reducing agent , i • CContent reducing agent , i • 12 i 44 + ∑ (M ore, h • CContentore, h ) • 12 h 44 + ∑ M slag forming material , j • CContent slag forming material , j • 12 j

(

)

(

(

)

− ∑ M product , k • CContent product , k • k

(

)

44 12

)

− ∑ M non − product outgoing stream, l • CContent non − product outgoing stream, l • l

44 12

Where: ECO2 = CO2 emissions frm ferroalloy production, tonnes Mreducing agent, i = mass of reducing agent i, tonnes CContentreducing agent, i = carbon content in reducing agent i, tonnes C/tonne reducing agent More, h = mass of ore h, tonnes CContentore, h = carbon content in ore h, tonnes C/tonne ore Mslag forming material, j = mass of slag forming material j, tonnes CContentslag forming material, j = carbon content in slag forming material j, tonnes C/tonne material Mproduct, k = mass of product k, tonnes CContentproduct, k = carbon content in product k, tonnes C/tonne product Mnon-product outgoing stream, l = mass of non-product outgoing stream l, tonnes CContentnon-product outgoing stream, l = carbon content in non-product outgoing stream l, tonnes C/tonne The constant 44/12 is the multiplication factor for the mass of CO2 emitted from each mass unit of total carbon used. The calculation will have good accuracy if analyses of total carbon in all reducing agents are available.

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Figure 4.9

Decision tree for estimation of CO 2 emissions from ferroalloy production Start

Are all data relating to the reducing agent and output streams available?

Yes

Calculate emissions using plant specific data. Box 3: Tier 3

No

Are national data available by process materials?

Yes

Calculate emissions using reducing agent specific emission factors. Box 2: Tier 2

No

Is this a key category1?

No

Calculate emissions using default emission factors and national production data. Box 1: Tier 1

Yes Collect data for the Tier 3 or the Tier 2 method. Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

METHODOLOGY FOR CH 4 AND DISCUSSION OF N 2 O EMISSIONS The heating of carbon materials in the furnace releases volatiles including methane. With open or semi-covered furnaces – predominantly used for FeSi and Si ferroalloy production - most of the volatiles will burn to CO2 above the charge, in the hood and off-gas channels, but some will remain un-reacted as CH4 and NMVOC. The amounts depend on the operation of the furnace. Sprinkle-charging will reduce the amounts of CH4 compared to batch-wise charging. Increased temperature in the hood (less false air) will reduce the content of CH4 further. The IPCC Guidelines outline several approaches for calculating CH4 emissions from FeSi- and Si- ferroalloy production. The choice of a good practice method depends on national circumstances as shown in the decision tree in Figure 4.10. The Tier 1 method calculates emissions from general emission factors applied to a country’s total ferroalloy production. The Tier 1 method is very simple, and it may lead to errors due to its reliance on assumptions rather than actual data. Therefore it should only be used when ferroalloy production is not a key category. The Tier 2 method calculates emissions from operation-specific emission factors. The Tier 3 method uses facility-specific emissions data. The errors associated with estimates or measurements of N2O emissions from the ferroalloys industry are very large and thus, a methodology is not provided.

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Figure 4.10

Decision tree for estimation of CH 4 emissions from FeSi and Si alloy production Start

Are emissions data available on a facility level?

Yes

Aggregate facilityspecific measured emissions data as basis for the Tier 3 method. Box 3: Tier 3

No

Are countryspecific FeSi and Si alloy process operations known ?

Yes

Calculate emissions using operation-specific emission factors. Box 2: Tier 2

No

Is this a key category1?

No

Multiply data by default emission factors. Box 1: Tier 1

Yes Collect data for the Tier 3 or the Tier 2 method. Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

Tier 1 method: FeSi and Si alloy production-based emission factors The simplest estimation method is to multiply default emission factors by Si-alloy product type. Total emissions are calculated according to: EQUATION 4.18 CH4 EMISSIONS FOR FERROALLOY PRODUCTION BY THE TIER 1 METHOD E CH 4 = ∑ (MPi • EFi ) i

Where: ECH4 = CH4 emissions, kg MPi = production of Si-alloy i, tonnes EFi = generic emission factor for Si-alloy i, kg CH4/ tonne specific Si-alloy product

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Tier 2 method: FeSi and Si alloy production-based, operation specific emission factors The Tier 2 method is also based on emission factors but unlike the Tier 1 method, these are operation specific.

Tier 3 method: Direct measurements Inventory compilers are strongly encouraged to measure CH4 emissions where emissions from ferroalloys industry is a key category.

4.3.2.2

C HOICE

OF EMISSION FACTORS

EMISSION FACTORS FOR CO 2 Tier 1 method: production-based emission factors When the only data available are national ferroalloy production statistics, it is good practice to use default emission factors. However, because of widely disparate factors depending on the type of ferroalloy production, it is necessary to determine how much tonnage is produced by which method and then to sum the product of the factors shown in Table 4.5 and the appropriate production tonnages. These factors are based on expert judgement using typical practice for the ferroalloy production scenarios listed. If any bio-carbon, except some woodchips for FeSi and Si-metal production, is used, the factors cannot be employed.

TABLE 4.5 GENERIC CO2 EMISSION FACTORS FOR FERROALLOY PRODUCTION (tonnes CO2/tonne product) Type of Ferroalloy

Emission Factor

Ferrosilicon 45% Si

2.5

Ferrosilicon 65 % Si

3.6

Ferrosillicon 75% Si

4.0

Ferrosillicon 90% Si

4.8

Ferromanganeses (7% C)

1.3

Ferromanganeses (1% C)

1.5

Silicomanganese

1.4

Silicon metal

5.0

Ferrochromium

1.3 (1.6 with sinter plant)

Source: IPCC (1997), IPCC (2000), Olsen (2004) and Lindstad (2004)

These default emission factors have been assessed by Olsen (2004) for the manganese alloys, Lindstad (2004) for the silicon alloys and by Olsen, Monsen and Lindstad (1998) for FeCr. For FeMn alloys the emission factors are based on production where the Mn containing raw materials are a mixture of oxide ores, carbonate ores and imported Mn-sinter. If the sinter is produced abroad it will not give any contribution to the national greenhouse gas inventory. Emission from sinter production must be reported where the production is located. The factor for FeSi90 and Si-metal is based on a Fix C consumption of 110 percent of the stoichiometric amount needed for reduction of SiO2. For the other FeSi-alloys the factor is based on 114 percent of the stoichiometric amount of Fix C.

Tier 2 method: production-based, raw material specific emission factors The emission factors for the reducing agents used in production of manganese and silicon alloys are given in Table 4.6. The factors have been assessed by Olsen (2004) for use in manganese alloys production and by Lindstad (2004) for use in silicon alloys.

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TABLE 4.6 CO2 EMISSION FACTORS FOR FERROALLOY PRODUCTION (tonnes CO2/tonne reducing agent) Reducing agent (usage)

Emission Factor

Coal (for FeSi and Si-metal)

3.1

Coal (for other ferroalloys)

* (See below)

Coke (for FeMn and SiMn)

3.2-3.3

Coke (for Si and FeSi)

3.3-3.4

Coke (for other ferroalloys)

* (See below)

Prebaked electrodes

3.54

Electrode paste

3.4

Petroleum coke

3.5

*: Inventory compilers are encouraged to use producer-specific values based on average blend of coal and/or coke for each ferroalloy producer. Source: Olsen (2004), Lindstad (2004)

Tier 3 method: calculations based on amounts and analyses of reducing agents For the Tier 3 method, it is necessary to determine the carbon contents of the reducing agents used in the production processes. But most ferroalloys producers analyse only on the basis of percentage of ash and volatiles, and calculate: Fix C % = 100 % – % Ash – % Volatiles. In that case, the total C-contents of reducing agents. is calculated by the following equation. EQUATION 4.19 CARBON CONTENTS OF FERROALLOY REDUCTING AGENTS CContent reducing agent ,i = FFixC ,i + Fvolatiles ,i • C v

Where: CContentreducing agent, i = carbon content in reducing agent i, tonnes C/tonne reducing agent FFixC,i = mass fraction of Fix C in reducing agent i, tonnes C/ tonne reducing agent Fvolatiles,i = mass fraction of volatiles in reducing agent i, tonnes volatiles/ tonne reducing agent Cv = carbon content in volatiles, tonnes C/tonne volatiles (Unless other information is available, Cv = 0.65 is used for coal and 0.80 for coke.)

EMISSION FACTORS FOR CH 4 Tier 1 method: FeSi and Si alloy production-based emission factors When the only data available are national ferroalloy production statistics, it is good practice to use default emission factors. However, because of the disparate factors depending on the type of ferroalloy production, it is necessary to determine how much tonnage is produced by which method and then to sum the product of the factors shown in Table 4.7 and the appropriate production tonnages. The default emission factors for CH4 is based on the averages of a small number of operation-specific measurements (shown in Table 4.7 for Tier 2) carried out by SINTEF and DNV mainly in 1995 and 1998 (FFF (2000)).

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TABLE 4.7 DEFAULT EMISSION FACTORS FOR CH4 (kg CH4/tonne product) Emission

Alloy

Emission Factor

CH4

Si-metal

1.2

FeSi 90

1.1

FeSi 75

1.0

FeSi 65

1.0

Source: FFF (2000)

Tier 2 method: FeSi and Si alloy production-based, operation specific emission factors The Tier 2 method is also based on emission factors but unlike the Tier 1 method, these factors are operation specific. The procedure is otherwise the same as that outlined in Equation 4.18, using values in Table 4.8. TABLE 4.8 EMISSION FACTORS FOR CH4 (kg CH4/tonne product) Emission

Alloy

Operation of Furnace Batch-charging

CH4

Sprinkle-charging 1)

Sprinkle-charging and >7500C 2)

Si-metal

1.5

1.2

0.7

FeSi 90

1.4

1.1

0.6

FeSi 75

1.3

1.0

0.5

FeSi 65

1.3

1.0

0.5

1

Sprinkle-charging is charging intermittently every minute.

2

Temperature in off-gas channel measured where the thermocouple cannot ‘see’ the combustion in the furnace hood.

Tier 3 method: Direct measurements Tier 3 is based on measurements rather than emission factors. The inventory compiler should consult guidance on plant-level measurements outlined in Volume 1, Chapter 2, and on QA/QC of measurements in Volume 1, Chapter 6.

4.3.2.3

C HOICE

OF ACTIVITY DATA

TIER 1 METHOD The Tier 1 method requires only the amount of ferroalloy produced in the country by product type. These data may be available from governmental agencies responsible for manufacturing statistics, business or industry trade associations, or individual ferroalloy companies. These tonnages can then be multiplied by the corresponding emission factors in Table 4.5 to estimate CO2 emissions from the sector and Table 4.7 to estimate CH4 emissions from the sector.

TIER 2 METHOD The Tier 2 method requires the total amount of reducing agent and other process materials used for ferroalloy production in the country, and knowledge of processes used. These data may be available from governmental agencies responsible for manufacturing or energy statistics, business or industry trade associations, or individual ferroalloy companies. These amounts can then be multiplied by the appropriate generic emission factors in Tables 4.6 and 4.8 and summed to determine total CO2 and CH4 emissions from the sector. However, activity data collected at the plant-level is preferred.

TIER 3 METHOD The Tier 3 method requires collection, compilation, and aggregation of facility-specific emissions data. These data may be available directly from companies.

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4.3.2.4

C OMPLETENESS

In estimating emissions from this source category, there is a risk of double-counting or omission in either the Industrial Processes or the Energy Sector. Since the primary use of carbon sources (coal, coke, limestone, dolomite etc.) is to produce ferroalloys, the emissions are considered to be industrial process emissions and should be reported as such. It should be noted that the risk of double counting is particularly high for the Tier 1 approach. Any deviation from reporting emissions as originating from an industrial process should be explicitly mentioned in the inventory, and a double-counting/completeness check should be performed.

4.3.2.5

D EVELOPING

A CONSISTENT TIME SERIES

Emissions from ferroalloy production should be calculated using the same method for every year in the time series. Where data are unavailable to support a Tier 3 method for all years in the time series, these gaps should be recalculated according to the guidance provided in Volume 1, Chapter 5.

4.3.3

Uncertainty assessment

Uncertainties for ferroalloy production result predominantly from uncertainties associated with activity data, and to a lesser extent from uncertainty related to the emission factor. Although some ferroalloys may be produced using wood or other biomass as a carbon source, information and data regarding these practices were not available. Emissions from ferroalloys produced with wood or other biomass would not be counted under this source because wood-based carbon is of biogenic origin. Emissions from ferroalloys produced with coking coal or graphite inputs would be counted in national trends, but may generate differing amounts of CO2 per unit of ferroalloy produced compared to the use of petroleum coke.

4.3.3.1

E MISSION

FACTOR UNCERTAINTIES

For Tier 3, actual emissions data would be expected to have less than 5 percent uncertainty. For Tier 2, the material-specific emission factors would be expected to be within 10 percent, which would provide less uncertainty overall than for Tier 1. Emission factors would be expected to be within 10 percent or less than 5 percent if plant-specific carbon content data are available. The default emission factors used in Tier 1 may have an uncertainty of 25 to 50 percent.

4.3.3.2

A CTIVITY

DATA UNCERTAINTIES

For Tier 1 the most important type of activity data is the amount of ferroalloy production by product type. National statistics should be available and likely have an uncertainty less than 5 percent. Tier 2 applied with plant-specific information on the amounts of reducing agents and process materials as applied in Tier 2 method should not exceed 5 percent uncertainty. TABLE 4.9 UNCERTAINTY RANGES Method

Data Source

Tier 1

National Production Data Default Emission Factors

< 5% < 25 %

Tier 2

Company-Derived Reducing Agent & Process Materials National Reducing Agent & Process Materials Data Company-Specific Emission Factors Material-Specific Default Emission Factors

< 5% < 5% < 5% < 10%

Tier 3

Company-Specific Measured CO2 Data

< 5%

4.40

Unertainty Range

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Metal Industry Emissions

4.3.4 4.3.4.1

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC)

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, Chapter 6, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventories agencies are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. In addition to the guidance in Volume 1, specific procedures of relevance to this source category are outlined below.

Review of emission factors Inventory compilers should compare aggregated national emission factors with the IPCC default factors in order to determine if the national factor is reasonable relative to the IPCC default. Differences between national factors and default factors should be explained and documented, particularly if they are representative of different circumstances.

Site-specific activity data check For site-specific data, inventory compilers should review inconsistencies between sites to establish whether they reflect errors, different measurement techniques, or result from real differences in emissions, operational conditions or technology. For ferroalloy production, inventory compilers should compare plant data with other plants. Inventory compilers should ensure that emission factors and activity data are developed in accordance with internationally recognised and proven measurement methods. If the measurement practices fail this criterion, then the use of these emissions or activity data should be carefully evaluated, uncertainty estimates reconsidered and qualifications documented. If there is a high standard of measurement and QA/QC is in place at most sites, then the uncertainty of the emissions estimates may be revised downwards.

Expert review Inventory compilers should include key industrial trade organisations associated with ferroalloy production in a review process. This process should begin early in the inventory development process to provide input to the development and review of methods and data acquisition Third party reviews are also useful for this source category, particularly related to initial data collection, measurement work, transcription, calculation and documentation.

Activity data check For all tier levels, inventory compilers should check with Volume 2, Chapter 2, Stationary Combustion of Energy Sector, to ensure that emissions from reducing agents and process materials (coal, coke, natural gas, etc.) are not double-counted or omitted. Inventory compilers should examine any inconsistency between data from different plants to establish whether these reflect errors, different measurement techniques or result from real differences in emissions, operational conditions or technology. This is particularly relevant to the plant-specific estimates of amounts of reducing agents or reported carbon content of process materials. Inventory compilers should compare aggregated plant-level estimates to industry totals for process materials consumption where such trade data are available.

4.3.4.2

R EPORTING

AND

D OCUMENTATION

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced.

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TIER 1 METHOD Besides reporting of estimated emissions, it is good practice to report the total ferroalloy production by process and corresponding emission factors used. In the corresponding table, it should be noted that reported emissions are only part of total emissions from the sector and the rest are reported elsewhere Volume 2, Chapter 2, Stationary Combustion of Energy Sector.

TIER 2 METHODS Good practice is to document the estimated or calculated emissions, all activity data, and corresponding emission factors and any assumptions or data justifying alternative emission factors. There should be a clear explanation of the linkage with the Fuel Combustion Sub-Sector estimate to demonstrate that double counting or missing emissions have not occurred.

TIER 3 METHOD Good practice is to document the calculated emissions and source of all data, taking into account the need to protect the confidentiality of data for specific facilities if the data are business-sensitive or of a proprietary nature. In addition, inventory compilers should for all tiers, document all information needed to reproduce the estimate, as well as the QA/QC procedures.

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4.4

PRIMARY ALUMINIUM PRODUCTION

4.4.1

Introduction

This section covers process emissions from primary aluminium production2. Worldwide, primary aluminium is produced exclusively by the Hall-Heroult electrolytic process. In this process, electrolytic reduction cells differ in the form and configuration of the carbon anode and alumina feed system and belong to one of four technology types: Centre-Worked Prebake (CWPB)3, Side-Worked Prebake (SWPB), Horizontal Stud Søderberg (HSS) and Vertical Stud Søderberg (VSS). The most significant process emissions are: (i) Carbon dioxide (CO2) emissions from the consumption of carbon anodes in the reaction to convert aluminium oxide to aluminium metal; (ii) Perfluorocarbons (PFCs) emissions of CF4 and C2F6 during anode effects. Also emitted are smaller amounts of process emissions, CO, SO2, and NMVOC. SF6 is not emitted during the electrolytic process and is only rarely used in the aluminium manufacturing process, where small quantities are emitted when fluxing specialized high magnesium aluminium alloys4. The decision trees in Figures 4.11 and 4.12 provide guidance for selecting a methodology estimating emissions from aluminium production. All inventory compilers in countries with aluminium production should be able to implement at a minimum level the Tier 1 method and thereby ensure completeness of reporting. Although this chapter presents default emission factors for both CO2 and PFC emissions, countries should make every effort to use higher Tier methods because emission rates can vary greatly, and the uncertainty associated with Tier 1 factors is very high. Aluminium smelters routinely collect the process data needed for calculation of Tier 2 emissions factors.

4.4.2 4.4.2.1

Methodological issues C HOICE

OF METHOD FOR CO 2 EMISSIONS FROM PRIMARY ALUMINIUM PRODUCTION

During normal operations, aluminium is produced at the cathode and carbon is consumed at the anode per the electrolytic reduction reaction: 2Al2O3 + 3C Æ 4Al + 3CO2

Most carbon dioxide emissions result from the electrolysis reaction of the carbon anode with alumina (Al2O3). The consumption of prebaked carbon anodes and Søderberg paste is the principal source of process related carbon dioxide emissions from primary aluminium production. Other sources of process related carbon dioxide emissions associated with Prebake anode baking account for less than 10 percent of the total non-energy related carbon dioxide emissions. The reactions leading to carbon dioxide emissions are well understood and the emissions are very directly connected to the tonnes of aluminium produced through the fundamental electrochemical equations for alumina reduction at a carbon anode and oxidation from thermal processes. Both of these fundamental processes

2

Emissions from the combustion of fossil fuels associated with primary aluminium production, bauxite mining, bauxite ore refining, and aluminium production from recycled sources are covered in Volume 2: Energy. Also, carbon dioxide emissions associated with production of electricity from fossil fuel combustion to produce aluminium are also covered in Volume 2.

3

Including Point Feed Prebake and Bar Broken Prebake cells.

4

A 2004 IAI survey found no evidence of SF6 being emitted from primary aluminium smelting through the Hall-Heroult electrolytic production process.

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producing carbon dioxide are included in process parameters routinely monitored at the production facilities, the net anode carbon consumed for Prebake facilities, or anode paste consumption for Søderberg facilities. For the CO2 emissions calculation, production data require technology differentiation as Søderberg or Prebake.There is no need for further differentiation as to the specific type of Søderberg or Prebake technology. The decision tree shown in Figures 4.11 describes good practice in choosing the CO2 inventory methodology appropriate for national circumstances.

Figure 4.11

Decision tree for calculation of CO 2 emissions from primary aluminium production Start

Are data available for anode or paste consumption?

Yes

Is facility specific anode or paste composition available1?

Yes

Calculate CO2 emissions using Tier 3. Box 3: Tier 3

No

No

Calculate CO2 emissions using Tier 2. Box 2: Tier 2

Is this a key category2?

No

Are production data available by technology3?

Yes

No

Collect process data.

Estimate annual production by technology.

Yes

Calculate CO2 emissions using Tier 1. Box 1: Tier 1

Note: 1. See International Aluminium Institute, The Aluminium Sector Greenhouse Gas Protocol, 2005. 2. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 3. For CO2 emissions calculation, the production data requires technology differentiation as Søderberg or Prebake. There is no need for further differentiation as to the specific type of Søderberg or Prebake technology.

Tier 1 method for CO 2 emissions The Tier 1 method for calculating CO2 emissions uses only broad cell technology characterizations (Prebake or Søderberg) as a lower order estimate of CO2 emissions from aluminium production. Given the uncertainty associated with the Tier 1 method, it is good practice to use higher tier methods if CO2 from primary aluminium is a key category. Total CO2 emissions are calculated according to Equation 4.20.

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EQUATION 4.20 PROCESS CO2 EMISSIONS FROM ANODE AND/OR PASTE CONSUMPTION (TIER 1 METHOD) E CO 2 = EFP • MPP + EFS • MPS

Where: ECO2 = CO2 emissions from anode and/or paste consumption, tonnes CO2 EFP = Prebake technology specific emission factor (tonnes CO2/tonne aluminium produced) MPP = metal production from Prebake process (tonnes Al) EFS = Søderberg technology specific emission factor (tonnes CO2/tonne aluminium produced) MPS = metal production from Søderberg process (tonnes Al)

Tier 2 or Tier 3 methods for CO 2 emissions For both the Prebake and Søderberg processes CO2 emissions are calculated using a mass balance approach that assumes that the carbon content of net anode consumption or paste consumption is ultimately emitted as CO2. The Tier 2 methods for both Prebake and Søderberg processes make use of typical industry values for impurities while the Tier 3 methods uses actual concentrations of impurities. The choice of method between the Tier 2 and Tier 3 method will depend on whether anode or paste composition data are available at the individual plant level.

CO 2 emissions for Prebake cells (CWPB and SWPB): The CO2 emissions for the Tier 2 and the Tier 3 method for Prebake cells are calculated according to Equation 4.21. Tier 3 requires specific operating facility data for all the components in Equation 4.21, whereas Tier 2 is based on default values for some of the components. Section 4.4.2.2 below provides more details on using these parameters. EQUATION 4.21 CO2 EMISSIONS FROM PREBAKED ANODE CONSUMPTION (TIER 2 AND TIER 3 METHODS) 100 − S a − Asha 44 • ECO 2 = NAC • MP • 100 12

Where: ECO2 = CO2 emissions from prebaked anode consumption, tonnes CO2 MP = total metal production, tonnes Al NAC = net prebaked anode consumption per tonne of aluminium, tonnes C/ tonne Al Sa = sulphur content in baked anodes, wt % Asha = ash content in baked anodes, wt % 44/12 = CO2 molecular mass: carbon atomic mass ratio, dimensionless Equation 4.21 should be applied to each Prebake smelter in the country and the results summed to arrive at total national emissions. It is possible to use a hybrid Tier 2/3 approach if data on ash or sulphur content are not available for each smelter. Emissions from the combustion of fossil fuels used in the production of baked anodes are covered in Volume 2: Energy. However, two other sources of CO2 emissions are associated with anode baking furnaces – the combustion of volatile matter released during the baking operation and the combustion of baking furnace packing material (coke). Equations 4.22 and 4.23 can be used for the calculation of such emissions.5

5

For additional information on the application of these equations to estimate emissions from combustion of volatile matter, see the IAI Greenhouse Gas Protocol (IAI, 2005a).

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EQUATION 4.22 CO2 EMISSIONS FROM PITCH VOLATILES COMBUSTION (TIER 2 AND TIER 3 METHODS) 44 ECO 2 = (GA − H w − BA − WT ) • 12

Where: ECO2 = CO2 emissions from pitch volatiles combustion, tonnes CO2 GA = initial weight of green anodes, tonnes Hw = hydrogen content in green anodes, tonnes BA = baked anode production, tonnes WT = waste tar collected, tonnes

EQUATION 4.23 CO2 EMISSIONS FROM BAKE FURNACE PACKING MATERIAL (TIER 2 AND TIER 3 METHODS) 100 − S pc − Ash pc 44 • ECO 2 = PCC • BA • 100 12

Where: ECO2 = CO2 emissions from bake furnace packing material, tonnes CO2 PCC = packing coke consumption, tonnes/tonne BA BA = baked anode production, tonnes Spc = sulphur content in packing coke, wt % Ashpc = ash content in packing coke, wt %

CO 2 emissions for Søderberg cells (VSS and HSS): The CO2 emissions for the Tier 2 and the Tier 3 method for Søderberg cells are calculated according to Equation 4.24. Tier 3 requires specific operating facility data for all the components in Equation 4.24, whereas Tier 2 is based on default values for some of the components. Section 4.4.2.2 below provides details on parameters to be used:. EQUATION 4.246 CO2 EMISSIONS FROM PASTE CONSUMPTION (TIER 2 AND TIER 3 METHODS) S p + Ash p + H p ⎛ CSM • MP BC − • PC • MP • ECO 2 = ⎜⎜ PC • MP − 1000 100 100 ⎝ S + Ashc 100 − BC ⎞ 44 − • PC • MP • c − MP • CD ⎟ • 100 100 ⎠ 12

Where: ECO2 = CO2 emissions from paste consumption, tonnes CO2 MP = total metal production, tonnes Al PC = paste consumption, tonnes/tonne Al CSM = emissions of cyclohexane soluble matter, kg/tonne Al BC = binder content in paste, wt % Sp = sulphur content in pitch, wt % 6

An acceptable alternative method is to use the parameter of 'pitch coking' in lieu of deducting measured or default values for Sp, Hp, Ashp and CSM from Equation 4.24. The pitch coking value is a commonly determined parameter for many facilities with Søderberg cells and standard methodology for performing the pitch coking test is described in ASTM D2416.

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Ashp = ash content in pitch, wt % Hp = hydrogen content in pitch, wt % Sc = sulphur content in calcined coke, wt % Ashc = ash content in calcined coke, wt % CD = carbon in skimmed dust from Søderberg cells, tonnes C/tonne Al 44/12 = CO2 molecular mass : carbon atomic mass ratio, dimensionless Equation 4.24 should be applied to each smelter in the country using the Søderberg process and the results summed to arrive at total national emissions. It is possible to use a hybrid Tier 2/3 approach if data on ash or sulphur content are not available for each smelter.

4.4.2.2

C HOICE

OF EMISSION FACTORS FOR PRIMARY ALUMINIUM PRODUCTION

CO 2

EMISSIONS FROM

Tier 1 method for CO 2 emissions Table 4.10 lists the default emission factors for CO2 per tonne of aluminium. The emission factors of 1.6 and 1.7 for Prebake and Søderberg technologies are based on International Aluminium Institute (IAI) global survey data (International Aluminium Institute, Life Cycle Assessment of Aluminium , 2000).

TABLE 4.10 TIER 1 TECHNOLOGY SPECIFIC EMISSION FACTORS FOR CALCULATING CARBON DIOXIDE EMISSIONS FROM ANODE OR PASTE CONSUMPTION

Technology

Emission Factor (tonnes CO2/tonne Al)

Uncertainty (+/-%)

Prebake7

1.6

10

Søderberg

1.7

10

Source: International Aluminium Institute, Life Cycle Assessment of Aluminium (IAI, 2000).

Tier 2 and Tier 3 emission factors for CO 2 emissions CO 2 emissions for Prebake cells (CWPB and SWPB): The most significant factors in Equation 4.21 are metal production and net anode consumption for Prebake technology. Both these parameters should be collected from individual operating facilities for use with the Tier 2 or the Tier 3. Other terms in the equation make minor adjustments for non-carbon components of the anodes (for example, sulphur and ash) and thus are not as critical. Tier 3 is based on the use of specific operating facility data for these minor components, whereas Tier 2 is based on default values listed in Tables 4.11 to 4.13. Tier 3 improves the accuracy of the results, but the improvement in accuracy is not expected to exceed 5 percent. Carbon consumed per tonne of aluminium produced is typically recorded by primary aluminium production facilities given its economic significance. Facilities using prebake cells refer to this consumption as ‘net anode or net carbon consumption,’ and those using Søderberg cells refer to it as ‘anode paste consumption.’

7

The emission factor for Prebake cells includes CO2 emissions from the combustion of pitch volatiles and packing coke from baking anodes.

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TABLE 4.11 DATA SOURCES AND UNCERTAINTIES FOR PARAMETERS USED IN TIER 2 OR 3 METHOD FOR CO2 EMISSIONS FROM PREBAKE CELLS (CWPB AND SWPB) , SEE EQUATION 4.21 Parameter

Tier 2 Method Data Source

Tier 3 Method Uncertainty (+/-%)

Data Source

Uncertainty (+/-%)

MP: total metal production (tonnes aluminium per year)

Individual facility records

2

Individual facility records

2

NAC: net anode consumption per tonne of aluminium (tonnes per tonne Al)

Individual facility records

5

Individual facility records

5

Use industry typical value, 2

50

Individual facility records

10

Use industry typical value, 0.4

85

Individual facility records

10

Sa: sulphur content in baked anodes (wt %) Asha: ash content in baked anodes (wt %) Source: IAI (2005b).

TABLE 4.12 DATA SOURCES AND UNCERTAINTIES FOR PARAMETERS USED IN TIER 2 OR 3 METHOD FOR CO2 EMISSIONS FROM PITCH VOLATILES COMBUSTION (CWPB AND SWPB) , SEE EQUATION 4.22 Parameter

Tier 2 Method Data Source

Tier 3 Method Uncertainty (+/-%)

Data Source

Uncertainty (+/-%)

GA: initial weight of green anodes processed (tonnes green anode per year)

Individual facility records

2

Individual facility records

2

Hw: Hydrogen content in green anodes (tonnes)

Use industry typical value, 0.005 • GA

50

Individual facility records

10

Individual facility records

2

Individual facility records

2

Use industry typical value, a) 0.005 • GA b) insignificant

50

Individual facility records

20

BA: Baked anode production (tonnes per year) WT: Waste tar collected (tonnes) a) Riedhammer furnaces b) All other furnaces Source: IAI (2005b).

TABLE 4.13 DATA SOURCES AND UNCERTAINTIES FOR PARAMETERS USED IN TIER 2 OR 3 METHOD FOR CO2 EMISSIONS FROM BAKE FURNACE PACKING MATERIAL (CWPB AND SWPB) , SEE EQUATION 4.23 Parameter

Tier 2 Method Data Source

Tier 3 Method Uncertainty (+/-%)

Data Source

Uncertainty (+/-%)

Use industry typical value, 0.015

25

Individual facility records

2

BA: Baked anode production (tonnes per year)

Individual facility records

2

Individual facility records

2

Spc: Sulphur content in packing coke (wt %)

Use industry typical value, 2

50

Individual facility records

10

Use industry typical value, 2.5

95

Individual facility records

10

PCC: Packing coke consumption (tonnes per tonne BA)

Ashpc: Ash content in packing coke (wt %) Source: IAI (2005b).

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CO 2 emissions for Søderberg cells (VSS and HSS): The binder content in paste, BC, typically varies by less than 1 percent and is part of operation practice by facility. It is an important term in Equation 4.24 because the carbon content of the pitch, which acts as a binder, is lower than that of the coke, which makes up the remainder of the paste. As was noted previously for Prebake anode consumption, the most important components of Equation 4.24 are the metal production and paste consumption. The other terms in Equation 4.24 make small corrections based on impurities and minor differences in carbon content of paste materials. Tier 3 is based on the use of specific operating facility data for these minor components, whereas Tier 2 is based on default values listed in Table 4.14. Tier 3 improves the accuracy of the results; however, the impact can be expected to be less than 5 percent on the result.

TABLE 4.14 DATA SOURCES AND UNCERTAINTIES FOR PARAMETERS USED IN TIER 2 OR 3 METHOD FOR CO2 EMISSIONS FROM SØDERBERG CELLS (VSS AND HSS) Parameter

Tier 2 Method Data Source

MP: total metal production (tonnes Al/year)

PC : paste consumption (tonnes per tonne Al)

CSM: emissions of cyclohexane soluble matter (kg per tonne Al)

BC: binder content in paste (wt %) Sp: sulphur content in pitch (wt %) Ashp: ash content in pitch (wt %) Hp: hydrogen content in pitch (wt %) Sc: sulphur content in calcined coke (wt %) Ashc: ash content in calcined coke (wt %) CD: carbon in dust from anode (tonnes of carbon in skim per tonne Al)

4.4.2.3

C HOICE

Tier 3 Method Data Uncertainty (+/-%)

Data Source

Data Uncertainty (+/-%)

Individual facility records

2

Individual facility records

2

Individual facility records

2-5

Individual facility records

2-5

30

Individual facility records

15

25

Individual facility records

5

20

Individual facility records

10

20

Individual facility records

10

50

Individual facility records

10

20

Individual facility records

10

50

Individual facility records

10

99

Individual facility records

30

Use industry typical value, HSS – 4.0 VSS – 0.5 Use industry typical value, Dry Paste – 24 Wet Paste – 27 Use industry typical value, 0.6 Use industry typical value, 0.2 Use industry typical value, 3.3 Use industry typical value, 1.9 Use industry typical value, 0.2 Use industry typical value, 0.01

OF METHOD FOR

PFC S

During electrolysis, alumina (Al2O3) is dissolved in a fluoride melt comprising about 80 weight percent cryolite (Na3AlF6). Perfluorocarbons (CF4 and C2F6 collectively referred to as PFCs) are formed from the reaction of the carbon anode with the cryolite melt during a process upset condition known as an ‘anode effect’. An anode effect occurs when the concentration of alumina in the electrolyte is too low to support the standard anode reaction.

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BOX 4.2 ANODE EFFECT DESCRIPTION

An anode effect is a process upset condition where an insufficient amount of alumina is dissolved in the electrolyte, causing voltage to be elevated above the normal operating range, resulting in the emission of PFC-containing gases. Both Tier 2 and Tier 3 for PFCs are based on plant-specific process data for anode effects, which are regularly collected. In choosing a method for PFCs, it should be noted that the uncertainty associated with higher tier methodologies is significantly lower than that for Tier 1, and therefore Tier 2 and Tier 3 are strongly recommended if this is a key category. Depending on the production technology type, the uncertainty of the methods for PFCs ranges from several hundred percent for the Tier 1 method to less than twenty percent for the Tier 3 method. The Tier 3 methodology for PFC inventory should be utilized with slope or overvoltage coefficients calculated from measurement data obtained using good measurement practices (U.S. EPA and IAI, 2003). Communication with primary aluminium producers will determine the availability of process data, which, in turn dictates the method used to calculate emissions. Plants routinely measure anode effect performance as anode effect minutes per cell-day or anode effect overvoltage. PFC emissions are directly related to anode effect performance via a coefficient, either the slope coefficient or the overvoltage coefficient. The decision tree shown in Figure 4.12 describes good practice in choosing the PFC inventory methodology appropriate for national circumstances. For high performing facilities that emit very small amounts of PFCs, the Tier 3 method will likely not provide a significant improvement in the overall facility GHG inventory in comparison with the Tier 2 Method. 8 Consequently, it is good practice to identify these facilities prior to selecting methods in the interest of prioritising resources. The parameters that identify these high performing facilities depend on the type of process data collected by the facility. High performing facilities are those that operate with less than 0.2 anode effect minutes per cell day when anode effect minutes are measured. When overvoltage is recorded, high performing facilities operate with less than 1.4 mV overvoltage. In addition, for these high performing facilities accurate measurement of the Tier 3 PFC coefficient is difficult because the very low frequency of anode effects requires an extended time to obtain statistically robust results. The status of a facility as a high performing facility should be assessed annually because economic factors, such as the restarts of production lines after a period of inactivity, or, process factors, such as periods of power curtailments might cause temporary increases in anode effect frequency. In addition, over time, facilities that might not at first meet the requirements for high performers may become high performing facilities through implementation of new technology or improved work practices. Note that in all cases, applying different Tiers for different years will require careful implementation to ensure time series consistency. For all other facilities, the Tier 3 approach is preferred because plant-specific coefficients will lead to estimates that are more accurate. If no PFC measurements have been made to establish a plant-specific coefficient, the Tier 2 Method can be used until measurements have been made and Tier 3 coefficients are established. Countries can use a combination of Tier 2 and Tier 3 depending on the type of data available from individual facilities.

Tier 1 method: Use of technology based default emission factors The Tier 1 method uses technology-based default emission factors for the four main production technology types (CWPB, SWPB, VSS and HSS). PFC emissions can be calculated according to Equation 4.25. The level of uncertainty in the Tier 1 method is much greater because individual facility anode effect performance, which is the key determinant of anode effects and thus PFC emissions, are not directly taken into account. Tier 1 can be consistent with good practice only when PFCs from primary aluminium is not a key category and when pertinent process data are not available from operating facilities.

8

The levels for the process parameters that define high performing facilities for PFC emissions are the combined result of the magnitude of, and, the uncertainty in the Tier 2 coefficient. The levels are calculated by using the positive and negative extremes of the 95% confidence limits for the Tier 2 coefficient as a proxy for the range of likely values for Tier 3 coefficients for these facilities. The potential difference is then assessed on the overall greenhouse gas emissions from a production facility considering both PFC and CO2 emissions. When facilities operate at or below the anode effect process parameter levels noted here for high performing facilities, the impact of moving from the Tier 2 method for PFCs to the Tier 3 method would not result in a change greater than 5% in overall GWP weighted GHG emissions. PFC emissions from high performing facilities account for less than 3% of global PFC emissions based on IAI 2004 anode effect survey data.

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EQUATION 4.25 PFC EMISSIONS (TIER 1 METHOD) ECF 4 = ∑ (EFCF 4, i • MPi )

and = ∑ (EFC 2 F 6, i • MPi ) i

EC 2 F 6

i

Where: ECF4 = emissions of CF4 from aluminium production, kg CF4 EC2F6 = emissions of C2F6 from aluminium production, kg C2F6 EFCF4,i = default emission factor by cell technology type i for CF4, kg CF4/tonne Al EFC2F6,i = default emission factor by cell technology type i for C2F6, kg C2F6/tonne Al MPi = metal production by cell technology type i, tonnes Al

Tier 2 and Tier 3 methods: based on anode effect performance There are two different equations for estimating individual plant CF4 emissions, which are both based on the relationship between anode effect and performance. These are the slope and overvoltage coefficient equations. Both types of coefficients are based on direct measurements of PFCs. Tier 2 makes use of an average coefficient from measurements at numerous facilities while Tier 3 is based on measurements at the individual facility. Because the process mechanisms that produce PFC emissions are similar for CF4 and C2F6, the two gases should be considered together when estimating PFC emissions. C2F6 emissions are calculated in all the methods described herein as a fraction of CF4 emissions. With an established relationship between anode effect process data and PFC emissions, process data collected on an on-going basis can be used to calculate PFC emissions in lieu of direct measurement of PFCs. The choice between the two estimation relationships depends on the process control technology in use. Equation 4.26 should be used when anode effect minutes per cell day are recorded and Equation 4.27 should be used when overvoltage data are recorded. Slope Coefficient: The slope coefficient represents the kg of CF4 per tonne of aluminium produced, divided by anode effect minutes per cell-day9. Since PFC emissions are measured per tonne of aluminium produced, it includes the effects of cell amperage and current efficiency, the two main factors determining the amount of aluminium produced in the cell. Equation 4.26 describes the slope method for both CF4 and C2F6. EQUATION 4.26 PFC EMISSIONS BY SLOPE METHOD (TIER 2 AND TIER 3 METHODS) ECF 4 = SCF 4 • AEM • MP

and EC 2 F 6 = ECF 4 • FC 2 F 6 / CF 4 Where: ECF4 = emissions of CF4 from aluminium production, kg CF4 EC2F6 = emissions of C2F6 from aluminium production, kg C2F6 SCF4 = slope coefficient for CF4, (kg CF4/tonne Al)/(AE-Mins/cell-day) AEM = anode effect minutes per cell-day, AE-Mins/cell-day MP = metal production, tonnes Al FC2F6/CF4 = weight fraction of C2F6/CF4, kg C2F6/kg CF4

9

The term ‘cell-day’ refers to the number of cells operating multiplied by the number of days of operation.

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Overvoltage Coefficient: Some process control systems characterize anode effects by calculating an Anode Effect Overvoltage10 (AEO) statistic. AEO is defined as the extra cell voltage above the target operating voltage, and this parameter has been shown to be a good predictor of PFC emissions when recorded by the process control system. The AEO process control technology is in use at many modern smelters. AEO is calculated by summing the product of time and voltage above the target operating voltage and dividing this figure by the time over which data were collected. EQUATION 4.27 PFC EMISSIONS BY OVERVOLTAGE METHOD (TIER 2 AND TIER 3 METHODS) AEO ECF 4 = OVC • • MP CE 100 EC 2 F 6

and = ECF 4 • FC 2 F 6 / CF 4

Where: ECF4 = emissions of CF4 from aluminium production, kg CF4 EC2F6 = emissions of C2F6 from aluminium production, kg C2F6 OVC = Overvoltage coefficient for CF4, (kg CF4/tonne Al)/mV AEO = anode effect overvoltage, mV CE = aluminium production process current efficiency expressed, percent (e.g., 95 percent) MP = metal production, tonnes Al F C2F6/CF4 = weight fraction of C2F6/CF4, kg C2F6/kg CF4

10

Computer control systems report either ‘positive’ or ‘algebraic’ overvoltage depending on the version of software used. Use of the expression ‘overvoltage’ should not be confused with the classical electrochemical terminology, which usually means the extra voltage needed for an electrochemical reaction to occur.

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Figure 4.12

Decision tree for calculation of PFC emissions from primary aluminium production Start

Are process data (AE minutes per cell day or AE overvoltage) available?

Yes

Do process data indicate a high performing facility?1

Yes

Calculate PFC emissions for high performing facilities using either Tier 2 or Tier 3. Box 4: Tier 3 or Tier 2

Collect process data5.

Yes

No

No

Are facility specific PFC coefficients available per good practice2?

Is this a key category4?

Yes

Box 3: Tier 3 No

No

Calculate PFC emissions using Tier 3 method.

Calculate PFC emissions using Tier 2 method3. Box 2: Tier 2

Are production data available by technology?

Yes

No

Estimate annual production by technology.

Calculate PFC emissions using Tier 1 method6. Box 1: Tier 1

Note: 1. High performing facilities emit so little PFCs that no significant improvement can be expected in the overall facility GHG inventory by using the Tier 3 method rather than the Tier 2 method. High performing facilities are defined, based on what process data are collected, as those that operate with less than 0.2 anode effect minutes per cell day, or, less than 1.4 mV overvoltage. In such facilities the improvement in accuracy in facility GHG inventory is less than 5% when moving from Tier 2 to Tier 3 methods for PFCs. 2. Good practices for obtaining facility specific PFC equation coefficients are detailed in the IAI GHG Protocol (IAI, 2005). 3. In this case, Tier 2 method should be used until site-specific Tier 3 coefficients become available and the Tier 3 method employed unless PFC emissions become immaterial, in which case facilities can choose to use either the Tier 2 or Tier 3 method. 4. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 5. For key categories, it is good practice to collect anode effect process data and production activity data at the individual production facility level. 6. Primary aluminium facilities regularly record activity data including metal production and anode effect process data facilitating, at a minimum, Tier 2 calculation method. Errors of magnitude of x10 can result from use of Tier 1 methods for PFCs.

4.4.2.4

C HOICE

OF EMISSION FACTORS FOR

PFC S

Tier 1: Technology based default emission factors Default emission factors for Tier 1 method are provided in Table 4.15.

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TABLE 4.15 DEFAULT EMISSION FACTORS AND UNCERTAINTY RANGES FOR THE CALCULATION OF PFC EMISSIONS FROM ALUMINIUM PRODUCTION BY CELL TECHNOLOGY TYPE (TIER 1 METHOD) Technology

CF4 a

C2F6 EFC2F6 (kg/tonne Al)

Uncertainty Range (%)d

-99/+380

0.04

-99/+380

1.6

-40/+150

0.4

-40/+150

VSS

0.8

-70/+260

0.04

-70/+260

HSS

0.4

-80/+180

0.03

-80/+180

EFCF4 (kg/tonne Al)

Uncertainty Range (%)

CWPB

0.4

SWPB

b

c

a

Default CF4 values calculated from median anode effect performance from 1990 IAI survey data (IAI, 2001).

b

Uncertainty based on the range of calculated CF4 specific emissions by technology from 1990 IAI anode effect survey data (IAI, 2001).

c

Default C2F6 values calculated from global average C2F6:CF4 ratios by technology, multiplied by the default CF4 emission factor.

d

Uncertainty range based on global average C2F6:CF4 ratios by technology, multiplied by calculated minimum and maximum specific CF4 emissions from 1990 IAI survey data (IAI, 2001).

Note: These default emission factors should only be used in the absence of Tier 2 or Tier 3 data.

Tier 2: PFC emission factor based on a technology specific relationship between anode effect performance and PFC emissions. The Tier 2 method is based on using either technology specific slope or overvoltage coefficients for the applicable reduction cell and process control technology as listed in Table 4.16.11 TABLE 4.16 TECHNOLOGY SPECIFIC SLOPE AND OVERVOLTAGE COEFFICIENTS FOR THE CALCULATION OF PFC EMISSIONS FROM ALUMINIUM PRODUCTION (TIER 2 METHOD)

Technologya

a

11

Slope Coefficient b, c [(kg PFC/tAl) / (AE-Mins/cellday)]

Overvoltage Coefficientb, c, d [(kg CF4/tAl ) / (mV)]

Weight Fraction C2F6 / CF4

CF4

Uncertainty (+/-%)

CF4

Uncertainty (+/-%)

C2F6/CF4

Uncertainty (+/-%)

CWPB

0.143

6

1.16

24

0.121

11

SWPB

0.272

15

3.65

43

0.252

23

VSS

0.092

17

NR

NR

0.053

15

HSS

0.099

44

NR

NR

0.085

48

Centre Worked Prebake (CWPB), Side Worked Prebake (SWPB), Vertical Stud Søderberg (VSS), Horizontal Stud Søderberg (HSS).

b

Source: Measurements reported to IAI, US EPA sponsored measurements and multiple site measurements (U.S. EPA and IAI, 2003).

c

Embedded in each Slope and Overvoltage coefficient is an assumed emissions collection efficiency as follows: CWPB 98%, SWPB 90%, VSS 85%, HSS 90%. These collection efficiencies have been assumed based on measured PFC collection fractions, measured fluoride gas collection efficiencies and expert opinion.

d

The noted coefficients reflect measurements made at some facilities recording positive overvoltage and others recording algebraic overvoltage. No robust relationship has yet been established between positive and algebraic overvoltage. Positive overvoltage should provide a better correlation with PFC emissions than algebraic overvoltage. Overvoltage coefficients are not relevant (NR) to VSS and HSS technologies.

These slope coefficients were derived from measurement of PFCs and correlating the measured PFC emissions to anode effect minutes per cell day at over one-hundred aluminium smelters. The values in Table 4.16 are the technology specific factors from measurement data available as of March 2005 when this document was developed. It is important to note Tier 2 slope coefficients are based on the anode-effect minutes per cell-day statistic as defined in the IAI GHG Protocol (IAI, 2005a). It is good practice to refer to the most current data for calculation of PFC emissions as noted in the IAI GHG Protocol and to the IPCC Emission Factor Database.

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Tier 3: PFC emission factor based on a facility specific relationship between anode effect performance and PFC emissions The Tier 3 method is based on a facility specific slope or anode effect overvoltage PFC coefficient. This coefficient characterizes the relationship between facility anode effect performance and measured PFC emissions from periodic or continuous measurements that are consistent with established measurement practices (U.S. EPA and IAI, 2003) and the International Aluminium Institute GHG Protocol (IAI, 2005a).

4.4.2.5

C HOICE

OF ACTIVITY DATA

Production statistics should be available from every facility to enable use of Tier 1 methods for both CO2 and PFC emissions. Uncertainty in the tonnes of aluminium produced is likely to be low in most countries. Given the expected universal availability of production data, production capacity data should only be used as a check on production statistics. Good practice methods for PFC emissions require accurate anode effect minutes per cell day data or accurate overvoltage (AEO) data for all cell types. Annual statistics should be based on the production-weighted average of monthly anode effect data. Both Tier 2 and Tier 3 utilize anode effect minutes per cell day or anode effect overvoltage, and aluminium production data. Individual aluminium companies or industry groups, national aluminium associations or the International Aluminium Institute, should be consulted to ensure that the data are available and in a useable format for inventory estimation. For CO2 emissions, all aluminium smelters collect data to support Tier 2 or Tier 3 methods. Søderberg smelters collect anode paste consumption data while Prebake smelters record baked anode consumption. The Tier 2 and Tier 3 methods use the same equation for calculation of CO2 emissions; however, the Tier 3 method uses facility specific composition data for anode materials while the Tier 2 method uses industry average anode composition data.

4.4.2.6

C OMPLETENESS

Primary aluminium facilities will generally have good records of tonnes of aluminium produced throughout the entire time series covered by the inventory. In addition, carbon consumption data are typically available over the same period. Anode effect process data may be incomplete over the entire time series and measures may have to be employed, such as those described in Section 4.4.2.7, Developing a Consistent Time Series, to calculate PFC emissions over some portions of the inventory period. Primary aluminium production also utilizes large amount of electricity and care should be exercised to avoid omissions of carbon dioxide associated with electricity input, or, to avoid double counting of this carbon dioxide.

4.4.2.7

D EVELOPING

A CONSISTENT TIME SERIES

Aluminium production statistics will typically be available for the entire history of the facility. Developing a consistent time series for carbon dioxide emissions should not be a problem since most facilities historically have measured and recorded anode or paste consumption. Where historic anode or paste consumption data are missing, carbon dioxide emissions can be estimated from aluminium production utilizing the Tier 1 method. A complete time series of PFC related activity data such as anode effect minutes per cell day or overvoltage gives the best time series results. Because PFC emissions only became a major focus area in the early 1990s for the global aluminium industry, some facilities may have limited information about the required anode effect data to implement Tier 2 or Tier 3 PFC inventory practices over the entire time covered by the inventory. Substantial errors and discontinuities can be introduced by reverting to Tier 1 methods for PFC emissions for years for which activity data are not available. The appropriateness of applying Tier 2 or Tier 3 PFC emission factors back in time to a given facility and availability of detailed process data vary with the specific conditions. Generally, backcasting of Tier 2 or Tier 3 methods using splicing or surrogate data are preferred over use of Tier 1 emission factors. Specifically, where only anode effect frequency data are available and anode effect duration data are unavailable, it is good practice to splice or backcast PFC emissions per tonne aluminium based on anode effect frequency data. Currently many facilities are making PFC measurements that facilitate implementation of Tier 3 PFC inventory methods. There are a number of issues that impact on whether Tier 3 PFC emission factors can be extrapolated to past inventory periods. Factors that should be considered include whether any technology upgrades have been implemented at the facility, whether there have been substantial changes in work practices, whether any changes in the calculation of underlying process data have occurred, and the quality of the measurements made to establish the Tier 3 coefficients. It is good practice to consult with representatives from the operating facilities, either directly or through regional or international organizations representing the industry

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to develop the best strategy for the specific group of operating locations included in the national inventory. Additional helpful information on splicing methods and details regarding constructing a time series for primary aluminium is available from IAI (IAI, 2005). Expert advice is also available from the International Aluminium Institute (London, UK) regarding greenhouse gas emissions and typical industry emissions from aluminium production.

4.4.3

Uncertainty assessment

There are major differences in the uncertainty for PFC emissions depending on the choice of Tier 1, Tier 2, or Tier 3 methods. The differences in uncertainty resulting from choice of method for carbon dioxide emissions are much smaller than for PFC emissions. There is no basis for country or regional differences in emissions resulting from aluminium production other than the differences that result from the specific type of production technologies and work practices in use in the country or region. These differences are reflected in the calculation methodologies described above.

4.4.3.1

E MISSION

FACTOR UNCERTAINTIES

The uncertainty in the emission factors for calculating carbon dioxide emissions from carbon anode or paste consumption should be less than ±5 percent for both the Tier 2 and Tier 3 methods, and less than ±10 percent for the Tier 1 method. The reactions leading to carbon dioxide emissions are well understood and the emissions are very directly connected to the tonnes of aluminium produced through the fundamental electrochemical equations for alumina reduction at a carbon anode and oxidation from thermal processes. Both of these fundamental processes producing carbon dioxide are included in process parameters routinely monitored at the production facilities, the net carbon consumed and/or paste consumption. The main source of uncertainty is in the net carbon consumed for Prebake technologies and paste consumption for Søderberg cells. These factors are both carefully monitored and are important factors in the economic performance of a facility. Improvements in accuracy of carbon dioxide emissions inventories can be achieved by moving from Tier 1 to Tier 2 methods because there is a range of performance of reduction facilities in the consumption of carbon anode materials. Less significant improvements in accuracy can be expected in choosing the Tier 3 method over the Tier 2 method. This is because the major factors in the calculation are the net anode carbon consumed or paste consumption and the production of aluminium. The uncertainty of both these components of the calculation equation is low, 2 to 5 percent, and these uncertainties dominate the overall calculation of carbon dioxide emissions in the Tier 2 and Tier 3 methods. Facility specific data are used in both Tier 2 and Tier 3 calculations for these parameters. The Tier 3 method refines the calculation to use actual composition of the carbon anode materials. While there can be considerable variability in the minor components of the anode materials this variability does not contribute significantly to the overall calculation of carbon dioxide emissions. In considering changes in uncertainty in PFC emissions inventory when moving from Tier 1 to Tier 2 and Tier 3 methods, there are major reductions in uncertainty when choosing the Tier 2 or Tier 3 methods over the Tier 1 method. The high level of uncertainty in the Tier 1 method results directly from the large variability in anode effect performance among operators using similar production technology. The Tier 1 method is based on using a single default coefficient for all operators by technology type. Since there can be variations in anode effect performance (frequency and duration) by factors of 10 among operators using the same technology (IAI, 2005c), use of the Tier 1 method can result in uncertainties of the same magnitude. There is less impact on uncertainty levels in choosing the Tier 3 method over the Tier 2 method; however, the level of uncertainty reduction depends on the cell technology type. The uncertainty for industry average coefficients ranges from +/-6 percent for CWPB, the most widely measured and used technology, to +/-44 percent for HSS. Both Tier 2 and Tier 3 methods are based on direct PFC measurements that establish a relationship between anode effect performance and PFC specific emissions. The Tier 2 method uses an industry average equation coefficient while the Tier 3 method uses a facility specific coefficient based on direct PFC measurements made at the facility. As more facility measurements are made, especially in those facilities operating with Søderberg technologies, the uncertainty in the average coefficients should be reduced. The lowest uncertainty for PFC emissions calculations is from the use of the Tier 3 method. However, to achieve this lower uncertainty in Tier 3 PFC calculations it is important to use good practices in making facility specific PFC measurements. These measurement good practices have been established and documented in a protocol available globally (USEPA/IAI, 2003). When properly established these Tier 3 coefficients will have an uncertainty of +/-15 percent at the time the coefficients are measured.

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4.4.3.2

A CTIVITY

DATA UNCERTAINTIES

There is very little uncertainty in the data for the annual production of aluminium, less than 1 percent. The uncertainty in recording carbon consumption as baked anode consumption or coke and paste consumption is estimated to be only slightly higher than for aluminium production, less than 2 percent. The other component of calculated facility specific emissions using Tier 2 or Tier 3 methods is the anode effect activity data, i.e., either anode effect minutes per cell day or anode effect overvoltage. These parameters are typically logged by the process control system as part of the operations of nearly all aluminium production facilities and the uncertainties in these data are low.

4.4.4 4.4.4.1

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC)

It is good practice at all primary aluminium production facilities to maintain records of all of the necessary activity data to support calculations of emissions factors as suggested in these guidelines. These records will include production of aluminium, anode effect performance and consumption of carbon materials used in either Prebake or Søderberg cells. In addition, the International Aluminium Institute maintains global summaries of aggregated activity data for these same parameters and regional data are available from regional aluminium associations. It is good practice to aggregate emissions estimates from each smelter to estimate total national emissions. However, if smelter-level production data are unavailable, smelter capacity data may be used along with aggregate national production to estimate smelter production. It is good practice to verify facility CO2 emission factors per tonne aluminium by comparison with the expected range of variation that would be predicted from the variation noted in Tables 4.10 and 4.11 for carbon dioxide specific emissions. Also, the underlying equation coefficients used for calculating PFC emission factors per tonne aluminium should be compared with those noted in Table 4.15. It is suggested that any inventory value outside the 95 percent confidence range of the data population variance be confirmed with the data source. Use of standard measurement methods improves the consistency of the resulting data and knowledge of the statistical properties of the data. For primary aluminium, the EPA/IAI Protocol for Measurement of Tetrafluoromethane (CF4) and Hexafluoroethane (C2F6) Emissions from Primary Aluminum Production is the internationally recognized standard (U.S. EPA and IAI, 2003). Inventory compilers should encourage plants to use this method for developing Tier 3 PFC equation coefficients. Significant differences between calculated coefficients based on PFC measurements and the industry average Tier 2 coefficients for similar reduction technology should elicit further review and checks on calculations. Large differences should be explained and documented. An international data set of anode effect performance, which can be used to identify outlier data, is available from the International Aluminium Institute. In addition, an up-to-date database of PFC measurements is also maintained by IAI and should be consulted when assessing the appropriateness of reported data. Inter-annual changes in emissions of carbon dioxide per tonne aluminium should not exceed +/-10 percent based on the consistency of the underlying processes that produce carbon dioxide. In contrast, inter-annual changes in emissions of PFCs per tonne of aluminium may change by values of up to +/- 100 percent. Increases in PFC specific emissions can result from process instability. Increases in anode effect frequency and duration can be the result of factors such as unanticipated power interruptions, changes in sources of alumina feed materials, cell operational problems, and increases in potline amperage to increase aluminium production. Decreases in PFC specific emissions can result from decreases in anode effect frequency and duration due to changes in the computer algorithms used in cell process control, upgrades in cell technology such as the installation of point feeders, improved work practices and better control of raw materials.

4.4.4.2

R EPORTING

AND

D OCUMENTATION

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Chapter 6, Quality Assurance and Quality Control, Internal Documentation and Archiving. Some examples of specific documentation and reporting relevant to this source category are provided below. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are

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transparent and steps in their calculation may be retraced. To improve transparency, it is good practice to report emissions for PFCs from aluminium production separately from other source categories. Additionally, it is good practice that CF4 and C2F6 emissions are reported separately on a mass basis. The supporting information necessary to ensure transparency in reported emissions estimates is shown in Table 4.17, Good practice Reporting Information for PFC Emissions from Aluminium Production by Tier, below. Much of the production and process data are considered proprietary by operators, especially where there is only one smelter in a country. It is good practice to exercise appropriate techniques, including aggregation of data, to ensure protection of confidential data.

TABLE 4.17 GOOD PRACTICE REPORTING INFORMATION FOR CALCULATING CO2 AND PFC EMISSIONS FROM ALUMINIUM PRODUCTION BY TIER

Data

Tier 3

Tier 2

Tier 1

PFCs Annual national production (by CWPB, SWPB, HSS, or VSS technology)

X

Annual production by smelter (by CWPB, SWPB, HSS, or VSS technology)

X

X

Anode Effect minutes per cell-day or Anode Effect Overvoltage (mV)

X

X

Facility specific emission coefficients linked to anode effect performance

X

Technology specific emission coefficients linked to anode effect performance

X

Default technology emission coefficients

X

Supporting documentation

X

X

X

CO2 Annual national production (by Prebake or Søderberg technology)

X

Annual production by smelter (by Prebake or Søderberg technology)

X

X

Net anode consumption for Prebake cells or paste consumption for Søderberg cells

X

X

Carbon material impurity levels and carbon dust for Søderberg cells

X

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4.5

MAGNESIUM PRODUCTION

4.5.1

Introduction

In the magnesium industry, there are a number of potential emission sources and gases. The amount and type of emission from the magnesium industry will reflect the raw material used for primary magnesium metal production and/or the type of cover gas mixture used in the casting and recycling foundries to prevent oxidation of molten magnesium. It is good practice to consider, in a disaggregated way if possible, all segments of the magnesium industry and their related emissions. A list of possible greenhouse gas emissions, which may be associated with primary, and secondary magnesium metal production and casting operations, is provided in Table 4.18. Primary magnesium refers to metallic magnesium derived from mineral sources. Primary magnesium can be produced either by electrolysis or a thermal reduction process. The raw materials used for primary magnesium production are dolomite, magnesite, carnalite, serpentine, brines or seawater. Processing of carbonate raw materials (magnesite and dolomite) will release CO2 during manufacturing. The CO2 is released during calcination of carbonate-based ores (dolomite/magnesite) - a ‘pre-treatment’ step to the main electrolytic/thermal reduction processes. This process is similar to the generation of CO2 in the mineral industry (see Chapter 2). Secondary magnesium production includes the recovery and recycling of metallic magnesium from a variety of magnesium containing scrap materials e.g., post consumer parts, machine cuttings, casting scraps, furnace residues, etc. Magnesium casting processes may involve metal from both primary production and secondary magnesium production. Magnesium casting processes involve handling of molten pure magnesium and/or molten high magnesium content alloys. Molten magnesium (also understood to mean high magnesium content alloys) maybe cast by a variety of methods including gravity casting, sand casting, die casting and others. All molten magnesium spontaneously burns in the presence of atmospheric oxygen. Production and casting of all magnesium metal requires a protection system to prevent burning. Among the various protection systems commonly used are those that use gaseous components with high GWP values, such as SF6, which typically escape to the atmosphere. Metallic magnesium cast from the various processes and sources all require protection methods and will therefore have similar potentials for GHG emissions.

TABLE 4. 18 POSSIBLE GHG EMISSIONS RELATED TO PRODUCTION AND PROCESSING OF MAGNESIUM PROCESS

POTENTIAL ASSOCIATED PROCESS GHG EMISSION SF6

HFC’s

CO2

Others*

Raw Materials Preparation for Primary Production Dolomite/Magnesite Based

-

-

X

-

Other Raw Materials

-

-

-

-

Primary ingot casting

X

X

X

X

Die casting

X

X

X

X

Gravity casting

X

X

X

X

Other casting methods

X

X

X

X

Secondary Mg Production**

X

X

X

X

Casting (primary & secondary)

*Others include fluorinated ketone and various fluorinated decomposition products e.g., PFCs ** Includes processes involving the recycle/recovery of metallic magnesium

Secondary magnesium production (recycling), handling, melting, and casting, molten metal is protected against oxidation throughout the process by using protection systems such as SF6 or SO2 containing cover gases (a

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carrier gas (commonly air and/or CO2) and SF6 or SO212) or, in some cases, flux. High-magnesium content alloys are also commonly protected using SF6 containing cover gases. Due to recent technological developments and a push towards the replacement of SF6, the magnesium industry has introduced alternative cover gases. It is foreseen that the two most common alternatives to SF6 in the next decade will be the fluorinated hydrocarbon HFC-134a and the fluorinated ketone FK 5-1-12 (C3F7C(O)C2F5), traded under the name Novec™61213, and that the individual magnesium producer’s/processor’s choice of cover gas will be strongly influenced by national/regional circumstances (Tranell et al., 2004).

CO 2 EMISSIONS FROM PRIMARY PRODUCTION As indicated in Table 4.18, the magnesium-containing ores which release CO2 during calcination are dolomite (Mg•Ca(CO3)2) and magnesite (MgCO3). For each kilogram of magnesium produced, theoretically 3.62 kg14 (dolomite) or 1.81 kg (magnesite) respectively of CO2, is emitted during calcination. The actual CO2 emissions per kilogram magnesium produced will be higher than the theoretical emission due to losses of magnesium in the process chain.

MAGNESIUM CASTING PROCESSES (PRIMARY & SECONDARY) In magnesium casting processes, the size and type of GHG emission will depend on the chosen cover gas system used to protect liquid magnesium. In addition to emissions of the active protection compound (SF6, HFC-134a or FK 5-1-12) in the cover gas itself – there may be emissions of various fluorinated decomposition products (e.g., PFCs) and potentially also the carrier gas (depending on choice of air and/or CO2 or N2).

SF 6 It has been a common assumption that SF6 in magnesium cover gas is inert and that hence, essentially all SF6 used in the magnesium industry will be emitted. However, recent independent studies (Bartos et al., 2003 and Tranell et al., 2004) demonstrate that SF6 does, to a certain degree, destruct in contact with liquid/gaseous magnesium at common magnesium holding/processing temperatures. The fraction of SF6 destroyed in the furnace, as well as the type/amount of secondary gas products generated from the reaction with magnesium, will depend on pertaining operating conditions such as SF6 concentration in cover gas, total cover gas flow-rate, size of reactive magnesium surface area, type of carrier gas used, furnace charging practises, etc.

HFC-134a, FK 5-1-12 and decomposition products (e.g., PFCs) Both HFC-134a and FK 5-1-12 are less thermodynamically stable (and thus have much lower GWP) than SF6. It is hence expected that these gases will decompose/react extensively in the contact with liquid/gaseous magnesium, leading to the production of various fluorinated gases (e.g., PFCs). Tranell et al., 2004 found that as a general rule of thumb, when SF6 is replaced by HFC-134a, less than half the amount of active fluorinated compound on a molar basis is needed to protect a given magnesium surface (under otherwise identical conditions). When SF6 replaces FK 5-1-12, less than a quarter of the quantity of active compound is needed. It was reported that, as is the case for SF6, the amount of active compound in the in-going cover gas destroyed in the furnace depends on conditions such as compound concentration in in-going cover gas, total cover gas flowrate, size of reactive magnesium surface area, type of carrier gas used, charging practises etc. It should be noted that emissions of PFCs as decomposition products would be more significant in terms of CO2 equivalent than FK 5-1-12 emissions, given their relative radiative effects15.

Carrier gases Many cover gas systems use CO2 as a carrier gas -alone or in combination with dry air- to dilute the active fluorinated compound and reduce the oxygen partial pressure in the furnace. It is a quantitatively reasonable assumption that all CO2 used in the cover gas is emitted as CO2. The amount of carbon dioxide cover gas used is much lower than the usual active agents in the cover gas system and can generally be disregarded.

12

Consistent with the scope of these Guidelines outlined in Volume 1, this chapter does not provide methods for estimating emissions of SO2.

13

FK 5-1-12 (C3F7C(O)C2F5), traded as Novec™612, is a fluorinated ketone produced by 3M (Milbrath, 2002).

14

This represents a case where the ore has a stoichiometric Mg/Ca ratio of 1.

15

The GWP value of FK 5-1-12 is not identified in the IPCC Third Assessment Report (IPCC, 2001), but it is estimated to be similar to that of CO2 according to the producer of this gas.

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4.5.2 4.5.2.1

Methodological issues C HOICE

OF METHOD

CO 2 EMISSIONS FROM PRIMARY PRODUCTION The choice of a good practice method for inventory preparation of carbon dioxide emissions from the primary magnesium (raw material) production segment will depend on national circumstances. The decision tree (see Figure 4.13, Decision Tree for Estimation of CO2 Emissions from Primary Magnesium Production) describes good practice in adapting the methods to these country-specific circumstances.

Tier 1 The Tier 1 method relies on national primary production data and knowledge of raw materials used in the country. National production data may not be publicly available as there are a limited number of countries producing magnesium and only a few individual producers - often only one in a country - often resulting in the designation of national production data as confidential. In the absence of national primary magnesium production statistics, industry associations, such as the International Magnesium Association (http://www.intlmag.org/), may be able to provide regional statistics. Failing other data, it may be possible to estimate primary magnesium production from annual national magnesium metal sales. This method has increased uncertainty, since it does not account for magnesium used in national product manufacturing. CO2 emissions are calculated using Equation 4.28. EQUATION 4.28 CO2 EMISSIONS FROM PRIMARY MAGNESIUM PRODUCTION (TIER 1)

(

)

ECO 2 = Pd • EFd + Pmg • EFmg • 10 −3

Where: ECO2 = CO2 emissions from primary magnesium production, Gg Pd = national primary magnesium production from dolomite, tonnes Pmg = national primary magnesium production from magnesite, tonnes EFd = Default emission factor for CO2 emissions from primary magnesium production from dolomite, tonne CO2/tonne primary Mg produced EFmg = Default emission factor for CO2 emissions from primary magnesium production from magnesite, tonne CO2/tonne primary Mg produced

Tier 2 The Tier 2 method for determining CO2 emissions from primary magnesium involves collecting company/plantspecific empirical emission factors, in addition to company specific production data. The company specific emission factors may differ substantially from the default emission factors depending on process materials handling. This collection should take place if the emissions are a key category. CO2 emissions are calculated using Equation 4.29. EQUATION 4.29 CO2 EMISSIONS FROM PRIMARY MAGNESIUM PRODUCTION (TIER 2) ECO 2 = ∑ ( Pi • EFi ) • 10 −3 i

Where: ECO2 = CO2 emissions from primary magnesium production, Gg Pi = primary magnesium produced in plant i, tonne EFi = company/plant-specific emission factor for CO2 emissions from primary magnesium production obtained from company/plant i, tonne CO2 /tonne primary Mg produced

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Tier 3 If actual measured CO2 emissions data are available from individual primary magnesium facilities, these data can be aggregated and used directly to account for national emissions.

MAGNESIUM CASTING PROCESSES (PRIMARY & SECONDARY) SF 6 The choice of a good practice method for inventory preparation of SF6 emissions from magnesium casting process segment will also depend on national circumstances. The decision tree (Figure 4.14, Decision Tree for Estimation of SF6 Emissions from Magnesium Processing) describes good practice in adapting the methods to these country-specific circumstances.

Tier 1 – default emission factors The Tier 1 method is based on the total amount of magnesium casting or handling in the country (Equation 4.30). The underlying assumption for the Tier 1 approach is that all SF6 consumption in the magnesium industry segment is emitted as SF6. As described in Section 4.5.1, this assumption will potentially overestimate the GHG emissions, but the overestimate will lie within the overall uncertainty range given in Section 4.5.3. The basic Tier 1 method uses a single value as a basis for the default emission calculation when SF6 is used for oxidation protection, despite the fact that SF6 consumption vary substantially between different casting operations and operators (sometimes orders of magnitude). The Tier 1 method should be used only when the inventory compiler has no knowledge of type of magnesium handling- or casting operation (recycling, billet casting or die-casting etc.) EQUATION 4.30 SF6 EMISSIONS FROM MAGNESIUM CASTING (TIER 1) E SF 6 = MGc • EFSF 6 • 10 −3

Where ESF6 = SF6 emissions from magnesium casting, tonnes MGc = total amount of magnesium casting or handling in the country, tonnes EFSF6 = default emission factor for SF6 emissions from magnesium casting, kg SF6/tonne Mg casting

Tier 2 – company-specific SF 6 consumption As for the Tier 1 method, the Tier 2 method also assumes that all SF6 consumed is subsequentlyemitted. Instead of the amount of magnesium casting, however, the Tier 2 method uses data on national (or sub-national) consumption of SF6 in the magnesium industry as reported by the industry or available through other sources such as national statistics (Equation 4.31). The most accurate application of the method is normally collection of direct data on SF6 consumption from all individual users of the gas in the magnesium industry. If no direct data are available, an alternative but a less accurate method is to estimate the share of annual national SF6 consumption attributable to the magnesium industry. This requires collecting annual data on national SF6 sales and assumes that all SF6 gas sold to the magnesium industry is emitted within the year. EQUATION 4.31 SF6 EMISSIONS FROM MAGNESIUM CASTING (TIER 2) E SF 6 = C SF 6

Where ESF6 = SF6 emissions from magnesium casting, tonnes CSF6 = consumption of SF6 in magnesium smelters and foundries, tonnes

Tier 3 – direct measurement approach If actual measured emission data are available from individual magnesium processing facilities, these data can be aggregated and used directly to account for national emissions. In such reporting, it is good practice to include destruction of SF6 and formation of secondary gas products.

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Figure 4.13

Decision tree for estimation of CO 2 emissions from raw materials calcination in the primary magnesium production process Start

Are carbonate raw materials used for primary Mg production?

No

Report Not occurring

Yes

Estimate emissions using the direct reporting method.

Yes

Are there direct reported data on CO2 emissions?

Box 4: Tier 3 No

Are there country/company-specific emission factors and production data available from companies?

Estimate emissions using country/company-specific emission factors and production data (direct and top-down) method.

Yes

Box 3: Tier 2 No

Is this a key category1?

No

Is national primary Mg production data and raw material source(s) known?

Yes

Estimate emissions using default emission factors. Box 2: Tier 1

Yes Collect data for the Tier 3 or the Tier 2 method

No

Estimate emissions using the national sales method. Box 1: Tier 1

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

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Figure 4.14

Decision tree for estimation of SF 6 emissions from magnesium processing Start

Identify segments of the Magnesium Industry.

For each segment, is SF6 used as a cover gas?

No

Continue to the next segment. If No for all segments, report Not occuring .

Yes

Estimate emissions using the direct reporting method.

Yes

Are there direct measured emission data related to SF6 use?

Box 4: Tier 3

No

Are there reported data on SF6 use from any segments?

Report emissions as equalling all SF6 use in segment.

Yes

Box 3: Tier 2

No

Is this a key category1?

No

Are there activity level and emission factor data?

Yes

Estimate emissions based on activity level and countryspecific or default emission factors. Box 2: Tier 1

Yes Collect data for the Tier 3 or the Tier 2 method

No

Estimate emissions using national statistics and default emission factor. Box 1: Tier 1

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

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HFC-134a, FK 5-1-12 and decomposition products (e.g., PFCs) As described in Section 4.5.1, the industrial use of fluorinated compounds other than SF6 for magnesium oxidation protection commenced in 2003-2004. As such, the industrial experience in using these compounds for magnesium protection purposes is yet very limited. Even individual plants will have little historic data, if any, on actual emissions of these other fluorinated compounds from their operations. While there is a general sense in industry that the volume use of these alternate gases will be less than SF6, there are no data available at this time on which to base emission factors. Hence, it is not possible at this time to develop an emission factor-based approach (Tier 1 or 2) for reporting emissions. However, if the GHG emission from the use of magnesium cover gases is a national key category, it is good practice to collect direct measurements or meaningful indirect measurements of GHG emissions (fugitive emissions of HFC134-a and FK 5-1-12 as well as emissions of PFCs as decomposition products) from magnesium foundries using HFC-134a or FK 5-1-12 as cover gases. This is consistent with the Tier 3 approach.

Carrier gases The contribution of carbon dioxide carrier gas used in protective cover gas systems is normally a small fraction of the global warming potential. In general, these emissions may be disregarded.

4.5.2.2

C HOICE

OF EMISSION FACTORS

CO 2 EMISSIONS FROM PRIMARY PRODUCTION Tier 1 – default emission factors As previously mentioned, the Tier 1 method calculates emissions from default emission factors applied to a country’s total primary magnesium production. The default emission factors (Table 4.19) take into account the type of material used and basic stoichiometric ratios which have been adjusted by empirical data for generic manufacturing process losses. The resulting emission of CO2 per tonne magnesium produced is considerably higher than the theoretical volume described in the Section 4.5.1.

TABLE 4.19 EMISSION FACTORS FOR ORE-SPECIFIC PRIMARY Mg METAL PRODUCTION Raw Material

tonnes CO2 emission/tonne primary Mg produced

Dolomite

5.13

Magnesite

2.83

Tier 2 – country/company-specific emission factors The Tier 2 method for estimating CO2 emissions from primary magnesium involves collecting company/plantspecific empirical emission factors. The company specific emission factors may differ substantially from the default emission factors depending on process materials handling. This collection should take place if the emissions are a key category.

Tier 3 – direct measurement approach If actual measured CO2 emissions data are available from individual primary magnesium facilities, these data can be aggregated and used directly to account for national emissions.

MAGNESIUM CASTING PROCESSES (PRIMARY & SECONDARY) SF 6 Tier 1 – default emission factors The underlying assumption for the Tier 1 approach is that all SF6 consumption in this industry segment is emitted, though. as described in Section 4.5.1, this assumption will potentially overestimate the GHG emissions. The Tier 1 method also assumes no knowledge of type of magnesium handling- or casting operation (recycling, billet casting or die-casting, etc.) Under recommended conditions for die-casting, the consumption rates are about 1 kg SF6 per tonne magnesium produced or smelted (Gjestland and Magers, 1996). Although the SF6

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consumption vary substantially between different casting operations and operators (sometimes orders of magnitude), the basic Tier 1 method uses this value as a basis for the default emission calculation when SF6 is used for oxidation protection. If the national magnesium manufacturing processes are well documented, a more accurate application of the Tier 1 method is to disaggregate production data and emission factors according to the various manufacturing processes. These emission factors should relate SF6 emissions to magnesium production at the same disaggregated level as the available activity data (e.g., national, sub-national). National emission factors based on plant measurements are preferable to international default factors because they reflect conditions specific to the country. Such information may be accessible through industry associations, surveys or studies.

TABLE 4.20 SF6 EMISSION FACTORS FOR MAGNESIUM CASTING Casting system

PROCESSES (TIER 1)

kg SF6 emission per tonne Mg casting

All Casting Processes

1.0

Source: Gjestland and Magers (1996)

Tier 2 – company-specific SF 6 consumption As for the Tier 1 method, the underlying principle for the Tier 2 method is that all SF6 consumed is emitted. In the Tier 2 method it is, however, assumed the national (or sub-national ) consumption of SF6 in the magnesium industry is reported by the industry or available through other sources such as national statistics. The most accurate application of the method is normally collection of direct data on SF6 consumption from all individual users of the gas in the magnesium industry. If no direct data are available, an alternative but a less accurate method is to estimate the share of annual national SF6 consumption attributable to the magnesium industry. This requires collecting annual data on national SF6 sales and assumes that all SF6 gas sold to the magnesium industry is emitted within the year.

Tier 3 – direct measurement approach If actual measured emission data are available from individual magnesium processing facilities, these data can be aggregated and used directly to account for national emissions. In such reporting, it is good practice to include destruction of SF6 and formation of secondary gas products.

HFC-134a, FK 5-1-12 and decomposition products (e.g., PFCs) As described above, there are little historic data upon which to base emission factors. However, if the GHG emission from the use of magnesium cover gases is a national key category, it is good practice, for inventory preparation purposes, to collect direct measurements and or reliable indirect measures of GHG emissions (fugitive emissions of HFC134-a and FK 5-1-12 as well as emissions of PFCs as decomposition products) from magnesium foundries using HFC-134a or FK 5-1-12 as cover gases. This may be considered a Tier 3 approach. Over time, it may be possible to use Tier 3 measurements as a means of developing emission factors that could be used for Tier 2.

Carrier gases As mentioned previously in this chapter, the contribution of carbon dioxide carrier gas used in protective cover gas systems is normally a small fraction of the global warming potential. In general it may be disregarded.

4.5.2.3

C HOICE

OF ACTIVITY DATA

CO 2 EMISSIONS FROM PRIMARY PRODUCTION For the Tier 1 method, inventory compilers need to obtain national primary production data and knowledge of raw material type used in the country. As discussed in Section 4.5.2.1, these data may not be publicly available and therefore be difficult to obtain, in particular for small-scale (particularly thermal reduction type) production units in developing countries. Approximate national magnesium production data may be available through industry associations such as the International Magnesium Association. For the Tier 2 method, inventory

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compilers need to collect primary magnesium production data and data on carbonate raw materials from each company/plant. With the Tier 3 method, activity data consists of direct measured and reported emissions.

MAGNESIUM CASTING PROCESSES (PRIMARY & SECONDARY) SF 6 For the Tier 1 method, it is good practice to disaggregate production data into segments using SF6, if possible, (e.g., primary production, recycling, billet casting, die casting, gravity casting, etc.) and apply available segmentspecific emission factors. Where disaggregated data are not available, more aggregated production data, possibly combining output from several different processes, may be used to provide an estimate. In the absence of SF6 consumption data or magnesium production data, the alternative is to collect annual national data on SF6 sales to the magnesium industry. SF6 producers may be able to provide these data directly, or they may be available from national statistics. It is good practice to consider data on consumption by other industries that use SF6 (e.g., electrical equipment) when estimating the share consumed by the magnesium industry. With the Tier 3 and 2 methods, the activity data are reported SF6 (and secondary gas product) emissions or SF6 consumption totals from each plant. For the Tier 1 method, national- or individual plant- magnesium production data are necessary. Where there is some direct reporting of SF6 use in a segment, it is good practice to assess the share of production represented by the plants that directly report. For the other plants, it is good practice to use production-based estimates of emissions.

HFC-134a, FK 5-1-12 and decomposition products (e.g., PFCs) With the Tier 3 method, activity data consists of direct measured and reported emissions. No Tier 1 or 2 method guidance is provided and hence, no activity data are necessary.

Carrier gases It is good practice in inventory reporting that the chosen activity data for carrier gases are analogous to those of the active compound used. I.e., if CO2 is used as carrier gas for SF6, the activity data of CO2 should reflect that of SF6. If CO2 is used as carrier for HFC-134a or FK 5-1-12, CO2 activity data should reflect HFC-134a or FK 5-112 activity data.

4.5.2.4

C OMPLETENESS

Incomplete direct reporting or incomplete activity data should not be a significant issue for primary production in developed countries. Typically, there are a small number of well-known primary magnesium producers in developed countries, and these producers are likely to keep good records. In developing countries, completeness issues generally arise in the casting segments, where facilities are more widely distributed, and have a wide range of capacities and technologies. Some plants may supply to niche markets not captured by national data sets. The inventory compiler should confirm the absence of estimates for these smaller industry segments rather than simply assuming they do not occur. It is also good practice to undertake periodic surveys of the industry and establish close links with international and local industry associations to check completeness of estimates. Because alternate (non-SF6) cover gas systems decompose to various fluorinated by-products, there may be some unaccounted global warming potential not described. This is not expected to be significant. Since an increasing fraction of the world’s primary production, as well as processing of magnesium, takes place in many small production units in countries with developing economies, completeness is expected to become a significant issue. Inventory compilers should be cautious of the potential for double counting emissions from calcination of magnesium carbonate raw materials during primary magnesium production and those emissions associated with calcining limestone, dolomite, and other carboneous minerals (see Chapter 2, Other Process Uses of Carbonates, in this volume.) All emissions associated with the calcination of carbonates for primary magnesium production should be reported as GHG emissions from magnesium production.

4.5.2.5

D EVELOPING

A CONSISTENT TIME SERIES

In terms of overall magnesium production statistics, these data will typically be available for the history of a plant. However, in some cases, historical production data may not be available due to lack of initial records or changes in the structure of the industry in the intervening period. In this case, production data from international sources may be used.

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There may be issues with establishing a consistent time series for CO2 emissions from primary magnesium production since these emissions may not have been reported prior to year 2006 (guidelines for reporting did not exist in the Revised 1996 IPCC Guidelines (IPCC, 1997)). For most primary magnesium production facilities it may, however, be assumed that the CO2 emission level is relatively constant over time on a per tonne magnesium produced basis. In terms of SF6 emissions, it is good practice for the Tier 1 approach to multiply historic activity data by subnational/national or default emission factor presently in use to establish consistent time series. It should be noted that plant specific emission factors would typically decrease over time due to environmental awareness, economic factors, and improved technologies and practices. Since the magnesium industry did not use HFC-134a and FK 5-1-12 cover gases to a significant extent in any country prior to 2003, historic emissions will likely be zero. Given the level of complexity in reporting emissions related to the use of these gases, developing consistent time series will be a challenge to inventory compilers. It is good practice to assess the appropriate historical emission factors following the guidance in Volume 1, Chapter 5. To ensure consistency over time, it is good practice to recalculate emissions estimates using previously used and new methods to ensure that any trends in emissions are real and not caused by changes in the estimation methodologies. Good practice is to document assumptions in all cases and archive them at the inventory compiler.

4.5.3

Uncertainty assessment

CO 2 EMISSIONS FROM PRIMARY PRODUCTION At the plant level, there should normally be well-documented raw material type/analysis and use, as well as tonnage magnesium produced. Directly-reported activity data, which are required for Tier 2 and 3 methods for all gases, are typically accurate to within less than 5 percent. At the national inventory level, the accuracy of magnesium production activity and emission data is comparable to that of other national production statistics (i.e., ±5 percent). Additional uncertainty is introduced through estimating the share of production not reporting directly.

MAGNESIUM CASTING PROCESSES (PRIMARY & SECONDARY) SF 6 In the Tier 1 approach, aggregating production from different secondary segments and using the default emission factor introduces uncertainty. For example, national data from casting operations may not be segregated into diecasting and gravity casting segments despite their potentially different SF6 emission rates. Thus, this approach gives by default a very rough approximation of real emissions. Given that different handling and casting operations may use concentrations of SF6 in cover gas that differ by orders of magnitude, uncertainties using the Tier 1 method may also range over orders of magnitude. For the Tier 1 and 2 methods, there is also a level of uncertainty associated with the assumption that 100 percent of the SF6 used is emitted. In a typical casting operation, the uncertainty in this assumption should be within 30 percent (Bartos et al., 2003). For the Tier 2 method, there is a very low uncertainty associated with SF6 use on a plant level, since SF6 use is measured easily and accurately from purchase data. (An uncertainty estimate of less than 5 percent is usually appropriate for directly reported data.)

For the Tier 3 method, uncertainties arise mainly from monitoring equipment calibration/accuracy. Typical gas analysis methods such as Fourier Transformed Infra Red Spectroscopy (FTIR) generally operate with an estimated accuracy of ± 10 percent. In addition to FTIR and similar analytical techniques, there will be further uncertainty caused by problems related to representative sampling and calibration that could raise the overall uncertainty of FTIR to ± 20 percent.

HFC-134a, FK 5-1-12 and decomposition products (e.g., PFCs) As with the Tier 3 method for SF6, main uncertainties are associated with monitoring equipment calibration/accuracy in processes using HFC-134a or FK 5-1-12 cover gases. Uncertainties are approximated to ± 10 percent.

Carrier gases The largest uncertainty is associated with the Tier 1 approach of considering CO2 emissions from cover gases negligible. This is particularly true if a facility uses a very CO2 rich carrier gas blend. Other tiers have the same uncertainties as related for SF6.

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4.5.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation

4.5.4.1

Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC)

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks, as outlined in Volume 1, Chapter 6, and quality assurance procedures may also be applicable, particularly for higher tier methods. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. The following section outlines additional procedures specific to magnesium production:

Comparison of emissions estimates using different approaches If emissions were calculated using data from individual plants, inventory compilers should compare the estimate to emissions calculated using national magnesium production data or (in the case of SF6) national consumption data attributed to magnesium use. The results of the comparison should be recorded and any discrepancies should be investigated.

Review of plant-level data The following plant-specific information should be archived to facilitate independent review: •

•

• • •

•

Magnesium production volumes and process types; Cover gas with global warming potential (SF6, HFC-134a, FK 5-1-12, CO2, etc.) consumption/composition or magnesium production (where factors are used); Plant-level QA/QC results (including documentation of sampling, measurement method, and measurement results for plant level data); Results of QA/QC conducted by any integrating body (e.g., industry association such as the International Magnesium Association.); Calculations and estimation method; and Where applicable, a list of assumptions in allocating national SF6 usage, HFC-134a, FK 5-1-12 or other cover gases of interest or production to plant level.

Inventory compilers should determine if national or international measurement standards were used for reporting of global warming cover gas (SF6, HFC-134a, FK 5-1-12, etc.) consumption or magnesium production data at the individual plants. If standard methods and QA/QC procedures were not followed, then use of these activity data should be reconsidered.

Review of national activity data QA/QC activities associated with the reference to magnesium production data should be evaluated and referenced. Inventory compilers should check if the trade association or agency that compiled the national production data used acceptable QA/QC procedures. If the QA/QC procedures are deemed acceptable, inventory compilers should reference the QC activity as part of the QA/QC documentation.

Assessment of emission factors Where company/country-specific factors are used, inventory compilers should review the level of QC associated with the underlying data. It is good practice that the inventory compiler cross-check national level default factors against plant-level factors to determine if these are representative.

Peer review Inventory compilers should involve magnesium industry experts in a thorough review of the inventory estimate, giving consideration to potential confidentiality issues. Historical production data may be less sensitive to public disclosure than current data and could be utilised for an external peer review of plant level emissions.

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Verification of SF 6 emissions data Inventory compilers should sum the amount of SF6 used by different industrial sectors (e.g., magnesium, electrical equipment) and compare this value with the total usage of SF6 in the country, obtained from import/export and production data. This provides an upper bound on the potential emissions.16

4.5.4.2

R EPORTING

AND

D OCUMENTATION

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced. To improve transparency, it is good practice to report emissions estimates from the magnesium source category separately by industry segments such as primary production, secondary production and casting. The following additional information can provide a reasonable degree of transparency in reporting:

Direct reporting •

•

Number of magnesium plants reporting;

•

Magnesium and magnesium products produced;

•

Use of other protective cover gases with global warming potential; and

•

The types of processes and manufacture employed;

•

SF6 emissions associated with the magnesium segment;

Emission factor data (and reference) for each protective cover gas with global warming potential.

National cover gas sales-based estimate of potential emissions •

•

National SF6 consumption (and reference);

•

National use of FK 5-1-12 assigned to the magnesium sector;

•

Estimate of percentage of national SF6, HFC-134a, FK 5-1-12, used in magnesium (and reference); and

•

National use of HFC-134a assigned to the magnesium sector;

•

Assumptions for allocating SF6 , HFC-134a, FK 5-1-12, used to magnesium;

Any other assumptions made.

In most countries, the magnesium industry will be represented by a small number of plants. In this industry, the activity level data and cover gas emissions (that are directly related to activity levels) may be considered confidential business information and public reporting may be subject to confidentiality considerations.

16

It may not always be the case that such aggregated consumption data will provide an upper limit on emissions. It is possible, depending on the national characteristics of the SF6 consuming industry that in some years actual emissions of SF6 may be greater than consumption of SF6. For instance, consumption in die casting of magnesium may be very low, there may not be much semiconductor manufacturing, but a considerable bank of SF6 in electrical equipment may have evolved through the years. In this case, leakage from bank combined with emissions resulting from decommissioning of equipment may lead to actual emissions that exceed consumption of SF6 (potential emissions). See also Section 8.2 on SF6 emissions from electrical equipment.

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4.6

LEAD PRODUCTION

4.6.1

Introduction

PRIMARY PRODUCTION PROCESSES There are two primary processes for the production of rough lead bullion from lead concentrates. The first type is sintering/smelting, which consists of sequential sintering and smelting steps and constitutes roughly 78 percent of the primary lead production. The second type is direct smelting, which eliminates the sintering step and constitutes the remaining 22 percent of primary lead production in the developed world. (Sjardin, 2003) In the sintering/smelting process, the initial sintering blends lead concentrates with recycled sinter, lime rock and silica, oxygen, and high-lead-content sludge to remove sulphur and volatile metals via combustion (Metallurgical Industry, 1995). The process, which produces a sinter roast that consists of lead oxide and other metallic oxides, results in the emission of sulphur dioxide (SO2) and energy-related carbon dioxide (CO2) from the natural gas used to ignite the lead oxides (DOE, 2002). The sinter roast is then put in a blast furnace along with ores containing other metals, air, smelter by-products, and metallurgical coke (Metallurgical Industry, 1995). The coke burns as it reacts with air and produces carbon monoxide (CO) that actually performs the reduction of the lead oxide by chemical reaction (DOE, 2002). The smelting process occurs in either a traditional blast furnace or an Imperial Smelting Furnace, and it is the reduction of the lead oxide during this process that produces CO2 emissions (Sjardin, 2003). The sintering process produces molten lead bullion (Metallurgical Industry, 1995). In the direct smelting process, the sintering step is skipped, and the lead concentrates and other materials are entered directly into a furnace in which they are melted and oxidized (Sjardin, 2003). A variety of furnaces are used for the direct smelting process, with the Isasmelt-Ausmelt, Queneau-Schumann-Lurgi, and Kaldo furnaces used for bath smelting and the Kivcet furnace used for flash smelting. A number of reducing agents, which include coal, metallurgical coke, and natural gas, are used in the process in different quantities for each furnace, which results in different levels of CO2 emissions for each type of furnace (Sjardin, 2003; LDA, 2002). The direct smelting process offers significant environmental and potential cost saving benefits through the avoidance of the sintering process and is therefore expected to constitute a growing portion of primary refinery lead production in the future (LDA, 2002).

SECONDARY PRODUCTION PROCESS The secondary production of refined lead amounts to the processing of recycled lead to prepare it for reuse. The vast majority of this recycled lead comes from scrapped lead acid batteries. The lead acid batteries are either crushed using a hammer mill and entered into the smelting process with or without desulphurization or they are smelted whole (Sjardin, 2003). Traditional blast furnaces, Imperial Smelting Furnaces, electric arc furnaces, electric resistance furnaces, reverbatory furnaces, Isasmelt furnaces, Queneau-Schumann-Lurgi furnaces, and Kivcet furnaces can all be used for the smelting of these batteries and other recycled scrap lead (Sjardin, 2003). As with the furnaces used for primary lead bullion production, these furnaces generate different levels of CO2 emissions from their use of differing types and quantities of reductants. The primary reductants are coal, natural gas, and metallurgical coke, although the electric resistance furnace uses petroleum coke (Sjardin, 2003).

4.6.2 4.6.2.1

Methodological Issues C HOICE

OF METHOD

The IPCC Guidelines outline three methods for calculating CO2 emissions from lead production. The choice of a good practice method depends on national circumstances as shown in the decision tree in Figure 4.15. The Tier 1 method calculates emissions from general emission factors applied to a country’s total lead production and is the least accurate. This method is appropriate only when lead production is not a key category. The Tier 2 method uses country specific process material data for both primary and secondary production processes multiplied by the appropriate carbon contents of process materials. The Tier 3 method requires facility-specific measured activity or emissions data.

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Figure 4.15

Decision tree for estimation of CO 2 emissions from lead production Start

Are plantspecific emissions or activity data available?

Yes

Calculate emissions using plant specific data. Box 3: Tier 3

No

Are national process material data available?

Yes

Calculate emissions using process material specific carbon contents. Box 2: Tier 2

No

Is this a key category1?

No

Calculate emissions using default emission factors and national production data. Box 1: Tier 1

Yes Collect data for the Tier 3 or the Tier 2 method.

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

TIER 1 METHOD The simplest estimation method is to multiply default emission factors by lead production. When the only data available are national lead production statistics, it is good practice to use default emission factors. Equation 4.32 calculates total carbon dioxide emissions from lead production by summing emissions by source and accounting for emissions from secondary feedstock pre-treatment. If it is not possible to differentiate the type of production process, the default emission factor should be used. The default emission factor assumes a that 80 percent of production (including both primary and secondary) is smelted using an Imperial Smelting Furnaces or blast furnaces, while the remaining 20 percent is smelted using the direct smelting method in the Kivcet, Ausmelt, and Queneau-Schumann-Lurgi furnaces. This assumption is consistent with global lead production data (Sjardin, 2003). EQUATION 4.32 CO2 EMISSIONS FROM LEAD PRODUCTION E CO 2 = DS • EFDS + ISF • EFISF + S • EFS

Where: ECO2 = CO2 emissions from lead production, tonnes DS = quantity of lead produced by Direct Smelting, tonnes

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EFDS = emission factor for Direct Smelting, tonne CO2/tonne lead product ISF = quantity of lead produced from the Imperial Smelting Furnace, tonnes EFISF = emission factor for Imperial Smelting Furnace, tonne CO2/tonne lead product S = quantity of lead produced from secondary materials, tonnes EFS = emission factor for secondary materials, tonne CO2/tonne lead product The CO2 emission factors used in Equation 4.32 are shown in Table 4.21.

TIER 2 METHOD The Tier 2 method recognizes that there are substantial differences in carbon dioxide emissions for lead production depending on the production methodology and the source of the raw materials, either from secondary sources such as recycled batteries, or, from primary production from ores. Secondary lead sources may be pretreated to remove impurities resulting in carbon dioxide emissions. Emissions can be calculated using country specific emission factors based on the use of reducing agents, furnace types and other process materials of interest. Factors can be developed based on carbon contents applicable to those materials. Table 4.22 provides carbon contents that can be used to derive country-specific factors. These data may be available from governmental agencies responsible for manufacturing or energy statistics, business or industry trade associations, or individual lead companies. Tier 2 is more accurate than Tier 1 because it takes into account the materials and the variety of furnace types used in the lead sector that contribute to CO2 emissions for a particular country rather than assuming worldwide industry-wide practices.

TIER 3 METHOD If actual directly measured CO2 emissions data are available from lead facilities, these data can be aggregated and used directly to account for national emissions using the Tier 3 method. Total national emissions will equal the sum of emissions reported from each facility. If facility emissions are not available, emissions should be calculated from plant-specific data for individual reducing agents and other process materials. To achieve a higher level of accuracy than Tier 2, it is good practice to develop emissions estimates at the plant-level because plants can differ substantially in their technology, specifically furnace technology. These data may be available from governmental agencies responsible for manufacturing or energy statistics, or from business or industry trade associations, but is preferably aggregated from data furnished by individual lead facilities.

4.6.2.2

C HOICE

OF EMISSION FACTORS

TIER 1 METHOD When the only data available are national lead production statistics, it is good practice to use default emission factor of 0.52 tonne of CO2/ tonne of lead (Sjardin 2003). This default should only be used when no information is available on the relative amounts of lead produced from primary and from secondary materials. If information is available, emissions should be calculated using the appropriate factors in Table 4.21. (Sjardin, 2003). The uncertainty in the default factor is high and varies depending on the mix of production methods and the percentage of secondary processing. In addition, the factor assumes that 80 percent of the world’s lead production (including both primary and secondary) is smelted using an Imperial Smelting Furnaces, while the remaining 20 percent is smelted using the direct smelting method in the Kivcet, Ausmelt, and QueneauSchumann-Lurgi furnaces (Sjardin, 2003). TABLE 4.21 GENERIC CO2 EMISSION FACTORS FOR LEAD PRODUCTION BY SOURCE AND FURNACE TYPE (tonnes CO2/tonne product) From Imperial Smelt Furnace (ISF) Production

From Direct Smelting (DS) Production

From Treatment of Secondary Raw Materials

Default Emission Factor (80% ISF, 20% DS)

0.59

0.25

0.2

0.52

Source: Sjardin (2003)

TIER 2 METHOD This method offers the opportunity to adjust emission factors to reflect variations from the presumed norms based on plant-specific data for the carbon content of these materials and based on furnace type. The default carbon contents in Table 4.22 should be used if an inventory compiler does not have information on conditions

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in lead facilities, but has detailed activity data for the process materials. The default values in Table 4.22 are derived from the default values in Tables 1.2 and 1.3 in Volume 2, Chapter 1 and should be referenced for further information. TABLE 4.22 MATERIAL-SPECIFIC CARBON CONTENT FOR LEAD PRODUCTION (kg carbon/kg ) Process Materials

Carbon Content

Blast Furnace Gas

0.17

Charcoal*

0.91

1

0.67

Coal

Coal Tar

0.62

Coke

0.83

Coke Oven Gas

0.47

Coking Coal

0.73 2

EAF Carbon Electrodes

0.82

3

EAF Charge Carbon Fuel Oil

0.83

4

0.86

Gas Coke

0.83

Natural Gas

0.73

Petroleum Coke

0.87

Source: References for carbon content data are included in Table 1.2 and 1.3 in Volume 2, Chapter 1. Notes: 1

Assumed other bituminous coal

2

Assumed 80 percent petroleum coke and 20 percent coal tar

3

Assumed coke oven coke

4

Assumed gas/diesel fuel

* The amount of CO2 emissions from charcoal can be calculated by using this carbon content value, but it should be reported as zero in national greenhouse gas inventories. (See Section 1.2 of Volume 1.)

TIER 3 METHOD The Tier 3 method is based on aggregated emission estimates or the application of the Tier 2 at a plant-specific level. The inventory compiler should ensure that each facility has documented the emission factors and carbon contents used, and that these emission factors are indicative of the processes and materials used at the facility. The Tier 3 method requires carbon contents and production/consumption mass rates for all of the process materials and off-site transfers such as those listed in Table 4.22. While Table 4.22 provides default carbon contents, it is good practice under Tier 3 to adjust these values to reflect variations at the plant level from default values represented in the table. The default factors listed in Table 4.22 are only appropriate for the Tier 3 method if plant-specific information indicates that they correspond to actual conditions. It is anticipated that for the Tier 3 method the plant-specific data would include both carbon content data and production/consumption mass rate data, and that therefore the default values in Table 4.22 would not be applied to the Tier 3 method in most instances.

4.6.2.3

C HOICE

OF ACTIVITY DATA

TIER 1 METHOD The Tier 1 method requires only the amount of lead produced in the country and if available, the amount produced by furnace type. These data may be available from governmental agencies responsible for manufacturing statistics, business or industry trade associations, or individual lead companies. These tonnages can then be multiplied by the corresponding emission factor in Table 4.21 to estimate CO2 emissions from the sector or the default factor if furnace type is unavailable.

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TIER 2 METHOD The Tier 2 method requires only the total amounts of reducing agents and other process materials used for lead production in the country. These data may be available from governmental agencies responsible for manufacturing or energy statistics, business or industry trade associations, or individual lead companies. These amounts can then be multiplied by the appropriate carbon contents in Table 4.22 and summed to determine total CO2 emission from the sector. However, activity data collected at the plant-level is preferred (Tier 3). If this is not a key category and data for total industry-wide reducing agents and process materials are not available, emissions can be estimated using the Tier 1 approach.

TIER 3 METHOD The Tier 3 method requires collection, compilation, and aggregation of facility-specific measured emissions or activity data. If emissions data are not available, the Tier 3 method requires activity data to be collected at the plant level and aggregated for the sector. The amounts of reducing agents and the type of furnace used are more accurately determined in this manner. These data may be available from governmental agencies responsible for manufacturing or energy statistics, or from business or industry trade associations, but are preferably aggregated from data furnished by individual lead facilities. This approach also allows for additional accuracy by allowing individual companies to provide more accurate plant-specific data and/or to use more relevant emission factors to reflect carbon contents and furnace types that may differ from the default factors used in the Tier 2 method.

4.6.2.4

C OMPLETENESS

In estimating emissions from this source category, there is a risk of double counting or omission in either the IPPU or the Energy Sector. As a general guide, all process emissions from lead production should be reported in the IPPU Sector.

4.6.2.5

D EVELOPING

A CONSISTENT TIME SERIES

Emissions from lead production should be calculated using the same method for every year in the time series. Where data are unavailable to support a Tier 3 method for all years in the time series, these gaps should be recalculated according to the guidance provided in Volume 1, Chapter 5, Time Series Consistency and Recalculation.

4.6.3

Uncertainty assessment

Uncertainty estimates for lead production result predominantly from uncertainties associated with activity data, and from uncertainty related to the emission factor. Table 4.23 provides an overview of the uncertainties for emission factors and activity data.

4.6.3.1

E MISSION

FACTOR UNCERTAINTIES

The default emission factors used in Tier 1 may have an uncertainty of ± 50 percent. Tier 2 carbon contents are expected to have an uncertainty of ± 15 percent. Tier 3 unit specific emission factors would be expected to be within 5 percent if plant-specific carbon content data are available.

4.6.3.2

A CTIVITY

DATA UNCERTAINTIES

National production statistics should be available and likely have an uncertainty of ± 10 percent. For Tier 2, the total amount of reducing agents and process materials used for lead production would likely be within 10 percent. Tier 3 requires plant-specific information on production data (about 5 percent uncertainty). In addition, actual emissions data for tier 3 would be expected to have ± 5 percent uncertainty.

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TABLE 4.23 UNCERTAINTY RANGES Method

Data Source

Tier 1

National Production Data Default Emission Factor Emission Factors by Process Type

± 10% ± 50% ± 20%

Tier 2

Amounts and Types of Reducing Agents Used Process Material Carbon Contents

± 10% ± 15%

Tier 3

Facility-Derived = Process Materials Data Facility-Specific Measured CO2 Data Facility-Specific Emission Factors

± 5% ± 5%

4.6.4 4.6.4.1

Uncertainty Range

± 5%

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC)

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, Chapter 6, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventories agencies are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. In addition to the guidance in Volume 1, Chapter 6, specific procedures of relevance to this source category are outlined below.

Review of emission factors Inventory compilers should compare aggregated national emission factors with the IPCC default factors in order to determine if the national factor is reasonable relative to the IPCC default. Differences between national factors and default factors should be explained and documented, particularly if they are representative of different circumstances.

Site-specific activity data check For site-specific data, inventory compilers should review inconsistencies between sites to establish whether they reflect errors, different measurement techniques, or result from real differences in emissions, operational conditions or technology. For lead production, inventory compilers should compare plant data with other plants. Inventory compilers should ensure that emission factors and activity data are developed in accordance with internationally recognised and proven measurement methods. If the measurement practices fail this criterion, then the use of these emissions or activity data should be carefully evaluated, uncertainty estimates reconsidered and qualifications documented. If there is a high standard of measurement and QA/QC is in place at most sites, then the uncertainty of the emissions estimates may be revised downwards.

Expert review Inventory compilers should include key industrial trade organisations associated with lead production in a review process. This process should begin early in the inventory development process to provide input to the development and review of methods and data acquisition Third party reviews are also useful for this source category, particularly related to initial data collection, measurement work, transcription, calculation and documentation.

Activity data check For all tier levels, inventory compilers should check with Volume 2: Energy to ensure that emissions from reducing agents and process materials (coal, coke, natural gas, etc.) are not double-counted or omitted.

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Inventory compilers should examine any inconsistency between data from different plants to establish whether these reflect errors, different measurement techniques or result from real differences in emissions, operational conditions or technology. This is particularly relevant to the plant-specific estimates of amounts of reducing agents or reported carbon content of process materials. Inventory compilers should compare aggregated plant-level estimates to industry totals for process materials consumption where such trade data are available.

4.6.4.2

R EPORTING

AND

D OCUMENTATION

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced.

TIER 1 METHOD Besides reporting of estimated emissions, it is good practice to report the total lead production by process and corresponding emission factors used.

TIER 2 METHOD Good practice is to document the estimated or calculated emissions, all activity data, and corresponding carbon contents any assumptions or data justifying alternative values. There should be a clear explanation of the linkage with the Volume 2, Energy, to demonstrate that double counting or missing emissions have not occurred.

TIER 3 METHOD Good practice is to document the calculated emissions and source of all data, taking into account the need to protect the confidentiality of data for specific facilities if the data are business-sensitive or of a proprietary nature. In addition, inventory compilers should for all tiers, document all information needed to reproduce the estimate, as well as the QA/QC procedures.

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4.7

ZINC PRODUCTION

4.7.1

Introduction

PRIMARY PRODUCTION PROCESSES There are three different types of primary zinc production. The first method is a metallurgical process called electro-thermic distillation. The process is used to combine roasted concentrate and secondary zinc products into a sinter feed that is burned to remove zinc, halides, cadmium, and other impurities. The resulting zinc oxide-rich sinter is combined with metallurgical coke in an electric retort furnace that reduces the zinc oxides and produces vaporized zinc which is captured in a vacuum condenser. The reduction results in the release of non-energy carbon dioxide (CO2) emissions. The electro-thermic distillation process is used in the United State and in Japan. (Sjardin, 2003; European IPPC Bureau, 2001) The second method of zinc production is a pyrometallurgical process involving the use of an Imperial Smelting Furnace, which allows for the simultaneous treatment of lead and zinc concentrates. The process results in the simultaneous production of lead and zinc and the release of non-energy CO2 emissions. The metallurgical coke/coal reductant used in this process must be allocated to lead and zinc production in order to perform an emission calculation without double counting. A mass based allocation results in a factor of 0.74 tonnes coke/tonne zinc. (Sjardin, 2003; European IPPC Bureau, 2001) The third zinc production method is the electrolytic process, which is a hydrometallurgical technique. In this process, zinc sulphide is calcined, resulting in the production of zinc oxide. The zinc oxide is then leached in sulphuric acid and purified to remove iron impurities, copper, and cadmium. The zinc is then drawn out of the solution using electrolysis. The electrolytic process does not result in non-energy CO2 emissions. (Sjardin 2003; European IPPC Bureau 2001)

SECONDARY PRODUCTION PROCESSES There are more than 40 hydrometallurgical and pyrometallurgical technologies that can be used to recover zinc metal from various materials. The preferred method for a given situation depends on the zinc source (contamination level and zinc concentration) and the desired end use for the recovered zinc. The process frequently consists of zinc concentration (through physical and/or chemical separation), sintering, smelting, and refining. In some cases, high grade zinc is removed from this process after physical concentration and consumed by other industries, including iron and steel manufacture, brass manufacture, and zinc die-casting, without going through the rest of the process steps. (Sjardin, 2003) The sintering, smelting, and refining steps are identical to the steps used in the primary zinc production process, so certain smelting processes are considered emissive, while the sintering and refining steps are considered non emissive from the perspective of non-energy CO2 emissions. When the concentration step involves the use of a carbon-containing reductant and high temperatures to volatilize or fume zinc from the source materials, the process could result in non-energy CO2 emissions. The Waelz Kiln and slag reduction or fuming processes are two such concentration methods. The Waelz Kiln process, which is used to concentrate zinc in flue dusts, sludges, slags, and other zinc-containing materials, involves the use of metallurgical coke as a reductant. However, the reduced zinc is re-oxided during the processes and the metallurgical coke also serves as a heat source during the process. The slag reduction or fuming process, which is used strictly to concentrate zinc in molten slags from copper and zinc smelting, involves the use of coal or another carbon source as a reductant. (Sjardin, 2003; European IPPC Bureau, 2001)

4.7.2 4.7.2.1

Methodological issues C HOICE

OF METHOD

The IPCC Guidelines outline several approaches for calculating CO2 emissions from zinc production. The choice of a good practice method depends on national circumstances as shown in the decision tree in Figure 4.16. The Tier 3 method may be used if facility-specific measured emissions data are available. Tier 2 method uses country specific emissions factors for both primary and secondary production processes. The Tier 1 method is very simple and it may lead to errors due to its reliance on assumptions rather than actual data. The Tier 1 method calculates emissions from general emission factors applied to a country’s total zinc production and is the least rigorous method. This method should only be used when zinc production is not a key category.

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TIER 1 METHOD The simplest estimation method is to multiply default emission factors by zinc product type (Equation 4.33). When the only data available are national zinc production statistics, it is good practice to use default emission factors. If material specific data are not available to calculate emissions using the Tier 2 methodology, but the process type is known, inventory compilers can calculate emissions using Equation 4.34. EQUATION 4.33 CO2 EMISSIONS FROM ZINC PRODUCTION (TIER 1) E CO 2 = Zn • EFdefault

Where: ECO2 = CO2 emissions from zinc production, tonnes Zn = quantity of zinc produced, tonnes EFdefault = default emission factor, tonnes CO2/tonne zinc produced

EQUATION 4.34 CO2 EMISSIONS FROM ZINC PRODUCTION (TIER 1) E CO 2 = ET • EFET + PM • EFPM + WK • EFWK

Where: ECO2 = CO2 emissions from zinc production, tonnes ET= quantity of zinc produced by electro-thermic distillation, tonnes EFET = emission factor for electro-thermic distillation, tonnes CO2/tonne zinc produced PM = quantity of zinc produced by pyrometallurgical process (Imperial Smelting Furnace Process) , tonnes EFPM = emission factor for pyrometallurgical process, tonnes CO2/tonne zinc produced WK = quantity of zinc produced by Waelz Kiln process, tonnes EFWK = emission factor for Waelz Kiln process, tonnes CO2/tonne zinc produced

TIER 2 METHOD Emission can be calculated using country specific emission factor based on aggregated plant statistics on the use of reducing agents, furnace types and other process materials of interest is developed based on default emission factors applicable to those materials. These data may be available from governmental agencies responsible for manufacturing or energy statistics, business or industry trade associations, or individual zinc companies. Tier 2 is more accurate than Tier 1 because it takes into account the materials and the variety of furnace types used in the zinc sector that contribute to CO2 emissions for a particular country rather than assuming industry-wide practices.

TIER 3 METHOD If actual measured CO2 emissions data are available from zinc facilities, these data can be aggregated and used directly to account for national emissions using the Tier 3 method.

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4.7.2.2

C HOICE

OF EMISSION FACTORS

TIER 1 METHOD The emission factor for the pyrometallurgaical process (Imperial Smelting Furnace) is an aggregate, weighted emission factor encompassing both primary and secondary zinc production in Europe (Sjardin, 2003), No data was available to determine an emission factor for the electro-thermic process. An emission factor based on the amount of coke consumed per tonne of EAF dust consumed in a Waelz Kiln furnace was developed based on the materials balance provided by Viklund-White (2000), wherein Viklund-White finds that 400 kg of coke are consumed for every metric tonne of EAF dust consumed.

TABLE 4.24 TIER 1 CO2 EMISSION FACTORS FOR ZINC PRODUCTION Process

Emission Factor

Source

Waelz Kiln (tonne of CO2/ tonne zinc)

3.66

Derived from Viklund-White C. (2000) The Use of LCA for the

Pyrometeallurgical (Imperial Smelting Furnace) (tonne of CO2/ tonne zinc)

0.43

Electro-thermic

Environmental Evaluation of the Recycling of Galvanized Steel. ISIJ International. Volume 40 No. 3: 292-299. Sjardin 2003. CO2 Emission Factors for Non-Energy Use in the Non-Ferrous Metal, Ferroalloys and Inorganics Industry. Copernicus Institute, Utrecht, The Netherlands. June 2003.

Unknown

Default Factor (tonne of CO2/ tonne zinc)

1.72

default factor is based on weighting of known emission factors (60% Imperial Smelting, 40% Waelz Kiln)

TIER 2 METHOD The Tier 2 method requires the calculation of a country specific emission factor based on the total amount of reducing agents and other carbon containing process materials used for zinc production in the country. These country specific emission factors should be based on aggregated plant statistics on the use of reducing agents, furnace types and other process materials of interest. An emission factor was developed based on the amount of metallurgical coke consumed per tonne of EAF dust consumed: 0.4 tonnes coke/ tonne EAF dust consumed (Viklund-White, 2000). If activity data are available, an emission factor of 1.23 tonnes of EAF dust per tonne of zinc could be used to calculate emissions. When producing zinc from EAF dust in a Waelz Kiln furnace, the complexities of the process suggest that emission factors are more accurate if they are based on the amount of EAF dust consumed rather than the total zinc produced . This is because the amount of reduction materials (metallurgical coke) consumed is directly dependent upon the amount, and zinc content, of the EAF dust consumed. Weighing equipment is used in the Waelz Kiln process to control the amount of metallurgical coke entered into the kiln (Sjardin 2003; European IPPC Bureau 2001).

4.7.2.3

C HOICE

OF ACTIVITY DATA

TIER 1 METHOD The Tier 1 method requires only the amount of zinc produced in the country, and if available, the process type. These data may be available from governmental agencies responsible for manufacturing statistics, business or industry trade associations, or individual zinc companies. These tonnages can then be multiplied by the default emission factors to estimate CO2 emissions.

TIER 2 METHOD The Tier 2 method requires the calculation of a country specific emission factor based on the total amount of reducing agents and other carbon containing process materials used for zinc production in the country. These data may be available from governmental agencies responsible for manufacturing or energy statistics, business or industry trade associations, or individual zinc companies. These country specific emission factors can then be multiplied by the production amount to determine total CO2 emission from the sector. If this is not a key category and data for total industry-wide reducing agents and process materials are not available, emissions can be estimated using the Tier 1.

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TIER 3 METHOD The Tier 3 method requires collection, compilation, and aggregation of facility-specific measured emissions data, if any. However, activity data collected at the plant-level can also be used, with separate emission factors for each plant multiplied by plant specific production. If this is not a key category and data for total industry-wide reducing agents and process materials are not available, emissions can be estimated using the Tier 1.

Figure 4.16

Decision tree for estimation of CO 2 emissions from zinc production Start

Are plantspecific emissions or activity data available?

Yes

Calculate process emissions using Tier 3 Method. Box 3: Tier 3

No

Are national data available for process materials used in zinc production?

Yes

Calculate process emissions using Tier 2 Method. Box 2: Tier 2

No

Is this a key category1?

No

Estimate process emissions using Tier 1 Method. Box 1: Tier 1

Yes Collect data for the Tier 3 or the Tier 2 method. Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

4.7.2.4

C OMPLETENESS

In estimating emissions from this source category, there is a risk of double-counting or omission in either the Industrial Processes or the Energy Sector. It is important to note that the Tier 1 emission factor assumes that the CO2 emissions from the combustion of various fuels used for production of heat in the calcining, sintering, leaching, purification smelting, and refining processes are captured within the CO2 from fossil fuel combustion emission category. In using the tier 2 or 3 methodologies, double-counting can be avoided. The largest source of potential double-counting, emissions from coke production, are calculated in Section 4.2 and reported in the Energy Sector.

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4.7.2.5

D EVELOPING

A CONSISTENT TIME SERIES

Emissions from zinc production should be calculated using the same method for every year in the time series. Where data are unavailable to support a Tier 3 method for all years in the time series, these gaps should be recalculated according to the guidance provided in Volume 1, General Guidance and Reporting.

4.7.3

Uncertainty assessment

Uncertainty estimates for zinc production result predominantly from uncertainties associated with activity data, and from uncertainty related to the emission factors. Table 4.25 provides an overview of the uncertainties for emission factors and activity data.

4.7.3.1

E MISSION

FACTOR UNCERTAINTIES

The default emission factors used in Tier 1 may have an uncertainty of ± 50 percent. Tier 2 country specific emission factors are expected to have an uncertainty of ± 15 percent. Tier 3 unit specific emission factors would be expected to be within 5 percent if plant-specific carbon content data are available.

4.7.3.2

A CTIVITY

DATA UNCERTAINTIES

National production statistics should be available and likely have an uncertainty of ± 10 percent. For Tier 2, the total amount of reducing agents and process materials used for lead production would likely be within 10 percent. Tier 3 actual emissions data would be expected to have ± 5 percent uncertainty.

TABLE 4.25 UNCERTAINTY RANGES Method

Data Source

Tier 1

National Production Data Default Emission Factors Process Specific Emission Factors

± 10% ± 50% ± 20%

Tier 2

National Reducing Agent & Process Materials Data Country Specific Emission Factors

± 10% ± 15%

Tier 3

Facility-Derived = Process Materials Data Facility-Specific Measured CO2 Data Facility-Specific Emission Factors

± 5% ± 5% ± 5%

4.7.4 4.7.4.1

Uncertainty Range

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC)

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. In addition to the guidance in Volume 1, specific procedures of relevance to this source category are outlined below.

Review of emission factors Inventory compilers should compare aggregated national emission factors with the IPCC default factor in order to determine if the national factor is reasonable relative to the IPCC default. Significant differences between national factors and the default factor should be explained and documented, particularly if they are representative of different circumstances.

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Site-specific activity data check For site-specific data, inventory compilers should review inconsistencies between sites to establish whether they reflect errors, different measurement techniques, or result from real differences in emissions, operational conditions or technology. For zinc production, inventory compilers should compare plant data with other plants. Inventory compilers should ensure that emission factors and activity data are developed in accordance with internationally recognised and proven measurement methods. If the measurement practices fail this criterion, then the use of these emissions or activity data should be carefully evaluated, uncertainty estimates reconsidered and qualifications documented. If there is a high standard of measurement and QA/QC is in place at most sites, then the uncertainty of the emissions estimates may be revised downwards.

Expert review Inventory compilers should include key industrial trade organisations associated with zinc production in a review process. This process should begin early in the inventory development process to provide input to the development and review of methods and data acquisition. Third party reviews are also useful for this source category, particularly related to initial data collection, measurement work, transcription, calculation and documentation.

Activity data check For all tier levels, inventory compilers should check to ensure that emissions from reducing agents and process materials (coal, coke, natural gas, etc.) are not double-counted as energy related emissions or omitted. Inventory compilers should examine any inconsistency between data from different plants to establish whether these reflect errors, different measurement techniques or result from real differences in emissions, operational conditions or technology. This is particularly relevant to the plant-specific estimates of amounts of reducing agents or reported carbon content of process materials. Inventory compilers should compare aggregated plant-level estimates to industry totals for process materials consumption where such trade data are available.

4.7.4.2

R EPORTING

AND

D OCUMENTATION

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced.

TIER 1 METHOD Besides reporting of estimated emissions, it is good practice to report the total zinc production by process and corresponding emission factors used.

TIER 2 METHOD Good practice is to document the estimated or calculated emissions, all activity data, and corresponding emission factors and any assumptions or data justifying alternative emission factors.

TIER 3 METHOD Good practice is to document the calculated emissions and source of all data, taking into account the need to protect the confidentiality of data for specific facilities if the data are business-sensitive or of a proprietary nature. In addition, inventory compilers should for all tiers, document all information needed to reproduce the estimate, as well as the QA/QC procedures

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References SECTION 4.2 EEA (2005). EMEP/CORINAIR. Emission Inventory Guidebook – 2005, European Environment Agency, Technical report No 30. Copenhagen, Denmark, (December 2005). Available from web site see: http://reports.eea.eu.int/EMEPCORINAIR4/en European IPPC Bureau (2001). Integrated Pollution Prevention and Control (IPPC) Best Available Techniques Reference Document on the Production of Iron and Steel, December 2001. http://eippcb.jrc.es/pages/FActivities.htm International Iron and Steel Institute (2004). Steel Statistical Yearbook 2004: International Iron and Steel Institute, COMMITTEE ON ECONOMIC STUDIES, Brussels. Pipatti, R. (2001). Greenhouse Gas Emissions and Removals in Finland, Report No. 2094, VTT Technical Research Centre of Finland, Espoo, 2001. http://virtual.vtt.fi/inf/pdf/tiedotteet/2001/T2094.pdf Schoenberger, H. (2000). European Conference on “The Sevilla Process: A Driver for Environmental Performance in Industry” Stuttgart, 6 and 7 April 2000, BREF on the Production of Iron and Steel conclusion on BAT, Dr. Harald Schoenberger, Regional State Governmental Office Freiburg, April 2000.

SECTION 4.3 FFF (2000). The Norwegian Ferroalloy Producers Research Association, “Emission factors standardized at meeting”, Oslo 2000. IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. Callander B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2000). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Penman J., Kruger D., Galbally I., Hiraishi T., Nyenzi B., Emmanuel S., Buendia L., Hoppaus R., Martinsen T., Meijer J., Miwa K., Tanabe K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. Lindstad, T. (2004). ‘CO2 Emissions from the Production of Silicon Alloys’, STF80A04019, SINTEF, Trondheim 2004. Olsen, S.E., Monsen, B.E. and Lindstad, T. (1998). ‘CO2 Emissions from the Production of Manganese and Chrome Alloys in Norway’, Electric Furnace Conference Proceedings Vol. 56, Iron & Steel Society, Warrendale PA 1998, pp 363-369. Olsen, S.E. (2004). ‘CO2 Emissions from the Production of Manganese Alloys in Norway’, STF80A04010, SINTEF, Trondheim 2004.

SECTION 4.4 IAI (2000). International Aluminium Institute, International Aluminium Institute (2000) ‘Life Cycle Assessment of Aluminium’ IAI (2001). International Aluminium Institute, Perfluorocarbon Emissions Reduction Programme 1990 - 2000, 2001, available at http://www.world-aluminium.org/iai/publications/documents/pfc2000.pdf. IAI (2005a). International Aluminium Institute, The Aluminium Sector Greenhouse Gas Protocol, http://www.world-aluminium.org/environment/climate/ghg_protocol.pdf, 2005. IAI (2005b). International Aluminium Institute, survey on composition of production materials, 2005 (unpublished) IAI (2005c). International Aluminium Institute, Annual Anode Effect Survey 2003, www.world-aluminium.org, 2005. U.S. EPA and IAI (2003), U.S. Environmental Protection Agency and International Aluminium Institute, USEPA/IAI Protocol for Measurement of PFCs from Primary Aluminium Production, EPA 43-R-03-006, May 2003.

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SECTION 4.5 Bartos, S., Kantamaneni, R., Marks, J. and Laush, C. (2003). “Measured SF6 Emissions from Magnesium Die Casting Operations,” Magnesium Technology 2003, Proceedings of The Minerals, Metals & Materials Society (TMS) Conference, March 2003. Gjestland, H. and Magers, D. (1996). “Practical usage of sulphur hexafluoride for melt protection in the magnesium die casting industry” Proceedings of the 53rd International Magnesium Conference, 1996, Ube City, Japan IPCC (2001). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J.T.,Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 881pp. Milbrath, D. (2002). “Development of 3M™ Novec™ 612 Magnesium Protection Fluid as a Substitute for SF6 Over Molten Magnesium,” International Conference on SF6 and the Environment: Emission Reduction Technologies, November 21-22, 2002, San Diego, CA. Tranell, G and Engh, T.A. (2004). “Alternatives to SF6 for the Magnesium Processor – A Technical, Environmental and Economic Assessment”, Proceedings of the 61st Annual International Magnesium Association Conference, May 2004, New Orleans, LA, USA.

SECTION 4.6 DOE (2002). Mining Industry of the Future: Energy and Environmental Profile of the U.S. Mining Industry. Prepared by BCS, Inc for the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, December 2002. LDA (2002). Technical Notes: Primary Extraction of Lead., Lead Development Association International. Internet: http://www.ldaint.org/technotes1.htm Metallurgical Industry (1995). AP 42, Fifth Edition, Volume I, Chapter 12, http://www.epa.gov/ttn/chief/ap42/ ch12/index.html Sjardin, M. (2003). CO2 Emission Factors for Non-Energy Use in the Non-Ferrous Metal, Ferroalloys and Inorganics Industry. Copernicus Institute, Utrecht, The Netherlands, June 2003.

SECTION 4.7 European IPPC Bureau (2001). Integrated Pollution Prevention and Control (IPPC) Best Available Techniques Reference Document on the Non Ferrous Metals Industries, December 2001. http://eippcb.jrc.es/pages/FActivities.htm Sjardin, M. (2003). CO2 Emission Factors for Non-Energy Use in the Non-Ferrous Metal, Ferroalloys and Inorganics Industry. Copernicus Institute, Utrecht, The Netherlands, June 2003. Viklund-White C. (2000). The Use of LCA for the Environmental Evaluation of the Recycling of Galvanized Steel. ISIJ International. Volume 40 No. 3: 292-299.

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Chapter 5: Non-Energy Products from Fuels and Solvent Use

CHAPTER 5

NON-ENERGY PRODUCTS FROM FUELS AND SOLVENT USE

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.1

Volume 3: Industrial Processes and Product Use

Authors Jos G. J. Olivier (Netherlands) Domenico Gaudioso (Italy), Michael Gillenwater (USA), Chia Ha (Canada), Leif Hockstad (USA), Thomas Martinsen (Norway), Maarten Neelis (Netherlands), Hi-chun Park (Republic of Korea), and Timothy Simmons (UK)

Contributing Author Martin Patel (Netherlands)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Non-Energy Products from Fuels and Solvent Use

Contents 5

Non-Energy Products from Fuels and Solvent Use.......................................................................................5.5 5.1

Introduction ...........................................................................................................................................5.5

5.2

Lubricant use.........................................................................................................................................5.6

5.2.1

Introduction ...................................................................................................................................5.6

5.2.2

Methodological issues ...................................................................................................................5.6

5.2.2.1

Choice of method.....................................................................................................................5.7

5.2.2.2

Choice of emission factors.......................................................................................................5.9

5.2.2.3

Choice of activity data .............................................................................................................5.9

5.2.2.4

Completeness...........................................................................................................................5.9

5.2.2.5

Developing a consistent time series .......................................................................................5.10

5.2.3 5.2.3.1

Emission factor uncertainties.................................................................................................5.10

5.2.3.2

Activity data uncertainties .....................................................................................................5.10

5.2.4

5.3

Uncertainty assessment ...............................................................................................................5.10

Quality Assurance and Quality Control (QA/QC), Reporting and Documentation.....................5.10

5.2.4.1

Quality Assurance and Quality Control .................................................................................5.10

5.2.4.2

Reporting and Documentation ...............................................................................................5.10

Paraffin wax use ..................................................................................................................................5.11

5.3.1

Introduction .................................................................................................................................5.11

5.3.2

Methodological issues .................................................................................................................5.11

5.3.2.1

Choice of method...................................................................................................................5.11

5.3.2.2

Choice of emission factors.....................................................................................................5.12

5.3.2.3

Choice of activity data ...........................................................................................................5.12

5.3.2.4

Completeness.........................................................................................................................5.13

5.3.2.5

Developing a consistent time series .......................................................................................5.13

5.3.3 5.3.3.1

Emission factor uncertainties.................................................................................................5.13

5.3.3.2

Activity data uncertainties .....................................................................................................5.13

5.3.4

5.4

Uncertainty assessment ...............................................................................................................5.13

Quality Assurance and Quality Control (QA/QC), Reporting and Documentation ................... 5.13

5.3.4.1

Quality Assurance and Quality Control .................................................................................5.13

5.3.4.2

Reporting and Documentation ...............................................................................................5.13

Asphalt production and use .................................................................................................................5.14

5.4.1

Introduction .................................................................................................................................5.14

5.4.2

Methodological issues .................................................................................................................5.15

5.4.3

Completeness ..............................................................................................................................5.16

5.4.4

Uncertainty assessment ...............................................................................................................5.16

5.4.5

Reporting and Documentation.....................................................................................................5.16

5.5

Solvent use ..........................................................................................................................................5.16

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Volume 3: Industrial Processes and Product Use

5.5.1

Introduction .................................................................................................................................5.16

5.5.2

Completeness ..............................................................................................................................5.17

5.5.3

Developing a consistent time series.............................................................................................5.17

5.5.4

Uncertainty assessment ...............................................................................................................5.17

References

.....................................................................................................................................................5.18

Equations Equation 5.1

Basic formula for calculating CO2 emissions from non-energy product uses .......................5.5

Equation 5.2

Lubricants – Tier 1 method ...................................................................................................5.7

Equation 5.3

Lubricants – Tier 2 method ...................................................................................................5.8

Equation 5.4

Waxes – Tier 1 method........................................................................................................5.11

Equation 5.5

Waxes – Tier 2 method........................................................................................................5.11

Figures Figure 5.1

Sectoral allocation of emissions from lubricants and waxes .................................................5.7

Figure 5.2

Decision tree for CO2 from non-energy uses of lubricants....................................................5.8

Figure 5.3

Decision tree for CO2 from non-energy uses of paraffin waxes ..........................................5.12

Tables Table 5.1

Non-energy product uses of fuels and other chemical products ............................................5.6

Table 5.2

Default oxidation fractions for lubricating oils, grease and lubricants in general .................5.9

Box Box 5.1

5.4

Asphalt production and use .................................................................................................5.14

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Non-Energy Products from Fuels and Solvent Use

5 NON-ENERGY PRODUCTS FROM FUELS AND

SOLVENT USE 5.1

INTRODUCTION

This section provides methods for estimating emissions from the first use of fossil fuels as a product for primary purposes other than i) combustion for energy purposes and ii) use as feedstock or reducing agent. Emissions from the latter two uses are accounted for by methods described in the chemical industry (Chapter 3) and in metal industry (Chapter 4). The products covered here comprise lubricants, paraffin waxes, bitumen/asphalt, and solvents. Emissions from further uses or disposal of the products after first use (i.e., the combustion of waste oils such as used lubricants) are to be estimated and reported in the Waste Sector when incinerated or in the Energy Sector when energy recovery takes place. Generally, the methods for calculating carbon dioxide (CO2) emissions from non-energy product uses follow a basic formula, in which the emission factor is composed of a carbon content factor and a factor that represents the fraction of fossil fuel carbon that is Oxidised During Use (ODU), e.g., actual co-combustion of the fraction of lubricants that slips into the combustion chamber of an engine). This concept is applied to oxidation during first use only of lubricants and paraffin waxes and not to subsequent uses (e.g., energy recovery): EQUATION 5.1 BASIC FORMULA FOR CALCULATING CO2 EMISSIONS FROM NON-ENERGY PRODUCT USES CO2 Emissions = ∑ (NEU i • CCi • ODU i ) • 44 / 12 i

Where: CO2 Emissions = CO2 emissions from non-energy product uses, tonne CO2 NEUi = non-energy use of fuel i, TJ CCi = specific carbon content of fuel i, tonne C/TJ (=kg C/GJ) ODUi = ODU factor for fuel i, fraction 44/12 = mass ratio of CO2/C The production and use of asphalt for road paving and roofing and the use of solvents derived from petroleum and coal are either not sources or are negligible sources of direct greenhouse gas emissions. They are, however, included in this chapter since they are sometimes substantial sources of non-methane volatile organic compounds (NMVOC) and carbon monoxide (CO) emissions which eventually oxidise to CO2 in the atmosphere. The resulting CO2 input can be estimated from the emissions of these non-CO2 gases (see Section 7.2.1.5 of Volume 1). While almost negligible for asphalt, for solvent use this may have some significance. Emissions from any other non-energy product of fossil fuels not described here should be reported under the subcategory 2D4 ‘Other’. There may be a risk that some of the CO2 emissions calculated for this source category could be partly accounted for elsewhere. Cases where this may occur are clearly indicated in the subsequent sections and should be crosschecked to avoid double counting. Methane (CH4) emissions from the activities covered in this chapter are expected to be minor or not to occur at all. Although some CH4 emissions occur from asphalt production and use for road paving, no method to estimate CH4 emissions is provided since these emissions are expected to be very negligible. Section 1.4 of Chapter 1 of this volume provides guidance for assessing consistency and completeness of carbon emissions from non-energy and feedstock use of fuels by (a) checking that non-energy use/feedstock requirements of processes included in the inventory are in balance with the non-energy use/feedstock supply as recorded in national energy statistics, (b) checking that total reported bottom-up calculated CO2 emissions from non-energy use/feedstock sources at different subcategory levels are complete and consistent, (c) documenting and reporting how these emissions are allocated in the inventory. The sources described in this chapter are part of the verification of completeness of fossil CO2 from non-energy sources and reporting of their allocation.

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Volume 3: Industrial Processes and Product Use

TABLE 5.1 NON-ENERGY PRODUCT USES OF FUELS AND OTHER CHEMICAL PRODUCTS Types of fuels used

Examples of non-energy uses

Gases covered in this chapter CO2

Lubricants

Lubricants used in transportation and industry; Section 5.2

X

Paraffin waxes

Candles, corrugated boxes, paper coating, board sizing, adhesives, food production, packaging; Section 5.3

X

NMVOC, CO

Bitumen; road oil and other petroleum diluents

Used in asphalt production for road paving and e.g., in roofing; Section 5.4

X

White spirit1, kerosene2, some aromatics

As solvent e.g. for surface coating (paint), dry cleaning; Section 5.5

X

5.2

LUBRICANT USE

5.2.1

Introduction

Lubricants are mostly used in industrial and transportation applications. Lubricants are produced either at refineries through separation from crude oil or at petrochemical facilities. They can be subdivided into (a) motor oils and industrial oils, and (b) greases, which differ in terms of physical characteristics (e.g., viscosity), commercial applications, and environmental fate.

5.2.2

Methodological issues

The use of lubricants in engines is primarily for their lubricating properties and associated emissions are therefore considered as non-combustion emissions to be reported in the IPPU Sector. However, in the case of 2stroke engines, where the lubricant is mixed with another fuel and thus on purpose co-combusted in the engine, the emissions should be estimated and reported as part of the combustion emissions in the Energy Sector (see Volume 2). It is difficult to determine which fraction of the lubricant consumed in machinery and in vehicles is actually combusted and thus directly results in CO2 emissions, and the fraction not fully oxidised that results firstly in NMVOC and CO emissions (except for the use in 2-stroke engines, which is excluded here). For this reason, these NMVOC and CO emissions are very seldom reported by countries in the emission inventories. Therefore, for calculating CO2 emissions the total amount of lubricants lost during their use is assumed to be fully combusted and these emissions are directly reported as CO2 emissions. Regulations and policies for the disposal of used oil in most OECD countries often restrict landfilling and dumping, and encourage the separate collection of used oil. A small proportion of lubricants oxidises during use, but the main contribution to emissions is when the waste lubricants are collected at the end of their use, in accordance with country-specific regulations, and subsequently combusted. These waste oil handling emissions, however, are to be reported in the Waste Sector (or in the Energy Sector when energy recovery takes place). Figure 5.1 illustrates this.

1

Also known as mineral turpentine, petroleum spirits, industrial spirit (‘SBP’).

2

Also known as paraffin or paraffin oils (UK, South Africa).

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Non-Energy Products from Fuels and Solvent Use

Figure 5.1

Sectoral allocation of emissions from lubricants and waxes Lubricants and Waxes

Primary Usage (i.e., lubrication or for coating)

Carbon released to atmosphere

Emissions reported in IPPU Sector

Carbon remaining in products

Secondary fate after Primary Usage

(i.e., Combusted for useful Heat/Energy)

(i.e., Disposed, landfilled, or incinerated)

Carbon released to atmosphere

Carbon released to atmosphere

Emissions reported in Energy Sector

Emissions reported in Waste Sector

Since CH4 and N2O emissions are very small in comparison to CO2, these can be neglected for the greenhouse gas calculation.

5.2.2.1

C HOICE

OF METHOD

There are two methodological tiers for determining emissions from the use of lubricants. Both Tier 1 and Tier 2 rely on essentially the same analytical approach, which is to apply emission factors to activity data on the amount of lubricant consumption in a country (in energy units, e.g., TJ). The Tier 2 method requires data on the quantities of different types of lubricants, excluding the amount used in 2-stroke engines, in combination with type-specific Oxidised During Use (ODU) factors to activity data, preferably country-specific, while the Tier 1 method relies on applying one default ODU factor to total lubricant activity data (see decision tree, Figure 5.2). Since the default ODU factor is four times smaller for greases than for lubricating oils, using a higher tier method will primarily capture the impact of using actual fractions of oils and greases in the emission calculation. It is considered good practice to use the Tier 2 method when this is a key category. Tier 1: CO2 emissions are calculated according to Equation 5.2 with aggregated default data for the limited parameters available and the ODU factor based on a default composition of oil and greases in total lubricant figures (in TJ units): EQUATION 5.2 LUBRICANTS – TIER 1 METHOD CO2 Emissions = LC • CC Lubricant • ODU Lubricant • 44 / 12 Where: CO2 Emissions = CO2 emissions from lubricants, tonne CO2 LC = total lubricant consumption, TJ CCLubricant = carbon content of lubricants (default), tonne C/TJ (= kg C/GJ) ODULubricant = ODU factor (based on default composition of oil and grease), fraction 44/12 = mass ratio of CO2/C

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Volume 3: Industrial Processes and Product Use

Tier 2: The Tier 2 method for lubricants relies on a similar equation, however detailed data on the quantities consumed per type of lubricants use (in energy units, e.g., TJ) and, preferably, country-specific emission factors should be used. The emission factors are composed of fuel type specific carbon content and the ODU factor: EQUATION 5.3 LUBRICANTS – TIER 2 METHOD CO2 Emissions = ∑ (LCi • CCi • ODU i ) • 44 / 12 i

Where: CO2 Emissions = CO2 emissions from lubricants, tonne CO2 LCi = consumption of lubricant type i, TJ CCi = carbon content of lubricant type i, tonne C/TJ (= kg C/GJ) ODUi = ODU factor for lubricant type i, fraction 44/12 = mass ratio of CO2/C Lubricant i refers to motor oils/industrial oils and greases separately, excluding the amount used in 2-stroke engines. In both tiers the carbon contents may be the default value for lubricants described in Volume 2 (Chapter 1, Table 1.3), or a country-specific value, if available. Figure 5.2

Decision tree for CO 2 from non-energy uses of lubricants Start

Are data collected for non-energy uses of lubricants, motor oils and greases?

No

Collect activity data.

Yes

Are country-specific statistics available on the fates and composition of lubricants, motor oils and greases?

Yes

Estimate CO2 emissions using countryspecific quantities of lubricants, excluding the amount used in 2-stroke engines (see Figure 5.1 in this Chapter and Volume 2, Chapter 3 on road transportation) Box 2: Tier 2

No Is Category 2D a key category1 and is Lubricant Use a significant subcategory?

No

Estimate CO2 emissions using the IPCC default oxidation fraction. Box 1: Tier 1

Yes Collect data for the Tier 2 method. Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

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Chapter 5: Non-Energy Products from Fuels and Solvent Use

5.2.2.2

C HOICE

OF EMISSION FACTORS

The emission factor is composed of a specific carbon content factor (tonne C/TJ) multiplied by the ODU factor. A further multiplication by 44/12 (the mass ratio of CO2/C) yields the emission factor (expressed as tonne CO2/TJ). For lubricants the default carbon contents factor is 20.0 kg C/GJ on a Lower Heating Value basis. (See Table 1.3 in Chapter 1 of Volume 2. Note that kg C/GJ is identical to tonne C/TJ.) It is assumed that use is combustion resulting in 100 percent oxidation to CO2, with no long-term storage of carbon in the form of ash or post-combustion residue. A small fraction of lubricating oils is oxidised during use (see Table 5.2). An even smaller fraction of greases are oxidised during use. Default ODU factors for oils (20 percent) and greases (5 percent) are based on limited available data (Table 5.2). Tier 1: Having only total consumption data for all lubricants (i.e., no separate data for oil and grease), the weighted average ODU factor for lubricants as a whole is used as default value in the Tier 1 method. Assuming that 90 percent of the mass of lubricants is oil and 10 percent is grease, applying these weights to the ODU factors for oils and greases yields an overall (rounded) ODU factor of 0.2 (Table 5.2). This ODU factor can then be applied to an overall carbon content factor, which may be country-specific or the default value for lubricants to determine national emission levels from this source when activity data on the consumption of lubricants is known (Equation 5.2). Tier 2: Those countries with specific details on the specific quantities of lubricants used as motor oils/industrial oils and as greases can apply different ODU factors, either the default values of 0.2 and 0.05, respectively, or their own ODU factors for lubricants and greases based on national knowledge. These default or country-specific ODU factors can then be multiplied with the country-specific carbon content factors or the single default IPCC carbon content factor for lubricants to determine national emission levels (Equation 5.3). TABLE 5.2 DEFAULT OXIDATION FRACTIONS FOR LUBRICATING OILS, GREASE AND LUBRICANTS IN GENERAL Default fraction in total lubricant a (%)

ODU factor

Lubricating oil (motor oil /industrial oils)

90

0.2

Grease

10

0.05

Lubricant / type of use

IPCC Default for total lubricants

b

0.2

a

Excluding the use in 2-stroke engines.

b

Assuming 90 percent lubricating oil consumption and 10 percent grease consumption and rounded to one significant digit.

Source: Rinehart (2000).

5.2.2.3

C HOICE

OF ACTIVITY DATA

Data on the non-energy use of lubricants are required to estimate emissions, with activity data expressed in energy units (TJ). To convert consumption data in physical units, e.g., in tonnes, into common energy units, e.g., in TJ (on a Lower Heating Value basis), calorific values are required (for specific guidance see Section 1.4.1.2 of Chapter 1 of Volume 2 on Energy). Basic data on non-energy products used in a country may be available from production, import and export data and on the energy/non-energy use split in national energy statistics. Additional information may need to be collected to determine the amount of lubricants being used in 2-stroke engines, which should be excluded from the Tier 2 calculation in this source category. For the Tier 2 method, the individual quantities applied as motor oil/industrial oils and as greases need to be separately known. For specific guidance on the data collection for lubricants used for 2-stroke engines, see Chapter 3 on Road Transport of Volume 2: Energy.

5.2.2.4

C OMPLETENESS

Emissions from the use of lubricants in 2-stroke engines should be accounted for in the Energy Sector. Any emissions that occur due to oxidation from post-use combustion or degradation after disposal should be accounted for separately in the Waste Sector (or Energy Sector, if combustion is used for energy recovery). To avoid double counting and to ensure completeness, the proper allocation of those emissions not related to the non-combustion usage of lubricants in the Energy and Waste Sectors should be cross-checked.

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Volume 3: Industrial Processes and Product Use

5.2.2.5

D EVELOPING

A CONSISTENT TIME SERIES

Emissions from lubricants should be calculated using the same method and data sets for every year in the time series.

5.2.3 5.2.3.1

Uncertainty assessment E MISSION

FACTOR UNCERTAINTIES

The default ODU factors developed are very uncertain, as they are based on limited knowledge of typical lubricant oxidation rates. Expert judgment suggests using a default uncertainty of 50 percent. The carbon content coefficients are based on two studies of the carbon content and heating value of lubricants, from which an uncertainty range of about ±3 percent is estimated (U.S.EPA, 2004).

5.2.3.2

A CTIVITY

DATA UNCERTAINTIES

Much of the uncertainty in emission estimates is related to the difficulty in determining the quantity of nonenergy products used in individual countries, for which a default of 5 percent may be used in countries with well developed energy statistics and 10-20 percent in other countries, based on expert judgement of the accuracy of energy statistics. If the amount of lubricants used in 2-stroke engines, which is to be subtracted from the total consumption used here, is not known, the uncertainty in the activity data will be higher and biased (too high). In countries where a large fraction of the use is in 2-stroke engines, the uncertainty range in the activity data in this section is much higher at the lower end, and can be estimated from the estimated share of 2-stroke engines in the national consumption total.

5.2.4 5.2.4.1

Quality Assurance and Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE

AND

Q UALITY C ONTROL

It is good practice to check the consistency of the total annual consumption figure with the production, import and export data. In addition, it is recommended to compare the amounts discarded, recovered and combusted and the amount used in 2-stroke engines, if available, with total consumption figures in the calculation to check the internal consistency of activity data and ODU factors used in the calculation of different source categories across sectors.

5.2.4.2

R EPORTING

AND

D OCUMENTATION

It is good practice to report and document: •

• •

• •

5.10

The total amount of lubricants produced, imported, exported, consumed, and discarded are to be reported when available. In addition, the amount used for 2-stroke engines and subtracted should be reported. If the latter information is not available or not used in the emission calculation this should be reported. When using the Tier 2 method, the consumption data should be reported per type of lubricant used in the calculation. If the default ODU factor is used, this should be noted in the reporting documentation. If a country-specific emission factor for lubricants was developed, in other words, if a country-specific ODU factor and/or country-specific carbon contents fraction is used, the corresponding data should be provided with an explanation of how this was measured. The allocation of CO2 emissions from lubricants in Table 1.6 on the allocation of CO2 from non-energy use of fossil fuels (see Chapter 1 of this volume).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Non-Energy Products from Fuels and Solvent Use

5.3

PARAFFIN WAX USE

5.3.1

Introduction

The category, as defined here, includes such products as petroleum jelly, paraffin waxes and other waxes, including ozokerite (mixtures of saturated hydrocarbons, solid at ambient temperature). Paraffin waxes are separated from crude oil during the production of light (distillate) lubricating oils. Paraffin waxes are categorised by oil content and the amount of refinement.

5.3.2

Methodological issues

Waxes are used in a number of different applications. Paraffin waxes are used in applications such as: candles, corrugated boxes, paper coating, board sizing, food production, wax polishes, surfactants (as used in detergents) and many others. Emissions from the use of waxes derive primarily when the waxes or derivatives of paraffins are combusted during use (e.g., candles), and when they are incinerated with or without heat recovery or in wastewater treatment (for surfactants). In the cases of incineration and wastewater treatment the emissions should be reported in the Energy or Waste Sectors, respectively (see Figure 5.1).

5.3.2.1

C HOICE

OF METHOD

There are two methodological tiers for determining emissions and storage from paraffin waxes. Both Tier 1 and Tier 2 rely on essentially the same analytical approach, which is to apply emission factors to activity data on the amount of paraffin waxes consumed in a country (in energy units, e.g., TJ). The Tier 2 method relies on determining the actual use of paraffin waxes and applying a country-specific ODU factor to activity data, while the Tier 1 method relies on applying default emission factors to activity data (see decision tree, Figure 5.3). Tier1: CO2 emissions are calculated according to Equation 5.4 with aggregated default data for the limited parameters available: EQUATION 5.4 WAXES – TIER 1 METHOD CO2 Emissions = PW • CCWax • ODU Wax • 44 / 12

Where: CO2 Emissions = CO2 emissions from waxes, tonne CO2 PW = total wax consumption, TJ CCWax = carbon content of paraffin wax (default), tonne C/TJ (= kg C/GJ) ODUWax = ODU factor for paraffin wax, fraction 44/12 = mass ratio of CO2/C Tier 2: The Tier 2 method for paraffin waxes relies on a similar equation, however detailed data on the quantities (possibly also on the types) of paraffin waxes produced (in energy units) and their respective use as well as country-specific emission factors should be used: EQUATION 5.5 WAXES – TIER 2 METHOD CO2 Emissions = ∑ (PWi • CCi • ODU i ) • 44 / 12 i

Where: CO2 Emissions = CO2 emissions from waxes, tonne CO2 PWi = consumption of was type i, TJ CCi = carbon content of wax type i, tonne C/TJ (= kg C/GJ) ODUi = ODU factor for wax type i, fraction 44/12 = mass ratio of CO2/C

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Figure 5.3

Decision tree for CO 2 from non-energy uses of paraffin waxes Start

Are data collected for non-energy uses of paraffin waxes?

No

Collect activity data.

Yes

Are country-specific statistics available on the fates of waxes?

Yes

Estimate CO2 emissions using a country-specific ODU factor. Box 2: Tier 2

No Is Category 2D a key category1 and is Paraffin Wax Use a significant subcategory?

No

Estimate CO2 emissions using the IPCC default ODU factor. Box 1: Tier 1

Yes Collect data for the Tier 2 method. Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

5.3.2.2

C HOICE

OF EMISSION FACTORS

A country-specific carbon content or default carbon content of 20.0 kg C/GJ (on a Lower Heating Value basis) should be applied. (See Table 1.3 in Chapter 1 of Volume 2. Note that kg C/GJ is identical to tonne C/TJ.) This default value is based on a combustion emission factor of 73.3 kg CO2/GJ (API, 2004). Tier 1: It can be assumed that 20 percent of paraffin waxes are used in a manner leading to emissions, mainly through the burning of candles, leading to a default ODU factor of 0.2 (Equation 5.4). Tier 2: Those countries with specific details on the uses of paraffin waxes in the country can determine their own country-specific ODU factors for waxes based on national knowledge of the combustion (Equation 5.5). These factors can be combined with either the default carbon contents listed above or a country-specific carbon contents if any are available.

5.3.2.3

C HOICE

OF ACTIVITY DATA

Data on the use of paraffin waxes are required to estimate emissions, with activity data expressed in energy units (TJ). To convert consumption data in physical units, e.g., in tonnes, into common energy units, e.g., in TJ (on a Lower Heating Value basis), calorific values are required (for specific guidance see Section 1.4.1.2 of Chapter 1 of Volume 2 on Energy). Basic data on non-energy products used in a country may be available from production, import and export data and on the energy/non-energy use split in national energy statistics. If the reported national statistics do not contain this as a separate fuel category but instead only show this as part of an

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Chapter 5: Non-Energy Products from Fuels and Solvent Use

aggregated ‘other oil products’ category, the national statistical agency should be contacted, since the oil product statistics are often collected at a more detailed level.

5.3.2.4

C OMPLETENESS

Emissions from incineration (without heat recovery) of wax coated boxes fall under the Waste Sector. Any emissions from paraffin waxes that are produced due to energy recovery should be reported in the Energy Sector.

5.3.2.5

D EVELOPING

A CONSISTENT TIME SERIES

Emissions from paraffin waxes should be calculated using the same method and data sets for every year in the time series. If a country-specific ODU factor is used, inventory compilers are encouraged to check whether the mix of applications with emissive and storage fates changes significantly over time. If that is the case, the ODU factors used per year should preferably reflect this change.

5.3.3 5.3.3.1

Uncertainty assessment E MISSION

FACTOR UNCERTAINTIES

The default emission factors are highly uncertain, because knowledge of national circumstances of paraffin wax fates is limited. Ideally, a Tier 2 method would be employed in which national data on the use and fates of waxes can be used as a surrogate to determine the quantities destined for an emissive fate versus storage fate. The default carbon content coefficient is subject to an uncertainty range of ±5 percent (U.S.EPA, 2004). However, the ODU factor is highly dependent on specific-country conditions and policies and the default value of 0.2 exhibits an uncertainty of about 100 percent.

5.3.3.2

A CTIVITY

DATA UNCERTAINTIES

Much of the uncertainty in emission estimates is related to the difficulty in determining the quantity of nonenergy products used and discarded in individual countries, for which a default of 5 percent may be used in countries with well developed energy statistics and 10-20 percent in other countries, based on expert judgement of the accuracy of energy statistics.

5.3.4 5.3.4.1

Quality Assurance and Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE

AND

Q UALITY C ONTROL

It is good practice to check the consistency of the total annual consumption figure with the production, import and export data. In addition, the amounts discarded, recovered and combusted, if available, may be compared with total consumption figures in the calculation to check the internal consistency of activity data and ODU factors used in the calculation of different source categories across sectors.

5.3.4.2

R EPORTING

AND

D OCUMENTATION

It is good practice to report and document country-specific emission factors, if these are used. •

•

If a country-specific emission factor for waxes was developed, in other words, if a country-specific ODU factor and/or country-specific carbon content fraction is used, the local value(s) with an explanation of their derivation should be provided . If the default ODU factor is used, this should be noted in the reporting documentation.

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5.4

ASPHALT PRODUCTION AND USE

5.4.1

Introduction

This source category comprises the non-combustion emissions from the production of asphalt in asphalt plants other than refineries and its application (such as paving and roofing operations as well as subsequent releases from the surfaces). It includes asphalt blowing for roofing. The production and use of asphalt results mainly in emissions of NMVOC, CO, SO2 and particulate matter, while the fate of the remaining hydrocarbons are stored in the product (much less than one per cent of the carbon is emitted). Emissions from the installation of roofing materials are assumed negligible. Emissions from the combustion of fuels needed to supply heat to the asphalt processes (production or heating of the asphalt mix) are covered under the Energy Sector. Asphalt is commonly referred to as bitumen, asphalt cement, or asphalt concrete or road oil and is mainly produced in petroleum refineries. In some countries the laid mixed product is also referred to as ‘asphalt’ but it also known as ‘macadam’. In view of the ambiguities created by differing nomenclatures, a single set of terms will be adopted here and applied uniformly in the text without implying any preferences for the terms used (see Box 5.1). BOX 5.1 ASPHALT PRODUCTION AND USE

The heavy black and very viscous organic liquid mainly produced from refineries and used as a feedstock for the road paving and roofing materials will be termed bitumen, to distinguish it from the products made from it. This also conforms to the terminology used in international energy statistics, which may provide some of the data required for emissions estimation. At normal temperatures bitumen is in a semi-solid state. It is processed and used as illustrated in the figure below. Heat

Bitumen ex refinery

Heat

Aggregates

Blown Asphalts

Hot Mix Asphalts

Liquefied Asphalts

Diluents (gas oil, fuel oil, etc.)

Cutback

Tack Coats

Air

Roofing materials

Emulsified

Water/ Soaps

Road paving

The diagram shows that bitumen may be heated and mixed with aggregate of various sizes, diluted with petroleum oils or water/soap emulsions, or heated and blown with air to polymerise/stabilise it and make it suitable for e.g., the treatment of roofing materials. These will be termed ‘asphalt processes’ and their products will be referred to as ‘asphalt products’

Bitumen and aggregates are mixed in either a fixed or mobile plant, usually within 30 to 50 km of the road surface paving site (EAPA, 2003). In industrialised countries typically 80 to 90 percent of bitumen is used for the manufacture of road surface paving (U.S.EPA, 2004). However, in developing countries with rapid infrastructural growth, the amount of bitumen used for roofing products may be of the same order of magnitude

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Chapter 5: Non-Energy Products from Fuels and Solvent Use

as those used for road paving (UNFCCC, 2004). Other uses of asphalt products are as binder or sealant in the production of roofing material, as a foundation sealant, and other industrial uses such as pipe coating. Direct greenhouse gas emissions, e.g., CO2 or CH4, associated with the production and use of asphalt are negligible since the majority of the light hydrocarbon compounds were extracted during the refining process to produce commercial fuels. From the EMEP/CORINAIR guidebook it can be concluded that CH4 emissions from hot mix asphalt and cutback asphalt and from the asphalt roofing industry are negligible (EEA, 2005). Greenhouse gas emissions from the use of recycled asphalt pavements as aggregate for new road paving are also negligible.

5.4.2

Methodological issues

Emission methodologies and default emission factors for NMVOC and CO are presented in the Road Paving (SNAP code 040610), Roofing Materials (SNAP code 040611) and Asphalt Blowing (SNAP code 060310) sections of the EMEP/CORINAIR Emission Inventory Guidebook (EEA, 2005). It is recommended that users refer to the guidebook when developing detailed NMVOC and CO estimates. (See also Volume 1, Chapter 7 of these Guidelines.) Note that in EMEP/CORINAIR the emissions from asphalt blowing for roofing are separately accounted for (under miscellaneous chemical product manufacture with SNAP code 060310). Limestone may be used as part of the aggregate in the asphalt. However, no CO2 is assumed to be released in the heating process (see Section 2.5, Other Process Uses of Carbonates, under Chapter 2 of this volume).

PRODUCTION AND USE OF ASPHALT FOR ROAD PAVING Asphalt paving consist of a mix of aggregate, sand, filler, bitumen and occasionally a number of additives. Asphalt road surfaces are, thus, composed of compacted aggregate and bitumen binder. Hot Mix Asphalt (HMA) is by far the most widely used, generally over 80 percent, and produces very few emissions (EAPA, 2003). Other types of road paving include cutback asphalt and emulsified asphalt, which are both liquefied asphalts (EEA, 2005). Cutback asphalts are liquefied by blending with petroleum solvents (diluents such as heavy residual oils, kerosene or naphtha solvents) and therefore show a relatively high level of emissions of CO and NMVOC due to the evaporation of the diluent. Therefore most emissions from road paving will arise from the use of cutback asphalts. Depending on the evaporation rate, three types are distinguished: Rapid-Cure (RC), using a naphtha or gasoline-type diluent of high volatility, Medium-Cure (MC) using a diluent of medium volatility and Slow-Cure (SC) cutback asphalt which use oils of low volatility. This is in contrast to so-called emulsified asphalt that contains mostly water and little or no solvent. The amount of diluent used is usually lower in warm countries than in the cooler climates, and hence lower emission factors may be expected in warm countries. Activity data for hot mix asphalt and production of cold mixes or ‘modified asphalt’ can be obtained for most European and several other industrialised countries from the European Asphalt Pavement Association (EAPA) or national paving and roofing associations such as the Asphalt Institute (EAPA, 2003; Asphalt Institute, 2004). Hot mix asphalt typically contains about 8 percent asphalt cement (bitumen) (EEA, 2005), but this may differ between countries (a figure of 5 percent has also been reported). For most industrialised countries the fraction of cutback asphalt is a few per cent, however several show shares of 5 percent to 12 percent, and exceptional shares up to 20 percent, or have none (EAPA, 2002; EAPA 2003; U.S. EPA, 2004). If the quantity of asphalt paved is not known but rather the area paved, a conversion factor of 100 kg asphalt/m2 road surface may be used to calculate the mass of asphalt produced. Gases are emitted from the asphalt plant (hot mix, cutback or emulsified), the road surfacing operations and subsequently by the road surface. The EMEP/CORINAIR Emission Inventory Guidebook provided processspecific uncontrolled emission factors for the different asphalt plants.

ASPHALT ROOFING The asphalt roofing industry produces saturated felt, roofing and siding shingles, roll roofing and sidings: asphalt shingles, smooth surfaced organic and asbestos felt roll roofing, mineral surfaced organic and asbestos felt roll roofing and sidings, asphalt saturated organic and asbestos felts, asphalt saturated and/or coated sheeting and asphalt compound. Most of these products are used in roofing and other building applications. Asphalt felt, roofing and shingle manufacture involves the saturation or coating of felt. Key steps in the total process include asphalt storage, asphalt blowing, felt saturation, coating and mineral surfacing, of which asphalt blowing is included here. Direct greenhouse gas emissions from asphalt roofing products are negligible compared to emissions such as NMVOC, CO and particulate matter. Asphalt blowing is the process of polymerising and stabilising asphalt to improve its weathering characteristics. Air blown asphalts are used in the production of asphalt roofing products. Blowing may take place in an asphalt

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Volume 3: Industrial Processes and Product Use

processing plant or an asphalt roofing plant (or in a refinery)3. Asphalt blowing leads to the highest emissions of NMVOC and CO, more than the other process steps. All asphalt used for non-paving applications has been blown (EEA, 2005).

5.4.3

Completeness

If no emissions are (explicitly) available for this source category, it should be checked whether they are already included elsewhere (e.g., in refinery emissions).

5.4.4

Uncertainty assessment

Although results from the use of more sophisticated methods are considered as the most accurate, the uncertainty for NMVOC and CO emissions from road paving and asphalt roofing may be in the range of ±25 percent and larger if the calculation was not based on detailed activity and control technology data (from −100 percent to +25 percent). The emission factors for NMVOC and CO for batch mix and drum mix HMA production have an uncertainty range of about ±50 percent, while the default factors for total HMA production and for cutback asphalt production and use will be about ±100 percent uncertain (i.e., between -50 percent and +100 percent). When country-specific emission factors are used for cutback asphalt production and paving, the uncertainty in the emission factors may be considerably smaller, e.g. in the range of ±50 percent.

Production data for HMA and cutback asphalt may be as accurate as ±10 percent, when based on data compiled by the asphalt production or construction industry. However, when activity data on cutback asphalt needs to be extrapolated, the uncertainties are very large, since it has been observed for a number of countries that the amount of cutback asphalt used can vary substantially from year to year; factors of two or more are not rare (EAPA, 2002; EAPA 2003; U.S. EPA, 2004). Also data on the mix of HMA production plant types and control technology applied as well as on the mix of cutback asphalt types (RC, MC, SC) will generally be less accurate than total production data. The uncertainty in production statistics of asphalt roofing material may be as accurate as ±10 percent if accounting is complete. If that is not the case, the uncertainty at the high end of the range could be as high as 100 percent or more.

The default fossil carbon content fraction of NMVOC from asphalt production and use for road paving varies between 40 to 50 percent by mass and is about 80 percent for NMVOC from asphalt roofing (calculated from the NMVOC speciation provided in the EMEP/CORINAIR Emission Inventory Guidebook).

5.4.5

Reporting and Documentation

The relatively small emissions from production and use of asphalt, including asphalt blowing, should be reported under the subcategory 2D4 ‘Other’ of this source category 2D ‘Non-Energy Products from Fuels and Solvent Use’.

5.5

SOLVENT USE

5.5.1

Introduction

The use of solvents manufactured using fossil fuels as feedstocks can lead to evaporative emissions of various non-methane volatile organic compounds (NMVOC), which are subsequently further oxidised in the atmosphere. Fossil fuels used as solvent are notably white spirit and kerosene (paraffin oil). White spirit is used as an extraction solvent, as a cleaning solvent, as a degreasing solvent and as a solvent in aerosols, paints, wood preservatives, lacquers, varnishes and asphalt products. In Western Europe about 60 percent of the total white spirit consumption is used in paints, lacquers and varnishes. White spirit is the most widely used solvent in the paint industry. 3

In UNECE inventories related emissions are accounted for under miscellaneous chemical product manufacture (separately for asphalt roofing manufacture/application and for asphalt blowing, SNAP codes 040610 and 060310) or under fugitive emissions from refineries (see EMEP/CORINAIR Emission Inventory Guidebook), but in the greenhouse gas inventory all emissions, including the precursor emissions, should be reported under the subcategory 2D4 ‘Other’.

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Chapter 5: Non-Energy Products from Fuels and Solvent Use

Methodologies for estimating these NMVOC emissions can be found in the EMEP/CORINAIR Emission Inventory Guidebook (EEA, 2005). This source category ‘solvent use’ is treated as a separate category because the nature of this source requires a somewhat different approach to emissions estimation than that used for calculating other emission categories. For this reason the 2006 IPCC Guidelines treats this also as a separate subcategory. In the EMEP/CORINAIR guidebook the subcategory ‘solvent and other product use’ group 6 of the Selected Nomenclature for sources of Air Pollution (SNAP) and is subdivided into five subcategories. Excluding the fifth: ‘other product use’ that refers to F-gases, N2O and ammonia, which are covered elsewhere in the IPPU Volume these are: •

•

• •

SNAP 0601: Paint application; SNAP 0602: Degreasing, dry cleaning and electronics; SNAP 0603: Chemical products manufacturing or processing. Including the processing of polyester, PVC, foams and rubber, manufacture of paints, inks, glues and adhesives and the finishing of textile SNAP 0604: Other use of solvents and related activities. Including such activities as ‘enduction’ (i.e., coating) of glass wool and mineral wool, printing industry, fat and oil extraction, uses of glues and adhesives, wood preservation, domestic solvent use (other than paint application) and vehicle underseal treatment and vehicle dewaxing.

Apart from emissions from road transport and, when occurring, production and handling of oil and biofuel combustion, this source category is often the largest source of national NMVOC emissions and its share may vary between 5 percent and 30 percent, with a global average of about 15 percent (Olivier and Berdowski, 2001).

5.5.2

Completeness

Emissions from this source category can be estimated using either a production-based or consumption-based approach. If total domestic sales figures of paints etc. are not available, apparent national consumption can be inferred from production, imports and exports. However, if trade statistics are not complete, this may introduce a significant uncertainty in the activity data. Thus, it is recommended that inventory compilers try to ensure that all significant evaporative uses of solvent and other product use are addressed by NMVOC emission estimates.

5.5.3

Developing a consistent time series

Usually for this source category only small annual changes are expected. However, when environmental policies are implemented to replace more toxic volatile compounds in solvents (e.g., with water,) both NMVOC emissions and the fossil carbon content of the NMVOC emissions may change over time.

5.5.4

Uncertainty assessment

The uncertainty of the NMVOC emissions will generally be quite large, e.g., about ±50 percent, except for countries that have developed a detailed inventory for these sources, in which case the uncertainty may be of the order of 25 percent. The default fossil carbon content fraction of NMVOC is 60 percent by mass, based on limited published national analyses of the speciation profile (U.S. EPA, 2002; Austria, 2004; Hungary, 2004; Klein Goldewijk et al., 2005). It may vary between 50 and 70 percent carbon by mass, so having an uncertainty of about ±10 percent. Country-specific fractions should have a lower uncertainty, e.g., ±5 percent.

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References API (2004). Compendium of Greenhouse Gas Emissions Methodologies for the Oil and Gas Industry, American Petroleum Institute (API), Table 4-2. Washington, DC, February 2004. Asphalt Institute (2004). Website http://www.asphaltinstitute.org/ai_pages/links/, visited 19 November 2004. Austria (2004). Austria’s National Inventory Report 2004. Submission under the United Nations Framework Convention on Climate Change, Umweltbundesamt, BE-244, Vienna. EAPA (2002). European Asphalt Pavement Association,, Asphalt in Figures 2002. Available at website http://www.eapa.org, visited 19 November 2004. EAPA (2003). European Asphalt Pavement Association,, Asphalt in Figures 2003. Available at website http://www.eapa.org, visited 19 November 2004. EEA (2005). “EMEP/CORINAIR. Emission Inventory Guidebook - 2005”, European Environment Agency, Technical report No 30. Copenhagen, Denmark, (December 2005). Available from web site see: http://reports.eea.eu.int/EMEPCORINAIR4/en Hungary (2004). Hungarian National Inventory Report for 2002. General Directorate for Environment, Nature and Water, UN Framework Convention on Climate Change, Directorate for Environmental Protection, Budapest. Klein Goldewijk, K., Olivier, J.G.J., Peters, J.A.H.W., Coenen, P.W.H.G. and Vreuls, H.H.J. (2005). Greenhouse Gas Emissions in the Netherlands 1990-2003. National Inventory Report 2005. RIVM Report no. 773201 009/2005. RIVM, Bilthoven. Olivier, J.G.J. and Berdowski, J.J.M. (2001). Global emissions sources and sinks. In: Berdowski, J., Guicherit, R. and B.J. Heij (eds.) "The Climate System", pp. 33-78. A.A. Balkema Publishers / Swets & Zeitlinger Publishers, Lisse, The Netherlands. ISBN 90 5809 255 0. Rinehart, T. (2000). Personal communication between Thomas Rinehart of U.S. Environmental Protection Agency, Office of Solid Waste, and Randall Freed of ICF Consulting, July 2000. UNFCCC (2004). Emissions data and National Inventory Reports. Website http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/2761. php visited 19 November 2004. U.S. EPA (2002). National Air Quality and Emissions Trends Report data, 1900-2000. United States Environmental Protection Agency (U.S. EPA), Research Triangle Park, NC. U.S. EPA (2004). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2002. United States Environmental Protection Agency (U.S. EPA), Washington, DC.

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Chapter 6: Electronics Industry Emissions

CHAPTER 6

ELECTRONICS INDUSTRY EMISSIONS

2006 IPCC Guidelines for National Greenhouse Gas Inventories

6.1

Volume 3: Industrial Processes and Product Use

Authors Scott Bartos (USA) Laurie S. Beu (USA), C. Shepherd Burton (USA), Charles L. Fraust (USA), Francesca Illuzzi (Italy), Michael T. Mocella (USA) and Sebastien Raoux (France/USA)

Contributing Authors Guido Agostinelli (Italy), Erik Alsema (Netherlands), Seung-Ki Chae (Republic of Korea), Vasilis M. Fthenakis (USA) , Joseph Van Gompel (USA) , Hideki Nishida (Japan), Takayuki Oogoshi (Japan) and Kurt T. Werner (USA)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Electronics Industry Emissions

Contents 6

Electronics Industry Emissions .....................................................................................................................6.5 6.1

Introduction ...........................................................................................................................................6.5

6.2

Methodological issues ...........................................................................................................................6.6

6.2.1

Choice of method ..........................................................................................................................6.6

6.2.1.1

Etching and CVD cleaning for semiconductors, liquid crystal displays, and photovoltaics....6.6

6.2.1.2

Heat transfer fluids ................................................................................................................6.13

6.2.2

Choice of emission factors ..........................................................................................................6.15

6.2.2.1

Etching and CVD cleaning for semiconductors, liquid crystal displays, and photovoltaics..6.15

6.2.2.2

Heat transfer fluids ................................................................................................................6.22

6.2.3

Choice of activity data.................................................................................................................6.22

6.2.4

Completeness ..............................................................................................................................6.24

6.2.5

Developing a consistent time series.............................................................................................6.25

6.3

Uncertainty assessment .......................................................................................................................6.25

6.3.1

Emission factor uncertainties ......................................................................................................6.25

6.3.2

Activity data uncertainties...........................................................................................................6.26

6.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...................................6.29

6.4.1

Quality Assurance/Quality Control (QA/QC) .............................................................................6.29

6.4.2

Reporting and Documentation.....................................................................................................6.29

References

.....................................................................................................................................................6.31

Equations Equation 6.1

Tier 1 method for estimation of the set of FC emissions.......................................................6.9

Equation 6.2

Tier 2a method for estimation of FC emissions...................................................................6.10

Equation 6.3

By-product emissions of CF4 ...............................................................................................6.10

Equation 6.4

By-product emissions of C2F6 .............................................................................................6.10

Equation 6.5

By-product emissions of chf3...............................................................................................6.10

Equation 6.6

By-product emissions of C3F8 .............................................................................................6.10

Equation 6.7

Tier 2b method for estimation of FC emissions ..................................................................6.11

Equation 6.8

By-product emissions of CF4 ...............................................................................................6.12

Equation 6.9

By-product emissions of C2F6 .............................................................................................6.12

Equation 6.10 By-product emissions of CHF3 ............................................................................................6.12 Equation 6.11 By-product emissions of C3F8 .............................................................................................6.12 Equation 6.12 Tier 1 method for estimation of total FC emissions from heat transfer fluids .....................6.14 Equation 6.13 Tier 2 method for estimation of FC emissions from heat transfer fluids .............................6.14

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Volume 3: Industrial Processes and Product Use

Figures Figure 6.1

Decision tree for estimation of FC emissions from electronics manufacturing.....................6.8

Figure 6.2

Decision tree for estimation of FC emissions from HT fluid loss from electronics manufacturing......................................................................................................................6.15

Tables Table 6.1

Information sources necessary for completing the tiered emission estimating methods for electronics manufacturing......................................................................................................6.7

Table 6.2

Tier 1 Gas-specific emission factors for FC emissions from electronics manufacturing ....6.16

Table 6.3

Tier 2 Default emission factors for FC emissions from semiconductor manufacturing ......6.17

Table 6.4

Tier 2 Default emission factors for FC emissions from LCD manufacturing......................6.18

Table 6.5

Tier 2 Default emission factors for FC emissions from PV manufacturing ........................6.19

Table 6.6

Tier 2a & 2b Default efficiency parameters for electronics industry FC emission reduction technologies.........................................................................................................................6.20

Table 6.7

Country total silicon (Si) and glass design capacities (Mm2) for 2003, 2004 and 2005 ......6.23

Table 6.8

Country total PV production capacitya for 2003, Mm2 ........................................................6.24

Table 6.9

Tier 2 default estimates of relative errors (%) for emission factor for FC emissions from semiconductor manufacturing, 95 percent confidence intervals..........................................6.27

Table 6.10

Tier 2 default estimates of relative errors (%) for emission factor for FC emissions from LCD manufacturing, 95 percent confidence intervals ..................................................................6.28

Table 6.11

Information necessary for full transparency of estimates of emissions from electronics manufacturing......................................................................................................................6.30

Box Box 6.1

6.4

Example for semiconductor manufacture ............................................................................6.13

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Electronics Industry Emissions

6 ELECTRONICS INDUSTRY EMISSIONS 6.1

INTRODUCTION

Several advanced electronics manufacturing processes utilise fluorinated compounds (FCs) for plasma etching intricate patterns, cleaning reactor chambers, and temperature control. The specific electronic industry sectors discussed in this chapter include semiconductor, thin-film-transistor flat panel display (TFT-FPD), and photovoltaic (PV) manufacturing (collectively termed ‘electronics industry’).1 The electronics industry currently emits both FCs that are gases at room temperature and FCs that are liquids at room temperature. The gases include CF4, C2F6, C3F8, c-C4F8, c-C4F8O, C4F6, C5F8, CHF3, CH2F2, nitrogen trifluoride (NF3), and sulfur hexafluoride (SF6), and are used in two important steps of electronics manufacturing: (i) plasma etching silicon containing materials and (ii) cleaning chemical vapour deposition (CVD) tool chamber-walls where silicon has deposited. 2 The majority of FC emissions results from limited utilisation efficiency (i.e., consumption) of the FC precursors during the etching or the cleaning process. In addition, a fraction of the fluorinated compounds used in the production process can be converted into by-product CF4 and in some instances into C2F6, CHF3 and C3F8.3 Also, formation of CF4 as a by-product of etching or cleaning carbon-containing low dielectric constant (low k) materials (or carbide) must be taken into account.4 In addition, F2, COF2, and ClF3 use may increase. These gases, although not in themselves contributors to global warming may lead to CF4 formation under some conditions. Electronics manufacturers use FCs for temperature control during certain processes. Also known as heat transfer fluids, these FCs are liquids at room temperature and have appreciable vapour pressures. Evaporative losses contribute to the total FC emissions. These evaporative losses occur during cooling of certain process equipment, during testing of packaged semiconductor devices and during vapour phase reflow soldering of electronic components to circuit boards. Evaporative losses do not appear to occur when liquid FCs are used to cool electronic components or systems during operation. In this application, the liquid FCs are contained in closed systems throughout the life of the product or system. More than 20 different liquid FCs are marketed, often as mixtures of fully fluorinated compounds, to the electronic sector.5 Because the CO2 equivalents of each liquid differ, each should be tracked and reported separately. The precise value of this conversion will be determined by the specific applicable reporting requirements.6,7 In addition, liquid FCs are occasionally used to clean TFTFPD panels during manufacture.

1

Recent comprehensive surveys of European and US PV manufacturers indicate that 40 to 50 percent of PV-manufacturers use relatively small quantities of FCs (predominantly CF4 during etching of crystalline silicon wafers and C2F6 during chamber cleaning after deposition of SiNx films). Global usage, according to these surveys for 2004, was approximately 30 Mtonnes CF4. While global FC use appears low in 2004, credible growth-forecasts of the PV industry are approximately 30 percent per year (and higher) for the foreseeable future. Morevoer, several reports extol the virtues of FC use as a means to increase manufacturing productivity and lower costs for silicon-based technologies (Shah et al., 2004; Maycock, 2005; Agostinelli et al., 2004 and Rentsch et al., 2005), Such expected growth rates and prospects for increase FC use motivate inclusion of FC emissions from PV manufacture in this chapter.

2

Although C5F8 does not currently have a global warming potential (GWP) recognized by the IPCC, C5F8 emissions are discussed in this chapter. C5F8 is a direct greenhouse gas and emissions can be estimated using methods and data described in this chapter. C5F8’s atmospheric lifetime is approximately 1 year, resulting in a relatively low GWP (Sekiya, 2003).

3

Emissions of C2F6 by-products have been observed from the decomposition of C4F6 molecules and may occur for other FC molecules with greater than two carbon atoms. Note that for most FC precursors, C2F6 formation as a by-product has not been observed. CHF3 formation has been reported when c-C4F8 is used as an etchant in TFT-FPD manufacture and C3F8 by-product emissions have been reported when C4F8O is used in chamber cleaning.

4

Low dielectric constant (low k) materials were first used as insulators for the interconnect structure of semiconductor chips at the 0.25μm node and below. Many low k materials contain carbon that may be removed as CF4 during etching of thin films or the cleaning of the CVD reactors used for low k deposition. CF4 may also be formed during cleaning of CVD reactors used for carbide deposition.

5

A relatively recent review summarises the uses of liquid FCs (heat transfer fluids), their chemical composition, GWPs, among other things. See Burton (2004a).

6

These materials are marketed under the trade names Fluorinert™ and Galden®. The Fluorinert™ materials are selected from fully fluorinated alkanes, ethers, tertiary amines and aminoethers and mixtures thereof to obtain the desired properties. The Galden® fluids span a range of fully fluorinated polyethers, called perfluoropolyethers (PFPEs), also selected for the desired properties.

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Volume 3: Industrial Processes and Product Use

6.2

METHODOLOGICAL ISSUES

6.2.1

Choice of method

6.2.1.1

E TCHING

AND CVD CLEANING FOR SEMICONDUCTORS , LIQUID CRYSTAL DISPLAYS , AND PHOTOVOLTAICS

Emissions vary according to the gases used in manufacturing different types of electronic devices, the process used (or more roughly, process type (e.g., CVD or etch)), the brand of process tool used, and the implementation of emission reduction technology. The choice of methods will depend on data availability and is outlined in the decision tree, see Figure 6.1, Decision Tree for Estimation of FC Emissions from Electronics Manufacturing. Emissions from liquid FCs are estimated using Tier 1, 2 and 3 approaches and are described separately in this section.8 Continuous (in-situ) emissions monitoring is currently considered a technically and economically unviable means to estimate emissions from this industry. FC emissions are periodically measured, however, during the development of new processes and tools, and after the establishment of commercial-ready process conditions (also known as centreline process conditions).9 The industry seeks, prior to the introduction of high-volume manufacturing, centreline process designs that minimize FC emissions. However, it must be noted that FC emissions can be affected by changes in process variables (e.g., pressure, temperature, plasma power, FC gas flow, processing time). Thus, the accuracy of the methods used for estimating emissions will be affected by eventual differences between the process used in production and the reference centreline process. In addition, the efficacy of FC emission control equipment depends on operating and maintaining the equipment according to the manufacturer’s specifications: Increased gas flows, improper temperature settings, and failure to perform required maintenance will individually and collectively negatively impact performance. The accuracy of estimated emissions depends on the method used. The Tier 1 method uses default values for all parameters and does not account for the use of emission control technology. The Tier 2a method uses companyspecific data on the proportion of gas used in processes with and without emission control technology, but does not distinguish between etching and cleaning, and uses default values for the other parameters. The Tier 2b method uses company-specific data on the proportion of gas used in etching versus cleaning and the proportion of gas used in processes with emission control technology, but relies on default values for some or all of the other parameters. The most rigorous method, Tier 3 method, requires a complete set of process-specific values rather than defaults. Table 6.1 summarises the data requirements for the tiered emissions estimating methods for electronics manufacturing.

7

Where a commercial mixture is used inventory compilers will need to ensure that the conversion of the mass of the mixture to CO2 equivalents uses the appropriate conversion factors.

8

The logic depicted in Figure 6.1 does not show the possibility of combining tiers to improve estimates of emissions. For example, improved estimates of emissions might be achieved by using Tier 3 for a specific gas and process and Tier 2b for other gases and processes instead of using only the Tier 2b method. Similarly, the Tier 2a and 2b methods might be combined to produce an improved estimate compared to using only Tier 2a. However, the Tier 1 method should not be combined with any other method.

9

Centreline conditions refer to the conditions under which equipment manufacturers standardise their equipment for sale. These are nominal specifications for gas flows, chamber pressure, processing time, plasma power, etc. It is common for semiconductor manufacturers to modify these conditions to optimise for particular needs.

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Chapter 6: Electronics Industry Emissions

TABLE 6.1 INFORMATION SOURCES NECESSARY FOR COMPLETING THE TIERED EMISSION ESTIMATING METHODS FOR ELECTRONICS MANUFACTURING

Annual Production Capacity

Downstream FC Emission Control

Process Gas Reactions and Destruction in Tool

Process Gas Entering Tool

Data

Tier 1

Tier 2a

Tier 2b

Tier 3

FCi,p = kg of gas i fed into specific process p or small set of common process tools (e.g., silicon nitride etch).

M

FCi,p = kg of gas i fed into broad process category (e.g., etching or CVD chamber cleaning).

M

M(etch) & M(CVD)

h = Fraction of gas remaining in shipping container after use (heel).

D

D

M

Ui,p = Use rate (fraction destroyed or transformed) for each gas i and process p.

D

D(etch) & D(CVD) a

M

BCF4,i,p, BC2F6,i,p, BCHF3,i,p and BC3F8,i,p = Emission factor for by-product emissions of CF4, C2F6, CHF3 and C3F8 respectively for gas i for each process.

D

D(etch) & D(CVD) a

M

ai,p = Fraction of gas i volume fed into processes with certified FC emission control technologies.

M

M

M

di,p = Fraction of gas i destroyed by the emission control technology.

D

Da

M

dCF4,p, dC2F6,p, dCHF3,p and dC3F8,p = Fraction of CF4, C2F6, CHF3 and C3F8 by-products respectively destroyed by the emission control technology.b

M

Cd = Annual manufacturing design capacity in surface area of substrate processed (e.g., silicon, glass). Cu = Fraction of annual capacity utilisation

M D/M

M = measure or acquire these values. D = Use default factors from guidance. a

When available and supportable, M values may be substituted for D values for Tier 2a and 2b. See conditions in Table 6.6.

b

There are no default values for Tier 2a and Tier 2b because the effect of by-products has been incorporated into the D-values for di,p for gas i.

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Volume 3: Industrial Processes and Product Use

Figure 6.1

Decision tree for estimation of FC emissions from electronics manufacturing Start

Are FC activity data available from electronics companies?

Do companies that report use process-specific emission factors?

Yes

Yes

Estimate emissions using the Tier 3 method. Box 4: Tier 3

No

Collect activity and emissions data from electronics companies.

Is Electronics Industry a key category1 and is this subcategory sinificant?

No

Do reporting companies track FC gas usage by process type (i.e., CVD clean and etch)?

Yes

Yes

Estimate emissions using the Tier 2b method. Box 3: Tier 2b

No No Are national data available on annual electronics production capacities by substrate area (e.g., silicon or glass)?

Yes

No

Develop or obtain data on annual production capacity by substrate area for each sector.

Estimate emissions using the Tier 2a method. Box 2: Tier 2a

Estimate emissions using the Tier 1 method. Box 1: Tier 1

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

TIER 1 METHOD – DEFAULT The Tier 1 method is the least accurate estimation method and should be used only in cases where companyspecific data are not available. The Tier 1 method, unlike the Tier 2 or 3 methods, is designed to give an aggregated estimate of FC emissions although its methodology appears to produce gas-specific emissions. Estimates are made simultaneously for all gases as listed in Table 6.2 and can only be used if reported as a complete set. The calculation of emissions relies on a fixed set of generic emissions factors. The members of the set differ depending on the sector (or class) of electronic products being manufactured (semiconductors, TFT-FPDs or PVcells). Each member of a set, which is a gas-specific emission factor, expresses an average emissions per unit of substrate area (e.g., silicon, TFT-FPD panel or PV-cell) consumed during manufacture. For any class of electronic products, the factors (members of the set) are multiplied by the annual capacity utilisation (Cu, a fraction) and the annual manufacturing design capacity (Cd, in units of giga square meters (Gm2)) of substrate processes. The product (Cu • Cd) is an estimate of the quantity of substrate consumed during electronics manufacture. The result is a set of annual emissions expressed in kg of the gases that comprise the set for each class of electronic products. Because the use of FCs varies widely during PV manufacture, a third factor to

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Electronics Industry Emissions

account for the proportion of PV manufacture that employs FC is needed to estimate FC emissions from the PV cells manufacturing. The Tier 1 formula is shown in Equation 6.1. EQUATION 6.1 TIER 1 METHOD FOR ESTIMATION OF THE SET OF FC EMISSIONS {FCi }n = {EFi • Cu • Cd • [CPV • δ + (1 − δ )]}n (i = 1, K, n ) Where: {FCi}n = emissions of FC gas i, mass of gas i Note: { }n denotes the set for each class of products (semiconductors, TFT-FPD or PV-cells) and n denotes the number of gases included in each set (six for semiconductors, three for TFT-FPD manufacture and two for PV-cells. See Table 6.2.). The estimates are only valid if made and reported for all members of the set using this Tier 1 methodology. EFi = FC emission factor for gas i expressed as annual mass of emissions per square meters of substrate surface area for the product class, (mass of gas i)/m2 Cu = fraction of annual plant production capacity utilisation, fraction Cd = annual manufacturing design capacity, Gm2of substrate processed, except for PV manufacturing which is Mm2 CPV = fraction of PV manufacture that uses FCs, fraction

δ = 1 when Equation 6.1 is applied to PV industry and zero when Equation 6.1 is applied to either semiconductor or TFT-FPD industries, dimensionless

This method does not account for differences among process types (etching versus cleaning), individual processes, or tools. It also does not account for the possible use of atmospheric emission-control devices. In using Tier 1, inventory compilers should not modify, in any way, the set of the FCs assumed in Table 6.2. Inventory compilers should not combine emissions estimated using Tier 1 method with emissions estimated using the Tier 2 or 3 methods. Neither may inventory compilers use, for example, the Tier 1 factor for CF4 to estimate the emissions of CF4 from semiconductors and combine it with the results of other FC gases from a Tier 2 or Tier 3 method. (See also Section 6.2.2.1.)

TIER 2a METHOD – PROCESS GAS-SPECIFIC PARAMETERS This method calculates emissions for each FC used on the basis of company-specific data on gas consumption and on emission control technologies. It uses industry-wide default values for the ‘heel’ or fraction of the purchased gas remaining in the shipping container after use (h), the fraction of the gas ‘used’ (destroyed or transformed) in the semiconductor or TFT-FPD manufacturing process, and the fraction of the gas converted into CF4 or C2F6 during the process. To use the Tier 2a method inventory compilers must have direct communication with industry (e.g., annual emissions reporting) to gather data and ensure that emission control technologies are installed and in use. Total emissions are equal to the sum of emissions from the gas FCi used in the production process plus the emissions of by-product CF4, C2F6, CHF3 and C3F8 resulting from use of the gas FCi., as shown in Equations 6.2, 6.3, 6.4, 6.5 and 6.6. Unlike the Tier 3 and 2b methods that are explained later in this section, the Tier 2a method does not distinguish between processes or process types (etching versus cleaning), individual processes or tools. The default emission factors represent weighted averages (based on expert judgments of weights), formed separately for each gas, over all etch and CVD processes. As discussed below in the section on emission factors, the Tier 2a method uses the emission factor for the process type (CVD or etch) in which the individual FC is most frequently used in the particular electronics sector. This method reflects a current trend where individual FCs tend to be used predominantly in particular process types (CVD or etch) throughout each industry. However, in countries with companies or plants that depart significantly from the industry-wide pattern of usage (e.g., by using a gas primarily in etch while others primarily use it in CVD), inventory compilers should evaluate the potential to introduce error by using the Tier 2a method rather than the Tier 2b method.

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EQUATION 6.2 TIER 2a METHOD FOR ESTIMATION OF FC EMISSIONS Ei = (1 − h ) • FCi • (1 − U i ) • (1 − ai • d i ) Where: Ei = emissions of gas i, kg FCi = consumption of gas i,(e.g., CF4, C2F6, C3F8, c-C4F8, c-C4F8O, C4F6, C5F8, CHF3, CH2F2, NF3, SF6), kg h = fraction of gas remaining in shipping container (heel) after use, fraction Ui = use rate of gas i (fraction destroyed or transformed in process), fraction ai = fraction of gas i volume used in processes with emission control technologies (company- or plantspecific), fraction di = fraction of gas i destroyed by the emission control technology, fraction

EQUATION 6.3 BY-PRODUCT EMISSIONS OF CF4 BPECF 4, i = (1 − h ) • BCF 4, i • FCi • (1 − ai • d CF 4 ) Where: BPECF4,i = by-product emissions of CF4 from the gas i used, kg BCF4,i = emission factor, kg CF4 created/kg gas i used dCF4 = fraction of CF4 by-product destroyed by the emission control technology, fraction

EQUATION 6.4 BY-PRODUCT EMISSIONS OF C2F6 BPEC 2 F 6, i = (1 − h ) • BC 2 F 6, i • FCi • (1 − ai • d C 2 F 6 ) Where: BPEC2F6,i = by-product emissions of C2F6 from the gas i used, kg BC2F6,i = emission factor, kg C2F6 created/kg gas i used dC2F6 = fraction of C2F6 by-product destroyed by the emission control technology, fraction

EQUATION 6.5 BY-PRODUCT EMISSIONS OF CHF3 BPECHF 3, i = (1 − h ) • BCHF 3, i • FCi • (1 − ai • d CHF 3 )

Where: BPECHF3,i = by-product emissions of CHF3 from the gas i used, kg BCHF3,i = emission factor, kg CHF3 created/kg gas i used dCHF3 = fraction of CHF3 by-product destroyed by the emission control technology, fraction

EQUATION 6.6 BY-PRODUCT EMISSIONS OF C3F8 BPEC 3 F 8, i = (1 − h ) • BC 3 F 8,i • FCi • (1 − ai • d C 3F 8 )

Where:

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Electronics Industry Emissions

BPEC3F8,i = by-product emissions of C3F8 from the gas i used, kg BC3F8,i = emission factor, kg C3F8 created/kg gas i used dC3F8 = fraction of C3F8 by-product destroyed by the emission control technology, fraction After estimating the emission of gas i (Ei) and the CF4, C2F6, CHF3 and C3F8 by-product emissions for each gas (BPECF4,i, BPEC2F6,i, BPECHF3,i and BPEC3F8,i), inventory compilers or companies should sum these emissions across all gases to estimate the total aggregate FC emissions.

TIER 2b METHOD – PROCESS TYPE-SPECIFIC PARAMETERS The Tier 2b method requires data on the aggregate quantities of each gas fed into all etching processes and all cleaning processes (FCi,p). Thus, it distinguishes only between broad process types (etching vs. CVD chamber cleaning), but it does not distinguish among the many possible individual processes or small sets of processes. Industry-wide default values can be used for any or all of the following: •

•

the fraction of the gas remaining in the shipping container after use termed the ‘heel’ (h);

•

the emission factor for CF4 by-product emissions in the process type (BCF4,i,p);

•

the emission factor for CHF3 by-product emissions in the process type (BCHF3,i,p)); and

• •

the fraction of the gas ‘used’ (destroyed or transformed) per process type (Ui,p);

the emission factor for C2F6 by-product emissions in the process type (BC2F6,i,p);

the emission factor for C3F8 by-product emissions in the process type (BC3F8,i,p).

Defaults are also presented (see Table 6.6) for the fraction of the gas destroyed by the emissions control technology by process type (di,p, dCF4,p, dC2F6,p, dCHF3,p and dC3F8,p). Unless emission control technologies are installed, the default value for ai,p, the fraction of gas volume fed into processes with emission control technologies, is zero. The default values for Ui,p, BCF4,i,p, BC2F6,i,p, BCHF3,i,p and BC3F8,i,p represent simple unweighted averages, formed separately for each gas, over all etch processes and over all CVD processes. Company or plant-specific emission factors may be substituted for default values when available. The equations account for the plant-specific use of emission-control devices, but do not account for differences among individual processes or tools or among manufacturing plants in their mix of processes and tools. Thus, Tier 2b estimates will be less accurate than Tier 3 estimates. Also, note that the Tier 2b method is applicable to semiconductor and TFT-FPD manufacture. Emissions resulting from the use of a specific FC (FCi) consist of emissions of FCi itself plus emissions of CF4, C2F6, CHF3 and C3F8 created as by-products during use of FCi. The following calculation should be repeated for each gas for each process type: EQUATION 6.7 TIER 2b METHOD FOR ESTIMATION OF FC EMISSIONS

[

(

) (

Ei = (1 − h ) • ∑ FCi , p • 1 − U i , p • 1 − ai , p • d i , p p

)]

Where: Ei = emissions of gas i, kg p = process type (etching vs. CVD chamber cleaning) FCi,p = mass of gas i fed into process type p (e.g., CF4, C2F6, C3F8, c-C4F8, c-C4F8O, C4F6, C5F8, CHF3, CH2F2, NF3, SF6), kg h = fraction of gas remaining in shipping container (heel) after use, fraction Ui,p= use rate for each gas i and process type p (fraction destroyed or transformed), fraction ai,p = fraction of gas i volume fed into process type p with emission control technologies (company-or plant-specific), fraction di,p = fraction of gas i destroyed by the emission control technology used in process type p (If more than one emission control technology is used in process type p, this is the average of the fraction destroyed by those emission control technologies, where each fraction is weighted by the quantity of gas fed into tools using that technology), fraction

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EQUATION 6.8 BY-PRODUCT EMISSIONS OF CF4

[

(

BPECF 4, i = (1 − h ) • ∑ BCF 4, i , p • FCi , p • 1 − ai , p • d CF 4, p p

)]

Where: BPECF4,i = by-product emissions of CF4 converted from the gas i used, kg BCF4,i,p = emission factor for by-product emissions of CF4 converted from gas i in process type p, kg CF4 created/kg gas i used dCF4,p = fraction of CF4 by-product destroyed by the emission control technology used in process type p (e.g., control technology type listed in Table 6.6), fraction

EQUATION 6.9 BY-PRODUCT EMISSIONS OF C2F6

[

(

BPEC 2 F 6, i = (1 − h ) • ∑ BC 2 F 6, i , p • FCi , p • 1 − ai , p • d C 2 F 6, p p

)]

Where: BPEC2F6,i = by-product emissions of C2F6 converted from the gas i used, kg BC2F6,i,p = emission factor for by-product emissions of C2F6 converted from gas i in process type p, kg C2F6 created/kg gas i used dC2F6,p = fraction of C2F6 by-product destroyed by the emission control technology used in process type p (e.g., control technology type listed in Table 6.6), fraction

EQUATION 6.10 BY-PRODUCT EMISSIONS OF CHF3

[

(

BPECHF 3, i = (1 − h ) • ∑ BCHF 3, i , p • FCi , p • 1 − ai , p • d CHF 3, p p

)]

Where: BPECHF3,i = by-product emissions of CHF3 converted from the gas i used, kg BCHF3,i,p = emission factor for by-product emissions of CHF3 converted from gas i in process type p, kg CHF3 created/kg gas i used dCHF3,p = fraction of CHF3 by-product destroyed by the emission control technology used in process type p (e.g., control technology type listed in Table 6.6), fraction

EQUATION 6.11 BY-PRODUCT EMISSIONS OF C3F8

[

(

BPEC 3 F 8, i = (1 − h ) • ∑ BC 3F 8, i , p • FCi , p • 1 − ai , p • d C 3F 8, p p

)]

Where: BPEC3F8,i = by-product emissions of C3F8 from the gas i used, kg BC3F8,i,p = emission factor for by-product emissions of C3F8 converted from gas i in process type p, kg C3F8 created/kg gas i used dC3F8,p = fraction of C3F8 by-product destroyed by the emission control technology used in process type p (e.g., control technology type listed in Table 6.6), fraction

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Chapter 6: Electronics Industry Emissions

Note that in certain etching or cleaning recipes, multiple FC precursors can be used concurrently and emissions of CF4, C2F6, CHF3 or C3F8 as by-products may originate from each of the individual FC precursor decomposition. In such cases, emissions of CF4, C2F6, CHF3 or C3F8 by-products should be reported as originating from the FC gas with the largest mass flow.

TIER 3 METHOD – PROCESS-SPECIFIC PARAMETERS The Tier 3 method also uses Equations 6.7, 6.8, 6.9, 6.10 and 6.11. However, this method requires companyspecific or plant-specific values for all the parameters used in these equations for each individual process or for each of small sets of processes (e.g., silicon nitride etching or plasma enhanced chemical vapour deposition (PECVD) tool chamber cleaning). Therefore, when using Equations 6.7, 6.8, 6.9, 6.10 and 6.11, inventory compilers need to interpret ‘p’ in these equations as a specific ‘Process’ (e.g., silicon nitride etching or plasma enhanced chemical vapour deposition (PECVD) tool chamber cleaning), not as ‘Process type’. For purposes of transparency and comparability, the values used for these emission parameters should be well documented (see Section 6.2.2).

CF 4 formation from C-containing films during semiconductor manufacturing The Tier 2a, Tier 2b and Tier 3 methods account for CF4 emissions formed during removal via etching of carbon-containing low dielectric constant (k) materials or cleaning CVD reactors containing low k or carbide films during semiconductor manufacture. CF4 may be formed even if the FC precursor does not contain carbon or if the FC precursor is not a greenhouse gas. For example, cleaning low k CVD reactors with NF3 will produce CF4 as a by-product. In these cases, Equation 6.7 should be used to report NF3 emissions and the result of Equation 6.8 should be used to reflect emissions of CF4 from the process. In those situations where F2, COF2, or ClF3 is used in chamber cleaning, CF4 may also be formed. In this case, CF4 emissions are estimated using Equation 6.8 and the results added to the total CF4 emissions obtained from Equation 6.7. In both cases, BCF4,i,p should be measured as the fraction of the mass of CF4 produced over the mass of clean or etch gas introduced in the reactor. After estimating emissions of each FC gas and emissions of CF4, C2F6, CHF3 and C3F8 as by-products, inventory compilers or companies should sum these emissions across all gases to arrive at an estimate of aggregate FC emissions from a particular process. BOX 6.1 EXAMPLE FOR SEMICONDUCTOR MANUFACTURE

For example, if a source used NF3 (for chamber cleaning and etch), CHF3 (etch) and CF4 (etch), the total emissions, if low k films were used, are estimated using Equation 6.7 for NF3, CHF3 and CF4 and Equation 6.8 for the formation of CF4 formed when removing low k films with NF3. In equation form, the total is: Total FC emissions = ENF3 + ECHF3 + ECF4 + BPECF4,NF3

6.2.1.2

H EAT

TRANSFER FLUIDS

There are two methods for estimating emissions from the use of heat transfer fluids. The choice of methods will depend on the availability of activity data on the use of heat transfer fluids, and is outlined in the decision tree (see Figure 6.2, Decision Tree for Estimation of FC Emissions from Heat Transfer Fluids, and see Section 1.5 of Chapter 1, Choosing between the Mass-Balance and Emission-Factor Approach).

TIER 1 – HEAT TRANSFER FLUIDS Tier 1 is appropriate when company-specific data are not available on heat transfer fluids. It is the less accurate of the two methods for estimating emissions from losses of heat transfer fluids. The method, unlike the Tier 2 method, gives an estimate of aggregate emissions - a weighted average emission across all liquid FCs that is expressed as the mass of C6F1410. The calculation relies on a generic emission factor that expresses the average

10

In the absence of GWP estimates, the appropriate GWP for C6F14 has been used as a proxy (to derive the default emission factor). (See the Inventory of U.S. Greenhouse Gas and Sinks: 1990-2003, the footnote to Table 4-58, page 166. (U.S. EPA, 2005))

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aggregate emissions per unit of silicon consumed during semiconductor manufacturing. The formula is shown in Equation 6.12. EQUATION 6.12 TIER 1 METHOD FOR ESTIMATION OF TOTAL FC EMISSIONS FROM HEAT TRANSFER FLUIDS FCliquid , total = EFl • Cu • Cd

Where: FCliquid, total = total FC emissions as expressed as the mass of C6F14, Mt C6F14 EFl = emission factor (aggregate FC emissions per Gm2 of silicon consumed during the period expressed as the mass of C6F14 (See Table 6.2.)), Mt C6F14/Gm2 Cu = average capacity utilisation for all semiconductor manufacturing facilities in the country during the period, fraction Cd = design capacity of semiconductor manufacturing facilities in the country, Gm2

TIER 2 METHOD – HEAT TRANSFER FLUIDS There is one Tier 2 method for estimating actual emissions from the use of each FC fluid. This method is a massbalance approach that accounts for liquid FC usage over an annual period. It is appropriate when companyspecific data are available. Over the course of a year, liquid FCs are used to fill newly purchased equipment and to replace FC fluid loss from equipment operation through evaporation. The Tier 2 method neglects fluid losses during filling new or existing equipment or when decommissioning old equipment (which is reasonable for these costly fluids).11 Inventory compilers should obtain from companies the chemical composition of the fluid(s) for which emissions are estimated. The method is expressed in Equation 6.13.

[

]

EQUATION 6.13 TIER 2 METHOD FOR ESTIMATION OF FC EMISSIONS FROM HEAT TRANSFER FLUIDS FCi = ρ i • I i , t −1 (l ) + Pi , t (l ) − N i , t (l ) + Ri ,t (l ) − I i , t (l ) − Di ,t (l )

Where: FCi = emissions of FCi, kg

ρ i = density of liquid FCi, kg/litre

Ii,t-1(l) = the inventory of liquid FCi at the end of the previous period, litres Pi,t(l) = net purchases of liquid FCi during the period (net of purchases and any returns), litres Ni,t(l) = total charge (or nameplate capacity) of new installed, litres Ri,t(l) = total charge (or nameplate capacity) of retired or sold equipment, litres Ii,t(l) = inventory of liquid FCi at end of the period, litres Di,t(l) = amount of FCi recovered and sent offsite from retired equipment during the period, litres

11

Prices for heat transfer fluids vary from $55 – 130/litre. 3M, a manufacturer of a popular heat transfer fluid estimates that a vintage 2 000 manufacturing plant may loose 1 900 litres/year via evaporation. Manufactures of testing equipment that use heat transfer fluids report loss rates of approximately 30 litres/year/system for newer designs that reduce evaporative losses and 50 litres/year/system for older designs.

6.14

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Electronics Industry Emissions

Figure 6.2

Decision tree for estimation of FC emissions from HT fluid loss from electronics manufacturing Start

Are heat transfer fluid loss data available from electronics manufacturing companies?

Yes

Estimate emissions using the Tier 2 method. Box 2: Tier 2

No

Is Electronics Industry a key category and is this subcategory significant? 1

Yes

Collect liquid FC use data from companies.

No Estimate emissions using the Tier 1 method. Box 1: Tier 1 Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees

6.2.2 6.2.2.1

Choice of emission factors12 E TCHING

AND CVD CLEANING FOR SEMICONDUCTORS , LIQUID CRYSTAL DISPLAYS , AND PHOTOVOLTAICS

TIER 1 The default emission factors for the Tier 1 method is presented in Table 6.2 below. In using Tier 1, it is not good practice to modify, in any way, the set of the FCs or the values of the emission factors assumed in Table 6.2. Inventory compilers should not combine emissions estimated using Tier 1 method with emissions estimated using the Tier 2 or 3 methods. For example, inventory compilers may not use the Tier 1 factor for CF4 to estimate the emissions of CF4 from semiconductors and combine it with the results of other FC gases from a Tier 2 or Tier 3 method. It should be also noted that the Tier 1 FC emission factors presented in Table 6.2 should not be used for any purpose other than estimating annual FC-aggregate emissions from semiconductor, TFT-FPD or PV manufacturing for compilation of the national greenhouse gas inventory.

12

Sources and methods for developing emissions factors, if not explicitly provided in Chapter 6, can be found in Burton (2006).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

6.15

Volume 3: Industrial Processes and Product Use

TABLE 6.2 TIER 1 GAS-SPECIFIC EMISSION FACTORS FOR FC EMISSIONS FROM ELECTRONICS MANUFACTURING Emission Factor (EF) (Mass per Unit Area of Substrate Processed)

Electronics Industry Sector Semiconductors, kg/m2 2

TFT-FPDs, g/m a

CF4

C2F6

CHF3

C3F8

NF3

SF6

C6F14

0.9

1.

0.04

0.05

0.04

0.2

NA

0.5

NA

NA

NA

0.9

4.

NA

5

0.2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

0.3

2

PV-cells , g/m

b

2

Heat Transfer Fluids , kg/m a

EFs adapted from unpublished work of Fthenakis, Alsema and Agostinelli. (Fthenakis ,2005) Note that factor is applicable only to silicon-specific technologies and is applied for abatement.

b

EF assumes HTFs have the same GWP and C6F14 represents a suitable proxy. The origin of this factor is described in Burton, 2004, and is based in part on the work of Tuma and Tousignant (2001).

TIER 2 As discussed above, emissions factors based on simple electronics production variables are not adequate to account for all of the factors that influence emissions. Data for each of the following parameters are necessary to prepare a reliable estimate: •

•

The gases used;

•

The brand of process tool used;

The process type (CVD or etch) used;

•

Emission reduction technology.

Default values have been developed for the parameters used in Tier 2a and 2b methods (See Figure 6.1) on the basis of direct measurements, literature, and expert judgement (see Tables 6.3, 6.4, and 6.5 Tier 2 Default Emission Factors for FCs Emissions from Semiconductor12, TFT-FPD13, and PV12 Manufacturing respectively). Given the difficulty in representing the diverse production conditions within the electronics industry, default emission parameters are inherently uncertain. While accuracy can be improved with larger sets of measured data and where factors are applied to similar processes using similar or identical chemical recipes, developing default factors necessarily involves some form of averaging across all of the data. Electronics industry specialists expect that rapid technical innovation by chemical and equipment suppliers and electronics manufacturers will result in major emission reductions in the future (i.e., 2006 onwards). As a result, emission factors for these categories should evolve to reflect these changes. The semiconductor and TFT-FPD industries have established mechanisms through the World Semiconductor Council and the World LCD Industry Cooperation Committee, respectively, to evaluate global emission factors. The PV industry may be considering establishing a mechanism for tracking its PFC emissions during PV manufacture. (Fthenakis, 2006) FC-use during PV manufacture may or may not increase. Existing evidence suggests that, should FC-use in this industry grow, efforts will be made to control their emissions (Agostinelli et al., 2004; Rentsch et al., 2005). Inventory compilers may wish to periodically consult with the industry to better understand global and national circumstances. Tables 6.3 and 6.4 include two entries for NF3: Remote-NF3 and NF3. The first refers to a cleaning method in which the film cleaning-agents formed from NF3 (F-atoms) are produced in a plasma upstream (remote) from the chamber being cleaned. The last, denoted as simply NF3, refers to an in-situ NF3 cleaning process that is analogous to the process for other cleaning gases like C2F6 and C3F8. The default value for the fraction of gas remaining in the shipping container (heel) is 0.10.

13

The emissions factors (EFs) for TFT-FPD manufacturing are simple (unweighted) averages developed from gas- and process-specific values published by Nishida et al. (2005).

6.16

2006 IPCC Guidelines for National Greenhouse Gas Inventories

0.2 NA NA

NA

NA

NA

BCF4

BC2F6

BC3F8

*

0.6 *

0.4 NA 0.1 NA NA

0.9

NA

NA

NA

NA

NA

CVD 1-Ui

Etch BCF4

Etch BC2F6

CVD BCF4

CVD BC2F6

CVD BC3F8

NA

NA

NA

NA

0.08

*

NA

0.06*

NA

NA

0.08

0.1

CH2F2

NA

NA

0.1

NA

NA

0.4

NA

NA

NA

0.1

0.4

C3F8

NA

NA

0.1

0.2

0.2

0.1

0.2*

NA

0.1

0.1

0.1

c-C4F8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Estimate reflects presence of low-k, carbide and multi-gas etch processes that may contain a C-containing FC additive

†

NA

NA

The default emission factors for F2 and COF2 may be applied to cleaning low-k CVD reactors with ClF3.

NA

NA

NA

0.2*

0.3

*

NA

0.1

NA

0.2

0.3

0.1

C4F6

Estimate includes multi-gas etch processes

NA

NA

NA

NA

NA

NA

0.2

NA

NA

NA

0.2

SF6

‡

NA

NA

0.1

†

†

0.02

NA

NA

0.2

0.2

NA

NA

0.09

0.2

NF3

NA

NA

0.02

NA

NA

NA

0.02

†

0.02

NF3 Remote

NA

NA

0.1

0.2

0.2

0.1

0.2

NA

0.04

0.1

0.1

C5F8

0.04

NA

0.1

NA

NA

0.1

NA

0.04

NA

0.1

0.1

C4F8O

Greenhouse Gases without TAR GWP

*

Notes: NA denotes not applicable based on currently available information

NA

NA

NA

NA

0.07

NA

0.4*

0.7*

0.4*

NA

NA

0.07

0.4

CHF3

Etch 1-Ui

Tier 2b

0.6

C2F6

0.9

CF4

1-Ui

Tier 2a

Process Gas (i)

Greenhouse Gases with TAR GWP

TABLE 6.3 TIER 2 DEFAULT EMISSION FACTORS FOR FC EMISSIONS FROM SEMICONDUCTOR MANUFACTURING

NA

NA

0.02

†

NA

NA

NA

NA

NA

NA

0.02

†

NA

F2

NA

NA

0.02†

NA

NA

NA

NA

NA

NA

0.02†

NA

COF2

Non-GHGs Producing FC By-products‡

6.17

Chapter 6: Electronics Industry Emissions

NA NA NA NA

NA

NA

NA

NA

BCF4

BCHF3

BC2F6

BC3F8

NA NA NA NA NA NA NA

NA

NA

NA

NA

NA

NA

NA

CVD 1-Ui

Etch BCF4

Etch BCHF3

Etch BC2F6

CVD BCF4

CVD BC2F6

CVD BC3F8

NA

NA

NA

0.05

NA

0.07

NA

0.2

NA

0.05

NA

0.07

0.2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

CH2F2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C3F8

NA

NA

NA

NA

0.02

0.009

NA

0.1

NA

NA

0.02

0.009

0.1

c-C4F8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Notes: NA denotes not applicable based on currently available information

NA

0.6

Etch 1-Ui

Tier 2b

NA

CHF3

NA

NA

NA

NA

NA

NA

0.03

NA

NA

NA

NA

NA

0.03

NF3 Remote

Greenhouse Gases with TAR GWP

NA

NA

NA

NA

NA

NA

0.3

NA

NA

NA

NA

NA

0.3

NF3

NA

NA

NA

NA

NA

NA

0.9

0.3

NA

NA

NA

NA

0.6

SF6

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C4F6

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C5F8

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C4F8O

Greenhouse Gases without TAR GWP

TABLE 6.4 TIER 2 DEFAULT EMISSION FACTORS FOR FC EMISSIONS FROM LCD MANUFACTURING

C2F6

0.6

CF4

1-Ui

Tier 2a

Process Gas (i)

Volume 3: Industrial Processes and Product Use

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

F2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

COF2

Non-GHGs Producing FC By-products

6.18

0.2 NA NA

NA

NA

NA

BCF4

BC2F6

BC3F8

0.6 0.2 NA 0.2 NA NA

NA

NA

NA

NA

NA

NA

CVD 1-Ui

Etch BCF4

Etch BC2F6

CVD BCF4

CVD BC2F6

CVD BC3F8

NA

NA

NA

NA

NA

NA

0.4

NA

NA

NA

0.4

CHF3

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

CH2F2

NA

NA

0.2

NA

NA

0.1

NA

NA

NA

0.2

0.4

C3F8

NA

NA

0.1

0.1

0.1

0.1

0.2

NA

0.1

0.1

0.2

c-C4F8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Notes: NA denotes not applicable based on currently available information

0.4

0.7

Etch 1-Ui

Tier 2b

0.6

C2F6

0.7

CF4

1-Ui

Tier 2a

Process Gas (i)

NA NA

NA

NA

NA

NA

0.3

NA

NA

NA

0.05

0.2

NF3

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NF3 Remote

Greenhouse Gases with TAR GWP

NA

NA

NA

NA

NA

0.4

0.4

NA

NA

NA

0.4

SF6

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C4F6

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C5F8

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C4F8O

Greenhouse Gases without TAR GWP

TABLE 6.5 TIER 2 DEFAULT EMISSION FACTORS FOR FC EMISSIONS FROM PV MANUFACTURING

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

F2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

COF2

Non-GHGs Producing FC By-products

6.19

Chapter 6: Electronics Industry Emissions

Volume 3: Industrial Processes and Product Use

TABLE 6.6 TIER 2a & 2b DEFAULT EFFICIENCY PARAMETERS FOR ELECTRONICS INDUSTRY FC EMISSION REDUCTION a,b,e

TECHNOLOGIES

CF4

C2F6

CHF3

C3F8

c-C4F8

NF3f

SF6

Destruction

0.9

0.9

0.9

0.9

0.9

0.95

0.9

Capture/Recoveryd

0.75

0.9

0.9

NT

NT

NT

0.9

Emission Control Technology c

a

Values are simple (unweighted) averages of destruction efficiencies for all abatement technologies. Emission factors do not apply to emission control technologies which cannot abate CF4 at destruction or removal efficiency (DRE) ≥ 85 percent when CF4 is present as an input gas or by-product and all other FC gases at DRE ≥ 90 percent. If manufacturers use any other type of emission control technology, its destruction efficiency is 0 percent when using the Tier 2 methods. b Tier 2 emission control technology factors are applicable only to electrically heated, fuelled-combustion, plasma, and catalytic devices that • are specifically designed to abate FCs, • are used within the manufacturer’s specified process window and in accordance with specified maintenance schedules and • have been measured and has been confirmed under actual process conditions, using a technically sound protocol, which accounts for known measurement errors including, for example, CF4 by-product formation during C2F6 as well as the effect of dilution, the use of oxygen or both in combustion abatement systems c Average values for fuelled combustion, plasma, and catalytic abatement technologies. d Average values for cryogenic and membrane capture and recovery technologies. e Vendor data verified by semiconductor manufacturers. Factors should only be used when an emission control technology is being utilised and maintained in accordance with abatement manufacturer specifications. f Use of NF3 in the etch process is typically small compared to CVD. The aggregate emissions of NF3 from etch and CVD under Tier 2b will usually not be greater than estimates made with Tier 2a or Tier 1 methods. NT = not tested.

Process tool emission factors The procedures for calculating process tool emission factors for Tier 2a and Tier 2b methods are identical. Process tool emission factors are defined as the amount of greenhouse gas emitted divided by the amount of greenhouse gas used in the process. The emission factors correspond to the ‘(1 – Ui)’ term in the Tier 2 formulas. For example, the emission factor of 0.9 for CF4 (see Table 6.3 above, Tier 2a value) means that 90 percent of the CF4 used in the process is emitted as CF4. By-product emission factors were also calculated. The major by-product emission of significance is CF4. While it is generally held that the only gases that emit significant amounts of CF4 as a by-product are C2F6 and C3F8, the data provided by tool manufacturers and chemical suppliers showed that CF4 is also formed from mixtures of gases (e.g., that contain CHF3 or CH2F2) and c-C4F8. As a result of this discussion, CF4 by-product emission factors were calculated for CHF3, CH2F2, C2F6, C3F8, c-C4F8 and C4F8O. For example, a value of 0.1for C3F8 (taken from Table 6.3 above, Tier 2a value) means that 10 percent of the C3F8 used is converted into CF4. However, C2F6 may also be emitted from the decomposition of molecules such as C4F6. As described previously, CF4 may also be formed when etching or cleaning chambers where carbon-containing films are present. In order to calculate the Tier 2b process tool emission factors, data were collected from process equipment and gas manufacturers. The data were collected according to process type (either Chemical Vapour Deposition (CVD) or etch) and also by type of gas (e.g. C2F6, CF4). The methods used to conduct the emissions testing were real time Quadrupole Mass Spectrometry (QMS) and Fourier Transform Infrared Spectroscopy (FTIR), the best known methods for measuring process tool emissions. Calibration standards (usually 1 percent mixtures with a balance of N2) were used to quantify the results. The measurement protocol and quality control requirements that were followed are outlined in the ‘Guidelines for Environmental Characterisation of Semiconductor Equipment.’ (Meyers et al., 2001) 14 The emission factors for Tier 2b (see Tables 6.3 and 6.4 above) are the simple (unweighted) average of the data collected for each gas for etch and CVD, rounded to one significant figure.12, 16 In order to determine the Tier 2a process tool emission factors, knowledge of the amounts of gas used in typical semiconductor manufacturing processes is required. The Tier 2a emission factors were obtained using weights provided by industry experts for the proportion of each gas used in etching and cleaning processes. For example, the Tier 2b emission factors for C2F6 (Table 6.3) are 0.5 (etch) and 0.6 (CVD). The distribution of C2F6 usage between etching and CVD chamber cleaning processes during semiconductor manufacture is 20:80. Applying these weights to each of the emission factors gives 0.6 for the Tier 2a factor for C2F6 to one significant figure.

14

These guidelines have also been adopted by flat panel display manufacturers for measuring FC-emissions during flat panel device manufacture.

6.20

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Electronics Industry Emissions

The corresponding distribution of SF6 usage in TFT-FPD manufacture is 50:50, which gives 0.6 for corresponding Tier 2a emission factor (Table 6.4).15 For Tier 3 emission factors, semiconductor manufacturers use company or plant-specific values rather than using default values as listed in Table 6.1 above. In order to assure the quality of emission factors, emission testing should be conducted in accordance with accredited methods.16 If a third-party supplier conducts the emissions testing, the semiconductor manufacturer should make sure that the third-party supplier is capable of meeting all of the requirements outlined in Revision 3.0 of the Equipment Environmental Characterisation Guidelines (SIA, 2000). Semiconductor manufacturers who use emission factors provided by the process tool equipment supplier should make sure that the emission factors are applicable to their specific manufacturing process. Manufacturing methods with process parameters (e.g., pressure, flow rate) that deviate from centreline conditions may have different emission factors than those provided by the tool manufacturer.

Emission control technology factors for Tier 2 methods Emissions control technologies are developing at a rapid pace along with electronics manufacturing technology. Default control technology emission factors in Table 6.6 are based on tests of control devices that have been optimised for specific processes and tools. Results are expected to vary across tools and gas flow rates. Emission factors are not applicable to all tools or processes in semiconductor, liquid crystal display, or photovoltaic manufacturing facilities. The Tier 2 default destruction efficiency parameters presented in Table 6.6 are only applicable when the inventory compiler can demonstrate through communication with facility managers and subsequent documentation that emissions control technologies are operated and maintained in accordance with manufacturer specifications. If companies use any other type of abatement device, they should assume that its destruction efficiency is 0 percent under the Tier 2 a and b methods. Assumptions for the emissions control technology emission factors for the Tier 2 (a & b) methods include: (i) Specific emissions control technologies are not listed; emission factors for each chemical were established based on results achieved during testing of emissions control technologies in semiconductor manufacturing applications; (ii) Emission factors should only be used when abatement is applied to emissions that fall within the operating range specified by the abatement manufacturer to meet or exceed the factors listed in Table 6.6; (iii) Emission factors apply only to that portion of emissions that pass through a properly operating and maintained control device; emission factors should not be applied when control device is bypassed, not operating according to manufacturer specifications, or not maintained in accordance with specifications. (iv) Emission factors do not apply to emission control technologies which cannot abate CF4 at a destruction removal efficiency (DRE) ≥ 85 percent when CF4 is present as an input gas or by-product and all other FC gases at DRE ≥ 90 percent. If manufacturers use any other type of emission control technology, its destruction efficiency is 0 percent when using the Tier 2 methods. The default Tier 2 emission control factors in Table 6.6, Default Efficiency Parameters for Electronics Industry FC Emission Reduction Technologies were calculated from data received from equipment suppliers, abatement technology suppliers and electronic device manufacturers. It should be noted that only data from abatement devices that were specifically designed to abate FCs were used in the calculation. Data were received from combustion abatement devices (all of which used some type of fuel), plasma abatement devices, electrically heated abatement devices, and catalytic abatement devices. The values presented in Table 6.6, Default Efficiency Parameters for Electronics Industry FC Emission Reduction Technologies, are the results of all of the data received for optimized technologies and for each input gas, rounded down to the next 5 percent (e.g., an average of 98 percent would be rounded down to 0.95). The averages were rounded down to reflect that (i) emissions control devices vary in their efficacy depending upon what gas they are optimised to destroy, and (ii) the efficacy of emission control devices depends on the type of tool they are installed on (150, 200 or 300mm wafers) and the amount of FC gas flown through that particular tool, and total exhaust flow through the emissions control device. An emission control device that can destroy 99 percent of a FC when it is optimised to destroy that FC on a certain tool may destroy less than 95 percent of that FC when it is optimised to destroy something else or when it is used on a tool for which it was not designed, or if the FC or total exhaust flow exceeds a certain limit. Electronics manufacturers and abatement tool manufacturers 15

The 50:50 SF6 usage rates represent an average for the leading TFT-FPD manufacturing regions of Japan, Republic of Korea and Taiwan. That proportion was provided by Nishida (2006) and Kim (2006).

16

One example of an internationally accredited testing method is Meyers et al. (2001).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

6.21

Volume 3: Industrial Processes and Product Use

should ensure that the abatement system installed is properly sized and maintained and that the emission control device can meet or exceed the default emission factor highlighted in Table 6.6.

6.2.2.2

H EAT

TRANSFER FLUIDS

The emission factor for the Tier 1 method is presented in Table 6.2. There is no emission factor for the Tier 2 method for estimating emissions from evaporation of heat transfer fluids.

6.2.3

Choice of activity data

Activity data for the electronics industry consists of data on gas sales and use or the annual amount of electronics substrate processed (e.g., m2 of silicon processed for semiconductors). For the more data-intensive Tier 2 methods, gas purchase data at the company or plant-level are necessary. For the Tier 1 methods, inventory compilers will need to determine the total surface area of electronic substrates processed for a given year. Silicon consumption may be estimated using an appropriate edition of the World Fab Watch (WFW) database, published quarterly by Semiconductor Equipment & Materials International (SEMI)17 . The database contains a list of plants (production as well as R&D, pilot plants, etc.) worldwide, with information about location, design capacity, wafer size and much more. Similarly, SEMI’s ‘Flat Panel Display Fabs on Disk’ database provides an estimate of glass consumption for global TFT-FPD manufacturing. The activity data in Table 6.7 reflect design capacity figures. Semiconductor and TFT-FPD manufacturing plants are not operated at design capacities for sustained periods, such as a full year. Instead, the capacity fluctuates depending on product demand. For semiconductor manufacturing, publicly available industry statistics show that the global annual average capacity utilisation during the period 1991 – 2000 varied between 76 and 91 percent, with an average value of 82 percent and most probable value of 80 percent. When country-specific capacity utilisation data are not available, the suggested capacity utilisation for semiconductor manufacturing is 80 percent. This should be used consistently for a time series of estimates. For TFT-FPD manufacturing, publicly available capacity utilisation data are not available. The TFT-FPD manufacturing industry, like the semiconductor manufacturing industry, lowers product prices to maintain the highest practical plant capacity utilisation. By analogy, therefore, it is suggested to use 80 percent to estimate substrate glass consumption using the design capacities provided in Table 6.7 for country TFT-FPD manufacturers. For PV manufacturing, published capacity utilisation data ranges between 77 – 92 percent, with the average for the years 2003 and 2004 of 86 percent. Therefore, 86 percent is the recommended default figure for Cu (see Equation 6.1) to use. When estimating emissions during PV manufacture, one should account for the fraction of the industry that actually employs FCs (CPV in Equation 6.1). Because recent surveys indicate that between 40 – 50 percent of PV manufacture actually uses FC, and the usage trend may be increasing, the recommended default value for CPV is 0.5. Table 6.7 summarises the capacity for 2003, 2004 and 2005 for countries, which in total, account for more than 90 percent of world capacity in 2003.

17

The term ‘fab’ is synonymous with clean room/manufacturing facility. Semiconductor and flat panel display manufacturing plants are often called fabrication plants, from which the abbreviation ‘fab’ follows.

6.22

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Electronics Industry Emissions

TABLE 6.7 COUNTRY TOTAL SILICON (Si) AND GLASS DESIGN CAPACITIES (Mm2) FOR 2003, 2004 AND 2005 Annual Si design capacities, Mm2

Annual Glass design capacities, Mm2

20031

20042

20052

20031

20042

20052

Australia

0.0008

0.0008

0.0008

NA

NA

NA

Austria

0.0201

0.0201

0.0201

NA

NA

NA

Belgium

0.0040

0.0040

0.0040

NA

NA

NA

Canada

0.0041

0.0041

0.0041

NA

NA

NA

China

0.1436

0.1982

0.3243

0.0432

0.0432

0.8154

Czech Republic

0.0057

0.0057

0.0057

NA

NA

NA

France

0.0653

0.0674

0.0674

NA

NA

NA

Germany

0.1622

0.1622

0.1622

NA

NA

NA

China, Hong Kong

0.0059

0.0059

0.0059

NA

NA

NA

Hungary

0.0006

0.0006

0.0006

NA

NA

NA

India

0.0128

0.0128

0.0128

NA

NA

NA

Ireland

0.0175

0.0430

0.0430

NA

NA

NA

Israel

0.0310

0.0310

0.0564

NA

NA

NA

Italy

0.0431

0.0431

0.0609

NA

NA

NA

Japan

0.9091

0.9235

0.9639

4.5746

5.3256

6.9201

Latvia

0.0019

0.0019

0.0019

NA

NA

NA

Malaysia

0.0284

0.0284

0.0284

NA

NA

NA

Netherlands

0.0301

0.0301

0.0301

0.0209

0.0209

0.0209

Republic of Belarus

0.0077

0.0077

0.0077

NA

NA

NA

Russia

0.0250

0.0250

0.0325

NA

NA

NA

South Korea

0.3589

0.3742

0.3937

5.8789

9.4679

12.4857

Singapore

0.1730

0.1730

0.1985

0.2821

0.2821

0.2821

Slovakia

0.0043

0.0043

0.0043

NA

NA

NA

South Africa

0.0021

0.0021

0.0021

NA

NA

NA

Sweden

0.0019

0.0019

0.0019

NA

NA

NA

Switzerland

0.0098

0.0098

0.0098

NA

NA

NA

Thailand

0.0000

0.0000

0.0094

NA

NA

NA

Turkey

0.0000

0.0000

0.0000

NA

NA

NA

United Kingdom

0.0597

0.0597

0.0936

NA

NA

NA

United States of America

0.6732

0.6921

0.7190

0.0000

0.0000

0.0000

Vietnam

0.0000

0.0000

0.0000

NA

NA

NA

Global Total

3.3206

3.4972

3.8849

15.0572

23.9959

33.7459

Country Totals

1

Country totals include fab in production

2

Country totals include fabs under construction and announced.

NA = not applicable. Sources: Extractions from World Fab Watch Database, January 2004 Edition for Semiconductor Manufacturing and Flat Panel Display Fabs on Disk Database (Strategic Marketing Associates, 2004a), October 2004 Edition for TFT-FPD Manufacturing (Strategic Marketing Associates, 2004b).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 3: Industrial Processes and Product Use

TABLE 6.8 COUNTRY TOTAL PV PRODUCTION CAPACITYa FOR 2003, Mm2 Australia

0.135

Austria

0.0307

Canada

0.0154

Denmark

0.00254

France

0.162

Germany

0.817

Italy

0.100

Japan

3.72

Norway

0.0138

Portugal

0.115

S. Korea

0.462

Spain

0.715

Sweden

0.377

Switzerland

0.00238

United Kingdom

0.0269

United States

1.02

a

Capacities for all PV manufacturing technologies, includes those that may not use FCs during PV manufacture; World average capacity utilisation for 2003 = 86%. Source: IEA, 2004. PV participating survey countries.

6.2.4

Completeness

Complete accounting of emissions from the semiconductor industry should be achievable in most countries because there are a limited number of companies and plants. There are four issues related to completeness that should be addressed: •

Other by-products: A number of transformation by-products are generated as a result of FC use for chamber cleaning and etching. As highlighted above, formation of CF4 and C2F6 can result from the decomposition of other FC gases. Also, CF4 formation has been observed in the cleaning of low k CVD chambers. In this case, the Tier 3 method should be used to accurately estimate emissions.

•

New chemicals: Completeness will be an issue in the future as the industry evaluates and adopts new chemical processes to improve its products. Industry-wide efforts to reduce FC emissions are also accelerating the review of new chemicals. Consequently, good practice for this industry is to incorporate a mechanism that accounts for greenhouse gases not listed in the IPCC Third Assessment Report (e.g., C4F6, C5F8, Fluorinerts™, and Galdens®). These new FC materials have high GWPs or may produce high GWP byproduct emissions.

•

Other sources: A small amount of FCs may be released during gas handling (e.g. distribution) and by sources such as research and development (e.g. university) scale plants and tool suppliers. These emissions are not believed to be significant (e.g., less than 1 percent of this industry’s total emissions).

•

18

Other products or processes: FC use has been identified in the electronics industry in emissive applications including: micro-electro-mechanical systems (MEMS),18 hard disk drive manufacturing, device testing (FC liquids), vapour phase reflow soldering,19 and precision cleaning.20

Emissions from micro-electro-mechanical systems (MEMS) manufacturing may be estimated using methods similar to those used for the other electronic sub sectors. Company-specific emission and abatement factors are required. Very small amounts of FCs are also used in and research and development laboratories/facilities.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Electronics Industry Emissions

6.2.5

Developing a consistent time series

Use of FCs by the semiconductor industry began in the late 1970s and accelerated significantly beginning in the early 1990s. Determining a base year emissions level may present difficulties because few data are available for emissions occurring before 1995. If historical emissions estimates were based on simple assumptions (e.g., use equals emissions), then these estimates could be improved by applying the methods described above. If historical data are not available to permit use of a Tier 3 or 2 method, then the Tier 1 method using default emission parameters can be used retrospectively. Both Tier 1 and Tier 2 could then be applied simultaneously for the years in which more data become available to provide a comparison or benchmark. This should be done according to the guidance provided in Volume 1, Chapter 5. In order to ensure a consistent emissions record over time, an inventory compiler should recalculate FC emissions for all years reported whenever emissions calculation procedures are changed (e.g., if an inventory compiler changes from the use of default values to actual values determined at the plant level). If plant-specific data are not available for all years in the time series, the inventory compiler will need to consider how current plant data can be used to recalculate emissions for these years. It may be possible to apply current plant-specific emission parameters to sales data from previous years, provided that plant operations have not changed substantially. Such a recalculation is required to ensure that any changes in emission trends are real and not an artefact of changes in procedure.

6.3

UNCERTAINTY ASSESSMENT

Use of the Tier 3 method will result in the least uncertain inventory. Given the limited number of plants and the close monitoring of production processes at the plant level, collection of data for use in Tier 2b or Tier 3 methods should be technically feasible. Inventory compilers should seek the advice of the industry on uncertainties, using the approaches to obtaining expert judgement outlined in Volume 1, Chapter 3. Of all the methods, Tier 1 is the most uncertain. Using a single factor to account for the FC emissions from the diversity of semiconductor products is a glaring simplification. The factors presented in Table 6.2 are heavily weighted toward the manufacture of advanced vintage-late-1990s memory and logic products, having 3 to 5 layers, respectively, manufactured on the silicon wafer. The factors for countries that are currently manufacturing products at the leading-edge of technology (and are not using measures to reduce FC emissions) would be larger, while countries that manufacture products that use older technologies or manufacture simpler devices would use the same or an even smaller factor. The Tier 1 emissions factors for TFT-FPD manufacturing represents a weighted average of the estimated aggregate PFC emissions per unit area of substrate glass consumed during TFT-FPD manufacture for the area where data were available (Burton, 2004b). The estimated emissions reported for Japan used Tier 2b factors for semiconductor manufacturing from Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (IPCC, 2000) in semiconductor manufacturing (Nishida et al., 2004). For emissions from Taiwan’s TFT-FPD manufacturers, the method for estimating emissions was not reported (Leu et al., 2004). However, subsequently Leu (2004) reported an aggregate emission factor having a similar magnitude to that developed by Burton (2004b). The uncertainty in the Tier 1 emissions factor for TFT-FPD manufacture is probably large, but not known at this time. When using Tier 3 method for semiconductor and TFT-FPD manufacturing, the resulting estimates of emissions will be more accurate than the Tier 2a, 2b or Tier 1 methods, on the order of ± 30 percent (95 percent confidence interval). Uncertainty in the efficacy of emission control technology appears to contribute most to this uncertainty, especially the variability in the uptime of emission control devices and in flow rates to emission control devices that may exceed device design limits. Estimates of emissions from using heat transfer fluids using the Tier 2 method will be more accurate than Tier 1 method, of the order of ± 20 percent (95 percent confidence interval).

6.3.1

Emission factor uncertainties

The uncertainties in the emission factors suggested for the Tier 2b and 2a methods are shown in Table 6.9 for semiconductor manufacturing and Table 6.10 for TFT-FPD manufacturing. The factors were developed 19

Emissions from vapour phase reflow soldering may be estimated to equal annual net FC purchases for maintaining vapour phase reflow soldering equipment.

20

Emissions from precision cleaning are to be accounted for in Section 7.2 (Solvents) of this Volume.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 3: Industrial Processes and Product Use

specifically for this guidance. For Tier 2b, relative errors for each entry (process and gas in the case of Tier 2b) were estimated as the standard deviation of the factors provided by an expert group, normalised to the simple (unweighted) average, rounded to one significant figure.12 The estimate for each value was then doubled to estimate the 95 percent confidence interval. The same procedure was used to estimate the relative errors for product-formation factors (B). The corresponding estimates for the Tier 2a method were derived for the Tier 2b estimates, using the estimates of gas usage employed in development of the emission factors (see Section 6.2.2 Tier 2). Tier 1 emission factors will have an uncertainty range that is skewed towards values close to zero extending up to 200 percent (95 percent confidence interval for semiconductor and TFT-FPD manufacture). Uncertainty estimates for PV manufacturing are not available.

6.3.2

Activity data uncertainties

Gas consumption constitutes the unit of activity to estimate emissions during semiconductor, TFT-FPD and PV manufacture for the Tier 2a and 2b methods. Gas consumption can be either measured or estimated from data on gas purchases, and requires knowledge of h, the unused gas returned to gas suppliers in the shipping containers. The uncertainties (95 percent confidence interval) in gas consumption and h, whether measured or estimated using expert judgment are shown in Table 6.10, Relative Errors (95 percent confidence interval) for Activity Data for Tier 2a and 2b Methods for Semiconductor and TFT-FPD Manufacture. For Tier 1 method, the unit of activity is substrate consumption. Uncertainties in the Tier 1 activity data are attributed principally to missing data entries in the WFW and FPD databases. An estimate of the reliability of entries derived from the WFW in Table 6.7 is ± 10 percent (95 percent confidence interval), which reflects errors due to missing and incorrect entries in the database. The 95 percent confidence interval in capacity utilisation over the 1991-2000 period is ± 12 percentage points (i.e., from 70 percent utilisation to 94 percent utilisation). The corresponding entries for TFT-FPD and PV manufacture are assumed to be similar to those for semiconductor manufacturing.

6.26

2006 IPCC Guidelines for National Greenhouse Gas Inventories

90 NA NA

NA

NA

NA

BCF4

BC2F6

BC3F8

30 200 NA 80 NA NA

10

NA

NA

NA

NA

NA

CVD 1-Ui

Etch BCF4

Etch BC2F6

CVD BCF4

CVD BC2F6

CVD BC3F8

NA

NA

NA

NA

300

NA

100

NA

NA

300

100

CHF3

NA

NA

NA

NA

200

NA

700

NA

NA

200

400

CH2F2

NA

NA

60

NA

NA

0.4

NA

NA

NA

60

20

C3F8

NA

NA

NA

NA

NA

NA

300

NA

NA

NA

300

SF6

NA

NA

NA

200

200

NA

300

NA

200

200

300

2006 IPCC Guidelines for National Greenhouse Gas Inventories

NA

NA

60

†

200

†

†

200

30

†

†

200

NA

200

100

†

80†

C5F8

40

NA

80

NA

NA

40

NA

40

NA

80

40

C4F8O

Greenhouse Gases without TAR GWP C4F6

Values that exceed 100% imply a distribution that is skewed towards values close to zero extending up to the value given.

NA

NA

200

NA

NA

70

300

NA

NA

200

70

NF3

Estimate relies on an analogy to c-C4F8 as the data for C5F8 were insufficient to estimate a confidence interval.

NA

NA

200

NA

NA

400

NA

NA

NA

200

400

NF3 Remote

†

NA

NA

60

200

200

30

200

NA

200

100

80

c-C4F8

*

Notes: NA denotes not applicable based on currently available information

100

60

Etch 1-Ui

Tier 2b

30

C2F6

15

CF4

1-Ui

Tier 2a

Process Gas (i)

Greenhouse Gases with TAR GWP

NA

NA

200

NA

NA

NA

NA

NA

NA

200

NA

F2

NA

NA

200

NA

NA

NA

NA

NA

NA

200

NA

COF2

Non-GHGs Producing FC By-products

TABLE 6.9 TIER 2 DEFAULT ESTIMATES OF RELATIVE ERRORS (%) FOR EMISSION FACTOR FOR FC EMISSIONS FROM SEMICONDUCTOR MANUFACTURING, 95 PERCENT CONFIDENCE INTERVALS*

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Chapter 6: Electronics Industry Emissions

NA NA NA

NA

NA

NA

BCF4

BCHF3

BC2F6

NA NA NA NA NA NA NA

NA

NA

NA

NA

NA

NA

NA

CVD 1-Ui

Etch BCF4

Etch BCHF3

Etch BC2F6

CVD BCF4

CVD BC2F6

CVD BC3F8

NA

NA

NA

40

NA

30

NA

8

40

NA

30

8

CHF3

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

CH2F2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C3F8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Notes: NA denotes not applicable based on currently available information

100

50

Etch 1-Ui

Tier 2b

NA

C2F6

50

CF4

1-Ui

Tier 2a

Process Gas (i)

NA

NA

NA

NA

20

40

NA

5

NA

20

40

5

c-C4F8

NA

NA

NA

NA

NA

NA

70

NA

NA

NA

NA

70

NF3 Remote

Greenhouse Gases with TAR GWP

NA

NA

NA

NA

NA

NA

20

60

NA

NA

NA

20

NF3

NA

NA

NA

NA

NA

NA

6

NA

NA

NA

NA

20

SF6

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C4F6

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C5F8

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

C4F8O

Greenhouse Gases without TAR GWP

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

F2

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

COF2

Non-GHGs Producing FC By-products

TABLE 6.10 TIER 2 DEFAULT ESTIMATES OF RELATIVE ERRORS (%) FOR EMISSION FACTOR FOR FC EMISSIONS FROM LCD MANUFACTURING, 95 PERCENT CONFIDENCE INTERVALS

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Chapter 6: Electronics Industry Emissions

6.4

QUALITY ASSURANCE/QUALITY CONTROL (QA/QC), REPORTING AND DOCUMENTATION

6.4.1

Quality Assurance/Quality Control (QA/QC)

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1 and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. Additional general guidance for higher tier QA/QC procedures is also included in Volume 1, Chapter 6. Due to the highly competitive nature of the semiconductor industry, provisions for handling confidential business information should be incorporated into the verification process. Methods used should be documented, and a periodic audit of the measurement and calculation of data should be considered. A QA audit of the processes and procedures should also be considered.

6.4.2

Reporting and Documentation

Care should be taken not to include emissions of HFCs used as ODS substitutes with those used in semiconductor manufacturing. It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced. Explicit reporting on emissions in this industry would improve the transparency and comparability of emissions. As a number of FCs gases are emitted by this industry, reporting by individual gas species rather than by chemical type would also improve the transparency and usefulness of this data. Efforts to increase transparency should take into account the protection of confidential business information related to specific gas use. Countrylevel aggregation of gas-specific emissions data should protect this information in countries with three or more manufacturers. Table 6.11, Information Necessary for Full Transparency of Estimates of Emissions from Semiconductor Manufacturing, shows the supporting information necessary for full transparency in reported emissions estimates. Good practice for Tier 3 is to document the development of company-specific emission factors, and to explain the deviation from the generic default values. Given confidentiality concerns, inventory compilers may wish to aggregate this information across manufacturers. In cases where manufacturers in a country have reported different emission or conversion factors for a given FC and process or process type, inventory compilers may provide the range of factors reported and used.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

6.29

Volume 3: Industrial Processes and Product Use

TABLE 6.11 INFORMATION NECESSARY FOR FULL TRANSPARENCY OF ESTIMATES OF EMISSIONS FROM ELECTRONICS MANUFACTURING

Data

Tier 1

Tier 2a

Tier 2b

Tier 3

Emissions of each FC (rather than aggregated for all FCs)

X

X

X

Sales/purchases of each FC

X X

X

X

X

2

Total surface area of electronics substrate processed (e.g., m silicon, m2 glass)

X

Capacity utilisation for semiconductor, TFT-FPD and PV manufacturing

X

Fraction of PV manufacturing capacity that uses FC gases

X

Mass of each FC used in each process or process type Fraction of each FC used in processes with emission control technologies

X

Use rate for each FC for each process or process type (This and following information is necessary only if default value is not used)

X

Fraction of each FC transformed into CF4 for each process or process type

X

Fraction of gas remaining in shipping container

X

Fraction of each FC destroyed by emission control technology

X

Fraction of CF4 by-product destroyed by emission control technology

X

6.30

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Electronics Industry Emissions

References Agnostinelli, G., Dekkers, H. F. W., DeWolf, S. and Beaucarne, G. (2004). “Dry Etching and Texturing Processes for Cystalline Silicon Solar Cells: Sustainability for Mass Production”, presented at the 19th European Photovoltaic Solar Energy Conference, Paris, 2004. Alsema, E. A., Bauman, A. E., Hill, R. and Patterson, M. H. (1997) “Health, Safety and Environmental Issues in Thin Film Manufacturing”, 14th European PV Solar Energy Conference, Barcelona, Spain. 1997. Burton, C. S. (2004a). “Uses and Air Emissions of Liquid PFC Heat Transfer Fluids from the Electronics Sector: Initial Findings”, report prepared for U. S. EPA’s Climate Change Division, October 2004. Burton, C. S. (2004b). “PFC Uses, Emissions, and Trends in FPD Manufacture: An Update”, draft report prepared for U. S. EPA’s Climate Change Division, June 2004. Burton, C. S. (2006). “Sources and Methods Used to Develop PFC Emission Factors from the Electronics Sector”, report prepared for U. S. EPA’s Climate Change Division, February 2006. Cowles, D. (1999) “Oxide Etch Tool Emissions Comparison for C5F8 and C4F8 Process Recipes”, presented at A Partnership for PFC Emissions Reductions, SEMICON Southwest 99, Austin, TX. October 1999. Fthenakis, V. (2005) Personal communication to S. Bartos on 5 February 2005 of data tables quantifying historical and current CF4 and C2F6 usage in PV manufacture for U. S. and Europe. Fthenakis, V. (2006) Personal communication to S. Burton and S. Bartos explaining proposal to begin monitoring FC emissions from European PV industry. Feb. 6, 2006. IEA (2004). ‘Trends in Photovoltaic Applications: Survey report of selected IEA Countries between 1992 – 2003’, Photovoltaic Power Systems Programme (PVPS), International Energy Agency, Report IEA-PVPS T1-13:2004, September 2004. IPCC (2000). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Penman J., Kruger D., Galbally I., Hiraishi T., Nyenzi B., Emmanuel S., Buendia L., Hoppaus R., Martinsen T., Meijer J., Miwa K., Tanabe K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. ITRS

(2004), “International Technology http://public.itrs.net/report.htm

Roadmap

for

Semiconductors”

available

at

Kim, D-H., (2006) 9 January 2006 Personal communication to Hideki Nishida identifying the historical average (50:50) proportion of SF6 usage for etching and CVD chamber-cleaning in Japanese, Korean and Taiwan TFT-FPD manufacture. Leu, C-H., (2004) “SF6 Abatement Strategy in Taiwan”, presented at SF6 Power Reduction Partnership for Electric Power Systems, Scottsdale, Az., 1-3 December 2004. Leu, C-H et al. (2004) “PFC emissions Abatement for TFT-LCD Industry in Taiwan”, available in the Proceedings of the 15th Annual Earth Technology Forum, Washington, D. C., 13-15 April 2004. Maycock, P. (2005) “PV market update: global PV production continues to increase”, Renewable Energy World, Vol. 8 (4), pp 86-99. Meyers, J., Maroulis, P., Reagan, B. and Green, D. (2001). “Guidelines for Environmental Characterization of Semiconductor Equipment”, Technology Transfer #01104197A-XFR, pub. International SEMATECH, Austin, Texas, USA. December 2004, See: www.sematech.org/docubase/document/4197axfr.pdf. Nishida, H. et al. (2004) “Voluntary PFC Emission Reduction in the LCD Industry”, available in the Proceedings of the 15th Annual Earth Technology Forum, Washington, D. C., 13 – 15 April 2004. Nishida, H., Marsumura, K., Kurokawa, H., Hoshino, A. and Masui, S. (2005), “PFC emission-reduction strategy for the LCD industry”, J. Society for Information Display, Vol 13, pp. 841-848 (2005). Nishda, H. (2006). 7 January 2006 Personal communication to D-H. Kim confirming historical average 50:50 proportion of SF6 usage for etching and CVD chamber-cleaning in Japan, Korean and Taiwan TFT-FPD manufacture. Phylipsen, G. J. M. and Alsema, E. A., (1995) “Environmental life-cycle assessment of multicrystalline silicon solar cell modules”, report prepared for Netherlands Agency for Energy and the Environment, Report No. 95057, September 1995.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 3: Industrial Processes and Product Use

Rentsch, J., Schetter C., Schlemm H., Roth, K. and Preu, R. (2005). “Industrialization of Dry Phosphorous Silicate Glass Etching and Edge Isolation for Crystalline Silicon Solar Cells”, Presented at the 20th European Photovoltaic Solar Energy Conference and Exhibition, Barcelona, Spain. 6-10 June, 2005. Sekiya, A. (2003). “Climate-Friendly Alternative Refrigerant and the Others: New Evaluations for sustainability”, The Earth Technologies Forum, Washington, D. C., 23 April, 2003. Shah, A., Meier, J., Buechel, A., Kroll, U., Steinhauser, J., Meillaud, F. and Schade, H. (2004). “Toward Very Low-Cost Mass Production of Thin-film silicon Photovoltaic (PV) Solar Modules on Glass“, presented at ICCG5 Conference in Saarbrucken, Germany, July 2004. SIA (2000). “Equipment Environmental Characterisation Guidelines”, Revision 3.0, Semiconductor Induistry Association (SIA), San Jose, California, USA, February 2000 Strategic Marketing Associates (2004a). WORLD FAB WATCH: The Industry’s Encyclopedia of Wafer Fabs Since 1994, January 2004 Edition. Strategic Marketing Associates (2004b). WORLD FAB WATCH: The Industry’s Encyclopedia of Wafer Fabs Since 1994, October 2004 Edition. Tuma, P.E. and Tousignant, L. (2001). “Reducing Emissions of PFC Heat Transfer Fluids,” Presented at Semicon West, San Francisco, July 2001. U.S. EPA (2005). U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas and Sinks: 19902003, EPA 430-R-05-003, April 2005.

6.32

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Emissions of Fluorinated Substitutes for Ozone Depleting Substances

CHAPTER 7

EMISSIONS OF FLUORINATED SUBSTITUTES FOR OZONE DEPLETING SUBSTANCES

2006 IPCC Guidelines for National Greenhouse Gas Inventories

7.1

Volume 3: Industrial Processes and Product Use

Authors Paul Ashford (UK) James A. Baker (USA), Denis Clodic (France), Sukumar Devotta (India), David Godwin (USA), Jochen Harnisch (Germany), William Irving (USA), Mike Jeffs (Belgium), Lambert Kuijpers (Netherlands), Archie McCulloch (UK), Roberto De Aguiar Peixoto (Brazil), Shigehiro Uemura (Japan), and Daniel P. Verdonik (USA)

Contributing Authors William G. Kenyon (USA), Sally Rand (USA), and Ashley Woodcock (UK)

7.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Emissions of Fluorinated Substitutes for Ozone Depleting Substances

Contents 7

Emissions of Fluorinated Substitutes for Ozone Depleting Substances ........................................................7.7 7.1

Introduction ...........................................................................................................................................7.7

7.1.1

Chemicals and relevant application areas covered ........................................................................7.7

7.1.2

General methodological issues for all ODS substitute applications ..............................................7.8

7.1.2.1

Overview of ODS substitute issues..........................................................................................7.8

7.1.2.2

Choice of method...................................................................................................................7.13

7.1.2.3

Choice of emission factors.....................................................................................................7.18

7.1.2.4

Choice of activity data ...........................................................................................................7.18

7.1.2.5

Completeness.........................................................................................................................7.20

7.1.2.6

Developing a consistent time series .......................................................................................7.20

7.1.3

Uncertainty assessment ...............................................................................................................7.21

7.1.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation for all ODS substitutes applications................................................................................................................7.21

7.2

7.1.4.1

Quality Assurance/Quality Control (QA/QC) for all ODS substitutes applications ..............7.21

7.1.4.2

Reporting and Documentation for all ODS substitutes applications......................................7.22

Solvents (non-aerosol).........................................................................................................................7.23

7.2.1

Chemicals covered in this application area .................................................................................7.23

7.2.2

Methodological issues .................................................................................................................7.23

7.2.2.1

Choice of method...................................................................................................................7.23

7.2.2.2

Choice of emission factors.....................................................................................................7.24

7.2.2.3

Choice of activity data ...........................................................................................................7.25

7.2.2.4

Completeness.........................................................................................................................7.26

7.2.2.5

Developing a consistent time series .......................................................................................7.26

7.2.3

Uncertainty assessment ...............................................................................................................7.26

7.2.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................7.27

7.3

7.2.4.1

Quality Assurance/Quality Control........................................................................................7.27

7.2.4.2

Reporting and Documentation ...............................................................................................7.27

Aerosols (propellants and solvents).....................................................................................................7.28

7.3.1

Chemicals covered in this application area .................................................................................7.28

7.3.2

Methodological issues .................................................................................................................7.28

7.3.2.1

Choice of method...................................................................................................................7.28

7.3.2.2

Choice of emission factors.....................................................................................................7.29

7.3.2.3

Choice of activity data ...........................................................................................................7.30

7.3.2.4

Completeness.........................................................................................................................7.30

7.3.2.5

Developing a consistent time series .......................................................................................7.31

7.3.3

Uncertainty assessment ...............................................................................................................7.31

7.3.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................7.31

2006 IPCC Guidelines for National Greenhouse Gas Inventories

7.3

Volume 3: Industrial Processes and Product Use

7.4

Quality Assurance/Quality Control (QA /QC).......................................................................7.31

7.3.4.2

Reporting and Documentation ...............................................................................................7.31

Foam blowing agents ..........................................................................................................................7.32

7.4.1

Chemicals covered in this application area .................................................................................7.32

7.4.2

Methodological issues .................................................................................................................7.33

7.4.2.1

Choice of method...................................................................................................................7.34

7.4.2.2

Choice of emission factors.....................................................................................................7.35

7.4.2.3

Choice of activity data ...........................................................................................................7.38

7.4.2.4

Summarising the primary methods ........................................................................................7.39

7.4.2.5

Completeness.........................................................................................................................7.41

7.4.2.6

Developing a consistent time series .......................................................................................7.41

7.4.3

Uncertainty assessment ...............................................................................................................7.41

7.4.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................7.41

7.5

7.4.4.1

Quality Assurance/Quality Control........................................................................................7.41

7.4.4.2

Reporting and Documentation ...............................................................................................7.42

Refrigeration and Air Conditioning.....................................................................................................7.43

7.5.1

Chemicals covered in this application area .................................................................................7.43

7.5.2

Methodological issues .................................................................................................................7.45

7.5.2.1

Choice of method...................................................................................................................7.45

7.5.2.2

Choice of emission factors.....................................................................................................7.51

7.5.2.3

Choice of activity data ...........................................................................................................7.53

7.5.2.4

Applying tier 2 methods – the example of mobile air conditioning (MAC) ..........................7.55

7.5.2.5

Completeness.........................................................................................................................7.58

7.5.2.6

Developing a consistent time series .......................................................................................7.58

7.5.3

Uncertainty assessment ...............................................................................................................7.58

7.5.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................7.58

7.6

7.5.4.1

Quality Assurance/Quality Control........................................................................................7.58

7.5.4.2

Reporting and Documentation ...............................................................................................7.59

Fire protection .....................................................................................................................................7.61

7.6.1

Chemicals covered in this application area .................................................................................7.61

7.6.2

Methodological issues .................................................................................................................7.61

7.6.2.1

Choice of method...................................................................................................................7.61

7.6.2.2

Choice of emission factors.....................................................................................................7.63

7.6.2.3

Choice of activity data ...........................................................................................................7.64

7.6.2.4

Completeness.........................................................................................................................7.64

7.6.2.5

Developing a consistent time series .......................................................................................7.64

7.6.3

Uncertainty assessment ...............................................................................................................7.64

7.6.4

Quality Assurance/Quality control (QA/QC), Reporting and Documentation ............................7.65

7.7

7.4

7.3.4.1

7.6.4.1

Quality Assurance/Quality Control........................................................................................7.65

7.6.4.2

Reporting and Documentation ...............................................................................................7.65

Other applications ...............................................................................................................................7.66

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Emissions of Fluorinated Substitutes for Ozone Depleting Substances

7.7.1

Chemicals covered in this application area .................................................................................7.66

7.7.2

Methodological issues .................................................................................................................7.66

7.7.2.1

Choice of method...................................................................................................................7.66

7.7.2.2

Choice of emission factors.....................................................................................................7.67

7.7.2.3

Choice of activity data ...........................................................................................................7.67

7.7.2.4

Completeness.........................................................................................................................7.67

7.7.2.5

Developing a consistent time series .......................................................................................7.68

7.7.3

Uncertainty assessment ...............................................................................................................7.68

7.7.4

Quality assurance/quality control (QA/QC), reporting and documentation ................................7.68

7.7.4.1

Quality assurance/quality control ..........................................................................................7.68

7.7.4.2

Reporting and documentation ................................................................................................7.68

References

.....................................................................................................................................................7.70

Equations Equation 7.1

Calculation of net consumption of a chemical in a specific application..............................7.14

Equation 7.2A Calculation of emissions of a chemical from a specific application....................................7.14 Equation 7.2B Calculation of emissions of a chemical from an application with banks.............................7.14 Equation 7.3

General mass balance equation for Tier 1b .........................................................................7.15

Equation 7.4

Summary emissions equation based on phases of the lifecycle...........................................7.17

Equation 7.5

Emission estimation method for solvent uses......................................................................7.23

Equation 7.6

Emission estimation method for aerosol uses......................................................................7.28

Equation 7.7

General emission-factor approach (a) for foams .................................................................7.33

Equation 7.8

Generic calculation method for emissions from open-celled foams....................................7.34

Equation 7.9

Determination of refrigerant emissions by mass balance ....................................................7.48

Equation 7.10 Summary of sources of emissions .......................................................................................7.49 Equation 7.11 Sources of emissions from management of containers........................................................7.49 Equation 7.12 Sources of emissions when charging new equipment..........................................................7.50 Equation 7.13 Sources of emissions during equipment lifetime .................................................................7.50 Equation 7.14 Emissions at system end-of-life...........................................................................................7.51 Equation 7.15 Verification of supply and demand assessments .................................................................7.58 Equation 7.16 Calculation of annual refrigerant market .............................................................................7.59 Equation 7.17 Time dependence of emissions from fire protection equipment..........................................7.61 Equation 7.18 Assessment of prompt emission sources from other applications .......................................7.67 Equation 7.19 Assessment of emissions from other contained applications...............................................7.67

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 3: Industrial Processes and Product Use

Figures Figure 7.1

Disaggregation of chemical data across an application .........................................................7.9

Figure 7.2

Decision tree for actual emissions from the solvents application ........................................7.25

Figure 7.3

Decision tree for actual emissions from the aerosol application .........................................7.29

Figure 7.4

Decision tree for emissions from the foam application .......................................................7.36

Figure 7.5

Example of spreadsheet calculation for Tier 1a method......................................................7.40

Figure 7.6

Decision tree for actual emissions from the refrigeration and air conditioning (RAC) application ...........................................................................................................................7.46

Figure 7.7

Example of spreadsheet calculation for Tier 1a/b assessments ...........................................7.47

Figure 7.8

Example of spreadsheet calculation for Tier 1 method .......................................................7.62

Figure 7.9

Decision tree for actual emissions from the fire protection application ..............................7.63

Figure 7.10

Decision tree for actual emissions from the other applications ...........................................7.69

Tables Table 7.1

Main application areas for HFCs and PFCs as ODS substitutes............................................7.8

Table 7.2

Overview of data requirements for different tiers and approaches ......................................7.13

Table 7.3

Example distribution of HFC/PFC use by application area (2002) .....................................7.16

Table 7.4

Use of HFCs in the foam blowing industry (foam product emissions by gas – ODS replacements).......................................................................................................................7.32

Table 7.5

Default emission factors for HFC from closed-cell foam....................................................7.35

Table 7.6

Default emission factors for HFC-134a and HFC-152a uses (foam sub-applications ) (IPCC/TEAP, 2005) ............................................................................................................7.37

Table 7.7

Default emission factors for HFC-245fa/HFC-365mfc/HFC-227ea uses (foam subapplication)..........................................................................................................................7.37

Table 7.8

Blends (many containing HFCs and/or PFCs).....................................................................7.44

Table 7.9

Estimates for charge, lifetime and emission factors for refrigeration and air-conditioning systems ................................................................................................................................7.52

Table 7.10

Good practice documentation for refrigeration and air-conditioning systems.....................7.60

Boxes

7.6

Box 7.1

Global and regional databases for ODS substitutes.............................................................7.11

Box 7.2

Tier 2a implementation for the foam application using globally or regionally derived data...........7.39

Box 7.3

Accounting for imports and exports of refrigerant and equipment......................................7.54

Box 7.4

Example of the application of a Tier 2a calculation for mobile air conditioning ................7.55

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Emissions of Fluorinated Substitutes for Ozone Depleting Substances

7 EMISSIONS OF FLUORINATED SUBSTITUTES FOR OZONE DEPLETING SUBSTANCES 7.1

INTRODUCTION

7.1.1

Chemicals and relevant application areas covered

Hydrofluorocarbons (HFCs) and, to a very limited extent, perfluorocarbons (PFCs), are serving as alternatives to ozone depleting substances (ODS) being phased out under the Montreal Protocol. Current and expected application areas of HFCs and PFCs include (IPCC/TEAP, 2005): •

•

refrigeration and air conditioning;

•

aerosols;

•

foam blowing; and

•

fire suppression and explosion protection;

•

solvent cleaning;

other applications1.

These major groupings of current and expected usage are referred to in this chapter as applications within the ODS substitutes category. This introduction (Section 7.1) provides a general framework for estimating emissions from ODS substitutes, and subsequent sections (Sections 7.2 through 7.7) provide more specialised guidance on the individual applications introduced above. Some of these applications themselves encompass products or uses with diverse emission characteristics, and countries will produce more rigorous estimates if they account for this diversity through the adoption of disaggregated assessments (higher tier). Additionally, the use of HFCs and PFCs in some applications, specifically rigid foam (typically closed-cell foam), refrigeration and fire suppression, can lead to the development of long-lived banks of material. The emission patterns from these uses can be particularly complex and methods employing disaggregated data sets are essential to generate accurate emissions estimates. Other applications, such as aerosols and solvent cleaning may have short-term inventories of stock but, in the context of emission estimation, can still be considered as sources of prompt emission. This statement also applies to flexible foams (typically open-cell foam). HFCs and PFCs are not controlled by the Montreal Protocol because they do not contribute to depletion of the stratospheric ozone layer. HFCs are chemicals containing only hydrogen, carbon, and fluorine. Prior to the Montreal Protocol and the phase-out of various ODS, the only HFCs produced were HFC-152a, which is a component of the refrigerant blend R-500, and HFC-23, a low temperature refrigerant which is a by-product of HCFC-22 2 production. HFC-134a entered production in 1991 and a variety of other HFCs have since been introduced and are now being used as ODS substitutes (IPCC/TEAP, 2005) among other applications. When collecting data on HFC and PFC consumption for reporting purposes, care needs to be taken to include those HFCs in blends, but, at the same time, to avoid including those components of a blend which are not required to be reported (e.g., CFCs and HCFCs). HFCs and PFCs have high global warming potentials (GWPs) and, in the case of PFCs, long atmospheric residence times. Table 7.1 gives an overview of the most important HFCs and PFCs (IPCC Second Assessment Report (IPCC, 1996); IPCC Third Assessment Report (IPCC, 2001); IPCC/TEAP, 2005), including their main application areas. The various HFCs and PFCs have very different potencies as greenhouse gases. PFCs have particularly high GWPs regardless of the integrated time horizon adopted because of their long atmospheric lifetimes. The consumption patterns relating to individual gases must be known, therefore, or estimated with reasonable accuracy, to achieve useful assessments for the contribution to global warming from emissions of these groups of chemicals.

1

HFCs and PFCs may also be used as ODS substitutes in sterilisation equipment, for tobacco expansion applications, and as solvents in the manufacture of adhesives, coating and inks.

2

HCFCs - hydrochlorofluorocarbons.

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Volume 3: Industrial Processes and Product Use

As CFCs, halons, carbon tetrachloride, methyl chloroform, and, ultimately, HCFCs are being finally phased out3, HFCs are being selectively used as replacements. PFCs are also being used, but only to a limited extent. Even though up to 75 percent of previous application of CFC may now be covered by non fluorocarbon technologies (IPCC/TEAP, 2005), HFC use is expected to continue to grow at least in the short term.

TABLE 7.1 MAIN APPLICATION AREAS FOR HFCS AND PFCS AS ODS SUBSTITUTES 1 Refrigeration and Air Conditioning

Fire Suppression and Explosion Protection

HFC-23

X

X

HFC-32

X

HFC-125

X

X

HFC-134a

X

X

HFC-143a

X

HFC-152a

X

HFC-227ea

X

X

HFC-236fa

X

X

Chemical

Aerosols

Foam Blowing

Other Applications2

X

X

X

X

X

X

X

Propellants

Solvents

Solvent Cleaning

HFC-245fa

X

HFC-365mfc

X

X

HFC-43-10mee

X

X

3

PFC-14 (CF4)

X

X X

X

PFC-116 (C2F6)

X

PFC-218 (C3F8) PFC-31-10 (C4F10)

X

4

PFC-51-14 (C6F14) 1

X

Several applications use HFCs and PFCs as components of blends. The other components of these blends are sometimes ODSs and/or non-greenhouse gases. Several HFCs, PFCs and blends are sold under various trade names; only generic designations are used in this chapter.

2

Other applications include sterilisation equipment, tobacco expansion applications, plasma etching of electronic chips (PFC-116) and as solvents in the manufacture of adhesive coatings and inks (Kroeze, 1995; U.S. EPA, 1992a).

3

PFC-14 (chemically CF4) is used as a minor component of a proprietary blend. Its main use is for semiconductor etching.

4

PFC-51-14 is an inert material, which has little or nil ability to dissolve soils. It can be used as a carrier for other solvents or to dissolve and deposit disk drive lubricants. PFCs are also used to test that sealed components are hermetically sealed.

7.1.2 7.1.2.1

General methodological issues for all ODS substitute applications O VERVIEW

OF

ODS

SUBSTITUTE ISSUES

LEVELS OF DATA AGGREGATION Each application discussed above can be divided into sub-applications. When selecting a method for estimating emissions, it is good practice to consider the number and relevance of sub-applications, the data availability, and the emission patterns. Applications with a high number of sub-applications (refrigeration has six major subapplications; foam has even more) will generally benefit from a higher level of disaggregation in their data sets, owing to the differences between the sub-applications. Accordingly, for rigorous emissions estimates, inventory compilers are likely to favour estimating emissions for each sub-application separately. In this chapter, such an

3

Refer to http://hq.unep.org/ozone/ for the phaseout schedules dictated under the Montreal Protocol.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Emissions of Fluorinated Substitutes for Ozone Depleting Substances

approach defines a Tier 2 method, whereas methods based on datasets aggregated at the application level are all classified as Tier 1. Even if few sub-applications exist, estimating emissions by sub-application may still be most appropriate owing to the differences in emission patterns, chemical use, data gathering methodologies, and/or data availability. Fire protection, for example, has only two major sub-applications, but each has unique emission characteristics and a disaggregated (Tier 2) method will produce better emission estimates. On the other hand, if emission patterns of sub-applications are similar and if data are difficult to collect in disaggregated form, estimating emissions at an aggregated application level (Tier 1) can be an appropriate approach to produce reliable emission estimates. For example, although several sub-applications exist within the aerosol propellants application, because the emission patterns and chemicals used are similar, estimating emissions at an application level may be sufficient to yield good results. Figure 7.1

Disaggregation of chemical data across an application Domestic production + Imports – Exports by product or equipment type (sub-application)

PRODUCT DATA* Average charge by chemical or blend type for each sub-application functional unit

Quantities of blends and chemicals consumed in year

CHEMICAL SALES DATA*

MATRIX Consumption by chemical type by subapplication

Chemicals imported/exported in products * Both required as a time-series

Product Data for Application Chemical Consumption,

Sub-Application 1

Sub-Application 2

Etc.

Bank, or Emissions

Domestic

Domestic + Imports

- Exports

Production

+ Imports

- Exports

Production

Chemical Data for Application

Chemical 1

Chemical 2

Complete each cell (some may be zero) using chemical and product

Chemical 3

data as available. For blends, separate data into individual

Blend A

chemical constituents. Determine if chemical data includes that chemical

Blend B

used in blends or not.

Etc.

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Volume 3: Industrial Processes and Product Use

TYPES OF DATA It is important early on in the estimation process to decide about how and from where data is to be collected. Data on chemical sales (sometimes referred to as top-down data) typically comes on a substance-by-substance basis, although even this can be complicated by the use of blends. Data on markets (sometimes referred to as bottom-up data) will tend to come in the form of equipment or product sales at the sub-application level, although this data will typically be influenced by the existence of imports and exports of such equipment or products. This data often need to be accompanied by an estimate of the share of the market that uses a particular technology. For example, different chemicals (including some not subject to reporting) may be used in the same sub-application. Additionally, the average amount of chemical used by each product type within the subapplication may vary. The two routes (chemicals and products) represent the two axes of a matrix and a disaggregated approach requires completion (or near completion) of that matrix (Figure 7.1). Completing this matrix is typically accomplished by using combinations of both types of data (i.e., both top-down and bottom-up data), comparing the results, and adjusting as appropriate.

DATA AVAILABILITY There are often difficulties in collecting data for both Tier 1 and Tier 2 methods if chemical suppliers at the national level believe that there are confidentiality implications arising from disclosure of information. In practice, this has been one of the major barriers to reliable emissions estimates at the national level. In order to overcome some of these constraints, there has, in recent years, been an effort to develop global and regional databases which provide information on historic and current activity (chemical consumption) data at the country level for specific applications and sub-applications. The value of this approach is that these data can be validated against chemical sales at regional, or even global, level and thereby avoids breeching confidentiality restrictions required by the suppliers. As these databases have developed, (for example, those developed under the oversight of the relevant UNEP Technical Options Committees under the Montreal Protocol) they have become increasingly sophisticated in their analyses of use patterns which are often well-understood at the subapplication level (see Box 7.1). This means that the two axes of the matrix described earlier can be addressed from these datasets and Tier 2 methods can be facilitated at a country level without a massive investment of resource. This activity data can then be combined with default emission factors or with country-specific emission factor data, if this is available, to derive appropriate emissions estimates. Of course, it is important to exercise care in making use of such databases and it is important to choose reputable well-documented sources. Nonetheless, the use of globally or regionally derived data of this type can deliver reliable estimates. An alternative strategy could be to use information generated from such a database to benchmark information collected nationally. In either case, it is important that data is generated in a form that will fit with relevant reporting requirements (e.g., the Common Reporting Format of the United Nations Framework Convention on Climate Change (UNFCCC)). These requirements may vary with time during the lifetime of these Guidelines. Accordingly, the structuring of activity datasets should be sufficiently flexible to deal with such changes. In some instances the complexity of the chemical and equipment supply chain can create additional challenges regarding data availability. As highlighted in Section 7.5, there are a range of containers that can be used to supply the mobile air conditioning market, from semi-bulk containers for OEMs; to intermediate containers for the average vehicle servicing centre (10-15kg); to small 300-500g cans for the do-it-yourself market. Since wastage levels will vary substantially between these differing supply-chain approaches, inventory compilers need to consider how to assess these losses in practice. The use of containers is not only limited to mobile air conditioning, but is often prevalent in other sectors of the refrigerant market, aerosols and in fire suppression. Inventory compilers could consider treating the supply of ODS substitutes as a separate element of the inventory. However, even if this route is taken, it will require detailed knowledge of the sub-applications to understand the range of sizes used and proportion of each. Accordingly, it is viewed as most appropriate to evaluate container losses (often termed heels) within each application and sub-application, although it would be good practice to compare estimated losses within different applications and sub-applications using the similar sized containers to ensure some uniformity of approach.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Emissions of Fluorinated Substitutes for Ozone Depleting Substances

BOX 7.1 GLOBAL AND REGIONAL DATABASES FOR ODS SUBSTITUTES

Global and regional databases are typically developed for specific applications by experts in the field. These experts often have good professional contacts with industry sources, and are familiar with access to relevant market studies and other reports that shed light onto the consumption patterns of regions and countries. From this knowledge base it is possible to cross-reference product data, either at regional level or even at global level, with chemical consumption data. It is common for such databases to predict future consumption as well as to assess current consumption. This makes them valuable also as a policy development tool. However, it is important that such databases are properly maintained and are regularly cross-checked with actual chemical consumption data whenever it becomes available in order to be assured that any new trends or other sources of discrepancy are accounted for and fully reconciled wherever possible. For example, individual members of the UNEP Technical Options Committees (TOCs) under the Montreal Protocol have prepared a number of global activity datasets that can assist countries in preparing estimates of ODS substitute emissions. Particularly relevant are the databases used to support the development of the IPCC/TEAP Special Report on Safeguarding the Ozone Layer and the Global Climate System: Issues Related to Hydrofluorocarbons and Perfluorocarbons (IPCC/TEAP, 2005), because information on the phase-out of ozone-depleting substances is directly relevant for estimating the phase-in of substitutes. The assumptions behind these datasets have been documented in a number of summary reports which can be found at http://epa.gov/ozone/snap/emissions/index.html (e.g., Clodic D., Palandre, L., McCulloch, A., Ashford, P. and Kuijpers, L.. ‘Determination of comparative HCFC and HFC emission profiles for the Foam and Refrigeration sectors until 2015.’ Report for ADEME and US EPA., 2004). These existing datasets have been regularly peer-reviewed by other experts from within the relevant TOCs and have been used by the Parties to the Montreal Protocol to assess transitions in chemical markets and chemical use patterns. If national data are difficult to obtain, countries can search the IPCC Emissions Factor Database (EFDB) for datasets such as those discussed above. All such databases should be structured to facilitate their use in inventory reporting. The EFDB is likely to become the home for a number of such global/regional databases in due course, either as additional sources for applications already covered or as new sources for applications not previously covered. Although inclusion of databases in the EFDB provides general assurance of due process, it is good practice for countries to ensure that all data taken from the EFDB are appropriate for their national circumstances and that peer review is sufficient for this complex area of activity.

TYPES OF EMISSION ESTIMATES In contrast with the earlier Guidelines, both Tier 1 and Tier 2 methods proposed in this chapter result in estimates of actual emissions rather than potential emissions. This reflects the fact that they take into account the time lag between consumption of ODS substitutes and emission, which, as noted previously, may be considerable in application areas such as closed cell foams, refrigeration and fire extinguishing equipment. A time lag results from the fact that a chemical placed in a new product may only slowly leak out over time, often not being released until end-of-life. A household refrigerator, for example, emits little or no refrigerant through leakage during its lifetime and most of its charge is not released until its disposal, many years after production. Even then, disposal may not entail significant emissions if the refrigerant and the blowing agent in the refrigerator are both captured for recycling or destruction. The potential emission method, in which emissions are assumed to equal the amount of virgin chemical consumed annually in the country minus the amount of chemical destroyed or exported in the year of consideration, is now presented only as a reference scenario in the QA/QC section. As noted above, the potential method does not take into account accumulation or possible delayed release4 of chemicals in various products and equipment, which means that, over the short term (e.g., 10-15 years), estimates may become very inaccurate. Therefore, it is not considered good practice to use the potential method for national estimates.5

4

Sometimes from types of equipment and products which have since converted out of halocarbon technologies.

5

The Conference of the Parties to the UNFCCC, at its third session, affirmed ‘… that the actual emissions of hydrofluorocarbons, perfluorocarbons and sulphur hexafluoride should be estimated, where data are available, and used for

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 3: Industrial Processes and Product Use

TIMING OF EMISSIONS AND THE SIGNIFICANCE OF BANKS In many applications ODS substitutes such as HFCs and PFCs serve their purpose only if they are contained (e.g., refrigeration and air conditioning), while in other applications, they are meant to be emitted (e.g., as an aerosol propellant). These differences are important to understand, so that the year in which emissions occur can be accurately assessed, and hence actual emissions can be accurately estimated. Where emissions occur within the first two years, they are usually referred to as prompt emissions. Examples of applications and sub-applications exhibiting prompt emissions include aerosols, aerosol solvents, open-cell foams and in some cases non-aerosol solvents. In general, emissions from applications or sub-applications exhibiting prompt emissions can be estimated by determining annual chemical consumption and then assuming all emissions occur within the first year or two of consumption. Thus, if chemical consumption is unknown prior to a certain date, emission estimates a year or two after that date will nonetheless be accurate and relatively little accuracy will be gained by searching for or estimating chemical consumption from prior years. Where delays in emission occur, the cumulative difference between the chemical that has been consumed in an application or sub-application and that which has already been released is known as a bank. Applications in which banks typically occur include refrigeration and air conditioning, fire protection, closed-cell foams, and often non-aerosol solvents. The definition of bank encompasses the presence of the chemical at all parts of the lifecycle and may even include waste streams. By way of example, blowing agent still present in foamed products which may have already been land-filled is still part of the bank, since it is chemical which has been consumed and still remains to be released. In practice, most equipment-related sub-applications (e.g. in refrigeration and fire protection) are unlikely to carry their charges into the waste stream and the total of the chemical contained in the equipment currently in use becomes a close approximation to the actual bank. Estimating the size of a bank in an application or sub-application is typically carried out by evaluating the historic consumption of a chemical and applying appropriate emission factors. Where more than one subapplication exists, but a Tier 1 method is being followed, a composite emission factor needs to be applied. However, in view of the uncertainties surrounding such composite emission factors, Tier 2 methods will always be preferred for applications with multiple sub-applications, particularly where these are dissimilar in nature. It is also sometimes possible to estimate the size of bank from a detailed knowledge of the current stock of equipment or products. A good example is in mobile air conditioning, where automobile statistics may be available providing information on car populations by type, age and even the presence of air conditioning. With knowledge of average charges, an estimate of the bank can be derived without a detailed knowledge of the historic chemical consumption, although this is still usually useful as a cross-check.

APPROACHES FOR EMISSION ESTIMATES Even among those applications which retain the chemicals over time, there are some significant distinctions. In some instances (e.g., refrigeration) the quantity of HFC or PFC is typically topped-up during routine servicing. If equipment were topped-up annually and the market was otherwise static (i.e., no growth in the equipment stock), the actual emissions would be consistent with consumption for that year. Under such circumstances, it is not necessary to know the precise equipment stock as long as the consumption of HFC or PFC is known by type at the sub-application level. This is the basis of the mass-balance approach which is referred to throughout this chapter as Approach B. More discussion on the mass-balance approach is found in Chapter 1, Section 1.5 of this volume. However, a mass-balance approach is not appropriate for other situations or for other products (e.g., foams) where consumption occurs only at the point of manufacture, while emissions may continue to a limited extent throughout the lifetime of the product. In such instances, it is usually better to revert to an emission-factor approach (i.e., methods based on activity (consumption) data and emission factors). Such methods can be operated at both aggregated (Tier 1) and disaggregated (Tier 2) levels and are referred to throughout this chapter as Approach A. Accordingly, a Tier 1a method will be an emission-factor approach with a low level of disaggregation, while a Tier 2b method will be a mass-balance approach with a relatively high degree of disaggregation (at least to the sub-application level). Further information on the choice between using a massbalance approach and an emission-factor approach is found in Chapter 1, Section 1.5. In general, mass-balance approaches are only considered for ODS substitutes stored or used in pressurised containers and so many applications do not consider Approach B at all. Where Approach B is considered (e.g., refrigeration and fire protection) the choice of method is discussed under that part of Chapter 7 addressing the application in question. Some methods described for these specific applications can have characteristics of both approaches, and the mass-balance approach can be used to cross-check and validate the results of an activity (consumption) data/emission factor approach. Accordingly, whilst the labelling conventions will remain unchanged throughout the reporting of emissions. Parties should make every effort to develop the necessary sources of data;’. (Decision 2/CP.3, Methodological issues related to the Kyoto Protocol)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 7: Emissions of Fluorinated Substitutes for Ozone Depleting Substances

to avoid confusion, it may be that some methods are labelled Tier 1a/b or Tier 2a/b because they are seen to contain elements of both approaches. This is most common in the case of Tier 1 methods where data is limited and one approach can be usefully used to cross-check the other. Table 7.2 below summarises what kind of data are required to implement different tiers and approaches.

TABLE 7.2 OVERVIEW OF DATA REQUIREMENTS FOR DIFFERENT TIERS AND APPROACHES Approach A (emission-factor approach)

•

Tier 2 (emission estimation at a disaggregated level)

•

Tier 1 (emission estimation at an aggregated level)

• •

Data on chemical sales and usage pattern by sub-application [country-specific or globally/regionally derived] Emission factors by sub-application [country-specific or default]

Data on chemical sales by application [country-specific or globally/regionally derived] Emission factors by application [countryspecific or (composite) default]

Approach B (mass-balance approach)

•

•

• •

Data on chemical sales by sub-application [country-specific or globally/regionally derived] Data on historic and current equipment sales adjusted for import/export by subapplication [country-specific or globally/regionally derived] Data on chemical sales by application [country-specific or globally/regionally derived] Data on historic and current equipment sales adjusted for import/export by application [country-specific or globally/regionally derived]

In the six sections that follow (Sections 7.2 to 7.7), decision trees are included for each application to assist in the identification of data needs and the selection of approach for individual sub-applications, where these exist.

7.1.2.2

C HOICE

OF METHOD

As already described, emissions of ODS substitutes can be estimated in a variety of ways with varying degrees of complexity and data intensity. This chapter provides less data-intensive Tier 1 methods, typically based on low levels of disaggregation, and more data-intensive Tier 2 methods, which require higher levels of disaggregation. A third Tier (Tier 3), based on actual monitoring and measurement of emissions from point sources, is technically possible for specific sub-applications but this is rarely, if ever, employed for ODS substitutes, because the individual point sources are widely disparate. Accordingly, Tier 3 methods are not addressed further in this chapter.

TIER 1 METHODS Tier 1 methods tend to be less data-intensive and less complex than Tier 2 because emission estimates are usually carried out at the application level rather than for individual products or equipment types. However, the approaches proposed vary considerably depending on the characteristics of the specific application. There can be Tier 1a approaches, Tier 1b approaches and, sometimes, combinations of the two (Tier 1 a/b). The latter is often the case where data are in short supply. Effectively, the output of a Tier 1a approach can be cross-checked using a Tier 1b method. In general, however, the simple methods tend to be based primarily on a Tier 1a approach (emission-factor approach) with the default emission factor being up to 100 percent for prompt release applications. For simpler Tier 1 approaches, the chemical sales data at the application level is usually sufficient. However, separating out individual components of blends can still represent a considerable challenge. Irrespective of the Tier 1 methodology chosen, countries will need to report emissions of individual HFCs and PFCs. Information on the practical use of the various commercial types of HFC/PFC refrigerants, blowing agents, solvents, etc. will therefore be required. Many of these products are mixtures of two or more HFCs and/or PFCs, and the composition of fluids for similar purposes may vary according to individual formulas developed by different chemical companies.

Tier 1a – Emission-factor approach at the application level Tier 1a relies on the availability of basic activity data at the application level, rather than at the level of equipment or product type (sub-application). This activity data may consist of annual chemical consumption data and, for applications exhibiting delayed emissions, banks derived therefrom. Once activity data have been established at the application level, composite emission factors (see Section 7.1.2.3) are then also applied at the

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application level. These more aggregated emission factors (e.g., all rigid foams) can be a composite or weighted average of the emission factors developed for Tier 2a covering individual equipment or product types, or can be a validated approximation approach (e.g., Gamlen et al. 1986). The calculation formula for Net Consumption within the Tier 1a method is as follows: EQUATION 7.1 CALCULATION OF NET CONSUMPTION OF A CHEMICAL IN A SPECIFIC APPLICATION Net Consumption = Production + Imports − Exports − Destruction

Net Consumption values for each HFC or PFC are then used to calculate annual emissions for applications exhibiting prompt emissions as follows: EQUATION 7.2A CALCULATION OF EMISSIONS OF A CHEMICAL FROM A SPECIFIC APPLICATION Annual Emissions = Net Consumption • Composite EF Where: Net Consumption = net consumption for the application Composite EF = composite emission factor for the application Note that, as discussed in the Choice of Activity Data section, inventory compilers may have access to chemical consumption data at the aggregate level rather than by application. In this case, it will be necessary as an early step to determine the share of total consumption represented by each application. In equation 7.1, Production refers to production of new chemical. Reprocessing of recovered fluid should not be included in consumption estimates. Imports and Exports include bulk chemicals but, for a Tier 1 method is unlikely to contain the quantity of chemical contained in products, such as refrigerators, air-conditioners, packaging materials, insulating foams, fire extinguishers etc. unless regional allocation system or other method of approximation has been used. The term composite emission factor refers to an emissions rate that summarises the emissions rates of different types of equipment, product or, more generally, sub-applications within an ODS application area. Composite emission factors should account for assembly, operation and, where relevant in the time-series, disposal emissions. Although destruction of virgin HFCs and PFCs is not currently practised widely, and may be technically difficult in some cases (UNEP TEAP Task Force on Destruction Technologies (UNEP-TEAP, 2002)), it should be included as a potential option to reduce consumption. It should be noted that destruction of virgin chemicals, as considered here, is distinct from the destruction of HFCs and PFCs in the end-of-life phase, which is strictly an emission reduction measure. By-product emissions during HFC/PFC production and fugitive emissions related to production and distribution have to be calculated separately. Even in simple Tier 1a methods, it is usually necessary to account for the potential development of banks, where these can occur. Banks are the amounts of chemical that have accumulated throughout the lifecycle, either in supply chains, products, equipment or even waste streams but which, as of the end of the most recent year, has not been emitted. At the application level, banks can be estimated using relatively straight-forward algorithms and assumptions provided that the historic Net Consumption is known for each year following the introduction of the substance or, where this period exceeds the average lifetime of the product or equipment, over that average lifetime. Relevant application level emission factors are then applied to the banks to deal with emissions during the lifetime of the products or equipment. This same process is carried out for Tier 2a methods but, in that case, at the sub-application level. More general information on banks is contained in Section 7.1.2.1. In cases where banks occur, Equation 7.2A is then modified to the following: EQUATION 7.2B CALCULATION OF EMISSIONS OF A CHEMICAL FROM AN APPLICATION WITH BANKS Annual Emissions = Net Consumption • Composite EFFY + Total Banked Chemical • Composite EFB

Where: Net Consumption = net consumption for the application

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Composite EFFY = composite emission factor for the application for first year Total Banked Chemical = bank of the chemical for the application Composite EFB = composite emission factor for the application for bank Composite emission factors are determined by taking an average of the applicable sub-application emission factors, weighted according to the activity in each sub-application. Sub-application emission factors can be country-specific where known or default. In practice, if sub-application data are known, inventory compilers would opt for a Tier 2 (disaggregated approach). If only application level data are known, representative composite emission factors from other studies or default composite emission factors provided in this chapter can be used.

Tier 1b – Mass-balance approach at the application level The mass balance approach also estimates emissions from assembly, operation, and disposal, but does not rely on emission factors. Instead, the method uses measured consumption (i.e., sales) of each chemical in the country or facility being considered. It is generally limited to ODS Substitutes contained in pressurised systems. The general equation is as follows6: EQUATION 7.3 GENERAL MASS BALANCE EQUATION FOR TIER 1b Emissions = Annual Sales of New Chemical − (Total Charge of New Equipment − Original Total Charge of Retiring Equipment )

Industry needs to purchase new chemical from manufacturers in order to replace leakage (i.e., emissions) from the current equipment stock, or the equipment will not function properly. If the equipment stock did not change from year to year, then annual chemical consumption alone would provide a reasonable estimate of actual leakage or emissions. The total equipment stock, and the chemical charge it contains, however, does change from year to year. Some amount of new equipment containing a chemical charge is introduced each year, and some amount of old equipment that was charged originally is retired each year. If the total chemical charge contained in all equipment is increasing as a result of this annual turnover, then total annual chemical consumption will overestimate emissions (i.e., the charge contained in new equipment is greater than the original charge of the retired equipment). Conversely, if the total chemical charge in all equipment is decreasing, then total annual chemical consumption will underestimate emissions. In order to make good use of data on annual sales of new chemical, it is therefore also necessary to estimate the total charge contained in new equipment, and the original charge contained in retiring equipment. The total charge of new equipment minus the original total charge of retiring equipment represents the net change in the charge of the equipment stock. (Using the mass balance approach, it is not necessary to know the total amount of each chemical in equipment stock in order to calculate emissions.) Where the net change is positive, some of the new chemical is being used to satisfy the increase in the total charge, and therefore cannot be said to replace emissions from the previous year. Industry also requires new chemical to replace destroyed gas and for stockpiles. Additionally, not all equipment will be serviced annually. Terms can be added to the general equation to account for these factors but are not typically adopted within simple Tier 1b methods. This approach is most directly applicable to the pressure equipment used in refrigeration and air conditioning, and fire protection applications because these are where chemical sales are most typically used to offset operational emissions. However, since the basic method is relatively simple to apply, it is more typical to extend the approach to the sub-application level (i.e., a Tier 2b method). Further elaboration and modification of this approach is provided in the description of each application. In practice, Tier 1b methods are most commonly used as a further cross-check to Tier 1a methods. Where basic Net Consumption data is lacking, regional and international databases and models have been developed that allocate regional chemical sales for different end uses (sub-applications) at a country level. These can therefore be used to source relevant data.

TIER 2 METHODS – APPLIED AT THE SUB-APPLICATION LEVEL There are two versions of the Tier 2 method, both of which result in emission calculations for each individual chemical and distinct types of products or equipment at the sub-application level or within a sub-application. The 6

Boundary conditions: If there is no net change in the total equipment charge, then annual sales are equal to emissions. If the net change in the total equipment charge is equal to annual sales, then emissions are zero.

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individual chemicals and products/equipment within the sub-application form the matrix referred to earlier in this section and their analysis is comparable with methods currently applied by the Alternative Fluorocarbons Environmental Acceptability Study (AFEAS) for CFCs, HCFCs and HFCs (McCulloch, Midgley and Ashford, 2001 and 2003; Ashford, Clodic, Kuijpers and McCulloch, 2004). Both versions of the Tier 2 methodology follow two general steps: i.

Calculation or estimation of the time series of net consumption of each individual HFC and PFC chemical at a relatively detailed product and equipment level to establish the consumption basis for emission calculations. (e.g., refrigerators, other stationary refrigeration/AC equipment, appliance foams, insulated panels, pipe insulation, etc.)

ii.

Estimation of emissions using the activity data and resulting bank calculations derived from step (i), and either emission factors that reflect the unique emission characteristics related to various processes, products and equipment (Tier 2a) or, relevant new and retiring equipment information at the subapplication level to support a mass balance approach. (Tier 2b).

The difference between Tier 2a and Tier 2b is the same as that for Tier 1a and Tier 1b – namely Tier 2a methods use an emission-factor approach while Tier 2b methods follow a mass-balance approach. Both, however, need to be operated at a level of disaggregation appropriate to a Tier 2 method, typically at least at the sub-application level. If the requisite data are available, a Tier 2 method is preferred for estimating emissions from ODS substitutes, particularly where the sub-applications within an overall application area are relatively heterogeneous. Some countries may already have the relevant information available to apply a Tier 2 methodology. Other countries might not have access to data for Tier 2 at present, but they are encouraged to establish routines to collect either country-specific or globally or regionally-derived activity data by chemical and sub-application within an application area (e.g., different types of refrigeration and air conditioning sub-applications). Tier 1, in contrast, requires data collection at a more aggregated application level (e.g., refrigeration and air conditioning in its totality). As a first step in using the Tier 2 method, countries may wish to make first order approximation of the information needed for step (i). This will give direction to more focused data collection efforts in certain application areas or sub-categories. Table 7.3 gives examples of HFC/PFC consumption distribution at the application level in 2002 among various application areas in selected countries. Since HFCs and PFCs have only recently entered the market in some applications, the relative size of consumption in each application will continue to change over time and should be updated regularly at a country level.

TABLE 7.3 EXAMPLE DISTRIBUTION OF HFC/PFC USE BY APPLICATION AREA (2002)a Country

Refrigeration Air Conditioning

Foam Blowing

Solventb

Fire Protectionb

Aerosol Propellantb

Other Applicationsb

Austria Denmark Norway Sweden United Kingdom

18% 81% 72% 48% 31%

81% 18% 11% 42% 22%

0% 0% 0% 0% 0%

1% 0% 16% 4% 9%

0% 1% 1% 6% 38%

0% 0% 0% 0% 0%

a b

UNFCCC Reported Data for 2002 as re-submitted in 2004 A zero declaration may not always reflect non-use but could reflect reporting under other categories.

Good practice guidance in this section deals with variations of the Tier 2 method. Tier 1 methods, covered previously, are generally seen as default methods where the application is not a key category and data availability is limited. (Exceptionally, for Fire Protection, Tier 1a method with country-specific activity data and emission factor will be used in the case it is identified as key category.) Each sub-section of Sections 7.2 to 7.7 discusses how to apply these methods to specific ODS applications, reviews existing data sources, and identifies gaps therein.

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Tier 2a – Emission-factor approach The country-specific data required for a Tier 2a approach are derived from the number of products and end-uses relevant to each sub-application in which ODS substitutes are contained and from which ODS substitutes are ultimately emitted. This approach seeks information on the number of equipment units or products that use these chemicals, average chemical charges, average service life, emission rates, recycling, disposal, and other pertinent parameters. This information is generally collected at the level of distinct groups of products or equipment (e.g., for rigid foams: integral skin, continuous panel, discontinuous panel, appliance, injected foam products and others). Annual emissions are then estimated as a function of these parameters through the life of the units or products by the application of emission factors that are relevant to the lifecycle phases. Since equipment and other products vary significantly in the amount of chemical used, service life, and emission rates, the characterisation of this equipment can be a resource intensive task. The longer-lived the end-use equipment or product, and the more diverse the types of equipment or product within a particular sub-application, the more complex the sourced data approach has to be in order to account for emissions. However, the approach can provide an accurate estimate of emissions if the data called for by the following equation are available for all relevant types and vintages of equipment or product: EQUATION 7.4 SUMMARY EMISSIONS EQUATION BASED ON PHASES OF THE LIFECYCLE Total Emissions of Each PFC or HFC = Assembly/Manufacturing Emissions + Operation Emissions + Disposal Emissions

Manufacturing or Assembly Emissions occur as fugitives when new equipment is filled for the first time with a chemical or when a product is manufactured. Operational Emissions from equipment and products occur as leaks or by diffusion during the use phase of the product or equipment (including servicing). In some cases, there can even be intentional releases during operation. Finally, Disposal Emissions can occur when the equipment or product reaches its end-of-life and is decommissioned and disposed of. In this case, the remaining HFC/PFC in the product or equipment may escape to the atmosphere, be recycled, or possibly destroyed. As with the Tier 1a method, there is a need to make provision for the development of banks in some applications. This can lead to complex multiple calculations at the sub-application level, since the dynamics of banks may vary considerably. However, because the individual algorithms rely on a simple sequential calculation of nonemitted consumption (i.e., consumption – emissions for each successive year), excellent emission assessments can result from a well-constructed and well-maintained national model. The need to update equipment and product inventories on an annual basis can be a major implementation challenge for inventory compilers with limited resources. This challenge is made somewhat easier because it may not be necessary to collect annual chemical consumption if a comprehensive set of other market parameters is available (e.g., number of domestic refrigerators produced, etc.) In some countries or regions, trade associations can be a significant source of such data. Otherwise, specific market research may be necessary. Where such market parameters are the primary source of activity data, the potential magnitude of errors that can be introduced by small discrepancies at unit level makes it good practice to carry out a chemical consumption data cross-check to act as a means of providing quality assurance. The relevant QA/QC sections of this chapter give guidance on how to conduct such cross-checks for each relevant application. In order to limit the burden of data management for both annual consumption data and the status of banks, it is possible to access international and regional databases of such information to gain the necessary inputs of globally or regionally validated data to maintain a national model. These databases can also help to overcome any confidentiality barriers that may exist in collecting and/or publishing data at a national level, particularly where the number of suppliers is low. More information on the use of such databases is contained in Section 7.1.2.4 and Box 7.1. Even where comprehensive country-specific activity data exists at a country level, it is good practice to benchmark outputs against assessments made from databases of globally or regionally derived data. This need not be done on an annual basis but could reasonably be conducted every 2-3 years. Significant discrepancies can then be analysed and suitable actions taken to reconcile differences.

Tier 2b – Mass-balance approach Tier 2 mass-balance approaches are similar to those described for Tier 1b, except that the process is applied at the sub-application level. This is a particularly valuable approach for the refrigeration sector where there are a significant number of relatively heterogeneous sub-applications. As is also the case for Tier 1 methods, it is not

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uncommon to see mass-balance approaches used in combination with emission-factor approaches to ensure that the outputs achieved are as robust as possible. Such approaches can realistically be described as hybrid Tier 2a/b methods and they will be identified as such, where they occur in the relevant application-specific sections that follow. Since mass-balance approaches also require activity data at the sub-application level, it may be more resourceefficient to use global or regional databases to obtain appropriate globally or regionally validated data. The same criteria for selection as set out for Tier 2a methods also apply for Tier 2b methods. Accordingly, equal care should be taken in selecting validated datasets.

7.1.2.3

C HOICE

OF EMISSION FACTORS

Emission factors are required for all methods following Approach A. In general terms, emission factors can be of two distinct types: 1.

Emission factors derived from actual measurements of products or equipment at a national level during the various phases of their lifecycle (country-specific),

2.

Emission factors inferred from wider regional or global sub-application experience (e.g., default).

or

The type of emission factor required will depend on the level of homogeneity within the sub-application, the Tier approach being implemented, the dependence of emission factors on field practices applied, the role of banks and the likelihood of specific national circumstances. In some cases, the application will be or may be reasonably considered to be totally emissive, in which case the net consumption for a given year will become the emissions estimate for that year (e.g., many aerosol applications). In such a situation, a default emission factor would normally be more than adequate. However, in most cases involving ODS substitutes, some delay in emission is anticipated. Accordingly emission factors may need to be more sophisticated, particularly when applied at the sub-application level (Tier 2). Because Tier 1 methods typically operate at the application level, it is necessary to use composite emission factors, which can be either based on weighted averages of known sub-application emission factors (countryspecific or default) or on validated approximation approaches. Since Tier 1 methods are intended to be simple in their application, inventory compilers have the option of using existing composite emission factors based on the work of others. The Tier 1a approaches outlined in Sections 7.2 to 7.7 make such provision. For Tier 2 methods, inventory compilers need to be aware of the specific circumstances surrounding the subapplications in their countries. Although product and equipment types can be similar throughout a region or, even globally, there can be significant differences in emission factors over the lifetime of the product or equipment. Such differences can arise from climatic factors, construction methods, regulatory approaches and, in particular, from servicing methods where these apply. An additional factor to be considered in many countries is the management of the disposal of products and equipment at the end of its service life, which can have a profound effect on the total emissions. The chemical remaining in systems at that stage can be 90 percent or more of the original quantity used. Specific issues related to emission factors are discussed in the relevant application sections. Therefore, inventory compilers should ensure that their derivation takes into consideration these potential sources of variation. This is often best done by comparing selections with those chosen by other countries with similar circumstances. Where emission factor variation is seen to be significant (e.g., distinction between developed and developing country experience with refrigeration equipment), the item is highlighted in relevant application-specific sections of this chapter. As an additional support to inventory compilers, the most significant emission factors are included in the Emissions Factor Database (EFDB) administered by IPCC. The extensive editorial review process ensures that listed emission factors in the EFDB are properly examined to insure their robustness. Since emission factors in the EFDB tend to be adjusted less frequently than globally or regionally derived activity data, the review process can usually keep up with developments, thereby ensuring that listed values are broadly current.

7.1.2.4

C HOICE

OF ACTIVITY DATA

For ODS substitutes, activity data consist of the net amount of each chemical consumed annually in a country in an application, sub-application or more detailed equipment/product type. When adopting a Tier 2a method, it is often necessary to collect activity data for the number of units of a particular equipment or product type in existence to estimate the amount of chemical consumed or in banks.

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Where banks of chemical are likely to occur, it is also necessary to have information on historical annual net consumption patterns, either since the year of introduction of the chemical or over the average lifetime of products or equipment within the application or sub-application. This allows for the calculation of the cumulative bank in cases where emission factors must then be applied (Tier 1a or Tier 2a methods). As noted previously, reprocessing of recovered fluid should not be included in consumption estimates. Imports and exports include not only bulk chemicals, but, for Tier 2 methods in particular, may also include the quantity of chemical contained in products, such as refrigerators, air-conditioners, packaging materials, insulating foams, fire extinguishers, etc., depending on whether regional allocation has been used or not. Usually, it is notoriously difficult to obtain data on HFCs and PFCs contained in equipment or products unless a specific customs regime has been set up to address this issue. This will only likely be practiced, if at all, in conjunction with the implementation of a Tier 2 method and is unlikely to be available for Tier 1 methods, making the availability of globally or regionally derived data particularly important, at least as a cross-check, if significant product or equipment trade is expected. Such globally or regionally derived net consumption (activity) data can be obtained from the datasets contained in regional and global databases. Under this approach, chemical sales data are sometimes assigned from wider regional consumption information on the basis of some geo-economic factor such as population, GDP or number of dwellings. When using this market-based allocation method, it might not be necessary to take account of HFCs and PFCs contained in products being imported or exported, if the regional treatment causes netting out of intra-regional trade (i.e., imports and exports of products containing HFCs and PFCs within a region are roughly balanced). Where extra-regional trade is significant, then the HFCs and PFCs contained in products will need more careful consideration. Since activity data will be more prone to annual change than emissions factors, the source of globally or regionally derived data used by the inventory compilers needs to be updated regularly. Reliable global databases carrying this information are regularly cross-checked with global sales data for individual chemicals and subapplications, thereby ensuring regular validation. When accessing such databases, it is good practice for inventory compilers to ensure that the information they are receiving has been so validated. As noted in Box 7.1, Global and Regional Databases for ODS Substitutes, inclusion in the IPCC EFDB will indicate general adherence to due process, but it is good practice for countries to ensure that all data taken from the EFDB are appropriate for their national circumstances.

Specific considerations when collecting country-specific activity data The collection of country-specific activity data requires an inventory of HFC/PFC net consumption for each chemical and, where emissions lag behind consumption, an inventory of banked chemicals. Some inventory compilers may have access to national data published in trade magazines or technical reports. If these data are not available directly, they can be estimated by means of a special study to estimate the inventory of existing units or chemicals. Expert panels can also facilitate the generation of this information. Care must be taken to ensure that the scope of any datasets cited is understood and that any remaining gaps are identified. Inventory compilers may also decide to conduct annual studies to update their inventories of different types of equipment/products. An alternative to this may be to calculate or estimate production growth for each one of the sub-applications under consideration. Data need to reflect new units that are introduced each year, and old or poorly functioning units that are retired. Data on national chemical use are more easily obtained than data for the amount of equipment responsible for emissions, provided that confidentiality constraints do not intervene. It is always good practice to obtain data on the total annual sales from the chemical manufacturers or importers. The best source of data on the total charge of new equipment is likely to be the equipment manufacturers or the trade associations that represent them. For the total charge of retiring equipment, it is essential to obtain information on or estimate (i) equipment/product lifetime, and (ii) either (a) the historical sales of equipment/product and the historical average charge size or formulation, or, (b) the growth rate of such sales and charge sizes over the period in question, where such information is known for the current year. Inventory compilers in countries that import all or the majority of new chemicals consumed are likely to encounter different issues of data availability than those in countries with significant domestic chemicals production. If the majority of chemicals are imported, either in bulk or in equipment and products, some form of import data will be necessary for calculating emissions. Ideally, customs officials should track and make available chemical import statistics. For some products, such as foam and aerosols, it may not be possible for customs officials to track the type of chemical in the product (e.g., Hydrocarbons vs. HFCs in aerosols), or the presence of the product in the imported equipment (e.g., closed cell foam in refrigerators). In such cases, it may be necessary to collect or estimate data with the assistance of major distributors and end-users. As noted previously in this Section, the ability to obtain relevant country-specific activity data and banking information on a consistent basis at a country level can be constrained by such issues as confidentiality, lack of

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downstream industry networks and lack of trade information in products-containing HFCs and/or PFCs. Reconciliation is therefore often better achieved at regional level or even global level in some cases. In making this comment, it should be noted that the use of country-specific and globally or regionally derived data is not specifically an ‘either/or’ choice. In many cases, the development of a country’s overall inventory may rely on a combination of data from both sources. In any event, the use of one to verify the other is actively encouraged as good practice.

Specific considerations when using the mass-balance approach (Tier 1b or 2b) Activity data for a mass balance approach (Tier 1b or 2b) focus on chemical deployment rather than sources of emissions. These activity data include annual sales of new chemical, the total charge of new equipment, and the total charge of retiring equipment. If these data are not available at the national level, then globally or regionally derived data can be used, as for Approach A (emission-factor approach). Since the mass-balance approach is generally reserved for pressurised equipment in the refrigeration, air conditioning and fire protection applications, it is useful to know that comprehensive global databases already exist for these.

Time dependency of data contained in these Guidelines The products and equipment in which ODS substitutes are used have changed significantly over time, and are expected to continue to change. As a result, where information on activity data and default emission factors is contained in these Guidelines, it should be noted that activity data will be a more volatile component than emission factors in determining overall emissions. Accordingly, any default activity information contained herein will ‘age’ more rapidly and will lead to greater inaccuracies with the passage of time unless appropriate adjustments are made for market growth in the interim. Global and regional databases for ODS substitutes noted in Box 7.1 will generally reflect these changes. Where ODS transitions are still in the future, the adoption of static activity data could lead to very significant errors in emission projections.

7.1.2.5

C OMPLETENESS

Completeness is assured to a large extent for ODS Substitutes as a result of well-documented use patterns for ODSs themselves and by the fact that activity data, assessed at the application and sub-application levels, can be validated against total chemical sales. This is particularly the case for those HFCs and PFCs which are only used as ODS substitutes. However, it is still important to be able to identify all potential HFCs and PFCs in use. Table 7.1 gives an overview of the main HFCs and PFCs to be considered, but this may not be exhaustive, particularly when it comes to the components of blends, which can often be complex in their composition. One set of emissions which is not covered routinely within this source category are those arising from chemical production itself. However, methods for assessing these emissions are covered within Chapter 3, Section 3.10. It is possible for emissions to exceed consumption (activity) in a given year owing to emission of previously accumulated banks and therefore completeness of emissions reporting can only be established in Tier 2 approaches by plotting cumulative emission versus cumulative activity for the total period over which consumption and resulting emissions have occurred (i.e., cumulative consumption equals cumulative emissions plus current bank less cumulative destruction).

7.1.2.6

D EVELOPING

A CONSISTENT TIME SERIES

Inventory compilers that have prepared basic (Tier 1) estimates in the past are encouraged to develop the capacity to prepare Tier 2 estimates in the future. It is good practice to ensure that only actual emission estimates are included in the same time series. Inventory compilers should recalculate historical emissions with the preferred actual method, if they change approaches. Since all Tier 1 and Tier 2 approaches are now actual emission methods, there is no problem in mixing these approaches for different applications or sub-applications. However, if potential emission methods have previously been used, the time series needs to be recalculated. If data are unavailable, the two methods should be reconciled to ensure consistency, following the guidance on recalculation provided in Volume 1, Chapter 5. It is good practice to provide full documentation for the recalculation, thereby ensuring transparency. Emission factors generally come from historical data on other chemicals (e.g., CFCs) used in established markets. These factors need to be adapted to new chemicals (e.g., ODS substitutes) where new uptake occurs. National data on base year deployment is now available (or can be calculated with known uncertainty).

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7.1.3

Uncertainty assessment

Over a long time (greater than 50 years in some applications) cumulative emissions of ODS substitutes within a country will tend to equal cumulative consumption in the same time frame unless significant end-of-life recovery has been practised. For a given year, the quantification of uncertainty for ODS is very difficult to estimate, due to the large number of different sources and the diversity of emission patterns. For the Tier 1b and 2b methods, the overall uncertainty will be directly related to quality and completeness of chemical sales and import data at either the application or the sub-application level. These factors will be equally important for Tier 1a methods but there will be additional sources of uncertainty arising from the use of composite emission factors and other assumptions required to complete specific algorithms. For the Tier 2a method, the uncertainty will reflect the completeness of the equipment survey, and the appropriateness of the emission factors developed at the subapplication level to characterise emissions. Further advice on uncertainties is provided in the separate sections on the six application areas that follow.

7.1.4

7.1.4.1

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation for all ODS substitutes applications Q UALITY A SSURANCE /Q UALITY C ONTROL (QA/QC) ALL ODS SUBSTITUTES APPLICATIONS

FOR

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from these applications or sub-applications. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. In addition to the guidance in Volume 1, specific procedures of relevance to this source category are outlined below. Even with such provisions in place to deal with activity data, the real emission data for a given year will never be exactly known, irrespective of the refinement of the estimation methods. Hence, cross checking of integrated emission figures against real net consumption of HFC/PFC, together with judgement of banking over the same period of time must be performed at regular intervals, and the input factors have to be adjusted to achieve agreement over time.

Comparison of emissions estimates using different approaches Inventory compilers should compare equipment/product based estimates at the sub-application level (Tier 2a) with the mass-balance Tier 1b or 2b approach, where appropriate, since emission factors at the product level have an inherent associated uncertainty. This technique will also minimise the possibility that certain end-uses are not accounted for in the equipment/product based approaches.

Potential emissions estima tes as a reference scenario Inventory compilers may also choose to use the potential emissions reference scenario as a check on the Tier 1 or Tier 2 actual estimates. Inventory compilers may consider developing accounting models that can reconcile potential and actual emissions estimates and may improve determination of country-specific emission factors over time. When taken alongside estimates of actual emission from determinations of atmospheric concentrations, this scenario can assist in monitoring the growth of banked greenhouse gases caused through delays in emission and, thereby, keeps track of likely future environmental burdens. This ultimate means of mass balance verification is particularly powerful for HFCs and PFCs because of their unique identities and lack of natural sources. Potential emissions of a certain chemical are equal to the amount of virgin chemical consumed annually in the country minus the amount of chemical recovered for destruction or export in the year of consideration. (See Annex 2 of this volume.) All chemicals consumed will eventually be emitted to the atmosphere over time if not permanently encapsulated, chemically converted, or destroyed7, and in the long term (in excess of 50 years for 7

The destruction of fluorocarbons can be costly but there are several destruction processes recommended by the Parties to the Montreal Protocol: liquid injection incineration; reactor cracking; gaseous/fume oxidation; rotary kiln incinerators; cement kilns; plasma destruction; municipal solid waste incinerators (foams only).

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some applications), cumulative potential emissions will equal cumulative actual emissions for those applications which ultimately cease use of HFCs and/or PFCs and where capture and destruction are not practised. Since accumulation is thought to be the dominant process at the present time in the major areas of usage, such as refrigeration and foams, potential emission calculations will strongly overestimate emissions and are inappropriate as a formal annual reporting method. The error is minimised when enough time has passed for HFC/PFC-containing equipment and products to begin to be retired, although, even then, the rate of subsequent emission may depend on the end-of-life strategy chosen. However, as long as emissions lag behind consumption and consumption continues to grow, the overestimation will persist. The error is zero only if there is no delay in emissions or if the consumption growth rate is zero for a long period of time.

National activity data check For the Tier 2 method, inventory compilers should evaluate the QA/QC procedures associated with estimating equipment and product inventories, whether country-specific, regionally or globally derived, to ensure that they meet the general procedures outlined in the QA/QC plan and that representative sampling procedures were used. This is particularly important for the ODS substitutes equipment/product types because of the large populations of equipment and products. For the Tier 1b (mass balance) method, inventory compilers should evaluate and reference QA/QC procedures conducted by the organisations responsible for producing chemical deployment information. Sales data may come from gas manufacturers, importers, distributors, or trade associations. If the QC associated with the secondary data is inadequate, then the inventory compiler should establish its own QC checks on the secondary data, reassess the uncertainty of the emissions estimates derived from the data, and reconsider how the data are used.

Emission factors check Emission factors used for the Tier 2a method should ideally be based on country-specific studies. Where such an approach is used, inventory compilers should compare these factors with the defaults and any values which may be contained in the EFDB or elsewhere in support of Tier 2a methods. They should determine if the countryspecific values are reasonable, given similarities or differences between the national circumstances surrounding the sub-application in question and those assumed within the defaults. Any differences between country specific factors and default factors should be explained and documented.

7.1.4.2

R EPORTING

AND D OCUMENTATION FOR ALL SUBSTITUTES APPLICATIONS

ODS

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. As discussed above, inventory compilers should prepare and report actual emissions estimates for as many subapplications as possible. This is now aided by the availability of globally or regionally derived activity data in regional and global databases (see Box 7.1) together with emission factors for several sub-applications contained in the EFDB. For those equipment/product types where it is not possible to prepare actual emissions estimates at the sub-application level (i.e., Tier 2 estimates), even with this additional support, inventory compilers should prepare and report actual emission estimates using Tier 1 methods at the application level. The balance between preservation of confidentiality and transparency of the data needs to be carefully addressed. Careful aggregation may solve some problems but will require that results are validated by other means (e.g., third party audit). Where data have been aggregated to preserve the confidentiality of proprietary information, qualitative explanations should be provided to indicate the method and approach for aggregation.

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7.2

SOLVENTS (NON-AEROSOL)

7.2.1

Chemicals covered in this application area

HFCs are now used in solvent applications to a much lower extent than CFC-113 was used prior to phase-out, and PFCs are still only very rarely used. HFC/PFC solvent uses occur in four main areas as follows: (i)

Precision Cleaning;

(ii)

Electronics Cleaning;

(iii)

Metal Cleaning;

(iv)

Deposition applications

HFCs are typically used in the form of an azeotrope or other blend for solvent cleaning. The most commonly used HFC solvent is HFC-43-10mee, with some use of HFC-365mfc, HFC-245fa (as an aerosol solvent8), and heptafluorocyclopentane (U.S. EPA, 2004b). This allows for tailoring the balance between effective cleaning and compatibility with materials of construction of the work-piece. The pure material does not have the cleaning power of CFC-113, since no chlorine atoms are present in the molecule. In general, perfluorocarbons have little use in cleaning, as they are essentially inert, have very high GWPs and have very little power to dissolve oils - except for fluoro-oils and fluoro-greases for even deposition of these materials as lubricants in disk drive manufacture. Accordingly, PFCs only find rare uses in the solvent sector as blanketing fluids for 2-propanol cleaning systems (per British Aerospace military section) or in the now obsolete Advanced Vapour Degreasing (AVD) heterogeneous co-solvent system. Such PFCs can be used as blanketing fluids to prevent the loss of the more costly primary fluids in dual-fluid vapour phase soldering systems. PFCs can be used as the only working fluid in single-fluid vapour phase soldering systems. In the component manufacturing sector, PFCs are used to test the hermeticity of sealed components. Further information on the use of PFCs in the electronics industry is found within Chapter 6 of this volume. In general, the major PFC manufacturers converted all former PFC users to HFC or hydrofluoroether (HFE) use in cleaning applications.

7.2.2 7.2.2.1

Methodological issues C HOICE

OF METHOD

Historically, emissions from solvent applications generally have been considered prompt emissions because 100 percent of the chemical is typically emitted within two years of initial use. (IPCC, 2000). In order to estimate emissions in such cases, it is necessary to know the total amount of each HFC or PFC chemical sold in solvent products each year. Emissions of HFCs and PFCs from solvent use in year t can be calculated as follows. EQUATION 7.5 EMISSION ESTIMATION METHOD FOR SOLVENT USES Emissions t = S t • EF + S t −1 • (1 − EF ) − Dt −1 Where: Emissionst = emissions in year t, tonnes St = quantity of solvents sold in year t, tonnes St–1 = quantity of solvents sold in year t–1, tonnes EF = emission factor (= fraction of chemical emitted from solvents in the year of initial use), fraction Dt–1 = quantity of solvents destroyed in year t–1, tonnes Table 7.1 indicates the known HFCs and PFCs used in solvent applications, although good practice is to research the country-specific situation in case any previously unidentified applications have arisen. The scope of

8

Emissions of aerosol solvents are included as an aerosol (see Section 7.3).

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the search is aided by the fact that the inventory compiler is only looking for applications where ODSs were previously used. The approach set out in Equation 7.5 can be applied as either a Tier 1a or a Tier 2a method, based as it is on Approach A (emission-factor approach). Whether the approach qualifies as a Tier 1 or Tier 2 method will depend on whether there are identifiable sub-applications within the solvent usages in the reporting country (e.g., the four main areas identified above). As trends have developed towards more controlled solvent environments, the need for a Tier 2 approach has increased. In some cases, there may be highly controlled sub-applications (e.g., in the precision electronics industry) where specific emission factors are fully characterised. These would be treated differently from more general solvent applications which may remain based on the default emission factor. It should be noted that Equation 7.5 assumes total release of solvent within two years regardless of the emission factor applied in year t. Additionally, there is no consideration of recovery and recycling, which may be a factor in some situations. However, it would be assumed that recovery and recycling would, in general, be reflected in reduced sales of virgin materials. Solvent recovered and subsequently destroyed is considered, but is an unlikely course in practice bearing in mind the cost of the chemicals involved.

7.2.2.2

C HOICE

OF EMISSION FACTORS

The emission factor EF represents the fraction of chemical emitted from solvents in year t. The product lifetime is assumed to be two years, and thus any amount not emitted during the first year is assumed by definition to be emitted during the second and possibly final year. A decision tree for estimating actual emissions is included in Figure 7.2, Decision Tree for Actual Emissions from the Solvents application. The data collection process is described in Section 7.2.2.3. In the absence of country-specific data, it is good practice to use a default emission factor of 50 percent of the initial charge/year for solvent applications. 9 In certain applications with new equipment incorporating low emission design features, it is very possible that much lower loss rates will be achieved and that emissions will occur over a period of more than two years. Alternative emission factors can be developed in such situations, using data on the use of such equipment and empirical evidence regarding alternative emission factors.10 Such country-specific emission factors should be documented thoroughly (Tier 2a). The ‘mix’ of hand operated batch cleaning systems and automated conveyorised systems within a country or region may result in very different emissions. Attention to proper work practices, setup of the work area and proper training of the workers will significantly lower solvent emissions. Within such groups (batch or conveyorised), there is a wide range of equipment age, low emission design sophistication, workpiece design, workpiece load size and maintenance diligence. All of these factors will affect emissions from a particular piece of equipment or region. Modifications for the recovery and recycling of solvents can be applied if an appropriate estimate of retrofitted equipment can be obtained. While HFC and PFC solvents may be recovered and recycled multiple times during their use owing to their high costs, in most emissive end uses (sub-applications) these chemicals will be released considerably more quickly after being placed in use than those in sealed refrigeration applications.

9

Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (IPCC, 2000).

10

As guidance, for sales to new equipment, approximately 10-20 percent may be emitted with the rest of the solvent used to fill the equipment. In subsequent years sales are for replenishment and can eventually be considered 100 percent emitted.

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Figure 7.2

Decision tree for actual emissions from the solvents application Start

Are equipment and solvent activity data (country-specific or globally or regionally derived) available at the sub-application level? Collect data at the subapplication level.

Yes

Box 3: Tier 2a

No No

Is this a key category1?

Yes

Yes

Are countryspecific emission factors available at the sub-application level for newer equipment with lower leak rates?

Calculate emissions of each HFC/PFC in each end-use, using sales data at the subapplication level, country-specific emission factors where available, and default factors for the remainder, taking into account the use of new equipment with lower leak rates.

No

Are any countryspecific emission factors available at the sub-application level?

No

Is there any domestic solvent production?

Yes

In each year, for each individual substance, obtain data from HFC/ PFC producers and importers/ exporters for gas sales to users.

Yes

Calculate emissions of each HFC/PFC in each end-use, using sales data at the subapplication level, country-specific emission factors where available, and default factors for the remainder. Box 2: Tier 2a

No In each year, for each individual substance, obtain solvent import data from customs statistics, solvent distributors or other globally or regionally derived data sources.

Calculate emissions of each HFC/PFC using production and import data at the application level. Box 1: Tier 1a

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

7.2.2.3

C HOICE

OF ACTIVITY DATA

Equation 7.5 should be applied to each chemical individually and, depending on the disaggregation in available data, it may be appropriate to assess net consumption of each chemical by sub-application (Tier 2a). Wherever possible, activity data should be collected directly from the suppliers of solvent or the users in support of either Tier 1a or 2a methods. However, where this is not possible, globally or regionally derived activity data at the application level or the sub-application level can be used where this is available. The activity data for this end-use are equal to the quantity of each relevant chemical sold as solvent in a particular year. Accordingly, data on both domestic and imported solvent quantities should be collected from suppliers. Depending on the character of the national solvent industry, this can then be cross-checked with users

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where this is practicable. In most countries, the end-users will be extremely diverse and a supplier-based approach would be most practical. Nonetheless, a combination of both approaches is often the most effective.

SUPPLIER DATA Supplier activity data refers to the amount of chemical solvent sold or imported annually into a country. Domestic solvent sales should be available directly from chemical manufacturers. As solvents are only produced in a few countries, most countries will import some or all of their consumption. Data on imported solvents can be collected from the exporting manufacturers, although information on exports to individual countries may be considered confidential. Alternatively, import statistics from customs agencies or the distributors of imported solvents can be used. Solvent import data are generally more easily obtained than aerosol import data because solvent is usually imported in bulk rather than in small containers. If specific emission factors are developed for particular types of equipment, it will be necessary to disaggregate the consumption data into these equipment classes. In general, this will require a bottom-up approach.

USER DATA User activity data include the number of pieces of equipment or canisters containing solvent and their charge. The bottom-up approach is suitable where large corporations consume most of the solvent sold, because it should be possible to obtain detailed solvent end-use data from a few large entities. The bottom-up approach may also be most appropriate when equipment-specific emission factors are available.

7.2.2.4

C OMPLETENESS

Completeness depends on the availability of activity data. Inventory compilers in countries without domestic solvent production may need to use expert judgement in estimating activity data, because import statistics are likely to be incomplete (see Volume 1, Chapters 2 and 3). There is a potential for double-counting with Volume 3 Chapter 6 which deals with HFC and PFC use in the electronics industry. This should not occur if care is taken to identify previous ODS consumption patterns. It is always good practice to cross-reference of both parts of a submission by inventory compilers to confirm that no double-counting has occurred. With respect to double-counting, care should also be taken where HFCs and PFCs acting as solvents are contained in aerosols. A clear policy should be established as to how these are accounted. It is normally good practice to account for these uses under consumption in aerosols to avoid problems in making distinctions between solvents and propellants, particularly where one chemical can act in both roles. This matter is covered further in Section 7.3. As noted in Section 7.2.2.1, it is also good practice to carry out some research to confirm that no HFCs or PFCs other than those listed in Table 7.1 are being used for solvent applications. Producers, importers and distributors should be able to confirm the situation.

7.2.2.5

D EVELOPING

A CONSISTENT TIME SERIES

Emissions from the solvent sector should be calculated using the same method and data sources for every year in the time series. Where consistent data are unavailable for any years in the time series, gaps should be recalculated according to the guidance provided in Volume 1, Chapter 5.

7.2.3

Uncertainty assessment

The assumption that all solvent may be emitted within approximately two years (50 percent in Year t and 50 percent in Year t+1) has been widely accepted by experts as a reasonable default (IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, 2000). However, the magnitude of the error caused by this assumption will depend on the nature of solvent usage patterns in the country being reported. In general, the default assumption will over-estimate emissions for a given year as leak tightness of equipment improves, although not on a cumulative basis unless destruction is being practised. Conversely, growth in the destruction of recovered or recycled solvent over time will influence the assumption of 100 percent eventual release. Activity data should be reliable at the application level because of the small number of chemical manufacturers, the high cost of the solvent, and the 100 percent emissive nature of the use over time in most applications. However, uncertainty at the sub-application level will depend largely on the quality of data provided by users and the level of completeness achieved in surveying them.

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7.2.4 7.2.4.1

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, Chapter 6, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this application. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. In addition to the guidance in Volume 1, specific procedures of relevance to this application are outlined below: •

•

For accurate quality control/assurance both top-down and end-use data should be compiled. To allow independent assessment of the level of quality of the data reporting, the number of manufacturers and distributors plus end users interviewed should be quantified. When applying emission factors and activity data specific to various solvent applications, the activity data should be obtained at the same level of detail.

7.2.4.2

R EPORTING

AND

D OCUMENTATION

Inventory compilers should report the emission factor used, and the empirical basis for any country-specific factors. For activity data, chemical sales and imports should be reported, unless there are confidentiality concerns arising from the limited number and location of manufacturers. (At present, for example, there may be only one producer of each compound.) Where there are less than three manufacturers of specific chemicals used as solvents, reporting could be aggregated into the aerosol section, because both are considered 100 percent emissive applications (see Section 7.3.4.2 below). In this case, to preserve confidentiality, emissions of individual gases should not be specified and emissions should be reported in CO2-equivalent tonnes.

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7.3

AEROSOLS (PROPELLANTS AND SOLVENTS)

7.3.1

Chemicals covered in this application area

Most aerosol packages contain hydrocarbon (HC) as propellants but, in a small fraction of the total, HFCs and PFCs may be used as propellants or solvents. Emissions from aerosols usually occur shortly after production, on average six months after sale. However, the period between manufacture and sale could vary significantly depending on the sub-application involved. During the use of aerosols, 100 percent of the chemical is emitted (Gamlen et al., 1986; U.S. EPA, 1992b). The 5 main sub-applications are as follows: (i)

Metered Dose Inhalers (MDIs);

(ii)

Personal Care Products (e.g., hair care, deodorant, shaving cream);

(iii)

Household Products (e.g., air-fresheners, oven and fabric cleaners);

(iv)

Industrial Products (e.g., special cleaning sprays such as those for operating electrical contact, lubricants, pipe-freezers);

(v)

Other General Products (e.g., silly string, tyre inflators, klaxons).

The HFCs currently used as propellants are HFC-134a, HFC-227ea, and HFC-152a, as shown in Table 7.1. The substances HFC-245fa, HFC-365mfc, HFC-43-10mee and a PFC, perfluorohexane, are used as solvents in industrial aerosol products. Of these, HFC-43-10mee is the most widely used.11 HFC-365mfc is also expected to be used within aerosols in the near future.

7.3.2 7.3.2.1

Methodological issues C HOICE

OF METHOD

Aerosol emissions are considered prompt because all the initial charge escapes within the first year or two after manufacture, typically six months after sale for most sub-applications. Therefore, to estimate emissions it is necessary to know the total amount of aerosol initially charged in product containers prior to sale. Emissions of each individual aerosol in year t can be calculated as follows: EQUATION 7.6 EMISSION ESTIMATION METHOD FOR AEROSOL USES Emissions t = S t • EF + S t −1 • (1 − EF ) Where: Emissionst = emissions in year t, tonnes St = quantity of HFC and PFC contained in aerosol products sold in year t, tonnes St–1 = quantity of HFC and PFC contained in aerosol products sold in year t–1, tonnes EF = emission factor (= fraction of chemical emitted during the first year), fraction This equation should be applied to each chemical individually. Wherever possible, activity data should be collected directly from the manufacturers or distributors of aerosols, ideally at the sub-application level to facilitate a Tier 2a approach. Globally or regionally derived activity data can be used to provide sub-application analysis where country-specific data does not exist. If data at the sub-application level is not available from either source, activity data at the application level should be obtained and applied using Equation 7.6 (Tier 1a). Since the lifetime of the product is assumed to be no more than two years, any amount not emitted during the first year must by definition be emitted during the second and final year. In reality, most emissions occur within the first year after product purchase, but Equation 7.6 rightly accounts for the lag period between the time of manufacture and the time of purchase and use. When applying Equation 7.6, however, care must be taken to define the Point of Sale which, for the purposes of emission estimation, is viewed as sales by the manufacturer to

11

HFC-43-10mee is used solely as a solvent, but is counted as an aerosol when delivered through aerosol canisters.

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the supply chain and not by the retailer to the end-user. This approach is most appropriate because sales data will normally be collected from manufacturers and major distributors. In contrast with the situation for solvents, there is rarely a need to account for recovery, recycling or destruction, since this is only likely to occur when stockpiled product becomes out-of-date. Under normal supply-chain management conditions this is a rare event. A decision tree for estimating actual emissions is included in Figure 7.3, Decision Tree for Actual Emissions from the Aerosol Application. The data collection process is described below. Figure 7.3

Decision tree for actual emissions from the aerosol application Start

Does the country produce general aerosol products and metered dose inhalers (MDIs) containing HFCs and PFCs?

Yes

No

Are activity data available at the sub-application level from local manufacturers, importers and/or global/ regional databases?

Yes

Are aerosol product and MDI import statistics available at the sub-application level?

Yes

Calculate emissions of each substance in each sub-application, using country-specific or globally/regionally derived activity data and appropriate emission factors. Box 2: Tier 2a

Collect data at the sub-application level.

Yes

No

Is this a key category1?

No

No

Calculate emissions from domestic and imported products for each chemical using activity data at the application level. Box 1: Tier 1a

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

7.3.2.2

C HOICE

OF EMISSION FACTORS

It is good practice to use a default emission factor of 50 percent of the initial charge per year for the broad spectrum of aerosol products when assessed at the application level (Tier 1a). This means that half the chemical charge escapes within the first year and the remaining charge escapes during the second year (Gamlen et al., 1986). Inventory compilers should use alternative emission factors only when empirical evidence is available for the majority of aerosol products at either the application level (Tier 1a) or the sub-application level (Tier 2a). In

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any event, the percentage emission factors should in general sum to 100 percent over the time during which it is assumed that the charge will escape. The development of country-specific emission factors should be documented thoroughly. General aerosol and MDI manufacturers may be able to provide data on process losses. As a general observation, the consistently emissive nature of aerosols makes the distinction between countryspecific and the default emission factor on the one hand and any differences between emission factors in the various sub-applications on the other hand, less influential on overall emissions estimates than is the case in other application areas. Therefore the benefit of following a more disaggregated Tier 2a approach in favour of a Tier 1a approach is less pronounced in the case of aerosols. Inventory compilers should therefore consider carefully how much resource to invest in developing a Tier 2 approach. However, there may be other reasons for keeping reporting of some sub-applications separate and it is expected many countries may wish to monitor emissions from MDIs separately to other general aerosols for policy reasons.

7.3.2.3

C HOICE

OF ACTIVITY DATA

For the Tier 1a method, the activity data required are the total quantity of each relevant chemical contained in all aerosol products consumed within a country (both domestic sales and imports). For countries that import 100 percent of aerosol products, activity data are equal to imports. Activity data for this application can be collected at the sub-application level using either a supplier-based or a user-based approach, depending on the availability and quality of the data (Tier 2a). The user-based approach requires data on the number of aerosol products sold and imported at the sub-application level (e.g., number of individual metered dose inhalers, hair care products, and tyre inflators), and the average charge per container. This may require globally or regionally derived activity data for some sectors of use. The supplier-based approach involves collecting aerosol and MDI chemical sales data directly from chemical manufacturers where their sales analysis is sufficiently robust at a country level. In many cases, a mix of both sources of data may be necessary. Domestic aerosol production: For countries with domestic production, general aerosol and MDI manufacturers can usually provide data on the quantity of aerosol products produced for consumption in the country, the number of aerosols exported, the average charge per aerosol, and the type of propellant or solvent used (i.e. which HFC/PFC). Total use of domestically produced aerosol products in each year can then be calculated as the number of aerosol products sold domestically in a given year times the charge of HFC/PFC in each product. Of course, imported aerosols will still need to be added to this assessment to provide the total picture. If subapplication data from indigenous aerosol producers are not available, domestic chemical producers can often provide data on the amount of HFCs sold to domestic manufacturers in metered dose inhalers, and aggregate sales data to producers of other aerosols (categories (ii), (iii), (iv) and (v) above). If domestic aerosol and MDI manufacturers import HFCs, information may also be sought from chemical importers or their overseas suppliers, although the latter may not be able to provide data on exports destined for individual countries because of confidential business concerns. Customs officials and chemical distributors are another possible source for chemical import data. Globally or regionally derived activity data may also have a role both to fill gaps in the existing dataset and to cross-check data obtained from aerosol manufacturers and chemical suppliers. Imported aerosol production: Most countries will import a significant share of their total aerosol products. Data on imports of HFC-containing general aerosols may be difficult to collect because official import statistics for aerosol products do not typically differentiate HFC-containing aerosols from others. When usable import statistics are unavailable from customs agencies, data may be available from product distributors and specific end-users. For example, in the case of MDIs, a limited number of pharmaceutical companies typically import products, and these companies can be surveyed to obtain the required information. Again globally or regionally derived activity data may be helpful in certain cases.

7.3.2.4

C OMPLETENESS

Completeness depends on the availability of activity data on each chemical to be covered. Section 7.3.1 (and Table 7.1) provides an assessment of HFCs and PFCs currently used, but inventory compilers should check the situation with in-country sources to confirm those chemical relevant to the local situation. Inventory compilers in countries without domestic aerosol production may need to use expert judgement in estimating activity data, because import statistics are likely to be incomplete (see Volume 1, Chapters 2 and 3), particularly with reference to the propellants and solvents contained. Globally or regionally validated activity databases may be particularly helpful in such instances, where these exist.

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7.3.2.5

D EVELOPING

A CONSISTENT TIME SERIES

Emissions from aerosols should be calculated using the same method and data sources for every year in the time series. Where consistent data are unavailable for any year in the time series, gaps should be recalculated according to the guidance provided in Volume 1, Chapter 5.

7.3.3

Uncertainty assessment

The use of HFCs in the general aerosol sector is typically larger than in the MDI sector. Data from HFC manufacturers and importers of sales to the general aerosol sector are, at the present time, not well-defined other than for HFC-134a on a global scale. These data can be improved through additional data collection activities and the development of global and regional databases. The diffuse nature of the general aerosol sector means that the acquisition of reliable bottom-up data (Tier 2a) requires specific study on a country basis through local industry experts, whose advice should be sought on uncertainties using the approaches to expert judgement outlined in Volume 1, Chapter 3. There are several sources of reliable data for the MDI sector, leading to a high level of confidence in the data reported that should be reflected in inventory emissions estimates. However, in reporting for a single country, the absence of reliable data for the general aerosol sector could mean that emission data could be over or under estimated by a factor of between one third and three times.

7.3.4 7.3.4.1

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL (QA /QC)

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and to organise an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, Chapter 6, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this application. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. In addition to the guidance in Volume 1, specific procedures of relevance to this application are outlined as follows. Aerosol manufacturing and importing data, on the one hand, and chemical supply data, on the other hand, can be used to cross-check one another during or after the development of an emission estimate. Data used to calculate emissions from year t–1 should be consistent with data used in the previous year’s inventory estimate, so the two-year total sums to 100 percent. If this is not the case, then the reason for the inconsistency should be reported. Data collection carried out in accordance with the section on data collection above should provide adequate quality control. To allow independent assessment of the level of quality of the data reporting, the number of manufacturers of aerosols plus importers should be quantified.

7.3.4.2

R EPORTING

AND

D OCUMENTATION

The emission estimate for metered dose inhalers may be reported separately from the emission estimate for other aerosols by some inventory compilers. In such cases, the specific emission factor used should be documented. If a country-specific emission factor is used in preference to a default factor, its development should be documented. Detailed activity data should be reported to the extent that it does not disclose confidential business information. Where some data are confidential, qualitative information should be provided on the types of aerosol products consumed, imported, and produced within the country. It is likely that the type of HFC used as a propellant or solvent and the sales of MDIs and general aerosols into individual countries could be viewed as confidential.12 Where there are less than three manufacturers of specific chemicals used as solvents, reporting could be aggregated into this section, because both are considered 100 percent emissive applications (see Section 7.2.4.2 above).

12

Quantification of use data for individual general aerosol sectors will enable more reliable future projections to be developed and emission reduction strategies to be considered.

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7.4

FOAM BLOWING AGENTS

7.4.1

Chemicals covered in this application area

Increasingly, HFCs are being used as replacements for CFCs and HCFCs in foams and particularly in insulation applications. Compounds that are being used include HFC-245fa, HFC-365mfc, HFC-227ea, HFC-134a, and HFC-152a, as shown in Table 7.1. The processes and applications for which these various HFCs are being used are shown in Table 7.4 with predominantly open-celled foams being in shaded rows.

TABLE 7.4 USE OF HFCS IN THE FOAM BLOWING INDUSTRY (FOAM PRODUCT EMISSIONS BY GAS – ODS REPLACEMENTS) Cell Type

O P E N

HFC Foam Blowing Agent Alternatives Sub-application HFC-134a

PU Flexible Moulded Foam PU Integral Skin Foam PU One Component Foam

PU Discontinuous Panel PU Appliance Foam PU Injected Foam

a a

a

a

a

HFC-365mfc (+ HFC-227ea)

a a

Phenolic Block Phenolic Laminate

a

a

a

a a

a

a

a

a

a a

a

a a

PU Continuous Laminate

Extruded Polystyrene

a a

PU Discontinuous Block

PU Spray Foam

a a

PU Continuous Block

PU Pipe-in-Pipe

a

HFC-245fa

PUa Flexible Foam

PU Continuous Panel

C L O S E D

HFC-152a

a

a

a

a a

a

a

a

PU denotes polyurethane

The division of foams into open-cell or closed-cell relates to the way in which blowing agent is lost from the products. For open-cell foam, emissions of HFCs used as blowing agents are likely to occur during the manufacturing process and shortly thereafter. In closed-cell foam, only a minority of emissions occur during the manufacturing phase. Emissions therefore extend into the in-use phase, with often the majority of emission not occurring until end-of-life (de-commissioning losses). Accordingly, emissions from closed cell foams can occur over a period of 50 years or even longer from the date of manufacture. Open-celled foams are used for applications such as household furniture cushioning, mattresses, automotive seating and for moulded products such as car steering wheels and office furniture. Closed-cell foams, on the other hand, are primarily used for insulating applications where the gaseous thermal conductivity of the chosen blowing agent (lower than air) is used to contribute to the insulating performance of the product throughout its lifetime.

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7.4.2

Methodological issues

The previous Guidelines presented an equation for calculating emissions from closed cell foam that accounted for the first two emission points (i.e., manufacturing and during the in-use phase). This remains generally sufficient to account for the early stages of HFC uptake. However, in order to prepare a complete estimate of emissions from this source, it is good practice to add third and fourth terms to the equation to account for decommissioning losses and chemical destruction, where data are available. Thus, the relevant equation is: EQUATION 7.7 GENERAL EMISSION-FACTOR APPROACH (A) FOR FOAMS Emissions t = M t • EFFYL + Bank t • EF AL + DLt − RDt Where: Emissionst = emissions from closed-cell foam in year t, tonnes Mt = total HFC used in manufacturing new closed-cell foam in year t, tonnes EFFYL = first year loss emission factor, fraction Bankt = HFC charge blown into closed-cell foam manufacturing between year t and year t-n, tonnes EFAL = annual loss emission factor, fraction DLt = docommissioning losses in year t = remaining losses of chemical at the end of service life that occur when the product/equipment is scrapped, calculated from the amount of remaining chemical and the end-of-life loss factor which depends on the type of end-of-life treatment adopted13, tonnes RDt = HFC emissions prevented by recovery and destruction of foams and their blowing agents in year t, tonnes n = product lifetime of closed-cell foam t = current year (t-n) = The total period over which HFCs used in foams could still be present It should be noted that Equation 7.7, although targeted at closed cell foams, can be equally applied to open celled foams. In this sense it is a universal equation for all foams. In the case of open-celled foams the first-year Loss Emission Factor is typically 100 percent and the equation simplifies to its first component only, which then further simplifies to Equation 7.8. Accordingly, where the nature of a foam is uncertain, Equation 7.7 should be applied to each chemical and major foam sub-application individually when pursuing a Tier 2a method. Since emission profiles vary substantially by sub-application within the overall foam application, there is significant incremental value in adopting a Tier 2 method wherever possible. Ideally, this should be achieved by the researching of individual country activities. However, in practice, the intra-regional trade in foams coupled with the significant difficulty in setting up systems to identify the blowing agents used in foams already manufactured, makes a method based on country-specific activity data very difficult to implement at the subapplication level. Recognising that both disaggregated activity data and the related emission factors may be difficult to obtain at a country level, there have been several efforts by the UNEP Foams Technical Options Committee (UNEP-FTOC, 1999; UNEP-FTOC, 2003) and others to provide globally or regionally derived activity data and default emissions factors by sub-application for CFCs, HCFCs and hydrocarbons (HCs). Although HFCs are only now being used significantly as additional ODS alternatives, it is expected that a similar approach can be carried forward for these chemicals, with emission factors being available within the EFDB in order to provide a helpful source of information for inventory compilers. Other databases are emerging from the original FTOC work for activity data, and will be particularly helpful for countries where the trade in productscontaining HFCs is significant, but difficult to track. As an additional methodological consideration, it should be noted that many of the emissions from closed-cell insulation foams arise from banks of blowing agent built up from previous years of consumption. This point was 13

Most decommissioning procedures will not result in the release of all remaining blowing agent. Even processing through an open auto-shredder has been found to result in emission of less than 50 percent of the remaining blowing agent at the point of processing (U.S. EPA/AHAM, 2005). Accordingly, blowing agent banks can accumulate further along the waste stream (e.g., landfills) – see Section 7.4.2.1.

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highlighted in the IPCC Special Report on Safeguarding the Ozone Layer and the Global Climate System: Issues Related to Hydrofluorocarbons and Perfluorocarbons (IPCC/TEAP, 2005) where it was noted that CFC emissions could continue from banked blowing agents in foams until the middle of the 21st century. This illustrates the importance of using an emissions estimation method which adequately reflects the development of banks. An additional characteristic of foam inventories is that a significant majority of emissions occur from closed cell foam at the point of decommissioning or thereafter. Therefore, inventory compilers should be careful to research decommissioning practices and any recovery and destruction practices within their country closely. As a further consequence, methods which assume total release of blowing agent over the manufacturing and use phases are likely to significantly over-estimate emissions for any given year. Accordingly, methods should typically assume complete release of blowing agent at decommissioning only where there is definite evidence to support this and should normally attribute emissions to subsequent years based on a more appropriate release function. The relevant columns in Tables 7.6 and 7.7 therefore represent ‘maximum potential’ losses. In practice such emissions are likely to be spread over a substantial number of years following decommissioning if the foam remains broadly intact (i.e., average particle size > 8mm) (U.S. EPA/AHAM, 2005). As a general observation, the mass-balance approach (Approach B) is inappropriate for foams since there is no mechanism by which such products are serviced in practice.

7.4.2.1

C HOICE

OF METHOD

Open-Cell Foam: Since HFCs used for open-cell foam blowing are released immediately, the emissions in almost all cases will occur in the country of manufacture. The only exception may be in the case of OCF (One Component Foams) where the filled container may be manufactured in one country but the emissions occur in another country because the containers are easily traded. Emissions are calculated according to the following equation:14 EQUATION 7.8 GENERIC CALCULATION METHOD FOR EMISSIONS FROM OPEN-CELLED FOAMS Emissions t = M t Where: Emissionst = emissions from open-cell foam in year t, tonnes Mt = total HFC used in manufacturing new open-cell foam in year t, tonnes This equation must be applied for each chemical used in open-celled foam applications. Although, there is little variation in emission factor across the open-cell sub-applications, it may still be advantageous to use a disaggregated Tier 2a method in order to make it easier to accurately assess net consumption activity data. Such an approach will naturally address the trade in one-component foams. Where there is little use of one-component foam, it could be logical to revert to a Tier 1a method where Equation 7.8 is applied at the application level. Closed-Cell Foam: Emissions from closed-cell foam occur at three distinct points, which have already been highlighted in Equation 7.7: (i)

First Year Losses from Foam Manufacture and Installation: These emissions occur where the product is manufactured or installed.

(ii)

Annual Losses (in-situ losses from foam use): Closed-cell foam will lose a fraction of its initial charge each year until decommissioning. These emissions occur where the product is used.

(iii)

Decommissioning Losses: Emissions upon decommissioning also occur where the product is used.

To implement an approach which captures these three phases it is necessary to collect current and historical data on annual chemical sales to the foam industry for the full length of time HFCs have been used in this application period up to and including the average lifetime of closed-cell foam (as long as 50 years). The import or export of foam formulations which already include HFCs should be also taken into account. Similarly, there should be adjustments made for articles such as domestic or commercial refrigerators and freezers or of construction sector applications such as sandwich panels, boards, blocks and insulated pipes which are produced in one country but may be used in another country.

14

For these applications, actual emissions of each chemical are equal to potential emissions.

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In earlier assessments the calculation of decommissioning losses has been based on the premise that all blowing agent remaining in a foam at end-of-life will be lost at the decommissioning stage. From an emissions standpoint, this is a worst case scenario, even for disposal methods which are not targeted at recovery and destruction (see footnote 13). In practice, recovery and destruction of blowing agent or direct destruction (e.g., incineration) will further alleviate these losses. Hence Equation 7.7 carries a fourth component to allow for HFC emissions prevented in this way. The UNEP TEAP Task Force Report on Foams End-of-Life (UNEP-TEAP, 2005) addresses the many of the potential ways in which foam blowing agent emissions can be avoided and introduces the concept of Recovery and Destruction Efficiency (RDE) to assess the effectiveness of such methods. Even where active recovery and destruction methods are not practised, it is still unlikely that all blowing agent will be released at end of life, particularly when foams are typically left in tact during disposal (e.g. during landfilling). Under these circumstances, a considerable proportion of the blowing agent will remain in the waste stream and an additional banked emission source will be established. Since the emission rates from such a bank will be lower than 100 percent, Equation 7.7 will over-estimate emissions where a significant proportion of the foam containing HFCs used in the country has already been decommissioned. Although it would be possible to envisage a fifth component to Equation 7.7 to address this element of emission, it is not deemed of sufficient relevance to warrant such an approach for the global phase of HFC use being covered by these Guidelines. However, some of the more sophisticated globally or regionally-derived assessments may address this issue. If it is not possible to collect data for potential losses upon decommissioning, it should be assumed that all chemical not emitted in manufacturing is emitted over the lifetime of the foam. However, particular care should be taken to check whether articles such as domestic or commercial refrigerators and freezers are exported to another country for re-use. Where the foam application cannot be disaggregated to the sub-application level and no globally or regionally derived activity data is available, a Tier 1a method needs to be followed. Good practice in the choice of a Tier 1 method is to assume that all closed cell foam emissions follow the Gamlen model (see Table 7.5)

TABLE 7.5 DEFAULT EMISSION FACTORS FOR HFC FROM CLOSED-CELL FOAM Emission Factor

Default Values

Product Lifetime

n = 20 years

First Year Losses

10% of the original HFC charge/year, although the value could drop to 5% if significant recycling takes place during manufacturing.

Annual Losses

4.5% of the original HFC charge/year

Source: Gamlen et al. (1986).

If both historical and current country-specific activity data is available for closed cell foams at the application level, it is possible to apply the Gamlen model to this information. However, the primary challenge for inventory compilers is usually in the characterisation of historic activity data at a country level. If such difficulties exist, it is usually possible to estimate activity data at a country level from the application of geo-economic factors provided that regional, globally or regionally-validated activity data are known. This approach is covered further in Section 7.4.2.3. Where net consumption activity data is available at the sub-application level, either from sources of countryspecific data or from globally or regionally derived activity datasets, it is good practice to use Tier 2 methods that reflect the level of disaggregation. This is particularly important for foams because of the heterogeneous nature of the various sub-applications involved. The decision tree in Figure 7.4 describes good practice in selecting methods for estimating emissions.

7.4.2.2

C HOICE

OF EMISSION FACTORS

As in other applications, the first choice for emission factors is to develop and use peer-reviewed and well documented country-specific data based on field research on each foam type (open cell and closed cell) in support of a Tier 2a approach. As noted previously, if no information is available at the sub-application level, emission factors can be adopted from the Emission Factor Database (EFDB) or from the data contained in this section. However, it should be noted that the data contained in this section will not be replaced with updated

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values in the same way as may happen for the EFDB. Either country-specific or globally/regionally-derived approaches will lead to the necessary assessment of decommissioning losses.15 Figure 7.4

Decision tree for emissions from the foam application Start

Perform a national end-use survey to determine closed cell and open cell foam applications.

Are country-specific or globally or regionally derived activity data available at the subapplication level?

Collect data at the subapplication level.

Yes

Yes

Are countryspecific emission factors available at the sub-application level?

Yes

Calculate emissions by substance and foam sub-application, using country-specific emission factors and available disaggregated data incorporating decommissioning losses and recovery and destruction adjustments, where appropriate. Box 2: Tier 2a

No No

Is this a key category1?

No

Are default emission factors available at the subapplication level?

Yes

Calculate emissions by substance and foam sub-application, using default emission factors and available disaggregated data incorporating decommissioning losses and recovery and destruction adjustments, where appropriate. Box 2: Tier 2a

No

Calculate emissions by substance, using country-specific or globally/regionally derived activity data at the application level and default emission factors. Box 1: Tier 1a

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

Table 7.6, Default Emission Factors for HFC-134a/HFC-152a (Foam Sub-Application) and Table 7.7, Default Emission Factors for HFC-245a/HFC-365mfc/HFC-227ea (Foam Sub-Application) lists default emission factors

15

It has also been noted that decommissioning may not necessarily involve total loss of blowing agent at that point, either because of a level of secondary use or because the item has been discarded intact (e.g., many refrigerators). These could be considered as some of the end-of-life management options available to nations, but are clearly less effective than proper destruction or recovery technologies. Emission models should focus proper attention to end-of-life issues.

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assumptions for the most important current closed-cell foam applications. Use of these factors will require data on chemical sales at the sub-application level for both current and historic consumption in order that the bank of chemical in equipment/products for these sub-applications is properly considered. TABLE 7.6 DEFAULT EMISSION FACTORS FOR HFC-134a AND HFC-152a (IPCC/TEAP, 2005)

USES (FOAM SUB-APPLICATIONS )

Product Life in years

First Year Loss %

Annual Loss %

Maximum Potential Endof-Life Loss %

Polyurethane – Integral Skin

12

95

2.5

0

Polyurethane – Continuous Panel

50

10

0.5

65

Polyurethane – Discontinuous Panel

50

12.5

0.5

62.5

Polyurethane – Appliance

15

7

0.5

85.5

15

12.5

0.5

80

50

95

2.5

0

Extruded Polystyrene (XPS)b - HFC-134a

50

25

0.75

37.5

Extruded Polystyrene (XPS) - HFC-152a

50

50

25

0

50

40

3

0

Sub-Application

Polyurethane – Injected One Component Foam (OCF)

Extruded Polyethylene (PE)

a

a

Source: a

Ashford and Jeffs (2004) assembled from UNEP FTOC Reports (UNEP-FTOC, 1999; UNEP-FTOC, 2003).

b

Vo and Paquet (2004): An Evaluation of Thermal Conductivity over time for Extruded Polystyrene Foams blown with HFC-134a and HCFC-142b

Some articles, such as reefers or insulated truck bodies, may spend almost all of their practical lives in transit between countries. Since these applications have very low in-use emissions it is reasonable if only the manufacturing and decommissioning losses are taken into account. TABLE 7.7 DEFAULT EMISSION FACTORS FOR HFC-245fa/HFC-365mfc/HFC-227ea USES (FOAM SUB-APPLICATION) HFC-245a/HFC-365mfc Applications

Product Life in years

First Year Loss %

Annual Loss %

Maximum Potential Endof-Life Loss %

Polyurethane – Continuous Panel

50

5

0.5

70

Polyurethane – Discontinuous Panel

50

12

0.5

63

Polyurethane – Appliance

15

4

0.25

92.25

Polyurethane – Injected

15

10

0.5

82.5

Polyurethane – Cont. Block

15

20

1

65

Polyurethane – Disc. Block for pipe sections

15

45

0.75

43.75

Polyurethane – Disc. Block for panels

50

15

0.5

60

Polyurethane – Cont. Laminate / Boardstock

25

6

1

69

Polyurethane – Spray

50

15

1.5

10

Polyurethane – Pipe-in-Pipe

50

6

0.25

81.5

Phenolic – Discontinuous Block

15

45

0.75

43.75

Phenolic – Discontinuous Laminate

50

10

1

40

Polyurethane – Integral Skin

12

95

2.5

0

Source: Ashford and Jeffs (2004) assembled from UNEP FTOC Reports (UNEP-FTOC, 1999; UNEP-FTOC, 2003)

If only aggregated chemical sales data for closed-cell foam are available and information on specific foam types cannot be obtained, the general default emission factors shown in Table 7.5 can be used in support of a Tier 1a

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method. 16 This replicates the previous Tier 2 guidance contained in the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 1997) but is now classified as a Tier 1a method following the exclusion of potential emission methods for ODS substitutes. Use of these default emission factors will result in 90 percent of the initial charges being emitted over twenty years of annual use, after the initial 10 percent during the first year.

7.4.2.3

C HOICE

OF ACTIVITY DATA

Two types of activity data are needed in order to prepare the emissions estimates: 1.

the amount of chemical used in foam manufacturing in a country and not subsequently exported, and

2.

the amount of chemical contained in foam imported into the country.

Data collection issues related to these two areas differ.

Chemical used in foam manufacture The amount of bulk chemicals used in the foam blowing industry should include both domestically produced and imported HFCs. Domestic chemical sales data to the foam industry should be available directly from chemical suppliers or foam manufacturers at the application level (Tier 1a) and may extend to a sub-application analysis (Tier 2a). As with other ODS substitute applications, imported chemical data may be available from customs officials or chemical distributors. Historic consumption data is required to build an adequate picture of the development of blowing agent banks. However, this does not apply to open-celled foams which lose their blowing agents in the first year. For opencell foam, all emissions will occur during manufacture, with the exception of the OCF sub-sector mentioned above. Thus, it is necessary to determine the share of chemical associated with the manufacture of open-celled foam. These data can be determined through an end-use survey, or approximated by reviewing similar end-use data gathered on CFCs and HCFCs.

Chemical contained in imported and exported foams Inventory compilers in countries that export closed-cell foam should subtract these volumes from their calculations of annual banks and ultimately decommissioning losses, since the in-use emissions will occur in the importing country. Data on the chemical charge of exported closed-cell foam may be available from large manufacturers. However, customs data itself is unlikely to yield relevant information on blowing agent type unless special provisions have been set up by the reporting country. Similarly, inventory compilers in countries that import products containing closed-cell foam, should include estimates of emissions from these imported products for completeness. Since the inventory compiler will have even less knowledge and control of products manufactured outside of the country than for those manufactured and subsequently exported, information on the blowing agents contained in closed-cell foam products imported is even more difficult to collect. Accordingly, inventory compilers in countries whose emissions occur only from imported closed-cell foam may need to use expert judgement in estimating this data (see Volume 1, Chapters 2 and 3). In the past, inventory compilers were not able to use international HFC production and consumption data sets to develop estimates of chemical contained in imported closed-cell foam because these data sets did not include regional use and trade pattern databases. For example, the Alternative Fluorocarbons Environmental Acceptability Study (AFEAS) statistics-gathering process compiled global activity data up until 1997 for HFC134a in the foam sector17 but regional breakdowns were unavailable. To help resolve this problem, some databases now contain national mechanisms to assist inventory compilers by taking advantage of international HFC/PFC consumption and emission data sets to access globally or regionally derived activity data and bank estimates for blowing agents contained in closed cell foams within their own countries. These can be applied within Tier 2a assessments and will provide estimated consumption and bank data at the sub-application level, to which the default emission factors contained in Tables 7.6 and 7.7 (or updated versions thereof carried in the EFDB or elsewhere) can be applied.

16

No emission factors are provided for open-cell foams because all emissions occur during the first year.

17

HFC-134a is the most commonly used HFC. AFEAS data can found at http://www.afeas.org.

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7.4.2.4

S UMMARISING

THE PRIMARY METHODS

Stepwise through the Tier 2a method using proxy data The Box 7.2 illustrates the typical steps required to implement a Tier 2a method using proxy data: BOX 7.2 TIER 2a IMPLEMENTATION FOR THE FOAM APPLICATION USING GLOBALLY OR REGIONALLY DERIVED DATA

There are up to 16 sub-applications in the foam sector. A reporting country needs to consider which of these sub-applications are relevant to its situation and carry out the following steps for each such process/application. Consumption 1.

Identify the tonnage of foam used in the process/application

2.

Establish the average density for the foam used in the process/application and hence foam volume (‘foam volume per unit’ is the commonly used metric for houses and buildings)

3.

Identify the number of houses built in the year or appliances manufactured/sold in the year to determine a nominal foam volume ratio (foam volume per unit)

4.

Assess growth trends in both the number of units and the foam volume ratio and apply these trends to estimate the tonnage of foam for previous and future years (i.e., years in which data from Step 3 might not be available).

5.

Assess the market split, or share of various blowing agents (chemicals) used for each process/application. Particular care should be taken when dealing with blends.

6.

Identify typical foam formulations for each blowing agent type and apply these formulations to the proportion of the process/application using that blowing agent.

7.

Multiply the foam tonnage by the formulation (weight/weight) and market share details to obtain blowing agent consumption by blowing agent type (typically up to 14 types).

8.

Cross-check with any sales information available on specific blowing agents at country-level.

Emissions-in-Use 9.

Establish the first year loss rate for the process/application. Multiply this loss rate by the chemical consumption to estimate losses emanating from this phase. These emissions should be added to the other sources of emission.

10. The balance of the non-emitted consumption for that year is added to the bank of blowing agent stored in that process/application. 11. Apply a linear emission rate to banked materials, thus eliminating the need to run parallel models based on the vintage of the bank contribution. 12. Apply the average in-use emission rate to the bank and add the resulting emissions to the emissions total. 13. Based on the predicted average product life, establish how much of the bank will be decommissioned in the current year and subtract it from the bank.

Decommissioning, recovery and destruction 14. There are a number of end-of-life options for foams, but good practice suggests that four major options should be considered: a. Re-use b. Landfill without shredding c. Shredding without recovery d. Total recovery and capture (including shredding with recovery, direct incineration etc.)

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15. The decommissioned portions of the banks for a given year should be ascribed to the four options outlined above in proportion to the practice of the country. 16. Emission factors during decommissioning and other end-of-life management steps should be established. These should then be applied to the decommissioned portions of the process/application. These emissions should be added to the other sources of emission. The maximum emission factors in Tables 7.6 and 7.7 should be applied only where instantaneous release can reasonably be assumed to occur. 17. Where emissions from end-of-life management may be on-going (e.g., re-use, landfill and shredding without recovery), further end-of-life banks should be established to keep track of accumulation of blowing agents and to estimate the on-going annual emissions from these sources. 18. Annual emission factors for each of these sources should be applied to the end-of-life banks. These emissions should be added to the other sources of emission.

Using the Tier 1a method based on Gamlen model As a more limited alternative, it is possible to use a Tier 1a method based on the Gamlen model (Table 7.5) to estimate emissions from the total bank of closed cell foam in a country. The following spreadsheet excerpt illustrates the method18: Example of spreadsheet calculation for Tier 1a method

Current Year Year of Introduction Emission in first year Emission in subsequent years

Belgium HFC-134a Leave blank to use database value Leave blank to use database value 2005 (Year for which estimate is made) 1993 10% (Emissions from manufacture and installation) 4.50% (Annual in-use losses)

Emissions from closed cell foams Emissions from open cell foams (Bank of agent in closed cell foams

55.4 tonnes 0.8 tonnes 635.9 tonnes) Emissions from Open Cell Foams

Year

0 1 2 3

2005 2004 2003 2002

interpolated consumption data

Age

Known Consumption data (tonnes)

Emissions from Closed Cell Foams

133.6

133.6 123.3 113.1 102.8

emission in first year

13.4 12.3 11.3 10.3

emission from bank

42.1 36.1 30.5 25.4

bank

635.9 557.8 482.9 411.6

total emission

55.4 48.4 41.8 35.7

0.828939

interpolated consumption data

Country Agent Closed Cell Foam Consumption Open Cell Foam Consumption

Known consumption data (tonnes)

Figure 7.5

0.8 0.7 0.6 0.5

emission in first year

0.8 0.7 0.6 0.5

In this instance, Belgium is estimated to have consumed 133.6 tonnes of HFC-134a for closed cell foams in 2005 and to have emitted 13.4 tonnes from first year foam manufacturing activities, and 42.1 tonnes emissions from the accrued bank of foams, making a total of 55.4 tonnes of HFC-134a from closed cell foams in 2005. This assessment is based on the understanding that HFC-134a was introduced substantively in Belgium in 1993, so the estimate includes 13 years of data. The overall approach, when based on regionally derived data, assumes that the average uptake of HFC-134a-based technologies in Europe is reflected in the country in question. This method has particular attraction for countries and regions that have low rates of foam consumption (e.g., developing countries), and where the foam volume ratio in buildings is low and emissions are likely to be minimal over the effective period of these Guidelines. However, for the regions consuming larger volumes of HFCs in buildings, Tier 2 methods are strongly recommended to avoid the misallocation of consumption and, in particular, emissions because of the assumptions implicit in the Gamlen model (Table 7.5) and the averaging of consumption patterns. 18

Where introduction is slow, the ‘year of introduction’ should be taken as the first year of substantive use.

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7.4.2.5

C OMPLETENESS

At least sixteen foam potential sub-applications and five potential chemicals used as blowing agents (HFC-134a, HFC-152a, HFC-245fa, HFC-365mfc and HFC-227ea) have been identified within the foam application. For completeness, inventory compilers should determine whether HFC blowing agents are used in each subapplication being practised in their countries, which suggests up to 80 theoretically possible combinations (see Table 7.4, Use of HFCs in the Foam Blowing Industry). In practice, this list reduces to 53 realistic potential chemical/application combinations, although there are some potential regional variations. It should also be noted that, at this stage, the methods described do not explicitly address the use of blends, although, in theory, this should be covered in the chemical-by-chemical assessment. The challenge, as with refrigerants (see Section 7.5) will be one of activity monitoring and reporting. The use of blends is undoubtedly increasing and may include combinations of, for example, HFC-245fa and HFC-365mfc. Blends of HFC-365mfc with minor proportions of HFC-227ea are also being introduced by one manufacturer. However, it is premature at this stage to assign different emission factors to such systems.

7.4.2.6

D EVELOPING

A CONSISTENT TIME SERIES

An inventory compiler should maintain a consistent method in assessing its emissions over the time period. If, for example, no system is established to monitor actual decommissioning at the outset of the inventory process, it will be very difficult to obtain data retrospectively if a change from globally or regionally-derived to countryspecific data is considered. This decision should therefore be the subject of careful consideration at the outset of the reporting process. Any recalculation of estimates should be done according to the guidance provided in Volume 1, Chapter 5. In contrast, changes in approach for Activity Level determinations will be easier to implement retrospectively.

7.4.3

Uncertainty assessment

For net consumption activity data, current sales data indicate that the global estimates are accurate to within 10 percent, regional estimates are in the 30 - 40 percent range, and the uncertainty of country specific consumption information may be more than 50 percent. It should be noted that the calculation of the total emissions for a year will be only partially dependent on the accuracy of estimates of new consumption in that year. The remainder of the emissions will arise from banked blowing agent in installed foam and from those foams decommissioned in that year. The estimation of these contributors will depend fundamentally on the accuracy of historic consumption data. Using Approach A (emission-factor approach), emission factors will add to the uncertainties, particularly if only default emission factors can be used. Since decommissioning will be the trigger for the majority of emissions in many cases, the product end-of-life assumptions may introduce the greatest degree of uncertainty. It is therefore very important that inventory compilers keep records of their estimates of HFC-containing products and develop some mechanism for monitoring actual decommissioning if possible. These records may help ensure that the summed emissions do not exceed total inputs over time.

7.4.4 7.4.4.1

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this application. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. One of the main concerns will be to ensure that the preservation of the integrity of regional and global data will be maintained by the summation of individual country estimates and a major part of the QA/QC review process will need to concern itself with this cross reference.

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7.4.4.2

R EPORTING

AND

D OCUMENTATION

Emissions factors should be reported, along with documentation for the development of country-specific data. Chemical sales to the foam blowing industry should be reported in a manner that addresses confidentiality concerns. Most confidentiality issues arising from any data collection process relate to the most highly concentrated activities. To deal with this, emissions from foam could be reported as a single number, provided that the development of the number could be reviewed under suitable terms of confidentiality. Of course, a declaration of consolidated emissions from manufacture (first year), use (product life) and decommissioning (end-of-life) will always be preferable to allow continued focus on improvements being made in each of these areas. If inventory compilers use activity data derived from global or regional databases, they should report the results of how they allocated emissions to the country level.

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7.5

REFRIGERATION AND AIR CONDITIONING

7.5.1

Chemicals covered in this application area

Refrigeration and air-conditioning (RAC) systems may be classified in up to six sub-application domains or categories (UNEP-RTOC, 2003), although less sub-applications are typically used at a single country level. These categories correspond to sub-applications that may differ by location and purpose, and are listed below: (i)

Domestic (i.e., household) refrigeration,

(ii)

Commercial refrigeration including different types of equipment, from vending machines to centralised refrigeration systems in supermarkets,

(iii)

Industrial processes including chillers, cold storage, and industrial heat pumps used in the food, petrochemical and other industries,

(iv)

Transport refrigeration including equipment and systems used in refrigerated trucks, containers, reefers, and wagons,

(v)

Stationary air conditioning including air-to-air systems, heat pumps, and chillers19 for building and residential applications,

(vi)

Mobile air-conditioning systems used in passenger cars, truck cabins, buses, and trains.20

For all these sub-applications, different HFCs are progressively replacing CFCs and HCFCs. For example, in developed and several developing countries, HFC-134a has replaced CFC-12 in domestic refrigeration, highpressure chillers and mobile air conditioning systems, and blends of HFCs such as R-407C (HFC-32/HFC125/HFC-134a) and R-410A (HFC-32/HFC-125) are replacing HCFC-22 mainly in stationary air conditioning. HFC blends R-404A (HFC-125/HFC-143a/HFC-134a) and R-507A (HFC-125/HFC-143a) have replaced R-502 (CFC-22/CFC-115) and HCFC-22 in commercial refrigeration. Other, non-HFC substances are also used to replace CFCs and HCFCs such as iso-butane (HC-600a) in domestic refrigeration or ammonia in industrial refrigeration. A large number of blends containing HFCs and/or PFCs are being used in Refrigeration and Air Conditioning applications. Table 7.8 shows the most common of these blends.

19

Comfort air conditioning in large commercial buildings (including hotels, offices, hospitals, universities, etc.) is commonly provided by water chillers coupled with an air handling and distribution system.

20

The sub-application of mobile air conditioning systems is likely to represent the largest share of HFC emissions within the Refrigeration and Air Conditioning application for many countries. See Section 7.5.2.4, Applying Tier 2 Methods – The Example Of Mobile Air Conditioning (MAC), for an example of how to calculate these emissions. The reader will see that limited information is needed to approximate these emissions, and essentially becomes a simple multiplication of an average emission factor and the number of cars with HFC air conditioning, and possibly adding emissions relating to container management, charging and end-of-life.

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TABLE 7.8 BLENDS (MANY CONTAINING HFCS AND/OR PFCS)

1

Blend

Constituents

Composition (%)

R-400 R-401A R-401B R-401C R-402A R-402B R-403A R-403B R-404A R-405A R-406A R-407A R-407B R-407C R-407D R-407E R-408A R-409A R-409B R-410A R-410B R-411A R-411B R-411C R-412A R-413A R-414A R-414B R-415A R-415B R-416A R-417A R-418A R-419A R-420A R-421A R-421B R-422A R-422B R-422C R-500 R-501 R-502 R-503 R-504 R-505 R-506 R-507A R-508A R-508B R-509A

CFC-12/CFC-114 HCFC-22/HFC-152a/HCFC-124 HCFC-22/HFC-152a/HCFC-124 HCFC-22/HFC-152a/HCFC-124 HFC-125/HC-290/HCFC-22 HFC-125/HC-290/HCFC-22 HC-290/HCFC-22/PFC-218 HC-290/HCFC-22/PFC-218 HFC-125/HFC-143a/HFC-134a HCFC-22/ HFC-152a/ HCFC-142b/PFC-318 HCFC-22/HC-600a/HCFC-142b HFC-32/HFC-125/HFC-134a HFC-32/HFC-125/HFC-134a HFC-32/HFC-125/HFC-134a HFC-32/HFC-125/HFC-134a HFC-32/HFC-125/HFC-134a HFC-125/HFC-143a/HCFC-22 HCFC-22/HCFC-124/HCFC-142b HCFC-22/HCFC-124/HCFC-142b HFC-32/HFC-125 HFC-32/HFC-125 HC-1270/HCFC-22/HFC-152a HC-1270/HCFC-22/HFC-152a HC-1270/HCFC-22/HFC-152a HCFC-22/PFC-218/HCFC-142b PFC-218/HFC-134a/HC-600a HCFC-22/HCFC-124/HC-600a/HCFC-142b HCFC-22/HCFC-124/HC-600a/HCFC-142b HCFC-22/HFC-152a HCFC-22/HFC-152a HFC-134a/HCFC-124/HC-600 HFC-125/HFC-134a/HC-600 HC-290/HCFC-22/HFC-152a HFC-125/HFC-134a/HE-E170 HFC-134a/HCFC-142b HFC-125/HFC-134a HFC-125/HFC-134a HFC-125/HFC-134a/HC-600a HFC-125/HFC-134a/HC-600a HFC-125/HFC-134a/HC-600a CFC-12/HFC-152a HCFC-22/CFC-12 HCFC-22/CFC-115 HFC-23/CFC-13 HFC-32/CFC-115 CFC-12/HCFC-31 CFC-31/CFC-114 HFC-125/HFC-143a HFC-23/PFC-116 HFC-23/PFC-116 HCFC-22/PFC-218

Should be specified1 (53.0/13.0/34.0) (61.0/11.0/28.0) (33.0/15.0/52.0) (60.0/2.0/38.0) (38.0/2.0/60.0) (5.0/75.0/20.0) (5.0/56.0/39.0) (44.0/52.0/4.0) (45.0/7.0/5.5/42.5) (55.0/14.0/41.0) (20.0/40.0/40.0) (10.0/70.0/20.0) (23.0/25.0/52.0) (15.0/15.0/70.0) (25.0/15.0/60.0) (7.0/46.0/47.0) (60.0/25.0/15.0) (65.0/25.0/10.0) (50.0/50.0) (45.0/55.0) (1.5/87.5/11.0) (3.0/94.0/3.0) (3.0/95.5/1.5) (70.0/5.0/25.0) (9.0/88.0/3.0) (51.0/28.5/4.0/16.5) (50.0/39.0/1.5/9.5) (82.0/18.0) (25.0/75.0) (59.0/39.5/1.5) (46.6/50.0/3.4) (1.5/96.0/2.5) (77.0/19.0/4.0) (88.0/12.0) (58.0/42.0) (85.0/15.0) (85.1/11.5/3.4) (55.0/42.0/3.0) (82.0/15.0/3.0) (73.8/26.2) (75.0/25.0) (48.8/51.2) (40.1/59.9) (48.2/51.8) (78.0/22.0) (55.1/44.9) (50.0/50.0) (39.0/61.0) (46.0/54.0) (44.0/56.0)

R-400 can have various proportions of CFC-12 and CFC-114. The exact composition needs to be specified, e.g., R-400 (60/40).

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7.5.2

Methodological issues

7.5.2.1

C HOICE

OF METHOD

As discussed in the introductory section to this chapter, both Tier 1 and Tier 2 result in estimates of actual emissions rather than estimates of potential emissions. Actual estimates, which account for the lag between consumption and emissions, are particularly important for both the refrigeration and air conditioning sector because of the potentially long retention of refrigerants in products and equipment utilised in these applications. The options available to the refrigeration and air conditioning application are shown in the decision tree shown in Figure 7.6.

TIER 1 Tier 1 a/b It is expected that the refrigeration and air conditioning will be a key category for many countries. The implication of this conclusion from Table 7.2 and the decision tree in Figure 7.6 is that either country-specific or globally or regionally derived activity data will be required at the sub-application (disaggregated) level in order to complete the reporting task. However, in the rare instances that the refrigeration and air conditioning application is much less significant, there should be available a suitable Tier 1 method for aggregated data. From experience of studying the dynamics of refrigerant consumption and banks in several countries (UNEPRTOC, 2003; Ashford, Clodic, Kuijpers and McCulloch, 2004; and supporting materials), it is possible to derive assumptions that allow for the assessment of the use of refrigerant that may help in assessing sales of a given refrigerant at a country level. Such a hybrid Tier 1a/b approach may use the following assumptions: 1.

Servicing of equipment containing the refrigerant does not commence until 3 years after the equipment is installed.

2.

Emissions from banked refrigerants average 15 percent annually across the whole RAC application area. This assumption is estimated to be a weighed average across all sub-applications, for which default emission factors are shown in Table 7.9.

3.

In a mature market two thirds of the sales of a refrigerant are used for servicing and one third is used to charge new equipment. A mature market is one in which ODS substitute-employing refrigeration equipment is in wide use, and there are relationships between suppliers and users to purchase and service equipment.

4.

The average equipment lifetime is 15 years. This assumption is also estimated to be a weighed average across all sub-applications.

5.

The complete transition to a new refrigerant technology will take place over a 10 year period. From experiences to date, this assumption is believed to be valid for a single chemical in a single country.

With these assumptions in place, it is possible to derive emissions, if the following data can be provided: •

•

Sales of a specific refrigerant in the year to be reported

•

Growth rate in sales of new equipment (usually assumed linear across the period of assessment)

•

Year of introduction of the refrigerant

•

Assumed percentage of new equipment exported Assumed percentage of new equipment imported

The Tier 1a/b method then back-calculates the development of banks of a refrigerant from the current reporting year to the year of its introduction. In mapping this period, the method also models the transition from sales to new equipment (100 percent initially) to the mature market position assumed based on experience to be 33 percent to new equipment and 67 percent to servicing requirements. It is assumed that the transition to new refrigerant technology is reflected identically in any imported equipment.

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Figure 7.6

Decision tree for actual emissions from the refrigeration and air conditioning (RAC) application Start

Box 3: Tier 2a Use Tier 2a method (Equations 7.10-7.14) Yes

Are the activity data disaggregated at the subapplication level (the 6 RAC areas) available?

Yes

Are the country-specific, regionally or globally derived emission factors available?

No Use Tier 2b method No

Box 2: Tier 2b

Is this a key category1?

Are country-specific or proxy activity data at the application level (RAC) available?

No

Yes

No

Obtain country-specific activity data disaggregated at the subapplication level (the 6 RAC areas).

Find appropriate sources of countryspecific, regionally or globally derived activity data at the application level.

Use default composite emission factors.

Are country-specific composite emission factors available?

No

Use Tier 1a/b method

Yes

Yes

Box 1: Tier 1 Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

The following spreadsheet example indicates how the Tier 1a/b method would estimate a seven-year time series of emissions of the selected refrigerant, following its initial introduction in 1998, with the knowledge that there were sales of 1 000 tonnes in 2005. The spreadsheet contained in the 2006 Guidelines CDROM mirrors this calculation, and globally or regionally derived datasets21 at both application and consolidated sub-application levels should be available at a country level to assist in completion of this spreadsheet. 21

As noted in Box 7.1, inclusion in the IPCC Emission Factor Database (EFDB) will indicate general adherence to due process, but it is good practice for countries to ensure that all data taken from the EFDB are appropriate for their national circumstances.

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Figure 7.7

Example of spreadsheet calculation for Tier 1a/b assessments

Tier 1 Refrigeration Argentina - HFC-143a HFC-

Summary Country: Argentina Agent: HFC-143a Year: 2005

a

Emission: 460.7 tonnes In Bank: 3071.1 tonnes

Current Year

2005 500

Use in current year - 2005 (tonnes)

Data Used Here

Production of HFC-143a Imports in current Year Exports in current year Total new agent to domestic market

800 200 0 1000

Year of Introduction of HFC-143a Growth Rate in New Equipment Sales

1998 3.0%

450 400 350 300 250 200 150 100 50

Tier 1 Defaults Assumed Equipment Lifetime (years)

15

Emission Factor from installed base

15%

% of HFC-143a destroyed at End-of-Life

0%

0 1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

1996 0 0 0

1997 0 0 0

1998 81 0 20

1999 167 0 42

2000 259 0 65

2001 355 0 89

2002 458 0 114

2003 566 0 141

2004 680 0 170

2005 800 0 200

2006 0 0 0

Total New Agent in Domestic Equipment

0

0

102

209

323

444

572

707

850

1000

0

Agent in Retired Equipment Destruction of agent in retired equipment Release of agent from retired equipment

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

Bank Emission

0 0

0 0

102 15

296 44

575 86

933 140

1365 205

1867 280

2437 365

3071 461

2610 #N/A

Estimated data for earlier years Production Agent in Exports Agent in Imports

In this hypothetical example, the production of a specific refrigerant are 800 tonnes with an additional 200 tonnes in imported equipment, in 2005 making a total consumption of 1 000 tonnes. Based on this consumption figure and knowledge of the year of introduction of the refrigerant, it can be seen that the Tier 1a/b method predicts emissions of 461 tonnes based on the development of banks over the previous seven years. The bank in 2005 is estimated at 3 071 tonnes. It should be noted that, while such methods allow for the estimation of emissions when data are difficult to obtain, it is still necessary to have an accurate assessment of country-specific or globally or regionally derived net consumption activity data. When the content of Table 7.8 is considered (particularly when some of these blends may be being imported in equipment) it is clear that there needs to be considerable knowledge of technology selection in the market. Refrigerant suppliers should be able to assist inventory compilers in this area, but the burden of developing high quality activity datasets may lead inventory compilers to the conclusion that Tier 2 options provide more value with little extra work. Indeed, where globally or regionally validated data activity is sought, this will normally be a reconstitution of disaggregated data originally at the sub-application level, so it might be most logical to take full advantage of that versatility and pursue a Tier 2 approach from the outset.

TIER 2 Overview The Tier 2a methodology: a)

Takes into account the phase out or the phase down of CFCs and HCFCs depending on the Montreal Protocol schedule and possible national or regional regulations, in order to establish the refrigerant choice for all applications;

b) Defines the typical refrigerant charge and the equipment lifetime per sub-application; c)

Defines the emission factors for refrigerant charge, during operation, at servicing and at end-of-life.

Calculation of emissions throughout the equipment lifetime requires deriving the total stock of equipment independent of their vintage. In doing so the refrigerant bank is established per sub-application.

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In order to achieve consistency it is suggested to derive the annual market of refrigerants from the refrigerant quantities charged in the brand new equipment and from the refrigerant quantities used for servicing of the total stock of equipment. The Tier 2b mass-balance approach relies on a knowledge of the annual sales of refrigerant, refrigerant destroyed and any changes in equipment stock that occur (i.e., new equipment sales and equipment decommissioned) on a sub-application basis. It does not require an absolute knowledge of equipment stocks or emission factors relating to each refrigeration and air conditioning sub-application. Examples of how the Tier 2 methodology may be applied are given in the remainder of this section.

Tier 2b - Mass-balance approach The mass-balance approach is particularly applicable to the Refrigeration and Air Conditioning application because of the significant servicing component required to maintain equipment. The general approach to Tier 2b is introduced in Chapter 1 of Volume 3. For the mass-balance approach, the four emission stages (charging, operation servicing and end-of-life) identified above are addressed in the following simplified equation: EQUATION 7.9 DETERMINATION OF REFRIGERANT EMISSIONS BY MASS BALANCE Emissions = Annual Sales of New Refrigerant − Total Charge of New Equipment

+ Original Total Charge of Retiring Equipment − Amount of Intentional Destruction

Annual Sales of New Refrigerant is the amount of a chemical introduced into the refrigeration sector in a particular country in a given year. It includes all chemical used to fill or refill equipment, whether the chemical is charged into equipment at the factory, charged into equipment after installation, or used to recharge equipment at servicing. It does not include recycled or reclaimed chemical. Total Charge of New Equipment is the sum of the full charges of all the new equipment that is sold in the country in a given year. It includes both the chemical required to fill equipment in the factory and the chemical required to fill the equipment after installation. It does not include charging emissions or chemical used to recharge equipment at servicing. Original Total Charge of Retiring Equipment is the sum of the full charges of all the retiring equipment decommissioned in a country in a given year. It assumes that the equipment will have been serviced right up to its decommissioning and will therefore contain its original charge. Amount of Intentional Destruction is that quantity of the chemical duly destroyed by a recognised destruction technology. In each country there is a stock of existing refrigeration equipment that contains an existing stock of refrigerant chemical (bank). Therefore, annual sales of new chemical refrigerant must be used for one of three purposes:

•

• •

To increase the size of the existing chemical stock (bank) in use (including retrofitting equipment from a previous chemical to the given chemical) To replace that fraction of last year’s stock of chemical that was emitted to the atmosphere (through, for example, leaks or servicing losses) To provide supply-chain priming or stockpiles

Since the third item in this list is rarely required in a steady-state market, it is not included in Equation 7.9. Terms to account for stockpiling and retrofitting could be added to Equation 7.9 if such situations exist. The difference between the total quantity of gas sold and the quantity of that gas used to increase the size of the chemical stock equals the amount of chemical emitted to the atmosphere. The increase in the size of the chemical stock is equal to the difference between the total charges of the new and retiring equipment. By using data on current and historical sales of gas, rather than emission factors referenced from literature, the equation reflects assembly, operation, and disposal emissions at the time and place where they occur. Default emission factors may not be accurate because emissions rates may vary considerably from country to country and even within a single country. As discussed in Chapter 1, Section 1.5 of Volume 3, one drawback of the mass-balance approach is that it can underestimate emissions when equipment stocks are growing, because there is a lag between the time the emissions occur and the time they are detected (through equipment servicing). This underestimate will be relatively large in countries where HFCs have been used in equipment for less than ten years, because much of

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the equipment will have leaked without ever being serviced. Thus, countries where HFCs have been used for less than ten years are encouraged to estimate emissions using alternative approaches. In general, the longer HFCs are used in a country, the smaller the underestimate associated with the mass-balance approach. Once equipment containing HFCs begins to retire, the underestimate declines to a low level. Equation 7.9 can be applied either to individual types of equipment (sub-applications), or more generally to all air conditioning and refrigeration equipment in a country (i.e., Tier 1b), depending on the level of disaggregation of available data. If disaggregated data are available, emissions estimates developed for each type of equipment and chemical are summed to determine total emissions for the application.

Tier 2a – Emission-factor approach In a Tier 2a calculation, refrigerant emissions at a year t from each of the six22 sub-applications of refrigeration and air conditioning systems are calculated separately. These emissions result from: •

• • •

Econtainers,t = emissions related to the management of refrigerant containers Echarge,t = emissions related to the refrigerant charge: connection and disconnection of the refrigerant container and the new equipment to be charged Elifetime,t = annual emissions from the banks of refrigerants associated with the six sub-applications during operation (fugitive emissions and ruptures) and servicing Eend-of-life,t = emissions at system disposal

All these quantities are expressed in kilograms and have to be calculated for each type of HFC used in the six different sub-applications.

E total ,t

EQUATION 7.10 SUMMARY OF SOURCES OF EMISSIONS = E containers ,t + E Charge,t + E lifetime ,t + E end − of −life ,t

Methods for estimating average emission rates for the above-mentioned sectors are outlined below and need to be calculated on a refrigerant by refrigerant basis for all equipment regardless of their vintage. If information on container and charging emissions is not available, inventory compilers can estimate these losses as a percent of the bank and revise the lifetime (operation plus servicing) emission factor in Equation 7.13 below to account for such losses.

Refrigerant management of containers The emissions related to the refrigerant container management comprises all the emissions related to the refrigerant transfers from bulk containers (typically 40 tonnes) down to small capacities where the mass varies from 0.5 kg (disposable cans) to 1 tonne (containers) and also from the remaining quantities - the so-called refrigerant heels (vapour and /or liquid) - left in the various containers, which are recovered or emitted. EQUATION 7.11 SOURCES OF EMISSIONS FROM MANAGEMENT OF CONTAINERS c Econtainers , t = RM t • 100 Where: Econtainers, t = emissions from all HFC containers in year t, kg RMt = HFC market for new equipment and servicing of all refrigeration application in year t, kg c = emission factor of HFC container management of the current refrigerant market, percent The emissions related to the complete refrigerant management of containers are estimated between 2 and 10 percent of the refrigerant market.

Refrigerant charge emissions of new equipment The emissions of refrigerant due to the charging process of new equipment are related to the process of connecting and disconnecting the refrigerant container to and from the equipment when it is initially charged.

22

More than six sub-applications can be used, depending on the level of disaggregated data available.

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EQUATION 7.12 SOURCES OF EMISSIONS WHEN CHARGING NEW EQUIPMENT E ch arg e , t = M t •

k 100

Where: Echarge, t = emissions during system manufacture/assembly in year t, kg Mt = amount of HFC charged into new equipment in year t (per sub-application), kg k = emission factor of assembly losses of the HFC charged into new equipment (per sub-application), percent Note: the emissions related to the process of connecting and disconnecting during servicing are covered in Equation 7.13. The amount charged (Mt) should include all systems which are charged in the country, including those which are produced for export. Systems that are imported pre-charged should not be considered. Typical range for the emission factor k varies from 0.1 to 3 percent. The emissions during the charging process are very different for factory assembled systems where the emissions are low (see Table 7.9) than for fielderected systems where emissions can be up to 2 percent.

Emissions during lifetime (operation and servicing) Annual leakage from the refrigerant banks represent fugitive emissions, i.e., leaks from fittings, joints, shaft seals, etc. but also ruptures of pipes or heat exchangers leading to partial or full release of refrigerant to the atmosphere. Besides component failures, such as compressor burn-out, equipment is serviced mainly when the refrigerating capacity is too low due to loss of refrigerant from fugitive emissions. Depending on the application, servicing will be done for instance every year or every three years, or sometimes not at all during the entire lifetime such as in domestic refrigeration sub-applications. For some sub-applications, leaks have to be fixed during servicing and refrigerant recovery may be necessary, so the recovery efficiency has to be taken into account when estimating emission factors. In addition, knowing the annual refrigerant needs for servicing per sub-application allows the determination of the national refrigerant market by adding the refrigerant quantities charged in new equipment (see Paragraph Quality assurance/Quality control). The following calculation formula applies: EQUATION 7.13 SOURCES OF EMISSIONS DURING EQUIPMENT LIFETIME x Elifetime, t = Bt • 100 Where: Elifetime, t = amount of HFC emitted during system operation in year t, kg Bt = amount of HFC banked in existing systems in year t (per sub-application), kg x = annual emission rate (i.e., emission factor) of HFC of each sub-application bank during operation, accounting for average annual leakage and average annual emissions during servicing, percent In calculating the refrigerant bank (Bt) all systems in operation in the country (produced domestically and imported) have to be considered on a sub-application by sub-application basis. Examples of typical leakage rates (x) for various types of equipment describing the respective refrigeration subapplications are given in Table 7.9.

Emissions at end-of-life The amount of refrigerant released from scrapped systems depends on the amount of refrigerant left at the time of disposal, and the portion recovered. From a technical point of view, the major part of the remaining fluid can be recovered, but recovery at end-of-life depends on regulations, financial incentives, and environmental consciousness. The following calculation formula (Equation 7.14) is applicable to estimate emissions at system disposal:

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EQUATION 7.14 EMISSIONS AT SYSTEM END-OF-LIFE

Eend −of −life, t = M t −d •

ηrec, d p • (1 − ) 100 100

Where: Eend-of-life, t = amount of HFC emitted at system disposal in year t, kg Mt-d = amount of HFC initially charged into new systems installed in year (t-d), kg p = residual charge of HFC in equipment being disposed of expressed in percentage of full charge, percent

ηrec,d = recovery efficiency at disposal, which is the ratio of recovered HFC referred to the HFC contained in the system, percent

In estimating the amount of refrigerant initially charged into the systems (M t-d), all systems charged in the country (for the domestic market) and systems imported pre-charged should be taken into account.

7.5.2.2

C HOICE

OF EMISSION FACTORS

Tier 1a/b method As explained within Section 7.5.2.1, Choice of Method, a composite emission factor is required to complete a Tier 1 method. Since the sub-applications within the refrigeration and air conditioning application are relatively heterogeneous, the validity of any single composite emission factor must be in doubt unless it takes into consideration the particular mix of sub-applications in the country. It is therefore good practice to develop composite emission factors on the basis of research within the country. The over-arching default emissions factor of 15 percent of the bank annually is used in the example of spreadsheet calculation contained in the 2006 Guidelines CDROM attached to these Guidelines.

Tier 2a method Good practice for choosing emission factors is to use country-specific data, based on information provided by equipment manufacturers, service providers, disposal companies, and independent studies. When national data are unavailable, inventory compilers should use the default emission factors shown in Table 7.9, Estimates for Charge, Lifetime and Emission Factors, which summarises best estimates of equipment charge, lifetime, and emission factors. These default values reflect the current state of knowledge about the industry, and are provided as ranges rather than point estimates. The lower end of the lifetime and emission factor ranges is intended to indicate the status within developed countries, while the upper end of each range is intended to indicate the status within developing countries. Inventory compilers should choose from the range according to country-specific conditions, and document the reasons for their choices. If data collected from the field cannot be broken down into the sub-applications as in Table 7.9, it is good practice to use expert judgement to estimate the relative share of each type of equipment, and calculate composite emission factors weighted according to that relative share, as proposed for Tier 1a/b, or use the emission factor appropriate to the most common type(s) of equipment.

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TABLE 7.9 ESTIMATES1 FOR CHARGE, LIFETIME AND EMISSION FACTORS FOR REFRIGERATION AND AIR-CONDITIONING SYSTEMS

Sub-application

Charge (kg)

Lifetimes (years)2

Factor in Equation

(M)

(d)

Domestic Refrigeration Stand-alone Commercial Applications Medium & Large Commercial Refrigeration Transport Refrigeration Industrial Refrigeration including Food Processing and Cold Storage Chillers Residential and Commercial A/C, including Heat Pumps Mobile A/C 1

Emission Factors (% of initial charge/year)3

End-of-Life Emission (%)

(k)

(x)

(ηrec,d)

Initial Emission

Operation Emission

Recovery Efficiency4

(p) Initial Charge Remaining

0.05 ≤ M ≤ 0.5

12 ≤ d ≤ 20

0.2 ≤ k ≤ 1

0.1 ≤ x ≤ 0.5

0 < ηrec,d < 70

0.2 ≤ M≤ 6

10 ≤ d ≤ 15

0.5 ≤ k ≤ 3

1 ≤ x ≤ 15

0 < ηrec,d < 70

50 ≤ M ≤ 2000

7 ≤ d ≤ 15

0.5 ≤ k ≤ 3

10 ≤ x ≤ 35

0 < ηrec,d < 70

3≤M≤8

6≤d≤9

0.2 ≤ k ≤ 1

15 ≤ x ≤ 50

0 < ηrec,d < 70

10 ≤ M ≤ 10,000

15 ≤ d ≤ 30

0.5 ≤ k ≤ 3

7 ≤ x ≤ 25

0 < ηrec,d < 90

50 < p < 100

10 ≤ M≤ 2000

15 ≤ d ≤ 30

0.2 ≤ k ≤ 1

2 ≤ x ≤ 15

0 < ηrec,d < 95

80 < p < 100

0.5 ≤ M≤ 100

10 ≤ d ≤ 20

0.2 ≤ k ≤ 1

1 ≤ x ≤ 10

0 < ηrec,d < 80

0.5 ≤ M ≤ 1.5

9 ≤ d ≤ 16

0.2 ≤ k ≤ 0.5

10 ≤ x ≤ 205

0 < ηrec,d < 50

0 < p < 80

0 < p < 80

50 < p < 100 0 < p < 50

0 < p < 80

0 < p < 50

Based on information contained in UNEP RTOC Reports (UNEP-RTOC, 1999; UNEP-RTOC, 2003)

2, 3

Lower value for developed countries and higher value for developing countries

4

The lower threshold (0%) highlights that there is no recovery in some countries.

5

Schwarz and Harnisch (2003) estimates leakage rates of 5.3% to 10.6%; these rates apply only to second generation mobile air conditioners installed in European models in 1996 and beyond.

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7.5.2.3

C HOICE

OF ACTIVITY DATA

Tier 1a/b method Inventory compilers in countries that manufacture refrigerant chemicals should estimate Annual Sales of New Refrigerant using information provided by chemical manufacturers. Data on imported chemical should be collected from customs statistics, importers, or distributors. Total Charge of New Equipment can be estimated using either: •

•

Information from equipment manufacturers/importers on the total charge of the equipment they manufacture or import; or Information from chemical manufacturers/importers on their sales to equipment manufacturers and distributors.

Ensure this information only includes sales as refrigerant, not feedstock or other uses. The difference between the total sales of new refrigerant and that charged in new equipment is assumed to be used for servicing. Where information on new equipment charges is unavailable, it can be assumed that, in a mature market, two thirds of refrigerant is used for servicing while one third is used for new equipment. However, the adoption of such assumptions must be accompanied by some justification about the state of the market and how well these assumptions are likely to apply.

Tier 2 methods Both Tier 2a and Tier 2b methods require the development of a matrix for each sub-application based on equipment type on the one hand and refrigerant type on the other hand. In order to derive the number of pieces of equipment for all the vintages, historic net consumption activity data is also required. The annual update of the matrix makes it possible to recalculate all emission types as detailed in Equations 7.10 to 7.14 each year. Moreover, the refrigerant choice has to be assessed on a year-by-year basis owing to changing national regulations (often relating to CFC and HCFC phase-out at different dates) and changing technological choices. In some countries HFC refrigerant regulations have started to enter into force. Where country-specific data cannot be analysed to this level, globally or regionally validated activity data can be obtained from reputable databases based on refrigerant charges and lifetimes provided in Table 7.9, for all subapplications, to facilitate Tier 2 methods. A number of refinements are usually necessary dependent on the particular circumstances of the country. Assistance for this can be obtained from application experts.

Other shared issues Whether collecting country-specific activity data in support of a Tier 1 or a Tier 2 method, inventory compilers must take care in dealing with refrigerant blends. Table 7.8 illustrates the complexity already existing and blends are only expected to increase in popularity as manufacturers of equipment seek for further improvements in performance, particularly in respect of energy efficiency. Where blends contain both HFCs and other components, only the reportable elements need to be considered. This is even the case for other components with significant GWPs (e.g., CFCs and HCFCs). Inventory compilers also need to consider how to monitor the movement of trade in equipment and products containing HFCs and/or PFCs. The Box 7.3 below sets out some of the measures required to estimate imports and exports adequately.

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BOX 7.3 ACCOUNTING FOR IMPORTS AND EXPORTS OF REFRIGERANT AND EQUIPMENT

In estimating Annual Sales of New Refrigerant, Total Charge of New Equipment, and Original Total Charge of Retiring Equipment, as required for Tier 2b, inventory compilers should account for imports and exports of both chemicals and equipment. This will ensure that they capture the actual domestic consumption of chemicals and equipment. For example, if a country imports a significant share of the HFC-134a that it uses, the imported quantity should be counted as part of Annual Sales. Alternatively, if a country charges and then exports a significant number of household refrigerators, the total charge of the exported refrigerators should be subtracted from the total charge of the household refrigerators manufactured in the country to obtain Total Charge of New Equipment. GENERAL APPROACH: In general, the quantity Annual Sales should be estimated using the following formula: Annual Sales

= Domestically Manufactured Chemical + Imported Bulk Chemical – Exported Bulk Chemical + Chemical Contained in Factory-Charged Imported Equipment – Chemical Contained in Factory-Charged Exported Equipment

All quantities should come from the year for which emissions are being estimated. Similarly, the quantity of Total Charge of New Equipment should be estimated using the following: Total Charge of New Equipment = Chemical to Charge Domestically Manufactured Equipment that is not Factory-Charged + Chemical to Charge Domestically Manufactured Equipment that is Factory-Charged + Chemical to Charge Imported Equipment that is not Factory-Charged + Chemical Contained in Factory-Charged Imported Equipment – Chemical Contained in Factory-Charged Exported Equipment

Original Total Charge of Retiring Equipment should be estimated the same way as Total Charge of New Equipment, except all quantities should come from the year of manufacture or import of the retiring equipment. SIMPLIFIED APPROACH: In estimating Annual Sales and Total Charge of New Equipment, it is possible to ignore the quantities of chemical imported or exported inside of factory-charged equipment if these quantities cancel out in the calculation of emissions. However, inventory compilers that use the simplified calculation should ensure that: (1) they treat imports and exports of factory-charged equipment consistently in estimating both Annual Sales and Total Charge New of Equipment; and (2) they continue to account for imports and exports of factory-charged equipment in estimating Original Total Charge of Retiring Equipment. As new equipment will eventually become retiring equipment, countries may wish to track imports and exports of factorycharged equipment even if this information is not strictly necessary to develop the current year’s estimate.

The simplified formula for Annual Sales is: Annual Sales

= Domestically Manufactured Chemicals + Imported Bulk Chemicals – Exported Bulk Chemicals

The simplified formula for Total Charge of New Equipment is: Total Charge of New Equipment = Chemicals to Charge Domestically Manufactured Equipment + Chemical to Charge Imported Equipment that is not Factory-Charged The full formula, accounting for imports and exports of pre-charged equipment, must be used to calculate Original Total Charge of Retiring Equipment.

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7.5.2.4

A PPLYING

TIER 2 METHODS AIR CONDITIONING (MAC)

–

THE EXAMPLE OF MOBILE

The Box 7.4 below sets out the step-by-step approach required to assess the emissions from the mobile air conditioning sub-application of a hypothetical country’s inventory. The method adopted is primarily a Tier 2a approach, although there are also elements which would be equally applicable to Tier 2b. This example, therefore, highlights the reality that pure approaches and methods are rare in practice. There will often be a mix of emission-factor approach and mass–balance approach as well as a mix of country-specific data and globally or regionally derived data. As pointed out in Section 7.1.2.1, one method, approach or dataset will often be used to cross-check another. This example also demonstrates that a detailed implementation of the Tier 2a method requires a significant amount information gathering about a sub-application. Once established, it is less burdensome to implement the approach in subsequent years. Also note that assumptions made are for example only; inventory compilers should collect country-specific information rather than using the assumptions shown.

BOX 7.4 EXAMPLE OF THE APPLICATION OF A TIER 2a CALCULATION FOR MOBILE AIR CONDITIONING

Introduction

National inventories and other studies to date show that emissions of HFC-134a from mobile air conditioners (MACs) contribute significantly to the Refrigeration and Air Conditioning (RAC) Application emissions and the ODS Substitutes Category emissions. For many countries, emissions from MACs will comprise 50 percent or more of the RAC emissions and possibly more than 50 percent of the total ODS Substitutes Category emissions. This is due to many factors, including: z z z z

The phaseout of ODSs to HFCs in MACs occurred earlier and more quickly than other SubApplications, such as residential (stationary) air conditioning and commercial refrigeration (supermarkets), which still rely substantially on ODSs. MACs are subject to extremes in terms of physical shock and vibration and hence emissions tend to be large. The lifetime of MACs tends to be shorter than many other RAC Sub-Applications, so that end-of-life emissions are seen earlier and equipment stocks relying on ODSs are replaced sooner with HFCs. Due to the small charge of refrigerant involved, recovery from MACs is often seen as uneconomical and hence is not often practiced during service and disposal.

In addition, data on vehicle purchases and registrations in a country are often known to a higher degree of quality or are easily obtained. Hence, it is good practice to estimate emissions from this Sub-Application. The following text describes how the general equations for the RAC Application can be applied to the MAC Sub-Application. Data Gathering and Assumptions

An accurate estimate of MAC emissions may be obtained by collecting some data at the SubApplication level and applying a few basic assumptions to simplify the data and calculations required, as follows: Refrigerant Type. It will be important to separate each data point by refrigerant, so that emissions of each refrigerant are calculated separately. For MACs, this may be simplified by the fact that all MACs produced since the mid- to late-1990s use HFC-134a as the refrigerant. However, CFC-12 was used in the past and still exists in some operating systems. Furthermore, for the future other refrigerants such as HFC-152a and R-744 (carbon dioxide) are being considered. Refrigerant Sold in Containers (RMt). For MACs, refrigerant generally comes in three basic types of containers – ‘bulk containers’ sent to vehicle manufacturers to fill new MACs, ‘small cans’ containing about 300-500 grams of refrigerant generally used by individuals servicing their own equipment, and ‘cylinders’ containing about 10-15 kilograms of refrigerant used by shops that service many vehicles. If one assumes no losses from bulk containers (see below), then in order to calculate Econtainers, one needs to know the total refrigerant sold in small cans (RMsc) and cylinders (RMcy). It will be important to distinguish the refrigerant sold into different Sub-Applications (e.g., HFC-134a is also used in the chillers and domestic refrigeration Sub-Applications) so that only the

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refrigerant sold for MACs is used in the calculations. This data may be obtained from small can packagers and refrigerant producers/distributors. Container Heels (c). For this example, we assume the heels from service containers are not recovered (e.g., the cylinders are discarded, not reused) and are csc = 20% for the small can and ccy = 2% for the cylinder. Because bulk refrigerant containers generally go back to the refrigerant producer and are refilled, we can assume there are no heels that would be emitted and hence cbulk = 0%. MACs Produced Each Year (Nt). If the number of MACs placed in service each year is not known, an estimate can be made by multiplying the number of cars placed in service each year by an estimate of the percentage that were sold with MACs. These data may be available from automobile manufacturers, MAC producers/suppliers, or government agencies involved in transportation, infrastructure and highway safety. If more than one type of refrigerant is used, it is important to separate each Nt into the different refrigerants, e.g., N1994 = N1994,CFC-12 + N1994,HFC-134a. Nominal Charge of Each MAC (mt). This factor would likely vary by the type of vehicle; for instance small passenger cars will likely have lower refrigerant charges than buses or larger cars, especially those with multiple evaporators. Likewise, this could vary over time, for instance decreasing as manufacturers make smaller systems for the same vehicle size, or increasing as larger cars and more multiple-evaporator units enter the market. For this example, we assume a constant over time at an average m = 0.7 kg, which is typical of small to medium-sized passenger cars. Refrigerant Charged into New Equipment (Mt). This is easily calculated as Mt = Nt • mt = 0.7 • Nt. Assembly Losses (k). This is used to calculate the Charge Emissions, also referred to as ‘First-Fill Emissions.’ The loss rate is often small, on the order of k = 0.5% or smaller. For simplicity, we assume k = 0 in this example. Lifetime (d). The presumed lifetime of a MAC. This variable can be based on national data and can be different for different types of MACs (passenger cars, buses, etc.) For this example, we assume the lifetime of all MACs is d = 12 years. Bank in Existing Equipment (B). The bank will be the amount of refrigerant in MACs put into service, minus the amount of refrigerant in MACs disposed, plus the amount of refrigerant used to service MACs, minus the amount that has leaked. In actuality, a given MAC will probably leak over several years before being serviced. Rather than attempting to account for this, for this example we apply Equation 7.13 which assumes all MACs are serviced each year such that the estimated charge of each MAC is the same as the nominal charge. The annual emission rate is averaged to account for this assumption. This will only produce small errors unless the year-toyear sales of MACs fluctuate widely. Hence the bank in any given year is the sum of the Refrigerant Charged into New Equipment each year from the current year back to the assumed average lifetime of the equipment. Thus, Bt = ∑ M t −i +1 d

i =1

For example, using d = 12 years, the bank in 2006 would be B2006 = M2006 + M2005 + M2004 + … + M1997 + M1996 + M1995. Annual Emission Rate (x). This factor accounts for both leaks from equipment as well as any emissions during service. Both of these items can be different for different types of MACs and can also vary by when the MAC was produced (i.e., older MACs may leak more than newer MACs). If annual servicing does not occur, the amount emitted at any servicing event needs to be average over the number of years between servicing event to obtain the annual rate. This amount is likely to vary considerably depending on national conditions and what type(s) of service is (are) performed. Whether recovery of the given charge before service is performed must be considered, and may be deduced in part by examining the amount of refrigerant sold in small cans versus cylinders. For this example, we assume that 15% of the nominal charge is leaked each year and 11% on average is emitted during servicing. Hence, x = 26%. Residual Charge in MACs Disposed (p). Assuming that the MAC is serviced the year before it is disposed, and that the annual emission rate is estimated, this is easily calculated as p = 1 – x. In our example, p = 1 – 26% = 0.74 Recovery Efficiency (nrec). If no regulations or incentives exist to require recovery of refrigerant

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from MACs disposed, then likely very little will occur. So, for this example, we assume that nrec = 0. Calculation of Different Types of Emissions

Now that these data have been gathered and assumptions have been made, calculating the emissions may be performed. An example for year t = 2006 follows: Container Emissions (Equation 7.11).

Econtainers , 2006 = RM cy , 2006 • ccy + RM sc, 2006 • csc = 0.02 • RM cy , 2006 + 0.2 • RM sc, 2006

Charging Emissions (Equation 7.11). Ech arg e, 2006 = M 2006 • k = 0

Lifetime (Operating and Servicing) Emissions (Equation 7.13). Eoperation , 2006 = B2006 • x = 0.26 • B2006 = 0.26 • ∑ M t − i +1 d

i =1

= 0.26 • ( M 2006 + M 2005 + M 2004 + L + M 1997 + M 1996 + M 1995 )

= 0.26 • m • ( N 2006 + N 2005 + N 2004 + L + N1997 + N1996 + N1995 )

= 0.26 • 0.7 • ( N 2006 + N 2005 + N 2004 + L + N1997 + N1996 + N1995 )

= 0.182 • ( N 2006 + N 2005 + N 2004 + L + N1997 + N1996 + N1995 )

End-of-Life Emissions (Equation 7.14).

Eend − of − life, 2006 = M 2006 − d • p • (1 − nrec ) = M 2006 −12 • 0.74 • (1 − 0) = 0.74 • M 1994 = 0.74 • 0.7 • N1994 = 0.518 • N1994

Calculation of Total Emissions

Total MAC Emissions (Equation 7.8).

Etotal , 2006 = Econtainers , 2006 + Ech arg e , 2006 + Elifetime , 2006 + E servicing , 2006 + Eend −of −life , 2006 = 0.02 • RM cy , 2006 + 0.2 • RM sc , 2006 + 0

+ 0.182 • ( N 2006 + N 2005 + N 2004 + L + N1997 + N1996 + N1995 ) + 0.518 • N1994

= 0.02 • RM cy , 2006 + 0.2 • RM sc , 2006

+ 0.182 • ( N 2006 + N 2005 + N 2004 + L + N1997 + N1996 + N1995 ) + 0.518 • N1994

The only unknowns are: • • •

RMsc – refrigerant (in kilograms) sold in small cans to service MACs, which may be obtained from small can packagers; RMcy – refrigerant (in kilograms) sold in cylinders to service MACs, which may be obtained from refrigerant producers/distributors; and, Nt – the number of MACs put in service each year, which may be available from automobile manufacturers, MAC producers/suppliers, or government agencies involved in transportation, infrastructure and highway safety.

If the emissions from refrigerant containers and from end-of-life are not included, for example if it is believed that service cylinders are completely evacuated and minimal MACs reach their end-oflife in the given year, this equation becomes simply an activity (the number of MACs) multiplied by an emission factor (annual emission rate times average charge size, in this case 0.182 kg per MAC). This calculation yields the total emissions in kilograms of refrigerant. Keeping each refrigerant separate and multiplying each sum by the refrigerant’s GWP will yield kilograms of CO2 equivalent emissions. Dividing by 1 billion (109) will yield emissions in teragrams of CO2 equivalent (TgCO2eq).

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7.5.2.5

C OMPLETENESS

Completeness for the Tier 1a/b method can be achieved if data for new refrigerants, and refrigerants in equipment that is retired in the current year, are available. For the Tier 2a and 2b methods, completeness depends on a thorough accounting of the existing equipment banks, and this may involve tracking large amounts of data.

7.5.2.6

D EVELOPING

A CONSISTENT TIME SERIES

Emissions from refrigeration and air conditioning should be calculated using the same method and data sources for every year in the time series. Where consistent data are unavailable for the more rigorous method for any years in the time series, these gaps should be recalculated according to the guidance provided in Volume 1, Chapter 5.

7.5.3

Uncertainty assessment

Table 7.8, Estimates for Charge, Lifetime and Emission Factors for Refrigeration and Air-Conditioning Systems, presents emission factor ranges that highlight the uncertainty associated with this sector. Generally, disaggregated methods (Tier 2) have less uncertainty than Tier 1 methods because of the heterogeneous nature of the sub-applications. Those Tier 2 methods that rely on emission factors (Tier 2a) have more uncertainty than mass balance methods that use chemical sales data (Tier 2b). This occurs largely because of the small unit size of most equipment and the potential for the multiplication of a small unit error. Inventory compilers should seek industrial advice on uncertainties, using the approaches to obtaining expert judgements outlined in Volume 1, Chapter 3.

7.5.4 7.5.4.1

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL

In order to conduct a quality control for Tier 2 method, it is possible, but not necessary in order to satisfy the requirements of good practice, to compare the annual national HFC refrigerant market as declared by the chemical manufacturers or the refrigerant distributors with the annual HFC refrigerant needs as derived by the Tier 2 method. Refrigerant will be needed for either charging new equipment or servicing existing equipment. What is needed (i.e., purchased) to charge equipment includes the refrigerant that is actually charged in the equipment plus any associated emissions (either during the charging process or from containers that are used for charging but not completely emptied before they are discarded). What is needed for service is refrigerant to replace that which is lost from existing equipment due to leaks and lost during servicing, as well as refrigerant from containers that are not completely emptied before they are discarded. The following formula leads to this verification.

RN t =

EQUATION 7.15 VERIFICATION OF SUPPLY AND DEMAND ASSESSMENTS

∑ (S prod _ t , j • mt , j ) + ∑ (M t , j • k j ) + ∑ (Bt , j • x j ) + RM t • c 6

6

6

j =1

j =1

j =1

Where: RNt = HFC refrigerant needs in year t, kg j = counter from 1 to 6 (or the number of sub-applications chosen for the Tier 2 method) Sprod_t,j = national production of equipment using HFC refrigerant for sub-application domain j in year t, number of equipment mt,j = initial average charge of HFC in sub-application j type of equipment, kg Mt,j = amount of HFC charged into the equipment of sub-application j at manufacturing in year t, kg kj = emission factor of assembly losses of the HFC charged into new equipment of sub-application j, fraction

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Bt,j = amount of HFC banked in existing systems of sub-application j in year t (per sub-application), kg xj = annual emission rate (i.e., emission factor) of HFC banked in sub-application j during operation, accounting for average annual leakage and average annual emissions during servicing, fraction RMt = HFC market for new equipment and servicing of all refrigeration sub-applications in year t, kg c = emission factor of HFC container management of the refrigerant market, fraction The first term corresponds to the refrigerant charge of new refrigerating and air conditioning system produced in the country at the current year t including exports. The second term corresponds to the refrigerant emitted during the initial charging of new refrigeration and air conditioning systems produced in the country at the current year t including exports. The third term corresponds to the refrigerant charge used for servicing, assuming the refrigerant emitted from leaks and during servicing is topped-off each year. The final term represents the refrigerant emitted from containers across the entire refrigeration and air conditioning market in the given year t. Refrigerant recovered and recharged directly to the same owner’s equipment are not seen as a need; however, refrigerant recovered and sent for reclamation is accounted for in the declared market. The annual refrigerant market as declared by chemical manufacturers or refrigerant distributors RD is calculated by Equation 7.16 EQUATION 7.16 CALCULATION OF ANNUAL REFRIGERANT MARKET RDt = R prod _ t − Rexp _ t + Rimp _ t + Rrecl _ t − Rdest _ t

Where: Rprod_t = quantities of HFC refrigerant production in the country, kg Rexp_t = quantities of HFC refrigerant produced in the country and exported, kg Rimp_t = quantities of imported HFC refrigerant, kg Rrecl_t = quantities of HFC refrigerant recovered and reprocessed for sale as reclaimed HFC refrigerant less quantities going to reclaimers that have not yet been sold, kg Rdest_t = quantities of HFC refrigerant destroyed, kg All quantities are calculated for the current year t. Comparing RNt that is the HFC refrigerant needs as derived from the inventory method and RDt the HFC refrigerant market as declared by refrigerant manufacturers and distributors gives a clear quality control of the inventory method, and also of the global emissions. RNt and RDt are calculated for each HFC type. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. It is difficult to provide adequate QA/QC procedures for the Tier 1 a/b method without carrying out a Tier 2 analysis to verify the choice of composite emission factor. Since this defeats the object of the Tier 1 approach, the most appropriate strategy is to seek external evaluation of the derivation of the composite emission factor where it is country-specific. An alternative will be to compare Tier 1 outputs with the predictions of regional or global databases.

7.5.4.2

R EPORTING

AND

D OCUMENTATION

The supporting information necessary to ensure transparency in reported emissions estimates is shown in Table 7.10, Good Practice Documentation for Refrigerating and Air-Conditioning Systems.

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TABLE 7.10 GOOD PRACTICE DOCUMENTATION FOR REFRIGERATION AND AIR-CONDITIONING SYSTEMS Data Source

Data to be Reported

Tier 1a/b

Tier 2a and 2b

Regulation for phase-out of CFCs and HCFCs

Schedule of phase out for charging of brand new equipment and for servicing

X

X

Government Statistics or Disposal Companies

Number of equipment disposed of for each type of application

X

X

Refrigerant Manufacturers and Distributors

All virgin refrigerants sold for charging new equipment and for servicing in the different sectors

X

X

Manufacturer Association or Marketing Studies

Equipment produced on a national level using HFC refrigerants (for the six sub-applications)

X

X

Import/Export Companies, Governement Statistics, Manufacturer Association or Marketing Studies

Number of equipment using HFCs (imported and exported) X

X

Government or Refrigerant Distributors

HFC refrigerants recovered for re-processing or for destruction

X

X

Manufacturer Association

Average equipment lifetime

NA

X

Manufacturer Association

Initial charge of systems

X

X

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7.6

FIRE PROTECTION

7.6.1

Chemicals covered in this application area

There are two general types of fire protection (fire suppression) equipment that use HFCs and/or PFCs as partial replacements for halons: portable (streaming) equipment, and fixed (flooding) equipment. HFCs, PFCs and more recently a fluoroketone are mainly used as substitutes for halons, typically halon 1301, in flooding equipment. PFCs played an early role in halon 1301 replacement but current use is limited to replenishment of previously installed systems. HFCs in portable (streaming) equipment, typically replacing halon 1211, are available but have achieved very limited market acceptance due primarily to their high cost. PFC use in new portable extinguishers is currently limited to a small proportion (few percent) in an HCFC blend. While actual emissions from the fire protection sub-sector are expected to be quite small, the use is normally non-emissive in provision of stand-by fire protection and is growing. This results in an accumulating bank of future potential emissions. HFCs and PFCs that might still be involved in fire protection are shown in Table 7.1.

7.6.2 7.6.2.1

Methodological issues C HOICE

OF METHOD

As with the refrigeration and air conditioning application, the fire protection application offers the possibility of using both Approach A (emission-factor approach) and Approach B (mass–balance approach). The latter is justified by the fact that a considerable proportion of net consumption is likely to be targeted at equipment servicing rather than new equipment. However, the fire protection application differs from the refrigeration and air conditioning application in that the sub-applications are less numerous and more homogeneous. This means that the Tier 1a or Tier 1b method may be sufficient to provide appropriate emissions reporting, although, to be strictly correct, the inclusion of end-of-life considerations would normally warrant a Tier 2 approach. However, as with both foam and refrigeration/air conditioning, it is necessary in the fire protection application to deal with the development and tracking of banks. This means that an historical time series of country-specific or globally or regionally derived activity data is required dating back to the introduction of any new HFC or PFC. Since HFCs and PFCs in fire protection are emitted over a period longer than one year, countries need to represent emissions from equipment charged during previous years. Choosing an annual production-based emission factor to reflect a multi-year emission process can lead to considerable error and is not considered good practice. Equation 7.17 indicates how the approach should be modified to consider the time dependence of the emissions and to consider what activity data could most likely be made available. EQUATION 7.17 TIME DEPENDENCE OF EMISSIONS FROM FIRE PROTECTION EQUIPMENT Emissions t = Bank t • EF + RRLt

Bank t =

and

∑ (Production i + Importsi − Exportsi − Destructioni − Emissionsi −1 ) − RRLt t

i =t 0

Where: Emissionst = emissions of agent from fire protection equipment in year t, tonnes Bankt = bank of agent in fire protection equipment in year t, tonnes EF = fraction of agent in equipment emitted each year (excluding emissions from retired equipment or otherwise removed from service), dimensionless RRLt = Recovery Release or Loss: emissions of agent during recovery, recycling or disposal at the time of removal from use of existing fire protection equipment in year t, tonnes

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Productiont = amount of newly supplied agent (i.e., excluding recycled agent) in fire protection equipment produced in year t, tonnes Importst = amount of agent in fire protection equipment imported in year t, tonnes Exportst = amount of agent in fire protection equipment exported in year t, tonnes Destructiont = amount of agent from retired fire protection equipment that is collected and destroyed, tonnes t = year for which emissions are being estimated (e.g., 2006, 2007, etc.) t0 = first year of chemical production and/or use i = counter from first year of chemical production and/or use t0 to current year t

It is good practice to apply Equation 7.17 to each individual greenhouse gas used in fire protection equipment. The calculation of the emissions must be performed for each year and applied to the next year’s calculation. With this background in mind, the decision tree for the fire protection application as set out in Figure 7.9 becomes very straight-forward. As with Tier 1 methods adopted in both foams and refrigeration and air conditioning, it is possible to create a simple spreadsheet that accounts for the development of banks and the subsequent emissions from them. The following spreadsheet extract provides an example: Figure 7.8

Example of spreadsheet calculation for Tier 1 method

Tier 1 FIRE PROTECTION Austria - HFC-227ea HFC-

Summary Country: Austria Agent: HFC-227ea Year: 2005

7ea

Emission: 27.1 tonnes In Bank: 678.4 tonnes

Current Year

2005 30

Use in current year - 2005 (tonnes)

Data Used Here

Production of HFC-227ea

120

Imports in current Year

80

Exports in current year Total new agent to domestic market

24 176

Year of Introduction of HFC-227ea Growth Rate in New Equipment Sales

1998 3.0%

25

20

15

10

5

Tier 1 Defaults Assumed Equipment Lifetime (years)

15

Emission Factor from installed base

4%

% of HFC-227ea destroyed at End-of-Life

0%

0 1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

1996 0 0 0

1997 0 0 0

1998 12 2 8

1999 25 5 17

2000 39 8 26

2001 53 11 36

2002 69 14 46

2003 85 17 57

2004 102 20 68

2005 120 24 80

2006 0 0 0

Total New Agent in Domestic Equipment

0

0

18

37

57

78

101

124

150

176

0

Agent in Retired Equipment Destruction of agent in retired equipment Release of agent from retired equipment

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 0

Bank Emission

0 0

0 0

18 1

54 2

109 4

183 7

276 11

389 16

523 21

678 27

651 #N/A

Estimated data for earlier years Production Agent in Exports Agent in Imports

It is intended that such a spreadsheet facilitates the calculation for the Fire Protection application, supported, where necessary, by activity data from an appropriate globally or regionally derived dataset23.

23

As noted in Box 7.1, inclusion in the IPCC Emission Factor Database (EFDB) will indicate general adherence to due process, but it is good practice for countries to ensure that all data taken from the EFDB are appropriate for their national circumstances.

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Figure 7.9

Decision tree for actual emissions from the fire protection application Start

Is there manufacture of fire protection agents in-country?

Yes

Collect current and historic activity data including import/export data.

Yes

Collect current and historic activity data including export data.

No

Is there manufacture of fire protection equipment incountry?

No

Combine with any current and historic data on imports of fire protection equipment.

Are reliable country-specific emission factor data available?

Collect data and develop country-specific emission factors.

Yes

Box 3: Tier 1a with country-specific EFs

No

Is this a key category1?

Use Tier 1a method with country-specific emission factors.

Yes

Yes

No

Are emission factor data available (e.g., from regional or global databases)?

No

Use Tier 1a method with emission factors derived from regional or global databases, etc. Box 2: Tier 1a with Efs derived from regional or global databases, etc.

Calculate emissions using Tier 1b method. Box 1: Tier 1b

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

7.6.2.2

C HOICE

OF EMISSION FACTORS

Experience gained during the phase-out of halons substances has taught some valuable lessons regarding use and emission patterns, and it is reasonable to expect that these lessons are relevant for greenhouse gases used for similar purposes. Fire protection equipment is designed to release its initial charge during an actual fire incident. A recent study shows that annual emissions from installed flooding systems are in the range of 2 ± 1 percent of the installed base (Verdonik and Robin, 2004). For halon 1211 portable extinguishers, the Halons Technical

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Options Committee (2003) estimated that the emission rate for 2000 is approximately twice that of fixed systems. Applying that factor provides a range of 2 to 6 percent (that is, 4% ±2%) of the in-use quantities. Given the nature of this application, there are opportunities to recover the gas at the end of life of the equipment (or whenever removed from service). The recovered gas may be destroyed or recycled. Therefore, the default assumption of zero end-of-life recovery may overestimate end-of-life emissions. The inventory compiler should establish contacts with relevant industries to collect information on recovery that may occur due to legislation, Industry Codes of Practice or other measures. It is good practice to document this information and report any assumptions. For those countries without a national Industry Code of Practice, it is good practice to assume that the agent will not be recovered at the end of the system life and is emitted. Typical lifetimes for flooding systems are 15 to 20 years. In specialised applications, such as aircraft and military systems, systems can remain in use for 25 to 35 years or longer (UNEP-HTOC, 1994).

7.6.2.3

C HOICE

OF ACTIVITY DATA

For countries that produce the fire protection agent, it is good practice to assign all of the production of that agent to that country unless known to have been 1) exported in bulk or 2) destroyed. For countries that do not produce the agent but produce and fill fire protection systems, all of the bulk agent imported into the country is considered to remain in the country unless known to have been 1) re-exported in bulk or 2) destroyed. Countries that do not produce the agent or systems would use the activity data developed by the producer countries to develop their inventory or, baring evidence of export into the country, estimate the emissions from fire protection as below the significance of their overall greenhouse gas emissions, i.e., essentially zero. This default methodology places the major responsibility on the countries that produce the agent or use them for the manufacture of systems. In order for producer countries to use this methodology, activity data would need to be developed on production, bulk imports and exports, and destruction. In summary, activity data comes from countries that are producers of fire protection agents or systems, with the exception of destruction. In order for the producer country to decrease the amount credited toward that country from production of agent, bulk exports must be demonstrated. These bulk exports while reducing the producer countries installed base would also serve as activity data for importing countries to determine their installed base.

7.6.2.4

C OMPLETENESS

Inventory compilers should ensure that all greenhouse gases used in the fire protection industry are included in the estimate. It is also necessary to apply Equation 7.17 beginning in the first year that greenhouse gas fire protection agents were employed in the country.

7.6.2.5

D EVELOPING

A CONSISTENT TIME SERIES

In some countries, historical activity data for the greenhouse gases charged into new equipment or used to service existing equipment may be difficult to determine because of the recent introduction of these substances. If inventory compilers use preliminary emission factors for these years based on historical data for halons, and then switch they should follow good practice in ensuring time series consistency, as described in Volume 1, Chapter 5.

7.6.3

Uncertainty assessment

On the global level, a high degree of certainty could be expected because assessments will be based on production and provides for a complete material balance. At any time, Aggregate Global Production will always equal Aggregate Global Emissions plus the Aggregate Total Contained in Equipment. There is more uncertainty in the country-specific data. A small error is built into the method as importing and exporting of filled systems is not included in the method. However, based on experience with halon and their greenhouse gas substitutes, accurate data on filled system import/export is likely not obtainable at a reasonable level of effort. Verdonik (2004) compared reports on halon consumption against the manufacturers’ reports of global halon production from CEFIC24, reported developing country production and reported CEIT25 production. The results 24

CEFIC – European Chemical Industry Association

25

CEIT – Countries with Economies in Transition

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were a standard deviation of 16 percent for developed countries, 15 percent for developing countries and 13 percent globally. It is anticipated that the uncertainty in HFC/PFC emission estimates would be comparable or higher than the uncertainty seen in halon consumption estimates.

7.6.4 7.6.4.1

Quality Assurance/Quality control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. This may involve direct reference to global or regional databases for parallel assessments which allow benchmarking. Additional quality control checks as outlined in Volume 1, Chapter 6, and quality assurance procedures would be applicable, if higher tier methods were used to determine emissions from this application. However, if this is not the case, the basic QA/QC approaches outlined in Volume 1, Chapter 6 can be adopted. In addition to the guidance in Volume 1, specific procedures of relevance to this application are outlined in the references at the end of the chapter.

7.6.4.2

R EPORTING

AND

D OCUMENTATION

Access to data such as chemical sales may depend on the ability of inventories to preserve confidentiality. The balance between preservation of confidentiality and transparency of the data is an important issue, especially in a low use application such as fire protection. These ODS substitutes are manufactured by a few producers, in quantities very much lower than ODS substitutes used in other applications. Careful aggregation of GWPweighted data may be a means to resolve this issue.

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7.7

OTHER APPLICATIONS

7.7.1

Chemicals covered in this application area

HFCs and PFCs represent a large range of gases whose properties make them attractive for a variety of niche applications not covered separately in this Chapter. These include electronics testing, heat transfer, dielectric fluid, medical applications and potentially many new applications not yet developed. There are also some historical uses of PFCs, as well as emerging use of HFCs, in these applications. These applications have leakage rates ranging from 100 percent emissive in year of application to around 1 percent per annum. However, this chapter is specifically focused on those uses of HFCs and PFCs which directly replace Ozone Depleting Substances and these are much more limited in scope. There is a need to be sure that double-counting does not occur with the electronics category covered in Chapter 6 of this Volume, including electronics testing, heat transfer and dielectric applications. Other double-counting is possible in the coverage of solvents or where HFCs and/or PFCs are contained as solvents in industrial aerosols. This is a prime example where the delineation between what is acting as an ODS Substitute and what is not can be very fine. To avoid confusion, this chapter has taken the approach that only those technology transitions which occur directly from ODSs to HFC and/or PFC technologies should be considered. Bearing in mind that ODS phase-out (both CFCs and HCFCs) is moving towards completion in developed countries, the number of new applications emerging is expected to be very limited. However, in theory at least, new applications could emerge right up until the final global phase-out of ODSs in 2040.

7.7.2 7.7.2.1

Methodological issues C HOICE

OF METHOD

The choice of good practice methods depends on national circumstances (see decision tree in Figure 7.10, Decision Tree for Actual Emissions from the Other Applications). When choosing a method for this application area, there is a need to consider whether to treat each Other Application as a separate application or whether to address them as a group. The former will lead to a series of Tier 2 methods, while the latter will lead to a single Tier 1 approach. The end-users for these niche applications will be extremely diverse. As a result, investigating each of these applications separately may not be feasible. Instead, it is suggested that these other miscellaneous applications be divided into highly emissive applications similar to solvents and aerosols, and less emissive contained applications similar to closed-cell foam and refrigerators. The breakdown of annual gas consumption going to either category should be determined by a survey of end-use applications. The split of usage will be: •

•

Emissive = X% of total consumption (where X would normally be expected to be typically >50%) Contained = (100 – X)% of total consumption

The consequence of this approach is that, depending on the number of sub-applications in each class, it could be possible to follow an exclusively Tier 1 approach or alternatively adopt a Tier 2 method. Since the primary differentiator is the rate of emission, and it is not known whether sub-applications will require servicing or not, it is recommended that exclusively Approach A (emission-factor approach) be used (i.e., Tier 1a and/or Tier 2a). Modelling of these two classes of sub-application is considered in turn.

EMISSIVE APPLICATIONS It is good practice to use a Tier 1a method, similar to the methods described for aerosols and solvents. During use of fluids in these applications, 100 percent of the chemical is emitted on average six months after sale. In other words emissions in year t can be calculated according to the equation for solvents and aerosols as follows:

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EQUATION 7.18 ASSESSMENT OF PROMPT EMISSION SOURCES FROM OTHER APPLICATIONS Emissions t = S t • EF + S t −1 • (1 − EF ) Where: Emissionst = emissions in year t, tonnes St = quantity of HFC and PFC sold in year t, tonnes St–1 = quantity of HFC and PFC sold in year t–1, tonnes EF = emission factor (= fraction of chemical emitted during the first year after manufacture), fraction The emission factor (EF) represents that fraction of chemical emitted during the first year after manufacture. By definition, emissions over two years must equal 100 percent. This equation should be applied to each chemical individually.

CONTAINED APPLICATIONS Certain applications have much lower loss rates. Where appropriate data are available, a separate emissions model will be required to adjust for this lower leakage rate. Where no data exist, globally or regionally derived activity data and emission factors can be used. Thus, the equation for annual emissions is as follows: EQUATION 7.19 ASSESSMENT OF EMISSIONS FROM OTHER CONTAINED APPLICATIONS Emissions = Product Manufacturing Emissions + Product Life Emissions + Product Disposal Emissions Where: Product Manufacturing Emissions = Annual Sales ● Manufacturing Emission Factors Product Life Emissions = Bank ● Leakage Rate Product Disposal Emissions = Annual Disposal ● Disposal Emission Factors

7.7.2.2

C HOICE

OF EMISSION FACTORS

Emission factors for those sub-applications with prompt emissions will follow similar selection criteria to those for solvents (Section 7.2.2.2) and aerosols (Section 7.3.2.2). Emission factors for contained sub-applications will depend on the particular nature of that sub-application. If a series of sub-applications is fairly homogeneous in nature it may still be possible to work with a composite emission factor and adopt a Tier 1a method. However, where there is considerable variation in the nature of contained sub-applications, it will be more appropriate to research these specifically, if appropriate emission factors are not available. In either case, the need for separate emission factors will lead to the adoption of a Tier 2a method.

7.7.2.3

C HOICE

OF ACTIVITY DATA

Activity data will always be difficult to establish for small niche applications and inventory compilers will be reliant on the co-operation of chemical suppliers to identify qualifying sub-applications. However, once identified, they should be relatively easy to quantify at a country level because they are likely to be fairly specialist in nature. As indicated in Figure 7.10, it is good practice to conduct an end-use survey periodically.

7.7.2.4

C OMPLETENESS

As noted in Section 7.7.2.3, the key challenge will be to keep updated with new Other Application as they emerge. Regular cross-reference with ODS Substitution Reviews (e.g., UNEP Technical & Economic Assessment Panel Reports) will assist in this respect.

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7.7.2.5

D EVELOPING

A CONSISTENT TIME SERIES

Emissions from Other Application should be calculated using the same method and data sources for every year in the time series. Where consistent data are unavailable for any year in the time series, gaps should be recalculated according to the guidance provided in Volume 1, Chapter 5.

7.7.3

Uncertainty assessment

There may be a wide range of other applications and therefore it is not possible to give default uncertainties for these sources. However, procedures should be put in place to assess levels of uncertainty in accordance with the practices outlined in Volume 1 Chapter 3.

7.7.4 7.7.4.1

Quality assurance/quality control (QA/QC), reporting and documentation Q UALITY

ASSURANCE / QUALITY CONTROL

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. This may involve direct reference to global or regional databases for parallel assessments which allow benchmarking. Additional quality control checks as outlined in Volume 1, Chapter 6, and quality assurance procedures may be applicable, if higher tier methods are used to determine emissions from these sub-applications. However, if this is not the case, the basic QA/QC approaches outlined in Volume 1, Chapter 6 can be adopted.

7.7.4.2

R EPORTING

AND DOCUMENTATION

The balance between preservation of confidentiality and transparency of the data is an important issue, especially in low-use sub-applications. Specialist ODS substitutes are often manufactured by only a few producers, in quantities very much lower than ODS substitutes used in other applications. Careful aggregation of GWPweighted data may be a means to resolve this issue.

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Figure 7.10

Decision tree for actual emissions from the other applications Start

Are the types of other uses in the country known already?

No

Perform an end-use survey of other applications using HFCs and PFCs.

Yes

Calculate emissions from contained applications, using sub-application emission factors, then sum emissions from contained applications and those from emissive applications.

Yes For each year, obtain data from chemical manufacturers and importers for sales of each HFC and PFC into other applications.

Separate activity data into emissive and contained applications. Calculate emissions from emissive applications using the appropriate equation (Tier 1a).

Are emission factors available for contained uses at the sub-application level?

Box 2: Tier 2a for contained applications, Tier 1a for emissive applications

No

Is this a key category1?

No

Yes

Calculate emissions from contained applications, using application level emission factors, then sum emissions from contained applications and those from emissive applications. Box 1: Tier 1a

Collect data on emission factors for contained uses at the sub-application level.

Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

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References Ashford, P., Clodic, D., Kuijpers, L. and McCulloch, A. (2004). Emission Profiles from the Foam and Refrigeration Sectors – Comparison with Atmospheric Concentrations, International Journal of Refrigeration, 2004. Ashford, P. and Jeffs, M. (2004). Development of Emissions Functions for Foams and their use in Emissions Forecasting, ETF Proceedings, April 2004. Clodic, D., Palandre, L., McCulloch, A., Ashford, P. and Kuijpers, L. (2004). Determination of comparative HCFC and HFC emission profiles for the Foam and Refrigeration sectors until 2015. Report for ADEME and US EPA. 2004. Gamlen P.H., Lane B.C., Midgley P.M. and Steed J.M (1986). The production and release to the atmosphere of CFCl3 and CF2 Cl2 (chlorofluorocarbons CFC-11 and CFC-12). Atmos. Environ. 20: 1077-1085. IPCC (1996). Climate Change 1995: Impacts, Adaptation, and Mitigation of Climate Change: ScientificTechnical Analysis. The Contribution of Working Group II to the Second Assessment Report of the Intergovernmental Panel on Climate Change. R. T. Watson, M. C. Zinyowera, R. H. Moss, (eds.), Cambridge University Press, Cambridge. IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. Callander B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2000). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Penman J., Kruger D., Galbally I., Hiraishi T., Nyenzi B., Emmanuel S., Buendia L., Hoppaus R., Martinsen T., Meijer J., Miwa K., Tanabe K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. IPCC (2001). Climate Change 2001: Mitigation: Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change, edited by Metz, B., Davidson, O., Swart, R. and Pan, J., Cambridge University Press, Cambridge. IPCC/TEAP (2005). IPCC/TEAP Special Report on Safeguarding the Ozone Layer and the Global Climate System: Issues Related to Hydrofluorocarbons and Perfluorocarbons. Prepared by Working I and III of the Intergovernmental Panel on Climate Change, and the Technology and Economic Assessment Panel [Metz, B., L. Kuijpers, S. Solomon, S. O. Andersen, O. Davidson, J. Pons, D. de Jager, T. Kestin, M. Manning, and L. A. Meyer (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 488 pp. Kroeze, C. (1995). Fluorocarbons and SF6: Global emission inventory and control. RIVM Report No. 773001007, Bilthoven, The Netherlands. McCulloch A., Ashford, P. and Midgley, P.M. (2001). Historic Emissions of Fluorotrichloromethane (CFC-11) Based on a Market Survey, Atmos. Environ., 35(26), 4387-4397 McCulloch A., Midgley, P.M. and Ashford, P. (2003). Releases of Refrigerant Gases (CFC-12, HCFC-22 and HFC-134a) to the Atmosphere, Atmos. Environ. 37(7), 889-902 Palandre L., Barrault, S. and Clodic,D. (2003). Inventaires et prévisions des fluides frigorigènes et de leurs émissions, France. Année 2001. Rapport pour l'ADEME, mai 2003. Palandre, L., Barrault, S. and Clodic, D. (2004). Inventaires et prévisions des émissions de fluides frigorigènes France - Année 2002. Rapport pour l'ADEME. Août 2004. Schwarz, W. and Harnisch, J. (2003). Establishing the leakage rates of Mobile Air Conditioners. Final report for the EC. Ref. B4-3040/2002/337136/MAR/C1. 17 April 2003. UNEP-FTOC (1999). 1998 Report of the Rigid and Flexible Foams Technical Options Committee, UNEP, Ozone Secretariat, 1999. UNEP-FTOC (2003). 2002 Report of the Rigid and Flexible Foams Technical Options Committee, UNEP, Ozone Secretariat, 2003. UNEP-HTOC (1994). Assessment Report of the Halons Technical Options Committee, Report prepared for the United Nations Environment Programme, Ozone Secretariat, Nairobi, Kenya, http://www.ozonelog.org. UNEP-HTOC (2003). Assessment Report of the Halons Technical Options Committee, Report for the United Nations Environment Programme, Ozone Secretariat, Nairobi, Kenya, http://www.ozonelog.org, 69 pp.

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UNEP-RTOC (1999). 1998 Report of the Refrigeration, Air Conditioning and Heat Pumps Technical Options Committee, 1998 Assessment, UNEP, Ozone Secretariat, Nairobi, Kenya, ISBN 92-807-1731-6 UNEP-RTOC (2003). 2002 Report of the Refrigeration, air Conditioning and Heat Pumps Technical Options Committee, 2002 Assessment, UNEP, Ozone Secretariat, Nairobi, Kenya, ISBN 92-807-2288-3 UNEP-TEAP (2002). April 2002 Report of the Technology and Economic Assessment Panel, Volume 3b, Report of the Task Force on Destruction Technologies. [S. Devotta, A. Finkelstein and L. Kuijpers (ed.)]. UNEP Ozone Secretariat, Nairobi, Kenya. UNEP-TEAP (2005). May 2005 Report of the Technology and Economic Assessment Panel, Volume 3, Report of the Task Force on Foam End-of-Life Issues, UNEP Ozone Secretariat, Nairobi, Kenya. U.S. EPA (1992a). U.S. Environmental Protection Agency, Risk Screen on the Use of Substitutes for Class I Ozone Depleting Substances Prepared in Support of the Significant New Alternatives Policy Program (SNAP), 1992. U.S. EPA (1992b). U.S. Environmental Protection Agency, Regulatory Impact Analysis: Compliance with Section 604 of the Clean Air Act for the Phaseout of Ozone Depleting Chemicals, 1992. U.S. EPA (2004a). U.S. Environmental Protection Agency, Analysis of International Costs to Abate HFC and PFC Emissions from Solvents (Preliminary Report), 2004 U.S. EPA (2004b). U.S. Environmental Protection Agency, The U.S. Solvent Cleaning Industry and the Transition to Non- Ozone Depleting Substances, http://www.epa.gov/ozone/snap/solvents/index.html U.S. EPA/AHAM (2005). Disposal of Refrigerators/Freezers in the US – State of Practice (Baumgartner W., Kjeldsen P. et al.), 2005 Verdonik, D.P. (2004). Modelling Emissions of HFCs and PFCs in the Fire Protection Sector, Proceedings of the Earth Technology Forum, Washington, DC, 2004, 13 pp. Verdonik, D.P. and Robin, M.L. (2004). Analysis of Emission Data, Estimates, and Modelling of Fire Protection Agents, Proceedings of the Earth Technology Forum, Washington, DC, 2004, 11 pp. Vo and Paquet (2004). An Evaluation of Thermal Conductivity over time for Extruded Polystyrene Foams blown with HFC-134a and HCFC-142b, ETF Proceedings, April 2004

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CHAPTER 8

OTHER PRODUCT MANUFACTURE AND USE

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Authors Sections 8.1, 8.2, and 8.3 Deborah Ottinger Schaefer (USA) Friedrich Plöger (Germany), Winfried Schwarz (Germany), Sven Thesen (USA), Ewald Preisegger (Germany), Ayite-Lo N. Ajavon (Togo), and Dadi Zhou (China) Section 8.4 Nigel Harper (UK)

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Chapter 8: Other Product Manufacture and Use

Contents 8

Other Product Manufacture and Use .............................................................................................................8.6 8.1

Introduction ...........................................................................................................................................8.6

8.2

Emissions of SF6 and PFCs from electrical equipment .........................................................................8.6

8.2.1

Introduction ...................................................................................................................................8.6

8.2.2

Methodological issues ...................................................................................................................8.7

8.2.2.1

Choice of method.....................................................................................................................8.7

8.2.2.2

Choice of emission factors.....................................................................................................8.14

8.2.2.3

Choice of activity data ...........................................................................................................8.17

8.2.2.4

Completeness.........................................................................................................................8.19

8.2.2.5

Developing a consistent time series .......................................................................................8.20

8.2.3

Uncertainty assessment ...............................................................................................................8.20

8.2.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................8.21

8.3

8.2.4.1

Quality Assurance/Quality Control........................................................................................8.21

8.2.4.2

Reporting and Documentation ...............................................................................................8.22

Use of SF6 and PFCs in other products ...............................................................................................8.23

8.3.1

Introduction .................................................................................................................................8.23

8.3.2

Methodological issues .................................................................................................................8.23

8.3.2.1

Choice of method...................................................................................................................8.23

8.3.2.2

Choice of emission factors.....................................................................................................8.32

8.3.2.3

Choice of activity data ...........................................................................................................8.33

8.3.2.4

Completeness.........................................................................................................................8.33

8.3.2.5

Developing a consistent time series .......................................................................................8.33

8.3.3

Uncertainty assessment ...............................................................................................................8.33

8.3.4

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................8.33

8.4

8.3.4.1

Quality Assurance/Quality Control........................................................................................8.33

8.3.4.2

Reporting and Documentation ...............................................................................................8.34

N2O from product uses ........................................................................................................................8.35

8.4.1

Introduction .................................................................................................................................8.35

8.4.2

Methodological issues .................................................................................................................8.36

8.4.2.1

Choice of method...................................................................................................................8.36

8.4.2.2

Choice of emission factors.....................................................................................................8.36

8.4.2.3

Choice of activity data ...........................................................................................................8.37

8.4.2.4

Completeness.........................................................................................................................8.37

8.4.2.5

Developing a consistent time series .......................................................................................8.37

8.4.3 8.4.3.1

Uncertainty assessment ...............................................................................................................8.37 Emission factor uncertainties.................................................................................................8.37

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8.4.3.2 8.4.4

Activity data uncertainties .....................................................................................................8.38 Quality Assurance/Quality Control (QA/QC), Reporting and Documentation ...........................8.38

References

.....................................................................................................................................................8.39

Annex 8A

Examples of Tier 3 national SF6 inventory systems ....................................................................8.41

Equations Equation 8.1

Default emission factor method.............................................................................................8.8

Equation 8.2

Equipment disposal emissions under country-specific emission factor method....................8.9

Equation 8.3

Tier 3 total emissions...........................................................................................................8.10

Equation 8.4A Equipment manufacturing emissions - pure mass-balance ..................................................8.10 Equation 8.4B Equipment manufacturing emissions - hybrid .....................................................................8.11 Equation 8.5A Equipment installation emissions - pure mass-balance .......................................................8.11 Equation 8.5B Equipment installation emissions - hybrid...........................................................................8.11 Equation 8.6A Equipment use emissions - pure mass-balance....................................................................8.11 Equation 8.6B Equipment use emissions - hybrid.......................................................................................8.12 Equation 8.7A Equipment disposal and final use emissions - pure mass-balance.......................................8.12 Equation 8.7B Equipment disposal and final use emissions - hybrid..........................................................8.12 Equation 8.8

Emissions from recycling of SF6 .........................................................................................8.13

Equation 8.9

Emissions from destruction of SF6 ......................................................................................8.13

Equation 8.10 Utility-level mass-balance approach....................................................................................8.14 Equation 8.11 Retiring nameplate capacity ................................................................................................8.18 Equation 8.12 Emissions from AWACS (default emission actor)..............................................................8.24 Equation 8.13 Emissions from AWACS (user mass-balance)....................................................................8.25 Equation 8.14 University and research particle accelerator emissions (country-level) ..............................8.27 Equation 8.15 University and research particle accelerator emissions (accelerator-level emission factor) ......................................................................................8.28 Equation 8.16 Total research accelerator emissions ...................................................................................8.28 Equation 8.17 Research accelerator emissions (accelerator-level mass-balance).......................................8.28 Equation 8.18 Industrial/medical accelerator emissions (country-level) ....................................................8.30 Equation 8.19 Adiabatic property applications...........................................................................................8.31 Equation 8.20 Double-glazed windows: assembly .....................................................................................8.31 Equation 8.21 Double-glazed windows: use...............................................................................................8.31 Equation 8.22 Double-glazed windows: disposal .......................................................................................8.31 Equation 8.23 Prompt emissions.................................................................................................................8.32 Equation 8.24 N2O emissions from other product uses ..............................................................................8.36

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Chapter 8: Other Product Manufacture and Use

Figures Figure 8.1

Decision tree for SF6 from electrical equipment....................................................................8.8

Figure 8.2

Decision tree for SF6 from AWACS ...................................................................................8.24

Figure 8.3

Decision tree for SF6 from research accelerators.................................................................8.27

Figure 8.4

Decision tree for industrial and medical particle accelerators .............................................8.29

Figure 8A.1

Example of Tier 3 approach: Germany, High-Voltage equipment ......................................8.42

Figure 8A.2

Example of Tier 3 approach: Germany, Medium-Voltage equipment ................................8.43

Tables Table 8.1

Avoiding double-counting or overlooking emissions: two examples..................................8.13

Table 8.2

Sealed pressure electrical equipment (MV Switchgear) containing SF6: default emission factors .......................................................................................................8.15

Table 8.3

Closed pressure electrical equipment (HV Switchgear) containing SF6: default emission factors .......................................................................................................8.16

Table 8.4

Gas insulated transformers containing SF6: default emission factors..................................8.16

Table 8.5

Uncertainties for default emission factors and lifetime .......................................................8.21

Table 8.6

Good practice reporting information for SF6 emissions from electrical equipment by tier.........................................................................................8.22

Table 8.7

SF6 emissions per plane per year .........................................................................................8.24

Table 8.8

National AWACS fleets ......................................................................................................8.25

Table 8.9

Average SF6 charge in a particle accelerator by process description...................................8.30

Table 8.10

Emission factor for each process description, (SF6 emissions from industrial and medical particle accelerators) .......................................................................................8.30

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8 OTHER PRODUCT MANUFACTURE AND USE 8.1

INTRODUCTION

This chapter outlines methods for estimating emissions of sulphur hexafluoride (SF6) and perfluorocarbons (PFCs) from the manufacture and use of electrical equipment and a number of other products. It also provides methods for estimating emissions of nitrous oxide (N2O) from several products. In most of these applications, the SF6, PFC, or N2O is deliberately incorporated into the product to exploit one or more of the physical properties of the chemical, such as the high dielectric strength of SF6, the stability PFCs, and the anaesthetic effect of N2O. However, the applications discussed here have a wide range of emission profiles, ranging from immediate and unavoidable release of all of the chemical (e.g., use of PFCs as atmospheric tracers) to largely avoidable, delayed release from leak-tight products after 40 years of use (e.g., manufacture and use of sealedpressure electrical equipment). The estimation methods presented in the chapter have been tailored to reflect these differences in emission profiles. Section 8.2 details methods for estimating SF6 and PFC emissions from electrical equipment. Section 8.3 details methods for estimating emissions from the manufacture and use of a wide variety of other industrial, commercial, and consumer products that contain SF6 and PFCs, excluding those discussed elsewhere in this volume (e.g., PFC emissions from electronics manufacturing, which are discussed in Chapter 6). (Please see the introduction to Section 8.3 for the list of excluded sources.) Finally, Section 8.4 discusses methods for estimating N2O emissions from anaesthetics, propellants, and other product uses.

8.2

EMISSIONS OF SF 6 AND PFCs FROM ELECTRICAL EQUIPMENT

8.2.1

Introduction

Sulphur hexafluoride (SF6) is used for electrical insulation and current interruption in equipment used in the transmission and distribution of electricity. Emissions occur at each phase of the equipment life cycle, including manufacturing, installation, use, servicing, and disposal. Most of the SF6 used in electrical equipment is used in gas insulated switchgear and substations (GIS) and in gas circuit breakers (GCB), though some SF6 is used in high voltage gas-insulated lines (GIL), outdoor gas-insulated instrument transformers and other equipment. The aforementioned applications may be divided into two categories of containment. The first category is ‘Sealed Pressure Systems’ or ‘Sealed-for-life Equipment’, which is defined as equipment that does not require any refilling (topping up) with gas during its lifetime and which generally contains less than 5 kg of gas per functional unit. 1 Distribution equipment normally falls into this category. The second category is ‘Closed Pressure Systems’, which is defined to include equipment that requires refilling (topping up) with gas during its lifetime. This type of equipment generally contains between 5 and several hundred kg per functional unit. Transmission equipment normally falls into this category. Both categories of equipment have lifetimes of more than 30 to 40 years. In Asia, significant quantities of SF6 are used in gas-insulated power transformers (GIT). Electrical equipment is the largest consumer and most important use of SF6, globally. It significantly contributes to worldwide SF6 emissions. However, the importance of this source varies considerably from region to region and from country to country. The emissions from this category depend not only on the installed (banked) or consumed quantities of SF6, but also very much on the tightness of the products and the handling processes applied. Regional average emission rates presently vary between far less than 1 percent to more than 10 percent. In general, emission rates have declined significantly since 1995. Targeted industry actions have reduced emissions by 50 to 90 percent in Europe and Asia (Ecofys, 2005; Aoyama, 2004). These actions include (1)

1

Formal definitions of ‘sealed-pressure system’ and ‘closed-pressure system’ are contained in International Electrotechnical Commission (IEC) Standard 60694. (IEC, 1996)

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Chapter 8: Other Product Manufacture and Use

designing equipment to require a smaller charge of SF6 and to be more leak tight and (2) improving handling processes and handling equipment for all life cycle stages.2 In some regions (e.g., North America and Japan), perfluorocarbons (PFCs) are used as dielectrics and heat transfer fluids in power transformers. PFCs are also used for retrofitting CFC-113 cooled transformers. One PFC used in this application is perfluorohexane (C6F14). In terms of both absolute and carbon-weighted emissions, PFC emissions from electrical equipment are generally believed to be much smaller than SF6 emissions from electrical equipment; however, there may be regional exceptions to this pattern.

8.2.2 8.2.2.1

Methodological issues C HOICE

OF METHOD

Emissions of SF6 from electrical equipment can be estimated in a variety of ways with varying degrees of complexity and data intensity. This section describes good practice for using a Tier 1 method (the default emission-factor approach), a Tier 2 method (the country-specific emission-factor approach), and a Tier 3 method (a hybrid that can use either mass-balance or emission-factor approaches for different life cycle stages, depending on country-specific circumstances). Generally, emissions estimates developed using the Tier 3 method, which is implemented at the facility level, will be the most accurate. Estimates developed using the Tier 1 method will be the least accurate. As is true for other emission sources, the tier selected will depend on data availability and whether or not the category is key. Figure 8.1, Decision Tree for SF6 from Electrical Equipment, summarises the process for choosing among Tiers 3, 2, and 1. Good practice in choosing between the mass-balance and emission-factor variants of the Tier 3 approach is discussed in detail in Section 1.5 of Chapter 1. This choice will depend both on data availability and on country-specific circumstances. As a first step in assessing the importance of SF6 emissions from electrical equipment and the other categories discussed in this chapter, inventory compilers are encouraged to contact chemical producers and suppliers as well as electrical equipment manufacturers and utilities and/or their industry associations. These organisations can provide basic information on chemical consumption and on equipment stocks and applications that can help the inventory compiler estimate emissions and identify sources that merit further investigation. They can also provide important advice and support in establishing more extensive data collection systems to support Tier 2 and Tier 3 estimates.

2

International Council on Large Electric Systems (CIGRE) has published a guide on SF6 handling, Guide for the Preparation of customized “Practical SF6 Handling Instructions,” Task Force B3.02.01, CIGRE Publication No.276, August 2005. (CIGRE, 2005)

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Decision tree for SF 6 from electrical equipment 1

Figure 8.1

Start

Can an annual survey of facilities that use SF6 be completed, gathering data by lifecycle stage?

Yes

Estimate emissions using Tier 3 Hybrid Life-Cycle Approach. Box 3: Tier 3

No

Are country-specific emission factors available?

Yes

Estimate emissions using Tier 2 CountrySpecific Emission Factor Approach. Box 2: Tier 2

Collect data for Tier 2 or 3 approaches,3

No

Yes

Is the Other Product Manufacture and Use a key category2, and is this subcategory significant?

No

Estimate emissions using the Tier 1 Default Emission Factor Approach. Box 1: Tier 1

Note: 1. In selecting an estimation method, it is good practice also to consider the criteria presented in Table 1.7, Chapter 1, Section 1.5 of this volume for choosing between the mass-balance and emission-factor variants of each tier. 2. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 3. It is good practice to contact National/Regional associations of utilities/users and manufacturers to collect, check, and aggregate actual and historical data.

TIER 1 METHOD – DEFAULT EMISSION FACTORS The Tier 1 approach is the simplest approach for estimating SF6 and PFC emissions from electrical equipment. (Henceforth in this section, ‘SF6’ will be used to denote ‘SF6 and/or PFCs.’) In this method, emissions are estimated by multiplying default regional emission factors by, as appropriate, the SF6 consumption of equipment manufacturers and/or by the nameplate SF6 capacity of the equipment at each life cycle stage beyond manufacturing in the country. The term Installation Emissions may be omitted if (1) installation emissions are not expected to occur (i.e., for closed-pressure equipment) or (2) installation emissions are included in the emission factor for emissions from Manufacturing or Use. Default emission factors are given in Tables 8.2 to 8.4. It is good practice to use the following equation: EQUATION 8.1 DEFAULT EMISSION FACTOR METHOD Total Emissions = Manufacturing Emissions + Equipment Installation Emissions + Equipment Use Emissions + Equipment Disposal Emissions Where:

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Chapter 8: Other Product Manufacture and Use Manufacturing emissions = Manufacturing Emission Factor • Total SF6 consumption by equipment manufacturers

Equipment installation emissions = Installation Emission Factor • Total nameplate capacity of new equipment filled on site (not at the factory).

Equipment use emissions = Use Emission Factor • Total nameplate capacity of installed equipment. The ‘use emission factor’ includes emissions due to leakage, servicing, and maintenance as well as failures Equipment disposal emissions = Total nameplate capacity of retiring equipment • Fraction of SF6 remaining at retirement

TIER 2 METHOD – COUNTRY-SPECIFIC EMISSION FACTOR METHOD The Tier 2 method uses the same basic equation as Tier 1, but requires reliable country-specific emission factors for each life cycle stage. Country-specific emission factors will be more accurate because they reflect the unique circumstances in which electrical equipment is used in a given country. In addition, if detailed data for equipment retirement are available, emissions due to retirement can be estimated more accurately. The expression for Equipment Disposal Emissions in the Tier 2 method includes terms accounting for SF6 recovery at retirement and disposal, as follows: EQUATION 8.2 EQUIPMENT DISPOSAL EMISSIONS UNDER COUNTRY-SPECIFIC EMISSION FACTOR METHOD

Equipment disposal emissions = Total nameplate capacity of retiring equipment • Fraction of SF6 remaining at retirement • (1 – fraction of retiring equipment whose SF6 is recovered • recovery efficiency • fraction of recovered SF6 recycled, reused with no further treatment, or destroyed*) *This final term is intended to account for emissions during chemical recycling and destruction. Note that to be considered Tier 2, estimates must be developed using only country-specific emission factors.

TIER 3 HYBRID METHOD – EMISSIONS BY LIFE CYCLE STAGE OF EQUIPMENT The Tier 3 method is the most accurate approach for estimating actual emissions of SF6 from electrical equipment. This method is detailed but flexible, accommodating a wide range of national circumstances. The method is implemented at the facility level and includes separate equations for each phase of the life cycle of equipment, including equipment manufacture, installation, use, and disposal. Depending on the type of equipment, the life cycle stage, and country-specific circumstances, either a mass-balance approach or country(or facility-) specific emission factors may be used. In general, it is good practice to use the mass-balance approach, except where (1) emission rates from a process are near or below the precision of the measurements required for the mass-balance approach (e.g., 3 percent of nameplate capacity per year or less), (2) equipment is never serviced during its lifetime (as is expected to be the case for sealed pressure equipment), or (3) equipment stocks are growing very rapidly, as may be the case in countries where electrical equipment has been introduced within the last 10-20 years. The hybrid approach enhances accuracy by permitting use of the mass-balance approach for some processes and life cycle stages and the emission-factor approach for other processes and life cycle stages. However, the combination of different approaches also introduces opportunities for double-counting or overlooking emissions. Inventory compilers need to be aware of this problem and take steps to avoid it. Table 8.1, Avoiding DoubleCounting or Overlooking Emissions, provides examples of both the problem and some potential solutions. The annex to this chapter (Annex 8A) briefly describes an example of the Tier 3 approach as it has been applied in Germany. This example is intended to illustrate rather than prescribe; the precise approach taken by any given country will depend on country-specific circumstances. Ideally, data are obtained for every equipment manufacturer, electricity transmission and distribution facility (utility), equipment disposer (which may be a manufacturer, electric utility, or other entity), and SF6 recycling or destruction facility in the country, and the emissions of all manufacturers, utilities, disposers, and recycling or destruction facilities are summed to develop the national estimate. The basic equation is:

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EQUATION 8.3 TIER 3 TOTAL EMISSIONS

Total Emissions = ∑ Equipment Manufacturing Emissions + ∑ Equipment Installation Emissions + ∑ Equipment Use Emissions

+ ∑ Equipment Disposal and Final Use Emissions

+ ∑ Emissions from SF6 Recycling and Destruction

Where: Equipment Manufacturing Emissions at the facility level can be estimated by Equations 8.4A and 8.4B. Equipment Installation Emissions at the facility level can be estimated by Equations 8.5A and 8.5B. Equipment Use Emissions at the facility level can be estimated by Equations 8.6A and 8.6B. Equipment Disposal and Final Use Emissions at the facility level can be estimated by Equations 8.7A and 8.7B. Emissions from SF6 Recycling and Destruction at the facility level can be estimated by Equations 8.8 and 8.9. In the above equation, national emissions for each phase are equal to the sum of the emissions of all equipment manufacturers, equipment users, equipment disposers, or SF6 recyclers/destroyers at that phase. In practice, it is not always possible to obtain data for every facility; in these cases countries may use one of the extrapolation methods discussed in Section 8.2.2.3, Choice of Activity Data.

Equipment manufacturing emissions Equipment manufacturing emissions can be estimated using either a pure mass-balance approach or a mixture (hybrid) of a mass-balance approach for some processes and an emission-factor based approach for others. The pure mass-balance approach is preferred except where a substantial fraction of a manufacturer’s emissions come from processes whose emission rates fall below the precision of the measurements required for the mass-balance approach (e.g., 3 percent of nameplate capacity per year or less). In these cases, it is good practice to use emission factors to estimate emissions from the processes with very low emission rates and to use the massbalance approach to estimate emissions from the other manufacturing processes. Pure mass-balance approach: Using the pure mass-balance approach, the total emissions of each equipment manufacturer can be estimated using the following equation: EQUATION 8.4A EQUIPMENT MANUFACTURING EMISSIONS - PURE MASS-BALANCE

Equipment Manufacturing Emissions = Decrease in SF6 Inventory + Acquisitions of SF6 − Disbursements of SF6

Where:

Decrease in SF6 Inventory = SF6 stored in containers at the beginning of the year – SF6 stored in containers at the end of the year Acquisitions of SF6 = SF6 purchased from chemical producers or distributors in bulk + SF6 returned by equipment users or distributors with or inside of equipment + SF6 returned to site after off-site recycling Disbursements of SF6 = SF6 contained in new equipment delivered to customers + SF6 delivered to equipment users in containers + SF6 returned to suppliers + SF6 sent off-site for recycling + SF6 destroyed Hybrid approach: This method first requires that manufacturers separate the gas flows associated with processes for which the mass-balance approach will be used from the gas flows associated with processes for which the emission-factor approach will be used. Emissions from the former can then be estimated using the approach outlined in Equation 8.4A. Emissions from the latter can be estimated by multiplying the total nameplate capacity of equipment undergoing each process (e.g., filling) by the country- or facility-specific emission factor for that process. Total emissions for each manufacturer are then estimated by summing the emissions from both sets of processes, using the following equation:

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EQUATION 8.4B EQUIPMENT MANUFACTURING EMISSIONS - HYBRID

Equipment Manufacturing Emissions = Equation 8.4 A

+ ∑ Nameplate capacity of equipment undergoing each process * • Emission factor for that process

* Excluding that covered by Equation 8.4A

Equipment installation emissions Equipment installation emissions may be estimated using either a mass-balance or an emission-factor approach. Again, the mass-balance approach is preferred except where emission rates are very low. Pure Mass-balance approach: Using the mass-balance approach, the total emissions of each equipment installer can be estimated using the following equation: EQUATION 8.5A EQUIPMENT INSTALLATION EMISSIONS - PURE MASS-BALANCE

Equipment Installation Emissions = SF6 used to fill equipment

− Nameplate capacity of new equipment

Hybrid approach: This method first requires that users separate the gas flows associated with equipment for which the mass-balance approach will be used from the gas flows associated with equipment for which the emission-factor approach will be used. Emissions from the former can then be estimated using the approach outlined in Equation 8.5A. Emissions from the latter can be estimated by multiplying the newly installed nameplate capacity of each equipment type by the country- or facility-specific installation emission factor for that type. Total emissions for each installer are then estimated by summing the emissions from both sets of equipments, using the following equation: EQUATION 8.5B EQUIPMENT INSTALLATION EMISSIONS - HYBRID

Equipment Installation Emissions = Equation 8.5A

+ ∑ Nameplate capacity of new equipment filled on site* • Installation emission factor * Excluding that covered by Equation 8.5A

Equipment use emissions Equipment use emissions may be estimated using either a pure mass-balance or a hybrid approach. The pure mass-balance approach is likely to be appropriate for countries where (1) electrical equipment that uses SF6 has been in use for 10-20 years or more, and (2) emissions from sealed-pressure systems are likely to be negligible. The hybrid approach is likely to be appropriate for other countries. Pure mass-balance approach: Using the pure mass-balance approach, the total emissions of each equipment user can be estimated using the following equation: EQUATION 8.6A EQUIPMENT USE EMISSIONS - PURE MASS-BALANCE

Equipment Use Emissions = SF6 used to recharge closed pressure equipment at servicing − SF6 recovered from closed pressure equipment at servicing

Hybrid approach: This method first requires that users separate the gas flows associated with equipment for which the mass-balance approach will be used from the gas flows associated with equipment for which the emission-factor approach will be used. Emissions from the former can then be estimated using the approach outlined in Equation 8.6A. Emissions from the latter can be estimated by multiplying the total nameplate capacity of each type of equipment by the country- or facility-specific emission factor for that type of equipment. The emission-factor approach is likely to be more accurate for sealed-pressure equipment everywhere and for all

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types of equipment in countries where electrical equipment has been used for less than 10-20 years. Total emissions for each user are then estimated by summing the emissions from both sets of equipment, using the following equation: EQUATION 8.6B EQUIPMENT USE EMISSIONS - HYBRID

Equipment Use Emissions = Equation 8.6A

+ ∑ Nameplate capacity of equipment installed * • Use emission factor * Excluding that covered by Equation 8.6A

Equipment disposal and final use emissions Equipment disposal and final use emissions may be estimated using either a pure mass-balance or a hybrid approach, based on country-specific circumstances. In both the pure mass-balance and hybrid approaches, emissions from closed-pressure equipment are estimated using a mass-balance equation. In the pure massbalance approach, emissions from sealed-pressure systems are also estimated using a mass-balance equation. In the hybrid approach, emissions from sealed-pressure systems are estimated using an emission-factor-based term. Pure mass-balance approach: In countries where the gas-collection infrastructure (including recovery equipment, technician training, and economic or legal incentives to recover) is not very well-developed or widely applied, it is good practice to use the pure mass-balance approach, as follows: EQUATION 8.7A EQUIPMENT DISPOSAL AND FINAL USE EMISSIONS - PURE MASS-BALANCE

Disposal and Final Use Emissions = Emissions from closed⋅ pressure equipment

+ Emissions from sealed⋅ pressure equipment (MB)

Where: Disposal and final use emissions from closed-pressure equipment = Nameplate capacity of retired closedpressure equipment – SF6 recovered from retired closed-pressure equipment, and Disposal and final use emissions from sealed-pressure equipment (MB) = Nameplate capacity of retired sealed-pressure systems – SF6 recovered from retired sealed-pressure systems Note that if the inventory compiler uses the emission-factor approach to estimate ‘use emissions’ from sealedpressure equipment, a term should be subtracted from the second equation to avoid double counting. See Table 8.1, Avoiding Double-Counting or Overlooking Emissions: Two Examples, for this term. Hybrid approach: In countries where the disposal of equipment is well controlled and understood (i.e., where an efficient gas collection infrastructure is in place) and where emissions from use of sealed-pressure equipment are accounted for under ‘use’ above, the hybrid approach may be used, as follows: EQUATION 8.7B EQUIPMENT DISPOSAL AND FINAL USE EMISSIONS - HYBRID

Disposal and Final Use Emissions = Emissions from closed pressure equipment

+ Emissions from sealed pressure equipment (EF )

Where: Disposal and final use emissions from closed-pressure equipment = Nameplate capacity of retired closedpressure equipment – SF6 recovered from retired closed-pressure equipment, and Disposal emissions from sealed-pressure equipment (EF) = [(Nameplate capacity of retired sealedpressure systems) – (Nameplate capacity of retired sealed-pressure systems • Use emission factor • Lifetime of equipment)] • (1 – fraction of retiring equipment whose SF6 is recovered • recovery efficiency) As noted above, emissions estimated using the above approach should be periodically checked, e.g., by using a pure mass-balance approach and/or assessing recovery frequency and practices. Inventory compilers should pay particular attention to the fraction of retiring equipment whose SF6 is recovered and to the fraction of the charge that is recovered when recovery is performed (‘recovery efficiency’). Even in countries where it is the norm to recover SF6 from retiring equipment, some venting may occur, and the venting of just a few percent of the SF6 in

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retiring equipment will drive emission rates far above the minimum that is technically achievable and that would otherwise be a reasonable basis for an emission factor.

Emissions from SF 6 recycling and destruction Some SF6 emissions occur after the chemical is recovered. These emissions include (1) emissions associated with recycling of SF6, and (2) emissions associated with the destruction of SF6. (Emissions associated with the shipment of SF6 to off-site recyclers or destruction facilities are considered negligible.) Emissions from recycling of SF6 are generally expected to be small — on the order of less than one percent of the total quantity fed into the recycling process. However, these emissions may be higher if state-of-the art handling equipment and practices are not used. In most cases, recycling is expected to occur on the site of the equipment manufacturer or user. In other cases, recycling may take place at a centralised recycling facility that is not associated with a chemical producer. Finally, recycling may take place on the premises of a chemical producer. Recycling emissions from chemical producers will be accounted for under chemical production (see Section 3.10 of this volume) and should not be included here. Emissions associated with the destruction of SF6 depend on the destruction efficiency of the process and the quantity of SF6 fed into the process. Given the high stability and dissociation temperature of SF6, the destruction efficiency may be as low as 90 percent. Thus, up to 10 percent of the SF6 fed into the destruction process could be emitted. The quantity of gas fed into the destruction process is generally expected to be small compared to that recycled. However, this may vary from country to country. It is good practice to develop country-specific emission factors for recycling and destruction that are based on full consideration of country-specific logistics and practices for SF6 recycling and destruction. Emissions from recycling of SF6 may be estimated using the following equation: EQUATION 8.8 EMISSIONS FROM RECYCLING OF SF6*

Emissions from Recycling = Recycling emission factor • Quantity SF6 fed into recycling process *Emissions from recycling that occurs at chemical production facilities should be excluded. Emissions from destruction of SF6 may be estimated using the following equation: EQUATION 8.9 EMISSIONS FROM DESTRUCTION OF SF6

Emissions from Destruction = Destruction emission factor • Quantity SF6 fed into destruction process

TABLE 8.1 AVOIDING DOUBLE-COUNTING OR OVERLOOKING EMISSIONS: TWO EXAMPLES Example 1 – Double Counting

Example 2 – Omission

Situation: An emission-factor approach is used to estimate emissions from sealed-pressure equipment during use, and a mass-balance approach is used to estimate emissions during disposal of sealed-pressure equipment.

Situation: A mass-balance approach is used to estimate emissions during use of closed-pressure equipment, but an emission-factor approach is used to estimate emissions during disposal of closed-pressure.

Potential problem: Emissions during use may be double-counted because some of the SF6 that is found to be missing when the equipment is disposed has already been counted as emitted during use.

Potential problem: Emissions that occur between the final servicing of the equipment and its disposal may be overlooked. These ‘final use’ emissions may account for a significant fraction of total use emissions, particularly if the equipment is refilled every 10 years or more.

Solution: Subtract lifetime use emissions (Nameplate capacity of retired sealed-pressure systems • Use emission factor • Lifetime of equipment) from emissions during disposal.

Solution: Use the mass-balance approach for both the use and disposal phases of the closed-pressure equipment life cycle.

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A special case of the Tier 3 method: the utility-level, pure mass-balance approach Countries that satisfy the good practice criteria for using the pure mass-balance approach beyond equipment manufacturing (i.e., countries where emissions during equipment installation, use, and disposal account for 3 percent or more of facility-level gas flows, where electrical equipment has been used for 10-20 years or more, and where emissions from sealed-pressure equipment are negligible), may, with little or no loss of accuracy, use a simplified version of the Tier 3 method to estimate emissions during equipment use. When summed together and reformulated in terms of facility-level gas flows, equations 8.5A, 8.6A, and 8.7A result in the following equation: EQUATION 8.10 UTILITY-LEVEL MASS-BALANCE APPROACH

User Emissions = Decrease in SF6 Inventory + Acquisitions of SF6 – Disbursements of SF6 – Net Increase in the Nameplate Capacity of Equipment Where: Decrease in SF6 Inventory = SF6 stored in containers at the beginning of the year – SF6 stored in containers at the end of the year Acquisitions of SF6 = SF6 purchased from chemical producers or distributors in bulk + SF6 purchased from equipment manufacturers or distributors with or inside of equipment + SF6 returned to site after off-site recycling Disbursements of SF6 = SF6 contained in equipment that is sold to other entities + SF6 returned to suppliers + SF6 sent off-site for recycling + SF6 destroyed Net Increase in Nameplate Capacity of Equipment = Nameplate Capacity of New Equipment – Nameplate Capacity of Retiring Equipment Although the utility-level approach is less detailed than the full life cycle approach, it is simple, and for those countries whose national circumstances permit its use, it provides estimates that are closely related to actual gas loss.

SF 6 EMISSIONS FROM MANUFACTURING OF ELECTRICAL COMPONENTS Some electrical equipment components may contain 1 percent or less by weight of SF6 in the insulating medium of the product. These components include but are not limited to medium voltage cast resin instrument transformers and high voltage bushings. In medium voltage (up to 52 kV) cast resin instrument transformers, SF6 is used to fill up micro-cavities in the resin insulation to improve the dielectric quality and durability of the product. In High Voltage (above 52 kV) bushings, SF6 is used as the blowing agent for the polyurethane resin in certain parts of the insulation system to improve the dielectric quality and durability of the product. SF6 emissions solely result from the casting/blowing process for the solid insulation of the product. All SF6 used is assumed to be emitted at the manufacturing stage. To estimate emissions from this source, the pure massbalance approach for equipment manufacturers (Equation 8.4A) may be used, setting the SF6 contained in new equipment equal to zero. Emission reduction measures focus on limiting losses/improving rate of recycling by suction devices and/or improved casting processes. SF6 in this type of high voltage bushings may be replaced by another blowing agent in the future.

8.2.2.2

C HOICE

OF EMISSION FACTORS

Because emission rates can vary not only from country to country but from facility to facility, inventory compilers using emission-factor based methods are encouraged to develop and use their own emission factors. Surveying a representative sample of equipment manufacturers and utilities within the country is an effective way to develop such factors. In general, it is good practice to document the evidence and reasoning supporting the selected emission factors, and to review these factors at least every 5 years. Factors that influence emission rates include the design of the equipment (which varies depending on when and where the equipment was manufactured), SF6-handling practices, availability of state-of-the-art handling equipment, SF6 prices, and regulations (e.g., recovery requirements). Variation of any one of these can change emission rates over time or among countries.

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TIER 1 METHOD Suggested default emission factors have been developed for some regions based on recent research. These factors are shown in Tables 8.2-8.4 below. It is good practice to select default emission factors from countries and regions with equipment designs and SF6handling practices similar to those of the country whose emissions are being estimated. Because Japan and Europe supply most of the global demand for electrical equipment, equipment designs are likely to be similar to those of either Japan or Europe. With the exception of the factors for the U.S., regional default emission factors are those documented for 1995, i.e., before any special industry actions for emission reduction were implemented. In Japan in 1995, approximately 70 percent of the SF6 used to test equipment during manufacture was recovered, and a similar percentage was recovered during equipment maintenance for equipment rated 110 kv or higher. (The 70 percent recovery fraction reflected recovery from an initial pressure of about 5 bars absolute to a final pressure of 1 to 1.5 bars absolute.) No gas was recovered from equipment rated lower than 110 kV (Maruyama et al., 2000). In Europe in 1995, gas supply systems for equipment manufacture were usually decentralised, and filling tubes were not self-closing. Gas was recovered to approximately 0.05 bars absolute during manufacturing and maintenance (Ecofys, 2005).

TABLE 8.2 SEALED PRESSURE ELECTRICAL EQUIPMENT (MV SWITCHGEAR) CONTAINING SF6: DEFAULT EMISSION FACTORS

Phase

Manufacturing (Fraction SF6 Consumption by Manufacturers)

Japan

c

Disposal (Fraction Nameplate Capacity of Disposed Equipment)

(Fraction per Year of Nameplate Capacity of All Equipment Installed)

Lifetime (years)

Fraction of charge remaining at retirement b

0.07

0.002

>35

0.93

0.29

0.007

Not reported

0.95

Region Europe a

Use (Includes leakage, major failures/arc faults and maintenance losses)

a

Source: ‘Reductions of SF6 Emissions from High and Medium Voltage Electrical Equipment in Europe,’ Ecofys, June, 2005.

b

This refers to the percentage of the original charge or nameplate capacity remaining in the equipment at end of life; it represents the fraction of the nameplate capacity potentially emitted before the equipment is recycled or disposed.

c

Based on data reported by the Federation of Electric Power Companies (FEPC) and the Japan Electrical Manufacturers’ Association (JEMA) (FEPC and JEMA, 2004). These organisations did not distinguish among equipment types in reporting average emission factors. The factors are therefore intended to be applied to all equipment types, including sealed pressure systems, closed pressure systems, and gas-insulated transformers.

Note: The emission factors above reflect the practices and technologies in place in 1995, i.e., before mitigation measures were implemented. References per footnotes a and c show how these developed further upon successive implementation of various voluntary measures later on. Another reference (Schwarz, 2006) relates state-of–the-art emission factors to mitigation measures in Germany.

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TABLE 8.3 CLOSED PRESSURE ELECTRICAL EQUIPMENT (HV SWITCHGEAR) CONTAINING SF6: DEFAULT EMISSION FACTORS

Phase

Manufacturing (Fraction SF6 Consumption by Manufacturers)

Japan U.S. a

d

Disposal (Fraction Nameplate Capacity of Disposed Equipment)

(Fraction per Year of Nameplate Capacity of All Equipment Installed)

Lifetime (years)

Fraction of charge remaining at retirement c

0.085b

0.026

>35

0.95

b

0.007

Not reported

0.95

>35

h

Region Europe a

Use (Includes leakage, major failures/arc faults and maintenance losses)

0.29

e

f

0.14

g

Source: ‘Reductions of SF6 Emissions from High and Medium Voltage Electrical Equipment in Europe,’ Ecofys, June, 2005.

b

Includes emissions from installation

c

This refers to the percentage of the original charge or nameplate capacity remaining in the equipment at end of life; it represents the fraction of the nameplate capacity emitted before the equipment is recycled or disposed.

d

Based on data eported by the Federation of Electric Power Companies (FEPC) and the Japan Electrical Manufacturers’ Association (JEMA) (FEPC and JEMA, 2004). These organisations reported average emission factors that include emissions from all equipment types, including sealed pressure systems, closed pressure systems, and gas-insulated transformers.

e

From the U.S. Inventory of Greenhouse Gases and Sinks, 1990-2002. (U.S. EPA, 2004). Value is from 1999, first year for which representative country-specific data were available.

f

No country-specific value available.

g

Includes emissions from installation.

h

Disposal emissions are included in use emission factor in the US.

Note: The emission factors above reflect the practices and technologies in place in 1995, i.e., before mitigation measures were implemented. References per footnotes a and d show how these developed further upon successive implementation of various voluntary measures later on. Schwarz (2006) relates state-of–the-art emission factors to mitigation measures in Germany.

TABLE 8.4 GAS INSULATED TRANSFORMERS CONTAINING SF6: DEFAULT EMISSION FACTORS Phase

Manufacturing (Fraction SF6 Consumption by Manufacturers)

Region Japan b

0.29

Use (Includes leakage, major failures/arc faults and maintenance losses)

Disposal (Fraction Nameplate Capacity of Disposed Equipment)

(Fraction per Year of Nameplate Capacity of All Equipment Installed)

Lifetime (years)

Fraction of charge remaining at retirement

0.007

Not reported

0.95

a

This refers to the percentage of the original charge or nameplate capacity remaining in the equipment at end of life; it represents the fraction of the nameplate capacity emitted before the equipment is recycled or disposed

b

Based on data reported by the Federation of Electric Power Companies (FEPC) and the Japan Electrical Manufacturers’ Association (JEMA) (FEPC and JEMA, 2004). These organisations did not distinguish among equipment types in reporting average emission factors. The factors are therefore intended to be applied to all equipment types, including sealed pressure systems, closed pressure systems, and gas-insulated transformers.

a

Note: The emission factors above reflect the practices and technologies in place in 1995, i.e., before mitigation measures were implemented. References per footnote b show how these developed further upon successive implementation of various voluntary measures later on. Schwarz (2006) relates state-of–the-art emission factors to mitigation measures in Germany.

TIER 2 METHOD Emission factors for the Tier 2 method are generally developed on the basis of data collected from representative manufacturers and utilities that track emissions by life cycle stage, essentially using the Tier 3, pure massbalance method at their facilities for at least one year. (The disposal emission factor should also account for emissions that occur downstream of the utility site, as discussed below.) These emissions by life cycle stage are then divided by the corresponding SF6 consumption or equipment capacity at that life cycle stage (i.e., SF6 consumption for manufacturing emissions, total existing equipment capacity for use emissions, and retiring

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equipment capacity for final use and disposal emissions) to develop emission factors. For example, to develop an emission factor for manufacturing, total emissions from the survey of manufacturers are summed and then divided by the total SF6 consumption of surveyed manufacturers. This emission factor can then be applied to the manufacturing sector as a whole, using national SF6 consumption by manufacturers. A similar approach can be used to estimate and apply emission factors for equipment use. The emission factor for disposal should fully account for three factors: (1) the recovery frequency (the fraction of equipment whose charge is recovered), (2) the recovery efficiency (the fraction of charge recovered when recovery is performed), and (3) the emissions from recycling and destruction of the recovered gas. The quantities in (1) and (2) will be automatically accounted for in emission factors based on use of the Tier 3 mass-balance method at representative utilities. However, the quantity in (3) reflects emissions that occur both on site and downstream of the utility/user. Thus, it must be accounted for separately. See the Tier 3 Method discussion below for guidance on estimating recycling and destruction emission factors. The facility-level variant of the Tier 3 approach may also be used to develop emission factors, but these will be applied at a more aggregated level, i.e., to equipment manufacturing and use (where the latter includes installation, use, and disposal) rather than to each lifecycle stage.

TIER 3 METHOD Because the Tier 3 method encourages the use of emission factors only when emission rates from processes are quite low (e.g., 3 percent of nameplate capacity per year or less) or when electrical equipment has only recently been introduced into a country, emission factors for this method may be difficult to measure directly using a mass-balance approach. To estimate Tier 3 emission factors, therefore, engineering studies may be used, identifying potential leak points and loss mechanisms and assigning probabilities and emission rates to these. Expected losses from service and maintenance should be factored into overall emission rates, as should losses from rare but catastrophic events that result in the loss of most of the equipment’s charge. Past experience with similar processes and designs should be considered. To ascertain and verify emission factors for use, surveys of equipment in the field may be carried out after several years of use, with the number of years determined by the expected leak rate and the limit of detection of the measuring equipment. Manufacturer statistics on equipment failure rates should be monitored to help ensure that catastrophic or gradual loss rates are not higher than expected. Disposal emissions are extremely sensitive to recovery frequencies (the fraction of equipment whose charge is recovered) and to recovery efficiencies (the fraction of charge recovered when recovery is performed, which, due to time considerations, may be lower than what is technically achievable). Thus, these should be monitored and documented carefully before establishing disposal emission factors. Emission factors for recycling of recovered SF6 may be based on professional judgement. Emission factors for destruction may be based on the rated destruction efficiency of the destruction technology, assuming that the technology is maintained and operated in a way that maintains its rated destruction efficiency.

8.2.2.3

C HOICE

OF ACTIVITY DATA

The activity data necessary to carry out the various estimation methods may be gathered from chemical manufacturers, equipment manufacturers, equipment users, and equipment disposers and/or their industry associations in the country or the region. The best source(s) of data vary depending upon the method and national circumstances.

TIER 1 METHOD SF6 consumption by equipment manufacturers: SF6 consumption by equipment manufacturers can be estimated using information from the manufacturers on their purchases of SF6, their returns of SF6 to chemical producers, and changes in their inventory of SF6 in containers. If information from equipment manufacturers is unavailable or incomplete, information from chemical producers and/or distributors on their sales to equipment manufacturers (less any returns) may be used. Namep la te capacity of new and retiring equipment: Nameplate capacity can be estimated using one or more of the following data sources: (1) information from equipment manufacturers/importers on the total nameplate capacity of the equipment they manufacture or import and export, (2) information from utilities on the total nameplate capacity of the equipment they install and retire each year, or (3) if information from (1) or (2) is not available, information from chemical manufacturers/importers on their sales of SF6 to equipment manufacturers. The first two data sources are preferable to the third, because gas sales to equipment manufacturers will differ from the nameplate capacity of new equipment installed in the country, particularly if equipment imports or exports are significant. In estimating the nameplate capacities of new and retiring equipment, inventory compilers should include the nameplate capacity of imported equipment and exclude the

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nameplate capacity of exported equipment. (See Section 7.5, Refrigeration, Box 7.1, Accounting for Imports and Exports of Refrigerant and Equipment, for a full discussion of how to treat imports and exports in estimating these quantities. This guidance is directly applicable to this category.) In the case of retiring equipment, capacity or sales information should be historical, starting in the year when the current year’s retiring equipment was built. Typical values for the lifetime of electrical equipment range from 30 to 40 years. If information on the total nameplate capacity of retiring equipment is not available, it can be estimated from new nameplate capacity, using the estimated annual growth rate of equipment capacity. In estimating the growth rate, it is good practice to consider both the number of pieces of equipment sold each year 3 and the average nameplate capacity of the equipment. The following equation can be used to estimate retiring nameplate capacity, if this information is not available directly: EQUATION 8.11 RETIRING NAMEPLATE CAPACITY

Retiring Nameplate Capacity = New Nameplate Capacity / ( 1 + g )L Where: L = equipment lifetime g = rate of growth According to a 2004 global survey, the average annual growth rate of SF6 sales to equipment manufacturers between 1970 and 2000 was approximately 9 percent. (Smythe, 2004). In the absence of country-specific information, a default factor of 9 percent may be used. To ta l namep la te capacity of insta lle d equipment: The total nameplate capacity of equipment can be estimated using the same data sources as are used to estimate the nameplate capacity for new and retiring equipment. If data from equipment manufacturers is used, it should include data on sales over the full lifetime of the equipment (30 to 40 years).

TIER 2 METHOD Quantities can be estimated as for Tier 1 above.

TIER 3 METHOD To implement the Tier 3 method, information must be gathered at two levels. At the facility level, gas flows must be tracked correctly according to the Tier 3 method. At the national level, information from facilities (manufacturers, users, and disposers of equipment) must be collected, checked, summed, and if necessary, extrapolated to include estimates of emissions from facilities in the country that do not collect data. Guidance regarding the information to be tracked by facilities is provided in the descriptions of the Tier 3 method above. Gas consumption may be measured by weighing gas cylinders before and after filling or recovery operations or at the beginning and end of the year or by using flow meters (e.g., during equipment manufacturing). At the national level, trade associations for equipment manufacturers and utilities can be very helpful in disseminating knowledge to their members regarding the Tier 3 approach and in helping their members to track and report data consistently and transparently. Trade associations can also act as third parties to aggregate confidential or sensitive data so that it can be released (in aggregate) to the public. Where trade associations are not active, national inventory compilers can facilitate the collection of information at the facility level, as well as the reporting and verification of this information, by developing model emission tracking protocols or by adopting existing industry protocols that embody the Tier 3 approach. These protocols can then be distributed to the manufacturers, users, and disposers of electrical equipment. Electronic protocols such as spreadsheets further facilitate the tracking, documentation, and reporting of emissions and minimize opportunities for arithmetic error. Because emission rates can vary from region to region and facility to facility, it is good practice to survey as many facilities as practical. In addition to manufacturers and utilities, countries should survey industrial sites and other non-utility sites if these contribute substantially to emissions from electrical equipment. If the number of facilities in a country is large (e.g., over 50), it may be difficult to achieve complete reporting. In these cases, countries may estimate emissions from non-reporting facilities by applying the Tier 2 method to these facilities

3

While the number of pieces of equipment sold each year has generally grown, the average nameplate capacity has generally declined.

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Chapter 8: Other Product Manufacture and Use

or by using alternative activity data as described in Chapter 2 of Volume 1, Approaches to Data Collection. Sector-specific considerations in selecting and using alternative activity data are discussed below. For sealed pressure equipment (which is widely dispersed among industrial users as well as utilities), manufacturers and distributors are likely to be the best source of complete information on national bank sizes and emission rates. To develop an accurate estimate, inventory compilers should survey manufacturers regarding their sales of equipment between the present and the time when currently retiring equipment was installed, or, if equipment has not yet begun to be retired, between the present and the time when the equipment was introduced into the country.

Sector-specific considerations in selecting and using alternative activity data for Tier 3 As discussed above, even when implementing a Tier 3 method it may not be possible to obtain data from all facilities. To obtain complete coverage of facilities, it is possible to use alternative activity data. For estimating emissions from non-reporting manufacturers, it may be possible to use the manufacturing capacity and/or collective market share (in terms of functional units) of the non-reporting manufacturers. For estimating emissions from non-reporting utilities, possible alternative data sets or drivers include (but are not limited to) the length of transmission lines, the combined length of transmission and distribution lines, or the number of substations of the non-reporting utilities. Transmission kilometres are likely to be a good predictor of emissions where most SF6 is used in high voltage transmission equipment, as in the U.S. (A discussion of how transmission kilometres are used to estimate emissions in the U.S. can be found in Volume 1, Chapter 2, Approaches to Data Collection.) Where a high percentage of SF6 is used in medium voltage distribution equipment or in gasinsulated substations, one of the other types of data may be appropriate. Wherever alternative data sets are used, it is important to derive emission factors from a representative set of facilities to ensure that the resulting estimate of national SF6 emissions is unbiased. Note that more than one factor may be appropriate, e.g., for different size utilities or for utilities in urban vs. rural locations. Because SF6 use and emission patterns can change over time, it is good practice to update the analysis and emission factor(s) at least every five years. (For example, emission rates may change as compact and leak-tight equipment replaces larger, leakier equipment and as sealed pressure equipment grows in importance.) In some cases, countries may be able to make use of emission factors developed in countries with similar electrical grids. In these cases, it is good practice to document the similarities between the grids before applying the emission factor from the other country.

8.2.2.4

C OMPLETENESS

Completeness for this source category requires accounting for emissions during the manufacture, use, and disposal of equipment, and during the recycling or destruction of SF6 recovered from equipment. Where Tier 3 methods are used, completeness requires that all significant SF6 users (manufacturers and utilities) be identified. When facility-level emissions data are not available from all of these users, emission estimates should be developed for them using one of the extrapolation methods described in Section 8.2.2.3, Choice of Activity Data. In the manufacturing sector, this requires assessing emissions from: •

•

•

•

Manufacture of gas insulated switchgear (GIS), gas circuit breakers (GCB), high voltage gas-insulated lines (GIL), outdoor gas-insulated instrument transformers, reclosers, switches, and ring main units of both types (sealed and closed pressure systems, respectively up to and above 52 kV), and other equipment including but not limited to cast resin instrument transformers and certain types of bushings using SF6 either as gas for the casting process or as a blowing agent; Manufacturers of gas-insulated power transformers (GIT); Minor SF6 users, including equipment remakers and servicing companies; The SF6 distribution chain from producers and distributors to manufacturing facilities.

In the utility and disposal sector, this requires accounting for all SF6 losses associated with: •

•

New electrical equipment installations;

•

Disposal of discarded electrical equipment;

•

Leakage, refill, maintenance, and equipment failures;

Recycling or destruction of SF6 recovered from equipment (but recycling emissions from chemical producers should be counted under chemical production, which is covered in Section 3.10 of this volume).

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It is good practice to identify and include industrial, military and small-utility applications if these are believed to contribute substantially to total emissions from the electrical equipment source category.

8.2.2.5

D EVELOPING

A CONSISTENT TIME SERIES

When estimating emissions from equipment users over a time series, it is necessary to consider SF6 emissions associated with the full set equipment at users’ sites for the years of interest. Thus, when using approaches based on banks and emission-factors (e.g., the Tier 2 approach), countries will require information on the capacity and emission rate of equipment purchased and installed for 30 to 40 years preceding the years of interest. In the user sector, if historical data are unavailable, good practice is to develop estimates using the top-down method, i.e., develop a model based on professional judgement by industry experts and inventory compilers and then calibrate as discussed below. Average leak rates for new equipment and the frequency of refill and routine maintenance all decreased from 1970 to 1995, and this trend has continued to the present. It is not good practice to apply current (post-2000) overall loss rates to historical years. Aggregate loss rates estimated from historical sales can be used in this case as well. On the manufacturing side, if historical data for developing base year emissions for 1990/1995 are not available, the top-down method calibrated to more accurate estimates for current years may be applied. Since SF6 handling practices of equipment manufacturers have changed substantially since 1995 (e.g., more gas is recovered), it is not good practice to apply current loss rates to historical estimates. Aggregate loss rates determined from global and regional sales and emission analyses may assist in providing an unbiased estimate for earlier years. It is good practice to recalculate emissions according to the guidance provided in Volume 1, Chapter 5, with all assumptions clearly documented.

8.2.3

Uncertainty assessment

When using the Tier 3 method, the resulting emissions estimates will have an accuracy of the order of ± 10 percent, and are likely to be more accurate than estimates developed using Tier 2 or Tier 1 methods.. If surveys are incomplete, the associated uncertainty will be greater. Particular sources of uncertainty may include: •

SF6 exported by equipment manufacturers (either in equipment or separately in containers);

•

SF6 imported by foreign equipment manufacturers (either in equipment or in containers);

•

SF6 returned to foreign recycling facilities;

•

Measurements of mass, density, and pressure (generally accurate to within one or two percent of the total quantity massed, but if emission rates are low, this may be a substantial percentage of those rates);

•

Emission factors;

•

Time lag between emissions and servicing;4

•

Lifetime of the equipment;

•

Regression error associated with any extrapolative approaches.

The estimated uncertainties in the default emission factors for the Tier 1 method are shown in Table 8.5, Uncertainties for Default Emission Factors for SF6 Emissions from Electrical Equipment. These values are based on the variation observed in emission factors in Europe. If the factors in Tables 8.2-8.4 are applied outside the countries and/or regions in which they were developed, uncertainties will be greater.

4

See Chapter 1 of this volume for a discussion of this issue.

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TABLE 8.5 UNCERTAINTIES FOR DEFAULT EMISSION FACTORS AND LIFETIME Phase

Manufacturi ng

Use (Includes leakage, major failures/arc faults and maintenance losses)

Lifetime (years)

Fraction of charge remaining at retirement

a

±20%

±20%

-20%/+40%

d

b

±30%

±30%

-10%/+40%

d

±30%

±30%

-10%/+40%

d

Equip -ment Type Sealed-Pressure

Closed-Pressure Gas Insulated Transformers c

Disposal

a

Estimated from ‘Reductions of SF6 Emissions from High and Medium Voltage Electrical Equipment in Europe,’ Ecofys, June, 2005;no uncertainties available from Japan; not relevant for USA..

b

Estimated from ‘Reductions of SF6 Emissions from High and Medium Voltage Electrical Equipment in Europe,’ Ecofys, June, 2005; U.S. emission factors have higher uncertainty for manufacturing (±70%) and slightly lower uncertainty for use (±15%) (U.S. Inventory of Greenhouse Gases and Sinks (U.S. EPA, 2004)). No uncertainties available from Japan.

c

Estimated by analogy with closed pressure systems; actual uncertainties may be somewhat higher. No uncertainties available from Japan.

d

No uncertainties available on fraction of charge remaining at retirement.

8.2.4

8.2.4.1

Quality Assurance/Quality Control (QA/QC), Reporting and Documentation Q UALITY A SSURANCE /Q UALITY C ONTROL

It is good practice to conduct quality control checks as outlined in Volume 1, Chapter 6, and an expert review of the emissions estimates. Additional quality control checks as outlined in Volume 1, and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source category. Inventory compilers are encouraged to use higher tier QA/QC for key categories as identified in Volume 1, Chapter 4. Additional procedures specific to electrical equipment are outlined below:

Comparison of emissions estimates using different approaches Inventory compilers should sum the facility-level data used as part of a bottom-up, Tier 3 method and crosscheck the data against national level emissions calculated using country-level data (the Tier 2 method) and/or country-level data with the IPCC default emission factors (the Tier 1 method). The Tier 2 method may similarly be checked against the Tier 1 method. Countries may also compare their results to those derived using a countrylevel mass-balance approach, as described in Equations 7.3 and 7.9 of Chapter 7. If countries do not have manufacturing facilities, they may also compare their estimates against potential emissions estimated using national apparent consumption data.

Review of facility-level emissions data In all instances where site-specific emissions data are obtained through surveys, inventory compilers should compare the emission rates between sites (adjusting for relative size or capacity) to identify significant outliers. They should investigate any outliers to determine if the differences can be explained or if there is an error in the reported emissions. As noted in Section 8.2.2.3, national inventory compilers can facilitate both the collection and verification of information at the facility level by distributing emission tracking protocols that embody the Tier 3 approach. Electronic protocols such as spreadsheets are particularly useful, as they minimize opportunities for arithmetic error. The calculations included in these protocols (whether electronic or not) can then be checked after they are submitted.

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Comparison of emission rates with those of other countries Inventory compilers should compare effective emission factors (loss rates) with values reported by other countries in the region, or with defaults published in the scientific literature for equipment with a similar design and similar level of emissions control. Transparent reporting, as outlined above, is essential for making international comparisons.

8.2.4.2

R EPORTING

AND

D OCUMENTATION

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Section 6.11. It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced. Some examples of specific documentation and reporting relevant to this source category ensuring transparency in reported emissions estimates are provided in Table 8.6, Good Practice Reporting Information for SF6 Emissions from Electrical Equipment by tier. Confidentiality issues may arise where there are limited numbers of manufacturers or utilities. In these cases, aggregated reporting for the total electrical equipment sector, or even total national SF6 applications, may be necessary. National or regional associations of users and manufacturers may be willing to collect, check, and aggregate data, particularly when they have collected such data historically. They can then report the aggregated information to the inventory compiler, resolving the problem of confidentiality. If survey responses cannot be released as public information, third party review of survey data may be necessary to support data verification efforts.

TABLE 8.6 GOOD PRACTICE REPORTING INFORMATION FOR SF6 EMISSIONS FROM ELECTRICAL EQUIPMENT BY TIER Data

Tier 3

Annual, country-wide consumption of SF6 by equipment manufacturers

Tier 2

Tier 1

X

X

Nameplate capacity of new equipment

X

X

X

Nameplate capacity of existing equipment

X*

X

X

Nameplate capacity of retiring equipment

X

X

X

SF6 destroyed

X

SF6 in inventory at beginning of year

X

SF6 in inventory at end of year

X

SF6 purchased by facility

X

SF6 sold or returned by facility

X

SF6 sent off-site for recycling

X

SF6 returned to site after recycling

X

SF6 used to fill new equipment

X

SF6 used to service equipment

X

SF6 recovered from retiring equipment

X

Emission/recovery factors

X*

X

Documentation for factors, if country-specific

X*

X

*Required for some variants of the methods.

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8.3

USE OF SF 6 AND PFCs IN OTHER PRODUCTS

8.3.1

Introduction

This source category excludes the following source categories that are addressed elsewhere in the 2006 Guidelines: •

•

Production of SF6 and PFCs (Section 3.10);

•

Primary and secondary production of magnesium and aluminium (Chapter 4); and

•

Production and use of electrical equipment (Section 8.2);

Semiconductor and flat panel display manufacturing (Chapter 6).

Identified remaining applications in this source category include:

•

•

•

• •

SF6 and PFCs used in military applications, particularly SF6 used in airborne radar systems, e.g., AWACS (Airborne Warning and Control System), and PFCs used as heat transfer fluids in high-powered electronic applications; SF6 used in equipment in university and research particle accelerators; SF6 used in equipment in industrial and medical particle accelerators; ‘Adiabatic’ applications utilising the low permeability through rubber of SF6 and some PFCs, e.g., car tires and sport shoe soles;

•

SF6 used in sound-proof windows;

•

PFCs used in cosmetics and in medical applications;

•

PFCs used as heat transfer fluids in commercial and consumer applications;

Other uses e.g. gas-air tracer in research and leak detectors.

8.3.2 8.3.2.1

Methodological issues C HOICE

OF METHOD

The good practice method is to use either consumption data from users of SF6 or PFCs or top-down import, export and consumption data from national SF6 producers and distributors, disaggregated by major type of SF6 or PFC application. Acquiring this data will entail a survey of all producers and distributors of SF6 and PFCs to identify total net SF6 and PFC consumption. Once the data are obtained, the amount of SF6 and PFC consumed by application in this source category should be estimated.

MILITARY APPLICATIONS SF 6 EMISSIONS FROM OPERATION OF AWACS SF6 is used as an insulating medium in the radar systems of military reconnaissance planes of the Boeing E-3A type, commonly known as AWACS. The purpose of the SF6 is to prevent electric flashovers in the hollow conductors of the antenna, in which high voltages of more than 135 kV prevail. When the plane ascends, SF6 is automatically released from the system and into the atmosphere to maintain the appropriate pressure difference between the system and the outside air. When the plane descends, SF6 is automatically charged into the system from an SF6 container on board. Most emissions occur during the pressure-balancing process on ascent, but emissions from system leakage can also occur during other phases of flight or during time on the ground. Annual emissions per plane have been estimated to be 740 kg, while the charge of each system is approximately 13 kg.

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Figure 8.2

Decision tree for SF 6 from AWACS Start

Are detailed acquisition and disbursement data available for this category?

Yes

Use Mass-Balance Tier 2 approach. Box 2: Tier 2

No

Is the Other Product Manufacture and Use a key category1, and is this subcategory significant?

No

Use Emission-Factor Tier 1 approach. Box 1: Tier 1

Yes Collect data for Tier 2 method. Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

Tier 1 method – SF 6 emissions per plane If a country does not have data on SF6 consumption by its AWACS, it may use a per-plane emission factor to estimate emissions. An emission factor of 740 kg per plane per year is presented in Table 8.7 below; this figure is based on estimates of SF6 emissions from NATO Boeing E-3As. Note that actual emissions per plane are strongly influenced by the average number of sorties (take-offs) per plane per year. More frequent sorties will raise the emission rate above 740 kg/plane; less frequent sorties will lower it. Leakage rates during flight or during time on the ground will also affect the emission rate. EQUATION 8.12 EMISSIONS FROM AWACS (DEFAULT EMISSION ACTOR)

User Emissions = 740 kg • Number of planes in AWACS fleet

TABLE 8.7 SF6 EMISSIONS PER PLANE PER YEAR Emissions per plane per year (kg SF6) 740 kg

Uncertainty ±100 kg

Source: Schwarz (2005)

Table 8.8 includes information on national AWACS fleets world wide (Boeing, 2005); like other activity data, it may quickly go out of date. Countries are in the best position to know the numbers of planes in their AWACS fleets.

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TABLE 8.8 NATIONAL AWACS FLEETS Country/ Organisation

USA

Japan

France

UK

Other NATO

Saudi Arabia

Total

No. AWACS

33

4

4

7

17

5

70

Source: Boeing (2005)

Tier 2 method – user mass-balance method The most accurate method for estimating SF6 emissions from AWACS is to track SF6 consumption by the systems. To do so, the following equations, which are similar to the utility-level variant of the Tier 3 method for electrical equipment, may be used. Note that for AWACS, acquisitions and disbursements of SF6 containers are likely to be considerably more important to the result than acquisitions and retirements of operating systems. EQUATION 8.13 EMISSIONS FROM AWACS (USER MASS-BALANCE)

User Emissions = Decrease in SF6 Inventory + Acquisitions of SF6 – Disbursements of SF6 – Net Increase in AWACS Fleet Charge Where: Decrease in SF6 Inventory = SF6 stored in containers at the beginning of the year – SF6 stored in containers at the end of the year Acquisitions of SF6 = SF6 purchased from chemical producers or distributors in bulk + SF6 purchased from AWACS manufacturers or distributors with or inside of new planes + SF6 returned to site after off-site recycling Disbursements of SF6 = SF6 contained in AWACS that are transferred to other entities + SF6 returned to suppliers + SF6 sent off-site for recycling + SF6 destroyed

Net Increase in AWACS Fleet Charge = 13 kg • (New AWACS – Retiring AWACS)

SF6 AND PFC EMISSIONS FROM OTHER MILITARY APPLICATIONS There is wide range of military applications using PFCs or SF6.5 Military electronics are believed to be an important and growing application of PFC heat transfer fluids, which are valued for their stability and dielectric properties. The fluids are used in ground and airborne radar (klystrons), avionics, missile guidance systems, ECM (Electronic Counter Measures), sonar, amphibious assault vehicles, other surveillance aircraft, lasers, SDI (Strategic Defense Initiative), and stealth aircraft. PFCs may also be used to cool electric motors, particularly in applications where noise reduction is valued, e.g., in ships and submarines. The specific PFCs used in these applications are believed to be similar to those identified as heat transfer fluids in electronics manufacturing in Chapter 6. Spray cooling, jet impingement cooling, and pool boiling appear to be the favoured systems for heat removal. In all of these cooling applications, the PFC is contained in a closed system, and neither replacement nor replenishment of the PFC liquid appears to be required. Thus, the greatest opportunities for emissions are the manufacture, maintenance, and, especially, the disposal of the equipment. SF6 is used in high-performance ground and airborne radar systems in their hollow conductors for transmission of high-frequency energy pulses at high voltages from the klystron. Another application of SF6 is as an oxidant of lithium in Stored Chemical Energy Propulsion System (SCEPS), e.g., in naval torpedoes and in infrared decoys (Koch, 2004). Apparently, these applications of SF6, like those of the PFC heat transfer fluids enumerated above, are generally more or less enclosed, but servicing and testing procedures may lead to emission. The use of SF6 for the quieting of torpedo propellers has also been reported (NIST, 1997). In addition, SF6 may be emitted as a by-product of the processing of nuclear material for the production of fuel and nuclear warheads. SF6 is known to be emitted from neutralising excess fluorine during the production of nuclear fuel for civilian applications (AREVA, 2005). 5

David Harris and James Hildebrandt, “Spray Cooling Electrical and Electronic Equipment,” COTS Journal, November 2003; C. Shepherd Burton, “Uses and Air Emissions of Liquid PFC Heat Transfer Fluids from the Electronics Sector,” Draft report prepared for Scott C. Bartos, U.S. Environmental Protection Agency.

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Although it is believed that the total amounts of SF6 and PFCs consumed and emitted in this sector may be significant, no data on quantities are publicly available so far. Therefore, inventory compilers should try to collect further information from the relevant authorities and, if possible, their suppliers. As noted above, the greatest opportunities for emissions from many of these applications appear to be the manufacture, maintenance, and disposal of the equipment. Thus, if inventory compilers can acquire information on emission rates during the manufacture, maintenance, and disposal of the equipment, along with the quantities of equipment manufactured, in use, and disposed, they can use the Tier 2 or Tier 3 method for electrical equipment to estimate emissions. For applications with different emissions profiles (e.g., prompt emissions), the appropriate equation from Section 8.2 may be used.

SF 6 EMISSIONS FROM UNIVERSITY AND RESEARCH PARTICLE ACCELERATORS SF6 is used in university and research operated particle accelerators as an insulating gas. Typically, high voltage equipment is contained and operated within a vessel filled with SF6 at a pressure exceeding atmospheric pressure. Charges range from five kilograms to over ten thousand kilograms, with typical charges falling between 500 and 3 000 kg. When the equipment requires maintenance, the SF6 is transferred into storage tanks. SF6 losses occur primarily during gas recovery and transfer, when pressure relief valves are actuated, and through slow leaks. Based on two recent studies annual SF6 losses range between 5 and 7 percent of vessel capacity per year and generally depend on the vessel opening frequency plus the efficiency of the recovery and transfer equipment. World banked capacity is roughly estimated to be 500 tonnes with annual SF6 emissions of 35 tonnes. Switzerland has developed a voluntary program to reduce SF6 emissions from particle accelerators. Suggestions and techniques for reducing SF6 emissions from these sources exist.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Other Product Manufacture and Use

Figure 8.3

Decision tree for SF 6 from research accelerators Start

Are detailed acquisition and disbursement data available for this category?

Yes

Use Accelerator-Level Mass-Balance approach. Box 3: Tier 3

No

Are data on individual accelerator charges available?

Yes

Use Accelerator-Level Emission-Factor approach. Box 2: Tier 2

No Is the Other Product Manufacture and Use a key category1, and is this subcategory significant?

No

Use Country-Level approach. Box 1: Tier 1

Yes Collect data for Tier 3 or Tier 2 method. Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

Tier 1 method – country-level method In cases where individual user accelerator charge data is unavailable, one extremely rough method involves determining the total number of university and research particle accelerators in the country and using several factors to determine the country-level annual emission rate as noted in Equation 8.14. For this Tier 1 method, the only data that requires collection is the total number of university and research particle accelerators in the given country. EQUATION 8.14 UNIVERSITY AND RESEARCH PARTICLE ACCELERATOR EMISSIONS (COUNTRY-LEVEL)

Emissions = (Number of university and research particle accelerators in the country) • (SF6 Use Factor) • (SF6 Charge Factor, kg) • (SF6 university and research particle accelerator Emission Factor) Where: Number of university and research particle accelerators in the country = The total number of university and research particle accelerators in the country. This rough method does not require countries to determine the number of accelerators that use SF6. To determine if a country has a particle accelerator, go to http://www-elsa.physik.uni-bonn.de/Informationen/accelerator_list.html

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SF6 Use Factor = 0.33 Approximately one third of university and research particle accelerators use SF6 as an insulator. SF6 Charge Factor = 2400 kg, SF6, the average SF6 charge in a university and research particle accelerator. SF6 university and research particle accelerator Emission Factor = 0.07, the average annual university and research particle accelerator emission rate as a fraction of the total charge.

Tier 2 method – accelerator-level emission-factor approach If data on the quantity of SF6 contained within each university and research accelerator are available, a default emission factor of 7 percent may be multiplied by the total SF6 charge contained in university and research accelerators in the country. The total country SF6 emission rate from university and research accelerators is therefore calculated from Equation 8.15. EQUATION 8.15 UNIVERSITY AND RESEARCH PARTICLE ACCELERATOR EMISSIONS (ACCELERATOR-LEVEL EMISSION FACTOR)

Total Emissions = SF6 university and research particle accelerator Emission Factor • ∑ Individual Accelerator Charges Where: SF6 university and research particle accelerator Emission Factor = 0.07, the average annual university and research particle accelerator emission rate as a fraction of the total charge. Individual User Accelerator Charges = SF6 contained within each university and research accelerator.

Tier 3 method –accelerator-level mass-balance method SF6 emissions from university and research facilities operating particle accelerators may be most accurately determined at the user level on an accelerator-by-accelerator basis. Emission calculations are estimated by tracking accelerator charge as well as SF6 consumption and disposal. As detailed in Equation 8.16, the total emissions are equal to the sum of the individual users’ emissions. Note, under this method, as the overall SF6 emission rate from particle accelerators is small compared to other SF6 uses, the associated SF6 lost in manufacturing is considered negligible and is not included in the calculation. EQUATION 8.16 TOTAL RESEARCH ACCELERATOR EMISSIONS

Total Emissions = ∑ Individual Accelerator Emissions

Each particle accelerator’s emissions can be calculated as follows: EQUATION 8.17 RESEARCH ACCELERATOR EMISSIONS (ACCELERATOR-LEVEL MASS-BALANCE)

Accelerator Emissions = Decrease in SF6 Inventory + Acquisitions of SF6 – Disbursements of SF6 – Net Increase in Accelerator Charge Where: Decrease in SF6 Inventory = SF6 stored in containers at the beginning of the year – SF6 stored in containers at the end of the year Acquisitions of SF6 = SF6 purchased from chemical producers or distributors in bulk + SF6 purchased from accelerator manufacturers or distributors with or inside of new accelerator components + SF6 returned to site after off-site recycling Disbursements of SF6 = SF6 contained in components transferred to other entities + SF6 returned to suppliers + SF6 sent off-site for recycling + SF6 destroyed Net Increase in Accelerator Charge = SF6 Charge of New Components – SF6 Charge of Retiring Components

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Chapter 8: Other Product Manufacture and Use

SF 6 EMISSIONS FROM INDUSTRIAL AND MEDICAL PARTICLE ACCELERATORS SF6 is used as an insulating gas in two types of industrial particle accelerators (low and high voltage) and also in medical (cancer therapy) particle accelerators, as is the case for university and research particle accelerators. However, the emission and charge factors for industrial and medical particle accelerators are different from those of university and research accelerators, as discussed below. Global banked capacity for industrial particle accelerators is roughly estimated to be 500 tonnes with annual SF6 emissions of 35 tonnes. Global banked capacity for medical (radiotherapy) particle accelerators is roughly estimated to be less than 5 tonnes with annual SF6 emissions of less than 5 tonnes. (Schwarz, 2005). Figure 8.4

Decision tree for industrial and medical particle accelerators Start

Are detailed acquisition and disbursement data available for this category?

Yes

Use User-Level MassBalance approach. Box 3: Tier 3

No

Are data on individual accelerator charges available?

Yes

Use User-Level EmissionFactor approach. Box 2: Tier 2

No

Is the Other Product Manufacture and Use a key category1, and is this subcategory significant?

No

Use Country-Level approach. Box 1: Tier 1

Yes Collect data for Tier 3 or Tier 2 method. Note: 1. See Volume 1 Chapter 4, Methodological Choice and Identification of Key Categories (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

Tier 1 method – country-level method In cases where individual user accelerator charge data is unavailable, one extremely rough method involves determining the total number of particle accelerators by process description in the country and using factors to

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determine the country level annual emission rate as noted in Equation 8.18. For this Tier 1 method, the only data that requires collection is the total number of particle accelerators which contain SF6 by process description in the given country. EQUATION 8.18 INDUSTRIAL/MEDICAL ACCELERATOR EMISSIONS (COUNTRY-LEVEL)

Emissions = (Number of particle accelerators that use SF6 by process description in the country) • (SF6 Charge Factor, kg) • (SF6 applicable particle accelerator Emission Factor) Where: Number of particle accelerators by type in the country = The total number of particle accelerators by type (industrial high voltage, industrial low voltage and radiotherapy) that use SF6 in the country, 1, 2, etc. (Only count particle accelerators that use SF6. This differs for the Tier 1 calculation for university and research particle accelerators) SF6 Charge Factor = The average SF6 charge in a particle accelerator by process description as noted below. SF6 particle accelerator Emission Factor = The average annual SF6 particle accelerator emission rate as a fraction of the total charge by process description.

TABLE 8.9 AVERAGE SF6 CHARGE IN A PARTICLE ACCELERATOR BY PROCESS DESCRIPTION Process Description

a

SF6 Charge Factor, kg

Industrial Particle Accelerators – high voltage (0.3-23 MV)

1300

Industrial Particle Accelerators –low voltage (18? and = 7 days of frost /year?

Start

No

MAT > 10? ?

Yes

Tropical Montane

Tropical Wet

Tropical Moist

Warm Temperate Moist

Elevation >1000m?

Yes

Yes

Yes

Yes

MAP:PET >1?

Yes

No

No

MAP > 2000mm?

Warm Temperate Dry

No

MAT >0? ?

MAP = 2000mm and > 1000mm?

Yes No MAP:PET >1?

No

Yes

Cool Temperate Moist

No

Tropical Dry Polar Moist

Boreal Moist Cool Temperate Dry

Yes

MAP:PET >1?

No

Yes

All Months Average 1?

No

No

Polar Dry

Boreal Dry

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 3A.5.3

Classification scheme for mineral soil types based on USDA taxonomy

Greater than 70% sand and less than 8% clay?

Start

Yes

Sandy Soils

No

Aquic Soil?

Yes

Wetland Soils

Yes

Volcanic Soils

No

Andisols?

No

Spodisols?

Yes

Spodic Soils

No

Low Activity Clay Soils

3.40

No

Mollisols, Vertisols, High-base status, Alfisols, Aridisols, Inceptisols?

Yes

High Activity Clay Soils

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Consistent Representation of Lands

Figure 3A.5.4

Classification scheme for mineral soil types based on World Reference Base for Soil Resources (WRB) classification.

Greater than 70% sand and less than 8% clay?

Start

Yes

Sandy Soils

No

Gleysols?

Yes

Wetland Soils

Yes

Volcanic Soils

No

Andisols?

No

Podzols?

Yes

Spodic Soils

No

Low Activity Clay Soils

No

Leptosols, Vertisols, Kastanozems, Chernozems, Phaeozems, Luvisols, Alisols, Albeluvisols, Solonetz, Calcisols, Gypsisols, Umbrisols, Cambisols, Regosols?

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Yes

High Activity Clay Soils

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References Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37(1), pp. 35-46. Darby, H.C. (1970). Doomsday Book – The first land utilization survey. The Geographical Magazine 42(6), pp. 416 – 423. FAO (1995). Planning for Sustainable use of Land Resources: Towards a New Type. Land and Water Bulletin 2, Food and Agriculture Organisation, Rome Italy, 60 pp. IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. Callander B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. Scott, C.T. and Kohl, M. (1994). Sampling with partial replacement and stratification. Forest Science 40 (1):3046. Singh, A. (1989). Digital change detection techniques using remotely sensed data. Int. J. Remote Sensing 10(6), pp. 989 – 1003. Swanson, B.E., Bentz, R.P. and Sofranco, A.J. (Eds.). (1997). Improving agricultural extension. A reference manual. Food and Agriculture Organization of the United Nations, Rome. USGS (2001). http://edcdaac.usgs.gov/glcc/

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Chapter 4: Forest Land

CHAPTER 4

FOREST LAND

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Volume 4: Agriculture, Forestry and Other Land Use

Authors Harald Aalde (Norway), Patrick Gonzalez (USA), Michael Gytarsky (Russian Federation), Thelma Krug (Brazil), Werner A. Kurz (Canada), Stephen Ogle (USA), John Raison (Australia), Dieter Schoene (FAO), and N.H. Ravindranath (India) Nagmeldin G. Elhassan (Sudan), Linda S. Heath (USA), Niro Higuchi (Brazil), Samuel Kainja (Malawi), Mitsuo Matsumoto (Japan), María José Sanz Sánchez (Spain), and Zoltan Somogyi (European Commission/Hungary)

Contributing Authors Jim B. Carle (FAO) and Indu K. Murthy (India)

4.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

Contents 4

Forest Land 4.1

Introduction ...........................................................................................................................................4.7

4.2

Forest Land Remaining Forest Land ...................................................................................................4.11

4.2.1 4.2.1.1

Choice of method...................................................................................................................4.11

4.2.1.2

Choice of emission factors.....................................................................................................4.14

4.2.1.3

Choice of activity data ...........................................................................................................4.15

4.2.1.4

Calculation steps for Tier 1....................................................................................................4.17

4.2.1.5

Uncertainty assessment..........................................................................................................4.19

4.2.2

Dead organic matter ....................................................................................................................4.20

4.2.2.1

Choice of method...................................................................................................................4.20

4.2.2.2

Choice of emission/removal factors.......................................................................................4.21

4.2.2.3

Choice of activity data ...........................................................................................................4.22

4.2.2.4

Calculation steps for Tier 1....................................................................................................4.22

4.2.2.5

Uncertainty assessment..........................................................................................................4.22

4.2.3

Soil carbon...................................................................................................................................4.23

4.2.3.1

Choice of method...................................................................................................................4.23

4.2.3.2

Choice of stock change and emission factors ........................................................................4.25

4.2.3.3

Choice of activity data ...........................................................................................................4.25

4.2.3.4

Calculation steps for Tier 1....................................................................................................4.26

4.2.3.5

Uncertainty assessment..........................................................................................................4.27

4.2.4

4.3

Biomass .......................................................................................................................................4.11

Non-CO2 greenhouse gas emissions from biomass burning........................................................4.27

4.2.4.1

Choice of method...................................................................................................................4.28

4.2.4.2

Choice of emissions factors ...................................................................................................4.28

4.2.4.3

Choice of activity data ...........................................................................................................4.28

4.2.4.4

Uncertainty assessment..........................................................................................................4.29

Land Converted to Forest Land...........................................................................................................4.29

4.3.1

Biomass .......................................................................................................................................4.30

4.3.1.1

Choice of method...................................................................................................................4.30

4.3.1.2

Choice of emission factors.....................................................................................................4.32

4.3.1.3

Choice of activity data ...........................................................................................................4.33

4.3.1.4

Calculation steps for Tier 1....................................................................................................4.34

4.3.1.5

Uncertainty assessment..........................................................................................................4.36

4.3.2

Dead organic matter ....................................................................................................................4.36

4.3.2.1

Choice of method...................................................................................................................4.37

4.3.2.2

Choice of emission/removal factors.......................................................................................4.37

4.3.2.3

Choice of activity data ...........................................................................................................4.38

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4.3.2.4

Calculation steps for Tier 1....................................................................................................4.38

4.3.2.5

Uncertainty assessment..........................................................................................................4.38

4.3.3 4.3.3.1

Choice of method...................................................................................................................4.39

4.3.3.2

Choice of stock change and emission factors ........................................................................4.40

4.3.3.3

Choice of activity data ...........................................................................................................4.41

4.3.3.4

Calculation steps for Tier 1....................................................................................................4.41

4.3.3.5

Uncertainty assessment..........................................................................................................4.42

4.3.4 4.4

Soil carbon...................................................................................................................................4.39

Non-CO2 greenhouse gas emissions from biomass burning........................................................4.42

Completeness, Time series, QA/QC, and Reporting and Documentation...........................................4.43

4.4.1

Completeness ..............................................................................................................................4.43

4.4.2

Developing a consistent times series ...........................................................................................4.43

4.4.3

Quality Assurance and Quality Control.......................................................................................4.44

4.4.4

Reporting and Documentation.....................................................................................................4.45

4.5

Tables ..................................................................................................................................................4.46

Annex 4A.1

Glossary for Forest Land .............................................................................................................4.72

Reference

.....................................................................................................................................................4.79

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

Figures Figure 4.1

Global ecological zones, based on observed climate and vegetation patterns (FAO, 2001).............................................................................................................4.9

Figure 4.2

Global forest and land cover 1995.......................................................................................4.10

Tables Table 4.1

Climate domains (FAO, 2001), climate regions (Chapter 3), and ecological zones (FAO, 2001) ..............................................................................................................4.46

Table 4.2

Forest and land cover classes...............................................................................................4.47

Table 4.3

Carbon fraction of aboveground forest biomass..................................................................4.48

Table 4.4

Ratio of below-ground biomass to above-ground biomass (R) ...........................................4.49

Table 4.5

Default biomass conversion and expansion factors (BCEF) ...............................................4.50

Table 4.6

Emission factors for drained organic soils in managed forests............................................4.53

Table 4.7

Above-ground biomass in forests ........................................................................................4.53

Table 4.8

Above-ground biomass in forest plantations .......................................................................4.54

Table 4.9

Above-ground net biomass growth in natural forests ..........................................................4.57

Table 4.10

Above-ground net biomass growth in tropical and sub-tropical forest plantations .............4.59

Table 4.11a

Above-ground net volume growth of selected forest plantation species .............................4.61

Table 4.11b

Mean annual increment (growth of merchantable volume) for some forest plantation species.................................................................................................................4.62

Table 4.12

Tier 1 estimated biomass values from Tables 4.7–4.11 (Except Table 4.11B)....................4.63

Table 4.13

Basic wood density (D) of tropical tree species...................................................................4.64

Table 4.14

Basic wood density (D) of selected temperate and boreal tree taxa ....................................4.71

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Volume 4: Agriculture, Forestry and Other Land Use

Boxes

4.6

Box 4.1

Levels of detail ......................................................................................................................4.8

Box 4.2

Biomass conversion and expansion factors for assessing biomass and carbon in forests ..................................................................................................................4.13

Box 4.3

Examples of good practice approach in identification of lands converted to Forest Land......................................................................................................................4.34

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

4 FOREST LAND 4.1

INTRODUCTION

This chapter provides methods for estimating greenhouse gas emissions and removals due to changes in biomass, dead organic matter and soil organic carbon on Forest Land and Land Converted to Forest Land. It builds on the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines) and the Good Practice Guidance for Land Use, Land-Use Change and Forestry (GPG-LULUCF). The chapter:

•

• • • •

addresses all five carbon pools identified in Chapter 1 and transfers of carbon between different pools within the same land areas; includes carbon stock changes on managed forests due to human activities such as establishing and harvesting plantations, commercial felling, fuelwood gathering and other management practices, in addition to natural losses caused by fire, windstorms, insects, diseases, and other disturbances; provides simple (Tier 1) methods and default values and outline approaches for higher tier methods for the estimation of carbon stock changes; provides methods to estimate non-CO2 greenhouse gas emissions from biomass burning (other non-CO2 emissions such as N2O emissions from soils are covered in Chapter 11); should be used together with generic description of methods and equations from Chapter 2, and the approaches for obtaining consistent area data described in Chapter 3.

The Guidelines provide methods for estimating and reporting sources and sinks of greenhouse gases only for managed forests, as defined in Chapter 1. Countries should consistently apply national definitions of managed forests over time. National definitions should cover all forests subject to human intervention, including the full range of management practices from protecting forests, raising plantations, promoting natural regeneration, commercial timber production, non-commercial fuelwood extraction, and abandonment of managed land. This chapter does not include harvested wood products (HWP) which are covered by Chapter 12 of this Volume. Managed Forest Land is partitioned into two sub categories and the guidance and methodologies are given separately in two sections: •

•

Section 4.2 Forest Land Remaining Forest Land Section 4.3 Land Converted to Forest Land

Section 4.2 covers the methodology that applies to lands that have been Forest Land for more than the transition period required to reach new soil carbon levels (default is 20 years). Section 4.3 applies to lands converted to Forest Land within that transition period. The 20-year interval is taken as a default length of transition period for carbon stock changes following land-use change. It is good practice to differentiate national forest lands by the above two categories. The actual length of transition period depends on natural and ecological circumstances of a particular country or region and may differ from 20 years. Unmanaged forests, which are brought under management, enter the inventory and should be included in the Land Converted to Forest Land. Unmanaged forests which are converted to other land uses enter the inventory under their post conversion land-use categories with the appropriate transition period for the new land-use category. If there are no data on land conversion and the period involved are available, the default assumption is that all managed forest land belongs to the category Forest Land Remaining Forest Land and greenhouse gas (GHG) emissions and removals are estimated using guidance given in Section 4.2.

Relevant carbon pools and non-CO 2 gases The relevant carbon pools and non-CO2 gases for which methods are provided are given below:

•

•

Biomass (above-ground and below-ground biomass)

•

Soil organic matter

•

Dead organic matter (dead wood and litter)

Non-CO2 gases (CH4, CO, N2O, NOX)

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Volume 4: Agriculture, Forestry and Other Land Use

The selection of carbon pools or non-CO2 gases for estimation will depend on the significance of the pool and tier selected for each land-use category.

Forest land-use classification Greenhouse gas emissions and removals per hectare vary according to site factors, forest or plantation types, stages of stand development and management practices. It is good practice to stratify Forest Land into various sub categories to reduce the variation in growth rate and other forest parameters and to reduce uncertainty (Box 4.1). As a default, the Guidelines use the most recent ecological zone (see Table 4.1 in Section 4.5 and Figure 4.1 in this chapter) and forest cover (see Table 4.2 in Section 4.5 and Figure 4.2 in this chapter) classifications, developed by the Food and Agriculture Organization (FAO, 2001). National experts should use more detailed classifications for their countries, if available and suitable, given the other data requirements.

BOX 4.1 LEVELS OF DETAIL

Stratification of forest types into homogeneous sub-categories, and if possible at regional or subregional level within a country, reduces the uncertainty of estimates of greenhouse gas emissions and removals. For simplicity and clarity, this chapter discusses estimation of emissions and removals at national level and for a relatively small number of subcategories of Forest Land. This level of detail is designed to match the available sources of default input data, carbon contents and other assumptions. It is important, however, for users of these Guidelines to understand that they are encouraged to carry out the greenhouse gas emissions inventory calculations at a finer level of detail, if possible. Many countries have more detailed information available about forests and land-use change than were used in constructing default values in this Chapter. These data should be used, if suitable, for the following reasons: 1. Geographic detail at regional rather than national level Experts may find that greenhouse gas estimation for various regions within a country are necessary to capture important geographic variations in ecosystem types, biomass densities, fractions of cleared biomass which are burnt, etc. 2. Finer detail by subcategory Experts may subdivide the recommended land-use categories and subcategories to reflect important differences in climate, ecology or species, forest types, land-use or forestry practices, fuelwood gathering patterns, etc. In all cases, working at finer levels of disaggregation does not change the basic nature of the method of estimations, although additional data and assumptions will generally be required beyond the defaults provided in this Chapter. Once greenhouse gas emissions are estimated, using the most appropriate level of detail determined by the national experts, results should also be aggregated up to the national level and the standard categories requested in these Guidelines. This will allow for comparability of results among all participating countries. Generally, the data and assumptions used for finer levels of detail should also be reported to ensure transparency and repeatability of methods.

Terminology The terminology used in the methods for estimating biomass stocks and changes need to be consistent with the terminologies and definitions used by the Food and Agriculture Organization (FAO). FAO is the main source of activity data and emission factors for forest and other land-use categories in Tier 1 level calculations. Examples of terminology from FAO are: biomass growth, mean annual increment, biomass loss, and wood-removal. The Glossary in Annex 4A.1 includes definitions of these terminologies.

4.8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land Figure 4.1

Global ecological zones, based on observed climate and vegetation patterns (FAO, 2001). Data for geographic information systems available at http://www.fao.org.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 4.2

4.10

Global forest and land cover 1995. Original spatial resolution of the forest data is 1 km 2 (analysis U.S. Geological Survey (Loveland et al., 2000) and FAO (2001)). Data for geographic information systems available at http://edc.usgs.gov.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

4.2

FOREST LAND REMAINING FOREST LAND

This section deals with managed forests that have been under Forest Land for over 20 years (default), or for over a country-specific transition period. Greenhouse gas inventory for Forest Land Remaining Forest Land (FF) involves estimation of changes in carbon stock from five carbon pools (i.e., above-ground biomass, belowground biomass, dead wood, litter, and soil organic matter), as well as emissions of non-CO2 gases. Methods for estimating greenhouse gas emissions and removals for lands converted to Forest Land in the past 20 years (e.g., from Cropland and Grassland) are presented in Section 4.3. The set of general equations to estimate the annual carbon stock changes on Forest Land are given in Chapter 2.

4.2.1

Biomass

This section presents methods for estimating biomass gains and losses. Gains include total (above-ground and below-ground) biomass growth. Losses are roundwood removal/harvest, fuelwood removal/harvest/gathering, and losses from disturbances by fire, insects, diseases, and other disturbances. When such losses occur, belowground biomass is also reduced and transformed to dead organic matter (DOM).

4.2.1.1

C HOICE

OF METHOD

Chapter 2 describes two methods, namely, Gain-Loss Method based on estimates of annual change in biomass from estimates of biomass gain and loss (Equation 2.7) and a Stock-Difference Method which estimates the difference in total biomass carbon stock at time t2 and time t1 (Equation 2.8). The biomass gain-loss method is applicable for all tiers although the stock-difference method is more suited to Tiers 2 and 3. This is because, in general, the stock-difference method will provide more reliable estimates for relatively large increases or decreases of biomass or where very accurate forest inventories are carried out. For areas with a mix of stands of different forest types, and/or where biomass change is very small compared to the total amount of biomass, the inventory error under the stock-difference method may be larger than the expected change. Unless periodic inventories give estimates on stocks of dead organic matter, in addition to growing stock, one should be aware that other data on mortality and losses will still be required for estimating the transfer to dead organic matter, harvested wood products and emissions caused by disturbances. Subsequent inventories must also allow identical area coverage in order to get reliable results when using the stock-difference method. The choice of using gain-loss or stock-difference method at the appropriate tier level will therefore be a matter of expert judgment, taking into account the national inventory systems, availability of data and information from ecological surveys, forest ownership patterns, activity data, conversion and expansion factors as well as costbenefit analysis. The decision tree as shown in Figure 1.2 in Chapter 1 should be used to guide choice between the Tiers. This promotes efficient use of available resources, taking into account whether the biomass of this category is a significant carbon pool or a key category as described in Volume 1, Chapter 4. Tier-1 Metho d ( Biomass Ga in- Lo ss Method) Tier 1 is feasible even when country-specific estimates of activity data and emission/removal factors are not available, and works when changes of the carbon pool in biomass on Forest Land Remaining Forest Land are relatively small. The method requires the biomass carbon loss to be subtracted from the biomass carbon gain (Equation 2.7). The annual change in carbon stocks in biomass can be estimated using the gain-loss method, where the annual increase in carbon stocks due to biomass growth and annual decrease in carbon stocks due to biomass losses are estimated: •

•

•

The annual increase in biomass carbon stock is estimated using Equation 2.9, where area under each forest sub-category is multiplied by mean annual increment in tonnes of dry matter per hectare per year. Since the biomass growth is usually in terms of merchantable volume or above-ground biomass, the belowground biomass is estimated with a below-ground biomass to above-ground biomass ratio (Equation 2.10). Alternatively, merchantable volume (m3) can be converted directly to total biomass using biomass conversion and expansion factors (BCEFI ), (Equation 2.10). If BCEFI values are not available and if the biomass expansion factor (BEF) and basic wood density (D) values are separately estimated, then the following conversion can be used: BCEFI = BEFI ● D

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Volume 4: Agriculture, Forestry and Other Land Use

•

•

•

•

Biomass Expansion Factors (BEFI) expand merchantable volume to total aboveground biomass volume to account for non-merchantable components of the tree, stand and forest. BEFI is dimensionless. The average above-ground biomass of forest areas affected by disturbances are given in Tables 4.7 and 4.8; net average annual above-ground biomass growth values are provided in Tables 4.9, 4.10, and 4.12; net volume annual increment values are provided in Tables 4.11A and 4.11B; wood density is given in Tables 4.13 and 4.14; and below-ground biomass to above-ground biomass ratios (R) are given in Table 4.4. Refer to Box 4.2 for detailed explanation on how to convert and expand volumes of growing stock, increment and wood removals to biomass. In some ecosystems, basic wood density (D) can influence spatial patterns of forest biomass (Baker et al., 2004b). Tier 1 users who do not have measurements of wood density at the desired sub-strata level can estimate wood density by estimating the proportion of total forest biomass contributed by the 2-3 dominant species and using species-specific wood density values (Tables 4.13 and 4.14) to calculate a weighted average wood density value. Annual biomass loss or decrease in biomass carbon stocks is estimated using Equation 2.11, which requires estimates of annual carbon loss due to wood removals (Equation 2.12), fuelwood removal (Equation 2.13) and disturbances (Equation 2.14). Transfer of biomass to dead organic matter is estimated using Equation 2.20, based on estimates of annual biomass carbon lost due to mortality (Equation 2.21), annual carbon transfer to slash (Equation 2.22). Biomass estimates are converted to carbon values using carbon fraction of dry matter (Table 4.3).

When either the biomass stock or its change in a category (or sub-category) is significant or a key category, it is good practice to select a higher tier methodology for estimation. The choice of Tier 2 or 3 method depends on the types and accuracy of data and models available, level of spatial disaggregation of activity data and national circumstances. If using activity data collected via Approach 1 (see Chapter 3), and it is not possible to use supplementary data to identify the amount of land converted from and to Forest Land, the inventory compiler should estimate C stocks in biomass on all Forest Land using the Tier 1 method described above for Forest Land Remaining Forest Land. Tier 2 Tier 2 can be used in countries where country-specific estimates of activity data and emission/removal factors are available or can be gathered at reasonable cost. Tier 2, same as Tier 1, uses Equations 2.7 to 2.14 (excluding Equation 2.8). Species-specific wood density values (Tables 4.13 and 4.14) permit the calculation of biomass from species-specific forest inventory data. It is possible to use the stock-difference method (Equation 2.8) at Tier 2 where the necessary country-specific data are available Tier 3 Tier 3 approach for biomass carbon stock change estimation allows for a variety of methods, including processbased models. Implementation may differ from one country to another, due to differences in inventory methods, forest conditions and activity data. Transparent documentation of the validity and completeness of the data, assumptions, equations and models used is therefore a critical issue at Tier 3. Tier 3 requires use of detailed national forest inventories when the stock-difference method is used (Equation 2.8). They may be supplemented by allometric equations and models (for example, Chambers et al. (2001) and Baker et al. (2004a) for the Amazon; Jenkins et al. (2004) and Kurz and Apps (2006) for North America; and Zianis et al. (2005) for Europe), calibrated to national circumstances that allow for direct estimation of biomass growth.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

BOX 4.2 BIOMASS CONVERSION AND EXPANSION FACTORS FOR ASSESSING BIOMASS AND CARBON IN FORESTS1

Forest inventories and operational records usually document growing stock, net annual increment or wood removals in m3 of merchantable volume. This excludes non-merchantable above-ground components such as tree tops, branches, twigs, foliage, sometimes stumps, and below-ground components (roots). Assessments of biomass and carbon stocks and changes, on the other hand, focus on total biomass, biomass growth and biomass removals (harvest), including non-merchantable components, expressed in tons of dry-weight. Several methods may be used to derive forest biomass and its changes. Above-ground biomass and changes can be derived in two ways, namely: (i) directly by measuring sample tree attributes in the field, such as diameters and heights, and applying, species-specific allometric equations or biomass tables based on these equations once or periodically. (ii) indirectly by transforming available volume data from forest inventories, e.g., merchantable volume of growing stock, net annual increment or wood removals (Somogyi et al., 2006). The latter approach may achieve the transformation by applying biomass regression functions, which usually express biomass of species or species groups (t/ha) or its rate of change, directly as a function of growing stock density (m3/ha), and age, eco-regions or other variables (Pan et al., 2004). More commonly than these biomass regression functions, a single, discrete transformation factors2 is applied to merchantable volume to derive above-ground biomass and its changes: (i) Biomass Expansion Factors (BEF) expand the dry weight3 of the merchantable volume of growing stock, net annual increment, or wood removals, to account for non-merchantable components of the tree, stand, and forest. Before applying such BEFs, merchantable volume (m3) must be converted to dry-weight (tonne) by multiplying with a conversion factor known as basic wood density (D) in (t/m3). BEFs are dimensionless since they convert between units of weight. This method gives best results, when the BEFs have actually been determined based on dry weights, and when locally applicable basic wood densities are well known. (ii) Biomass Conversion and Expansion Factors (BCEF) combine conversion and expansion. They have the dimension (t/m3) and transform in one single multiplication growing stock, net annual increment, or wood removals (m3) directly into above-ground biomass, above-ground biomass growth, or biomass removals (t). BCEFs are more convenient. They can be applied directly to volume-based forest inventory data and operational records without the need of having to resort to basic wood densities. They provide best results, when they have been derived locally, based directly on merchantable volume. Mathematically, BCEF and BEF are related by: BCEF = BEF ● D Application of this equation requires caution because basic wood density and biomass expansion factors tend to be correlated. If the same sample of trees was used to determine D, BEF or BCEF, conversion will not introduce error. If, however, basic wood density is not known with certainty, transforming one into the other might introduce error, as BCEF implies a specific but unknown basic wood density. Ideally, all conversion and expansion factors would be derived or their applicability checked locally.

1

Please see glossary (Annex 4A.1) for definitions of terms.

2

While these transformation factors are usually applied in discrete form, they can also be expressed and depicted as continuous functions of growing stock density, age, or other variables.

3

In some applications, biomass expansion factors expand dry-weight of merchantable components to total biomass, including roots, or expand merchantable volume to above-ground or total biomass volume (Somogyi et al., 2006). As used in this document, biomass expansion factors always transform dry-weight of merchantable volume including bark to above-ground biomass, excluding roots.

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Volume 4: Agriculture, Forestry and Other Land Use

Both BEF and BCEF tend to decrease as a function of stand age, as growing stock density (volume of growing stock per ha) increases. This is because of the increasing ratio of merchantable volume to total volume. The decrease is rapid at low growing stock densities or for young stands and levels out for older stands and higher stand densities. The GPG-LULUCF provided only average default BEF values, together with wide ranges, and general guidance on how to select applicable values for specific countries from these ranges. To facilitate selection of more reliable default values, this document provides default factors as a function of growing stock density in Table 4.5. Since more comprehensive and more recent data were found in the literature, Table 4.5 contains BCEF defaults only. Countries that possess country-specific basic wood densities and BEF on a consistent basis may apply them to calculate country-specific BCEF using the formula given above. BCEF or BEF that apply to growing stock and net annual increment are different. In this document, the following symbols are used: BCEFS: biomass conversion and expansion factor applicable to growing stock; transforms merchantable volume of growing stock into above-ground biomass. BCEFI: biomass conversion and expansion factor applicable to net annual increment; transforms merchantable volume of net annual increment into above-ground biomass growth. BCEFR: biomass conversion and expansion factors applicable to wood removals; transforms merchantable biomass to total biomass (including bark). BCEFR and BEFR for wood and fuelwood removal will be larger than that for growing stock due to harvest loss (see Annex 4A.1 Glossary). If a country specific value for harvest loss is not known, defaults are 10% for hardwoods and 8% for conifers (Kramer and Akca, 1982). Default conversion and expansion factors for wood removals can be derived by dividing BCEFS by (1– 0.08) for conifers and (1-0.1) for broadleaves. It is good practice to estimate growing stock biomass, above-ground biomass growth and aboveground biomass removals by strata; to document these strata; and to aggregate results ex post. Methods described above will yield above-ground biomass and its changes. Results must be expanded to total biomass via applicable below-ground biomass to above-ground biomass ratios.

4.2.1.2

C HOICE

OF EMISSION FACTORS

The Gain-Loss Method requires the above-ground biomass growth, biomass conversion and expansion factor (BCEF), BEF, and/or basic wood densities according to each forest type and climatic zone in the country, plus emission factors related to biomass loss, including losses due to wood removals, fuelwood removals and disturbances.

Annual biomass carbon gain, ΔC G Mea n abo ve-g round b ioma ss gro wth ( in cr em ent), G W Tier 1 Default values of the above-ground biomass growth (GW) which are provided in Tables 4.9, 4.10 and 4.12 can be used at Tier 1. If available, it is good practice to use other regional default values for different forest types more relevant to the country. Tier 2 Tier 2 method uses more country-specific data to calculate the above-ground biomass growth, GW from countryspecific net annual increment of growing stock (IV). Tables 4.11a and 4.11b provide default values for IV. Combined default biomass conversion and expansion factor (BCEFI) of Iv are provided in Table 4.5. Separate data on biomass expansion factor for increment (BEFI) and basic wood density (D) can also be used to convert the available data to GW. Tables 4.13 and 4.14 provide default values for basic wood density. Tier 3 Under Tier 3, process-based estimation will have access to detailed forest inventory or monitoring system with data on growing stock and past and projected net annual increment and functions relating to growing stock or net annual increment directly to biomass and biomass growth. It is also possible to derive net annual increment by process simulation. Specific carbon fraction and basic wood density should also be incorporated. Forest inventories usually provide conditions of forest growing stock and net annual increment in the year of the inventory. When the year of inventory does not coincide with the year of reporting, interpolated or extrapolated

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

net annual increment or increment estimated by models (i.e., model capable of simulating forest dynamics), should be used along with data on harvesting and disturbances to update inventory data to the year of interest. Belo w-g round b iomass g rowth ( increment) Tier 1 Below-ground carbon stock changes, as a default assumption consistent with the 1996 IPCC Guidelines, can be zero. Alternatively, default values for below-ground biomass to above-ground biomass ratios (R) are to be used to estimate below-ground biomass growth. Default values are provided in Table 4.4. Strictly, these ratios of below-ground biomass to above-ground biomass are only valid for stocks, but no appreciable error is likely to obtain if they are applied to above-ground biomass growth over short periods. Tier 2 Country-specific below-ground biomass to above-ground biomass ratios should be used to estimate belowground biomass for different forest types. Tier 3 For preference, below-ground biomass should be directly incorporated in models for calculating total biomass increment and losses. Alternatively, nationally or regionally determined below-ground biomass to above-ground biomass ratios or regression models (e.g., Li et al., 2003) may be used.

Annual carbon loss in biomass, ΔC L Bioma ss lo ss due to wood remova ls, L w o o d - r e m o v a l s and L f u e l w o o d When computing carbon loss through biomass removals, the following factors are needed: Wood removal (H), fuelwood removal as trees or parts of trees (FG), basic wood density (D), below-ground biomass to aboveground biomass ratio (R), carbon fraction (CF), BCEF for wood removals. While all wood removals represent a loss for the forest biomass pool, Chapter 12 provides guidance for estimating annual change in carbon stocks in harvested wood products. D isturba nces, L d i s t u r b a n c e The estimate of other losses of carbon requires data on areas affected by disturbances (Adisturbance) and the biomass of these forest areas (BW). Above ground biomass estimates of forest types affected by disturbance are required, along with below-ground biomass to above-ground biomass ratio and fraction of biomass lost in disturbance. Chapter 2, Tables 2.4, 2.5 and 2.6 provide fuel biomass consumption values, emission factors, and combustion factors needed for estimating proportion of biomass lost in fires and proportion to be transferred to dead organic matter under higher tiers. Tier 1 The average biomass varies with the forest types and management practices. The default values are given in Tables 4.9 and 4.10. In the case of fire, both CO2 and non-CO2 emissions occur from combusted fuels of aboveground biomass including understory. Fire may consume a high proportion of understory vegetation. In the case of other disturbances, a fraction of above ground biomass is transferred to dead organic matter and under Tier 1, all biomass in area subjected to disturbance is assumed to be emitted in the year of disturbance. Tier 2 Under Tier 2, biomass changes due to disturbances will be taken into account by forest category, type of disturbance and intensity. Average values for biomass are obtained from country-specific data. Tier 3 In addition to calculating losses similar to Tier 2, Tier 3 can also adopt models, which typically employ spatially referenced or spatially explicit information on the year and type of disturbance.

4.2.1.3

C HOICE

OF ACTIVITY DATA

Area of manag ed Fo rest La nd All tiers require information on areas of managed Forest Land according to different forest types, climate, management systems, and regions. Tier 1 Tier 1 uses data of forest area which can be obtained through national statistics, from forest agencies (which may have information on areas of different management practices), conservation agencies (especially for areas managed for natural regeneration), municipalities, survey and mapping agencies. Cross-checks should be made to ensure complete and consistent representation for avoiding omissions or double counting as specified in Chapter 3. If no country data are available, aggregate information can be obtained from international data

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Volume 4: Agriculture, Forestry and Other Land Use

sources (FAO, 1995; FAO, 2001; TBFRA, 2000). It is good practice to verify, validate, and update the FAO data using national sources. Tier 2 Tier 2 uses country-defined national data sets, according to different forest types, climate, management systems, and regions, with a resolution sufficient to ensure appropriate representation of land areas in line with provisions of Chapter 3 of this volume. Approach 2 of Chapter 3 is relevant for Tier 2. Tier 3 Tier 3 uses country-specific data on managed Forest Land from different sources, notably national forest inventories, registers of land use and land-use changes, or remote sensing. These data should give a full accounting of all land-use transitions to Forest Land and disaggregate along climate, soil, and vegetation types. Geo-referenced area under different forest types may be used to track changes in area under different land-use types, using Approach 3 of Chapter 3. Wood remova ls The inventory requires data on wood removals, including fuelwood removals and biomass losses due to disturbances, in order to calculate biomass stock changes and carbon pool transfers. In addition to wood removals for industrial purposes, there may also be wood removals for small scale processing or direct sales to consumers from land owners. This quantity may not be included in official statistics and may need to be estimated by survey. Fuelwood from branches and tops of felled trees must be subtracted from transfers to the dead wood pool. Salvage of wood from areas affected by disturbances must also be subtracted from biomass, to ensure that no double counting occurs in Tier 1 inventories in which the biomass in areas affected by disturbances is already assumed released to the atmosphere. In using production statistics, users must pay careful attention to the units involved. It is important to check whether the information in the original data is reported in biomass, volumes underbark or overbark to ensure that expansion factors are used only where appropriate and in a consistent way. Unless restricted to Approach 1 land representation without supplementary data, so that all forest land is counted under Forest Land Remaining Forest Land, wood removals from Forest Land being converted to another land use should not be included in losses reported for Forest Land Remaining Forest Land since these losses are reported in the new land-use category. If the statistics on wood removals do not provide stratification on lands, then an amount of biomass approximating the biomass loss from lands converted from Forest Land should be subtracted from the total wood removals. Extraction of roundwood is published in the UNECE/FAO Timber Bulletin and by FAO Yearbook of Forest Products. The latter is based primarily on data provided by the countries. In the absence of official data, FAO provides an estimate based on the best information available. Usually, the FAO yearbook appears with a twoyear time lag. Tier 1 FAO data can be used as a Tier 1 default for H in Equation 2.12 in Chapter 2. The roundwood data include all wood removed from forests which are reported in cubic meters underbark. The underbark data need conversion to overbark before using BCEFR. Conversion from underbark to overbark volumes is done by using bark percentages. Tier 2 Country-specific data should be used. Tier 3 Country-specific wood removals data from different forest categories should be used at the spatial resolution chosen for reporting Fuelwood remo va l Estimation of carbon losses due to fuelwood removal requires annual volume of fuelwood removed (FG) and basic wood density (D). Fuelwood is produced in different ways in countries and varies from ordinary timber harvesting, to using parts of trees, to gathering of dead wood. Fuelwood constitutes the largest component of biomass loss for many countries, thus reliable estimates are needed for such countries. . If possible, fuelwood removal from Forest Land Remaining Forest Land and that coming from Forest Land conversion to other uses should be separated. Tier 1 FAO provides statistics on fuelwood and charcoal removals for all countries. FAO statistics are based on what is provided by the concerned ministries/ departments in the countries and in some cases may not account fully for the entire fuelwood and charcoal removal due to the limitations of national data collection and reporting systems. Thus, under Tier 1, FAO statistics can be used directly but should be checked for completeness by the national

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

source of data for the FAO such as the Ministry of Forests or Agriculture or any statistical organization. FAO or any national estimates should be supplemented from regional surveys or local studies on fuelwood consumption, since fuelwood is collected from multiple sources; forests, timber processing residues, farms, homesteads, village commons, etc. If more complete information is available nationally, it should be used. Tier 2 Country-specific data should be used, if available. Regional surveys of fuelwood removals can be used to verify and supplement the national or FAO data source. At the national level, aggregate fuelwood removals can be estimated by conducting regional level surveys of rural and urban households at different income levels, industries and establishments. Tier 3 Fuelwood removals data from national level studies should be used at the resolution required for the Tier 3 model, including the non-commercial fuelwood removals. Fuelwood removal should be linked to forest types and regions. Different methods of fuelwood removal from Forest Land Remaining Forest Land should be accounted at regional or disaggregated level through surveys. The source of fuelwood should be identified to ensure that no double counting occurs. D isturba nces A database on rate and impact of natural disturbances by type, for all European countries (Schelhaas et al., 2001), can be found at: http://www.efi.fi/ A UNEP database on global burnt area can be found at: http://www.grid.unep.ch/ However, one should note that the UNEP database is only valid for year 2000. In many countries inter-annual variability in burnt area is large, so these figures will not provide a representative average. Many countries maintain their own disturbance statistics e.g., Stocks et al. (2002) which can be employed in Tier 2 or Tier 3 approaches (Kurz and Apps, 2006). The FRA2005 (FAO, 2005) should also be examined for data on disturbances.

4.2.1.4

C ALCULATION

STEPS FOR

T IER 1

T he fo llo wing s umma r iz e s s tep s for e s tima ting cha nge in c arbo n s to ck s in bio mas s (∆ C B ) u s ing t he d e f a u l t me t ho d s: Step 1: Using guidance from Chapter 3 (approaches in representing land areas), categorise the area (A) of Forest Land Remaining Forest Land into forest types of different climatic or ecological zones, as adopted by the country. As a point of reference, Annex 3A.1 of GPG-LULUCF (IPCC, 2003) provides national-level data of forest area and annual change in forest area by region and by country as a means of comparison. Alternatively FAO also periodically provides area data; Step 2: Estimate the annual biomass gain in Forest Land Remaining Forest Land (∆CG) using estimates of area and biomass growth, for each forest type and climatic zone in the country available using Equations 2.9 and 2.10 in Chapter 2; Step 3: Estimate the annual carbon loss due to wood removals (Lwood-removals) using Equation 2.12 in Chapter 2; Step 4: Estimate annual carbon loss due to fuelwood removal (Lfuelwood) using Equation 2.13 in Chapter 2; Step 5: Estimate annual carbon loss due to disturbance (Ldisturbance) using Equation 2.14 in Chapter 2, avoid double counting of losses already covered in wood removals and fuelwood removals; Step 6: From the estimated losses in Steps 3 to 5, estimate the annual decrease in carbon stocks due to biomass losses (∆CL) using Equation 2.11 in Chapter 2; Step 7: Estimate the annual change in carbon stocks in biomass (∆CB) using Equation 2.7 in Chapter 2.

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Example. The following example shows Gain-Loss Method (Tier 1) calculations of annual change in carbon stocks in biomass (∆CB), using Chapter 2, Equation 2.7 (∆CB = (∆CG – ∆CL)), for a hypothetical country in temperate continental forest zone of Europe (Table 4.1, Section 4.5): -

the area of Forest Land Remaining Forest Land (A) within the country is 100,000 ha (see Chapter 3 for area categorization);

-

it is a 25-year-old pine forest, average above-ground growing stock volume is 40 m3 ha-1;

-

the merchantable round wood harvest over bark (H) is 1,000 m3 yr-1;

-

whole trees fuel wood removal (FGtrees) is 500 m3 yr-1;

-

area of insect disturbance is 2,000 ha yr-1 with above-ground biomass affected 4.0 tonne d.m. ha-1.

Annual gain in biomass (∆CG) is a product of mean annual biomass increment (GTOTAL), area of

land (A) and carbon fraction of dry matter (CF); Equation 2.9 in Chapter 2 (ΔCG = ∑ij (A ● GTOTAL ● CF). GTOTAL is calculated using Chapter 2, Equation 2.10 for given values of annual aboveground biomass growth (GW), below-ground biomass to above-ground biomass ratio (R), and default data tables in Section 4.5. For the hypothetical country, GW

= 4.0 tonnes d.m. ha-1 yr-1 (Table 4.9);

R

= 0.29 tonne d.m. (tonne d.m.)-1 for above-ground biomass of 50 to 150 t ha-1 (Table 4.4 with reference to Table 4.7 for above ground biomass);

GTOTAL

= 4.0 tonnes d.m. ha-1 yr-1 ● (1 + 0.29) = 5.16 tonnes d.m. ha-1 yr-1 (Equation 2.10); and

CF

= 0.47 tonne C (tonne d.m.)-1 (Table 4.3).

Consequently, (Equation 2.9): ∆CG = 100,000 ha ● 5.16 tonnes d.m. ha-1 yr-1 ● 0.47 tonne C (tonne dm)-1 = 242,520 tonnes C yr-1. Biomass loss (∆CL) is a sum of annual loss due to wood removals (Lwood-removals), fuel wood gathering (Lfuelwood) and disturbances (Ldisturbance), Equation 2.11 in Chapter 2. Wood removal (Lwood-removals) is calculated with Equation 2.12, Chapter 2, merchantable round wood over bark (H), biomass conversion expansion factor (BCEFR), bark fraction in harvested wood (BF), below-ground biomass to above-ground biomass ratio (R), carbon fraction of dry matter (CF) and default tables, Section 4.5. For the hypothetical country, BCEFR

= 1.11 tonnes d.m. m-3 (Table 4.5 with reference to growing stock volume 40 m3 ha-1);

BF

= 0.1 tonne d.m. (tonne d.m.)-1. R = 0.29 tonne d.m. (tonne d.m.)-1 for above-ground biomass 50 to 150 t ha-1 (Table 4.4, for above-ground biomass refer to Table 4.7); and

CF

= 0.47 tonne C (tonne d.m.)-1 (Table 4.3).

Lwood-removals = 1,000 m3 yr-1 ● 1.11 tonnes d.m. m-3 (1 + 0.29 + 0.1) ● 0.47 tonne C (tonne d.m.)-1 = 725.16 tonnes C yr-1 (Equation 2.12). Fuelwood removal (Lfuelwood) is calculated using Equation 2.13, Chapter 2, wood removals as whole trees (FGtrees), biomass conversion expansion factor (BCEFR), below-ground biomass to aboveground biomass ratio (R), carbon fraction of dry matter (CF) and default tables in Section 4.5. For the hypothetical country, BCEFR

= 1.11 tonnes d.m. m-3 (Table 4.5 with reference to growing stock volume 40 m3 ha-1);

R

= 0.29 tonne d.m. (tonne dm)-1 for above-ground biomass 50 to 150 t ha-1 (Table 4.4, for above-ground biomass refer to Table 4.7); and

CF

= 0.47 tonne C (tonne dm)-1 (Table 4.3).

Lfuelwood = 500 m3 yr-1 ● 0.75 tonne d.m. m-3 (1 + 0.29) ● 0.47 tonne C (tonne d.m.)-1 = 336.50 tonne C yr-1 (Equation 2.13).

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Chapter 4: Forest Land

Annual carbon loss in biomass due to disturbances (Ldisturbance) is calculated using Equation 2.14, Chapter 2, area of disturbances (Adisturbance), average above-ground biomass affected (BW), belowground biomass to above-ground biomass ratio (R), carbon fraction of dry matter (CF), fraction of biomass lost in disturbance (fd) and default tables in Section 4.5. For the hypothetical country, R

= 0.29 tonne d.m. (tonne dm)-1 for above-ground biomass 50 to 150 t ha-1 (Table 4.4, for above-ground biomass refer to Table 4.7);

CF

= 0.47 tonne C (tonne dm)-1 (Table 4.3); and fd = 0.3

Ldisturbance = 2,000 ha yr-1 ● 4.0 tonnes d.m. ha-1 (1 + 0.29) ● 0.47 tonne C (tonne dm)-1 ● 0.3 = 1,455.12 tonnes C yr-1 (Equation 2.14) Annual decrease in carbon stocks due to biomass losses (∆CL), ∆CL = 725.16 tonnes C yr-1 + 336.50 tonnes C yr-1 + 1,455.12 tonnes C yr-1 = 2,516.78 tonnes C yr-1 (Equation 2.11) Annual change in carbon stocks in biomass (∆CB) Using Chapter 2, Equation 2.7 (∆CB = (∆CG – ∆CL)), ∆CB = 242,520 tonnes C yr-1 – 2,516.78 tonnes C yr-1 = 240,003.22 tonnes C yr-1

4.2.1.5

U NCERTAINTY

ASSESSMENT

This section considers source-specific uncertainties relevant to inventory estimates made for Forest Land Remaining Forest Land. Estimating country-specific and/or disaggregated values requires more accurate information on uncertainties than given below. Volume 1, Chapter 3 provides information on uncertainties associated with sample-based studies. The literature available on uncertainty estimates on emission factors and activity data is limited. Emission and remo va l fa ctors FAO (2006) provides uncertainty estimates for forest carbon factors; basic wood density (10 to 40%); annual increment in managed forests of industrialized countries (6 %); growing stock (industrialized countries 8%, nonindustrialized countries 30%); combined natural losses for industrialized countries (15%); wood and fuelwood removals (industrialized countries 20%). In Finland, the uncertainty of basic wood density of pine, spruce and birch trees is under 20% in studies of Hakkila (1968, 1979). The variability between forest stands of the same species should be lower or at most the same as for individual trees of the same species. In Finland, the uncertainty of biomass expansion factors for pine, spruce, and birch was approximately 10% (Lehtonen et al., 2003). In eight Amazon tropical forest inventory plots, combined measurement errors led to errors of 10-30% in estimates of basal area change over periods of less than 10 years (Phillips et al., 2002). The major sources of uncertainty of wood density and biomass expansion factors are stand age, species composition, and structure. To reduce uncertainty, countries are encouraged to develop country- or regionspecific biomass expansion factors and BCEFs that fit their conditions. In case country- or regional-specific values are unavailable, the sources of default parameters should be checked and their correspondence with specific conditions of a country should be examined. The causes of variation of annual increment include climate, site growth conditions, and soil fertility. Artificially regenerated and managed stands are less variable than natural forests. The major ways to improve accuracy of estimates are associated with application of country-specific or regional increment stratified by forest type. If the default values of increment are used, the uncertainty of estimates should be clearly indicated and documented. Tier 3 approaches can use growth curves stratified by species, ecological zones, site productivity and management intensity. Similar approaches are routinely used in timber supply planning models and this information can be incorporated into carbon accounting models (e.g., Kurz et al., 2002). Data on commercial fellings are relatively accurate, although they may be incomplete or biased due to illegal fellings and underreporting due to tax regulations. Traditional wood that is gathered and used directly, without being sold, is not likely to be included in any statistics. Countries must carefully consider these issues. The amount of wood removed from forests after storm breaks and pest outbreaks varies both in time and volume. No default data can be provided on these types of losses. The uncertainties associated with these losses can be estimated from the amount of damaged wood directly withdrawn from the forest or using data on damaged wood

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Volume 4: Agriculture, Forestry and Other Land Use

subsequently used for commercial and other purposes. If fuelwood gathering is treated separately from fellings, the relevant uncertainties might be high, due to high uncertainty associated with traditional gathering. Activity da ta Area data should be obtained using the guidance in Chapter 3 or from FAO (2000). Industrialized countries estimated an uncertainty in forest area estimates of approximately 3% (FAO, 2000).

4.2.2

Dead organic matter

The general description of methods for estimating changes in carbon stocks in dead organic matter (DOM) pools (litter and dead wood) has been provided in Chapter 2. This section focuses on methods for estimating carbon stock changes in dead organic matter pools for Forest Land Remaining Forest Land. Tier 1 methods assume that the net carbon stock changes in DOM pools are zero because the simple input and output equations used in Tier 1 methods are not suitable to capture the DOM pool dynamics. Countries that want to quantify DOM dynamics need to develop Tier 2 or 3 methodologies. The countries where DOM is a key category should adopt higher tiers and estimate DOM changes. The dead wood (DW) pool contains carbon in coarse woody debris, dead coarse roots, standing dead trees, and other dead material not included in the litter or soil carbon pools. Estimating the size and dynamics of the dead wood pool poses many practical limitations, particularly related to field measurements. The uncertainties associated with estimates of the rate of transfer from the DW pool to the litter and soil pools, and emissions to the atmosphere are generally high. The amount of dead wood is highly variable between stands, both in managed (Duvall and Grigal, 1999; Chojnacky and Heath, 2002) and unmanaged lands (Spies et al., 1988). Amounts of dead wood depend on the time since last disturbance, the type of the last disturbance, losses during disturbances, the amount of biomass input (mortality) at the time of the disturbance (Spies et al., 1988), natural mortality rates, decay rates, and management (Harmon et al., 1986). Net litter accumulation rates can be estimated using the stock-difference method or the gain-loss method. The latter requires an estimate of the balance of the annual amount of litterfall (which includes all leaves, twigs and small branches, fruits, flowers, roots, and bark) minus the annual rate of litter decomposition. In addition, disturbances can add and remove carbon from the litter pool, influencing the size and composition of the litter pool. The litter dynamics during the early stages of stand development depend on the type and intensity of the last disturbance. Where disturbance has transferred biomass to DOM pools (e.g., wind-throw or insect kill), litter pools can be decreasing until losses are compensated by litter inputs. Where disturbance has removed litter (e.g., wildfire), litter pools can be increasing in the early stages of stand development if litter input exceeds decay. Management such as timber harvesting, slash burning, and site preparation alter litter properties (Fisher and Binkley, 2000), but there are few studies clearly documenting the effects of management on litter carbon (Smith and Heath, 2002).

4.2.2.1

C HOICE

OF METHOD

The decision tree in Figure 2.3 in Chapter 2 provides guidance in the selection of the appropriate tier level for the implementation of estimation procedures. The choice of method is described jointly for dead wood and Litter since the equations are identical for both, but the estimates are calculated separately for each of the two pools. The estimation of changes in carbon stocks in DOM pool requires estimates of changes in carbon stocks of dead wood and litter pools (refer to Equation 2.17 of Chapter 2). Tier 1 The Tier 1 method assumes that the dead wood and litter carbon stocks are in equilibrium so that the changes in carbon stock in the DOM pools are assumed to be zero. Countries experiencing significant changes in forest types, disturbance or management regimes in their forests are encouraged to develop domestic data to quantify the impacts from these changes using Tier 2 or 3 methodologies and to report the resulting stock changes and non-CO2 emissions. T i e r s 2 a nd 3 Two general methods are available for estimating the carbon stock changes in dead wood and litter. Similar methods exist for the estimation of biomass carbon stock changes, and the choice of method for estimating DOM changes may be affected by the choice of method for biomass carbon stock change estimation. Gain-Loss Method: The Gain-Loss method uses a mass balance of inputs to and losses from the dead wood and litter pools to estimate stock changes over a specified period. This involves estimating the area of managed Forest Land Remaining Forest Land and the average annual transfer of carbon stock into and out of dead wood and litter pools (Equation 2.18 in Chapter 2). To reduce uncertainty, the area under Forest Land Remaining

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

Forest Land can be further stratified by climate or ecological zones, and classified by forest type, productivity, disturbance regime, management practice, or other factors that affect dead wood and litter carbon pool dynamics. Estimation of the net balance requires calculation on a per hectare basis of the annual transfers into the dead wood and litter pools from stem mortality, litterfall and turnover, and the losses from decomposition. In addition, in areas subject to management activities or natural disturbances, dead wood and litter will be added in the form of biomass residues, and transferred through harvest (salvage of standing dead trees), burning or other mechanisms. It is good practice that the stratification of Forest Land adopted for DOM be identical to that used for the estimation of changes in biomass carbon stocks (Section 4.2.1). Stock-Difference Method: This involves estimating the area of managed Forest Land Remaining Forest Land, determining the dead wood and litter carbon stocks at two points of time and the calculation of the difference between the two carbon stock estimates (Equation 2.19 in Chapter 2). The annual carbon stock change for the inventory year is obtained by dividing the change in carbon stock by the period (years) between the two measurements. Method 2 is only feasible for countries which have forest inventories based on sample plots. Calculating carbon stock changes as the difference of carbon stocks at two points in time requires that the area at time t1 and t2 is identical to ensure that reported carbon stocks are not the result of changes in area. For Tiers 2 and 3 methods, both options, are data intensive and require field measurements and models for their implementation. Such models can build on the knowledge and information compiled for the simulation of forest dynamics as used in the timber supply planning process (e.g. Kurz et al., 2002, and Kurz and Apps, 2006).

4.2.2.2

C HOICE

OF EMISSION / REMOVAL FACTORS

Tier 1 By default, it is assumed that the carbon stocks in the DOM pools in Forest Land Remaining Forest Land are stable. Carbon-dioxide emissions originating from dead wood and litter pools during wildfire are assumed to be zero, and accumulation of carbon in dead wood and litter pools during regrowth is also not counted. Non- CO2 emissions from wildfire, including CH4 and CO are estimated in Tier 1. T i e r s 2 a nd 3 The parameter fBLol is the fraction of total biomass left to decay on the ground, see Chapter 2, Equation 2.20. Resolution and accuracy of the transferred carbon will correspond to the expansion factors applied in calculating losses. Tier 2 estimation of fBlol requires national data on average proportions of carbon left after disturbances. When national data are incomplete, Chapter 2 provides two tables: •

•

Default values of combustion factor to be used as (1– fBL) in case the country has good growing stock biomass data; in this case the proportion lost is used; see Table 2.6

Default values of biomass removals to be used as [MB • (1– fBL)] in case the growing stock biomass data are not reliable. MB is the mass of fuel available for combustion (see Table 2.4 and Equation 2.27 in Chapter 2).

Country-specific values for transfer of carbon in live trees that are harvested to harvest residues can be derived from national expansion factors, taking into account the forest type (coniferous/broadleaved/ mixed), the rate of biomass utilization, harvesting practices and the amount of damaged trees during harvesting operations. Both harvest and natural disturbances add biomass to dead wood and litter pools. Other management practices (such as burning of harvest residues) and wildfire remove carbon from dead wood and litter pools. If the area under each management practice and type of forest affected by disturbance are known, then disturbance matrices (see Chapter 2, Table 2.1; Kurz et al., 1992) can be used to define for each disturbance type the proportion of each biomass, dead organic matter, and soil carbon pool that is transferred to other pools, to the atmosphere, or removed from the forest during harvest. Tier 3 estimation of fBlol, will require more detailed knowledge of the proportion of rapid emissions from disturbances such as fires and windstorms. Data should be obtained by on-site measurements or from studies of similar disturbances. Disturbance matrices (see Chapter 2, Table 2.1) have been developed to define, for each disturbance type, the proportion of biomass (and all other carbon pools) that is transferred to other carbon pools, released to the atmosphere, or transferred to harvested wood products (Kurz et al., 1992). Disturbance matrices ensure conservation of carbon when calculating the immediate impacts of harvest or disturbances on ecosystem carbon. Tier 3 methods rely on more complex forest carbon accounting models that track the rates of input and losses from dead organic matter pools for each forest type, productivity, and age-class. Where comprehensive forest inventories exist, that include re-measurement of dead organic matter pools, estimates of carbon stock changes

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Volume 4: Agriculture, Forestry and Other Land Use

can also be derived using the stock-difference approach described in Equation 2.19 in Chapter 2. It is good practice that inventory-based approaches with periodic sampling follow the principles set out in Chapter 3, Annex 3A.3. Inventory-based approaches can be coupled with models to capture the dynamics of all forest carbon pools. Tier 3 methods provide estimates of greater certainty than lower tiers and feature a greater link between the dynamics of biomass and dead organic matter carbon pools. Other important parameters in modelling dead wood and litter carbon budgets are decay rates, which may vary with the forest type and climatic conditions, and forest management practices (e.g., controlled broadcast burning or thinning and other forms of partial harvest).

4.2.2.3

C HOICE

OF ACTIVITY DATA

Countries using a Tier 1 method require no activity data for estimation of changes in carbon stock in DOM in Forest Land Remaining Forest Land. Countries using higher tiers require activity data on the areas of Forest Land Remaining Forest Land classified by major forest types, management practices, and disturbance regimes. Total forest area and all other activity data should be consistent with that reported under other sections of this chapter, notably under biomass section of Forest Land Remaining Forest Land (Section 4.2.1). Country-specific activity data on the area annually affected by harvest and disturbances can be derived from national monitoring programs. The assessment of changes in carbon stock in DOM is greatly facilitated if this information can be used in conjunction with national soil and climate data, vegetation inventories, and other geophysical data. Data sources will vary according to a country’s forest management system. Data can be compiled from individual contractors or companies, regulation bodies and governmental agencies responsible for forest inventory and management, and from research institutions. Data formats vary widely, and include, among others, activity reports submitted regularly within incentive programs or as required by regulations, forest management inventories and from monitoring programs using remotely sensed imagery (Wulder et al., 2004).

4.2.2.4

C ALCULATION

STEPS FOR

T IER 1

Since Tier 1 assumes no change in DOM for Forest Land Remaining Forest Land, guidance on calculations steps is not relevant.

4.2.2.5

U NCERTAINTY

ASSESSMENT

Tier 1 by definition assumes stable carbon stocks so formal uncertainty analysis is not appropriate. In fact the assumption is almost never true at the stand level and unlikely to be true in general, although the resulting error could be small for a forested landscape because increases in some stands could be off-set by decreases in others, but for the entire landscape or country, dead organic matter pools can be either increasing or decreasing. An understanding of the types of changes that are occurring in the forests of a country can provide some qualitative insight into the direction of change in dead organic matter pools. For example, in some countries biomass growing stocks are increasing because harvest and disturbance losses are smaller than growth increments. It is likely that dead organic matter pools are also increasing, even if the rate of increase cannot be known unless a Tier 2 or 3 estimation method is used. Countries that use methods that assume all carbon losses occur in the year of disturbance are likely to overestimate disturbance losses in the years of above-average disturbances, and underestimate true emissions in years of below-average disturbances. Countries with fairly constant harvest or disturbance rates that rely on such methods are likely to be closer to the actual net carbon stock changes. The uncertainty of estimates using higher Tier methods must be evaluated for each country using expert judgment. It is fair to assume that the uncertainty in the estimates of changes of carbon stock in dead organic matter is generally larger than that of the estimates of changes in carbon stock in biomass since, in most countries, considerably more data are available on biomass stocks than on dead organic matter stocks. Moreover, models that describe biomass dynamics are generally more advanced than models of dead organic matter dynamics. Given the increased importance of understanding the non-timber components of forest ecosystems, many countries have revised their inventory procedures. More data on dead organic matter carbon stocks and their dynamics are becoming available, which will allow inventory agencies to better identify, quantify and reduce uncertainties in dead organic matter estimates in the years to come.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

4.2.3

Soil carbon

This section elaborates on estimation procedures and good practices for estimating change in forest soil C stocks. It does not include forest litter, which is a dead organic matter pool. Separate guidance is provided for two types of forest soils: 1) mineral forest soils, and 2) organic forest soils. The organic C content of mineral forest soils (to 1 m depth) typically varies between 20 to over 300 tonnes C ha-1 depending on the forest type and climatic conditions (Jobbagy and Jackson, 2000). Globally, mineral forest soils contain approximately 700 Pg C (Dixon et al., 1994), but soil organic C pools are not static due to differences between C inputs and outputs over time. Inputs are largely determined by the forest productivity, the decomposition of litter and its incorporation into the mineral soil and subsequent loss through mineralization/respiration (Pregitzer, 2003). Other losses of soil organic C occur through erosion or the dissolution of organic C that is leached to groundwater or loss through overland flow. A large proportion of input is from above-ground litter in forest soils so soil organic matter tends to concentrate in the upper soil horizons, with roughly half of the soil organic C in the upper 30 cm layer. The C held in the upper profile is often the most chemically decomposable, and the most directly exposed to natural and anthropogenic disturbances. This section only deals with soil C and does not address decomposing litter (i.e., dead organic matter, see Section 4.2.2). Human activities and other disturbances such as changes in forest type, productivity, decay rates and disturbances can alter the C dynamics of forest soils. Different forest management activities, such as rotation length; choice of tree species; drainage; harvest practices (whole tree or sawlog, regeneration, partial cut or thinning); site preparation activities (prescribed fires, soil scarification); and fertilization, affect soil organic C stocks (Harmon and Marks, 2002; Liski et al., 2001; Johnson and Curtis, 2001). Changes in disturbance regimes, notably in the occurrence of severe forest fires, pest outbreaks, and other stand-replacing disturbances are also expected to alter the forest soil C pool (Li and Apps, 2002; de Groot et al., 2002). In addition, drainage of forest stands on organic soils reduces soil C stocks. General information and guidelines on estimating changes soil C stocks are found in Chapter 2, Section 2.3.3, and needs to be read before proceeding with the specific guidelines dealing with forest soil C stocks. Changes in soil C stocks associated with forests are computed using Equation 2.24 in Chapter 2, which combines the change in soil organic C stocks for mineral soils and organic soils; and stock change for soil inorganic C pools (Tier 3 only). This section elaborates on estimation procedures and good practices for estimating change in forest soil C organic stocks (Note: It does not include forest litter, i.e., dead organic matter). Separate guidance is provided for two types of forest soils: 1) mineral forest soils, and 2) organic forest soils. See Section 2.3.3.1 for general discussion on soil inorganic C (no additional information is provided in the Forest Land discussion below). To account for changes in soil C stocks associated with Forest Land Remaining Forest Land, countries need to have, at a minimum, estimates of the total Forest Land area at the beginning and end of the inventory time period, stratified by climate region and soil type. If land-use and management activity data are limited, Approach 1 activity data (see Chapter 3) can be used as the basis for a Tier 1 approach, but higher Tiers are likely to need more detailed records or knowledge of country experts about the approximate distribution of forest management systems. Forest Land classes must be stratified according to climate regions and major soil types, which can be accomplished with overlays of suitable climate and soil maps.

4.2.3.1

C HOICE

OF METHOD

Inventories can be developed using Tier 1, 2 or 3 approaches, and countries may choose to use different tiers for mineral and organic soils. Decision trees are provided for mineral soils (Figure 2.4) and organic soils (Figure 2.5) in Chapter 2 to assist inventory compilers with selection of the appropriate tier for their soil C inventory. Min era l so ils In spite of a growing body of literature on the effect of forest types, management practices and other disturbances on soil organic C, the available evidence remains largely site- and study-specific, but eventually may be generalized based on the influence of climatic conditions, soil properties, the time scale of interest, taking into consideration sampling intensity and effects across different soil depth increments (Johnson and Curtis, 2001; Hoover, 2003; Page-Dumroese et al., 2003). However, the current knowledge remains inconclusive on both the magnitude and direction of C stock changes in mineral forest soils associated with forest type, management and other disturbances, and cannot support broad generalizations. Tier 1 Due to incomplete scientific basis and resulting uncertainty, it is assumed in the Tier 1 method that forest soil C stocks do not change with management. Furthermore, if using Approach 2 or 3 activity data (see Chapter 3), it is not necessary to compute C stock changes for mineral soils (i.e., change in SOC stocks is 0).

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Volume 4: Agriculture, Forestry and Other Land Use

If using activity data collected via Approach 1 (see Chapter 3), and it is not possible to identify the amount of land converted from and to Forest Land, then the inventory compiler should estimate soil C stocks for Forest Land using the areas at and the end of the year for which the inventory is being estimated, and the difference estimates the uptake or less of forest soil. The changes in soil C stocks for Forest Land are summed with the changes in stocks for other land uses to estimate the influence of land-use change. If the compiler does not compute a stock for Forest Land, it is likely to create systematic errors in the inventory. For example, land converted from Forest Land to Cropland or Grassland will have a soil C stock estimated in the final year of the inventory, but will have no stock in the first year of the inventory (when it was forest). Consequently, conversion to Cropland or Grassland is estimated as a gain in soil C because the soil C stocks are assumed to be 0 in the Forest Land, but not in Cropland and Grassland. This would introduce a bias into the inventory estimates. SOC0 and SOC0-T are estimated for the top 30 cm of the soil profile using Equation 2.25 (Chapter 2). Note that areas of exposed bedrock in Forest Land are not included in the soil C stock calculation (assume a stock of 0). Tier 2 Using Equation 2.25 (Chapter 2) soil organic C stocks are computed based on reference soil C stocks and country-specific stock change factors for forest type (FI), management (FMG) and natural disturbance regime (FD). Note that the stock change factor for natural disturbance regime (FD) is substituted for the land-use factor (FLU) in Equation 2.25. In addition, country-specific information can be incorporated to better specify reference C stocks, climate regions, soil types, and/or the land management classification system. Tier 3 Tier 3 approaches will require considerable knowledge and data allowing for the development of an accurate and comprehensive domestic estimation methodology, including evaluation of model results and implementation of a domestic monitoring scheme and/or modelling tool. The basic elements of a country-specific approach are (adapted from Webbnet Land Resource Services Pty ltd, 1999): •

•

• •

Stratification by climatic zones, major forest types and management regimes coherent with those used for other C pools in the inventory, especially biomass; Determination of dominant soil types in each stratum; Characterization of corresponding soil C pools, identification of determinant processes in SOC input and output rates and the conditions under which these processes occur; and Determination and implementation of suitable methods to estimate carbon stock changes from forest soils for each stratum on an operational basis, including model evaluation procedures; methodological considerations are expected to include the combination of monitoring activities – such as repeated forest soil inventories - and modelling studies, and the establishment of benchmark sites. Further guidance on good soil monitoring practices is available in the scientific literature (Kimble et al., 2003, Lal et al., 2001, McKenzie et al., 2000). It is good practice for models developed or adapted for this purpose to be peer-reviewed, and validated with observations representative of the ecosystems under study and independent from the calibration data.

O rganic so ils Tier 1 Currently, only C emissions due to drainage of forest organic soils are addressed in the Tier 1 method due to data limitations and lack of sufficient knowledge that constrain the development of a more refined default methodology. Using Equation 2.26 (Chapter 2), drained forest organic soils are stratified by climate type, and then multiplied by a climate-specific emission factor to derive an estimate of annual C emissions. Areas converted to Forest Land can be included in the total area estimate, in using Approach 1 land representation, without supplementary data, to be able to identify land-use changes. Tier 2 For Tier 2, the same basic equation is used as in Tier 1 (Equation 2.26), but country-specific information is incorporated to better specify emission factors, climate regions, and/or develop a forest classification scheme, relevant for organic soils. Tier 3 Tier 3 methodology involves the estimation of CO2 emissions associated with management of forested organic soils, including all anthropogenic activities likely to alter the hydrological regime, surface temperature, and vegetation composition of forested organic soils; and major disturbances such as fires.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

4.2.3.2

C HOICE

OF STOCK CHANGE AND EMISSION FACTORS

Min era l so ils Tier 1 It is not necessary to compute the stock estimates for Forest Land Remaining Forest Land with Approach 2 or 3 activity data (see Chapter 3). If using Approach 1 activity data, stock change factors, including input, management and disturbance regime, are equal to 1 using the Tier 1 approach. Consequently, only reference C stocks are needed to apply the method, and those are provided in Table 2.3 of Chapter 2. Tier 2 In a Tier 2 approach, stock change factors are derived based on a country-specific classification scheme for management, forest types, and natural disturbance regimes. A Tier 2 approach should also include the derivation of country-specific reference C stocks, and a more detailed classification of climate and soils than the default categories provided with the Tier 1 method. It is good practice to focus on the factors that have the largest overall effect, taking into account the impact on forest SOC and the extent of affected forests. Management practices can be coarsely labeled as intensive (e.g., plantation forestry) or extensive (e.g., natural forest); these categories can also be redefined according to national circumstances. The development of stock change factors is likely to be based on intensive studies at experimental sites and sampling plots involving replicated, paired site comparisons (Johnson et al., 2002; Olsson et al., 1996; see also the reviews by Johnson and Curtis, 2001; and Hoover, 2003). In practice, it may not be possible to separate the effects of a different forest types, management practices and disturbance regimes, in which case some stock change factors can be combined into a single modifier. If a country has well-documented data for different forest types under different management regimes, it might be possible to derive soil organic C estimates directly without using reference C stocks and adjustment factors. However, a relationship to the reference C stocks must be established so that the impact of land-use change can be computed without artificial increases or decreases in the C stocks due to a lack of consistency in the methods across the various land-use categories (i.e., Forest Land, Cropland, Grassland, Settlements, and Other Land). Inventories can also be improved by deriving country-specific reference C stocks (SOCref), compiled from published studies or surveys. Such values are typically obtained through the development and/or compilation of large soil profile databases (Scott et al., 2002; Siltanen et al., 1997). Additional guidance for deriving stock change factors and reference C stocks is provided in Section 2.3.3.1 (Chapter 2). Tier 3 Constant stock change rate factors per se are less likely to be estimated in favor of variable rates that more accurately capture land-use and management effects. See Section 2.3.3.1 (Chapter 2) for further discussion. O rganic so ils Tier 1 Default emission factors are provided in Table 4.6 of Section 4.5, to estimate the loss of C associated with drainage of organic soils. Tier 2 Tier 2 approaches involve the derivation of emission factors from country-specific data. The main consideration is whether forests types or management in addition to climate regions will be subdivided into finer classes. These decisions will depend on experimental data that demonstrate significant differences in C loss rates. For example, drainage classes can be developed for various forest management systems. In addition, management activities may disrupt the C dynamics of the underlying organic soils. Harvest, for example, may cause a rise in the water table due to reduced interception, evaporation and transpiration (Dubé et al., 1995). Tier 3 Constant emission rate factors per se are less likely to be estimated in favor of variable rates that more accurately capture land-use and management effects. See Section 2.3.3.1 (Chapter 2) for further discussion.

4.2.3.3

C HOICE

OF ACTIVITY DATA

Min era l so ils Tier 1 For the Tier 1 approach, it is assumed that forest soil C stocks do not change with management, and therefore it is not necessary to classify forest into various types, management classes or natural disturbance regimes. However, if using Approach 1 activity data (see Chapter 3), environmental data will be needed to classify the

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

country into climate regions and soil types in order to apply the appropriate reference C stocks to Forest Land. A detailed description of the default climate classification scheme is given in Chapter 3, Annex 3A.5. If the information needed to classify climate types is not available from national databases, there are international sources of climate data such as United Nations Environmental Program. Data will also be needed to classify soils into the default categories provided in Chapter 3, and if national data are not available to map the soil types, international soils data provide a reasonable alternative, such as the FAO Soils Map of the World. Tier 2 Activity data for the Tier 2 approach consist of the major forest types, management practices, disturbance regimes and the areas to which they apply. It is preferable for the data to be linked with the national forest inventory, where one exists, and/or with national soil and climate databases. Typical changes include: conversion of unmanaged to managed forest; conversion of native forest into a new forest type; intensification of forest management activities, such as site preparation, tree planting and rotation length changes; changes in harvesting practices (bole vs. whole-tree harvesting; amount of residues left on-site); frequency of disturbances (pest and disease outbreaks, flooding, fires, etc). Data sources will vary according to a country’s forest management system, but could include individual contractors or companies, statutory forest authorities, research institutions and agencies responsible for forest inventories. Data formats vary widely, and include, among others, activity reports, forest management inventories and remote sensing imagery. In addition, Tier 2 should involve a finer stratification of environmental data than the Tier 1 approach, including climate regions and soil types, which would likely be based on national climate and soils data. If a finer classification scheme is utilized in a Tier 2 inventory, reference C stocks will also need to be derived for the more detailed set of climate regions and soil types, and the land management data will need to be stratified based on the country-specific classification. Tier 3 For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or more detailed data on the combinations of climate, soil, topographic and management data are needed, relative to the Tiers 1 and 2 methods, but the exact requirements will be dependent on the model or measurement design. O rganic so ils Tier 1 Forests are not stratified into various systems using Tier 1 methods. However, land areas do need to be stratified by climate region and soil type (see Chapter 3 for guidance on soil and climate classification) so that organic soils may be identified and the appropriate default emission factor applied. Tier 2 Tier 2 approaches may involve a finer stratification of management, forest type or disturbance regime, in a manner consistent with the country-specific emission factors for organic soils. For example, forest systems will need to be stratified by drainage if management factors are derived by drainage class. However it is good practice for the classification to be based on empirical data that demonstrates significant differences in rates of C change for the proposed categories. In addition, Tier 2 approaches should involve a finer stratification of climate regions. Tier 3 For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or more detailed data on the combinations of climate, soil, topographic and management data are needed, relative to the Tiers 1 and 2 methods, but the exact requirements will be dependent on the model or measurement design.

4.2.3.4

C ALCULATION

STEPS FOR

T IER 1

Min era l so ils Since Tier 1 assumes no change in mineral soil C stocks for Forest Land Remaining Forest Land, guidance on calculations steps are not provided. O rganic so ils Step 1: Estimate the area of drained organic soils under managed forest in each climatic region of the country for each year or for the last year in each time period of the inventory (e.g., emissions over an inventory time period between 1990 and 2000 would be based on the land-use in 2000, assuming land-use and management are only known for these two years during the inventory time period). Step 2: Select the appropriate emission factor (EF) for annual losses of CO2 (from Table 4.6). Step 3: Estimate total emissions by summing the product of area (A) multiplied by the emission factor (EF) for all climate zones.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

4.2.3.5

U NCERTAINTY

ASSESSMENT

Three broad sources of uncertainty exists in soil C inventories: 1) uncertainties in land-use and management activity and environmental data; 2) uncertainties in reference soil C stocks if using Tier 1 or 2 approaches (mineral soils only); and 3) uncertainties in the stock change/emission factors for Tier 1 or 2 approaches, model structure/parameter error for Tier 3 model-based approaches, or measurement error/sampling variability associated with Tier 3 measurement-based inventories. In general, precision of an inventory is increased (i.e., smaller confidence ranges) with more sampling to estimate values for the three broad categories. In addition, reducing bias (i.e., improve accuracy) is more likely through the development of a higher Tier inventory that incorporates country-specific information. For Tier 1, uncertainties are provided with the reference C stocks in the first footnote of Table 2.3 (Chapter 2), and emission factor uncertainties for organic soils are provided in Table 4.6, Section 4.5. Uncertainties in landuse and management data will need to be addressed by the inventory compiler, and then combined with uncertainties for the default factors and reference C stocks (mineral soils only) using an appropriate method, such as simple error propagation equations. Refer to Section 4.2.1.5 for uncertainty estimate for land area estimates. However, it is good practice for the inventory compiler to derive uncertainties from country-specific activity data instead of using a default level. Default reference C stocks for mineral soils and emission factors for organic soils can have inherently high uncertainties, particularly bias, when applied to specific countries. Defaults represent globally averaged values of land-use and management impacts or reference C stocks that may vary from region-specific values (Powers et al., 2004; Ogle et al., 2006). Bias can be reduced by deriving country-specific factors using Tier 2 method or by developing a Tier 3 country-specific estimation system. The underlying basis for higher Tier approaches will be research in the country or neighbouring regions that address the effect of land use and management on soil C. In addition, it is good practice to further minimize bias by accounting for significant within-country differences in land-use and management impacts, such as variation among climate regions and/or soil types, even at the expense of reduced precision in the factor estimates (Ogle et al., 2006). Bias is considered more problematic for reporting stock changes because it is not necessarily captured in the uncertainty range (i.e., the true stock change may be outside of the reported uncertainty range if there is significant bias in the factors). Uncertainties in land-use activity statistics may be improved through a better national system, such as developing or extending a ground-based survey with additional sample locations and/or incorporating remote sensing to provide additional coverage. It is good practice to design a classification that captures the majority of land-use and management activity with a sufficient sample size to minimize uncertainty at the national scale. For Tier 2 methods, country-specific information is incorporated into the inventory analysis for purposes of reducing bias. For example, Ogle et al. (2003) utilized country-specific data to construct probability distribution functions for US specific factors, activity data and reference C stocks for agricultural soils. It is good practice to evaluate dependencies among the factors, reference C stocks or land-use and management activity data. In particular, strong dependencies are common in land-use and management activity data because management practices tend to be correlated in time and space. Combining uncertainties in stock change/emission factors, reference C stocks and activity data can be done using methods such as simple error propagation equations or Monte-Carlo procedures. Tier 3 models are more complex and simple error propagation equations may not be effective at quantifying the associated uncertainty in resulting estimates. Monte Carlo analyses are possible (Smith and Heath, 2001), but can be difficult to implement if the model has many parameters (some models can have several hundred parameters) because joint probability distribution functions must be constructed quantifying the variance as well as covariance among the parameters. Other methods are also available such as empirically-based approaches (Monte et al., 1996), which use measurements from a monitoring network to statistically evaluate the relationship between measured and modelled results (Falloon and Smith, 2003). In contrast to modelling, uncertainties in measurement-based Tier 3 inventories can be determined from the sample variance, measurement error and other relevant sources of uncertainty.

4.2.4

Non-CO 2 greenhouse gas emissions from biomass burning

Both uncontrolled (wildfires) and managed (prescribed) fires can have a major impact on the non-CO2 greenhouse gas emissions from forests. In Forest Land Remaining Forest Land, emissions of CO2 from biomass burning also need to be accounted for because they are generally not synchronous with rates of CO2 uptake. This is especially important after stand replacing wildfire, and during cycles of shifting cultivation in tropical regions.

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Where the type of forest changes (e.g., conversion of natural forests to plantation forests), there may be net emissions of CO2 from biomass burning during the initial years, in particular if significant woody biomass is burnt during the conversion. Over time, however, the impacts are not as great as those that result from Forest Land Converted to Cropland or Grassland. Fire emissions during land-use conversion are reported in the new land-use category unless restricted Approach 1 land area representation is being used without supplementary data to enable land use conversions to be identified explicitly, in which case fire emissions from Forest Land should all be included in the Forest Land Remaining Forest Land category. The general method for estimating greenhouse gas emissions in Forest Land Remaining Forest Land, and in Land Converted to Forest Land is described in Equation 2.27 in Chapter 2. Default tables for Tier 1 approach or components of a Tier 2 approach are provided in that Section 2.4 of Chapter 2.

4.2.4.1

C HOICE

OF METHOD

It is good practice that countries choose the appropriate Tier for reporting greenhouse gas emissions from fire, based on the decision tree in Figure 2.6 in Chapter 2. Where fire is a key category, emphasis should be on using a Tier 2 or Tier 3 approach. For prescribed fires, country-specific data are required to generate reliable estimates of emissions, since activity data, in general, are poorly reflected in global data sets. In Forest Land, both the CO2 emissions due to biomass burning and the CO2 removals resulting from vegetation regrowth need to be accounted for when estimating the net carbon flux.

4.2.4.2

C HOICE

OF EMISSIONS FACTORS

The mass of fuel available for combustion (MB of Equation 2.27) is critical for estimating the non-CO2 emissions. Default data to support estimation of emissions under a Tier 1 approach are given in Tables 2.4 to 2.6 in Chapter 2. Countries need to judge how their vegetation types correspond with the broad vegetation categories described in the default tables. Guidance for this is provided in Chapter 3 (Consistent Representation of Lands). Countries using Tier 2 are likely to have national data at disaggregated level on MB, according to forest types and management systems. Tier 3 estimation requires spatial estimates of MB according to different forest types, regions and management systems. Tier 3 estimation methods can also distinguish fires burning at different intensities, resulting in different amounts of fuel consumption.

4.2.4.3

C HOICE

OF ACTIVITY DATA

Estimates of area burnt in Forest Land Remaining Forest Land are needed. A global database exists that covers the area burnt annually by fires but this will not provide reliable data for the area burnt annually by prescribed fires in individual countries. It is good practice to develop national estimates of the area burnt and the nature of the fires especially how they affect forest carbon dynamics (e.g., effects on tree mortality) to improve the reliability of national inventories. Countries using Tier 2 are likely to have access to national estimates. Tier 3 estimation requires regional and forest type specific estimates of area subjected to fire and fire intensity.

Summary of steps for calculating greenhouse gas emissions from biomass burning using Equation 2.27 in Chapter 2: Step 1: Using guidance from Chapter 3 (approaches in representing land-use areas), categorise the area of Forest Land Remaining Forest Land into forest types of different climatic or ecological zones, as adopted by the country for Equation 2.27. Obtain estimates of A (area burnt) from global database or from national sources. Step 2: Estimate the mass of fuel (MB) available for combustion, in tonnes/ha, which includes biomass, litter and dead wood. Step 3: Select combustion factor Cf (default values are in Table 2.6, Chapter 2). Step 4: Multiply MB and Cf to provide an estimate of the amount of fuel combusted. If MB or Cf is unknown, defaults for the product of MB and Cf are given in Table 2.4. Step 5: Select emission factors Gef (default factors are in Table 2.5, Chapter 2). Step 6: Multiply parameters A, MB, Cf, (or MB and Cf, Table 2.4) and Gef to obtain the quantity of greenhouse gas emission from biomass burning. Repeat the steps for each greenhouse gas.

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Chapter 4: Forest Land

4.2.4.4

U NCERTAINTY

ASSESSMENT

Country-specific uncertainty estimates are to be estimated for Forest Land Remaining Forest Land. These result from the product of the uncertainties associated with activity data (area burnt) and the emission factors. It is good practice to provide error estimates (e.g., ranges, standard errors) and not to use country-specific data (for example, if it is of a limited nature) or approaches, unless this leads to a reduction in uncertainties compared with a Tier 1 approach.

4.3

LAND CONVERTED TO FOREST LAND

This section provides methodological guidance on annual estimation of emissions and removals of greenhouse gases, which occur on lands converted to Forest Land from different land-uses, including Cropland, Grassland, Wetlands, Settlements, and Other land, through afforestation and reforestation, either by natural or artificial regeneration (including plantations). The emissions and removals on abandoned lands, which are regenerating to forest due to human activities, should be also estimated under this section. It substitutes the method described under categories 5A, 5C, and 5D of the IPCC Guidelines. Land is converted to Forest Land by afforestation and reforestation, either by natural or artificial regeneration (including plantations). The anthropogenic conversion includes promotion of natural re-growth (e.g., by improving the water balance of soil by drainage), establishment of plantations on non-forest lands or previously unmanaged Forest land, lands of settlements and industrial sites, abandonment of croplands, pastures or other managed lands, which re-grow to forest. Unmanaged forests are not considered as anthropogenic greenhouse gas sources or sinks, and are excluded from inventory calculations. Where these unmanaged forests are affected by human activities such as planting, thinning, promotion of natural regeneration or others, they change status and become managed forests, reported under the category Land Converted to Forest Land, whose greenhouse gas emissions and removals should be included in the inventory and estimated with the use of the guidance in this section. Land conversion may result in an initial loss of carbon due to changes in biomass, dead organic matter, and soil carbon. But natural regeneration or plantation practices lead to carbon accumulation and that is related to changes in the area of plantations and their biomass stocks. Converted areas are considered Forest Land, if, following conversion, they correspond to definition of forest adopted by the country. Land Converted to Forest Land is covered in this section of the national greenhouse gas inventory until the time the soil carbon in new forests reach a stable level. A default period of 20 years4 is suggested. Forest ecosystems may require a certain time to return to the level of biomass, stable soil and litter pools of undisturbed state. With this in mind and as a practical matter, the default 20-year time interval is suggested. Countries also have an option to extend the length of transition period. After 20 years or other time interval chosen, the converted lands become forest, i.e., the land areas are transferred from the Land Converted to Forest Land category to Forest Land Remaining Forest Land (Section 4.2), where areas still becoming established can be treated as a separate stratum if necessary. Logging followed by regeneration or re-growth should be considered under Forest Land Remaining Forest Land category, since no land-use change is involved. Some abandoned lands may be too infertile, saline, or eroded for forest re-growth to occur. In this case, either the land remains in its current state or it may further degrade and lose organic matter. Those lands that remain constant with respect to carbon flux can be ignored. However, in some countries, the degradation of abandoned lands may be a significant problem and could be an important source of CO2. Where lands continue to degrade, both above-ground biomass and soil carbon may decline rapidly, e.g., due to erosion. The carbon in eroded soil could be re-deposited in rivers, lakes or other lands downstream. For countries with significant areas of such lands, this issue should be considered in a more refined calculation. Classification of land: Land Converted to Forest Land can be classified based on climate domain and ecological zones and forest crown cover classes. The carbon stock varies with climate, biome or forest type, species mix, management practices, etc. It is good practice to stratify lands into homogenous sub-categories (see Chapter 3) to reduce uncertainty in estimates of greenhouse gas emissions. The estimation of emissions and removals of carbon from land-use conversion to Forest Land is divided into three sub-sections: Change in Carbon Stocks in Biomass (Section 4.3.1), Change in Carbon Stocks in Dead Organic Matter (Section 4.3.2) and Change in Carbon Stocks in Soils (Section 4.3.3). The annual changes in carbon stocks on Land Converted to Forest Land are calculated using Equations 2.2 and 2.3 of Chapter 2 on the

4

It is clear that most forest ecosystems will take longer than 100 years to return to the level of biomass, soil and litter pools in undisturbed state; however human-induced activities can enhance the rate of return to stable state of carbon stocks. With this in mind and as a practical matter, the default 20-year time interval is suggested to capture the establishment of the forest ecosystems. Countries also have the option to extend the length of the transition period, though a consistent transition period will be required for the land use matrix system of land area representation to work properly.

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basis of annual changes in carbon stocks in biomass, dead organic matter (including dead wood and litter) and soil. Changes in carbon stock in Land Converted to Forest Land are estimated using: •

•

•

annual change in carbon stocks in above- and below-ground biomass annual change in carbon stocks in dead organic matter that includes dead wood and litter annual change in carbon stocks in soils

The approach for calculation of non-CO2 emissions is described in Section 4.3.4 based on methods given in Chapter 2. Application of these methods will only be possible if using Approach 2 or 3 land area representation as set out in Chapter 3, or Approach 1 data with supplementary data to enable land-use conversions to be identified. The actions to be taken in this case have already been identified in Section 4.2 above (Forest Land Remaining Forest Land).

4.3.1

Biomass

This section presents methodological guidance for calculation of emissions and removals of CO2 by changes in biomass on Land Converted to Forest Land. It substitutes the methodology provided for reporting on “Changes in Forest and Other Woody Biomass Stocks” and “Abandonment of Managed Lands” categories of the IPCC Guidelines as applied to newly established forests.

4.3.1.1

C HOICE

OF METHOD

This section presents methodological guidance for calculation of emissions and removals of CO2 by changes in above-ground and below-ground biomass on Land Converted to Forest Land. Based on key category analysis, activity data and resources available, three tier methods are suggested to estimate changes in biomass stocks. The decision tree in Figure 1.3 in Chapter 1 illustrates good practice approach for choosing the method to calculate CO2 emissions and removals in biomass on Land Converted to Forest Land. Tier 1 Annual change in carbon stocks in biomass is estimated with the use of Equation 2.7 in Chapter 2. Tier 1 follows the default approach. It implies the use of default parameters provided in Section 4.5. This approach can be also applied, if the data on previous land uses are not available, which may be the case, when areas are estimated using Approach 1 from Chapter 3. It implies the use of default parameters in Tables 4.1 through 4.14. Annual increase in carbon stocks in biomass, ∆CG. The calculations of ∆CG should be made according to Equation 2.9 in Chapter 2. As the growth rate of trees strongly depends on management regime, a distinction should be made between intensively (e.g., plantation forestry) and extensively (naturally re-growing stands with reduced or minimum human intervention) managed forests. The intensively and extensively managed forests can be further stratified based on climate, species, management practices, etc. Hence, the annual increase in carbon stocks can be estimated separately for intensively and extensively managed forests, using Equation 2.9 twice. First, for intensively managed forests using relevant area (AI) and the relevant mean annual biomass growth (GTotal) for intensively managed forests and second, for extensively managed forests by using appropriate area (AE) and mean annual biomass growth (GTotal) data for extensively managed forests. GTOTAL is calculated using Equation 2.10, Chapter 2, and default data tables in Section 4.5. The intensively managed and extensively managed forests can be further stratified based on climate, species, forest management practices, etc. The default data for extensively and intensively managed forests from the tables should be chosen with regard to tree species composition and climatic region. The default data for extensively and intensively managed forests should be taken from Section 4.5, correspondingly. Annual decrease in carbon stocks in biomass due to losses, ∆CL. Biomass loss due to wood removal (Lwood-removals), fuelwood removal (Lfuelwood) and disturbances (Ldisturbance) attributed to Land Converted to Forest Land, is estimated using Equation 2.11 in Chapter 2. The loss of biomass due to wood removal (Lwood-removals) is estimated with the use of Equation 2.12, of Chapter 2, and default values of basic wood density and the data on round wood logging, biomass conversion expansion factor, below-ground biomass to above-ground biomass ratio (R) and carbon fraction of dry matter (CF), provided in Section 4.5 tables. The biomass loss due to fuelwood removal (Lfuelwood) is estimated using Equation 2.13, fuelwood collecting data and relevant BCEFR for growing stock, R and CF from default tables in Section 4.5. The (Ldisturbance) could be estimated using Equation 2.14, in Chapter 2, area of disturbance, average growing stock biomass of land areas affected by disturbances and appropriate R and CF from default tables in Section 4.5.

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Chapter 4: Forest Land

The ∆CL should be assumed 0, if no data on losses are available (for Equation 2.11). To prevent double accounting or omission, consistent reporting of biomass loss should be maintained in Sections 4.2.1 and 4.3.1. Tier 2 The Tier 2 method is similar to Tier 1, but it uses nationally derived data and more disaggregated activity data and allows for more precise estimates of changes in carbon stocks in biomass. The net annual CO2 removals are calculated as a sum of increase in biomass due to biomass growth on converted lands, changes due to actual conversion (difference between biomass stocks before and after conversion) and losses on converted lands (Equations 15 and 16, Chapter 2). In addition to default values, the application of Tier 2 (Equation 2.15) requires national data on: i) area annually converted to forest; ii) average annual growth in carbon stocks in biomass per ha on converted lands, obtained e.g., from forest inventories (no default data can be provided); iii) change in biomass carbon when non-forest land becomes Forest Land; and iv) emissions due to loss of biomass on converted land. The approach may require data on previous land uses as well as knowledge of land-use change matrix (see Table 3.4 in Chapter 3) and carbon stocks on those lands. ∆CG should be estimated using Equation 2.9, where the area (A) of Land Converted to Forest Land should be considered separately along with respective mean annual increments for intensively and extensively managed forests (further categorized based on species, climate, etc.) and summed up. Average annual increment in biomass for managed forests is calculated in accordance with Tier 2 method as in Section 4.2.1, Forest Land Remaining Forest Land and Equation 2.10, Chapter 2, based on country-specific data on average annual biomass growth in merchantable volume per ha on land converted to forests (obtained e.g., from forest inventories) and on basic wood density, biomass conversion and expansion factors and below-ground to above-ground biomass ratio. ∆CCONVERSION accounts for the initial change in biomass stocks resulting from the land-use conversion, e.g., part of biomass may be withdrawn through land clearing, restocking or other human-induced activities applied on land prior to artificial or natural regeneration. These changes in carbon stocks in biomass are calculated with the use of Equation 2.16 in Chapter 2. This requires estimates of biomass stocks on land type i before (BBEFOREi) and after (BAFTERi ) the conversion in tonnes d.m. ha-1, area of land-use i converted to Forest Land (∆ATO_FORESTi) in a certain year, and the carbon fraction of dry matter (CF). The calculation of ∆CCONVERSION may be applied separately to account for different carbon stocks occurring on specific types of land (ecosystems, site types, etc.) before the transition. The ∆ATO_FORESTi refers to the particular inventory year for which the calculations are made. ∆CL is estimated using Equation 2.11 in Chapter 2. Biomass loss due to wood removal (Lwood-removals), fuelwood removal (Lfuelwood), and disturbances (Ldisturbance) should be estimated with the use of Equations 2.12 to 2.14, in Chapter 2. Inventory compilers are encouraged to develop country-specific wood density and BEF or BCEF values for growing stock increment and harvests to apply them in Equation 2.12 (for Tier 2 calculations). Chapter 2 describes the method for calculation of biomass losses from fuelwood gathering (Lfuelwood) and disturbances (Ldisturbance). The ∆CL should be assumed 0, if no data on losses are available. It is good practice to ensure consistent reporting on biomass losses between Sections 4.2.2 and 4.2.3 to avoid over- and underestimates due to double counting or omissions. Tier 3 Tier 3 should be used when land conversion to Forest Land is a key category and leading to a significant change of carbon stocks. It can follow the same equations and steps as Tier 2 or can use more complex methods and models, but in either case, it can make use of substantial national methods and country-specific data. The Equations 2.15 and 2.16 can be expanded on the basis of finer geographical scale and sub-division to forest type, species, and land type before conversion. Country-defined methodologies may be based on regular forest inventory or geo-referenced data and (or) models for accounting for changes in biomass. National activity data can have high resolution and be available for all categories of converted lands and forest types established on them. It is good practice to describe and document the methodology in accordance with Volume 1, Chapter 8 (Reporting Guidance and Tables). Transf er of b io mass to d ead orga nic ma tter During the process of conversion of land to Forest Land as well as during the process of extraction of biomass through felling, the non-commercial component of the biomass is left on the forest floor or transferred to dead organic matter. Refer to Section 4.3.2 for description of the method and the assumptions about the fate of dead organic matter.

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4.3.1.2

C HOICE

OF EMISSION FACTORS

Annua l increase in ca rbon sto cks in b iomass, ∆ C G The calculations distinguish between two broad management practices: intensive (e.g., plantation forestry with site preparation, planting of selected species and fertilization) and extensive (natural regeneration with minimum human intervention). These categories can also be refined according to national circumstances, for example based on stand origin (e.g., natural or artificial regeneration, restocking, promotion of natural re-growth, etc.), climate, species, management practice, etc. Tier 1 The methods for calculation of total biomass require above-ground and below-ground biomass pools (for pool descriptions, refer to Chapter 1). The tables in Section 4.5 represent default values of average annual growth in above-ground biomass for intensively (plantations) and extensively (naturally regenerated) managed forests, biomass conversion and expansion factors, below-ground biomass to above-ground biomass ratio and carbon fraction of dry matter (CF). The below-ground biomass to above-ground biomass ratio should be used to account for below-ground biomass in total biomass estimations. Basic wood density and biomass expansion factors, which allow for calculation of ∆CG as described in Section 4.2.1 Forest Land Remaining Forest Land. It is good practice to explore any regional or otherwise relevant default values to the country. Tier 2 It is good practice to determine, wherever possible, annual increment values, below-ground biomass to aboveground biomass, basic wood density, and biomass conversion and expansion factors appropriate for national conditions and use them in calculations under Tier 2. These categories can also be refined according to national circumstances, for example based on stand origin (natural or artificial regeneration, restocking, promotion of natural re-growth, etc.), climate, species composition, and management regime. The further stratifications may refer to tree species composition, management regime, stand age, climatic region and soil type, etc. Countries are encouraged to obtain specific biomass increment and expansion factors through research efforts. Additional guidance is provided in Section 4.2.1. Tier 3 The increment in biomass carbon stocks can be estimated based on country-specific annual biomass growth and carbon fraction in biomass data that come from forest inventories, sample plots, research and (or) models. The inventory compilers should ensure that the models and forest inventory data have been appropriately documented and described in line with the requirements highlighted in Volume 1, Chapter 8. Cha nge in b ioma ss stocks o n land b efo re and af ter co nversion, ∆C C O N V E R S I O N The calculations of biomass stocks before and after conversion should be made with the use of values consistent with other land uses. For example, comparable values of carbon stock should be used to estimate initial carbon stock for Grassland converted to Forest Land and for changes in biomass for Grassland Remaining Grassland. Tier 1 No estimate of ∆CCONVERSION is required for Tier 1 calculations. Tier 2 It is good practice to obtain and use, wherever possible, country-specific data on biomass stocks on land before and after conversion. The estimates should be consistent with those used in calculations of carbon stock changes in Cropland, Grassland, Wetlands, Settlements and Other Land, and should be obtained from national agencies or surveys. Tier 2 may imply the use of a combination of country-specific and default data. For default biomass stock values on land before the conversion, refer to other sections of this Volume. Tier 3 Estimates and calculations should be performed based on forest inventory and or model data. Forest inventory, models and data should be documented in line with procedures outlined in Volume 1, Chapter 8. Cha nge in ca rbon stocks in bio mass due to lo sses, ∆ C L Wood removal, fuelwood removal, and natural disturbances such as windfall, fires, and insect outbreaks result in loss of carbon on Land Converted to Forest Land that should be reported in accordance with good practice approach provided in Section 4.2.1. The good practice approach provided in Section 4.2.1 for estimating losses of carbon is fully applicable and should be used for appropriate calculations under Section 4.2.2. If changes in carbon stocks are derived from regular forest inventories, the losses from wood removal and disturbances will be covered without a need to report on them separately. It is good practice to ensure consistent reporting on losses of biomass between Sections 4.2.1 and 4.2.2 to avoid double counting or omissions. The data on logging of round wood should be taken from national sources or FAO. It should be noted that FAO data on logging is in merchantable round wood over bark. Bark fraction in harvested wood (BF) should be

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Chapter 4: Forest Land

applied to account for bark in wood removals with harvest. If logging is significant in the country, the inventory compilers are encouraged to use national harvest data or derive country-specific BF values. In most countries, information on area disturbed is not likely to be available by the two sub-categories, Forest Land Remaining Forest Land and Land Converted to Forest Land sub-categories. Given that the latter is, in most cases, much smaller than the former, all disturbances can be applied to Forest Land Remaining Forest Land, or the disturbed area can be pro-rated in proportion to the two land sub-categories. Fuelwood consumption data are not normally reported separately for Forest Land Remaining Forest Land and Land Converted to Forest Land. Then it is likely that the default fuelwood data is likely to be reported in Forest Land Remaining Forest Land. The reporting of fuelwood should be cross-checked between the two land subcategories to avoid double counting by checking with reporting of fuelwood in Forest Land Remaining Forest Land.

4.3.1.3

C HOICE

OF ACTIVITY DATA

Ar ea of land con v er ted to for es t, ∆A T O _ F O R E S T All tiers require information on areas converted to Forest Land over the 20 years prior to the inventory year. After 20 years or other time interval chosen, the lands converted to Forest Land, as defined in the country, should be transferred to and accounted for under Section 4.2 (Forest Land Remaining Forest Land). The same area data should be used for Sections 4.3.2 (Change in Carbon Stocks in Dead Organic Matter), Section 4.3.3 (Change in Carbon Stocks in Soils), and Section 4.3.4 (Non-CO2 Greenhouse Gas Emissions). If possible, these areas should be further disaggregated to take into consideration major soil types and biomass densities on land before and after conversion. Box 4.3 gives examples of a good practice approach in identification of lands converted to Forest Land. Subject to national data availability, the inventory compilers can also choose good practice approach on the basis of approaches provided in Chapter 3. Different biomass growth rates should be used for calculations of biomass stocks for forests naturally re-growing on abandoned lands and for forest plantations. To undertake calculations under Tiers 2 and 3, inventory compilers are encouraged to obtain information on types of previous land uses for lands converted to Forest Land. Tier 1 Activity data can be obtained through national statistics, from forestry agencies (information on areas of different management practices), conservation agencies (naturally regenerated areas), municipalities, survey and mapping agencies. Expert judgment may be used to assess whether new forests are predominantly intensively or extensively managed, if no recorded data are available. If the data on intensively and extensively managed areas of forests become available, these should be used for further partitioning areas to obtain more accurate estimates. Cross-checks should be applied to ensure complete and consistent representation of data to avoid omissions or double counting. If no country data are available, aggregate information can be obtained from international data sources (FAO, 2001; TBFRA, 2000). Tier 2 Areas under different land uses subjected to conversion during a given year or over a period of years should be available. They can come from national data sources and a land-use change matrix or its equivalent that covers all possible transitions to Forest Land. Country-defined national data sets should have a resolution sufficient to ensure appropriate representation of land areas in line with provisions of Chapter 3 of this Volume. It is important to estimate area converted to forest through natural regeneration and plantation approach. Tier 3 National activity data on land conversion to Forest Land through natural and artificial regeneration should be available from different sources, notably national forest inventories, registers of land use and land-use changes and remote sensing, as described in Chapter 3 of this Volume. These data should give a full accounting of all land-use transitions to Forest Land and can be further disaggregated along climate, soil, and vegetation types. Area under plantations is usually available according to species and age of the stand.

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BOX 4.3 EXAMPLES OF GOOD PRACTICE APPROACH IN IDENTIFICATION OF LANDS CONVERTED TO FOREST LAND

National land management systems can allow for identification of land-use changes, and the land census systems implemented in many countries also enables consistent representation and timely tracking changes in land use. The national inventory compilers should take the data from land management systems or censuses and use them as the basis for identification of converted lands. The land conversion data may be obtained directly from companies, private owners, ministries and agencies, which undertake particular activities over converted lands. In some countries, special accounting systems have been designed to estimate emissions and removals over converted lands. The Australia National Carbon Accounting System (NCAS) is an example of a good practice approach in identification of land conversion. The NCAS is a sophisticated model-based tool that comprises data from resource census, field studies, and remote sensing. It operates at high spatial and temporal scales. The NCAS addresses all sectors of activity in land systems, including carbon pools and all greenhouse gases as affected by human-induced activities. It allows for tracking afforestation and reforestation activities within the territory of the country along with estimating emissions and removals relevant to them. As soon as the new data enter the NCAS, the inventory data are updated continuously. Design and implementation of the NCAS and its components has been subjected to extensive peer review and Quality Assurance/Quality Control regime (AGO, 2002). Similar systems are being developed in New Zealand (Stephens et al., 2005; Trotter et al., 2005), Canada (Kurz and Apps, 2006), and other countries. The use of such land management systems contributes to development of high quality inventories and reduces the levels of uncertainty within the sector.

4.3.1.4

C ALCULATION

STEPS FOR

T IER 1

T he fo llo wing s umma r iz e s s tep s for e s tima ting cha nge in c arbo n s to ck s in bio mas s (∆ C B ) u s ing t he d e f a u l t me t ho d s Step 1: Estimate area converted to Forest Land (during the period 20 years before the year of the inventory) from other land-use categories such as Cropland, Grassland, and Settlements. Refer to Chapter 3 for detailed approaches for estimating Land Converted to Forest Land. Step 2: Disaggregate the area converted to Forest Land according to intensively managed forest (through plantation forestry) and extensively managed forest (through natural regeneration) based on the approach used for conversion. Step 3: Calculate the initial biomass loss associated with the land conversion, ∆CCONVERSION (Equation 2.16). This can be stratified by land conversion methods. Step 4: Estimate the annual increase in carbon stocks in biomass due to growth on Land Converted to Forest Land (∆CG), for intensively managed forests at species and other sub-category level, using Equations 2.9 and 2.10 in Chapter 2. Estimate annual increment of biomass at species and other sub-category level. Step 5: Estimate the annual increase in carbon stocks in biomass growing on Land Converted to Forest Land (∆CG), for extensively managed forests at species and other sub-category level, using Equations 2.9 and 2.10 in Chapter 2. Step 6: Estimate annual loss or decrease in biomass (Lwood-removals) due to commercial fellings (industrial wood and sawn logs) using Equation 2.12 in Chapter 2. Step 7: Estimate biomass loss due to fuelwood removal (Lfuelwood) on Land Converted to Forest Land using Equation 2.13 in Chapter 2. Step 8: Estimate annual carbon loss due to disturbance or other losses (Ldisturbance) using Equation 2.14 in Chapter 2. Step 9: Estimate the total loss of biomass carbon due to wood removal, fuelwood removal, and disturbance (∆CL) using Equation 2.11 in Chapter 2. Step 10: Estimate the annual change in carbon stock in biomass (∆CB) on Land Converted to Forest Land using Equation 2.15 in Chapter 2.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

Example. The following example shows Gain-Loss method (Tier 1) calculations of annual change in carbon stocks in biomass (∆CB in Equation 2.7, Chapter 2) for a hypothetical country in temperate continental forest zone of Europe (Table 4.1, Section 4.5). The area of non-forest land converted to Forest Land (A) within the country is 1,000 ha (see Chapter 3 for area categorization). The new forest is intensively managed 9-year-old pine plantation, average above-ground growing stock volume is 10 m3 ha-1. Thinning removed 100 m3 yr-1 of merchantable round wood over bark (H); 50 m3 yr-1 of whole trees (FGtrees) were removed as fuel wood. The area of insect disturbance (Adisturbance) is 50 ha yr-1 with 1.0 tonne d.m. ha-1 of above-ground biomass affected (BW). Annual gain in biomass (∆CG) is a product of mean annual biomass increment (GTOTAL), area of land converted to Forest Land (A) and carbon fraction of dry matter (CF), Equation 2.9, Chapter 2. GTOTAL is calculated using annual above-ground biomass increment (GW), below-ground biomass to above-ground biomass ratio (R), (Equation 2.10, Chapter 2) and default data tables, Section 4.5. For the hypothetical country, GW

= 4.0 tonnes d.m. ha-1 yr-1 (Table 4.12); and

R

= 0.40 tonne d.m. (tonne d.m.)-1 for above-ground biomass 600 m

Polar

P

all months 10°C

Warm temperate dry Warm temperate moist or dry

Temperate

Boreal

Polar

4-8 months at a temperature >10°C

≤ 3 months at a temperature >10°C all months

precipitation

seasonally dry: winter rains, dry summer semi-arid: evaporation >precipitation

oceanic climate: coldest month >0°C continental climate: coldest month precipitation

Climate domain: Area of relatively homogenous temperature regime, equivalent to the Köppen-Trewartha climate groups (Köppen, 1931). Climate region: Areas of similar climate defined in Chapter 3 for reporting across different carbon pools. Ecological zone: Area with broad, yet relatively homogeneous natural vegetation formations that are similar, but not necessarily identical, in physiognomy. Dry month: A month in which Total Precipitation (mm) ≤ 2 x Mean Temperature (ºC).

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.2 FOREST AND LAND COVER CLASSES Forest or land cover class

Definition

Forest

Land spanning more than 0.5 hectare with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use. Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate. Includes: areas with bamboo and palms provided that height and canopy cover criteria are met; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectare and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Excludes: tree stands in agricultural production systems, for example in fruit plantations and agroforestry systems. The term also excludes trees in urban parks and gardens.

Other wooded land

Land not classified as “Forest”, spanning more than 0.5 hectare; with trees higher than 5 meters and a canopy cover of 5-10 percent, or trees able to reach these thresholds in situ; or with a combined cover of shrubs, bushes and trees above 10 percent. It does not include land that is predominantly under agricultural or urban land use.

Other land

All land that is not classified as Forest or Other Wooded Land. Includes: agricultural land, meadows and pastures, built-up areas, barren land, etc; areas classified under the subcategory ‘Other Land with tree cover’.

Other land with tree cover

Land classified as Other Land, spanning more than 0.5 hectare with a canopy cover of more than 10 percent of trees able to reach a height of 5 meters at maturity. Includes: groups of trees and scattered trees in agricultural landscapes, parks, gardens, and around buildings (provided that the area, height and canopy cover criteria are met); tree plantations established mainly for other purposes than wood, such as fruit orchards and palm plantations.

Source: FAO, 2006. Global Forest Resources Assessment 2005 – progress towards sustainable forest management. FAO Forestry Paper No. 147. Rome.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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TABLE 4.3 CARBON FRACTION OF ABOVEGROUND FOREST BIOMASS Domain Default value

Tropical and Subtropical

Temperate and Boreal

4.48

Part of tree

Carbon fraction, (CF) [tonne C (tonne d.m.)-1]

All

0.47

References McGroddy et al., 2004 Andreae and Merlet, 2001; Chambers et al., 2001; McGroddy et al., 2004; Lasco and Pulhin, 2003

All

0.47 (0.44 - 0.49)

wood

0.49

Feldpausch et al., 2004

wood, tree d < 10 cm

0.46

Hughes et al., 2000

wood, tree d ≥ 10 cm

0.49

Hughes et al., 2000

foliage

0.47

Feldpausch et al., 2004

foliage, tree d < 10 cm

0.43

Hughes et al., 2000

foliage, tree d ≥ 10 cm

0.46

Hughes et al., 2000

All

0.47 (0.47 - 0.49)

Andreae and Merlet, 2001; Gayoso et al., 2002; Matthews, 1993; McGroddy et al., 2004

broad-leaved

0.48 (0.46 - 0.50)

Lamlom and Savidge, 2003

conifers

0.51 (0.47 - 0.55)

Lamlom and Savidge, 2003

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

RATIO OF BELOW-GROUND

Domain

Ecological zone

TABLE 4.4 BIOMASS TO ABOVE-GROUND BIOMASS (R)

Above-ground biomass

Tropical rainforest

Tropical moist deciduous forest

Tropical Tropical dry forest

0.37 above-ground biomass 125 tonnes ha-1 above-ground biomass 20 tonnes ha-1

Tropical shrubland

Subtropical

Subtropical dry forest

above-ground biomass 125 tonnes ha-1 above-ground biomass 20 tonnes ha-1

Subtropical steppe

Temperate

Temperate continental forest, Temperate mountain systems

Boreal

Boreal coniferous forest, Boreal tundra woodland, Boreal mountain systems

Fittkau and Klinge, 1973 Mokany et al., 2006

0.24 (0.22 - 0.33)

Mokany et al., 2006

0.56 (0.28 - 0.68)

Mokany et al., 2006

0.28 (0.27 - 0.28)

Mokany et al., 2006 Poupon, 1980

0.27 (0.27 - 0.28)

Singh et al., 1994

0.20 (0.09 - 0.25)

Mokany et al., 2006

0.24 (0.22 - 0.33)

Mokany et al., 2006

0.56 (0.28 - 0.68)

Mokany et al., 2006

0.28 (0.27 - 0.28)

Mokany et al., 2006

0.32 (0.26 - 0.71)

Mokany et al., 2006

no estimate available

Subtropical mountain systems

Temperate oceanic forest,

References

0.20 (0.09 - 0.25)

0.40

Tropical mountain systems

Subtropical humid forest

R [tonne root d.m. (tonne shoot d.m.)-1]

conifers above-ground biomass < 50 tonnes ha-1 conifers above-ground biomass 50-150 tonnes ha-1 conifers above-ground biomass > 150 tonnes ha-1 Quercus spp. aboveground biomass >70 tonnes ha-1 Eucalyptus spp. aboveground biomass < 50 tonnes ha-1 Eucalyptus spp. aboveground biomass 50-150 tonnes ha-1 Eucalyptus spp. aboveground biomass > 150 tonnes ha-1 other broadleaf aboveground biomass < 75 tonnes ha-1 other broadleaf aboveground biomass 75-150 tonnes ha-1 other broadleaf aboveground biomass >150 tonnes ha-1 above-ground biomass 75 tonnes ha-1

2006 IPCC Guidelines for National Greenhouse Gas Inventories

0.40 (0.21 - 1.06)

Mokany et al., 2006

0.29 (0.24 - 0.50)

Mokany et al., 2006

0.20 (0.12 - 0.49)

Mokany et al., 2006

0.30 (0.20 - 1.16)

Mokany et al., 2006

0.44 (0.29 - 0.81)

Mokany et al., 2006

0.28 (0.15 - 0.81)

Mokany et al., 2006

0.20 (0.10 - 0.33)

Mokany et al., 2006

0.46 (0.12 - 0.93)

Mokany et al., 2006

0.23 (0.13 - 0.37)

Mokany et al., 2006

0.24 (0.17 - 0.44)

Mokany et al., 2006

0.39 (0.23 - 0.96)

Li et al., 2003; Mokany et al., 2006

0.24 (0.15 - 0.37)

Li et al., 2003; Mokany et al., 2006

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DEFAULT BIOMASS CONVERSION AND

TABLE 4.5 3 -1 EXPANSION FACTORS (BCEF), TONNES BIOMASS (M OF WOOD VOLUME)

BCEF for expansion of merchantable growing stock volume to above-ground biomass (BCEFS), for conversion of net annual increment (BCEFI) and for conversion of wood and fuelwood removal volume to above-ground biomass removal (BCEFR) Climatic zone

Forest type

pines

larch Boreal firs and spruces

hardwoods

Growing stock level (m3)

BCEF 100

BCEFS

1.2 (0.85-1.3)

0.68 (0.5-0.72)

0.57 (0.52-0.65)

0.5 (0.45-0.58)

BCEFI

0.47

0.46

0.46

0.463

BCEFR

1.33

0.75

0.63

0.55

BCEFS

1.22 (0.9-1.5)

0.78 (0.7-0.8)

0.77 (0.7-0.85)

0.77 (0.7-0.85)

BCEFI

0.9

0.75

0.77

0.77

BCEFR

1.35

0.87

0.85

0.85

BCEFS

1.16 (0.8-1.5)

0.66 (0.55-0.75)

0.58 (0.5-0.65)

0.53 (0.45-0.605)

BCEFI

0.55

0.47

0.47

0.464

BCEFR

1.29

0.73

0.64

0.59

BCEFS

0.9 (0.7-1.2)

0.7 (0.6-0.75)

0.62 (0.53-0.7)

0.55 (0.5-0.65)

BCEFI

0.65

0.54

0.52

0.505

BCEFR

1.0

0.77

0.69

0.61

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Chapter 4: Forest Land DEFAULT BIOMASS CONVERSION AND

TABLE 4.5 (CONTINUED) 3 -1 EXPANSION FACTORS (BCEF), TONNES BIOMASS (M OF WOOD VOLUME)

BCEF for expansion of merchantable growing stock volume to above-ground biomass (BCEFS), for conversion of net annual increment (BCEFI) and for conversion of wood and fuelwood removal volume to above-ground biomass removal (BCEFR) Climatic zone

Forest type

hardwoods

Growing stock level (m3)

BCEF 200

BCEFS

3.0 (0.8-4.5)

1.7 (0.8-2.6)

1.4 (0.7-1.9)

1.05 (0.6-1.4)

BCEFI

1.5

1.3

0.9

0.6

0.8 (0.551.1)

BCEFR

3.33

1.89

1.55

1.17

0.48 0.89

Temperate

pines

other conifers

BCEFS

1.8 (0.6 -2.4)

1.0 (0.65 -1.5)

0.75 (0.6-1.0)

0.7 (0.4-1.0)

0.7 (0.4-1.0)

BCEFI

1.5

0.75

0.6

0.67

0.69

BCEFR

2.0

1.11

0.83

0.77

0.77

BCEFS

3.0 (0.7-4.0)

1.4 (0.5-2.5)

1.0 (0.5-1.4)

0.75 (0.4-1.2)

BCEFI

1.0

0.83

0.57

0.53

0.7 (0.350.9)

BCEFR

3.33

1.55

1.11

0.83

0.60 0.77

hardwoods Mediterranean, dry tropical, subtropical conifers

80

BCEFS

5.0 (2.0-8.0)

1.9 (1.0-2.6)

0.8 (0.6-1.4)

0.66 (0.4-0.9)

BCEFI

1.5

0.5

0.55

0.66

BCEFR

5.55

2.11

0.89

0.73

BCEFS

6.0 (3.0-8.0)

1.2 (0.5-2.0)

0.6 (0.4-0.9)

0.55 (0.4-0.7)

BCEFI

1.5

0.4

0.45

0.54

BCEFR

6.67

1.33

0.67

0.61

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4. Agriculture, Forestry and Other Land Use

DEFAULT BIOMASS CONVERSION AND

TABLE 4.5 (CONTINUED) 3 -1 EXPANSION FACTORS (BCEF), TONNES BIOMASS (M OF WOOD VOLUME)

BCEF for expansion of merchantable growing stock volume to above-ground biomass (BCEFS), for conversion of net annual increment (BCEFI) and for conversion of wood and fuelwood removal volume to above-ground biomass removal (BCEFR) Climatic zone

Forest type

conifers Humid tropical natural forests

Growing stock level (m3)

BCEF 200

BCEFS

4.0 (3.0-6.0)

1.75 (1.4-2.4)

1.25 (1.0-1.5)

1.0 (0.8-1.2)

0.8 (0.7-1.2)

0.76 (0.6-1.0)

0.7 (0.6-0.9)

0.7 (0.6-0.9)

BCEFI

2.5

0.95

0.65

0.55

0.53

0.58

0.66

0.70

BCEFR

4.44

1.94

1.39

1.11

0.89

0.84

0.77

0.77

BCEFS

9.0 (4.0-12.0)

4.0 (2.5-4.5)

2.8 (1.4-3.4)

2.05 (1.2-2.5)

1.7 (1.2-2.2)

1.5 (1.0-1.8)

1.3(0.9-1.6)

BCEFI

4.5

1.6

1.1

0.93

0.9

0.87

0.86

0.95 (0.71.1)

BCEFR

10.0

4.44

3.11

2.28

1.89

1.67

1.44

0.85 1.05

Note: Lower values of the ranges for BCEFS apply if growing stock definition includes branches, stem tops and cull trees; upper values apply if branches and tops are not part of growing stock, minimum top diameters in the definition of growing stock are large, inventoried volume falls near the lower category limit or basic wood densities are relatively high. Continuous graphs, functional forms and updates with new studies can be found at the forest- and climate- change website at: http://www.fao.org/forestry/ Average BCEF for inhomogeneous forests should be derived as far as possible as weighted averages. It is good practice to justify the factors chosen. To apply BCEFI, an estimate of the current average growing stock is necessary. It can be derived from FRA 2005 at http://www.fao.org/forestry/ BCEFR values are derived by dividing BCEFS by 0.9 Sources: Boreal forests: Alexeyev V.A. and R.A. Birdseye, 1998; Fang J. and Z.M. Wang, 2001; temperate forests: Fang J. et al., 2001; Fukuda M. et al., 2003; Schroeder P. et al., 1997; Snowdon P. et.al., 2000; Smith J. et. al., 2002; Brown S., 1999; Schoene D. and A. Schulte, 1999; Smith J. et al., 2004; Mediterranean forests: Vayreda et al., 2002; Gracia et al., 2002; tropical forests: Brown S. et al., 1989; Brown S. and A. Lugo, 1992; Brown S., 2002; Fang J.Y., 2001.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Chapter 4: Forest Land

TABLE 4.6 EMISSION FACTORS FOR DRAINED ORGANIC SOILS IN MANAGED FORESTS Emission factors (tonnes C ha-1 yr-1) Climate Values

Ranges

Tropical

1.36

0.82 – 3.82

Temperate

0.68

0.41 – 1.91

Boreal

0.16

0.08 – 1.09

Source: GPG-LULUCF, Table 3.2.3

TABLE 4.7 ABOVE-GROUND BIOMASS IN FORESTS Domain

Ecological zone

Tropical rain forest

Tropical moist deciduous forest

Tropical

Tropical dry forest

Tropical shrubland

Tropical mountain systems Subtropical humid forest Subtropical dry forest Subtropical Subtropical steppe

Subtropical mountain systems

Continent

Above-ground biomass (tonnes d.m. ha-1)

Africa

310 (130-510)

North and South America

300 (120-400)

Asia (continental) Asia (insular) Africa North and South America Asia (continental) Asia (insular) Africa North and South America Asia (continental) Asia (insular) Africa North and South America Asia (continental) Asia (insular) Africa North and South America Asia (continental) Asia (insular) North and South America Asia (continental) Asia (insular) Africa North and South America Asia (continental) Asia (insular) Africa North and South America Asia (continental) Asia (insular) Africa North and South America Asia (continental) Asia (insular)

280 (120-680) 350 (280-520) 260 (160-430) 220 (210-280) 180 (10-560) 290 120 (120-130) 210 (200-410) 130 (100-160) 160 70 (20-200) 80 (40-90) 60 70 40-190 60-230 50-220 50-360 220 (210-280) 180 (10-560) 290 140 210 (200-410) 130 (100-160) 160 70 (20-200) 80 (40-90) 60 70 50 60-230 50-220 50-360

2006 IPCC Guidelines for National Greenhouse Gas Inventories

References IPCC, 2003 Baker et al., 2004a; Hughes et al., 1999 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 Sebei et al., 2001 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 Montès et al., 2002 IPCC, 2003 IPCC, 2003 IPCC, 2003

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TABLE 4.7 (CONTINUED) ABOVE-GROUND BIOMASS IN FORESTS Domain

Ecological zone

Continent Europe

Temperate oceanic forest

Temperate

Temperate continental forest

Temperate mountain systems

Boreal coniferous forest

Boreal

Boreal tundra woodland

Boreal mountain systems

Above-ground biomass (tonnes d.m. ha-1) 120

References

New Zealand

360 (210-430)

South America

180 (90-310)

Asia, Europe (≤20 y) Asia, Europe (>20 y) North and South America (≤20 y) North and South America (>20 y) Asia, Europe (≤20 y) Asia, Europe (>20 y) North and South America (≤20 y) North and South America (>20 y)

20 120 (20-320)

Hessl et al., 2004; Smithwick et al., 2002 Hall et al., 2001 Gayoso and Schlegel, 2003; Battles et al., 2002 IPCC, 2003 IPCC, 2003

60 (10-130)

IPCC, 2003

130 (50-200)

IPCC, 2003

100 (20-180) 130 (20-600)

IPCC, 2003 IPCC, 2003

50 (20-110)

IPCC, 2003

130 (40-280)

IPCC, 2003

North America

660 (80-1200)

Asia, Europe, North America Asia, Europe, North America (≤20 y) Asia, Europe, North America (>20 y) Asia, Europe, North America (≤20 y) Asia, Europe, North America (>20 y)

10-90

Gower et al., 2001

3-4

IPCC, 2003

15-20

IPCC, 2003

12-15

IPCC, 2003

40-50

IPCC, 2003

TABLE 4.8 ABOVE-GROUND BIOMASS IN FOREST PLANTATIONS Domain

Ecological zone

Tropical rain forest

Tropical

Tropical moist deciduous forest

4.54

Continent Africa broadleaf > 20 y Africa broadleaf ≤ 20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia broadleaf Asia other Africa broadleaf > 20 y Africa broadleaf ≤ 20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia broadleaf Asia other

Above-ground biomass (tonnes d.m. ha-1) 300 100 200 60 200 300 240 150 220 130 150 80 120 40 90 270 120 100 180 100

References IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 Kraenzel et al., 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 Stape et al., 2004 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.8 (CONTINUED) ABOVE-GROUND BIOMASS IN FOREST PLANTATIONS Domain

Ecological zone

Tropical dry forest

Tropical shrubland

Tropical mountain systems

Subtropical humid forest

Subtropical Subtropical dry forest

Continent Africa broadleaf > 20 y Africa broadleaf ≤ 20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia broadleaf Asia other Africa broadleaf Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia broadleaf Asia other Africa broadleaf > 20 y Africa broadleaf ≤ 20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia broadleaf Asia other Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia broadleaf Asia other Africa broadleaf > 20 y Africa broadleaf ≤ 20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia broadleaf Asia other

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Above-ground biomass (tonnes d.m. ha-1) 70 30 60 20 90 110 90 60 90 60 20 20 15 60 60 50 30 40 30 60-150 40-100 30-100 10-40 30-120 60-170 30-130 30-80 40-150 25-80 140 270 120 100 180 100 70 30 60 20 110 110 90 60 90 60

References IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 Stape et al., 2004 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003

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TABLE 4.8 (CONTINUED) ABOVE-GROUND BIOMASS IN FOREST PLANTATIONS Domain

Ecological zone

Subtropical steppe

Subtropical mountain systems

Temperate oceanic forest

Continent

Above-ground biomass (tonnes d.m. ha-1)

Africa broadleaf Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia broadleaf > 20 y Asia broadleaf ≤ 20 y Asia coniferous > 20 y Asia coniferous ≤ 20 y Africa broadleaf > 20 y Africa broadleaf ≤ 20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia broadleaf Asia other Asia, Europe, broadleaf > 20 y Asia, Europe, broadleaf ≤ 20 y Asia, Europe, coniferous > 20 y Asia, Europe, coniferous ≤ 20 y North America

20 20 15 60 60 50 30 80 10 20 100-120 60-150 40-100 30-100 10-40 30-120 60-170 30-130 30-80 40-150 25-80

New Zealand

150-350

South America Asia, Europe, broadleaf > 20 y Asia, Europe, broadleaf ≤ 20 y Asia, Europe, coniferous > 20 y Asia, Europe, coniferous ≤ 20 y North America South America Asia, Europe > 20 y Asia, Europe ≤ 20 y North America Asia, Europe > 20 y Asia, Europe ≤ 20 y North America

90-120

200 30 150-250 40 50-300

Temperate

Temperate continental forest and mountain systems

Boreal

Boreal coniferous forest and mountain systems Boreal tundra woodland

4.56

200 15 150-200 25-30 50-300 90-120 40 5 40-50 25 5 25

References IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 Hinds and Reid, 1957; Hall and Hollinger, 1997; Hall, 2001 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.9 ABOVE-GROUND NET BIOMASS GROWTH IN NATURAL FORESTS

Domain

Ecological zone

Tropical rain forest

Tropical moist deciduous forest

Tropical Tropical dry forest

Tropical shrubland

Tropical mountain systems

Subtropical humid forest Subtropical Subtropical dry forest

Continent

Above-ground biomass growth (tonnes d.m. ha-1 yr-1)

Africa (≤20 y) Africa (>20 y)

10 3.1 (2.3-3.8)

North America

0.9-18

South America (≤20 y) South America (>20 y) Asia (continental ≤20 y) Asia (continental >20 y) Asia (insular ≤20 y) Asia (insular >20 y) Africa (≤20 y) Africa (>20 y) North and South America (≤20 y) North and South America (>20 y) Asia (continental ≤20 y) Asia (continental >20 y) Asia (insular ≤20 y) Asia (insular >20 y) Africa (≤20 y) Africa (>20 y) North and South America (≤20 y) North and South America (>20 y) Asia (continental ≤20 y) Asia (continental >20 y) Asia (insular ≤20 y) Asia (insular >20 y) Africa (≤20 y) Africa (>20 y) North and South America (≤20 y) North and South America (>20 y) Asia (continental ≤20 y) Asia (continental >20 y) Asia (insular ≤20 y) Asia (insular >20 y) Africa (≤20 y) Africa (>20 y) North and South America (≤20 y) North and South America (>20 y) Asia (continental ≤20 y) Asia (continental >20 y) Asia (insular ≤20 y) Asia (insular >20 y) North and South America (≤20 y) North and South America (>20 y) Asia (continental ≤20 y) Asia (continental >20 y) Asia (insular ≤20 y) Asia (insular >20 y) Africa (≤20 y) Africa (>20 y) North and South America (≤20 y)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

11 3.1 (1.5-5.5) 7.0 (3.0-11.0) 2.2 (1.3-3.0) 13 3.4 5 1.3 7.0 2.0 9.0 2.0 11 3.0 2.4 (2.3-2.5) 1.8 (0.6-3.0) 4.0 1.0 6.0 1.5 7.0 2.0 0.2-0.7 0.9 (0.2-1.6) 4.0 1.0 5.0 1.3 (1.0-2.2) 2.0 1.0 2.0-5.0 1.0-1.5 1.8-5.0 0.4-1.4 1.0-5.0 0.5-1.0 3.0-12 1.0-3.0 7.0 2.0 9.0 2.0 11 3.0 2.4 (2.3-2.5) 1.8 (0.6-3.0) 4.0

Reference IPCC, 2003 IPCC, 2003 Clark et al., 2003 ; Hughes et al., 1999 Feldpausch et al., 2004 Malhi et al., 2004 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 Harmand et al., 2004 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 Nygård et al., 2004 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003

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TABLE 4.9 (CONTINUED) ABOVE-GROUND NET BIOMASS GROWTH IN NATURAL FORESTS

Domain

Ecological zone

Subtropical steppe

Subtropical mountain systems

Temperate oceanic forest Temperate

Boreal

4.58

Temperate continental forest

Continent

Above-ground biomass growth (tonnes d.m. ha-1 yr-1)

Reference

North and South America (>20 y) Asia (continental ≤20 y) Asia (continental >20 y) Asia (insular ≤20 y) Asia (insular >20 y) Africa (≤20 y) Africa (>20 y) North and South America (≤20 y) North and South America (>20 y) Asia (continental ≤20 y) Asia (continental >20 y) Asia (insular ≤20 y) Asia (insular >20 y) Africa (≤20 y) Africa (>20 y) North and South America (≤20 y) North and South America (>20 y) Asia (continental ≤20 y) Asia (continental >20 y) Asia (insular ≤20 y) Asia (insular >20 y) Europe North America New Zealand South America Asia, Europe, North America (≤20 y) Asia, Europe, North America (>20 y)

1.0 6.0 1.5 7.0 2.0 1.2 (0.8-1.5) 0.9 (0.2-1.6) 4.0 1.0 5.0 1.3 (1.0-2.2) 2.0 1.0 2.0-5.0 1.0-1.5 1.8-5.0 0.4-1.4 1.0-5.0 0.5-1.0 3.0-12 1.0-3.0 2.3 15 (1.2-105) 3.5 (3.2-3.8) 2.4-8.9

IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003

4.0 (0.5-8.0)

IPCC, 2003

4.0 (0.5-7.5)

IPCC, 2003 IPCC, 2003

Hessl et al., 2004 Coomes et al., 2002 Echevarria and Lara, 2004

Temperate mountain systems Boreal coniferous forest Boreal tundra woodland

Asia, Europe, North America

3.0 (0.5-6.0)

Asia, Europe, North America

0.1-2.1

Asia, Europe, North America

0.4 (0.2-0.5)

IPCC, 2003

Boreal mountain systems

Asia, Europe, North America (≤20 y) Asia, Europe, North America (>20 y)

1.0-1.1

IPCC, 2003

1.1-1.5

IPCC, 2003

Gower et al., 2001

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.10 ABOVE-GROUND NET BIOMASS GROWTH IN TROPICAL AND SUB-TROPICAL FOREST PLANTATIONS

Domain

Ecological zone

Tropical rain forest

Tropical moist deciduous forest

Tropical Tropical dry forest

Tropical shrubland

Tropical mountain systems

Subtropical humid forest

Subtropical Subtropical dry forest

Continent Africa Pinus sp. ≤ 20 y Africa other ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia Eucalyptus sp. Asia other Africa Eucalyptus sp. >20 y Africa Eucalyptus sp. ≤20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Africa other ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia Africa Eucalyptus sp. ≤20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Africa other ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia Eucalyptus sp. Asia other Africa Eucalyptus sp. >20 y Africa Eucalyptus sp. ≤20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Africa other > 20 y Africa other ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Asia Africa Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia Eucalyptus sp. Asia other Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia Africa Eucalyptus sp. ≤20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Africa other ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia Eucalyptus sp. Asia other

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Above-ground biomass growth (tonnes d.m. ha-1 yr-1) 20 6 (5-8) 20 (6-40) 20 15 20 (5-35) 5 (4-8) 5 (2-8) 25 20 15 10 9 (3-15) 16 7 (4-10) 8 (4-12) 6-20 8 13 10 8 10 (4-20) 20 (6-30) 7 (4-10) 8 (4-12) 10 (3-12) 15 (5-25) 7 (2-13) 8 (5-14) 5 (3-7) 2.5 3 (0.5-6) 10 15 20 5 6 (1-12) 10 10 (8-18) 10 2 4 3 5 (1-10) 20 (6-32) 7 (4-10) 8 (4-12) 10 (3-12) 8 13 10 8 10 (4-20) 20 (6-30) 7 (4-10) 8 (4-12) 10 (3-12) 15 (5-25) 7 (2-13)

References IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 Stape et al., 2004 IPCC, 2003 IPCC, 2003 Lugo et al., 1990 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003

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TABLE 4.10 (CONTINUED) ABOVE-GROUND NET BIOMASS GROWTH IN TROPICAL AND SUB-TROPICAL FOREST PLANTATIONS

Domain

Ecological zone

Subtropical steppe

Subtropical mountain systems

Temperate oceanic forest

Temperate

Temperate continental forest and mountain systems

Boreal

Boreal coniferous forest and mountain systems Boreal tundra woodland

4.60

Continent Africa Eucalyptus sp. >20 y Africa Eucalyptus sp. ≤20 y Africa Pinus sp. > 20 y Africa Pinus sp. ≤ 20 y Africa other > 20 y Africa other ≤ 20 y Americas Eucalyptus sp. Americas Pinus sp. Asia Africa Americas Eucalyptus sp. Americas Pinus sp. Americas Tectona grandis Americas other broadleaf Asia Eucalyptus sp. Asia other Asia, Europe, broadleaf > 20 y Asia, Europe, broadleaf ≤ 20 y Asia, Europe, coniferous > 20 y Asia, Europe, coniferous ≤ 20 y North America New Zealand South America Asia, Europe, broadleaf > 20 y Asia, Europe, broadleaf ≤ 20 y Asia, Europe, coniferous > 20 y Asia, Europe, coniferous ≤ 20 y North America South America Asia, Europe > 20 y Asia, Europe ≤ 20 y North America Asia, Europe > 20 y Asia, Europe ≤ 20 y North America

Above-ground biomass growth (tonnes d.m. ha-1 yr-1) 8 (5-14) 5 (3-7) 2.5 3 (0.5-6) 10 15 20 5 6 (1-12) 10 10 (8-18) 10 2 4 3 5 (1-10)

References IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003 IPCC, 2003

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.11A ABOVE-GROUND NET VOLUME GROWTH OF SELECTED FOREST PLANTATION SPECIES Tree species

Above-ground net volume growth (m3 ha-1 y-1)

Acacia auriculiformis

6 - 20

Acacia mearnsii

14 - 25

Araucaria angustifolia

8 - 24

Araucaria cunninghamii

10 - 18

Casuarina equisetifolia

6 - 20

Casuarina junghuhniana

7 - 11

Cordia alliadora

10 - 20

Cupressus lusitanica

8 - 40

Dalbergia sissoo

5-8

Eucalyptus camaldulensis

15 - 30

Eucalyptus deglupta

14 - 50

Eucalyptus globulus

10 - 40

Eucalyptus grandis

15 - 50

Eucalyptus robusta

10 - 40

Eucalyptus saligna

10 - 55

Eucalyptus urophylla

20 - 60

Gmelina arborea

12 - 50

Leucaena leucocephala

30 - 55

Pinus caribaea v. caribaea

10 - 28

Pinus caribaea v. hondurensis

20 - 50

Pinus oocarpa

10 - 40

Pinus patula

8 - 40

Pinus radiata

10 - 50

Swietenia macrophylla

7 - 30

Tectona grandis

6 - 18

Terminalia ivorensis

8 - 17

Terminalia superba

10 - 14

Source: Ugalde and Perez, 2001

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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TABLE 4.11B MEAN ANNUAL INCREMENT (GROWTH OF MERCHANTABLE VOLUME) Planted forest type/ region

Tree species

FOR SOME FOREST PLANTATION SPECIES

Mean annual increment (MAI) over rotation (m3 ha-1 yr-1) MAI min

MAI max

Acacia mellifera Acacia nilotica Acacia senegal Acacia seyal Ailanthus excelsa Bamboo bamboo Cupressus spp. Eucalyptus spp. Khaya spp. Tectona grandis Eucalyptus camaldulensis Pinus spp.

2.2 15.0 1.4 2.0 6.6 5.0 15.0 12.0 8.5 2.5 21.0 4.0

4.0 20.0 2.6 6.0 9.4 7.5 24.0 14.0 12.0 3.5 43.0 15.0

Tectona grandis Xylia xylocapa Acacia spp. Araucaria angustifolia Eucalyptus spp. Hevea brasiliensis Mimosa scabrella Pinus spp. Populus spp.

7.3 3.0 15.0 15.0 20.0 10.0 10.0 25.0 10.0

17.3 8.8 30.0 30.0 70.0 20.0 25.0 40.0 30.0

Tectona grandis

15.0

35.0

4.0 1.9 12.5 1.1 1.8 1.2 1.5 1.2 1.5 0.9

6.1 3.5 20.0 2.4 3.2 3.7 2.4 1.5 1.7 1.0

Acacia mellifera Acacia nilotica Acacia senegal Acacia seyal Ailanthus spp. Bamboo bamboo Cupressus spp. Eucalyptus spp.

2.0 13.0 1.4 1.9 6.0 4.0 14.0 10.0

6.0 21.0 2.8 4.3 12.0 8.0 20.0 14.0

Khaya spp. Tectona grandis

7.0 5.0

16.0 8.0

Productive plantations

Africa

Asia

South America

Productive, semi-natural forests Acacia albida Acacia mellifera Acacia nilotica Acacia senegal Acacia seyal Africa Acacia tortilis Acacia tortilis var siprocarpa Balanites aegyptiaca Sclerocarya birrea Ziziphus mauritiana Protective plantations

Africa

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Chapter 4: Forest Land

TABLE 4.11B (CONTINUED) MEAN ANNUAL INCREMENT (GROWTH OF MERCHANTABLE VOLUME) Planted forest type/ region

FOR SOME FOREST PLANTATION SPECIES

Mean annual increment (MAI) over rotation (m3 ha-1 yr-1)

Tree species

Protective Semi-natural plantations Acacia albida Acacia mellifera Acacia nilotica Acacia senegal Acacia seyal Africa Acacia tortilis Acacia tortilis var siprocarpa Balanites aegyptiaca Sclerocarya birrea Ziziphus mauritiana

MAI min

MAI max

4.0 1.7 12.0 1.1 1.8 1.3 1.6 1.2 1.5 0.9

6.2 3.2 15.0 2.4 3.3 3.5 2.4 1.5 1.7 1.0

Source: FAO at http://www.fao.org/forestry/

TABLE 4.12 TIER 1 ESTIMATED BIOMASS VALUES FROM TABLES 4.7–4.11 (EXCEPT TABLE 4.11B) (VALUES ARE APPROXIMATE; USE ONLY FOR TIER 1)

Climate domain

Tropical

Subtropical

Temperate

Boreal

Ecological zone

Above-ground biomass in natural forests (tonnes d.m. ha-1)

Above-ground biomass in forest plantations (tonnes d.m. ha-1)

Above-ground net biomass growth in natural forests (tonnes d.m. ha-1 yr-1)

Above-ground net biomass growth in forest plantations (tonnes d.m. ha-1 yr-1)

Tropical rain forest

300

150

7.0

15.0

Tropical moist deciduous forest

180

120

5.0

10.0

Tropical dry forest

130

60

2.4

8.0

Tropical shrubland

70

30

1.0

5.0

Tropical mountain systems

140

90

1.0

5.0

Subtropical humid forest

220

140

5.0

10.0

Subtropical dry forest

130

60

2.4

8.0

Subtropical steppe

70

30

1.0

5.0

Subtropical mountain systems

140

90

1.0

5.0

Temperate oceanic forest

180

160

4.4

4.4

Temperate continental forest

120

100

4.0

4.0

Temperate mountain systems

100

100

3.0

3.0

Boreal coniferous forest

50

40

1.0

1.0

Boreal tundra woodland

15

15

0.4

0.4

Boreal mountain systems

30

30

1.0

1.0

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TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Adina cordifolia 0.58-0.59 Asia 5 Aegle marmelo 0.75 Asia 5 Afzelia bipidensis 0.67-0.79 Africa 3 Agathis sp. 0.44 Asia 5 Aglaia llanosiana 0.89 Asia 5 Agonandra brasiliensis 0.74 Americas 4 Aidia ochroleuca 0.78 Africa 5 Alangium longiflorum 0.65 Asia 5 Albizia sp. 0.52 Americas 5 Albizzia amara 0.70 Asia 5 Albizzia falcataria 0.25 Asia 5 Alcornea sp. 0.34 Americas 5 Aldina heterophylla 0.73 Americas 4 Aleurites trisperma 0.43 Asia 5 Alexa grandiflora 0.59 Americas 4 Alexa imperatricis 0.52 Americas 4 Allophyllus africanus 0.45 Africa 5 Alnus ferruginea 0.38 Americas 5 Alnus japonica 0.43 Asia 5 Alphitonia zizyphoides 0.50 Asia 5 Alphonsea arborea 0.69 Asia 5 Alseodaphne longipes 0.49 Asia 5 Alstonia congensis 0.33 Africa 5 Amburana cearensis 0.43 Americas 1 Amoora sp. 0.60 Asia 5 Amphimas 0.63 Africa 5 pterocarpoides Anacardium excelsum 0.41 Americas 4 Anacardium giganteum 0.44 Americas 4 Anadenanthera 0.86 Americas 4 macrocarpa Andira inermis 0.64 Americas 4 Andira parviflora 0.69 Americas 4 Andira retusa 0.67 Americas 5 Aniba amazonica 0.52-0.56 Americas 1 Aniba canelilla 0.92 Americas 4 Aningeria robusta 0.44-0.53 Africa 3 Anisophyllea 0.63 Africa 5 obtusifolia Anisophyllea zeylanica 0.46 Asia 5 Anisoptera sp. 0.54 Asia 5 Annonidium mannii 0.29 Africa 5 Anogeissus latifolia 0.78-0.79 Asia 5 Anopyxis klaineana 0.74 Africa 5 Anthocephalus 0.33-0.36 Asia 5 chinensis Anthocleista keniensis 0.50 Africa 5 Anthonotha 0.78 Africa 5 macrophylla Anthostemma 0.32 Africa 5 aubryanum Antiaris africana 0.38 Americas 5 Antiaris sp. 0.38 Africa 5 Antidesma pleuricum 0.59 Asia 5 Antrocaryon 0.50 Africa 5 klaineanum Apeiba aspera 0.28 Americas 1 Apeiba echinata 0.36 Americas 5 Apeiba peiouma 0.20 Americas 4 Aphanamiris 0.52 Asia 5 perrottetiana Apuleia leiocarpa 0.70 Americas 1 Apuleia molaris 0.76 Americas 4 Araucaria bidwillii 0.43 Asia 5 Ardisia cubana 0.62 Americas 1 Artocarpus comunis 0.70 Americas 5 Artocarpus sp. 0.58 Asia 5 Aspidosperma album 0.76 Americas 4

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Aspidosperma 0.67 Americas 1 macrocarpon Aspidosperma 0.86 Americas 4 obscurinervium Astronium gracile 0.73 Americas 4 Astronium graveolens 0.75 Americas 4 Astronium lecointei 0.73 Americas 5 Astronium ulei 0.71 Americas 4 Astronium urundeuva 1.21 Americas 4 Aucoumea klaineana 0.31-0.48 Africa 3 Autranella congolensis 0.78 Africa 5 Azadirachta sp. 0.52 Asia 5 Bagassa guianensis 0.69 Americas 4 Baillonella toxisperma 0.70 Africa 3 Balanites aegyptiaca 0.63 Africa 5 Balanocarpus sp. 0.76 Asia 5 Banara guianensis 0.61 Americas 5 Baphia kirkii 0.93 Africa 5 Barringtonia edulis 0.48 Asia 5 Basiloxylon exelsum 0.58 Americas 5 Bauhinia sp. 0.67 Asia 5 Beilschmiedia louisii 0.70 Africa 5 Beilschmiedia nitida 0.50 Africa 5 Beilschmiedia sp. 0.61 Americas 5 Beilschmiedia tawa 0.58 Asia 5 Berlinia sp. 0.58 Africa 5 Berrya cordifolia 0.78 Asia 5 Bertholletia excelsa 0.62 Americas 4 Bischofia javanica 0.54-0.62 Asia 5 Bixa arborea 0.32 Americas 4 Bleasdalea vitiensis 0.43 Asia 5 Blighia welwitschii 0.74 Africa 5 Bocoa sp. 0.42 Americas 1 Bombacopsis quinata 0.39 Americas 1 Bombacopsis sepium 0.39 Americas 5 Bombax costatum 0.35 Africa 3 Bombax paraense 0.39 Americas 1 Borojoa patinoi 0.52 Americas 5 Boswellia serrata 0.50 Asia 5 Bowdichia 0.39 Americas 2 coccolobifolia Bowdichia crassifolia 0.39 Americas 2 Bowdichia nitida 0.79 Americas 4 Bowdichia virgilioides 0.52 Americas 2 Brachystegia sp. 0.52 Africa 5 Bridelia micrantha 0.47 Africa 5 Bridelia squamosa 0.50 Asia 5 Brosimum acutifolium 0.55 Americas 4 Brosimum alicastrum 0.69 Americas 4 Brosimum guianense 0.96 Americas 4 Brosimum lactescens 0.70 Americas 1 Brosimum 0.58 Americas 4 parinarioides Brosimum potabile 0.53 Americas 4 Brosimum rubescens 0.87 Americas 4 Brosimum utile 0.40-0.49 Americas 1 Brysenia adenophylla 0.54 Americas 5 Buchenavia capitata 0.63 Americas 4 Buchenavia huberi 0.79 Americas 4 Buchenavia latifolia 0.45 Asia 5 Buchenavia oxycarpa 0.72 Americas 4 Buchenavia viridiflora 0.88 Americas 1 Bucida buceras 0.93 Americas 5 Bursera serrata 0.59 Asia 5 Bursera simaruba 0.29-0.34 Americas 5 Butea monosperma 0.48 Asia 5 Byrsonima coriacea 0.64 Americas 5 Byrsonima spicata 0.61 Americas 4

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Chapter 4: Forest Land

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Byrsonima 0.33 Americas 2 verbascifolia Cabralea canjerana 0.55 Americas 4 Caesalpinia sp. 1.05 Americas 5 Calophyllum 0.53 Americas 4 brasiliense Calophyllum sp. 0.46 Americas 1 Calophyllum sp. 0.53 Asia 5 Calpocalyx klainei 0.63 Africa 5 Calycarpa arborea 0.53 Asia 5 Calycophyllum 0.74 Americas 1 spruceanum Campnosperma 0.37 Americas 1 panamensis Cananga odorata 0.29 Asia 5 Canarium sp. 0.44 Asia 5 Canthium monstrosum 0.42 Asia 5 Canthium 0.63 Africa 5 rubrocostratum Carallia calycina 0.66 Asia 5 Carapa guianensis 0.55 Americas 4 Carapa procera 0.59 Africa 5 Cariniana integrifolia 0.49 Americas 4 Cariniana micrantha 0.64 Americas 4 Caryocar glabrum 0.65 Americas 1 Caryocar villosum 0.72 Americas 4 Casearia battiscombei 0.50 Africa 5 Casearia sp. 0.62 Americas 5 Cassia javanica 0.69 Asia 5 Cassia moschata 0.71 Americas 5 Cassia scleroxylon 1.01 Americas 4 Cassipourea euryoides 0.70 Africa 5 Cassipourea malosana 0.59 Africa 5 Castanopsis 0.51 Asia 5 philippensis Casuarina equisetifolia 0.81 Americas 5 Casuarina equisetifolia 0.83 Asia 5 Casuarina nodiflora 0.85 Asia 5 Catostemma commune 0.50 Americas 1 Cecropia sp. 0.36 Americas 5 Cedrela odorata 0.42 Americas 1 Cedrela odorata 0.38 Asia 5 Cedrela sp. 0.40-0.46 Americas 5 Cedrela toona 0.43 Asia 5 Cedrelinga 0.45 Americas 1 catenaeformis Ceiba pentandra 0.18-0.39 Africa 3 Ceiba pentandra 0.28 Americas 4 Ceiba pentandra 0.23 Asia 5 Ceiba samauma 0.57 Americas 1 Celtis luzonica 0.49 Asia 5 Celtis schippii 0.59 Americas 1 Celtis sp. 0.59 Africa 5 Centrolobium sp. 0.65 Americas 5 Cespedesia 0.63 Americas 5 macrophylla Cespedesia spathulata 0.54 Americas 1 Chaetocarpus 0.80 Americas 5 schomburgkianus Chisocheton pentandrus 0.52 Asia 5 Chlorophora excelsa 0.48-0.66 Africa 3 Chlorophora tinctoria 0.73 Americas 4 Chloroxylon swietenia 0.76-0.80 Asia 5 Chorisia integrifolia 0.28 Americas 1 Chrysophyllum 0.56 Africa 5 albidum Chukrassia tabularis 0.57 Asia 5 Citrus grandis 0.59 Asia 5 Clarisia racemosa 0.59 Americas 4

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Cleidion speciflorum 0.50 Asia 5 Cleistanthus eollinus 0.88 Asia 5 Cleistanthus 0.87 Africa 5 mildbraedii Cleistocalyx sp. 0.76 Asia 5 Cleistopholis patens 0.36 Africa 5 Clusia rosea 0.67 Americas 5 Cochlospermum 0.27 Asia 5 gossypium Cochlospermum 0.26 Americas 5 orinocensis Cocos nucifera 0.50 Asia 5 Coda edulis 0.78 Africa 5 Coelocaryon preussii 0.56 Africa 5 Cola sp. 0.70 Africa 5 Colona serratifolia 0.33 Asia 5 Combretodendron 0.57 Asia 5 quadrialatum Conopharyngia holstii 0.50 Africa 5 Copaifera officinalis 0.61 Americas 1 Copaifera pubifora 0.56 Americas 1 Copaifera religiosa 0.50 Africa 5 Copaifera reticulata 0.63 Americas 4 Cordia alliodora 0.48 Americas 5 Cordia bicolor 0.49 Americas 4 Cordia gerascanthus 0.74 Americas 5 Cordia goeldiana 0.48 Americas 4 Cordia millenii 0.34 Africa 5 Cordia platythyrsa 0.36 Africa 5 Cordia sagotii 0.50 Americas 4 Cordia sp. 0.53 Asia 5 Corynanthe pachyceras 0.63 Africa 5 Corythophora rimosa 0.84 Americas 4 Cotylelobium sp. 0.69 Asia 5 Couepia sp. 0.70 Americas 5 Couma macrocarpa 0.50 Americas 4 Couratari guianensis 0.54 Americas 4 Couratari multiflora 0.47 Americas 4 Couratari oblongifolia 0.49 Americas 4 Couratari stellata 0.63 Americas 4 Crataeva religiosa 0.53 Asia 5 Cratoxylon arborescens 0.40 Asia 5 Croton megalocarpus 0.57 Africa 5 Croton xanthochloros 0.48 Americas 5 Cryptocarya sp. 0.59 Asia 5 Cryptosepalum staudtii 0.70 Africa 5 Ctenolophon 0.78 Africa 5 englerianus Cubilia cubili 0.49 Asia 5 Cullenia excelsa 0.53 Asia 5 Cupressus lusitanica 0.43-0.44 Americas 5 Curatella americana 0.41 Americas 2 Cylicodiscus 0.80 Africa 5 gabonensis Cynometra alexandri 0.74 Africa 5 Cynometra sp. 0.80 Asia 5 Cyrilla racemiflora 0.53 Americas 5 Dacrycarpus imbricatus 0.45-0.47 Asia 5 Dacrydium sp. 0.46 Asia 5 Dacryodes buttneri 0.44-0.57 Africa 3 Dacryodes excelsa 0.52-0.53 Americas 5 Dacryodes sp. 0.61 Asia 5 Dactyodes colombiana 0.51 Americas 5 Dalbergia paniculata 0.64 Asia 5 Dalbergia retusa. 0.89 Americas 5 Dalbergia stevensonii 0.82 Americas 5 Daniellia oliveri 0.53 Africa 3 Declinanona calycina 0.47 Americas 5 Decussocarpus vitiensis 0.37 Asia 5

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TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Degeneria vitiensis 0.35 Asia 5 Dehaasia triandra 0.64 Asia 5 Dendropanax arboreum 0.40 Americas 4 Desbordesia pierreana 0.87 Africa 5 Detarium senegalensis 0.63 Africa 5 Dialium excelsum 0.78 Africa 5 Dialium guianense 0.88 Americas 4 Dialium sp. 0.80 Asia 5 Dialyanthera sp. 0.36-0.48 Americas 5 Diclinanona calycina 0.47 Americas 4 Dicorynia ghuianensis 0.65 Americas 4 Dicorynia paraensis 0.60 Americas 5 Didelotia africana 0.78 Africa 5 Didelotia letouzeyi 0.50 Africa 5 Didymopanax sp. 0.74 Americas 5 Dillenia sp. 0.59 Asia 5 Dimorphandra mora 0.99 Americas 5 Dinizia excelsa 0.86 Americas 4 Diospyros sp. 0.82 Africa 5 Diospyros sp. 0.47 Americas 1 Diospyros sp. 0.70 Asia 5 Diplodiscus paniculatus 0.63 Asia 5 Diploon cuspidatum 0.85 Americas 4 Diplotropis martiusii 0.74 Americas 1 Diplotropis purpurea 0.78 Americas 4 Dipterocarpus caudatus 0.61 Asia 5 Dipterocarpus 0.56 Asia 5 eurynchus Dipterocarpus gracilis 0.61 Asia 5 Dipterocarpus 0.62 Asia 5 grandiflorus Dipterocarpus kerrii 0.56 Asia 5 Dipterocarpus 0.57 Asia 5 kunstlerii Dipterocarpus sp. 0.61 Asia 5 Dipterocarpus 0.52 Asia 5 warburgii Dipteryx odorata 0.93 Americas 4 Dipteryx polyphylla 0.87 Americas 4 Discoglypremna 0.32 Africa 5 caloneura Distemonanthus 0.58 Africa 5 benthamianus Dracontomelon sp. 0.50 Asia 5 Dryobalanops sp. 0.61 Asia 5 Drypetes sp. 0.63 Africa 5 Drypetes variabilis 0.71 Americas 4 Dtypetes bordenii 0.75 Asia 5 Durio sp. 0.53 Asia 5 Dussia lehmannii 0.59 Americas 5 Dyera costulata 0.36 Asia 5 Dysoxylum 0.49 Asia 5 quercifolium Ecclinusa bacuri 0.59 Americas 4 Ecclinusa guianensis 0.63 Americas 5 Ehretia acuminata 0.51 Africa 5 Elaeocarpus serratus 0.40 Asia 5 Emblica officinalis 0.80 Asia 5 Enantia chlorantha 0.42 Africa 5 Endiandra laxiflora 0.54 Asia 5 Endlicheria sp. 0.50 Americas 1 Endodesmia 0.66 Africa 5 calophylloides Endopleura uchi 0.78 Americas 4 Endospermum sp. 0.38 Asia 5 Entandrophragma utile 0.53-0.62 Africa 3 Enterolobium 0.34 Americas 4 cyclocarpum

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Enterolobium 0.35 Asia 5 cyclocarpum Enterolobium 0.40 Americas 4 maximum Enterolobium 0.78 Americas 4 schomburgkii Eperua falcata 0.78 Americas 4 Epicharis cumingiana 0.73 Asia 5 Eribroma oblongum 0.60 Africa 5 Eriocoelum 0.50 Africa 5 microspermum Eriotheca 0.45 Americas 4 longipedicellata Erisma uncinatum 0.47 Americas 1 Erismadelphus ensul 0.56 Africa 5 Erythrina sp. 0.23 Americas 5 Erythrina subumbrans 0.24 Asia 5 Erythrina vogelii 0.25 Africa 5 Erythrophleum 0.70-0.88 Africa 3 ivorense Erythrophloeum 0.65 Asia 5 densiflorum Eschweilera amazonica 0.90 Americas 4 Eschweilera coriacea 0.78 Americas 4 Eschweilera ovata 0.81 Americas 4 Eschweilera sagotiana 0.79 Americas 4 Eucalyptus citriodora 0.64 Asia 5 Eucalyptus deglupta 0.34 Asia 5 Eucalyptus robusta 0.51 Americas 5 Eugenia sp. 0.65 Asia 5 Eugenia stahlii 0.73 Americas 5 Euxylophora paraensis 0.70 Americas 4 Fagara macrophylla 0.69 Africa 5 Fagara sp. 0.69 Americas 5 Fagraea sp. 0.73 Asia 5 Ficus benjamina 0.65 Asia 5 Ficus insipida 0.50 Americas 1 Ficus iteophylla 0.40 Africa 5 Fumtumia latifolia 0.45 Africa 5 Gallesia integrifolia 0.51 Americas 1 Gambeya sp. 0.56 Africa 5 Ganua obovatifolia 0.59 Asia 5 Garcinia myrtifolia 0.65 Asia 5 Garcinia punctata 0.78 Africa 5 Garcinia sp. 0.75 Asia 5 Gardenia turgida 0.64 Asia 5 Garuga pinnata 0.51 Asia 5 Genipa americana 0.51 Americas 4 Gilletiodendron 0.87 Africa 5 mildbraedii Gluta sp. 0.63 Asia 5 Glycydendron 0.66 Americas 4 amazonicum Gmelina arborea 0.41-0.45 Asia 5 Gmelina vitiensis 0.54 Asia 5 Gonocaryum 0.64 Asia 5 calleryanum Gonystylus punctatus 0.57 Asia 5 Gossweilerodendron 0.40 Africa 5 balsamiferum Goupia glabra 0.68 Americas 1 Grewia tiliaefolia 0.68 Asia 5 Guarea cedrata 0.48-0.57 Africa 3 Guarea chalde 0.52 Americas 5 Guarea guidonia 0.68 Americas 4 Guarea kunthiana 0.60 Americas 1 Guatteria decurrens 0.52 Americas 1 Guatteria olivacea 0.51 Americas 4 Guatteria procera 0.65 Americas 4

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TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Guazuma ulmifolia 0.50-0.52 Americas 5 Guibourtia demeusii 0.70-0.84 Africa 3 Guillielma gasipae 0.95-1.25 Americas 5 Gustavia speciosa 0.34 Americas 1 Hannoa klaineana 0.28 Africa 5 Hardwickia binata 0.73 Asia 5 Harpullia arborea 0.62 Asia 5 Harungana 0.45 Africa 5 madagascariensis Helicostylis tomentosa 0.72 Americas 4 Heritiera sp. 0.56 Asia 5 Hernandia Sonora 0.29 Americas 5 Hevea brasiliensis 0.49 Americas 4 Hevea brasiliensis 0.53 Asia 5 Hexalobus crispiflorus 0.48 Africa 5 Hibiscus tiliaceus 0.57 Asia 5 Hieronyma chocoensis 0.59-0.62 Americas 1 Hieronyma laxiflora 0.55 Americas 1 Himatanthus articulatus 0.38 Americas 2 Hirtella davisii 0.74 Americas 5 Holoptelea grandis 0.59 Africa 5 Homalanthus 0.38 Asia 5 populneus Homalium sp. 0.70 Africa 5 Homalium sp. 0.76 Asia 5 Hopea acuminata 0.62 Asia 5 Hopea sp. 0.64 Asia 5 Huberodendron patinoi 0.50 Americas 1 Humiria balsamifera 0.66 Americas 4 Humiriastrum excelsum 0.75 Americas 4 Humiriastrum procera 0.70 Americas 5 Hura crepitans 0.36 Americas 4 Hyeronima 0.64 Americas 4 alchorneoides Hyeronima laxiflora 0.59 Americas 5 Hylodendron 0.78 Africa 5 gabonense Hymenaea courbaril 0.77 Americas 1 Hymenaea davisii 0.67 Americas 5 Hymenaea oblongifolia 0.62 Americas 1 Hymenaea parvifolia 0.95 Americas 4 Hymenolobium 0.64 Americas 4 excelsum Hymenolobium 0.65 Americas 4 modestum Hymenolobium 0.67 Americas 4 pulcherrimum Hymenostegia 0.78 Africa 5 pellegrini Inga alba 0.62 Americas 4 Inga edulis 0.51 Americas 1 Inga paraensis 0.82 Americas 4 Intsia palembanica 0.68 Asia 5 Irvingia grandifolia 0.78 Africa 5 Iryanthera grandis 0.55 Americas 4 Iryanthera sagotiana 0.57 Americas 4 Iryanthera trocornis 0.72 Americas 4 Jacaranda copaia 0.33 Americas 4 Joannesia heveoides 0.39 Americas 4 Julbernardia globiflora 0.78 Africa 5 Kayea garciae 0.53 Asia 5 Khaya ivorensis 0.40-0.48 Africa 3 Kingiodendron 0.48 Asia 5 alternifolium Klainedoxa gabonensis 0.87 Africa 5 Kleinhovia hospita 0.36 Asia 5 Knema sp. 0.53 Asia 5 Koompassia excelsa 0.63 Asia 5

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Koordersiodendron 0.65-0.69 Asia 5 pinnatum Kydia calycina 0.72 Asia 5 Lachmellea speciosa 0.73 Americas 5 Laetia procera 0.63 Americas 1 Lagerstroemia sp. 0.55 Asia 5 Lannea grandis 0.50 Asia 5 Lecomtedoxa klainenna 0.78 Africa 5 Lecythis idatimon 0.77 Americas 4 Lecythis lurida 0.83 Americas 4 Lecythis pisonis 0.84 Americas 4 Lecythis poltequi 0.81 Americas 4 Lecythis zabucaja 0.86 Americas 4 Letestua durissima 0.87 Africa 5 Leucaena leucocephala 0.64 Asia 5 Licania macrophylla 0.76 Americas 4 Licania oblongifolia 0.88 Americas 4 Licania octandra 0.77 Americas 4 Licania unguiculata 0.88 Americas 1 Licaria aritu 0.80 Americas 4 Licaria cannella 1.04 Americas 4 Licaria rigida 0.73 Americas 4 Lindackeria sp. 0.41 Americas 5 Linociera domingensis 0.81 Americas 5 Lithocarpus soleriana 0.63 Asia 5 Litsea sp. 0.40 Asia 5 Lonchocarpus sp. 0.69 Americas 5 Lophira alata 0.84-0.97 Africa 3 Lophopetalum sp. 0.46 Asia 5 Lovoa trichilioides 0.45 Africa 5 Loxopterygium sagotii 0.56 Americas 5 Lucuma sp. 0.79 Americas 5 Luehea sp. 0.50 Americas 5 Lueheopsis duckeana 0.62 Americas 4 Mabea piriri 0.59 Americas 5 Macaranga denticulata 0.53 Asia 5 Machaerium sp. 0.70 Americas 5 Maclura tinctoria 0.71 Americas 1 Macoubea guianensis 0.40 Americas 5 Madhuca oblongifolia 0.53 Asia 5 Maesopsis eminii 0.41 Africa 5 Magnolia sp. 0.52 Americas 5 Maguira sclerophylla 0.57 Americas 5 Malacantha sp. 0.45 Africa 5 Mallotus philippinensis 0.64 Asia 5 Malouetia duckei 0.57 Americas 4 Mammea africana 0.62 Africa 5 Mammea americana 0.62 Americas 5 Mangifera indica 0.55 Americas 5 Mangifera sp. 0.52 Asia 5 Manilkara amazonica 0.85 Americas 4 Manilkara bidentata 0.87 Americas 1 Manilkara huberi 0.93 Americas 4 Manilkara lacera 0.78 Africa 5 Maniltoa minor 0.76 Asia 5 Maquira sclerophylla 0.57 Americas 4 Marila sp. 0.63 Americas 5 Markhamia platycalyx 0.45 Africa 5 Marmaroxylon 0.81 Americas 4 racemosum Mastixia philippinensis 0.47 Asia 5 Matayba domingensis 0.70 Americas 5 Matisia hirta 0.61 Americas 5 Mauria sp. 0.31 Americas 1 Maytenus sp. 0.71 Americas 5 Melanorrhea sp. 0.63 Asia 5 Melia dubia 0.40 Asia 5 Melicope triphylla 0.37 Asia 5 Meliosma macrophylla 0.27 Asia 5

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TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Melochia umbellata 0.25 Asia 5 Memecylon 0.77 Africa 5 capitellatum Metrosideros collina 0.70-0.76 Asia 5 Mezilaurus itauba 0.70 Americas 4 Mezilaurus lindaviana 0.68 Americas 4 Michelia sp. 0.43 Asia 5 Michropholis sp. 0.61 Americas 5 Microberlinia 0.70 Africa 5 brazzavillensis Microcos coriaceus 0.42 Africa 5 Microcos stylocarpa 0.40 Asia 5 Micromelum 0.64 Asia 5 compressum Micropholi guyanensis 0.65 Americas 4 Micropholi venulosa 0.67 Americas 4 Milletia sp. 0.72 Africa 5 Milliusa velutina 0.63 Asia 5 Mimusops elengi 0.72 Asia 5 Minquartia guianensis 0.76 Americas 1 Mitragyna parviflora 0.56 Asia 5 Mitragyna stipulosa 0.47 Africa 5 Monopetalanthus 0.44-0.53 Africa 3 heitzii Mora excelsa 0.80 Americas 4 Mora gonggrijpii 0.78 Americas 1 Mora megistosperma 0.63 Americas 1 Mouriri barinensis 0.78 Americas 1 Mouriria sideroxylon 0.88 Americas 5 Musanga cecropioides 0.23 Africa 5 Myrciaria floribunda 0.73 Americas 5 Myristica platysperma 0.55 Americas 4 Myristica sp. 0.53 Asia 5 Myroxylon balsamum 0.78 Americas 1 Myroxylon peruiferum 0.78 Americas 1 Nauclea diderrichii 0.63 Africa 5 Nealchornea yapurensis 0.61 Americas 1 Nectandra rubra 0.57 Americas 5 Neesia sp. 0.53 Asia 5 Neonauclea bernardoi 0.62 Asia 5 Neopoutonia 0.32 Africa 5 macrocalyx Neotrewia cumingii 0.55 Asia 5 Nesogordonia 0.65 Africa 5 papaverifera Ochna foxworthyi 0.86 Asia 5 Ochroma pyramidale 0.30 Asia 5 Ochtocosmus africanus 0.78 Africa 5 Ocotea guianensis 0.63 Americas 4 Ocotea neesiana 0.63 Americas 4 Octomeles sumatrana 0.27-0.32 Asia 5 Odyendea sp. 0.32 Africa 5 Oldfieldia africana 0.78 Africa 5 Ongokea gore 0.72 Africa 5 Onychopetalum 0.61 Americas 4 amazonicum Ormosia coccinea 0.61 Americas 1 Ormosia paraensis 0.67 Americas 4 Ormosia schunkei 0.57 Americas 1 Oroxylon indicum 0.32 Asia 5 Otoba gracilipes 0.32 Americas 1 Ougenia dalbergiodes 0.70 Asia 5 Ouratea sp. 0.66 Americas 5 Oxystigma oxyphyllum 0.53 Africa 5 Pachira acuatica 0.43 Americas 5 Pachyelasma 0.70 Africa 5 tessmannii Pachypodanthium 0.58 Africa 5 staudtii

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Palaquium sp. 0.55 Asia 5 Pangium edule 0.50 Asia 5 Paraberlinia bifoliolata 0.56 Africa 5 Parashorea stellata 0.59 Asia 5 Paratecoma peroba 0.60 Americas 5 Paratrophis glabra 0.77 Asia 5 Parinari excelsa 0.68 Americas 4 Parinari glabra 0.87 Africa 5 Parinari montana 0.71 Americas 4 Parinari rodolphii 0.71 Americas 4 Parinari sp. 0.68 Asia 5 Parkia multijuga 0.38 Americas 4 Parkia nitada 0.40 Americas 4 Parkia paraensis 0.44 Americas 4 Parkia pendula 0.55 Americas 4 Parkia roxburghii 0.34 Asia 5 Parkia ulei 0.40 Americas 4 Pausandra trianae 0.59 Americas 1 Pausinystalia 0.56 Africa 5 brachythyrsa Pausinystalia sp. 0.56 Africa 5 Payena sp. 0.55 Asia 5 Peltogyne paniculata 0.89 Americas 4 Peltogyne paradoxa 0.91 Americas 4 Peltogyne 0.89 Americas 1 porphyrocardia Peltophorum 0.62 Asia 5 pterocarpum Pentace sp. 0.56 Asia 5 Pentaclethra macroloba 0.43 Americas 1 Pentaclethra 0.78 Africa 5 macrophylla Pentadesma butyracea 0.78 Africa 5 Persea sp. 0.40-0.52 Americas 5 Peru glabrata 0.65 Americas 5 Peru schomburgkiana 0.59 Americas 5 Petitia domingensis 0.66 Americas 5 Phaeanthus 0.56 Asia 5 ebracteolatus Phyllanthus discoideus 0.76 Africa 5 Phyllocladus 0.53 Asia 5 hypophyllus Phyllostylon 0.77 Americas 4 brasiliensis Pierreodendron 0.70 Africa 5 africanum Pinus caribaea 0.51 Americas 5 Pinus caribaea 0.48 Asia 5 Pinus insularis 0.47-0.48 Asia 5 Pinus merkusii 0.54 Asia 5 Pinus oocarpa 0.55 Americas 5 Pinus patula 0.45 Americas 5 Piptadenia communis 0.68 Americas 4 Piptadenia grata 0.86 Americas 1 Piptadenia suaveolens 0.75 Americas 4 Piptadeniastrum 0.56 Africa 5 africanum Piratinera guianensis 0.96 Americas 5 Pisonia umbellifera 0.21 Asia 5 Pithecellobium 0.56 Americas 5 guachapele Pithecellobium 0.36 Americas 1 latifolium Pithecellobium saman 0.49 Americas 1 Pittosporum 0.51 Asia 5 pentandrum Plagiostyles africana 0.70 Africa 5 Planchonia sp. 0.59 Asia 5 Platonia insignis 0.70 Americas 5

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TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Platymiscium sp. 0.71-0.84 Americas 5 Podocarpus oleifolius 0.44 Americas 1 Podocarpus rospigliosii 0.57 Americas 1 Podocarpus sp. 0.43 Asia 5 Poga oleosa 0.36 Africa 5 Polyalthia flava 0.51 Asia 5 Polyalthia suaveolens 0.66 Africa 5 Polyscias nodosa 0.38 Asia 5 Pometia sp. 0.54 Asia 5 Poulsenia armata 0.37-0.44 Americas 1 Pourouma sp. 0.32 Americas 5 Pouteria anibifolia 0.66 Americas 1 Pouteria anomala 0.81 Americas 4 Pouteria caimito 0.87 Americas 4 Pouteria guianensis 0.90 Americas 4 Pouteria manaosensis 0.64 Americas 4 Pouteria oppositifolia 0.65 Americas 4 Pouteria villamilii 0.47 Asia 5 Premna angolensis 0.63 Africa 5 Premna tomentosa 0.96 Asia 5 Prioria copaifera 0.40-0.41 Americas 5 Protium heptaphyllum 0.54 Americas 4 Protium tenuifolium 0.65 Americas 4 Pseudolmedia laevigata 0.62-0.63 Americas 1 Pseudolmedia laevis 0.71 Americas 1 Pteleopsis hylodendron 0.63 Africa 5 Pterocarpus marsupium 0.67 Asia 5 Pterocarpus soyauxii 0.62-0.79 Africa 3 Pterocarpus vernalis 0.57 Americas 1 Pterogyne nitens 0.66 Americas 4 Pterygota sp. 0.52 Africa 5 Pterygota sp. 0.62 Americas 1 Pycnanthus angolensis 0.40-0.53 Africa 3 Qualea albiflora 0.50 Americas 5 Qualea brevipedicellata 0.69 Americas 4 Qualea dinizii 0.58 Americas 5 Qualea lancifolia 0.58 Americas 4 Qualea paraensis 0.67 Americas 4 Quararibea asterolepis 0.45 Americas 1 Quararibea bicolor 0.52-0.53 Americas 1 Quararibea cordata 0.43 Americas 1 Quassia simarouba 0.37 Americas 4 Quercus alata 0.71 Americas 5 Quercus costaricensis 0.61 Americas 5 Quercus eugeniaefolia 0.67 Americas 5 Quercus sp. 0.70 Asia 5 Radermachera pinnata 0.51 Asia 5 Randia cladantha 0.78 Africa 5 Raputia sp. 0.55 Americas 5 Rauwolfia macrophylla 0.47 Africa 5 Rheedia sp. 0.60 Americas 1 Rhizophora mangle 0.89 Americas 4 Ricinodendron 0.20 Africa 5 heudelotii Rollinia exsucca 0.52 Americas 4 Roupala moniana 0.77 Americas 4 Ruizierania albiflora 0.57 Americas 4 Saccoglottis gabonensis 0.74 Africa 5 Saccoglottis guianensis 0.77 Americas 4 Salmalia malabarica 0.32-0.33 Asia 5 Samanea saman 0.45-0.46 Asia 5 Sandoricum vidalii 0.43 Asia 5 Santiria trimera 0.53 Africa 5 Sapindus saponaria 0.58 Asia 5 Sapium ellipticum 0.50 Africa 5 Sapium luzontcum 0.40 Asia 5 Sapium marmieri 0.40 Americas 1 Schefflera morototoni 0.36 Americas 1 Schizolobium parahyba 0.40 Americas 1

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Schleichera oleosa 0.96 Asia 5 Schrebera arborea 0.63 Africa 5 Schrebera swietenoides 0.82 Asia 5 Sclerolobium 0.62 Americas 4 chrysopyllum Sclerolobium paraense 0.64 Americas 4 Sclerolobium 0.65 Americas 4 peoppigianum Scleronema 0.61 Americas 4 micranthum Sclorodophloeus 0.68 Africa 5 zenkeri Scottellia coriacea 0.56 Africa 5 Scyphocephalium 0.48 Africa 5 ochocoa Scytopetalum tieghemii 0.56 Africa 5 Semicarpus anacardium 0.64 Asia 5 Serialbizia acle 0.57 Asia 5 Serianthes melanesica 0.48 Asia 5 Sesbania grandiflora 0.40 Asia 5 Shorea assamica forma 0.41 Asia 5 philippinensis Shorea astylosa 0.73 Asia 5 Shorea ciliata 0.75 Asia 5 Shorea contorta 0.44 Asia 5 Shorea palosapis 0.39 Asia 5 Shorea plagata 0.70 Asia 5 Shorea polita 0.47 Asia 5 Shorea robusta 0.72 Asia 5 Shorea sp. (balau) 0.70 Asia 5 Shorea sp. (dark red 0.55 Asia 5 meranti) Shorea sp. (light red 0.40 Asia 5 meranti) Sickingia sp. 0.52 Americas 5 Simaba multiflora 0.51 Americas 5 Simarouba amara 0.36 Americas 1 Simira sp. 0.65 Americas 1 Sindoropsis letestui 0.56 Africa 5 Sloanea guianensis 0.79 Americas 5 Sloanea javanica 0.53 Asia 5 Sloanea nitida 1.01 Americas 4 Soymida febrifuga 0.97 Asia 5 Spathodea campanulata 0.25 Asia 5 Spondias lutea 0.38 Americas 4 Spondias mombin 0.31-0.35 Americas 1 Spondias purpurea 0.40 Americas 4 Staudtia stipitata 0.75 Africa 5 Stemonurus luzoniensis 0.37 Asia 5 Sterculia apetala 0.33 Americas 4 Sterculia pruriens 0.46 Americas 4 Sterculia rhinopetala 0.64 Africa 5 Sterculia speciosa 0.51 Americas 4 Sterculia vitiensis 0.31 Asia 5 Stereospermum 0.62 Asia 5 suaveolens Strephonema 0.56 Africa 5 pseudocola Strombosia 0.71 Asia 5 philippinensis Strombosiopsis 0.63 Africa 5 tetrandra Strychnos potatorum 0.88 Asia 5 Stylogyne sp. 0.69 Americas 5 Swartzia fistuloides 0.82 Africa 5 Swartzia laevicarpa 0.61 Americas 1 Swartzia panacoco 0.97 Americas 4 Swietenia macrophylla 0.43 Americas 1 Swietenia macrophylla 0.49-0.53 Asia 5

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TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREE -3 SPECIES (OVEN-DRY TONNES (MOIST M ))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Swintonia foxworthyi 0.62 Asia 5 Swintonia sp. 0.61 Asia 5 Sycopsis dunni 0.63 Asia 5 Symphonia globulifera 0.58 Africa 5 Symphonia globulifera 0.58 Americas 1 Syzygium cordatum 0.59 Africa 5 Syzygium sp. 0.69-0.76 Asia 5 Tabebuia rosea 0.54 Americas 1 Tabebuia serratifolia 0.92 Americas 1 Tabebuia stenocalyx 0.55-0.57 Americas 5 Tachigalia 0.53 Americas 4 myrmecophylla Talisia sp. 0.84 Americas 5 Tamarindus indica 0.75 Asia 5 Tapirira guianensis 0.50 Americas 4 Taralea oppositifolia 0.80 Americas 1 Tectona grandis 0.50-0.55 Asia 5 Terminalia amazonica 0.65 Americas 1 Terminalia citrina 0.71 Asia 5 Terminalia copelandii 0.46 Asia 5 Terminalia ivorensis 0.40-0.59 Africa 3 Terminalia microcarpa 0.53 Asia 5 Terminalia nitens 0.58 Asia 5 Terminalia oblonga 0.73 Americas 1 Terminalia pterocarpa 0.48 Asia 5 Terminalia superba 0.40-0.66 Africa 3 Terminalia tomentosa 0.73-0.77 Asia 5 Ternstroemia 0.53 Asia 5 megacarpa Tessmania africana 0.85 Africa 5 Testulea gabonensis 0.60 Africa 5 Tetragastris altissima 0.74 Americas 4 Tetragastris panamensis 0.76 Americas 4 Tetrameles nudiflora 0.30 Asia 5 Tetramerista glabra 0.61 Asia 5 Tetrapleura tetraptera 0.50 Africa 5 Thespesia populnea 0.52 Asia 5 Thyrsodium guianensis 0.63 Americas 4 Tieghemella africana 0.53-0.66 Africa 3 Toluifera balsamum 0.74 Americas 5 Torrubia sp. 0.52 Americas 5 Toulicia pulvinata 0.63 Americas 5 Tovomita guianensis 0.60 Americas 5 Trattinickia sp. 0.38 Americas 5 Trema orientalis 0.31 Asia 5 Trema sp. 0.40 Africa 5 Trichilia lecointei 0.90 Americas 4 Trichilia prieureana 0.63 Africa 5 Trichilia propingua 0.58 Americas 5 Trichoscypha arborea 0.59 Africa 5 Trichosperma 0.41 Americas 5 mexicanum Trichospermum richii 0.32 Asia 5 Triplaris cumingiana 0.53 Americas 5 Triplochiton 0.28-0.44 Africa 3 scleroxylon. Tristania sp. 0.80 Asia 5 Trophis sp. 0.44 Americas 1 Turpinia ovalifolia 0.36 Asia 5 Vantanea parviflora 0.86 Americas 4 Vatairea guianensis 0.70 Americas 4 Vatairea paraensis 0.78 Americas 4 Vatairea sericea 0.64 Americas 4 Vateria indica 0.47 Asia 5 Vatica sp. 0.69 Asia 5 Vepris undulata 0.70 Africa 5 Virola michelii 0.50 Americas 4 Virola reidii 0.35 Americas 1 Virola sebifera 0.37 Americas 1

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004; 3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992 Species Density Continent Reference Vismia sp. 0.41 Americas 5 Vitex doniana 0.40 Africa 5 Vitex sp. 0.52-0.57 Americas 5 Vitex sp. 0.65 Asia 5 Vitex stahelii 0.60 Americas 5 Vochysia densiflora 0.29 Americas 1 Vochysia ferruginea 0.37 Americas 1 Vochysia guianensis 0.53 Americas 4 Vochysia lanceolata 0.49 Americas 1 Vochysia macrophylla 0.36 Americas 1 Vochysia maxima 0.47 Americas 4 Vochysia melinonii 0.51 Americas 4 Vochysia obidensis 0.50 Americas 4 Vochysia surinamensis 0.66 Americas 4 Vouacapoua americana 0.79 Americas 4 Warszewicsia coccinea 0.56 Americas 5 Wrightia tinctorea 0.75 Asia 5 Xanthophyllum 0.63 Asia 5 excelsum Xanthoxylum 0.46 Americas 5 martinicensis Xanthoxylum sp. 0.44 Americas 5 Xylia xylocarpa 0.73-0.81 Asia 5 Xylopia frutescens 0.64 Americas 5 Xylopia nitida 0.57 Americas 4 Xylopia staudtii 0.36 Africa 5 Zanthoxylum rhetsa 0.33 Asia 5 Zizyphus sp. 0.76 Asia 5

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TABLE 4.14 BASIC WOOD DENSITY (D) OF SELECTED TEMPERATE AND BOREAL TREE TAXA D [oven-dry tonnes (moist m-3)]

Source

Abies spp.

0.40

2

Acer spp.

0.52

2

Alnus spp.

0.45

2

Betula spp.

0.51

2

Fagus sylvatica

0.58

2

Fraxinus spp.

0.57

2

Larix decidua

0.46

2

Picea abies

0.40

2

Picea sitchensis

0.40

3

Pinus pinaster

0.44

4

Pinus radiata

0.38 (0.33 - 0.45)

1

Pinus strobus

0.32

2

Pinus sylvestris

0.42

2

Populus spp.

0.35

2

Prunus spp.

0.49

2

Pseudotsuga menziesii

0.45

2

Quercus spp.

0.58

2

Salix spp.

0.45

2

Tilia spp.

0.43

2

Taxon

1 = Beets et al., 2001 2 = Dietz, 1975 3 = Knigge and Shulz, 1966 4 = Rijsdijk and Laming, 1994

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Annex 4A.1

Glossary for Forest Land

Terminology for stocks and changes in forests as defined in this volume Component

State

Increase

Decrease from harvest

growing stock

net annual increment

removals

growing stock biomass

increment biomass

removals biomass

above-ground biomass growth below-ground biomass growth

above-ground biomass removals below-ground biomass1 removals

total biomass growth

biomass removals

Merchantable volume Biomass in the merchantable volume Total above-ground biomass Total below-ground biomass Total above-ground and below-ground biomass Carbon

above-ground biomass below-ground biomass total biomass

carbon in … (in any of the compartments above, e.g., carbon in growing stock or biomass removals), or in litter, dead wood and soil organic matter

ABOVE-GROUND BIOMASS All biomass of living vegetation, both woody and herbaceous, above the soil including stems, stumps, branches, bark, seeds, and foliage. Note: In cases where forest understory is a relatively small component of the above-ground biomass carbon pool, it is acceptable for the methodologies and associated data used in some tiers to exclude it, provided the tiers are used in a consistent manner throughout the inventory time series.

ABOVE-GROUND BIOMASS GROWTH Oven-dry weight of net annual increment (s.b.) of a tree, stand or forest plus oven-dry weight of annual growth of branches, twigs, foliage, top and stump. The term “growth” is used here instead of “increment”, since the latter term tends to be understood in terms of merchantable volume.

AFFORESTATION2 The direct human-induced conversion of land that has not been forested for a period of at least 50 years to forested land through planting, seeding and/or the human-induced promotion of natural seed sources.

AGROFORESTRY A land-use system that involves deliberate retention, introduction, or mixture of trees or other woody perennials in crop and animal production systems to take advantage of economic or ecological interactions among the components (Dictionary of Forestry, helms, 1998, Society of American Foresters).

BASIC WOOD DENSITY Ratio between oven dry mass and fresh stem-wood volume without bark.

BELOW-GROUND BIOMASS All biomass of live roots. Fine roots of less than (suggested) 2mm diameter are often excluded because these often cannot be distinguished empirically from soil organic matter or litter.

B I O M A S S C O N V E R S I O N A N D E X P A N S I O N F A C T O R (BCEF) A multiplication factor that coverts merchantable volume of growing stock, merchantable volume of net annual increment, or merchantable volume of wood-removal and fuelwood-removals to above-ground biomass, aboveground biomass growth, or biomass removals, repectively. Biomass conversion and expansion factors for 1

Occurs in some cases, e.g., where root stocks (walnut) or entire root systems are removed (biomass harvesting).

2

In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision -/CMP.1 (Land Use, Land-use Change and Forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

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growing stock (BCEFS), for net annual increment (BCEFI), and for wood-removal and fuelwood-removals (BCEFR) usually differ. As used in these guidelines, they account for above-ground components only. For more detail see Box 4.2.

B I O M A S S E X P A N S I O N F A C T O R (BEF) A multiplication factor that expands the dry-weight of growing stock biomass, increment biomass, and biomass of wood-removal or fuelwood-removals to account for non-merchantable or non-commercial biomass components, such as stump, branches, twigs, foliage, and, sometimes, non-commercial trees. Biomass expansion factors usually differ for growing stock (BEFS), net annual increment (BEFI), and wood-removal and fuelwoodremovals (BEFR). As used in these guidelines, biomass expansion factors account for above-ground components only. For more detail see Box 4.2.

BIOMASS REMOVALS Biomass of wood-removal and firewood-removals (s.b.) plus oven-dry weight of branches, twigs, foliage of the trees or stands removed.

CANOPY COVER See crown cover

CARBON CONTENT Absolute amount of carbon in a pool or parts of it.

CARBON FRACTION Tonnes of carbon per tonne of biomass dry matter.

CARBON IN… See table above; absolute amount in tonnes, obtained by multiplying amount of biomass in respective component by the applicable carbon fraction, usually 50%.

CARBON STOCK The quantity of carbon in a pool.

CARBON STOCK CHANGE The carbon stock in a pool changes due to gains and losses. When losses exceed gains, the stock decreases, and the pool acts as a source; when gains exceed losses, the pools accumulate carbon, and the pools act as a sink.

CLOSED FOREST Formations where trees, in the various stories and the undergrowth, cover a high proportion of the ground (>40%).

CONVERSION Change of one land use to another.

CONVERSION FACTOR Multiplier that transforms the measurement units of an item without affecting its size or amount. For example, basic wood density is a conversion factor that transforms green volume of wood into dry weight.

CROWN COVER The percentage of the ground covered by a vertical projection of the outermost perimeter of the natural spread of the foliage (cannot exceed 100%).

DEAD WOOD Includes all non-living woody biomass not contained in the litter, either standing, lying on the ground, or in the soil. Dead wood includes wood lying on the surface, dead roots, and stumps, larger than or equal to 10cm in diameter (or the diameter specified by the country).

DEAD WOOD BIOMASS All non-living woody biomass not contained in the litter, either standing, lying on the ground, or in the soil. Dead wood includes wood lying on the surface, dead roots down to a diameter of 2mm, and stumps larger than or equal to 10cm in diameter or any other diameter used by the country.

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DEFORESTATION3 The direct human-induced conversion of forested land to non-forested land.

DISTURBANCE A disturbance is defined as an environmental fluctuation and destructive event that disturb forest health, structure, and/or change resources or physical environment at any given spatial or temporal scale. Disturbances that affect health and vitality which include biotic agents such as insects and diseases, and abiotic agents such as fire, pollution, and extreme weather conditions (see also below, mortality and other disturbance).

DISTURBANCE BY DISEASES Disturbances caused by diseases attributable to pathogens such as bacteria, fungi, phytoplasma, or virus.

DISTURBANCE BY FIRE Disturbance caused by wildfire regardless of whether it broke out inside or outside the Forest. A wildfire is any unplanned and uncontrolled wildland fire which, regardless of ignition source, may require suppression response.

DISTURBANCE BY INSECTS Disturbance caused by insect pests that are detrimental to tree health.

DRY (FOREST) Moisture regimes for boreal and temperate zones are defined by the ratio of mean annual precipitation (MAP) and potential evapotranspiration (PET): Dry (MAP/PET < 1) and Wet (MAP/PET > 1); and for tropical zones by precipitation alone: Dry (MAP < 1,000mm), Moist (MAP: 1,000-2,000mm) and Wet (MAP > 2,000mm).

D R Y M A T T E R ( D.M. ) Dry matter refers to biomass that has been dried to an oven-dry state, often at 70ºC.

FELLINGS Volume (over bark) of all trees, living or dead, above a 10cm diameter at breast height, felled annually in forests or other wooded land. It includes volume of all felled trees whether or not they are removed. It includes silvicultural and pre-commercial thinning and cleanings of trees of more than 10cm diameter, left in the forest, and natural losses that are recovered. Note: In these guidelines, only the terms “wood-removal” and “fuelwood-removals” are used, consistent with GFRA 2005. Removals are generally a subset of fellings.

FOREST4 Forest is a minimum area of land of 0.05 – 1.0 hectares with tree crown cover (or equivalent stocking level) of more than 10 – 30 per cent with trees with the potential to reach a minimum height of 2 – 5 metres at maturity in situ. A forest may consist either of closed forest formations where trees of various storeys and undergrowth cover a high portion of the ground or open forest. Young natural stands and all plantations which have yet to reach a crown density of 10 – 30 per cent or tree height of 2 – 5 metres are included under forest, as are areas normally forming part of the forest area which are temporarily unstocked as a result of human intervention such as harvesting or natural causes but which are expected to revert to forest.

FOREST INVENTORY System for measuring the extent, quantity, and condition of a forest, usually by sampling: 1.

A set of objective sampling methods designed to quantify the spatial distribution, composition, and rates of change of forest parameters within specified levels of precision for the purpose of management;

2.

The listing of data from such a survey. May be made of all forest resources including trees and other vegetation, fish, insects, and wildlife, as well as street trees and urban forest trees.

3

In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision -/CMP.1 (Land Use, Land-use Change and Forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

4

In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision -/CMP.1 (Land Use, Land-use Change and Forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

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FOREST LAND This category includes all land with woody vegetation consistent with thresholds used to define Forest Land in the national greenhouse gas inventory. It also includes systems with a vegetation structure that currently fall below, but in situ could potentially reach the threshold values used by a country to define the Forest Land category.

FOREST MANAGEMENT5 A system of practices for stewardship and use of forest land aimed at fulfilling relevant ecological (including biological diversity), economic and social functions of the forest in a sustainable manner.

FOREST PLANTATION Forest stands established by planting or/and seeding in the process of afforestation or reforestation. They are either of introduced species (all planted stands), or intensively managed stands of indigenous species, which meet all the following criteria: one or two species at planting, even age class, and regular spacing.

FUELWOOD-REMOVAL The wood removed for energy production purposes, regardless of whether for industrial, commercial, or domestic use. Fuel wood includes wood collected or removed directly from forest or other wooded land for energy purposes only. It excludes fuelwood which is produced as a by-product or residual matter from the industrial processing of round wood. It includes removal from fellings in an earlier period and from trees killed or damaged by natural causes. It also includes removal by local people or owners for their own use.

GROWING STOCK Volume over bark of all living trees more than X cm in diameter at breast height. It includes the stem from ground level or stump height up to a top diameter of Y cm, and may also include branches to a minimum diameter of W cm. Countries indicate the three thresholds (X, Y, W in cm) and the parts of the tree that are not included in the volume. Countries also indicate whether the reported figures refer to volume above ground or above stump. The diameter is measured at 30cm above the end of the buttresses if these are higher than 1 meter. It includes windfallen living trees and excludes smaller branches, twigs, foliage, flowers, seeds, and roots.

GROWING STOCK BIOMASS Oven-dry weight of the growing stock (s.a.).

HARVEST LOSS Difference between the assessed merchantable volume of growing stock and the actual volume of the harvested timber. Due to different measurement rules for standing and felled timber, losses are from bucking, breakage, defect.

INCREMENT BIOMASS Oven-dry weight of (merchantable) net annual increment of a tree, stand, or forest.

INTENSIVE FOREST MANAGEMENT A regime of forest management, where silvicultural practices define the structure and composition of forest stands. A formal or informal forest management plan exists. A forest is not under intensive management, if mainly natural ecological processes define the structure and composition of stands.

INTRODUCED SPECIES A species introduced outside of its normal past and current distribution.

LITTER Includes all non-living biomass with a size greater than the limit for soil organic matter (suggested 2mm) and less than the minimum diameter chosen for dead wood (e.g., 10cm), lying dead, in various states of decomposition above or within the mineral or organic soil. This includes the litter layer as usually defined in soil

5

Forest management has particular meaning under the Marrakesh Accords, which may require subdivision of the managed forest as described in Chapter 4.

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typologies. Live fine roots above the mineral or organic soil (of less than the minimum diameter limit chosen for below-ground biomass) are included in litter where they cannot be distinguished from it empirically.

L O W A C T I V I T Y C L A Y (LAC) S O I L S Soils with low activity clay (LAC) minerals are highly weathered soils dominated by 1:1 clay mineral and amorphous iron and aluminium oxides (in FAO classification included: Acrisols, Nitosols, Ferrasols).

MANAGED FOREST A managed forest is forest land subjected to conditions defined for managed land.

MANAGED LAND Managed land is land where human interventions and practices have been applied to perform production, ecological or social functions.

MERCHANTABLE VOLUME Mechantable volume is the volume overbark of all trees defined using the conditions described for growing stocks. Further, this can be applied to growing stocks as well as net annual increment and wood removals.

MOIST (FOREST) Moisture regimes for boreal and temperate zones are defined by the ratio of mean annual precipitation (MAP) and potential evapotranspiration (PET): Dry (MAP/PET < 1) and Wet (MAP/PET > 1); and for tropical zones by precipitation alone: Dry (MAP < 1,000mm), Moist (MAP: 1,000-2,000mm) and Wet (MAP > 2,000mm).

MORTALITY Trees dying naturally from competition in the stem-exclusion stage of a stand or forest. As used here, mortality does not include losses due to disturbances (s.a.).

NATURAL FOREST A forest composed of indigenous trees and not classified as a forest plantation.

NATURAL REGENERATION Re-establishment of a forest stand by natural means i.e., by natural seeding or vegetative regeneration. It may be assisted by human intervention e.g., by scarification of the soil or fencing to protect against wildlife or domestic animal grazing.

NET ANNUAL INCREMENT Average annual volume of gross increment over the given reference period minus mortality (s.a.), of all trees to a specified minimum diameter at breast height. As used here, it is not net of losses due to disturbances (s.a.).

ORGANIC SOILS Soils are organic if they satisfy the requirements 1 and 2, or 1 and 3 below (FAO, 1998): 1) Thickness of organic horizon greater than or equal to 10cm. A horizon of less than 20cm must have 12 percent or more organic carbon when mixed to a depth of 20cm. 2) Soils that are never saturated with water for more than a few days must contain more than 20 percent organic carbon by weight (i.e., about 35 percent organic matter). 3) Soils are subject to water saturation episodes and has either: a.

At least 12 percent organic carbon by weight (i.e., about 20 percent organic matter) if the soil has no clay; or

b.

At least 18 percent organic carbon by weight (i.e., about 30 percent organic matter) if the soil has 60% or more clay; or

c.

An intermediate, proportional amount of organic carbon for intermediate amounts of clay.

OTHER DISTURBANCE Disturbance caused by factors other than fire, insects, or diseases. May include areas affected by drought, flooding, windfalls, acid rain, etc.

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PEAT SOIL (ALSO HISTOSOL) A typical wetland soil with a high water table and an organic layer of at least 40cm thickness (poorly drained organic soil).

POOL/CARBON POOL A reservoir. A system which has the capacity to accumulate or release carbon. Examples of carbon pools are forest biomass, wood products, soils, and the atmosphere. The units are in mass.

REFORESTATION6 Direct human-induced conversion of non-forested land to forested land through planting, seeding and/or the human-induced promotion of natural seed sources, on land that was forested but that has been converted to nonforested land. For the first commitment period, reforestation activities will be limited to reforestation occurring on those lands that did not contain forest on 31 December 1989.

REMOVAL BIOMASS Oven dry weight of wood removals.

REVEGETATION7 A direct human-induced activity to increase carbon stocks on sites through the establishment of vegetation that covers a minimum area of 0.05 hectares and does not meet the definitions of afforestation and reforestation contained here.

ROOT-SHOOT RATIO Ratio of below-ground biomass to above-ground biomass; applies to above-ground biomass, above-ground biomass growth, biomass removals and may differ for these components.

ROUNDWOOD All round wood felled or otherwise harvested and removed; it comprises all wood obtained from removals e.g., quatities removed from forests and from trees outside forests, including wood recovered from natural felling and logging losses during a period. In the production statistics, it represents the sum of fuelwood, including wood for charcoal, saw-and veneer logs, pulpwood and other industrial roundwood. In the trade statistics, it represents the sum of industrial roundwood, and fuelwood, including wood for charcoal. It is reported in cubic meters excluding bark.

SANDY SOILS Includes all soils (regardless of taxonomic classification) having > 70% sand and < 8% clay (based on standard textural measurements (in FAO classification include: Arenosols, sandy Regosols)).

SAVANNA Savannas are tropical and subtropical formations with continuous grass cover, occasionally interrupted by trees and shrubs. Savannas are found in Africa, Latin America, Asia and Australia.

SEASONAL (FOREST) Semi-deciduous forests with a distinct wet and dry season and rainfall between 1,200 and 2,000 mm per year.

STAND–REPLACING DISTURBANCES Major disturbances which kill or remove all the existing trees above the forest floor vegetation. Minor disturbances leave some of the pre-disturbance trees alive.

SHRUB Woody perennial plants, generally more than 0.5 meters and less than 5 meters in height at maturity and without definite crown. Height limits for trees and shrubs should be interpreted with flexibility, particularly the minimum tree and maximum shrub height, which may vary between 5 and 7 meters.

6

In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision -/CMP.1 (Land Use, Land-use Change and Forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

7

In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision -/CMP.1 (Land Use, Land-use Change and Forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

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SOIL CARBON Organic carbon in mineral and organic soils (including peat) to a specified depth chosen by the country and applied consistently through the time series. Live fine roots of less than 2mm (or other value chosen by the country as diameter limit for below-ground biomass) are included with soil organic matter where they cannot be distinguished from it empirically.

SOIL ORGANIC MATTER Includes organic carbon in mineral soils to a specified depth chosen by the country and applied consistently through the time series. Live and dead fine roots and DOM within the soil, that are less than the minimum diameter limit (suggested 2mm) for roots and DOM, are included with soil organic matter where they cannot be distinguished from it empirically. The default for soil depth is 30cm and guidance on determining countryspecific depths is given in Chapter 2.3.3.1.

SPODIC SOILS Soils exhibiting strong podzolization (in FAO classification includes many Podzolic groups).

TOTAL BIOMASS Growing stock biomass of trees, stands or forests plus biomass of branches, twigs, foliage, seeds, stumps, and sometimes, non-commercial trees. Differentiated into above-ground biomass and below-ground biomass (s.a.). If there is no misunderstanding, possible also just to use “biomass” with the same meaning.

TOTAL BIOMASS GROWTH Biomass of the net annual increment (s.a.) of trees, stands, or forests, plus the biomass of the growth of branches, twigs, foliage, seeds, stumps, and sometimes, non-commercial trees. Differentiated into above-ground biomass growth and below-ground biomass growth (s.a.). If there is no misunderstanding, possible also just to use “biomass growth” with the same meaning. The term “growth” is used here instead of “increment”, since the latter term tends to be understood in terms of merchantable volume.

TREE A woody perennial with a single main stem, or in the case of coppice with several stems, having a more or less definitive crown. Includes bamboos, palms, and other woody plants meeting the above criteria.

VOLUME OVERBARK Growing stock or merchantable wood measured outside, that is including the bark. Bark adds 5-25% of total volume, depending on tree diameter and bark thickness of species. The weighted average bark percentage calculated from the data of TBFRA 2000 is 11% of the volume outside bark.

VOLUME UNDERBARK Growing stock or merchantable wood without the bark. See above.

WET (FOREST) Moisture regimes for boreal and temperate zones are defined by the ratio of mean annual precipitation (MAP) and potential evapotranspiration (PET): Dry (MAP/PET < 1) and Wet (MAP/PET > 1); and for tropical zones by precipitation alone: Dry (MAP < 1,000mm), Moist (MAP: 1,000-2,000mm) and Wet (MAP > 2,000mm).

WOODY BIOMASS Biomass from trees, bushes and shrubs, for palms, bamboos not strictly correct in the botanical sense.

WOOD FUEL Also wood-based fuels, wood-derived biofuels. All types of biofuels originating directly or indirectly from woody biomass.

WOOD-REMOVAL The wood removed (volume of round wood over bark) for production of goods and services other than energy production (fuelwood). The term removal differs from fellings as it excludes felled trees left in the forest. It includes removal from fellings of an earlier period and from trees killed or damaged by natural causes. It also includes removal by local people or owners for their own use. As the term “removal” is used in the context of climate change to indicate sequestration of greenhouse gases from the atmosphere, removal in the context of forest harvesting should always be used as “wood-removal or fuelwood-removal” to avoid misunderstandings.

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References Australian Greenhouse Gas Office (AGO) (2002). Greenhouse Gas Emissions from Land Use Change in Australia: An Integrated Application of the National Carbon Accounting System (2002). Andreae, M.O. and Merlet, P. (2001). Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles 15: 955-966. Armentano, T.V. and Menges, E.S. (1986). Patterns of change in the carbon balance of organic soil-wetlands of the temperate zone. Journal of Ecology 74: 755-774. Baker, T.R., Phillips, O.L., Malhi, Y., Almeida, S., Arroyo, L., Di Fiore, A., Erwin, T., Higuchi, N., Killeen, T.J., Laurance, S.G., Laurance, W.F., Lewis, S.L., Monteagudo, A., Neill, D.A., Vargas, P.N., Pitman, N.C.A., Silva, J.N.M. and Martínez, R.V. (2004a). Increasing biomass in Amazonian forest plots. Philosophical Transactions of the Royal Society of London B 359: 353-365. Baker, T.R., Phillips, O.L., Malhi, Y., Almeida, S., Arroyo, L., Di Fiore, A., Erwin, T., Killeen, T.J., Laurance, S.G., Laurance, W.F., Lewis, S.L., Lloyd, J., Monteagudo, A., Neill, D.A., Patiño, S., Pitman, N.C.A., Silva, J.N.M. and Martínez, R.V. (2004b). Variation in wood density determines spatial patterns in Amazonian forest biomass. Global Change Biology 10: 545-562. Barbosa, R.I. and Fearnside, P.M. (2004). Wood density of trees in open savannas of the Brazilian Amazon. Forest Ecology and Management 199: 115-123. Battles, J.J., Armesto, J.J., Vann, D.R., Zarin, D.J., Aravena, J.C., Pérez, C. and Johnson, A.H. (2002). Vegetation composition, structure, and biomass of two unpolluted watersheds in the Cordillera de Piuchué, Chiloé Island, Chile. Plant Ecology 158: 5-19. Beets, P.N., Gilchrist, K. and Jeffreys, M.P. (2001). Wood density of radiata pine: Effect of nitrogen supply. Forest Ecology and Management 145: 173-180. Bhatti, J.S., Apps, M.J., and Jiang, H. (2001). Examining the carbon stocks of boreal forest ecosystems at stand and regional scales. In: Lal R. et al. (eds.) Assessment Methods for Soil Carbon, Lewis Publishers, Boca Raton FL, pp. 513-532. Cairns, M.A., Brown, S., Helmer, E.H. and Baumgardner, G.A. (1997). Root biomass allocation in the world’s upland forests. Oecologia 111: 1-11. Cannell, M.G.R. (1982). World forest biomass and primary production data. Academic Press, New York, NY. Centre Technique Forestier Tropical (CTFT) (1989). Memento du Forestier, 3e Édition. Ministère Français de la Coopération et du Développement, Paris, France. Chambers, J.Q., Tribuzy, E.S., Toledo, L.C., Crispim, B.F., Higuchi, N., dos Santos, J., Araújo, A.C., Kruijt, B., Nobre, A.D. and Trumbore, S.E. (2004). Respiration from a tropical forest ecosystem: Partitioning of sources and low carbon use efficiency. Ecological Applications 14: S72-S88. Chambers, J.Q., dos Santos, J., Ribeiro, R.J., and Higuchi, N. (2001a). Tree damage, allometric relationships, and above-ground net primary production in a tropical forest. Forest Ecology and Management 152: 7384. Chambers, J.Q., Schimel, J.P. and Nobre, A.D. (2001b). Respiration from coarse wood litter in Central Amazon Forests. Biogeochemistry 52: 115-131. Clark, D.A., Piper, S.C., Keeling, C.D. and Clark, D.B. (2003). Tropical rain forest tree growth and atmospheric carbon dynamics linked to interannual temperature variation during 1984-2000. Proceedings of the National Academy of Sciences of the USA 100: 5852-5857. de Groot, W.J., Bothwell, P.M., Carlsson, D.H. and Logan, K.A. (2003). Simulating the effects of future fire regimes on western Canadian boreal forests. Journal of Vegetation Science 14: 355-364 DeWalt, S.J. and Chave, J. (2004). Structure and biomass of four lowland Neotropical forests. Biotropica 36: 719. Dietz, P. (1975). Dichte und Rindengehalt von Industrieholz. Holz Roh- Werkstoff 33: 135-141. Dixon, R.K., Brown, S., Houghton, R.A., Solomon, A.M., Trexler, M.C. and Wisniewski, J. (1994). Carbon pools and flux of global forest ecosystems. Science 263(1544): 185-190. Dong, J., Kaufmann, R.K., Myneni, R.B., Tucker, C.J., Kauppi, P.E., Liski, J., Buermann, W., Alexeyev, V. and Hughes, M.K. (2003). Remote sensing estimates of boreal and temperate forest woody biomass: Carbon pools, sources, and sinks. Remote Sensing of Environment 84: 393-410.

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Dubé, S., Plamondon, A.P. and Rothwell, R.L. (1995). Watering up after clear-cutting on forested wetlands of the St. Lawrence lowland. Water Resources Research 31:1741-1750. Echeverria, C. and Lara, A. (2004). Growth patterns of secondary Nothofagus obliqua-N. alpina forests in southern Chile. Forest Ecology and Management 195: 29-43. Ellert, B.H., Janzen, H.H. and McConkey, B.G. (2001). Measuring and comparing soil carbon storage. In: R. Lal, J.M. Kimble, R.F. Follett and B.A. Stewart (eds.). Soil Management for Enhancing Carbon Sequestration. CRC Press, Boca Raton, FL., pp. 593-610. Falloon, P. and Smith, P. (2003). Accounting for changes in soil carbon under the Kyoto Protocol: need for improved long-term data sets to reduce uncertainty in model projections. Soil Use and Management, 19, 265-269. Fearnside, P.M. (1997). Wood density for estimating forest biomass in Brazilian Amazonia. Forest Ecology and Management 90: 59-87. Feldpausch, T.R., Rondon, M.A., Fernandes, E.C.M. and Riha, S.J. (2004). Carbon and nutrient accumulation in secondary forests regenerating on pastures in central Amazonia. Ecological Applications 14: S164-S176. Filipchuk, A.N., Strakhov, V.V., Borisov, B.A. et al. (2000). A Brief National Overview on Forestry Sector and Wood Products: Russian Federation. UN ECE, FAO. New York, Geneva. ECE/TIM/SP/18, p. 94 (In Russian). Fittkau, E.J. and Klinge, N.H. (1973). On biomass and trophic structure of the central Amazonian rainforest ecosystem. Biotropica 5: 2-14. Food and Agriculture Organization (FAO) 2001. Global forest resources assessment 2000. FAO, Rome, Italy. Food and Agriculture Organization (FAO) 2006. Global forest resources assessment 2005. FAO, Rome, Italy. Gayoso, J. and Schlegel, B. (2003). Estudio de línea de base de carbono: Carbono en bosques nativos, matorrales y praderas de la Décima Región de Chile. Universidad Austral de Chile, Valdivia, Chile. Gayoso, J., Guerra, J. and Alarcón, D. (2002). Contenido de carbono y funciones de biomasa en especies natives y exóticas. Universidad Austral de Chile, Valdivia, Chile. Gower, S.T., Krankina, O., Olson, R.J., Apps, M., Linder, S. and Wang, C. (2001). Net primary production and carbon allocation patterns of boreal forest ecosystems. Ecological Applications 11: 1395-1411. Hall, G.M.J. (2001). Mitigating an organization's future net carbon emissions by native forest restoration. Ecological Applications 11: 1622-1633. Hall, G.M.J. and Hollinger, D. Y. (1997). Do the indigenous forests affect the net CO2 emission policy of New Zealand? New Zealand Forestry 41: 24-31. Hall, G.M.J., Wiser, S.K., Allen, R.B., Beets, P.N. and Goulding, C.J. (2001). Strategies to estimate national forest carbon stocks from inventory data: The 1990 New Zealand baseline. Global Change Biology 7: 389-403. Harmand, J.M., Njiti, C.F., Bernhard-Reversat, F. and Puig, H. (2004). Aboveground and belowground biomass, productivity and nutrient accumulation in tree improved fallows in the dry tropics of Cameroon. Forest Ecology and Management 188: 249-265. Harmon, M.E. and Marks, B. (2002). Effects of silvicultural practices on carbon stores in Douglas-fir-western hemlock forests in the Pacific Northwest, USA: results from a simulation model. Canadian Journal of Forest Research 32 (5): 863-877. Harmon, M.E., Franklin, J.F., Swanson, F.J., Sollins, P., Gregory, S.V., Lattin, J.D., Anderson, N.H., Cline, S.P., Aumen, N.G., Sedell, J.R., Lienkaemper, G.W., Cromack, J.R. and Cummins, K.W. (1986). Ecology of coarse woody debris in temperate ecosystems. Advances in Ecological Research 15: 133–302. Hessl, A.E., Milesi, C., White, M.A., Peterson, D.L. and Keane, R. (2004). Ecophysiological parameters for Pacific Northwest trees. U.S. Department of Agriculture, Forest Service, Portland, OR. Hinds, H.V. and Reid, J.S. (1957). Forest trees and timbers of New Zealand. New Zealand Forest Service Bulletin 12: 1-221. Hughes, R.F., Kauffman, J.B. and Jaramillo, V.J. (1999). Biomass, carbon, and nutrient dynamics of secondary forests in a humid tropical region of México. Ecology 80: 1892-1907. Hughes, R.F., Kauffman, J.B. and Jaramillo-Luque, V.J. (2000). Ecosystem-scale impacts of deforestation and land use in a humid tropical region of México. Ecological Applications 10: 515-527.

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IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. Callander B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2003). Good Practice Guidance for Land Use, Land-Use Change and Forestry. Penman J., Gytarsky M., Hiraishi T., Krug, T., Kruger D., Pipatti R., Buendia L., Miwa K., Ngara T., Tanabe K., Wagner F. (Eds).Intergovernmental Panel on Climate Change (IPCC), IPCC/IGES, Hayama, Japan. Jenkins, J.C., Chojnacky, D.C., Heath, L.S. and Birdsey, R.A. (2004). Comprehensive database of diameterbased biomass regressions for North American tree species. U.S. Department of Agriculture, Forest Service, Newtown Square, PA. Jobbagy, E.G. and Jackson, R.B. (2000). The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecological Applications 19(2):423-436. Johnson, D.W., and Curtis, P.S. (2001). Effects of forest management on soil C and N storage: meta analysis. Forest Ecology and Management 140: 227-238. Johnson, D.W., Knoepp, J.D. and Swank, W.T. (2002). Effects of forest management on soil carbon: results of some long-term resampling studies. Environment Pollution 116: 201-208. Knigge, W. and Schulz, H. (1966). Grundriss der Forstbenutzung. Verlag Paul Parey, Hamburg, Berlin. Köppen, W. (1931). Grundriss der Klimakunde. Walter deGruyter Co., Berlin, Germany. Kraenzel, M., Castillo, A., Moore, T. and Potvin, C. (2003). Carbon storage of harvest-age teak (Tectona grandis) plantations, Panama. Forest Ecology and Management 173: 213-225. Kurz, W.A., Apps, M.J., Banfield, E. and Stinson, G. (2002). Forest carbon accounting at the operational scale. The Forestry Chronicle 78: 672-679. Kurz, W.A. and Apps, M.J. (2006). Developing Canada's national forest carbon monitoring, accounting and reporting system to meet the reporting requirements of the Kyoto Protocol. Mitigation and Adaptation Strategies for Global Change 11(1): 33-43. Kurz, W.A., Apps, M.J., Webb, T.M. and McNamee, P.J. (1992). The carbon budget of the Canadian forest sector: phase I. Forestry Canada, Northwest Region. Information Report NOF-X-326, 93 pp. Kurz, W.A., Beukema, S.J. and Apps, M.J. (1998). Carbon budget implications of the transition from natural to managed disturbance regimes in forest landscapes. Mitigation and Adaptation Strategies for Global Change 2:405-421. Kurz, W.A., Beukema, S.J. and Apps, M.J. (1996). Estimation of root biomass and dynamics for the carbon budget model of the Canadian forest sector. Can. J. For. Res. 26:1973-1979. Lamlom, S.H. and Savidge, R.A. (2003). A reassessment of carbon content in wood: variation within and between 41 North American species. Biomass and Bioenergy 25: 381-388. Lasco, R.D. and Pulhin, F.B. (2003). Philippine forest ecosystems and climate change: Carbon stocks, rate of sequestration and the Kyoto Protocol. Annals of Tropical Research 25: 37-51. Levy, P.E., Hale, S.E. and Nicoll, B.C. (2004). Biomass expansion factors and root:shoot ratios for coniferous tree species in Great Britain. Forestry 77: 421-430. Li, C. and Apps, M.J. (2002). Fire Regimes and the Carbon Dynamics of Boreal Forest Ecosystems. In Shaw C. and Apps MJ (Eds). The role of Boreal Forests and Forestry in the Global Carbon Budget, Northern Forestry Centre Report Fo42-334/2000E, 107-118. Li, C., Kurz, W.A., Apps, M.J. and Beukema, S.J. (2003). Belowground biomass dynamics in the Carbon Budget Model of the Canadian Forest Sector: recent improvements and implications for the estimation of NPP and NEP. Canadian Journal of Forest Research 33: 126-136. Liski, J., Pussinen, A., Pingoud, K., Makipaa, R. and Karjalainen, T. (2001). Which rotation length is favourable to carbon sequestration? Canadian Journal of Forest Research 31: 2004-2013. Loveland, T.R, Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, Z., Yang, L. and Merchant, J.W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1-km AVHRR data. International Journal of Remote Sensing 21: 1303-1330. Lugo, A.E., Wang, D. and Bormann, F.H. (1990). A comparative analysis of biomass production in five tropical tree species. Forest Ecology and Management 31: 153-166.

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Malhi, Y., Baker, T.R., Phillips, O.L., Almeida, S., Alvarez, E., Arroyo, L., Chave, J., Czimczik, C.I., Di Fiore, A., Higuchi, N., Killeen, T.J., Laurance, S.G., Laurance, W.F., Lewis, S.L., Montoya, L.M.M., Monteagudo, A., Neill, D.A., Vargas, P.N., Patiño, S., Pitman, N.C.A., Quesada, C.A., Salomãos, R., Silva, J.N.M., Lezama, A.T., Martínez, R.V., Terborgh, J., Vinceti, B. and Lloyd, J. (2004). The aboveground coarse wood productivity of 104 Neotropical forest plots. Global Change Biology 10: 563-591. Matthews, G.A.R. (1993). The carbon content of trees. UK Forestry Commission, Edinburgh, UK. McGroddy, M.E., Daufresne, T. and Hedin, L.O. (2004). Scaling of C:N:P stoichiometry in forests worldwide: Implications of terrestrial Redfield-type ratios. Ecology 85: 2390-2401. McKenzie, N.J., Cresswell, H.P., Ryan, P.J. and Grundy, M. (2000). Opportunities for the 21st century: Expanding the horizons for soil, plant, and water analysis. Communications in Soil Science and Plant Analysis 31: 1553-1569. Mokany, K., Raison, J.R. and Prokushkin, A.S. (2006). Critical analysis of root:shoot ratios in terrestrial biomes. Global Change Biology 12: 84-96. Monte, L, Hakanson, L., Bergstrom, U., Brittain, J. and Heling, R. (1996). Uncertainty analysis and validation of environmental models: the empirically based uncertainty analysis. Ecological Modelling 91:139-152. Montès, N., Bertaudière-Montes, V., Badri, W., Zaoui, E.H. and Gauquelin, T. (2002). Biomass and nutrient content of a semi-arid mountain ecosystem: the Juniperus thurifera L. woodland of Azzaden Valley (Morocco). Forest Ecology and Management 166: 35-43. Nygård, R., Sawadogo, L. and Elfving, B. (2004). Wood-fuel yields in short-rotation coppice growth in the north Sudan savanna in Burkina Faso. Forest Ecology and Management 189: 77-85. Ogle, S.M., Breidt, F.J., Eve, M.D. and Paustian, K. (2003). Uncertainty in estimating land use and management impacts on soil organic carbon storage for U.S. agricultural lands between 1982 and 1997. Global Change Biology 9:1521-1542. Ogle, S.M., Breidt, F.J. and Paustian, K. (2006). Bias and variance in model results associated with spatial scaling of measurements for parameterization in regional assessments. Global Change Biology 12:516523. Post, W.M. and Kwon, K.C. (2000). Soil carbon sequestration and land-use change: processes and potential. Global Change Biology 6:317-327. Poupon, H. (1980). Structure et dynamique de la strate ligneuse d’une steppe Sahélienne au nord du Sénégal. Office de la Recherche Scientifique et Technique Outre-Mer, Paris, France. Powers, J.S., Read, J.M., Denslow, J.S. and Guzman, S.M. (2004). Estimating soil carbon fluxes following landcover change: a test of some critical assumptions for a region in Costa Rica. Global Change Biology 10:170-181. Pregitzer, K.S. (2003). Woody plants, carbon allocation and fine roots. New Phytologist 158 (3): 421-424. Reyes, G., Brown, S., Chapman, J. and Lugo, A.E. (1992). Wood densities of tropical tree species. U.S. Department of Agriculture, Forest Service, New Orleans, LA. Rijsdijk, J.F. and Laming, P.B. (1994). Physical and related properties of 145 timbers. Kluwer Academic Publishers, Dordrecht, Netherlands. Saldarriaga, J.G., West, D.C., Tharp, M.L. and Uhl, C. (1988). Long term chronosequence of forest succession in the upper Rio Negro of Colombia and Venezuela. Journal of Ecology 76: 938-958. Scott, N.A., Tate, K.R., Giltrap, D.J., et al. (2002). Monitoring land-use change effects on soil carbon in New Zealand: quantifying baseline soil carbon stocks. Environmental Pollution 116: 167-186. Sebei, H., Albouchi, A., Rapp, M. and El Aouni, M.H. (2001). Évaluation de la biomasse arborée et arbustive dans une séquence de dégradation de la suberaie à Cytise de Kroumirie (Tunisie). Annals of Forest Science 58: 175-191. Siltanen, et al. (1997). A soil profile and organic carbon data base for Canadian forest and tundra mineral soils. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta. Singh, K. and Misra, R. (1979). Structure and Functioning of Natural, Modified and Silvicultural Ecosystems in Eastern Uttar Pradesh. Banras Hindu University, Varanasi, India. Singh, S.S., Adhikari, B.S. and Zobel, D.B. (1994). Biomass, productivity, leaf longevity, and forest structure in the central Himalaya. Ecological Monographs 64: 401-421.

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Smith, J.E. and Heath, L.S. (2001). Identifying influences on model uncertainty: an application using a forest carbon budget model. Environmental Management 27:253-267. Smithwick, E.A.H., Harmon, M.E., Remillard, S.M., Acker, S.A. and Franklin, J.F. (2002). Potential upper bounds of carbon stores in forests of the Pacific Northwest. Ecological Applications 12: 1303-1317. Somogyi, Z., Cienciala, E., Mäkipää, R., Muukkonen, P., Lehtonen, A. and Weiss, P. (2006). Indirect methods of large-scale forest biomass estimation. European Journal of Forest Research. DOI: 10.1007/s103420060125-7. Stape, J.L., Binkley, D. and Ryan, M.G. (2004). Eucalyptus production and the supply, use and efficiency of use of water, light and nitrogen across a geographic gradient in Brazil. Forest Ecology and Management 193: 17-31. Stephens, P., Trotter, C., Barton, J., Beets, P., Goulding, C., Moore, J., Lane, P. and Payton, I. (2005). Key elements in the development of New Zealand’s carbon monitoring, accounting and reporting system to meet Kyoto Protocol LULUCF good practice guidance, Poster paper presented at IUFRO World Congress, Brisbane Australia, August 2005. Stocks, B.J., Mason, J.A., Todd, J.B., Bosch, E.M., Wotton, B.M., Amiro, B.D., Flannigan, M.D., Hirsch, K.G., Logan, K.A., Martell, D.L., and Skinner, W.R. (2002). “Large forest fires in Canada, 1959 – 1997”, Journal of Geophysical Research, 107, 8149 [printed 108(D1), 2003]. Trotter, C., Barton, J., Beets, P., Goulding, C., Lane, P., Moore, J., Payton, I., Rys, G., Stephens, P., Tate, K. and Wakelin, S. (2005). New Zealand’s approach to forest inventory under the UNFCCC and Kyoto Protocol. Proceedings of the International Workshop of Forest Inventory for the Kyoto Protocol (Eds Matsumoto, M. and Kanomata, H.), pp. 33–43, published by: Division of Policy and Economics, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki, 305-8687, Japan. Trotter, C.M. (1991). Remotely sensed data as an information source for Geographical Information Systems in natural resource management. International Journal of Geographical Information Systems 5, No. 2, 225240. Ugalde, L. and Perez, O. (2001). Mean annual volume increment of selected industrial forest planatation species. Food and Agriculture Organization, Rome, Italy. VandenBygaart, A.J., Gregorich, E.G., Angers, D.A., et al. (2004). Uncertainty analysis of soil organic carbon stock change in Canadian cropland from 1991 to 2001. Global Change Biology 10:983-994. Wulder, M., Kurz, W.A. and Gillis, M. (2004). National level forest monitoring and modeling in Canada, Progress in Planning, Volume 61:365-381. Zianis, D., Muukkonen, P., Mäkipää, R. and Mencuccini, M. (2005). Biomass and stem volume equations for tree species in Europe. Silva Fennica, Monographs 4. 63. p.

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Chapter 5: Cropland

CHAPTER 5

CROPLAND

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.1

Volume 4: Agriculture, Forestry and Other Land Use

Authors Rodel D. Lasco (Philippines), Stephen Ogle (USA), John Raison (Australia), Louis Verchot (ICRAF/USA), Reiner Wassmann (Germany), and Kazuyuki Yagi (Japan) Sumana Bhattacharya (India), John S. Brenner (USA), Julius Partson Daka (Zambia), Sergio P. González (Chile), Thelma Krug (Brazil), Yue Li (China), Daniel L. Martino (Uruguay), Brian G. McConkey (Canada), Pete Smith (UK), Stanley C. Tyler (USA), and Washington Zhakata (Zimbabwe)

Contributing Authors Ronald L. Sass (USA) and Xiaoyuan Yan (China)

5.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

Contents 5

Cropland 5.1

Introduction ...........................................................................................................................................5.6

5.2

Cropland Remaining Cropland..............................................................................................................5.7

5.2.1 5.2.1.1

Choice of methods ...................................................................................................................5.7

5.2.1.2

Choice of emission factors.......................................................................................................5.8

5.2.1.3

Choice of activity data ...........................................................................................................5.10

5.2.1.4

Calculation steps for Tier 1 and Tier 2 ..................................................................................5.11

5.2.1.5

Uncertainty assessment..........................................................................................................5.12

5.2.2

Dead organic matter ....................................................................................................................5.12

5.2.2.1

Choice of method...................................................................................................................5.13

5.2.2.2

Choice of emission/removal factors.......................................................................................5.13

5.2.2.3

Choice of activity data ...........................................................................................................5.14

5.2.2.4

Calculation steps for Tiers 1 and 2 ........................................................................................5.14

5.2.2.5

Uncertainty assessment..........................................................................................................5.15

5.2.3

Soil carbon...................................................................................................................................5.15

5.2.3.1

Choice of method...................................................................................................................5.15

5.2.3.2

Choice of stock change and emission factors ........................................................................5.16

5.2.3.3

Choice of activity data ...........................................................................................................5.19

5.2.3.4

Calculation steps for Tier 1....................................................................................................5.22

5.2.3.5

Uncertainty assessment..........................................................................................................5.23

5.2.4

5.3

Biomass .........................................................................................................................................5.7

Non-CO2 greenhouse gas emissions from biomass burning........................................................5.24

5.2.4.1

Choice of method...................................................................................................................5.24

5.2.4.2

Choice of emission factors.....................................................................................................5.24

5.2.4.3

Choice of activity data ...........................................................................................................5.25

5.2.4.4

Uncertainty assessment..........................................................................................................5.25

Land Converted to Cropland ...............................................................................................................5.25

5.3.1

Biomass .......................................................................................................................................5.26

5.3.1.1

Choice of methods .................................................................................................................5.26

5.3.1.2

Choice of emission/removal factors.......................................................................................5.28

5.3.1.3

Choice of activity data ...........................................................................................................5.29

5.3.1.4

Calculation steps for Tiers 1 and 2 ........................................................................................5.30

5.3.1.5

Uncertainty assessment..........................................................................................................5.30

5.3.2

Dead organic matter ....................................................................................................................5.30

5.3.2.1

Choice of method...................................................................................................................5.31

5.3.2.2

Choice of emission/removal factors.......................................................................................5.32

5.3.2.3

Choice of activity data ...........................................................................................................5.33

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.3

Volume 4: Agriculture, Forestry and Other Land Use

5.3.2.4

Calculation steps for Tiers 1 and 2 ........................................................................................5.33

5.3.2.5

Uncertainty assessment..........................................................................................................5.35

5.3.3 5.3.3.1

Choice of method...................................................................................................................5.35

5.3.3.2

Choice of stock change and emission factors ........................................................................5.36

5.3.3.3

Choice of activity data ...........................................................................................................5.37

5.3.3.4

Calculation steps for Tier 1....................................................................................................5.38

5.3.3.5

Uncertainty assessment..........................................................................................................5.39

5.3.4

5.4

Soil carbon...................................................................................................................................5.35

Non-CO2 greenhouse gas emissions from biomass burning........................................................5.39

5.3.4.1

Choice of method...................................................................................................................5.39

5.3.4.2

Choice of emission factors.....................................................................................................5.40

5.3.4.3

Choice of activity data ...........................................................................................................5.40

5.3.4.4

Uncertainty assessment..........................................................................................................5.41

Completeness, Time series, QA/QC, and Reporting ...........................................................................5.41

5.4.1

Completeness ..............................................................................................................................5.41

5.4.2

Developing a consistent time series.............................................................................................5.42

5.4.3

Quality Assurance and Quality Control.......................................................................................5.43

5.4.4

Reporting and Documentation.....................................................................................................5.43

5.5

Methane Emissions from Rice Cultivation..........................................................................................5.44

5.5.1

Choice of method ........................................................................................................................5.44

5.5.2

Choice of emission and scaling factors .......................................................................................5.48

5.5.3

Choice of activity data.................................................................................................................5.51

5.5.4

Uncertainty assessment ...............................................................................................................5.52

5.5.5

Completeness, Time series, QA/QC, and Reporting ...................................................................5.52

Annex 5A.1

Estimation of default stock change factors for mineral soil C emissions/removals for cropland............................................................................................................................5.54

References

.....................................................................................................................................................5.55

Equations

5.4

Equation 5.1

CH4 emissions from rice cultivation ....................................................................................5.45

Equation 5.2

Adjusted daily emission factor ............................................................................................5.48

Equation 5.3

Adjusted CH4 emission scaling factors for organic amendments ........................................5.50

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

Figures Figure 5.1

Classification scheme for cropland systems .......................................................................5.21

Figure 5.2

Decision tree for CH4 emissions from rice production ........................................................5.47

Tables Table 5.1

Default coefficients for above-ground woody biomass and harvest cycles in cropping systems containing perennial species .................................................................5.9

Table 5.2

Potential C storage for agroforestry systems in different ecoregions of the world ...........................................................................................................................5.9

Table 5.3

Default above-ground biomass for various types of perennial croplands..............................5.9

Table 5.4

Examples of perennial cropland subcategories which a country may have.........................5.10

Table 5.5

Relative stock change factors (FLU, FMG, and FI) (over 20 years) for different management activities on cropland.......................................................................5.17

Table 5.6

Annual emission factors (EF) for cultivated organic soils...................................................5.19

Table 5.7

Example of a simple disturbance matrix (Tier 2) for the impacts of land conversion activities on carbon pools ..........................................................................5.27

Table 5.8

Default biomass carbon stocks removed due to land conversion to cropland .....................5.28

Table 5.9

Default biomass carbon stocks present on Land Converted to Cropland in the year following conversion .........................................................................................5.28

Table 5.10

Soil stock change factors (FLU, FMG, FI) for land-use conversions to Cropland...................5.37

Table 5.11

Default CH4 baseline emission factor assuming no flooding for less than 180 days prior to rice cultivation, and continuously flooded during rice cultivation without organic amendments ............................................................................................................5.49

Table 5.12

Default CH4 emission scaling factors for water regimes during the cultivation period relative to continuously flooded fields .....................................................................5.49

Table 5.13

Default CH4 emission scaling factors for water regimes before the cultivation period .......5.50

Table 5.14

Default conversion factor for different types of organic amendment ..................................5.51

Boxes Box 5.1

Relevant carbon pools for cropland.......................................................................................5.6

Box 5.2

Conditions influencing CH4 emissions from rice cultivation...............................................5.46

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.5

Volume 4: Agriculture, Forestry and Other Land Use

5 CROPLAND 5.1

INTRODUCTION

This section provides a tiered methodology for estimating and reporting greenhouse gas emissions from croplands. Cropland includes arable and tillable land, rice fields, and agroforestry systems where the vegetation structure falls below the thresholds used for the Forest Land category, and is not expected to exceed those thresholds at a later time. Cropland includes all annual and perennial crops as well as temporary fallow land (i.e., land set at rest for one or several years before being cultivated again). Annual crops include cereals, oils seeds, vegetables, root crops and forages. Perennial crops include trees and shrubs, in combination with herbaceous crops (e.g., agroforestry) or as orchards, vineyards and plantations such as cocoa, coffee, tea, oil palm, coconut, rubber trees, and bananas, except where these lands meet the criteria for categorisation as Forest Land. Arable land which is normally used for cultivation of annual crops but which is temporarily used for forage crops or grazing as part of an annual crop-pasture rotation (mixed system) is included under cropland. The amount of carbon stored in and emitted or removed from permanent cropland depends on crop type, management practices, and soil and climate variables. For example, annual crops (cereals, vegetables) are harvested each year, so there is no long-term storage of carbon in biomass. However, perennial woody vegetation in orchards, vineyards, and agroforestry systems can store significant carbon in long-lived biomass, the amount depending on species type and cultivar, density, growth rates, and harvesting and pruning practices. Carbon stocks in soils can be significant and changes in stocks can occur in conjunction with soil properties and management practices, including crop type and rotation, tillage, drainage, residue management and organic amendments. Burning of crop residue produces significant non-CO2 greenhouse gases and the calculation methods are provided. There is separate guidance for Cropland Remaining Cropland (CC) and Land Converted to Cropland (LC) because of the difference in carbon dynamics. Land-use conversions to Cropland from Forest Land, Grassland and Wetlands usually result in a net loss of carbon from biomass and soils as well as N2O to the atmosphere. However, Cropland established on previously sparsely vegetated or highly disturbed lands (e.g., mined lands) can result in a net gain in both biomass and soil carbon. Some changes, especially those dealing with soil carbon, may take place in periods of time longer than one year. The guidance covers the carbon pools shown in Box 5.1. The term land-use conversion refers only to lands coming from one type of use into another. In cases where existing perennial cropland is replanted to the same or different crops, the land use remains Cropland; therefore, the carbon stock changes should be estimated using the methods for Cropland Remaining Cropland, as described in Section 5.2 below. BOX 5.1 RELEVANT CARBON POOLS FOR CROPLAND

Biomass -

Above-ground biomass

-

Below-ground biomass

Dead organic matter -

Dead wood

-

Litter

Soils (soil organic matter) The new features of the 2006 IPCC Guidelines relative to 1996 IPCC Guidelines are the following:

•

•

the whole Cropland section is new;

•

methane emissions from rice are included in the Cropland category;

•

•

5.6

biomass carbon and soil carbon are in the same section;

non-CO2 gas emissions from biomass burning (Cropland Remaining Cropland and Land Converted to Cropland) are also included in the Cropland chapter; and default values are provided for biomass on Cropland and agroforestry areas.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

5.2

CROPLAND REMAINING CROPLAND

This section provides guidelines on greenhouse gas inventory for croplands that have not undergone any landuse conversion for a period of at least 20 years as a default period 1. Section 5.3 provides guidelines on Land Converted to Cropland more recently than this. The annual greenhouse gas emissions and removals from Cropland Remaining Cropland include: •

•

Estimates of annual change in C stocks from all C pools and sources; and Estimates of annual emission of non-CO2 gases from all pools and sources.

The changes in carbon stocks in Cropland Remaining Cropland are estimated using Equation 2.3.

5.2.1 5.2.1.1

Biomass C HOICE

OF METHODS

Carbon can be stored in the biomass of croplands that contain perennial woody vegetation including, but not limited to, monocultures such as coffee, oil palm, coconut, rubber plantations, fruit and nut orchards, and polycultures such as agroforestry systems. The default methodology for estimating carbon stock changes in woody biomass is provided in Chapter 2, Section 2.2.1. This section elaborates this methodology with respect to estimating changes in carbon stocks in biomass in Cropland Remaining Cropland. The change in biomass is only estimated for perennial woody crops. For annual crops, increase in biomass stocks in a single year is assumed equal to biomass losses from harvest and mortality in that same year - thus there is no net accumulation of biomass carbon stocks. Changes in carbon in cropland biomass (ΔCCCB) may be estimated from either: (a) annual rates of biomass gain and loss (Chapter 2, Equation 2.7) or (b) carbon stocks at two points in time (Chapter 2, Equation 2.8). The first approach (gain-loss method) provides the default Tier 1 method and can also be used at Tier 2 or 3 with refinements described below. The second approach (the stock-difference method) applies either at Tier 2 or Tier 3, but not Tier 1. It is good practice to improve inventories by using the highest feasible tier given national circumstances. It is good practice for countries to use a Tier 2 or Tier 3 method if carbon emissions and removals in Cropland Remaining Cropland is a key category and if the sub-category of biomass is considered significant. It is good practice for countries to use the decision tree in Figure 2.2 in Chapter 2 to identify the appropriate tier to estimate changes in carbon stocks in biomass. Tier 1 The default method is to multiply the area of perennial woody cropland by a net estimate of biomass accumulation from growth and subtract losses associated with harvest or gathering or disturbance (according to Equation 2.7 in Chapter 2). Losses are estimated by multiplying a carbon stock value by the area of cropland on which perennial woody crops are harvested. Default Tier 1 assumptions are: all carbon in perennial woody biomass removed (e.g., biomass cleared and replanted with a different crop) is emitted in the year of removal; and perennial woody crops accumulate carbon for an amount of time equal to a nominal harvest/maturity cycle. The latter assumption implies that perennial woody crops accumulate biomass for a finite period until they are removed through harvest or reach a steady state where there is no net accumulation of carbon in biomass because growth rates have slowed and incremental gains from growth are offset by losses from natural mortality, pruning or other losses. Under Tier 1, default factors shown in Table 5.1, are applied to nationally derived estimates of land areas. Tier 2 Two methods can be used for Tier 2 estimation of changes in biomass. Method 1 (also called the Gain-Loss Method) requires the biomass carbon loss to be subtracted from the biomass carbon increment for the reporting year (Chapter 2, Equation 2.7). Method 2 (also called the Stock-Difference Method) requires biomass carbon stock inventories for a given land-use area at two points in time (Chapter 2, Equation 2.8). A Tier 2 estimate, in contrast, will generally develop estimates for the major woody crop types by climate zones, using country-specific carbon accumulation rates and stock losses where possible or country-specific estimates of carbon stocks at two points in time. Under Tier 2, carbon stock changes are estimated for above-ground and 1

Countries using higher tier methods may use different time periods depending on the time taken for carbon stocks to equilibrate after change in land use.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

below-ground biomass in perennial woody vegetation. Tier 2 methods involve country-specific or regionspecific estimates of biomass stocks by major cropland types and management system, and estimates of stock change as a function of major management system (e.g., dominant crop, productivity management). To the extent possible, it is good practice for countries to incorporate changes in perennial crop or tree biomass using country-specific or region-specific data. Where data are missing, default data may be used. Tier 3 A Tier 3 estimate will use a highly disaggregated Tier 2 approach or a country-specific method involving process modelling and/or detailed measurement. Tier 3 involves inventory systems using statistically-based sampling of carbon stocks over time and/or process models, stratified by climate, cropland type and management regime. For example, validated species-specific growth models that incorporate management effects such as harvesting and fertilization, with corresponding data on management activities, can be used to estimate net changes in cropland biomass carbon stocks over time. Models, perhaps accompanied by measurements like those in forest inventories, can be used to estimate stock changes and extrapolate to entire cropland areas, as in Tier 2. Key criteria in selecting appropriate models are that they are capable of representing all of the management practices that are represented in the activity data. It is critical that the model be validated with independent observations from country-specific or region-specific field locations that are representative of climate, soil and cropland management systems in the country.

5.2.1.2

C HOICE

OF EMISSION FACTORS

Emission and removal factors required to estimate the changes in carbon stocks include (a) annual biomass accumulation or growth rate, and (b) biomass loss factors which are influenced by such activities as removal (harvesting), fuelwood gathering and disturbance.

Above-ground woody biomass growth rate Tier 1 Tables 5.1 to 5.3 provide estimates of biomass stocks and biomass growth rates and losses for major climatic regions and agricultural systems. However, given the large variation in cropping systems, incorporating trees or tree crops, it is good practice to seek national data on above-ground woody biomass growth rate. Tier 2 Annual woody biomass growth rate data can be, at a finer or disaggregated scale, based on national data sources for different cropping and agroforestry systems. Rates of change in annual woody biomass growth rate should be estimated in response to changes in specific management/land-use activities (e.g., fertilization, harvesting, thinning). Results from field research should be compared to estimates of biomass growth from other sources to verify that they are within documented ranges. It is important, in deriving estimates of biomass accumulation rates, to recognize that biomass growth rates will occur primarily during the first 20 years following changes in management, after which time the rates will tend towards a new steady-state level with little or no change occurring unless further changes in management conditions occur. Tier 3 For Tier 3, highly disaggregated factors for biomass accumulation are needed. These may include categorisation of species, specific for growth models that incorporate management effects such as harvesting and fertilization. Measurement of above-ground biomass, similar to forest inventory with periodic measurement of above-ground biomass accumulation, is necessary.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

TABLE 5.1 DEFAULT COEFFICIENTS FOR ABOVE-GROUND WOODY BIOMASS AND HARVEST CYCLES IN CROPPING SYSTEMS CONTAINING PERENNIAL SPECIES

Climate region

Above-ground biomass carbon stock at harvest (tonnes C ha-1)

Harvest /Maturity cycle (yr)

Biomass accumulation rate (G) (tonnes C ha-1 yr-1)

Biomass carbon loss (L) (tonnes C ha-1 yr-1)

Error range1

Temperate (all moisture regimes)

63

30

2.1

63

+ 75%

Tropical, dry

9

5

1.8

9

+ 75%

Tropical, moist

21

8

2.6

21

+ 75%

Tropical, wet

50

5

10.0

50

+ 75%

Note: Values are derived from the literature survey and synthesis published by Schroeder (1994). 1

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

TABLE 5.2 POTENTIAL C STORAGE FOR AGROFORESTRY SYSTEMS IN DIFFERENT ECOREGIONS OF THE WORLD Eco-region

System

Above-ground biomass (tonnes ha-1)

Range (tonnes ha-1)

Humid tropical high Humid tropical low Dry lowlands Humid tropical Dry lowlands Humid topical Humid tropical high Humid tropical low Dry lowlands Humid tropical low

Agrosilvicultural Agrosilvicultural Agrosilvicultural Agrosilvicultural Agrosilvicultural Silvopastoral Silvopastoral Silvopastoral Silvopastoral Silvopastoral

41.0 70.5 117.0 120.0 75.0 39.5 143.5 151.0 132.5 16.5

29 - 53 39 - 102 39 - 195 12 - 228 68 - 81 28 - 51 133 - 154 104 - 198 90 - 175 15 - 18

Region Africa S America S America SE Asia SE Asia Australia N America N America N America N Asia

Source: Albrecht and Kandji, 2003

TABLE 5.3 DEFAULT ABOVE-GROUND BIOMASS FOR VARIOUS TYPES OF PERENNIAL CROPLANDS (TONNES HA-1) Cropland type

Region

Above-ground biomass

Oil Palm Mature rubber Young rubber Young cinnamon (7 years) Coconut Improved fallow 2-year fallow 1-year fallow 6-year fallow (average) Alley cropping Multistorey system Jungle rubber Gmelina-cacao

SE Asia SE Asia SE Asia SE Asia SE Asia

Range

Error

136.0 178.0 48.0 68.0 196.0

62 - 202

78 90

E Africa E Africa SE Asia SE Asia

35.0 12.0 16.0 2.9

27 - 44 7 - 21 4 - 64 1.5 - 4.5

SE Asia SE Asia

304.0 116.0

16 - 80 47

2006 IPCC Guidelines for National Greenhouse Gas Inventories

40 89

References

Palm et al., 1999 Wasrin et al., 2000 Siregar & Gintings, 2000 Lasco et al., 2002

105

Albrecht and Kandji, 2003 Albrecht and Kandji, 2003 Lasco and Suson, 1999 Lasco et al., 2001

17 53

Tomich et al., 1998 Lasco et al., 2001

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Volume 4: Agriculture, Forestry and Other Land Use

Below-ground biomass accumulation Tier 1 The default assumption is that there is no change in below-ground biomass of perennial trees in agricultural systems. Default values for below-ground biomass for agricultural systems are not available. Tier 2 This includes the use of actually measured below-ground biomass data from perennial woody vegetation. Estimating below-ground biomass accumulation is recommended for Tier 2 calculation. Root-to-shoot ratios show wide ranges in values at both individual species (e.g., Anderson et al., 1972) and community scales (e.g., Jackson et al., 1996; Cairns et al., 1997). Limited data is available for below ground biomass thus, as far as possible, empirically-derived root-to-shoot ratios specific to a region or vegetation type should be used. Tier 3 This includes the use of data from field studies identical to forest inventories and modelling studies, if stock difference method is adopted.

Biomass losses from removal, fuelwood and disturbance Tier 1 The default assumption is that all biomass lost is assumed to be emitted in the same year. Biomass removal, fuelwood gathering and disturbance loss data from cropland source are not available. FAO provides total roundwood and fuelwood consumption data, but not separated by source (e.g., Cropland, Forest Land, etc.). It is recognized that statistics on fuelwood are extremely poor and uncertain worldwide. Default removal and fuelwood gathering statistics (discussed in Chapter 4, Section 4.2) may include biomass coming from cropland such as when firewood is harvested from home gardens. Thus, it is necessary to ensure no double counting of losses occurs. If no data are available for roundwood or fuelwood sources from Cropland, the default approach will include losses in Forest Land (Section 4.2) and will exclude losses from Cropland. T i e r s 2 a nd 3 National level data at a finer scale, based on inventory studies or production and consumption studies according to different sources, including agricultural systems, can be used to estimate biomass loss. These can be obtained through a variety of methods, including estimating density (crown coverage) of woody vegetation from air photos (or high resolution satellite imagery) and ground-based measurement plots. Species composition, density and above-ground vs. below-ground biomass can vary widely for different cropland types and conditions and thus it may be most efficient to stratify sampling and survey plots by cropland types. General guidance on survey and sampling techniques for biomass inventories is given in Chapter 3, Annex 3A.3.

5.2.1.3

C HOICE

OF ACTIVITY DATA

Activity data in this section refer to estimates of land areas of growing stock and harvested land with perennial woody crops. The area data are estimated using the approaches described in Chapter 3. They should be regarded as strata within the total cropland area (to keep land-use data consistent) and should be disaggregated depending on the tier used and availability of growth and loss factors. Examples of Cropland subcategories are given in Table 5.4. TABLE 5.4 EXAMPLES OF PERENNIAL CROPLAND SUBCATEGORIES WHICH A COUNTRY MAY HAVE Broad subcategories

Specific subcategories

Fruit orchards

Mango, Citrus, Apple

Plantation crops

Rubber, Coconut, Oil palm, Coffee, Cacao

Agroforestry systems

Hedgerow cropping (alley cropping), Improved fallow, Multi-storey systems, Home gardens, Boundary planting, Windbreaks

Tier 1 Under Tier 1, annual or periodic surveys are used in conjunction with the approaches outlined in Chapter 3 to estimate the average annual area of established perennial woody crops and the average annual area of perennial woody crops that are harvested or removed. The area estimates are further sub-divided into general climate regions or soil types to match the default biomass gain and loss values. Under Tier 1 calculations, international statistics such as FAO databases, and other sources can be used to estimate the area of land under perennial woody crops.

5.10

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

Tier 2 Under Tier 2, more detailed annual or periodic surveys are used to estimate the areas of land in different classes of perennial woody biomass crops. Areas are further classified into relevant sub categories such that all major combinations of perennial woody crop types and climatic regions are represented with each area estimate. These area estimates must match any country-specific biomass carbon increment and loss values developed for the Tier 2 method. If country-specific finer resolution data are only partially available, countries are encouraged to extrapolate to the entire land base of perennial woody crops using sound assumptions from best available knowledge. Tier 3 Tier 3 requires high-resolution activity data disaggregated at sub-national to fine grid scales. Similar to Tier 2, land area is classified into specific types of perennial woody crops by major climate and soil categories and other potentially important regional variables (e.g., regional patterns of management practices). Furthermore, it is good practice to relate spatially explicit area estimates with local estimates of biomass increment, loss rates, and management practices to improve the accuracy of estimates.

5.2.1.4

C ALCULATION

STEPS FOR

T IER 1

AND

T IER 2

Summary of steps for estimating change in carbon stocks in biomass in Cropland Remaining Cropland (∆C B ) using the Tier 1 and Tier 2 methods Using the worksheets for Cropland (see Annex 1 –AFOLU Worksheets), calculate the change in biomass carbon stocks of Cropland Remaining Cropland: Step 1: Enter the subcategories of Cropland for the reporting year Typically, there are various types of Cropland with woody perennial cover in a country with varying biomass stocks and increments. Examples of these are: fruit orchards (e.g., mango, citrus), agricultural plantations (e.g., coconut, rubber) and agroforestry farms. Step 2: For each sub-category, enter the annual area of Cropland with perennial woody biomass The area (A) in hectares of each sub-category of Cropland can usually be obtained from national land-use agencies, Ministry of Agriculture, and Ministry of Natural Resources. Possible sources of data include: satellite images, aerial photography and land-based surveys, and FAO database. Step 3: For each sub-category, enter the mean annual carbon stocks in the biomass accumulation (in tonnes C ha yr-1) of perennial woody biomass The annual growth rates (ΔCG) for each sub-category of Cropland, from the biomass accumulation rates G in Table 5.1, are entered in the appropriate column of worksheets. Step 4: For each sub-category, enter the annual carbon stocks in biomass losses (in tonnes C ha yr-1) If there is harvesting, the amount of carbon stocks from the biomass harvested (ΔCL) is entered in the appropriate column. This can be estimated by multiplying the default above woody above ground biomass for various croplands in Table 5.3 by the default carbon density of 0.5 tonne C/tonne biomass. Step 5: Calculate the annual change of carbon stocks in biomass for each sub-category The annual change of carbon stocks in biomass (ΔCB) is calculated using Equation 2.7 in Chapter 2. Step 6: Calculate the total change in carbon stocks (ΔCB) by adding up all the values of the subcategory estimates.

Example 1: In the inventory year, 90,000 hectares of perennial woody crops are cultivated in a tropical moist environment, while 10,000 ha are subjected to harvesting. The immature perennial woody cropland area accumulates carbon at a rate of approximately 2.6 tonnes of above ground C ha-1 yr-1. The area harvested looses all carbon in biomass stocks in the year of removal. Default carbon stock losses for a tropical moist perennial woody cropland are 21 tonnes C ha-1 yr-1. From these values, an estimated 234,000 tonnes C accumulates per year and 210,000 tonnes C are lost. Using Equation 2.7 in Chapter 2, the net change in carbon stocks (above-ground) in the tropical moist environment are 24,000 tonnes C yr-1.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.11

Volume 4: Agriculture, Forestry and Other Land Use

5.2.1.5

U NCERTAINTY

ASSESSMENT

The following discussion provides guidance on approaches for assessing uncertainty associated with estimates of biomass carbon for each tier method. Tier 1 The sources of uncertainty when using the Tier 1 method include the degree of accuracy in land area estimates (see Chapter 3) and in the default biomass carbon increment and loss rates. Uncertainty is likely to be low ( 20 year) annual cropping of wetlands (paddy rice). Can include double-cropping with nonflooded crops. For paddy rice, tillage and input factors are not used.

Perennial/ Tree Crop

All

Dry and Moist/ Wet

1.00

+ 50%

Long-term perennial tree crops such as fruit and nut trees, coffee and cacao.

Dry

0.93

+ 11%

Moist/ Wet

0.82

+ 17%

Tropical montane4

n/a

0.88

+ 50%

All

Dry and Moist/ Wet

1.00

NA

Dry

1.02

+ 6%

Moist

1.08

+ 5%

Dry

1.09

+ 9%

Moist/ Wet

1.15

+ 8%

n/a

1.09

+ 50%

Dry

1.10

+ 5%

Moist

1.15

+ 4%

Dry

1.17

+ 8%

Moist/ Wet

1.22

+ 7%

n/a

1.16

+ 50%

Level

Longterm cultivated

Set aside (< 20 yrs)

Full

Tropical

Temperate/ Boreal and Tropical

Temperate/ Boreal

Tillage (FMG)

Reduced

Tropical

Tropical montane4 Temperat e/ Boreal

Tillage (FMG)

No-till

Tropical

Tropical montane4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Description

Represents area that has been continuously managed for >20 yrs, to predominantly annual crops. Input and tillage factors are also applied to estimate carbon stock changes. Land-use factor was estimated relative to use of full tillage and nominal (‘medium”) carbon input levels.

Represents temporary set aside of annually cropland (e.g., conservation reserves) or other idle cropland that has been revegetated with perennial grasses.

Substantial soil disturbance with full inversion and/or frequent (within year) tillage operations. At planting time, little (e.g., 30% coverage by residues at planting.

Direct seeding without primary tillage, with only minimal soil disturbance in the seeding zone. Herbicides are typically used for weed control.

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 5.5 (CONTINUED) RELATIVE STOCK CHANGE FACTORS (FLU, FMG, AND FI) (OVER 20 YEARS) FOR DIFFERENT MANAGEMENT ACTIVITIES ON CROPLAND

Factor value type

Input (FI)

Input (FI)

Input (FI)

Input (FI)

Temper -ature regime

Moisture regime1

IPCC defaults

Error2,3

Temperate/ Boreal

Dry

0.95

+ 13%

Moist

0.92

+ 14%

Dry

0.95

+ 13%

Moist/ Wet

0.92

+ 14%

Tropical montane4

n/a

0.94

+ 50%

Medium

All

Dry and Moist/ Wet

1.00

NA

Dry

1.04

+ 13%

High

Temperate/ Boreal and Tropical

Moist/ Wet

1.11

+ 10%

n/a

1.08

+ 50%

Dry

1.37

+ 12%

Moist/ Wet

1.44

+ 13%

n/a

1.41

+ 50%

Level

Low

without manure

High – with manure

Tropical

Tropical montane4 Temperate/ Boreal and Tropical

Tropical montane4

Description

Low residue return occurs when there is due to removal of residues (via collection or burning), frequent barefallowing, production of crops yielding low residues (e.g., vegetables, tobacco, cotton), no mineral fertilization or Nfixing crops.

Representative for annual cropping with cereals where all crop residues are returned to the field. If residues are removed then supplemental organic matter (e.g., manure) is added. Also requires mineral fertilization or N-fixing crop in rotation. Represents significantly greater crop residue inputs over medium C input cropping systems due to additional practices, such as production of high residue yielding crops, use of green manures, cover crops, improved vegetated fallows, irrigation, frequent use of perennial grasses in annual crop rotations, but without manure applied (see row below).

Represents significantly higher C input over medium C input cropping systems due to an additional practice of regular addition of animal manure.

1

Where data were sufficient, separate values were determined for temperate and tropical temperature regimes; and dry, moist, and wet moisture regimes. Temperate and tropical zones correspond to those defined in Chapter 3; wet moisture regime corresponds to the combined moist and wet zones in the tropics and moist zone in temperate regions.

2

+ two standard deviations, expressed as a percent of the mean; where sufficient studies were not available for a statistical analysis to derive a default, uncertainty was assumed to be + 50% based on expert opinion. NA denotes ‘Not Applicable’, where factor values constitute defined reference values, and the uncertainties are reflected in the reference C stocks and stock change factors for land use.

3

This error range does not include potential systematic error due to small sample sizes that may not be representative of the true impact for all regions of the world.

4

There were not enough studies to estimate stock change factors for mineral soils in the tropical montane climate region. As an approximation, the average stock change between the temperate and tropical regions was used to approximate the stock change for the tropical montane climate.

Note: See Annex 5A.1 for the estimation of default stock change factors for mineral soil C emissions/removals for Cropland.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

TABLE 5.6 ANNUAL EMISSION FACTORS (EF) FOR CULTIVATED ORGANIC SOILS IPCC default (tonnes C ha-1 yr-1)

Error 2

Boreal/Cool Temperate

5.0

+ 90%

Warm Temperate

10.0

+ 90%

Tropical/Sub-Tropical

20.0

+ 90%

Climatic temperature regime1

1

Climate classification is provided in Chapter 3.

2

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean. Estimates are based on Glenn et al., 1993; Kasimir-Klemedtsson et al., 1997; Freibauer and Kaltschmitt, 2001; Leifeld et al., 2005; Augustin et al., 1996; Nykänen et al., 1995; Maljanen et al., 2001, 2004; Lohila et al., 2004; Ogle et al., 2003; Armentano and Menges, 1986.

5.2.3.3

C HOICE

OF ACTIVITY DATA

Mineral soils Tier 1 Cropland systems are classified by practices that influence soil C storage. The default management classification system is provided in Figure 5.1. Inventory compilers should use this classification to categorize management systems in a manner consistent with the default Tier 1 stock change factors. This classification may be further developed for Tiers 2 and 3 approaches. In general, practices that are known to increase C storage, such as irrigation, mineral fertilization, organic amendments, cover crops and high residue yielding crops, have higher inputs, while practices that decrease C storage, such as residue burning/removal, bare fallow, and low residue crop varieties, have lower inputs. These practices are used to categorize management systems and then estimate the change in soil organic C stocks. Practices should not be considered that are used in less than 1/3 of a given cropping sequence (i.e., crop rotation), which is consistent with the classification of experimental data used to estimate the default stock change factors. Rice production, perennial croplands, and set-aside lands (i.e., lands removed from production) are considered unique management systems (see below). Each of the annual cropping systems (low input, medium input, high input, and high input w/organic amendment) are further subdivided based on tillage management. Tillage practices are divided into no-till (direct seeding without primary tillage and only minimal soil disturbance in the seeding zone; herbicides are typically used for weed control), reduced tillage (primary and/or secondary tillage but with reduced soil disturbance that is usually shallow and without full soil inversion; normally leaves surface with >30% coverage by residues at planting) and full tillage (substantial soil disturbance with full inversion and/or frequent, within year tillage operations, while leaving 20 yrs)?

Yes

Low C input

No

Annual crop with residues removed or burned1?

Yes

Organic amendment?

No

Yes

Annual crop with low residue2 or rotation with bare fallow?

Medium C input

No Practice increasing C input3?

Medium C input

Yes No

Yes

High C input with organic amendment

Low C input

No

Annual crop with no N mineral fertilization or N-fixing crop?

Yes

Practice increasing C input4?

Medium C input

Yes

No Annual crop with organic amendment?

Yes Set-aside

No

Annual crop with practice increasing C input4?

Yes

High C input

No No

Non-cropland systems (e.g., Forest land, Grassland)

Yes

Converted into another managed land use?

Yes

Converted into continuous perennial cover5? No

Medium C input

Note: 1: Does not typically include grazing of residues in the field. 2: e.g. cotton, vegetables and tobacco. 3: Practices that increase C input above the amount typically generated by the low residues yielding varieties such as using organic amendments, cover crops/green manures, and mixed crop/grass systems. 4: Practices that increase C input by enhancing residue production, such as using irrigation, cover crops/green manures, vegetated fallows, high residue yielding crops, and mixed crop/grass systems. 5 Perennial cover without frequent harvest. Note: Only consider practices, such as irrigation, residue burning/removal, mineral fertilizers, N-fixing crops, organic amendment, cover crops/green manures, low residue crop, or fallow, if used in at least 1/3 of cropping rotation sequence.

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5.2.3.4

C ALCULATION

STEPS FOR

T IER 1

Mineral soils The steps for estimating SOC0 and SOC(0-T) and net soil C stock change per ha for Cropland Remaining Cropland on mineral soils are as follows: Step 1: Organize data into inventory time periods based on the years in which activity data were collected (e.g., 1990 to 1995, 1995 to 2000, etc.) Step 2: Determine the amount Cropland Remaining Cropland by mineral soil types and climate regions in the country at the beginning of the first inventory time period. The first year of the inventory time period will depend on the time step of the activity data (0-T; e.g., 5, 10 or 20 years ago). Step 3: Classify each Cropland into the appropriate management system using Figure 5.1. Step 4: Assign a native reference C stock values (SOCREF) from Table 2.3 based on climate and soil type. Step 5: Assign a land-use factor (FLU), management factor (FMG) and C input levels (FI) to each Cropland based on the management classification (Step 2). Values for FLU, FMG and FI are given in Table 5.5. Step 6: Multiply the factors (FLU, FMG, FI) by the reference soil C stock (SOCREF) to estimate an ‘initial’ soil organic C stock (SOC(0-T)) for the inventory time period. Step 7: Estimate the final soil organic C stock (SOC0) by repeating Steps 1 to 5 using the same native reference C stock (SOCREF), but with land-use, management and input factors that represent conditions for each cropland in the last (year 0) inventory year. Step 8: Estimate the average annual change in soil organic C stocks for Cropland Remaining Cropland (∆CMineral) by subtracting the ‘initial’ soil organic C stock (SOC(0-T)) from the final soil organic C stock (SOC0), and then dividing by the time dependence of the stock change factors (i.e., 20 years using the default factors). If an inventory time period is greater than 20 years, then divide by the difference in the initial and final year of the time period. Step 9: Repeat steps 2 to 8 if there are additional inventory time periods (e.g., 1990 to 2000, 2001 to 2010, etc.). A numerical example is given below for Cropland Remaining Cropland on mineral soils, using Equation 2.25 and default reference C stocks (Table 2.3) and stock change factors (Table 5.5).

Example: The following example shows calculations for aggregate areas of cropland soil carbon stock change. In a warm temperate wet climate on Mollisol soils, there are 1Mha of permanent annual cropland. The native reference carbon stock (SOCREF) for the region is 88 tonnes C ha-1. At the beginning of the inventory calculation period (in this example, 10 yrs earlier in 1990), the distribution of cropland systems were 400,000 ha of annual cropland with low carbon input levels and full tillage and 600,000 ha of annual cropland with medium input levels and full tillage. Thus, initial soil carbon stocks for the area were: 400,000 ha ● (88 tonnes C ha-1 ● 0.69 ● 1 ● 0.92) + 600,000 ha ● (88 tonnes C ha-1 ● 0.69 ● 1 ● 1) = 58.78 million tonnes C. In the last year of the inventory time period (in this example, the last year is 2000), there are: 200,000 ha of annual cropping with full tillage and low C input, 700,000 ha of annual cropping with reduced tillage and medium C input, and 100,000 ha of annual cropping with no-till and medium C input. Thus, total soil carbon stocks in the inventory year are: 200,000 ha ● (88 tonnes C ha-1 ● 0.69 ● 1 ● 0.92) + 700,000 ha ● (88 tonnes C ha-1 ● 0.69 ● 1.08 ● 1) + 100,000 ha ● (88 tonnes C ha-1 ● 0.69 ● 1.15 ● 1) = 64.06 million tonnes C. Thus, the average annual stock change over the period for the entire area is: 64.06 – 58.78 = 5.28 million tonnes/20 yr = 264,000 tonnes C per year soil C stock increase (Note: 20 years is the time dependence of the stock change factor, i.e., factor represents annual rate of change over 20 years).

Organic soils The steps for estimating the loss of soil C from drained organic soils are as follows: Step 1: Organize data into inventory time periods based on the years in which activity data were collected (e.g., 1990 to 1995, 1995 to 2000, etc.)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

Step 2: Determine the amount of Cropland Remaining Cropland on organic soils for the last year of each inventory time period. Step 3: Assign the appropriate emission factor (EF) for annual losses of CO2 based on climate (from Table 5.6). Step 4: Estimate total emissions by summing the product of area (A) multiplied by the emission factor (EF) for all climate zones. Step 5: Repeat for additional inventory time periods.

A numerical example is given below for Cropland Remaining Cropland on drained organic soils, using Equation 2.26 and default emission factors (Table 5.6).

Example: The following example shows calculations for aggregate areas of cropland soil carbon stock change. In a warm temperate wet climate on Histosols, there are 0.4 Mha of permanent annual cropland on drained organic soils. The emission factor for this climate is 10.0 tonnes C ha-1 yr-1. Thus, annual soil carbon stock change for organic soils during the inventory time period is: 400,000 ha ● 10.0 tonnes C ha-1 = 4.0 million tonnes C yr-1.

5.2.3.5

U NCERTAINTY

ASSESSMENT

Three broad sources of uncertainty exist in soil C inventories: 1) uncertainties in land-use and management activity, and environmental data; 2) uncertainties in reference soil C stocks if using a Tier 1 or 2 approach (mineral soils only); and 3) uncertainties in the stock change/emission factors for Tier 1 or 2 approaches, model structure/parameter error for Tier 3 model-based approaches, or measurement error/sampling variability associated with Tier 3 measurement-based inventories. In general, precision of an inventory is increased and confidence ranges are smaller with more sampling to estimate values for the three board categories, while reducing bias (i.e., improve accuracy) is more likely to occur through the development of a higher Tier inventory that incorporates country-specific information. For Tier 1, uncertainties are provided with the reference C stocks in the first footnote in Table 2.3, stock change factors in Table 5.5, and emission factor for organic soils in Table 5.6. Uncertainties in land-use and management data will need to be addressed by the inventory compiler, and then combined with uncertainties for the default factors and reference C stocks (mineral soils only) using an appropriate method, such as simple error propagation equations. If using aggregate land-use area statistics for activity data (e.g., FAO data), the inventory agency may have to apply a default level of uncertainty for the land area estimates (±50%). It is good practice for the inventory compiler to derive uncertainties from country-specific activity data instead of using a default level. Default reference C stocks and stock change factors for mineral soils and emission factors for organic soils can have inherently high uncertainties, particularly bias, when applied to specific countries. Defaults represent globally averaged values of land-use and management impacts or reference C stocks that may vary from regionspecific values (Powers et al., 2004; Ogle et al., 2006). Bias can be reduced by deriving country-specific factors using a Tier 2 method or by developing a Tier 3 country-specific estimation system. The underlying basis for higher Tier approaches will be experiments in the country or neighbouring regions that address the effect of land use and management on soil C. In addition, it is good practice to further minimize bias by accounting for significant within-country differences in land-use and management impacts, such as variation among climate regions and/or soil types, even at the expense of reduced precision in the factor estimates (Ogle et al., 2006). Bias is considered more problematic for reporting stock changes because it is not necessarily captured in the uncertainty range (i.e., the true stock change may be outside of the reported uncertainty range if there is significant bias in the factors). Uncertainties in land-use activity statistics may be reduced through a better national system, such as developing or extending a ground-based survey with additional sample locations and/or incorporating remote sensing to provide additional coverage. It is good practice to design a classification that captures the majority of land-use and management activity with a sufficient sample size to minimize uncertainty at the national scale. For Tier 2 methods, country-specific information is incorporated into the inventory analysis for purposes of reducing bias. For example, Ogle et al. (2003) utilized country-specific data to construct probability distribution functions for US specific factors, activity data and reference C stocks for agricultural soils. It is good practice to

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Volume 4: Agriculture, Forestry and Other Land Use

evaluate dependencies among the factors, reference C stocks or land-use and management activity data. In particular, strong dependencies are common in land-use and management activity data because management practices tend to be correlated in time and space. Combining uncertainties in stock change/emission factors, reference C stocks and activity data can be done using methods such as simple error propagation equations or Monte-Carlo procedures to estimate means and standard deviations for the change in soil C stocks (Ogle et al., 2003; Vanden Bygaart et al., 2004). Tier 3 models are more complex and simple error propagation equations may not be effective at quantifying the associated uncertainty in resulting estimates. Monte Carlo analyses are possible (Smith and Heath, 2001), but can be difficult to implement if the model has many parameters (some models can have several hundred parameters) because joint probability distribution functions must be constructed quantifying the variance as well as covariance among the parameters. Other methods are also available such as empirically-based approaches (Monte et al., 1996), which use measurements from a monitoring network to statistically evaluate the relationship between measured and modelled results (Falloon and Smith, 2003). In contrast to modelling, uncertainties in measurement-based Tier 3 inventories can be estimated from the sample variance, measurement error and other relevant sources of uncertainty.

5.2.4

Non-CO 2 greenhouse gas emissions from biomass burning

Non-CO2 emissions from Cropland Remaining Cropland (particularly CH4, CO, NOx and N2O) are usually associated with burning of agriculture residues, which vary by country, crop, and management system. CO2 emissions from biomass burning do not have to be reported, since the carbon released during the combustion process is assumed to be reabsorbed by the vegetation during the next growing season. The percentage of the agricultural crop residues burnt on-site, which is the mass of fuel available for burning, should be estimated taking into account the fractions removed before burning due to animal consumption, decay in the field, and use in other sectors (e.g., biofuel, domestic livestock feed, building materials, etc.). This is important to eliminate the possibility of double counting. The methodology for estimating non-CO2 emissions from biomass burning in Cropland Remaining Cropland follows the generic formulation in Equation 2.27 in Chapter 2. The estimates should be based on annual data.

5.2.4.1

C HOICE

OF METHOD

The decision tree in Figure 2.6 in Chapter 2 provides general guidance on the choice of the appropriate Tier to be used. The method of estimation of greenhouse gas emission from biomass burning involves the use of Equation 2.27 (Chapter 2). Under a Tier 1 approach, the activity data are normally highly aggregated, and combustion and emissions factors are the default values provided in Chapter 2. Under a Tier 2, estimates are generally developed for the major crop types by climate zone, using country-specific residue accumulation rates and country-specific combustion and emission estimates. Tier 3 is a very country-specific method involving process modelling and/or detailed measurement. All countries should strive for improving inventory and reporting approaches by applying the highest Tier possible, given national circumstances. If burning in Cropland Remaining Cropland is a key category, countries should use either Tier 2 or Tier 3 method.

5.2.4.2

C HOICE

OF EMISSION FACTORS

Tier 1 Countries applying a Tier 1 method should replace quantities MB and Cf in Equation 2.27 in Chapter 2 by the appropriate default fuel consumption value (MB x Cf) in Table 2.4. The default emission factors to be used are provided in Table 2.5 for each greenhouse gas of interest. Tier 2 This method expands Tier 1 to include use of country-specific available fuel, combustion and emission factors. Countries may estimate the amount of available fuel from crop production statistics and from the ratio of crop yield and residue produced. Field studies are needed to estimate the fractions of crop residue removed from field (as fuel or fodder) and left as residue for burning for different crop systems. Countries should focus on the most dominant crops being burnt or the systems with relatively high biomass per hectare and levels of emissions per unit of land (e.g., sugarcane, cotton).

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

Tier 3 This tier makes use of models based on country-specific parameters, using national inventory data to ensure that no burning of crop residues is omitted. Tier 3 depends on the field measurement of the amount of residues burnt on-site for different cropping systems under different climate zones and management systems, based on sampling methods described in Chapter 3 (Annex 3A.3). Countries should prioritize the development of country-specific combustion and emission factors, by focusing on the most dominant crop residues being burnt.

5.2.4.3

C HOICE

OF ACTIVITY DATA

Tier 1 Activity data includes estimates of land areas under the crop types for which agricultural residues are normally burnt. This can be obtained in consultation with national agricultural governmental sectors, in the lack of objective data from satellite imagery, for example. Countries can also estimate the crop area planted from the annual crop production and an estimate of the average productivity per hectare. If no national estimates are available, FAO statistics can be used. It is good practice to cross check FAO data with national sources. Tier 2 Under a Tier 2 method, countries should use more disaggregated area estimates (e.g., major crop types by climate zone) with country-specific and crop management system-specific residue accumulation rates. This can be accomplished through the use of more detailed annual or periodic surveys to estimate the areas of land under different crop classes. Areas should be further classified into relevant categories such that all major combinations of crop types and climatic regions are represented, with individual area estimates provided. Tier 3 Tier 3 requires high-resolution activity data disaggregated at sub-national to fine grid scales. Similar to Tier 2, land area is classified into specific types of crops by major climate and soil categories and other potentially important regional variables (e.g., regional patterns of management practices) to be used in models. Countries should strive to obtain spatially explicit area estimates to facilitate complete coverage of the cropland and ensure that areas are not over- nor under-estimated. Additionally, spatially explicit area estimates can be related to locally relevant emission rates and management impacts, improving the accuracy of the estimates. Area data for different cropping systems used should be consistent with area used in earlier sections (Biomass, Dead organic matter), though residues may be burnt on only a part of the total area.

5.2.4.4

U NCERTAINTY

ASSESSMENT

Estimates of the area planted under each crop type for which residues are normally burnt may be highly uncertain. Global statistics of crop production, which may be an indirect way to estimate area planted, if not updated on a yearly basis, may be very uncertain. The fraction of the agricultural residue that is burnt in the field is possibly the variable with most uncertainty. Tier 2 estimates are more precise, being based on country-specific parameters. It is good practice to provide error estimates (i.e., standard deviation, standard error, ranges) for country-specific combustion and emission factors and areas burnt.

5.3

LAND CONVERTED TO CROPLAND

Globally, about 50% of the total land surface has been transformed by direct human action, 20% of land ecosystems have been converted to permanent croplands, and 25% of the world’s forests have been cleared for various uses such as crop cultivation and pastures (Moore, 2002). Area under cropland has been increasing in some parts of the world to meet growing food and fibre demands. Most of the expansion of cropland in the last two decades has occurred in Southeast Asia, parts of South Asia, the Great Lakes region of eastern Africa and the Amazon Basin (Millennium Ecosystems Assessment, 2005). During the same period, forest destruction in the tropics averaged 12 million hectares per year according to Environmental Group Limited (http://www.environmental.com.au/). Deforestation rate during the 1990’s averaged 14.6 million ha per year. Conversion to Cropland is a leading land-use change following tropical deforestation. Greenhouse gas emissions and removals from Land Converted to Cropland can be a key source for many countries. Estimation of annual greenhouse gas emissions and removals from Land Converted to Cropland includes the following: •

Estimates of annual change in C stocks from all C pools and sources: o o o

Biomass (above-ground and below-ground biomass); Dead organic matter (dead wood and litter); Soils (soil organic matter).

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Estimates of non-CO2 gases (CH4, CO, N2O, NOx) from burning of above-ground biomass and DOM

5.3.1 5.3.1.1

Biomass C HOICE

OF METHODS

This section provides guidance on methods for calculating carbon stock change in biomass due to the conversion of land from natural conditions and other uses to Cropland, including deforestation and conversion of pasture and grazing lands to Cropland. The methods require estimates of carbon in biomass stocks prior to and following conversion, based on estimates of the areas of lands converted during the period between land-use surveys. As a result of conversion to Cropland, it is assumed (in Tier 1) that the dominant vegetation is removed entirely leading to emissions, resulting in near zero amounts of carbon remaining in biomass. Some type of cropping system is planted soon thereafter increasing the amount of carbon stored in biomass. The difference between initial and final biomass carbon pools is used to calculate carbon stock change from land-use conversion; and in subsequent years accumulations and losses in perennial woody biomass in Cropland are counted using methods in Section 5.2.1 (Cropland Remaining Cropland). It is good practice to consider all carbon pools (i.e., above ground and below ground biomass, dead organic matter, and soils) in estimating changes in carbon stocks in Land Converted to Cropland. Currently, there is insufficient information to provide a default approach with default parameters to estimate carbon stock change in dead organic matter (DOM) pools2. DOM is unlikely to be important except in the year of conversion. It is assumed that there will be no DOM in Cropland. In addition, the methodology below considers only carbon stock change in above-ground biomass since limited data are available on below-ground carbon stocks in perennial Cropland. The IPCC Guidelines describe increasingly sophisticated alternatives that incorporate greater detail on the areas of land converted, carbon stocks on lands, and loss of carbon resulting from land conversions. It is good practice to adopt the appropriate tier depending on key source analysis, data availability and national circumstances. All countries should strive for improving inventory and reporting approaches by advancing to the highest tier possible given national circumstances. It is good practice for countries to use a Tier 2 or Tier 3 approach if carbon emissions and removals in Land Converted to Cropland is a key category and if the sub-category of biomass is considered significant based on principles outlined in Volume 1, Chapter 4. Countries should use the decision tree in Figure 1.3 to help with the choice of method. Land Converted to Cropland is likely to be a key category for many countries and further biomass is likely to be a key source. Tier 1 The Tier 1 method follows the approach in Chapter 4 (Forest Land) where the amount of biomass that is cleared for cropland is estimated by multiplying the area converted in one year by the average carbon stock in biomass in the Forest Land or Grassland prior to conversion. It is good practice to account completely for all land conversions to Cropland. Thus, this section elaborates on the method such that it includes different initial uses, including but not limited to forests. Equation 2.15 in Chapter 2 summarises the major elements of a first-order estimation of carbon stock change from land-use conversion to Cropland. Average carbon stock change on a per hectare basis is estimated for each type of conversion. The average carbon stock change is equal to the carbon stock change due to the removal of biomass from the initial land use (i.e., carbon in biomass immediately after conversion minus the carbon in biomass prior to conversion), plus carbon stocks from one year of growth in Cropland following conversion. It is necessary to account only for any woody vegetation that replaces the vegetation that was cleared during land-use conversion. The GPG-LULUCF combines carbon in biomass after conversion and carbon in biomass that grows on the land following conversion into a single term. In this method, they are separated into two terms, BAFTER and ΔCG to increase transparency. At Tier 1, carbon stocks in biomass immediately after conversion (BAFTER) are assumed to be zero, since the land is cleared of all vegetation before planting crops. Average carbon stock change per hectare for a given land-use conversion is multiplied by the estimated area of lands undergoing such a conversion in a given year. In subsequent years, change in biomass of annual crops is considered zero because carbon gains in biomass from annual growth are offset by losses from harvesting. Changes in biomass of perennial woody crops are counted following the methodology in Section 2.3.1.1 (Change in carbon stocks in biomass in land remaining in a landuse category). 2

Any litter and dead wood pools (estimated using the methods described in Chapter 2, Section 2.3.2) should be assumed oxidized following land conversion.

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Chapter 5: Cropland

The default assumption for Tier 1 is that all carbon in biomass removed is lost to the atmosphere through burning or decay processes either on-site or off-site. Tier 1 calculations do not differentiate immediate emissions from burning and other conversion related losses. Tier 2 The Tier 2 calculations are structurally similar to Tier 1, with the following distinctions. First, Tier 2 relies largely on country-specific estimates of the carbon stocks in initial and final land uses rather than the default data. Area estimates for Land Converted to Cropland are disaggregated according to original vegetation (e.g., from Forest Land or Grassland) at finer spatial scales to capture regional and crop systems variations in countryspecific carbon stocks values. Second, Tier 2 may modify the assumption that carbon stocks immediately following conversion are zero. This enables countries to take into account land-use transitions where some, but not all, vegetation from the original land use is removed. Third, under Tier 2, it is good practice to apportion carbon losses to burning and decay processes if applicable. Emissions of carbon dioxide occur as a result of burning and decay in land-use conversions. Further, non-CO2 trace gas emissions occur as a result of burning. By partitioning losses to burning and decay, countries can also calculate non-CO2 trace gas emissions from burning (Section 5.3.4). The immediate impacts of land conversion activities on the five carbon stocks can be summarized in a disturbance matrix, which describes the retention, transfers and releases of carbon in the pools in the original ecosystem following conversion to Cropland. A disturbance matrix defines for each pool the proportion that remains in that pool and the proportion that is transferred to other pools. A small number of transfers are possible, and are outlined in a disturbance matrix in Table 5.7. The disturbance matrix ensures consistency of the accounting of all carbon pools. Biomass transfers to dead wood and litter can be estimated using Equation 2.20. Tier 3 The Tier 3 method is similar to Tier 2, with the following distinctions: i) rather than relying on average annual rates of conversion, countries can use direct estimates of spatially disaggregated areas converted annually for each initial and final land use; ii) carbon densities and soil carbon stock change are based on locally specific information, which makes possible a dynamic link between biomass and soil; and iii) biomass volumes are based on actual inventories. The transfer of biomass, to dead wood and litter following land-use conversion can be estimated using Equation 2.20.

TABLE 5.7 EXAMPLE OF A SIMPLE DISTURBANCE MATRIX (TIER 2) FOR THE IMPACTS OF LAND CONVERSION ACTIVITIES ON CARBON POOLS

To From

Aboveground biomass

Belowground biomass

Dead wood

Litter

Soil organic matter

Harvested wood products

Atmosphere

Sum of row (must equal 1)

Above-ground biomass Below-ground biomass Dead wood Litter Soil organic matter Enter the proportion of each pool on the left side of the matrix that is transferred to the pool at the top of each column. All of the pools on the left side of the matrix must be fully accounted, so the values in each row must sum to 1. Impossible transitions are blacked out.

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5.3.1.2

C HOICE

OF EMISSION / REMOVAL FACTORS

The emission/removal factors needed for the default method are: carbon stocks before conversion in the initial land use and after conversion to Cropland; and growth in biomass carbon stock from one year of cropland growth. Tier 1 Default biomass carbon stock in initial land-use categories (BBEFORE) mainly Forest Land and Grassland are provided in Table 5.8. Initial land-use based carbon stocks should be obtained for different Forest Land or Grassland categories based on biome type, climate, soil management systems, etc. It is assumed that all biomass is cleared when preparing a site for cropland use, thus, the default for BAFTER is 0 tonne C ha-1. In addition, a value is needed for carbon stocks after one year of growth in crops planted after conversion (ΔCG). Table 5.9 provides defaults for ΔCG. Separate defaults are provided for annual non-woody crops and perennial woody crops. For lands planted in annual crops, the default value of ΔCG is 5 tonnes of C per hectare, based on the original IPCC Guidelines recommendation of 10 tonnes of dry biomass per hectare (dry biomass has been converted to tonnes carbon in Table 5.9). The total accumulation of carbon in perennial woody biomass will, over time, exceed that of the default carbon stock for annual cropland. However, default values provided in this section are for one year of growth immediately following conversion, which usually give lower carbon stocks for perennial woody crops compared to annual crops.

TABLE 5.8 DEFAULT BIOMASS CARBON STOCKS REMOVED DUE TO LAND CONVERSION TO CROPLAND Carbon stock in biomass before conversion (BBefore) (tonnes C ha-1)

Error range #

See Chapter 4 Tables 4.7 to 4.12 for carbon stocks in a range of forest types by climate regions. Stocks are in terms of dry matter. Multiply values by a carbon fraction (CF) 0.5 to convert dry matter to carbon.

See Section 4.3 (Land Converted to Forest Land)

See Chapter 6 for carbon stocks in a range of grassland types by climate regions.

+ 75%

Land-use category

Forest Land

Grassland

# Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

TABLE 5.9 DEFAULT BIOMASS CARBON STOCKS PRESENT ON LAND CONVERTED TO CROPLAND IN THE YEAR FOLLOWING CONVERSION

Carbon stock in biomass after one year (ΔCG) (tonnes C ha-1)

Error range#

5.0

+ 75%

Temperate (all moisture regimes)

2.1

+ 75%

Tropical, dry

1.8

+ 75%

Tropical, moist

2.6

+ 75%

Tropical, wet

10.0

+ 75%

Crop type by climate region Annual cropland Perennial cropland

#

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

Tier 2 Tier 2 methods should include some country-specific estimates for biomass stocks and removals due to land conversion, and also include estimates of on-site and off-site losses due to burning and decay following land conversion to Cropland. These improvements can take the form of systematic studies of carbon content and emissions and removals associated with land uses and land-use conversions within the country and a reexamination of default assumptions in light of country-specific conditions.

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Chapter 5: Cropland

Default parameters for emissions from burning and decay are provided. However, countries are encouraged to develop country-specific coefficients to improve the accuracy of estimates. The IPCC Guidelines use a general default of 0.5 for the proportion of biomass burnt on-site for both Forest Land and Grassland conversions. Research studies suggest that the fraction is highly variable and could be as low as 0.2 (Fearnside, 2000; Barbosa and Fearnside, 1996; and Fearnside, 1990). Updated default proportions of biomass burnt on-site are provided in Chapter 4 (Forest Land) for a range of forest vegetation classes. These defaults should be used for transitions from Forest Land to Cropland. For non-forest initial land uses, the default proportion of biomass left on-site and burnt is 0.35. This default takes into consideration research, which suggests the fraction should fall within the range 0.2 to 0.5 (e.g., Fearnside, 2000; Barbosa and Fearnside, 1996; and Fearnside, 1990). It is good practice for countries to use 0.35 or another value within this range, provided that the rationale for the choice is documented. There is no default value for the amount of biomass taken off-site and burnt; countries will need to develop a proportion based on national data sources. In Chapter 4 (Forest Land), the default proportion of biomass oxidized as a result of burning is 0.9, as originally stated in the GPG-LULUCF. The method for estimating emissions from decay assumes that all biomass decays over a period of 10 years. For reporting purposes countries have two options: 1) report all emissions from decay in one year, recognizing that in reality they occur over a 10 year period, and 2) report all emission from decay on an annual basis, estimating the rate as one tenth of the totals. If countries choose the latter option, they should add a multiplication factor of 0.10 to the equation. Tier 3 Under Tier 3, all parameters should be country-defined using measurements and monitoring for more accurate values rather than the defaults. Process based models and decay functions can also be used.

5.3.1.3

C HOICE

OF ACTIVITY DATA

All tiers require estimates of land areas converted to Cropland. The same area estimates should be used for both biomass and soil C calculations on Land Converted to Cropland. Higher tiers require greater specificity of areas. At a minimum, the area of Forest Land and natural Grassland converted to Cropland should be identified separately for all tiers. This implies at least some knowledge of the land uses prior to conversion. This may also require expert judgment if Approach 1 in Chapter 3 of these guidelines is used for land area identification. Tier 1 Separate estimates are required of areas converted to Cropland from initial land uses (i.e., Forest Land, Grassland, Settlements, etc.) to final crop land type (i.e., annual or perennial) (ATO_OTHERS). For example, countries should estimate separately the area of tropical moist forest converted to annual cropland, tropical moist forest converted to perennial cropland, tropical moist Grassland converted to perennial cropland, etc. Although, to allow other pools to equilibrate and for consistency with land area estimation overall, land areas should remain in the conversion category for 20 years (or other period reflecting national circumstances) following conversion. The methodology assumes that area estimates are based on a one-year time frame, which is likely to require estimation on the basis of average rates on land-use conversion, determined by measurements estimates made at longer intervals. If countries do not have these data, partial samples may be extrapolated to the entire land base or historic estimates of conversions may be extrapolated over time based on the judgement of country experts. Under Tier 1 calculations, international statistics such as FAO databases, IPCC GPG Reports and other sources, supplemented with sound assumptions, can be used to estimate the area of Land Converted to Cropland from each initial land use. For higher tier calculations, country-specific data sources are used to estimate all possible transitions from initial land use to final crop type. Tier 2 It is good practice for countries to use actual area estimates for all possible transitions from initial land use to final crop type. Full coverage of land areas can be accomplished either through analysis of periodic remotely sensed images of land-use and land cover patterns, through periodic ground-based sampling of land-use patterns, or hybrid inventory systems. If finer resolution country-specific data are partially available, countries are encouraged to use sound assumptions from best available knowledge to extrapolate to the entire land base. Historic estimates of conversions may be extrapolated over time based on the judgment of country experts. Tier 3 Activity data used in Tier 3 calculations should be a full accounting of all land-use transitions to Cropland and be disaggregated to account for different conditions within a country. Disaggregation can occur along political (county, province, etc.), biome, climate, or on a combination of such parameters. In many cases, countries may have information on multi-year trends in land conversion (from periodic sample-based or remotely sensed inventories of land use and land cover). Periodic land-use change matrix need to be developed giving the initial and final land-use areas at disaggregated level based on remote sensing and field surveys.

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5.3.1.4

C ALCULATION

STEPS FOR

T IERS 1

AND

2

The following summarizes steps for estimating change in carbon stocks in biomass (∆C B ) using the default methods Using the worksheet provided for Land Converted to Cropland (see Annex 1, AFOLU Worksheets), calculate the change in biomass carbon stocks in Land Converted to Cropland as follows: Step 1: Enter the subcategories of croplands for the reporting year. The subcategories of croplands used in Section 5.2 may also be used to fill out the appropriate column in the worksheet. Step 2: For each sub-category, enter the annual area of land converted to Cropland (ATO_OTHERS). Data for annual area may be obtained from various sources such as the Ministry of Forestry, Ministry of Agriculture, Ministry of Planning, or Mapping Office within a country. Step 3: For each sub-category, enter the carbon stocks in biomass immediately after conversion to Cropland (BAFTER), in tonnes C ha-1. Biomass and carbon data may be default values or country-specific values. Step 4: For each sub-category, enter the carbon stocks in biomass immediately before conversion to Cropland (BBEFORE), in tonnes C ha-1. Biomass and carbon data may be default values or country-specific values. Step 5: Calculate the carbon stocks change per area (CCONVERSION) for the type of conversion when land is converted to Cropland (Equation 2.16). Step 6: Obtain the values for change in carbon stocks from one year of cropland growth (ΔCG) and the decrease in biomass carbon due to losses ((ΔCL) using Table 5.1. Enter the values in the appropriate column. Step 7: Calculate the annual change in carbon stocks in biomass in Land Converted to Cropland (ΔCB) using Equation 2.15. Step 8: Sum up all the annual changes in carbon stocks in biomass.

5.3.1.5

U NCERTAINTY

ASSESSMENT

Tier 1 The sources of uncertainty in this method are from the use of global or national average rates of conversion and from estimates of land areas converted to Cropland. In addition, reliance on default parameters for carbon stocks in initial and final conditions contributes to relatively high degrees of uncertainty. The default values in this method have error ranges associated with them. A published compilation of research on carbon stocks in agroforestry systems was used to derive the default data provided in Section 5.2 (Schroeder, 1994). While defaults were derived from multiple studies, their associated uncertainty ranges were not included in the publication. Therefore, a default uncertainty level of +75% of the carbon stock has been assumed based on expert judgement. Land Converted to Cropland is likely to be a key source category for many countries and all efforts should be made to reduce uncertainty. Tier 2 The Tier 2 method uses at least some country-defined defaults, which will improve the accuracy of estimates, because they better represent conditions relevant to the country. Use of country-specific values should entail sufficient sample sizes and or use of expert judgment to estimate uncertainties. This, together with uncertainty estimates on activity data derived using the advice in Chapter 3, should be used in the approaches to uncertainty analysis as described in Volume 1, Chapter 3 of this report. Tier 3 Activity data from a land-use and management inventory system should provide a basis to assign estimates of uncertainty to areas associated with land-use changes. Combining emission and activity data and their associated uncertainties can be done using Monte-Carlo procedures to estimate means and confidence intervals for the overall inventory. The uncertainty is likely to be less than for other tiers since estimates of carbon stock changes are based on more measurements and more refined models.

5.3.2

Dead organic matter

Forest Land, Grassland, Settlements, and other land-use categories could be potentially converted to Cropland which, in general will have little or no dead wood or litter, with the exception of agroforestry systems. Methods are provided for two types of dead organic matter pools: 1) dead wood, and 2) litter. Chapter 1of this report provides detailed definitions of these pools.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

Dead wood is a diverse pool which is difficult to measure, with associated uncertainties about rates of transfer to litter, soil, or emissions to the atmosphere. Litter accumulation depends on litterfall, which includes all leaves, twigs and small branches, fruits, flowers, and bark, minus the rate of decomposition. The litter mass is also influenced by the time since the last disturbance, and the type of disturbance. During the early stages of cropland development, litter increases rapidly. Management such as vegetation harvesting and burning dramatically alter litter stocks, but there are very few studies clearly documenting the effects of management on litter carbon. In general, croplands will have little or no dead wood or litter, and therefore these pools can often be assumed to approach zero after conversion, the exception being agroforestry systems which may be accounted either under Cropland or Forest Land, depending upon definitions adopted by countries for reporting. It is likely that the same will be true of many land uses prior to conversion, so that corresponding carbon pools prior to conversion can also be assumed to be zero. The exceptions are forest, agro-forests, and wetlands converted to Cropland, which could have significant carbon in DOM pools, as well as forest areas around settlements that may have been defined as Settlements based on nearby use rather than land cover. Estimating change in carbon stocks in DOM for lands converted to Cropland under higher tiers requires a twophase approach. During the first phase, there is often an abrupt change in DOM associated with the land-use change, particularly then the change is deliberate and associated with land preparation operations (e.g., clearing and burning). The second phase accounts for decay and accumulation processes during a transition period to a new steady-state system. At some point in time, the cropland ecosystem should reach an equilibrium at which time it can be considered Cropland Remaining Cropland and accounted for under that category. The transition period should be 20 years, but some countries can determine the appropriate transition period more accurately at higher tiers. To account for the transition period, lands converted to Cropland should be treated as annual cohorts. That is, land converted in a given year should be accounted for with Phase 1 methods in the year of conversion, and with Phase 2 methods for the subsequent 19 years. At the end of the 20 year period, the land area for that given year is added to the land area being accounted under the Cropland Remaining Cropland category.

5.3.2.1

C HOICE

OF METHOD

The decision tree in Figure 2.3 in Chapter 2 provides assistance in the selection of the appropriate tier level for the implementation of estimation procedures. Estimation of changes in carbon stocks in DOM requires an estimate of changes in stocks of dead wood and changes in litter stocks (refer to Equation 2.17 in Chapter 2). Each of the DOM pools (dead wood and litter) is to be treated separately, but the method for each pool is the same. Tier 1 A Tier 1 approach involves estimating the area of each type of land conversion using only the major conversion categories (e.g., Forest Land to Cropland). The immediate and abrupt carbon stock change (Phase 1) in dead wood and litter due to conversion of other lands to Cropland under Tier 1 will be estimated using Equation 2.23 in Chapter 2. C0 in Equation 2.23 is likely to be zero and there is no need to divide Ton. The Tier 1 default assumes removal of all dead wood and litter during conversion and that there is no dead wood or litter that remains or accumulates in Land Converted to Cropland. Countries where this assumption is known to be false (e.g., where slash and burn agriculture is widely practiced) are encouraged to use a higher tier when accounting for lands converted to Cropland. Additionally, it is assumed that croplands achieve their steady-state biomass during the first year following conversion. Thus, for Tier 1, Phase 2 has no transition period and lands converted to Cropland are transferred to Cropland Remaining Cropland in the second year following conversion. There are no default values available for dead wood or litter in most systems. For forests, there are no global default values for dead wood, but there are values for litter (Table 2.2 in Chapter 2). These values are in terms of tonnes C ha-1, not in terms of litter stocks. Countries should make best estimates and use local data from forestry and agricultural research institutes to provide best estimates of the dead wood and litter in the initial system prior to conversion. Tier 2 Tier 2 approaches require greater disaggregation than that used in Tier 1. Activity data should be reported by management regimes. Tier 2 also employs the two-phase approach described above. As recommended above in the biomass section, the immediate impacts of land conversion activities on the five carbon stocks can be summarized in a disturbance matrix. The disturbance matrix describes the retention, transfers and releases of carbon in the pools in the original ecosystem following conversion to Cropland. A disturbance matrix defines the proportion of the carbon stock that remains in that pool and the proportion that is

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transferred to other pools. A small number of transfers are possible, and are outlined in the disturbance matrix in Table 5.7. Use of a disturbance matrix ensures consistency of the accounting of all carbon pools. The immediate and abrupt carbon stock change in dead wood due to conversion of other lands to Cropland under Tiers 2 and 3 will be estimated using Equation 2.23 in Chapter 2 as suggested in Tier 1. During the transition period, pools that gain or lose carbon often have a non-linear loss or accumulation curve that can be represented through successive transition matrices. For Tier 2, a linear change function can be assumed; a Tier 3 approach based upon this method should use the true shapes of the curves. These curves should be applied to each cohort that is under transition during the reporting year to estimate the annual change in the dead wood and litter carbon pools. For the calculation of changes in dead wood and litter carbon during the transition phase, two methods are suggested: Method 1 (Also called the Gain-Loss Method, Equation 2.18 in Chapter 2): Method 1 involves estimating the area of each type of land conversion and the average annual transfer into and out of dead wood and litter stocks. This requires an estimate of area under Land Converted to Cropland according to different climate or cropland types, management regime, or other factors significantly affecting dead wood and litter carbon pools and the quantity of biomass transferred into dead wood and litter stocks as well as the quantity of biomass transferred out of the dead wood and litter stocks on per hectare basis according to different cropland types. Method 2 (Also called the Stock-Difference Method, Equation 2.19 in Chapter 2): Method 2 involves estimating the area of Land Converted to Cropland and then estimating dead wood and litter stocks at two periods of time, t1 and t2. The dead wood and litter stock changes for the inventory year are obtained by dividing the stock changes by the period (years) between two measurements. The stock difference method is feasible for countries, which have periodic inventories. This method is more suitable for countries adopting Tier 3 methods. Tier 3 methods are used where countries have country-specific emission factors, and substantial national data. Country-defined methodology may be based on detailed inventories of permanent sample plots for their croplands and/or models. Tier 3 For Tier 3, countries should develop their own methodologies and parameters for estimating changes in DOM. These methodologies may be derived from both methods specified above, or may be based on other approaches. The method used needs to be clearly documented. Method 2 may be suitable for countries adopting Tier 3 methods. Tier 3 methods are used where countries have country-specific emission factors, and substantial national data. Country-defined methodology may be based on detailed inventories of permanent sample plots for their grasslands and/or models.

5.3.2.2

C HOICE

OF EMISSION / REMOVAL FACTORS

Carbon Fraction: The carbon fraction of dead wood and litter is variable and depends on the stage of decomposition. Wood is much less variable than litter and a value of 0.50 tonne C (tonne d.m.)-1 can be used for the carbon fraction. Tier 1 For Tier 1, it is assumed that the dead wood and litter carbon stocks in lands converted to Cropland are all lost during the conversion and that there is no accumulation of new DOM in the Cropland after conversion. Countries experiencing significant conversions of other ecosystems to Cropland that have a significant component of dead wood or litter (e.g., slash and burn systems for clearing land, agroforestry, etc.) are encouraged to develop domestic data to quantify this impact and report it under Tier 2 or 3 methodologies. Tier 2 It is good practice to use country-level data on dead wood and litter for different Cropland categories, in combination with default values, if country or regional values are not available for some conversion categories. Country-specific values for transfer of carbon from live trees and other crops that are harvested to harvest residues and decomposition rates, in the case of Method 1 (Gain-Loss Method), or the net change in DOM pools, in the case of Method 2 (Stock-Difference Method), can be derived from domestic expansion factors, taking into account the Cropland type, the rate of biomass utilization, harvesting practices and the amount of damaged vegetation during harvesting operations. Country-specific values for disturbance regimes should be derived from scientific studies. Tier 3 National level disaggregated DOM carbon estimates should be determined as part of a national land-use inventory, national level models, or from a dedicated greenhouse gas inventory programme, with periodic

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

sampling according to the principles set our in Chapter 3 Annex 3A.3. Inventory data can be coupled with modelling studies to capture the dynamics of all Cropland carbon pools. Tier 3 methods provide estimates of greater certainty than lower tiers and feature a greater link between individual carbon pools. Some countries have developed disturbance matrices that provide a carbon reallocation pattern among different pools for each type of disturbance. Other important parameters in a modelled DOM carbon budget are decay rates, which may vary with the type of wood and microclimatic conditions, and site preparation procedures (e.g., controlled broadcast burning, or burning of piles).

5.3.2.3

C HOICE

OF ACTIVITY DATA

Activity data should be consistent with the activity data used for estimating changes in biomass on land areas converted to Cropland. This can be obtained, consistent with the general principles set out in Chapter 3 and as described earlier through national statistics, from forest services, conservation agencies, municipalities, survey and mapping agencies. Cross-checks should be made to ensure complete and consistent representation of annually converted lands in order to avoid possible omissions or double counting. Data should be disaggregated according to the general climatic categories and Cropland types. Tier 3 inventories will require more comprehensive information on the establishment of new croplands, with refined soil classes, climates, and spatial and temporal resolution. All changes having occurred over the number of years selected as the transition period should be included with transitions older than the transition period (default 20 years) reported as a subdivision of Cropland Remaining Cropland. All tiers require estimates of land areas converted to Cropland. The same area data should be used for biomass calculations, dead organic matter and the soil carbon estimates. If necessary, area data used in the soils analysis can be aggregated to match the spatial scale required for lower order estimates of biomass; however, at higher tiers, stratification should take account of major soil types. Area data should be obtained using the methods described in Chapter 3. Higher tiers require greater detail but the minimum requirement for inventories to be consistent with the IPCC Guidelines is that the areas of forest conversion can be identified separately. This is because forest will usually have higher carbon density before conversion. This implies that at least partial knowledge of the land-use change matrix, and therefore, where Approaches 1 and 2 from Chapter 3 are used to estimate land area are being used, supplementary surveys may be needed to identify the area of land being converted from Forest Land to Cropland. As pointed out in Chapter 3, where surveys are being set up, it will often be more accurate to determine directly areas undergoing conversion, than to estimate these from the differences in total land areas under particular uses at different times.

5.3.2.4

C ALCULATION

STEPS FOR

T IERS 1

AND

2

Tier 1 Step 1: Determine the categories of land conversion to be used in this assessment and the representative area of conversion by year (Aon). Area data should be obtained using the methods described in Chapter 3. Higher tiers require greater detail but the minimum requirement for inventories to be consistent with the IPCC Guidelines when using Tier 1 is that the areas of Forest Land conversion to Cropland can be identified separately. Step 2: For each activity category, determine the dead wood and litter stocks (separately) per hectare prior to conversion (ΔCo). Step 3: For each activity category, determine the stocks in the dead wood and litter (separately) per hectare for the particular type of cropland after conversion (ΔCn). For Tier 1, dead wood and litter stocks following conversion are assumed to be equal to zero. Step 4: Calculate the net change of dead wood and litter stocks per hectare for each type of conversion by subtracting the initial stocks from the final stocks. A negative value indicates a loss in the stock. Step 5: Convert the net change in the individual stock to units of tonnes C ha-1 by multiplying the net stock change by the carbon fraction of that stock (0.40 tonne C (tonne d.m.)-1 for litter, and 0.50 tonne C (tonne d.m.)-1 for dead wood). Step 6: Multiply the net change in each C stock by the area converted during the reporting year, to get the annual change in carbon stocks in dead wood and litter (ΔCDOM).

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Tier 2 Step 1: Determine the categories of land conversion to be used in this assessment and the representative area of conversion by year. When calculating for lands in the transition phase, representative areas for each category at different stages of conversion are required. Step 2: Abrupt changes

•

• • • •

•

Determine the activity categories to be used in this assessment and the representative areas. The category consists of definitions of the type of conversion and, if applicable, the nature of management of the previous land cover and cropland management, for example: ‘conversion of logged tropical seasonal forest to cereal crops’. For each activity category, determine the dead wood and litter stocks (separately) per hectare prior to conversion. For each activity category, determine the stocks in the dead wood and litter (separately) per hectare following one year of conversion to Cropland. Calculate the net change of dead wood and litter stocks per hectare for each type of conversion by subtracting the initial stocks from the final stocks. A negative value indicates a loss in the stock. Convert the net change in the individual stock to units of tonnes C ha-1 as mentioned in Tier 1. Multiply the net change in each C stock by the area converted during the reporting year.

Step 3: Transitional changes •

• • • •

• •

Determine the activity categories and cohorts to be used in this assessment and the representative areas. The category consists of definitions of the type of conversion and, if applicable, the nature of management of the previous land cover and cropland management, for example: ‘conversion of logged tropical seasonal forest to cattle pasture using exotic grasses’. Determine the annual change rate for dead wood and litter stocks (separately) by activity type using either Method 1 (Gain-Loss Method) or Method 2 (Stock-Difference Method) (see below) for each cohort of lands that are currently in the transition phase between conversion and a new steady-state cropland system. Determine the dead wood and litter stocks in the cohort during the previous year (usually taken from the previous inventory). Calculate the change in dead wood and litter stocks for each cohort by adding the net change rate to the previous year’s stocks. Convert the net change in the individual stock to units of tonnes C ha-1 as described in Tier 1. Multiply the net change in each C stock by the area in each cohort for the reporting year.

Method 1 (Gain-Loss Method; see Equation 2.18 in Chapter 2) •

•

• •

Determine the average annual inputs of dead wood and litter (separately). Determine the average annual losses of dead wood and litter (separately). Determine the net change rate in dead wood and litter by subtracting the outputs from the inputs. A Tier 2 approach requires country-specific and cropping system-specific stock change factors and the best available local data should be used (and documented).

Method 2 (Stock-Difference Method; see Equation 2. 19 in Chapter 2) • •

5.34

Determine the inventory time interval, the average stocks of dead wood and litter at the initial inventory, and the average stocks of dead wood and litter at the final inventory. Use these figures to calculate the net change in dead wood and litter stocks by subtracting the initial stock from the final stock and dividing this difference by the number of years between inventories. A negative value indicates a loss in the stock. A Tier 2 approach requires country-specific and cropping system-specific stock change factors and the best available local data should be used (and documented).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

5.3.2.5

U NCERTAINTY

ASSESSMENT

This section considers source-specific uncertainties relevant to estimates made for lands converted to Cropland. Sources of uncertainty include the degree of accuracy in land area estimates, carbon increase and loss, carbon stock, fraction of land area burnt, and expansion factor terms. Error estimates (i.e., standard deviations, standard error, or ranges) must be calculated for each of the country-defined terms used in a basic uncertainty assessment.

Emission factor uncertainties These will be the same as the uncertainties associated with estimation of the litter and dead organic matter stocks per unit area on the previous land use. Uncertainties need not be estimated where zero carbon density in litter and dead organic matter pools is assumed for Cropland. Where this is not the case, uncertainties should be assessed by analysis of local data and should both exceed a factor of about 2.

Activity data uncertainties Area data and estimates of uncertainty should be obtained using the methods in Chapter 3. Tiers 2 and 3 approaches may also use higher resolution activity data, such as area estimates for different climatic regions or for cropland management systems within national boundaries. This will reduce uncertainty levels when associated with carbon accumulation factors defined at the same resolution.

5.3.3

Soil carbon

Land is typically converted to Cropland from native lands, managed Forest Land and Grassland, but occasionally conversions can occur from Wetlands and seldom Settlements. Regardless of soil type (i.e., mineral or organic), the conversion of land to Cropland will, in most cases, result in a loss of soil C for some years following conversion (Mann, 1986; Armentano and Menges, 1986; Davidson and Ackerman, 1993). Possible exceptions are irrigation of formerly arid lands and conversion of degraded lands to Cropland. General information and guidance for estimating changes in soil C stocks are provided in Section 2.3.3 of Chapter 2 (including equations), and that section needs to be read before proceeding with a consideration of specific guidelines dealing with cropland soil C stocks. The total change in soil C stocks for Land Converted to Cropland is estimated using Equation 2.24 (Chapter 2), which combines the change in soil organic C stocks (SOC stocks) for mineral soils and organic soils; and stock changes associated with soil inorganic C pools (Tier 3 only). This section provides specific guidance for estimating soil organic C stock changes; see Section 2.3.3.1 for discussion on soil inorganic C (no additional guidance is provided in the Cropland section below). To account for changes in soil C stocks associated with Land Converted to Cropland, countries need to have, at a minimum, estimates of the areas of Land Converted to Cropland during the inventory time period. If land-use and management data are limited, aggregate data, such as FAO statistics, can be used as a starting point, along with knowledge of country experts of the approximate distribution of land-use types being converted and their associated management. If the previous land uses and conversions are not unknown, SOC stocks changes can still be computed using the methods provided in Cropland Remaining Cropland, but the land base area will likely be different for croplands in the current year relative to the initial year in the inventory. It is critical, however, that the total land area across all land-use sectors be equal over the inventory time period (e.g., 7 million ha may be converted from Forest Land and Grassland to Cropland during the inventory time period, meaning that croplands will have an additional 7 Million ha in the last year of the inventory, while grasslands and forests will have a corresponding loss of 7 Million ha in the last year). Land Converted to Cropland is stratified according to climate regions and major soil types, which could either be based on default or countryspecific classifications. This can be accomplished with overlays of climate and soil maps, coupled with spatiallyexplicit data on the location of land conversions.

5.3.3.1

C HOICE

OF METHOD

Inventories can be developed using a Tier 1, 2 or 3 approach with each successive tier requiring more detail and resources than the previous one. It is also possible that countries will use different tiers to prepare estimates for the separate subcategories of soil C (i.e., soil organic C stocks changes in mineral soils and organic soils; and stock changes associated with soil inorganic C pools). Decision trees are provided for mineral soils (Figure 2.4) and organic soils (Figure 2.5) in Section 2.3.3.1 (Chapter 2) to assist inventory compilers with selection of the appropriate tier for their soil C inventory.

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Mineral soils Tier 1 Soil organic C stock changes for mineral soils can be estimated for land-use conversion to Cropland using Equation 2.25 in Chapter 2. For Tier 1, the initial (pre-conversion) soil organic C stock (SOC(0-T)) and C stock in the last year of the inventory time period (SOC0) are computed from the default reference soil organic C stocks (SOCREF) and default stock change factors (FLU, FMG, FI). Annual rates of stock changes are estimated as the difference in stocks (over time) divided by the time dependence (D) of the Cropland stock change factors (default is 20 years). Tier 2 The Tier 2 method for mineral soils also uses Equation 2.25 in Chapter 2, but incorporates country-specific reference C stocks and/or stock change factors, and possibly more disaggregated land-use activity and environmental data. Tier 3 Tier 3 methods will involve more detailed and country-specific models and/or measurement-based approaches along with highly disaggregated land-use and management data. Tier 3 approaches estimate soil C change from land-use conversions to Cropland, and may employ models, data sets and/or monitoring networks. If possible, it is recommended that Tier 3 methods be integrated with estimates of biomass removal and the post-clearance treatment of plant residues (including woody debris and litter), as variation in the removal and treatment of residues (e.g., burning, site preparation) will affect C inputs to soil organic matter formation and C losses through decomposition and combustion. It is important that models be evaluated with independent observations from country-specific or region-specific field locations that are representative of the interactions of climate, soil and cropland management on post-conversion change in soil C stocks.

Organic soils Tier 1 and Tier 2 Land Converted to Cropland on organic soils within the inventory time period is treated the same as long-term cropped organic soils. Carbon losses are computed using Equation 2.26 (Chapter 2). Additional guidance on the Tiers 1 and 2 approaches are given in the Cropland Remaining Cropland section (Section 5.2.3). Tier 3 A Tier 3 approach will involve more detailed and country-specific models and/or measurement-based approaches along with highly disaggregated land-use and management data (see mineral soils above for further discussion).

5.3.3.2

C HOICE

OF STOCK CHANGE AND EMISSION FACTORS

Mineral soils Tier 1 For native unmanaged land, as well as for managed forest lands, settlements and nominally managed grasslands with low disturbance regimes, soil C stocks are assumed equal to the reference values (i.e., land-use, disturbance (forests only), management and input factors equal 1), while it will be necessary to apply the appropriate stock change factors to represent previous land-use systems that are not the reference condition, such as improved and degraded grasslands. It will also be necessary to apply the appropriate stock change factor to represent input and management effects on soil C storage in the new cropland system. Default reference C stocks are found in Table 2.3 (Chapter 2). See the appropriate land-use chapter for default stock change factors. In the case of transient land-use conversions to Cropland, the stock change factors are given in Table 5.10, and depend on the length of the fallow (vegetation recovery) cycle in a shifting cultivation system, representing an average soil C stock over the crop-fallow cycle. Mature fallow denotes situations where the non-cropland vegetation (e.g., forests) recovers to a mature or near mature state prior to being cleared again for cropland use, whereas in shortened fallow, vegetation recovery is not attained prior to re-clearing. If land already in shiftingcultivation is converted to permanent Cropland (or other land uses), the stock change factors representing shifting cultivation would provide the ‘initial’ C stocks (SOC(0-T)) in the calculations using Equation 2.25 (Chapter 2).

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Chapter 5: Cropland

TABLE 5.10 SOIL STOCK CHANGE FACTORS (FLU, FMG, FI) FOR LAND-USE CONVERSIONS TO CROPLAND Factor value type

Level

Land use

Land use

Land-use, Management, & Input Land-use, Management, & Input Land-use, Management, & Input #

Climate regime

IPCC default

Error

Definition

#

Native forest or grassland (non-degraded) Shifting cultivation – Shortened fallow

All

1

NA

Tropical

1

NA

Tropical

0.64

+ 50%

Shifting cultivation – Mature fallow

Tropical

0.8

+ 50%

Represents native or long-term, nondegraded and sustainably managed forest and grasslands. Permanent shifting cultivation, where tropical forest or woodland is cleared for planting of annual crops for a short time (e.g., 3-5 yr) period and then abandoned to regrowth.

Managed forest

(default value is 1)

Managed grassland

(See default values in Table 6.2)

Cropland

(See default values in Table 5.5)

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean. NA denotes ‘Not Applicable’, where factor values constitute defined reference values.

Tier 2 Estimation of country-specific stock change factors is probably the most important development associated with the Tier 2 approach. Differences in soil organic C stocks among land uses are computed relative to a reference condition, using land-use factors (FLU). Input factors (FI) and management factors (FMG) are then used to further refine the C stocks of the new cropland system. Additional guidance on how to derive these stock change factors is given in Croplands Remaining Croplands, Section 5.2.3.2. See the appropriate chapter for specific information regarding the derivation of stock change factors for other land-use categories (Forest Land in Section 4.2.3.2, Grassland in 6.2.3.2, Settlements in 8.2.3.2, and Other Land in 9.3.3.2). Reference C stocks can also be derived from country-specific data in a Tier 2 approach. However, reference values should be consistent across the land uses (i.e., Forest Land, Cropland, Grassland, Settlements, Other Land), and thus must be coordinated among the various teams conducting soil C inventories for AFOLU. Tier 3 Constant stock change rate factors per se are less likely to be estimated in favor of variable rates that more accurately capture land-use and management effects. See Chapter 2, Section 2.3.3.1 for further discussion.

Organic soils Tier 1 and Tier 2 Land Converted to Cropland on organic soils within the inventory time period is treated the same as long-term cropped organic soils. Tier 1 emission factors are given in Table 5.6, while Tier 2 emission factors are derived from country-specific or region-specific data. Tier 3 Constant emission rate factors per se are less likely to be estimated in favor of variable rates that more accurately capture land-use and management effects. See Chapter 2, Section 2.3.3.1 for further discussion.

5.3.3.3

C HOICE

OF ACTIVITY DATA

Mineral soils Tier 1 and Tier 2 For purposes of estimating soil carbon stock change, area estimates of Land Converted to Cropland should be stratified according to major climate regions and soil types. This can be based on overlays with suitable climate and soil maps and spatially-explicit data of the location of land conversions. Detailed descriptions of the default climate and soil classification schemes are provided in Chapter 3, Annex 3A.5. Specific information is provided

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in the each of the land-use chapters regarding treatment of land-use/management activity data (Forest Land in Section 4.2.3.3, Cropland in 5.2.3.3, Grassland in 6.2.3.3, Settlements in 8.2.3.3, and Other Land in 9.3.3.3). One critical issue in evaluating the impact of Land Converted to Cropland on soil organic C stocks is the type of land-use and management activity data. Activity data gathered using Approach 2 or 3 (see Chapter 3 for discussion about approaches) provide the underlying basis for determining the previous land use for Land Converted to Cropland. In contrast, aggregate data (Approach 1, Chapter 3) only provide the total amount of area in each land at the beginning and end of the inventory period (e.g., 1985 and 2005). Approach 1 data are not sufficient to determine specific transitions. In this case all Cropland will be reported in the Cropland Remaining Cropland category and in effect transitions become step changes across the landscape. This makes it particularly important to achieve coordination among all land sectors to ensure that the total land base is remaining constant over time, given that some land area will be lost and gained within individual sectors during each inventory year due to land-use change. Tier 3 For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or more detailed data on the combinations of climate, soil, topographic and management data are needed, relative to Tier 1 or 2 methods, but the exact requirements will be dependent on the model or measurement design.

Organic soils T i e r s 1 a nd 2 Land Converted to Cropland on organic soils within the inventory time period is treated the same as long-term cropped organic soils, and guidance on activity data is discussed in Section 5.2.3.3. Tier 3 Similar to mineral soils, Tier 3 approaches will likely require more detailed data on the combinations of climate, soil, topographic and management data. Relative to Tier 1 or 2 methods, the exact requirements will be dependent on the model or measurement design.

5.3.3.4

C ALCULATION

STEPS FOR

T IER 1

Mineral soils The steps for estimating SOC0 and SOC(0-T) and net soil C stock change per ha of Land Converted to Cropland on mineral soils are as follows: Step 1: Organize data into inventory time periods based on the years in which activity data were collected (e.g., 1990 to 1995, 1995 to 2000, etc.) Step 2: Determine the amount of Land Converted to Cropland by mineral soil types and climate regions in the country at the beginning of the first inventory time period. The first year of the inventory time period will depend on the time step of the activity data (0-T; e.g., 5, 10 or 20 years ago). Step 3: For Grassland converted to Cropland, classify previous grasslands into the appropriate management system using Figure 6.1. No classification is needed for other land uses at the Tier 1 level. Step 4: Assign native reference C stock values (SOCREF) from Table 2.3 based on climate and soil type. Step 5: Assign a land-use factor (FLU), management factor (FMG) and C input levels (FI) to each grassland based on the management classification (Step 2). Values for FLU, FMG and FI are given in Table 6.2 for grasslands. Values are assumed to be 1 for all other land uses. Step 6: Multiply the factors (FLU, FMG, FI) by the reference soil C stock to estimate an ‘initial’ soil organic C stock (SOC(0-T)) for the inventory time period. Step 7: Estimate the final soil organic C stock (SOC0) by repeating Steps 1 to 5 using the same native reference C stock (SOCREF), but with land-use, management and input factors that represent conditions for the cropland in the last (year 0) inventory year. Step 8: Estimate the average annual change in soil organic C stocks for land converted to Cropland (∆CMineral) by subtracting the ‘initial’ soil organic C stock (SOC(0-T)) from the final soil organic C stock (SOC0), and then dividing by the time dependence of the stock change factors (i.e., 20 years using the default factors). Note: if an inventory time period is greater than 20 years, then divide by the difference in the initial and final year of the time period. Step 9: Repeat Steps 2 to 8 if there are additional inventory time periods (e.g., 1990 to 2000, 2001 to 2010, etc.). Note that Land Converted to Cropland will retain that designation for 20 years. Therefore, inventory time

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periods that are less than 20 years may need to refer to the previous inventory time period to evaluate if a parcel of land is considered Land Converted to Cropland or Cropland Remaining Cropland. A numerical example is given below for Forest Land converted to Cropland on mineral soils, using Equation 2.25 and default reference C stocks (Table 2.3) and stock change factors (Table 5.5). Example: For a forest on volcanic soil in a tropical moist environment: SOCRef = 70 tonnes C ha-1. For all forest soils (and for native grasslands) default values for stock change factors (FLU , FMG , FI) are all 1; thus SOC(0-T) is 70 tonnes C ha-1. If the land is converted into annual cropland, with intensive tillage and low residue C inputs then SOC0 = 70 tonnes C ha-1 ● 0.48 ● 1 ● 0.92 = 30.9 tonnes C ha-1. Thus the average annual change in soil C stock for the area over the inventory time period is calculated as (30.9 tonnes C ha-1 – 70 tonnes C ha-1) / 20 yrs = -2.0 tonnes C ha-1 yr-1.

Organic soils Calculation steps and example are the same as described in Section 5.2.3.4 above.

5.3.3.5

U NCERTAINTY

ASSESSMENT

Uncertainty analyses for Land Converted to Cropland are fundamentally the same as Cropland Remaining Cropland. Three broad sources of uncertainty exists: 1) uncertainties in land-use and management activity and environmental data; 2) uncertainties in reference soil C stocks if using a Tier 1 or 2 approach (mineral soils only); and 3) uncertainties in the stock change/emission factors for Tier 1 or 2 approaches, model structure/parameter error for Tier 3 model-based approaches, or measurement error/sampling variability associated with a Tier 3 measurement-based inventories. See the uncertainty section in Cropland Remaining Cropland for additional discussion (Section 5.2.3.5).

5.3.4

Non-CO 2 greenhouse gas emissions from biomass burning

Greenhouse gas emissions from conversion of non-cropland, particularly Forest Land and Grassland to Cropland, are likely to be key source category for many countries. Greenhouse gas emissions from Land Converted to Cropland occur from incomplete combustion of biomass and dead organic matter (DOM) in the initial land-use category before conversion. CO2 emissions are accounted for in the new land-use category (Land Converted to Cropland). The most significant non-CO2 emissions in this section arise from conversion of Forest Land to Cropland, but it may also occur as a result of the conversion from Grassland to Cropland. It is very unlikely that Cropland originates from conversion of the other land-use categories (Settlements, Wetlands, or Other Land). In the tropics, it is common practice to burn the forest residues successively, until most (or all) of the forest residues and DOM is cleared, and agriculture can be established. In some places, up to three or four burnings are necessary. Part of the above-ground forest biomass removed during the process of conversion of Forest Land to Cropland may be transferred to harvested wood products, and an amount may be removed from the site to be used as fuel wood (hence, burnt off-site). Whatever remains is normally burnt on-site. Methods for estimating CO2 emissions from fire for Land Converted to Cropland are described in Section 2.4 in Chapter 2. Non-CO2 emissions from biomass burning in unmanaged Forest Land, if followed by a land-use conversion, shall be reported, since the converted land is considered to be managed land. The approach to be used to estimate non-CO2 emissions from biomass burning in Land Converted to Cropland is essentially the same as for Cropland Remaining Cropland.

5.3.4.1

C HOICE

OF METHOD

The decision tree in Figure 2.6 in Chapter 2 provides guidance on the choice of the Tier level to be applied by countries when reporting non-CO2 emissions from Land Converted to Cropland. Countries experiencing significant scale conversion of non-cropland, particularly from Forest Land, to cropland should strive to adopt Tier 2 or 3 methods.

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The choice of method is directly related to the availability of national data on the area of converted land burnt, the mass of fuel available, and combustion and emission factors. When using higher tiers, country-specific data on the mass of available fuel is used to represent the amount of biomass removed for conversion, and transferred to harvested wood product (if applicable), removed for fuel use and burnt off-site. Countries should strive to report using a Tier 2 or Tier 3 method whenever greenhouse gas emissions from biomass burning in Land Converted to Cropland is a key category. If models have been developed and validated, countries should apply a Tier 3 method even in those cases where Land Converted to Cropland is not a key category.

5.3.4.2

C HOICE

OF EMISSION FACTORS

Tier 1 The mass of fuel combusted is critical for estimating greenhouse gas emissions. Default data to support estimation of emissions under a Tier 1 approach are given in Tables 2.4 – 2.6 in Chapter 2. Countries need to judge how their vegetation types relate to the broad vegetation categories described in the default tables. For Tier 1, it should be assumed that all of the carbon in above-ground biomass and DOM in the previous land category is lost immediately after conversion. Default values for biomass prior to conversion can be found in the chapters relating to the respective land uses (e.g., default factors for Forest Land are to be found in the chapter dealing with biomass in Forest Land). For calculation of non-CO2 emissions, estimates of the amount of fuel actually burnt (Table 2.4) should be used. Tier 2 In a Tier 2 method, country-specific estimates of mass of fuel available should be used. Data should be disaggregated according to forest types, in the case of Forest Land converted to Cropland. Combustion and emission factors that reflect better the national conditions (climate zone, biome, burning conditions) should be developed and uncertainty ranges provided. In addition, unlike Tier 1, where it is assumed that all of the carbon in above-ground and DOM is lost immediately after conversion, in a Tier 2 method the transfers of biomass to harvested wood products and fuelwood (burnt off-site) should be estimated to provide a more reliable estimate of the mass of fuel available for combustion. Tier 3 Under a Tier 3, all the parameters required for estimating CO2 and non-CO2 emissions should be developed nationally for different land types subjected to conversion to Cropland.

5.3.4.3

C HOICE

OF ACTIVITY DATA

The activity data needed to estimate non-CO2 emissions from biomass burning refers to the area affected by this activity. Countries shall stratify the area converted to Cropland by Forest Land and Grassland converted, since the amount of fuel available for burning may present large variations from one category of land use to another. The most critical conversion is from Forest Land to Cropland, due to large biomass involved per hectare. It is good practice to ensure the area data used for non-CO2 estimation is consistent with that used for biomass and DOM sections. Tier 1 Countries applying a Tier 1 method should estimate the areas converted to Cropland from initial land uses (Forest Land, Grassland, etc.). Countries using Approach 1 of Chapter 3 should strive to further stratify Land Converted to Cropland from different land-use categories. The conversion should be estimated on a yearly basis. Estimates can be derived by applying a rate of conversion to Cropland to the total area cropped annually. The rate can be estimated on the basis of historical knowledge, judgement of country experts, and/or from samples of converted areas and assessment of the final land use. Alternatively, estimates can be derived using data from international sources, such as FAO, to estimate the area of Forest Land and Grassland area annually converted, and using expert judgement to estimate the portion of this area converted to Cropland. Tier 2 Countries should, where possible, use actual area estimates for all possible conversions to Cropland. Multitemporal remotely sensed data of adequate resolution should provide better estimates of land-use conversion than the approaches used in Tier 1. The analysis may be based on full coverage of the territory or on representative sample areas selected, from which estimates of the area converted to Cropland in the entire territory can be derived. Tier 3 The activity data in Tier 3 should be based on the Approach 3 method presented in Chapter 3, where the total annual area converted to Cropland (from Forest Land, Grassland, or other land-use category) is estimated. It is

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good practice to develop a land-use change matrix as suggested in Chapter 3, in a spatially explicit manner. The data should be disaggregated according to type of biome, climate, soils, political boundaries, or a combination of these parameters.

5.3.4.4

U NCERTAINTY

ASSESSMENT

Tier 1 The sources of uncertainty arise from: (i) use of global or national average rates of conversion or coarse estimates of land areas converted to Cropland; (ii) estimate of the area converted that is burnt; (iii) mass of available fuel; and (iv) combustion and emission factors. Uncertainties associated with emission and combustion factors are provided, and those related to items (i) and (ii) can vary significantly depending on the method used in their estimation. As a result of these uncertainties it is unlikely that the estimate of area burnt will be known to better than 20% and the emissions per unit area to within a factor of 2 using Tier 1 methods. Tier 2 The use of area estimates produced from more reliable sources (remotely sensed data, sample approach) will improve the accuracy relative to Tier 1 and Approach 1 of Chapter 3. These sources will also provide better estimates of the areas that are converted and burnt. Taking into account the biomass transferred to harvested wood products or removed from the site as fuelwood, and the biomass left on-site to decay, will eliminate a bias (overestimation) in the estimates. Estimates of emission or combustion factors, if accompanied by error ranges (in the form of standard deviation), will allow uncertainty associated with Land Converted to Cropland to be assessed. Tier 3 Uncertainty is less and is dependent on the accuracy of remote sensing and field surveys, and of the modelling approach used and associated data inputs.

5.4

COMPLETENESS, TIME SERIES, QA/QC, AND REPORTING

Material presented here supplements the general guidance on these issues that is provided in Volume 1.

5.4.1

Completeness

Tier 1 A complete Cropland inventory for Tier 1 has three elements: 1) carbon stock changes and non-CO2 (CH4, CO, N2O, NOx) emissions from biomass burning have been estimated for all Land Converted to Cropland and Cropland Remaining Cropland during the inventory time period, 2) inventory analysis addressed the impact of all management practices described in the Tier 1 methods, and 3) the analysis accounted for climatic and soil variation that impacts emissions and removals (as described for Tier 1). The latter two elements require assignment of management systems to cropland areas and stratification by climate regions and soil types. It is good practice for countries to use the same area classifications for biomass and soil pools in addition to biomass burning (to the extent that classifications are needed for these source categories). This will ensure consistency and transparency, allow for efficient use of land surveys and other data collection tools, and enable the explicit linking between carbon dioxide emissions and removals in biomass and soil pools, as well as non-CO2 emissions from biomass burning. For biomass and soil C stock estimations, a cropland inventory should address the impact of land-use change (Land Converted to Cropland) and management. However, in some cases, activity data or expert knowledge may not be sufficient to estimate the effects of agroforestry, crop rotation practices, tillage practices, irrigation, manure application, residue management, etc. In those cases, countries may proceed with an inventory addressing land use alone, but the results will be incomplete and omission of management practices must be clearly identified in the reporting documentation for purposes of transparency. If there are omissions, it is good practice to collect additional activity data for future inventories, particularly if biomass or soil C is a key source category. C stock changes may not be computed for some cropland areas if greenhouse gas emissions and removals are believed to be insignificant or constant through time, such as non-woody cropland where there are no management or land-use changes. In this case, it is good practice for countries to document and explain the reason for omissions.

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For biomass burning, non-CO2 greenhouse gases should be estimated for all major categories of crop residues, taking care to account for removal of residues from the field for other purposes such as energy production, and for losses of residues resulting from grazing and decomposition during the period between harvests and burning operations. Where there is conversion of Forest Land to Cropland, the emissions from the burning of DOM and cleared tree biomass should be included. Tier 2 A complete Tier 2 inventory has similar elements as Tier 1 but incorporates country-specific data: to estimate C stock change factors, reference soil C stocks, residue estimates (fuel load), combustion and emission factors for biomass burning; and to develop climate descriptions and soil categories in addition to improve management system classifications. Moreover, it is good practice for a Tier 2 inventory to incorporate country-specific data for each component. Inventories are still considered complete, however, if they combine country- specific data with Tier 1 defaults. Tier 3 In addition to the Tiers 1 and 2 considerations, completeness of Tier 3 inventories will depend on the components of the country-specific evaluation system. In practice, Tier 3 inventories are likely to fully account for emissions and removals from croplands using more, finely resolved data on climate, soils, biomass burning and management systems. It is good practice for inventory compilers to describe and document the elements of the country-specific system, demonstrating the completeness of the approach and data sources. If gaps are identified, it is good practice to gather additional data and further develop the country-specific system.

5.4.2

Developing a consistent time series

Tier 1 Consistent time series are essential for evaluating trends in emissions or removals. In order to maintain consistency, compilers should apply the same classifications and factors over the entire inventory time period, including climate, soil types, management system classifications, C stock change factors, reference soil C stocks, residue estimates (fuel load), combustion factors, and non-CO2 emission factors. Defaults are provided for all of these characteristics and so consistency should not be an issue. In addition, the land base should also remain consistent through time, with the exception of Land Converted to Cropland or Cropland converted to other land uses. Countries should use consistent sources of activity data on land use, management and biomass burning, throughout the inventory. Sampling approaches, if used, should be maintained for the duration of the inventory time period to ensure a consistent approach. If subcategories are created, countries should keep transparent records of how they are defined and apply them consistently throughout the inventory. In some cases, sources of activity data, definitions or methods may change over time with availability of new information. Inventory compilers should determine the influence of changing data or methods on trends, and if deemed significant, emissions and removals should be re-calculated for the time series using methods provided in Chapter 5 of Volume 1. For C stock changes, one key element in producing a consistent time series is to ensure consistency between C stocks for lands converted to Cropland that were reported in previous reporting periods and the state of those stocks reported for those lands that are remaining Cropland in the current reporting period. For example, if 50 tonnes of the above-ground live biomass was transferred to the dead organic matter pool for land converted from Forest Land to Cropland in the previous reporting period, reporting in this period must assume that the starting carbon stocks in the dead organic matter pool was 50 tonnes for those lands. Tier 2 In addition to the issues discussed under Tier 1, there are additional considerations associated with introduction of country-specific information. Specifically, it is good practice to apply new values or classifications derived from country-specific information across the entire inventory and re-calculate the time series. Otherwise, positive or negative trends in C stocks or biomass burning may be partly due to changes associated with the inventory methods at some point in the time series, and not representative of actual trends. It is possible that new country-specific information may not be available for the entire time series. In those cases, it is good practice to demonstrate the effect of changes in activity levels versus updated country-specific data or methods. Guidance on recalculation for these circumstances is presented in Chapter 5 of Volume 1. Tier 3 Similar to Tiers 1 and 2, it is good practice to apply the country-specific estimation system throughout the entire time series. Inventory agencies should use the same measurement protocols (sampling strategy, method, etc.) and/or model-based system throughout the inventory time period.

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5.4.3

Quality Assurance and Quality Control

Tier 1 It is good practice to implement Quality Assurance/Quality Controls with internal and external review of Cropland inventory data. Internal reviews should be conducted by the agency in charge of the inventory, while external review is conducted by other agencies, experts or groups who are not directly involved with the compilation. Internal review should focus on the inventory implementation process to ensure that: 1) activity data have been stratified appropriately by climate regions and soil types; 2) management classifications/descriptions have been applied appropriately; 3) activity data have been properly transcribed into the worksheets or inventory computation software; and 4) C stock change factors, reference soil C stocks, residue estimates (fuel load), and biomass burning combustion and emission factors have been assigned appropriately. Quality Assurance/Quality Control measures may involve visual inspection as well as built-in program functions to check data entry and results. Summary statistics can also be helpful, such as summing areas by strata within worksheets to determine if they are consistent with land-use statistics. Total areas should remain constant over the inventory period, and areas by strata should only vary by land-use or management classification (climate and soil areas should remain constant). External reviews need to consider the validity of the inventory approach, thoroughness of inventory documentation, methods explanation and overall transparency. It is important to evaluate if the total area of cropland is realistic, and reviewers should cross-check area estimates across land-use categories (i.e., Forest Land, Cropland, Grassland, etc.) to ensure that the sum of the entire land base for a country is equal across every year in the inventory time period. Tier 2 In addition to the Quality Assurance/Quality Controls measures under Tier 1, the inventory agency should review the country-specific climate regions, soil types, management system classifications, C stock change factors, reference C stocks, residue estimates (fuel load), combustion factors and/or non-CO2 emission factors for biomass burning. If using factors based on direct measurements, the inventory agency should review the measurements to ensure that they are representative of the actual range of environmental and management conditions, and were developed according to recognized standards (IAEA, 1992). If accessible, it is good practice to compare country-specific factors with Tier 2 stock change and emission factors used by other countries with comparable circumstances, in addition to the IPCC defaults. Given the complexity of emission and removal trends, specialist in the field should be involved in the external review to critique the residue fuel load estimates, stock change factors, combustion and emission factors, as well as country-specific climate regions, soil types, and/or management system descriptions. Tier 3 Country-specific inventory systems will likely need additional Quality Assurance/Quality Control measures, but this will be dependent on the systems that are developed. It is good practice to develop a Quality Assurance/Quality Control protocol that is specific to the country’s advanced inventory system, archive the reports, and include summary results in reporting documentation.

5.4.4

Reporting and Documentation

Tier 1 In general, it is good practice to document and archive all information required to produce the national inventory estimates. For Tier 1, inventory compilers should document activity data trends and uncertainties for croplands. Key activities include land-use change, use of mineral fertilizers, agroforestry practices, organic amendments, tillage management, cropping rotations, residue management (including burning), irrigation practices, extent of mixed cropping systems, water management in rice systems, and land-use change. It is good practice to archive actual databases, such as agricultural census data, and procedures used to process the data (e.g., statistical programs); definitions used to categorize or aggregate activity data; and procedures used to stratify activity data by climate regions and soil types (for Tier 1 and Tier 2). The worksheets or inventory software should be archived with input/output files that were generated to produce the results. In cases where activity data are not available directly from databases or multiple data sets were combined, the information, assumptions and procedures that were used to derive the activity data should be described. This documentation should include the frequency of data collection and estimation, and uncertainty. Use of expert knowledge should be documented and correspondences archived.

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It is good practice to document and explain trends in biomass and soil C stocks, as well as biomass burning in terms of the land-use and management activity. Changes in biomass stocks should be linked directly to land use or changes in agroforestry practices, while trends in soil C stocks may be due to land use or shifts in key management activities, as described above. Biomass burning emissions from residues will depend on the extent to which burning is used to prepare fields for planting. Significant fluctuations in emissions between years should be explained. Countries need to include documentation on completeness of their inventory, issues related to time series consistency or lack thereof, and a summary of Quality Assurance/Quality Control measures and results. Tier 2 In addition to the Tier 1 considerations, inventory compilers should document the underlying basis for countryspecific C stock change factors, reference soil C stocks, residue estimated (fuel loads), combustion and emission factors for biomass burning, management system classifications, climate regions and/or soil types. Furthermore, it is good practice to archive metadata and data sources for information used to estimate country-specific values. Reporting documentation should include the country-specific factors (i.e., means and uncertainties). It is good practice to include a discussion in the inventory report about differences between country-specific factors and Tier 1 defaults as well as Tier 2 factors from regions with similar circumstances as the reporting country. If different emission factors, parameters and methods are used for different years, the reasons for these changes should be explained and documented. In addition, inventory agencies should describe country-specific classifications of management, climate and/or soil types, and it is recommended that improvements in the inventory methods based on the new classifications be documented. For example, tillage management practices may be subdivided into additional categories beyond the Tier 1 classes (i.e., reduced, no-till and full tillage), but further subdivisions will only improve inventory estimates if the stock change or emission factors differ significantly among the new categories. When discussing trends in emissions and removals, a distinction should be made between changes in activity levels and changes in methods from year to year, and the reasons for these changes need to be documented. Tier 3 Tier 3 inventory will need similar documentation about activity data and emission/removal trends as lower tier approaches, but additional documentation should be included to explain the underlying basis and framework of the country-specific estimation system. With measurement-based inventories, it is good practice to document the sampling design, laboratory procedures and data analysis techniques. Measurement data should be archived, along with results from data analyses. For Tier 3 approaches that use models, it is good practice to document the model version and provide a model description, as well as permanently archive copies of all model input files, source code and executable programs.

5.5

METHANE EMISSIONS FROM RICE CULTIVATION

Anaerobic decomposition of organic material in flooded rice fields produces methane (CH4), which escapes to the atmosphere primarily by transport through the rice plants (Takai, 1970; Cicerone and Shetter, 1981; Conrad, 1989; Nouchi et al., 1990). The annual amount of CH4 emitted from a given area of rice is a function of the number and duration of crops grown, water regimes before and during cultivation period, and organic and inorganic soil amendments (Neue and Sass, 1994; Minami, 1995). Soil type, temperature, and rice cultivar also affect CH4 emissions. These new guidelines for computing CH4 emissions incorporate various changes as compared to the 1996 Guidelines and the GPG2000, namely (i) revision of emission and scaling factors derived from updated analysis of available data, (ii) use of daily – instead of seasonal – emission factors to allow more flexibility in separating cropping seasons and fallow periods, (iii) new scaling factors for water regime before the cultivation period and timing of straw incorporation, and (iv) inclusion of Tier 3 approach in line with the general principles of the 2006 revision of guidelines. The revised guidelines also maintain the separate calculation of N2O emission from rice cultivation (as one form of managed soil) which is dealt with in Chapter 11.

5.5.1

Choice of method

The basic equation to estimate CH4 emissions from rice cultivation is shown in Equation 5.1. CH4 emissions are estimated by multiplying daily emission factors by cultivation period3 of rice and annual harvested areas4. In its 3

In the case of a ratoon crop, ‘cultivation period’ should be extended by the respective number of days.

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most simple form, this equation is implemented using national activity data (i.e., national average cultivation period of rice and area harvested) and a single emission factor. However, the natural conditions and agricultural management of rice production may be highly variable within a country. It is good practice to account for this variability by disaggregating national total harvested area into sub-units (e.g., harvested areas under different water regimes). Harvested area for each sub-unit is multiplied by the respective cultivation period and emission factor that is representative of the conditions that define the sub-unit (Sass, 2002). With this disaggregated approach, total annual emissions are equal to the sum of emissions from each sub-unit of harvested area.

EQUATION 5.1 CH4 EMISSIONS FROM RICE CULTIVATION CH 4 Rice =

∑ ( EFi, j , k • ti , j , k • Ai , j , k • 10 −6 )

i, j, k

Where: CH4 Rice

= annual methane emissions from rice cultivation, Gg CH4 yr-1

EFijk

= a daily emission factor for i, j, and k conditions, kg CH4 ha-1 day-1

tijk = cultivation period of rice for i, j, and k conditions, day Aijk = annual harvested area of rice for i, j, and k conditions, ha yr-1 i, j, and k = represent different ecosystems, water regimes, type and amount of organic amendments, and other conditions under which CH4 emissions from rice may vary The different conditions that should be considered include rice ecosystem type, flooding pattern before and during cultivation period, and type and amount of organic amendments. Other conditions such as soil type, and rice cultivar can be considered for the disaggregation if country-specific information about the relationship between these conditions and CH4 emissions are available. The rice ecosystem types and water regimes during cultivation period are listed in Table 5.12. If the national rice production can be subdivided into climatic zones with different production systems (e.g., flooding patterns), Equation 5.1 should be applied to each region separately. The same applies if rice statistics or expert judgments are available to distinguish management practices or other factors along administrative units (district or province). In addition, if more than one crop is harvested during a given year, emissions should be estimated for each cropping season taking into account possible differences in cultivation practice (e.g., use of organic amendments, flooding pattern before and during the cultivation period). The decision tree in Figure 5.2 guides inventory agencies through the process of applying the good practice IPCC approach. Implicit in this decision tree is a hierarchy of disaggregation in implementing the IPCC method. Within this hierarchy, the level of disaggregation utilised by an inventory agency will depend upon the availability of activity and emission factor data, as well as the importance of rice as a contributor to its national greenhouse gas emissions. The specific steps and variables in this decision tree, and the logic behind it, are discussed in the text that follows the decision tree. Tier 1 Tier 1 applies to countries in which either CH4 emissions from rice cultivation are not a key category or countryspecific emission factors do not exist. The disaggregation of the annual harvest area of rice needs to be done for at least three baseline water regimes including irrigated, rainfed, and upland. It is encouraged to incorporate as many of the conditions (i, j, k, etc.) that influence CH4 emissions (summarized in Box 5.2) as possible. Emissions for each sub-unit are adjusted by multiplying a baseline default emission factor (for field with no preseason flooding for less than 180 days prior to rice cultivation and continuously flooded fields without organic amendments, EFc) by various scaling factors as shown in Equation 5.2. The calculations are carried out for each water regime and organic amendment separately as shown in Equation 5.1.

4

In case of multiple cropping during the same year, ‘harvested area’ is equal to the sum of the area cultivated for each cropping.

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BOX 5.2 CONDITIONS INFLUENCING CH4 EMISSIONS FROM RICE CULTIVATION

The following rice cultivation characteristics should be considered in calculating CH4 emissions as well as in developing emission factors: Regional differences in rice cropping practices: If the country is large and has distinct agricultural regions with different climate and/or production systems (e.g., flooding patterns), a separate set of calculations should be performed for each region. Multiple crops: If more than one crop is harvested on a given area of land during the year, and the growing conditions vary among cropping seasons, calculations should be performed for each season. Water regime: In the context of this chapter, water regime is defined as a combination of (i) ecosystem type and (ii) flooding pattern. Ecosystem type: At a minimum, separate calculations should be undertaken for each rice ecosystem (i.e., irrigated, rainfed, and deep water rice production). Flooding pattern: Flooding pattern of rice fields has a significant effect on CH4 emissions (Sass et al., 1992; Yagi et al., 1996; Wassmann et al., 2000). Rice ecosystems can further be distinguished into continuously and intermittently flooded (irrigated rice), and regular rainfed, drought prone, and deep water (rainfed), according to the flooding patterns during the cultivation period. Also, flooding pattern before cultivation period should be considered (Yagi et al., 1998; Cai et al., 2000; 2003a; Fitzgerald et al., 2000). Organic amendments to soils: Organic material incorporated into rice soils increases CH4 emissions (Schütz et al., 1989; Yagi and Minami, 1990; Sass et al., 1991). The impact of organic amendments on CH4 emissions depends on type and amount of the applied material which can be described by a dose response curve (Denier van der Gon and Neue, 1995; Yan et al., 2005). Organic material incorporated into the soil can either be of endogenous (straw, green manure, etc.) or exogenous origin (compost, farmyard manure, etc.). Calculations of emissions should consider the effect of organic amendments. Other conditions: It is known that other factors, such as soil type (Sass et al., 1994; Wassmann et al., 1998; Huang et al., 2002), rice cultivar (Watanabe and Kimura, 1998; Wassmann and Aulakh, 2000), sulphate containing amendments (Lindau et al., 1993; Denier van der Gon and Neue, 2002), etc., can significantly influence CH4 emissions. Inventory agencies are encouraged to make every effort to consider these conditions if country-specific information about the relationship between these conditions and CH4 emissions is available.

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Figure 5.2

Decision tree for CH 4 emissions from rice production

Start

Are there different agroecological zones in the country?

Yes

Calculate emissions for each agro-ecological zone.

Yes

Calculate emissions for each cropping (i.e., dry season-, wet season-, early-, single-, late-cropping).

Yes

Calculate emissions using country-specific methods for higher level of disaggregation as basis for the Tier 3 method.

No Are there multiple rice cropping during the same year? No

Are countryspecific methods, including modelling or direct measurement approach, available?

Box 3: Tier 3 No

Are country-specific emission factors available for different water regime?

Yes

Calculate emissions using the Tier 2 method. Box 2: Tier 2

No Is rice production a key source category1?

Yes

Collect data for Tier 2 or Tier 3 method.

No

Calculate emissions using the Tier 1 default emission factor and scaling factors together with activity data for harvested area and cultivation period. Box 1: Tier 1

Note: 1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

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EQUATION 5.2 ADJUSTED DAILY EMISSION FACTOR EFi = EFc • SFw • SF p • SFo • SFs ,r Where: EFi = adjusted daily emission factor for a particular harvested area EFc = baseline emission factor for continuously flooded fields without organic amendments SFw = scaling factor to account for the differences in water regime during the cultivation period (from Table 5.12) SFp = scaling factor to account for the differences in water regime in the pre-season before the cultivation period (from Table 5.13) SFo = scaling factor should vary for both type and amount of organic amendment applied (from Equation 5.3 and Table 5.14) SFs,r = scaling factor for soil type, rice cultivar, etc., if available

Tier 2 Tier 2 applies the same methodological approach as Tier 1, but country-specific emission factors and/or scaling factors should be used. These country-specific factors are needed to reflect the local impact of the conditions (i, j, k, etc.) that influence CH4 emissions, preferably being developed through collection of field data. As for Tier 1 approach, it is encouraged to implement the method at the most disaggregated level and to incorporate the multitude of conditions (i, j, k, etc.) that influence CH4 emissions. Tier 3 Tier 3 includes models and monitoring networks tailored to address national circumstances of rice cultivation, repeated over time, driven by high-resolution activity data and disaggregated at sub-national level. Models can be empirical or mechanistic, but must in either case be validated with independent observations from country or region-specific studies that cover the range of rice cultivation characteristics (Cai et al., 2003b; Li et al., 2004; Huang et al., 2004). Proper documentation of the validity and completeness of the data, assumptions, equations and models used is therefore critical. Tier 3 methodologies may also take into account inter-annual variability triggered by typhoon damage, drought stress, etc. Ideally, the assessment should be based on recent satellite data.

5.5.2

Choice of emission and scaling factors

Tier 1 A baseline emission factor for no flooded fields for less than 180 days prior to rice cultivation and continuously flooded during the rice cultivation period without organic amendments (EFc) is used as a starting point. The IPCC default for EFc is 1.30 kg CH4 ha-1 day-1 (with error range of 0.80 - 2.20, Table 5.11), estimated by a statistical analysis of available field measurement data (Yan et al., 2005, the data set used in the analysis is available at a web site5). Scaling factors are used to adjust the EFc to account for the various conditions discussed in Box 5.2, which result in adjusted daily emission factors (EFi) for a particular sub-unit of disaggregated harvested area according to Equation 5.2. The most important scaling factors, namely water regime during and before cultivation period and organic amendments, are represented in Tables 5.12, 5.13, and 5.14, respectively, through default values. Country-specific scaling factors should only be used if they are based on well-researched and documented measurement data. It is encouraged to consider soil type, rice cultivar, and other factors if available.

5

http://www.jamstec.go.jp/frcgc/

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Chapter 5: Cropland

TABLE 5.11 BASELINE EMISSION FACTOR ASSUMING NO FLOODING FOR LESS THAN 180 DAYS PRIOR TO RICE CULTIVATION, AND CONTINUOUSLY FLOODED DURING RICE CULTIVATION WITHOUT ORGANIC AMENDMENTS

DEFAULT CH4

-1

Emission factor

Error range

1.30

0.80 - 2.20

-1

CH4 emission (kg CH4 ha d )

Source: Yan et al., 2005

Water regime during the cultivation period (SFw): Table 5.12 provides default scaling factors and error ranges reflecting different water regimes. The aggregated case refers to a situation when activity data are only available for rice ecosystem types, but not for flooding patterns (see Box 5.2). In the disaggregated case, flooding patterns can be distinguished in the form of three subcategories as shown in Table 5.12. It is good practice to collect more disaggregated activity data and apply disaggregated case SFw whenever possible.

TABLE 5.12 DEFAULT CH4 EMISSION SCALING FACTORS FOR WATER REGIMES DURING THE CULTIVATION PERIOD RELATIVE TO CONTINUOUSLY FLOODED FIELDS

Aggregated case Water regime

Scaling factor (SFw)

Error range

Scaling factor (SFw)

Error range

Upland a

0

-

0

-

1

0.79 - 1.26

0.60

0.46 - 0.80

Intermittently flooded – multiple aeration

0.52

0.41 - 0.66

Regular rainfed

0.28

0.21 - 0.37

0.25

0.18 - 0.36

0.31

ND

Continuously flooded Irrigated b

Disaggregated case

Intermittently flooded – single aeration

Rainfed and deep water c

Drought prone

0.78

0.27

0.62 - 0.98

0.21 - 0.34

Deep water ND: not determined a

Fields are never flooded for a significant period of time.

b

Fields are flooded for a significant period of time and water regime is fully controlled. • Continuously flooded: Fields have standing water throughout the rice growing season and may only dry out for harvest (end-season drainage). • Intermittently flooded : Fields have at least one aeration period of more than 3 days during the cropping season. - Single aeration: Fields have a single aeration during the cropping season at any growth stage (except for end-season drainage). - Multiple aeration: Fields have more than one aeration period during the cropping season (except for end-season drainage).

c

Fields are flooded for a significant period of time and water regime depends solely on precipitation. • Regular rainfed: The water level may rise up to 50 cm during the cropping season. • Drought prone: Drought periods occur during every cropping season. • Deep water rice: Floodwater rises to more than 50 cm for a significant period of time during the cropping season.

Note: Other rice ecosystem categories, like swamps and inland, saline or tidal wetlands may be discriminated within each sub-category. Source: Yan et al., 2005

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Water regime before the cultivation period (SFp): Table 5.13 provides default scaling factors for water regime before the cultivation period which can be used when country-specific data are unavailable. This table distinguishes three different water regimes prior to rice cultivation, namely: 1.

Non-flooded pre-season < 180 days, which often occurs under double cropping of rice;

2.

Non-flooded pre-season > 180 days, e.g., single rice crop following a dry fallow period; and

3.

Flooded pre-season in which the minimum flooding interval is set to 30 days; i.e., shorter flooding periods (usually done to prepare the soil for ploughing) will not be included in this category.

When activity data for the pre-season water status are not available, aggregated case factors can be used. It is good practice to collect more disaggregated activity data and apply disaggregated case of SFp. Scaling factors for additional water regimes can be applied if country-specific data are available.

TABLE 5.13 DEFAULT CH4 EMISSION SCALING FACTORS FOR WATER REGIMES BEFORE THE CULTIVATION PERIOD Aggregated case

Water regime prior to rice cultivation (schematic presentation showing flooded periods as shaded)

Scaling factor (SFp)

Error range

Disaggregated case Scaling factor (SFp)

Error range

1

0.88 - 1.14

0.68

0.58 - 0.80

1.90

1.65 - 2.18

< 180 d

Non flooded preseason 180 d

Non flooded preseason >180 d

CROP

1.22

1.07 - 1.40

> 30 d

Flooded preseason (>30 d)a,b

CROP

a

Short pre-season flooding periods of less than 30 d are not considered in selection of SFp

b

For calculation of pre-season emission see below (section on completeness)

Source: Yan et al., 2005

Organic amendments (SFo): It is good practice to develop scaling factors that incorporate information on the type and amount of organic amendment applied (compost, farmyard manure, green manure, and rice straw). On an equal mass basis, more CH4 is emitted from amendments containing higher amounts of easily decomposable carbon and emissions also increase as more of each organic amendment is applied. Equation 5.3 and Table 5.14 present an approach to vary the scaling factor according to the amount of different types of amendment applied. Rice straw is often incorporated into the soil after harvest. In the case of a long fallow after rice straw incorporation, CH4 emissions in the ensuing rice-growing season will be less than the case that rice straw is incorporated just before rice transplanting (Fitzgerald et al., 2000). Therefore, the timing of rice straw application was distinguished. An uncertainty range of 0.54-0.64 can be adopted for the exponent 0.59 in Equation 5.3.

EQUATION 5.3 ADJUSTED CH4 EMISSION SCALING FACTORS FOR ORGANIC AMENDMENTS ⎛ ⎞ SFo = ⎜⎜1 + ∑ ROAi • CFOAi ⎟⎟ i ⎝ ⎠

0.59

Where: SFo = scaling factor for both type and amount of organic amendment applied ROAi = application rate of organic amendment i, in dry weight for straw and fresh weight for others, tonne ha-1 CFOAi = conversion factor for organic amendment i (in terms of its relative effect with respect to straw applied shortly before cultivation) as shown in Table 5.14.

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Chapter 5: Cropland

TABLE 5.14 DEFAULT CONVERSION FACTOR FOR DIFFERENT TYPES OF ORGANIC AMENDMENT Conversion factor (CFOA)

Error range

1

0.97 - 1.04

Straw incorporated long (>30 days) before cultivationa

0.29

0.20 - 0.40

Compost

0.05

0.01 - 0.08

Farm yard manure

0.14

0.07 - 0.20

Green manure

0.50

0.30 - 0.60

Organic amendment Straw incorporated shortly ( 100km2) should be available and will probably be accurate to within 10 percent. Where national database on dams are not available, and other information is used, the flooded land areas retained behind dams will probably have an uncertainty of more than 50 percent, especially for countries with large flooded land areas. Detailed information on the location, type and function of smaller dams may be also difficult to obtain, though statistical inference may be possible based on the size distribution of reservoirs for which data are available. Reservoirs are created for a variety of reasons that influence the availability of data, and, consequently, the uncertainty on surface area is dependent on country specific conditions. Uncertainty in biomass stocks is discussed in Chapters 4, 5 and 6.

7.4

COMPLETENESS, TIME SERIES CONSISTENCY, AND QA/QC

7.4.1

Completeness

Complete greenhouse gas inventories will include estimates of emissions from the two types of managed wetlands as described in Sections 7.2 and 7.3 above, unless these wetland types do not occur on the national territory. As in other land categories, countries are encouraged to monitor the fate of managed wetlands, and avoid doublecounting with lands in other categories. It is good practice to document the extent of reservoir areas. Once peatlands are brought under peat extraction, they remain managed peatlands even after peat extraction activities have ceased, until they are converted to another use. Rewetting of soils, or the return of the water table to predrainage levels, do not change the status of peatlands. See Section 7.5 “Future Methodological Development” for additional discussion of restored peatlands. Countries using advanced methods and data should take care not to report greenhouse gas emissions already accounted for in other AFOLU chapters, or in other Volumes of these guidelines. In particular, wetlands may receive non-point source effluents and sediments with high nutrient contents; organic or inorganic N, and organic C emitted from these wetlands may have already been included in the estimation methodologies for Forest Land or Cropland, or the Waste Sector. When there is evidence of such non-point source of carbon or nitrogen to wetlands, it is good practice to ensure that the associated greenhouse gas emissions are reported under the proper inventory sectors and categories; countries are encouraged to develop, compile or use the available information in order to avoid biased estimates.

7.4.2

Developing a consistent time series

General guidance on consistency in time series can be found in Volume 1, Chapter 5 (Time Series Consistency). The emission estimation method should be applied consistently to every year in the time series, at the same level of spatial disaggregation. Moreover, when country-specific data are used, national inventories agency should use the same measurement protocol (sampling strategy, method, etc.) throughout the time series. If this is not possible, the guidance on interpolation techniques and recalculation in Volume 1, Chapter 5 should be followed. Differences in emissions between inventory years should be explained, e.g., by demonstrating changes in areas of peatlands or flooded lands, by updated emission factors.

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7.4.3

Quality Assurance and Quality Control (QA/QC)

Quality assurance/quality control (QA/QC) procedures should be developed and implemented as outlined in Volume 1, Chapter 6 of this report. The development of additional, category-specific quality control and quality assurance activities may also be applicable (Volume 1, Chapter 6), particularly if higher tier methods are used to quantify emissions from this source category. Where country-specific emission factors are being used, they should be based on high quality experimental data, developed using a rigorous measurement programme, and be adequately documented. It is, at present, not possible to crosscheck emissions estimates from organic soils managed for peat extraction with other measurement methods. However, the inventory agency should ensure that emission estimates undergo quality control by:

•

•

•

cross-referencing reported country-specific emissions factors with default values, and values published in the scientific literature or reported by other countries; checking the accuracy of activity data with data of peat industries and peat production; and assessing the plausibility of estimates against those of other countries with comparable circumstances.

7.4.4

Reporting and Documentation

It is appropriate to document and archive all information required to produce the national emission/removal inventory estimates as outlined in Volume 1, Chapter 8 of these Guidelines.

EMISSION FACTORS The scientific basis of new country-specific emission factors, parameters and models should be fully described and documented. This includes defining the input parameters and describing the process by which the emission factors, parameters and models were derived, as well as describing sources of uncertainties.

ACTIVITY DATA Sources of all activity data used in the calculations (data sources, databases and soil map references) should be recorded, plus (subject to any confidentiality considerations) communication with industry. This documentation should cover the frequency of data collection and estimation, and estimates of accuracy and precision, and reasons for significant changes in emission levels.

TREND ANALYSIS Significant fluctuations in emissions between years should be explained. A distinction should be made between changes in activity levels and changes in emission factors, parameters and methods from year to year, and the reasons for these changes documented. If different emission factors, parameters and methods are used for different years, the reasons for this should be explained and documented.

7.5

FUTURE METHODOLOGICAL DEVELOPMENT

Other types of managed wetlands may emit or sequester significant amounts of greenhouse gases, notably restored or constructed wetlands. Restored wetlands are wetlands which have been drained and perhaps converted to other uses in the past, but have recently been restored back to functioning wetland ecosystems by raising the water table to pre-drainage levels. In recent decades, public, non-profit and other programs in numerous countries have begun to restore former wetlands and construct others from uplands. A primary purpose is to reduce the runoff from agricultural fields and settlements which causes eutrophication, algal blooms, and hypoxic dead zones in lakes, estuaries, and enclosed bays and seas. Other important benefits include reducing flood damage, stabilizing shorelines and river deltas, retarding saltwater seepage, recharging aquifers, and improving wildlife, waterfowl, and fish habitat. Most operational wetland restorations have occurred since 1990. The technical literature describes programs or projects in some 15 countries in North America, Europe, Asia, and Australia and New Zealand, in particular the river deltas. This literature suggests that wetland ecosystems can be restored, but over variable periods of time and with variable resemblance to natural wetland ecosystems. Currently, there is no available compilation of the global area of wetland restoration and construction. The IPCC Special Report on Land Use, Land-Use Change and Forestry estimates that maximum areas available for restoration are in the range of 30 to 250 Mha (Watson et al., 2000).

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Chapter 7: Wetlands

At the time of preparation of these Guidelines, published studies based on observational data are too recent and limited to develop default emission factors for any of the major greenhouse gases--- CO2, CH4 or N2O. Better understanding of the biogeochemical fluxes within drainage basins will be needed to prevent double-counting emissions due to fertilizer application and waste treatment. Hence, the estimation of greenhouse gas emissions and removals from restored or constructed wetlands remains an area for further development. An increase in CH4 emissions is expected to occur upon the rewetting of organic soils. A first approximation of CH4 emissions on rewetted organic soils with a forest cover is from 0 to 60 kg CH4 ha-1 yr-1 in temperate and boreal climates, and from 280 to 1260 kg CH4 ha-1 yr-1 in tropical climates (Bartlett and Harriss, 1993). However, in the short term these emissions may not return to their pre-drainage levels (Tuittila et al., 2000; Komulainen et al, 1998). The effect of non-point nutrient sources to flooded lands (reservoirs) also remains poorly documented. Countries using advanced, domestic approaches should implement cross-sectoral checks, ideally using mass-balance, to ensure that the fate of all carbon and nitrogen released in a watershed is properly accounted for. The lack of observational data from reservoirs in Asia is a notable gap in the data samples used to develop CO2 emission factors for flooded land. It may be possible, in future editions of these guidelines, to incorporate more information from this region.

References SECTION 7.2: PEATLANDS EXTRACTION

MANAGED

OR

BEING

CONVERTED

FOR

PEAT

Alm, J., Saario, S., Nykänen, H., Silvola, J. and Martikainen, P.J. (1999). Winter CO2, CH4, and N2O fluxes on some natural and drained boreal peatlands. Biogeochemistry 44: 163-186. Bartlett, K.B. and Harriss, R.C. (1993). Review and assessment of methane emissions from wetlands. Chemosphere 26:261-320. Canadian Sphagnum Peat Moss Association (2004). Harvesting Peat in Canada http://www.peatmoss.com/ Cicerone, R.J. and Oremland, R.S. (1988). Biogeochemical aspects of atmospheric methane. Global Biogeochemical Cycles 2: 288-327. Cleary, J., Roulet, N.T. and Moore, T.R. (2005). Greenhouse gas emissions from Canadian peat extraction, 1990-2000: A life-cycle analysis. Ambio 34(6):456-461. IPCC (2003). Good Practice Guidance for Land Use, Land-Use Change and Forestry. Penman J., Gytarsky M., Hiraishi T., Krug, T., Kruger D., Pipatti R., Buendia L., Miwa K., Ngara T., Tanabe K., Wagner F. (Eds).Intergovernmental Panel on Climate Change (IPCC), IPCC/IGES, Hayama, Japan. International Peat Society (2004). Environmental Assessment of Peat Production www.peatsociety.fi Joosten, H. (2004). The IMCG Global Peatland Database. http://www.imcg.net/gpd/ Joosten, H. and Clarke, D. (2002). Wise Use of Mires and Peatlands.Internation Mire Conservation Group and International Peat Society, Saarijarvi, Finland, 304 p. Klemedtsson, L., Von Arnold, K., Weslien, P. and Gundersen, P. (2005). Soil CN ratio as a scalar parameter to predict nitrous oxide emissions. Global Change Biology 11:1142-1147 Komulainen, V.-M., Nykänen, H., Martikainen, P.J. and Laine, J. (1998). Short-term effect of restoration on vegetation change and methane emissions from peatlands drained for forestry in Southern Finland. Can. J. For. Res. 28:402-411. Komulainen, V-M., Tuittila, E-S., Vasander, H. and Laine, J. (1999). Restoration of drained peatlands in southern Finland : initial effects on vegetation change and CO2 balance. J. Appl. Ecol. 36:634-648. Laine, J. and Minkkinen, K. (1996). Effect of forest drainage on the carbon balance of a mire--a case study. Scandinavian Journal of Forest Research. 11: 307-312. Laine, J., Silvola, J., Tolonen, K., Alm, J., Nykänen, H., Vasander, H., Sallantaus, T., Savolainen, I., Sinisalo, J. and Martikainen, P.J. (1996). Effect of water-level drawdown on global climatic warming--northern peatlands. Ambio. 25: 179-184. Lappalainen, E. (1996). Global Peat Resources.International Peat Society Saarijarvi, Finland, 368 p. LUSTRA (2002). Land-use Strategies for Reducing Net Greenhouse Gas Emissions. Annual Report 2002 Uppsala, Sweden.162 p.

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Martikainen, P.J., Nykänen, H., Alm, J. and Silvola, J. (1995). Change in fluxes of carbon dioxide, methane, and nitrous oxide due to forest drainage of mire sites of diferent trophy, . Plant and Soil 169: 571-577. Minkkinen, K., Korhonen, R., Savolainen, I. and Laine, J. (2002). Carbon balance and radiative forcing of Finnish peatlands 1990-2100 the impact of forestry drainage. Global Change Biology 8: 785-799. Mitsch, W.J. and Gosselink, J.G. (2000). Wetlands.3rd ed, . Wiley, New York, 920 p. Moore, T.R. and Knowles, R. (1989). The influence of water table levels on methane and carbon dioxide emissions from peatland soils. Canadian Journal of Soil Science 69 (1): p. 33-38. Nilsson, K. and Nillson, M. (2004). The Climate Impact of Energy Peat Utilsation in Sweden--the Effect of Former Land-Use and After Treatment. IVL Swedish Environmental Research Institute. Report B1606. Stockholm, 91 p. Petrone, R.M., Waddington, J.M. and Price, J.S. (2003). Ecosystem-scale flux of CO2 from a restored vacuum harvested peatland. Wetlands Ecology and Management 11:419-432. Ramsar (1996). The Ramsar Convention definition of "wetland" and classification system for wetland type. Appendix A of Strategic framework and guidelines for the future development of the list of wetlands of international Importance of the Convention on Wetlands (Ramsar, Iran, 1971). Available at www.ramsar.org/key_guide_list_e.htm. Regina, K., Nykänen, H., Silvola, J. and Martikainen, P.J. (1996). Fluxes of nitrous oxide from boreal peatlands as affected by peatland type, water table level and nitrification capacity. Biogeochemistry 35: 401-418. Sirin, A and Minayeva, T. eds (2001). Peatlands of Russia: towards the analyses of sectoral information GEOS, Moscow, 190 pp. (in Russian). Strack, M., Waddington, J.M. and Tuittila, E.-S. (2004). Effect of water table drawdown on northern peatland methane dynamics: implications for climate change. Global Biogeochemical Cycles 18, GB4003. Sundh, I., Nilsson, M., Mikkala, C., Granberg, G. and Svensson, B.H. (2000). Fluxes of methane and carbon dioxide on peat-mining areas in Sweden. Ambio. 29: 499-503. US Geological Survey (2004). US Minerals Yearbook. www.usgs.gov/minerals/pubs/commodity/peat Waddington, J.M. and McNeil, P. (2002). Peat oxidation in an abandoned cutover peatland. Can.J.Soil Sci. 82:279-286. Waddington, J.M., Warner, K.D. and Kennedy, G.W. (2002). Cutover peatlands: a persistent source of atmospheric CO2. Global Biogeochemical Cycles 16(1) 10:1029é2001GB001398 Waddington, J.M. and Price, J.S. (2000). Effect of peatland drainage, harvesting, and restoration on atmospheric water and carbon exchange. Physical Geography 21(5):433-451. Watson, R.T., Noble, I.R., Bolin, B., Ravindranath, N.H., Verardo D.J. and Dokken D.J. (Eds.) (2000). Special Report of the IPCC on Land Use, Land-Use Change, and Forestry. Cambridge University Press, UK. pp 375 World Energy Council (2004). http://www.worldenergy.org/wec-geis/publications/reports/ser/peat/peat.asp

SECTION 7.3: FLOODED LAND Bartlett, K.B. and Harriss, R.C. (1993). Review and assessment of methane emissions from wetlands. Chemosphere 26:261-320. International Commission on Large Dams (ICOLD) (1998). World register of Dams 1998. Paris. International Comittee on large Dams (Ed.). Metadatabase. Komulainen, V-M., Tuittila, E-S., Vasander, H. and Laine, J. (1999). Restoration of drained peatlands in southern Finland : initial effects on vegetation change and CO2 balance. J. Appl. Ecol. 36:634-648. Tuittila, E-S., Komulainen, V-M., Vasander, H., Nykänen, H., Martikainen, P.J. and Laine, J. (2000). Methane dynamics of a restored cut-away peatland. Global Change Biology, 6: 569 Watson, R.T., Noble, I.R., Bolin, B., Ravindranath, N.H., Verardo, D.J. and Dokken, D.J. (Eds.) (2000). Special Report of the IPCC on Land Use, Land-Use Change, and Forestry. Cambridge University Press, UK. pp 375 WCD (2000). Dams and Development a new framework for Decision-Making, The report of the World Commission on Dams, Earthscan Publications Ltd, London and Sterling, VA, 356 p.

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Chapter 8: Settlements

CHAPTER 8

SETTLEMENTS

2006 IPCC Guidelines for National Greenhouse Gas Inventories

8.1

Volume 4: Agriculture, Forestry and Other Land Use

Authors Jennifer C. Jenkins (USA), Hector Daniel Ginzo (Argentina), Stephen M. Ogle (USA), and Louis V. Verchot (ICRAF/USA) Mariko Handa (Japan) and Atsushi Tsunekawa (Japan)

8.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Settlements

Contents 8

Settlements 8.1

Introduction ...........................................................................................................................................8.5

8.2

Settlements Remaining Settlements ......................................................................................................8.6

8.2.1 8.2.1.1

Choice of method.....................................................................................................................8.6

8.2.1.2

Choice of emission/removal factors.........................................................................................8.8

8.2.1.3

Choice of activity data ...........................................................................................................8.10

8.2.1.4

Uncertainty assessment..........................................................................................................8.12

8.2.2

Choice of method...................................................................................................................8.12

8.2.2.2

Choice of emission/removal factors.......................................................................................8.13

8.2.2.3

Choice of activity data ...........................................................................................................8.13

8.2.2.4

Uncertainty assessment..........................................................................................................8.14 Soil carbon...................................................................................................................................8.14

8.2.3.1

Choice of method...................................................................................................................8.15

8.2.3.2

Choice of stock change and emission factors ........................................................................8.15

8.2.3.3

Choice of activity data ...........................................................................................................8.16

8.2.3.4

Uncertainty assessment..........................................................................................................8.17

Land Converted to Settlements ...........................................................................................................8.17

8.3.1

Biomass .......................................................................................................................................8.18

8.3.1.1

Choice of method...................................................................................................................8.18

8.3.1.2

Choice of emission/removal factors.......................................................................................8.18

8.3.1.3

Choice of activity data ...........................................................................................................8.19

8.3.1.4

Uncertainty assessment..........................................................................................................8.20

8.3.2

Dead organic matter ....................................................................................................................8.20

8.3.2.1

Choice of method...................................................................................................................8.20

8.3.2.2

Choice of emission/removal factors.......................................................................................8.21

8.3.2.3

Choice of activity data ...........................................................................................................8.21

8.3.2.4

Uncertainty assessment..........................................................................................................8.23

8.3.3

8.4

Dead organic matter ....................................................................................................................8.12

8.2.2.1

8.2.3

8.3

Biomass .........................................................................................................................................8.6

Soil carbon...................................................................................................................................8.23

8.3.3.1

Choice of method...................................................................................................................8.23

8.3.3.2

Choice of stock change and emission factor..........................................................................8.24

8.3.3.3

Choice of activity data ...........................................................................................................8.25

8.3.3.4

Uncertainty assessment..........................................................................................................8.25

Completeness, Time series, QA/QC, and Reporting ...........................................................................8.25

8.4.1

Completeness ..............................................................................................................................8.25

8.4.2

Developing a consistent time series.............................................................................................8.26

2006 IPCC Guidelines for National Greenhouse Gas Inventories

8.3

Volume 4: Agriculture, Forestry and Other Land Use

8.4.3

Inventory Quality Assurance/Quality Control.............................................................................8.26

8.4.4

Reporting and Documentation.....................................................................................................8.26

8.5

Basis for future methodological development.....................................................................................8.27

References

...............................................................................................................................................8.28

Equations Equation 8.1

Annual carbon change in live biomass pools in Settlements Remaining Settlements ...........8.6

Equation 8.2

Annual biomass increment based on total crown cover area .................................................8.7

Equation 8.3

Annual biomass growth based on number of individual woody plants in broad classes .......8.7

Tables

8.4

Table 8.1

Tier 2a default crown cover area-based growth rates (CRW) for urban tree crown cover by region ...................................................................................................8.9

Table 8.2

Tier 2b default average annual carbon accumulation per tree in urban trees by species classes ........................................................................................................8.10

Table 8.3

Default activity data by potential natural vegetation (PNV) type for percent tree cover.....8.11

Table 8.4

Default biomass carbon stocks removed due to land conversion to settlements .................8.19

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Settlements

8 SETTLEMENTS 8.1

INTRODUCTION

This Chapter provides methods for estimating carbon stock changes and greenhouse gas emissions and removals associated with changes in biomass, dead organic matter (DOM), and soil carbon on lands classified as settlements. Settlements are defined in Chapter 3 as including all developed land -- i.e., residential, transportation, commercial, and production (commercial, manufacturing) infrastructure of any size, unless it is already included under other land-use categories. The land-use category Settlements includes soils, herbaceous perennial vegetation such as turf grass and garden plants, trees in rural settlements, homestead gardens and urban areas. Examples of settlements include land along streets, in residential (rural and urban) and commercial lawns, in public and private gardens, in golf courses and athletic fields, and in parks, provided such land is functionally or administratively associated with particular cities, villages or other settlement types and is not accounted for in another land-use category. See Chapter 3 for area reporting guidelines and for definitions of the six land-use categories. Roughly 2% of Earth’s terrestrial surface is covered by urban areas which are home to over 3 billion people. Over half of the world population currently lives in cities; this number is projected to double within 50 years (Crane and Kinzig, 2005). In many regions, land classified as urban, based on population density or city boundaries, is just a subset of land that can be classified as settlements using the criteria described above. These areas of less-dense settlement may extend well beyond the officially-defined border of a city, and in many regions their areas are expanding quickly (Elvidge et al., 2004; Gallo et al., 2004; Theobald, 2004). In areas that are primarily rural, even if land uses are not changing quickly, land devoted to residential uses can occupy a significant portion of the landscape. Transitions of Forest Land, Cropland, and Grassland to Settlements can have important impacts on carbon stocks and fluxes (Imhoff et al., 2000; Milesi et al., 2003). Vegetation management in settlements may result in gains, losses, or transfers of carbon amongst the relevant pools. For example, branches removed during pruning or turfgrass clippings (biomass losses) may be left on site (transfer to litter), disposed of as solid waste (transfer to waste), or burned (emitted). Emissions of the relevant greenhouse gases are accounted for in the appropriate sections of the present guidance. For example, Table 2.3 in Chapter 2, Volume 5 (Waste), includes wood/ yard waste in national-scale statistics describing the fate of municipal solid waste at the national scale. Biomass removed as fuelwood from trees in settlements and used as fuel is accounted for in the Energy Sector. The net effect of conversion or management leading to increment, on the one hand, or to loss (such as from burning and decay), on the other, determines the overall C balance in settlements. Soils and DOM in Settlements Remaining Settlements or in Land Converted to Settlements may be sources or sinks of CO2, depending on previous land use, topsoil burial or removal during development, current management, particularly with respect to nutrient and water applications, and the type and amount of vegetation cover interspersed among roads, buildings and associated infrastructure (Goldman et al., 1995; Jo, 2002; Pouyat et al., 2002; Qian and Follett, 2002; Kaye et al., 2004; Kaye et al., 2005). The 1996 IPCC Guidelines covered above-ground biomass in trees in rural settlements, but not other settlement categories and pools. The 2006 IPCC Guidelines differ from those in the GPG-LULUCF as follows: •

• •

• •

The discussion and detailed methodologies have been moved from the Appendix to the main text and considered as greenhouse gas emission source or removal sector; The discussion and methodologies have been expanded to include the five biomass pools described in Chapter 1; Tier 1 default methodologies are presented; Additional data appropriate for Tiers 2 and 3 have been published since the GPG-LULUCF and are included here; and An expanded discussion on developing and applying country-specific Tier 2 and Tier 3 methodologies and values is included, including methods to work with more detailed activity data.

The carbon pools estimated for Settlements are above-ground and below-ground biomass, DOM, and soils. Sections 8.2 and 8.3 respectively describe methodology to estimate changes in carbon stocks for Settlements Remaining Settlements, and to estimate carbon stocks on Land Converted to Settlements. The methodology in the second section is broadly applicable to Land Converted to Settlements from any other type of land.

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8.2

SETTLEMENTS REMAINING SETTLEMENTS

This category refers to all classes of urban formations that have been in use as settlements (e.g., areas that are functionally or administratively associated with public or private land in cities, villages, or other settlement types), since the last time data were collected. Emissions and removals of CO2 in this category are estimated by the subcategories of changes in carbon stocks in biomass (both woody and perennial non-woody components), in DOM, and in soils, as summarized in Equation 2.3 in Chapter 2. The biomass pool in settlements has woody and herbaceous components. For woody biomass, carbon stock change is calculated as the difference between biomass increment and biomass loss due to management activities. For herbaceous biomass (such as turfgrass or garden plants) in Settlements Remaining Settlements, the carbon stock change in biomass can usually be assumed to be zero. The DOM pool in settlements contains dead wood and litter from both woody and herbaceous components. For the woody vegetation, changes in this pool can be quantified as production of coarse and fine litter from woody plants. For herbaceous vegetation, annual production of DOM is estimated as the accumulation of thatch plus production of herbaceous material such as garden waste and yard trimmings. Greenhouse gas emissions associated with Waste Sector are estimated in Volume 5 (Waste) and therefore the methods in this Chapter describe only those components of annual production that can reasonably be expected to stay on-site. Soil C pools vary with time depending on the balance between C inputs from plant litter and other forms of organic matter and C outputs resulting from decomposition, erosion and leaching. Estimating the impact of settlement management on soil C storage will be particularly important in countries with a large portion of land in cities and towns, or high rates of settlement expansion. For mineral soils, the impact of settlement land use and management on soil C stocks can be estimated based on differences in storage among settlement cover classes relative to a reference condition, such as native lands. Although organic soils are less commonly used for settlements, C is emitted from these soils if they are drained for development due to enhanced decomposition, similar to the effect of drainage for agricultural purposes (Armentano, 1986). In addition, peat may be harvested from organic soils during settlement development, which will also generate emissions to the atmosphere.

8.2.1 8.2.1.1

Biomass C HOICE

OF METHOD

The general method for biomass carbon stock change in Settlements Remaining Settlements follows the approach in Equation 2.7 in Chapter 2. This method estimates changes in biomass carbon stocks, accounting for gains in carbon stocks in biomass as a result of growth minus losses in carbon stocks as a result of pruning and mortality. Depending on the relative magnitudes of the increment and loss terms, the average annual changes in biomass carbon stocks in settlements may be positive or negative. Biomass change in Settlements Remaining Settlements is the sum of biomass change in three components: trees, shrubs, and herbaceous perennials (e.g., turfgrass and garden plants), as described in Equation 8.1. EQUATION 8.1 ANNUAL CARBON CHANGE IN LIVE BIOMASS POOLS IN SETTLEMENTS REMAINING SETTLEMENTS ΔC B = ΔCTrees + ΔC Shrubs + ΔC Herbs Where: ΔCB = annual carbon accumulation attributed to biomass increment in Settlements Remaining Settlements, tonnes C yr-1 ΔCTrees = annual carbon accumulation attributed to biomass increment in trees in Settlements Remaining Settlements, tonnes C yr-1 ΔCShrubs = annual carbon accumulation attributed to biomass increment in shrubs in Settlements Remaining Settlements, tonnes C yr-1 ΔCHerbs = annual carbon accumulation attributed to biomass increment in herbaceous biomass in Settlements Remaining Settlements, tonnes C yr-1 Depending on the availability of relevant activity data and appropriate emission factors, any of the methodological tiers described below can be used. Figure 2.2 in Chapter 2 also provides guidance for the identification of the appropriate tier to estimate changes in carbon in biomass.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Settlements

Tier 1 Tier 1 assumes no change in carbon stocks in live biomass in Settlements Remaining Settlements, in other words, that the growth and loss terms balance. If the category Settlements Remaining Settlements is determined to be a key category, then a country should collect appropriate activity data and/or develop emission factors appropriate to the region and adopt Tier 2 or 3. Tier 2 There are two options for Tier 2 estimation of changes in biomass in Settlements Remaining Settlements. Tier 2a uses changes in carbon stocks per unit of plant crown cover area as a removal factor, and Tier 2b uses changes in carbon stocks per number of plants as a removal factor. The choice of method will depend on availability of activity data. Tier 2a and Tier 2b both provide methods for estimating ΔCG in Equation 2.7 (Gain-Loss Method). This is appropriate for countries lacking a continuous inventory in Settlements Remaining Settlements. The main perennial types are trees, shrubs, and herbaceous perennials (such as turfgrass and garden plants). The methods as presented here set the change in biomass of herbaceous annuals to zero in Settlements Remaining Settlements on the basis that growth of herbaceous biomass (whether perennial or annual herbaceous vegetation) is equal to loss from harvest or mortality. Countries may choose to define tree and woody perennial types as appropriate and each types may be further divided into classes defined according to species, climate zone, seasonality, or other criteria as appropriate and if data are available. Tie r 2a : C ro wn cov e r a rea m ethod This method is represented by Equation 8.2 and should be used when data are available on total area of crown cover in perennial types (j) and their classes (i) in Settlements Remaining Settlements. EQUATION 8.2 ANNUAL BIOMASS INCREMENT BASED ON TOTAL CROWN COVER AREA ΔCG = ∑ ATi , j • CRWi , j i, j

Where:

ΔCG = annual carbon accumulation attributed to biomass increment in Settlements Remaining Settlements, tonnes C yr-1 ATij = total crown cover area of class i in woody perennial1 type j, ha CRWij = crown cover area-based growth rate of class i in woody perennial type j, tonnes C (ha crown cover)-1 yr-1

Tie r 2 b: Ind iv idua l p lan t 2 gro wth met hod The method is represented by Equation 8.3 and should be used where data on the number of woody plants by broad species class in Settlements Remaining Settlements are available. It is possible, when making estimates for trees, to convert between the methods used in Tiers 2a and 2b by assuming that an individual tree in an urban area covers approximately 50 m2 crown area when mature (cf. Akbari, 2002). EQUATION 8.3 ANNUAL BIOMASS GROWTH BASED ON NUMBER OF INDIVIDUAL WOODY PLANTS IN BROAD ΔCG = ∑ NTi , j • Ci , j CLASSES i, j

Where:

ΔCG = annual carbon accumulation due to live biomass increment in Settlements Remaining Settlements, tonnes C yr-1 NTij = number of individuals of class i in perennial type j Cij = annual average carbon accumulation per class i of perennial type j, tonnes C yr-1 per individual

1

References to woody perennials include trees unless otherwise specified.

2

References to plants include trees unless otherwise specified.

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Tier 3 Tier 3 approaches can be based either on Tier 2 methods above (Equations 8.2 and 8.3) with more detailed measurements of parameters at disaggregated level for different settlement systems such as parks, rural or urban residential areas, avenues, etc., or on a stock difference approach based on Equation 2.8. Changes in carbon stocks are estimated at two points in time, where the changes account for biomass carbon gains and losses. The generic approach for this method calls for forest-specific biomass expansion factors (BEFs) which do not apply to settlements. Countries wishing to use the stock difference method to estimate biomass change in Settlements Remaining Settlements should consider using allometric methods such as those based on individual tree diameter at breast height (dbh) (Jenkins et al., 2004), adjusted for open-grown trees as described above, rather than forest specific BEFs for estimating tree biomass.

8.2.1.2

C HOICE

OF EMISSION / REMOVAL FACTORS

Few allometric biomass equations exist specifically for trees or shrubs in urban settings (Nowak, 1996; Jo, 2002) so investigators have tended to apply equations derived for forest trees, adjusting the resulting biomass with a coefficient (such as 0.80 [Nowak, 1994; Nowak and Crane, 2002]) intended to take account of the allometry of open-grown trees in cities where above-ground biomass for a given diameter is typically lower than that of forest-grown trees (Nowak, 1996). Allometric equations for some shrub species exist, but have not routinely been applied to urban settings (Smith and Brand, 1983; Nowak et al., 2002 for shrub leaf biomass estimates). Below-ground tree biomass can be derived from above-ground biomass by multiplying the latter by an estimated root: shoot ratio, as described by Cairns et al. (1997) and applied for urban settings by Nowak et al. (2002). See Chapter 4 (Forest Land) for examples of root: shoot ratios (R) (also called below-ground to above-ground biomass ratio) often used in forest settings. Ratios appropriate to the region of interest can be assumed to apply without modification to settlements. Tree growth and mortality in settlements can be affected by urban conditions such as variations in local air quality, atmospheric deposition, enhanced atmospheric CO2 concentrations, and reduced air exchange in the root zone due to impermeable paving surfaces (e.g., Pouyat et al., 1995; Idso et al., 1998; Idso et al., 2001; Gregg et al., 2003; Pouyat and Carreiro, 2003). Therefore, the values and equations used to predict tree growth in settlements at higher tiers should, to the extent feasible, allow for the surrounding environment and the condition of the trees. Carbon stored in the woody components of trees makes up the largest compartment of standing biomass stocks and annual biomass increment in settlements. Data are still sparse, though availability is increasing. For example, Nowak and Crane (2002) estimated on a citywide basis that the net annual carbon storage by trees in cities in the conterminous USA ranged from 600 to 32,200 tonnes C yr-1. Jo (2002) found that the amount of C sequestered annually in three Korean cities varied from 2,900 to 40,300 tonnes. In Australia, Brack (2002) estimated that the amount of C sequestered by trees in Canberra between 2008 and 2012 would be 6,000 tonnes C yr-1. Clearly, the estimates depend on the definition and hence extent of the settlement areas being considered. The variation is less per unit land area; for ten cities in the United States, measurements of C stored in woody biomass ranged from 150 to 940 kg C ha-1 yr-1 (Nowak and Crane, 2002) and for three Korean cities annual C stored in woody biomass varied from 530 to 800 kg C ha-1 yr-1 (Jo, 2002). Trees in urban lawns in Colorado (USA) stored 1,590 kg C ha-1 yr-1 (Kaye et al., 2005). There is still less variation in estimates of annual C storage per unit of tree crown cover. Nowak and Crane (2002) found that annual sequestration rates ranged from 0.12 to 0.26 kg C m-2 crown cover yr-1, while Brack (2002) used a model to estimate that annual sequestration in Canberra between 2008 and 2012 would be 0.27 kg C m-2 yr-1. Tier 1 This method assumes, probably conservatively, that changes in biomass carbon stocks due to growth in biomass are fully offset by decreases in carbon stocks due to removals (i.e., by harvest, pruning, clipping) from both living and from dead biomass (e.g., fuelwood, broken branches, etc.). Therefore, in a Tier 1 approach ΔCG = ΔCL and for all plant components, and ΔCB = 0 in Equation 2.7.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Settlements

Tier 2 Trees Tier 2 calls for parameter values for CRWij (Equation 8.2) and Cij (Equation 8.3). A default removal factor for tree biomass (CRW) of 2.9 tonnes C (ha crown cover)-1 yr -1 is usually suitable for Tier 2a (see Table 8.1). This estimate is based on a sample of ten US cities, with values that ranged from 1.8 to 3.4 tonnes C (ha crown cover)1 yr-1 (Nowak and Crane, 2002). Values appropriate to national circumstances can also be developed. Using Tier 2b, the removal factor is Cij. Table 8.2 provides defaults carbon accumulation rates for tree species classes for use at Tier 2b. These estimates are based on various allometric equations and limited field data from urban areas in the USA, and are averages for trees of all sizes (not just mature trees). Tiers 2a and 2b methods provide biomass estimates for total combined above-ground and below-ground woody biomass. If required belowground biomass can be estimated separately using a root: shoot ratio of 0.26 (Nowak et al., 2002). For Tiers 2a and 2b, the default assumption for ΔCL where the average age of the tree population is less than or equal to 20 years is zero. This is based on the assumption that urban trees are net sinks for carbon when they are actively growing and that the active growing period (AGP) is roughly 20 years, depending on tree species, planting density, and location. Thereafter, the method assumes that the accumulation of carbon in biomass slows with age, and thus for trees older than the AGP, increases in biomass carbon are assumed to be offset by losses from pruning and mortality. For trees older than the AGP this is conservatively accounted for by setting ΔCGwood = ΔCLwood. Countries can define AGP depending on their circumstances.

Other woody perennial types Countries may, for any perennial type, develop their own values for CRWij (in Equation 8.2) and Cij (in Equation 8.3). A conservative assumption of no change in any of these components (i.e., CRWij = 0 and Cij = 0) can also be applied.

Tiers 2a and 2b both assume no change in herbaceous biomass. Using this method, ΔCGHerbs = ΔCLHerbs and ΔCB is based on the difference between increment and losses in woody biomass only.

Tier 3 For Tier 3, countries should develop plant type-specific biomass increment factors appropriate to national circumstances. Country-specific parameters and growth equations should be based on the dominant climate zones and particular species composition of the major settlements areas in a country, before making estimates for less extensive settlements. If country-specific biomass increment parameters are developed from estimates of biomass on a dry matter basis, they need conversion to units of carbon using either a default carbon fraction (CF) of 0.5 tonne C (tonne d.m.)-1, or a carbon fraction that is more appropriate to circumstances.

Under higher tiers, the assumptions for ΔCL should be evaluated and modified to address national circumstances better. For instance, countries may have information on age-dependent and/ or species-specific carbon losses in settlement trees. In this case, countries should develop a loss term and document the resources and rationale used in its development. If a country adopts the stock-difference method (Equation 2.8), it should have representative sampling and periodic measurement system to estimate the changes in biomass carbon stocks.

TABLE 8.1 TIER 2A DEFAULT CROWN COVER AREA-BASED GROWTH RATES (CRW) FOR URBAN TREE CROWN COVER BY REGION Region

Default annual carbon accumulation per ha tree crown cover [tonnes C (ha crown cover)-1 yr-1]

United States (global default)

2.9 a

Australia

3.6 b

a

Nowak and Crane 2002; average of 10 US cities.

b

Brack 2002; modelling analysis in Canberra.

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TABLE 8.2 TIER 2B DEFAULT AVERAGE ANNUAL CARBON ACCUMULATION PER TREE IN URBAN TREES BY SPECIES CLASSES

Default annual carbon accumulation per tree (tonnes C yr-1)

Broad species class Aspen

0.0096

Soft maple

0.0118

Mixed hardwood

0.0100

Hardwood maple

0.0142

Juniper

0.0033

Cedar/larch

0.0072

Douglas fir

0.0122

True fir/Hemlock

0.0104

Pine

0.0087

Spruce

0.0092

Source: D. Nowak (2002; personal communication)

8.2.1.3

C HOICE

OF ACTIVITY DATA

Tier 1 No activity data are needed. Tier 2 The activity data needed to implement a Tier 2 method are either ATij, area of crown cover for each class within a perennial type (Equation 8.2), or NTij, number of individual plants in each class within a perennial type (Equation 8.3). Crown cover is defined as the percent of ground covered by a vertical projection of the outermost perimeter of the natural spread of the foliage. For Tier 2a, crown cover area data (ATij) can be obtained from aerial photographs of urban areas, provided expertise in photo interpretation, image sampling and area measurement (Nowak et al., 1996) are available. Values in percent crown cover should be converted to total crown cover area for use in Equation 8.2 by multiplying the percent crown cover by the total area of the plants (trees or shrubs) within the outermost perimeter. If data are not available to determine percent crown cover, then default activity data can be used. This approach takes advantage of the fact that settlements found in different biomes, as defined by different potential natural vegetation or PNV (Kuchler, 1969), have been found to have similar values for percent tree cover, total greenspace, and canopy greenspace (Nowak et al., 1996) (Table 8.3). Settlements found in regions where the PNV is forest, for example, have substantially higher percent tree cover values than do settlements found in regions where the PNV is desert (Table 8.3). In Table 8.3, percent total greenspace is the proportion of land area covered by vegetation or soil (i.e., not impervious surfaces or water), and canopy greenspace is the proportion of that greenspace filled with tree canopies (calculated as percent canopy cover/ percent total greenspace). The default data on percent tree crown cover should be multiplied by the settlement area and used with the default growth rates in Table 8.1, in a simplified version of Equation 8.2, to estimate the annual carbon accumulation in the tree perennial type. Data on percent total greenspace and percent canopy greenspace in Table 8.3 are not needed for a Tier 2 approach to estimate biomass carbon stocks, but may be useful for cross-checking. For Tier 2b, records of plant populations, disaggregated into species or broad species classes, may be obtained from municipal agencies caring for urban vegetation, or from sampling methods. Tier 3 Under Tier 3, the type of activity data to be collected depends on the methodological approaches used. If the stock-difference method is used, then it is necessary to disaggregate and estimate area under different types of vegetation types (parks, rural or urban settlements, avenues, playgrounds, etc) using remote sensing techniques in different climate or economic development indicators. The higher the tier to be used, the more disaggregated will be the activity data, and the more precise the estimation methods. The area sampling methods described in Chapter 3, Annex 3A.3 can be used for this.

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Chapter 8: Settlements

TABLE 8.3 DEFAULT ACTIVITY DATA BY POTENTIAL NATURAL VEGETATION (PNV) TYPE (KUCHLER, 1969) FOR PERCENT TREE COVER

Potential Natural Vegetation (PNV)

Percent tree cover (+ S.E.)

Percent total greenspace (+ S.E.)

Percent canopy greenspace (+ S.E.)

Forest

31.1 (+ 2.6)

58.4 (+ 2.9)

50.9 (+ 3.3)

Grassland

18.9 (+ 1.5)

54.8 (+ 2.1)

32.9 (+ 2.3)

Desert

9.9 (+ 2.4)

64.8 (+ 4.2)

16.9 (+ 4.6)

Source: Nowak et al. (1996)

STEP-BY-STEP SUMMARY OF METHOD FOR ESTIMATING CHANGES IN BIOMASS STOCKS Tier 1 The Tier 1 methodology assumes no change in biomass carbon stocks in Settlements Remaining Settlements. Tier 2 Method A: Crown cover area method Step 1: Define the total crown cover area in each of the woody perennial types in the settlement. If data are not available for all types, the method may be applied for trees only, setting the area in other perennial types to zero. Default activity data for tree cover can be applied using Table 8.3. To estimate total tree crown cover for a settlement falling in a region where the PNV is grassland, for example, multiply the total land area in the settlement by the 18.9%, which is the average percent tree cover for settlements found in areas where the PNV is grassland, from Table 8.3. The total crown cover area of all vegetation (including trees) is calculated as (total greenspace area = percent greenspace x settlement area) and the aggregated crown cover of the other perennial vegetation types is the difference between the total greenspace area and the tree crown cover area. Step 2: Calculate ΔCG for each of the perennial types, using Equation 8.2. The tree crown cover area value obtained in Step 1 should be used for the tree perennial type. Countries may apply a default value of CRW for trees from Table 8.1; should develop and apply their own values for CRWij. Default values are available only for CRW for the tree component of vegetation. If CRW values for other perennial types do not exist and cannot be developed, or if activity data for these types do not exist these parameters may be set to zero, and the tree component of biomass growth only be estimated. Step 3: Calculate ΔCL for plant components, to be used in Equation 2.7 in Chapter 2. For the tree component of vegetation, it is good practice to set this value equal to zero where the average age of the tree population is less than or equal to the active growing period (AGP; see Section 8.2.1.2). If the average age of trees is greater than the AGP, then either assume ΔCG = ΔCL or use situation-specific data. In the absence of data to the contrary, assume that ΔCG = ΔCL for shrubs and herbaceous plants. Step 4: Use the values obtained for ΔCG and ΔCL in Equation 2.7 in Chapter 2 to quantify the total change in biomass carbon in Settlements Remaining Settlements.

Method B: Individual plant growth method Step 1: Estimate the number of plants in Settlements Remaining Settlements for each perennial type (e.g., trees, shrubs, and herbaceous plants). If data are not available for all of the perennial types, a minimum approach is to use data for trees only, setting the number of plants in other perennial types to zero. There are no default activity data for this method. Step 2: Using Equation 8.3, multiply each estimate by the appropriate rate of carbon increment per plant (Cij,) to obtain the amount of carbon sequestered annually. Default Cij values for trees can be found in Table 8.2; there are no default values for shrubs or herbaceous species. Countries may choose to apply their own values if appropriate, or set the missing values to zero and produce estimates for trees only. Step 3: As in Equation 8.2, sum the amount of carbon sequestered, ΔCG, by each perennial type over all classes present in Settlements Remaining Settlements.

Step 4: Use the estimate of ΔCG in Equation 2.7 in Chapter 2 to estimate the annual change in carbon stock in biomass. For trees, set ΔCL = 0 if the average age of the tree population is less than or equal to the active growing period (AGP); if average age of trees is greater than the AGP (Section 8.2.1.2), then either assume ΔCG = ΔCL or use situation-specific data.

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Tier 3 A Tier 3 approach requires more detailed information than the Tier 2 approach, such as: •

•

accounting for different land uses within settlements (residential, recreational, industrial, etc.);

•

fate of pruned and dead wood and other biomass transferred to the DOM pool; and

•

detailed estimates and models for growth and longevity of the most important plant species;

other items as appropriate for national circumstances.

8.2.1.4

U NCERTAINTY

ASSESSMENT

Tier 1 Assessment of uncertainty is not required, because the change in living biomass is set to zero. Tier 2 and Tier 3 The overall uncertainty of any estimate of the change in the carbon stock of living biomass will be a combination of the individual uncertainties of its component terms. These will be influenced by the heterogeneity between and within land-use urban types, and also by the intensity and frequency of the stewardship of plants in both public and private spaces. The uncertainty is likely to be high since there is limited experience in measuring carbon stock change in urban and rural settlements. The few studies done on the CO2 sink capacity of cities differ in methodology and scope, but the overall relative uncertainty of the estimate of changes in carbon stocks is unlikely to be less than 30-50% about the mean.

8.2.2

Dead organic matter

Most of the changes in carbon stocks associated with dead organic matter (DOM) will be associated with changes in tree cover in settlements. Methods are provided for two types of DOM pools: 1) dead wood and 2) litter. Chapter 1of this volume provides detailed definitions of these pools. Dead wood is a diverse pool with many practical problems for measuring in the field and associated uncertainties about rates of transfer to litter, soil, or emissions to the atmosphere. Amounts of dead wood depend on the time of last disturbance, the amount of input (mortality) at the time of the disturbance, natural mortality rates, decay rates, and management. Litter accumulation is a function of the annual amount of litterfall, which includes all leaves, twigs and small branches, fruits, flowers, and bark, minus the annual rate of decomposition. The litter mass is influenced by the time since the last disturbance, and the type of disturbance. Management such as wood and grass collection, burning, and grazing dramatically alter litter properties, but there are few studies clearly documenting the effects. In herbaceous perennial turfgrass communities, thatch accumulates in a thin layer at the soil surface. The depth of this layer depends on the balance between accumulation (grass production) and decomposition, which varies substantially with climate and management regime. While the function of this layer has been recognized (Raturi et al., 2004), there are so far no published data on the overall impact of carbon accumulation in this DOM pool on landscape-level. As a result, these Guidelines acknowledge the potential importance of thatch in the DOM in settlements but assume that inputs equal outputs so that the net change in carbon stock is zero. No studies have yet been published on the accumulation rate of dead wood in settlements, though some studies have described the production of leaf litter in settlements (cf. Jo and McPherson, 1995). In the only measured data on this component of carbon flux, Kaye et al. (2005) found that leaf and shrub litter in residential lawns in Colorado (USA) totalled 49 g C m-2 yr-1, or roughly 13% of total above-ground productivity (383 g C m-2 yr-1). Since the rate of soil respiration in settlements is typically quite high compared to native landscapes (Koerner and Klopatek, 2002; Kaye et al., 2005), it is likely that fine litterfall decays quickly. The conservative approach, therefore, is to set the accumulation rate of the litter component of DOM to zero.

8.2.2.1

C HOICE

OF METHOD

Estimation of changes in carbon stocks in DOM requires an estimate of changes in stocks of dead wood and changes in litter stocks (refer to Equation 2.17 of Chapter 2). Each of the DOM pools is treated separately, but the method for determining changes in each pool is the same. The decision tree in Chapter 2, Figure 2.3 helps select the appropriate tier.

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Chapter 8: Settlements

Tier 1 Tier 1 assumes that the dead wood and litter stocks are at equilibrium, and so there is no need to estimate the carbon stock changes for these pools. Countries experiencing significant changes in tree cover in settlements are encouraged to develop national data to quantify this change and report it under Tier 2 or 3 methodologies. T i e r s 2 a nd 3 Tiers 2 and 3 allow for calculation of changes in dead wood and litter carbon due to changes in tree cover. Two methods are suggested for estimating associated carbon stock changes. Method 1 (Also called the Gain-Loss Method, Equation 2.18 in Chapter 2): This involves estimating the area of settlement categories and the average annual transfer into and out of dead wood and litter stocks. It requires an estimate of area under Settlements Remaining Settlements according to different climate or ecological zones or settlement types, disturbance regime, management regime, or other factors significantly affecting dead wood and litter carbon pools. It also requires the quantity of biomass transferred into dead wood and litter stocks as well as the quantity of biomass transferred out of the dead wood and litter stocks on per hectare basis according to different settlement types. Method 2 (Also called the Stock-Difference Method, Equation 2.19 in Chapter 2): This method involves estimating the area of settlements and the dead wood and litter stocks at two periods of time, t1 and t2. The dead wood and litter stock changes for the inventory year are obtained by dividing the stock changes by the period (years) between two measurements. The Stock-Difference Method is feasible for countries, which have periodic inventories in settlements. This method is more suitable for countries adopting Tier 3 methods. Tier 3 methods are used where countries have country-specific emission factors, and substantial national data. Country-defined methodology may be based on detailed inventories of permanent sample plots for their settlements and/or models.

8.2.2.2

C HOICE

OF EMISSION / REMOVAL FACTORS

Carbon fraction: The carbon fraction of dead wood and litter is variable, particularly for litter, and depends on the stage of decomposition. A value of 0.50 tonne C (tonne d.m.)-1 can be used as a default in both cases. Tier 1 Emission factors are unnecessary. Tier 2 It is good practice to use country-level data on DOM for different settlement categories, in combination with default values, if country or regional values are not available for some settlement categories. Country-specific values for the transfer of carbon from live trees and grasses that are harvested to harvest residues and decomposition (in the case of the Gain-Loss Method), or the net change in DOM pools (in the case of the StockDifference Method), can be derived taking into account domestic expansion factors, settlement types, the rate of biomass utilization, mortality, management and harvesting practices, and the amount of damaged vegetation during management and harvesting operations. Tier 3 Countries should develop their own methodologies and parameters for estimating changes in DOM. These methodologies may be derived from Methods 1 or 2 specified above, or may be based on other modelling or sampling approaches (see sampling methods set out in Chapter 3, Annex 3A.3).

8.2.2.3

C HOICE

OF ACTIVITY DATA

Activity data consist of areas of Settlements Remaining Settlements summarised by major settlement types. Total settlement areas should be consistent with those reported under other sections of this chapter, notably under the biomass section of Settlements Remaining Settlements. The assessment of changes in DOM will be greatly facilitated if this information can be used in conjunction with national soils and climate data, vegetation inventories, and other geophysical data.

Step-by-step summary of method for estimating changes in DOM carbon stocks Tier 1 Tier 1 assumes DOM inputs and outputs are equal so there are no net annual changes in dead wood or litter carbon stocks and no further assessment is needed. Tier 2 or Tier 3 (M ethod 1, Ga in-Loss M ethod) Each of the DOM pools (dead wood and litter) is to be treated separately, but the method for each pool is the same.

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Step 1: Determine the categories to be used in this assessment and the representative area. The category consists of definitions of the type of settlements. Area data should be obtained using the methods described in Chapter 3. Step 2: Identify values from inventories or scientific studies for the average inputs and outputs of dead wood or litter for each category. No default factors exist for inputs and outputs from these pools, so countries should use locally available data. Calculate the net change in the DOM pools by subtracting the outputs from the inputs. Negative values indicate a net decrease in the stock (Equation 2.18). Step 3: Determine the net change in DOM carbon stocks for each category. Multiply the change in DOM stocks by the carbon fraction of the dead wood or litter to determine the net change in dead wood and litter carbon stocks. Step 4: Determine the total change in the DOM carbon pools for each category by multiplying the representative area of each category by the net change in DOM carbon stocks for that category. Step 5: Determine the total change in carbon stocks in DOM by taking the sum of the total changes in DOM across all categories.

Tier 2 or Tier 3 (M ethod 2, S t o c k - D if f e re n c e M et h o d ) Each of the DOM pools is to be treated separately, but the method for each pool is the same. Step 1: Determine the settlement categories and area as described in Step 1 above. Step 2: From the inventory data, identify the inventory time interval, the average stock of DOM at the initial inventory (t1), and the average stock of DOM at the final inventory (t2). Use these figures to calculate the net annual change in DOM stocks by subtracting the DOM stock at t1 from the DOM stock at t2 and dividing this difference by the time interval. A negative value indicates a decrease in the DOM stock (Equation 2.19). Step 3: Determine the net change in DOM carbon stocks for each category. Determine the net change in DOM carbon stocks by multiplying the net change in DOM stocks for each category by the carbon fraction of the DOM. Step 4: Determine the total change in the DOM carbon pool for each activity category by multiplying the representative area of each activity category by the net change in DOM carbon stocks for that category. Step 5: Determine the total change in carbon stocks in DOM by taking the sum of the total changes in DOM across all activity categories.

8.2.2.4

U NCERTAINTY

ASSESSMENT

There is no need to estimate uncertainty under Tier 1, since DOM pools are assumed to be stable. For Tiers 2 and 3 estimates, sources of uncertainty include the degree of accuracy in land area estimates, carbon increment and loss, carbon stocks, and expansion factor terms. Area data and estimates of uncertainty should be obtained using the methods in Chapter 3 which provides default uncertainties associated with the different approaches. Uncertainties associated with carbon stocks and other parameter values are likely to be at least a factor of three unless country-specific data are available from well designed surveys.

8.2.3

Soil carbon

Soils in settlements may be sources or sinks of CO2 depending on previous land use, soil burial or collection during development, and current management, particularly with respect to nutrient and water applications in addition to the type and amount of vegetation cover interspersed among roads, buildings and associated infrastructure (Goldman et al., 1995; Pouyat et al., 2002; Jo, 2002; Qian and Follett, 2002; Kaye et al., 2004). Only a few studies have been conducted at the time of writing that evaluate the effect of settlement management on soil C, and most of the focus has been on North America (e.g., Pouyat et al., 2002), making it difficult to generalize. For example, there are likely to be large differences that have not been well studied between settlements in developed countries and developing countries. Estimating the impact of settlement management on soil C storage will be particularly important in countries with a large portion of land in cities and towns, or high rates of settlement expansion. For mineral soils, the impact of settlement land use and management on soil C stocks can be estimated based on differences in storage among settlement management classes relative to a reference condition, such as other managed land uses, or native lands. Settlement management classes could include turf grass (e.g., lawns and golf courses), urban woodlands, gardens, refuse areas (e.g., garbage dumps), barren areas (exposed soil), and infrastructure (e.g.,

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Chapter 8: Settlements

roadways, houses, and buildings). Although organic soils are less commonly used for settlement development, C is emitted from these soils if they are drained due to enhanced decomposition, similar to the effect of drainage for agricultural purposes (Armentano and Menges, 1986). General information and guidelines for estimating changes in soil C stocks are found in Chapter 2, Section 2.3.3, and should be reviewed before proceeding with specific guidelines dealing with settlements. The total change in soil C stocks for settlements is computed using Equation 2.24 in Chapter 2, which combines the change in soil organic C stocks for mineral soils and organic soils; and stock changes for soil inorganic C pools (Tier 3 only). The next section provides specific guidance on estimating the soil organic C stock change in settlements. For general discussion on soil inorganic C, no additional information is provided in the settlements discussion below. To account for changes in soil C stocks associated with Settlements Remaining Settlements, countries need to have estimates of the relevant settlement area, stratified by climate region and soil type. More detailed inventory estimations can be made through ground-based surveys and/or periodic analyses of remote sensing imagery to determine settlement management classes (e.g., turf grass, urban woodlands, gardens, refuse areas, barren areas and infrastructure). Inventories can be developed using Tier 1, 2 or 3 approaches, with Tier 3 requiring more detail and resources. It is also possible that countries will use different tiers to prepare estimates for the separate components in this source category, which includes mineral soils and organic soils in addition to soil inorganic C pools, if using a Tier 3 approach. Figures 2.4 and 2.5 in Chapter 2 are decision trees that provide guidance for identification of appropriate tier to estimate changes in carbon stocks in mineral soils and organic soils, respectively.

8.2.3.1

C HOICE

OF METHOD

Mineral soils Tier 1 It is assumed in the Tier 1 method that inputs equal outputs so that settlement soil C stocks do not change in Settlements Remaining Settlements. Tier 2 The Tier 2 approach for mineral soils uses Equation 2.25 in Chapter 2; involves country- or region-specific reference C stocks and/or stock change factors and possibly suitably disaggregated land-use activity and environmental data. Tier 3 Tier 3 is an advanced method for estimating soil C stocks associated with settlement cover classes, such as a dynamic model or measurement/monitoring network. Few if any models or measurement systems have been developed for estimating soil C stocks in settlements that would be considered a Tier 3 method. This should be considered if settlement soil C is considered a key source category. Additional guidance on Tier 3 approaches is given in Chapter 2, Section 2.3.3

Organic soils T i e r s 1 a nd 2 Settlements are unlikely to be built on deep organic soils, but if needed, emissions can be computed using Equation 2.26 in Chapter 2. A Tier 2 approach will incorporate country-specific information to estimate emission factors, in addition to a settlement cover classification. However, it is also optional in the Tier 2 approach to use a more detailed classification of climate and soils than the default categories. Tier 3 Tier 3 approaches for organic soils will include more detailed management systems integrating dynamic models and/or measurement networks. Additional guidance on Tier 3 approaches is given in Chapter 2, Section 2.3.3.

8.2.3.2

C HOICE

OF STOCK CHANGE AND EMISSION FACTORS

Mineral soils Tier 1 It is assumed in the Tier 1 method that inputs equal outputs so that settlement soil C stocks do not change in Settlements Remaining Settlements. Tier 2 Since defaults are unavailable, Tier 2 requires estimation of country-specific stock change factors. Equation 2.25 in Chapter 2 uses three levels of stock change factor depending on the land use, the management within the

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land use, and the level of inputs The inventory compiler should define management classes relevant to settlements (such as turf grass); derive stock change factors for land use (FLU) based on the C storage for each class relative to the reference condition which is likely to be native lands. Management factors (FMG) give flexibility to specify the way land use is managed (such as for sports fields or ornamental use) and input factors (FI) can be used to represent the influence of management on C of input such as watering or fertilization practices. Tier 3 Tier 3 requires some combination of detailed process models and data gathering, with a sampling strategy and periodic re-sampling, to capture land-use and management effects. See Chapter 2, Section 2.3.3.1 for further discussion.

Organic soils Tier 1 If soils are drained and the peat is not removed, the emissions can be computed using emission factors for cultivated organic soils, due to deep drainage in settlements similar to croplands. If the peat is removed the carbon should be assumed to be released in the year of removal (see Chapter 5, Cropland). Tier 2 Emission factors are derived from country-specific experimental data in a Tier 2 approach. It is good practice for emission factors to be derived for specific settlement management classes and/or a finer classification of climate regions, assuming the new categories capture significant differences in C loss rates. Additional guidance is given in Chapter 2, Section 2.3.3.1. Tier 3 The advice is the same as that given above for mineral soils.

8.2.3.3

C HOICE

OF ACTIVITY DATA

Mineral soils Tier 1 It is assumed in the Tier 1 method that inputs equal outputs so that settlement soil C stocks do not change in Settlements Remaining Settlements. Tier 2 For the Tier 2 level, activity data consist of areas for settlements subdivided by climate, soil type, and /or management classes, as needed, to correspond with the stock change factors described above. Municipality records may be useful for determining the proportion of various management classes (e.g., shopping areas, subdivisions, businesses, parks, schools, etc.), augmented with knowledge of country experts about the approximate distribution of settlement classes (i.e., turf grass, urban woodlands, gardens, refuse areas, barren areas and infrastructure). Tier 2 approaches may involve a finer stratification of environmental data, including climate regions and soil types, provided the corresponding stock change factors have been developed. Tier 3 The activity data for application of dynamic models and/or a direct measurement-based inventory will characterise climate, soil, topographic and management regime, depending on the model or sampling design.

Organic soils Tier 1 The total area of cultivated organic soils in settlements, stratified by climate region to correspond to Table 5.6 in Chapter 5 or Table 6.3 in Chapter 6, is needed. A default can be obtained by multiplying total urban area, as a function of climate region, by the area proportion of greenspace from Table 8.3 above. Tier 2 Tier 2 approaches for organic soils will involve more detailed specification of management classes, and possibly finer division of those classes by drainage or climate regions. Stratification should be based on empirical data demonstrating significant differences in C loss rates for the proposed classes. Tier 3 The advice is the same as that given above for mineral soils.

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Chapter 8: Settlements

8.2.3.4

U NCERTAINTY

ASSESSMENT

Uncertainties in soil C inventories are related at Tiers 1 and 2 to representation of 1) land-use and management activities; 2) mineral soil reference C stocks; and 3) stock change and emission factors. Tier 3 uncertainties depend on model structure and parameters, or measurement error/sampling strategy. Uncertainty is generally reduced by more sampling and use of a higher Tier estimates incorporating country-specific information. Uncertainties in reference C stocks and emission factors are indicated in Table 2.3 in Chapter 2; Tables 5.5 and 5.6 in Chapter 5; and Tables 6.2 and 6.3 in Chapter 6. Uncertainties in land-use and management data will need to be assessed by the inventory compiler, and combined with uncertainties for the default factors and reference C stocks using an appropriate method, such as simple error propagation equations. If using aggregate land-use area statistics for activity data (e.g., FAO data), the inventory compiler may have to apply a default level of uncertainty for the land area estimates (+50%). However, it is good practice for the inventory compiler to derive uncertainties from country-specific activity data instead of using a default level. Default reference C stocks for mineral soils and emission factors for organic soils can have high uncertainties, when applied to specific countries. Defaults represent globally averaged values of land-use and management impacts or reference C stocks that may vary from region-specific values (Powers et al., 2004; Ogle et al., 2006). Bias can be reduced by deriving country-specific factors using Tier 2 method or by developing a Tier 3 countryspecific estimation system. The underlying basis for higher Tier approaches will be research in the country or neighbouring regions that address the effect of land use and management on soil C. It is good practice to minimize bias by accounting for significant within-country differences in land-use and management impacts, such as variation among climate regions and/or soil types, even at the expense of reduced precision in the factor estimates (Ogle et al., 2006). Bias is more problematic for reporting stock changes because it is not necessarily captured in the uncertainty range (i.e., the true stock change may be outside of the reported uncertainty range if there is significant bias in the factors). Precision in land-use activity statistics may be improved through a better national system, such as developing or extending a ground-based survey with additional sample locations and/or incorporating remote sensing to provide additional coverage. It is good practice to design a classification that captures the majority of land-use and management activity with a sufficient sample size to minimize uncertainty at the national scale. For Tier 2 methods, country-specific information is incorporated into the inventory analysis for purposes of reducing bias. For example, Ogle et al. (2003) utilized country-specific data to construct probability density functions for US specific factors, activity data and reference C stocks for agricultural soils. It is good practice to evaluate dependencies among the factors, reference C stocks or land-use and management activity data. In particular, strong dependencies are common in land-use and management activity data because management practices tend to be correlated in time and space. Tier 3 models are more complex and simple error propagation equations may not be effective at quantifying the associated uncertainty in resulting estimates. Monte Carlo analyses are possible (Smith and Heath, 2001), but can be difficult to implement if the model has many parameters (some models can have several hundred parameters) because joint probability density functions must be constructed quantifying the variance as well as covariance among the parameters. Other methods are also available such as empirically-based approaches (Monte et al., 1996), which use measurements from a monitoring network to statistically evaluate the relationship between measured and modelled results (Falloon and Smith, 2003). In contrast to modelling, uncertainties in measurement-based Tier 3 inventories can be determined directly from the sample variance, measurement error and other relevant sources of uncertainty.

8.3

LAND CONVERTED TO SETTLEMENTS

Conversion of Forest Land, Cropland, Grassland etc. to Settlements, leads to emissions and removals of greenhouse gases. Methods for estimating change in carbon stocks associated with land-use conversions are explained in Chapters 2, 4, 5 and 6 of this volume. The decision tree (see Figure 1.3 in Chapter 1) and the same basic methods can be applied to estimate change in carbon stocks in Forest Land, Cropland and Grassland converted to Settlements. Depending on the magnitude of carbon stocks in the previous land-use category, land converted to Settlements may experience a relatively rapid loss of carbon in the first year, followed by a more gradual increase in carbon pools subsequently. Forest Land converted to Settlements, for example, would normally be characterized by this abrupt change followed by a gradual increase in carbon stocks. If carbon stocks in the previous land use were lower than in settlements, this abrupt transition would not take place in the first year. For example, abandoned Cropland converted to Settlements would experience only the gradual carbon stock increase and not the initial abrupt transition.

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The methods described can have sometimes been simplified by estimating the effects of conversion in a single year followed by application of the methods described above for Settlements Remaining Settlements. However, where this is done, the land area should be kept in the conversion state for the transition period adopted. Otherwise, there are likely to be difficulties with maintaining the consistency of the land-use matrix. Where Approach 1 is used in its simplest form for land area representation (see Chapter 3) and no supplementary information is available that will allow the previous land uses to be inferred, only the total area of settlements will be known as a function of time and the previous land uses will not be known. Under these circumstances, the biomass stocks before the conversion (Bbefore) cannot be estimated and Equation 2.16 cannot be applied. In this case land converted to Settlements will have to be estimated with land remaining Settlements and the emissions or removals from conversion to Settlements as well as other land-use changes will be represented as step changes in the remaining categories rather than properly allocated to the conversions consistent with the land-use change matrix. In effect transitions become step changes across the landscape. This makes it particularly important to achieve coordination among each sector to ensure the total land base is remaining constant over time, given that some land area will be lost and gained within individual sectors during each inventory year due to land-use change.

8.3.1 8.3.1.1

Biomass C HOICE

OF METHOD

The general approach for calculating the immediate change in live biomass accruing from the conversion to Settlements is represented by Equations 2.15 and 2.16 in Chapter 2. The mean annual biomass increment resulting from the transition is represented by the difference between the biomass in the settlement land-use category immediately after the transition (BAfter) and the biomass in the previous category (BBefore). This method follows the approach in the Guidelines for other land-use transitions: the annual change in carbon stock in biomass due to land conversion is estimated (using Equation 2.16) by multiplying the area converted annually to settlements by the difference in carbon stocks between biomass in the system prior to conversion (BBefore) and that in the settlements after conversion (BAfter). Tier 1 For Tier 1, in the initial year following conversion to the settlement land use, the most conservative approach is to set BAfter to zero, meaning that the process of development of settlements causes carbon stocks to be entirely depleted. To do this it is necessary to add growth during the year of inventory (∆CG) and subtract loss (∆CL) to obtain the net change in carbon stocks on land converted to Settlements (Equation 2.15). Tier 2 At Tier 2, country-specific carbon stocks can be applied to activity data disaggregated to a level of detail adapted to national circumstances. At the higher tiers, the area of each land-use or land cover type converted to another type in a settlement (examples of land use and land cover types are described in Section 8.2) should be recorded, because that area is associated with the amount of carbon both before and after the conversion. Settlement landuse or land cover types are likely to differ in carbon density. Tier 3 At Tier 3, countries can use the stock difference method (Equation 2.8) or other advanced estimation methods that may involve complex models and highly disaggregated activity data including, if available, more detailed information about BAfter on a country- or biome-specific basis.

8.3.1.2

C HOICE

OF EMISSION / REMOVAL FACTORS

Tier 1 Tier 1 methods require estimates of the biomass of the land use before conversion and after conversion. It is assumed that all biomass is cleared when preparing a site for settlements, thus, the default for biomass immediately after conversion is 0 tonnes ha-1. Table 8.4 provides default values for biomass before conversion (BBefore).

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Chapter 8: Settlements

TABLE 8.4 DEFAULT BIOMASS CARBON STOCKS REMOVED DUE TO LAND CONVERSION TO SETTLEMENTS Carbon stock in biomass before conversion (BBefore) (tonnes C ha-1)

Land-use category

Error range #

Forest Land

See Chapter 4, Tables 4.7 to 4.12 for carbon stocks in a range of forest types by climate regions. Stocks are in terms of dry matter. Multiply values by a carbon fraction (CF) 0.5 to convert dry matter to carbon.

See Section 4.3 (Land Converted to Forest Land)

Grassland

See Table 6.4, Chapter 6 for carbon stocks in a range of grassland types by climate regions.

+ 75%

Cropland

For cropland containing annual crops: Use default of 4.7 tonnes of carbon ha-1 or 10 tonnes of dry matter ha-1 (see Chapter 6, Section 6.3.1.2)

± 75%.

#

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

T i e r s 2 a nd 3 Tier 2 methods replace the default data by country-specific data and Tier 3 involves detailed modelling or measurement data relevant to the conversion processes.

8.3.1.3

C HOICE

OF ACTIVITY DATA

Activity data for estimating changes in biomass on land areas converted to Settlements can be obtained, consistent with the general principles set out in Chapter 3, through national statistics, from forest services, conservation agencies, municipalities, survey and mapping agencies. Cross-checks should be made to ensure complete and consistent representation of annually converted lands in order to avoid possible omissions or double counting. Data should be disaggregated according to the general climatic categories and settlements types. Tier 3 inventories will require more comprehensive information on the establishment of new settlements, with refined soil classes, climates, and spatial and temporal resolution. All changes having occurred over the number of years selected as the transition period should be included with transitions older than the transition period (default 20 years) reported as a subdivision of Settlements Remaining Settlements. Higher tiers require greater detail but the minimum requirement for inventories to be consistent with the IPCC Guidelines is that the areas of Forest Land conversion can be identified separately. This is because forest will usually have higher carbon density before conversion. This implies that at least partial knowledge of the land-use change matrix, and therefore, where Approaches 1 and 2 from Chapter 3 are used to estimate land area, supplementary surveys may be needed to identify the area of land being converted from Forest Land to Settlements. As pointed out in Chapter 3, where surveys are being set up, it will often be more accurate to seek to establish directly, areas undergoing conversion, than to estimate these from the differences in total land areas under particular uses at different times.

Step by step method for implementation Tier 1 Use default values for Bbefore from respective land-use category chapter (Forest Land, Grassland, etc) and assume that BAfter equals zero in Equation 2.16. Step 1: Apply Equation 2.16 to each land-use type converted to settlement lands; Step 2: Add up the biomass changes over all the land-use types; and Step 3: Multiply the result by 44/12 to obtain the amount of CO2 equivalents emitted (the sum obtained in Step 2 will be a negative number) from the land conversion. Tier 2 The typical steps to implement a Tier 2 method are: Step 1: Use the methods described in Chapter 3, including where relevant cadastral and planning records or the analysis of remote sensing images (or both), to estimate the change in area between the present and the last area survey. Step 2: Define — as a first approximation — settlement land-use types on the basis of the proportion of greenspace. For instance, three tentative land-use classes could be: Low (less than 33% greenspace), Medium (from 33 to less than 66% greenspace), and High (more than 66% greenspace). Each one of those classes can be

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assigned with an average carbon content, obtained from the species surveyed in similarly defined classes for accounting biomass changes in Section 8.2. Step 3: Draw a land-use conversion area matrix for the land-use transitions defined in Step 2. Step 4: Estimate with equations the biomass stocks of the defined land-use types and the converted land-use types (to obtain BBefore and BAfter), apply Equation 2.16 to each non-empty cell of the land-use change matrix, add up the changes in carbon stocks, and multiply the sum by 44/12 to obtain the emission/removal of CO2 equivalents. Step 5: Calculate ∆CG, using either Method A or Method B in Section 8.2.1, Settlements Remaining Settlements (the choice of method will depend on the applicability of the emission and removal factors, as well as the availability of activity data). This will be used in Equation 2.15. Step 6: Calculate ∆CL, using Methods as described in Section 8.2.1.3, Settlements Remaining Settlements. Step 7: Calculate the change in carbon stocks in live biomass resulting from the land-use transition to Settlements, accounting for the biomass increment, biomass losses, and biomass change due to land-use conversion as given in Equation 2.15.

8.3.1.4

U NCERTAINTY

ASSESSMENT

See guidance in Section 8.2.1.4.

8.3.2

Dead organic matter

Methods are provided for two types of DOM pools: 1) dead wood and 2) litter. Chapter 1of this report provides definitions of these pools and Section 8.2.2 DOM in the context of settlements. Some land converted to Settlements will not have an abrupt transition (e.g., Cropland that is abandoned and converted to Settlements). In this case, Phase 1 methods will not be appropriate and there will be a gradual transition in DOM pools to a new equilibrium. When this type of conversion occurs, the whole conversion accounting can be treated with Phase 2 methods.

8.3.2.1

C HOICE

OF METHOD

Estimation of changes in carbon stocks in DOM requires separate estimates of changes in stocks of dead wood and changes in litter stocks (refer to Equation 2. 17 of Chapter 2). The decision tree in Figure 2.3 in Chapter 2 helps select the appropriate tier to use. Tier 1 Tier 1 default assumes all carbon contained in dead wood and litter is lost during conversion and does not take account of any subsequent accumulation. Tier 2 Tier 2 approaches require greater disaggregation than that used in Tier 1. The immediate and abrupt carbon stock change in dead wood due to conversion of other lands to Settlements under Tiers 2 and 3 will be estimated using Equation 2.23, where Co is set to zero and Ton is set at 1 year. Tier 2 assumes a linear change function, although during the transition period, pools that gain or lose C often have a non-linear loss or accumulation curve that can be represented at Tier 3 through successive transition matrices. For the calculation of changes in dead wood and litter carbon during the transition phase, two methods are suggested: Method 1 (Also called the Gain-Loss Method, Equation 2.18 in Chapter 2): This method involves estimating the area of each type of land conversion and the average annual transfer into and out of dead wood and litter stocks. This requires an estimate of area under land converted to Settlements according to different climate or ecological zones or settlement types, disturbance regime, management regime, or other factors significantly affecting dead wood and litter carbon pools and the quantity of biomass transferred into dead wood and litter stocks as well as the quantity of biomass transferred out of the dead wood and litter stocks on per hectare basis according to different settlement types. Method 2 (Also called the Stock-Difference Method, Equation 2.19 in Chapter 2): This method involves estimating the area of land converted to Settlements and the dead wood and litter stocks at two periods of time, t1 and t2. The dead wood and litter stock changes for the inventory year are obtained by dividing the stock changes

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Chapter 8: Settlements

by the period (years) between two measurements. The stock-difference method is feasible for countries, which have periodic inventories. Tier 3 For Tier 3, countries should develop their own methodologies and parameters for estimating changes in DOM. These methodologies may be derived from Methods 1 or 2 specified above, or may be based on other approaches. The method used needs to be clearly documented. A Tier 3 approach should use or be consistent with the true shapes of the loss or accumulation curves. These curves should be applied to each cohort that is under transition during the reporting year to estimate the annual change in the dead wood and litter carbon pools.

8.3.2.2

C HOICE

OF EMISSION / REMOVAL FACTORS

Carbon fraction: The carbon fraction of dead wood and litter is variable and depends on the stage of decomposition. Wood is much less variable than litter and a value of 0.50 tonne C (tonne d.m.)-1 can be used for the carbon fraction. Litter values in settlements range from 0.30 to 0.50. When country- or ecosystem-specific data are not available we suggest a carbon fraction value of 0.40 for litter. Tier 1 Dead wood and litter carbon stocks in lands converted to Settlements are assumed all lost during the conversion and there is assumed to be no subsequent accumulation of new DOM in the settlements after conversion. Default values for forest litter prior to conversion are provided in Table 2.2 in Chapter 2 but there are no default values available for dead wood or litter in most systems. Countries should seek estimates and use local data from forestry and agricultural research institutes to provide best estimates of the dead wood and litter in the initial system prior to conversion, or use the defaults in Table 2.2 in the absence of other information. Carbon stocks in litter and dead wood pools in all non-forest land categories are assumed to be zero. Countries experiencing significant conversions of other ecosystems to settlements are encouraged to develop domestic data to quantify this impact and report it under Tier 2 or 3 methodologies. Tier 2 It is good practice to use country-level data on dead wood and litter for different settlements categories, in combination with default values if country or regional values are not available for some conversion categories. Country-specific values for the transfer of carbon from live trees and grasses that are harvested to harvest residues and decomposition rates, in the case of the Gain-Loss Method, or the net change in DOM pools in the case of the Stock-Difference Method, can be derived from domestic expansion factors, taking into account the settlements type, the rate of biomass utilization, harvesting practices and the amount of damaged vegetation during harvesting operations. Country-specific values for disturbance regimes should be derived from scientific studies. Tier 3 National level disaggregated DOM carbon estimates should be determined as part of a national inventory, national level models, or from a dedicated greenhouse gas inventory programme, with periodic sampling according to the principles set out in Chapter 3, Annex 3A.3. Inventory data can be coupled with modelling studies to capture the dynamics of all settlements carbon pools. Tier 3 methods provide estimates of greater certainty than lower tiers and feature a greater link between individual carbon pools. Some countries have developed disturbance matrices that provide a carbon reallocation pattern among different pools for each type of disturbance. Other important parameters in a modelled DOM carbon budget are decay rates, which may vary with the type of wood and microclimatic conditions, and site preparation procedures (e.g., controlled broadcast burning, or burning of piles).

8.3.2.3

C HOICE

OF ACTIVITY DATA

The activity data should be the same as that used for biomass and described in Section 8.3.1.3

Step-by-step summary of method for estimating changes in DOM stocks Tier 1 Step 1: Determine the categories of land conversion to be used in this assessment and the representative area of conversion by year. Area data should be obtained using the methods described in Chapter 3. Higher tiers require greater detail but the minimum requirement for inventories to be consistent with the IPCC Guidelines, when using Tier 1, is that the areas of forest conversion are identified separately. Step 2: For each activity category, determine the dead wood and litter stocks (separately) per hectare prior to conversion (see Table 2.2 in Chapter 2 for default values).

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Step 3: For each activity category, determine the stocks in the dead wood and litter (separately) per hectare for the particular type of settlement. For Tier 1, dead wood and litter stocks following conversion are assumed to be equal to zero. Step 4: Calculate the net change of dead wood and litter stocks per hectare for each type of conversion by subtracting the initial stocks from the final stocks. A negative value indicates a loss in the stock (Equation 2.23). Step 5: Convert the net change in the individual stock to units of tonnes C ha-1 by multiplying the net stock change by the carbon fraction of that stock (0.40 tonne C (tonne d.m.)-1 for litter and 0.50 tonne C (tonne d.m.)-1 for dead wood). Step 6: Multiply the net change in each C stock by the area converted during the reporting year.

T i e r s 2 a nd 3 Step 1: Determine the categories of land conversion to be used in this assessment and the representative area of conversion by year. When calculating for lands in the transition phase, representative areas for each category at different stages of conversion are required. Higher tiers require greater detail but the minimum requirement for inventories to be consistent with the IPCC Guidelines is that the areas of forest conversion are identified separately. Step 2: Abrupt changes •

• • • • •

Determine the activity categories to be used in this assessment and the representative areas. The activity category consists of definitions of the type of conversion and, if applicable, the nature of management of the previous land cover and settlements management, for example: ‘conversion of logged tropical seasonal forest to cattle pasture using exotic grasses’. Area data should be obtained using the methods described in Chapter 3. For each activity category, determine the dead wood and litter stocks (separately) per hectare prior to conversion. For each activity category, determine the stocks in the dead wood and litter (separately) per hectare following one year of conversion to Settlements. Calculate the net change of dead wood and litter stocks per hectare for each type of conversion by subtracting the initial stocks from the final stocks. A negative value indicates a loss in the stock. Convert the net change in the individual stock to units of tonnes C ha-1 by multiplying the net stock change by the carbon fraction of that stock (0.40 tonne C (tonne d.m.)-1 for litter and 0.50 tonne C (tonne d.m.)-1 for dead wood). Multiply the net change in each C stock by the area converted during the reporting year.

Step 3: Transitional changes •

• • • • •

8.22

Determine the categories and cohorts to be used in this assessment and the representative areas. The category consists of definitions of the type of conversion and, if applicable, the nature of management of the previous land cover and settlements type. Area data should be obtained using the methods described in Chapter 3. Determine the annual change rate for dead wood and litter stocks (separately) by activity type using either the Gain-Loss Method or the Stock-Difference Method (see below) for each cohort of lands that are currently in the transition phase between conversion and a new steady-state. Determine the dead wood and litter stocks in the cohort during the previous year (usually taken from the previous inventory). Calculate the change in dead wood and litter stocks for each cohort by adding the net change rate to the previous year’s stocks. Convert the net change in the individual stock to units of tonnes C ha-1 by multiplying the net stock change by the carbon fraction of that stock (0.40 tonne C (tonne d.m.)-1 for litter and 0.50 tonne C (tonne d.m.)-1 for dead wood). Multiply the net change in each C stock by the area in each cohort for the reporting year.

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Chapter 8: Settlements

Ga i n- Los s M ethod • Determine the average annual inputs of dead wood and litter (separately).

•

•

Determine the average annual losses of dead wood and litter (separately). Determine the net change rate in dead wood and litter by subtracting the outputs from the inputs.

S t o c k - D if f er e n c e M et h o d • Determine the inventory time interval, the average stocks of dead wood and litter at the initial inventory, and the average stocks of dead wood and litter at the final inventory. •

•

Use these figures to calculate the net change in dead wood and litter stocks by subtracting the initial stock from the final stock and dividing this difference by the number of years between inventories. A negative value indicates a loss in the stock. A Tier 3 approach requires country- or region-specific expansion factors. There are no default expansion factors for Tier 2, and the best available local data should be used (and documented).

8.3.2.4

U NCERTAINTY

ASSESSMENT

Uncertainty at Tier 1 is the same as the uncertainty in the carbon stock on the area of land subject to annual conversion. DOM changes are subsequently assumed to be zero, and no associated uncertainty is needed at Tier 1 after the initial transition. For Tiers 2 and 3 estimates, sources of uncertainty include the degree of accuracy in land area estimates, carbon increment and loss, carbon stocks, amount of carbon burned, and expansion factor terms. Area data and estimates of uncertainty should be obtained using the methods in Chapter 3 which provide default uncertainties associated with the different approaches. Uncertainties associated with carbon stocks and other parameter values are likely to be at least a factor of three unless country-specific data are available from well designed surveys.

8.3.3

Soil carbon

Land conversion to Settlements occurs with development and expansion of cities and towns on former Forest Land, Cropland, Grassland, Wetlands, and Other Land. These conversions change soil C stocks due to mechanical disturbance of the soil; soil burial or collection during development; type and amount of vegetated cover; in addition to the new management regime, particularly with respect to nutrient and water applications. General information and guidelines for estimating changes in soil C stocks are found in Chapter 2, Section 2.3.3 (including equations). The total change in soil C stocks for Land Converted to Settlements is computed using Equation 2.24, which combines the change in soil organic C stocks for mineral soils and organic soils; and stock changes associated with soil inorganic C pools (for Tier 3 only). To account for changes in soil C stocks associated with land converted to Settlements, countries need to have estimates of the areas of land converted to Settlements during the inventory time period, stratified by climate region and soil type. If aggregate land-use data are used and specific conversions among uses are not known, soil organic C (SOC) stock changes can still be computed using the methods provided in Settlements Remaining Settlements, but the land-base area will then probably be different for settlements in the current year relative to the initial year in the inventory, and the dynamics of the transition will be less well represented. Chapter 3 (Consistent representation of lands) emphasises the importance of maintaining consistency in total land area.

8.3.3.1

C HOICE

OF METHOD

Inventories can be developed using Tier 1, 2 or 3 approaches, with each successive Tier requiring more detail and resources than the previous one. It is also possible that countries may use different tiers to prepare estimates for the separate sub-categories of soil C (i.e., soil organic C stocks changes in mineral soils and organic soils, and stock changes associated with soil inorganic C pools, are estimated at Tier 3). Decision trees are provided for mineral soils (Figure 2.4) and organic soils (Figure 2.5) in Section 2.3.3.1 (Chapter 2) to help selection of the appropriate tiers.

Mineral soils Tier 1 Change in soil organic C stocks can be estimated for mineral soils with land-use conversion to Settlements using Equation 2.25 in Chapter 2. For Tier 1, the initial (pre-conversion) soil organic C stock (SOC(0-T)) and C stock in the last year of the inventory time period (SOC0) are determined from the common set of reference soil organic

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C stocks (SOCREF) and default stock change factors (FLU, FMG, FI). Areas of exposed bedrock in Forest Land or the previous land use are not included in the soil C stock calculation (assume a stock of 0). Annual rates of emissions (source) or removals (sink) are calculated as the difference in stocks (over time) divided by the time dependence (D) of the stock change factors (default is 20 years). Tier 2 The Tier 2 approach for mineral soils also uses Equation 2.25 in Chapter 2, but involves country- or regionspecific reference C stocks and/or stock change factors and possibly more disaggregated land-use activity and environmental data. Removal, translocation or burial of soil C during development is a particular issue for settlements. To the extent that soil C is not decomposed during the development phase and resides deeper in the profile, is translocated to another area, or possibly used as a commodity. It is good practice for Tier 2 stock change factor to be adjusted to reflect the reduction in loss of C to the atmosphere as CO2. Tier 3 Tier 3 methods will involve more detailed and country-specific models and/or measurement-based approaches along with highly disaggregated land-use and management data. It is good practice that Tier 3 approaches for estimating soil C change from land-use conversions to Settlements, employ models, data sets and/or monitoring networks that are capable of representing transitions over time from other land uses, including Forest Land, Grassland, Cropland or other lands. Tier 3 methods need to be integrated with estimates of biomass removal and the post-clearance treatment of plant residues (including woody debris and litter), as variation in the removal and treatment of residues (e.g., burning, site preparation) will affect C inputs to soil organic matter formation and C losses through decomposition and combustion. Models should be validated with independent observations from country- or region-specific field locations that are representative of the interactions of climate, soil and management on post-conversion change in soil C stocks.

Organic soils T i e r s 1 a nd 2 Land converted to Settlements on organic soils within the inventory time period is treated the same as Settlements Remaining Settlements. Carbon losses are computed using Equation 2.26 in Chapter 2. Additional guidance on Tiers 1 and 2 approaches are given in Section 8.2.3.1. Tier 3 As with mineral soils, a Tier 3 approach will involve country-specific models and/or measurement-based approaches along with highly disaggregated land-use and management data.

8.3.3.2

C HOICE

OF STOCK CHANGE AND EMISSION FACTOR

Mineral soils Tier 1 Default reference C stocks are found in Table 2.3 of Chapter 2, and stock change factors for previous land uses can be found in the relevant Chapters (for Forest Land in Section 4.2.3.2, Cropland in 5.2.3.2, Grassland in 6.2.3.2, and Other Land in 9.3.3.2). Default stock change factors for land use after conversion (Settlements) are not needed for the Tier 1 method for Settlements Remaining Settlements because the default assumption is that inputs equal outputs and therefore no net change in soil carbon stocks occur once the settlement is established. Conversions, however, may entail net changes and it is good practice to use the following assumptions: (i)

for the proportion of the settlement area that is paved over, assume product of FLU, FMG and FI is 0.8 times the corresponding product for the previous land use (i.e., 20% of the soil carbon relative to the previous land use will be lost as a result of disturbance, removal or relocation);

(ii)

for the proportion of the settlement area that is turfgrass, use the appropriate values for improved grassland from Table 6.2, Chapter 6;

(iii)

for the proportion of the settlement area that is cultivated soil (e.g., used for horticulture) use the no-till FMG values from Table 5.5 (Chapter 5) with FI equal to 1; and

(iv)

for the proportion of the settlement area that is wooded assume all stock change factors equal 1.

Tier 2 Estimation of country-specific stock change factors is probably the most important development associated with the Tier 2 approach. Differences in soil organic C stocks among land uses are computed relative to a reference condition, using land-use factors (FLU). Input factor (FI) and management factor (FMG) are then used to further refine the C stocks of the settlement management classes. Additional guidance on how to derive these stock change factors is given in Settlements Remaining Settlements, Section 8.2.3.2. See the appropriate section for specific information regarding the derivation of stock change factors for other land-use sectors (Forest Land in

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Chapter 8: Settlements

Chapter 4, Cropland in Chapter 5, Grassland in Chapter 6, and Other Land in Chapter 9). Reference C stocks can also be derived from country-specific data in a Tier 2 approach and should of course be consistent across the land uses (i.e., Forest Land, Cropland, Grassland, Settlements, Other Land), and therefore coordinated among the various teams conducting soil C inventories for AFOLU. Tier 3 Constant emission rate factors per se are less likely to be estimated in favor of variable rates that more accurately capture land-use and management effects. See Chapter 2, Section 2.3.3.1 for further discussion.

Organic soils T i e r s 1 a nd 2 Land converted to Settlements on organic soils within the inventory period is treated the same as Settlements Remaining Settlements. Tier 2 emission factors are derived from country- or region-specific data; additional guidance is given in Section 8.2.3.2. Tier 3 Constant emission rate factors per se are less likely to be estimated in favor of variable rates that more accurately capture land-use and management effects. See Chapter 2, Section 2.3.3.1 for further discussion.

8.3.3.3

C HOICE

OF ACTIVITY DATA

Mineral soils T i e r s 1 a nd 2 The amount of land converted to Settlements, stratified by climate region and soil type, is needed to estimate the appropriate stocks at the Tier 1 level. This can be based on overlays with suitable climate and soil maps and spatially-explicit data of the location of land conversions. Detailed descriptions of the default climate and soil classification schemes are provided in Chapter 3. In the absence of specific information, default area within the settlements that is paved over should be estimated as the non-greenspace proportion of the total area, using the data in Table 8.3, and the same Table can be used to partition the greenspace area into wooded areas and nonwooded areas. The latter may be assumed all to be turfgrass unless data are available on the area otherwise cultivated. Tier 3 For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or more detailed data on the combinations of climate, soil, topographic and management data are needed, but the exact requirements will depend on the model or measurement design.

Organic soils T i e r s 1 a nd 2 Land converted to Settlements on organic soils within the inventory time period is treated the same as Settlements Remaining Settlements, and guidance on activity data is discussed in Section 8.2.3.3. Tier 3 As with mineral soils, Tier 3 approaches will likely require more detailed data on the combinations of climate, soil, topographic and management data, relative to Tier 1 or 2 methods, but the exact requirements will be dependent on the model or measurement design.

8.3.3.4

U NCERTAINTY

ASSESSMENT

See guidance in Section 8.2.3.4.

8.4

COMPLETENESS, TIME SERIES, QA/QC, AND REPORTING

8.4.1

Completeness

It is good practice for soil C inventories to track the changes in total area over time, and if using a Tier 2 or 3 approach, the inventory should track areas associated with the major management classes (e.g., turf grass, urban woodlands, gardens, refuse areas, barren areas and infrastructure). The total area covered by the settlement inventory methodology is the sum of land in Settlements Remaining in Settlements and Land Converted to

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Settlements during the time period. This inventory methodology may not include some settlement areas where greenhouse gas emissions and removals are believed to be insignificant or constant through time because of little or no change in settlement management or no significant change in management inputs. However, countries are encouraged to track through time the total area of land in settlements within country boundaries, keeping transparent records on which portions are used to estimate carbon dioxide emissions and removals. In this case, it is good practice for countries to document and explain the difference in the area that is included in the inventory computations and the total settlement area in the land base.

8.4.2

Developing a consistent time series

To maintain a consistent time series, it is good practice for countries to apply the same inventory methods over the entire reporting time period, including definitions for land-use and settlement systems, area included in a C inventory, and calculation method. If changes are made, it is good practice to keep transparent records of the changes, and then re-calculate the C stock changes over the entire inventory time period. Guidance on recalculation under these circumstances is given in Volume 1, Chapter 5. Consistent estimation and reporting also requires common definitions of management activities, climate and soil types across the entire time series for the period of the inventory.

8.4.3

Inventory Quality Assurance/Quality Control

It is good practice to implement quality control checks and external expert review of inventory estimates and data. Specific attention is expected to be paid to country-specific estimates of stock change and emission factors, ensuring that they are based on high quality data and verifiable expert opinion. Specific QA/QC checks across the settlements methodology include: Settlements Remaining Settlements: It is good practice for settlement areas to be consistent for reporting of biomass stock and soil stock changes. Settlements may include areas where soil stock changes are accounted for but biomass changes are assumed to be zero (e.g., where non-woody biomass is largely absent), areas where both biomass and soil stocks are changing (e.g., development of a park), and areas where neither biomass nor soil stocks are changing (e.g., infrastructure and barren areas). To increase transparency and eliminate errors, it is good practice to report the total settlement area regardless of whether stocks are changing. Land Converted to Settlements: Aggregate area totals for land converted to Settlements are expected to be the same in the biomass and soils estimations. While biomass and soil pools may be disaggregated to different levels of detail, it is good practice to use the same general categories for disaggregating the area data. For all soil C stock change estimates, it is expected that the total areas will be the same for each climate-soil type combination at the beginning (year(0-T)) and the last year (year(0)) of the inventory time period, unless it has been demonstrated that some portion of the land base has been incorporated into another land-use sector or gained from another sector. Ultimately, the sum of the entire land base for a country, which includes each sector, must be equal across every year in the inventory time period.

8.4.4

Reporting and Documentation

It is good practice to maintain and archive all information used to produce national inventory estimates including: (i) data sources, databases, data sources for information used to estimate country-specific factors as well as the procedures used to estimate factors; (ii) activity data and definitions used to categorize or aggregate the activity data; and (iii) climate region classifications and soil types (for Tier 1 and Tier 2) must be clearly documented. For Tier 3 approaches using modelling, it is good practice to document the model version and provide a model description, in addition to permanently archiving copies of all model input files, source code and executable programs.

Reporting tables and worksheets The categories described in this Chapter can be reported using the reporting tables in Volume 1, Chapter 8. The estimates under the Settlements category can be compared with the reporting categories in the IPCC Guidelines as follows: •

8.26

Carbon dioxide emissions and removals in woody biomass in Settlements Remaining Settlements to IPCC reporting category 5A and Land Converted to Settlements in IPCC reporting category 5B; and IPCC reporting categories 4E and 4F for non-CO2 gases;

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 8: Settlements • •

Carbon dioxide emissions and removals in soils in Settlements Remaining Settlements to IPCC reporting categories 5D and 5E for CO2, and IPCC reporting category 4D for non-CO2 gases; and Carbon dioxide emissions and removals resulting from land-use conversions to Settlements to IPCC reporting category 5B for biomass, IPCC reporting categories 5D and 5E for soils; and IPCC reporting categories 4D, 4E, and 4F for non-CO2 gases.

Worksheets are provided in Annex 1 for calculating greenhouse gases emissions and removals (Tier 1 methods) for Settlements.

8.5

BASIS FOR FUTURE METHODOLOGICAL DEVELOPMENT

Gaps in this methodology exist because sufficient data are not available to quantify all of the pools and fluxes of greenhouse gases in settlements. Obvious gaps include: •

• •

•

•

Methodology for estimating emissions of non-CO2 greenhouse gases (N2O and CH4); Detailed methodology to account for carbon stocks other than live biomass and soils (specifically, dead wood and litter); Discussion of carbon stocks and fluxes from turfgrass and turf management; Discussion of carbon stocks and fluxes from gardens and other herbaceous plants; and A generalized methodology to account for different classes of settled lands, with different amounts of woody and non-woody vegetation and different types of management.

Non-CO2 greenhouse gases. While some evidence exists to support the idea that nitrous oxide fluxes may be enhanced in urban areas relative to the native condition (Kaye et al., 2004), this result likely depends on the native condition (i.e., the climate and region in which the settlement is located) and the management regime typically applied in that settled area. Additional data are required before conclusions about the impact of settlement on non-CO2 greenhouse gas fluxes can be drawn. Dead wood and litter. Dead wood is a class variously composed of fallen or pruned branches or trees, or dead standing trees not yet replaced with live individuals. This dead wood may be burned or disposed of as solid waste, used for composting, left to decay either in-site or off-site. This material is treated in this methodology as a loss from the live biomass term. Because dead wood is likely to be carried off-site in settlements (rather than left on-site to decay as in forests), a more detailed methodology developed in the future might account for the proportion of dead wood taken to landfills, disposed of in compost piles, burned, or left on-site to decay. The portion taken to landfills or composted might be treated as harvested wood products (HWP) or as waste, both of which are treated in other sections of the Guidelines. Turfgrass and turf management. Turfgrass biomass consists of roots, stubble, thatch, and above-ground components. Though estimates of turfgrass productivity have been published (Falk, 1976; Falk, 1980; Qian et al., 2003), grass decomposes quickly and there is little information about the overall accumulation of biomass in the longer-lived components of turf biomass. Turfgrass allocation to the above-ground and below-ground components also depends on the management and mowing regime. Because of the lack of generalizable information on this topic, as well as the lack of activity data quantifying the area covered by turfgrass in settlements, there is currently no detailed methodology describing carbon removed by turf systems. A more detailed methodology would require additional information on turf productivity, turfgrass turnover, and allocation to different plant components as it varies with management regime. Of course, the activity data required to implement this methodology would include information on management regimes and the proportion of settlements covered by turfgrass. Gardens and other herbaceous plants. Similar to the situation with turfgrass, information does not exist describing the annual biomass accumulation and allocation of garden plants to different above-ground and below-ground parts. Similarly, information is not available describing the variation in plant productivity with management regime. Activity data required to implement a more detailed methodology would include information on management regimes and the proportion of settlement area covered by this type of vegetation. These are mainly garden plants, so sampling them in private gardens presents the additional problem of their likely disturbance and consequent denial of access to them (cf. Jo and McPherson, 1995). Land classes. A more detailed methodology would benefit from a consistent set of definitions of land classes within settlements, that could be applied to any country regardless of its climate, native vegetation, or typical settlement regime. This would make settlements parallel to other land uses – Forest Land, Grassland, Cropland,

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Wetlands – which are easily defined based on a set of measurable and objective parameters. Some research has been applied in this direction (Theobald, 2004), but current classifications are inconsistent. While the rate of carbon sequestration per unit of tree crown cover is fairly consistent, for example, the overall rate of carbon storage per unit of settlement area depends entirely on the relative amounts of tree and turfgrass cover within that settlement. This land classification would be part of the set of activity data collected by countries, and the detailed methodology could be developed and applied consistently based on those land cover data. This type of land-use classification would also enable countries to account for changes in carbon storage resulting from management changes within areas broadly classified as settlements. For example, when vacant plots are developed, the adventitious vegetation remaining in the non-built areas might be replaced with landscape species differing in ability to store carbon.

References Akbari, H. (2002). Shade trees reduce building energy use and CO2 emissions from power plants. Environmental Pollution 116:S119-S124. Armentano, T.V. and Menges, E.S. (1986). Patterns of change in the carbon balance of organic soil-wetlands of the temperate zone. Journal of Ecology 74:755-774. 1986. Brack, C.L. (2002). Pollution mitigation and carbon sequestration by an urban forest. Environmental Pollution 116:S195-S200. Cairns, M.A., Brown, S., Helmer, E.H. and Baumgardner, G.A. (1997). Root biomass allocation in the world's upland forests. Oecologia 111:1-11. Crane, P. and Kinzig, A. (2005). Nature in the metropolis. Science 308:1225-1225. Elvidge, C.D., Milesi, C., Dietz, J.B., Tuttle, B.T., Sutton, P.C., Nemani, R. and Vogelmann, J.E. (2004). U.S. constructed area approaches the size of Ohio. EOS - Transactions of the American Geophysical Union 85:233-234. Falk, J. (1980). The primary productivity of lawns in a temperate environment. Journal of Applied Ecology 17:689-696. Falk, J.H. (1976). Energetics of a suburban lawn ecosystem. Ecology 57:141-150. Gallo, K.P., Elvidge, C.D., Yang, L. and Reed, B.C. (2004). Trends in night-time city lights and vegetation indices associated with urbanization within the conterminous USA. International Journal Of Remote Sensing 25:2003-2007. Goldman, M.B., Groffman, P.M., Pouyat, R.V., McDonnell, M.J. and Pickett, S.T.A. (1995). CH4 uptake and N availability in forest soils along an urban to rural gradient. Soil Biology and Biochemistry 27:281-286. Gregg, J.W., Jones, C.G. and Dawson, T.E. (2003). Urbanization effects on tree growth in the vicinity of New York City. Nature 424:183-187. Idso, C., Idso, S. and Balling, R.J. (1998). The urban CO2 dome of Phoenix, Arizona. Physical Geography 19:95-108. Idso, C., Idso, S. and Balling, R.J. (2001). An intensive two-week study of an urban CO2 dome. Atmospheric Environment 35:995-1000. Imhoff, M., Tucker, C., Lawrence, W. and Stutzer, D. (2000). The use of multisource satellite and geospatial data to study the effect of urbanization on primary productivity in the United States. IEEE Transactions on Geoscience and Remote Sensing 38:2549-2556. IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. Callander B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2003). Good Practice Guidance for Land Use, Land-Use Change and Forestry. Penman J., Gytarsky M., Hiraishi T., Krug, T., Kruger D., Pipatti R., Buendia L., Miwa K., Ngara T., Tanabe K., Wagner F. (Eds).Intergovernmental Panel on Climate Change (IPCC), IPCC/IGES, Hayama, Japan. Jenkins, J., Chojnacky, D., Heath, L. and Birdsey, R. (2004). Comprehensive database of diameter-based biomass regressions for North American tree species. General Technical Report NE-, USDA Forest Service Northeastern Research Station, Newtown Square, PA.

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Jo, H. (2002). Impacts of urban greenspace on offsetting carbon emissions for middle Korea. Journal of Environmental Management 64:115-126. Jo, H. and McPherson, E. (1995). Carbon storage and flux in urban residential greenspace. Journal of Environmental Management 45:109-133. Kaye, J., Burke, I., Mosier, A. and Guerschman, J. (2004). Methane and nitrous oxide fluxes from urban soils to the atmosphere. Ecological Applications 14:975-981. Kaye, J.P., McCulley, R.L. and Burke, I.C. (2005). Carbon fluxes, nitrogen cycling, and soil microbial communities in adjacent urban, native and agricultural ecosystems. Global Change Biology 11:575-587. Koerner, B., and Klopatek, J. (2002). Anthropogenic and natural CO2 emission sources in an arid urban environment. Environmental Pollution 116:S45-S51. Kuchler, A. (1969). Potential natural vegetation. US Geological Survey Map, Sheet 90, Washington, DC. Milesi, C., Elvidge, C.D., Nemani, R.R., and Running, S.W. (2003). Assessing the impact of urban land development on net primary productivity in the southeastern United States. Remote Sensing Of Environment 86:401-410. Nowak, D. (1996). Estimating leaf area and leaf biomass of open-grown deciduous urban trees. Forest Science 42:504-507. Nowak, D. and Crane, D. (2002). Carbon storage and sequestration by urban trees in the United States. Environmental Pollution 116:381-389. Nowak, D., Crane, D.E., Stevens, J.C. and Ibarra, M. (2002). Brooklyn's urban forest. General Technical Report NE-290, USDA Forest Service Northeastern Research Station, Newtown Square, PA. Nowak, D.J., Rowntree, R.A., McPherson, E.G., Sisinni, S.M., Kerkmann, E.R. and Stevens, J.C. (1996). Measuring and analyzing urban tree cover. Landscape and Urban Planning 36:49-57. Pouyat, R. and Carreiro, M. (2003). Controls on mass loss and nitrogen dynamics of oak leaf litter along an urban-rural land-use gradient. Oecologia 135:288-298. Pouyat, R., Groffman, P., Yesilonis, I. and Hernandez, L. (2002). Soil carbon pools and fluxes in urban ecosystems. Environmental Pollution 116:S107-S118. Pouyat, R.V., McDonnell, M.J. and Pickett, S.T.A. (1995). Soil characteristics of oak stands along an urban-rural land-use gradient. Journal of Environmental Quality 24:516-526. Qian, Y., Bandaranayake, W., Parton, W., Mecham, B., Harivandi, M. and Mosier, A. (2003). Long-term effects of clipping and nitrogen management in turfgrass on soil organic carbon and nitrogen dynamics: The CENTURY model simulation. Journal of Environmental Quality 32:1695-1700. Qian, Y. and Follett, R. (2002). Assessing soil carbon sequestration in turfgrass systems using long-term soil testing data. Agronomy Journal 94:930-935. Raturi, S., Islam, K.R., Carroll, M.J. and Hill, R.L. (2004). Thatch and soil characteristics of cool- and warmseason turfgrasses. Communications In Soil Science And Plant Analysis 35:2161-2176. Smith, W.B. and Brand, G.J. (1983). Allometric biomass equations for 98 species of herbs, shrubs, and small trees. Research Note NC-299, USDA Forest Service North Central Forest Experiment Station, St. Paul, MN. Theobald, D.M. (2004). Placing exurban land-use change in a human modification framework. Frontiers in Ecology and the Environment 2:139-144.

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Chapter 9: Other Land

CHAPTER 9

OTHER LAND

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Volume 4: Agriculture, Forestry and Other Land Use

Authors Jennifer C. Jenkins (USA), Hector D. Ginzo (Argentina), and Stephen Ogle (USA)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 9: Other Land

Contents 9

Other Land 9.1

Introduction ...........................................................................................................................................9.4

9.2

Other Land Remaining Other Land.......................................................................................................9.4

9.3

Land Converted to Other Land..............................................................................................................9.4

9.3.1

Biomass .........................................................................................................................................9.4

9.3.1.1

Choice of method.....................................................................................................................9.4

9.3.1.2

Choice of emission/removal factors.........................................................................................9.5

9.3.1.3

Choice of activity data .............................................................................................................9.5

9.3.1.4

Uncertainty assessment............................................................................................................9.6

9.3.2

Dead organic matter ......................................................................................................................9.6

9.3.3

Soil carbon.....................................................................................................................................9.7

9.4

9.3.3.1

Choice of method.....................................................................................................................9.7

9.3.3.2

Choice of stock change and emission factors ..........................................................................9.7

9.3.3.3

Choice of activity data .............................................................................................................9.8

9.3.3.4

Uncertainty assessment............................................................................................................9.8

Completeness, Time series, QA/QC, and Reporting .............................................................................9.9

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Volume 4: Agriculture, Forestry and Other Land Use

9 OTHER LAND 9.1

INTRODUCTION

Chapter 3 of this Volume defines Other Land to include bare soil, rock, ice, and all land areas that do not fall into any of the other five land-use categories treated in Chapters 4 to 8. Other Land is often unmanaged, and in that case changes in carbon stocks and non-CO2 emissions and removals are not estimated. Guidance is provided for the case of Land Converted to Other Land. This is because the conversion is associated with changes in carbon stocks or non-CO2 emissions, most importantly those associated with conversions from Forest Land. Emissions and removals from this land should continue to be estimated following the conversion, as described below. Inclusion also enables checking the overall consistency of land area and tracking conversions to and from Other Land.

9.2

OTHER LAND REMAINING OTHER LAND

Emissions and removals on Land Converted to Other Land class are estimated using the methods described below, which also cover land remaining Other Land after conversion. All areas of Other Land Remaining Other Land should be included in the land-use change matrix as described in Chapter 3 as a check on the overall area. Emissions from land converted to bare soil as a result of development of settlements should of course be included in the Settlements land-use category (See Chapter 8, Settlements).

9.3

LAND CONVERTED TO OTHER LAND

This section provides guidance on methods for estimating carbon stock changes for Land Converted to Other Land. In general this is unlikely to be a key category, if it takes place at all, but land can be converted to Other Land, e.g., as a result of deforestation with subsequent severe degradation, release of carbon stocks and associated emissions. Figure 1.3 in Chapter 1 provides the decision tree which can be used to identify the appropriate tier-level for Land Converted to Other Land. The fundamental equation for estimating change in carbon stocks associated with land-use conversions was introduced in Section 2.3.1.2 in Chapter 2. This basic method can be applied to estimate change in carbon stocks in Forest Land, Cropland, Grassland, Wetlands, and Settlements converted to Other Land. Extensions to the method are provided for the subsequent treatment of these land areas following the transition period to the Other Land category.

9.3.1

Biomass

The method requires estimates of carbon in biomass stocks prior to conversion, based on estimates of the areas of land converted during the period between land-use surveys. As a result of conversion to Other Land, it is assumed that the dominant vegetation is removed entirely, resulting in no carbon remaining in biomass after conversion. The difference between initial and final biomass carbon pools is used to calculate change in carbon stocks due to land-use conversion. In subsequent years, accumulations and losses in living biomass in Other Land is considered to be zero. Figure 2.2 provides the decision tree for identification of appropriate tier to estimate changes in carbon stocks in biomass.

9.3.1.1

C HOICE

OF METHOD

The basic method (Equation 2.16 in Chapter 2) summarises how to estimate the change in carbon stocks in biomass on Land Converted to Other Land. Average change in carbon stocks on a per area basis are estimated to be equal to the change in carbon stocks due to the removal of living biomass from the initial land uses. Tier 1 A Tier 1 method follows the approach in Equation 2.16 in Chapter 2 where the amount of above-ground biomass that is removed is estimated by multiplying the area (e.g., forest area) converted annually to Other Land by the average carbon content of biomass in the land prior to conversion (BBEFORE). In this case, BAFTER in Equation

9.4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 9: Other Land

2.16 is set to zero by default. The default assumption for the Tier 1 calculation is that all carbon in biomass (less harvested wood products removed from the area) is released to the atmosphere immediately (i.e., in the first year after conversion) through decay processes either on- or off-site. Tier 2 A Tier 2 method can be developed and used if country-specific data on carbon stocks before conversion to Other Land (i.e., BBEFORE in Equation 2.16) are obtainable. BAFTER remains at zero. In addition, under Tier 2, carbon losses can be apportioned to specific conversion processes, such as burning or harvesting. This allows for more accurate estimation of non-CO2 greenhouse gas emissions. A portion of biomass removed is sometimes used as wood products or as fuel wood. Chapter 2, Section 2.4 provides the basic method for estimating non-CO2 greenhouse gas emissions from biomass burning. Chapter 12 provides guidance for estimation techniques for carbon stored in harvested wood products. Tier 3 A Tier 3 method requires more detailed data/information than the Tier 2 approach, e.g.,: •

•

•

•

Geo-referenced disaggregated areas converted annually are used for each land use converted to Other Land; Carbon densities are based on locally specific information and; and Biomass stock values are based on inventories and/or the model estimations. Where data are available, Tier 3 methods may be used to track the dynamic behaviour of carbon stocks and greenhouse gas emissions following conversion. Where the land remains in a vegetation-free state (due to severe degradation), there will generally be a continuing decline in carbon stocks. If this is not the case, countries should consider whether the land should be classified under another land use, as indicated in Chapter 3.

9.3.1.2

C HOICE

OF EMISSION / REMOVAL FACTORS

Tier 1 Default parameters are provided for biomass stocks before conversion to enable countries with limited data resources to estimate emissions and removals from this source. The method requires the estimation of carbon stocks before conversion for the initial land use (BBEFORE) and assumes that the carbon stock after conversion (BAFTER) is zero. Tables provided in Chapters 4, 5, 6, 7 and 8 of this report, for average above-ground biomass volume and below-ground to above-ground biomass ratio in different land uses, can be used to estimate carbon stocks before conversion. Tier 2 The Tier 2 method requires country-specific information, which may be obtained, for example, through systematic studies of biomass carbon stocks in the various land-use categories. The default carbon stock values mentioned above can be applied to some parameters in a Tier 2 approach. Default parameters for emissions from biomass burning are provided in Chapter 2 Section 2.4. However, inventory compilers are encouraged to develop country-specific coefficients to improve the accuracy of estimates. BAFTER is set to zero. Tier 3 Under Tier 3, all model parameters should be country-specific and at a disaggregated level, and/or biomass stocks derived from periodic inventories should be used.

9.3.1.3

C HOICE

OF ACTIVITY DATA

All tiers require estimate of the area of land converted to Other Land over a time period that is consistent with land-use surveys and the period used for conversions in the land-use change matrix. Chapter 3 provides guidance on the use of different types of data representing land so that they are applied as appropriately and consistently as possible in inventory calculations. The same aggregate area estimates should be used for both biomass and soil in the calculations of change in carbon stocks on land converted to Other Land. As described below, higher tiers require greater specificity of areas. Tier 1 For a Tier 1 approach, activity data on areas of different land-use categories converted to Other Land are needed. If countries do not have these data, partial samples may be extrapolated to the entire land base, or historic

2006 IPCC Guidelines for National Greenhouse Gas Inventories

9.5

Volume 4: Agriculture, Forestry and Other Land Use

estimates of conversions may be extrapolated over time based on expert judgment. Forest areas converted to Other Land are particularly important. Tier 2 Under Tier 2, inventory compilers should use actual area estimates for transitions from various land-use categories to Other Land. Full coverage of land areas can be accomplished either through analysis of periodic remotely sensed images of land-use and land cover patterns, through periodic ground-based sampling of land-use patterns, or hybrid inventory systems (Chapter 3, Annex 3A.3 provides guidance on sampling). Tier 3 The activity data used should allow full accounting of all land-use category transitions to Other Land and should be disaggregated to account for different conditions within a country. Disaggregation can occur along political boundaries (county, province, etc.), biome, climate, or on a combination of these parameters. In many cases, information on multi-year trends in land conversion may be available (from periodic sample-based or remotely sensed inventories of land use and land cover).

9.3.1.4

U NCERTAINTY

ASSESSMENT

Tier 1 Under Tier 1, the sources of uncertainty are the use of global or national averages for biomass carbon stocks in Forest Land or Other Land uses before conversion, and coarse estimates of areas converted to Other Land. Areas should be estimated using the methods outlined in Chapter 3. Carbon stocks will have the uncertainties associated with their estimation in the relevant section of the Guidelines. In the absence of other estimates, a default uncertainty level of +75% of the estimated mean CO2 emission may be assumed. Tier 2 Actual area estimates for Land Converted to Other Land will enable more transparent accounting and allow experts to identify gaps and double counting of land areas. The Tier 2 method uses at least some country-specific values, which will improve the accuracy of estimates, provided they better represent conditions relevant to the country. When country-specific values are developed, inventory compilers should use sufficient sample sizes and techniques to minimize standard errors. Probability density functions (i.e., providing mean and variance estimates) can be derived for all country-parameters. Such data can be used in more advanced uncertainty analyses such as Monte Carlo simulations. Volume 1, Chapter 3 of this report can be referred for guidance on developing such analyses. At a minimum, Tier 2 approaches should provide error ranges for each countryspecific parameter. Tier 3 Activity data should provide a basis to assign estimates of uncertainty to areas associated with land conversion. Combining emission/removal factors and activity data and their associated uncertainties can be done using Monte Carlo procedures to estimate means and confidence intervals for the overall inventory.

9.3.2

Dead organic matter

T i e r s 1 a nd 2 For Tiers 1 and 2, it is assumed that no carbon remains in biomass or dead organic matter after conversion to Other Land. All biomass carbon stocks are assumed to be emitted in the year of conversion, thus there is no accumulation of DOM stocks. Under Tier 1, DOM in the various land-use categories are not estimated and thus there are no emissions or removals by sinks related to DOM to be estimated with conversions to Other Land. Under Tier 2, if countries estimate DOM stocks for land-use categories that are subject to conversion to Other Land, then (as for biomass) all DOM is assumed to be emitted in the year of conversion to Other Land. Tier 3 At Tier 3, estimates should incorporate country-specific data on DOM.

9.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 9: Other Land

9.3.3

Soil carbon

For Land Converted to Other Land, inventory compilers should estimate the change in carbon stocks in mineral soils under the initial land use relative to Other Land. Conversion of land to Other Land will result in a release of organic carbon previously held in soil if the conversion is to impervious surfaces such as bare rock. General information and guidance on estimating changes in soil C stocks are provided in Chapter 2, Section 2.3.3 (including equations), and needs to be reviewed before proceeding with a consideration of specific guidelines below. The total change in soil C stocks for land converted to Grassland is estimated using Equation 2.24 for the change in soil organic C stocks for mineral soils and organic soils; and stock changes associated with soil inorganic C pools (Tier 3 only). This section provides specific guidance for estimating mineral soil organic C stock changes. It is assumed that the stock changes in organic soils are minimal because drainage is unlikely in “Other Lands” However, methods are provided in Section 2.3.3 (Chapter 2) to estimate stock changes for organic soils in addition to soil inorganic C.

9.3.3.1

C HOICE

OF METHOD

Inventories can be developed using a Tier 1, 2 or 3 approach, with each successive tier requiring more detail and resources than the previous one. Decision trees are provided for mineral soils (Figure 2.4, in Chapter 2) to assist inventory compilers with selection of the appropriate tier. The approach at Tier 1 is that soil carbon stocks will decline to zero after conversion. If this is not the case, then the land should probably be classified under one of the Other Land uses. For Tier 2, country-specific estimates for C stocks on land that has been converted to Other Land should be used or the dynamics of carbon stocks in soils can be tracked at Tier 3 using country-specific data.

Mineral soils Tier 1 Using Equation 2.25 in Chapter 2, the change in soil organic C stocks are estimated for mineral soils accounting for the impact of land-use conversion to Other Land. Annual rates of emissions (source) or removals (sink) are estimated based on the difference in stocks (over time) for the initial and last year divided by the time dependence of the stock change factors (D, default is 20 years). Tier 2 The Tier 2 method for mineral soils also uses Equation 2.25, but involves country- or region-specific reference C stocks and/or stock change factors and more disaggregated land-use activity and environmental data. Tier 3 Tier 3 methods will involve more detailed and country-specific models and/or measurement-based approaches along with highly disaggregated land-use and management data. It is good practice that Tier 3 approaches estimating soil C change from land-use conversions to Other Land, employ models, data sets and/or monitoring networks that are capable of representing transitions over time from other land uses, including Forest Land, Grassland, Cropland, and possibly Settlements. Tier 3 methods should, where possible, be integrated with estimates of biomass removal and the post-clearance treatment of plant residues (including woody debris and litter), as variation in the removal and treatment of residues (e.g., burning, site preparation) will affect C inputs to soil organic matter formation and C losses through decomposition and combustion.

9.3.3.2

C HOICE

OF STOCK CHANGE AND EMISSION FACTORS

Mineral soils Tier 1 The initial (pre-conversion) soil organic C stock (SOC(0-T)) is computed from the default reference soil organic C stock (SOCREF) and stock change factor for land-use systems (FLU). The reference C stock at the end of the 20 year default transition period is assumed to be zero. See the appropriate section for specific information regarding the derivation of pre-conversion stock change factors for other land-use sectors (Forest Land in Section 4.2.3.2, Cropland in 5.2.3.2, Grassland in 6.2.3.2, and Settlements in 8.2.3.2).

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Volume 4: Agriculture, Forestry and Other Land Use

Tier 2 A Tier 2 approach can be implemented in which country-specific data are used to derive reference C stock and stock change factors (SOC(0-T), FLU, FMG, FI) that better represent conditions in different types of Other Land. Country-specific reference stocks at the end of the 20 year period can also be applied. Subsequently, emissions and removals are set to zero. See the appropriate section for specific information regarding the derivation of preconversion stock change factors for other land-use sectors (Forest Land in Section 4.2.3.2, Cropland in 5.2.3.2, Grassland in 6.2.3.2, and Settlements in 8.2.3.2). Reference values should be consistent across land-use sectors (i.e., Forest Land, Cropland, Grassland, Settlements, and Other Land), which requires coordination among the various teams conducting soil C inventories for AFOLU. Tier 3 Model parameters will be determined using country-specific data or soil stocks measures; using soil inventories with representative sampling as set out in Chapter 3.

9.3.3.3

C HOICE

OF ACTIVITY DATA

Mineral soils T i e r s 1 a nd 2 For purposes of estimating soil carbon stock change, area estimates of land-use conversions to Other Land should be stratified according to major climate regions and soil types. If such information has not already been compiled, an initial approach would be to overlay available land cover/land-use maps (of national origin or from global datasets such as IGBP_DIS) with soil and climate maps of national origin or global sources, such as the FAO Soils Map of the World and climate data from the United Nations Environmental Program. Detailed descriptions of the default climate and soil classification schemes are provided in Chapter 3. Soil types are classified based on taxonomic description and textural data, and climate regions are based on mean annual temperatures and precipitation, elevation and potential evapotranspiration. See corresponding sections dealing with each land-use category for sector-specific information on activity data (Forest Land in Section 4.2.3.3, Cropland in 5.2.3.3, Grassland in 6.2.3.3, and Settlements in 8.2.3.3). Activity data gathered using Approach 2 or 3 (see Chapter 3) provide the underlying basis for determining the previous land use for Land Converted to Other Land, but in its basic form at least, aggregate data (Approach 1) do not reveal specific transitions. In this case, conversions to Other Land will be reported with the Other Land Remaining Other Land and in effect transitions become step changes across the landscape. This makes it particularly important to achieve coordination among categories of land use to ensure consistency over time. Tier 3 For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or more detailed data on the combinations of climate, soil, topographic and management data are needed, relative to Tier 1 or 2 methods, but the exact requirements will be dependent on the model or measurement design.

9.3.3.4

U NCERTAINTY

ASSESSMENT

Uncertainties in estimating soil C stock changes in Land Converted to Other Land are due to: (i) uncertainties in land-use and management activity prior to conversion; (ii) uncertainties in reference soil C stocks if using a Tier 1 or 2 approach for mineral soils only; and (iii) uncertainties in the stock change/emission factors for Tier 1 or 2 approaches (or equivalently for Tier 3, uncertainties due to model structure or parameter values, or in measurements with sample-based inventories). Uncertainties may be large at Tier 1 where global or nationally aggregated statistics on land conversion are used, and because of reliance on default reference carbon stocks. Table 3.7 in Chapter 3 provides default uncertainties ranges associated with the different approaches to area estimation, and the uncertainty in carbon stock estimation could exceed +50% at Tier 1.

9.8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 9: Other Land

9.4

COMPLETENESS, TIME SERIES, QA/QC, AND REPORTING

COMPLETENESS The total area of Other Land covered by the inventory methodology is the sum of Other Land Remaining Other Land and Land Converted to Other Land during the time period. Inventory compilers are encouraged to track through time the total area of land classified as Other Land within country boundaries, keeping transparent records on which portions are used to estimate change in carbon stocks. All land area in a country should be included in the reporting even if an inventory of emissions and removals has not been compiled for a portion of the land base, such as Other Land.

DEVELOPING A CONSISTENT TIME SERIES To maintain a consistent time series, it is good practice for countries to apply the same inventory methods over the entire reporting time period, including definitions for land uses, area included in a C inventory, and calculation method. It is good practice to keep transparent records of any changes, and then re-calculate the C stock changes over the entire inventory time period. Guidance on re-calculation under these circumstances is given in Volume 1, Chapter 5. Consistent estimation and reporting of “Other Lands” also requires common definitions of climate and soil types across the entire time series for the period of the inventory.

INVENTORY QUALITY ASSURANCE/QUALITY CONTROL (QA/QC) It is good practice to implement quality control checks and external expert review of inventory estimates and data. Specific attention should be paid to country-specific estimates of stock change factors and emission factors to ensure that they are based on high quality data and verifiable expert opinion.

REPORTING AND DOCUMENTATION It is good practice to maintain and archive all information used to produce national inventory estimates. Metadata and data sources for information used to estimate country-specific parameters should be documented, and both mean and variance estimates provided. Actual databases and procedures used to process the data (e.g., statistical programs) to estimate country-specific factors should be archived. Activity data and definitions used to categorise or aggregate the activity data should be documented and archived.

REPORTING TABLES AND WORKSHEETS The categories described in this section can be reported using the reporting tables in Volume1, Chapter 8. The estimates for carbon dioxide emissions and removals from soils resulting from Land Converted to Other Land are reported in IPCC Reporting Category 5D, changes in soil carbon. Worksheets are provided in Annex 1 for calculating emissions and removals of CO2 from Land Converted to Other Land.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

9.9

Chapter 10: Emissions from Livestock and Manure Management

CHAPTER 10

EMISSIONS FROM LIVESTOCK AND MANURE MANAGEMENT

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.1

Volume 4: Agriculture, Forestry and Other Land Use

Authors Hongmin Dong (China), Joe Mangino (USA), and Tim A. McAllister (Canada) Jerry L. Hatfield (USA), Donald E. Johnson (USA), Keith R. Lassey (New Zealand), Magda Aparecida de Lima (Brazil), and Anna Romanovskaya (Russian Federation)

Contributing Authors Deborah Bartram (USA), Darryl Gibb (Canada), and John H. Martin, Jr. (USA)

10.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

Contents 10 Emissions from Livestock and Manure Management 10.1

Introduction .........................................................................................................................................10.7

10.2

Livestock Population and Feed Characterisation ................................................................................10.7

10.2.1

Steps to define categories and subcategories of livestock ...........................................................10.7

10.2.2

Choice of method ........................................................................................................................10.8

10.2.3

Uncertainty assessment .............................................................................................................10.23

10.2.4

Characterisation for livestock without species: Specific emission estimation methods ............10.23

10.3

Methane Emissions from Enteric Fermentation ................................................................................10.24

10.3.1

Choice of method ......................................................................................................................10.24

10.3.2

Choice of emission factors ........................................................................................................10.26

10.3.3

Choice of activity data...............................................................................................................10.33

10.3.4

Uncertainty assessment .............................................................................................................10.33

10.3.5

Completeness, Time series, Quality Assurance/Quality Control and Reporting .......................10.33

10.4

Methane Emissions from Manure Management................................................................................10.35

10.4.1

Choice of method ......................................................................................................................10.35

10.4.2

Choice of emission factors ........................................................................................................10.37

10.4.3

Choice of activity data...............................................................................................................10.48

10.4.4

Uncertainty assessment .............................................................................................................10.48

10.4.5

Completeness, Time series, Quality assurance / Quality control and Reporting .......................10.50

10.5

N2O Emissions from Manure Management.......................................................................................10.52

10.5.1

Choice of method ......................................................................................................................10.53

10.5.2

Choice of emission factors ........................................................................................................10.57

10.5.3

Choice of activity data...............................................................................................................10.61

10.5.4

Coordination with reporting for N2O emissions from managed soils........................................10.64

10.5.5

Uncertainty assessment .............................................................................................................10.66

10.5.6

Completeness, Time series, Quality assurance/Quality control and Reporting .........................10.68

10.5.7

Use of worksheets .....................................................................................................................10.69

Annex 10A.1 Data underlying methane default emission factors for enteric fermentation .............................10.71 Annex 10A.2 Data underlying methane default emission factors for Manure Management ...........................10.74 References

...................................................................................................................................................10.84

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.3

Volume 4: Agriculture, Forestry and Other Land Use

Equations Equation 10.1 Annual average population..................................................................................................10.8 Equation 10.2 Coefficient for calculating net energy for maintenance.....................................................10.13 Equation 10.3 Net energy for maintenance..............................................................................................10.15 Equation 10.4 Net energy for activity (for cattle and buffalo)..................................................................10.16 Equation 10.5 Net energy for activity (for sheep).....................................................................................10.16 Equation 10.6 Net energy for growth (for cattle and buffalo) ..................................................................10.17 Equation 10.7 Net energy for growth (for sheep) .....................................................................................10.17 Equation 10.8 Net energy for lactation (for beef cattle, dairy cattle and buffalo).....................................10.18 Equation 10.9 Net energy for lactation for sheep (milk production known).............................................10.18 Equation 10.10 Net energy for lactation for sheep (milk production unknown).........................................10.19 Equation 10.11 Net energy for work (for cattle and buffalo)......................................................................10.19 Equation 10.12 Net energy to produce wool (for sheep) ............................................................................10.19 Equation 10.13 Net energy for pregnancy (for cattle/buffalo and sheep) ...................................................10.20 Equation 10.14 Ratio of net energy available in a diet for maintenance to digestible energy consumed ...10.20 Equation 10.15 Ratio of net energy available for growth in a diet to digestible energy consumed ............10.21 Equation 10.16 Gross energy for cattle/buffalo and sheep .........................................................................10.21 Equation 10.17 Estimation of dry matter intake for growing and finishing cattle ......................................10.22 Equation 10.18a Estimation of dry matter intake for mature beef cattle .....................................................10.22 Equation 10.18b Estimation of dry matter intake for mature dairy cows ....................................................10.22 Equation 10.19 Enteric fermentation emissions from a livestock category ................................................10.28 Equation 10.20 Total emissions from livestock enteric fermentation.........................................................10.28 Equation 10.21 CH4 emission factors for enteric fermentation from a livestock category .........................10.31 Equation 10.22 CH4 emissions from manure management.........................................................................10.37 Equation 10.23 CH4 emission factor from manure management................................................................10.41 Equation 10.24 Volatile solid excretion rates .............................................................................................10.42 Equation 10.25 Direct N2O emissions from manure management .............................................................10.54 Equation 10.26 N losses due to volatilisation from manure management ..................................................10.54 Equation 10.27 Indirect N2O emissions due to volatilisation of N from manure management ..................10.56 Equation 10.28 N losses due to leaching from manure management systems............................................10.56 Equation 10.29 Indirect N2O emissions due to leaching from manure management..................................10.57 Equation 10.30 Annual N excretion rates ...................................................................................................10.57 Equation 10.31 Annual N excretion rates (Tier 2)......................................................................................10.58 Equation 10.32 N intake rates for cattle......................................................................................................10.58 Equation 10.33 N retained rates for cattle...................................................................................................10.60 Equation 10.34 Managed manure N available for application to managed soils, feed, fuel or construction uses ...............................................................................................................10.65

10.4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

Figures Figure 10.1

Decision tree for livestock population characterisation.......................................................10.9

Figure 10.2

Decision Tree for CH4 Emissions from Enteric Fermentation ..........................................10.25

Figure 10.3

Decision tree for CH4 emissions from Manure Management ............................................10.36

Figure 10.4

Decision tree for N2O emissions from Manure Management (Note 1) .............................10.55

Tables Table 10.1

Representative livestock categories...................................................................................10.11

Table 10.2

Representative feed digestibility for various livestock categories.................................10.14

Table 10.3

Summary of the equations used to estimate daily gross energy intake for Cattle, Buffalo and Sheep ...........................................................................................10.15

Table 10.4

Coefficients for calculating net energy for maintenance ( NEm ).......................................10.16

Table 10.5

Activity coefficients corresponding to animal’s feeding situation ....................................10.17

Table 10.6

Constants for use in calculating NEg for Sheep .................................................................10.18

Table 10.7

Constants for use in calculating NEp in Equation 10.13 ....................................................10.20

Table 10.8

Examples of NEma content of typical diets fed to Cattle for estimation of dry matter intake in Equations 10.17 and 10.18 ...............................................................10.23

Table 10.9

Suggested emissions inventory methods for enteric fermentation.....................................10.27

Table 10.10

Enteric fermentation emission factors for Tier 1 method ..................................................10.28

Table 10.11

Tier 1 enteric fermentation emission factors for Cattle .....................................................10.29

Table 10.12

Cattle/buffalo CH4 conversion factors (Ym ) .....................................................................10.30

Table 10.13

Sheep CH4 conversion factors (Ym)...................................................................................10.31

Table 10.14

Manure management methane emission factors by temperature for Cattle, Swine, and Buffalo ........................................................................................................................10.38

Table 10.15

Manure management methane emission factors by temperature for Sheep, Goats, Camels, Horses, Mules and Asses, and Poultry.................................................................10.40

Table 10.16

Manure management methane emission factors for Deer, Reindeer, Rabbits, and fur-bearing animals .....................................................................................................10.41

Table 10.17

MCF values by temperature for manure management systems .........................................10.44

Table 10.18

Definitions of manure management systems .....................................................................10.49

Table 10.19

Default values for nitrogen excretion rate ........................................................................10.59

Table 10.20

Default values for the fraction of nitrogen in feed intake of livestock that is retained by the different livestock species/categories (fraction N-intake retained by the animal) ......................................................................................................10.60

Table 10.21

Default emission factors for direct N2O emissions from manure management.................10.62

Table 10.22

Default values for nitrogen loss due to volatilisation of NH3 and NOx from manure management.......................................................................................................................10.65

Table 10.23

Default values for total nitrogen loss from manure management ......................................10.67

Table 10A.1

Data for estimating Tier 1 enteric fermentation CH4 emission factors for Dairy Cows in Table 10.11................................................................................................10.72

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.5

Volume 4: Agriculture, Forestry and Other Land Use

Table 10A.2

Data for estimating Tier 1 enteric fermentation CH4 emission factors for Other Cattle in Table 10.11 ...............................................................................................10.73

Table 10A.3

Data for estimating Tier 1 enteric fermentation CH4 emission factors for Buffalo ..........10.75

Table 10A-4

Manure management methane emission factor derivation for Dairy Cows.......................10.77

Table 10A-5

Manure management methane emission factor derivation for Other Cattle ......................10.78

Table 10A-6

Manure management methane emission factor derivation for Buffalo..............................10.79

Table 10A-7

Manure management methane emission factor derivation for Market Swine ...................10.80

Table 10A-8

Manure management methane emission factor derivation for Breeding Swine ................10.81

Table 10A-9

Manure management methane emission factor derivation for Other Animals .................10.82

10.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

10 EMISSIONS FROM LIVESTOCK AND MANURE MANAGEMENT 10.1

INTRODUCTION

This chapter provides guidance on methods to estimate emissions of methane from Enteric Fermentation in livestock, and methane and nitrous oxide emissions from Manure Management. CO2 emissions from livestock are not estimated because annual net CO2 emissions are assumed to be zero – the CO2 photosynthesized by plants is returned to the atmosphere as respired CO2. A portion of the C is returned as CH4 and for this reason CH4 requires separate consideration. Livestock production can result in methane (CH4) emissions from enteric fermentation and both CH4 and nitrous oxide (N2O) emissions from livestock manure management systems. Cattle are an important source of CH4 in many countries because of their large population and high CH4 emission rate due to their ruminant digestive system. Methane emissions from manure management tend to be smaller than enteric emissions, with the most substantial emissions associated with confined animal management operations where manure is handled in liquid-based systems. Nitrous oxide emissions from manure management vary significantly between the types of management system used and can also result in indirect emissions due to other forms of nitrogen loss from the system. The calculation of the nitrogen loss from manure management systems is also an important step in determining the amount of nitrogen that will ultimately be available in manure applied to managed soils, or used for feed, fuel, or construction purposes – emissions that are calculated in Chapter 11, Section 11.2 (N2O emissions from managed soils). The methods for estimating CH4 and N2O emissions from livestock require definitions of livestock subcategories, annual populations and, for higher Tier methods, feed intake and characterisation. The procedures employed to define livestock subcategories, develop population data, and characterize feed are described in Section 10.2 (Livestock Population and Feed Characterisation). Suggested feed digestibility coefficients for various livestock categories have been provided to help estimation of feed intake for use in calculation of emissions from enteric and manure sources. A coordinated livestock characterisation as described in Section 10.2 should be used to ensure consistency across the following source categories: •

•

Section 10.3 - CH4 emissions from Enteric Fermentation;

•

Section 10.5 - N2O emissions from Manure Management (direct and indirect);

•

Section 10.4 - CH4 emissions from Manure Management;

Chapter 11, Section 11.2 - N2O emissions from Managed Soils (direct and indirect).

10.2

LIVESTOCK POPULATION AND FEED CHARACTERISATION

10.2.1

Steps to define categories and subcategories of livestock

Good practice is to identify the appropriate method for estimating emissions for each source category, and then base the characterisation on the most detailed requirements identified for each livestock species. The livestock characterisation used by a country will probably undergo iterations as the needs of each source category are assessed during the emissions estimation process (see Figure 10.1, Decision Tree for Livestock Population Characterisation). The steps are: •

•

Identify livestock species applicable to each emission source category: The livestock species that contribute to more than one emission source category should first be listed. These species are typically: cattle, buffalo, sheep, goats, swine, horses, camels, mules/asses, and poultry. Review the emission estimation method for each relevant source category: For the source categories of Enteric Fermentation and Manure Management, identify the emission estimating method for each species for that source category. For example, enteric fermentation emissions from cattle, buffalo, and sheep should each be examined to assess whether the trend or level of emissions warrant a Tier 2 or Tier 3 emissions estimate. Similarly, manure management methane emissions from cattle, buffalo, swine, and poultry should

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•

be examined to determine whether the Tier 2 or Tier 3 emissions estimate is appropriate. Existing inventory estimates can be used to conduct this assessment. If no inventory has been developed to date, Tier 1 emission estimates should be calculated to provide initial estimates for conducting this assessment. See Volume 1, Chapter 4 (Methodological Choice and Identification of Key Categories) for guidance on the general issues of methodological choice. Identify the most detailed characterisation required for each livestock species: Based on the assessments for each species under each source category, identify the most detailed characterisation required to support each emissions estimate for each species. Typically, the ‘Basic’ characterisation can be used across all relevant source categories if the enteric fermentation and manure sources are both estimated with their Tier 1 methods. An ‘Enhanced’ characterisation should be used to estimate emissions across all the relevant sources if the Tier 2 method is used for either enteric fermentation or manure.

10.2.2

Choice of method

TIER 1: BASIC CHARACTERISATION FOR LIVESTOCK POPULATIONS Basic characterisation for Tier 1 is likely to be sufficient for most animal species in most countries. For this approach it is good practice to collect the following livestock characterisation data to support the emissions estimates: Livestock species and categories: A complete list of all livestock populations that have default emission factor values must be developed (e.g., dairy cows, other cattle, buffalo, sheep, goats, camels, llamas, alpacas, deer, horses, rabbits, mules and asses, swine, and poultry) if these categories are relevant to the country. More detailed categories should be used if the data are available. For example, more accurate emission estimates can be made if poultry populations are further subdivided (e.g., layers, broilers, turkeys, ducks, and other poultry), as the waste characteristics among these different populations varies significantly. Annual population: If possible, inventory compilers should use population data from official national statistics or industry sources. Food and Agriculture Organisation (FAO) data can be used if national data are unavailable. Seasonal births or slaughters may cause the population size to expand or contract at different times of the year, which will require the population numbers to be adjusted accordingly. It is important to fully document the method used to estimate the annual population, including any adjustments to the original form of the population data as it was received from national statistical agencies or from other sources. Annual average populations are estimated in various ways, depending on the available data and the nature of the animal population. In the case of static animal populations (e.g., dairy cows, breeding swine, layers), estimating the annual average population may be as simple as obtaining data related to one-time animal inventory data. However, estimating annual average populations for a growing population (e.g., meat animals, such as broilers, turkeys, beef cattle, and market swine) requires more evaluation. Most animals in these growing populations are alive for only part of a complete year. Animals should be included in the populations regardless if they were slaughtered for human consumption or die of natural causes. Equation 10.1 estimates the annual average of livestock population. EQUATION 10.1 ANNUAL AVERAGE POPULATION ⎛ NAPA ⎞ AAP = Days _alive • ⎜ ⎟ ⎝ 365 ⎠ Where: AAP = annual average population NAPA = number of animals produced annually Broiler chickens are typically grown approximately 60 days before slaughter. Estimating the average annual population as the number of birds grown and slaughtered over the course of a year would greatly overestimate the population, as it would assume each bird lived the equivalent of 365 days. Instead, one should estimate the average annual population as the number of animals grown divided by the number of growing cycles per year. For example, if broiler chickens are typically grown in flocks for 60 days, an operation could turn over approximately 6 flocks of chickens over the period of one year. Therefore, if the operation grew 60,000 chickens in a year, their average annual population would be 9,863 chickens. For this example the equation would be: Annual average population = 60 days ● 60,000 / 365 days / yr = 9,863 chickens

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Chapter 10: Emissions from Livestock and Manure Management

Figure 10.1

Decision tree for livestock population characterisation

Start

Identify livestock species applicable to each category.

Review the emission estimation methods for each of the categories1.

Identify whether a basic or enhanced characterisation is required for each livestock species based on key category analyses2.

Ask for each livestock species: "Are data available to support the level of detail required for the characterisation?"

Can data be collected to support the level of characterisation?

No

No

Set the level of the characterisation to the available data. Box 1

Yes Yes Perform the characterisation at the required level of detail.

Collect the data required to support the characterisation. Yes

Box 2 Note: 1: These categories include: CH4 Emission from Enteric Fermentation, CH4 Emission from Manure Management, and N2O Emission from Manure Management. 2: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

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Dairy cows and milk production: The dairy cow population is estimated separately from other cattle (see Table 10.1). Dairy cows are defined in this method as mature cows that are producing milk in commercial quantities for human consumption. This definition corresponds to the dairy cow population reported in the FAO Production Yearbook. In some countries the dairy cow population is comprised of two well-defined segments: (i) high-producing (also called improved) breeds in commercial operations; and (ii) low-producing cows managed with traditional methods. These two segments can be combined, or can be evaluated separately by defining two dairy cow categories. However, the dairy cow category does not include cows kept principally to produce calves for meat or to provide draft power. Low productivity multi-purpose cows should be considered as other cattle. Dairy buffalo may be categorized in a similar manner to dairy cows. Data on the average milk production of dairy cows are also required. Milk production data are used in estimating an emission factor for enteric fermentation using the Tier 2 method. Country-specific data sources are preferred, but FAO data may also be used. These data are expressed in terms of kilograms of whole fresh milk produced per year per dairy cow. If two or more dairy cow categories are defined, the average milk production per cow is required for each category.

TIER 2: ENHANCED CHARACTERISATION FOR LIVESTOCK POPULATIONS The Tier 2 livestock characterisation requires detailed information on: •

• •

Definitions for livestock subcategories; Livestock population by subcategory, with consideration for estimation of annual population as per Tier 1; and Feed intake estimates for the typical animal in each subcategory.

The livestock population subcategories are defined to create relatively homogenous sub-groupings of animals. By dividing the population into these subcategories, country-specific variations in age structure and animal performance within the overall livestock population can be reflected. The Tier 2 characterisation methodology seeks to define animals, animal productivity, diet quality and management circumstances to support a more accurate estimate of feed intake for use in estimating methane production from enteric fermentation. The same feed intake estimates should be used to provide harmonised estimates of manure and nitrogen excretion rates to improve the accuracy and consistency of CH4 and N2O emissions from manure management.

Definitions for livestock subcategories It is good practice to classify livestock populations into subcategories for each species according to age, type of production, and sex. Representative livestock categories for doing this are shown in Table 10.1. Further subcategories are also possible: •

•

• •

Cattle and buffalo populations should be classified into at least three main subcategories: mature dairy, other mature, and growing cattle. Depending on the level of detail in the emissions estimation method, subcategories can be further classified based on animal or feed characteristics. For example, growing / fattening cattle could be further subdivided into those cattle that are fed a high-grain diet and housed in dry lot vs. those cattle that are grown and finished solely on pasture. Subdivisions similar to those used for cattle and buffalo can be used to further segregate the sheep population in order to create subcategories with relatively homogenous characteristics. For example, growing lambs could be further segregated into lambs finished on pasture vs. lambs finished in a feedlot. The same approach applies to national goat herds. Subcategories of swine could be further segregated based on production conditions. For example, growing swine could be further subdivided into growing swine housed in intensive production facilities vs. swine that are grown under free-range conditions. Subcategories of poultry could be further segregated based on production conditions. For example, poultry could be divided on the basis of production under confined or free-range conditions.

For large countries or for countries with distinct regional differences, it may be useful to designate regions and then define categories within those regions. Regional subdivisions may be used to represent differences in climate, feeding systems, diet, and manure management. However, this further segregation is only useful if correspondingly detailed data are available on feeding and manure management system usage by these livestock categories.

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TABLE 10.1 REPRESENTATIVE LIVESTOCK CATEGORIES1,2 Main categories Mature Dairy Cow or Mature Dairy Buffalo

Other Mature Cattle or Mature Non-dairy Buffalo

• •

Subcategories High-producing cows that have calved at least once and are used principally for milk production Low-producing cows that have calved at least once and are used principally for milk production

Females: • •

Cows used to produce offspring for meat Cows used for more than one production purpose: milk, meat, draft

Males: • •

Growing Cattle or Growing Buffalo

Mature Ewes Other Mature Sheep (>1 year) Growing Lambs

Mature Swine

Growing Swine

Chickens

• • • •

• • •

• • • • • •

• • • • • • • •

Turkeys

Ducks Others (for example)

1 2

• • • • •

• • • • • • • • •

Bulls used principally for breeding purposes Bullocks used principally for draft power Calves pre-weaning Replacement dairy heifers Growing / fattening cattle or buffalo post-weaning Feedlot-fed cattle on diets containing > 90 % concentrates Breeding ewes for production of offspring and wool production Milking ewes where commercial milk production is the primary purpose No further sub-categorisation recommended Intact males Castrates Females Sows in gestation Sows which have farrowed and are nursing young Boars that are used for breeding purposes Nursery Finishing Gilts that will be used for breeding purposes Growing boars that will be used for breeding purposes Broiler chickens grown for producing meat Layer chickens for producing eggs, where manure is managed in dry systems (e.g., high-rise houses) Layer chickens for producing eggs, where manure is managed in wet systems (e.g., lagoons) Chickens under free-range conditions for egg or meat production Breeding turkeys in confinement systems Turkeys grown for producing meat in confinement systems Turkeys under free-range conditions for meat production Breeding ducks Ducks grown for producing meat Camels Mules and Asses Llamas, Alpacas Fur bearing animals Rabbits Horses Deer Ostrich Geese

Source IPCC Expert Group Emissions should only be considered for livestock species used to produce food, fodder or raw materials used for industrial processes.

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For each of the representative animal categories defined, the following information is required: •

•

•

annual average population (number of livestock or poultry as per calculations for Tier 1); average daily feed intake (megajoules (MJ) per day and / or kg per day of dry matter); and methane conversion factor (percentage of feed energy converted to methane).

Generally, data on average daily feed intake are not available, particularly for grazing livestock. Consequently, the following general data should be collected for estimating the feed intake for each representative animal category: •

•

weight (kg);

•

feeding situation: confined, grazing, pasture conditions;

• •

•

•

•

•

average weight gain per day (kg)1;

milk production per day (kg/day) and fat content (%)2; average amount of work performed per day (hours day-1); percentage of females that give birth in a year3; wool growth; number of offspring; and feed digestibility (%).

Feed intake estimates Tier 2 emissions estimates require feed intakes for a representative animal in each subcategory. Feed intake is typically measured in terms of gross energy (e.g., megajoules (MJ) per day) or dry matter (e.g., kilograms (kg) per day). Dry matter is the amount of feed consumed (kg) after it has been corrected for the water content in the complete diet. For example, consumption of 10 kg of a diet that contains 70% dry matter would result in a dry matter intake of 7 kg. To support the enteric fermentation Tier 2 method for cattle, buffalo, and sheep (see Section 10.3), detailed data requirements and equations to estimate feed intake are included in guidance below. Constants in the equations have been combined to simplify overall equation formats. The remainder of this subsection presents the typical data requirements and equations used to estimate feed intake for cattle, buffalo, and sheep. Feed intake for other species can be estimated using similar country-specific methods appropriate for each. For all estimates of feed intake, good practice is to:

•

•

Collect data to describe the animal’s typical diet and performance in each subcategory; Estimate feed intake from the animal performance and diet data for each subcategory.

In some cases, the equations may be applied on a seasonal basis, for example under conditions in which livestock gain weight in one season and lose weight in another. This approach may require a more refined variation of Tier 2 or more complex Tier 3 type methodology. The following animal performance data are required for each animal subcategory to estimate feed intake for the subcategory: •

Weight (W), kg: Live-weight data should be collected for each animal subcategory. It is unrealistic to perform a complete census of live-weights, so live-weight data should be obtained from representative sample studies or statistical databases if these already exist. Comparing live-weight data with slaughterweight data is a useful cross-check to assess whether the live-weight data are representative of country conditions. However, slaughter-weight data should not be used in place of live-weight data as it fails to account for the complete weight of the animal. Additionally, it should be noted that the relationship between live-weight and slaughter-weight varies with breed and body condition. For cattle, buffalo and

1

This may be assumed to be zero for mature animals.

2

Milk production data are required for dairy animals. These can be estimated for non-dairy animals providing milk to young, where data are available.

3

This is only relevant for mature females.

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•

•

• •

•

mature sheep, the yearly average weight for each animal category (e.g., mature beef cows) is needed. For young sheep, weights are needed at birth, weaning, one year of age or at slaughter if slaughter occurs within the year. Average weight gain per day (WG), kg day-1: Data on average weight gain are generally collected for feedlot animals and young growing animals. Mature animals are generally assumed to have no net weight gain or loss over an entire year. Mature animals frequently lose weight during the dry season or during temperature extremes and gain weight during the following season. However, increased emissions associated with this weight change are likely to be small. Reduced intakes and emissions associated with weight loss are largely balanced by increased intakes and emissions during the periods of gain in body weight. Mature weight (MW), kg: The mature weight of the adult animal of the inventoried group is required to define a growth pattern, including the feed and energy required for growth. For example, mature weight of a breed or category of cattle or buffalo is generally considered to be the body weight at which skeletal development is complete. The mature weight will vary among breeds and should reflect the animal’s weight when in moderate body condition. This is termed ‘reference weight’ (ACC, 1990) or ‘final shrunk body weight’ (NRC, 1996). Estimates of mature weight are typically available from livestock specialists and producers. Average number of hours worked per day: For draft animals, the average number of hours worked per day must be determined. Feeding situation: The feeding situation that most accurately represents the animal subcategory must be determined using the definitions shown below (Table 10.5). If the feeding situation lies between the definitions, the feeding situation should be described in detail. This detailed information may be needed when calculating the enteric fermentation emissions, because interpolation between the feeding situations may be necessary to assign the most appropriate coefficient. Table 10.5 defines the feeding situations for cattle, buffalo, and sheep. For poultry and swine, the feeding situation is assumed to be under confinement conditions and consequently the activity coefficient (Ca )is assumed to be zero as under these conditions very little energy is expended in acquiring feed. Activity coefficients have not been developed for freeranging swine or poultry, but in most instances these livestock subcategories are likely to represent a small proportion of the national inventory. Mean winter temperature (ºC): Detailed feed intake models consider ambient temperature, wind speed, hair and tissue insulation and the heat of fermentation (NRC, 2001; AAC, 1990) and are likely more appropriate in Tier 3 applications. A more general relationship adapted from North America data suggest adjusting the Cfi of Equation 10.3 for maintenance requirements of open-lot fed cattle in colder climates according to the following equation (Johnson, 1986): EQUATION 10.2 COEFFICIENT FOR CALCULATING NET ENERGY FOR MAINTENANCE Cf i (in _ cold ) = Cf i + 0.0048 • (20 − °C )

Where: Cfi = a coefficient which varies for each animal category as shown in Table 10.4 (Coefficients for calculating NEm), MJ day-1 kg-1 °C = mean daily temperature during winter season Considering the average temperature during winter months, net energy for maintenance (NEm) requirements may increase by as much as 30% in northern North America. This increase in feed use for maintenance is also likely associated with greater methane emissions. •

• •

Average daily milk production (kg day-1): These data are for milking ewes, dairy cows and buffalo. The average daily production should be calculated by dividing the total annual production by 365, or reported as average daily production along with days of lactation per year, or estimated using seasonal production divided by number of days per season. If using seasonal production data, the emission factor must be developed for that seasonal period. Fat content (%): Average fat content of milk is required for lactating cows, buffalo, and sheep producing milk for human consumption. Percent of females that give birth in a year: This is collected only for mature cattle, buffalo, and sheep.

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Number of off spring produced per year: This is relevant to female livestock that have multiple births per year (e.g., ewes). Feed digestibility (DE%): The portion of gross energy (GE) in the feed not excreted in the faeces is known as digestible feed. The feed digestibility is commonly expressed as a percentage (%) of GE or TDN (total digestible nutrients). That percentage of feed that is not digested represents the % of dry matter intake that will be excreted as faeces. Typical digestibility values for a range of livestock classes and diet types are presented in Table 10.2 as a guideline. For ruminants, common ranges of feed digestibility are 45-55% for crop by-products and range lands; 55-75% for good pastures, good preserved forages, and grain supplemented forage-based diets; and 75-85% for grain-based diets fed in feedlots. Variations in diet digestibility results in major variations in the estimate of feed needed to meet animal requirements and consequently associated methane emissions and amounts of manure excreted. It is also important to note that digestibility, intake, and growth are co-dependent phenomena. For example, a low digestibility will lead to lower feed intake and consequently reduced growth. Conversely, feeds with high digestibility will often result in a higher feed intake and increased growth. A 10% error in estimating DE will be magnified to 12 to 20% when estimating methane emissions and even more (20 to 45%) for manure excretion (volatile solids). Digestibility data should be based on measured values for the dominant feeds or forages being consumed by livestock with consideration for seasonal variation. In general, the digestibility of forages decreases with increasing maturity and is typically lowest during the dry season. Due to significant variation, digestibility coefficients should be obtained from local scientific data wherever possible. Although a complete census of digestibility is considered unrealistic, at a minimum digestibility data from research studies should be consulted. While developing the digestibility data, associated feed characteristic data should also be recorded when available, such as measured values for Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), crude protein, and the presence of anti-nutritional factors (e.g., alkaloids, phenolics, % ash). NDF and ADF are feed characteristics measured in the laboratory that are used to indicate the nutritive value of the feed for ruminant livestock. Determination of these values can enable DE to be predicted as defined in the recent dairy NRC (2001). The concentration of crude protein in the feed can be used in the process of estimating nitrogen excretion (Section 10.5.2). Average annual wool production per sheep (kg yr-1): The amount of wool produced in kilograms (after drying out but before scouring) is needed to estimate the amount of energy allocated for wool production.

REPRESENTATIVE Main categories Swine

Cattle and other ruminants

Poultry

1

10.14

• • • •

TABLE 10.2 FEED DIGESTIBILITY FOR VARIOUS LIVESTOCK CATEGORIES Class Mature Swine – confinement Growing Swine - confinement Swine – free range

• •

Feedlot animals fed with > 90% concentrate diet; Pasture fed animals; Animals fed – low quality forage

• • • • •

Broiler Chickens –confinement Layer Hens – confinement Poultry – free range Turkeys – confinement Geese – confinement

• • •

Digestibility (DE%) 70 - 80% 80 - 90% 50 - 70% 1

•

75 - 85%

• •

55 - 75% 45 - 55%

• • • • •

85 - 93% 70 - 80% 55 - 90% 1 85 - 93% 80 - 90%

The range in digestibility of feed consumed by free-range swine and poultry is extremely variable due to the selective nature of these diets. Often it is likely that the amount of manure produced in these classes will be limited by the amount of feed available for consumption as opposed to its degree of digestibility. In instances where feed is not limiting and high quality feed sources are readily accessible for consumption, digestibility may approach values that are similar to those measured under confinement conditions.

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Gross energy calculations Animal performance and diet data are used to estimate feed intake, which is the amount of energy (MJ/day) an animal needs for maintenance and for activities such as growth, lactation, and pregnancy. For inventory compilers who have well-documented and recognised country-specific methods for estimating intake based on animal performance data, it is good practice to use the country-specific methods. The following section provides methods for estimating gross energy intake for the key ruminant categories of cattle, buffalo and sheep. The equations listed in Table 10.3 are used to derive this estimate. If no country-specific methods are available, intake should be calculated using the equations listed in Table 10.3. As shown in the table, separate equations are used to estimate net energy requirements for sheep as compared with cattle and buffalo. The equations used to calculate GE are as follows:

TABLE 10.3 SUMMARY OF THE EQUATIONS USED TO ESTIMATE DAILY GROSS ENERGY INTAKE FOR CATTLE, BUFFALO AND SHEEP Metabolic functions and other estimates

Equations for cattle and buffalo

Equations for sheep

Maintenance (NEm)

Equation 10.3

Equation 10.3

Activity (NEa)

Equation 10.4

Equation 10.5

Growth (NEg)

Equation 10.6

Equation 10.7

Lactation (NEl)*

Equation 10.8

Equations 10.9 and 10.10

Draft Power (NEwork)

Equation 10.11

NA

NA

Equation 10.12

Pregnancy (NEp)*

Equation 10.13

Equation 10.13

Ratio of net energy available in diet for maintenance to digestible energy consumed (REM)

Equation 10.14

Equation 10.14

Ratio of net energy available for growth in a diet to digestible energy consumed (REG)

Equation 10.15

Equation 10.15

Gross Energy

Equation 10.16

Equation 10.16

Wool Production (NEwool)

Source: Cattle and buffalo equations based on NRC (1996) and sheep based on AFRC (1993). NA means ‘not applicable’. * Applies only to the proportion of females that give birth.

Net energy for maintenance: (NEm ) is the net energy required for maintenance, which is the amount of energy needed to keep the animal in equilibrium where body energy is neither gained nor lost (Jurgen, 1988).

NET

EQUATION 10.3 ENERGY FOR MAINTENANCE

NEm = Cf i • (Weight )0.75

Where: NEm = net energy required by the animal for maintenance, MJ day-1 Cfi = a coefficient which varies for each animal category as shown in Table 10.4 (Coefficients for calculating NEm), MJ day-1 kg-1 Weight = live-weight of animal, kg Net energy for activity: (NEa) is the net energy for activity, or the energy needed for animals to obtain their food, water and shelter. It is based on its feeding situation rather than characteristics of the feed itself. As presented in Table 10.3, the equation for estimating NEa for cattle and buffalo is different from the equation used for sheep. Both equations are empirical with different definitions for the coefficient Ca.

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EQUATION 10.4 NET ENERGY FOR ACTIVITY (FOR CATTLE AND BUFFALO) NEa = Ca • NE m Where: NEa = net energy for animal activity, MJ day-1 Ca = coefficient corresponding to animal’s feeding situation (Table 10.5, Activity coefficients) NEm = net energy required by the animal for maintenance (Equation 10.3), MJ day-1

EQUATION 10.5 NET ENERGY FOR ACTIVITY (FOR SHEEP) NEa = Ca • (weight ) Where: NEa = net energy for animal activity, MJ day-1 Ca = coefficient corresponding to animal’s feeding situation (Table 10.5), MJ day-1 kg-1 weight = live-weight of animal, kg For Equations 10.4 and 10.5, the coefficient Ca corresponds to a representative animal’s feeding situation as described earlier. Values for Ca are shown in Table 10.5. If a mixture of these feeding situations occurs during the year, NEa must be weighted accordingly.

TABLE 10.4 COEFFICIENTS FOR CALCULATING NET ENERGY FOR MAINTENANCE ( NEM ) Animal category

Cfi (MJ d-1 kg-1)

Comments

Cattle/Buffalo (non-lactating cows)

0.322

Cattle/Buffalo (lactating cows)

0.386

This value is 20% higher for maintenance during lactation

Cattle/Buffalo (bulls)

0.370

This value is 15% higher for maintenance of intact males

Sheep (lamb to 1 year)

0.236

This value can be increased by 15% for intact males

Sheep (older than 1 year)

0.217

This value can be increased by 15% for intact males.

Source: NRC (1996) and AFRC (1993).

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TABLE 10.5 ACTIVITY COEFFICIENTS CORRESPONDING TO ANIMAL’S FEEDING SITUATION Situation

Definition

Ca

Cattle and Buffalo (unit for Ca is dimensionless) Stall

Animals are confined to a small area (i.e., tethered, pen, barn) with the result that they expend very little or no energy to acquire feed.

0.00

Pasture

Animals are confined in areas with sufficient forage requiring modest energy expense to acquire feed.

0.17

Grazing large areas

Animals graze in open range land or hilly terrain and expend significant energy to acquire feed.

0.36

Sheep (unit for Ca = MJ d-1 kg-1) Housed ewes

Animals are confined due to pregnancy in final trimester (50 days).

0.0090

Grazing flat pasture

Animals walk up to 1000 meters per day and expend very little energy to acquire feed.

0.0107

Grazing hilly pasture

Animals walk up to 5,000 meters per day and expend significant energy to acquire feed.

0.0240

Housed fattening lambs

Animals are housed for fattening.

0.0067

Source: NRC (1996) and AFRC (1993).

Net energy for growth: (NEg) is the net energy needed for growth (i.e., weight gain). Equation 10.6 is based on NRC (1996). Equation 10.7 is based on Gibbs et al. (2002). Constants for conversion from calories to joules and live to shrunk and empty body weight have been incorporated into the equation. EQUATION 10.6 NET ENERGY FOR GROWTH (FOR CATTLE AND BUFFALO) ⎛ BW ⎞ NE g = 22.02 • ⎜ ⎟ ⎝ C • MW ⎠

0.75

• WG1.097

Where: NEg = net energy needed for growth, MJ day-1 BW = the average live body weight (BW) of the animals in the population, kg C = a coefficient with a value of 0.8 for females, 1.0 for castrates and 1.2 for bulls (NRC, 1996) MW = the mature live body weight of an adult female in moderate body condition, kg WG = the average daily weight gain of the animals in the population, kg day-1

(

(

))

EQUATION 10.7 NET ENERGY FOR GROWTH (FOR SHEEP) WGlamb • a + 0.5b BWi + BW f NE g = 365 Where: NEg = net energy needed for growth, MJ day-1 WGlamb = the weight gain (BWf – BWi), kg yr-1 BWi = the live bodyweight at weaning, kg

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BWf = the live bodyweight at 1-year old or at slaughter (live-weight) if slaughtered prior to 1 year of age, kg a, b = constants as described in Table 10.6. Note that lambs will be weaned over a period of weeks as they supplement a milk diet with pasture feed or supplied feed. The time of weaning should be taken as the time at which they are dependent on milk for half their energy supply. The NEg equation used for sheep includes two empirical constants (a and b) that vary by animal species/category (Table 10.6).

TABLE 10.6 CONSTANTS FOR USE IN CALCULATING NEG FOR SHEEP a (MJ kg-1)

b (MJ kg-2)

Intact males

2.5

0.35

Castrates

4.4

0.32

Females

2.1

0.45

Animal species/category

Source: AFRC (1993).

Net energy for lactation: (NEl ) is the net energy for lactation. For cattle and buffalo the net energy for lactation is expressed as a function of the amount of milk produced and its fat content expressed as a percentage (e.g., 4%) (NRC, 1989): EQUATION 10.8 NET ENERGY FOR LACTATION (FOR BEEF CATTLE, DAIRY CATTLE AND BUFFALO) NE1 = Milk • (1.47 + 0.40 • Fat ) Where: NEl = net energy for lactation, MJ day-1 Milk = amount of milk produced, kg of milk day-1 Fat = fat content of milk, % by weight. Two methods for estimating the net energy required for lactation (NEl) are presented for sheep. The first method (Equation 10.9) is used when the amount of milk produced is known, and the second method (Equation 10.8) is used when the amount of milk produced is not known. Generally, milk production is known for ewes kept for commercial milk production, but it is not known for ewes that suckle their young to weaning. With a known amount of milk production, the total annual milk production is divided by 365 days to estimate the average daily milk production in kg/day (Equation 10.9). When milk production is not known, AFRC (1990) indicates that for a single birth, the milk yield is about 5 times the weight gain of the lamb. For multiple births, the total annual milk production can be estimated as five times the increase in combined weight gain of all lambs birthed by a single ewe. The daily average milk production is estimated by dividing the resulting estimate by 365 days as shown in Equation 10.10. EQUATION 10.9 NET ENERGY FOR LACTATION FOR SHEEP (MILK PRODUCTION KNOWN) NE1 = Milk • EVmilk Where: NEl = net energy for lactation, MJ day-1 Milk = amount of milk produced, kg of milk day-1

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EVmilk = the net energy required to produce 1 kg of milk. A default value of 4.6 MJ/kg (AFRC, 1993) can be used which corresponds to a milk fat content of 7% by weight

EQUATION 10.10 NET ENERGY FOR LACTATION FOR SHEEP (MILK PRODUCTION UNKNOWN) ⎡ (5 • WGwean ) ⎤ NE1 = ⎢ ⎥ • EVmilk 365 ⎣ ⎦ Where: NEl = net energy for lactation, MJ day-1 WG wean = the weight gain of the lamb between birth and weaning, kg EVmilk = the energy required to produce 1 kg of milk, MJ kg-1. A default value of 4.6 MJ kg-1 (AFRC, 1993) can be used. Net energy for work: (NEwork ) is the net energy for work. It is used to estimate the energy required for draft power for cattle and buffalo. Various authors have summarised the energy intake requirements for providing draft power (e.g., Lawrence, 1985; Bamualim and Kartiarso, 1985; and Ibrahim, 1985). The strenuousness of the work performed by the animal influences the energy requirements, and consequently a wide range of energy requirements have been estimated. The values by Bamualim and Kartiarso show that about 10 percent of a day’s NEm requirements are required per hour for typical work for draft animals. This value is used as follows: EQUATION 10.11 NET ENERGY FOR WORK (FOR CATTLE AND BUFFALO) NEwork = 0.10 • NEm • Hours Where: NEwork = net energy for work, MJ day-1 NEm = net energy required by the animal for maintenance (Equation 10.3), MJ day-1 Hours = number of hours of work per day

Net energy for wool production: (NEwool ) is the average daily net energy required for sheep to produce a year of wool. The NEwool is calculated as follows: EQUATION 10.12 NET ENERGY TO PRODUCE WOOL (FOR SHEEP) • Productionwool ⎞ ⎛ EV NE wool = ⎜ wool ⎟ 365 ⎝ ⎠ Where: NEwool = net energy required to produce wool, MJ day-1 EVwool = the energy value of each kg of wool produced (weighed after drying but before scouring), MJ kg-1. A default value of 24 MJ kg-1 (AFRC, 1993) can be used for this estimate. Productionwool = annual wool production per sheep, kg yr-1 Net energy for pregnancy: (NEp) is the energy required for pregnancy. For cattle and buffalo, the total energy requirement for pregnancy for a 281-day gestation period averaged over an entire year is calculated as 10% of NEm. For sheep, the NEp requirement is similarly estimated for the 147-day gestation period, although the percentage varies with the number of lambs born (Table 10.7, Constant for Use in Calculating NEp in Equation 10.13). Equation 10.13 shows how these estimates are applied.

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EQUATION 10.13 NET ENERGY FOR PREGNANCY (FOR CATTLE/BUFFALO AND SHEEP) NE p = C pregnancy • NEm Where: NEp = net energy required for pregnancy, MJ day-1 Cpregnancy = pregnancy coefficient (see Table 10.7) NEm = net energy required by the animal for maintenance (Equation 10.3), MJ day-1

TABLE 10.7 CONSTANTS FOR USE IN CALCULATING NEP IN EQUATION 10.13 Animal category

Cpregnancy

Cattle and Buffalo

0.10

Sheep Single birth

0.077

Double birth (twins)

0.126

Triple birth or more (triplets)

0.150

Source: Estimate for cattle and buffalo developed from data in NRC (1996). Estimates for sheep developed from data in AFRC (1993), taking into account the inefficiency of energy conversion.

When using NEp to calculate GE for cattle and sheep, the NEp estimate must be weighted by the portion of the mature females that actually go through gestation in a year. For example, if 80% of the mature females in the animal category give birth in a year, then 80% of the NEp value would be used in the GE equation below. To determine the proper coefficient for sheep, the portion of ewes that have single births, double births, and triple births is needed to estimate an average value for Cpregnancy. If these data are not available, the coefficient can be calculated as follows: •

•

If the number of lambs born in a year divided by the number of ewes that are pregnant in a year is less than or equal to 1.0, then the coefficient for single births can be used. If the number of lambs born in a year divided by the number of ewes that are pregnant in a year exceeds 1.0 and is less than 2.0, calculate the coefficient as follows: Cpregnancy = [(0.126 • Double birth fraction) + (0.077 • Single birth fraction)]

Where: Double birth fraction = [(lambs born / pregnant ewes) – 1] Single birth fraction = [1 – Double birth fraction]

Ratio of net energy available in diet for maintenance to digestible energy consumed (REM): For cattle, buffalo and sheep, the ratio of net energy available in a diet for maintenance to digestible energy consumed (REM ) is estimated using the following equation (Gibbs and Johnson, 1993): EQUATION 10.14 RATIO OF NET ENERGY AVAILABLE IN A DIET FOR MAINTENANCE TO DIGESTIBLE ENERGY

(

) [

]

⎡ ⎛ 25.4 ⎞⎤ REM = ⎢1.123 − 4.092 • 10 −3 • DE % + 1.126 • 10 −5 • (DE % )2 − ⎜ ⎟⎥ ⎝ DE % ⎠⎦ ⎣ CONSUMED

Where:

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REM = ratio of net energy available in a diet for maintenance to digestible energy consumed DE% = digestible energy expressed as a percentage of gross energy

Ratio of net energy available for growth in a diet to digestible energy consumed (REG): For cattle, buffalo and sheep the ratio of net energy available for growth (including wool growth) in a diet to digestible energy consumed (REG ) is estimated using the following equation (Gibbs and Johnson, 1993):

) [

]

EQUATION 10.15 RATIO OF NET ENERGY AVAILABLE FOR GROWTH IN A DIET TO DIGESTIBLE ENERGY CONSUMED

(

⎡ ⎛ 37.4 ⎞⎤ REG = ⎢1.164 − 5.160 • 10 −3 • DE % + 1.308 • 10 −5 • (DE % )2 − ⎜ ⎟⎥ ⎝ DE % ⎠⎦ ⎣

Where: REG = ratio of net energy available for growth in a diet to digestible energy consumed DE% = digestible energy expressed as a percentage of gross energy

Gross energy, GE: As shown in Equation 10.16, GE requirement is derived based on the summed net energy requirements and the energy availability characteristics of the feed(s). Equation 10.16 represents good practice for calculating GE requirements for cattle and sheep using the results of the equations presented above.

In using Equation 10.16, only those terms relevant to each animal category are used (see Table 10.3). EQUATION 10.16 GROSS ENERGY FOR CATTLE/BUFFALO AND SHEEP

⎡ ⎛ NEm + NE a + NE1 + NE work + NE p ⎢ ⎜⎜ REM GE = ⎢ ⎝ ⎢ DE % ⎢ 100 ⎢⎣

⎞ ⎛ NE g + NE wool ⎟+⎜ ⎟ ⎜ REG ⎠ ⎝

⎞⎤ ⎟⎥ ⎟ ⎠⎥ ⎥ ⎥ ⎥⎦

Where: GE = gross energy, MJ day-1 NEm = net energy required by the animal for maintenance (Equation 10.3), MJ day-1 NEa = net energy for animal activity (Equations 10.4 and 10.5), MJ day-1 NEl = net energy for lactation (Equations 10.8, 10.9, and 10.10), MJ day-1 NEwork = net energy for work (Equation 10.11), MJ day-1 NEp = net energy required for pregnancy (Equation 10.13), MJ day-1 REM = ratio of net energy available in a diet for maintenance to digestible energy consumed (Equation 10.14) NEg = net energy needed for growth (Equations 10.6 and 10.7), MJ day-1 NEwool = net energy required to produce a year of wool (Equation 10.12), MJ day-1 REG = ratio of net energy available for growth in a diet to digestible energy consumed (Equation 10.15) DE%= digestible energy expressed as a percentage of gross energy Once the values for GE are calculated for each animal subcategory, the feed intake in units of kilograms of dry matter per day (kg day-1) should also be calculated. To convert from GE in energy units to dry matter intake (DMI), divide GE by the energy density of the feed. A default value of 18.45 MJ kg-1 of dry matter can be used if feed-specific information is not available. The resulting daily dry matter intake should be in the order of 2% to 3% of the body weight of the mature or growing animals. In high producing milk cows, intakes may exceed 4% of body weight.

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Feed intake estimates using a simplified Tier 2 method Prediction of DMI for cattle based on body weight and estimated dietary net energy concentration (NEma) or digestible energy values (DE%): It is also possible to predict dry matter intake for mature and growing cattle based on body weight of the animal and either the NEma concentration of the feed (NRC, 1996) or DE%. Dietary NEma concentration can range from 3.0 to 9.0 MJ kg-1 of dry matter. Typical values for high, moderate and low quality diets are presented in Table 10.8. These figures can also be used to estimate NEma values for mixed diets based on estimate of diet quality. For example, a mixed forage-grain diet could be assumed to have a NEma value similar to that of a high-quality forage diet. A mixed grain-straw diet could be assumed to have a NEma value similar to that of a moderate quality forage. Nutritionists within specific geographical areas should be able to provide advice with regard to the selection of NEma values that are more representative of locally fed diets.

Dry matter intake for growing and finishing cattle is estimated using the following equation:

(

)⎤⎥

EQUATION 10.17 ESTIMATION OF DRY MATTER INTAKE FOR GROWING AND FINISHING CATTLE ⎡ 0.2444 • NE ma − 0.0111 • NE ma 2 − 0.472 DMI = BW 0.75 • ⎢ NE ma ⎢⎣

⎥⎦

Where: DMI = dry matter intake, kg day-1 BW = live body weight, kg NEma = estimated dietary net energy concentration of diet or default values in Table 10.8, MJ kg-1 Dry matter intake for mature beef cattle is estimated using the following equation:

(

)⎤⎥

EQUATION 10.18a ESTIMATION OF DRY MATTER INTAKE FOR MATURE BEEF CATTLE ⎡ 0.0119 • NE ma 2 + 0.1938 DMI = BW 0.75 • ⎢ NE ma ⎢⎣

⎥⎦

Where: DMI = dry matter intake, kg day-1 BW = live body weight, kg NEma = estimated dietary net energy concentration of diet or default values given in Table 10.8, MJ kg-1 For mature dairy cows consuming low quality, often tropical forages, the following alternative equation for estimating dry matter intake based on DE% can be used (NRC, 1989): EQUATION 10.18b ESTIMATION OF DRY MATTER INTAKE FOR MATURE DAIRY COWS ⎡ ⎛ (5.4 • BW ) ⎞ ⎤ ⎟ ⎥ ⎢ ⎜ 500 ⎠ ⎥ ⎝ ⎢ DMI = ( )⎞⎥ DE − 100 % ⎢⎛ ⎟⎥ ⎢⎜ 100 ⎠⎦ ⎣⎝

Where: DMI = dry matter intake, kg day-1 BW = live body weight, kg DE%= digestible energy expressed as a percentage of gross energy (typically 45-55% for low quality forages)

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Equations 10.17, 10.18a, and 10.18b provide a good check to the main Tier 2 method to predict feed intake. They can be viewed as asking ‘what is an expected intake for a given diet quality?’ and used to independently predict DMI from BW and diet quality (NEma or DE%). In contrast, the main Tier 2 method predicts DMI based on how much feed must be consumed to meet estimated requirements (i.e., NEm and NEg) and does not consider the biological capacity of the animal to in fact consume the predicted quantity of feed. Consequently, the simplified Tier 2 method can be used to confirm that DMI values derived from the main Tier 2 method are biologically realistic. These estimates are also subject to the cross check that dry matter intake should be in the order of 2% to 3% of the bodyweight of the mature or growing animals.

TABLE 10.8 EXAMPLES OF NEMA CONTENT OF TYPICAL DIETS FED TO CATTLE FOR ESTIMATION OF DRY MATTER INTAKE IN EQUATIONS 10.17 AND 10.18 Diet type

NEma (MJ (kg dry matter)-1)

High grain diet > 90%

7.5 - 8.5

High quality forage (e.g., vegetative legumes & grasses )

6.5 - 7.5

Moderate quality forage (e.g., mid season legume & grasses)

5.5 - 6.5

Low quality forage (e.g., straws, mature grasses)

3.5 - 5.5

Source: Estimates obtained from predictive models in NRC (1996), NEma can also be estimated using the equation: NEma = REM x 18.45 x DE% / 100.

10.2.3

Uncertainty assessment

The first step in collecting data should be to investigate existing national statistics, industry sources, research studies and FAO statistics. The uncertainty associated with populations will vary widely depending on source, but should be known within +20%. Often, national livestock population statistics already have associated uncertainty estimates in which case these should be used. If published data are not available from these sources, interviews of key industry and academic experts can be undertaken. Estimates of digestibility are also particularly important in Tier 2 estimates of gross energy intake. Uncertainty estimates for digestibility estimates may be as high as +20%. Volume 1, Chapter 3 (Uncertainties) describes how to elicit expert judgement for uncertainty ranges. Similar expert elicitation protocols can be used to obtain the information required for the livestock characterisation if published data and statistics are not available.

10.2.4

Characterisation for livestock without species: Specific emission estimation methods

Some countries may have domesticated livestock for which there are currently no Tier 1 or Tier 2 emissions estimating methods (e.g., llamas, alpacas, wapiti, emus, and ostriches). Good practice in estimating emissions from these livestock is to first assess whether their emissions are likely to be significant enough to warrant characterising them and developing country-specific emission factors. Volume 1, Chapter 4 (Methodological Choice and Identification of Key Categories) presents guidance for assessing the significance of individual source categories within the national inventory. Similar approaches can be used to assess the importance of subsource categories (i.e. species) within a source category. If the emissions from a particular sub-species are determined to be significant, then country-specific emission factors should be developed, and a characterisation should be performed to support the development of the emission factors. Research into the estimation of emission levels from these non-characterized species should be encouraged. The data and methods used to characterise the animals should be well documented. As emissions estimation methods are not available for these animals, approximate emission factors based on ‘order of magnitude calculations’ are appropriate for conducting the assessment of the significance of their emissions. One approach for developing the approximate emission factors is to use the Tier 1 emissions factor for an animal with a similar digestive system and to scale the emissions factor using the ratio of the weights of the animals raised to the 0.75 power. The Tier 1 emission factors can be classified by digestive system as follows:

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•

Ruminant animals: Cattle, Buffalo, Sheep, Goats, Camels

•

Poultry: Chickens, Ducks, Turkeys, Geese

•

Non-ruminant herbivores: Horses, Mules/Asses

Non-poultry monogastric animals: Swine

For example, an approximate enteric fermentation methane emissions factor for alpacas could be estimated from the emissions factor for sheep (also a ruminant animal) as follows: Approximate emissions factor = [(alpaca weight) / (sheep weight)]0.75 • sheep emissions factor Similarly, an approximate manure methane emissions factor for ostriches could be estimated using the Tier 1 emission factor for chickens. Approximate emission factors developed using this method can only be used to assess the significance of the emissions from the animals, and are not considered sufficiently accurate for estimating emissions as part of a national inventory.

10.3

METHANE EMISSIONS FROM ENTERIC FERMENTATION

Methane is produced in herbivores as a by-product of enteric fermentation, a digestive process by which carbohydrates are broken down by micro-organisms into simple molecules for absorption into the bloodstream. The amount of methane that is released depends on the type of digestive tract, age, and weight of the animal, and the quality and quantity of the feed consumed. Ruminant livestock (e.g., cattle, sheep) are major sources of methane with moderate amounts produced from non-ruminant livestock (e.g., pigs, horses). The ruminant gut structure fosters extensive enteric fermentation of their diet. Digestive system

The type of digestive system has a significant influence on the rate of methane emission. Ruminant livestock have an expansive chamber, the rumen, at the fore-part of their digestive tract that supports intensive microbial fermentation of their diet which yields several nutritional advantages including the capacity to digest cellulose in their diet. The main ruminant livestock are cattle, buffalo, goats, sheep, deer and camelids. Non-ruminant livestock (horses, mules, asses) and monogastric livestock (swine) have relatively lower methane emissions because much less methane-producing fermentation takes place in their digestive systems. Feed intake

Methane is produced by the fermentation of feed within the animal's digestive system. Generally, the higher the feed intake, the higher the methane emission. Although, the extent of methane production may also be affected by the composition of the diet. Feed intake is positively related to animal size, growth rate, and production (e.g., milk production, wool growth, or pregnancy). To reflect the variation in emission rates among animal species, the population of animals should be divided into subgroups, and an emission rate per animal is estimated for each subgroup. Types of population subgroups are provided in Section 10.2 (Livestock and Feed Characterisation). The amount of methane emitted by a population subgroup is calculated by multiplying the emission rate per animal by the number of animals within the subgroup. Natural wild ruminants are not considered in the derivation of a country’s emission estimate. Emissions should only be considered from animals under domestic management (e.g., farmed deer, elk, and buffalo).

10.3.1

Choice of method

It is good practice to choose the method for estimating methane emissions from enteric fermentation according to the decision tree in Figure 10.2. The method for estimating methane emission from enteric fermentation requires three basic steps:

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Figure 10.2

Decision Tree for CH 4 Emissions from Enteric Fermentation

Start

Do you have a country-specific Tier 3 methodology?

Yes

Estimate emissions for the species using Tier 3 approach. Box 3: Tier 3

No

Is enhanced livestock characterization available?

No

Is enteric fermentation a key category1 and is the species significant2?

No

Estimate emissions for the species using Tier 1 approach. Box 1: Tier 1

Yes Yes Collect enhanced species characterization data for Tier 2 approach.

Estimate emissions for the species using Tier 2 approach. Box 2: Tier 2 Note: 1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 2: As a rule of thumb, a livestock species would be significant if it accounts for 25-30% or more of emissions from the source category.

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Step 1: Divide the livestock population into subgroups and characterize each subgroup as described in Section 10.2. It is recommended that national experts use annual averages estimated with consideration for the impact of production cycles and seasonal influences on population numbers. Step 2: Estimate emission factors for each subgroup in terms of kilograms of methane per animal per year. Step 3: Multiply the subgroup emission factors by the subgroup populations to estimate subgroup emission, and sum across the subgroups to estimate total emission.

These three steps can be performed at varying levels of detail and complexity. This chapter presents the following three approaches: Tier 1 A simplified approach that relies on default emission factors either drawn from the literature or calculated using the more detailed Tier 2 methodology. The Tier 1 method is likely to be suitable for most animal species in countries where enteric fermentation is not a key source category, or where enhanced characterization data are not available. When approximate enteric emissions are derived by extrapolation from main livestock categories they should be considered to be a Tier 1 method. Tier 2 A more complex approach that requires detailed country-specific data on gross energy intake and methane conversion factors for specific livestock categories. The Tier 2 method should be used if enteric fermentation is a key source category for the animal category that represents a large portion of the country’s total emissions. Tier 3 Some countries for which livestock emissions are particularly important may wish to go beyond the Tier 2 method and incorporate additional country-specific information in their estimates. This approach could employ the development of sophisticated models that consider diet composition in detail, concentration of products arising from ruminant fermentation, seasonal variation in animal population or feed quality and availability, and possible mitigation strategies. Many of these estimates would be derived from direct experimental measurements. Although countries are encouraged to go beyond the Tier 2 method presented below when data are available, these more complex analyses are only briefly discussed here. A Tier 3 method should be subjected to a wide degree of international peer review such as that which occurs in peer-reviewed publications to ensure that they improve the accuracy and / or precision of estimates.

Countries with large populations of domesticated animal species for which there are no IPCC default emission factors (e.g., llamas and alpacas) are encouraged to develop national methods that are similar to the Tier 2 method and are based on well-documented research (if it is determined that emissions from these livestock are significant). The approach is described in Section 10.2.4 under the heading ‘Characterisation for livestock without species-specific emission estimation methods’ for more information. Table 10.9 summarises the suggested approaches for the livestock emissions included in this inventory.

10.3.2

Choice of emission factors

Tier 1 Approach for methane emissions from Enteric Fermentation This Tier 1 method is simplified so that only readily-available animal population data are needed to estimate emissions. Default emission factors are presented for each of the recommended population subgroups. Each step is discussed in turn. Step 1: Animal population

The animal population data should be obtained using the approach described in Section 10.2. Step 2: Emission factors

The purpose of this step is to select emission factors that are most appropriate for the country's livestock characteristics. Default emission factors for enteric fermentation have been drawn from previous studies, and are organised by region for ease of use. The data used to estimate the default emission factors for enteric fermentation are presented in Annex 10A.1 at the end of this section.

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TABLE 10.9 SUGGESTED EMISSIONS INVENTORY METHODS FOR ENTERIC FERMENTATION Livestock

Suggested emissions inventory methods

Dairy Cow

Tier 2a/Tier 3

Other Cattle

Tier 2a/Tier 3

Buffalo

Tier 1/Tier 2

Sheep

Tier 1/Tier 2

Goats

Tier 1

Camels

Tier 1

Horses

Tier 1

Mules and Asses

Tier 1

Swine

Tier 1

Poultry

Not developed

Other (e.g., Llamas, Alpacas, Deer) a

Tier 1

The Tier 2 method is recommended for countries with large livestock populations. Implementing the Tier 2 method for additional livestock subgroups may be desirable when the category emissions are a large portion of total methane emissions for the country.

Table 10.10 shows the enteric fermentation emission factors for each of the animal species except cattle. As shown in the table, emission factors for sheep and swine vary for developed and developing countries. The differences in the emission factors are driven by differences in feed intake and feed characteristic assumptions (see Annex 10A.1). Table 10.11 presents the enteric fermentation emission factors for cattle. A range of emission factors is shown for typical regional conditions. As shown in the table, the emission factors vary by over a factor of four on a per head basis. While the default emission factors shown in Table 10.11 are broadly representative of the emission rates within each of the regions described, emission factors vary within each region. Animal size and milk production are important determinants of emission rates for dairy cows. Relatively smaller dairy cows with low levels of production are found in Asia, Africa, and the Indian subcontinent. Relatively larger dairy cows with high levels of production are found in North America and Western Europe. Animal size and population structure are important determinants of emission rates for other cattle. Relatively smaller other cattle are found in Asia, Africa, and the Indian subcontinent. Also, many of the other cattle in these regions are young. Other cattle in North America, Western Europe and Oceania are larger, and young cattle constitute a smaller portion of the population. To select emission factors from Tables 10.10 and 10.11, identify the region most applicable to the country being evaluated. Scrutinise the tabulations in Annex 10A.1 to ensure that the underlying animal characteristics such as weight, growth rate and milk production used to develop the emission factors are similar to the conditions in the country. The data collected on the average annual milk production by dairy cows should be used to help select a dairy cow emission factor. If necessary, interpolate between dairy cow emission factors shown in the table using the data collected on average annual milk production per head. Note that using the same Tier 1 emission factors for the inventories of successive years means that no allowance is being made for changing livestock productivity, such as increasing milk productivity or trend in live weight. If it is important to capture the trend in methane emission that results from a trend in livestock productivity, then livestock emissions can become a key source category based on trend and a Tier 2 calculation should be used.

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TABLE 10.10 ENTERIC FERMENTATION EMISSION FACTORS FOR TIER 1 METHOD1 (KG CH4 HEAD-1 YR-1) Developed countries

Developing countries

Liveweight

Buffalo

55

55

300 kg

Sheep

8

5

65 kg - developed countries; 45 kg - developing countries

Goats

5

5

40 kg

Camels

46

46

570 kg

Horses

18

18

550 kg

Mules and Asses

10

10

245 kg

Deer

20

20

120 kg

Alpacas

8

8

65 kg

Swine

1.5

1.0

Poultry

Insufficient data for calculation

Insufficient data for calculation

Other (e.g., Llamas)

To be determined1

To be determined1

Livestock

All estimates have an uncertainty of +30-50%. Sources: Emission factors for buffalo and camels from Gibbs and Johnson (1993). Emission factors for other livestock from Crutzen et al., (1986), Alpacas from Pinares-Patino et al., 2003; Deer from Clark et al., 2003 . 1

One approach for developing the approximate emission factors is to use the Tier 1 emissions factor for an animal with a similar digestive system and to scale the emissions factor using the ratio of the weights of the animals raised to the 0.75 power. Liveweight values have been included for this purpose. Emission factors should be derived on the basis of characteristics of the livestock and feed of interest and should not be restricted solely to within regional characteristics.

Step 3: Total emission

To estimate total emission, the selected emission factors are multiplied by the associated animal population (Equation 10.19) and summed (Equation 10.20): EQUATION 10.19 ENTERIC FERMENTATION EMISSIONS FROM A LIVESTOCK CATEGORY ⎛ N (T ) Emissions = EF(T ) • ⎜⎜ 6 ⎝ 10

⎞ ⎟ ⎟ ⎠

Where: Emissions = methane emissions from Enteric Fermentation, Gg CH4 yr-1 EF(T) = emission factor for the defined livestock population, kg CH4 head-1 yr-1 N(T) = the number of head of livestock species / category T in the country T = species/category of livestock

EQUATION 10.20 TOTAL EMISSIONS FROM LIVESTOCK ENTERIC FERMENTATION

Total CH 4 Enteric = ∑ Ei i

Where: Total CH4Enteric = total methane emissions from Enteric Fermentation, Gg CH4 yr-1 Ei = is the emissions for the ith livestock categories and subcategories

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TABLE 10.11 TIER 1 ENTERIC FERMENTATION EMISSION FACTORS FOR CATTLE1

Regional characteristics

North America: Highly productive commercialized dairy sector feeding high quality forage and grain. Separate beef cow herd, primarily grazing with feed supplements seasonally. Fast-growing beef steers/heifers finished in feedlots on grain. Dairy cows are a small part of the population. Western Europe: Highly productive commercialised dairy sector feeding high quality forage and grain. Dairy cows also used for beef calf production. Very small dedicated beef cow herd. Minor amount of feedlot feeding with grains. Eastern Europe: Commercialised dairy sector feeding mostly forages. Separate beef cow herd, primarily grazing. Minor amount of feedlot feeding with grains. Oceania: Commercialised dairy sector based on grazing. Separate beef cow herd, primarily grazing rangelands of widely varying quality. Growing amount of feedlot feeding with grains. Dairy cows are a small part of the population. Latin America: Commercialised dairy sector based on grazing. Separate beef cow herd grazing pastures and rangelands. Minor amount of feedlot feeding with grains. Growing non-dairy cattle comprise a large portion of the population. Asia: Small commercialised dairy sector. Most cattle are multi-purpose, providing draft power and some milk within farming regions. Small grazing population. Cattle of all types are smaller than those found in most other regions. Africa and Middle East: Commercialised dairy sector based on grazing with low production per cow. Most cattle are multi-purpose, providing draft power and some milk within farming regions. Some cattle graze over very large areas. Cattle are smaller than those found in most other regions. Indian Subcontinent: Commercialised dairy sector based on crop by-product feeding with low production per cow. Most bullocks provide draft power and cows provide some milk in farming regions. Small grazing population. Cattle in this region are the smallest compared to cattle found in all other regions.

Cattle category

Emission factor 2,3 (kg CH4 head-1 yr-1)

Dairy

128

Average milk production of 8,400 kg head-1 yr-1.

Other Cattle

53

Includes beef cows, bulls, calves, growing steers/heifers, and feedlot cattle.

Dairy

117

Average milk production of 6,000 kg head-1 yr-1.

Other Cattle

57

Includes bulls, calves, and growing steers/heifers.

Dairy

99

Average milk production of 2,550 kg head-1 yr-1.

Other Cattle

58

Includes beef cows, bulls, and young.

Dairy

90

Average milk production of 2,200 kg head-1 yr-1.

Other Cattle

60

Includes beef cows, bulls, and young.

Dairy

72

Average milk production of 800 kg head-1 yr-1

Other Cattle

56

Includes beef cows, bulls, and young.

Dairy

68

Average milk production of 1,650 kg head-1 yr-1

Other Cattle

47

Includes multi-purpose cows, bulls, and young

Dairy

46

Average milk production of 475 kg head-1 yr-1

Other Cattle

31

Includes multi-purpose cows, bulls, and young

Dairy

58

Average milk production of 900 kg head-1 yr-1

Other Cattle

27

Includes cows, bulls, and young. Young comprise a large portion of the population

Comments

1

Emission factors should be derived on the basis of the characteristics of the cattle and feed of interest and need not be restricted solely to within regional characteristics.

2

IPCC Expert Group, values represent averages within region, where applicable the use of more specific regional milk production data is encouraged. Existing values were derived using Tier 2 method and the data in Tables 10 A.1 and 10A. 2.

3

The following assumptions have been made in deriving these values: i) mature weights of animals have been used; ii) cows have been assumed to be non-lactating as lactation levels were low and, iii) the mix of bulls and castrates among "males" was undetermined as Cfi value for castrates was not specified.

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Tier 2 Approach for methane emissions from Enteric Fermentation The Tier 2 method is applied to more disaggregated livestock population categories and used to calculate emission factors, as opposed to default values. The key considerations for the Tier 2 method are the development of emission factors and the collection of detailed activity data. Step 1: Livestock population

The animal population data and related activity data should be obtained following the approach described in Section 10.2. Step 2: Emission factors

When the Tier 2 method is used, emission factors are estimated for each animal category using the detailed data developed in Step 1. The emission factors for each category of livestock are estimated based on the gross energy intake and methane conversion factor for the category. The gross energy intake data should be obtained using the approach described in Section 10.2. The following two sub-steps need to be completed to calculate the emission factor under the Tier 2 method: 1. Obtaining the methane conversion factor (Ym)

The extent to which feed energy is converted to CH4 depends on several interacting feed and animal factors. If CH4 conversion factors are unavailable from country-specific research, the values provided in Table 10.12, Cattle/Buffalo CH4 conversion factors, can be used for cattle and buffalo. These general estimates are a rough guide based on the general feed characteristics and production practices found in many developed and developing countries. When good feed is available (i.e., high digestibility and high energy value) the lower bounds should be used. When poorer feed is available, the higher bounds are more appropriate. A CH4 conversion factor of zero is assumed for all juveniles consuming only milk (i.e., milk-fed lambs as well as calves). Due to the importance of Ym in driving emissions, substantial ongoing research is aimed at improving estimates of Ym for different livestock and feed combinations. Such improvement is most needed for animals fed on tropical pastures as the available data are sparse. For example, a recent study (Kurihara et al., 1999) observed Ym values outside the ranges described in Table 10.12.

TABLE 10.12 CATTLE/BUFFALO CH4 CONVERSION FACTORS (YM ) Livestock category

Ym b

Feedlot fed Cattle a

3.0% + 1.0%

Dairy Cows (Cattle and Buffalo) and their young

6.5% + 1.0%

Other Cattle and Buffaloes that are primarily fed low quality crop residues and byproducts

6.5% + 1.0%

Other Cattle or Buffalo – grazing

6.5% + 1.0%

a b

When fed diets contain 90 percent or more concentrates. The ± values represent the range.

Source: IPCC Expert Group.

Regional, national and global estimates of enteric methane generation rely on small scale determinations both of Ym and of the influence of feed and animal properties upon Ym. Traditional methods for measuring Ym include the use of respiration calorimeters for housing individual animals (Johnson and Johnson, 1995). A tracer technique using SF6 enables methane emissions from individual animals to be estimated under both housed or grazing conditions (Johnson et al., 1994). The results of recent measurements have been surveyed by Lassey ( 2006) who also examines the "upscaling" of such measurements to national and global inventories. It is also important to examine the influences of feed properties and animal attributes on Ym. Such influences are important to better understand the microbiological mechanisms involved in methanogenesis with a view to designing emission abatement strategies, as well as to identify different values for Ym according to animal husbandry practices. To date, the search for such influences is equivocal, and consequently there is little variability evident both in the values reported in Table 10.12 as supported by the recent survey of Ym measurements in the literature (Lassey, 2006).

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Table 10.13 proposes a common Ym value for all mature sheep irrespective of feed quality, but with different values for mature and juvenile sheep with demarcation at 1 year of age. These values are based on data by Lassey et al. (1997), Judd et al. (1999) and Ulyatt et al. (2002a, 2002b, 2005) and while consistent with measurements by other researchers (Murray et al., 1978; Leuning et al., 1999), may not span the full range of pastures to be found. The median value is appropriate for most applications, but for poor quality feed the upper limits may be more appropriate, and for high-digestibility high-energy feeds the lower limits may be used.

TABLE 10.13 SHEEP CH4 CONVERSION FACTORS (YM) Ym a

Category

a

Lambs ( 1 month

17%

19% 20% 22% 25% 27%

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29%

32%

35%

39%

42%

46%

50%

55%

60%

65%

71%

78%

80%

Judgement of IPCC Expert Group in combination with Mangino et al. (2001). Note that the ambient temperature, not the stable temperature is to be used for determining the climatic conditions. When pits used as fed-batch storage/digesters, MCF should be calculated according to Formula 1.

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TABLE 10.17 (CONTINUED) MCF VALUES BY TEMPERATURE FOR MANURE MANAGEMENT SYSTEMS MCFs by average annual temperature (°C) Systema

Cool ≤ 10

Anaerobic digester

11

< 1 month

Cattle and Swine deep bedding (cont.)

> 1 month

13

14

15

16

17

18

19

21

22

23

24

25

26

27

≥ 28

0-100%

0-100%

0-100%

10%

10%

10%

Judgement of IPCC Expert Group in combination with Safley et al. (1992).

30%

Judgement of IPCC Expert Group in combination with Moller et al. (2004). Expect emissions to be similar, and possibly greater, than pit storage, depending on organic content and moisture content.

3%

17%

20

Source and comments

Warm

Should be subdivided in different categories, considering amount of recovery of the biogas, flaring of the biogas and storage after digestion. Calculation with Formula 1.

Burned for fuel

Cattle and Swine deep bedding

12

Temperate

19%

20%

3%

22%

25%

27%

29%

32%

35%

39%

42%

46%

50%

55%

60%

65%

71%

78%

80%

Judgement of IPCC Expert Group combination with Mangino et al. (2001).

in

Composting - In-vesselb

0.5%

0.5%

0.5%

Judgement of IPCC Expert Group and Amon et al. (1998). MCFs are less than half of solid storage. Not temperature dependant.

Composting - Static pileb

0.5%

0.5%

0.5%

Judgement of IPCC Expert Group and Amon et al. (1998). MCFs are less than half of solid storage. Not temperature dependant.

Composting - Intensive windrowb

0.5%

1.0%

1.5%

Judgement of IPCC Expert Group and Amon et al. (1998). MCFs are slightly less than solid storage. Less temperature dependant.

Composting – Passive windrowb

0.5%

1.0%

1.5%

Judgement of IPCC Expert Group and Amon et al. (1998). MCFs are slightly less than solid storage. Less temperature dependant.

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TABLE 10.17 (CONTINUED) MCF VALUES BY TEMPERATURE FOR MANURE MANAGEMENT SYSTEMS MCFs by average annual temperature (°C) Systema

Cool ≤ 10

11

12

Temperate 13

14

15

16

17

18

19

20

21

Source and comments

Warm 22

23

24

25

26

27

≥ 28

Poultry manure with litter

1.5%

1.5%

1.5%

Judgement of IPCC Expert Group. MCFs are similar to sol id storage but with generally constant warm temperatures.

Poultry manure without litter

1.5%

1.5%

1.5%

Judgement of IPCC Expert Group. MCFs are similar to dry lot at a warm climate.

0%

MCFs are near zero. Aerobic treatment can result in the accumulation of sludge which may be treated in other systems. Sludge requires removal and has large VS values. It is important to identify the next management process for the sludge and estimate the emissions from that management process if significant.

Aerobic treatment

0%

0%

Formula 1 (Timeframe for inputs should reflect operating period of digester): MCF = [{CH4 prod - CH4 used - CH4 flared + (MCFstorage /100 * Bo * VSstorage * 0.67 )}/ (Bo* VSstorage * 0.67)] *100 Where: CH4 prod = methane production in digester , (kg CH4) . Note: When a gas tight coverage of the storage for digested manure is used, the gas production of the storage should be included. CH4 used = amount of methane gas used for energy, (kg CH4) CH4 flared = amount of methane flared, (kg CH4) MCFstorage = MCF for CH4 emitted during storage of digested manure (%) VSstorage = amount of VS excreted that goes to storage prior to digestion (kg VS) When a gas tight storage is included: MCFstorage = 0 ; otherwise MCFstorage = MCF value for liquid storage a

Definitions for manure management systems are provided in Table 10. 18.

b

Composting is the biological oxidation of a solid waste including manure usually with bedding or another organic carbon source typically at thermophilic temperatures produced by microbial heat production.

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10.4.3

Choice of activity data

There are two main types of activity data for estimating CH4 emissions from manure management: (1) animal population data; and (2) manure management system usage data. The animal population data should be obtained using the approach described in Section 10.2. As noted in Section 10.2, it is good practice to conduct a single livestock characterisation that will provide the activity data for all emissions sources relying on livestock population data. It is important to note, however, that the level of disaggregation in the livestock population data required to estimate emissions from manure management, may differ from those used for other sources, such as Enteric Fermentation. For example, for some livestock population species/categories, such as cattle, the enhanced characterisation required for the Tier 2 enteric fermentation estimate could be aggregated to broader categories that are sufficient for this source category. For other livestock species, such as swine, it may be preferable to have more disaggregation of weight categories for manure management calculations than for enteric fermentation. However, consistency in total livestock categories should be retained throughout the inventory. Inventory agencies in countries with varied climatic conditions are encouraged to obtain population data for each major climatic zone. In addition, where possible, the associated annual average temperature for locations where livestock manure is managed in liquid-based systems (e.g., pits, tanks, and lagoons) should be obtained. This will allow more specific selection of default factors or MCF values for those systems more sensitive to temperature changes. Ideally, the regional population breakdown can be obtained from published national livestock statistics, and the temperature data from national meteorological statistics. If regional data are not available, experts should be consulted regarding regional production (e.g., milk, meat, and wool) patterns or land distribution, which may provide the required information to estimate the regional animal distributions. To implement the Tier 2 method, the portion of manure managed in each manure management system must also be collected for each representative animal species. Table 10.18 summarizes the main types of manure management systems. Quantitative data should be used to distinguish whether the system is judged to be a solid storage or liquid/slurry. The borderline between dry and liquid can be drawn at 20% dry matter content. Note that in some cases, manure may be managed in several types of manure management systems. For example, manure flushed from a dairy freestall barn to an anaerobic lagoon may first pass through a solids separation unit where some of the manure solids are removed and managed as a solid. Therefore, it is important to carefully consider the fraction of manure that is managed in each type of system. The best means of obtaining manure management system distribution data is to consult regularly published national statistics. If such statistics are unavailable, the preferred alternative is to conduct an independent survey of manure management system usage. If the resources are not available to conduct a survey, experts should be consulted to obtain an opinion of the system distribution. Volume 1, Chapter 2 Approaches to Data Collection describes how to elicit expert judgement. Similar expert elicitation protocols can be used to obtain manure management system distribution data.

10.4.4

Uncertainty assessment

EMISSION FACTORS There are large uncertainties associated with the default emission factors for Tier 1 (see Tables 10.14 to 10.16). The uncertainty range for the default factors is estimated to be +30%. Improvements achieved by Tier 2 methodologies are estimated to reduce uncertainty ranges in the emission factors to +20%. Accurate and welldesigned emission measurements from well characterised types of manure and manure management systems can help reduce these uncertainties further. These measurements must account for temperature, moisture conditions, aeration, VS content, duration of storage, and other aspects of treatment. The default values may have a large uncertainty for an individual country because they may not reflect the specific manure management conditions present within the country. Uncertainties can be reduced by developing and using MCF, Bo, and VS values that reflect country/region specific conditions.

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TABLE 10.18 DEFINITIONS OF MANURE MANAGEMENT SYSTEMS System

Definition

Pasture/Range/Paddock

The manure from pasture and range grazing animals is allowed to lie as deposited, and is not managed.

Daily spread

Manure is routinely removed from a confinement facility and is applied to cropland or pasture within 24 hours of excretion.

Solid storage

The storage of manure, typically for a period of several months, in unconfined piles or stacks. Manure is able to be stacked due to the presence of a sufficient amount of bedding material or loss of moisture by evaporation.

Dry lot

A paved or unpaved open confinement area without any significant vegetative cover where accumulating manure may be removed periodically.

Liquid/Slurry

Manure is stored as excreted or with some minimal addition of water in either tanks or earthen ponds outside the animal housing, usually for periods less than one year.

Uncovered anaerobic lagoon

A type of liquid storage system designed and operated to combine waste stabilization and storage. Lagoon supernatant is usually used to remove manure from the associated confinement facilities to the lagoon. Anaerobic lagoons are designed with varying lengths of storage (up to a year or greater), depending on the climate region, the volatile solids loading rate, and other operational factors. The water from the lagoon may be recycled as flush water or used to irrigate and fertilise fields.

Pit storage below animal confinements

Collection and storage of manure usually with little or no added water typically below a slatted floor in an enclosed animal confinement facility, usually for periods less than one year.

Anaerobic digester

Animal excreta with or without straw are collected and anaerobically digested in a large containment vessel or covered lagoon. Digesters are designed and operated for waste stabilization by the microbial reduction of complex organic compounds to CO2 and CH4, which is captured and flared or used as a fuel.

Burned for fuel

The dung and urine are excreted on fields. The sun dried dung cakes are burned for fuel.

Cattle and Swine deep bedding

As manure accumulates, bedding is continually added to absorb moisture over a production cycle and possibly for as long as 6 to 12 months. This manure management system also is known as a bedded pack manure management system and may be combined with a dry lot or pasture.

Composting - invessela

Composting, typically in an enclosed channel, with forced aeration and continuous mixing.

Composting - Static pilea

Composting in piles with forced aeration but no mixing.

Composting - Intensive Composting in windrows with regular (at least daily) turning for mixing and aeration. windrowa Composting - Passive windrowa

Composting in windrows with infrequent turning for mixing and aeration.

Poultry manure with litter

Similar to cattle and swine deep bedding except usually not combined with a dry lot or pasture. Typically used for all poultry breeder flocks and for the production of meat type chickens (broilers) and other fowl.

May be similar to open pits in enclosed animal confinement facilities or may be designed and Poultry manure without operated to dry the manure as it accumulates. The latter is known as a high-rise manure litter management system and is a form of passive windrow composting when designed and operated properly.

Aerobic treatment

a

The biological oxidation of manure collected as a liquid with either forced or natural aeration. Natural aeration is limited to aerobic and facultative ponds and wetland systems and is due primarily to photosynthesis. Hence, these systems typically become anoxic during periods without sunlight.

Composting is the biological oxidation of a solid waste including manure usually with bedding or another organic carbon source typically at thermophilic temperatures produced by microbial heat production.

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ACTIVITY DATA – LIVESTOCK POPULATIONS See Section 10.2 Livestock and Feed Characterisation for discussion on uncertainty of animal population and characterisation data.

ACTIVITY DATA – MANURE MANAGEMENT SYSTEM USAGE The uncertainty of the manure management system usage data will depend on the characteristics of each country's livestock industry and how information on manure management is collected. For example, for countries that rely almost exclusively on one type of management system, such as pasture and range, the uncertainty associated with management system usage data can be 10% or less. However, for countries where there is a wide variety of management systems used with locally different operating practices, the uncertainty range in management system usage data can be much higher, in the range of 25% to 50%, depending on the availability of reliable and representative survey data that differentiates animal populations by system usage. Preferably, each country should estimate the uncertainty associated with their management system usage data by using the methods described in Volume 1, Chapter 3.

10.4.5

Completeness, Time series, Quality assurance / Quality control and Reporting

A complete inventory should estimate CH4 emissions from all systems of manure management for all livestock species/categories identified in Section 10.2. Countries are encouraged to use manure management system definitions that are consistent with those presented in Table 10.18 to ensure that all types of systems are being accounted for. Population data should be cross-checked between main reporting mechanisms (such as FAO and national agricultural statistics databases) to ensure that information used in the inventory is complete and consistent. Because of the widespread availability of the FAO database of livestock information, most countries should be able to prepare, at a minimum, Tier 1 estimates for the major livestock categories. For more information regarding the completeness of livestock characterisation, see Section 10.2. Developing a consistent time series of emission estimates for this source category requires, at a minimum, collection of an internally consistent time series of livestock population statistics. General guidance on the development of a consistent time series is addressed in Volume 1, Chapter 5 (Time Series Consistency). If significant changes in manure management practices have occurred over time, the Tier 1 method will not provide an accurate time series of emissions (since the Tier 1 default factors are based on a historical set of parameters), and the Tier 2 method should be considered. When developing a time series for the Tier 2 method it is also necessary to collect country-specific manure management system data. In cases when manure management system data are not available for some period during the time series, trends can be used to extrapolate data from a sample area or region to the entire country, if climatic conditions are similar (i.e., temperature and rainfall). National livestock experts from government, industry, or universities should be consulted where possible to develop trends in management system usage and characteristics. If the emission estimation method has changed, historical data that are required by the current method should be collected and used to recalculate emissions for that period. If such data are not available, it may be appropriate to create a trend with recent data and use the trend to back-estimate management practices for the time series. For example, it may be known that certain livestock industries are converting to more intensive management systems in lieu of grazing. Historically, this changeover should be captured in the time series of emissions, through modifications to the manure management system allocation. It may be necessary to base this allocation on expert judgment from national experts where extensive survey data are not available. Volume 1, Chapter 5 provides additional guidance on how to address recalculation issues. Also, Section 10.2 suggests approaches for the animal population aspects. The inventory text should thoroughly explain how the change in farm practices or implementation of mitigation measures has affected the time series of activity data or emission factors. It is good practice to implement general quality control checks as outlined in Volume 1, Chapter 6, Quality Assurance/Quality Control and Verification, and expert review of the emission estimates. Additional quality control checks and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source. The general QA/QC related to data processing, handling, and reporting should be supplemented with procedures discussed below.

ACTIVITY DATA CHECK •

The inventory agency should review livestock data collection methods, in particular checking that livestock subspecies data were collected and aggregated correctly. The data should be cross-checked with previous years to ensure the data are reasonable and consistent with the expected trend. Inventory agencies should document data collection methods, identify potential areas of bias (e.g., systematic under-reporting of

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•

•

animal populations to statistical agencies by individual livestock owners), and evaluate the representativeness of the data. Manure management system allocation should be reviewed on a regular basis to determine if changes in the livestock industry are being captured. Conversion from one type of management system to another, and technical modifications to system configuration and performance, should be captured in the system modeling for the affected livestock. National agricultural policy and regulations may have an effect on parameters that are used to calculate manure emissions, and should be reviewed regularly to determine what impact they may have. For example, guidelines to reduce manure runoff into water bodies may cause a change in management practices, and thus affect the MCF value for a particular livestock category. Consistency should be maintained between the inventory and ongoing changes in agricultural practices.

REVIEW OF EMISSION FACTORS •

• •

•

If using the Tier 1 method (using default IPCC emission factors), the inventory agency should evaluate how well the default VS excretion rates, Bo values, and manure management practices represent the defined animal population and manure characteristics of the country. This should be done by reviewing the background information from Tables 10A-4 to 10A-9 to see how well the default input parameters match the inventory area. If there is not a good match, substitution of more appropriate country-specific parameters can be used to develop an improved emission factor. If using the Tier 2 method, the inventory agency should cross-check the country-specific parameters (e.g., VS excretion rates, Bo, and MCF) against the IPCC defaults. Significant differences between countryspecific parameters and default parameters should be explained and documented. If using the Tier 2 method, derivation of VS rates should be compared to background assumptions used for the enteric fermentation Tier 2 inventory where applicable. For example, the gross energy and digestible energy components used in the enteric fermentation inventory can be used to cross-check independentlyderived VS rates. Application of Equation 10.24 (Volatile solid excretion rates) can be used in this case for such a cross-comparison on ruminants. For all animals, on a gross basis, VS rates should be consistent with the feed intake of the animal (i.e., waste energy should not exceed intake energy) and be consistent with the range of DE% values reported in Section 10.2, Table 10.2 of this report. Whenever possible, available measurement data, even if they represent only a small sample of systems, should be reviewed relative to assumptions for MCF values and CH4 production estimates. Representative measurement data may provide insights into how well current assumptions predict CH4 production from manure management systems in the inventory area, and how certain factors (e.g., temperature, system configuration, retention time) are affecting emissions. Because of the relatively small amount of measurement data available for these systems worldwide, any new results can improve the understanding of these emissions and possibly their prediction.

EXTERNAL REVIEW •

The inventory agency should utilise experts in manure management and animal nutrition to conduct expert peer review of the methods and data used. While these experts may not be familiar with greenhouse gas emissions, their knowledge of key input parameters to the emission calculation can aid in the overall verification of the emissions. For example, animal nutritionists can evaluate VS production rates to see if they are consistent with feed utilization research for certain livestock species. Practicing farmers can provide insights into actual manure management techniques, such as storage times and mixed-system usage. Wherever possible, these experts should be completely independent of the inventory process in order to allow a true external review.

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Chapter 6 (Quality assurance/Quality control and Verification). When country-specific data (e.g., emission factors, manure management practices, and manure characteristics such as VS and Bo) have been used, the derivation of or references for these data should be clearly documented and reported along with the inventory results under the appropriate IPCC source category. To improve transparency, emission estimates from this source category should be reported along with the activity data and emission factors used to determine the estimates. The following information should be documented: •

All activity data (e.g., livestock population data by species/category and by region), including sources used, complete citations for the statistical database from which data were collected, and (in cases where activity

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•

• •

•

• •

data were not available directly from databases) the information and assumptions that were used to derive the activity data. Climatic conditions (e.g., average temperature during manure storage) in regions if applicable. Manure management system data, by livestock species/category and by region, if applicable. If manure management systems different than those defined in this chapter are used, these should be described. The frequency of data collection, and estimates of accuracy and precision. Emission factors documentation, including: (i)

References for the emission factors that were used (IPCC default or otherwise); and

(ii)

The scientific basis of these emission factors and methods, including definition of input parameters and description of the process by which these emission factors and methods are derived, as well as describing sources and magnitudes of uncertainties. (In inventories, in which country- or regionspecific emission factors were used or in which new methods other than those described here were used).

If the Tier 1 method is used, all default emission factors used in the emissions estimation for the specific livestock population species/category. If the Tier 2 method is used, documentation of emission factor calculation components, including: (i)

VS and Bo values for all livestock population species/category in inventory, whether countryspecific, region-specific, or IPCC default; and

(ii)

MCF values for all manure management systems used, whether country-specific or IPCC default.

10.5

N 2 O EMISSIONS FROM MANURE MANAGEMENT

The section describes how to estimate the N2O produced, directly and indirectly, during the storage and treatment of manure before it is applied to land or otherwise used for feed, fuel, or construction purposes. The term ‘manure’ is used here collectively to include both dung and urine (i.e., the solids and the liquids) produced by livestock. The N2O emissions generated by manure in the system ‘pasture, range, and paddock’ occur directly and indirectly from the soil, and are therefore reported under the category ‘N2O Emissions from Managed Soils’ (see Chapter 11, Section 11.2). The emissions associated with the burning of dung for fuel are to be reported under ‘Fuel Combustion’ (see Volume 2: Energy), or under ‘Waste Combustion’ (see Volume 5: Waste) if burned without energy recovery. Direct N2O emissions occur via combined nitrification and denitrification of nitrogen contained in the manure. The emission of N2O from manure during storage and treatment depends on the nitrogen and carbon content of manure, and on the duration of the storage and type of treatment. Nitrification (the oxidation of ammonia nitrogen to nitrate nitrogen) is a necessary prerequisite for the emission of N2O from stored animal manures. Nitrification is likely to occur in stored animal manures provided there is a sufficient supply of oxygen. Nitrification does not occur under anaerobic conditions. Nitrites and nitrates are transformed to N2O and dinitrogen (N2) during the naturally occurring process of denitrification, an anaerobic process. There is general agreement in the scientific literature that the ratio of N2O to N2 increases with increasing acidity, nitrate concentration, and reduced moisture. In summary, the production and emission of N2O from managed manures requires the presence of either nitrites or nitrates in an anaerobic environment preceded by aerobic conditions necessary for the formation of these oxidized forms of nitrogen. In addition, conditions preventing reduction of N2O to N2, such as a low pH or limited moisture, must be present. Indirect emissions result from volatile nitrogen losses that occur primarily in the forms of ammonia and NOx. The fraction of excreted organic nitrogen that is mineralized to ammonia nitrogen during manure collection and storage depends primarily on time, and to a lesser degree temperature. Simple forms of organic nitrogen such as urea (mammals) and uric acid (poultry) are rapidly mineralized to ammonia nitrogen, which is highly volatile and easily diffused into the surrounding air (Asman et al., 1998; Monteny and Erisman, 1998). Nitrogen losses begin at the point of excretion in houses and other animal production areas (e.g., milk parlors) and continue through on-site management in storage and treatment systems (i.e., manure management systems). Nitrogen is also lost through runoff and leaching into soils from the solid storage of manure at outdoor areas, in feedlots and where animals are grazing in pastures. Pasture losses are considered separately in Chapter 11, Section 11.2, N2O Emissions from Managed Soils, as are emissions of nitrogen compounds from grazing livestock.

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Due to significant direct and indirect losses of manure nitrogen in management systems it is important to estimate the remaining amount of animal manure nitrogen available for application to soils or for use in feed, fuel, or construction purposes. This value is used for calculation N2O emissions from managed soils (see Chapter 11, Section 11.2). The methodology to estimate manure nitrogen that is directly applied to soils, or available for use in feed, fuel, or construction purposes is described in this chapter under Section 10.5.4 “Coordination with reporting for N2O emissions from managed soils".

10.5.1

Choice of method

The level of detail and methods chosen for estimating N2O emissions from manure management systems will depend upon national circumstances and the decision tree in Figure 10.4 describes good practice in choosing a method accordingly. The following sections describe the different tiers referenced in the decision tree for calculating direct and indirect N2O emissions from manure management systems.

Direct N 2 O emissions from Manure Management Tier 1 The Tier 1 method entails multiplying the total amount of N excretion (from all livestock species/categories) in each type of manure management system by an emission factor for that type of manure management system (see Equation 10.25). Emissions are then summed over all manure management systems. The Tier 1 method is applied using IPCC default N2O emission factors, default nitrogen excretion data, and default manure management system data (see Annex 10A.2, Tables 10A-4 to 10A-8 for default management system allocations). Tier 2 A Tier 2 method follows the same calculation equation as Tier 1 but would include the use of country-specific data for some or all of these variables. For example, the use of country-specific nitrogen excretion rates for livestock categories would constitute a Tier 2 methodology. Tier 3 A Tier 3 method utilizes alternative estimation procedures based on a country-specific methodology. For example, a process-based, mass balance approach which tracks nitrogen throughout the system starting with feed input through final use/disposal could be utilized as a Tier 3 procedure. Tier 3 methods should be well documented to clearly describe estimation procedures.

To estimate emissions from manure management systems, the livestock population must first be divided into categories that reflect the varying amounts of manure produced per animal as well as the manner in which the manure is handled. This division of manure by type of system should be the same as that used to characterize methane emissions from manure management (see Section 10.4). For example, if Tier 1 default emission factors are used for calculating CH4 emissions, then the manure management systems usage data from Tables 10A-4 to 10A-8 should be applied. Detailed information on how to characterise the livestock population for this source is provided in Section 10.2. The following five steps are used to estimate direct N2O emissions from Manure Management: Step 1: Collect population data from the Livestock Population Characterisation; Step 2: Use default values or develop the annual average nitrogen excretion rate per head (Nex(T)) for each defined livestock species/category T; Step 3: Use default values or determine the fraction of total annual nitrogen excretion for each livestock species/category T that is managed in each manure management system S (MS(T,S)); Step 4: Use default values or develop N2O emission factors for each manure management system S (EF3(S)); and Step 5: For each manure management system type S, multiply its emission factor (EF3(S)) by the total amount of nitrogen managed (from all livestock species/categories) in that system, to estimate N2O emissions from that manure management system. Then sum over all manure management systems.

In some cases, manure nitrogen may be managed in several types of manure management systems. For example, manure flushed from a dairy freestall barn to an anaerobic lagoon may first pass through a solids separation unit where some of the manure nitrogen is removed and managed as a solid. Therefore, it is important to consider carefully the fraction of manure nitrogen that is managed in each type of system. The calculation of direct N2O emissions from manure management is based on the following equation:

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EQUATION 10.25 DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT

(

⎡ ⎡ N 2OD ( mm ) = ⎢∑ ⎢∑ N (T ) • Nex(T ) • MS (T , S ) ⎣ S ⎣T

)⎤⎥ • EF3(S ) ⎤⎥ • 44 28 ⎦

⎦

Where: N2OD(mm) = direct N2O emissions from Manure Management in the country, kg N2O yr-1 N(T) = number of head of livestock species/category T in the country Nex(T) = annual average N excretion per head of species/category T in the country, kg N animal-1 yr-1 MS(T,S) = fraction of total annual nitrogen excretion for each livestock species/category T that is managed in manure management system S in the country, dimensionless EF3(S) = emission factor for direct N2O emissions from manure management system S in the country, kg N2O-N/kg N in manure management system S S = manure management system T = species/category of livestock 44/28 = conversion of (N2O-N)(mm) emissions to N2O(mm) emissions There may be losses of nitrogen in other forms (e.g., ammonia and NOx) as manure is managed on site. Nitrogen in the volatilized form of ammonia may be deposited at sites downwind from manure handling areas and contribute to indirect N2O emissions (see below). Countries are encouraged to consider using a mass balance approach (Tier 3) to track the manure nitrogen excreted, managed on site in manure management systems, and ultimately applied to managed soils. The estimation of the amount of manure nitrogen which is directly applied to managed soils or otherwise available for use as feed, fuel or construction purposes is described in the Section 10.5.4, Coordination with reporting for N2O emissions from managed soils. See Chapter 11, Section 11.2 for procedures to calculate N2O emissions from managed manure nitrogen applied to soils.

Indirect N 2 O emissions from Manure Management Tier 1 The Tier 1 calculation of N volatilisation in forms of NH3 and NOx from manure management systems is based on multiplication of the amount of nitrogen excreted (from all livestock categories) and managed in each manure management system by a fraction of volatilised nitrogen (see Equation 10.26). N losses are then summed over all manure management systems. The Tier 1 method is applied using default nitrogen excretion data, default manure management system data (see Annex 10A.2, Tables 10A-4 to 10A-8) and default fractions of N losses from manure management systems due to volatilisation (see Table 10.22): EQUATION 10.26 N LOSSES DUE TO VOLATILISATION FROM MANURE MANAGEMENT

(

)

⎡ ⎡ ⎤⎤ ⎛ FracGasMS ⎞ N volatilization−MMS = ∑ ⎢∑ ⎢ N (T ) • Nex(T ) • MS (T ,S ) • ⎜ ⎥⎥ ⎟ 100 ⎠ (T ,S ) ⎥⎦ ⎥⎦ ⎝ S ⎢T ⎢ ⎣ ⎣

Where: Nvolatilization-MMS = amount of manure nitrogen that is lost due to volatilisation of NH3 and NOx, kg N yr-1 N(T) = number of head of livestock species/category T in the country Nex(T) = annual average N excretion per head of species/category T in the country, kg N animal-1 yr-1 MS(T,S) = fraction of total annual nitrogen excretion for each livestock species/category T that is managed in manure management system S in the country, dimensionless FracGasMS = percent of managed manure nitrogen for livestock category T that volatilises as NH3 and NOx in the manure management system S, %

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Figure 10.4

Decision tree for N 2 O emissions from Manure Management (Note 1)

Start

Do you have a country-specific Tier 3 methodology?

Yes

Estimate emissions using Tier 3 method. Box 3: Tier 3

No

Is a Tier 2 livestock population characterization, available and do you have country-specific N excretion rates, fractions of N losses, EFs and management system usage data?

No

Collect data for Tier 2 method.

Is N2O from manure management a key category2 and is the species a significant share of emissions3?

Yes

No

Yes (all or some)

Estimate direct and indirect N2O emissions using Tier 2 method with available country-specific inputs. Box 2: Tier 2

Estimate direct and indirect N2O emissions using Tier 1 method and IPCC defaults. Box 1: Tier 1

Note: 1: N2O emissions from manure management systems include both direct and indirect sources 2: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 3: As a rule of thumb, a livestock species would be significant if it accounts for 25-30% or more of emissions from the source category.

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The indirect N2O emissions from volatilisation of N in forms of NH3 and NOx (N2OG(mm)) are estimated using Equation 10.27: EQUATION 10.27 INDIRECT N2O EMISSIONS DUE TO VOLATILISATION OF N FROM MANURE MANAGEMENT 44 N 2OG ( mm) = (N volatilization − MMS • EF4 ) • 28

Where: N2OG(mm) = indirect N2O emissions due to volatilization of N from Manure Management in the country, kg N2O yr-1 EF4 = emission factor for N2O emissions from atmospheric deposition of nitrogen on soils and water surfaces, kg N2O-N (kg NH3-N + NOx-N volatilised)-1 ; default value is 0.01 kg N2O-N (kg NH3-N + NOx-N volatilised)-1 , given in Chapter 11, Table 11.3

Tier 2 Countries may wish to develop a Tier 2 methodology for better consideration of national circumstances and to reduce uncertainty of estimates as much as possible. As for direct N2O emission from manure management, a Tier 2 method would follow the same calculation equation as Tier 1 but include the use of country-specific data for some or all of these variables. For example, the use of country-specific nitrogen excretion rates for livestock categories would constitute a Tier 2 method. National NH3 emission inventories developed by some countries could be used for Tier 2 estimation of nitrogen volatilisation from manure management systems. A Tier 2 method would require more detailed characterisation of the flow of nitrogen throughout the animal housing and manure management systems used in the country. Double counting of emissions associated with the application of managed manure should be avoided, as well as manure associated with pasture and grazing operations, which should be calculated and reported under Chapter 11, Section 11.2 (N2O emissions from managed soils).

There are extremely limited measurement data on leaching and runoff losses from various manure management systems. The greatest N losses due to runoff and leaching typically occur where animals are on a drylot. In drier climates, runoff losses are smaller than in high rainfall areas and have been estimated in the range from 3 to 6% of N excreted (Eghball and Power, 1994). Studies by Bierman et al. (1999) found nitrogen lost in runoff was 5 to 19% of N excreted and 10 to 16% leached into soil, while other data show relatively low loss of nitrogen through leaching in solid storage (less than 5% of N excreted) but greater loss could also occur (Rotz, 2004). Further research is needed in this area to improve the estimated losses and the conditions and practices under which such losses occur. Equation 10.28 should only be used where there is country-specific information on the fraction of nitrogen loss due to leaching and runoff from manure management systems available. Therefore, estimation of N losses from leaching and runoff from manure management should be considered part of a Tier 2 or Tier 3method. Nitrogen that leaches into soil and/or runs off during solid storage of manure at outdoor areas or in feedlots is derived as follows: EQUATION 10.28 N LOSSES DUE TO LEACHING FROM MANURE MANAGEMENT SYSTEMS

(

)

⎡ ⎡ ⎤⎤ ⎛ FracleachMS ⎞ N leaching −MMS = ∑ ⎢∑ ⎢ N (T ) • Nex(T ) • MS (T ,S ) • ⎜ ⎥⎥ ⎟ 100 ⎠ (T ,S ) ⎥⎦ ⎥⎦ ⎝ S ⎢T ⎢ ⎣ ⎣

Where: Nleaching-MMS = amount of manure nitrogen that leached from manure management systems, kg N yr-1 N(T) = number of head of livestock species/category T in the country Nex(T) = annual average N excretion per head of species/category T in the country, kg N animal-1 yr-1 MS(T,S) = fraction of total annual nitrogen excretion for each livestock species/category T that is managed in manure management system S in the country, dimensionless FracleachMS = percent of managed manure nitrogen losses for livestock category T due to runoff and leaching during solid and liquid storage of manure (typical range 1-20%)

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The indirect N2O emissions from leaching and runoff of nitrogen from manure management systems (N2OL(mm)) are estimated using Equation 10.29: EQUATION 10.29 INDIRECT N2O EMISSIONS DUE TO LEACHING FROM MANURE MANAGEMENT 44 N 2OL ( mm) = N leaching − MMS • EF5 • 28

(

)

Where: N2OL(mm) = indirect N2O emissions due to leaching and runoff from Manure Management in the country, kg N2O yr-1 EF5 = emission factor for N2O emissions from nitrogen leaching and runoff, kg N2O-N/kg N leached and runoff (default value 0.0075 kg N2O-N (kg N leaching/runoff)-1, given in Chapter 11, Table 11.3

Tier 3 To reduce uncertainty of the estimates, a Tier 3 method could be developed with country-specific emission factors for volatilisation and nitrogen leaching and runoff based on actual measurements.

All losses of N through manure management systems (both direct and indirect) need to be excluded from the amount of manure N that is available for application to soils and which is reported in Chapter 11, Section 11.2 N2O Emissions from Managed Soils. Refer to Section 10.5.4, Coordination with reporting for N2O emissions from managed soils, for guidance on calculating total N losses from manure management systems.

10.5.2

Choice of emission factors

Annual average nitrogen excretion rates, Nex ( T ) Tier 1 Annual nitrogen excretion rates should be determined for each livestock category defined by the livestock population characterisation. Country-specific rates may either be taken directly from documents or reports such as agricultural industry and scientific literature, or derived from information on animal nitrogen intake and retention (as explained below). In some situations, it may be appropriate to use excretion rates developed by other countries that have livestock with similar characteristics.

If country-specific data cannot be collected or derived, or appropriate data are not available from another country, the IPCC default nitrogen excretion rates presented in Table 10.19 can be used. These rates are presented in units of nitrogen excreted per 1000 kg of animal per day. These rates can be applied to livestock sub-categories of varying ages and growth stages using a typical average animal mass (TAM) for that population sub-category, as shown in Equation 10.30. EQUATION 10.30 ANNUAL N EXCRETION RATES TAM Nex(T ) = N rate (T ) • • 365 1000

Where: Nex(T) = annual N excretion for livestock category T, kg N animal-1 yr-1 Nrate(T) = default N excretion rate, kg N (1000 kg animal mass)-1 day-1 (see Table 10.19) TAM(T) = typical animal mass for livestock category T, kg animal-1 Default TAM values are provided in Tables 10A-4 to 10A-9 in Annex 10A.2. However, it is preferable to collect country-specific TAM values due to the sensitivity of nitrogen excretion rates to different weight categories. For example, market swine may vary from nursery pigs weighing less than 30 kilograms to finished pigs that weigh over 90 kilograms. By constructing animal population groups that reflect the various growth stages of market pigs, countries will be better able to estimate the total nitrogen excreted by their swine population.

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When estimating the Nex(T) for animals whose manure is classified in the manure management system burned for fuel (Table 10.21, Default emission factors for direct N2O emissions from Manure Management), it should be kept in mind that the dung is burned and the urine stays in the field. As a rule of thumb, 50% of the nitrogen excreted is in the dung and 50% is in the urine. If the burned dung is used as fuel, then emissions are reported under the IPCC category Fuel Combustion (Volume 2: Energy), whereas if the dung is burned without energy recovery the emissions should be reported under the IPCC category Waste Incineration (Volume 5: Waste). Tier 2 The annual amount of N excreted by each livestock species/category depends on the total annual N intake and total annual N retention of the animal. Therefore, N excretion rates can be derived from N intake and N retention data. Annual N intake (i.e., the amount of N consumed by the animal annually) depends on the annual amount of feed digested by the animal, and the protein content of that feed. Total feed intake depends on the production level of the animal (e.g., growth rate, milk production, draft power). Annual N retention (i.e., the fraction of N intake that is retained by the animal for the production of meat, milk, or wool) is a measure of the animal's efficiency of production of animal protein from feed protein. Nitrogen intake and retention data for specific livestock species/categories may be available from national statistics or from animal nutrition specialists. Nitrogen intake can also be calculated from data on feed and crude protein intake developed in Section 10.2. Default N retention values are provided in Table 10.20, Default values for the fraction of nitrogen in feed taken in by animals that is retained by the different animal species/categories. Rates of annual N excretion for each livestock species/category (Nex(T)) are derived as follows:

(

)

EQUATION 10.31 ANNUAL N EXCRETION RATES (TIER 2) Nex(T ) = N int ake(T ) • 1 − N retention (T )

Where: Nex(T) = annual N excretion rates, kg N animal-1 yr-1 Nintake(T) = the annual N intake per head of animal of species/category T , kg N animal-1 yr-1 Nretention(T) = fraction of annual N intake that is retained by animal of species/category T, dimensionless

Exam p le of Tier 2 metho d fo r est ima t ing n itrog en ex cr etion for ca ttle Nitrogen excretion may be calculated based on the same dietary assumptions used in modelling enteric fermentation emissions (see Section 10.2). The amount of nitrogen excreted by cattle can be estimated as the difference between the total nitrogen taken in by the animal and the total nitrogen retained for growth and milk production. Equations 10.32 and 10.33 can be used to calculate the variables for nitrogen intake and nitrogen retained for use in Equation 10.31. The total nitrogen intake rate is derived as follows: EQUATION 10.32 N INTAKE RATES FOR CATTLE N intake(T )

⎛ CP % ⎞ GE ⎜⎜ 100 ⎟⎟ = • 18.45 ⎜ 6.25 ⎟ ⎟ ⎜ ⎠ ⎝

Where: Nintake(T) = daily N consumed per animal of category T, kg N animal-1 day-1 GE = gross energy intake of the animal, in enteric model, based on digestible energy, milk production, pregnancy, current weight, mature weight, rate of weight gain, and IPCC constants, MJ animal-1 day-1 18.45 = conversion factor for dietary GE per kg of dry matter, MJ kg-1. This value is relatively constant across a wide range of forage and grain-based feeds commonly consumed by livestock. CP% = percent crude protein in diet, input 6.25 = conversion from kg of dietary protein to kg of dietary N, kg feed protein (kg N)-1

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Chapter 10: Emissions from Livestock and Manure Management TABLE 10.19 DEFAULT VALUES FOR NITROGEN EXCRETION RATE a (KG N (1000 KG ANIMAL MASS)-1 DAY-1) Region Category of animal North America

Western Europe

Eastern Europe

Oceania

Latin America

Africa

Middle East

Asia

Dairy Cattle

0.44

0.48

0.35

0.44

0.48

0.60

0.70

0.47

Other Cattle

0.31

0.33

0.35

0.50

0.36

0.63

0.79

0.34

0.50

0.68

0.74

0.73

1.64

1.64

1.64

0.50

Market

0.42

0.51

0.55

0.53

1.57

1.57

1.57

0.42

Breeding

0.24

0.42

0.46

0.46

0.55

0.55

0.55

0.24

0.83

0.83

0.82

0.82

0.82

0.82

0.82

0.82

Hens >/= 1 yr

0.83

0.96

0.82

0.82

0.82

0.82

0.82

0.82

Pullets

0.62

0.55

0.60

0.60

0.60

0.60

0.60

0.60

Other Chickens

0.83

0.83

0.82

0.82

0.82

0.82

0.82

0.82

Broilers

1.10

1.10

1.10

1.10

1.10

1.10

1.10

1.10

Turkeys

0.74

0.74

0.74

0.74

0.74

0.74

0.74

0.74

Swine

b

Poultry

0.83

0.83

0.83

0.83

0.83

0.83

0.83

0.83

Sheep

Ducks

0.42

0.85

0.90

1.13

1.17

1.17

1.17

1.17

Goats

0.45

1.28

1.28

1.42

1.37

1.37

1.37

1.37

Horses (and mules, asses)

0.30

0.26

0.30

0.30

0.46

0.46

0.46

0.46

Camelsc

0.38

0.38

0.38

0.38

0.46

0.46

0.46

0.46

0.32

0.32

0.32

0.32

0.32

0.32

0.32

0.32

4.59

4.59

4.59

4.59

4.59

4.59

4.59

4.59

8.10

8.10

8.10

8.10

8.10

8.10

8.10

8.10

12.09

12.09

12.09

12.09

12.09

12.09

12.09

12.09

c

Buffalo

Mink and Polecat 1 d )

-1

(kg N head yr

Rabbits (kg N head-1 yr-1) -1

-1 d

Fox and Racoon (kg N head yr )

-

The uncertainty in these estimates is +50%. a Summarized from 1996 IPCC Guidelines, 1997; European Environmental Agency, 2002; USA EPA National NH3 Inventory Draft Report, 2004; and data of GHG inventories of Annex I Parties submitted to the Secretariat UNFCCC in 2004. b Nitrogen excretion for swine are based on an estimated country population of 90% market swine and 10% breeding swine. c Modified from European Environmental Agency, 2002. d Data of Hutchings et al., 2001.

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TABLE 10.20 DEFAULT VALUES FOR THE FRACTION OF NITROGEN IN FEED INTAKE OF LIVESTOCK THAT IS RETAINED BY THE DIFFERENT LIVESTOCK SPECIES/CATEGORIES (FRACTION N-INTAKE RETAINED BY THE ANIMAL) Nretention(T) (kg N retained/animal/year) (kg N intake/animal/year)-1

Livestock category Dairy Cows

0.20

Other Cattle

0.07

Buffalo

0.07

Sheep

0.10

Goats

0.10

Camels

0.07

Swine

0.30

Horses

0.07

Poultry

0.30

The uncertainty in these estimates is +50%. Source: Judgement of IPCC Expert Group (see Co-chairs, Editors and Experts; N2O emissions from Manure Management).

The total nitrogen retained is derived as follows: EQUATION 10.33 N RETAINED RATES FOR CATTLE

N retention (T )

⎡ ⎡ ⎡ ⎛ Milk PR% ⎞ ⎤ ⎢WG • ⎢268 − ⎛⎜ 7.03 • NE g Milk • ⎟⎥ ⎜ ⎜ ⎢ WG ⎢⎣ 100 ⎝ ⎠⎥ + ⎢ ⎝ =⎢ ⎢ 1000 6.38 ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ 6.25 ⎦ ⎣ ⎣

⎞⎤ ⎤ ⎟⎥ ⎥ ⎟ ⎠⎥⎦ ⎥ ⎥ ⎥ ⎥ ⎦

Where: Nretention(T) = daily N retained per animal of category T, kg N animal-1 day-1 Milk = milk production, kg animal-1 day-1 (applicable to dairy cows only) Milk PR% = percent of protein in milk, calculated as [1.9 + 0.4 ● %Fat], where %Fat is an input, assumed to be 4% (applicable to dairy cows only) 6.38 = conversion from milk protein to milk N, kg Protein (kg N)-1 WG = weight gain, input for each livestock category, kg day-1 268 and 7.03 = constants from Equation 3-8 in NRC (1996) NEg = net energy for growth, calculated in livestock characterisation, based on current weight, mature weight, rate of weight gain, and IPCC constants, MJ day-1 1000 = conversion from grams per kilogram, g kg-1 6.25 = conversion from kg dietary protein to kg dietary N, kg Protein (kg N)-1 Annual nitrogen excretion data are also used for the calculation of direct and indirect N2O emissions from managed soils (see Chapter 11, Section 11.2, N2O emissions from managed soils). The same rates of N excretion, and methods of derivation, that are used to estimate N2O emissions from Manure Management should be used to estimate N2O emissions from managed soils.

Emission factors for direct N 2 O emissions from Manure Management The best estimate will be obtained using country-specific emission factors that have been fully documented in peer reviewed publications. It is good practice to use country-specific emission factors that reflect the actual duration of storage and type of treatment of animal manure in each management system that is used. Good

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practice in the derivation of country-specific emission factors involves the measurement of emissions (per unit of manure N) from different management systems, taking into account variability in duration of storage and types of treatment. When defining types of treatment, conditions such as aeration and temperature should be taken into account. If inventory agencies use country-specific emission factors, they are encouraged to provide justification for these values via peer-reviewed documentation. If appropriate country-specific emission factors are unavailable, inventory agencies are encouraged to use the default emission factors presented in Table 10.21, Default emission factors for direct N2O emissions from Manure Management. This table contains default emission factors by manure management system. Note that emissions from liquid/slurry systems without a natural crust cover, anaerobic lagoons, and anaerobic digesters are considered negligible based on the absence of oxidized forms of nitrogen entering these systems combined with the low potential for nitrification and denitrification to occur in the system.

Emission factors for indirect N 2 O emissions from Manure Management In order to estimate indirect N2O emissions from Manure Management, two fractions of nitrogen losses (due to volatilization and leaching/runoff), and two indirect N2O emissions factors associated with these losses (EF4 and EF5) are needed. Default values for volatilization N losses are presented in the Table 10.22. Values represent average rates for N loss in the forms of NH3 and NOx, with most of the loss in the form of NH3. Ranges reflect values that appear in the literature. The values represent conditions without any significant nitrogen control measures in place. Countries are encouraged to develop country-specific values, particularly related to ammonia losses where component emissions may be well characterized as part of larger air quality assessments and where emissions may be affected by nitrogen reduction strategies. For example, detailed methodologies for estimating NH3 and other nitrogen losses using mass balance/mass flow procedures are described in the EMEP/CORINAIR Atmospheric Inventory Guidebook, Chapter 1009 (European Environmental Agency, 2002). The fraction of manure nitrogen that leaches from manure management systems (FracleachMS) is highly uncertain and should be developed as a country-specific value applied in Tier 2 method. Default values for EF4 (N volatilisation and re-deposition) and EF5 (N leaching/runoff) are given in Chapter 11, Table 11.3 (Default emission, volatilisation and leaching factors for indirect soil N2O emissions).

10.5.3

Choice of activity data

There are two main types of activity data for estimating N2O emissions from manure management systems: (1) livestock population data, and (2) manure management system usage data.

Livestock population data, N ( T ) The animal population data should be obtained using the approach described in Section 10.2. If using default nitrogen excretion rates to estimate N2O emissions from manure management systems, a Tier 1 livestock population characterisation is sufficient. To estimate N2O emissions from Manure Management using calculated nitrogen excretion rates, a Tier 2 characterisation must be performed. As noted in Section 10.2, good practice in characterising livestock populations is to conduct a single characterisation that will provide the activity data for all emissions sources that depend on livestock population data.

Manure management system usage data, MS ( T , S ) The manure management system usage data used to estimate N2O emissions from Manure Management should be the same as those that are used to estimate CH4 emissions from Manure Management (see Table 10.18 for a summary of the main types of manure management systems). The portion of manure managed in each manure management system must be collected for each representative livestock category. Note that in some cases, manure may be managed in several types of manure management systems. For example, manure flushed from a dairy freestall barn to an anaerobic lagoon may first pass through a solids separation unit where some of the manure solids are removed and managed as a solid. Therefore, it is important to carefully consider the fraction of manure that is managed in each type of system. The best means of obtaining manure management system distribution data is to consult regularly published national statistics. If such statistics are unavailable, the preferred alternative is to conduct an independent survey of manure management system usage. If the resources are not available to conduct a survey, experts should be consulted to obtain an opinion of the system distribution. If country-specific manure management system usage data are not available, default values should be used. The IPCC default values for dairy cows, other cattle, buffalo, swine (market and breeding swine), and poultry should be taken from Tables 10A-4 through 10A-8 of Annex 10A.2. Manure from other animal categories is typically managed in pastures and grazing operations.

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TABLE 10.21 DEFAULT EMISSION FACTORS FOR DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT

System

EF3 [kg N2O-N (kg Nitrogen excreted)-1]

Definition

Uncertainty ranges of EF3

Sourcea

Pasture/Range/ Paddock

The manure from pasture and range grazing animals is allowed to lie as is, and is not managed.

Direct and indirect N2O emissions associated with the manure deposited on agricultural soils and pasture, range, paddock systems are treated in Chapter 11, Section 11.2, N2O emissions from managed soils.

Daily spread

Manure is routinely removed from a confinement facility and is applied to cropland or pasture within 24 hours of excretion. N2O emissions during storage and treatment are assumed to be zero. N2O emissions from land application are covered under the Agricultural Soils category.

Not applicable

Judgement by IPCC Expert Group (see Co-chairs, Editors and Experts; N2O emissions from Manure Management).

Solid storageb

The storage of manure, typically for a period of several months, in unconfined piles or stacks. Manure is able to be stacked due to the presence of a sufficient amount of bedding material or loss of moisture by evaporation.

0.005

Factor of 2

Judgement of IPCC Expert Group in combination with Amon et al. (2001), which shows emissions ranging from 0.0027 to 0.01 kg N2O-N (kg N)-1.

Dry lot

A paved or unpaved open confinement area without any significant vegetative cover where accumulating manure may be removed periodically. Dry lots are most typically found in dry climates but also are used in humid climates.

0.02

Factor of 2

Judgement of IPCC Expert Group in combination with Kulling (2003).

0.005

Factor of 2

Judgement of IPCC Expert Group in combination with Sommer et al. (2000).

Not applicable

Judgement of IPCC Expert Group in combination with the following studies: Harper et al. (2000), Lague et al. (2004), Monteny et al. (2001), and Wagner-Riddle and Marinier (2003). Emissions are believed negligible based on the absence of oxidized forms of nitrogen entering systems in combination with low potential for nitrification and denitrification in the system.

Not applicable

Judgement of IPCC Expert Group in combination with the following studies: Harper et al. (2000), Lague et al. (2004), Monteny et al. (2001), and Wagner-Riddle and Marinier (2003). Emissions are believed negligible based on the absence of oxidized forms of nitrogen entering systems in combination with low potential for nitrification and denitrification in the system.

With natural crust cover

Liquid/Slurry

Manure is stored as excreted or with some minimal addition of water to facilitate handling and is stored in either tanks or earthen ponds.

Without natural crust cover

Uncovered anaerobic lagoon

Anaerobic lagoons are designed and operated to combine waste stabilization and storage. Lagoon supernatant is usually used to remove manure from the associated confinement facilities to the lagoon. Anaerobic lagoons are designed with varying lengths of storage (up to a year or greater), depending on the climate region, the volatile solids loading rate, and other operational factors. The water from the lagoon may be recycled as flush water or used to irrigate and fertilise fields.

Pit storage below animal confinements

Collection and storage of manure usually with little or no added water typically below a slatted floor in an enclosed animal confinement facility.

10.62

0

0

0

0.002

Factor of 2

Judgement of IPCC Expert Group in combination with the following studies: Amon et al. (2001), Kulling (2003), and Sneath et al. (1997).

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.21 (CONTINUED) DEFAULT EMISSION FACTORS FOR DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT

System

Anaerobic digester

Burned for fuel or as waste

Cattle and swine deep bedding

EF3 [kg N2O-N (kg Nitrogen excreted)-1]

Definition

Anaerobic digesters are designed and operated for waste stabilization by the microbial reduction of complex organic compounds to CH4 and CO2, which is captured and flared or used as a fuel.

0

Uncertainty ranges of EF3

Sourcea

Not applicable

Judgement of IPCC Expert Group in combination with the following studies: Harper et al. (2000), Lague et al. (2004) Monteny et al. (2001), and Wagner-Riddle and Marinier (2003). Emissions are believed negligible based on the absence of oxidized forms of nitrogen entering systems in combination with low potential for nitrification and denitrification in the system.

The dung is excreted on fields. The sun dried dung cakes are burned for fuel.

The emissions associated with the burning of the dung are to be reported under the IPCC category 'Fuel Combustion' if the dung is used as fuel and under the IPCC category 'Waste Incineration' if the dung is burned without energy recovery.

Urine N deposited on pasture and paddock

Direct and indirect N2O emissions associated with the urine deposited on agricultural soils and pasture, range, paddock systems are treated in Chapter 11, Section 11.2, N2O emissions from managed soils.

As manure accumulates, bedding is continually added to absorb moisture over a production cycle and possibly for as long as 6 to 12 months. This manure management system also is known as a bedded pack manure management system and may be combined with a dry lot or pasture.

No mixing

Active mixing

Factor of 2

Average value based on Sommer and Moller (2000), Sommer (2000), Amon et al. (1998), and Nicks et al. (2003).

0.07

Factor of 2

Average value based on Nicks et al. (2003) and Moller et al. (2000). Some literature cites higher values to 20% for well maintained, active mixing, but those systems included treatment for ammonia which is not typical.

0.01

Composting In-Vesselc

Composting, typically in an enclosed channel, with forced aeration and continuous mixing.

0.006

Factor of 2

Judgement of IPCC Expert Group. Expected to be similar to static piles.

Composting Static Pilec

Composting in piles with forced aeration but no mixing.

0.006

Factor of 2

Hao et al. (2001).

Composting Intensive Windrowc

Composting in windrows with regular turning for mixing and aeration.

0.1

Factor of 2

Judgement of IPCC Expert Group. Expected to be greater than passive windrows and intensive composting operations, as emissions are a function of the turning frequency.

Composting Passive Windrowc

Composting in windrows with infrequent turning for mixing and aeration.

0.01

Factor of 2

Hao et al. (2001).

Poultry manure with litter

Similar to deep bedding systems. Typically used for all poultry breeder flocks and for the production of meat type chickens (broilers) and other fowl.

Factor of 2

Judgement of IPCC Expert Group based on the high loss of ammonia from these systems, which limits the availability of nitrogen for nitrification/denitrification.

Poultry manure without litter

May be similar to open pits in enclosed animal confinement facilities or may be designed and operated to dry the manure as it accumulates. The latter is known as a high-rise manure management system and is a form of passive windrow composting when designed and operated properly.

Factor of 2

Judgement of IPCC Expert Group based on the high loss of ammonia from these systems, which limits the availability of nitrogen for nitrification/denitrification.

0.001

0.001

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TABLE 10.21 (CONTINUED) DEFAULT EMISSION FACTORS FOR DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT

System

Aerobic treatment

a

EF3 [kg N2O-N (kg Nitrogen excreted)-1]

Definition

The biological oxidation of manure collected as a liquid with either forced or natural aeration. Natural aeration is limited to aerobic and facultative ponds and wetland systems and is due primarily to photosynthesis. Hence, these systems typically become anoxic during periods without sunlight.

Natural aeration systems

Forced aeration systems

0.01

0.005

Uncertainty ranges of EF3

Sourcea

Factor of 2

Judgement of IPCC Expert Group. Nitrification-denitrification is used widely for the removal of nitrogen in the biological treatment of municipal and industrial wastewaters with negligible N2O emissions. Limited oxidation may increase emissions compared to forced aeration systems.

Factor of 2

Judgement of IPCC Expert Group. Nitrification-denitrification is used widely for the removal of nitrogen in the biological treatment of municipal and industrial wastewaters with negligible N2O emissions.

Also see Dustan (2002), which compiled information from some of the original references cited.

b

Quantitative data should be used to distinguish whether the system is judged to be a solid storage or liquid/slurry. The borderline between dry and liquid can be drawn at 20% dry matter content. c Composting is the biological oxidation of a solid waste including manure usually with bedding or another organic carbon source typically at thermophilic temperatures produced by microbial heat production.

10.5.4

Coordination with reporting for N 2 O emissions from managed soils

Following storage or treatment in any system of manure management, nearly all the manure will be applied to land. The emissions that subsequently arise from the application of the manure to soil are to be reported under the category N2O emissions from managed soils. The methods for estimating these emissions are discussed in Chapter 11, Section 11.2. In estimating N2O emissions from managed soils, the amount of animal manure nitrogen that is directly applied to soils, or available for use in feed, fuel, or construction purposes, are considered. A significant proportion of the total nitrogen excreted by animals in managed systems (i.e., all livestock except those in pasture and grazing conditions) is lost prior to final application to managed soils or for use as feed, fuel, or for construction purposes. In order to estimate the amount of animal manure nitrogen that is directly applied to soils, or available for use in feed, fuel, or construction purposes (i.e., the value which is used in Chapter 11, Equation 11.1 or 11.2), it is necessary to reduce the total amount of nitrogen excreted by animals in managed systems by the losses of N through volatilisation (i.e., NH3, N2 and NOx), conversion to N2O and losses through leaching and runoff. Where organic forms of bedding material (straw, sawdust, chippings, etc.) are used, the additional nitrogen from the bedding material should also be considered as part of the managed manure N applied to soils. Bedding is typically collected with the remaining manure and applied to soils. It should be noted, however, that since mineralization of nitrogen compounds in beddings occurs more slowly compared to manure and the concentration of ammonia fraction in organic beddings is negligible, both volatilization and leaching losses during storage of bedding are assumed to be zero (European Environmental Agency, 2002).

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TABLE 10.22 DEFAULT VALUES FOR NITROGEN LOSS DUE TO VOLATILISATION OF NH3 AND NOX FROM MANURE MANAGEMENT

Animal type

Swine

Dairy Cow

Poultry

Other Cattle

Other

c

Manure management system (MMS) a

N loss from MMS due to volatilisation of N-NH3 and N-NOx (%) b FracGasMS (Range of FracGasMS)

Anaerobic lagoon

40% (25 – 75)

Pit storage

25% (15 – 30)

Deep bedding

40% (10 – 60)

Liquid/slurry

48% (15 – 60)

Solid storage

45% (10 – 65)

Anaerobic lagoon

35% (20 – 80)

Liquid/Slurry

40% (15 – 45)

Pit storage

28% (10 – 40)

Dry lot

20% (10 – 35)

Solid storage

30% (10 – 40)

Daily spread

7% (5 – 60)

Poultry without litter

55% (40 – 70)

Anaerobic lagoon

40% (25 – 75)

Poultry with litter

40% (10 – 60)

Dry lot

30% (20 – 50)

Solid storage

45% (10 – 65)

Deep bedding

30% (20 – 40)

Deep bedding

25% (10 – 30)

Solid storage

12% (5 – 20)

a

Manure Management System here includes associated N losses at housing and final storage system.

b

Volatilization rates based on judgement of IPCC Expert Group and following sources: Rotz ( 2003), Hutchings et al. (2001), and U.S EPA (2004).

c

Other includes sheep, horses, and fur-bearing animals.

The estimate of managed manure nitrogen available for application to managed soils, or available for use in feed, fuel, or construction purposes is based on the following equation: EQUATION 10.34 MANAGED MANURE N AVAILABLE FOR APPLICATION TO MANAGED SOILS, FEED, FUEL OR

N MMS _ Avb

(

)

CONSTRUCTION USES

⎧ ⎡⎡ ⎛ Frac LossMS ⎪ ⎢ ⎢ N (T ) • Nex(T ) • MS(T , S ) • ⎜1 − 100 = ∑ ⎨ ∑ ⎢⎣ ⎝ S ⎪(T ) ⎢ N (T ) • MS(T , S ) • N beddingMS ⎩ ⎣

[

]

⎞⎤ ⎤ ⎫ ⎟⎥ + ⎥ ⎪ ⎠⎦ ⎥ ⎬ ⎥⎪ ⎦⎭

Where: NMMS_Avb = amount of managed manure nitrogen available for application to managed soils or for feed, fuel, or construction purposes, kg N yr-1 N(T) = number of head of livestock species/category T in the country Nex(T) = annual average N excretion per animal of species/category T in the country, kg N animal-1 yr-1

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MS(T,S) = fraction of total annual nitrogen excretion for each livestock species/category T that is managed in manure management system S in the country, dimensionless FracLossMS = amount of managed manure nitrogen for livestock category T that is lost in the manure management system S, % (see Table 10.23) NbeddingMS = amount of nitrogen from bedding (to be applied for solid storage and deep bedding MMS if known organic bedding usage), kg N animal-1 yr-1 S = manure management system T = species/category of livestock

Bedding materials vary greatly and inventory compilers should develop values for NbeddingMS based on the characteristics of bedding material used in their livestock industries. Limited data from scientific literature indicates the amount of nitrogen contained in organic bedding material applied for dairy cows and heifers is usually around 7 kg N animal-1 yr-1, for other cattle is 4 kg N animal-1 yr-1, for market and breeding swine is around 0.8 and 5.5 kg N animal-1 yr-1, respectively. For deep bedding systems, the amount of N in litter is approximately double these amounts (Webb, 2001; Döhler et al., 2002). Table 10.23 presents default values for total nitrogen losses from manure management systems. These default values include losses that occur from the point of excretion, including animal housing losses, manure storage losses, and losses from leaching and runoff at the manure storage system where applicable. For example, values provided for dairy anaerobic lagoon systems include nitrogen losses that occur in the dairy barn and milking parlour prior to the collection and treatment of manure, as well as those that occur from the lagoon. There is a high level of variability in the range of total nitrogen losses from managed manure systems. As shown in Table 10.23, the majority of these are due to volatilization losses, primarily ammonia losses that occur rapidly following the excretion of the manure. However, losses also occur in the form of NO3, N2O, and N2 as well from leaching and runoff that occurs where manure is stored in piles. The values in Table 10.23 reflect average values for typical housing/storage combinations for each animal category. Countries are encouraged to develop countryspecific values, particularly related to ammonia losses where component emissions may be well characterised for local practices as part of larger air quality assessments and where emissions may be affected by nitrogen reduction strategies. Countries may wish to develop an alternative approach for better consideration of national circumstances and to reduce uncertainty of estimates as much as possible. This approach would entail more detailed characterisation of the flow of nitrogen through the components of the animal housing and manure management systems used in the country, accounting for any mitigation activity (e.g., the use of covers over slurry tanks), and consideration of local practices, such as type of bedding material used.

10.5.5

Uncertainty assessment

EMISSION FACTORS – NITROGEN EXCRETION RATES Uncertainty ranges for the default N excretion rates are estimated at about +50% (Source: Judgement by IPCC Expert Group). The uncertainty ranges for the default N retention values provided here are also +50% (see Table 10.20). If inventory agencies derive N excretion rates using accurate in-country statistics on N intake and N retention, the uncertainties associated with the N excretion rates may be reduced substantially. The degree of uncertainty may be further reduced by using direct emission measurements of nitrogen losses from specific manure management systems.

EMISSION FACTORS – DIRECT N 2 O EMISSIONS There are large uncertainties associated with the default emission factors for this source category (–50% to +100%). Accurate and well-designed emission measurements from well characterised types of manure and manure management systems can help reduce these uncertainties. These measurements must account for temperature, moisture conditions, aeration, manure N content, metabolisable carbon, duration of storage, and other aspects of treatment.

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TABLE 10.23 DEFAULT VALUES FOR TOTAL NITROGEN LOSS FROM MANURE MANAGEMENT

Animal category

Manure management system a

Total N loss from MMS b FracLossMS (Range of FracLossMS)

Anaerobic lagoon Swine

Dairy Cow

Poultry

Other Cattle

Other c

78% (55 – 99)

Pit storage

25% (15 – 30)

Deep bedding

50% (10 – 60)

Liquid/Slurry

48% (15 – 60)

Solid storage

50% (20 – 70)

Anaerobic lagoon

77% (55 – 99)

Liquid/Slurry

40% (15 – 45)

Pit storage

28% (10 – 40)

Dry lot

30% (10 – 35)

Solid storage

40% (10 – 65)

Daily spread

22% (15 – 60)

Poultry without litter

55% (40 – 70)

Anaerobic lagoon

77% (50 – 99)

Poultry with litter

50% (20 – 80)

Dry lot

40% (20 – 50)

Solid storage

50% (20 – 70)

Deep bedding

40% (10 – 50)

Deep bedding

35% (15 – 40)

Solid storage

15% (5 – 20)

a

Manure Management System here includes associated N losses at housing and final storage system.

b

Total N loss rates based on judgement of IPCC Expert Group and following sources: Rotz ( 2003), Hutchings et al. (2001), and U.S EPA (2004). Rates include losses in forms of NH3, NOx, N2O, and N2 as well from leaching and runoff from solid storage and dry lots. Values represent average rates for typical housing and storage components without any significant nitrogen control measures in place. Ranges reflect values that appear in the literature. Where measures to control nitrogen losses are in place, alternative rates should be developed to reflect those measures.

c

Other includes sheep, horses, and fur-bearing animals.

EMISSION FACTORS – INDIRECT N 2 O EMISSIONS Uncertainty ranges for default N losses due to volatilisation of NH3 and NOx and total N losses from manure management systems are presented in the Tables 10.22 and 10.23, respectively. The uncertainty associated with default emission factor for nitrogen volatilisation and re-deposition (EF4) is given in Table 11.3 of Chapter 11. The uncertainty range for the default emission factor for leaching and runoff (EF5) is also provided in Table 11.3. Caution should be taken when developing country-specific emission factors for volatilisation and re-deposition of nitrogen, since direct measurements could include transboundary atmospheric transport.

ACTIVITY DATA – LIVESTOCK POPULATIONS See Section 10.2 (Livestock Population and Feed Characterisation) for discussion on uncertainty of animal population and feed characterisation data.

ACTIVITY DATA – MANURE MANAGEMENT SYSTEM USAGE The uncertainty of the manure management system usage data will depend on the characteristics of each country's livestock industry and how information on manure management is collected. For example, for countries that rely almost exclusively on one type of management system, such as dry lot, the uncertainty associated with management system usage data can be 10% or less. However, for countries where there is a wide variety of management systems used with locally different operating practices, the uncertainty in management system usage data can be much higher, in the range of 25% to 50%, depending on the availability of reliable and representative survey data that differentiates animal populations by system usage. Preferably, each country should estimate the uncertainty associated with their management system usage data by using the methods described in Volume 1, Chapter 3.

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10.5.6

Completeness, Time series, Quality assurance/Quality control and Reporting

A complete inventory should estimate N2O emissions from all systems of manure management for all livestock species/categories. Countries are encouraged to use manure management system definitions that are consistent with those presented in Table 10.18. Population data should be cross-checked between main reporting mechanisms (such as FAO and national agricultural statistics databases) to ensure that information used in the inventory is complete and consistent. Because of the widespread availability of the FAO database of livestock information, most countries should be able to prepare, at a minimum, Tier 1 estimates for the major livestock categories. For more information regarding the completeness of livestock characterisation, see Section 10.2. Developing a consistent time series of emission estimates for this source category requires, at a minimum, the collection of an internally consistent time series of livestock population statistics. General guidance on the development of a consistent time series is addressed in Volume 1, Chapter 5 of this report. In most countries, the other two activity data sets required for this source category (i.e., N excretion rates and manure management system usage data), as well as the manure management emission factors, will be kept constant for the entire time series. However, in some cases, there may be reasons to modify these values over time. For example, farmers may alter livestock feeding practices which could affect nitrogen excretion rates. A particular system of manure management may change due to operational practices or new technologies such that a revised emission factor is warranted. These changes in practices may be due to the implementation of explicit greenhouse gas mitigation measures, or may be due to changing agricultural practices without regard to greenhouse gases. Regardless of the driver of change, the parameters and emission factors used to estimate emissions must reflect the change. The inventory text should thoroughly explain how the change in farm practices or implementation of mitigation measures has affected the time series of activity data or emission factors. It is good practice to implement general quality control checks as outlined in Volume 1, Chapter 6, Quality Assurance/Quality Control and Verification, and expert review of the emission estimates. Additional quality control checks and quality assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions from this source. The general QA/QC related to data processing, handling, and reporting should be supplemented with procedures discussed below:

Activity data check •

•

•

• •

The inventory agency should review livestock data collection methods, in particular checking that livestock subspecies data were collected and aggregated correctly with consideration for the duration of production cycles. The data should be cross-checked with previous years to ensure the data are reasonable and consistent with the expected trend. Inventory agencies should document data collection methods, identify potential areas of bias, and evaluate the representativeness of the data. Manure management system allocation should be reviewed on a regular basis to determine if changes in the livestock industry are being captured. Conversion from one type of management system to another, and technical modifications to system configuration and performance, should be captured in the system modelling for the affected livestock. National agricultural policy and regulations may have an effect on parameters that are used to calculate manure emissions, and should be reviewed regularly to determine what impact they may have. For example, guidelines to reduce manure runoff into water bodies may cause a change in management practices, and thus affect the N distribution for a particular livestock category. Consistency should be maintained between the inventory and ongoing changes in agricultural practices. If using country-specific data for Nex(T) and MS(T,S), the inventory agency should compare these values to the IPCC default values. Significant differences, data sources, and methods of data derivation, should be documented. The nitrogen excretion rates, whether default or country-specific values, should be consistent with feed intake data as determined through animal nutrition analyses.

Review of emission factors •

•

The inventory agency should evaluate how well the implied N2O emission factors and nitrogen excretion rates compare with alternative national data sources and with data from other countries with similar livestock practices. Significant differences should be investigated. If using country-specific emission factors, the inventory agency should compare them to the default factors and note differences. The development of country-specific emission factors should be explained and documented, and the results peer-reviewed by independent experts.

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•

Whenever possible, available measurement data, even if they represent only a small sample of systems, should be reviewed relative to assumptions for N2O emission estimates. Representative measurement data may provide insights into how well current assumptions predict N2O production from manure management systems in the inventory area, and how certain factors (e.g., feed intake, system configuration, retention time) are affecting emissions. Because of the relatively small amount of measurement data available for these systems worldwide, any new results can improve the understanding of these emissions and possibly their prediction.

External review •

The inventory agency should utilise experts in manure management and animal nutrition to conduct expert peer review of the methods and data used. While these experts may not be familiar with greenhouse gas emissions, their knowledge of key input parameters to the emission calculation can aid in the overall verification of the emissions. For example, animal nutritionists can evaluate N production rates to see if they are consistent with feed utilization research for certain livestock species. Practicing farmers can provide insights into actual manure management techniques, such as storage times and mixed-system usage. Wherever possible, these experts should be completely independent of the inventory process in order to allow a true external review.

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Volume 1, Chapter 6, Quality Assurance/Quality Control and Verification. When country-specific emission factors, fractions of N losses, N excretion rates, or manure management system usage data have been used, the derivation of or references for these data should be clearly documented and reported along with the inventory results under the appropriate IPCC source category. N2O emissions from different types of manure management systems have to be reported according to categories in Table 10.18. N2O emissions from all types of manure management systems are to be reported under Manure Management, with two exceptions: •

•

Emissions from the manure management system for pasture, range, and paddock are to be reported under the IPCC source category N2O emissions from managed soils because this manure is deposited directly on soils by the livestock. Emission from the manure management system burned for fuel, are to be reported under the IPCC category Fuel Combustion if the dung is used as fuel and under the IPCC category Waste Incineration if the dung is burned without energy recovery. It should be noted, however, if the urine nitrogen is not collected for burning it must be reported under N2O emissions from pasture, range, and paddock animals.

10.5.7

Use of worksheets

Use the worksheets for Livestock N2O contained in Annex 1 (AFOLU Worksheets) to calculate and report inventory information for default methodologies described in Section 10.5 N2O emission from manure management. The following is a summary of the step-by-step instructions to follow when completing the worksheets. Note that columns are referred to using the symbols of the variables that both appear in the equations, as well as in column headings of the worksheets. Step 1: Calculation of N excretion from manure management systems (see worksheet for category Manure Management: Direct N2O emissions from Manure Management, Category code 3A2, Sheet 1 of 1). Make extra copies of the worksheet and complete one for each manure management systems (MMS). Step 1A: Collect population data from the Livestock Population Characterisation and enter corresponding values in column N(T); Step 1B: Use default values for Nrate and TAM (Equation 10.30 and using data from Table 10.19 and Tables 10A-4 to 10A-9) or develop the annual average nitrogen excretion rate per head (Nex(T)) for each defined livestock species/category T and enter these values in columns Nrate and TAM, or Nex(T), respectively; Step 1C: Enter in column MS(T,S) default values (see Tables 10A-4 through 10A-8 of Annex 10A.2) or determine the fraction of total annual nitrogen excretion for each livestock species/category T that is managed in each manure management system S (MS(T,S)); Step 1D: Multiply the number of heads (column N(T)) by the value of N excretion rate per head (Nex(T)) for each livestock species/category T (column Nex(T)) and by the fraction of manure nitrogen per MMS (column MS(T,S)) in order to estimate total nitrogen excretion for each MMS in kilograms per year (column NEMMS). Enter the results in column NEMMS of this sheet, and in column NEMMS of Sheet 1of 2 and Sheet 2 of 2 for worksheets under category Indirect N2O emissions from Manure Management, Category code 3C6.

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Step 2: Calculation of direct N2O emissions from manure management systems (see worksheet for category Manure Management: Direct N2O emissions from Manure Management, Category code 3A2, Sheet 1 of 1). Step 2A: Use default values (see Table 10.21) or develop direct N2O emission factors for each manure management system S (EF3(S)) and enter corresponding emission factor in the column EF3(S); Step 2B: For each manure management system type S, multiply its emission factor (column EF3(S)) by the amount of nitrogen managed (column NEMMS) in that system, to estimate direct N2O emissions per MMS. Note that emissions estimates should be reported in kg of N2O. Enter the results in the column N2OD(mm) of this sheet. Step 3: Calculation of indirect N2O emissions from manure management systems (see worksheet for category Indirect N2O emissions from Manure Management, Category code 3C6, Sheet 1 of 2). Make extra copies of the worksheet using one for each MMS). Step 3A: Enter in column FracGasMS default values (see Table 10.22) or determine country-specific fraction of managed livestock manure nitrogen that volatilises as NH3 and NOx for each defined livestock species/category T per each MMS (FracGasMS); Step 3B: Multiply the fraction of manure nitrogen that volatilises as NH3 and NOx (column FracGasMS) by the total amount of nitrogen excreted in each MMS per livestock categories (column NEMMS) to estimate amount of manure nitrogen that is lost due to volatilisation of NH3 and NOx (Nvolatilizations-MMS); Step 3C: Use default value (see Table 11.3, Chapter 11, Section 11.2 N2O emissions from managed soils) or develop country-specific emission factor for indirect N2O emission from atmospheric deposition of nitrogen on soils and water surfaces and enter the emission factor in the column EF4; Step 3D: Multiply the amount of manure nitrogen that is lost due to volatilisation of NH3 and NOx (column Nvolatilizations-MMS) by the emission factor (column EF4), to calculate annual indirect N2O emissions per MMS. Note that emissions estimates should be reported in kg of N2O. Enter the results in the column N2OG(mm) of this sheet. Step 4: Calculation of manure N that is available for application to soils or for use in feed, fuel or construction purposes from manure management systems (see worksheet for category Indirect N2O emissions from Manure Management, Category code 3C6, Sheet 2 of 2). Make extra copies of the worksheet using one for each MMS). Step 4A: Enter in column FraclossMS default values (see Table 10.23) or develop country-specific fraction of total nitrogen loss from manure managed in each MMS for each livestock species/category T (FraclossMS); Step 4B: If country-specific values for organic bedding usage are available for solid storage or deep bedding MMS, calculate the amount of N from bedding by multiplying the number of animals associated with these two systems by the N content in bedding per animal. Enter results obtained in the column NbeddingMS. Step 4C: Calculate managed manure N available for application to managed soils, feed, fuel or construction using Equation 10.34 and enter obtained results in column NMMS_Avb. Then sum over all manure management systems. This value is used for calculation of N2O emissions from managed soils (see worksheets in Annex 1).

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Annex 10A.1 Data underlying methane default emission factors for Enteric Fermentation This annex presents the data used to develop the default emission factors for methane emissions from Enteric Fermentation. The Tier 2 method was implemented with these data to estimate the default emission factors for cattle and buffalo.

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TABLE 10A.1 DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS FOR DAIRY COWS IN TABLE 10.11 Weight, kg

Weight gain, kg day-1

Feeding Situation

Milk, kg day-1

Work, hr day-1

%Pregnant

Digestibility of feed (DE%)

CH4 conversion factor (Ym)

North Americaa

600

0

Stall fed

23.0

0

90%

75%

6.5%

Western Europe

600

0

Stall fed

16.4

0

90%

70%

6.5%

b

550

0

Stall fed

7.0

0

80%

60%

6.5%

500

0

Pasture/Range

6.0

0

80%

60%

6.5%

400

0

Pasture/Range

2.2

0

80%

60%

6.5%

350

0

Stall fed

4.5

0

80%

60%

6.5%

275

0

Stall fed

1.3

0

67%

60%

6.5%

275

0

Stall fed

2.5

0

50%

55%

6.5%

Regions

Eastern Europe Oceania

c

Latin America

d

Asiae Africa & Middle East f

Indian Subcontinent a

Based on estimates for the United States.

b

Based on estimates for the former USSR.

c

Based on average estimate for region.

d

Based on estimates for Brazil.

e

Based on estimates for China.

f

Based on estimates for India.

Source: Gibbs and Johnson (1993).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.72

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10A.2 DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS FOR OTHER CATTLE IN TABLE 10.11 Subcategory

Weight, kg

Weight gain, kg day-1

Feeding situation

Milk, kg day-1

Work, hr day-1

%Pregnant

Digestibility of feed (DE%)

CH4 conversion factor (Ym)

Day weighted population mix %

Emission factors, kg CH4 head-1 yr-1

North Americaa Mature females

500

0.0

Pasture/Range

3.3

0.0

80%

60%

6.5%

36%

76

Mature males

800

0.0

Pasture/Range

0.0

0.0

0%

60%

6.5%

2%

81

Calves on milk

100

0.9

Pasture/Range

0.0

0.0

0%

NA

0.0%

16%

0

Calves on forage

185

0.9

Pasture/Range

0.0

0.0

0%

65%

6.5%

8%

48

65%

6.5%

17%

55

11%

66

Growing heifers/steers

265

0.7

Pasture/Range

0.0

0.0

0%

Replacement/growing

375

0.4

Pasture/Range

0.0

0.0

0%

60%

6.5%

Feedlot cattle

415

1.3

Stall fed

0.0

0.0

0%

75%

3.0%

11%

33

Mature males

600

0.0

Pasture/Range

0.0

0.0

0%

60%

6.5%

22%

66

Replacement/growing

400

0.4

Pasture/Range

0.0

0.0

0%

60%

6.5%

54%

73

Calves on milk

230

0.3

Pasture/Range

0.0

0.0

0%

65%

0.0%

15%

0

Calves on forage

230

0.3

Pasture/Range

0.0

0.0

0%

65%

6.5%

8%

35

Western Europe

Eastern Europeb Mature females

500

0.0

Pasture/Range

3.3

0.0

67%

60%

6.5%

30%

75

Mature males

600

0.0

Pasture/Range

0.0

0.0

0%

60%

6.5%

22%

66

Young

230

0.4

Pasture/Range

0.0

0.0

0%

60%

6.5%

48%

45

Oceaniac

a

Mature females

400

0.0

Pasture/Range

2.4

0.0

67%

55%

6.5 %

51%

71

Mature males

450

0.0

Pasture/Range

0.0

0.0

0%

55%

6.5%

11%

61

Young

200

0.3

Pasture/Range

0.0

0.0

0%

55%

6.5%

38%

46

Based on estimates for the United States; . b Based on estimates for the former USSR;

2006 IPCC Guidelines for National Greenhouse Gas Inventories

c

Based on average estimate for region.

10.73

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10A.2 (CONTINUED) DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS FOR OTHER CATTLE IN TABLE 10.11 Weight,

Subcategory

kg

Weight gain, kg day-1

Feeding situation

Milk, kg day-1

Work, hr day-1

%Pregnant

Digestibility of feed (DE%)

CH4 conversion factor (Ym)

Day weighted population mix %

Emission factors, kg CH4 head-1 yr-1

Latin Americad Mature females

400

0.0

Large areas

1.1

0.0

67%

60%

6.5%

37%

64

Mature males

450

0.0

Large areas

0.0

0.0

0%

60%

6.5%

6%

61

Young

230

0.3

Large areas

0.0

0.0

0%

60%

6.5%

58%

49

Asiae Mature females- Farming

325

0.0

Stall fed

1.1

0.55

33%

55%

6.5%

27%

50

Mature females- Grazing

300

0.0

Pasture/Range

1.1

0.00

50%

60%

6.5%

9%

46

Mature males-Farming

450

0.0

Stall fed

0.0

1.37

0%

55%

6.5%

24%

59

Mature males-Grazing

400

0.0

Pasture/Range

0.0

0.00

0%

60%

6.5%

8%

48

Young

200

0.2

Pasture/Range

0.0

0.00

0%

60%

6.5%

32%

36

Africa Mature females

200

0.0

Stall fed

0.3

0.55

33%

55%

6.5%

13%

32

Draft bullocks

275

0.0

Stall fed

0.0

1.37

0%

55%

6.5%

13%

41

Mature females- Grazing

200

0.0

Large areas

0.3

0.00

33%

55%

6.5%

6%

41

Bulls- Grazing

275

0.0

Large areas

0.0

0.00

0%

55%

6.5%

25%

49

Young

75

0.1

Pasture/Range

0.0

0.00

0%

60%

6.5%

44%

16

Indian Subcontinentf Mature females

125

0.0

Stall fed

0.6

0.00

33%

50%

6.5%

40%

28

Mature males

200

0.0

Stall fed

0.0

2.74

0%

50%

6.5%

10%

42

80

0.1

Stall fed

0.0

0.00

0%

50%

6.5%

50%

23

Young d

e

f

Based on estimates for the Brazil.; Based on estimates for the China.; Based on estimates for India; Source: Gibbs and Johnson (1993)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.74

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10A.3 DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 Subcategory

Weight,

Weight gain,

kg

kg day-1

Feeding situation

Milk,

Work,

kg day-1

hr day-1

EMISSION FACTORS FOR BUFFALO

%Pregnant

Digestibility of feed (DE%)

CH4 conversion factor (Ym)

Day weighed population mix %

Emissions factors, kg CH4 head-1 yr-1

Indian Subcontinenta Adult males

350 - 550

0.00

Stall fed

0.00

1.37

0%

55%

6.5%

14%

55 - 77

Adult females

250 - 450

0.00

Stall fed

2.70

0.55

33%

55%

6.5%

40%

57 - 80

Young

100 - 300

0.15

Stall fed

0.00

0.00

0%

55%

6.5%

46%

23 - 50

Other Countriesb Adult males

350 - 550

0.00

Stall fed

0.00

1.37

0%

55%

6.5%

45%

55 - 77

Adult females

250 - 450

0.00

Stall fed

0.00

0.55

25%

55%

6.5%

45%

45 - 67

Young

100 - 300

0.15

Stall fed

0.15

0.00

0%

55%

6.5%

10%

23 - 50

a

Based on estimates for India.

b

Based on estimates for China.

Source: Gibbs and Johnson (1993).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Annex 10A.2 Data underlying methane default emission factors for Manure Management This annex presents the data used to develop the default emission factors for methane emissions from Manure Management. The Tier 2 method was implemented with these data to estimate the default emission factors for each livestock category.

10.76

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management Table 10A-4 Manure Management Methane Emission Factor Derivation for Dairy Cows

Annual Average Temperature (°C) Cool

Temp

Warm

Lagoon1 66% 68% 70% 71% 73% 74% 75% 76% 77% 77% 78% 78% 78% 79% 79% 79% 79% 80% 80%

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Liquid/ Slurry1 17% 19% 20% 22% 25% 27% 29% 32% 35% 39% 42% 46% 50% 55% 60% 65% 71% 78% 80%

Manure Management System MCFs Pasture/ Range/ Daily Drylot Paddock Spread Digester 1.0% 1.0% 0.1% 10.0% 1.0% 1.0% 0.1% 10.0% 1.0% 1.0% 0.1% 10.0% 1.0% 1.0% 0.1% 10.0% 1.0% 1.0% 0.1% 10.0% 1.5% 1.5% 0.5% 10.0% 1.5% 1.5% 0.5% 10.0% 1.5% 1.5% 0.5% 10.0% 1.5% 1.5% 0.5% 10.0% 1.5% 1.5% 0.5% 10.0% 1.5% 1.5% 0.5% 10.0% 1.5% 1.5% 0.5% 10.0% 1.5% 1.5% 0.5% 10.0% 1.5% 1.5% 0.5% 10.0% 1.5% 1.5% 0.5% 10.0% 1.5% 1.5% 0.5% 10.0% 2.0% 2.0% 1.0% 10.0% 2.0% 2.0% 1.0% 10.0% 2.0% 2.0% 1.0% 10.0%

Solid Storage 2.0% 2.0% 2.0% 2.0% 2.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 5.0% 5.0% 5.0%

Burned for Fuel 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0%

Other 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Emission Factors kg CH4 per head per year

Dairy Cow Characteristics Region

Massa

Bob

VSc

kg

m3CH4/kg VS

kg/hd/day

d

North America 604 0.24 5.4 Western Europe 600 0.24 5.1 Eastern Europe 550 0.24 4.5 Oceania 500 0.24 3.5 Latin America 400 0.13 2.9 Africa 275 0.13 1.9 Middle East 275 0.13 1.9 Asia 350 0.13 2.8 Indian Subcontinent 275 0.13 2.6 a Average dairy cow mass for each region (default estimates are ±10%) b Bo estimates are ±15%

Manure Management System Usage (MS%)

Cool 10

15.0% 0.0% 0.0% 16.0% 0.0% 0.0% 0.0% 4.0% 0.0%

27.0% 35.7% 17.5% 1.0% 1.0% 0.0% 1.0% 38.0% 1.0%

26.3% 36.8% 60.0% 0.0% 1.0% 1.0% 2.0% 0.0% 0.0%

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

10.8% 20.0% 18.0% 76.0% 36.0% 83.0% 80.0% 20.0% 27.0%

18.4% 7.0% 2.5% 8.0% 62.0% 5.0% 2.0% 29.0% 19.0%

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2.0% 1.0%

0.0% 0.0% 0.0% 0.0% 0.0% 6.0% 17.0% 7.0% 51.0%

2.6% 0.5% 2.0% 0.0% 0.0% 4.0% 0.0% 0.0% 0.0%

48 21 11 23 1 1 2 9 5

11 50 23 12 24 1 1 2 10 5

12 53 25 13 25 1 1 2 10 5

Temperate 13 55 27 14 26 1 1 2 11 5

14 58 29 15 26 1 1 2 12 5

15 63 34 20 27 1 1 2 13 5

16 65 37 21 28 1 1 2 14 5

17 68 40 22 28 1 1 2 15 5

18 71 43 23 28 1 1 2 16 5

19 74 47 25 29 1 1 2 17 5

20 78 51 27 29 1 1 2 18 5

Warm

21 81 55 28 29 1 1 2 20 5

22 85 59 30 29 1 1 2 21 5

23 89 64 33 29 1 1 2 23 5

24 93 70 35 30 1 1 2 24 5

25 98 75 37 30 1 1 2 26 5

26

27

28

105 83 42 31 2 1 2 28 5

110 90 45 31 2 1 3 31 6

112 92 46 31 2 1 3 31 6

1 Lagoon and Liquid/Slurry MCFs are calculated based on the van't Hoff-Arrhenius Average VS production per head per day for the average dairy cow (default estimates are equation relating temperature to biological activity. Lagoon MCFs are also calculated based on longer (up to a year) retention times. [Mangino, et. al (2001)] ±20%) c

d

For North America, "Other" manure management system MCFs represent deep pits, which have the same MCF values as Liquid/Slurry. Emission Factors (EF) for each region are calculated based on eq.10.23.

Sources: For North America, dairy cow mass values are from Safley (2000) and VS values are estimated based on an analysis of feed data from Petersen et.al (2003). North American manure management system usage values are estimated using data from the 1992 and 1997 USDA's Census of Agriculture and National Animal Health Monitoring System Reports. Bo values are from Morris (1976) and Bryant, et.al. (1976). For Western and Eastern Europe manure management system usage, mass and VS values based on the analysis of national GHG inventories of Annex I countires submitted to the secretariat UNFCCC in 2004. For the rest of the world, the detailed information for dairy cows are developed in Gibbs and Johnson (1993), and manure management system usage and Bo estimates are from Safley et. al (1992). Methane conversion factor data are from Woodbury and Hashimoto (1993). MCFs for lagoons and liquid/slurry systems are based on data obtained from an analysis of these systems in the United States.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Table 10A-5 Manure Management Methane Emission Factor Derivation for Other Cattle Manure Management System MCFs Pasture/ Annual Average Temperature (°C) Liquid/ Solid Range/ Daily Burned 1 1 Slurry Storage Drylot Paddock Spread Digester for Fuel Other Lagoon Cool 10 66% 17% 2.0% 1.0% 1.0% 0.1% 10.0% 10.0% 1.0% 11 68% 19% 2.0% 1.0% 1.0% 0.1% 10.0% 10.0% 1.0% 12 70% 20% 2.0% 1.0% 1.0% 0.1% 10.0% 10.0% 1.0% 13 71% 22% 2.0% 1.0% 1.0% 0.1% 10.0% 10.0% 1.0% 14 73% 25% 2.0% 1.0% 1.0% 0.1% 10.0% 10.0% 1.0% Temp 15 74% 27% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% 16 75% 29% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% 17 76% 32% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% 18 77% 35% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% 19 77% 39% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% 20 78% 42% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% 21 78% 46% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% 22 78% 50% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% 23 79% 55% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% 24 79% 60% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% 25 79% 65% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 1.0% Warm 26 79% 71% 5.0% 2.0% 2.0% 1.0% 10.0% 10.0% 1.0% 27 80% 78% 5.0% 2.0% 2.0% 1.0% 10.0% 10.0% 1.0% 28 80% 80% 5.0% 2.0% 2.0% 1.0% 10.0% 10.0% 1.0% Emission Factors kg CH4 per head per year

Other Cattle Characteristics Region

a

B ob

Mass

VS

c

Manure Management System Usage (MS%)

Cool

3

kg m CH4/kg VS kg/hd/day North America 389 0.19 2.4 Western Europe 420 0.18 2.6 Eastern Europe 391 0.17 2.7 Oceania 330 0.17 3.0 Latin America 305 0.1 2.5 Africa 173 0.1 1.5 Middle East 173 0.1 1.5 Asia 319 0.1 2.3 Indian Subcontinent 110 0.1 1.4 a Average other cattle mass for each region (default estimates are ±25%) b Bo estimates are ±15%

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

0.2% 25.2% 22.5% 0.0% 0.0% 0.0% 0.0% 0.0% 1.0%

0.0% 39.0% 44.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

18.4% 0.0% 0.0% 9.0% 0.0% 1.0% 1.0% 46.0% 4.0%

81.5% 32.0% 20.0% 91.0% 99.0% 95.0% 79.0% 50.0% 22.0%

0.0% 1.8% 0.0% 0.0% 0.0% 1.0% 2.0% 2.0% 20.0%

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.0%

0.0% 0.0% 0.0% 0.0% 0.0% 3.0% 17.0% 2.0% 53.0%

0.0% 2.0% 13.5% 0.0% 1.0% 0.0% 2.0% 0.0% 0.0%

Temperate

Warm

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 6 7 7 8 8 10 11 12 13 14 15 16 17 18 20 21 24 25 26 6 6 7 7 8 9 10 11 11 12 13 14 15 16 18 19 21 23 23 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

1

Lagoon and Liquid/Slurry MCFs are calculated based on the van't Hoff-Arrhenius Average VS production per head per day for the average non-dairy cow (default estimates equation relating temperature to biological activity. Lagoon MCFs are also calculated are ±35%) based on longer (up to a year) retention times. [Mangino, et. al (2001)] c

Emission Factors (EF) for each region are calculated based on eq.10.23.

Sources: For North America, other cattle mass are from Safley (2000) and USDA's Agricultural Waste Management Field Handbook and VS values are estimated based on an analysis of feed data from Petersen, et.al (2003). North American manure management system usage values are estimated using data from the 1992 and 1997 USDA's Census of Agriculture and National Animal Health Monitoring System Reports. Bo data are values reported in Hashimoto (1981). For Western and Eastern Europe manure management system usage, average mass, Bo, and VS values based on the analysis of national GHG inventories of Annex I countires submitted to the secretariat UNFCCC in 2004. For the rest of the world, the detailed information for cattle are developed in Gibbs and Johnson (1993), and manure management system usage and Bo estimates are from Safley et. al (1992). Methane conversion factor data are from Woodbury and Hashimoto (1993). MCFs for lagoons and liquid/slurry systems are based on data obtained from an analysis of these systems in the United States.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.78

Chapter 10: Emissions from Livestock and Manure Management Table 10A-6 Manure Management Methane Emission Factor Derivation for Buffalo Manure Management System MCFs Pasture/ Annual Average Temperature (°C) Liquid/ Solid Range/ Daily Burned 1 1 Lagoon Slurry Storage Drylot Paddock Spread Digester for Fuel Cool 10 66% 17% 2.0% 1.0% 1.0% 0.1% 10.0% 10.0% 11 68% 19% 2.0% 1.0% 1.0% 0.1% 10.0% 10.0% 12 70% 20% 2.0% 1.0% 1.0% 0.1% 10.0% 10.0% 13 71% 22% 2.0% 1.0% 1.0% 0.1% 10.0% 10.0% 14 73% 25% 2.0% 1.0% 1.0% 0.1% 10.0% 10.0% Temp 15 74% 27% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 16 75% 29% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 17 76% 32% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 18 77% 35% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 19 77% 39% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 20 78% 42% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 21 78% 46% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 22 78% 50% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 23 79% 55% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 24 79% 60% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% 25 79% 65% 4.0% 1.5% 1.5% 0.5% 10.0% 10.0% Warm 26 79% 71% 5.0% 2.0% 2.0% 1.0% 10.0% 10.0% 27 80% 78% 5.0% 2.0% 2.0% 1.0% 10.0% 10.0% 28 80% 80% 5.0% 2.0% 2.0% 1.0% 10.0% 10.0%

Other 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Emission Factors kg CH4 per head per year

Buffalo Characteristics Region

Mass kg

a

VS

Bo 3

m CH4/kg VS (not applicable) 0.1 0.1 (not applicable) 0.1 (not applicable) 0.1 0.1 0.1

b

Manure Management System Usage (MS%)

Cool

kg/hd/day

North America Western Europe 380 Eastern Europe 380 Oceania Latin America 380 Africa Middle East 380 Asia 380 Indian Subcontinent 295 a Average buffalo mass for each region b Average VS production per head per day for the average buffalo

3.9 3.9

10 0% 0%

20% 24%

0% 0%

3.9

0%

0%

0%

3.9 3.9 3.1

0% 0% 0%

0% 0% 0%

0% 0% 0%

(not applicable) 79% 0% 0% 29% (not applicable) 0% 99% (not applicable) 0% 20% 41% 50% 4% 19%

0% 0%

0% 0%

0% 0%

0% 47%

4 5

11 4 5

12 5 5

Temperate 13 5 6

14 5 6

15 6 7

16 7 8

0%

0%

0%

1%

1

1

1

1

1

1

1

19% 4% 21%

0% 0% 1%

42% 5% 55%

19% 0% 0%

4 1 4

4 1 4

4 1 4

4 1 4

4 1 4

5 2 5

5 2 5

Warm

17

18 19 20 21 22 23 24 25 26 27 28 Not Applicable 7 8 9 9 10 11 12 13 14 15 16 17 8 9 10 11 11 12 13 15 16 17 19 19 Not Applicable 1 1 1 1 1 1 1 1 1 2 2 2 Not Applicable 5 5 5 5 5 5 5 5 5 5 5 5 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5

1 Lagoon and Liquid/Slurry MCFs are calculated based on the van't Hoff-Arrhenius equation relating temperature to biological activity. Lagoon MCFs are also calculated based on longer (up to a year) retention times. [Mangino, et. al (2001)]

Emission Factors (EF) for each region are calculated based on eq.10.23.

Sources: The detailed information for buffalo are developed in Gibbs and Johnson (1993),and manure management system usage and Bo estimates are from Safley et. al (1992). Methane conversion factor data are from Woodbury and Hashimoto (1993). MCFs for lagoons and liquid/slurry systems are based on data obtained from an analysis of these systems in the United States.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Table 10A-7 Manure Management Methane Emission Factor Derivation for Market Swine Manure Management System MCFs Annual Average Temperature (°C) Cool

Temp

Warm

Liquid/ Solid Lagoon1 Slurry1 Storage Drylot 66% 17% 2.0% 1.0% 68% 19% 2.0% 1.0% 70% 20% 2.0% 1.0% 71% 22% 2.0% 1.0% 73% 25% 2.0% 1.0% 74% 27% 4.0% 1.5% 75% 29% 4.0% 1.5% 76% 32% 4.0% 1.5% 77% 35% 4.0% 1.5% 77% 39% 4.0% 1.5% 78% 42% 4.0% 1.5% 78% 46% 4.0% 1.5% 78% 50% 4.0% 1.5% 79% 55% 4.0% 1.5% 79% 60% 4.0% 1.5% 79% 65% 4.0% 1.5% 79% 71% 5.0% 2.0% 80% 78% 5.0% 2.0% 80% 80% 5.0% 2.0%

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Pit Pit 1 month 3.0% 17% 3.0% 19% 3.0% 20% 3.0% 22% 3.0% 25% 3.0% 27% 3.0% 29% 3.0% 32% 3.0% 35% 3.0% 39% 3.0% 42% 3.0% 46% 3.0% 50% 3.0% 55% 3.0% 60% 3.0% 65% 30.0% 71% 30.0% 78% 30.0% 80%

Daily Spread Digester 0.1% 10.0% 0.1% 10.0% 0.1% 10.0% 0.1% 10.0% 0.1% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 1.0% 10.0% 1.0% 10.0% 1.0% 10.0%

Other 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Emission Factors kg CH4 per head per year

Market Swine Characteristics Region

Massa

B ob

VSc

kg m3CH4/kg VS kg/hd/day North America 46 0.48 0.27 Western Europe 50 0.45 0.3 Eastern Europe 50 0.45 0.3 Oceania 45 0.45 0.28 Latin America 28 0.29 0.3 Africa 28 0.29 0.3 Middle East 28 0.29 0.3 Asia 28 0.29 0.3 Indian Subcontinent 28 0.29 0.3 a Average marker swine mass for each region (default estimates are ±20%) b Bo estimates are ±15%

Manure Management System Usage (MS%) 32.8% 8.7% 3.0% 54.0% 0.0% 0.0% 0.0% 0.0% 9.0%

18.5% 0.0% 0.0% 0.0% 8.0% 6.0% 14.0% 40.0% 22.0%

4.2% 13.7% 42.0% 3.0% 10.0% 6.0% 0.0% 0.0% 16.0%

4.0% 0.0% 0.0% 15.0% 41.0% 87.0% 69.0% 54.0% 30.0%

0.0% 2.8% 24.7% 0.0% 0.0% 1.0% 0.0% 0.0% 3.0%

40.6% 69.8% 24.7% 0.0% 0.0% 0.0% 17.0% 0.0% 0.0%

Cool 0.0% 2.0% 0.0% 0.0% 2.0% 0.0% 0.0% 0.0% 9.0%

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 7.0% 8.0%

0.0% 3.0% 5.7% 28.0% 40.0% 0.0% 0.0% 0.0% 3.0%

Temperate

Warm

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 10 11 11 12 12 13 13 14 15 15 16 17 18 18 19 20 22 23 23 6 6 7 7 8 9 9 10 11 11 12 13 14 15 16 18 19 21 21 3 3 3 3 3 4 4 4 4 5 5 5 6 6 6 7 10 10 10 11 11 12 12 12 13 13 13 13 13 13 13 13 13 13 13 13 13 13 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2 2 3 3 3 3 4 4 4 5 5 5 6 2 2 2 2 2 3 3 3 3 4 4 4 5 5 5 6 6 7 7 2 2 3 3 3 3 3 3 4 4 4 4 4 5 5 5 6 6 6

1 Lagoon and Liquid/Slurry MCFs are calculated based on the van't Hoff-Arrhenius Average VS production per head per day for the average market swine (default estimates equation relating temperature to biological activity. Lagoon MCFs are also calculated based on longer (up to a year) retention times. [Mangino, et. al (2001)] are ±25%) c

Emission Factors (EF) for each region are calculated based on eq.10.23.

Sources: For North America, mass, VS, and Bo values are from Safley (2000), USDA's Agricultural Waste Management Field Handbook, and Hashimoto (1984), respectively. North American manure management system usage data are estimated using data from the 1992 and 1997 USDA's Census of Agriculture and National Animal Health Monitoring System Reports. For Western and Eastern Europe manure management system usage, mass of animals, Bo and VS values based on the analysis of national GHG inventories of Annex I countires submitted to the secretariat UNFCCC in 2004. For the rest of the world, swine feed intake data are from Crutzen et. al (1986), and manure management system usage and Bo estimates are from Safley et. al (1992). Methane conversion factor data are from Woodbury and Hashimoto (1993). MCFs for lagoons and liquid/slurry systems are based on data obtained from an analysis of these systems in the United States.

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Chapter 10: Emissions from Livestock and Manure Management Table 10A-8 Manure Management Methane Emission Factor Derivation for Breeding Swine Manure Management System MCFs Annual Average Temperature (°C) Cool

Temp

Warm

Liquid/ Solid Pit Pit Lagoon1 Slurry1 Storage Drylot 1 month 66% 17% 2.0% 1.0% 3.0% 17% 68% 19% 2.0% 1.0% 3.0% 19% 70% 20% 2.0% 1.0% 3.0% 20% 71% 22% 2.0% 1.0% 3.0% 22% 73% 25% 2.0% 1.0% 3.0% 25% 74% 27% 4.0% 1.5% 3.0% 27% 75% 29% 4.0% 1.5% 3.0% 29% 76% 32% 4.0% 1.5% 3.0% 32% 77% 35% 4.0% 1.5% 3.0% 35% 77% 39% 4.0% 1.5% 3.0% 39% 78% 42% 4.0% 1.5% 3.0% 42% 78% 46% 4.0% 1.5% 3.0% 46% 78% 50% 4.0% 1.5% 3.0% 50% 79% 55% 4.0% 1.5% 3.0% 55% 79% 60% 4.0% 1.5% 3.0% 60% 79% 65% 4.0% 1.5% 3.0% 65% 79% 71% 5.0% 2.0% 30.0% 71% 80% 78% 5.0% 2.0% 30.0% 78% 80% 80% 5.0% 2.0% 30.0% 80%

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Daily Spread Digester 0.1% 10.0% 0.1% 10.0% 0.1% 10.0% 0.1% 10.0% 0.1% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 0.5% 10.0% 1.0% 10.0% 1.0% 10.0% 1.0% 10.0%

Other 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% Emission Factors kg CH4 per head per year

Breeding Swine Characteristics Region

Mass

a

Bob

VS

c

kg m3CH4/kg VS kg/hd/day North America 198 0.48 0.5 Western Europe 198 0.45 0.46 Eastern Europe 180 0.45 0.5 Oceania 180 0.45 0.5 Latin America 28 0.29 0.3 Africa 28 0.29 0.3 Middle East 28 0.29 0.3 Asia 28 0.29 0.3 Indian Subcontinent 28 0.29 0.3 a Average breed swine mass for each region (default estimates are ±20%) b Bo estimates are ±15% c

Manure Management System Usage (MS%) 32.8% 8.7% 3.0% 54.0% 0.0% 0.0% 0.0% 0.0% 9.0%

Average VS production per head per day for the average breed swine (default estimates are ±25%)

18.5% 0.0% 0.0% 0.0% 8.0% 6.0% 14.0% 40.0% 22.0%

4.2% 13.7% 42.0% 3.0% 10.0% 6.0% 0.0% 0.0% 16.0%

4.0% 0.0% 0.0% 15.0% 41.0% 87.0% 69.0% 54.0% 30.0%

0.0% 2.8% 24.7% 0.0% 0.0% 1.0% 0.0% 0.0% 3.0%

40.6% 69.8% 24.7% 0.0% 0.0% 0.0% 17.0% 0.0% 0.0%

Cool 0.0% 2.0% 0.0% 0.0% 2.0% 0.0% 0.0% 0.0% 9.0%

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 7.0% 8.0%

0.0% 3.0% 5.7% 28.0% 40.0% 0.0% 0.0% 0.0% 3.0%

Temperate

Warm

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 19 20 21 22 23 24 26 27 28 29 31 32 34 35 37 39 41 44 45 9 10 10 11 12 13 14 15 16 17 19 20 22 23 25 27 29 32 33 4 5 5 5 5 6 7 7 7 8 8 9 9 10 11 12 16 17 17 20 20 21 21 22 22 23 23 23 23 23 24 24 24 24 24 24 24 24 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2 2 3 3 3 3 4 4 4 5 5 5 6 2 2 2 2 2 3 3 3 3 4 4 4 5 5 5 6 6 7 7 2 2 3 3 3 3 3 3 4 4 4 4 4 5 5 5 6 6 6

1 Lagoon and Liquid/Slurry MCFs are calculated based on the van't Hoff-Arrhenius equation relating temperature to biological activity. Lagoon MCFs are also calculated based on longer (up to a year) retention times. [Mangino, et. al (2001)]

Emission Factors (EF) for each region are calculated based on eq.10.23.

Sources: For North America, mass, VS, and Bo values are from Safley (2000), USDA's Agricultural Waste Management Field Handbook, and Hashimoto (1984), respectively. North American manure management system usage data are estimated using data from the 1992 and 1997 USDA's Census of Agriculture and National Animal Health Monitoring System Reports. For Western and Eastern Europe manure management system usage, mass of animals, Bo and VS values based on the analysis of national GHG inventories of Annex I countires submitted to the secretariat UNFCCC in 2004. For the rest of the world, swine feed intake data are from Crutzen et. al (1986), and manure management system usage and Bo estimates are from Safley et. al (1992). Methane conversion factor data are from Woodbury and Hashimoto (1993). MCFs for lagoons and liquid/slurry systems are based on data obtained from an analysis of these systems in the United States.

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Layers (wet)

Broilers

Turkeys

Ducks

Developing

Poultry Developed Layers (dry)

Developing

0.10 0.10 0.10 0.10 0.10 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.20 0.20 0.20

Mule/Asses

238 0.7 5.96 4 1.72 0.26

130 0.7 3.25 4 0.94 0.33

130 0.7 3.25 4 0.94 0.26

1.8 NR NR NR 0.02 0.39

1.8 NR NR NR 0.02 0.39

0.9 NR NR NR 0.01 0.36

6.8 NR NR NR 0.07 0.36

2.7 NR NR NR 0.02 0.36

NR NR NR NR 0.02 0.24

1.0% 1.0% 1.0% 1.0% 1.0% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 2.0% 2.0% 2.0%

1.0% 1.0% 1.0% 1.0% 1.0% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 2.0% 2.0% 2.0%

1.0% 1.0% 1.0% 1.0% 1.0% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 2.0% 2.0% 2.0%

1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5%

65% 68% 70% 73% 74% 75% 76% 76% 77% 78% 78% 78% 78% 79% 79% 80% 80% 80% 80%

1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5%

1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5%

1.0% 1.0% 1.0% 1.0% 1.0% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 2.0% 2.0% 2.0%

1.0% 1.0% 1.0% 1.0% 1.0% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 2.0% 2.0% 2.0%

1.09 1.09 1.09 1.09 1.09 1.64 1.64 1.64 1.64 1.64 1.64 1.64 1.64 1.64 1.64 1.64 2.19 2.19 2.19

0.76 0.76 0.76 0.76 0.76 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.52 1.52 1.52

0.60 0.60 0.60 0.60 0.60 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 1.20 1.20 1.20

0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

1.13 1.18 1.21 1.26 1.28 1.30 1.31 1.32 1.33 1.35 1.35 1.36 1.36 1.37 1.38 1.38 1.38 1.39 1.39

0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09

0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Developed

0.19 0.19 0.19 0.19 0.19 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.37 0.37 0.37

Developing

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Developed

1.0% 1.0% 1.0% 1.0% 1.0% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 2.0% 2.0% 2.0%

Developing

1.0% 1.0% 1.0% 1.0% 1.0% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 2.0% 2.0% 2.0%

Developed

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Developing

Developing 28 0.5 0.7 8 0.32 0.13

Country

Developed 48.5 0.60 1.08 8.00 0.40 0.19

Developed

Table 10A-9 Manure Management Methane Emission Factor Derivation Sheep Goats Camels Horses

Animal

Animal Characteristics Mass (kg) Digest (%) Intake/d (kg Feed) % Ash (Dry Basis) VS/day (kg VS) Bo (m3/kg VS) Cool

Temperate Annual Average Temperature (°C)

Warm

Cool

Temperate

Annual Average Temperature (°C)

Warm

38.5 30 217 217 377 0.6 0.5 0.5 0.5 0.7 0.76 0.76 5.42 5.42 5.96 8 8 8 8 4 0.3 0.35 2.49 2.49 2.13 0.18 0.13 0.26 0.21 0.3 Manure Management System MCFs 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% Emission Factors (kg CH4 per head per year) 0.13 0.11 1.58 1.28 1.56 0.13 0.11 1.58 1.28 1.56 0.13 0.11 1.58 1.28 1.56 0.13 0.11 1.58 1.28 1.56 0.13 0.11 1.58 1.28 1.56 0.20 0.17 2.37 1.92 2.34 0.20 0.17 2.37 1.92 2.34 0.20 0.17 2.37 1.92 2.34 0.20 0.17 2.37 1.92 2.34 0.20 0.17 2.37 1.92 2.34 0.20 0.17 2.37 1.92 2.34 0.20 0.17 2.37 1.92 2.34 0.20 0.17 2.37 1.92 2.34 0.20 0.17 2.37 1.92 2.34 0.20 0.17 2.37 1.92 2.34 0.20 0.17 2.37 1.92 2.34 0.26 0.22 3.17 2.56 3.13 0.26 0.22 3.17 2.56 3.13 0.26 0.22 3.17 2.56 3.13

NR = Not reported. Emission factors, except for poultry, were developed from feed intake values and feed digestibilities used to develop the enteric fermentation emission factors (see Appendix 10A.1). MCFs and Bo values are reported in Woodbury and Hashimoto (1993). All manure except for Layers (wet) is assumed to be managed in dry systems, which is consistent with the manure management system usage reported in Woodbury and Hashimoto (1993). Poultry for developed countries was subdivided into five categories. Layers (dry) represent layers in a "without bedding" waste management system; Layers (wet) represent layers in an anaerobic lagoon waste managemnet system. Estimates of animal mass are ±30%, VS values are ±50% and Bo values are ±15%

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TABLE 10A-9 (CONTINUED) MANURE MANAGEMENT METHANE EMISSION FACTOR DERIVATION FOR OTHER ANIMALS Animal Characteristics Animal

Manure management system MCF

Emission factors (kg CH4 head-1 yr-1)

Mass (kg)

VS (kg VS day-1)

Bo (m3 kg VS)

Deer a

NR

NR

NR

NR

0.22

Reindeer b

NR

0.39

0.19

2.0%

0.36

Rabbits c

1.60

0.10

0.32

1.0%

0.08

Fur-bearing animals b

NR

0.14

0.25

8.0%

0.68

Ostrich b

NR

1.16

0.25

8.0%

5.67

a

Sneath (1997) cited in the GHG inventory of United Kingdom.

b

Estimations of Agricultural University of Norway, Institute of Chemistry and Biotechnology, Section for Microbiology.

c

Data obtained from GHG inventory of Italy, 2004.

NR = not reported

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References SECTION 10.2 LIVESTOCK POPULATION AND FEED CHARACTERISATION AAC (Australian Agricultural Council) (1990). Feed Standards for Australian Livestock Ruminants. Commonwealth Scientific and Industrial Research Organization (CSIRO) Publications, East Melbourne, Victoria, Australia. AFRC Technical Committee on Responses to Nutrients (1990). Nutritive Requirements of Ruminant Animals: Energy. Rep. 5, CAB International, Wallingford, U.K. Agricultural and Food Research Council (AFRC) Technical Committee on Responses to Nutrients (1993). Energy and Protein Requirements of Ruminants. 24-159, CAB International, Wallingford, U.K. Bamualim, A. and Kartiarso (1985). ‘Nutrition of draught animals with special reference to Indonesia.’ In: Draught Animal Power for Production. J.W. Copland (ed.). Australian Centre for International agricultural Research (ACIAR), Proceedings Series No. 10. ACIAR, Canberra, A.C.T., Australia. Food and Agriculture Organisation (FAO) (1999). Statistical Database. Gibbs, M.J. and Johnson, D.E. (1993). "Livestock Emissions." In: International Methane Emissions, US Environmental Protection Agency, Climate Change Division, Washington, D.C., U.S.A. Gibbs, M.J., Conneely, D., Johnson, D., Lassey, K.R. and Ulyatt, M.J. (2002). CH4 emissions from enteric fermentation. In: Background Papers: IPCC Expert Meetings on Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, p 297–320. IPCC-NGGIP, Institute for Global Environmental Strategies (IGES), Hayama, Kanagawa, Japan. Ibrahim, M.N.M. (1985). ‘Nutritional status of draught animals in Sri Lanka.’ In: Draught Animal Power for Production, J.W. Copland (ed.). ACIAR (Australian Centre for International Agricultural Research) Proceedings Series No. 10. ACIAR, Canberra, A.C.T., Australia. Jurgen, M.H. (1988). Animal Feeding and Nutrition, Sixth Edition, Kendall/Hunt Publishing Company, Dubuque, Iowa, U.S.A. Lawrence, P.R. (1985). ‘A review of nutrient requirements of draught oxen.’ In: Draught Animal Power for Production. J.W. Copland (ed.). ACIAR (Australian Centre for International Agricultural Research) Proceedings Series No. 10. ACIAR, Canberra, A.C.T., Australia. National Research Council (NRC) (1984). Nutrient Requirements of Beef Cattle, National Academy Press, Washington, D.C. U.S.A. NRC (1989). Nutrient Requirements of Dairy Cattle, 6th, National Academy Press, Washington, D.C. U.S.A. NRC (1996). Nutrient Requirements of Beef Cattle, National Academy Press, Washington, D.C. U.S.A. NRC (2001). Nutrient Requirements of Dairy Cattle, 7th Ed., Nat. Acad. Press, Washington, DC.

SECTION 10.3 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERIC FERMENTATION Clark, H., Brookes, I. and Walcroft, A. (2003). Enteric methane emissions from New Zealand ruminants 19992001 calculated using an IPCC Tier 2 approach. http://www.climatechange.govt.nz/resources/reports/nirapr03/. Crutzen, P.J., Aselmann, I. and Seiler, W. (1986). "Methane Production by Domestic Animals, Wild Ruminants, Other Herbivorous Fauna, and Humans," Tellus 38B:271-284. Diarra, B. (1994). Net energy value of soybean hulls as feed for sheep. Dissertation. Colorado State University, Ft Collins, CO. Donovan, K. and Baldwin, L. (1999). Results of the AAMOLLY model runs for the Enteric Fermentation Model. University of California, Davis. Hindrichsen, I., Kreuzer, M., Machmuller, A., Knudsen, K. E., Madsen, J. and Wettstein, H.R. (2003). Methane release and energy expenditure of dairy cows fed concentrates characterized by different carbohydrates. In: Prog. in Res. En. & Prot. Metabol. (Souffrant, W.B, and CC. Metges, eds.) Wageningen Acad. Pub, The Netherlands, EAAP Publ. 109:413-416. Johnson, K., Huyler, M., Westberg, H., Lamb, B. and Zimmerman, P. (1994). Measurement of methane emissions from ruminant livestock using a SF6 tracer technique. Environmental. Sci. Tech., 28: 359-362.

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Chapter 10: Emissions from Livestock and Manure Management

Johnson, K.A. and Johnson, D.E. (1995). Methane emissions from cattle. J. Anim. Sci., 73: 2483-2492 Judd, M.J., Kelliher, F.M., Ulyatt, M.J., Lassey, K.R., Tate, K.R., Shelton, I.D., Harvey, M.J. and Walker, C.F. (1999). Net methane emissions from grazing sheep, Global Change Biol., 5, pp. 647–657. Kujawa, M. (1994). Energy partitioning in steers fed cottonseed hulls or sugar beet pulp. Dissertation, Colorado State University, Ft Collins, CO. Kurihara, M., Magner, T., Hunter, R.A. and McCrabb, G.J. (1999). Methane production and energy partition of cattle in the tropics. British Journal of Nutrition, 81, pp. 227-234. Lassey, K.R. (2006). Livestock methane emission: from the individual grazing animal through national inventories to the global methane cycle. Agric. For. Meteorol. (in press). Lassey, K.R., Ulyatt, M.J., Martin, R.J., Walker, C.F. and Shelton, I.D. (1997). Methane emissions measured directly from grazing livestock in New Zealand, Atmos. Environ., 31, pp. 2905-2914. Leuning, R., Baker, S.K., Jamie, I.M., Hsu, C.H., Klein, L., Denmead, O.T. and Griffith, D.W.T. (1999). Methane emission from free-ranging sheep: a comparison of two measurement methods, Atmos. Environ., 33, pp. 1357–1365. Murray, B.R., Bryant, A.M. and Leng, R.A. (1978). Methane production in the rumen and lower gut of sheep given lucerne chaff: effect of level of intake, Br. J. Nutr., 39, pp. 337-345. National Research Council (NRC) (1989). Nutrient Requirements of Dairy Cattle, 6th Ed., Nat. Acad. Press, Washington, DC. National Research Council (NRC) (1996). Nutrient Requirements of Beef Cattle, 7th Edit., Nat. Acad. Press, Washington, DC. National Research Council (NRC) (2001). Nutrient Requirements of Dairy Cattle, 7th Ed., Nat. Acad. Press, Washington, DC. Pinares-Patino, C.S., Ulyatt, M.J., Waghorn, G.C., Lassey, K.R., Barry, T.N., Holmes, C.W. and Johnson, D.E. (2003). Methane emission by alpaca and sheep fed on Lucerne hay or grazed on pastures of perennial ryegrass/white clover or birds foot trefoil. J. Agric. Sci. 140:215-226. Ulyatt, M.J., Lassey, K.R., Shelton, I.D. and Walker, C.F. (2002a). “Seasonal variation in methane emission from dairy cows and breeding ewes grazing ryegrass/white clover pasture in New Zealand.” New Zealand Journal of Agricultural Research 45:217–226. Ulyatt, M.J., Lassey, K.R., Shelton, I.D. and Walker, C.F. (2002b). “Methane emission from dairy cows and wether sheep fed subtropical grass-dominant pastures in midsummer in New Zealand.” New Zealand Journal of Agricultural Research 45:227–234. Ulyatt, M.J., Lassey, K.R., Shelton, I.D. and Walker, C.F. (2005). Methane emission from sheep grazing four pastures in late summer in New Zealand. New Zealand Journal Agricultural Research 48: 385-390.

SECTION 10.4 METHANE EMISSIONS FROM MANURE MANAGEMENT Amon, B., Amon, Th., Boxberger, J. and Pollinger, A. (1998). Emissions of NH3, N2O, and CH4 from composted and anaerobically stored farmyard manure. Pages 209-216 in Martinez J, Maudet M-N (eds) Ramiran 98, Proc. 8th Int. Conf. on the FAO ESCORENA Network on Recycling of Agricultural, Municipal and Industrial Residues in Agriculture. Rennes, France. Amon, B., Amon, Th., Boxberger, J. and Alt, Ch. (2001). Emissions of NH3, N2O, and CH4 from dairy cows housed in a farmyard manure tying stall (Housing, Manure Storage, Manure Spreading). Nutrient Cycling in Agroecosystems, 60: pp. 103-113. ASAE (1999). ASAE Standards 1999, 46th Edition. American Society of Agricultural Engineers, St. Joseph, MI. Hashimoto, A. and Steed, J. (1993). Methane emissions from typical U.S. livestock manure management systems. Draft report prepared for ICF Incorporated under contract to the Global Change Division of the Office of Air and Radiation, US Environmental Protection Agency, Washington, D.C. Hill, D.T. (1982). Design of digestion systems for maximum methane production. Transactions of the ASAE, 25(1): pp. 226-230. Hill, D.T. (1984). Methane productivity of the major animal types. Transactions of the ASAE 27(2): pp. 530-540. IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton J.T., Meira Filho L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. Callander B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France.

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Mangino, J., Bartram, D. and Brazy, A. (2001). Development of a methane conversion factor to estimate emissions from animal waste lagoons. Presented at U.S. EPA’s 17th Annual Emission Inventory Conference, Atlanta GA, April 16-18, 2002. Moller, H.B., Sommer, S.G. and Ahring, B. (2004). Biological degradation and greenhouse gas emissions during pre-storage of liquid animal manure. Journal of Environmental Quality, 33: pp. 27-36. Peterson, K. and Jacobs, H. (2003). 1990-2002 Volatile solids and Nitrogen excretion rates deliverable under EPA Contract No. GS-10F-0124J, Task Order 004-02. Memorandum to EPA from ICF Consulting. August 28, 2003. Safley, L.M., Casada, M.E., Woodbury, J.W. and Roos, K.F. (1992). Global Methane Emissions from Livestock and Poultry Manure. US Environmental Protection Agency, Global Change Division, Washington, D.C., February 1992, EPA/400/1091/048. Sneath, R.W., Phillips, V.R., Demmers, G.M., Burgess, L.R. and Short, J.L. (1997). Long Term Measurements of Greenhouse Gas Emissions from UK Livestock Buildings. Bio-Engineering Division, Silsoe Research Institute, Wrest Park, Silsoe, Bedford, MK45 4HS. Livestock Environment: Proceedings of the Fifth International Symposium. Bloomington MN. May 29-31, 1997. Sommer, S.G., Petersen, S.O. and Sogaard, H.T. (2000). Greenhouse gas emissions from stored livestock slurry. Journal of Environmental Quality, 29: pp. 744-751. Steed Jr, J. and Hashimoto, A.G. (1994). Methane emissions from typical manure management systems. Bioresource Technology 50: pp. 123-130. USDA (1996). Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH). Part 651, U.S. Department of Agriculture, Natural Resources Conservation Service. July. Woodbury, J.W. and Hashimoto, A. (1993). Methane Emissions from Livestock Manure. In International Methane Emissions, US Environmental Protection Agency, Climate Change Division, Washington, D.C., U.S.A. Zeeman, G. (1994). Methane production/emission in storages for animal manure. Fertilizer Research 37: 207211, 1994. Kluwer Academic Publishers, Netherlands.

SECTION 10.5 NITROUS OXIDE EMISSIONS FROM MANURE MANAGEMENT Amon, B., Amon, Th., Boxberger, J. and Pollinger, A. (1998). Emissions of NH3, N2O, and CH4 from composted and anaerobically stored farmyard manure. Pages 209-216 in Martinez J, Maudet M-N (eds) Ramiran 98, Proc. 8th Int. Conf. on the FAO ESCORENA Network on Recycling of Agricultural, Municipal and Industrial Residues in Agriculture. Rennes, France. Amon, B., Amon, Th., Boxberger, J. and Alt, Ch. (2001). Emissions of NH3, N2O, and CH4 from dairy cows housed in a farmyard manure tying stall (Housing, Manure Storage, Manure Spreading). Nutrient Cycling in Agroecosystems, 60: pp. 103-113. Asman, W.A.H., Sutton, M.A. and Schjoerring, J.K. (1998). Ammonia: emission, atmospheric transport and deposition. New Phytol., 139, p. 27-48 Bierman, S., Erickson, G.E., Klopfenstein, T.J., Stock, R.A. and Shain, D.H. (1999). Evaluation of nitrogen and organic matter balance in the feedlot as affected by level and source of dietary fiber. J. Anim. Sci. 77:1645-1653. Döhler, H., Eurich-Menden, B., Dämmgen, U., Osterburg, B., Lüttich, M., Bergschmidt, A., Berg, W., Brunsch, R. (2002). BMVEL/UBA-Ammoniak-Emissionsinventar der deutschen Landwirtschaft und Minderungsszenarien bis zum Jahre 2010. Texte 05/02. Umweltbundesamt, Berlin. Dustan, A. (2002). Review of methane and nitrous oxide emission factors in cold climates. Institutet for jordbruks-och miljoteknik, JTI-rapport, Lantbruk & Industri, 299. Eghball, B. and Power, J.F. (1994). Beef cattle feedlot manure management. J. Soil Water Cons. 49:113-122. European Environmental Agency (2002). Joint EMEP/CORINAIR Atmospheric Emission Inventory Guidebook, 3rd ed., July 2002, Copenhagen. Groot Koerkamp, P.W.G., Speelman, L. and Metz, J.H.M. (1998). Effect of type of aviary, manure and litter handling on the emission kinetics of ammonia from layer houses. Br. Poult. Sci., 39, p. 379-392. Hao, X., Chang, C., Larney, F.J. and Travis, G.R. (2001). Greenhouse gas emissions during cattle feedlot manure composting. Journal Environmental Quality 30: pp. 376-386.

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Harper, L.A., Sharpe, R.R. and Parkin, T.B. (2000). Gaseous emissions from anaerobic swine lagoons: Ammonia, Nitrous Oxide, and Dinitrogen Gas. Journal of Environmental Quality 29: pp. 1356-1365. Hutchings, N.J., Sommer, S.G., Andersen, J.M. and Asman, W.A.H. (2001). A detailed ammonia emission inventory for Denmark. Atmospheric Environment, 35, p. 1959-1968. Külling, D.R., Menzi, H., Sutter, F., Lischer, P. and Kreuzer, M. (2003). Ammonia, nitrous oxide and methane emissions from differently stored dairy manure derived from grass- and hay-based rations. Nutrient Cycling in Agroecosystems, 65: pp. 13-22. Lague, C., Fonstad, T. A., Marquis, A., Lemay, S.P., Godbout, S. and Joncas, R. (2004). Greenhouse Gas Emissions from Swine Operations in Québec and Saskatchewan: Benchmark Assessments. Climate Change Funding Initiative in Agriculture (CCFIA), Canadian Agricultural Research Council, Ottawa, ON. Meisinger, J.J. and Jokela, W.E. (2000). Ammonia Volatilization from Dairy and Poultry Manure. In: Managing Nutrients and Pathogens from Animal Agriculture. Natural Resource, Agriculture, and Engineering Service, Ithaca, NY. March 28-30, 2000. NRAES-130, p.334-354. Moller, H.B., Sommer, S.G. and Anderson, B.H. (2000). Nitrogen mass balance in deep litter during the pig fattening cycle and during composting. Journal of Agricultural Science, Cambridge 137:235-250. Monteny G. J., Groesetein C. M. and Hilhorst M. A. (2001). Interactions and coupling between emissions of methane and nitrous oxide from animal husbandry. Nutrient Cycling in Agroecosystems, 60: pp. 123-132. Monteny, G.J. and Erisman, J.W. (1998). Ammonia emissions from dairy cow buildings: A review of measurement techniques, influencing factors and possibilities for reduction. Neth. J. Agric. Sci., 46, p. 225-247. Moreira, V.R. and Satter, L.D. (2004). Estimating nitrogen loss from dairy farms. Pedology. National Research Council (NRC) (1996). Nutrient Requirements of Beef Cattle, 7th Revised Ed., Nat. Acad. Press, Washington., DC Nicks, B., Laitat, M., Vandenheede, M., Desiron, A., Verhaege, C. and Canart, B. (2003). Emissions of Ammonia, Nitrous Oxide, Methane, Carbon Dioxide, and Water Vapor in the Raising of Weaned Pigs on Straw-Based and Sawdust-Based Deep Litters. Animal Research Journal, 52: pp. 299-308. Rotz, C.A. (2004). Management to reduce nitrogen losses in animal production. J. Anim. Sci. 82(E. Suppl.):E119-E137. Sneath, R.W., Phillips, V.R., Demmers, G.M., Burgess, L.R. and Short, J.L. (1997). Long Term Measurements of Greenhouse Gas Emissions from UK Livestock Buildings. Bio-Engineering Division, Silsoe Research Institute, Wrest Park, Silsoe, Bedford, MK45 4HS. Livestock Environment: Proceedings of the Fifth International Symposium. Bloomington MN. May 29-31, 1997. Sommer, S.G. and Moller, H.B. (2000). Emission of greenhouse gases during composting of deep litter from pig production – effect of straw content. Journal of Agricultural Science, Cambridge 134:327-335. Sommer, S.G., Petersen, S.O. and Søgaard, H.T. (2000). Greenhouse gas emission from stored livestock slurry. Journal of Environmental Quality 29: pp. 744-751. US EPA (2004). National Emission Inventory – Ammonia Emissions from Animal Husbandry Operations, Draft Report. January 30, 2004. Wagner-Riddle, C. and Marinier, M. (2003). Improved Greenhouse Gas Emission Estimates from Manure Storage Systems. Prepared for Climate Change Funding Initiative in Agriculture, Final Project Report, Component 2-3 Projects, Climate Change Science and Technology. Webb, J. (2001). Estimating the potential for ammonia emissions from livestock excreta and manures. Environ. Pollut. 111, p. 395-406.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

CHAPTER 11

N2O EMISSIONS FROM MANAGED SOILS, AND CO2 EMISSIONS FROM LIME AND UREA APPLICATION

2006 IPCC Guidelines for National Greenhouse Gas Inventories

11.1

Volume 4: Agriculture, Forestry and Other Land Use

Authors Cecile De Klein (New Zealand), Rafael S.A. Novoa (Chile), Stephen Ogle (USA), Keith A. Smith (UK), Philippe Rochette (Canada), and Thomas C. Wirth (USA) Brian G. McConkey (Canada), Arvin Mosier (USA), and Kristin Rypdal (Norway)

Contributing Authors Margaret Walsh (USA) and Stephen A. Williams (USA)

11.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Contents 11 N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application 11.1

Introduction .........................................................................................................................................11.5

11.2

N2O Emissions from Managed Soils...................................................................................................11.5

11.2.1

11.2.1.1

Choice of method...................................................................................................................11.6

11.2.1.2

Choice of emission factors...................................................................................................11.10

11.2.1.3

Choice of activity data .........................................................................................................11.12

11.2.1.4

Uncertainty assessment........................................................................................................11.16

11.2.2

Indirect N2O emissions..............................................................................................................11.19

11.2.2.1

Choice of method.................................................................................................................11.19

11.2.2.2

Choice of emission, volatilisation and leaching factors.......................................................11.23

11.2.2.3

Choice of activity data .........................................................................................................11.23

11.2.2.4

Uncertainty assessment........................................................................................................11.24

11.2.3 11.3

Direct N2O emissions ..................................................................................................................11.6

Completeness, Time series, QA/QC..........................................................................................11.25

CO2 Emissions from Liming .............................................................................................................11.26

11.3.1

Choice of method ......................................................................................................................11.27

11.3.2

Choice of emission factors ........................................................................................................11.29

11.3.3

Choice of activity data...............................................................................................................11.29

11.3.4

Uncertainty assessment .............................................................................................................11.29

11.3.5

Completeness, Time series, QA/QC..........................................................................................11.30

11.4

CO2 Emissions from Urea Fertilization.............................................................................................11.32

11.4.1

Choice of method ......................................................................................................................11.32

11.4.2

Choice of emission factor..........................................................................................................11.34

11.4.3

Choice of activity data...............................................................................................................11.34

11.4.4

Uncertainty assessment .............................................................................................................11.34

11.4.5

Completeness, Time series consistency, QA/QC ......................................................................11.35

Annex 11A.1 References for crop residue data in Table 11.2 .........................................................................11.37 References

...................................................................................................................................................11.53

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11.3

Volume 4: Agriculture, Forestry and Other Land Use

Equations Equation 11.1 Direct N2O emissions from managed soils (Tier 1).............................................................11.7 Equation 11.2 Direct N2O emissions from managed soils (Tier 2)...........................................................11.10 Equation 11.3 N from organic N additions applied to soils (Tier 1).........................................................11.12 Equation 11.4 N from animal manure applied to soils (Tier 1) ................................................................11.13 Equation 11.5 N in urine and dung deposited by grazing animals on pasture, range and paddock (Tier 1)................................................................................................11.13 Equation 11.6 N from crop residues and forage/pasture renewal (Tier 1) ................................................11.14 Equation 11.7 Dry-weight correction of reported crop yields ..................................................................11.15 Equation 11.7A Alternative approach to estimate FCR (using Table 11.2) ..................................................11.15 Equation 11.8 N mineralised in mineral soils as a result of loss of soil C through change in land use or management (Tiers 1 and 2)........................................................................11.16 Equation 11.9 N2O from atmospheric deposition of N volatilised from managed soils (Tier 1) ..............11.21 Equation 11.10 N2O from N leaching/runoff from managed soils in regions where leaching/runoff occurs (Tier 1) ...................................................................................................................11.21 Equation 11.11 N2O from atmospheric deposition of N volatilised from managed soils (Tier 2) ..............11.22 Equation 11.12 Annual CO2 emissions from lime application ...................................................................11.27 Equation 11.13 Annual CO2 emissions from urea application ....................................................................11.32

Figures Figure 11.1

Schematic diagram illustrating the sources and pathways of N that result in direct and indirect N2O emissions from soils and waters. ...............................................11.8

Figure 11.2

Decision tree for direct N2O emissions from managed soils ...............................................11.9

Figure 11.3

Decision tree for indirect N2O emissions from managed soils ..........................................11.20

Figure 11.4

Decision tree for identification of appropriate tier to estimate CO2 emissions from liming........................................................................................................................11.28

Figure 11.5

Decision tree for identification of appropriate tier to estimate CO2 emissions from urea fertilisation ........................................................................................................11.33

Tables Table 11.1

Default emission factors to estimate direct N2O emissions from managed soils...............11.11

Table 11.2

Default factors for estimation of N added to soils from crop residues ..............................11.17

Table 11.3

Default emission, volatilisation and leaching factors for indirect soil N2O emissions ......11.24

11.4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

11 N 2 O EMISSIONS FROM MANAGED SOILS, AND CO 2 EMISSIONS FROM LIME AND UREA APPLICATION 11.1

INTRODUCTION

Chapter 11 provides a description of the generic methodologies to be adopted for the inventory of nitrous oxide (N2O) emissions from managed soils, including indirect N2O emissions from additions of N to land due to deposition and leaching, and emissions of carbon dioxide (CO2) following additions of liming materials and urea-containing fertiliser. Managed soils1 are all soils on land, including Forest Land, which is managed. For N2O, the basic three-tier approach is the same as used in the IPCC Good Practice Guidance for Land Use, Land-use Change and Forestry (GPG-LULUCF) for Grassland and Cropland, and in the IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (GPG2000) for agricultural soils while relevant parts of the GPG-LULUCF methodology have been included for Forest Land. Because the methods are based on pools and fluxes that can occur in all the different land-use categories and because in most cases, only national aggregate (i.e., non-land use specific) data are available, generic information on the methodologies, as applied at the national level is given here, including: •

•

• •

a general framework for applying the methods, and appropriate equations for the calculations; an explanation of the processes governing N2O emissions from managed soils (direct and indirect) and CO2 emissions from liming and urea fertilisation, and the associated uncertainties; and choice of methods, emission factors (including default values) and activity data, and volatilisation and leaching factors. If activity data are available for specific land-use categories, the equations provided can be implemented for specific land-use categories.

The changes in the 2006 IPCC Guidelines, relative to 1996 IPCC Guidelines, include the following: •

•

provision of advice on estimating CO2 emissions associated with the use of urea as a fertilizer;

•

extensive literature review leading to revised emission factors for nitrous oxide from agricultural soils; and

•

full sectoral coverage of indirect N2O emissions;

removal of biological nitrogen fixation as a direct source of N2O because of the lack of evidence of significant emissions arising from the fixation process.

11.2

N 2 O EMISSIONS FROM MANAGED SOILS

This section presents the methods and equations for estimating total national anthropogenic emissions of N2O (direct and indirect) from managed soils. The generic equations presented here can also be used for estimating N2O within specific land-use categories or by condition-specific variables (e.g., N additions to rice paddies) if the country can disaggregate the activity data to that level (i.e., N use activity within a specific land use). Nitrous oxide is produced naturally in soils through the processes of nitrification and denitrification. Nitrification is the aerobic microbial oxidation of ammonium to nitrate, and denitrification is the anaerobic microbial reduction of nitrate to nitrogen gas (N2). Nitrous oxide is a gaseous intermediate in the reaction sequence of denitrification and a by-product of nitrification that leaks from microbial cells into the soil and ultimately into the atmosphere. One of the main controlling factors in this reaction is the availability of inorganic N in the soil. This methodology, therefore, estimates N2O emissions using human-induced net N additions to soils (e.g., synthetic or organic fertilisers, deposited manure, crop residues, sewage sludge), or of mineralisation of N in soil organic matter following drainage/management of organic soils, or cultivation/land-use change on mineral soils (e.g., Forest Land/Grassland/Settlements converted to Cropland).

1

Managed land is defined in Chapter 1, Section 1.1.

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Volume 4: Agriculture, Forestry and Other Land Use

The emissions of N2O that result from anthropogenic N inputs or N mineralisation occur through both a direct pathway (i.e., directly from the soils to which the N is added/released), and through two indirect pathways: (i) following volatilisation of NH3 and NOx from managed soils and from fossil fuel combustion and biomass burning, and the subsequent redeposition of these gases and their products NH4+ and NO3- to soils and waters; and (ii) after leaching and runoff of N, mainly as NO3-, from managed soils. The principal pathways are illustrated in Figure 11.1. Direct emissions of N2O from managed soils are estimated separately from indirect emissions, though using a common set of activity data. The Tier 1 methodologies do not take into account different land cover, soil type, climatic conditions or management practices (other than specified above). Neither do they take account of any lag time for direct emissions from crop residues N, and allocate these emissions to the year in which the residues are returned to the soil. These factors are not considered for direct or (where appropriate, indirect) emissions because limited data are available to provide appropriate emission factors. Countries that have data to show that default factors are inappropriate for their country should utilise Tier 2 equations or Tier 3 approaches and include a full explanation for the values used.

11.2.1

Direct N 2 O emissions

In most soils, an increase in available N enhances nitrification and denitrification rates which then increase the production of N2O. Increases in available N can occur through human-induced N additions or change of land-use and/or management practices that mineralise soil organic N. The following N sources are included in the methodology for estimating direct N2O emissions from managed soils: •

•

synthetic N fertilisers (FSN);

•

urine and dung N deposited on pasture, range and paddock by grazing animals (FPRP);

organic N applied as fertiliser (e.g., animal manure, compost, sewage sludge, rendering waste) (FON);

•

• •

N in crop residues (above-ground and below-ground), including from N-fixing crops 2 and from forages during pasture renewal 3 (FCR); N mineralisation associated with loss of soil organic matter resulting from change of land use or management of mineral soils (FSOM); and drainage/management of organic soils (i.e., Histosols) 4 (FOS).

11.2.1.1

C HOICE

OF METHOD

The decision tree in Figure 11.2 provides guidance on which tier method to use. Tier 1 In its most basic form, direct N2O emissions from managed soils are estimated using Equation 11.1 as follows:

2

Biological nitrogen fixation has been removed as a direct source of N2O because of the lack of evidence of significant emissions arising from the fixation process itself (Rochette and Janzen, 2005). These authors concluded that the N2O emissions induced by the growth of legume crops/forages may be estimated solely as a function of the above-ground and below-ground nitrogen inputs from crop/forage residue (the nitrogen residue from forages is only accounted for during pasture renewal). Conversely, the release of N by mineralisation of soil organic matter as a result of change of land use or management is now included as an additional source. These are significant adjustments to the methodology previously described in the 1996 IPCC Guidelines.

3

The nitrogen residue from perennial forage crops is only accounted for during periodic pasture renewal, i.e. not necessarily on an annual basis as is the case with annual crops.

4

Soils are organic if they satisfy the requirements 1 and 2, or 1 and 3 below (FAO, 1998): 1. Thickness of 10 cm or more. A horizon less than 20 cm thick must have 12 percent or more organic carbon when mixed to a depth of 20 cm; 2. If the soil is never saturated with water for more than a few days, and contains more than 20 percent (by weight) organic carbon (about 35 percent organic matter); 3. If the soil is subject to water saturation episodes and has either: (i) at least 12 percent (by weight) organic carbon (about 20 percent organic matter) if it has no clay; or (ii) at least 18 percent (by weight) organic carbon (about 30 percent organic matter) if it has 60 percent or more clay; or (iii) an intermediate, proportional amount of organic carbon for intermediate amounts of clay (FAO, 1998).

11.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

EQUATION 11.1 DIRECT N2O EMISSIONS FROM MANAGED SOILS (TIER 1) N 2ODirect −N = N 2O −N N inputs + N 2O−N OS + N 2O−N PRP ⎡[(FSN + FON + FCR + FSOM ) • EF1 ] + ⎤ N 2O−N N inputs = ⎢ ⎥ ⎣[(FSN + FON + FCR + FSOM )FR • EF1FR ]⎦

Where:

N 2O−N OS

( ( (

) ( ) (

)

⎡ FOS , CG ,Temp • EF2CG ,Temp + FOS , CG ,Trop • EF2CG ,Trop + ⎤ ⎢ ⎥ = ⎢ FOS , F ,Temp , NR • EF2 F ,Temp, NR + FOS , F ,Temp, NP • EF2 F ,Temp , NP + ⎥ ⎢ ⎥ ⎢⎣ FOS , F ,Trop • EF2 F ,Trop ⎥⎦

[

)

)

]

N 2O−N PRP = (FPRP, CPP • EF3PRP,CPP ) + (FPRP, SO • EF3 PRP, SO ) Where:

N2ODirect –N = annual direct N2O–N emissions produced from managed soils, kg N2O–N yr-1 N2O–NN inputs = annual direct N2O–N emissions from N inputs to managed soils, kg N2O–N yr-1 N2O–NOS = annual direct N2O–N emissions from managed organic soils, kg N2O–N yr-1 N2O–NPRP = annual direct N2O–N emissions from urine and dung inputs to grazed soils, kg N2O–N yr-1 FSN = annual amount of synthetic fertiliser N applied to soils, kg N yr-1 FON = annual amount of animal manure, compost, sewage sludge and other organic N additions applied to soils (Note: If including sewage sludge, cross-check with Waste Sector to ensure there is no double counting of N2O emissions from the N in sewage sludge), kg N yr-1 FCR = annual amount of N in crop residues (above-ground and below-ground), including N-fixing crops, and from forage/pasture renewal, returned to soils, kg N yr-1 FSOM = annual amount of N in mineral soils that is mineralised, in association with loss of soil C from soil organic matter as a result of changes to land use or management, kg N yr-1 FOS = annual area of managed/drained organic soils, ha (Note: the subscripts CG, F, Temp, Trop, NR and NP refer to Cropland and Grassland, Forest Land, Temperate, Tropical, Nutrient Rich, and Nutrient Poor, respectively) FPRP = annual amount of urine and dung N deposited by grazing animals on pasture, range and paddock, kg N yr-1 (Note: the subscripts CPP and SO refer to Cattle, Poultry and Pigs, and Sheep and Other animals, respectively) EF1 = emission factor for N2O emissions from N inputs, kg N2O–N (kg N input)-1(Table 11.1) EF1FR is the emission factor for N2O emissions from N inputs to flooded rice, kg N2O–N (kg N input)-1 (Table 11.1) 5 EF2 = emission factor for N2O emissions from drained/managed organic soils, kg N2O–N ha-1 yr-1; (Table 11.1) (Note: the subscripts CG, F, Temp, Trop, NR and NP refer to Cropland and Grassland, Forest Land, Temperate, Tropical, Nutrient Rich, and Nutrient Poor, respectively) EF3PRP = emission factor for N2O emissions from urine and dung N deposited on pasture, range and paddock by grazing animals, kg N2O–N (kg N input)-1; (Table 11.1) (Note: the subscripts CPP and SO refer to Cattle, Poultry and Pigs, and Sheep and Other animals, respectively)

5

When the total annual quantity of N applied to flooded paddy rice is known, this N input may be multiplied by a lower default emission factor applicable to this crop, EF1FR (Table 11.1) (Akiyama et al., 2005) or, where a country-specific emission factor has been determined, by that factor instead. Although there is some evidence that intermittent flooding (as described in Chapter 5.5) can increase N2O emissions, current scientific data indicate that EF1FR also applies to intermittent flooding situations.

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 11.1

Schematic diagram illustrating the sources and pathways of N that result in direct and indirect N 2 O emissions from soils and waters

Note: Sources of N applied to, or deposited on, soils are represented with arrows on the left-hand side of the graphic. Emission pathways are also shown with arrows including the various pathways of volatilisation of NH3 and NOx from agricultural and non-agricultural sources, deposition of these gases and their products NH4+ and NO3-, and consequent indirect emissions of N2O are also illustrated. “Applied Organic N Fertilisers” include animal manure, all compost, sewage sludge, tankage, etc. “Crop Residues” include above- and below-ground residues for all crops (non-N and N fixing) and from perennial forage crops and pastures following renewal. On the lower right-hand side is a cut-away view of a representative sections of managed land; Histosol cultivation is represented here.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Figure 11.2

Decision tree for direct N 2 O emissions from managed soils Start

For each N source ask: Do you have country-specific activity data1?

Yes

Is this a key category2 and is this N source significant3?

No

Obtain country-specific data.

Yes

Do you have rigorously documented country-specific emission factors for EF1, EF2, and/or EF3PRP?

No

Estimate emissions using Tier 1 equations, default emission factors, FAO activity data for mineral N fertiliser use and livestock populations, and expert opinion on other activity data. No

Box 1: Tier 1

Yes

Estimate emissions using Tier 2 equation and available countryspecific emission factors, or Tier 3 methods. Box 3: Tier 2 or 3

Estimate emissions using the Tier 1 default emission factor value and country-specific activity data. Box 2: Tier 1

Note: 1: N sources include: synthetic N fertiliser, organic N additions, urine and dung deposited during grazing, crop/forage residue, mineralisation of N contained in soil organic matter that accompanies C loss from soils following a change in land use or management and drainage/management of organic soils. Other organic N additions (e.g., compost, sewage sludge, rendering waste) can be included in this calculation if sufficient information is available. The waste input is measured in units of N and added as an additional source sub-term under FON in Equation 11.1 to be multiplied by EF1. 2: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 3: As a rule of thumb, a sub-category would be significant if it accounts for 25-30% of emissions from the source category.

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Conversion of N2O–N emissions to N2O emissions for reporting purposes is performed by using the following equation: N2O = N2O–N ● 44/28 Tier 2 If more detailed emission factors and corresponding activity data are available to a country than are presented in Equation 11.1, further disaggregation of the terms in the equation can be undertaken. For example, if emission factors and activity data are available for the application of synthetic fertilisers and organic N (FSN and FON) under different conditions i, Equation 11.1 would be expanded to become 6: EQUATION 11.2 DIRECT N2O EMISSIONS FROM MANAGED SOILS (TIER 2) N 2ODirect −N = ∑ (FSN + FON )i • EF1i + (FCR + FSOM ) • EF1 + N 2O−N OS + N 2O−N PRP i

Where: EF1i = emission factors developed for N2O emissions from synthetic fertiliser and organic N application under conditions i (kg N2O–N (kg N input)-1); i = 1, …n. Equation 11.2 may be modified in a variety of ways to accommodate any combination of N source-, crop type-, management-, land use-, climate-, soil- or other condition-specific emission factors that a country may be able to obtain for each of the individual N input variables (FSN, FON, FCR, FSOM, FOS, FPRP). Conversion of N2O–N emissions to N2O emissions for reporting purposes is performed by using the following equation: N2O = N2O–N ● 44/28 Tier 3 Tier 3 methods are modelling or measurement approaches. Models are useful because in appropriate forms they can relate the soil and environmental variables responsible for N2O emissions to the size of those emissions. These relationships may then be used to predict emissions from whole countries or regions for which experimental measurements are impracticable. Models should only be used after validation by representative experimental measurements. Care should also be taken to ensure that the emission estimates developed through the use of models or measurements account for all anthropogenic N2O emissions.7 Guidance that provides a sound scientific basis for the development of a Tier 3 Model-based Accounting System is given in Chapter 2, Section 2.5.

11.2.1.2

C HOICE

OF EMISSION FACTORS

T i e r s 1 a nd 2 Three emission factors (EF) are needed to estimate direct N2O emissions from managed soils. The default values presented here may be used in the Tier 1 equation or in the Tier 2 equation in combination with country-specific emission factors. The first EF (EF1) refers to the amount of N2O emitted from the various synthetic and organic N applications to soils, including crop residue and mineralisation of soil organic carbon in mineral soils due to land-use change or management. The second EF (EF2) refers to the amount of N2O emitted from an area of drained/managed organic soils, and the third EF (EF3PRP) estimates the amount of N2O emitted from urine and dung N deposited by grazing animals on pasture, range and paddock. Default emission factors for the Tier 1 method are summarised in Table 11.1.

6

It is important to note that Equation 11.2 is just one of many possible modifications to Equation 11.1 when using the Tier 2 method. The eventual form of Equation 11.2 will depend upon the availability of condition-specific emission factors and the ability to which a country can disaggregate its activity data.

7

Natural N2O emissions on managed land are assumed to be equal to emissions on unmanaged land. These latter emissions are very low. Therefore, nearly all emissions on managed land are considered anthropogenic. Estimates using the IPCC methodology are of the same magnitude as total measured emissions from managed land. The so-called 'background' emissions estimated by Bouwman (1996) (i.e., approx. 1 kg N2O–N/ha/yr under zero fertiliser N addition) are not “natural” emissions but are mostly due to contributions of N from crop residue. These emissions are anthropogenic and accounted for in the IPCC methodology.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

In the light of new evidence, the default value for EF1 has been set at 1% of the N applied to soils or released through activities that result in mineralisation of organic matter in mineral soils 8. In many cases, this factor will be adequate, however, there are recent data to suggest that this emission factor could be disaggregated based on (1) environmental factors (climate, soil organic C content, soil texture, drainage and soil pH); and (2) management-related factors (N application rate per fertiliser type, type of crop, with differences between legumes, non-leguminous arable crops, and grass) (Bouwman et al., 2002; Stehfest and Bouwman, 2006). Countries that are able to disaggregate their activity data from all or some of these factors may choose to use disaggregated emission factors with the Tier 2 approach.

TABLE 11.1 DEFAULT EMISSION FACTORS TO ESTIMATE DIRECT N2O EMISSIONS FROM MANAGED SOILS Emission factor

Default value

Uncertainty range

EF1 for N additions from mineral fertilisers, organic amendments and crop residues, and N mineralised from mineral soil as a result of loss of soil carbon [kg N2O–N (kg N)-1]

0.01

0.003 - 0.03

EF1FR for flooded rice fields [kg N2O–N (kg N)-1]

0.003

0.000 - 0.006

EF2 CG, Temp for temperate organic crop and grassland soils (kg N2O–N ha-1)

8

2 - 24

EF2 CG, Trop for tropical organic crop and grassland soils (kg N2O–N ha-1)

16

5 - 48

EF2F, Temp, Org, R for temperate and boreal organic nutrient rich forest soils (kg N2O–N ha-1)

0.6

0.16 - 2.4

EF2F, Temp, Org, P for temperate and boreal organic nutrient poor forest soils (kg N2O–N ha-1)

0.1

0.02 - 0.3

8

0 - 24

EF3PRP, CPP for cattle (dairy, non-dairy and buffalo), poultry and pigs [kg N2O–N (kg N)-1]

0.02

0.007 - 0.06

EF3PRP, SO for sheep and ‘other animals’ [kg N2O–N (kg N)-1]

0.01

0.003 - 0.03

EF2F, Trop for tropical organic forest soils (kg N2O–N ha-1)

Sources: EF1: Bouwman et al. 2002a,b; Stehfest & Bouwman, 2006; Novoa & Tejeda, 2006 in press; EF1FR: Akiyama et al., 2005; EF2CG, Temp, EF2CG, Trop, EF2F,Trop: Klemedtsson et al., 1999, IPCC Good Practice Guidance, 2000; EF2F, Temp: Alm et al., 1999; Laine et al., 1996; Martikainen et al., 1995; Minkkinen et al., 2002: Regina et al., 1996; Klemedtsson et al., 2002; EF3, CPP, EF3, SO: de Klein, 2004.

The default value for EF2 is 8 kg N2O–N ha-1 yr-1 for temperate climates. Because mineralisation rates are assumed to be about 2 times greater in tropical climates than in temperate climates, the emission factor EF2 is 16 kg N2O–N ha-1 yr-1 for tropical climates 9. Climate definitions are given in Chapter 3, Annex 3A.5. The default value for EF3PRP is 2% of the N deposited by all animal types except ‘sheep’ and ‘other’ animals. For these latter species, a default emission factor of 1% of the N deposited may be used 10.

8

The value of EF1 has been changed from 1.25% to 1%, as compared to the 1996 IPCC Guidelines, as a result of new analyses of the available experimental data (Bouwman et al., 2002a,b; Stehfest and Bouwman, 2006; Novoa and Tejeda, 2006 in press). These analyses draw on a much larger number of measurements than were available for the earlier study that gave rise to the previous value used for EF1 (Bouwman, 1996). The mean value for fertiliser- and manure-induced emissions calculated in these reviews is close to 0.9%; however, it is considered that, given the uncertainties associated with this value and the inclusion in the inventory calculation of other contributions to the nitrogen additions (e.g., from crop residues and the mineralisation of soil organic matter), the round value of 1% is appropriate.

9

The values of EF2, for both temperate and tropical climates, have been changed from the values provided in the 1996 IPCC Guidelines to those contained in the GPG2000.

10

The addition of a default emission factor for sheep is a change from the 1996 IPCC Guidelines. The default emission factor value for EF3PRP has been disaggregated for different animal types based on a recent review on N2O emissions from urine and dung depositions (de Klein, 2004). This review indicated that the emission factor for sheep is lower than that for cattle and that a value of 1% of the nitrogen deposited is more appropriate. Reasons for the lower EF3PRP for sheep include more even urine distribution (smaller and more frequent urinations), and smaller effects on soil compaction during grazing.

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11.2.1.3

C HOICE

OF ACTIVITY DATA

T i e r s 1 a nd 2 This section describes generic methods for estimating the amount of various N inputs to soils (FSN, FON, FPRP, FCR, FSOM, FOS) that are needed for the Tier 1 and Tier 2 methodologies (Equations 11.1 and 11.2).

Applied synthetic fertiliser (FSN) The term FSN refers to the annual amount of synthetic N fertiliser applied to soils 11. It is estimated from the total amount of synthetic fertiliser consumed annually. Annual fertiliser consumption data may be collected from official country statistics, often recorded as fertiliser sales and/or as domestic production and imports. If countryspecific data are not available, data from the International Fertilizer Industry Association (IFIA) (http://www.fertilizer.org/ifa/statistics.asp) on total fertiliser use by type and by crop, or from the Food and Agriculture Organisation of the United Nations (FAO): (http://faostat.fao.org/) on synthetic fertiliser consumption, can be used. It may be useful to compare national statistics to international databases such as those of the IFIA and FAO. If sufficient data are available, fertiliser use may be disaggregated by fertiliser type, crop type and climatic regime for major crops. These data may be useful in developing revised emission estimates if inventory methods are improved in the future. It should be noted that most data sources (including FAO) might limit reporting to agricultural N uses, although applications may also occur on Forest Land, Settlements, or other lands. This unaccounted N is likely to account for a small proportion of the overall emissions. However, it is recommended that countries seek out this additional information whenever possible. Applied organic N fertilisers (FON) The term “applied organic N fertiliser” (FON) refers to the amount of organic N inputs applied to soils other than by grazing animals and is calculated using Equation 11.3. This includes applied animal manure, sewage sludge applied to soil, compost applied to soils, as well as other organic amendments of regional importance to agriculture (e.g., rendering waste, guano, brewery waste, etc.). Organic N fertiliser (FON) is calculated using Equation 11.3: EQUATION 11.3 N FROM ORGANIC N ADDITIONS APPLIED TO SOILS (TIER 1) FON = FAM + FSEW + FCOMP + FOOA Where: FON = total annual amount of organic N fertiliser applied to soils other than by grazing animals, kg N yr-1 FAM = annual amount of animal manure N applied to soils, kg N yr-1 FSEW = annual amount of total sewage N (coordinate with Waste Sector to ensure that sewage N is not double-counted) that is applied to soils, kg N yr-1 FCOMP = annual amount of total compost N applied to soils (ensure that manure N in compost is not double-counted), kg N yr-1

There are no or very limited data for N2O emission factors of other animal types, and the emission factor for poultry and swine remains at 2% of nitrogen deposited. However, a value of 1% of the nitrogen deposited may be used for animals classified as ‘other animals’ which includes goats, horses, mules, donkeys, camels, reindeer, and camelids, as these are likely to have nitrogen excretion rates and patterns that are more similar to sheep than to cattle. The review further suggested that a disaggregation of EF3PRP for dung vs. urine nitrogen could also be considered. However, this is difficult to implement as it is unlikely that countries have the required information readily available to assess excretion rates in urine and dung. However, this approach may be considered by countries that use a higher tier methodology. Finally, the review revealed that current information is insufficient or inconclusive to allow for disaggregation of EF3PRP based on climate region, soil type or drainage class, and/or grazing intensity. 11

For the Tier 1 approach, the amounts of applied mineral nitrogen fertilisers (FSN) and of applied organic nitrogen fertilisers (FON) are no longer adjusted for the amounts of NH3 and NOx volatilisation after application to soil. This is a change from the methodology described in the 1996 IPCC Guidelines. The reason for this change is that field studies that have determined N2O emission factors for applied N were not adjusted for volatilisation when they were estimated. In other words, these emission factors were determined from: fertiliser-induced N2O–N emitted / total amount of N applied, and not from: fertiliser-induced N2O–N emitted / (total amount of N applied – NH3 and NOx volatilised). As a result, adjusting the amount of N input for volatilisation before multiplying it with the emission factor would in fact underestimate total N2O emissions. Countries using Tier 2 or Tier 3 approaches should be aware that correction for NH3/NOx volatilisation after mineral or organic N application to soil may be required depending on the emission factor and/or the inventory methodology used.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

FOOA = annual amount of other organic amendments used as fertiliser (e.g., rendering waste, guano, brewery waste, etc.), kg N yr-1

The term FAM is determined by adjusting the amount of manure N available (NMMS_Avb; see Equation 10.34 in Chapter 10) for the amount of managed manure used for feed (FracFEED), burned for fuel (FracFUEL), or used for construction (FracCNST) as shown in Equation 11.4. Data for FracFUEL, FracFEED, FracCNST can be obtained from official statistics or a survey of experts. However, if these data are not available use NMMS_Avb as FAM without adjusting for FracFUEL, FracFEED, FracCNST.

[ (

EQUATION 11.4 N FROM ANIMAL MANURE APPLIED TO SOILS (TIER 1)

FAM = N MMS Avb • 1 − FracFEED + FracFUEL + FracCNST

)]

Where: FAM = annual amount of animal manure N applied to soils, kg N yr-1 NMMS_Avb = amount of managed manure N available for soil application, feed, fuel or construction, kg N yr-1 (see Equation 10.34 in Chapter 10) FracFEED = fraction of managed manure used for feed FracFUEL = fraction of managed manure used for fuel FracCNST = fraction of managed manure used for construction

Urine and dung from grazing animals (FPRP) The term FPRP refers to the annual amount of N deposited on pasture, range and paddock soils by grazing animals. It is important to note that the N from managed animal manure applied to soils is included in the FAM term of FON. The term FPRP is estimated using Equation 11.5 from the number of animals in each livestock species/category T (N(T)), the annual average amount of N excreted by each livestock species/category T (Nex(T)), and the fraction of this N deposited on pasture, range and paddock soils by each livestock species/category T (MS(T,PRP)). The data needed for this equation can be obtained from the livestock chapter (see Chapter 10, Section 10.5). Equation 11.5 provides an estimate of the amount of N deposited by grazing animals: EQUATION 11.5 N IN URINE AND DUNG DEPOSITED BY GRAZING ANIMALS ON PASTURE, RANGE AND PADDOCK (TIER 1) FPRP = ∑ N (T ) • Nex(T ) • MS(T , PRP ) T

[(

)

]

Where: FPRP = annual amount of urine and dung N deposited on pasture, range, paddock and by grazing animals, kg N yr-1 N(T) = number of head of livestock species/category T in the country (see Chapter 10, Section 10.2) Nex(T) = annual average N excretion per head of species/category T in the country, kg N animal-1 yr-1 (see Chapter 10, Section 10.5) MS(T,PRP) = fraction of total annual N excretion for each livestock species/category T that is deposited on pasture, range and paddock12 (see Chapter 10, Section 10.5)

Crop residue N, including N-fixing crops and forage/ pasture renewal, returned to soils, (FCR) The term FCR refers to the amount of N in crop residues (above-ground and below-ground), including N-fixing crops, returned to soils annually 13. It also includes the N from N-fixing and non-N-fixing forages mineralised 12

In the livestock section, pasture, range and paddock is referred to as one of the manure management systems denoted as “S”.

13

The equation to estimate FCR has been modified from the previous 1996 IPCC Guidelines to account for the contribution of the below-ground nitrogen to the total input of nitrogen from crop residues, which previously was ignored in the estimate of FCR. As a result, FCR now represents a more accurate estimate of the amount of nitrogen input from crop residue, which

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during forage or pasture renewal 14. It is estimated from crop yield statistics and default factors for above-/belowground residue:yield ratios and residue N contents. In addition, the method accounts for the effect of residue burning or other removal of residues (direct emissions of N2O from residue burning are addressed under Chapter 2, Section 2.4. Because different crop types vary in residue:yield ratios, renewal time and N contents, separate calculations should be performed for major crop types and then N values from all crop types are summed up. At a minimum, it is recommended that crops be segregated into: 1) non-N-fixing grain crops (e.g., maize, rice, wheat, barley); 2) N-fixing grains and pulses (e.g., soybean, dry beans, chickpea, lentils); 3) root and tuber crops (e.g., potato, sweet potato, cassava); 4) N-fixing forage crops (alfalfa, clover); and 5) other forages including perennial grasses and grass/clover pastures. Equation 11.6 provides the equation to estimate N from crop residues and forage/pasture renewal, for a Tier 1 approach.

(

)

EQUATION 11.6 N FROM CROP RESIDUES AND FORAGE/PASTURE RENEWAL (TIER 1) ⎧⎪Crop(T ) • Area(T ) − Area burnt (T ) • C f • Frac Renew(T ) •⎫⎪ FCR = ∑ ⎨ ⎬ T ⎪ ⎩ R AG (T ) • N AG (T ) • 1 − Frac Remove(T ) + RBG (T ) • N BG (T ) ⎪⎭

[

(

)

]

Where: FCR = annual amount of N in crop residues (above and below ground), including N-fixing crops, and from forage/pasture renewal, returned to soils annually, kg N yr-1 Crop(T) = harvested annual dry matter yield for crop T, kg d.m. ha-1 Area(T) = total annual area harvested of crop T, ha yr-1 Area burnt (T) = annual area of crop T burnt, ha yr-1 Cf = combustion factor (dimensionless) (refer to Chapter 2, Table 2.6) FracRenew (T) = fraction of total area under crop T that is renewed annually 15. For countries where pastures are renewed on average every X years, FracRenew = 1/X. For annual crops FracRenew = 1 RAG(T) = ratio of above-ground residues dry matter (AGDM(T)) to harvested yield for crop T (Crop(T)), kg d.m. (kg d.m.)-1, = AGDM(T) ● 1000 / Crop(T) (calculating AGDM(T) from the information in Table 11.2) NAG(T) = N content of above-ground residues for crop T, kg N (kg d.m.) -1, (Table 11.2) FracRemove(T) = fraction of above-ground residues of crop T removed annually for purposes such as feed, bedding and construction, kg N (kg crop-N)-1. Survey of experts in country is required to obtain data. If data for FracRemove are not available, assume no removal. RBG(T) = ratio of below-ground residues to harvested yield for crop T, kg d.m. (kg d.m.)-1. If alternative data are not available, RBG(T) may be calculated by multiplying RBG-BIO in Table 11.2 by the ratio of total above-ground biomass to crop yield ( = [(AGDM(T) ● 1000 + Crop(T)) / Crop(T)], (also calculating AGDM(T) from the information in Table 11.2). NBG(T) = N content of below-ground residues for crop T, kg N (kg d.m.)-1, (Table 11.2)

T = crop or forage type Data on crop yield statistics (yields and area harvested, by crop) may be obtained from national sources. If such data are not available, FAO publishes data on crop production: (http://faostat.fao.org/). Since yield statistics for many crops are reported as field-dry or fresh weight, a correction factor can be applied to estimate dry matter yields (Crop(T)) where appropriate (Equation 11.7). The proper correction to be used is dependent on the standards used in yield reporting, which may vary between countries. Alternatively, the default values for dry matter content given in Table 11.2 may be used.

makes it possible to assess the contribution to residue nitrogen arising from the growth of forage legumes such as alfalfa, where the harvesting of virtually all the above-ground dry matter results in no significant residue except the root system. 14

The inclusion of nitrogen from forage or pasture renewal is a change from previous 1996 IPCC Guidelines.

15

This term is included in the equation to account for N release and the subsequent increases in N2O emissions (e.g., van der Weerden et al., 1999; Davies et al., 2001), from renewal/cultivation of grazed grass or grass/clover pasture and other forage crops.

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Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

EQUATION 11.7 DRY-WEIGHT CORRECTION OF REPORTED CROP YIELDS Crop(T ) = Yield Fresh(T ) • DRY Where: Crop(T) = harvested dry matter yield for crop T, kg d.m. ha-1 Yield_Fresh(T) = harvested fresh yield for crop T, kg fresh weight ha-1 DRY = dry matter fraction of harvested crop T, kg d.m. (kg fresh weight)-1

The regression equations in Table 11.2 may also be used to calculate the total above-ground residue dry matter, and the other data in the table then permit the calculation in turn of the N in the above-ground residues, the below-ground dry matter, and the total N in the below-ground residues. The total N addition, FCR, is the sum of the above-and below-ground N contents. With this approach, FCR is given by Equation 11.7A:

(

)

EQUATION 11.7A ALTERNATIVE APPROACH TO ESTIMATE FCR (USING TABLE 11.2)

⎧⎪ AGDM (T ) • Area(T ) − Area burnt(T ) • CF • FracRenew(T ) •⎫⎪ FCR = ∑ ⎨ ⎬ ⎪⎭ T ⎪ ⎩ N AG (T ) • 1 − FracRemove(T ) + RBG − BIO (T ) • N BG (T )

[

(

)

]

An improvement on this approach for determining FCR (i.e., Tier 2) would be the use of country-specific data rather than the values provided in Table 11.2, as well as country-specific values for the fraction of above-ground residue burned.

Mineralised N resulting from loss of soil organic C stocks in mineral soils through land-use change or management practices (FSOM) 16 The term FSOM refers to the amount of N mineralised from loss in soil organic C in mineral soils through landuse change or management practices. As explained in Chapter 2, Section 2.3.3, land-use change and a variety of management practices can have a significant impact on soil organic C storage. Organic C and N are intimately linked in soil organic matter. Where soil C is lost through oxidation as a result of land-use or management change, this loss will be accompanied by a simultaneous mineralisation of N. Where a loss of soil C occurs, this mineralised N is regarded as an additional source of N available for conversion to N2O (Smith and Conen, 2004); just as mineral N released from decomposition of crop residues, for example, becomes a source. The same default emission factor (EF1) is applied to mineralised N from soil organic matter loss as is used for direct emissions resulting from fertiliser and organic N inputs to agricultural land. This is because the ammonium and nitrate resulting from soil organic matter mineralisation is of equal value as a substrate for the microorganisms producing N2O by nitrification and denitrification, no matter whether the mineral N source is soil organic matter loss from land-use or management change, decomposition of crop residues, synthetic fertilisers or organic amendments. (Note: the opposite process to mineralisation, whereby inorganic N is sequestered into newly formed SOM, is not taken account of in the calculation of the mineralisation N source. This is because of the different dynamics of SOM decomposition and formation, and also because reduced tillage in some circumstances can increase both SOM and N2O emission.) For all situations where soil C losses occur (as calculated in Chapter 2, Equation 2.25) the Tier 1 and 2 methods for calculating the release of N by mineralisation are shown below:

Calculation steps for estimating changes in N supply from mineralisation Step 1: Calculate the average annual loss of soil C (∆CMineral, LU) for the area, over the inventory period, using Equation 2.25 in Chapter 2. Using the Tier 1 approach, the value for ∆CMineral, LU will have a single value for all land-uses and management systems. Using Tier 2, the value for ∆CMineral, LU will be disaggregated by individual land-use and/or management systems. Step 2: Estimate the N mineralised as a consequence of this loss of soil C (FSOM), using Equation 11.8:

16

The inclusion of the term FSOM is a change from the previous 1996 IPCC Guidelines, which did not include the N from mineralisation associated with a loss of soil organic C.

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Volume 4: Agriculture, Forestry and Other Land Use

EQUATION 11.8 N MINERALISED IN MINERAL SOILS AS A RESULT OF LOSS OF SOIL C THROUGH CHANGE IN LAND USE OR MANAGEMENT (TIERS 1 AND 2)

FSOM =

⎡⎛ ⎤ 1⎞ ∑ ⎢⎜ ΔCMineral , LU • ⎟ • 1000⎥

LU ⎣⎝

R⎠

⎦

Where: FSOM = the net annual amount of N mineralised in mineral soils as a result of loss of soil carbon through change in land use or management, kg N ∆CMineral, LU = average annual loss of soil carbon for each land-use type (LU ), tonnes C (Note: for Tier 1, ∆Cmineral, LU will have a single value for all land-uses and management systems. Using Tier 2 the value for ΔCmineral, LU will be disaggregated by individual land-use and/or management systems. R = C:N ratio of the soil organic matter. A default value of 15 (uncertainty range from 10 to 30) for the C:N ratio (R) may be used for situations involving land-use change from Forest Land or Grassland to Cropland, in the absence of more specific data for the area. A default value of 10 (range from 8 to 15) may be used for situations involving management changes on Cropland Remaining Cropland. C:N ratio can change over time, land use, or management practice 17. If countries can document changes in C:N ratio, then different values can be used over the time series, land use, or management practice. LU = land-use and/or management system type Step 3: For Tier 1, the value for FSOM is calculated in a single step. For Tier 2, FSOM is calculated by summing across all land-uses and/or management system types (LU).

Countries that are not able to estimate gross changes of mineral soil C will create a bias in the N2O estimate, and it is good practice to acknowledge this limitation in the reporting documentation. It is also good practice to use specific data for the C:N ratios for the disaggregated land areas, if these are available, in conjunction with the data for carbon changes. Area of drained/managed organic soils (FOS) The term FOS refers to the total annual area (ha) of drained/managed organic soils (see footnote 4 for definition). This definition is applicable for both the Tier 1 and Tier 2 methods. For all land uses, the areas should be stratified by climate zone (temperate and tropical). In addition, for temperate Forest Land the areas should be further stratified by soil fertility (nutrient rich and nutrient poor). The area of drained/managed organic soils (FOS) may be collected from official national statistics. Alternatively, total areas of organic soils from each country are available from FAO (http://faostat.fao.org/), and expert advice may be used to estimate areas that are drained/managed. For Forest Land, national data will be available at soil survey organisations and from wetland surveys, e.g., for international conventions. In case no stratification by soil fertility is possible, countries may rely on expert judgment.

11.2.1.4

U NCERTAINTY

ASSESSMENT

Uncertainties in estimates of direct N2O emissions from managed soils are caused by uncertainties related to the emission factors (see Table 11.1 for uncertainty ranges), natural variability, partitioning fractions, activity data, lack of coverage of measurements, spatial aggregation, and lack of information on specific on-farm practices. Additional uncertainty will be introduced in an inventory when emission measurements that are not representative of all conditions in a country are used. In general, the reliability of activity data will be higher than that of the emission factors. As an example, further uncertainties may be caused by missing information on observance of laws and regulations related to handling and application of fertiliser and manure, and changing management practices in farming. Generally, it is difficult to obtain information on the actual observance of laws and possible emission reductions achieved as well as information on farming practices. For more detailed guidance on uncertainty assessment refer to Volume 1, Chapter 3.

17

Information on C:N ratios in forest and cropped soils may be found in the following references: Aitkenhead-Peterson et al., 2005; Garten et al., 2000; John et al., 2005; Lobe et al., 2001; Snowdon et al., 2005, and other references cited by these authors.

11.16

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

TABLE 11.2 DEFAULT FACTORS FOR ESTIMATION OF N ADDED TO SOILS FROM CROP RESIDUES a

Crop

Dry matter fraction of harvested product (DRY)

Slope

Major crop types Grains Beans & pulsesb Tubersc Root crops, otherd N-fixing forages Non-N-fixing forages Perennial grasses Grass-clover mixtures

± 2 s.d. as % of mean

± 2% ± 19% ± 69% ± 19% ± 50% default

0.88 0.91 0.22 0.94 0.90

1.09 1.13 0.10 1.07 0.3

0.90

0.3

0.90

0.3

0.90

0.3

± 50% default

0.87 0.89 0.89 0.89 0.89 0.89 0.89 0.90 0.89 0.88

1.03 1.51 1.61 1.29 0.95 0.98 0.91 1.43 0.88 1.09

± 3% ± 3% ± 3% ± 5% ±19% ± 8% ± 5% ± 18% ± 13% ± 50% default

Individual crops Maize Wheat Winter wheat Spring wheat Rice Barley Oats Millet Sorghum Ryee

R2 adj.

N content of above-ground residues (NAG)

Ratio of belowground residues to above-ground biomass (RBG-BIO)

N content of below-ground residues (NBG)

Above-ground residue dry matter AGDM(T) (Mg/ha): AGDM(T) = Crop(T) * slope(T) + intercept(T)

± 50% default ± 50% default

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Intercept

± 2 s.d. as % of mean

0.88 0.85 1.06 1.54 0

± 6% ± 56% ± 70% ± 41% -

0.65 0.28 0.18 0.63 -

0.006 0.008 0.019 0.016 0.027

0.22 (± 16%) 0.19 (± 45%) 0.20 (± 50%) 0.20 (± 50%) 0.40 (± 50%)

0.009 0.008 0.014 0.014 0.022

0

-

-

0.015

0.54 (± 50%)

0.012

0

-

-

0.015

0.80 (± 50%)l

0.012

0

-

-

0.025

0.80 (± 50%)l

0.016p

0.61 0.52 0.40 0.75 2.46 0.59 0.89 0.14 1.33 0.88

± 19% ± 17% ± 25% ± 26% ± 41% ± 41% ± 8% ± 308% ± 27% ± 50% default

0.76 0.68 0.67 0.76 0.47 0.68 0.45 0.50 0.36 -

0.006 0.006 0.006 0.006 0.007 0.007 0.007 0.007 0.007 0.005

0.22 (± 26%) 0.24 (± 32%) 0.23 (± 41%) 0.28 (± 26%) 0.16 (± 35%) 0.22 (± 33%) 0.25 (± 120%) NA NA NA

0.007 0.009 0.009 0.009 NA 0.014 0.008 NA 0.006 0.011

11.17

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 11.2 (CONTINUED) DEFAULT FACTORS FOR ESTIMATION OF N ADDED TO SOILS FROM CROP RESIDUES a

Crop

Soyabeanf Dry beang Potatoh Peanut (w/pod)i Alfalfaj Non-legume hayj

Dry matter fraction of harvested product (DRY)

0.91 0.90 0.22 0.94 0.90 0.90

Above-ground residue dry matter AGDM(T) (Mg/ha): AGDM(T) = Crop(T) * slope(T) + intercept(T) Slope

0.93 0.36 0.10 1.07 0.29k 0.18

± 2 s.d. as % of mean

± 31% ± 100% ± 69% ± 19% ± 31% ± 50% default

Intercept

± 2 s.d. as % of mean

1.35 0.68 1.06 1.54 0 0

R2 adj.

N content of above-ground residues (NAG)

0.16 0.15 0.18 0.63 -

0.008 0.01 0.019 0.016 0.027 0.015

± 49% ± 47% ± 70% ± 41% -

Ratio of belowground residues to above-ground biomass (RBG-BIO)

0.19 (± 45%) NA 0.20 (± 50%)m NA 0.40 (± 50%)n 0.54 (± 50%)n

N content of below-ground residues (NBG)

0.008 0.01 0.014 NA 0.019 0.012

a

Source: Literature review by Stephen A. Williams, Natural Resource Ecology Laboratory, Colorado State University. (Email: [email protected]) for CASMGS (http://www.casmgs.colostate.edu/). A list of the original references is given in Annex 11A.1.

b

The average above-ground residue:grain ratio from all data used was 2.0 and included data for soya bean, dry bean, lentil, cowpea, black gram, and pea.

c

Modelled after potatoes.

d

Modelled after peanuts.

e

No data for rye. Slope and intercept values are those for all grain. Default s.d.

f

The average above-ground residue:grain ratio from all data used was 1.9.

g

Ortega, 1988 (see Annex 11A.1). The average above-ground residue:grain ratio from this single source was 1.6. default s.d. for root:AGB.

h

The mean value for above-ground residue:tuber ratio in the sources used was 0.27 with a standard error of 0.04.

I

The mean value for above-ground residue: pod yield in the sources used was 1.80 with a standard error of 0.10.

j

Single source. Default s.d. for root:AGB.

k

This is the average above-ground biomass reported as litter or harvest losses. This does not include reported stubble, which averaged 0.165 x Reported Yields. Default s.d.

l

Estimate of root turnover to above-ground production based on the assumption that in natural grass systems below-ground biomass is approximately equal to twice (one to three times) the above-ground biomass and that root turnover in these systems averages about 40% (30% to 50%) per year. Default s.d.

m

This is an estimate of non-tuber roots based on the root:shoot values found for other crops. If unmarketable tuber yield is returned to the soil then data are derived from Vangessel and Renner, 1990 (see Annex 11A.1) (unmarketable yield = 0.08 * marketable yield = 0.29 * above-ground biomass) suggest that the total residues returned might then be on the order of 0.49 * above-ground biomass. Default s.d.

n

This is an estimate of root turnover in perennial systems. Default s.d.

p

It is assumed here that grass dominates the system by 2 to 1 over legumes.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

11.18

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

11.2.2

Indirect N 2 O emissions

In addition to the direct emissions of N2O from managed soils that occur through a direct pathway (i.e., directly from the soils to which N is applied), emissions of N2O also take place through two indirect pathways (as illustrated above in Section 11.2). The first of these pathways is the volatilisation of N as NH3 and oxides of N (NOx), and the deposition of these gases and their products NH4+ and NO3- onto soils and the surface of lakes and other waters. The sources of N as NH3 and NOx are not confined to agricultural fertilisers and manures, but also include fossil fuel combustion, biomass burning, and processes in the chemical industry (see Volume 1, Chapter 7, Section 7.3). Thus, these processes cause N2O emissions in an exactly analogous way to those resulting from deposition of agriculturally derived NH3 and NOx, following the application of synthetic and organic N fertilisers and /or urine and dung deposition from grazing animals. The second pathway is the leaching and runoff from land of N from synthetic and organic fertiliser additions, crop residues 18, mineralisation of N associated with loss of soil C in mineral and drained/managed organic soils through land-use change or management practices, and urine and dung deposition from grazing animals. Some of the inorganic N in or on the soil, mainly in the NO3- form, may bypass biological retention mechanisms in the soil/vegetation system by transport in overland water flow (runoff) and/or flow through soil macropores or pipe drains. Where NO3- is present in the soil in excess of biological demand, e.g., under cattle urine patches, the excess leaches through the soil profile. The nitrification and denitrification processes described at the beginning of this chapter transform some of the NH4+ and NO3- to N2O. This may take place in the groundwater below the land to which the N was applied, or in riparian zones receiving drain or runoff water, or in the ditches, streams, rivers and estuaries (and their sediments) into which the land drainage water eventually flows. This methodology described in this Chapter addresses the following N sources of indirect N2O emissions from managed soils arising from agricultural inputs of N: •

• •

•

•

synthetic N fertilisers (FSN); organic N applied as fertiliser (e.g., applied animal manure 19, compost, sewage sludge, rendering waste and other organic amendments) (FON); urine and dung N deposited on pasture, range and paddock by grazing animals (FPRP); N in crop residues (above- and below-ground), including N-fixing crops and forage/pasture renewal returned to soils (FCR) 20; and N mineralisation associated with loss of soil organic matter resulting from change of land use or management on mineral soils (FSOM).

The generic Tier 1 and Tier 2 methods described below can be used to estimate aggregate total indirect N2O emissions from agricultural N additions to managed soils for an entire country. If a country is estimating its direct N2O from managed soils by land-use category, the indirect N2O emissions can also be estimated by the same disaggregation of land-use categories using the equations presented below with activity data, partitioning fractions, and/or emission factors specific for each land-use category. The methodology for estimating indirect N2O emissions from combustion-related and industrial sources is described in Volume 1, Chapter 7, Section 7.3.

11.2.2.1

C HOICE

OF METHOD

Refer to the decision tree in Figure 11.3 (Indirect N2O Emissions) for guidance on which Tier method to use.

18

The inclusion of crop residues as an N input into the leaching and runoff component is a change from the previous IPCC Guidelines.

19

Volatilisation and subsequent deposition of nitrogen from the manure in manure management systems is covered in the manure management section of this Volume.

20

Nitrogen from these components is only included in the leaching/run-off component of indirect N2O emission.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

11.19

Volume 4: Agriculture, Forestry and Other Land Use

Figure 11.3

Decision tree for indirect N 2 O emissions from managed soils Start

For each agricultural N source1, for both volatilization and leaching/runoff, ask: Do you have country-specific activity data?

Yes

Is this a key category2 and is this N source significant3?

No

Obtain countryspecific data.

Yes No

For each N source, do you have rigorously documented country-specific emission factors (EF4 or EF5) and as appropriate rigorously documented country-specific partitioning fractions (FracGASF,FracGASM, FracLEACH) values?

Do you have rigorously documented country-specific EF values (EF4 or EF5) and as appropriate rigorously documented countryspecific partitioning fractions (FracGASF, FracGASM, FracLEACH) values?

Yes

Yes

Estimate emissions using Tier 2 equation, country-specific activity data and country-specific emission factors and partitioning fractions, or Tier 3 method.

Estimate emissions with Tier 2 equation, using a mix of country-specific and other available data and country-specific emission and partitioning factors.

No

Box 2: Tier 2

Box 4: Tier 2 or 3

Estimate emissions using Tier 1 or Tier 2 equation, country-specific activity data and a mix of county-specific or default emission factors and partitioning fractions. Box 3: Tier 1 or 2

No

Estimate emissions using the Tier 1 equation with default emission and partitioning factors and available activity data. Box 1: Tier 1

Note: 1: N sources include: synthetic N fertilizer, organic N additions, urine and dung depositions, crop residue, N mineralization/immobilization associated with loss/gain of soil C on mineral soils as a result of land use change or management practices (crop residue and N mineralization/immobilization is only accounted for in the indirect N2O emissions from leaching/runoff). Sewage sludge or other organic N additions can be included if sufficient information is available. 2: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 3: As a rule of thumb, a sub-source category would be significant if it accounts for 25-30% of emissions from the source category.

11.20

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Tier 1 Volatilisation, N2O(ATD) The N2O emissions from atmospheric deposition of N volatilised from managed soil are estimated using Equation 11.9: EQUATION 11.9 N2O FROM ATMOSPHERIC DEPOSITION OF N VOLATILISED FROM MANAGED SOILS (TIER 1) N 2O( ATD ) −N = [(FSN • FracGASF ) + ((FON + FPRP ) • FracGASM )] • EF4

Where: N2O(ATD)–N = annual amount of N2O–N produced from atmospheric deposition of N volatilised from managed soils, kg N2O–N yr-1 FSN = annual amount of synthetic fertiliser N applied to soils, kg N yr-1 FracGASF = fraction of synthetic fertiliser N that volatilises as NH3 and NOx, kg N volatilised (kg of N applied)-1 (Table 11.3) FON = annual amount of managed animal manure, compost, sewage sludge and other organic N additions applied to soils, kg N yr-1 FPRP = annual amount of urine and dung N deposited by grazing animals on pasture, range and paddock, kg N yr-1 FracGASM = fraction of applied organic N fertiliser materials (FON) and of urine and dung N deposited by grazing animals (FPRP) that volatilises as NH3 and NOx, kg N volatilised (kg of N applied or deposited)-1 (Table 11.3) EF4 = emission factor for N2O emissions from atmospheric deposition of N on soils and water surfaces, [kg N–N2O (kg NH3–N + NOx–N volatilised)-1] (Table 11.3) Conversion of N2O(ATD)-N emissions to N2O emissions for reporting purposes is performed by using the following equation: N2O(ATD) = N2O(ATD) –N • 44/28

Leaching/Runoff, N2O(L) The N2O emissions from leaching and runoff in regions where leaching and runoff occurs are estimated using Equation 11.10: EQUATION 11.10 N2O FROM N LEACHING/RUNOFF FROM MANAGED SOILS IN REGIONS WHERE LEACHING/RUNOFF OCCURS (TIER 1) N 2O( L ) −N = (FSN + FON + FPRP + FCR + FSOM ) • Frac LEACH − ( H ) • EF5

Where: N2O(L)–N = annual amount of N2O–N produced from leaching and runoff of N additions to managed soils in regions where leaching/runoff occurs, kg N2O–N yr-1 FSN = annual amount of synthetic fertiliser N applied to soils in regions where leaching/runoff occurs, kg N yr-1 FON = annual amount of managed animal manure, compost, sewage sludge and other organic N additions applied to soils in regions where leaching/runoff occurs, kg N yr-1 FPRP = annual amount of urine and dung N deposited by grazing animals in regions where leaching/runoff occurs, kg N yr-1 (from Equation 11.5) FCR = amount of N in crop residues (above- and below-ground), including N-fixing crops, and from forage/pasture renewal, returned to soils annually in regions where leaching/runoff occurs, kg N yr-1 FSOM = annual amount of N mineralised in mineral soils associated with loss of soil C from soil organic matter as a result of changes to land use or management in regions where leaching/runoff occurs, kg N yr-1 (from Equation 11.8)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

FracLEACH-(H) = fraction of all N added to/mineralised in managed soils in regions where leaching/runoff occurs that is lost through leaching and runoff, kg N (kg of N additions)-1 (Table 11.3) EF5 = emission factor for N2O emissions from N leaching and runoff, kg N2O–N (kg N leached and runoff)-1 (Table 11.3) Note: If a country is able to estimate the quantity of N mineralised from organic soils, then include this as an additional input to Equation 11.10. Conversion of N2O(L)–N emissions to N2O emissions for reporting purposes is performed by using the following equation: N2O(L) = N2O(L)–N • 44/28

Tier 2 If more detailed emission, volatilisation or leaching factors are available to a country than are presented in Table 11.3, further disaggregation of the terms in the equations can also be undertaken. For example, if specific volatilisation factors are available for the application of synthetic fertilisers (FSN) under different conditions i, Equation 11.9 would be expanded to become 21: EQUATION 11.11 N2O FROM ATMOSPHERIC DEPOSITION OF N VOLATILISED FROM MANAGED SOILS (TIER 2)

(

)

⎫ ⎧ N 2O( ATD ) −N = ⎨∑ FSN i • FracGASFi + [(FON + FPRP ) • FracGASM ]⎬ • EF4 ⎭ ⎩i

Where: N2O(ATD)–N = annual amount of N2O–N produced from atmospheric deposition of N volatilised from managed soils, kg N2O–N yr-1 FSNi = annual amount of synthetic fertiliser N applied to soils under different conditions i, kg N yr-1 FracGASFi = fraction of synthetic fertiliser N that volatilises as NH3 and NOx under different conditions i, kg N volatilised (kg of N applied)-1 FON = annual amount of managed animal manure, compost, sewage sludge and other organic N additions applied to soils, kg N yr-1 FPRP = annual amount of urine and dung N deposited by grazing animals on pasture, range and paddock, kg N yr-1 FracGASM = fraction of applied organic N fertiliser materials (FON) and of urine and dung N deposited by grazing animals (FPRP) that volatilises as NH3 and NOx, kg N volatilised (kg of N applied or deposited)-1 (Table 11.3) EF4 = emission factor for N2O emissions from atmospheric deposition of N on soils and water surfaces, [kg N–N2O (kg NH3–N + NOx–N volatilised)-1] (Table 11.3) Note: If a country is able to estimate the quantity of N mineralised from drainage/management of organic soils then include this as one of the N inputs into the Tier 2 modification of Equation 11.10. Conversion of N2O(ATD)–N emissions to N2O(ATD) emissions for reporting purposes is performed by using the following equation: N2O(ATD) = N2O(ATD)–N • 44/28

21

It is important to note that Equation 11.11 is just one of many possible modifications to Equation 11.9, and is also meant to illustrate how Equation 11.10 could be modified, when using the Tier 2 method. The eventual form of Equation 11.11 will depend upon the availability of land use and/or condition-specific partitioning fractions and/or emission factors and the ability to which a country can disaggregate its activity data.

11.22

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application

Tier 3 Tier 3 methods are modelling or measurement approaches. Models are useful as they can relate the variables responsible for the emissions to the size of those emissions. These relationships may then be used to predict emissions from whole countries or regions for which experimental measurements are impracticable. For more information refer to Chapter 2, Section 2.5, where guidance is given that provides a sound scientific basis for the development of a Tier 3 Model-based Accounting System.

11.2.2.2

C HOICE

OF EMISSION , VOLATILISATION AND LEACHING FACTORS

The method for estimating indirect N2O emissions includes two emission factors: one associated with volatilised and re-deposited N (EF4), and the second associated with N lost through leaching/runoff (EF5). The method also requires values for the fractions of N that are lost through volatilisation (FracGASF and FracGASM) or leaching/runoff (FracLEACH-(H)). The default values of all these factors are presented in Table 11.3. Note that in the Tier 1 method, for humid regions or in dryland regions where irrigation (other than drip irrigation) is used, the default FracLEACH-(H) is 0.30. For dryland regions, where precipitation is lower than evapotranspiration throughout most of the year and leaching is unlikely to occur, the default FracLEACH is zero. The method of calculating whether FracLEACH-(H) = 0.30 should be applied is given in Table 11.3. Country-specific values for EF4 should be used with great caution because of the special complexity of transboundary atmospheric transport. Although inventory compilers may have specific measurements of N deposition and associated N2O flux, in many cases the deposited N may not have originated in their country. Similarly, some of the N that volatilises in their country may be transported to and deposited in another country, where different conditions that affect the fraction emitted as N2O may prevail. For these reasons the value of EF4 is very difficult to determine, and the method presented in Volume 1, Chapter 7, Section 7.3 attributes all indirect N2O emissions resulting from inputs to managed soils to the country of origin of the atmospheric NOx and NH3, rather than the country to which the atmospheric N may have been transported.

11.2.2.3

C HOICE

OF ACTIVITY DATA

In order to estimate indirect N2O emissions from the various N additions to managed soils, the parameters FSN, FON, FPRP, FCR, FSOM need to be estimated. Applied synthetic fertiliser (FSN) The term FSN refers to the annual amount of synthetic fertiliser N applied to soils. Refer to the activity data section on direct N2O emissions from managed soils (Section 11.2.1.3) and obtain the value for FSN. Applied organic N fertilisers (FON) The term FON refers to the amount of organic N fertiliser materials intentionally applied to soils. Refer to the activity data section on direct N2O emissions from managed soils (Section 11.2.1.3) and obtain the value for FON. Urine and dung from grazing animals (FPRP) The term FPRP refers to the amount of N deposited on soil by animals grazing on pasture, range and paddock. Refer to the activity data section on direct N2O emissions from managed soils (Section 11.2.1.3) and obtain the value for FPRP. Crop residue N, including N from N-fixing crops and forage/pasture renewal, returned to soils (FCR) The term FCR refers to the amount of N in crop residues (above- and below-ground), including N-fixing crops, returned to soils annually. It also includes the N from N-fixing and non-N-fixing forages mineralised during forage/pasture renewal. Refer to the activity data section on direct N2O emissions from managed soils (Section 11.2.1.3) and obtain the value for FCR. Mineralised N resulting from loss of soil organic C stocks in mineral soils (FSOM) The term FSOM refers to the amount of N mineralised from the loss of soil organic C in mineral soils through land-use change or management practices. Refer to the activity data section on direct N2O emissions from managed soils (Section 11.2.1.3) and obtain the value for FSOM.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

11.23

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 11.3 DEFAULT EMISSION, VOLATILISATION AND LEACHING FACTORS FOR INDIRECT SOIL N2O EMISSIONS Default value

Uncertainty range

EF4 [N volatilisation and re-deposition], kg N2O–N (kg NH3–N + NOX–N volatilised)-1 22

0.010

0.002 - 0.05

EF5 [leaching/runoff], kg N2O–N (kg N leaching/runoff) -1 23

0.0075

0.0005 0.025

FracGASF [Volatilisation from synthetic fertiliser], (kg NH3–N + NOx–N) (kg N applied) –1

0.10

0.03 - 0.3

FracGASM [Volatilisation from all organic N fertilisers applied , and dung and urine deposited by grazing animals], (kg NH3–N + NOx–N) (kg N applied or deposited) –1

0.20

0.05 - 0.5

0.30

0.1 - 0.8

Factor

FracLEACH-(H) [N losses by leaching/runoff for regions where Σ(rain in rainy season) - Σ (PE in same period) > soil water holding capacity, OR where irrigation (except drip irrigation) is employed], kg N (kg N additions or deposition by grazing animals)-1

Note: The term FracLEACH previously used has been modified so that it now only applies to regions where soil water-holding capacity is exceeded, as a result of rainfall and/or irrigation (excluding drip irrigation), and leaching/runoff occurs, and redesignated as FracLEACH-(H). In the definition of FracLEACH-(H) above, PE is potential evaporation, and the rainy season(s) can be taken as the period(s) when rainfall > 0.5 * Pan Evaporation. (Explanations of potential and pan evaporation are available in standard meteorological and agricultural texts). For other regions the default FracLEACH is taken as zero.

11.2.2.4

U NCERTAINTY

ASSESSMENT

Uncertainties in estimates of indirect N2O emissions from managed soils are caused by uncertainties related to natural variability and to the emission, volatilization and leaching factors (see Table 11.3 for uncertainty ranges), activity data, and lack of measurements. Additional uncertainty will be introduced in an inventory when values for these factors that are not representative of all conditions in a country are used. In general, the reliability of activity data will be higher than that of the emission, volatilisation and leaching factors. As with direct emissions, further uncertainties may be caused by missing information on observance of laws and regulations related to handling and application of fertiliser and manure, and changing management practices in farming. Generally, it is difficult to obtain information on the actual observance of laws and possible emission reductions achieved as well as information on farming practices. Uncertainties in emission factors are nevertheless likely to dominate and uncertainty ranges are indicated in the tabulations above. For more detailed guidance on uncertainty assessment refer to Volume 1, Chapter 3.

22

The uncertainty range has been widened, in view of results showing that emissions from some environments, particularly deciduous forests receiving high rates of N deposition from the atmosphere, are substantially higher than those previously reported (e.g., Butterbach-Bahl et al., 1997; Brumme et al., 1999; Denier van der Gon and Bleeker, 2005)., while there is also clear evidence that EFs can be very low (80%) of the BC that is produced by a fire event remains proximal to the site where it was formed. It is then incorporated into the soil where it can remain for long periods of time. However, BC can also be transported via fluvial and atmospheric pathways to marine sediments, with the majority moving through the fluvial system. This results in most of the particulate BC transported to the oceans being deposited on the coastal shelves, while a smaller portion continues on to the deeper ocean sediments. Another fraction of the particulate BC produced is dispersed into the atmosphere. With residence times that can exceed 7days, much of this component of BC is transported to the oceans and ultimately contributes to the BC fraction of deep ocean sediments, where it is very stable. Over the past few decades BC concentrations in the earth’s atmosphere and biosphere have become of interest because, in aerosol form, they are strong absorbers of solar radiation. They can provide a record of palaeoenvironments in sediment and ice cores, and they may also be a sizable contributor of oxygen to the atmosphere over geological time frames. BC, in particular the charcoal component, is also important because it represents one of the few ways that carbon can be rendered relatively inert, such that it can not easily recombine with oxygen to form CO2. Hence, there is a strong potential for BC to act as a significant removal (sink) of carbon from the more rapid bio-atmospheric carbon cycle to the slower (long-term) geological carbon cycle (e.g., Graetz and Skjemstad, 2003; Schmidt, 2004; Druffel, 2004).

Role of black carbon in the global C budget In a recent review of the formation and persistence of BC in terrestrial ecosystems, Forbes et al. (2006) provided a revised estimate of the formation of BC from vegetation fires and fossil fuel burning of 50 - 270 Tg yr-1. This is a very large C flux and a key question is thus whether the rate of annual BC formation exceeds the amount of C released from the large pool of BC that is already accumulated in terrestrial and marine ecosystems. Whilst it is currently not possible to definitively answer this question, it is important to continue research that will enable a methodology to be developed in the future for accounting for BC in greenhouse gas inventories, and for better understanding the role of BC in the global C budget. Forbes et al. (2006) also identified a set of important issues to be addressed in order to make development of a reliable methodology possible. They identified the need to describe rates of BC formation in a consistent way and suggested it should be expressed as a percentage of the amount of C consumed (CC) by fire. They found that when expressed this way (BC/CC), the rates of BC formation were 100km2) should be available and will probably be accurate to within 10%. Where national database on dams are not available, and other information is used, the flooded land areas retained behind dams will probably have an uncertainty of more than 50%, especially for countries with large flooded land areas. Detailed information on the location, type and function of smaller dams may be also difficult to obtain, though statistical inference may be possible based on the size distribution of reservoirs for which data are available. Reservoirs are created for a variety of reasons that influence the availability of data, and, consequently, the uncertainty on surface area is dependent on countryspecific conditions.

References Åberg, J., Bergström, A.K., Algesten, G., Söderback, K. and Jansson, M. (2004). A comparison of the carbon balances of a natural lake (L. Östräsket) and a hydroelectric reservoir (L. Skinnmuddselet) in northern Sweden, Water Research, 28, 531-538. Abril, G., Guérin, F., Richard, S., Delmas, R., Galy-Lacaux, C., Gosse, P., Tremblay, A., Varfalvy, L., dos Santos, A.M. and Matvienko, B. (2005). Carbon dioxide and methane emissions and the carbon budget of a 10-years old tropical reservoir (Petit-Saut, French Guiana). Global Biogeochemical Cycle (in press). Bergström, A.K., Algesten, G., Sobek, S., Tranvik, L. and Jansson, M. (2004). Emission of CO2 from hydroelectric reservoirs in northern Sweden, Arch. Hydrobiol., 159, 1, 25-42. Cole, J.J. and Caraco, N.F. (2001). Carbon in catchments: connecting terrestrial carbon losses with aquatic metabolism. Marine and Freshwater Research, 52:101-110 Delmas, R.. Richard, S., Guérin, F., Abril, G., Galy-Lacaux, C., Delon, C. and Grégoire, A. (2005). Long Term Greenhouse Gas Emissions from the Hydroelectric Reservoir of Petit Saut (French Guiana) and Potential Impacts. In Tremblay, A., L. Varfalvy, C. Roehm and M. Garneau (Eds.). Greenhouse gas Emissions: Fluxes and Processes, Hydroelectric Reservoirs and Natural Environments. Environmental Science Series, Springer, Berlin, Heidelberg, New York, pp. 293-312. dos Santos, M.A. (2000). Inventário emissões de gases de efeito estufa derivadas de Hidréletricas, PhD. Dissertation, University of Rio de Janeiro, Rio de Janeiro, Brazil, 154p. Duchemin, E., Lucotte, M., Canuel, R. and Soumis, N. (2006). First assessment of CH4 and CO2 emissions from shallow and deep zones of boreal reservoirs upon ice break-up, Lakes and Reservoirs: Research and Management, 11:9-19. Duchemin É. (2000). Hydroelectricity and greenhouse gases: Emission evaluation and identification of biogeochemical processes responsible for their production, PhD. Dissertation, Université du Québec à Montréal, Montréal (Québec), Canada, 321 p (available on CD-ROM). Duchemin, É., Lucotte, M., Canuel, R. and Chamberland, A. (1995). Production of the greenhouse gases CH4 and CO2 by hydroelectric reservoirs of the boreal region, Global Biogeochemical Cycles, 9, 4, 529-540. Duchemin, É., Lucotte, M., Canuel, R., Almeida Cruz, D., Pereira, H.C., Dezincourt, J. and Queiroz, A.G. (2000). Comparison of greenhouse gas emissions from an old tropical reservoir and from other reservoirs worldwide, Verh. Internat. Verein. Limnol., 27, 3, 1391-1395. Duchemin, É., Canuel, R., Ferland, P. and Lucotte, M. (1999). Étude sur la production et l’émission de gaz à effet de serre par les réservoirs hydroélectriques d’Hydro-Québec et des lacs naturels (Volet 2), Scientific report, Direction principal Planification Stratégique - Hydro-Québec, 21046-99027c, 48p. Fearnside, P.M. (2002). Greenhouse gas emissions from a hydroelectric reservoir (Brazil’s Tucurui dam) and the energy policy implications, Water Air and Soil Pollution 133, 1-4, 69-96.

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Galy-Lacaux, C. (1996). Modifications des échanges de constituants mineurs atmosphériques liées à la création d'une retenue hydroélectrique. Impact des barrages sur le bilan du méthane dans l'atmosphère, PhD dissertation, Université Paul Sabatier, Toulouse (France), 200 p. Galy-Lacaux, C., Delmas, R., Jambert, C., Dumestre, J.-F., Labroue, L., Richard, S. and Gosse, P. (1997). Gaseous emissions and oxygen consumption in hydroelectric dams: a case study in French Guyana, Global Biogeochemical Cycles, 11, 4, 471-483. Hélie, J.F. (2004). Geochemistry and fluxes of organic and inorganic in aquatic systems of eastern Canada: examples of the St-Lawrence River and Robert-Bourassa reservoir: Isotopic approach, PhD. Dissertation, Université du Québec à Montréal, Montréal (Québec), Canada, 205p. Houel, S. (2003). Dynamique de la matière organique terrigène dans les réservoirs boréaux, PhD. Dissertation, Université du Québec à Montréal, Montréal (Québec), Canada, 121p. Huttunen, J.T., Alm, J., Liikanen, A., Juutinen, S., Larmola, T., Hammar, T., Silvola, J. and Martikainen, P.J. (2003). Fluxes of methane, carbon dioxide and nitrous oxide in boreal lakes and potential anthropogenic effects on the aquatic greenhouse gas emissions, Chemosphere, 52, 609-621 Huttunen, J.T., Väisänen, T.S., Hellsten, S.K., Heikkinen, M., Nykänen, H., Jungner, H., Niskanen, A., Virtanen, M.O., Lindqvist, O.V., Nenonen, O.S. and Martikainen, P.J. (2002). Fluxes of CH4, CO2, and N2O in hydroelectric reservoir Lokka and Porttipahta in the northern boreal zone in Finland, Global Biogeochemical Cycles, 16, 1, doi:10.1029/2000GB001316. International Commission on Large Dams (ICOLD) (1998). World register of Dams 1998. Paris. International Comittee on large Dams (Ed.). Metadatabase. Keller, M. and Stallard, R.F. (1994). Methane emission by bubbling from Gatun lake, Panama, J. Geophys. Res., 99, D4, 8307-8319. Rosa, L.P., Schaeffer, R. and Santos, M.A. (1996). Are hydroelectric dams in the Brazalian Amazon significant sources of greenhouse gases? Environmental Conservation, 66, No. 1: 2-6. Cambridge University Press. Rosa, L.P., Santos, M.A., Matvienko, B., Santos, E.O. and Sisar, E. (2004). Greehouse gas emissions from hydroelectric reservoirs in tropical Regions, Climatic Change, 66: 9-21. Rosa, L.P., Matvienko Sikar, B., dos Santos, M.A., Matvienko Sikar, E. (2002). Emissoes de dioxido de carbono e de metano pelos reservatorios hydroelectricos brasileiros, Relatorio de referencia – Inventorio brasileiro de emissoes antropicas de gase de efeito de estufa, Ministerio da Ciencia e tecnologia, Brazil, 199p. Schlellhase, H.U. (1994). B.C. Hydro Strategic R&D; Carbon project - Reservoir case study, Powertech Labs inc., Final Report, 1-57. Smith, L.K. and Lewis, W.M. (1992). Seasonality of methane emissions from five lakes and associated wetlands of the Colorado Rockies, Global Biogeochemical Cycles, 6, 4, 323-338 Soumis, N., Lucotte, M., Duchemin, É., Canuel, R., Weissenberger, S., Houel, S. and Larose, C. (2005). Hydroelectric reservoirs as anthropogenic sources of greenhouse gases. In Water Encyclopedia. Volume 3: Surface and agricultural water, sous la dir. de J. H. Lehr et J. Keeley. p. 203-210. Hoboken, NJ: John Wiley & Sons. Soumis, N., Duchemin, É., Canuel, R. and Lucotte, M. (2004). Greenhouse gas emissions from reservoirs of the western United States, Global Biogeochem. Cycles, 18, GB3022, doi:10.1029/2003GB002197. St-Louis, V., Kelly, C.A., Duchemin, É., Rudd, J.W.M. and Rosenberg, D.M. (2000). Reservoir surfaces as sources of greenhouse gases: A global estimate, Bioscience, 50, 9, 766-775. Tavares de Lima, I. (2005). Biogeochemical distinction of methane releases from two Amazon hydroreservoirs, Chemosphere, In Press Tavares de Lima, I. (2002). Emissoa de metano em reservatorio hidreletricos amazonicos atraves de leis de potencia (Methane emission from Amazonian hydroelectric reservoirs through power laws), PhD Dissertation, Universidade de Sao Paulo, Sao Paulo, Brazil, 119 p. Therrien, J. (2005). Aménagement hydroélectrique de l'Eastmain-1 – Étude des gaz à effet de serre en milieux aquatiques 2003-2004. Rapport de GENIVAR Groupe Conseil Inc. à la Société d'énergie de la Baie James. 48 p. et annexes. Therrien, J. (2004). Flux de gaz à effet de serre en milieux aquatiques - Suivi 2003. Rapport de GENIVAR Groupe Conseil Inc. présenté à Hydro-Québec. 52 p. et annexes.

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Appendix 2: Estimating CO2 emissions from lands converted to permanently flooded lands

Therrien, J., Tremblay, A. and Jacques, R. (2005). CO2 Emissions from Semi-arid Reservoirs and Natural Aquatic Ecosystems. In Tremblay, A., L. Varfalvy, C. Roehm et M. Garneau (Eds.). Greenhouse Gas Emissions: Fluxes and Processes, Hydroelectric Reservoirs and Natural Environments. Environmental Science Series, Springer, Berlin, Heidelberg, New York, pp. 233-250. Tremblay, A., Therrien, J., Hamlin, B., Wichmann, E. and LeDrew, L. (2005). GHG Emissions from Boreal Reservoirs and Natural Aquatic Ecosystems. In Tremblay, A., L. Varfalvy, C. Roehm and M. Garneau (Eds.). Greenhouse gas Emissions: Fluxes and Processes, Hydroelectric Reservoirs and Natural Environments. Environmental Science Series, Springer, Berlin, Heidelberg, New York, pp. 209-231. WCD (2000). Dams and Development a New Framework for Decision-Making, The Report of the World Commission on Dams, Earthscan Publications Ltd, London and Sterling, VA, 356 p.

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Appendix 3: CH4 Emissions from Flooded Land: Basis for Future Methodological Development

Appendix 3

CH4 Emissions from Flooded Land: Basis for Future Methodological Development

This Appendix provides a basis for future methodological development rather than complete guidance. Flooded Land may emit CH4 in significant quantities, depending on a variety of characteristic such as age and depth of reservoirs, land-use prior to flooding, climate, and management practices. In contrast with CO2 emissions, CH4 emissions are highly variable spatially and temporally. Current measurements of CH4 fluxes from Flooded Land are not sufficiently comprehensive to support the development of accurate default emission factors (especially for bubbles emissions and degassing emissions). In addition, data are not available for countries with substantial surface area cover by reservoirs, as India, China, and Russia. Measurement studies do not indicate that the time elapsed since flooding has a significant influence on CH4 fluxes from boreal and temperate reservoirs. The opposite is true in tropical regions where time elapsed since flooding may have a significant influence on both diffusive, bubbles CH4 and degassing emissions. This trend was observed only on Petit-Saut reservoir in French Guiana (Abril et al., 2005); however, some old tropical reservoirs show high bubbles emissions (Duchemin et al., 2000; Stallard and Keller, 1994). The model developed on Petit-Saut reservoir predicted very well the dissolved CH4 concentrations in an Ivory Coast reservoir (Galy-Lacaux et al., 1998). Evidence suggests that in Flooded Land, CH4 was generally produced exclusively from flooded soils; the production of this gas could sustain measured fluxes at the water-air interface (Houel, 2003; Duchemin, 2000; Abril et al., 2005).

3a.1

Flooded Land Remaining Flooded Land

This section provides information on how to estimate CH4 emissions from Flooded Land Remaining Flooded Land. This information is drawn from available literature and is intended to be useful to countries that wish to develop preliminary estimates of CH4 emissions from this source. Countries with potentially significant CH4 emissions from Flooded Land seeking to report these emissions should consider the development of countryspecific emission factors to reduce overall uncertainty. Guidance on the development of such factors is provided in Box 2a.1 in Appendix 2.

3 A .1.1

CH 4 EMISSIONS FROM F LOODED L AND R EMAINING F LOODED L AND

METHODOLOGICAL ISSUES Post-flooded CH4 emissions can occur via the following pathways:

•

• •

Diffusive emissions, due to molecular diffusion across the air-water interface; Bubble emissions, or gas emissions from the sediment through the water column via bubbles; this is a very important pathway for CH4 emissions, especially in temperate and tropical regions; Degassing emissions, or emissions resulting from a sudden change in hydrostatic pressure, as well as the increased air/water exchange surface after reservoir waters flow through a turbine and/or a spillway (Hélie, 2004; Soumis et al., 2004; Delmas et al., 2005); this is a very important pathway for CH4 emissions from young tropical reservoirs.

The Tier 1 approach only covers diffusive emissions. Tier 2 includes a term for estimating CH4 bubble emissions, and if applicable, separate consideration of ice-free and ice-covered periods. Tier 3 methods refer to any detailed measurement-based approach that includes an estimate of all relevant CH4 fluxes from Flooded Land, which also includes degassing emissions, and considers the depth, the geographical localization and water temperature of the reservoir for its entire life-time. Tier 3 methods are not outlined further in this chapter, but countries should refer to Box 2a.1 in Appendix 2 on the derivation of country-specific emission factors as a resource for implementing a Tier 3 approach. Table 3a.1 summarizes the coverage of the three tiers and CH4 emission pathway.

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TABLE 3A.1 SUMMARY OF METHODS AND EMISSIONS COVERAGE •

Tier 1

• •

Tier 2

•

Tier 3

CH4 Diffusive emissions Diffusive emissions Bubble emissions All emissions

The following section describes the Tier 2 and Tier 1 approaches for CH4 emissions.

CHOICE OF METHOD Methane can be emitted from flooded lands through release of bubbles, by diffusion and by degassing. The decision tree in Figure 3a.1 guides inventory compilers through the processes of selecting an appropriate approach for CH4 emissions from Flooded Land. Tier selection and the level of spatial and temporal disaggregation implemented by inventory compilers will depend upon the availability of activity data and emission factors, as well as the importance of reservoirs as contributors to national greenhouse gas emissions. Country-specific scientific evidence and data are always preferable to Tier 1 default data. Tier 1 The Tier 1 method for estimating CH4 emissions from Flooded Land includes only diffusion emissions during ice-free period. Emissions during the ice-cover period are assumed to be zero. Equation 3a.1 can be used with measured emissions provided in Table 3a.2 and country-specific total area of flooded land:

EQUATION 3A.1 CH4 EMISSIONS FROM FLOODED LANDS (TIER 1) CH 4 EmissionWWflood = P • E (CH 4 ) diff • A flood _ total _ surface • 10 −6

Where: CH4 emissionsWW flood = total CH4 emissions from Flooded Land, Gg CH4 yr-1 P = ice-free period, days yr-1 (usually 365 for annual inventory estimates, or less in country with ice-cover period)) E(CH4)diff = averaged daily diffusive emissions, kg CH4 ha-1 day-1 Aflood, total surface = total flooded surface area, including flooded land, lakes and rivers, ha

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Appendix 3: CH4 Emissions from Flooded Land: Basis for Future Methodological Development

Figure 3a.1

Decision Tree for CH 4 Emissions from Flooded Land Remaining Flooded Land

Start

Are country-specific seasonally integrated emission factors available for major type of reservoirs?

Estimate emissions from all relevant emission pathways using country-specific emission factors (Tier 3).

Yes

Box 4: Tier 3 No

Are country-specific CH4 diffusive and bubble emissions data available for major type of reservoirs?

No

Are flooded lands a key category1 and is CH4 significant2?

No

Estimate emissions using default emission factors (Tier 1). Box 1: Tier 1

Yes

Yes

Are country-specific CH4 degassing data available for major type of reservoirs?

Determine seasonally integrated CH4 emission factors for diffusive and bubble emissions for major type of reservoirs.

No

Yes Estimate diffusive, bubble and degassing using country-specific emission factors (Tier 2). Box 2: Tier 2

Estimate diffusive and bubble emissions only, using country-specific emission factors (Tier 2). Box 3: Tier 1/2

Note: 1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 2: A subcategory is significant if it accounts for 25-30% of emissions/removals for the overall category.

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Tier 2 A Tier 2 approach for CH4 emissions requires country-specific emission factors for diffusive and bubble emissions, and if applicable, accounts for different rates of diffusion and bubble emissions during the ice-free and ice-covered periods. Flooded land area may also be disaggregated by climatic zone, or any relevant parameter listed in Box 2a.1 in Appendix 2. This approach is described in Equation 3a.2.

EQUATION 3A.2 CH4 EMISSIONS FROM FLOODED LANDS (TIER 2)

CH 4 EmissionsWWflood

( (

)

⎡ P f • Ef (CH 4 ) diff • A flood , surface + ⎤ ⎢ ⎥ = ⎢ P f • Ef (CH 4 ) bubble • A flood , surface + ⎥ ⎢ ⎥ ⎣⎢ Pi • E i (CH 4 ) diff + E i (CH 4 ) bubble • A flood , surface ⎦⎥

(

) )

Where: CH4 emissionsWW flood = total CH4 emissions from Flooded Land per year, kg CH4 yr-1 Pf = ice-free period, days yr-1 Pi = period with ice cover, days yr-1 Ef(CH4)diff = averaged daily diffusive emissions from air water-interface during the ice-free period, kg CH4 ha-1 day-1 Ef(CH4)bubble = averaged daily bubbles emissions from air water-interface during the ice-free period, kg CH4 ha-1 day-1 Ei(CH4)diff = diffusive emissions related to the ice-cover period, kg CH4 ha-1 day-1 Ei(CH4)bubble = bubbles emissions related to the ice-cover period, kg CH4 ha-1 day-1 Aflood, surface = total flooded surface area, including flooded land, lakes and rivers, ha

CHOICE OF EMISSION FACTORS Tier 1 The key default values for Tier 1 are emission factors for CH4 via the diffusion pathway. Table 3a.2 provides measured emissions for various climate zones. To the extent possible given available research, these measured emissions integrate spatial (intra reservoir and regional variations) and temporal variations (dry/rainy and other seasonal, inter-annual variations) in the emissions from reservoirs. Default emission factors should be used in Tier 1 for the ice-free period only. During complete ice-cover period, CH4 emissions are assumed to be zero. When default data are not available, countries should use the closest default emission factors value (emissions of the most similar climatic region). Tier 2 Under Tier 2, country-specific emission factors should be used instead of default factors to the extent possible. Additional estimates of winter emissions and CH4 bubble emissions are also needed, which will require the development of country-specific emission factors. It is anticipated that a mix of default values and countryspecific emission factors will be used when the latter do not cover the full range of environmental and management conditions. The development of country-specific emission factors is discussed in Box 2a.1 in Appendix 2. The derivation of country-specific factors should be clearly documented, and published in peer reviewed literature.

CHOICE OF ACTIVITY DATA Several different types of activity data may be needed to estimate flooded land emissions, depending on the tier being implemented and the known sources of spatial and temporal variability within the national territory. These activity data types correspond to the same data required for CO2 emissions as described in Section 7.3.2.

Flooded land area Country-specific data on flooded land area are required for all tiers to estimate diffusive and bubble emissions. Alternatively, countries can obtain an estimate of their flooded land area from a drainage basin cover analysis, from a national dam database, from the International Commission on Large Dams (ICOLD, 1998) or from the

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Appendix 3: CH4 Emissions from Flooded Land: Basis for Future Methodological Development

World Commission on Dams report (WCD, 2000). Since flooded land area could change rapidly, countries should use updated and recent data. Tier 2 and Tier 3 approaches preferably rely on a national database to track reservoirs surface area. This database should also include other parameters as reservoir depth, year of flooding, reservoir localization (see Box 2a.1 in Appendix 2).

Period of ice-free cover/Period of ice-cover Under Tiers 2 and 3, the periods during which the reservoirs are ice-free or ice-covered are required to estimate emissions of CH4 emissions. These data can be obtained from national meteorological services.

Outflow/Spillway volume Under Tier 3, flooded land outflow and spillway volume are required to estimate degassing emissions of CH4.

CH 4 concentrations upstream and downstream of dams Under Tier 3, CH4 concentrations upstream and downstream of dams would be needed for estimating degassing emissions. Information on how to measure these data can be obtained from the references cited in Box 2a.1 in Appendix 2.

TABLE 3A.2 CH4 MEASURED EMISSIONS FOR FLOODED LAND Diffusive Emissions (ice-free period) Ef(CH4)diff (kg CH4 ha-1 day-1)

Climate

References

Median

Min

Max

Nm

Nres

Polar/Boreal, wet

0.086

0.011

0.3

253

13

Blais 2005; Tremblay et al. 2005; Therrien, 2004; Therrien, 2005; Huttunen et al., 2002; Lambert, 2002; Duchemin, 2000

Cold temperate, moist

0.061

0.001

0.2

233

10

Tremblay et al., 2005; Therrien, 2004; Blais, 2005; Lambert, 2002; Duchemin et al., 1999

Warm temperate, moist

0.150

- 0.05

1.1

416

16

Tremblay et al., 2005; Soumis et al., 2004; Duchemin, 2000; Smith and Lewis, 1992

Warm temperate, dry

0.044

0.032

0.09

135

5

Therrien et al., 2005; Therrien, 2004; Soumis et al., 2004

Tropical, wet

0.630

0.067

1.3

303

6

Tavares de lima, 2005; Abril et al., 2005; Therrien, 2004; Rosa et al., 2002; Tavares de lima et al., 2002; Duchemin et al., 2000; Galy-Lacaux et al., 1997; Galy-Lacaux, 1996; Keller and Stallard, 1994

Tropical, dry

0.295

0.070

1.1

230

5

Rosa et al., 2002; Dos Santos, 2000

The values in the second column represent the medians of CH4 emissions reported in the literature, which themselves are arithmetic means of flux measured above individual reservoirs. The medians are used because the frequency distributions of underlying flux measurements are not normal, and their arithmetic means are already skewed by extreme values. Min and Max values are, respectively, the lowest and highest of all individual measurements within a given climate region; these are provided as an indication of variability only. Nm = number of measurements; Nres = number of reservoirs sampled. These measurements may include non-anthropogenic emissions (e.g., emissions from carbon in the upstream basin) and possible double counting of anthropogenic emissions (e.g., waste water from urban areas in the region of the reservoir) and so may overestimate the emissions.

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UNCERTAINTY ASSESSMENT The two largest sources of uncertainty in the estimation of CH4 emissions from Flooded Land are the quality of emission factors for the various pathways (diffusive, bubble and degassing) and estimates of the flooded land areas.

Emission factors As shown in Table 3a.2, average diffusive emissions can vary by an order of magnitude in boreal and temperate regions, and by one to three orders of magnitude in tropical regions. The same variability in bubble emissions is observed in all regions (about one order of magnitude). Therefore, the use of any default emission factor will result in high uncertainty. CH4 degasssing emissions are also an important source of uncertainty. Degassing emissions are important component of GHG emissions from flooded tropical lands (Galy-lacaux et al., 1997), accounting for more than 40% of the total GHG emissions from a nine year old reservoir (Delmas et al., 2005). However, for many reservoirs degassing emissions are small or negligible (Duchemin, 2000; Soumis et al., 2004). Hence, until additional knowledge becomes available on the dynamics of CH4 degassing emissions, estimation should be conducted on a case-by-case basis. To reduce the uncertainties on emissions factors, countries should develop appropriate, statistically-valid sampling strategies that take into account natural variability of the ecosystem under study (Box 2a.1 in Appendix 2). When applicable, the distinction between ice-free and ice-covered periods may be a significant improvement in accuracy (Duchemin et al., 2005). Those sampling strategy should include enough sampling stations per reservoir, enough reservoirs and sampling periods. The number of sampling stations should be determined using recognized statistical approach. Moreover, countries should consider factors included in Box 2a.1 in Appendix 2.

Flooded land surface area Information on the flooded area retained behind larger dams (>100 km2) should be available and will probably be uncertain by approximately 10%, especially in countries with large dams and hydroelectric reservoirs. For countries with many flooded lands and where national databases are not available, flooded area retained behind dams will probably be uncertain by more than 50%. Detailed information on the location, type and function of smaller dams may be also difficult to obtain, though statistical inference may be possible based on the size distribution of reservoirs for which data are available. In addition, reservoirs are created for variety of reasons that influence the availability of data, and, consequently, the uncertainty on surface area is dependent on countryspecific conditions.

3a.2

Land Converted to Flooded Land

With the actual knowledge, for Land Converted to Flooded Land, it is suggested to use measured emissions in Table 3a.2. Inventory compilers should use Tier 1, Tier 2, and Tier 3 methods described in Section 3a.1 to estimate CH4 emissions from Land Converted to Flooded Land.

References Abril, G., Guérin, F., Richard, S., Delmas, R., Galy-Lacaux, C., Gosse, P., Tremblay, A., Varfalvy, L., dos Santos, M.A. and Matvienko, B. (2005). Carbon dioxide and methane emissions and the carbon budget of a 10-years old tropical reservoir (Petit-Saut, French Guiana), Global Biogeochemical Cycle, 19, doi:10292005GB002457. Blais, A.-M. (2005). Étude des gaz à effet de serre en milieux aquatiques Relevés de terrain 2005. Rapport d'Environnement Illimité à Hydro-Québec Production. 30 p. and annexes. Delmas, R., Richard, S., Guérin, F., Abril, G., Galy-Lacaux, C., Delon, C. and Grégoire, A. (2005). Long Term Greenhouse Gas Emissions from the Hydroelectric Reservoir of Petit Saut (French Guiana) and Potential Impacts. In Tremblay, A., L. Varfalvy, C. Roehm and M. Garneau (Eds.). Greenhouse gas Emissions: Fluxes and Processes, Hydroelectric Reservoirs and Natural Environments. Environmental Science Series, Springer, Berlin, Heidelberg, New York, pp. 293-312. dos Santos, M.A. (2000). Inventário emissões de gases de efeito estufa derivadas de Hidréletricas, PhD. Dissertation, University of Rio de Janeiro, Rio de Janeiro, Brazil, 154p. Duchemin, E., Lucotte, M., Canuel, R. and Soumis, N. (2006). First assessment of CH4 and CO2 emissions from shallow and deep zones of boreal reservoirs upon ice break-up, Lakes and Reservoirs: Research and Management, 11:9-19.

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Appendix 3: CH4 Emissions from Flooded Land: Basis for Future Methodological Development

Duchemin, É. (2000). Hydroelectricity and greenhouse gases: Emission evaluation and identification of biogeochemical processes responsible for their production, PhD. Dissertation, Université du Québec à Montréal, Montréal (Québec), Canada, 321 p (available on CD-ROM). Duchemin, É., Lucotte, M., Canuel, R. and Chamberland, A. (1995). Production of the greenhouse gases CH4 and CO2 by hydroelectric reservoirs of the boreal region, Global Biogeochemical Cycles, 9, 4, 529-540. Duchemin, É., Lucotte, M., Canuel, R., Almeida Cruz, D., Pereira, H.C., Dezincourt, J. and Queiroz, A.G. (2000). Comparison of greenhouse gas emissions from an old tropical reservoir and from other reservoirs worldwide, Verh. Internat. Verein. Limnol., 27, 3, 1391-1395. Duchemin, É., Canuel, R., Ferland, P. and Lucotte, M. (1999). Étude sur la production et l’émission de gaz à effet de serre par les réservoirs hydroélectriques d’Hydro-Québec et des lacs naturels (Volet 2), Scientific report, Direction principal Planification Stratégique - Hydro-Québec, 21046-99027c, 48p. Galy-Lacaux, C. (1996). Modifications des échanges de constituants mineurs atmosphériques liées à la création d'une retenue hydroélectrique. Impact des barrages sur le bilan du méthane dans l'atmosphère, PhD dissertation, Université Paul Sabatier, Toulouse (France), 200 p. Galy-Lacaux, C., Delmas, R., Jambert, C., Dumestre, J.-F., Labroue, L., Richard, S. and Gosse, P. (1997). Gaseous emissions and oxygen consumption in hydroelectric dams: a case study in French Guyana, Global Biogeochemical Cycles, 11, 4, 471-483. Galy-Lacaux, C., Delmas, R., Kouadio, G., Richard, S. and Gosse, P. (1998). Long-term greenhouse gas emissions from hydroelectric reservoirs in tropical forest regions, Global Biogeochemical Cycles, 13, 2, 503-517. Hélie, J.F. (2004). Geochemistry and fluxes of organic and inorganic in aquatic systems of eastern Canada: examples of the St-Lawrence River and Robert-Bourassa reservoir: Isotopic approach, PhD. Dissertation, Université du Québec à Montréal, Montréal (Québec), Canada, 205p. Houel, S. (2003). Dynamique de la matière organique terrigène dans les réservoirs boréaux, PhD. Dissertation, Université du Québec à Montréal, Montréal (Québec), Canada, 121p. Huttunen, J.T., Väisänen, T.S., Hellsten, S.K., Heikkinen, M., Nykänen, H., Jungner, H., Niskanen, A., Virtanen, M.O., Lindqvist, O.V., Nenonen, O.S. and Martikainen, P.J. (2002). Fluxes of CH4, CO2, and N2O in hydroelectric reservoir Lokka and Porttipahta in the northern boreal zone in Finland, Global Biogeochemical Cycles, 16, 1, doi:10.1029/2000GB001316. International Commission on Large Dams (ICOLD) (1998). World Register of Dams 1998. Paris. International Comittee on large Dams (Ed.). Metadatabase. Keller, M. and Stallard, R.F. (1994). Methane emission by bubbling from Gatun lake, Panama, J. Geophys. Res., 99, D4, 8307-8319. Lambert, M. (2002). Campagne d'échantillonnage sur les émissions de gaz à effet de serre des réservoirs et des lacs environnants - Rapport de terrain 2001. Rapport présenté à la Direction Barrage et environnement par la Direction Environnement, Hydro-Québec, 108 p and appendix. Rosa, L.P., Matvienko Sikar, B., dos Santos, M.A., and Matvienko Sikar, E. (2002). Emissoes de dioxido de carbono e de metano pelos reservatorios hydroelectricos brasileiros, Relatorio de referencia – Inventorio brasileiro de emissoes antropicas de gase de efeito de estufa, Ministerio da Ciencia e tecnologia, Brazil, 199p. Smith, L.K. and Lewis, W.M. (1992). Seasonality of methane emissions from five lakes and associated wetlands of the Colorado Rockies, Global Biogeochemical Cycles, 6, 4, 323-338 Soumis, N., Duchemin, É., Canuel, R. and Lucotte, M. (2004). Greenhouse gas emissions from reservoirs of the western United States, Global Biogeochem. Cycles, 18, GB3022, doi:10.1029/2003GB002197. Tavares de Lima, I. (2005). Biogeochemical distinction of methane releases from two Amazon hydroreservoirs, Chemosphere, (in press) Tavares de Lima, I. (2002). Emissoa de metano em reservatorio hidreletricos amazonicos atraves de leis de potencia (Methane emission from Amazonian hydroelectric reservoirs through power laws), PhD Dissertation, Universidade de Sao Paulo, Sao Paulo, Brazil, 119 p. Therrien, J. (2004). Flux de gaz à effet de serre en milieux aquatiques - Suivi 2003. Rapport de GENIVAR Groupe Conseil Inc. présenté à Hydro-Québec. 52 p. et annexes.

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Volume 4: Agriculture, Forestry and Other Land Use

Therrien, J., Tremblay, A. and Jacques, R. (2005). CO2 Emissions from Semi-arid Reservoirs and Natural Aquatic Ecosystems. In Tremblay, A., L. Varfalvy, C. Roehm et M. Garneau (Eds.). Greenhouse Gas Emissions: Fluxes and Processes, Hydroelectric Reservoirs and Natural Environments. Environmental Science Series, Springer, Berlin, Heidelberg, New York, pp. 233-250. Tremblay, A., Therrien, J., Hamlin, B., Wichmann, E. and LeDrew, L. (2005). GHG Emissions from Boreal Reservoirs and Natural Aquatic Ecosystems. In Tremblay, A., L. Varfalvy, C. Roehm and M. Garneau (Eds.). Greenhouse gas Emissions: Fluxes and Processes, Hydroelectric Reservoirs and Natural Environments. Environmental Science Series, Springer, Berlin, Heidelberg, New York, pp. 209-231. WCD (2000). Dams and Development a New Framework for Decision-Making, The report of the World Commission on Dams, Earthscan Publications Ltd, London and Sterling, VA, 356 p.

Ap3.8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

A report prepared by the Task Force on National Greenhouse Gas Inventories (TFI) of the IPCC and accepted by the Panel but not approved in detail

Whilst the information in this IPCC Report is believed to be true and accurate at the date of going to press, neither the authors nor the publishers can accept any legal responsibility or liability for any errors or omissions. Neither the authors nor the publishers have any responsibility for the persistence of any URLs referred to in this report and cannot guarantee that any content of such web sites is or will remain accurate or appropriate.

Published by the Institute for Global Environmental Strategies (IGES), Hayama, Japan on behalf of the IPCC © The Intergovernmental Panel on Climate Change (IPCC), 2006. When using the guidelines please cite as: IPCC 2006, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). Published: IGES, Japan.

IPCC National Greenhouse Gas Inventories Programme Technical Support Unit ℅ Institute for Global Environmental Strategies 2108 -11, Kamiyamaguchi Hayama, Kanagawa JAPAN, 240-0115 Fax: (81 46) 855 3808 http://www.ipcc-nggip.iges.or.jp

Printed in Japan ISBN 4-88788-032-4

VOLUME 5

WASTE

Coordinating Lead Authors Riitta Pipatti (Finland) and Sonia Maria Manso Vieira (Brazil)

Review Editors Dina Kruger (USA) and Kirit Parikh (India)

Table of Contents

Contents Volume 5

Waste

Chapter 1

Introduction

Chapter 2

Waste Generation, Composition and Management Data

Chapter 3

Solid Waste Disposal

Chapter 4

Biological Treatment of Solid Waste

Chapter 5

Incineration and Open Burning of Waste

Chapter 6

Wastewater Treatment and Discharge

Annex 1

Worksheets

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Waste.v

Chapter 1: Introduction

CHAPTER 1

INTRODUCTION

2006 IPCC Guidelines for National Greenhouse Gas Inventories

1.1

Volume 5: Waste

Authors Riitta Pipatti (Finland) and Sonia Maria Manso Vieira (Brazil)

1.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 1: Introduction

Contents 1

Introduction 1.1

Introduction ......................................................................................................................................... 1.4

References .......................................................................................................................................................... 1.5

Figure Figure 1.1

Structure of Waste Sector .................................................................................................... 1.4

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Volume 5: Waste

1 INTRODUCTION 1.1

INTRODUCTION

The Waste volume gives methodological guidance for estimation of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions from following categories: •

•

Solid waste disposal (Chapter 3),

•

Incineration and open burning of waste (Chapter 5),

•

Biological treatment of solid waste (Chapter 4),

Wastewater treatment and discharge (Chapter 6).

Chapter 3, Solid Waste Disposal, provides also a methodology for estimating changes in carbon stored in solid waste disposal sites (SWDS), which is reported as an information item in the Waste Sector (see also Volume 4, AFOLU, Chapter 12, Harvested Wood Products). Chapter 2, Waste Generation, Composition and Management Data, gives general guidance of data collection for solid waste management including disposal, biological treatment, waste incineration and open burning of waste. Categories and activities of the Waste Sector and their definitions can be found in Table 8.2 in Chapter 8 of Volume1, General Guidance and Reporting. It is good practice to apply these categories in reporting as fully as possible. Figure 1.1 shows the structure of categories within the Waste Sector and coding of their IPCC categories. Figure 1.1

Structure of Waste Sector

1

ENERGY

2 INDUSTRIAL PROCESSES AND PRODUCT USE National Greenhouse Gas Inventory

3 AGRICULTURE, FORESTRY, AND OTHER LAND USE 4A1 Managed Waste Disposal Sites 4A Solid Waste Disposal

4A2 Unmanaged Waste Disposal Sites 4A3 Uncategorised Waste Disposal Sites

4B Biological Treatment of Solid Waste

4 WASTE

4C Incineration and Open Burning of Waste

4D Wastewater Treatment and Discharge

4C1 Waste Incineration 4C2 Open Burning of Waste 4D1 Domestic Wastewater Treatment and Discharge 4D2 Industrial Wastewater Treatment and Discharge

4E Other

5 OTHER

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Chapter 1: Introduction

Typically, CH4 emissions from SWDS are the largest source of greenhouse gas emissions in the Waste Sector. CH4 emissions from wastewater treatment and discharge may also be important. Incineration and open burning of waste containing fossil carbon, e.g., plastics, are the most important sources of CO2 emissions in the Waste Sector. All greenhouse gas emissions from waste-to-energy, where waste material is used directly as fuel or converted into a fuel, should be estimated and reported under the Energy Sector. The guidance given in Chapter 5 of this Volume is generally valid for waste burning with or without energy recovery. CO2 is also produced in SWDS, wastewater treatment and burning of non-fossil waste, but this CO2 is of biogenic origin and is therefore not included as a reporting item in this sector.1 In the Energy Sector, CO2 emissions resulting from combustion of biogenic materials, including CO2 from waste-to-energy applications, are reported as an information item. Nitrous oxide is produced in most treatments addressed in the Waste volume. The importance of the N2O emissions varies much depending on the type of treatment and conditions during the treatment. Waste and wastewater treatment and discharge can also produce emissions of non-methane volatile organic compounds (NMVOCs), nitrogen oxides (NOx), and carbon monoxide (CO) as well as of ammonia (NH3). However, specific methodologies for the estimation of emissions for these gases are not included in this Volume, and the readers are guided to refer to guidelines developed under the Convention of Long Range Transboundary Air Pollution (EMEP/CORINAIR Guidebook, EEA, 2005) and EPA's Compilation of Air Pollutant Emissions Factors (U.S.EPA, 1995). The NOx and NH3 emissions from the Waste Sector can cause indirect N2O emissions. NOx is produced mainly in burning of waste, while NH3 in composting. Overall, the indirect N2O from the Waste Sector are likely to be insignificant. However, when estimates of NOx and NH3 emissions are available, it is good practice to estimate the indirect N2O emissions for complete reporting (see Chapter 7 of Volume 1). The scope of the Waste Volume is similar to the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 Guidelines, IPCC, 1997) and the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (GPG2000, IPCC, 2000). Following new subcategories have been added to complement the guidance to cover all major waste management practices: •

• •

Biological treatment of solid waste: Guidance for estimation of CH4 and N2O emissions from biological treatment (composting, anaerobic digestion in biogas facilities) has been included in Chapter 4, Biological Treatment of Solid Waste. Open burning of waste: Guidance to estimate emissions from open burning of waste as well as for estimation of CH4 emissions from incineration complements the previous guidance on waste incineration in Chapter 5, Incineration and Open Burning of Waste. Septic tanks and latrines: Methods to estimate CH4 and N2O emissions from septic tanks and latrines as well as from discharge of wastewater into waterways are included in Chapter 6, Wastewater Treatment and Discharge.

References EEA (2005). EMEP/CORINAIR. Emission Inventory Guidebook – 2005. European Environment Agency. URL: http://reports.eea.eu.int/EMEPCORINAIR4/en IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2000). Good Practice Guidance and Uncertianty Management in National Greenhouse Gas Inventories. Penman, J., Kruger, D., Galbally, I., Hiraishi, T., Nyenzi, B., Enmanuel, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K. and Tanabe, K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. U.S.EPA (1995). U.S. EPA's Compilation of Air Pollutant Emissions Factors, AP-42, Edition 5. http://www.epa.gov/ttn/chief/ap42/. United States Environmental Protection Agency.

1

CO2 emissions of biogenic origin are either covered by the methodologies and reported as carbon stock change in the AFOLU Sector, or do not need to be accounted for because the corresponding CO2 uptake by vegetation is not reported in the inventory (e.g., annual crops).

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Chapter 2: Waste Generation, Composition and Management Data

CHAPTER 2

WASTE GENERATION, COMPOSITION AND MANAGEMENT DATA

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.1

Volume 5: Waste

Authors Riitta Pipatti (Finland), Chhemendra Sharma (India), Masato Yamada (Japan) Joao Wagner Silva Alves (Brazil), Qingxian Gao (China), G.H. Sabin Guendehou (Benin), Matthias Koch (Germany), Carlos López Cabrera (Cuba), Katarina Mareckova (Slovakia), Hans Oonk (Netherlands), Elizabeth Scheehle (USA), Alison Smith (UK), Per Svardal (Norway), and Sonia Maria Manso Vieira (Brazil)

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Chapter 2: Waste Generation, Composition and Management Data

Contents 2

Waste Generation, Composition and Management Data 2.1

Introduction ......................................................................................................................................... 2.4

2.2

Waste generation and management data ............................................................................................. 2.4

2.2.1

Municipal Solid Waste (MSW) ................................................................................................... 2.5

2.2.2

Sludge .......................................................................................................................................... 2.7

2.2.3

Industrial waste ........................................................................................................................... 2.8

2.2.4

Other waste ................................................................................................................................ 2.10

2.3

Waste composition ............................................................................................................................ 2.11

2.3.1

Municipal Solid Waste (MSW) ................................................................................................. 2.11

2.3.2

Sludge ........................................................................................................................................ 2.15

2.3.3

Industrial waste ......................................................................................................................... 2.15

2.3.4

Other waste ................................................................................................................................ 2.16

Annex 2A.1

Waste Generation and Management Data - by country and regional averages ......................... 2.17

References ......................................................................................................................................................... 2.21

Tables Table 2.1

MSW generation and treatment data - regional defaults ...................................................... 2.5

Table 2.2

Industrial waste generation in selected countries ................................................................. 2.9

Table 2.3

MSW composition data by percent - regional defaults ...................................................... 2.12

Table 2.4

Default dry matter content, DOC content, total carbon content and fossil carbon fraction of different MSW components ................................................. 2.14

Table 2.5

Default DOC and fossil carbon content in industrial waste ............................................... 2.16

Table 2.6

Default DOC and fossil carbon contents in other waste .................................................... 2.16

Table 2A.1

MSW generation and management data - by country and regional averages .................... 2.17

Boxes Box 2.1

Example of activity data collection for estimation of emissions from solid waste treatment based on waste stream analysis by waste type ..................................................................... 2.6

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Volume 5: Waste

2 WASTE GENERATION, COMPOSITION AND MANAGEMENT DATA 2.1

INTRODUCTION

The starting point for the estimation of greenhouse gas emissions from solid waste disposal, biological treatment and incineration and open burning of solid waste is the compilation of activity data on waste generation, composition and management. General guidance on the data collection for solid waste disposal, biological treatment and incineration and open burning of waste is given in this chapter in order to ensure consistency across these waste categories. More detailed guidance on choice of activity data, emission factors and other parameters needed to make the emission estimates is given under Chapter 3, Solid Waste Disposal, Chapter 4, Biological Treatment of Solid Waste, and in Chapter 5, Incineration and Open Burning of Waste. Solid waste generation is the common basis for activity data to estimate emissions from solid waste disposal, biological treatment, and incineration and open burning of waste. Solid waste generation rates and composition vary from country to country depending on the economic situation, industrial structure, waste management regulations and life style. The availability and quality of data on solid waste generation as well as subsequent treatment also vary significantly from country to country. Statistics on waste generation and treatment have been improved substantially in many countries during the last decade, but at present only a small number of countries have comprehensive waste data covering all waste types and treatment techniques. Historical data on waste disposal at SWDS are necessary to estimate methane (CH4) emissions from this category using the First Order Decay method (see Chapter 3 Solid Waste Disposal, Section 3.2.2). Very few countries have data on historical waste disposal going back several decades. Solid waste is generated from households, offices, shops, markets, restaurants, public institutions, industrial installations, water works and sewage facilities, construction and demolition sites, and agricultural activities (emissions from manure management as well as on-site burning of agricultural residues are treated in the Agriculture, Forestry and Other Land Use (AFOLU) Volume). It is good practice to account for all types of solid waste when estimating waste-related emissions in the greenhouse gas inventory. Solid waste management practices include: collection, recycling, solid waste disposal on land, biological and other treatments as well as incineration and open burning of waste. Although recycling (material recovery)1 activities will affect the amounts of waste entering into other management and treatment systems, the impact on emissions due to recycling (e.g., changes in emissions in production processes and transportation) is covered under other sectors and will not be addressed here in more detail.

2.2

WASTE GENERATION AND MANAGEMENT DATA

Guidance on how to collect data on waste generation and management practices is given separately for municipal solid waste (MSW), sludge, industrial and other waste. Default definitions for these categories are given below. These default definitions are used in the subsequent methodological guidance. The definitions are transparent to allow for country-specific modifications, as waste categorisation varies much from country to country, and can encompass different waste components.2 If the available data used in the inventory cover only certain waste types or sources (e.g., municipal waste), this limited availability should be documented clearly in the inventory report and efforts should be made to complement the data to cover all waste types. In the Section 2.3 Waste Composition, default compositions are given for these default waste categories. The default compositions are used as the basis for the calculations for Tier 1 methods.

1

Recycling is often defined to encompass also waste-to-energy activities and biological treatment. For practical reasons a more narrow definition is used here: Recycling is defined as recovery of material resources (typically paper, glass, metals and plastics, sometimes wood and food waste) from the waste stream.

2

Some countries do not use these broad waste categories but a more detailed classification, e.g., the Regulation of the European Parliament and Council on waste statistics (EC no 2150/2002) that does not include municipal solid waste as a category.

2.4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Waste Generation, Composition and Management Data

2.2.1

Municipal Solid Waste (MSW)

Municipal waste is generally defined as waste collected by municipalities or other local authorities. However, this definition varies by country. Typically, MSW includes: •

Household waste;

•

Garden (yard) and park waste; and

•

Commercial/institutional waste.

The regional default composition data for MSW is given in Section 2.3.1.

Default data Region-specific default data on per capita MSW generation and management practices are provided in Table 2.1. These data are estimated based on country-specific data from a limited number of countries in the regions (see Annex 2A.1). These data are based on weight of wet waste3 and can be assumed to be applicable for the year 2000. Waste generation per capita for subsequent or earlier years can be estimated using the guidance on how to estimate historical emissions from SWDS in Chapter 3, Section 3.2.2, and the methods for extrapolation and interpolation using drivers in Chapter 6, Time Series Consistency, in Volume 1, General Guidance and Reporting. TABLE 2.1 MSW GENERATION AND TREATMENT DATA - REGIONAL DEFAULTS Region

MSW Generation Rate1, 2, 3 (tonnes/cap/yr)

Fraction of MSW disposed to SWDS

Fraction of MSW incinerated

Fraction of MSW composted

Fraction of other MSW management, unspecified4

0.37 0.21 0.27

0.55 0.74 0.59

0.26 0.09

0.01 0.05 0.05

0.18 0.21 0.27

0.29

0.69

-

-

0.31

0.38 0.64 0.52 0.56

0.90 0.47 0.85 0.47

0.04 0.24 0.05 0.22

0.01 0.08 0.05 0.15

0.02 0.20 0.05 0.15

0.49 0.21 0.26 0.65

0.83 0.50 0.54 0.58

0.02 0.01 0.06

0.003 0.06

0.15 0.50 0.46 0.29

0.69

0.85

-

-

0.15

Asia Eastern Asia South-Central Asia South-East Asia Africa5 Europe Eastern Europe Northern Europe Southern Europe Western Europe America Caribbean Central America South America North America Oceania6

3

1

Data are based on weight of wet waste.

2

To obtain the total waste generation in the country, the per-capita values should be multiplied with the population whose waste is collected. In many countries, especially developing countries, this encompasses only urban population.

3

The data are default data for the year 2000, although for some countries the year for which the data are applicable was not given in the reference, or data for the year 2000 were not available. The year for which the data are collected, where available, is given in the Annex 2A.1.

4

Other, unspecified, includes data on recycling for some countries.

5

A regional average is given for the whole of Africa as data are not available for more detailed regions within Africa.

6

Data for Oceania are based only on data from Australia and New Zealand.

Wet waste is not treated before measuring, while dry weight is estimated after drying waste under certain temperature, ventilation and time conditions before measuring. In the conversions in this Volume (see e.g., Table 2.4) the assumption is that no moisture is left in the dry matter.

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Volume 5: Waste

Country-specific data It is good practice that countries use data on country-specific MSW generation, composition and management practices as the basis for their emission estimation. Country-specific data on MSW generation and management practices can be obtained from waste statistics, surveys (municipal or other relevant administration, waste management companies, waste association organisations, other) and research projects (World Bank, OECD, ADB, JICA, U.S.EPA, IIASA, EEA, etc.). Large countries with differences in waste generation and treatment within the domestic regions are encouraged to use data from these regions to the extent possible. Additional guidance on data collection in general and on waste surveys is given in Chapter 2, Approaches to Data Collection, in Volume 1.

Data from waste stream analyses MSW treatment techniques are often applied in a chain or in parallel. A more accurate but data intensive approach to data collection is to follow the streams of waste from one treatment to another taking into account the changes in composition and other parameters that affect emissions. Waste stream analyses should be combined with high quality country-specific data on waste generation and management. The approach is often complemented with modelling. When using this approach, it is good practice to verify the data using separately collected data on MSW generation, treatment and disposal, especially in cases where they are based largely on modelling. This method is only more accurate than the approaches given above if countries have good quality, detailed data on each end point and have verified the information. An example of applying the approach for estimating the amount of paper waste disposed at SWDS is given in Box 2.1, Example of Activity Data Collection for Estimation of Emissions from Solid Waste Treatment Based on Waste Stream Analysis by Waste Type. Using this approach following all waste streams in the country would provide activity data for all solid waste treatment and disposal (including waste incineration and open burning of waste). The data needed for the approach could be estimated based on surveys to industry, households and waste management companies/facilities, complemented with statistical data on MSW generation, treatment and disposal.

BOX 2.1 EXAMPLE OF ACTIVITY DATA COLLECTION FOR ESTIMATION OF EMISSIONS FROM SOLID WASTE TREATMENT BASED ON WASTE STREAM ANALYSIS BY WASTE TYPE

Waste streams begin at the point of generation, flow through collection and transportation, separation for resource recovery, treatment for volume reduction, detoxification, stabilisation, recycling and/or energy recovery and terminate at SWDS. Waste streams are country-specific. Traditionally most solid waste has been disposed at SWDS in many countries. Recent growing recognition of the need for resource conservation and environmental protection has increased solid waste recycling and treatment before disposal in developed countries. In developing countries, recovery of valuable material at collection, during transportation and at SWDSs has been common. Degradable organic carbon (DOC) is one of the main parameters affecting the CH4 emissions from solid waste disposal. DOC is estimated based on the waste composition, and varies for different waste fractions. Accurate estimates of the amount of waste and amount of DOC in waste (DOCm) disposed at SWDS could be achieved by sampling waste at the gate of SWDS and measuring DOCm in that waste, or specifying the waste stream for each waste type and/or source. Intermediate processes in the waste stream can significantly change physical and chemical properties of waste, including moisture and DOCm. DOCm in waste at SWDS will differ considerably from that at generation, depending on the treatment before the disposal. For those countries that do not have reliable data based on measurements on DOCm disposed at SWDS, the analysis on the change in mass of moisture and DOCm during earlier treatment for each waste type, could provide a method to avoid over-/under-estimating the CH4 emissions at SWDS.

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Chapter 2: Waste Generation, Composition and Management Data

BOX 2.1 (CONTINUED) EXAMPLE OF ACTIVITY DATA COLLECTION FOR ESTIMATION OF EMISSIONS FROM SOLID WASTE TREATMENT BASED ON WASTE STREAM ANALYSIS BY WASTE TYPE

Paper Waste Generation Total 1000 (Mois. 200) DOCm 400

Resource Recovery Total 500 (Mois. 100) DOCm 200

Stream A (composting) Total 100 -> 80 (Mois. 20 ->20) Compost DOCm 40 ->20 50% reduction of DOCm Stream B (incineration) Total 200 -> 40 (Mois. 40 ->4) Ash DOCm 80 ->0 80% reduction of Total Mass 90% reduction of Mois. 100% reduction of DOC m

Use on Land Total 40 (Mois. 10) DOCm 10

SWDS total 270 (Mois. 44) DOCm 90

Stream C (disposal) Total 200 -> 190 (Mois. 40 ->30) DOCm 80 ->80 25% loss of Mois. during reshipment & transportation

Note 1: ‘Mois.’ means moisture and DOCm is the mass of degradable organic carbon. Note 2: Values in each box give the weight of the total mass (Total), moisture (Mois.) and DOCm in mass units (tonnes or kilograms or other).

The figure above shows an example of a paper waste flow chart for analysis of change in DOCm in waste during the treatment before disposal. Some portion of paper waste would be recovered as material, and be diverted from the waste management flow. The DOCm in paper waste is reduced by intermediate processes, such as composting and incineration before disposal at the SWDS. Mass of total waste, DOCm and moisture at the exit of each process can be given by multiplying mass of these components at the entrance by reduction rates of the process. In this figure the changes of mass are studied for paper waste solely, although the treatment steps would usually include also other waste types. Incineration will remove most of the moisture, but the ash will be re-wetted to avoid the fly loss during transportation and loading into SWDS. Greenhouse gas emissions from other categories than SWDS (i.e., resource recovery, composting, incineration and use on land) should be estimated under guidelines in relevant chapters. The estimates in this figure are based on expert judgement only as an example. To apply this approach national statistics on municipal waste generation and treatment streams, country-specific parameters on waste composition and fraction moisture as well as DOC estimates for each waste type are needed for precise estimation. It may be difficult to obtain all these data and parameters in many countries. If country-specific reduction rates of moisture and DOCm at each intermediate treatment step before disposal at SWDS can be obtained, estimated DOCm disposed into SWDS will be more precise than when based on data measured at generation.

2.2.2

Sludge

Sludge from domestic and industrial wastewater treatment plants is addressed as a separate waste category in this Volume. In some countries, sludge from domestic wastewater treatment is included in MSW and sludge from industrial wastewater treatment in industrial waste. Countries may also include all sludge in industrial waste. When country-specific categorisation is used, it should be documented transparently. The emissions from sludge treatment at wastewater treatment facilities are treated in Chapter 6, Wastewater Treatment and Discharge. Chapters 3, 4 and 5 consider disposal, composting (and anaerobic digestion of sludge

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Volume 5: Waste

with other organic solid waste) and incineration of sludge, respectively. Sludge that is applied on agricultural land is considered in Volume 4, Agriculture, Forestry and Other Land Use, Chapter 11, Section 11.2, N2O Emissions from Managed Soils. Double counting of the emissions between the different categories should be avoided. The amount of organic matter removed from wastewater treatment as sludge (see Equation 6.1 in Chapter 6) due to disposal into SWDS, composting, incineration or use in agriculture should be consistent with the amounts reported under these categories. Default data for sludge generation, disposal into SWDS, composting or incineration are not given here.4 If no country-specific data are available, the reporting of the emissions is covered by the methodology in Chapter 6. Default values for degradable organic carbon content in sludge are given in Section 2.3 Waste Composition, in this chapter.

2.2.3

Industrial waste

In some countries, significant quantities of organic industrial solid waste are generated. 5 Industrial waste generation and composition vary depending on the type of industry and processes/technologies in the concerned country. Countries apply various categorisations for industrial waste. For example, construction and demolition waste can be included in industrial waste, in MSW, or defined as a separate category. The default categorisation used here assumes construction and demolition waste are part of the industrial waste. In many countries industrial solid waste is managed as a specific stream and the waste amounts are not covered by general waste statistics. OECD (see e.g., OECD, 2002) collects statistical data on industrial waste generation and treatment. These statistics are published periodically. In most developing countries industrial wastes are included in the municipal solid waste stream, therefore, it is difficult to obtain data of the industrial waste separately. Industrial solid waste disposal data may be obtained by surveys or from national statistics. Only those industrial wastes which are expected to contain DOC and fossil carbon should be considered for the purpose of emission estimation from waste. Construction and demolition waste is mainly inert (concrete, rubble, etc.) but may contain some DOC (see Section 2.3.3) in wood and some fossil carbon in plastics. Recycling and reduction using different technologies applied to industrial waste prior to disposal in SWDS or incineration should be taken into account, where data are available.

Default data Industrial waste generation data (total industrial waste generation and data for manufacturing industries and construction waste) are given in Table 2.2 for some countries. The total amount includes also other waste types than those from manufacturing industries and construction. The data are based on weight of wet waste. Although significant amounts of industrial waste are generated, the rates of recycling/reuse are often high, and the fraction of degradable organic material from industrial waste disposed at solid waste disposal sites is often less than that of MSW. Incineration of industrial waste may take place in significant amounts, however this will vary from country to country. Composting or other biological treatment is restricted to waste from industries producing food and other putrescible waste. Countries for which no national data on industrial waste generation can be obtained and whose data are not given in Table 2.2, are encouraged to use data from countries, or a cluster of countries, with similar circumstances. Chapter 2, Approaches to Data Collection, in Volume 1 gives general guidance on data collection. The data in Table 2.2 do not include data on industrial waste management practices. When country-specific data on industrial waste management are not available from other sources, the management can be assumed to follow the same pattern as management of MSW (see Table 2.1). For more accurate data, the inventory compilers are encouraged to contact relevant sources of information in the country, such as governmental agencies and local authorities responsible for industrial waste management as well as industrial organisations.

4

For some European countries, data on sewage waste disposal is collected by Eurostat (2005).

5

The default values provided in Table 2.1 do not include industrial solid waste.

2.8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Waste Generation, Composition and Management Data

TABLE 2.2 INDUSTRIAL WASTE GENERATION IN SELECTED COUNTRIES (1,000 tonnes per year) Region/ Country Asia China Japan Singapore Republic of Korea Israel Europe Austria Belgium Bulgaria Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Malta Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland Turkey UK Oceania Australia New Zealand

Total

Manufacturing Industries

Construction

1 004 280 120 050

76 240

39 810

28 750

14 284 14 144 3 145 1 600 9 618 2 950

27 500 9 046 7 142 5 083 3 220

15 281 98 000 47 960 6 680 2 605 10 5 361 35 392 422 25 17 595 415 58 975 8 356 797 6 715 1 493 20 308 18 690 1 470 1 166 50 000

1 420

1 423.5 1 000

1 261.5

1 103

37 040 1 750

231 000 1 800 707 3 651 27 291 7 206 23 800 4 143 85 223

6 390 72 000 10 NR

Data are based on weight of wet waste. The data are default data for the year 2000, although for some countries the year for which the data are applicable was not given in the reference, or data for the year 2000 were not available. References: Environmental Statistics Yearbook of China (2003) Eurostat (2005) Latvian Environment Agency (2004) OECD (2002) National environmental agency, Singapore (2001) Estonian Environment Information Centre (2003) Statistics Finland (2005) Milleubalans (2005)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.9

Volume 5: Waste

Country-specific industrial waste generation data Some countries have statistical data on industrial waste generation and management. It is good practice to use country-specific data on industrial waste generation, waste composition (see Section 3.2.2) as well as management practices as the basis for the emission estimation. The data should to the extent possible be collected by industry types. If the available data cover only part of industry or industrial waste types, this limited availability should be documented clearly in the inventory report, as well as efforts made to complement the data to cover all industrial waste.

Data for the waste stream analyses Approaches following the streams of waste from one treatment to another taking the changes in composition and other parameters affecting the emissions discussed in Section 2.2.1 could be used also for industrial waste. Data could be collected using surveys or be collected plant-by-plant.

2.2.4

Other waste

Clinical waste: These wastes include materials like plastic syringes, animal tissues, bandages, cloths, etc. Some countries choose to include these items in the MSW. Clinical waste is usually incinerated. However, some clinical waste may be disposed in SWDS. No regional or country-specific default data are given for clinical waste generation and management. In most countries, the amount of greenhouse gas emissions due to clinical waste appears to be insignificant. Default DOC and fossil carbon content in clinical waste are given in Section 2.3.4, Table 2.6. Hazardous waste: Waste oil, waste solvents, ash, cinder and other wastes with hazardous nature, such as flammability, explosiveness, causticity, and toxicity, are included in hazardous waste. Hazardous wastes are generally collected, treated and disposed separately from non-hazardous MSW and industrial waste streams. Some hazardous wastes are incinerated and can contribute to the fossil CO2 emissions from incineration (see Chapter 5) (Eurostat, 2005)6. Neutralisation and cement solidification are also treatment processes for hazardous waste. These processes applied together to organic sludge or other liquid-like waste with hazardous nature can reduce (or delay) greenhouse gas emissions at SWDS by isolation. In many countries it is prohibited to dispose hazardous waste at SWDS without pre-treatment. Emissions from solid waste disposal of hazardous waste are likely to be small. No regional or country-specific default data are given for hazardous waste generation and management. Default DOC and fossil carbon content in hazardous waste are given in Section 2.3.4, Table 2.6. Agricultural waste: Manure management and burning of agricultural residues are considered in the AFOLU Volume. Agricultural waste which will be treated and/or disposed with other solid waste may however be included in MSW or industrial waste. For example, such waste may include manure, agricultural residues, dead body of live stock, plastic film for greenhouse and mulch.

6

Eurostat (2005) collects data based on national statistics from European countries on hazardous waste generation and treatment.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Waste Generation, Composition and Management Data

2.3

WASTE COMPOSITION

2.3.1

Municipal Solid Waste (MSW)

Waste composition is one of the main factors influencing emissions from solid waste treatment, as different waste types contain different amount of degradable organic carbon (DOC) and fossil carbon. Waste compositions, as well as the classifications used to collect data on waste composition in MSW vary widely in different regions and countries. In this Volume, default data on waste composition in MSW are provided for the following waste types: (1)

food waste

(2)

garden (yard) and park waste

(3)

paper and cardboard

(4)

wood

(5)

textiles

(6)

nappies (disposable diapers)

(7)

rubber and leather

(8)

plastics

(9)

metal

(10)

glass (and pottery and china)

(11)

other (e.g., ash, dirt, dust, soil, electronic waste)

Waste types from (1) to (6) contain most of the DOC in MSW. Ash, dust, rubber and leather contain also certain amounts of non-fossil carbon, but this is hardly degradable. Some textiles, plastics (including plastics in disposable nappies), rubber and electronic waste contain the bulk part of fossil carbon in MSW. Paper (with coatings) and leather (synthetic) can also include small amounts of fossil carbon. Regional and country-specific default data on waste composition in MSW are given in Table 2.3. These data are based on weight of wet waste. Table 2.3 does not give default data for garden and park waste and nappies. In the Tier 1 default method these waste fractions can be assumed to be zero, i.e., they can be assumed to be encompassed by the other waste types.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.11

40.3

43.5

41.1

South-Central Asia

South-Eastern Asia

Western Asia & Middle East

43.4

51.1 23

40.4

Middle Africa

Northern Africa

Southern Africa

Western Africa

23.8

36.9

24.2

Northern Europe

Southern Europe

Western Europe

67.5

Rest of Oceania

43.8

44.9

46.9

Central America

South America

Caribbean

17.0

17.1

13.7

23.2

6.0

30.0

27.5

17.0

30.6

21.8

9.8

25

16.5

16.8

7.7

18.0

12.9

11.3

18.8

Paper/cardboard

2.4

4.7

13.5

6.2

2.5

24.0

11.0

10.6

10.0

7.5

4.4

15

2

6.5

7.0

9.8

9.9

7.9

3.5

Wood

5.1

2.6

2.6

3.9

2.0

4.7

1.0

2.5

2.5

1.7

2.9

2.7

2.5

3.5

Textiles

1.9

0.7

1.8

1.4

1.4

1.1

0.6

0.9

0.8

1.0

Rubber/leather

TABLE 2.3 MSW COMPOSITION DATA BY PERCENT - REGIONAL DEFAULTS

2006 IPCC Guidelines for National Greenhouse Gas Inventories

33.9

North America

America

36.0

Australia and New Zealand

Oceania

30.1

Eastern Europe

Europe

53.9

Eastern Africa

Africa

26.2

Food waste

Eastern Asia

Asia

Region

Volume 5: Waste

9.9

10.8

6.7

8.5

13.0

6.2

3.0

4.5

4.5

5.5

6.3

7.2

6.4

14.3

Plastic

5.0

2.9

2.6

4.6

7.0

3.6

1.0

3.5

3.5

1.8

1.3

3.3

3.8

2.7

Metal

5.7

3.3

3.7

6.5

8.0

10.0

2

2.0

2.3

2.2

4.0

3.5

3.1

Glass

3.5

13.0

12.3

9.8

14.6

1.5

1.5

11.6

5.4

16.3

21.9

7.4

2.12

Other

2006 IPCC Guidelines for National Greenhouse Gas Inventories

López et al. (2002)

MAG/SSERNMA/DOA-PNUD/UNITAR (1999)

U.S. EPA (1997)

Ministry of Science and Technology, Brazil (2002)

López, C. (2006). Personal Communication.

Ministerio de Desarrollo Social y Medio Ambiente/Secretaría de Desarrollo Sustentable y Política Ambiental (1999)

OPS/OMS (1997)

JICA (1991)

Monreal (1998)

BID/OPS/OMS (1997)

U.S. EPA (2002)

CONADE/SEDUE (1992); INE/SMARN (2000)

www.climatechange.govt.nz/resources/reports/nir-apr04

www.defra.gov.uk/environment/statistics/wastats/mwb0203/wbch04.htm

Shimura et al. (2001)

Vishwanathan and Trakler (2003a and b)

Hoornweg (1999)

Doorn and Barlaz (1995)

Sources:

Note 2: The region-specific values are calculated from national, partly incomplete composition data. The percentages given may therefore not add up to 100%. Some regions may not have data for some waste types blanks in the table represent missing data.

Note 1: Data are based on weight of wet waste of MSW without industrial waste at generation around year 2000.

TABLE 2.3 (CONTINUED) MSW COMPOSITION DATA BY PERCENT - REGIONAL DEFAULTS

2.13

Chapter 2: Waste Generation, Composition and Management Data

Volume 5: Waste

Default values for DOC and fossil carbon content in different waste types is given in Table 2.4. Table 2.4 gives default values also for garden and park waste, and disposable nappies. These waste types were not included in Table 2.3 due to lack of data. All fractions in the Table 2.4 are given as percentages.

TABLE 2.4 DEFAULT DRY MATTER CONTENT, DOC CONTENT, TOTAL CARBON CONTENT AND FOSSIL CARBON FRACTION OF DIFFERENT MSW COMPONENTS MSW component

Dry matter DOC content content in % in % of wet waste of wet weight 1

DOC content in % of dry waste

Total carbon content in % of dry weight

Fossil carbon fraction in % of total carbon

Default

Default

Range

Default

Range 2

Default

Range

Paper/cardboard

90

40

36 - 45

44

40 - 50

46

42 - 50

1

0-5

Textiles 3

80

24

20 - 40

30

25 - 50

50

25 - 50

20

0 - 50

Food waste

40

15

8 - 20

38

20 - 50

38

20 - 50

-

-

43

39 - 46

50

46 - 54

50

46 - 54

-

-

20

18 - 22

49

45 - 55

49

45 - 55

Range

Wood Garden and Park waste Nappies

40

24

18 - 32

60

44 - 80

70

54 - 90

10

10

Rubber and Leather

84

(39) 5

(39) 5

(47) 5

(47) 5

67

67

20

20

Plastics

100

-

-

-

-

75

67 - 85

100

95 - 100

6

100

-

-

-

-

NA

NA

NA

NA

Glass 6

100

-

-

-

-

NA

NA

NA

NA

90

-

-

-

-

3

0-5

100

50 - 100

Metal

Other, inert waste

85

4

Default

40

0

0

1

The moisture content given here applies to the specific waste types before they enter the collection and treatment. In samples taken from collected waste or from e.g., SWDS the moisture content of each waste type will vary by moisture of co-existing waste and weather during handling.

2

The range refers to the minimum and maximum data reported by Dehoust et al., 2002; Gangdonggu, 1997; Guendehou, 2004; JESC, 2001; Jager and Blok, 1993; Würdinger et al., 1997; and Zeschmar-Lahl, 2002.

3

40 percent of textile are assumed to be synthetic (default). Expert judgement by the authors.

4

This value is for wood products at the end of life. Typical dry matter content of wood at the time of harvest (that is for garden and park waste) is 40 percent. Expert judgement by the authors.

5

Natural rubbers would likely not degrade under anaerobic condition at SWDS (Tsuchii et al., 1985; Rose and Steinbüchel, 2005).

6

Metal and glass contain some carbon of fossil origin. Combustion of significant amounts of glass or metal is not common.

DOC values for different waste types, which are derived from analyses based on sampling during waste collection at SWDS or at incineration facilities, may include impurities, e.g., traces of food in glass and plastic waste. Carbon contents of paper, textiles, nappies, rubber and plastic may also be different between countries and at different time periods. These analyses may therefore result in DOC estimates different from those given in Table 2.4. It is good practice to use DOC values consistently with the way the waste composition data are derived. The best composition data can be obtained by routine monitoring at the gate of SWDS or incineration and other treatment facilities. If these data are not available, composition data obtained at generation and/or transportation, treatment and recycling facilities can be used for disposed DOC estimations using waste stream analysis (see Box 2.1). Waste can be sampled at pits in waste treatment facilities, at loading yards in transportation stations and SWDS. Composition data of disposed waste can be obtained from field sampling at SWDS. The amount of waste (typically more than 1 m3 for a representative sample) should be separated manually into each item and weighed by item in order to obtain wet weight composition. A certain amount of each item should be reduced and sampled by quartering and used for chemical analysis including moisture and DOC. Samples should be taken on different days of the week. MSW composition will vary by city in a same country. It will also vary by the day of the week, season and year in the same city. National representative (or average) composition data should be obtained from sampling at several typical cities on same days of the week in each season. Sampling at SWDS on rainy days will change moisture content (i.e., wet weight composition) significantly, and needs attention in interpretation of that in annual data.

2.14

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Waste Generation, Composition, and Management Data

Analyses to determine the national waste composition should be based on appropriate sampling methods (see Volume 1, Chapter 2, Approaches to Data Collection) and be repeated periodically to cover changes in waste generation and management. The sampling methods, frequency of sampling and implications on time series should be documented. The default DOC values given in Table 2.4 are used in estimating CH4 emissions from and carbon stored in SWDS (see Chapter 3). The default total carbon contents and fossil carbon fractions for estimating fossil CO2 emissions from incineration and open burning are also given in Table 2.4.

2.3.2

Sludge

The DOC content in sludge will vary depending on the wastewater treatment method producing the sludge, and also be different for domestic and industrial sludge. For domestic sludge, the default DOC value (as percentage of wet waste assuming a default dry matter content of 10 percent) is 5 percent (range 4 - 5 percent, which means that the DOC content would be 40-50 percent of dry matter). A rough default value of 9 percent DOC (assuming the dry matter content to be 35 percent) can be used for industrial sludge, when country and/or industry-specific is not available. The default DOC value applies for total industrial sludge in a country. Sewage, food industry, textile industry and chemical industry will generate organic sludge. DOC is also found in sludge from water work and dredging. The DOC in sludge can vary much by industry type. Examples of carbon contents in some organic sludge (percentage of dry matter) in Japan are: 27 percent for pulp and paper industry, 30 percent for food industry and 52 percent for chemical industry (Yamada et al., 2003).

2.3.3

Industrial waste

The average composition of industrial waste is very different from the average composition of MSW, and varies by type of industry, although many of the waste types can be included in both of industrial waste and MSW. DOC and fossil carbon in industrial waste is mainly found in the same waste types as in MSW. DOC is found in paper and cardboard, textiles, food and wood. Synthetic leather, rubber, and plastics are major sources of fossil carbon. Waste oils and solvents are also important sources of fossil carbon in industrial liquid waste. Paper and cardboard and plastics will be generated at various industries mainly from office work and by packaging waste. Wood will be found in wastes from pulp and paper, wood manufacturing industries and construction and demolition activities. Food, beverage and tobacco industry will be the major source of food waste. Details of product and/or activity of each industry are different country by country. In order to estimate the DOC and fossil carbon in industrial waste, surveys on waste generation and composition at representative industries and estimation of unit generation of certain composition per economic driver, such as production, floor area and employee number, can be used. Non-hazardous waste (like office waste and waste from catering) from industrial activities is sometimes included in MSW. Double counting of the emissions should be avoided. Table 2.5 provides default values of DOC and fossil carbon contents in industrial waste by industry type per amount waste produced. The default values are only for process waste generated at the facilities (e.g., office waste is assumed to be included in MSW). Countries are encouraged to collect and use national data where available as the default data are very uncertain. The guidance given above and in Chapter 2 of Volume 1 can be used to develop data collection systems for industrial waste. The DOC and fossil carbon contents can be determined using the same sampling methods as for MSW.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.15

Volume 5: Waste

TABLE 2.5 DEFAULT DOC AND FOSSIL CARBON CONTENT IN INDUSTRIAL WASTE (PERCENTAGE IN WET WASTE PRODUCED)1 Industry type

DOC

Fossil carbon

Total carbon

Water content 2

Food, beverages and tobacco (other than sludge)

15

-

15

60

Textile

24

16

40

20

Wood and wood products

43

-

43

15

Pulp and paper (other than sludge)

40

1

41

10

-

80

80

0

17

56

16

Petroleum products, Solvents, Plastics Rubber

(39)

3

Construction and demolition

4

20

24

0

Other 4

1

3

4

10

Source: Expert Judgement; Pipatti et al. 1996; Yamada et al. 2003. 1

The default values apply only for process waste from the industries, office and other similar waste are assumed to be included in MSW.

2

Note that water contents of industrial wastes vary enormously, even within a single industry.

3

Natural rubbers would likely not degrade under anaerobic condition at SWDS (Tsuchii, et al., 1985; Rose and Steinbüchel, 2005).

4

These values can be used also as defaults for total waste from manufacturing industries, when data on waste production by industry type are not available. Waste from mining and quarrying should be excluded from the calculations as the amounts can be large and the DOC and fossil carbon contents are likely to be negligible.

2.3.4

Other waste

Default values for DOC and fossil carbon for hazardous waste and clinical waste are given in Table 2.6. The values should be applied only for total amounts of hazardous and clinical waste generated in the country. Major part of hazardous waste would be generated as sludge or liquid-like nature, as well as ash, cinder and slug which are dry nature.

TABLE 2.6 DEFAULT DOC AND FOSSIL CARBON CONTENTS IN OTHER WASTE (PERCENTAGE IN WET WASTE PRODUCED) Waste type

DOC

Hazardous waste

NA

Clinical waste

15

Fossil carbon 5 - 50 25

1

Total carbon NA 40

Water Content 10 - 90 1 35

NA = not available Sources: Expert Judgement; IPCC 2000 1

The higher fossil carbon value is for waste with lower water content. When no data on the water content are available, the mean value of the range should be used.

2.16

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Waste Generation, Composition, and Management Data

Annex 2A.1 Waste Generation and Management Data - by country and regional averages Table 2A.1 in this Annex shows MSW generation and management data for some countries whose data are available. Regional defaults for waste generation and treatment that are provided in Table 2.1 in Chapter 2 are derived based on the information from this table. The data are applicable as default data for the year 2000. For comparison, data on waste generation and disposal to SWDS from the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines) are also given in the table. TABLE 2A.1 MSW GENERATION AND MANAGEMENT DATA - BY COUNTRY AND REGIONAL AVERAGES

Region /Country

MSW 1, 2 Generation Rate

MSW 1, 2, 3 Generation Rate

Fraction of MSW disposed to SWDS

Fraction Fraction of Fraction of Fraction of of MSW other MSW MSW MSW Source disposed management, incinerated composted 5 to SWDS unspecified

IPCC -1996 Year 2000 IPCC-1996 values 4 (tonnes/cap/yr) values 4 (tonnes/cap/yr )

Asia Eastern Asia China Japan Rep. of Korea Southern and Central Asia Bangladesh India Nepal Sri Lanka South-eastern Asia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Africa Africa 6 Egypt Sudan South Africa Nigeria Europe Eastern Europe Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Poland

0.41 0.41 0.12 0.12

0.37 0.27 0.47 0.38 0.21 0.18 0.17 0.18 0.32

0.38 0.38 0.60 0.60

0.55 0.97 0.25 0.42

0.26 0.02 0.72 0.04

0.01 0.01 0.02

0.74

-

0.05

0.95 0.70 0.40 0.90

0.20

0.18 0.01 0.54

1 2, 31 3

0.21 0.05 0.10 0.60 0.10

4 4 4 4

0.27

0.59

0.09

0.05

0.27

0.28 0.25 0.30 0.16 0.19 0.40 0.40 0.20

0.80 0.40 0.70 0.60 0.62 0.20 0.80 0.60

0.05

0.10

0.05

0.10

0.05 0.60 0.15 0.40 0.28 0.22 0.05 0.40

4 4 4 4 4, 5 6 4 4

0.29

0.69 0.70 0.82 0.90 0.40

0.31 0.30 0.18 0.10 0.60

4 7 4 4

0.02 0.00 0.00 0.06 0.02 0.00 0.02 0.00 0.00

8 8 8 8 8 8 8 8

0.29 1.00

0.38 0.52 0.33 0.44 0.45 0.27 0.31 0.32

0.9 1.00 1.00 0.75 0.98 0.92 0.92 1.00 0.98

2006 IPCC Guidelines for National Greenhouse Gas Inventories

0.10 0.58 0.05

0.04 0.00 0.00 0.14 0.00 0.08 0.04 0.00 0.00

0.10

0.01 0.00 0.00 0.04 0.00 0.00 0.02 0.00 0.02

2.17

Volume 5: Waste

TABLE 2A.1 (CONTINUED) MSW GENERATION AND MANAGEMENT DATA - BY COUNTRY AND REGIONAL AVERAGES

Region /Country

MSW 1, 2 Generation Rate

MSW 1, 2, 3 Generation Rate

Fraction of MSW disposed to SWDS

Fraction Fraction of Fraction of Fraction of of MSW other MSW MSW MSW Source disposed management, incinerated composted 5 to SWDS unspecified

IPCC -1996 Year 2000 IPCC-1996 values 4 (tonnes/cap/yr) values 4 (tonnes/cap/yr )

Romania 0.36 Russian 0.32 0.34 Federation Slovakia 0.32 Slovenia 0.51 Northern Europe 0.64 Denmark 0.46 0.67 Finland 0.62 0.50 Iceland 1.00 Norway 0.51 0.62 Sweden 0.37 0.43 Southern Europe 0.52 Cyprus 0.68 Greece 0.31 0.41 Italy 0.34 0.50 Malta 0.48 Portugal 0.33 0.47 Spain 0.36 0.60 Turkey 0.50 Western Europe 0.45 0.56 Austria 0.34 0.58 Belgium 0.40 0.47 France 0.47 0.53 Germany 0.36 0.61 Ireland 0.31 0.60 Luxemburg 0.49 0.66 Netherlands 0.58 0.62 Switzerland 0.40 0.40 UK 0.69 0.57 Central, South America and Caribbean states Caribbean 0.49 Bahamas 0.95 Cuba 0.21 Dominican 0.25 Republic St. Lucia 0.55 Central America 0.21 Costa Rica 0.17 Guatemala Honduras Nicaragua South America South America Argentina Bolivia

2.18

0.94

0.2 0.77 0.75 0.44

0.93 0.88 0.86 0.85 0.57 0.4 0.43 0.46 0.66 1.0 0.35 0.67 0.23 0.90

1.00

0.00

0.00

0.00

8

0.71

0.19

0.00

0.10

9

1.00 0.90 0.47 0.10 0.61 0.86 0.55 0.23 0.85 1.00 0.91 0.70 1.00 0.69 0.68 0.99 0.47 0.30 0.17 0.43 0.30 0.89 0.27 0.11 1.00 0.82

0.00 0.00 0.24 0.53 0.1 0.06 0.15 0.39 0.05 0.00 0.00 0.07 0.00 0.19 0.07 0.00 0.22 0.10 0.32 0.33 0.24 0.00 0.55 0.36 0.00 0.07

0.00 0.08 0.08 0.16 0.07 0.01 0.09 0.10 0.05 0.00 0.01 0.14 0.00 0.05 0.16 0.01 0.15 0.37 0.23 0.12 0.17 0.01 0.18 0.28 0.00 0.03

0.00 0.02 0.20 0.22 0.22 0.06 0.22 0.29 0.05 0.00 0.08 0.09 0.00 0.07 0.09 0.00 0.15 0.23 0.28 0.13 0.29 0.11 0.00 0.25 0.00 0.08

8 8

8 8 8 8 8 8 8 8 8

0.83 0.7 0.90

0.02

0.15 0.3 0.1

10 11

0.90

0.06

0.04

12

0.17 0.50

13

0.83 0.50

8 8 8 8 8 8 8 8 8 8 8 8

0.22

0.40

0.60

0.15 0.28

0.40 0.70

0.60 0.30

14, 15 16, 17, 18 4 4

0.26 0.28 0.16

0.54 0.59 0.70

0.46 0.41 0.30

4 19

0.01

0.003

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Waste Generation, Composition, and Management Data

TABLE 2A.1 (CONTINUED) MSW GENERATION AND MANAGEMENT DATA - BY COUNTRY AND REGIONAL AVERAGES

Region /Country

MSW 1, 2 Generation Rate

MSW 1, 2, 3 Generation Rate

Fraction of MSW disposed to SWDS

Fraction Fraction of Fraction of Fraction of of MSW other MSW MSW MSW Source disposed management, incinerated composted 5 to SWDS unspecified

IPCC -1996 Year 2000 IPCC-1996 values 4 (tonnes/cap/yr) values 4 (tonnes/cap/yr )

Brazil Chile Colombia Ecuador Paraguay (Asuncion) Peru Uruguay Venezuela North America North America Canada Mexico USA Oceania Oceania Australia New Zealand

0.18 0.26 0.22

0.80 0.40 0.31 0.40

0.05

0.03

0.12 0.60 0.69 0.60

20, 21 4 22 23

0.44

0.40

0.60

24

0.20 0.26 0.33

0.53 0.72 0.50

0.47 0.28 0.50

4, 25 26, 27 28

0.70

0.65

0.69

0.58

0.06

0.06

0.29

0.66

0.49

0.75

0.71

0.04

0.19

0.06

0.73

0.31 1.14

0.62

0.49 0.55

0.14

1.00 1.00 1.00

0.85 1.00 0.70

0.47 0.46 0.49

0.69 0.69

0.51 0.31

29, 30, 31 32, 33 34

0.15 0.30

4, 31 4

1

Data are based on weight of wet waste. To obtain the total waste generation in the country, the per-capita values should be multiplied with the population whose waste is collected. In many countries, especially developing countries, this encompasses only urban population. 3 The data are default data for the year 2000, although for some countries the year for which the data are applicable was not given in the reference, or data for the year 2000 were not available. The year for which the data are collected is given below with source of the data, where available. 4 Values shown in this column are the ones included in the 1996 IPCC Guidelines. 5 Other, unspecified, includes data on recycling for some countries. 6 A regional average is given for the whole of Africa as data are not available for more detailed regions within Africa.

2

Source

Year

1

Urban Construction Statistics Yearbook of China – Year 2000 (2001). Ministry of Chinese Construction. Chinese Construction Industry Publication Company.

2

OECD Environment Directorate, OECD Environmental Data 2002, Waste.

3

1) '97 National Status of Solid Waste Generation and Treatment , the Ministry of Environment, Korea, 1998.

Ministry of Environment, Japan (1992-2003): Waste of Japan, http://www.env.go.jp/recycle/waste/ippan.html.

2) '96 National Status of Solid Waste Generation and Treatment , the Ministry of Environment, Korea, 1997. 3) Korea Environmental Yearbook, the Ministry of Environment, Korea, 1990. 4

Doorn and Barlaz, 1995, Estimate of global methane emissions from landfills and open dumps, EPA-600/R-95-019, Office of Research & Development, Washington DC, USA.

5

Shimura et al. (2001).

6

2001

7

8 9

National Environmental Agency, Singapore (www.nea.gov.sg. ) and www.acrr.org/resourcecities/waste_resources/europe_waste.htm. Ministry of Environment and Physical Development, Higher Council for Environment and Natural Resources, Sudan (2003), Sudan's First National Communications under the United Nations Framework Convention on Climate Change.

2000

Eurostat (2005). Waste Generated and Treated in Europe. Data 1995-2003. European Commission - Eurostat, Luxemburg. 131p. Problems of waste management in Russia: Not-for-Profit Partnership “Waste Management – Strategic Ecological Initiative” http://www.sagepub.com/journalsProdEditBoards.nav?prodId=Journal201691.

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Volume 5: Waste

TABLE 2A.1 (CONTINUED) MSW GENERATION AND MANAGEMENT DATA- BY COUNTRY AND REGIONAL AVERAGES Source

Year

10 11

The Bahamas Environment, Science and Technology Commission (2001). Commonwealth of the Bahamas. First National Communication on Climate Change. Nassu, New Providence, April 2001, 121pp. 1990

12

13

OPS/OMS (1997). Análisis Sectorial de Residuos Sólidos en Cuba. Serie Análisis 1. Sectoriales No. 13, Organización Panamericana de la Salud, 206 pp., 2. López, C., et al. (2002). República de Cuba. Inventario Nacional de Emisiones y Absorciones de Gases de Invernadero (colectivo de autores). Reporte para el Año 1996/Actualización para los Años 1990 y 1994. CD-ROM Vol. 01. Instituto de Meteorología-AMA-CITMA. La Habana, 320 pp. ISBN: 959-02-0352-3. Secretaría de Estado de Medio Ambiente y Recursos Naturales (2004). República Dominicana. Primera Comunicación Nacional a la Convención Marco de Naciones Unidas sobre Cambio Climático. UNEP/GEF, Santo Domingo, Marzo de 2004, 163 pp.

1990

Ministry of Planning, Development, Environment and Housing (2001). Saint Lucias’s Initial National Communication on Climate Change, UNEP/GEF, 306 pp.

14

Lammers, P. E. M., J. F. Feenstra, A. A. Olstroorn (1998). Country/Region-Specific Emission Factors in National Greenhouse Gas Inventories. UNEP/Institute for Environmental Studies Vrije Universiteit, 112 pp.

15

Ministerio de Recursos Naturales, Energía y Minas (1995). Inventario Nacional de Fuentes y Sumideros de Gases con Efecto Invernadero en Costa Rica. MRNEM, Instituto Meteorológico Nacional, San José, Septiembre 1995.

16

Ministerio de Ambiente y Recursos Naturales (2001). República de Guatemala. Primera Comunicación Nacional sobre Cambio Climático..

17

JICA (Agencia Japonesa de Cooperación Internacional) (1991). Estudio sobre el Manejo de los Desechos Sólidos en el Area Metropolitana de la Ciudad de Guatemala. Volumen 1.

18

Guatemala de la Asunción, diciembre 2001, 127 p.,OPS/OMS (1995). Análisis Sectorial de Residuos Sólidos en Guatemala, Diciembre 1995, 183 pp.

19

1990

Fondo Nacional de Desarrollo (FNDR). Cantidad de RSM dispuestos en RSA-años 1996 y 1997, La Paz, Bolivia., 2. Ministerio de Desarrollo Sostenible y Medio Ambiente/Secretaría Nacional de Recursos Naturales y Medio Ambiente (1997). Inventariación de Emisiones de Gases de Efecto Invernadero. Bolivia – 1990. MDSMA/SNRNMA/SMA/PNCC/U.S. CSP, La Paz, 1997.

20

Ministry of Science and Technology, Brazil (2002). First Brazilian Inventory of Anthropogenic Greenhouse Gas Emissions. Background Reports. Methane Emissions from Waste Treatment and Disposal. CETESB. 1990 and 1994, Brazília, DF, 85 pp.

21

CETESB (1992). Companhia de Tecnologia de Saneamiento Ambiental. Programa de gerenciamiento de residuos sólidos domiciliares e de services de saúde. PROLIXO, CETESB; Sao Paulo, 29 pp., IBGE: Instituto Brasileiro de Geografía e Estadística. http://www.ibge.gov.br/home/estadistica/populacao/atlassaneamiento/pdf/mappag59.pdf in November 2004.

22

1990

23

Ministerio de Medio Ambiente/IDEAM (1999). República de Colombia. Inventario Nacional de Fuentes y Sumideros de Gases de Efecto Invernadero. 1990. Módulo Residuos, Santa Fe de Bogotá, DC, Marzo de 1999, 14 pp. BID/OPS/OMS (1997). Diagnóstico de la Situación del Manejo de los Residuos Sólidos Municipales en América Latina y el Caribe., Doorn and Barlaz, 1995, Estimate of global methane emissions from landfills and open dumps, EPA-600/R-95-019, Office of Research & Development, Washington DC, USA.

24

1990

MAG/SSERNMA/DOA – PNUD/UNITAR (1999). Paraguay: Inventario Nacional de Gases de Efecto Invernadero por Fuentes y Sumideros. Año 1990. Proyecto PAR GLO/95/G31. Asunción, Noviembre 1999, 90 pp.

25

1990 1994 1998

Estudios CEPIS-OPS y/o Estudio Sectorial de Residuos Sólidos del Perú. Ditesa/OPS., Lammers, P. E. M., J. F. Feenstra, A. A. Olstroorn (1998). Country/Region-Specific Emission Factors in National Greenhouse Gas Inventories. UNEP/Institute for Environmental Studies Vrije Universiteit, 112 pp.

26

Ministerio de Vivienda, Ordenamiento Territorial y Medio Ambiente/Dirección Nacional de Medio Ambiente/Unidad de Cambio Climático (1998). Uruguay. Inventario Nacional de Emisiones Netas de Gases de Efecto Invernadero 1994/Estudio Comparativo de Emisiones Netas de Gases de Efecto Invernadero para 1990 y 1994. Montevideo, Noviembre de 1998, 363pp.

27

OPS/OMS (1996). Análisis Sectorial de Residuos Só,Ministerio de Vivienda, Ordenamiento Territorial y Medio Ambiente/Dirección Nacional de Medio Ambiente/Unidad de Cambio Climático (2004). Uruguay. Segunda Comunicación a la CMNUCC. 330p. lidos en Uruguay. Plan Regional de Inversiones en Medio Ambiente y Salud, Marzo 1996.

28

2000

Ministerio del Ambiente y de los Recursos Naturales Renovables. Ministerio de Energía y Minas (1996). Venezuela. Inventario de Emisiones de Gases de Efecto Invernadero. Año 1990. GEF/UNEP/U.S CSP.

29

1992

Organization for Economic Cooperation and Development (OECD) http://www.oecd.org/dataoecd/11/15/24111692.PDF

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Waste Generation, Composition, and Management Data

TABLE 2A.1 (CONTINUED) MSW GENERATION AND MANAGEMENT DATA- BY COUNTRY AND REGIONAL AVERAGES Source

Year

30

The Fraser Institute, Environmental Indicators, 4th Edition (2000). http://oldfraser.lexi.net/publications/critical_issues/2000/env_indic/section_05.html.

31

UNFCCC Secretariat, Working paper No.3 (g) (2000). Expert report, prepared for the UNFCCC secretariat, 20 February 2000.

32

1992

http://www.oecd.org/dataoecd/11/15/24111692.PDF.

33

INE/SMARN (2000). Inventario Nacional de Emisiones de Gases de Invernadero 1994-1998, Ciudad de Mexico, Octubre 2000, 461 p.

34

Waste generation from: BioCycle (January 2004). "14th Annual BioCycle Nationwide Survey: The State of Garbage in America", Waste disposition from: BioCycle (December 2001). "13th Annual BioCycle Nationwide Survey: The State of Garbage in America"; Personal Communication: Elizabeth Scheele, U.S. EPA.

References BID/OPS/OMS (1997). Diagnóstico de la Situación del Manejo de los Residuos Sólidos Municipales en América Latina y el Caribe. CONADE/SEDUE (1992). Informe de la Situación General en Materia de Equilibrio Ecológico y Protección al Ambiente 1989-1990. (Actualizado por la Dirección General de Servicios Urbanos, DDF, 1992.Dehoust, G., Gebhardt, P., Gärtner, S. (2002). Der Beitrag der thermischen Abfallbehandlung zu Klimaschutz, Luftreinhaltung und Ressourcenschonung [The contribution of thermal waste treatment to climate change mitigation, air quality and resource management]. For: Interessengemeinschaft der Betreiber Thermischer Abfallbehandlungsanlagen in Deutschland (ITAD). Öko-Institut, Darmstadt 2002 [In German]. Dehoust, G., et al. (2002). Dehoust, G., Gebhardt, P., Gärtner, S., Der Beitrag der thermischen Abfallbehandlung zu Klimaschutz, Luftreinhaltung und Ressourcenschonung [The contribution of thermal waste treatment to climate change mitigation, air quality and resource management]. For: Interessengemeinschaft der Betreiber Thermischer Abfallbehandlungsanlagen in Deutschland (ITAD). Öko-Institut, Darmstadt 2002 [In German]. Doorn, M. and Barlaz, M. (1995). Estimate of global methane emissions from landfills and open dumps, EPA600/R-95-019, Office of Research & Development, Washington DC, USA. Environmental Statistics Yearbook of China (2003). URL:http://www.cnemc.cn/stat/indexs.asp?id=15 (in Chinese) Estonian Environment Information Centre. (2003). URL: http://www.keskkonnainfo.ee/english/waste Eurostat (2005). Waste Generated and Treated in Europe. Data 1995-2003, European Commission -Eurostat, Luxemburg. 131 p. Gangdonggu Go"mi (1997). Study on the situation of wastes discharge in Gangdonggu. (Institute of Metropolitan), Seoul (University of Seoul) 1997.2 Guendehou, G.H.S. (2004). Open-Burning of Waste. Discussion Paper. Fifth Authors/Experts Meeting : Waste, 2-4 November 2004, Ottawa, Canada, in the Preparation of the 2006 IPCC National Greenhouse Gas Inventories Guidelines. Hoornweg, D. T. L. (1999). What A Waste: Solid Waste Management in Asia, The International Bank for Reconstruction and Development, The World Bank, p 42. INE/SMARN. (2000). Inventario Nacional de Emisiones de Gases de Invernadero 1994-1998. Ciudad de Mexico, Octubre 2000. 461 p. IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander B.A. (Eds), Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

Jager, D. de and Blok, K. (1993). Koolstofbalans van het avfalsysteem in Nederland [Carbon balance of the waste management system in the Netherlands]. For: Rijksinstituut vor Volksgezondheid en Mileuhygiene RIVM. Ecofys, Utrecht [In Dutch]. JESC (2001). Fact Book: Waste Management & Recycling in JAPAN, Japan Environmental Sanitation Center, Kanagawa. JICA (1991). Estudio sobre el Manejo de los Desechos Sólidos en el Area Metropolitana de la Ciudad de Guatemala. Volumen 1. Agencia Japonesa de Cooperación Internacional. Latvian Environment Agency (2004). Economy-wide Natural Resources Flow Assessment (in Latvian: Resursu patēriņa novērtējums), pages 84-85, The Ministry of the Environment of the Republic of Latvia, Riga. ISBN (in English) 9984-9557-6-1 (URL: http://www.lvgma.gov.lv/produkti/rpn2004/MFA.pdf) López, C., et al. (2002). República de Cuba. Inventario Nacional de Emisiones y Absorciones de Gases de Invernadero (colectivo de autores). Reporte para el Año 1996/Actualización para los Años 1990 y 1994. CD-ROM Vol. 01. Instituto de Meteorología-AMA-CITMA. La Habana, 320 pp. ISBN: 959-02-0352-3. López, C. (2006). Personal Communication. MAG/SSERNMA/DOA – PNUD/UNITAR (1999). Paraguay: Inventario Nacional de Gases de Efecto Invernadero por Fuentes y Sumideros. Año 1990. Proyecto PAR GLO/95/G31. Asunción, Noviembre 1999, 90 pp Milleubalans (2005). Milleu en Natuur Planbureau. ISBN 90-6969-120-6. Ministerio de Desarrollo Social y Medio Ambiente/Secretaría de Desarrollo Sustentable y Política Ambiental (1999). Inventario de Emisiones de Gases de Efecto Invernadero de la República Argentina. Año 1997. Manejo de Residuos. Buenos Aires, Octubre 1999, p 146. Ministry of Environment, Japan (1992-2003). Waste of Japan, URL: http://www.env.go.jp/recycle/waste/ippan.html Ministry of Environment, Korea (1998). '97 National Status of Solid Waste Generation and Treatment’, Korea. URL: http://www.me.go.kr/ (in Korea) Ministry of Environment, Korea (1997). '96 National Status of Solid Waste Generation and Treatment’, Korea. URL: http://www.me.go.kr/ (in Korea) Ministry of Environment, Korea (1990). Korea Environmental Yearbook, Korea. URL: http://www.me.go.kr/ (in Korea) Ministry of Science and Technology, Brazil (2002). First Brazilian Inventory of Anthropogenic Greenhouse Gas Emissions. Background Reports. Methane Emissions from Waste Treatment and Disposal. CETESB. 1990 and 1994, Brazília, DF, 85 pp. Monreal, J. C. (1998). Gestión de Residuos Sólidos en América Latina y el Caribe. OEA. Programa Interamericano de Cooperación en Tecnologías Ambientales en Sectores Claves de la Industria. URL: http://www.idrc/industry/brazil_s9htlm. National Environmental Agency, Singapore (2001). URL: www.nea.gov.sg, and www.acrr.org/resourcecities/waste_resources/europe_waste.htm OECD (2002). OECD Environmental Data. Waste. Compendium 2002. Environmental Performance and Information Division, Environment Directorate, Organization for Economic Cooperation and Development (OECD), Working Group on Environmental Information and Outlooks. 27 p. URL: http://www.oecd.org OPS/OMS (1997). Análisis Sectorial de Residuos Sólidos en Cuba. Serie Análisis 1. Sectoriales No. 13, Organización Panamericana de la Salud, 206 pp., 2. Pipatti, R., Hänninen, K., Vesterinen, R., Wihersaari, M. and Savolainen, I. (1996). Impact of waste management alternative on greenhouse gas emissions, Espoo, VTT Julkaisuja - Publikationer. 85 p. (In Finnish) Rose, K. and Steinbüchel, A. (2005). ‘Biodegradation of natural rubber and related compounds: recent insights into a hardly understood catabolic capability of microorganisms’, Applied and Environmental Microbiology, June 2005, 2803-2812. Shimura, S., Yokota, I. and Nitta, Y. (2001). Research for MSW Flow Analysis in Developing Nations. J. Mater cycles waste manag., 3, p. 48-59 Statistics Finland (2005). Environmental Statistics. Environment and Natural Resources. 2005:2, Helsinki, 208 p.

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Chapter 2: Waste Generation, Composition, and Management Data

Tsuchii, A., Suzuki, T. and Takeda, K. (1985). ‘Microbial degradation of natural rubber vulacnizates’, Applied and Environmental Microbiology, Oct. 1985, p. 965-970. UNFCCC Secretariat (2000). Working paper No.3 (g), Expert report, prepared for the UNFCCC secretariat, 20 February 2000. U.S.EPA (1997). Evaluation of Emissions from the Open Burning of Household Waste in Barrels, Volume1, Technical Report, United States Environmental Protection Agency (U.S. EPA), Control Technology Center. U.S.EPA (2002). Solid Waste Management and Greenhouse Gases, 2nd Ed, United States Environmental Protection Agency (U.S. EPA), EPA530-R-02-006. Vishwanathan,C. and Trakler, J. (2003a). ‘Municipal solid waste management in Asia’, ARPPET Report, Asian Institute of Technology. Vishwanathan,C. and Trakler, J. (2003b). Municipal solid waste management in Asia: A comparative analysis. In Proc. of the workshop on Sustainable landfill management, 3-5 Dec. 2003, Anna University, p 5 & 40. Würdinger, E., et al. (1997) Studie über die energetische Nutzung der Biomasseanteile in Abfällen [Study on the energy recovery of the biomass fraction in waste]. For: Bayerisches Staatsministerium für Landesentwicklung und Umweltfragen. Bayerisches Institut für Abfallforschung (BifA), Würdinger, E., Wagner, J., Tränkler, J., Rommel, W. Augsburg 1997 (In German). Yamada, M., Ishigaki, T., Tachio, K. and Inue, Y. (2003). Carbon flow and landfill methane emissions in Japanese waste stream. Sardinia 2003, Nineth International Waste Management and Landfill Symposium, Cagliari, Italy. Zeschmar-Lahl, B. (2002). Die Klimarelevanz der Abfallwirtschaft im Freistaat Sachsen [The relevance of climate change for waste management in the federal state of Saxonia]. For: Sächsisches Ministerium für Umwelt und Landwirtschaft. BZL, Oyten 2002 (In German).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Chapter 3: Solid Waste Disposal

CHAPTER 3

SOLID WASTE DISPOSAL

2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.1

Volume 5: Waste

Authors Riitta Pipatti (Finland), Per Svardal (Norway) Joao Wagner Silva Alves (Brazil), Qingxian Gao (China), Carlos López Cabrera (Cuba), Katarina Mareckova (Slovakia), Hans Oonk (Netherlands), Elizabeth Scheehle (USA), Chhemendra Sharma (India), Alison Smith (UK), and Masato Yamada (Japan)

Contributing Authors Jeffrey B. Coburn (USA), Kim Pingoud (Finland), Gunnar Thorsen (Norway), and Fabian Wagner (Germany)

3.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Solid Waste Disposal

Contents 3

Solid Waste Disposal 3.1

Introduction ......................................................................................................................................... 3.6

3.2

Methodological issues ......................................................................................................................... 3.6

3.2.1

Choice of method ........................................................................................................................ 3.6

3.2.2

Choice of activity data ............................................................................................................... 3.12

3.2.3

Choice of emission factors and parameters ............................................................................... 3.13

3.3

Use of measurement in the estimation of CH4 emissions from SWDS ............................................. 3.20

3.4

Carbon stored in SWDS .................................................................................................................... 3.23

3.5

Completeness .................................................................................................................................... 3.23

3.6

Developing a consistent time series .................................................................................................. 3.24

3.7

Uncertainty assessment ..................................................................................................................... 3.24

3.7.1

Uncertainty attributable to the method ...................................................................................... 3.24

3.7.2

Uncertainty attributable to data ................................................................................................. 3.25

3.8

QA/QC, Reporting and Documentation ............................................................................................ 3.28

References ..................................................................................................................................................... 3.29 Annex 3A.1

First Order Decay Model ........................................................................................................... 3.32

References ..................................................................................................................................................... 3.40

2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.3

Volume 5: Waste

Equations Equation 3.1

CH4 emission from SWDS .................................................................................................. 3.8

Equation 3.2

Decomposable DOC from waste disposal data .................................................................... 3.9

Equation 3.3

Transformation from DDOCm to Lo ................................................................................... 3.9

Equation 3.4

DDOCm accumulated in the SWDS at the end of year T .................................................... 3.9

Equation 3.5

DDOCm decomposed at the end of year T .......................................................................... 3.9

Equation 3.6

CH4 generated from decayed DDOCm .............................................................................. 3.10

Equation 3.7

Estimates DOC using default carbon content values ......................................................... 3.13

Equation 3A1.1

Differential equation for first order decay ...................................................................... 3.32

Equation 3A1.2

First order decay equation .............................................................................................. 3.32

Equation 3A1.3

DDOCm remaining after 1 year of decay ...................................................................... 3.32

Equation 3A1.4

DDOCm decomposed after 1 year of decay ................................................................... 3.33

Equation 3A1.5

DDOCm decomposed in year T ..................................................................................... 3.33

Equation 3A1.6

Relationship between half-life and reaction rate constant .............................................. 3.33

Equation 3A1.7

FOD equation for decay commencing after 3 months .................................................... 3.33

Equation 3A1.8

DDOCm decomposed in year of disposal (3 month delay) ............................................ 3.33

Equation 3A1.9

DDOCm dissimilated in year (t) (3 month delay) .......................................................... 3.33

Equation 3A1.10 Mass of degradable organic carbon accumulated at the end of year T ........................... 3.34 Equation 3A1.11 Mass of degradable organic carbon decomposed in year T ............................................ 3.34 Equation 3A1.12 DDOCm remaining at end of year of disposal ............................................................... 3.35 Equation 3A1.13 DDOCm decomposed during year of disposal ............................................................... 3.35 Equation 3A1.14 DDOCm accumulated at the end of year T .................................................................... 3.36 Equation 3A1.15 DDOCm decomposed in year T ..................................................................................... 3.36 Equation 3A1.16 Calculation of decomposable DOCm from waste disposal data .................................... 3.36 Equation 3A1.17 CH4 generated from decomposed DDOCm ................................................................... 3.36 Equation 3A1.18 CH4 emitted from SWDS ............................................................................................... 3.37 Equation 3A1.19 Calculation of long-term stored DOCm from waste disposal data ................................. 3.37 Equation 3A1.20 First order rate of reaction equation ............................................................................... 3.38 Equation 3A1.21 IPCC 1996 Guidelines equation for DOC reacting in year T ......................................... 3.38 Equation 3A1.22 IPCC 2000GPG FOD equation for DDOCm reacting in year T .................................... 3.39 Equation 3A1.23 FOD with disposal rate D(t) ........................................................................................... 3.39 Equation 3A1.24 Degradable organic carbon accumulated during a year .................................................. 3.40 Equation 3A1.25 CH4 generated during a year .......................................................................................... 3.40

3.4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Solid Waste Disposal

Figures Figure 3.1

Decision Tree for CH4 emissions from Solid Waste Disposal Sites .................................... 3.7

Figure 3A1.1

Error introduced by not fully integrating the rate of reaction curve .................................. 3.38

Figure 3A1.2

Effect of error in the GPG2000 equation ........................................................................... 3.39

Tables Table 3.1

SWDS classification and Methane Correction Factors (MCF) .......................................... 3.14

Table 3.2

Oxidation factor (OX) for SWDS ...................................................................................... 3.15

Table 3.3

Recommended default methane generation rate (k) values under Tier 1 ........................... 3.17

Table 3.4

Recommended default Half-life (t1/2) values (yr) under Tier 1 .......................................... 3.18

Table 3.5

Estimates of uncertainties associated with the default activity data and parameters in the FOD method for CH4 emissions from SWDS ......................................................... 3.27

Table 3A1.1

New FOD calculating method ........................................................................................... 3.35

Boxes Box 3.1

Direct measurements from gas collection systems to estimate FOD model parameters ... 3.20

Box 3.2

Direct measurements of methane emissions from the SWDS surface ............................... 3.22

2006 IPCC Guidelines for National Greenhouse Gas Inventories

3.5

Volume 5: Waste

3 SOLID WASTE DISPOSAL 3.1

INTRODUCTION

Treatment and disposal of municipal, industrial and other solid waste produces significant amounts of methane (CH4). In addition to CH4, solid waste disposal sites (SWDS) also produce biogenic carbon dioxide (CO2) and non-methane volatile organic compounds (NMVOCs) as well as smaller amounts of nitrous oxide (N2O), nitrogen oxides (NOx) and carbon monoxide (CO). CH4 produced at SWDS contributes approximately 3 to 4 percent to the annual global anthropogenic greenhouse gas emissions (IPCC, 2001). In many industrialised countries, waste management has changed much over the last decade. Waste minimisation and recycling/reuse policies have been introduced to reduce the amount of waste generated, and increasingly, alternative waste management practices to solid waste disposal on land have been implemented to reduce the environmental impacts of waste management. Also, landfill gas recovery has become more common as a measure to reduce CH4 emissions from SWDS. Decomposition of organic material derived from biomass sources (e.g., crops, wood) is the primary source of CO2 released from waste. These CO2 emissions are not included in national totals, because the carbon is of biogenic origin and net emissions are accounted for under the AFOLU Sector. Methodologies for NMVOCs, NOx and CO are covered in guidelines under other conventions such as the UNECE Convention on Long Range Transboundary Air Pollution (CLRTAP). Links to these methodologies are provided in Chapter 1 of this volume, and additional information in Chapter 7 of Volume 1. No methodology is provided for N2O emissions from SWDS because they are not significant. The Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 Guidelines, IPCC, 1997) and the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (GPG2000, IPCC, 2000) described two methods for estimating CH4 emissions from SWDS: the mass balance method (Tier 1) and the First Order Decay (FOD) method (Tier 2). In this Volume, the use of the mass balance method is strongly discouraged as it produces results that are not comparable with the FOD method which produces more accurate estimates of annual emissions. In place of the mass balance method, this chapter provides a Tier 1 version of the FOD method including a simple spreadsheet model with step-by-step guidance and improved default data. With this guidance, all countries should be able to implement the FOD method.

3.2

METHODOLOGICAL ISSUES

3.2.1

Choice of method

The IPCC methodology for estimating CH4 emissions from SWDS is based on the First Order Decay (FOD) method. This method assumes that the degradable organic component (degradable organic carbon, DOC) in waste decays slowly throughout a few decades, during which CH4 and CO2 are formed. If conditions are constant, the rate of CH4 production depends solely on the amount of carbon remaining in the waste. As a result emissions of CH4 from waste deposited in a disposal site are highest in the first few years after deposition, then gradually decline as the degradable carbon in the waste is consumed by the bacteria responsible for the decay. Transformation of degradable material in the SWDS to CH4 and CO2 is by a chain of reactions and parallel reactions. A full model is likely to be very complex and vary with the conditions in the SWDS. However, laboratory and field observations on CH4 generation data suggest that the overall decomposition process can be approximated by first order kinetics (e.g., Hoeks, 1983), and this has been widely accepted. IPCC has therefore adopted the relatively simple FOD model as basis for the estimation of CH4 emissions from SWDS. Half-lives for different types of waste vary from a few years to several decades or longer. The FOD method requires data to be collected or estimated for historical disposals of waste over a time period of 3 to 5 half-lives in order to achieve an acceptably accurate result. It is therefore good practice to use disposal data for at least 50 years as this time frame provides an acceptably accurate result for most typical disposal practices and conditions. If a shorter time frame is chosen, the inventory compiler should demonstrate that there will be no significant underestimation of the emissions. These Guidelines provide guidance on how to estimate historical waste disposal data (Section 3.2.2, Choice of Activity Data), default values for all the parameters of the FOD model

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(Section 3.2.3, Choice of Emission Factors and Parameters), and a simple spreadsheet model to assist countries in using the FOD method. Three tiers to estimate the CH4 emissions from SWDS are described: Tier 1: The estimations of the Tier 1 methods are based on the IPCC FOD method using mainly default activity data and default parameters. Tier 2: Tier 2 methods use the IPCC FOD method and some default parameters, but require good quality country-specific activity data on current and historical waste disposal at SWDS. Historical waste disposal data for 10 years or more should be based on country-specific statistics, surveys or other similar sources. Data are needed on amounts disposed at the SWDS. Tier 3: Tier 3 methods are based on the use of good quality country-specific activity data (see Tier 2) and the use of either the FOD method with (1) nationally developed key parameters, or (2) measurement derived country-specific parameters. The inventory compiler may use country-specific methods that are of equal or higher quality to the above defined FOD-based Tier 3 method. Key parameters should include the half-life, and either methane generation potential (Lo) or DOC content in waste and the fraction of DOC which decomposes (DOCf ). These parameters can be based on measurements as described in Box 3.1. A decision tree for choosing the most appropriate method appears in Figure 3.1. It is good practice for all countries to use the FOD method or a validated country-specific method, in order to account for time dependence of the emissions. The FOD method is briefly described in Section 3.2.1.1 and in more detail in Annex 3A.1. A spreadsheet model has been developed by the IPCC to assist countries in implementing the FOD: IPCC Spreadsheet for Estimating Methane Emissions from Solid Waste Disposal Sites (IPCC Waste Model) 1.The IPCC Waste Model is described in more detail below and can be modified and used for all tiers. Decision Tree for CH 4 emissions from Solid Waste Disposal Sites

Figure 3.1

Start

Are good quality country-specific activity data on historical and current waste disposal1 available?

No

Are country-specific models or key parameters2 available?

Yes

Collect current waste disposal data and estimate historical data using guidance in Section 3.2.2.

Is solid waste disposal on land a key category 3?

Yes

Estimate emissions using country-specific methods or IPCC FOD method with country-specific key parameters and good quality country-specific activity data. Box 3: Tier 3

No

Yes

Estimate emissions using the IPCC FOD method with default parameters and good quality country-specific activity data. Box 2: Tier 2

No

Estimate Emissions using the IPCC FOD method with default data to fill in missing country-specific data. Box 1: Tier 1

Note: 1. Good quality country-specific activity data mean country-specific data on waste disposed in SWDS for 10 years or more. 2. Key parameters mean DOC/Lo, DOCf and half-life time. 3. See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 1

See the attached spreadsheets in Excel format. .

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3.2.1.1

F IRST O RDER D ECAY (FOD)

METHANE EMISSIONS The CH4 emissions from solid waste disposal for a single year can be estimated using Equations 3.1. CH4 is generated as a result of degradation of organic material under anaerobic conditions. Part of the CH4 generated is oxidised in the cover of the SWDS, or can be recovered for energy or flaring. The CH4 actually emitted from the SWDS will hence be smaller than the amount generated. EQUATION 3.1 CH4 EMISSION FROM SWDS

⎤ ⎡ CH 4 Emissions = ⎢∑ CH 4 generated x ,T − RT ⎥ • ( 1 − OX T ) ⎦ ⎣x Where: CH4 Emissions

=

CH4 emitted in year T, Gg

T

=

inventory year

x

=

waste category or type/material

RT

=

recovered CH4 in year T, Gg

OXT

=

oxidation factor in year T, (fraction)

The CH4 recovered must be subtracted from the amount CH4 generated. Only the fraction of CH4 that is not recovered will be subject to oxidation in the SWDS cover layer.

METHANE GENERATION The CH4 generation potential of the waste that is disposed in a certain year will decrease gradually throughout the following decades. In this process, the release of CH4 from this specific amount of waste decreases gradually. The FOD model is built on an exponential factor that describes the fraction of degradable material which each year is degraded into CH4 and CO2. One key input in the model is the amount of degradable organic matter (DOCm) in waste disposed into SWDS. This is estimated based on information on disposal of different waste categories (municipal solid waste (MSW), sludge, industrial and other waste) and the different waste types/material (food, paper, wood, textiles, etc.) included in these categories, or alternatively as mean DOC in bulk waste disposed. Information is also needed on the types of SWDS in the country and the parameters described in Section 3.2.3. For Tier 1, default regional activity data and default IPCC parameters can be used and these are included in the spreadsheet model. Tiers 2 and 3 require country-specific activity data and/or country-specific parameters. The equations for estimating the CH4 generation are given below. As the mathematics are the same for estimating the CH4 emissions from all waste categories/waste types/materials, no indexing referring to the different categories/waste materials/types is used in the equations below. The CH4 potential that is generated throughout the years can be estimated on the basis of the amounts and composition of the waste disposed into SWDS and the waste management practices at the disposal sites. The basis for the calculation is the amount of Decomposable Degradable Organic Carbon (DDOCm) as defined in Equation 3.2. DDOCm is the part of the organic carbon that will degrade under the anaerobic conditions in SWDS. It is used in the equations and spreadsheet models as DDOCm. The index m is used for mass. DDOCm equals the product of the waste amount (W), the fraction of degradable organic carbon in the waste (DOC), the fraction of the degradable organic carbon that decomposes under anaerobic conditions (DOCf), and the part of the waste that will decompose under aerobic conditions (prior to the conditions becoming anaerobic) in the SWDS, which is interpreted with the methane correction factor (MCF).

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EQUATION 3.2 DECOMPOSABLE DOC FROM WASTE DISPOSAL DATA DDOCm = W • DOC • DOC f • MCF

Where: DDOCm =

mass of decomposable DOC deposited, Gg

W

=

mass of waste deposited, Gg

DOC

=

degradable organic carbon in the year of deposition, fraction, Gg C/Gg waste

DOCf

=

fraction of DOC that can decompose (fraction)

MCF

=

CH4 correction factor for aerobic decomposition in the year of deposition (fraction)

Although CH4 generation potential (Lo) 2 is not used explicitly in these Guidelines, it equals the product of DDOCm, the CH4 concentration in the gas (F) and the molecular weight ratio of CH4 and C (16/12). EQUATION 3.3 TRANSFORMATION FROM DDOCm TO LO Lo = DDOCm • F • 16 / 12

Where: Lo

=

CH4 generation potential, Gg CH4

DDOCm =

mass of decomposable DOC, Gg

F

=

fraction of CH4 in generated landfill gas (volume fraction)

16/12

=

molecular weight ratio CH4/C (ratio)

Using DDOCma (DDOCm accumulated in the SWDS) from the spreadsheets, the above equation can be used to calculate the total CH4 generation potential of the waste remaining in the SWDS.

FIRST ORDER DECAY BASICS With a first order reaction, the amount of product is always proportional to the amount of reactive material. This means that the year in which the waste material was deposited in the SWDS is irrelevant to the amount of CH4 generated each year. It is only the total mass of decomposing material currently in the site that matters. This also means that when we know the amount of decomposing material in the SWDS at the start of the year, every year can be regarded as year number 1 in the estimation method, and the basic first order calculations can be done by these two simple equations, with the decay reaction beginning on the 1st of January the year after deposition.

(

)

EQUATION 3.4 DDOCm ACCUMULATED IN THE SWDS AT THE END OF YEAR T DDOCmaT = DDOCmd T + DDOCmaT −1 • e − k

(

)

EQUATION 3.5 DDOCm DECOMPOSED AT THE END OF YEAR T DDOCm decompT = DDOCmaT −1 • 1 − e − k

2

In the 2006 Guidelines Lo (Gg CH4 generated) is estimated from the amount of decomposable DOC in the SWDS. The equation in GPG2000 is different as Lo is estimated as Gg CH4 per Gg waste disposed, and the emissions are obtained by multiplying with the mass disposed.

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Where: T

=

inventory year

DDOCmaT

=

DDOCm accumulated in the SWDS at the end of year T, Gg

DDOCmaT-1

=

DDOCm accumulated in the SWDS at the end of year (T-1), Gg

DDOCmdT

=

DDOCm deposited into the SWDS in year T, Gg

DDOCm decompT =

DDOCm decomposed in the SWDS in year T, Gg

k

=

reaction constant, k = ln(2)/t1/2 (y-1)

t1/2

=

half-life time (y)

The method can be adjusted for reaction start dates earlier than 1st of January in the year after deposition. Equations and explanations can be found in Annex 3A.1.

CH 4 generated from decomposable DDOCm The amount of CH4 formed from decomposable material is found by multiplying the CH4 fraction in generated landfill gas and the CH4 /C molecular weight ratio. EQUATION 3.6 CH4 GENERATED FROM DECAYED DDOCm

CH 4 generatedT = DDOCm decompT • F • 16 / 12

Where: CH4 generatedT

=

amount of CH4 generated from decomposable material

DDOCm decompT =

DDOCm decomposed in year T, Gg

F

fraction of CH4, by volume, in generated landfill gas (fraction)

16/12

= =

molecular weight ratio CH4/C (ratio)

Further background details on the FOD, and an explanation of differences with the approaches in previous versions of the guidance (IPCC, 1997; IPCC, 2000), are given in Annex 3A.1.

SIMPLE FOD SPREADSHEET MODEL The simple FOD spreadsheet model (IPCC Waste Model) has been developed on the basis of Equations 3.4 and 3.5 shown above. The spreadsheet keeps a running total of the amount of decomposable DOC in the disposal site, taking account of the amount deposited each year and the amount remaining from previous years. This is used to calculate the amount of DOC decomposing to CH4 and CO2 each year. The spreadsheet also allows users to define a time delay between deposition of the waste and the start of CH4 generation. This represents the time taken for substantial CH4 to be generated from the disposed waste (see Section 3.2.3 and Annex 3A.1). The model then calculates the amount of CH4 generated from the DDOCm, and subtracts the CH4 recovered and CH4 oxidised in the cover material (see Annex 3A.1 for equations) to give the amount of CH4 emitted. The IPCC Waste Model provides two options for the estimation of the emissions from MSW, that can be chosen depending on the available activity data. The first option is a multi-phase model based on waste composition data. The amounts of each type of degradable waste material (food, garden and park waste 3 , paper and cardboard, wood, textiles, etc.) in MSW are entered separately. The second option is single-phase model based on bulk waste (MSW). Emissions from industrial waste and sludge are estimated in a similar way as for bulk MSW. Countries that choose to use the spreadsheet model may use either the waste composition or the bulk waste option, depending on the level of data available. When waste composition is relatively stable, both options give similar results. However when rapid changes in waste composition occur, options might give different

3

‘garden waste’ may also be called ‘yard waste’ in US English.

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outputs. For example, changes in waste management, such as bans to dispose food waste or degradable organic materials, can result in rapid changes in the composition of waste disposed in SWDS. Both options can be used for estimating the carbon in harvested wood products (HWP) that is long-term stored in SWDS (see Volume 4, Chapter 12, Harvested Wood Products). If no national data are available on bulk waste, it is good practice to use the waste composition option in the spreadsheets, using the provided IPCC default data for waste composition. In the spreadsheet model, separate values for DOC and the decay half-life may be entered for each waste category and in the waste composition option also for each waste type/material. The decay half-life can also be assumed to be the same for all waste categories and/or waste types. The first approach assumes that decomposition of different waste types/materials in a SWDS is completely independent of each other; the second approach assumes that decomposition of all types of waste is completely dependent on each other. At the time of writing these Guidelines, no evidence exists that one approach is better than the other (see Section 3.2.3, Halflife). The spreadsheet calculates the amount of CH4 generated from each waste component on a different worksheet. The methane correction factor (MCF – see Section 3.2.3) is entered as a weighted average for all disposal sites in the country. MCF may vary by time to take account of changes in waste management practices (such as a move towards more managed SWDS or deeper sites). Finally, the amount of CH4 generated from each waste category and type/material is summed, and the amounts of CH4 recovered and oxidised in the cover material are subtracted (if applicable), to give an estimate of total CH4 emissions. For the bulk waste option, DOC can be a weighted average for MSW. The spreadsheet model is most useful to Tier 1 methods, but can be adapted for use with all tiers. For Tier 1 the spreadsheets can estimate the activity data from population data and disposal data per capita (for MSW) and GDP (industrial waste), see Section 3.2.2 for additional guidance. When Tier 2 and 3 approaches are used, countries can extend the spreadsheet model to meet their own demands, or create their own models. The spreadsheet model can be extended with more sheets to calculate the CH4 emissions if needed. MCF, OX and DOC for bulk waste can be made to vary over time. The same can easily be done to other parameters like DOCf. New half-lives will require new CH4 calculating sheets. Countries with good data on industrial waste can add new CH4 calculating sheets and calculate the CH4 emissions separately for different types of industrial waste. When the spreadsheet model is modified or countries-specific models are used, key assumptions and parameters should be transparently documented. Details on how to use the spreadsheet model can be found in the Instructions spreadsheet. The model can be copied from the 2006 Guidelines CDROM or downloaded from the IPCC NGGIP website < http://www.ipcc-nggip.iges.or.jp/ >.

Modelling different geographical or climate regions It is possible to estimate CH4 generation in different geographical regions of the country. For example, if the country contains a hot and wet region and a hot and dry region, the decay rates will be different in each region.

Dealing with different waste categories Some users may find that their national waste statistics do not match the categories used in the model (food, garden and park waste, paper and cardboard, textiles and others as well as industrial waste). Where this is the case, the spreadsheet model will need to be modified to correspond to categorisation used by the country, or country-specific waste types will need to be re-classified into the IPCC categories. For example, clothes, curtain, and rugs are included in textiles, kitchen waste is similar to food waste, and straw and bamboo are similar to wood. The national statistics may contain a category called street sweepings. The user should estimate the composition of this waste. For example, it may be 50 percent inert material, 10 percent food, 30 percent paper and 10 percent garden and park waste. The street sweepings category can then be divided into these IPCC categories and added on to the waste already in these categories. In a similar manner, furniture can be divided into wood, plastic or metal waste, and electronics to metal, plastic and glass waste. This can all be done in a separate worksheet set up by the inventory compiler.

Adjusting waste composition at generation to waste composition at SWDS The user should establish whether national waste composition statistics refer to the composition of waste generated or waste received at SWDS. The default waste composition statistics presented here are the composition of waste generated, not waste sent to SWDS. The composition should therefore be adjusted if necessary to take account of the impact of recycling or composting activities on the composition of the waste sent to SWDS. This could be best done in a separate spreadsheet set up by the inventory compiler, to estimate

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the amounts of each waste material generated, then subtract estimates of the amount of each waste material recycled, incinerated or composted, and work out the new composition of the residual waste sent to SWDS.

Open burning of waste at SWDS Open burning at SWDS is common in many developing countries. The amount of waste (and DDOCm) available for decay at SWDS should be adjusted to the amount burned. Chapter 5 provides methods how to estimate the amount of waste burned. The estimation of emissions from SWDS should be consistent with estimates for open burning of waste at the disposal sites.

3.2.2

Choice of activity data

Activity data consist of the waste generation for bulk waste or by waste component and the fraction of waste disposed to SWDS. Waste generation is the product of the per capita waste generation rate (tonnes/capita/yr) for each component and population (capita). Chapter 2 gives guidance on the collection of data on waste generation and waste composition as well as waste management practices. Regional default values for MSW can be found in Table 2.1 for the generation rate and the fraction disposed in SWDS, and Table 2.3 for the waste composition. For industrial waste default data can be found in Table 2.2. To achieve accurate emission estimates in national inventories it is usually necessary to include data on solid waste disposal (amount, composition) for 3 to 5 half-lives (see Section 3.2.3) of the waste deposited at the SWDS, and specifications of different half-lives for different components of the waste stream or for bulk waste by SWDS type (IPCC, 2000). Changes in waste management practices (e.g., site covering/capping, leachate drainage improvement, compacting, and prohibition of hazardous waste disposal together with MSW) should also be taken into account when compiling historical data. The FOD methods require data on solid waste disposal (amounts and composition) that are collected by default for 50 years. Countries that do not have historical statistical data, or equivalent data on solid waste disposal that go back for the whole period of 50 years or more will need to estimate these data using surrogates (extrapolation with population, economic or other drivers). The choice of the method will depend on the availability of data in the country. For countries using default data on MSW disposal on land, or for countries whose own data do not cover the past 50 years, the missing historical data can be estimated to be proportional to urban population4 (or total population when historical data on urban population are not available, or in cases where waste collection covers the whole population). For countries having national data on MSW generation, management practices and composition over a period of years (Tier 2 FOD), analyses on the drivers for solid waste disposal are encouraged. The historical data could be proportional to economic indicators, or combinations of population and economic indicators. Trend extrapolation could also produce good results. Waste management policies to reduce waste generation and to promote alternatives to solid waste disposal should be taken into account in the analyses. Data on industrial production (amount or value of production, preferably by industry type, depending on availability of data) are recommended as surrogate for the estimation of disposal of industrial waste (Tier 2). When production data are not available, historical disposal of industrial waste can be estimated proportional to GDP or other economic indicators. GDP is used as the driver in the Tier 1 method. Historical data on urban population (or total population), GDP (or other economic indicators) and statistics in industrial production can be obtained from national statistics. International databases can help when national data are not available, for example:

•

Population data (1950 onwards with five-year intervals) can be found in UN Statistics (see http://esa.un.org/unpp/).

•

GDP data (1970 onwards, annual data at current prices in national currency) can be found in UN Statistics (see http://unstats.un.org/unsd/snaama/selectionbasicFast.asp).

For those years data are not available interpolation or extrapolation can be used. Alternative methods have been put forward in literature and can be used when they can be shown to give better estimates than the above-mentioned default methods. The choice of method and surrogate, and the reasoning behind the choice, should be documented transparently in the inventory report. The use of surrogate methods, interpolation and extrapolation as means to derive missing data is described in more detail in Chapter 6, Time Series Consistency, in Volume 1.

4

The choice between urban population and total population should be guided by the coverage of waste collection. When data on coverage of waste collection is not available, the recommendation is to use urban population as the driver.

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3.2.3

Choice of emission factors and parameters

DEGRADABLE ORGANIC CARBON (DOC) Degradable organic carbon (DOC) is the organic carbon in waste that is accessible to biochemical decomposition, and should be expressed as Gg C per Gg waste. The DOC in bulk waste is estimated based on the composition of waste and can be calculated from a weighted average of the degradable carbon content of various components (waste types/material) of the waste stream. The following equation estimates DOC using default carbon content values:

EQUATION 3.7 ESTIMATES DOC USING DEFAULT CARBON CONTENT VALUES DOC = ∑ ( DOCi • Wi ) i

Where: DOC

=

fraction of degradable organic carbon in bulk waste, Gg C/Gg waste

DOCi

=

fraction of degradable organic carbon in waste type i e.g., the default value for paper is 0.4 (wet weight basis)

Wi

=

fraction of waste type i by waste category e.g., the default value for paper in MSW in Eastern Asia is 0.188 (wet weight basis)

The default DOC values for these fractions for MSW can be found in Table 2.4 and for industrial waste by industry in Table 2.5 in Chapter 2 of this Volume. A similar approach can be used to estimate the DOC content in total waste disposed in the country. In the spreadsheet model, the estimation of the DOC in MSW is needed only for the bulk waste option, and is the average DOC for the MSW disposed in the SWDS, including inert materials. The inert part of the waste (glass, plastics, metals and other non-degradable waste, see defaults in Table 2.3 in Chapter 2.) is important when estimating the total amount of DOC in MSW. Therefore it is advised not to use IPCC default waste composition data together with country-specific MSW disposal data, without checking that the inert part is close to the inert part in the IPCC default data. The use of country-specific values is encouraged if data are available. Country-specific values can be obtained by performing waste generation studies, sampling at SWDS combined with analysis of the degradable carbon content within the country. If national values are used, survey data and sampling results should be reported (see also Section 3.2.2 for activity data and Section 3.8 for reporting).

FRACTION OF DEGRADABLE ORGANIC CARBON WHICH DECOMPOSES (DOC f ) Fraction of degradable organic carbon which decomposes (DOCf ) is an estimate of the fraction of carbon that is ultimately degraded and released from SWDS, and reflects the fact that some degradable organic carbon does not degrade, or degrades very slowly, under anaerobic conditions in the SWDS . The recommended default value for DOCf is 0.5 (under the assumption that the SWDS environment is anaerobic and the DOC values include lignin, see Table 2.4 in Chapter 2 for default DOC values) (Oonk and Boom, 1995; Bogner and Matthews, 2003). DOCf value is dependent on many factors like temperature, moisture, pH, composition of waste, etc. National values for DOCf or values from similar countries can be used for DOCf, but they should be based on well-documented research. The amount of DOC leached from the SWDS is not considered in the estimation of DOCf. Generally the amounts of DOC lost with the leachate are less than 1 percent and can be neglected in the calculations5. Higher tier methodologies (Tier 2 or 3) can also use separate DOCf values defined for specific waste types. There is some literature giving information about anaerobic degradability (DOCf) for material types (Barlaz, 5

In countries with high precipitation rates the amount of DOC lost through leaching may be higher. In Japan, where the precipitation is high, SWDS with high penetration rate, have been found to leach significant amounts of DOC (sometimes more than 10 percent of the carbon in the SWDS) (Matsufuji et al., 1996).

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2004; Micales & Skog, 1997; US EPA, 2002; Gardner et al., 2002). The reported degradabilities especially for wood, vary over a wide range and is yet quite inconclusive. They may also vary with tree species. Separate DOCf values for specific waste types imply the assumption that degradation of different types of waste is independent of each other. As discussed further, below under Half-Life, scientific knowledge at the moment of writing these Guidelines is not yet conclusive on this aspect. Hence the use of waste type specific values for DOCf can introduce additional uncertainty to the estimates in cases where the data on waste composition are based on default values, modelling or estimates based on expert judgement. Therefore, it is good practice to use DOCf values specific to waste types only when waste composition data are based on representative sampling and analyses.

METHANE CORRECTION FACTOR (MCF) 6 Waste disposal practices vary in the control, placement of waste and management of the site. The CH4 correction factor (MCF) accounts for the fact that unmanaged SWDS produce less CH4 from a given amount of waste than anaerobic managed SWDS. In unmanaged SWDS, a larger fraction of waste decomposes aerobically in the top layer. In unmanaged SWDS with deep disposal and/or with high water table, the fraction of waste that degrades aerobically should be smaller than in shallow SWDS. Semi-aerobic managed SWDS are managed passively to introduce air to the waste layer to create a semi-aerobic environment within the SWDS. The MCF in relation to solid waste management is specific to that area and should be interpreted as the waste management correction factor that reflects the management aspect it encompasses. An MCF is assigned to each of four categories, as shown in Table 3.1. A default value is provided for countries where the quantity of waste disposed to each SWDS is not known. A country’s classification of its waste sites into managed or unmanaged may change over a number of years as national waste management policies are implemented. The Fraction of Solid Waste Disposed to Solid Waste Disposal Sites (SWF) and MCF reflect the way waste is managed and the effect of site structure and management practices on CH4 generation. The methodology requires countries to provide data or estimates of the quantity of waste that is disposed of to each of four categories of solid waste disposal sites (Table 3.1). Only if countries cannot categorise their SWDS into four categories of managed and unmanaged SWDS, the MCF for ‘uncategorised SWDS’ can be used.

TABLE 3.1 SWDS CLASSIFICATION AND METHANE CORRECTION FACTORS (MCF) Type of Site Managed – anaerobic

Methane Correction Factor (MCF) Default Values 1

1.0

Managed – semi-aerobic 2

0.5

Unmanaged 3 – deep ( >5 m waste) and /or high water table

0.8

4

Unmanaged – shallow ( 20°C)

Boreal and Temperate (MAT ≤ 20°C)

Type of Waste

Dry (MAP/PET < 1)

Wet (MAP/PET > 1)

Dry (MAP < 1000 mm)

Moist and Wet (MAP ≥ 1000 mm)

Default

Range2

Default

Range2

Default

Range2

Default

Range2

Paper/textiles waste

0.04

0.033,5 – 0.053,4

0.06

0.05 – 0.073,5

0.045

0.04 – 0.06

0.07

0.06 – 0.085

Wood/ straw waste

0.02

0.013,4 – 0.036,7

0.03

0.02 – 0.04

0.025

0.02 – 0.04

0.035

0.03 – 0.05

Moderately degrading waste

Other (non – food) organic putrescible/ Garden and park waste

0.05

0.04 – 0.06

0.1

0.06 – 0.18

0.065

0.05 – 0.08

0.17

0.15 – 0.2

Rapidly degrading waste

Food waste/Sewage sludge

0.06

0.05 – 0.08

0.1854

0.13,4 – 0.29

0.085

0.07 – 0.1

0.4

0.17 – 0.710

0.05

0.04 – 0.06

0.09

0.088 – 0.1

0.065

0.05 – 0.08

0.17

0.1511 – 0.2

Slowly degrading waste

Bulk Waste 1

The available information on the determination of k and half-lives in tropical conditions is quite limited. The values included in the table, for those conditions, are indicative and mostly have been derived from the assumptions described in the text and values obtained for temperate conditions.

2

The range refers to the minimum and maximum data reported in literature or estimated by the authors of the chapter. It is included, basically, to describe the uncertainty associated with the default value.

3

Oonk and Boom (1995).

4

IPCC (2000).

5

Brown et al. (1999). A near value (16 yr) was used, for slow degradability, in the GasSim model verification (Attenborough et al., 2002).

6

Environment Canada (2003).

7

In this range are reported longer half-lives values (up to 231 years) that were not included in the table since are derived from extremely low k values used in sites with mean daily temperature < 0ºC (Levelton, 1991).

8

Estimated from RIVM (2004).

9

Value used for rapid degradability, in the GasSim model verification (Attenborough et al., 2002);

10

Estimated from Jensen and Pipatti (2003).

11

Considering t1/2 = 4 - 7 yr as characteristic values for most developing countries in a tropical climate. High moisture conditions and higly degradable waste.

*Adapted from: Chapter 3 in GPG-LULUCF (IPCC, 2003). MAT – Mean annual temperature; MAP – Mean annual precipitation; PET – Potential evapotranspiration. MAP/PET is the ratio of MAP to PET. The average annual MAT, MAP and PET during the time series should be selected to estimate emissions and indicated by the nearest representative meteorological station.

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TABLE 3.4 RECOMMENDED DEFAULT HALF-LIFE (t1/2) VALUES (YR) UNDER TIER 1 (Derived from k values obtained in experimental measurements, calculated by models, or used in greenhouse gas inventories and other studies) Climate Zone* Tropical1 (MAT > 20°C)

Boreal and Temperate (MAT ≤ 20°C)

Type of Waste

Dry (MAP/PET < 1) Default

Range2

Wet (MAP/PET > 1)

Dry (MAP < 1000 mm)

Moist and Wet (MAP ≥ 1000 mm)

Default

Range2

Default

Range2

Default

Range2

3,5

Paper/textiles waste

17

14 – 233,4

12

10 – 143,5

15

12 – 17

10

8 – 12

Wood/ straw waste

35

233,4 – 696,7

23

17 – 35

28

17 – 35

20

14 – 23

Moderately degrading waste

Other (non – food) organic putrescible/ Garden and park waste

14

12 – 17

7

6 – 98

11

9 – 14

4

3–5

Rapidly degrading waste

Food waste/Sewage sludge

12

9 – 14

44

33,4 – 69

8

6 – 10

2

110 – 4

14

12 – 17

7

6 – 98

11

9 – 14

4

3 – 511

Slowly degrading waste

Bulk Waste 1

The available information on the determination of k and half-lives in tropical conditions is quite limited. The values included in the table, for those conditions, are indicative and mostly have been derived from the assumptions described in the text and values obtained for temperate conditions.

2

The range refers to the minimum and maximum data reported in literature or estimated by the authors of the chapter. It is included, basically, to describe the uncertainty associated with the default value.

3

Oonk and Boom (1995).

4

IPCC (2000).

5

Brown et al. (1999). A near value (16 yr) was used, for slow degradability, in the GasSim model verification (Attenborough et al., 2002).

6

Environment Canada (2003).

7

In this range are reported longer half-lives values (up to 231 years) that were not included in the table since are derived from extremely low k values used in sites with mean daily temperature < 0ºC (Levelton,1991).

8

Estimated from RIVM (2004).

9

Value used for rapid degradability, in the GasSim model verification (Attenborough et al., 2002).

10

Estimated from Jensen and Pipatti (2003).

11

Considering t1/2 = 4 - 7 yr as characteristic values for most developing countries in a tropical climate. High moisture conditions and higly degradable waste.

*Adapted from: Chapter 3 –GPG-LULUCF (IPCC, 2003). MAT – Mean annual temperature; MAP – Mean annual precipitation; PET – Potential evapotranspiration. MAP/PET is the ratio of MAP to PET. The average annual MAT, MAP and PET during the time series should be selected to estimate emissions and indicated by the nearest representative meteorological station.

METHANE RECOVERY (R) CH4 generated at SWDS can be recovered and combusted in a flare or energy device. The amount of CH4 which is recovered is expressed as R in Equation 3.1. If the recovered gas is used for energy, then the resulting greenhouse gas emissions should be reported under the Energy Sector. Emissions from flaring are however not significant, as the CO2 emissions are of biogenic origin and the CH4 and N2O emissions are very small, so good practice in the waste sector does not require their estimation. However, if it is wished to do so these emissions should be reported under the waste sector. A discussion of emissions from flares and more detailed information are given in Volume 2, Energy, Chapter 4.2. Emissions from flaring are not treated at Tier 1.

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The default value for CH4 recovery is zero. CH4 recovery should be reported only when references documenting the amount of CH4 recovery are available. Reporting based on metering of all gas recovered for energy and flaring, or reporting of gas recovery based on the monitoring of produced amount of electricity from the gas (considering the availability of load factors, heating value and corresponding heat rate, and other factors impacting the amount of gas used to produce the monitored amount of electricity) is consistent with good practice. Estimating the amount of CH4 recovered using more indirect methods should be done with great care, using substantiated assumptions. Indirect methods might be based on the number of SWDS in a country with CH4 collection or the total capacity of utilisation equipment or flaring capacity sold. When CH4 recovery is estimated on the basis of the number of SWDS with landfill gas recovery a default estimate of recovery efficiency would be 20 percent. This is suggested due to the many uncertainties in using this methodology. There have been some measurements of efficiencies at gas recovery projects, and reported efficiencies have been between 10 and 85 percent Oonk and Boom (1995) measured efficiencies at closed, unlined SWDS to be in between 10 and 80 percent, the average over 11 SWDS being 37 percent. More recently Scharff et al. (2003) measured efficiencies at four SWDS to be 9 percent, 50 percent, 55 percent and 33 percent. Spokas et al. (2006) and Diot et al. (2001) recently measured efficiencies above 90 percent. In general, high recovery efficiencies can be related to closed SWDS, with reduced gas fluxes, well-designed and operated recovery and thicker and less permeable covers. Low efficiencies can be related to SWDS with large parts still being in exploitation and with e.g., temporary sandy covers. Country-specific values may be used but significant research would need to be done to understand the impact on recovery of following parameters: cover type, percentage of SWDS covered by recovery project, presence of a liner, open or closed status, and other factors. When the amount of CH4 recovered is based on the total capacity of utilisation equipment or flares sold, an effort should be made in order to identify what part of this equipment is still operational. A conservative estimate of amount of CH4 generated could be based on an inventory of the minimum capacities of the operational utilisation equipment and flares. Another conservative approach is to estimate total recovery as 35 percent of the installed capacities. Based on Dutch and US studies (Oonk, 1993; Scheehle, 2006), recovered amounts varied from 35 to 70 percent of capacity rates. The reasons for the range included (i) running hours from 95 percent down to 80 percent, due to maintenance or technical problems; (ii) overestimated gas production and as result oversized equipment; (iii) back-up flares being largely inactive. The higher rates took these considerations already into account when estimating capacity. If a country uses this method for flaring, care must be taken to ensure that the flare is not a back-up flare for a gas-to-energy project. Flares should be matched to SWDS wherever possible to ensure that double counting does not occur. In all cases, the recovered amounts should be reported as CH4, not as landfill gas, as landfill gas contains only a fraction of CH4. The basis for the reporting should be clearly documented. When reporting is based on the number of SWDS with landfill gas recovery or the total capacity of utilisation equipment, it is essential that all assumptions used in the estimation of the recovery are clearly described and justified with country-specific data and references.

DELAY TIME In most solid waste disposal sites, waste is deposited continuously throughout the year, usually on a daily basis. However, there is evidence that production of CH4 does not begin immediately after deposition of the waste. At first, decomposition is aerobic, which may last for some weeks, until all readily available oxygen has been used up. This is followed by the acidification stage, with production of hydrogen. The acidification stage is often said to last for several months. After which there is a transition period from acidic to neutral conditions, when CH4 production starts. The period between deposition of the waste and full production of CH4 is chemically complex and involves successive microbial reactions. Time estimates for the delay time are uncertain, and will probably vary with waste composition and climatic conditions. Estimates of up to one year have been given in the literature (Gregory et al., 2003; Bergman, 1995; Kämpfer and Weissenfels, 2001; Barlaz, 2004). The IPCC provides a default value of six months for the time delay (IPCC, 1997). This is equivalent to a reaction start time of 1st of January in the year after deposition, when the average residence time of waste in the SWDS has been six months. However, the uncertainty of this assumption is at least 2 months. The IPCC Waste Model allows the user to change the default delay of six months to a different value. It is good practice to choose a delay time of between zero and six months. Values outside this range should be supported by evidence.

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3.3

USE OF MEASUREMENT IN THE ESTIMATION OF CH 4 EMISSIONS FROM SWDS

The FOD model and other methods for estimating CH4 generation at SWDS are constructed using scientific knowledge as well as assumptions on microbial metabolism under anaerobic conditions in the SWDS. As with all models, validation that includes some form of direct measurements to compare model predictions to actual measurements increases the user’s confidence in the model and can be used to refine and improve the model predictions. These measurements can also be used to validate a model by comparing model predictions to CH4 generation rates developed from measurements and to document the choice of country-specific values for parameters used in the model in preparing national inventories. Measurements can be measured amounts of gas recovered in the gas collection system (in combination with an estimate of the recovery efficiency), measured amounts of diffuse CH4 emissions to air and combinations of both. Several studies have used measurement data from gas collection systems to develop estimates of the parameters needed for the FOD model (such as the decay rate constant and CH4 generation potential) for specific SWDS, for classes of SWDS in specific regions, and for application to SWDS on a national basis (Oonk and Boom, 1995; Huitric et al., 1997; SWANA, 1998; SCS Engineers, 2003; U.S. EPA, 1998; U.S. EPA, 2005). The technique uses statistical procedures to develop best fit values for the model parameters, such as a nonlinear regression that evaluates model parameters in an iterative fashion to find the best estimate for the model parameters, based on the smallest sum of squared errors. With sufficient site-specific detail and an adequate large database of SWDS, the statistical analysis can identify the effects of variations in waste composition, geographical location, rainfall, and other factors on appropriate values for the model parameters. For example, several studies have found that the decay rate constant increases with precipitation (U.S. EPA, 2005). The use of direct measurements of extracted amounts of gas to estimate FOD model parameters is one option for the good practice of developing country-specific values. This technique was used to develop some of the default values for half-life presented in Table 3.4. It is applicable for those countries with accurate measurement data from landfill gas collection systems for a representative set of SWDS with well known amounts, composition and age-distribution of waste deposited. If site-specific CH4 collection data are used to estimate parameters for the FOD model for the national inventory, it is good practice to ensure that the SWDS used in the analysis are representative of all SWDS in the country in terms of the major factors that affect the values of the parameters and CH4 emissions. Additional details on this technique are provided in Box 3.1.

BOX 3.1 DIRECT MEASUREMENTS FROM GAS COLLECTION SYSTEMS TO ESTIMATE FOD MODEL PARAMETERS

The key element in developing estimates of the parameters for the FOD model is a representative database of landfills that has the following characteristics: (i)

Contain types of wastes representative of landfills nationwide,

(ii)

Include a range of sizes, waste age, and geographical regions (especially if the effect of precipitation is to be evaluated),

(iii)

Have site-specific measurements of the landfill gas (LFG) collection rate and percent CH4 that include seasonal variations over time (covering at least one year and preferably longer),

(iv)

Have site-specific measurements of annual waste acceptance rates or total waste in place and year the landfill opened (i.e., the waste in place or average annual acceptance rate for the area of the landfill under the influence of the collection system,

(v)

Include site-specific estimates of percent recovery (based on design and operational characteristics or other information), and

(vi)

Include annual average precipitation (if this effect is to be evaluated).

Accuracy of direct measurements of LFG flow rate, percent CH4, and annual waste disposal rates can be better than ±10 percent. The most significant source of error in using the direct measurement of CH4 collection rates to estimate CH4 generation rates is the determination of LFG collection efficiency. However, this error can be reduced and controlled if collection rate data are used only for landfills that are known or can be shown to have efficient and well-maintained collection systems and cover materials.

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BOX 3.1 (CONTINUED)

The use of a collection efficiency will need to be researched and justified in order to be used with confidence. Several factors must be considered, such as the type of final cover, surface monitoring conducted on a regular basis showing low levels or no detectable CH4, and a program of corrective action if CH4 is detected (e.g., performing maintenance to improve the integrity of the cover or increasing the vacuum of collection wells). The estimate of collection efficiency can be based on site-specific considerations and adjusted to the upper or lower end of the range after considering these factors. The overall error and effect on the final results would tend to be lower when averaged over a large database of landfills because the errors would tend to cancel when using an unbiased midrange estimate. Although surface measurements can be used to detect CH4 as noted above, the use of surface measurements at the landfill to directly estimate collection efficiency is only recommended when the limitations of methods are fully taken into account, as discussed in more detail in the following section that describe the difficulties and inaccuracies of such measurements. Effects to take into account when measuring collection efficiencies are (i) CH4 oxidation, that reduces the ratios of amount of CH4 emitted and (ii) solution of CO2 in the water phase in the waste or in the top-layer, when comparing the ratio of CH4 and CO2 emissions and CH4 and CO2 recovery. Once a representative database has been established, measurements and collection efficiencies are estimated, the measurement data can be analyzed to determine country or region specific parameters. If a country has good waste composition data by landfill, this information could be used together with measurements and modelling to deduce parameters such as DDOC. For a country with less reliable waste composition data, parameters may have to be estimated at a broader level, considering Lo and k instead of more waste type specific parameters. It is not recommended for a country to directly estimate national emissions from measurements. Using measurements to deduce national level parameters based on the characteristics of the landfills analysed is the preferred approach to incorporating measurement data from collection systems.

Direct measurements of CH4 emissions at the SWDS surface (rather than measuring CH4 collection or generation) at a specific SWDS can in principle be of similar value for validating the FOD model parameters and developing national inventory estimates. In practice there are however limitations for several reasons: (i)

Monitoring and measuring CH4 emissions at the SWDS’s surface is a demanding task, and there are no generally agreed or standardised methods available for routine or long-term monitoring because the emissions come from a large area and vary throughout the year.

(ii)

There are very few representative data available from direct measurements of CH4 emissions for individual SWDS, much less to give good estimates for national emission inventories. It is therefore at the moment considered good practice to use emission estimates from individual sites based on monitoring and measurements only if the representativeness of the monitoring can be justified. If site-specific emissions data are used to estimate national emissions, it is good practice to group all SWDS in the country according to their characteristics and to base the national estimate on representative emission behaviour in each group.

Atmospheric emissions measurement techniques, their difficulties, and other considerations are discussed in more detail in Box 3.2.

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BOX 3.2 DIRECT MEASUREMENTS OF METHANE EMISSIONS FROM THE SWDS SURFACE

Surface landfill gas (LFG) emissions are highly variable both spatially and temporally. Emissions vary on a daily basis as a result of changes in air-pressure and due to rainfall which affects the permeability of the top-layer. On top of that there is a seasonal variation in emissions as a result of reduced oxidation in winter. Additionally, emissions vary over the sections of the SWDS, due to differences in waste amounts, age and composition. Due to the high horizontal permeability, compared to vertical permeability, the slopes of a SWDS generally have higher emissions than the upper surface. On a more local scale, emissions are highly variable due to regions of reduced permeability in the subsurface and due to cracks in the surface. As a result, emissions at locations a few metres away from each other can vary over a factor 1000. Measurement of diffuse CH4 emissions in this context should give an indication of annual average emissions from the entire SWDS. So, temporal and seasonal fluctuation of gas emission (Maurice and Lagerkvist, 1997; Park and Shin, 2001) should be considered as part of the evaluation of sitespecific data. The data collection period should be sufficient to cover temporal variation at the site. Seasonal variation might be comparably easily taken into consideration. When performing measurements of diffuse emissions, one should realise that one measures the flux after oxidation, which can be a significant part of the percent of CH4 generated that is not recovered. Several techniques for direct measurement at the surface and/or below and above-ground have been proposed. The most important techniques are: (i)

Static or forced flux chamber measurements,

(ii)

Mass balance methods,

(iii)

Micrometeorological measurements,

(iv)

Plume measurements.

The flux chamber method has been widely applied to measure the CH4 flux on the SWDS surface (e.g., Park and Shin, 2001; Mosher et al., 1999; UK Environment Agency, 2004). A drawback of this method is the necessity of large number of measuring points in order to obtain reliable estimates of total emissions, which makes the method very labour intensive and thus expensive. There are a number of ways to improve the accuracy or reduce the number of measurements required, e.g., to expand the estimates from a smaller section to the whole SWDS through geostatistical methods (Börjesson et al., 2000; Spokas et al., 2003) or to identify the main emitting zones by observing cracks, stressed vegetation, interfaces between capped zone, edges and slope condition, etc. (UK Environment Agency, 2004), or to use portable gas-meter, olfaction or surface temperature as a first indicator (Yamada et al, 2005). In the mass-balance method emissions are obtained by measuring the flux through an imaginary vertical plane on the SWDS by interpreting of wind velocity and the CH4 concentrations at different heights over the SWDS surface. This plane can be both one-dimensional (Oonk and Boom, 1995; Scharff et al., 2003) or two-dimensional. The advantage of this method is that it is easily automated and can measure emissions from a large surface (in many case the whole SWDS) for longer period of times (weeks to months). Another advantage is that the both CH4 and CO2 emissions can be obtained which gives information on CH4 oxidation and collection efficiencies. The disadvantage of the method is its limited scope (250 m) which makes it hard to measure emissions from the largest SWDS. In the micrometeorological method emissions are measured as a flux through an imaginary horizontal plane and recalculated as vertical fluxes. CH4 concentrations above the SWDS are used in combination with information on air transport and mixing at the scale of a few m3 (hence micrometeorology, Fowler and Duyzer, 1989). Laurila et al. (2005) propose the micrometeorological Eddy-covariance method as suitable for estimation of landfill gas emission, with advantages of easy automation which enables measurements over longer periods of time and the simultaneous monitoring of CH4 and CO2 emissions. The drawback of the method seems to be its limited footprint (about 25 m), as a result of which it might not produce representative emissions from the entire SWDS.

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BOX 3.2 (CONTINUED)

Plume measurements are designed to measure the emissions from an entire SWDS by measuring the difference in CH4 flux in a transect screen downwind and upwind from the SWDS. Emissions might be assessed comparing increase in CH4 concentrations with tracer concentrations (e.g., from a known amount of N2O or SF6 released on the SWDS) or using a dispersion model. Variations of this method are used around the world by Czepiel et al. (1996), Savanne et al. (1997), Galle et al., (1999) and Hensen and Scharff (2001). The advantage of the method is its accuracy and its possibility to measure emissions from the entire SWDS, this being very effective to cope with spatial variation. However, the method is very expensive and normally only applied for one or a few specific days. Therefore the result seems to be not representative for the annual average emissions from the site (Scharff et al., 2003). For this reason Scharff et al. (2003) developed a stationary version of the mobile plume measurement (SPM) for plume measurements around a SWDS for longer times. At this moment (2006), there is no scientific agreement on what methodology is preferred to obtain annual average emissions from an entire SWDS. Intercomparisons of methods are performed by Savanne et al. (1995) and Scharff et al. (2003) and the conclusion is more or less that no single method can deal with spatial and temporal variability and is yet affordable. According to Scharff et al. (2003) the mass-balance method and the static plume method are the best candidates for further development and validation. However there has been little scientific discussion on this conclusion at the moment of writing of the Guidelines.

3.4

CARBON STORED IN SWDS

Some carbon will be stored over long time periods in SWDS. Wood and paper decay very slowly and accumulate in the SWDS (long-term storage). Carbon fractions in other waste types decay over varying time periods (see Half-life under Section 3.2.3.) The amount of carbon stored in the SWDS can be estimated using the FOD model (see Annex 3A.1). The longterm storage of carbon in paper and cardboard, wood, garden and park waste is of special interest as the changes in carbon stock in waste originating from harvested wood products which is reported in the AFOLU volume (see Chapter 12, Harvested Wood Products). The FOD model of this Volume provides these estimates as a byproduct. The waste composition option calculates the long-term stored carbon from wood, paper and cardboard, and garden and park waste in the SWDS, as this is simply the portion of the DOC that is not lost through decay (the equations to estimate the amount are given in Annex 3A.1). When using the bulk waste option it is necessary to estimate the appropriate portion of DOC originating from harvested wood products in the total DOC of the waste, before finding the amounts of long-term stored carbon. When country-specific estimates are not available, the IPCC default fractions of paper and cardboard, wood, and garden and park waste can be used. The long-term stored carbon in SWDS is reported as an information item in the Waste sector. The reported value for waste derived from harvested wood products (paper and cardboard, wood and garden and park waste) is equal to the variable 1B, ∆CHWP SWDS DC, i.e., the carbon stock change of HWP from domestic consumption disposed into SWDS of the reporting country used in Chapter 12, Harvested Wood Products, of the AFOLU Volume. This parameter as well as the annual CH4 emissions from disposal of HWP in the country can be estimated with the FOD model (see sheet HWP in the spreadsheet).

3.5

COMPLETENESS

Previous versions of the IPCC Guidelines have focused on emissions from MSW disposal sites, although inventory compilers were encouraged to consider emissions from other waste types. However, it is now recognised that there is often a significant contribution to emissions from other waste types. The 2006 Guidelines therefore provide default data and methodology for estimating the generation and DOC content of the following waste types: •

•

•

Municipal Solid Waste (MSW) – the default definition and composition is given in Chapter 2, Sewage sludge ( from both municipal and industrial sewage treatment), Industrial solid waste (including waste from wood and paper industries and construction and demolition waste, which may be largely inert materials, but also include wood as a source of DDOCm),

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Residues from mechanical-biological treatment plants (see Chapter 4, Biological Treatment of Solid Waste).

Countries should provide their own estimates of the fractions of these waste types disposed in SWDS, incinerated or recycled. Waste types addressed elsewhere in the 2006 Guidelines: •

Emissions from manure management (included in the AFOLU sector.)

Waste management types to include: •

•

Managed SWDS, Unmanaged SWDS (open dumps, including above-ground piles, holes in the ground and dumping into natural features such as ravines).

Waste management types addressed elsewhere in the 2006 Guidelines: •

•

•

Emissions from incineration (Chapter 5 of this Volume), Emissions from open burning at SWDS (Chapter 5 of this Volume), Emissions from biological treatment of solid waste including centralised composting facilities, and home composting (Chapter 4 of this Volume).

Closed SWDS continue to emit CH4. This is automatically accounted for in the FOD method because historical waste disposal data are used. All of the management types listed above should be included in this sector where they occur to a significant extent.

3.6

DEVELOPING A CONSISTENT TIME SERIES

Two major changes from the 1996 Guidelines are introduced in the 2006 Guidelines. These are: •

•

Replacing the old default (mass balance) method with the first-order decay (FOD) method, Inclusion of industrial waste and other non-MSW categories for all countries.

Both of these changes may require countries to recalculate their results for previous years, so that the time series will be consistent. The new spreadsheet provided for the IPCC FOD method automatically calculates emissions for all past years. However, it is important to ensure that the data input into the model form a consistent time series. The FOD model requires historical data as far back as 1950, so this is a significant task. Guidance is given in Section 3.2.2 to enable countries to estimate past MSW and industrial waste disposal based on urban population, GDP and other drivers. As waste statistics generally improve over time, countries may find that country-specific data are available for recent years but not for the whole time series. It is good practice to use country-specific data where possible. Where default data and country-specific data are mixed in a time series, it is important to check for consistency. It may also be necessary to use backward extrapolation or splicing techniques to reconcile the two datasets. The general guidance on these techniques is given in Chapter 6 of Volume 1 (Time Series Consistency).

3.7

UNCERTAINTY ASSESSMENT

There are two areas of uncertainty in the estimate of CH4 emissions from SWDS: (i) the uncertainty attributable to the method; and (ii) the uncertainty attributable to the data (activity data and parameters).

3.7.1

Uncertainty attributable to the method

The FOD model consists of a pre-exponential term, describing the amount of CH4 generated throughout the lifetime of the SWDS, and an exponential term that describes how this CH4 is generated over time. Therefore the uncertainties in using the FOD model can be divided into uncertainties in the total amount of CH4 formed throughout the life-time of the SWDS and uncertainties in the distribution of this amount over the years.

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The uncertainties in the total amount of CH4 formed during the life-time of the SWDS stem from uncertainties in the amount and the composition of the waste disposed in SWDS (W and DOC), the decomposition (DOCf) and the CH4 correction factor (MCF). These uncertainties are addressed hereafter. The uncertainties in distribution of CH4 generation over the years are highly dependent on the specific situation. When amounts of waste disposed and waste management practices only slowly develop over the years, the uncertainty due to the model will be low. For example, when decomposition is slower than expected, an underestimation of CH4 formation in 2005 from waste disposed in 1990 will be counteracted by an overestimation of amounts formed from waste disposed in e.g., 2000. However, when the annual amounts of waste or waste composition change significantly, errors in the model are of importance. The best way of evaluating the error due to the model in a specific case can be obtained from the model by performing a sensitivity analysis, varying the k-values within the error ranges assumed (see Table 3.5 for default uncertainty values) or in a Monte Carlo analysis using the model and varying all relevant variables. The use of the mass balance method, which was the default (Tier 1) method in previous versions of the IPCC guidance, tends to lead to over-estimation of emissions in cases where the trend is for increased disposal of waste to SWDS over time. It was assumed that all CH4 would be released in the same year that the waste was deposited. The use of the FOD method removes this error and reduces the uncertainty associated with the method. However, it is important to remember that the FOD method is a simple model of a very complex and poorly understood system. Uncertainty arises from the following sources: •

• •

Decay of carbon compounds to CH4 involves a series of complex chemical reactions and may not always follow a first-order decay reaction. Higher order reactions may be involved, and reaction rates will vary with conditions at the specific SWDS. Reactions may be limited by restricted access to water and local variations in populations of bacteria. SWDS are heterogeneous. Conditions such as temperature, moisture, waste composition and compaction vary considerably even within a single site, and even more between different sites in a country. Selection of ‘average’ parameter values typical for a whole country is difficult. Use of the FOD method introduces additional uncertainty associated with decay rates (half-lives) and historical waste disposal amounts. Neither of these are well understood or thoroughly researched.

However, it is likely that the main source of uncertainty lies in selection of values for parameters for the model, rather than with the methodology of the model itself.

3.7.2

Uncertainty attributable to data

This source of uncertainty is simply the uncertainty attributable to each of the parameter inputs. The uncertainty attributable to the data can be classified into activity data and parameters.

3.7.2.1

U NCERTAINTIES

ASSOCIATED WITH ACTIVITY DATA

The quality of CH4 emission estimates is directly related to the quality and availability of the waste generation, composition and management data used to derive these estimates. The activity data in the waste sector include the total municipal solid waste, total industrial waste, waste composition, and the fraction of solid waste sent to solid waste disposal sites. The uncertainty in waste disposal data depends on how the data is obtained. Uncertainty can be reduced when the amounts of waste in the SWDS are weighed. If the estimates are based on waste delivery vehicle capacity or visual estimation, uncertainty will be higher. Estimates based default activity data will have the highest uncertainties. If waste scavenging takes place at the SWDS, it needs to be taken into account with the waste disposal data, otherwise, the uncertainty in waste disposal data will increase. Scavenging will also increase uncertainties in the composition of waste disposed in the SWDS, and hence also the total DOC in the waste. Uncertainty estimates for the default model parameters are given in Table 3.5. The estimates are based on expert judgement. Waste generation may be estimated from population (or urban population) and per-capita waste generation rates. Uncertainty can be introduced if the population does not match the population whose waste is collected. Typically, in many countries, waste is only collected from urban populations. Urban population could fluctuate daily or seasonally by migration of the workforce.

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3.7.2.2

U NCERTAINTIES

ASSOCIATED WITH PARAMETERS

M et ha ne cor r e ct io n f a c t o r ( M CF ) There are two sources of uncertainty in the MCF. •

•

Uncertainty in the value of the MCF for each type of site (managed-anaerobic, managed-semi-aerobic, unmanaged deep and/or high water table, unmanaged shallow): These MCF values are based on one experimental study and expert judgement and not on measured data. Uncertainty in the classification of sites into the different site types: For example, the distinction between deep and shallow sites (5 m depth of waste) is based on expert opinion. Inevitably, few, if any, countries will be able to classify their unmanaged waste disposal sites into deep and shallow based on measured data. It can also be difficult to determine the sites that meet the IPCC criteria for managed sites.

D egra dable o rganic carbon (DOC) There are two sources of uncertainty in DOC values. •

•

Uncertainty in setting the DOC for different types of waste types/materials (paper, food, etc.): There are few studies of DOC, and different types of paper, food, wood and textiles can have very different DOC values. The water content of the waste also has an influence. DOC for industrial waste is very poorly known. Uncertainty in the waste composition affects estimates of total DOC in the SWDS: Waste composition varies widely even within countries (for example, between urban and rural populations, between households on different incomes, and between seasons) as well as between countries.

Fra ct ion of deg rada b le o rganic car bon wh ich de com pos es (DOC f ) The uncertainty in DOCf is very high. There have been few studies, and it is difficult to replicate real SWDS conditions in experimental studies. Fra ct ion of C H 4 in landfill gas (F) The CH4 fraction of generated landfill gas, F, is usually taken to be 0.5, but can vary between 0.5 and 0.55, depending on several factors (see Section 3.2.3). The uncertainty in this figure is relatively low, as F depends largely on the stoichiometry of the chemical reaction producing CH4. The concentration of CH4 in recovered landfill gas may be lower than the actual value because of potential dilution by air, so F values estimated in this way will not necessarily be representative. M ethane recovery (R ) CH4 recovery is the amount of CH4 generated at SWDS that is recovered and burned in a flare or energy recovery device. The uncertainty depends on the method used to estimate recovered CH4. The uncertainty is likely to be relatively small compared to other uncertainties if metering is used. If other methods are used, for example by estimating the efficiency of CH4 recovery equipment, the uncertainty will be larger. (See Section 3.2.3.) Ox idat ion fa ctor (OX ) The oxidation factor is very uncertain because it is difficult to measure, varies considerably with the thickness and nature of the cover material, atmospheric conditions and climate, the flux of methane, and the escape of methane through cracks/fissures in the cover material. Field and laboratory studies which determine oxidation of CH4 only through uniform and homogeneous soil layers may lead to over-estimations of oxidation in landfill cover soils. The ha lf-life There is high uncertainty in the estimates of half-life because it is difficult to measure decay rates under conditions equivalent to those prevailing in real SWDS. Also, since there is considerable variation in half-life with waste composition, climate and site type, it is difficult to select values representative of a whole country.

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Uncertainty estimates for MSWT (total MSW generated) and MSWF (fraction of MSWT disposed at SWDS) and the default model parameters are given in Table 3.5. The estimates are based on expert judgement.

TABLE 3.5 ESTIMATES OF UNCERTAINTIES ASSOCIATED WITH THE DEFAULT ACTIVITY DATA AND PARAMETERS IN THE FOD METHOD FOR CH4 EMISSIONS FROM SWDS Activity data and emission factors

Uncertainty Range

Total Municipal Solid Waste (MSWT)

Country-specific: 30% is a typical value for countries which collect waste generation data on regular basis. ±10% for countries with high quality data (e.g., weighing at all SWDS and other treatment facilities). For countries with poor quality data: more than a factor of two.

Fraction of MSWT sent to SWDS (MSWF) Total uncertainty of Waste composition

Degradable Organic Carbon (DOC)7

Fraction of Degradable Organic Carbon Decomposed (DOCf) Methane Correction Factor (MCF) = 1.0 = 0.8 = 0.5 = 0.4 = 0.6 Fraction of CH4 in generated Landfill Gas (F) = 0.5

±10% for countries with high quality data (e.g., weighing at all SWDS). ±30% for countries collecting data on disposal at SWDS. For countries with poor quality data: more than a factor of two.

±10% for countries with high quality data (e.g., regular sampling at representative SWDS). ±30% for countries with country-specific data based on studies including periodic sampling. For countries with poor quality data: more than a factor of two.

For IPCC default values : ±20% For country-specific values: Based on representative sampling and analyses: ±10%

For IPCC default value (0.5): ± 20% For country-specific value ± 10% for countries based on the experimental data over longer time periods. For IPCC default value： –10%, +0% ±20% ±20% ±30% –50%, +60%

For IPCC default value: ±5%

Methane Recovery (R)

The uncertainty range will depend on how the amounts of CH4 recovered and flared or utilised are estimated: ± 10% if metering is in place. ± 50% if metering is not in place.

Oxidation Factor (OX)

Include OX in the uncertainty analysis if a value other than zero has been used for OX itself. In this case the justification for a non-zero value should include consideration of uncertainties.

half-life ( t1/2 )

Ranges for the IPCC default values are provided in Table 3.4. Country-specific values should include consideration of uncertainties.

Source: Expert judgement by Lead Authors of the Chapter.

7

The uncertainty range given applies to the DOC content in bulk waste. The ranges for DOC for different waste components in MSW given in Table 2.4 can be used to estimate the uncertainties for these components.

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3.8

QA/QC, Reporting and Documentation

It is good practice to document and archive all information required to produce the national emissions inventory estimates as outlined in Chapter 6, Quality Assurance and Quality Control and Verification, in Volume 1, General Guidance and Reporting. Some examples of specific documentation and reporting relevant to this source category are provided below. •

•

• •

•

Countries using the IPCC FOD model should include the model in the reporting. Countries using other methods or models should provide similar data (description of the method, key assumptions and parameters). If country-specific data are used for any part of the time series, it should be documented. The distribution of waste to managed and unmanaged sites for the purpose of MCF estimation should also be documented with supporting information. If CH4 recovery is reported, an inventory of known recovery facilities is desirable. Flaring and energy recovery should be documented separately from each other. Changes in parameters from year to year should be clearly explained and referenced.

It is not practical to include all documentation in the national inventory report. However, the inventory should include summaries of methods used and references to source data such that the reported emissions estimates are transparent and steps in their calculation may be retraced. It is good practice to conduct quality control checks and an expert review of the emissions estimates as outlined in Chapter 6 of Volume 1, Quality Assurance and Quality Control, and Verification. Inventory compilers should cross-check country-specific values for MSW generated, industrial waste generated and waste composition against the default IPCC values, to determine whether the national parameters used are considered reasonable relative to the IPCC default values. Where survey and sampling data are used to compile national values for solid waste activity data, QC procedures should include: (i)

Reviewing survey data collection methods, and checking the data to ensure that they were collected and aggregated correctly. Inventory compilers should cross-check the data with previous years to ensure the data are reasonable.

(ii)

Evaluating secondary data sources and referencing QA/QC activities associated with the secondary data preparation. This is particularly important for solid waste data, since most of these data are originally prepared for purposes other than greenhouse gas inventories.

Inventory compilers should provide the opportunity for experts to review input parameters. Inventory compilers should compare national emission rates with those of similar countries that have comparable demographic and economic attributes. Inventory compilers should study significant discrepancies to determine if they represent errors in the calculation or actual differences.

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References Facultad de Ingenieria de la Universidad Nacional Del Centro De La Provincia De Buenos Aires (2004). Olavarria Landfill Gas Recovery Project, Buenos Aires, Argentina. August 2004. http://www.dnv.is/certification/climatechange/Upload/PDD_Olavarria_2004-0521.pdf#search=%22olavarria%20landfill%20gas%20recovery%20project%22 Attenborough, G. M., Hall, D. H., Gregory, R. G. and McGoochan, L. (2002). GasSim: Landfill Gas Risk Assessment Model. In: Conference Proceedings SITA Environmental TrustSponsored by SITA Environmental Trust and Organics Limited. ISBN 0-9539301. Barlaz, M. (2004). Critical Rewiew of Forest Products Decomposition in Municipal Solid Waste Landfills. NCASI Technical Bulletin, no 872, March 2004. Bergman, H. (1995). Metanoxidation i täckskikt på avfallsupplag. (Methane oxidation in waste deposition covers). Licentiate thesis 1995:14L, Tekniska Högskolan i Luleå, ISSN 0280-8242. (In Swedish) Bogner, J. and Matthews, E. (2003). ‘Global methane emissions from landfills: New methodology and annual estimates 1980 – 1996’, Global Biogeochemical Cycles, Vol. 17, No. 2. Brown, K. A., Smith, A., Burnley, S. J., Campbell, D.J.V., King, K. and Milton, M.J.T. (1999). Methane Emissions from UK Landfills, AEA Technology, AEAT-5217, Culham. Börjesson, G., Danielsson, A. and Svensson, B.H. (2000). ‘Methane fluxes from a Swedish landfill determined by geostatistical treatment of static chamber measurements’, Environ Sci Technol 34: 4044-4050. Czepiel, P.M., Mosher, B., Harriss, R., Shorter, J.H., McManus, J.B., Kolb, C.E., Allwine, E. and Lamb, B. (1996). ‘Landfill methane emissions measured by enclosure and atmospheric tracer methods’, J Geophys Res D101: 16711-16719. Diot M., Bogner, J., Chanton, J., Guerbois, M., Hébé, I., Moreau le Golvan, Y., Spokas, K. and Tregourès, A. (2001). LFG mass balance: a key to optimize LFG recovery, in Proceedings of the Eighth International Waste Management and Landfill Symposium Sardinia 2001, S. Margherita di Pula (Cagliari, Italia), October 1-5, 2001. Environment Canada (2003). Canada’s Greenhouse Gas Inventory 1990-2001. 8. Waste. Greenhouse Gas Division August 2003. The Green line Environment Canada’s Worldwide Web Site. Environment Canada (2004). Landfill Gas Capture and Combustion Quantification Protocol. Avaliable at: www.ec.gc.ca/pdb/ghg/lfg_protocol_e.cfm. Fowler, D. and Duyzer, J.H. (1989). Micrometeorological techniques for the measurement of trace gas exchange, Exchange between terrestrial ecosystems and the atmosphere, Andreae, M.O., Schimel, D.S. Eds., John Wiley & Sons, pp. 189-207. Galle B., Samuelsson, J., Börjesson, G. and Svensson, H. (1999). Measurement of methane emissions from landfills using FTIR spectroscopy. Sardinia 1999, Seventh International Waste Management and Landfill Symposium. Vol. IV, 47-54. Gardner, W.D., Ximenes, F., Cowie, A., Marchant, J.F., Mann, S. and Dods, K. (2002). Decomposition of wood products in the Lucas Heights landfill facility. Presented at the Third Australian Conference on 'Life Cycle Assessment – “Life Cycle Decision-making for Sustainability”. Queensland, Australia, 17 – 19 July, 2002. State Forests of New South Wales, Sydney, Australia. URL:http://www.greenhouse.crc.org.au/crc/ecarbon/enews/gardner.pdf Gregory, R.G., Gillet, A.G. and Bradley, D. (2003). ‘Methane emissions from landfill sites in the UK’, LQM Report 443/1, January 2003. Hensen, A. and Scharff, H. (2001). Methane emission estimates from landfills obtained with dynamic plume measurements, Water, Air and Soil Pollution, Focus, 1(5-6): 455-464. Hoeks, J. (1983). Significance of biogas reduction in waste tips, Waste management and research, 1, pp. 323325 Huitric, R. and Soni, R. (1997). Making the most of LFG projection models, Proceedings from SWANA's 20th annual LFG symposium, Monterey California, USA IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds), Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France.

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IPCC (2000). Good Practice Guidance and Uncertianty Management in National Greenhouse Gas Inventories. Penman J., Kruger D., Galbally I., Hiraishi T., Nyenzi B., Enmanuel S., Buendia L., Hoppaus R., Martinsen T., Meijer J., Miwa K. and Tanabe K. (Eds), Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. IPCC (2001). Summary for Policymakers and Technical Summary of Climate Change 2001: Mitigation. Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Bert Metz et al. eds. Cambridge University Press, Cambridge, United Kingdom IPCC (2003). Good Practice Guidance for Land Use, land-Use Change and Forestry. Penman, J., Gytarsky, M., Hiraishi, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. and Wagner, F. (Eds), Intergovernmental Panel on Climate Change (IPCC), IPCC/IGES, Hayama, Japan. Jensen, J. E. and Pipatti, R. (2002). ‘CH4 Emissions from Solid Waste Disposal’, Background Papers. IPCC Expert Meetings on Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, ), Intergovernmental Panel on Climate Change (IPCC)-National Greenhouse Gas Inventories Programme (NGGIP), IGES, Hayama, Japan, pp 419-465. Kämpfer, P. and Weissenfels, W. (2001). Biologische Behandlung organisher Abfälle, Springer, Berlin. Laurila, T., Tuovinen, J.P., Lohila, A., Hatakka, J., Aurela, M., Thum, T., Pihlatie, M., Rinne, J. and Vesala, T. (2005). ‘Measuring methane emissions from a landfill using a cost-effective micrometeorological method’, Geoph. Res. Let., Vol. 32, L19808, Levelton, B.H. (1991). Inventory of Methane Emissions from Landfills in Canada. Levelton & Associates. Prepared for Environment Canada, June 1991. Matsufuji, Y., Kobayashi, H., Tanaka, A., Ando, S., Kawabata, T. and Hanashima, M. (1996). ‘Generation of greenhouse gas effect gases by different landfill types and methane gas control’, Proceedings of 7th ISWA International Congress and Exhibition, 1996:10, No. 1, p. 253-254. Maurice, C. and Lagerkvist, A. (1997). ‘Seasonal variation of landfill gas emissions’, Sardinia 1997 Sixth International Waste Management and Landfill Symposium,Vol IV, pp. 87-93. Micales, J.A. and Skog, K.E. (1997). ‘The decomposition of forest products in landfills’, International Biodeterioration and Biodegradation 39(2-3): pp. 145-158 Mosher, B., Czepiel, P., Harriss, R., Shorter, J.H., Kolb, C.E., McManus, J.B., Allwine, E. and Lamb, B. (1999). ‘Methane emission at nine landfill sites in the northeastern United States’, Environ Sci Technol 33: 20882094. Oonk, H. (1993). Overzicht van stortgasprojecten in Nederland (Overview of Dutch Landfill gas projects), March 1993. TNO, Apeldoorn, The Netherlands. Oonk, H. and Boom, T. (1995). ‘Landfill gas formation, recovery and emissions’, TNO-report R95-203, TNO. Appeldoorn, The Netherlands. Park J.W. and Shin H.C. (2001). ‘Surface methane emission of landfill gas from solid waste landfill’, Atmospheric Environment 35, 3445-3451 Pelt, R., Bass, R.L., Heaton, R. E., White, Ch., Blackard, A., Burklin, C. and Reisdorph, A. (1998). User’s Manual Landfill Gas Emissions Model Version 2.0. U.S. Environmental Protection Agency, Washington, D.C. February 1998, 94 pp. RIVM. (2004). Netherlands’s National GHG Inventory Report. 8. Waste. RIVM Report 773201008, 8 pp. Savanne, D., Arnaud, A., Beneito, A., Berne, P., Burkhalter, R., Cellier, P., Gonze, M.A., Laville, P., Levy, F., Milward, R., Pokryszka, Z., Sabroux, J.C., Tauziede, C. and Tregoures, A. (1997). ‘Comparison of different methods for measuring landfill methane emissions’, Sardinia 1997 Sixth International Waste Management and Landfill Symposium, Vol IV, pp. 81-86. Scheehle, E. (2006). Personal Communication. SCS Wetherill Environmental (2003). New Zealand’s Greenhouse Gas Inventory 1990-2002. Chapter 8: Waste. New Zealand Climate Change Office. Scharff, H., Martha, A., van Rijn, D.M.M., Hensen, A., v.d. Bulk, W.C.M., Flechard, C., Oonk H., Vroon, R., de Visscher, A. and Boeckx, P. (2003). A comparison of measurement methods to determine landfill methane emissions, report by Afvalzorg Deponie B.V., Haarlem, the Netherlands. Spokas, K., Graff, C., Morcet, M. and Aran, C. (2003). Implications of the spatial variability of landfill emission rates on geospatial analyses. Waste Management. 23: 599-607.

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Spokas, K., Bogner, J., Chanton, J., Morcet, M., Aran, C., Graff, C., Moreau-le-Golvan, Y. and Hebe, I. (2006). ‘Methane mass balance at three landfill sites: What is the efficiency of capture by gas collection systems?’ Waste Management, 26: 516-525. SWANA (1998). Comparison of Models for Predicting Landfill Methane Recovery, Publication No. GRLG0075, March, Solid Waste Association of North America (SWANA). UK Environment Agency (2004). Guidance on monitoring landfill gas surface emissions. URL: http://www.environment-agency.gov.uk/subjects/waste/?lang=_e U.S. EPA (2005). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2004. Annex 3.14. Methodology for Estimating CH4 Emissions from Landfills. April 2005, U.S. Environmental Protection Agency (U.S. EPA). U.S. EPA (2002). Solid Waste Management and Greenhouse Gases, 2nd Ed, EPA530-R-02-006, U.S. Environmental Protection Agency (U.S. EPA). U.S. EPA (1998). Compilation of Air Pollutant Emission Factors AP-42, Fifth Edition, Volume1: Stationary Point and Area Sources. Chapter 2: Solid waste Disposal. Section 2.4.4.1. U.S. Environmental Protection Agency (U.S. EPA), November1998. U.S. EPA (1995). Compilation of Air Pollutant Emissions Factors, AP-42, Edition 5. U.S. Environmental Protection Agency (U.S. EPA).URL: http://www.epa.gov/ttn/chief/ap42/ Yamada, M., Ishigaki, T., Endo, K., Inoue, Y., Nagamori, M., Ono Y. and Ono Y. (2005). Distribution of temperature and methane flux on landfill surface. Sardinia 2005, Tenth International Waste Management and Landfill Symposium, Cagliari, Italy

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Annex 3A.1 First Order Decay Model 3A1.1

INTRODUCTION

The first order decay (FOD) model introduced in Chapter 3 is the default method for calculating methane (CH4) emissions from solid waste disposal sites (SWDS). This Annex provides the supplementary information on this model: •

• •

•

•

mathematical basis for the FOD model (see Section 3A1.2), key issues in the model, such as the estimation of the mass of degradable organic carbon available for anaerobic decomposition at SWDS (DDOCm) (Section 3A1.2) and the delay time from disposal of waste in the SWDS till the decomposition starts (Section 3A1.3), introduction of the spreadsheet model developed to facilitate the use of the FOD method (3A1.4), how to estimate the long-term storage of carbon in SWDS (Section 3A1.5), different approaches to the FOD model, including an explanation of the differences between the current and earlier IPCC methods (Section 3A1.6).

3A1.2

FIRST ORDER DECAY (FOD) MODEL – BASIC THEORY

The basis for a first order decay reaction is that the reaction rate is proportional to the amount of reactant remaining (Barrow and Gordon, 1996), in this case the mass of degradable organic carbon decomposable under anaerobic conditions (DDOCm). The DDOCm reacted over a period of time dt is described by the differential equation 3A.1.1: EQUATION 3A1.1 DIFFERENTIAL EQUATION FOR FIRST ORDER DECAY d (DDOCm ) = −k • DDOCm • dt

Where: DDOCm =

mass of degradable organic carbon (DOC) in the disposal site at time t

k

decay rate constant in y-1

=

The solution to this equation is the basic FOD equation. EQUATION 3A1.2 FIRST ORDER DECAY EQUATION DDOCm = DDOCm0 • e − kt

Where: DDOCm =

mass of degradable organic carbon that will decompose under anaerobic conditions in disposal site at time t

DDOCm0 =

mass of DDOC in the disposal site at time 0, when the reaction starts

k

=

decay rate constant in y-1

t

=

time in years.

Substituting t =1 into Equation 3A1.2 shows that at the end of year 1 (the year after disposal), the amount of DDOCm remaining in the disposal site is: EQUATION 3A1.3 DDOCM REMAINING AFTER 1 YEAR OF DECAY

At t = 1, DDOCm = DDOCm0 • e − k

The DDOCm decomposed into CH4 and CO2 at the end of year 1 (DDOCm decomp) will then be:

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EQUATION 3A1.4 DDOCm DECOMPOSED AFTER 1 YEAR OF DECAY

At t = 1, DDOCm decomp = DDOCm0 • ( 1 − e − k )

The equation for the general case, for DDOCm decomposed in period T 8 between time (t−1) and t, will be: EQUATION 3A1.5 DDOCm DECOMPOSED IN YEAR T

DDOCm decompT = DDOCm0 • ⎡e − k (t −1) − e − kt ⎤ ⎢⎣ ⎦⎥

Equations 3A1.4 and 3A1.5 are based on the mass balance over the year. In Section 3.2.3, the parameter half-life time of the decay is discussed. Half-life is the time it takes for the amount of reaction to be reduced by 50 percent. The relationship between half-life time and the reaction rate constant k is found by substituting DDOCm in Equation 3A1.2 with 1/2DDOCm0, and t with t1/2: EQUATION 3A1.6 RELATIONSHIP BETWEEN HALF-LIFE AND REACTION RATE CONSTANT k = In(2) / t1 / 2

3A1.3

CHANGING THE TIME DELAY IN THE FOD EQUATION

In most SWDS, waste is disposed continuously throughout the year, usually on a daily basis. However, there is evidence that production of CH4 does not begin immediately after disposal of the waste (see Section 3.2.3 in Chapter 3). Equations 3A1.3 and 3A1.4 assume that the decay reaction starts on January 1 in the year after disposal, i.e., an average six month delay before the reaction commences. The equations can easily be transformed to model an earlier start to the decay reaction, i.e., start of the decay reaction in the year of disposal. This is done by moving the e-kt curve backwards along the time axis. For example, to model a reaction start on the first of October in the year of disposal (i.e., an average time delay of three months before the decay reaction commences, instead of six months), Equation 3A1.2 will be transformed into the following: EQUATION 3A1.7 FOD EQUATION FOR DECAY COMMENCING AFTER 3 MONTHS DDOCm = DDOCm0 • e − k ( t + 0.25 )

Then there will be two solutions, one for the year of disposal and one for the rest of the years:

(

)

EQUATION 3A1.8 DDOCm DECOMPOSED IN YEAR OF DISPOSAL (3 MONTH DELAY) DDOCm decompY = DDOCm0 • 1 − e −0.25k

EQUATION 3A1.9 DDOCm DISSIMILATED IN YEAR (T) (3 MONTH DELAY)

DDOCm decompT = DDOCm0 • ⎡ e − k ( T − 0.75 ) − e − k ( T + 0.25 ) ⎤ ⎢⎣ ⎦⎥

8

T denotes the year for which the estimate is done in relation to deposition year.

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Where: DDOCm decompY

=

DDOCm decompT

=

DDOCm decomposed in year of disposal

DDOCm decomposed in year T (from point t−1 to point t on time axis)

T

=

year from point t−1 to t on the time axis, where year 1 is the year after disposal.

Y

=

disposal year

The same can be done to find the equations for reaction start within the year after disposal.

3A1.3.1

Disposal profile

The method presented here assumes that CH4 production from all of the waste disposed during the first year (Year Y) begins on the 1st of January on the year after disposal. Year 1 is defined as the year after disposal. Some inaccuracy will be introduced by the fact that, in reality, waste disposed at the beginning of the year will begin to produce CH4 earlier, and waste disposed at the end of the year will begin to produce CH4 later. Comparison of results calculated with the simple FOD method presented here and the exact day-by-day method, which is presented in Section 3A1.6.3, has been used to evaluate this error. With a half-life time of 10 years, evaluating CH4 emissions with the exact method gives a decay profile only 1 day difference from the simplified version of the method. With a half-life time of 3 years, the simple method gives 3.5 days difference from the exact method. Even with a half-life time of 1 year, the difference between the exact and simple methods is just 10 days. The error introduced by the assumption in this simple method is very small in comparison with other uncertainties in the parameters, especially given that the uncertainty in delay time is at least two months.

3A1.4

SPREADSHEET FOD MODEL

In order to estimate CH4 emissions for all solid waste disposal sites in a country, one method is to model the emissions from the waste disposed in each year as a separate row in a spreadsheet. In the IPCC Waste Model, CH4 formation is calculated separately for each year of disposal, and the total amount of CH4 generated is found by a summation at the end. A typical example, for six years of disposal of 100 units of DDOCm each year, with a decay rate constant of 0.1 (half-life time of 6.9 years), and CH4 generation beginning in the year after disposal, is shown in the table below. The figures in the table are the DDOCm decomposed from that waste each year, from which the CH4 emissions can be calculated. When considered over a period of 50 years, which is necessary for the FOD method, this leads to a rather large calculation matrix. The spreadsheet uses a more compact and elegant approach to the calculations. This is done by adding the DDOCm disposed into the disposal site in one year to the DDOCm left over from the previous years. The CH4 emission for the next year is then calculated from this ‘running total’ of the DDOCm remaining in the site. In this way, the full calculation for one year can be done in only three columns, instead of having one column for each year (see Table 3A1.1). The basis for this approach lies in the first order reaction. With a first order reaction the amount of product (here DDOCm decomposed) is always proportional to the amount of reactant (here DDOCm). This means that the time of disposal of the DDOCm is irrelevant to the amount of CH4 generated each year - it is just the total DDOCm remaining in the site that matters. This also means that when we know the amount of DDOCm in the SWDS at the start of the year, every year can be regarded as year number 1 in the estimation method, and all calculation can be done by these two simple equations:

(

)

EQUATION 3A1.10 MASS OF DEGRADABLE ORGANIC CARBON ACCUMULATED AT THE END OF YEAR T DDOCmaT = DDOCmdT + DDOCmaT −1 • e − k

(

)

EQUATION 3A1.11 MASS OF DEGRADABLE ORGANIC CARBON DECOMPOSED IN YEAR T DDOCm decompT = DDOCmaT −1 • 1 − e − k

Where: the decay reaction begins on the 1st of January the year after disposal.

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DDOCmaT

=

DDOCm accumulated in the SWDS at the end of year T

DDOCmdT

=

mass of DDOC disposed in the SWDS in year T

DDOCmaT-1

=

DDOCm accumulated in the SWDS at the end of year (T−1)

DDOCm decompT =

DDOCm decomposed in year T

TABLE 3A1.1 NEW FOD CALCULATING METHOD

3A1.4.1

year

DDOCm disposed

DDOCm accumulated

DDOCm decomposed

0

100

100

0

1

100

190.5

9.5

2

100

272.4

18.1

3

100

346.4

25.9

4

100

413.5

33.0

5

100

474.1

39.3

6

100

529.0

45.1

Introducing a different time delay into the spreadsheet model

The table and equations presented above assume that anaerobic decomposition of DDOCm to CH4 begins on 1st of January in the year after disposal (an average delay of 6 months before the decay reaction begins). If the anaerobic decomposition is set to start earlier than this, i.e., in the year of disposal, separate calculations will have to be made for the year of disposal. As the mathematics of every waste category or waste type/fraction is the same, only parameters are different, indexing for different waste categories and types/fractions are omitted in the equations 3A1.12-17, and 3A1.19: EQUATION 3A1.12 DDOCm REMAINING AT END OF YEAR OF DISPOSAL DDOCm remT = DDOCmd T • e − k • ( 13 − M ) / 12

(Column F in CH4 calculating sheets in the spreadsheet model)

EQUATION 3A1.13 DDOCm DECOMPOSED DURING YEAR OF DISPOSAL

DDOCm decT = DDOCmdT • ⎡ 1 − e − k • ( 13 − M ) / 12 ⎤ ⎢⎣ ⎥⎦

(Column G in the CH4 calculating sheets in the spreadsheet model) Where: DDOCm remT =

DDOCm disposed in year T which still remains at the end of year T (Gg)

DDOCmdT

DDOCm disposed in year T (Gg)

=

DDOCm decT =

DDOCm disposed in year T which has decomposed by the end of year T (Gg)

T

=

year T (inventory year)

M

=

month when reaction is set to start, equal to the average delay time + 7 (month)

k

=

rate of reaction constant (y-1)

Equations 3A1.10 and 3A1.11 will then become:

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(

EQUATION 3A1.14 DDOCm ACCUMULATED AT THE END OF YEAR T

DDOCmaT = DDOCm remT + DDOCmaT −1 • e − k

)

(Column H in the CH4 calculating sheets in spreadsheet model)

EQUATION 3A1.15 DDOCm DECOMPOSED IN YEAR T

(

DDOCm decompT = DDOCm decT + DDOCmaT −1 • 1 − e − k

)

(Column I in the CH4 calculating sheets in the spreadsheet model) Where: DDOCmaT

=

DDOCmaT-1

=

DDOCm accumulated in the SWDS at the end of year T, Gg

DDOCm accumulated in the SWDS at the end of year (T−1), Gg

DDOCm decompT =

DDOCm decomposed in year T, Gg

The spreadsheets are based on Equations 3A1.12 to 3A1.15. If the reaction start is set to the first of January the year after disposal, this is equivalent to an average time delay of 6 months (month 13). Equations 3A1.14 and 3A1.15 will then be identical to Equations 3A1.10 and 3A1.11.

3A1.4.2

Calculating DDOCm from amount of waste disposed

Data on waste disposal is entered into the spreadsheet. The data can be given by waste type (waste composition option) or as bulk waste. In the waste composition option, waste is split by waste type/material (paper and cardboard, food garden and park waste, wood, textiles and other waste). In the bulk waste option, waste is split only by main waste category (MSW and industrial waste). Not all DOCm entering the site will decompose under the anaerobic conditions in the SWDS. The parameter DOCf is the fraction of DOCm which will actually degrade in the SWDS (see Section 3.2.3 in Chapter 3). The decomposable DOCm (DDOCm) entering the SWDS is calculated as follows: EQUATION 3A1.16 CALCULATION OF DECOMPOSABLE DOCm FROM WASTE DISPOSAL DATA DDOCmdT = WT • DOC • DOC f • MCF

(Column D in the CH4 calculating sheet in the spreadsheet model) Where: DDOCmdT

=

WT

=

mass of waste disposed in year T, Gg

DOC

=

Degradable organic carbon in disposal year (fraction), Gg C/Gg waste

DOCf

=

fraction of DOC that can decompose in the anaerobic conditions in the SWDS (fraction)

MCF

=

CH4 correction factor for year of disposal (fraction) (see Section 3.2.3)

3A1.4.3

DDOCm disposed in year T, Gg

Calculating CH 4 generation from DDOCm decomposed

The amount of CH4 generated from the DDOCm which decomposes is calculated as follows: EQUATION 3A1.17 CH4 GENERATED FROM DECOMPOSED DDOCm

CH 4 generatedT = DDOCm decompT • F • 16 / 12

(Column J in the CH4 calculating sheets in the spreadsheet model) Where: CH4 generatedT

3.36

= amount of CH4 generated from the DDOCm which decomposes

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Solid Waste Disposal

DDOCm decompT = DDOCm decomposed in year T, Gg F

=

fraction of CH4, by volume, in generated landfill gas

16/12

=

molecular weight ratio CH4/C (ratio).

The CH4 generated by each category of waste disposed is added to get total CH4 generated in each year. Finally, emissions of CH4 are calculated by subtracting first the CH4 gas recovered from the disposal site, and then CH4 oxidised to carbon dioxide in the cover layer. EQUATION 3A1.18 CH4 EMITTED FROM SWDS

⎞ ⎛ CH 4 emittedT = ⎜⎜ ∑ CH 4 generated x,T − RT ⎟⎟ • ( 1 − OX T ) ⎠ ⎝ x

(The final result calculating column in the Results sheet) Where: CH4 emittedT

=

CH4 emitted in year T, Gg

x

=

waste type/material or waste category

RT

=

CH4 recovered in year T, Gg

OXT =

3A1.5

Oxidation factor in year T, (fraction)

CARBON STORED IN SWDS

Only part of the DOCm in waste disposed in SWDS will decay into both CH4 and CO2. An MCF value lower than 1, means that part of the DOCm will decompose aerobically to CO2, but not to CH4. The DOCm available for anaerobic decay will not decompose completely either. The decomposing part of this DOCm (DDOCmd) is given in Equation 3A1.16. The part of DOCm that will not decompose will be stored long-term in the SWDS, which will then be: EQUATION 3A1.19 CALCULATION OF LONG-TERM STORED DOCm FROM WASTE DISPOSAL DATA

(

)

DOCm long -term stored T = WT • DOC • 1 − DOC f • MCF

Using the default value for DOCf = 0.5, 50 percent of the disposed DOCm will remain there for long term. Equation 19 describes the annual increase in the stock of long-term stored carbon in the SWDS. The long-term stored carbon in harvested wood products (HWP) disposed in SWDS (see Chapter 12 in the AFOLU volume) can be estimated using this equation. For the waste composition option, the amount of DOCm which is long-term stored in HWP waste disposed in SWDS is calculated directly from material information in the Activity sheet. Using the bulk waste option, the fraction of waste originating from HWP needs to be estimated first. If this is not known, the regional or country-specific default fractions for paper and cardboard, garden and park and wood waste can be used (see Section 2.3). The calculations are performed in the spreadsheet model in the sheet called ‘Stored C’ and ‘HWP’.

3A1.6

DIFFERENT FOD APPROACHES

Different FOD approaches have been used to estimate the CH4 emissions from SWDS. The differences between the approach used in these Guidelines, previous IPCC approaches and the so-called exact FOD method are discussed below. The approach used in this Volume has been chosen mainly for the following reasons:

•

•

the method describes the FOD reaction mathematically more accurately than the previous IPCC approaches,

•

it is easy to use in a spreadsheet model,

•

it is easy to understand,

it gives, as a by-product, an estimate of changes in carbon stored in SWDS (annual changes in carbon stock, for both long-term and short-term storage as the mass-balance of conversions of carbon into CH4 and CO2 in the SWDS are maintained by the approach).

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3A1.6.1

1996 Guidelines - The rate of reaction approach

In the Revised 1996 IPCC guidelines (1996 Guidelines, (IPCC, 1997)) the estimation of the CH4 emissions from SWDS was based on the rate of reaction equation. This is a common way of looking at the mass transformation in a chemical reaction. This is obtained by differentiating Equation 3A1.2 with respect to time: EQUATION 3A1.20 FIRST ORDER RATE OF REACTION EQUATION

DDOCm reaction rate = − d (DDOCm ) / dt = k • DDOCm0 • e − kt

The rate of reaction equation shows the rate of reaction at any time, and the rate of reaction moves along a curve. Therefore it has to be integrated to find the amount of reacted DDOCm over a period of time. We want to find the DDOCm decomposed into CH4 and CO2 per calendar year. The start is year number 1 going from point 0 to point 1 on the time axis. Year number 1 is associated to point 1 on the time axis. Therefore the integration has to be performed from t−1 to t, which leads to an equation identical to Equation 3A1.5. However, the equation presented in the 1996 Guidelines (Equation 4, Chapter 6) is: EQUATION 3A1.21 IPCC 1996 GUIDELINES EQUATION FOR DOC REACTING IN YEAR T DDOCm decompT = k • DDOCm0 • e − kt

In fact, this is the rate of reaction equation. Effectively this means that the yearly CH4 production is calculated from the rate of reaction at the end of the year. This is an approximation which involves summing a series of rectangles under the rate of reaction curve, instead of accurately integrating the whole area under the curve. An error is introduced by the approximation; the small triangles shown on the top of the columns in Figure 3A1.1 are neglected, and mass balance over the year is not obtained. The method based on the equation in the 1996 Guidelines using a half-life time of 10 years would give results 3.5 percent lower than the full mass balance calculations used in these Guidelines (see equations 3A.1.4-5). However, where the method in the 1996 Guidelines is used with half life times developed specifically for this method, calculations will be correct. Figure 3A1.1

3A1.6.2

Error introduced by not fully integrating the rate of reaction curve

IPCC 2000 Good Practice Guidance

In the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (GPG2000, IPCC, 2000), Equation 5.1, a normalisation factor A is introduced into the rate of reaction equation. When this ‘normalisation factor’ is multiplied into Equation 5.1 the result is a solved integral:

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 3: Solid Waste Disposal

EQUATION 3A1.22 IPCC 2000GPG FOD EQUATION FOR DDOCm REACTING IN YEAR T DDOCm decompT = DDOCm0 • ⎡ e − kt − e − k (t +1) ⎤ ⎢⎣ ⎥⎦

This is equivalent to the correct equation (Equation 3A1.5) as it integrates the decay curve. However, for year 1 it integrates from point 1 to point 2 on the time axis, and therefore the CH4 formed in the first year of reaction is not counted (see Figure 3A1.2). This means that with a half life time of 10 years the GPG2000 equation calculates results that are 7 percent lower than those calculated with approach taking the full mass balance into account. Figure 3A1.2

Effect of error in the GPG2000 equation

The intention of the normalisation factor has obviously been to fill in the small triangles on top of the columns in Figure 3A1.1. It fails because the normalisation factor used is equivalent to an integration going from point t to (t +1) on the time axis. As the integration using year number as a basis has to go from t-1 to 1, the normalisation factor filling in the whole area under the rate of reaction curve would be A = ((1/e-k) − 1)/k.

3A1.6.3

Mathematically Exact First-Order Decay Model

The First Order Decay (FOD) model as described above can be shown to be mathematically equivalent to a model for which the total amount of DOC is assumed to be disposed at a single point in time in each disposal year, i.e., on a single date. If there is no delay in the commencement of the decay process, this date would be the middle of the year, i.e., 1st of July, with a delay of 6 months the assumed reaction start with the full amount of material is 31st December/1st January. This assumption, though counter-intuitive, leads to numerical errors that are small compared to the uncertainty in the understanding of the chemical processes, activity data, emission factors and other parameters of the emission calculation. An alternative formulation of the FOD method is presented here for completeness. The delay in the commencement of the decay process can be represented, and simple recursive formulations can be given. Equation 3A1.23 represents the formulation of the FOD with disposal rate D(t). The first term in the bracket represents the inflow into the carbon pool in the SWDS (disposal), the second term represents the outflow from the site (carbon in form of CH4); the sum of the two terms represents the overall change in carbon stock in the SWDS. EQUATION 3A1.23 FOD WITH DISPOSAL RATE D(t)

dDOCm(t ) = [ D(t ) − k • DDOCm(t )

]

dt

Where: dDDOCm(t)

=

change in DDOCm at time t

D(t)

=

DDOCm disposal rate at time t

DDOCm(t)

=

DDOCm available at time t for decay

If there is a delay of Δ years in the commencement of the decay process after the DDOCm has been disposed, it will be necessary to distinguish the part of the stock that is available for decay, to which Equation 3A1.23

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Volume 5: Waste

applies, and the inert part of the stock. For a disposal rate D(t) that is constant during each disposal year (and equal to the amount of DDOC disposed during that year divided by one year) it can be shown that the carbon stocks at the end of year i can be expressed in terms of the carbon stocks at the end of year i−1 and the amounts of disposal in year i and year i−1 (Pingoud and Wagner, 2006): EQUATION 3A1.24 DEGRADABLE ORGANIC CARBON ACCUMULATED DURING A YEAR

DDOCma( i + 1) = a • DDOCma( i ) + b • DDOCmd ( i − 1) + c • DDOCmd ( i )

Where: DDOCma (i)

=

DDOCm stock in the SWDS at the beginning of year i, Gg C

DDOCmd (i)

=

DDOCm disposed during year i, Gg C

e

-k

(constant)

a

=

b

=

c

=

1/k • (1-e

Δ

=

delay constant, in years (between 0 and 1 years)

1/k • (e

-e ) − Δ • e-k (constant)

-k(1-Δ) -k

) + Δ (constant)

-k(1-Δ)

For an immediately starting decay (Δ=0), the constant b is equal to zero, so that Equation 3A1.24 reduces to an equation that relates the carbon pool in a given year i to the carbon pool in the previous year i-1 and the amount of DOC being deposited during year i. It can further be shown (Pingoud and Wagner, 2006) that this form can be used to calculate recursively the corresponding CH4 produced in a given year: EQUATION 3A1.25 CH4 GENERATED DURING A YEAR

CH 4 gen( i ) = q • [a' • DDOCma( i ) − b' • DDOCmd ( i − 1 ) + c' • DDOCmd ( i )]

Where: CH4 gen (i)

=

CH4 generated during year i, Gg C

DDOCma(i)

=

DDOC stock in the SWDS at the beginning of year i, Gg C

DDOCmd(i)

=

DDOC disposed during year i, Gg C

q

=

MCF • F • 16/12

a’

=

1−e

b’

=

c’

=

-k

1/k • (e

= 1 − a (constant)

-e ) − Δ • e-k = b (constant)

-k(1-Δ) -k

1− Δ − 1/k • (1-e

-k(1-Δ)

) = 1 − c (constant)

References Pingoud, K. and Wagner, F. (2006). Methane emissions from landfills and decay of harvested wood products: the first order decay revisited. IIASA Interim Report IR-06-004. Barrow, Gordon M. (1996). Physical Chemistry, Mc Graw Hill, NewYork, 6th ed. IPCC (2000). Good Practice Guidance and Uncertianty Management in National Greenhouse Gas Inventories. Penman, J., Kruger D., Galbally, I., Hiraishi, T., Nyenzi, B., Enmanuel, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K. and Tanabe, K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Biological Treatment of Solid Waste

CHAPTER 4

BIOLOGICAL TREATMENT OF SOLID WASTE

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.1

Volume 5: Waste

Authors Riitta Pipatti (Finland) Joao Wagner Silva Alves (Brazil), Qingxian Gao (China), Carlos López Cabrera (Cuba), Katarina Mareckova (Slovakia), Hans Oonk (Netherlands), Elizabeth Scheehle (USA), Chhemendra Sharma (India), Alison Smith (UK), Per Svardal (Norway), and Masato Yamada (Japan)

4.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Biological Treatment of Solid Waste

Contents 4

Biological Treatment of Solid Waste 4.1

Methodological issues ......................................................................................................................... 4.4

4.1.1

Choice of method ........................................................................................................................ 4.5

4.1.2

Choice of activity data ................................................................................................................. 4.6

4.1.3

Choice of emission factors .......................................................................................................... 4.6

4.2

Completeness ...................................................................................................................................... 4.7

4.3

Developing a consistent time series .................................................................................................... 4.7

4.4

Uncertainty assessment ....................................................................................................................... 4.7

4.5

QA/QC ................................................................................................................................................ 4.7

4.6

Reporting and Documentation ............................................................................................................. 4.7

References ........................................................................................................................................................... 4.8

Equations Equation 4.1

CH4 emissions from biological treatment ............................................................................ 4.5

Equation 4.2

N2O emissions from biological treatment ............................................................................ 4.5

Tables Table 4.1

Default emission factors for CH4 and N2O emissions from biological treatment of waste ...................................................................................... 4.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

4 BIOLOGICAL TREATMENT OF SOLID WASTE 4.1

METHODOLOGICAL ISSUES

Composting and anaerobic digestion of organic waste, such as food waste, garden (yard) and park waste and sludge, is common both in developed and developing countries. Advantages of the biological treatment include: reduced volume in the waste material, stabilisation of the waste, destruction of pathogens in the waste material, and production of biogas for energy use. The end products of the biological treatment can, depending on its quality, be recycled as fertiliser and soil amendment, or be disposed in SWDS. Anaerobic treatment is usually linked with methane (CH4) recovery and combustion for energy, and thus the greenhouse gas emissions from the process should be reported in the Energy Sector. Anaerobic sludge treatment at wastewater treatment facilities is addressed in Chapter 6, Wastewater Treatment and Discharge, and emissions should be reported under the categories of Wastewater. However, when sludge from wastewater treatment is transferred to an anaerobic facility which is co-digesting sludge with solid municipal or other waste, any related CH4 and nitrous oxide (N2O) emissions should be reported under this category, biological treatment of solid waste. Where these gases are used for energy, then associated emissions should be reported in the Energy Sector. Composting is an aerobic process and a large fraction of the degradable organic carbon (DOC) in the waste material is converted into carbon dioxide (CO2). CH4 is formed in anaerobic sections of the compost, but it is oxidised to a large extent in the aerobic sections of the compost. The estimated CH4 released into the atmosphere ranges from less than 1 percent to a few per cent of the initial carbon content in the material (Beck-Friis, 2001; Detzel et al., 2003; Arnold, 2005). Composting can also produce emissions of N2O. The range of the estimated emissions varies from less than 0.5 percent to 5 percent of the initial nitrogen content of the material (Petersen et al., 1998; Hellebrand 1998; Vesterinen, 1996; Beck-Friis, 2001; Detzel et al., 2003). Poorly working composts are likely to produce more both of CH4 and N2O (e.g., Vesterinen, 1996). Anaerobic digestion of organic waste expedites the natural decomposition of organic material without oxygen by maintaining the temperature, moisture content and pH close to their optimum values. Generated CH4 can be used to produce heat and/or electricity, wherefore reporting of emissions from the process is usually done in the Energy Sector. The CO2 emissions are of biogenic origin, and should be reported only as an information item in the Energy Sector. Emissions of CH4 from such facilities due to unintentional leakages during process disturbances or other unexpected events will generally be between 0 and 10 percent of the amount of CH4 generated. In the absence of further information, use 5 percent as a default value for the CH4 emissions. Where technical standards for biogas plants ensure that unintentional CH4 emissions are flared, CH4 emissions are likely to be close to zero. N2O emissions from the process are assumed to be negligible, however, the data on these emissions are very scarce. Mechanical-biological (MB) treatment of waste is becoming popular in Europe. In MB treatment, the waste material undergoes a series of mechanical and biological operations that aim to reduce the volume of the waste as well as stabilise it to reduce emissions from final disposal. The operations vary by application. Typically, the mechanical operations separate the waste material into fractions that will under go further treatment (composting, anaerobic digestion, combustion, recycling). These may include separation, shredding and crushing of the material. The biological operations include composting and anaerobic digestion. The composting can take place in heaps or in composting facilities with optimisation of the conditions of the process as well as filtering of the produced gas. The possibilities to reduce the amount of organic material to be disposed at landfills are large, 40 60 percent (Kaartinen, 2004). Due to the reduced amount in material, organic content and biological activity, the MB-treated waste will produce up to 95 percent less CH4 than untreated waste when disposed in SWDS. The practical reductions have been smaller and depend on the type and duration of MB treatments in question (see e.g., Binner, 2002). CH4 and N2O emissions during the different phases of the MB treatment depend on the specific operations and the duration of the biological treatment. Overall, biological treatment of waste affects the amount and composition of waste that will be deposited in SWDS. Waste stream analyses (see example in Box 3.1) are recommended methodologies for estimating the impact of the biological treatment on emissions from SWDS. The estimation of CH4 and N2O emissions from biological treatment of solid waste involves following steps:

4.4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Biological Treatment of Solid Waste

Step 1:

Collect data on the amount and type of solid waste which is treated biologically. Data on composting and anaerobic treatment should be collected separately, where possible. Regional default data on composting are provided in Table 2.1 in Chapter 2, and country-specific data for some countries can be found in Annex 2A.1 of this Volume. Anaerobic digestion of solid waste can be assumed to be zero where no data are available. The default data should be used only when country-specific data are not available (see also Section 4.1.2).

Step 2:

Estimate the CH4 and N2O emissions from biological treatment of solid waste using Equations 4.1 and 4.2. Use default or country-specific emission factors in accordance with the guidance as provided in Sections 4.1.1, 4.1.2 and 4.1.3.

Step 3:

Subtract the amount of recovered gas from the amount of CH4 generated to estimate net annual CH4 emissions, when CH4 emissions from anaerobic digestion are recovered.

Consistency between CH4 and N2O emissions from composting or anaerobic treatment of sludge and emissions from treatment of sludge reported in the Wastewater Treatment and Discharge category should be checked. Also, if emissions from anaerobic digestion are reported under Biological Treatment of Solid Waste, the inventory compilers should check that these emissions are not also included under the Energy Sector. Relevant information on activity data collection, choice of emission factor and method used in estimating the emissions should be documented following the guidance in Section 4.6.

4.1.1

Choice of method

The CH4 and N2O emissions of biological treatment can be estimated using the default method given in Equations 4.1 and 4.2 shown below:

EQUATION 4.1 CH4 EMISSIONS FROM BIOLOGICAL TREATMENT CH 4 Emissions = ∑ (M i • EFi ) • 10 −3 − R i

Where: CH4 Emissions

=

total CH4 emissions in inventory year, Gg CH4

Mi

=

mass of organic waste treated by biological treatment type i, Gg

EF

=

emission factor for treatment i, g CH4/kg waste treated

i

=

composting or anaerobic digestion

R

=

total amount of CH4 recovered in inventory year, Gg CH4

When CH4 emissions from anaerobic digestion are reported, the amount of recovered gas should be subtracted from the amount CH4 generated. The recovered gas can be combusted in a flare or energy device. The amount of CH4 which is recovered is expressed as R in Equation 4.1. If the recovered gas is used for energy, then also the resulting greenhouse gas emissions from the combustion of the gas should be reported under Energy Sector. The emissions from combustion of the recovered gas are however not significant, as the CO2 emissions are of biogenic origin, and the CH4 and N2O emissions are very small so good practice in the Waste Sector does not require their estimation. However, if it is wished to estimate such emissions, the emissions from flaring should be reported under the Waste Sector. A discussion of emissions from flaring and more detailed information are given in Volume 2, Energy, Chapter 4.2. Emissions from flaring are not treated at Tier 1.

EQUATION 4.2 N2O EMISSIONS FROM BIOLOGICAL TREATMENT N 2 O Emissions = ∑ (M i • EFi ) • 10 −3 i

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

Where: N2O Emissions

=

total N2O emissions in inventory year, Gg N2O

Mi

=

mass of organic waste treated by biological treatment type i, Gg

EF

=

emission factor for treatment i, g N2O/kg waste treated

i

=

composting or anaerobic digestion

Three tiers for this category are summarised below. Tier 1:

Tier 1 uses the IPCC default emission factors.

Tier 2:

Country-specific emission factors based on representative measurements are used for Tier 2.

Tier 3:

Tier 3 methods would be based on facility or site-specific measurements (on-line or periodic).

4.1.2

Choice of activity data

Activity data on biological treatment can be based on national statistics. Data on biological treatment can be collected from municipal or regional authorities responsible for waste management, or from waste management companies. Table 2.1 in Chapter 2, Waste Generation, Composition and Management Data, gives regional default values on biological treatment. Country-specific default values for some countries can be found in Annex 2A.1 of this Volume. These data can be used as a starting point. It is good practice that countries use national, annually or periodically collected data, where available.

4.1.3

Choice of emission factors

4.1.3.1

T IER 1

The emissions from composting, and anaerobic digestion in biogas facilities, will depend on factors such as type of waste composted, amount and type of supporting material (such as wood chips and peat) used, temperature, moisture content and aeration during the process. Table 4.1 gives default factors for CH4 and N2O emissions from biological treatment for Tier 1 method.

TABLE 4.1 DEFAULT EMISSION FACTORS FOR CH4 AND N2O EMISSIONS FROM BIOLOGICAL TREATMENT OF WASTE Type of biological treatment

CH4 Emission Factors (g CH4/kg waste treated)

N2O Emission Factors (g N2O/kg waste treated)

on a dry weight basis

on a wet weight basis

on a dry weight basis

on a wet weight basis

Composting

10 (0.08 - 20)

4 (0.03 - 8)

0.6 (0.2 - 1.6)

0.3 (0.06 - 0.6)

Anaerobic digestion at biogas facilities

2 (0 - 20)

1 (0 - 8)

Assumed negligible

Assumed negligible

Remarks

Assumptions on the waste treated: 25-50% DOC in dry matter, 2% N in dry matter, moisture content 60%. The emission factors for dry waste are estimated from those for wet waste assuming a moisture content of 60% in wet waste.

Sources: Arnold, M.(2005) Personal communication; Beck-Friis (2002); Detzel et al. (2003); Petersen et al. 1998; Hellebrand 1998; Hogg, D. (2002); Vesterinen (1996).

Emission from MB treatment can be estimated using the default values in Table 4.1 for the biological treatment. Emissions during mechanical operations can be assumed negligible.

4.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Biological Treatment of Solid Waste

4.1.3.2

T IER 2

AND

T IER 3

In Tier 2, the emissions factors should be based on representative measurements that cover relevant biological treatment options applied in the country. In Tier 3, emission factors would be based on facility/site-specific measurements (on-line or periodic).

4.2

COMPLETENESS

Reporting on CH4 and N2O emissions from biological treatment, where present, will complement the reporting of emissions from SWDS and burning of waste and contribute to full coverage of all sources in the Waste Sector. This will be particularly important in countries for which biological treatment is, or is becoming, significant.

4.3

DEVELOPING A CONSISTENT TIME SERIES

As the methodological guidance for estimating and reporting of emissions from biological treatment was not included in the previous IPCC Guidelines, it is recommended that the whole time series is estimated using the same methodology. The activity data for earlier years may not be available in all countries. Also current data on biological treatment may not be collected on an annual basis. The methods for obtaining missing data are described in Volume 1, Chapter 5, Time Series Consistency. The default emission factors are based on limited amount of studies. The data availability is expected to improve in coming years. It is good practice to use updated scientific information to improve emission factors when it becomes available. Then, the estimates for the whole times series should be recalculated accordingly.

4.4

UNCERTAINTY ASSESSMENT

The uncertainty in activity data will depend on how the data are collected. The uncertainty estimates for waste generation and the fraction of waste treated biologically can be estimated in the same manner as for MSW disposed at SWDS (see Table 3.5). The uncertainties will depend on the quality of data collection in the country. Uncertainties in the default emission factors can be estimated using the ranges given in Table 4.1. Uncertainties in country-specific emission factors will depend on the sampling design and measurement techniques used to determine the emission factors.

4.5

QA/QC

The requirements on QA/QC addressed in Section 3. 8 in Chapter 3, Solid Waste Disposal, are also applicable for biological treatment of waste.

4.6

REPORTING AND DOCUMENTATION

It is good practice to document and archive all information required to produce the national greenhouse gas inventory as outlined in Section 6.11 of Chapter 6, QA/QC and Verification, in Volume 1 of these Guidelines. A few examples of specific documentation and reporting relevant to this category are outlined in the following paragraphs. •

• •

•

The sources of activity data should be described and referenced. The information on the collection frequency and coverage (e.g., whether composting at households is included or not) should be given. Information on types of waste (e.g., food waste, garden and park waste) composted or treated anaerobically should be provided, if available. Country-specific emission factors should be justified and referenced. In cases where reporting of biological treatment will be split under several sectors and/or categories, the reporting should be clarified under all relevant sectors/categories, to avoid double counting or omissions.

The worksheets developed for the estimation of the greenhouse gas emissions from biological treatment are included at the end of this Volume. These worksheets include information on activity data and emission factors used to calculate the estimates.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

References Arnold, M. (2005). Espoo: VTT Processes: Unpublished material from measurements from biowaste composts. (Personal communication). Beck-Friis, B.G. (2001). Emissions of ammonia, nitrous oxide and methane during composting of organic household waste. Uppsala: Swedish University of Agricultural Sciences. 331 p. (Doctoral Thesis). Binner, E. (2002). The impact of Mechanical-Biological Pretreatment on the Landfill Behaviour of Solid Wastes. Workshop Biowaste. Brussels, 8-10.04.2002. 16 p. Detzel, A., Vogt, R., Fehrenbach, H., Knappe, F. and Gromke, U. (2003). Anpassung der deutschen Methodik zur rechnerischen Emissionsermittlung und internationale Richtlinien: Teilbericht Abfall/Abwasser. IFEU Institut - Öko-Institut e.V. 77 p. Hellebrand, H.J. (1998). ‘Emissions of nitrous oxide and other trace gases during composting of grass and green waste’, J. agric, Engng Res., 69:365-375. Hogg, D., Favoino, E., Nielsen, N.,Thompson, J., Wood, K., Penschke, A., Economides, D. and Papageorgiou, S., (2002). Economic analysis of options for managing biodegradable municipal waste, Final Report to the European Commission, Eunomia Research & Consulting, Bristol, UK. Kaartinen, T. (2004). Sustainable disposal of residual fractions of MSW to future landfills. Espoo: Technical University of Helsinki. (Master of Science Thesis). In Finnish. Petersen, S.O., Lind, A.M. and sommer, S.G. (1998). ‘Nitrogen and organic matter losses during storage of cattle and pig manure’, J. Agric. Sci., 130: 69-79. Vesterinen, R. (1996): Impact of waste management alternatives on greenhouse gas emissions: Greenhouse gas emissions from composting. Jyväskylä: VTT Energy. Research report ENE38/T0018/96. (In Finnish). 30p.

4.8

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Incineration and Open Burning of Waste

CHAPTER 5

INCINERATION AND OPEN BURNING OF WASTE

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.1

Volume 5: Waste

Authors G.H. Sabin Guendehou (Benin), Matthias Koch (Germany) Leif Hockstad (USA), Riitta Pipatti (Finland), and Masato Yamada (Japan)

5.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Incineration and Open Burning of Waste

Contents 5

Incineration and Open Burning of Waste 5.1

Introduction ..................................................................................................................................................... 5.5

5.2

Methodological issues ..................................................................................................................................... 5.6

5.2.1

Choice of method for estimating CO2 emissions ................................................................................... 5.6

5.2.2

Choice of method for estimating CH4 emissions ................................................................................. 5.11

5.2.3

Choice of method for estimating N2O emissions ................................................................................. 5.13

5.3

Choice of activity data .................................................................................................................................. 5.15

5.3.1

Amount of waste incinerated ................................................................................................................ 5.15

5.3.2

Amount of waste open-burned ............................................................................................................. 5.16

5.3.3

Dry matter content ................................................................................................................................ 5.17

5.4

Choice of emission factors ............................................................................................................................ 5.18

5.4.1

CO2 emission factors ............................................................................................................................ 5.18

5.4.2

CH4 emission factors ............................................................................................................................ 5.20

5.4.3

N2O emission factors ............................................................................................................................ 5.21

5.5

Completeness ................................................................................................................................................ 5.22

5.6

Developing a consistent time series .............................................................................................................. 5.23

5.7

Uncertainty assessment ................................................................................................................................. 5.23

5.7.1

Emission factor uncertainties ............................................................................................................... 5.23

5.7.2

Activity data uncertainties .................................................................................................................... 5.24

5.8

QA/QC, Reporting and Documentation ....................................................................................................... 5.24

5.8.1

Inventory Quality Assurance/Quality Control (QA/QC) .................................................................... 5.24

5.8.2

Reporting and Documentation ............................................................................................................. 5.25

References ...................................................................................................................................................................... 5.25

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.3

Volume 5: Waste

Equations Equation 5.1

CO2 emission estimate based on the total amount of waste combusted ....................................... 5.7

Equation 5.2

CO2 emission estimate based on the MSW composition .............................................................. 5.7

Equation 5.3

CO2 emission from incineration of fossil liquid waste ................................................................ 5.10

Equation 5.4

CH4 emission estimate based on the total amount of waste combusted ..................................... 5.12

Equation 5.5

N2O emission estimate based on the waste input to the incinerators .......................................... 5.14

Equation 5.6

N2O emission estimate based on influencing factors .................................................................. 5.14

Equation 5.7

Total amount of municipal solid waste open-burned .................................................................. 5.16

Equation 5.8

Dry matter content in MSW ........................................................................................................ 5.17

Equation 5.9

Total carbon content in MSW ...................................................................................................... 5.19

Equation 5.10

Fossil carbon fraction (FCF) in MSW ......................................................................................... 5.19

Figures Figure 5.1

Decision Tree for CO2 emissions from incineration and open burning of waste ......................... 5.9

Figure 5.2

Decision Tree for CH4 and N2O emissions from incineration/open-burning of waste .............. 5.12

Tables Table 5.1

Overview of data sources of different tier levels ............................................................................ 10

Table 5.2

Default data for CO2 emission factors for incineration and open burning of waste ...................... 18

Table 5.3

CH4 emission factors for incineration of MSW ............................................................................. 20

Table 5.4

N2O emission factors for incineration of MSW ............................................................................. 21

Table 5.5

N2O emission factors for incineration of sludge and industrial waste ........................................... 21

Table 5.6

Default N2O emission factors for different types of waste and management practices ................ 22

Boxes Box 5.1

5.4

Example of estimating MSWB ..................................................................................................... 5.17

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Incineration and Open Burning of Waste

5 INCINERATION AND OPEN BURNING OF WASTE 5.1

INTRODUCTION

Waste incineration is defined as the combustion of solid and liquid waste in controlled incineration facilities. Modern refuse combustors have tall stacks and specially designed combustion chambers, which provide high combustion temperatures, long residence times, and efficient waste agitation while introducing air for more complete combustion. Types of waste incinerated include municipal solid waste (MSW), industrial waste, hazardous waste, clinical waste and sewage sludge 1 . The practice of MSW incineration is currently more common in developed countries, while it is common for both developed and developing countries to incinerate clinical waste. Emissions from waste incineration without energy recovery are reported in the Waste Sector, while emissions from incineration with energy recovery are reported in the Energy Sector, both with a distinction between fossil and biogenic carbon dioxide (CO2) emissions. The methodology described in this chapter is applicable in general both to incineration with and without energy recovery. Co-firing of specific waste fractions with other fuels is not addressed in this chapter, as co-firing is covered in Volume 2, Energy. Emissions from agricultural residue burning are considered in the AFOLU Sector, Chapter 5 of Volume 4. Open burning of waste can be defined as the combustion of unwanted combustible materials such as paper, wood, plastics, textiles, rubber, waste oils and other debris in nature (open-air) or in open dumps, where smoke and other emissions are released directly into the air without passing through a chimney or stack. Open burning can also include incineration devices that do not control the combustion air to maintain an adequate temperature and do not provide sufficient residence time for complete combustion. This waste management practice is used in many developing countries while in developed countries open burning of waste may either be strictly regulated, or otherwise occur more frequently in rural areas than in urban areas. Incineration and open burning of waste are sources of greenhouse gas emissions, like other types of combustion. Relevant gases emitted include CO2, methane (CH4) and nitrous oxide (N2O). Normally, emissions of CO2 from waste incineration are more significant than CH4 and N2O emissions. Consistent with the 1996 Guidelines (IPCC, 1997), only CO2 emissions resulting from oxidation, during incineration and open burning of carbon in waste of fossil origin (e.g., plastics, certain textiles, rubber, liquid solvents, and waste oil) are considered net emissions and should be included in the national CO2 emissions estimate. The CO2 emissions from combustion of biomass materials (e.g., paper, food, and wood waste) contained in the waste are biogenic emissions and should not be included in national total emission estimates. However, if incineration of waste is used for energy purposes, both fossil and biogenic CO2 emissions should be estimated. Only fossil CO2 should be included in national emissions under Energy Sector while biogenic CO2 should be reported as an information item also in the Energy Sector. Moreover, if combustion, or any other factor, is causing long term decline in the total carbon embodied in living biomass (e.g., forests), this net release of carbon should be evident in the calculation of CO2 emissions described in the Agriculture, Forestry and Other Land Use (AFOLU) Volume of the 2006 Guidelines. This chapter provides guidance on methodological choices for estimating and reporting CO2, CH4 and N2O emissions from incineration and open burning of all types of combustible waste. Where possible, default values for activity data, emission factors and other parameters are provided. Traditional air pollutants from combustion - non-methane volatile organic compounds (NMVOCs), carbon monoxide (CO), nitrogen oxides (NOx), sulphur oxides (SOx) - are covered by existing emission inventory systems. Therefore, the IPCC does not provide new methodologies for these gases here, but recommends that national experts or inventory compilers use existing published methods under international agreements. Some key examples of the current literature providing methods include EMEP/CORINAIR Guidebook (EMEP 2004), US EPA's Compilation of Air Pollutant Emissions Factors, AP-42, Fifth Edition (USEPA, 1995), EPA Emission Inventory Improvement Program Technical Report Series, Vol. III Chapter 16: Open Burning (USEPA, 2001). The estimation of indirect N2O emissions, resulting from the conversion of nitrogen deposition to soils due to NOx emissions from waste incineration and open burning, is addressed in Section 5.4.3 of this chapter. General background

1

Waste generation, composition and management practices, including waste incineration and open burning, are addressed in detail in Chapter 2 of this volume.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

and information on the reporting of the indirect N2O emissions is given in Chapter 7, Precursors and Indirect Emissions, of Volume 1, General Guidance and Reporting.

5.2

METHODOLOGICAL ISSUES

The choice of method will depend on national circumstances, including whether incineration and open burning of waste are key categories in the country, and to what extent country- and plant-specific information is available or can be gathered. For waste incineration, the most accurate emission estimates can be developed by determining the emissions on a plant-by-plant basis and/or differentiated for each waste category (e.g., MSW, sewage sludge, industrial waste, and other waste including clinical waste and hazardous waste). The methods for estimating CO2, CH4 and N2O emissions from incineration and open burning of waste vary because of the different factors that influence emission levels. Estimation of the amount of fossil carbon in the waste burned is the most important factor determining the CO2 emissions. The non-CO2 emissions are more dependent on the technology and conditions during the incineration process. Intentional burning of waste on solid waste disposal sites is sometimes used as a management practice in some countries. Emissions from this practice and those from unintentional fires (accidental fires on solid waste disposal sites) should be estimated and reported according to the methodology and guidance provided for open burning of waste. The general approach to calculate greenhouse gas emissions from incineration and open burning of waste is to obtain the amount of dry weight of waste incinerated or open-burned (preferably differentiated by waste type) and to investigate the related greenhouse gas emission factors (preferably from country-specific information on the carbon content and the fossil carbon fraction). For CO2 emissions from incineration and open burning of waste, the basic approach is given here as an example of a consecutive approach: • •

•

•

•

Identify types of wastes incinerated/open-burned: MSW, sewage sludge, industrial solid waste, and other wastes (especially hazardous waste and clinical waste) incinerated/open-burned. Compile data on the amount of waste incinerated/open-burned including documentation on methods used and data sources (e.g., waste statistics, surveys, expert judgement): Regional default data are also provided in Table 2.1 in Chapter 2, Waste Generation, Composition and Management Data, and country-specific data for a limited number of countries in Annex 2A.1 of this Volume. The default data should be used only when country-specific data are not available. For open burning, the amount of waste can be estimated based on demographic data. This is addressed in Section 5.3.2. Use default values provided on dry matter content, total carbon content, fossil carbon fraction and oxidation factor (see Section 5.4.1.3) for different types of wastes: For MSW, preferably identify the waste composition and calculate the respective dry matter content, total carbon content, and fossil carbon fraction using default data provided for each MSW component (plastic, paper, etc) in Section 2.3, Waste composition, of this Volume. Calculate the CO2 emissions from incineration and open burning of solid wastes. Provide data in the worksheets given in Annex 1 of this Volume.

For other waste types and other greenhouse gases, the approach usually does not differentiate as much as for the MSW in terms of waste composition. Detailed guidance on the choice of method, activity data and emission factors for all major types of waste to estimate the emissions from relevant waste incineration and burning practices is outlined in the following sections.

5.2.1

Choice of method for estimating CO2 emissions

The common method for estimating CO2 emissions from incineration and open burning of waste is based on an estimate of the fossil carbon content in the waste combusted, multiplied by the oxidation factor, and converting the product (amount of fossil carbon oxidised) to CO2. The activity data are the waste inputs into the incinerator or the amount of waste open-burned, and the emission factors are based on the oxidised carbon content of the waste that is of fossil origin. Relevant data include the amount and composition of the waste, the dry matter content, the total carbon content, the fossil carbon fraction and the oxidation factor. The following sections describe the tiers to be applied for the estimation of CO2 emissions from incineration and open burning of waste. The tiers differ to what extent the total amount of waste, the emission factors and parameters used are default (Tier 1), country-specific (Tier 2a, Tier 2b) or plant-specific (Tier 3).

5.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Incineration and Open Burning of Waste

5.2.1.1

T IER 1

The Tier 1 method is a simple method used when CO2 emissions from incineration/open burning are not a key category. Data on the amount of waste incinerated/open-burned are necessary 2 . Default data on characteristic parameters (such as dry matter content, carbon content and fossil carbon fraction) for different types of waste (MSW, sewage sludge, industrial waste and other waste such as hazardous and clinical waste) are provided in Table 5.2 in this chapter and Tables 2.3 to 2.6 in Section 2.3, on waste composition in Chapter 2 of this Volume. The calculation of the CO2 emissions is based on an estimate of the amount of waste (wet weight) incinerated or open-burned taking into account the dry matter content, the total carbon content, the fraction of fossil carbon and the oxidation factor. The method based on the total amount of waste combusted is outlined in Equation 5.1, and the method based on the MSW composition is given in Equation 5.2. It is preferable to apply Equation 5.2 for MSW, but if the required MSW data are not available, Equation 5.1 should be used instead. EQUATION 5.1 CO2 EMISSION ESTIMATE BASED ON THE TOTAL AMOUNT OF WASTE COMBUSTED CO2 Emissions = ∑ ( SWi • dmi • CFi • FCFi • OFi ) • 44 / 12 i

Where: CO2 Emissions

= CO2 emissions in inventory year, Gg/yr

SWi

=

total amount of solid waste of type i (wet weight) incinerated or open-burned, Gg/yr

dmi

=

dry matter content in the waste (wet weight) incinerated or open-burned, (fraction)

CFi

=

fraction of carbon in the dry matter (total carbon content), (fraction)

FCFi

=

fraction of fossil carbon in the total carbon, (fraction)

OFi

=

oxidation factor, (fraction)

44/12

=

conversion factor from C to CO2

i

=

type of waste incinerated/open-burned specified as follows: MSW: municipal solid waste (if not estimated using Equation 5.2), ISW: industrial solid waste, SS: sewage sludge, HW: hazardous waste, CW: clinical waste, others (that must be specified)

If the activity data of wastes are available on a dry matter basis, which is preferable, the same equation can be applied without specifying the dry matter content and the wet weight separately. Also if a country has data on the fraction of fossil carbon in the dry matter, it does not need to provide CFi and FCFi separately but instead it should combine them into one component. For MSW, it is good practice to calculate the CO2 emissions on the basis of waste types/material (such as paper, wood, plastics) in the waste incinerated or open-burned as shown in Equation 5.2. EQUATION 5.2 CO2 EMISSION ESTIMATE BASED ON THE MSW COMPOSITION

(

)

CO2 Emissions = MSW • ∑ WF j • dm j • CF j • FCF j • OF j • 44 / 12 j

Where: CO2 Emissions

2

= CO2 emissions in inventory year, Gg/yr

MSW

=

total amount of municipal solid waste as wet weight incinerated or open-burned, Gg/yr

WFj

=

fraction of waste type/material of component j in the MSW (as wet weight incinerated or openburned)

dmj

=

dry matter content in the component j of the MSW incinerated or open-burned, (fraction)

CFj

=

fraction of carbon in the dry matter (i.e., carbon content) of component j

The methodology is addressed under Section 5.3, Choice of Activity data, and Chapter 2, Waste Generation, Composition and Management.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

FCFj

=

fraction of fossil carbon in the total carbon of component j

OFj

=

oxidation factor, (fraction)

44/12

=

conversion factor from C to CO2

=

component of the MSW incinerated/open-burned such as paper/cardboard, textiles, food waste, wood, garden (yard) and park waste, disposable nappies, rubber and leather, plastics, metal, glass, other inert waste.

with:

∑ WF j = 1 j

j

If data by waste type/material are not available, the default values for waste composition given in Section 2.3 Waste composition could be used. If CO2 emissions from incineration and open burning of waste is a key category, it is good practice to apply a higher tier.

5.2.1.2

T IER 2

The Tier 2 method is based on country-specific data regarding waste generation, composition and management practices. Here, Equations 5.1 and 5.2 are also applied, as outlined for the Tier 1 method. It is good practice to use the Tier 2 method when CO2 emission from incineration and open burning of waste is a key category or when more detailed data are available or can be gathered. Tier 2a requires the use of country-specific activity data on the waste composition and default data on other parameters for MSW (Equation 5.2). For other categories of waste, country-specific data on the amounts are required (Equation 5.1). Country-specific MSW composition, even if using default data on other parameters, will reduce uncertainties compared to the use of aggregated MSW statistics. A Tier 2a method for open burning of waste could incorporate annual surveys on the amounts and the composition of waste burned by households, authorities and companies responsible for the waste management. Tier 2b requires country-specific data on the amount of waste incinerated/open-burned by waste type (Equation 5.1) or MSW composition (Equation 5.2), dry matter content, carbon content, fossil carbon fraction and oxidation factor, in addition to country-specific waste composition data. If these data are available, an estimate according to Tier 2b will have lower uncertainty than Tier 2a. A Tier 2b method for open burning of waste could incorporate annual and detailed surveys on the amounts and the composition of waste burned by households, authorities and companies responsible for the waste management described in Tier 2a, with a combined measurement programme for emission factors related to the practices of open burning in the country. It is good practice to implement those measurement programmes in different periods of the year to allow consideration of all seasons since emission factors depend on the combustion conditions. For example, in some countries where there is a rainy season and open burning is practised, more waste is burned during the dry season because of better burning conditions. Under these circumstances emission factors may vary with season. In any case, all country-specific methods, activity data and parameters used should be described and justified in a transparent manner. The documentation should include descriptions on any experimental procedures, measurements and analyses made as well as information on atmospheric parameters such as temperature, wind, and rainfall in the case of open burning.

5.2.1.3

T IER 3

The Tier 3 method utilises plant-specific data to estimate CO2 emissions from waste incineration. It is good practice at this tier level to consider parameters affecting both the fossil carbon content and the oxidation factor. Factors affecting the oxidation factor include: •

•

•

•

5.8

type of installation/technology: fixed bed, stoker, fluidised bed, kiln, operation mode: continuous, semi-continuous, batch type, size of the installation, parameters such as the carbon content in the ash.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Incineration and Open Burning of Waste

The total fossil CO2 emissions from waste incineration are calculated as the sum of all plant-specific fossil CO2 emissions. It is good practice to include all waste types and the entire amount incinerated as well as all types of incinerators in the inventory. The estimation is done similarly as in the Tier 1 and Tier 2 methods and at the end, the CO2 emissions from all plants, installations and other subcategories are added up to estimate the total emissions from waste incineration in the country. The decision tree in Figure 5.1 gives guidance on the choice of method. The choice will depend on the national circumstances and the availability of data. Management practices in the decision tree are related to incineration and open burning. Figure 5.1

Decision Tree for CO 2 emissions from incineration and open burning of waste Start

Are plant-specific data and/or data for different management practices available?

Estimate CO2 emissions from plant- and/or management-specific data.

Yes

Box 4: Tier 3

No

Are country-specific data on waste generation, composition and management practices available?

Yes

Are country-specific data on emission factors for waste management practices available? Yes

Collect countryspecific data.

No

Yes

Are CO2 emissions from waste incineration or open burning a key category 1?

Estimate CO2 emissions using country-specific data and emission factors. No

Box 3: Tier 2b

No Estimate the total amount of waste incinerated / openburned and the waste fractions in MSW.

Estimate CO2 emissions using country-specific data and default emission factors. Box 2: Tier 2a Estimate CO2 emissions using the total amount estimated above and default data on emission factors. Box 1: Tier 1

1. See Volume 1 Chapter 4, “Methodological Choice and Identification of Key Categories” (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.9

Volume 5: Waste

The following Table 5.1 gives an overview on Tier levels at which default values or country-specific data are to be applied for calculating CO2 emissions.

TABLE 5.1 OVERVIEW OF DATA SOURCES OF DIFFERENT TIER LEVELS

Total waste amount (W)

Waste fraction (WF): % of each component mainly for MSW

Dry matter content (dm)

Carbon fraction (CF)

Fossil carbon fraction (FCF)

Oxidation factor (OF)

Tier 3

plant- / managementspecific

plant- / managementspecific

plant- / managementspecific

plant- / managementspecific

plant- / managementspecific

plant- / managementspecific

Tier 2b

countryspecific

countryspecific

countryspecific

countryspecific

default / countryspecific

default / countryspecific

Tier 2a

countryspecific

countryspecific

default

default

default

default

Tier 1

default / countryspecific

default

default

default

default

default

Data sources

Tiers

5.2.1.4

CO 2

EMISSIONS FROM INCINERATION OF FOSSIL LIQUID WASTE

Fossil liquid waste is here defined as industrial and municipal residues, based on mineral oil, natural gas or other fossil fuels. It includes waste formerly used as solvents and lubricants. It does not include wastewater, unless it is incinerated (e.g., because of a high solvent content). Biogenic liquid waste, e.g., waste oil from food processing, does not need to be accounted for, unless biogenic and fossil oil are mixed and a significant portion of its carbon is of fossil origin. Fossil liquid waste is here considered as a specific type of waste, for which combustion is a common management practice. In some countries it is not incinerated together with solid waste (e.g., hazardous waste) but treated separately. Fossil liquid waste is in many cases not taken into account in the waste statistics, because in some countries they are not included as part of the main waste streams discussed in Section 5.2.1.1. Fossil liquid waste is not taken into account in Section 5.2.1.1 to 5.2.1.3 because the equations are not applicable for this type of waste. Unless fossil liquid waste is included in other types of waste (e.g., industrial waste, hazardous waste), the emissions need to be calculated separately. Consistent with the reporting guidance, emissions from incineration of fossil liquid waste are reported in the Energy Volume when it is used for energy purposes. CO2 emissions from incineration of fossil liquid waste can be estimated using Equation 5.3.

EQUATION 5.3 CO2 EMISSION FROM INCINERATION OF FOSSIL LIQUID WASTE CO2 Emissions = ∑ ( ALi • CLi • OFi ) • 44 / 12 i

Where: CO2 Emissions

5.10

= CO2 emissions from incineration of fossil liquid waste, Gg

ALi

=

amount of incinerated fossil liquid waste type i, Gg

CLi

=

carbon content of fossil liquid waste type i, (fraction)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Incineration and Open Burning of Waste

OFi

=

oxidation factor for fossil liquid waste type i, (fraction)

44/12

=

conversion factor from C to CO2

If the amount of fossil liquid waste is in terms of volume, this should be converted into mass using the density. If no information on the density of fossil liquid waste in the country is available, the default density provided can be used. Three tiers to estimate the CO2 emissions from incineration of fossil liquid waste are described as: Tier 1: The default values are provided in Table 5.2. Tier 2: Country-specific data on amount of fossil liquid waste incinerated, carbon content and country-specific oxidation factor are required at this tier, for each type of fossil liquid waste. Tier 3: Plant-specific data should be used if available. The required data are the same as for Tier 1 and Tier 2. Estimates should consider all plants incinerating fossil liquid waste as well as the total amount of fossil liquid waste incinerated.

5.2.2

Choice of method for estimating CH4 emissions

CH4 emissions from incineration and open burning of waste are a result of incomplete combustion. Important factors affecting the emissions are temperature, residence time, and air ratio (i.e., air volume in relation to the waste amount). The CH4 emissions are particularly relevant for open burning, where a large fraction of carbon in the waste is not oxidised. The conditions can vary much, as waste is a very heterogeneous and a low quality fuel with variations in its calorific value. In large and well-functioning incinerators, CH4 emissions are usually very small. It is good practice to apply the CH4 emission factors provided in Chapter 2, Stationary Combustion, of Volume 2. Methane can also be generated in the waste bunker of incinerators if there are low oxygen levels and subsequent anaerobic processes in the waste bunker. This is only the case where wastes are wet, stored for long periods and not well agitated. Where the storage area gases are fed into the air supply of the incineration chamber, they will be incinerated and emissions will be reduced to insignificant levels (BREF, 2005). Figure 5.2 shows the decision tree for CH4 and N2O emissions from the incineration and open burning of waste.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.11

Volume 5: Waste

Figure 5.2

Decision Tree for CH 4 and N 2 O emissions from incineration/open-burning of waste

Start

Are plantspecific or management practice-specific data available?

Yes

Estmate CH4 / N2O emissions from plant- or management practicespecific data. Box 3: Tier 3

No

Are country-specific data by waste type, technology or management practice available? Collect countryspecific data

No

Yes

Is CH4 / N2O emission from incineration / openburning of waste key category1?

Yes

Estimate CH4 / N2O emissions from country-specific data. Box 2: Tier 22

No Estimate CH4 / N2O emission using amount estimated above and default emission factors.

Estimate total amount of wastes incinerated or open-burned.

Box 1: Tier 12 1. See Volume 1 Chapter 4, “Methodological Choice and Identification of Key Categories”, (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees. 2. The Tier 1 and Tier 2 methods follow the same approach but differ to the extent country-specific data are applied.

5.2.2.1

T IER 1

The calculation of CH4 emissions is based on the amount of waste incinerated/open-burned and on the related emission factor as shown in Equation 5.4. EQUATION 5.4 CH4 EMISSION ESTIMATE BASED ON THE TOTAL AMOUNT OF WASTE COMBUSTED CH 4 Emissions = ∑ (IWi • EFi ) • 10 −6 i

5.12

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Incineration and Open Burning of Waste

Where: CH4 Emissions = CH4 emissions in inventory year, Gg/yr IWi

=

amount of solid waste of type i incinerated or open-burned, Gg/yr

EFi

=

aggregate CH4 emission factor, kg CH4/Gg of waste

-6

10

=

conversion factor from kilogram to gigagram

i

=

category or type of waste incinerated/open-burned, specified as follows: MSW: municipal solid waste, ISW: industrial solid waste, HW: hazardous waste, CW: clinical waste, SS: sewage sludge, others (that must be specified)

The amount and composition of waste should be consistent with the activity data used for estimating CO2 emissions from incineration/open burning. Default emission factors are provided under Section 5.4.2, CH4 emission factors, for incineration and open burning of waste. If the CH4 emissions from incineration or open burning of waste are key categories, it is good practice to use a higher tier.

5.2.2.2

T IER 2

Tier 2 is similar to Tier 1 but takes country-specific data into account. Tier 2 also follows Equation 5.4, as Tier 1. Inventory compilers should use country-specific data including activity data, emission factors by waste, technology or management practice. Countries with a high proportion of open burning or batch-type/semi-continuous incinerators should consider further investigation of CH4 emission factors.

5.2.2.3

T IER 3

It is good practice to use the Tier 3 method when plant-specific data are available. All incinerators should be considered and their emissions summed. Figure 5.2 provides a general decision tree for estimating CH4 emissions from incineration and open burning of waste. The best results will be obtained if country-specific or plant-specific CH4 emission factors are available. Information on CH4 from incineration and open burning of waste to satisfy the requirement of Tier 3 method is currently scant. If detailed monitoring shows that the concentration of a greenhouse gas in the discharge from a combustion process is equal to or less than the concentration of the same gas in the ambient intake air to the combustion process then emissions may be reported as zero. Reporting these emissions as ‘negative emissions’ would require continuous highquality monitoring of both the air intake and the atmospheric emissions.

5.2.3

Choice of method for estimating N2 O emissions

Nitrous oxide is emitted in combustion processes at relatively low combustion temperatures between 500 and 950 °C. Other important factors affecting the emissions are the type of air pollution control device, type and nitrogen content of the waste and the fraction of excess air (BREF, 2005; Korhonen et al., 2001; Löffler et al., 2002; Kilpinen, 2002; Tsupari et al., 2005). N2O emissions from the combustion of fossil liquid waste can be considered negligible, unless country-specific data indicate otherwise. Figure 5.2 provides a general decision tree for the estimation of N2O emissions from incineration and open burning of waste. The most accurate results will be obtained if N2O emissions are determined for each plant based on the plantspecific monitoring data, and then summed.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

5.2.3.1

T IER 1

The calculation of N2O emissions is based on the waste input to the incinerators or the amount of waste open-burned and a default emission factor. This relationship is summarised in the following Equation 5.5: EQUATION 5.5 N2O EMISSION ESTIMATE BASED ON THE WASTE INPUT TO THE INCINERATORS N 2O Emissions =

∑ (IWi • EFi ) • 10 −6 i

Where: N2O Emissions = N2O emissions in inventory year, Gg/yr IWi

=

amount of incinerated/open-burned waste of type i , Gg/yr

EFi

=

N2O emission factor (kg N2O/Gg of waste) for waste of type i

-6

10

=

conversion from kilogram to gigagram

i

=

category or type of waste incinerated/open-burned, specified as follows: MSW: municipal solid waste, ISW: industrial solid waste, HW: hazardous waste, CW: clinical waste, SS: sewage sludge, others (that must be specified)

The amount and composition of waste should be consistent with the activity data used for the calculation of CO2 and CH4 emissions. Default emission factors are provided in Section 5.4.3. However, inventory compilers should be aware that default emission factors for N2O emissions from incineration and open burning of waste have a relatively high level of uncertainty. The use of country-specific data are preferable, if they meet quality assurance and quality control criteria outlined in Section 5.8 and in Chapter 6, QA/QC and Verification, in Volume 1. If N2O emissions from incineration or open burning of waste are key categories, it is good practice to use a higher tier.

5.2.3.2

T IER 2

Tier 2 uses the same method as for the Tier 1, however, country-specific data are used to obtain emission factors. Where practical, N2O emission factors should be derived from emission measurements. Where measured data are not available, other reliable means can be used to develop emission factors. Emission factors for N2O differ with type of facility and type of waste. Emission factors for fluidised-bed plants are higher than those for plants with grate furnaces. Emission factors for MSW are usually lower than for sewage sludge. Ranges of N2O emission factors reflect abatement techniques, such as the injection of ammonia or urea used in some NOx abatement technologies that may increase emissions of N2O, temperature, and the residence time of the waste in the incinerator. Tier 2 is applicable when country-specific emission factors are available but no detailed information on a plant-byplant basis or further differentiated by management practices are available.

5.2.3.3

T IER 3

Tier 3 methods are based on site-specific data on flue gas concentrations. Equation 5.6 indicates the relevant factors of influence and enables to estimate N2O emissions. EQUATION 5.6 N2O EMISSION ESTIMATE BASED ON INFLUENCING FACTORS N 2 O Emissions = ∑ ( IWi • ECi • FGVi ) • 10 −9 i

Where: N2O Emissions = IWi

5.14

=

N2O emissions in inventory year, Gg/yr

amount of incinerated waste of type i, Gg/yr

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Chapter 5: Incineration and Open Burning of Waste

ECi

=

N2O emission concentration in flue gas from the incineration of waste type i, mg N2O/m3

FGVi

=

flue gas volume by amount of incinerated waste type i, m3/Mg

10-9

=

conversion to gigagram

i

=

category or type of waste incinerated/open-burned, specified as follows: MSW: municipal solid waste, ISW: industrial solid waste, HW: hazardous waste, CW: clinical waste, SS: sewage sludge, others (that must be specified)

Tier 3 outlines the most detailed and accurate approach, where data on a plant-by-plant basis or for various management practices are available. It requires data on the flue gas volume and concentration of N2O emissions in the flue gas. Continuous emission monitoring is technically feasible, but not necessarily cost-effective. Periodic measurements should be conducted sufficiently often to account for the variability of N2O generation (i.e., due to the nitrogen content in the waste), and different types of incinerator operating conditions (e.g., combustion temperature, with or without daily shut down).

5.3

CHOICE OF ACTIVITY DATA

General guidance for activity data collection for solid waste treatment and disposal as well as default values on waste generation, management practices and composition are given in Chapter 2, Waste Generation, Composition and Management. Activity data needed in the context of incineration and open burning of waste includes the amount of waste incinerated or open-burned, the related waste fractions (composition) and the dry matter content. As the type of waste combusted and the applied management practice are relevant for the CO2, CH4 and N2O emissions, the choice of activity data section is outlined according to the common factors related to activity data and not separately for each of the emitted gases. In addition, the waste composition is particularly relevant for the CO2 emissions. The N2O emissions are mainly determined by technology, combustion temperature and waste composition. Completeness of combustion (temperature, oxygen, residence time) is particularly relevant for the CH4 emissions. The N content and technology-specific activity data are related to higher tiers, and country-specific schemes to collect the data (surveys to plants, research projects, etc.) need to be established. The composition of MSW generated in the country can be used as a default for MSW incinerated or open-burned when data by management practice are not available. More accurate emission estimates can be obtained if data on the composition of waste incinerated or open-burned are available (Tier 2). It is good practice to make a distinction between composition of wastes incinerated/open-burned and the composition of all waste delivered to the waste management system, if data are available. If a certain waste type/material in MSW (e.g., paper waste) or industrial waste is incinerated separately, country-specific data on the incinerated or open-burned fraction should be determined taking this into account. Particular attention should be paid to the representativeness of the country-specific data. Ideally, the data used should be representative for the waste incinerated and open-burned. If such data are not available, country-specific data without differentiation by waste type or incineration technology used are still more appropriate than default data. Results of sampling, measurements and waste sorting studies applied in the data collection should be documented transparently and quality assurance and quality control practices outlined in Section 5.8 should be applied. In developing countries, basic data on amount of waste and treatment practices may not be available. Waste incineration in some developing countries is likely to take place only in minor quantities. Therefore, emissions from open burning of waste should be considered in detail (see Section 5.3.2), while emissions from incineration should also be quantified if expected to be relevant. If emissions from incineration are assumed to be negligible, the reasons for the assumption should clearly be explained and documented by the inventory compiler.

5.3.1

Amount of waste incinerated

Obtaining data on the amount of waste incinerated is a prerequisite for preparing an emission inventory for incineration of waste. Many countries that use waste incineration should have plant-specific data on the amount of MSW and other types of waste incinerated. For hazardous and clinical wastes, the activity data may be difficult to obtain since waste incinerated in some of these plants (e.g., on-site incinerators in chemical and pharmaceutical industry) may not be included in waste statistics. For these waste types, even though plant-specific data may not be available, overall data for total waste incinerated may be available from the waste administration. The default data given in Chapter 2, Section 2.2 on waste generation and management data (see particularly Tables 2.1, 2.3, and 2.4) and Annex 2A.1: Waste generation and management data – by country and regional averages, from the

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respective region or neighbouring countries with similar conditions could be used when country-specific data are not available. It is good practice to apply accurate boundaries system for the distinction to report emissions under the energy, waste or industry sections. Also, agricultural residue burning should be reported in the AFOLU Sector. See Section 5.8.2, Reporting and Documentation.

5.3.2

Amount of waste open-burned

The amount of waste open-burned is the most important activity data required for estimating emissions from open burning of waste. In most countries statistics may not be available. Where the data on waste amount are not available, alternative methods such as data from period surveys, research project or expert judgement can be used to estimate total amount of waste burned together with appropriate explanation and documentation. Extrapolation and interpolation can be used to obtain estimates for years for which no data are available. Population and economic data can be used as drivers. Equation 5.7 below can be used to estimate the total amount of MSW open-burned. EQUATION 5.7 TOTAL AMOUNT OF MUNICIPAL SOLID WASTE OPEN-BURNED MSWB = P • Pfrac • MSWP • B frac • 365 • 10 −6

Where: MSWB =

Total amount of municipal solid waste open-burned, Gg/yr

P

=

population (capita)

Pfrac

=

fraction of population burning waste, (fraction)

MSWP =

per capita waste generation, kg waste/capita/day

Bfrac

=

fraction of the waste amount that is burned relative to the total amount of waste treated, (fraction)

365

=

number of days by year

-6

=

conversion factor from kilogram to gigagram

10

Fraction of population burning waste (P f r a c ) Open burning includes regularly burning and sporadically burning. Regularly burning means that this is the only practice used to eliminate waste. Sporadically burning means that this practice is used in addition to other practices and therefore open burning is not the only practice used to eliminate waste. For example, when waste is not collected or is burned for other reasons such as cost avoidance. For countries that have well functioning waste collection systems in place, it is good practice to investigate whether any fossil carbon is open-burned. In a developed country, Pfrac can be assumed to be the rural population for a rough estimate. In a region where urban population exceeds 80 percent of total population, one can assume no open burning of waste occurs. In a developing country, mainly in urban areas, Pfrac can be roughly estimated as being the sum of population whose waste is not collected by collection structures and population whose waste is collected and disposed in open dumps that are burned. In general, it is preferable to apply country- and regional-specific data on waste handling practices and waste streams.

Fraction of waste amount open-burned (B f r a c ) Bfrac means the fraction of waste for which carbon content is converted to CO2 and other gases. When all the amount of waste is burned Bfrac could be considered equal to 1 (an oxidation factor related to the combustion efficiency is applied later to estimate emissions using Equation 5.1 or 5.2). However, in some cases, mainly when a substantial quantity of waste in open dumps is burned, a relatively large part of waste is left unburned (in open dumps the fraction not compacted often burns). In this situation Bfrac should be estimated using survey or research data available, or expert judgement, and applied in the Equation 5.7 (here also an oxidation factor is applied later to estimate emissions using Equation 5.1 or 5.2).

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Chapter 5: Incineration and Open Burning of Waste

When open burning is practiced, countries are encouraged to undertake surveys in order to estimate Pfrac and Bfrac and then MSWB using the Equation 5.7. Box 5.1 gives an example of estimating MSWB .

BOX 5.1 EXAMPLE OF ESTIMATING MSWB

In a country of population P inhabitants, 15 percent of the population burns waste in the backyard (barrels or on the ground) and 20 percent sends waste to open-dumps that are burned. Therefore, Pfrac = 35 percent. The remainder 65percent are eliminated through other waste treatment systems. The example calculation is as follows: MSWP =

0.57 kg waste/capita/day

Bfrac

=

0.6 (default value suggested for burning of open dumps based on expert judgment considering the fact that 0.4 is suggested as default value for MCF of unmanaged shallow SWDS).

For P

=

1 500 000 inhabitants, the total amount of waste open-burned is:

MSWB =

65.54 Gg/yr

National statistics on population and per capita waste generation exist in many countries and can be used. Data on population, per capita waste generation and waste composition used should be consistent with those reported under the categories of Solid Waste Disposal and Biological Treatment of Solid Waste. Population data are usually available from national statistics, international databases such as those of United Nations also provide international population statistics (UN, 2002) can be used where national statistics are not available (see Section 3.2.2). The amount of fossil liquid waste combusted can include both by incineration and by open burning (see Section 5.2.1.4). The amount does not need to be differentiated by type of management practice, as the default methodology is applicable to both practices (see also Chapter 2).

5.3.3

Dry matter content

An important distinction needs to be made between dry weight and wet weight of waste, because the water content of waste can be substantial. Therefore, the dry matter content of the waste or waste fraction is an important parameter to be determined. The weight of waste incinerated should be converted from wet weight to dry weight, if the related emission factors refer to dry weight. The dry matter content of waste can range from below 50 percent in countries with a higher percentage of food waste to 60 percent in countries with higher fractions of paper-based and fossil carbon-based wastes. Detailed procedures for determination of the dry matter content are being developed in the document PrEN (2001). Table 2.4 in Section 2.3 provides default data on dry matter content for different waste types/material that can be used to estimate dry matter content in MSW. This can be done using Equation 5.8. EQUATION 5.8 DRY MATTER CONTENT IN MSW

dm = ∑ ( WFi • dmi i

)

Where: dm

=

total dry matter content in the MSW

WFi

=

fraction of component i in the MSW

dmi

=

dry matter content in the component i.

It is important to notice that Equation 5.8 is a part of Equation 5.2.

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5.4

CHOICE OF EMISSION FACTORS

Emission factors in the context of incineration and open burning of waste relate the amount of greenhouse gas emitted to the weight of waste incinerated or open-burned. In the case of CO2, this applies data on the fractions of carbon and fossil carbon in the waste. For CH4 and N2O, this primarily depends on the treatment practice and the combustion technology. For the estimation of CO2, CH4 and N2O emissions from incineration and open burning of waste, guidance on choice of the emission factors is outlined in the following sections.

5.4.1

CO 2 emission factors

It is generally more practical to estimate CO2 emissions from incineration and open burning of waste using calculations based on the carbon content in the waste, instead of measuring the CO2 concentration. Default values for parameters related to emission factors are shown in Table 5.2. Each of these factors is discussed in detail in the sections below3. TABLE 5.2 DEFAULT DATA FOR CO2 EMISSION FACTORS FOR INCINERATION AND OPEN BURNING OF WASTE Management practice

Parameters

MSW

Industrial Waste (%)

Clinical Waste (%)

Sewage Sludge (%)

Fossil liquid waste (%)

Note 4

Note 5

Dry matter content in % of wet weight

see Note 1

NA

NA

NA

NA

Total carbon content in % of dry weight

see Note 1

50

60

40 − 50

80

Fossil carbon fraction in % of total carbon content

see Note 2

90

40

0

100

incineration

100

100

100

100

100

Open- burning (see Note 3)

58

NO

NO

NO

NO

Oxidation factor in % of carbon input NA: Not Available, NO: Not Occurring

Note 1: Use default data from Table 2.4 in Section 2.3 Waste composition and equation 5.8 (for dry matter), Equation 5.9 (for carbon content) and Equation 5.10 (for fossil carbon fraction). Note 2: Default data by industry type is given in Table 2.5 in Section 2.3 Waste composition. For estimation of emissions, use equations mentioned in Note 1. Note 3: When waste is open-burned, refuse weight is reduced by approximately 49 to 67 percent (US-EPA, 1997, p.79). A default value of 58 percent is suggested. Note 4: See Section 2.3.2 Sludge in Chapter 2. Note 5: The total carbon content of fossil liquid waste is provided in percent of wet weight and not in percent of dry weight (GIO, 2005). References: GPG2000 (IPCC, 2000), Lead Authors of the 2006 Guidelines, Expert judgement.

5.4.1.1

T OTAL

CARBON CONTENT

While a fraction of the carbon in waste incinerated or open-burned is derived from biomass raw materials (e.g., paper, and food waste), part of the total carbon is plastics or other products made from fossil fuel. Table 5.2 in this section and Section 2.3 in Chapter 2 provide default carbon fractions for waste types and MSW waste fractions respectively. Further details on the fraction of fossil carbon are provided below. Inventory compilers can use data on composition of MSW and the default data on total carbon content for different waste types/material of MSW provided in Section 2.3 of Chapter 2 to estimate the total carbon content in MSW (see Equation 5.9).

3

The parameters total carbon content in percent of dry weight and fossil carbon fraction in percent of total carbon content could be combined to the parameter: fossil carbon content in percent of dry weight.

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EQUATION 5.9 TOTAL CARBON CONTENT IN MSW CF = ∑ ( WFi • CFi i

)

Where: CF

=

total carbon content in MSW

WFi

=

fraction of component i in the MSW

CFi

=

carbon content in the waste type/material i in MSW

This is also reflected in Equation 5.2.

5.4.1.2

F OSSIL

CARBON FRACTION

In estimating emissions from incineration and open burning of waste, the desired approach is to separate carbon in the waste into biomass and fossil fuel based fractions. For the purposes of calculating anthropogenic CO2 emissions from incineration and open burning of waste, the amount of fossil carbon in the waste should be determined. The fraction of fossil carbon will differ for different waste categories and types of waste. The carbon in MSW and clinical waste is of both biogenic and fossil origin. In sewage sludge the fossil carbon usually can be neglected while the carbon in hazardous waste is usually of fossil origin. Default data for these waste categories and different waste types/materials included in MSW are provided in Table 5.2 and in Chapter 2, Section 2.3. Where plant-specific data are available, the exact composition of the waste being incinerated should be collected and used in CO2 emission calculations. If such data are not readily available, country-specific data may be used. This type of data will most likely be in the form of general surveys of the country-specific waste stream. The survey should contain not only the composition, but also the fate of the waste streams (i.e., the percentage of a particular waste type, which is incinerated/open-burned). Different fossil fuel-based waste products will contain different percentages of fossil carbon. For each waste stream, an analysis should be performed for each waste type. In general, plastics will represent the waste type being incinerated with the highest fossil carbon fraction. In addition, the fossil carbon content of toxics, synthetic fibres and synthetic rubbers is particularly relevant. A certain amount of tire waste is also considered as source of fossil carbon, since tires can be composed of synthetic rubbers or carbon black. If neither plant-specific waste types nor country-specific waste stream information are available, Section 2.3 in Chapter 2 provides default fossil carbon fractions for the most relevant waste fractions in MSW as well as for specific types of industrial waste and other waste (including hazardous waste and clinical waste). The fractions of fossil and biogenic carbon are likely to change considerably in the future because of recent waste legislation adopted in some countries. Such programmes will influence the total waste flow incinerated, as well as the fossil carbon content of the waste incinerated/open-burned. It is good practice, under Tier 2a, that inventory compilers use country-specific data on composition of MSW and default values provided in Chapter 2, Section 2.3, to estimate fossil carbon fraction (FCF) in MSW using Equation 5.10. EQUATION 5.10 FOSSIL CARBON FRACTION (FCF) IN MSW

FCF = ∑ ( WFi • FCFi i

)

Where: FCF

=

total fossil carbon in the MSW

WFi

=

fraction of waste type i in the MSW

FCFi

=

fraction of fossil carbon in the waste type i of the MSW

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5.4.1.3

OXIDATION FACTOR

When waste streams are incinerated or open-burned most of the carbon in the combustion product oxidises to CO2. A minor fraction may oxidise incompletely due to inefficiencies in the combustion process, which leave some of the carbon unburned or partly oxidised as soot or ash. For waste incinerators it is assumed that the combustion efficiencies are close to 100 percent, while the combustion efficiency of open burning is substantially lower. If oxidation factors of waste incineration below 100 percent are applied, these need to be documented in detail with the data source provided. Table 5.2 presents default oxidation factors by management practices and waste types. If the CO2 emissions are determined on a technology- or plant-specific basis in the country, it is good practice to use the amount of ash (both bottom ash and fly ash) as well as the carbon content in the ash as a basis for determining the oxidation factor.

5.4.2

CH 4 emission factors

CH4 emissions from waste incineration are much dependent on the continuity of the incineration process, the incineration technology, and management practices. The most detailed observations have been made in Japan (GIO, 2004), where the following CH4 emission factors based on technology and operation mode are obtained. Continuous incineration includes incinerators without daily start-up and shutdown. Batch type and semi-continuous incineration mean that the incinerator is usually started-up and shutdown at least once a day. These differences in operation are at the origin of difference in emission factors. It is sometimes observed that the concentrations of CH4 in the exhaust gas of the furnace are below the CH4 concentrations in intake gas of the incinerator (GIO, 2005). Because of the low concentrations and high uncertainties it is here good practice to apply an emission factor of zero (see Section 5.2.2.3). For continuous incineration of MSW and industrial waste, it is good practice to apply the CH4 emission factors provided in Volume 2, Chapter 2, Stationary Combustion. For other MSW incinerators (semi-continuous and batch type), Table 5.3 shows CH4 emission factors reported by GIO, Japan. The CH4 emission factors of other industrial waste incinerators are differentiated by waste type, rather than technology (GIO, 2005). In Japan, the CH4 emission factors of waste oil and of sludge are 0.56 g CH4/t wet weight and 9.7 g CH4/t wet weight, respectively.

TABLE 5.3 CH4 EMISSION FACTORS FOR INCINERATION OF MSW CH4 Emission Factors

Type of incineration/technology

(kg/Gg waste incinerated on a wet weight basis) Continuous incineration Semi-continuous incineration Batch type incineration

stoker

0.2

fluidised bed

Note1

stoker

~0 6

fluidised bed

188

stoker

60

fluidised bed

237

Note 1: In the study cited for this emission factor, the measured CH4 concentration in the exhaust air was lower than the concentration in ambient air. Source: Greenhouse Gas Inventory Office of Japan, GIO 2004.

For open burning of waste, a CH4 emission factor of 6500 g / t MSW wet weight has been reported (EIIP, 2001). This factor should be applied as a default, unless another CH4 emission factor seems more appropriate. If country-specific data are available, these should be applied instead and the method used to derive them as well as the data sources need to be documented in detail.

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5.4.3

N 2 O emission factors

Nitrous oxide emissions from waste incineration are determined by a function of the type of technology and combustion conditions, the technology applied for NOx reduction as well as the contents of the waste stream. As a result, emission factors can vary from site to site. Several countries have reported N2O emissions from waste incineration in their national inventory reports. Table 5.4 shows examples of emission factors that have been used for incineration of MSW. The differences in the emission factors are mainly caused by varying technologies in the context of NOx removal.

TABLE 5.4 N2O EMISSION FACTORS FOR INCINERATION OF MSW Country

Emission factor for MSW (g N2O/t MSW incinerated)

Weight basis

Stocker

47

wet weight

Fluidised bed

67

wet weight

41

wet weight

68

wet weight

Type of Incineration / Technology

Japan 1

Continuous incineration

Semi-continuous incineration Stocker Fluidised bed Batch type incineration

Stoker Fluidised bed

Germany 2 Netherlands

3

Austria 4

56

wet weight

221

wet weight

8

wet weight

20

wet weight

12

wet weight

1

GIO, 2005. Johnke 2003. 3 Spakman 2003. 4 Anderl et al. 2004. 2

Table 5.5 shows the example of N2O emission factors used for estimate emissions from incineration of sludge and industrial waste. TABLE 5.5 N2O EMISSION FACTORS FOR INCINERATION OF SLUDGE AND INDUSTRIAL WASTE Country Japan1

Type of Waste

Type of Incineration / Technology

Waste paper, waste wood

10

waste oil

Germany2 1 2

Emission factor for Industrial Waste (g N2O / t waste) 9.8

Weight basis wet weight wet weight

waste plastics

170

wet weight

sludge (except sewage sludge)

450

wet weight

dehydrated sewage sludge

900

wet weight

high molecular weight flocculant

fluidised bed incinerator at normal temperature

1 508

wet weight

high molecular weight flocculant

fluidised bed incinerator at high temperature

645

wet weight

high molecular weight flocculant

multiple hearth

882

wet weight

other flocculant

882

wet weight

lime sludge

294

wet weight

sewage sludge

990

dry weight

industrial waste

420

wet weight

GIO 2005. Johnke 2003.

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It is good practice to apply these if no country-specific information is available. For open burning of waste, only information on emissions from burning of agricultural residues is available. The approach for agricultural residues is outlined in Volume 4, Section 2.4 in Chapter 2 Non CO2 emissions, and Section 11.2 (N2O emissions from managed soils) in Chapter 11. Assuming an N/C ratio of 0.01 (Crutzen and Andrea, 1990), an emission factor of up to 0.15 g N2O / kg dry matter is obtained as N2O emission factor for agricultural residues. Because it is expected that the nitrogen content of household waste is towards the higher end of the nitrogen content of agricultural wastes, this emission factor for agricultural wastes is suggested here to be used as default value for N2O emissions from open-burning of waste. Based on the current information available and the emission factors provided in Table 5.4 and 5.5, Table 5.6 provides N2O default emission factors for different types of waste and management practices.

TABLE 5.6 DEFAULT N2O EMISSION FACTORS FOR DIFFERENT TYPES OF WASTE AND MANAGEMENT PRACTICES Emission factor Type of waste Technology / Management practice weight basis (g N2O / t waste) continuous and semi-continuous incinerators 50 wet weight MSW MSW

batch-type incinerators

60

wet weight

MSW

open burning

150

dry weight

Industrial waste

all types of incineration

100

wet weight

Sludge (except sewage sludge)

all types of incineration

450

wet weight

Sewage sludge

incineration

990

dry weight

900

wet weight

Source: Expert judgement by lead authors of this chapter of 2006 Guidelines

It is good practice to apply these if no country-specific information is available. NOx can be transformed to N2O in the atmosphere. Therefore, NOx emissions from incineration and open burning of waste can be relevant sources of indirect N2O emissions. When the country has information on NOx emissions, it is good practice to estimate the indirect N2O emissions using the guidance in Chapter 7 Ozone Precursors, SO2 and Indirect Emissions of Volume 1.

5.5

COMPLETENESS

Completeness depends on the reporting of types and amounts of waste incinerated or open-burned. If the method is implemented at the facility-level and then summed across facilities, it is good practice to ensure that all waste incineration plants are covered. Inventory compilers should make efforts to report all waste types arising in their country as well as associated management practices. When different types of waste are incinerated together, it is good practice to estimate emissions from each type of waste separately and report them following guidance provided in this chapter. It should be noted that there are possibilities of double counting CO2 emissions because waste is often incinerated in facilities with energy recovery capabilities. Also, waste can be used as substitute fuel in industrial plants other than waste incineration plants (e.g., in cement and brick kilns, and blast furnaces). In order to avoid double counting or misallocation, guidance provided in this chapter for estimating and reporting emissions from incineration between Waste and Energy Sectors should be followed. For open burning of waste, it could be difficult to determine the total amount of waste burned because reliable statistics are often unavailable. Inventory compilers should consider data that fall outside the official statistics in order to avoid underestimation of emissions. If household waste is open-burned in rural areas (villages, etc.) this should be considered. Open-burning on solid waste disposal sites has an effect to reduce degradable organic carbon (DOC). The reduction in the DOC available for decay, and hence the reduction in future CH4 emissions, can be roughly estimated, at Tier 1, as the product of the amount of waste burned on landfills and the corresponding average DOC. Actually, open burning on landfills is a more complex issue since it would affect some important parameters such as humidity, availability of nutrients, and availability of micro organisms (likely killed by fire or change in their metabolism) to some extent and this would influence subsequent CH4 emissions from landfill at least for a given period. At higher tiers (e.g., Tier 2) countries should strive for improving estimate of emissions arising from this practice as well as its effect on DOC.

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Chapter 5: Incineration and Open Burning of Waste

To check whether completeness has been achieved, a diagram showing waste stream and distribution between management practices could be drawn. This could also facilitate the process of QA/QC.

5.6

DEVELOPING A CONSISTENT TIME SERIES

Emissions of greenhouse gas from incineration and open-burning of waste should be calculated using the same method and data sets consistently for every year in the time series, at the same level of disaggregation. Where country-specific data are used, it is good practice to use the same coefficients and methods for equivalent calculations at all points in the time series. Where consistent data are not available for the same method for any years in the time series, these gaps should be filled according to the guidance provided in Volume 1, Chapter 5 Time Series Consistency, Section 5.3, Resolving data gaps. Activity data may only be available every few years. To achieve time series consistency, various methods such as interpolation, extrapolation from longer time series or trends should be used. (See Chapter 5 of Volume 1.)

5.7

UNCERTAINTY ASSESSMENT

Section 2.3 in Chapter 2, Table 2.4 provides typical ranges as well as single values for parameters relevant for the calculation of CO2 emissions from incineration and open burning of waste. Examples of CH4 and N2O emission factors of some countries are outlined in Section 5.4.2 and Section 5.4.3 respectively. It is good practice that inventory compilers calculate the uncertainty as 95 percent confidence interval for country-defined parameters. Also uncertainty estimates based on expert judgement or the default uncertainty estimates can be used. More recent information could have a lower uncertainty because it reflects changing practices, technical developments, or changing fractions (biogenic and fossil) of incinerated waste. This should form the basis of the inventory uncertainty assessment. Volume 1, Chapter 3, Uncertainties, provides advice on quantifying uncertainties in practice. It includes eliciting and using expert judgements, which in combination with empirical data can provide overall uncertainty estimates. Estimates of emissions from open burning can be highly uncertain due to lack of information mainly in developing countries. The use of country-specific data may introduce additional uncertainty in the following areas:

•

•

If surveys on waste composition are used, the interpretation of definitions of solid waste and surveys may differ, which due to a variety of sources of varying reliability and accuracy. Emission factors for N2O and CH4 for solid waste combustion facilities may span an order of magnitude, reflecting considerable variability in the processes from site to site. Control/removal efficiency can also be uncertain, e.g., due to controls in place to reduce NOx.

5.7.1

Emission factor uncertainties

There is a high level of uncertainty related to the separation of biogenic and fossil carbon fractions in the waste. This uncertainty is mainly related to the uncertainties in waste composition. The major uncertainty associated with CO2 emissions estimate is related to the estimation of the fossil carbon fraction (see Section 3.7 on uncertainty assessment in Chapter 3 of this Volume). Uncertainties associated with CO2 emission factors for open burning depend on uncertainties related to fraction of dry matter in waste open-burned, fraction of carbon in the dry matter, fraction of fossil carbon in the total carbon, combustion efficiency, and fraction of carbon oxidised and emitted as CO2. A default value of ± 40 percent is proposed for countries relying on default data on the composition in their calculations. Direct measurement or monitoring of emissions of N2O and CH4 has less uncertainty. For continuous and periodic emission monitoring, uncertainty depends on the accuracy of measurement instruments and methods used. These are likely to be in order of ± 10 percent. For periodic measurement, uncertainty will also depend on the sampling strategy and frequency, and the uncertainties will be much higher. If default values for N2O and CH4 emission factors are used, uncertainty ranges have been estimated to be ± 100 percent or more.

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5.7.2

Activity data uncertainties

In many developed countries where the amount of waste incinerated is based on waste statistics or plant specific data, uncertainties on the amount of incinerated waste are estimated around ± 5 percent on a wet weight basis. The uncertainty could be higher for some waste types, such as clinical waste. The conversion of waste amounts from wet weight to dry weight adds additional uncertainty. Depending on the frequency and the accuracy of the dry weight determination, this uncertainty varies substantially. The uncertainty of the dry matter content may therefore range between ± 10 percent up to ± 50 percent and even more. When waste statistics are insufficient, population, per capita waste generation, and fraction of waste burned are parameters to be considered for estimating amount of waste open-burned. Uncertainties can be particularly high for the amount of waste generated per capita and the fraction of waste burned. For the countries using the default values for waste generation and management data given in the Section 2.2 in Chapter 2, the uncertainty values for activity data provided in Table 3.5 in Chapter 3 can be used also for incineration. Estimates on the total carbon content and fraction of fossil carbon can be estimated using the ranges given in Table 2.4 in Chapter 2, Section 2.3.

5.8

QA/QC, REPORTING AND DOCUMENTATION

5.8.1

Inventory Quality Assurance/Quality Control (QA/QC)

Quality assurance and quality control checks as outlined in Chapter 6 of Volume 1 should be used when estimating emissions from incineration and open burning of waste. Furthermore, transparency can be improved by the provision of clear documentation and explanations of work undertaken in the following areas. R ev iew of act iv ity data • Inventories compilers should review data collection methods, check data and compare them with other data sources. Data should also be checked with previous year to ensure consistency over time. This includes mainly amount of waste incinerated/open-burned and dry matter content.

•

Diagram of distribution of waste according to management practices should be developed to ensure that the total amount of waste generated is the same as the sum of waste recycled and treated under different management practices.

R ev ie w of em is sio n fa cto rs • Inventory compilers should compare country-specific or plant-specific values of the carbon content of waste, the fossil carbon as fraction of total carbon, and the efficiency of combustion for the incinerator to the default values provided. When there is difference, they should check whether sound explanation is provided. R ev ie w o f d i r e ct e mi s sio n m ea s u r em en t s • Where direct measurement data are available, inventory compilers should confirm that internationally recognised standard methods were used for measurements. If the measurement practices fail this criterion, then the use of these emissions data should be carefully evaluated.

•

Where emissions are measured directly, inventory compilers should compare plant-level factors among plants, and also with IPCC defaults. They should review any significant differences between factors. This is particularly true for hazardous and clinical waste, because these wastes are often not quantified on a plant basis and can vary significantly from plant to plant.

Consistency of act iv ity data a nd emissions fa ctors • The activity data, the emission factors and related factors need to be related to the quantity of waste in a consistent manner: e.g., wet weight or dry weight. Otherwise conversion factors (e.g., dry matter content) need to be applied.

•

5.24

The applied data and factors should preferably refer to the same or similar system boundaries. For example, if one component in an equation relates to rural waste, another to waste in large cities, these should be used in a consistent manner.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Incineration and Open Burning of Waste

5.8.2

Reporting and Documentation

It is good practice to document and archive all information required to produce the national greenhouse gas inventory as outlined in Section 6.11 of Chapter 6 in Volume 1. A few examples of specific documentation and reporting relevant to this category are outlined in the following paragraphs. While documentation is important, it is not practical or necessary to include all documentation in a greenhouse gas inventory report. However, the inventory should include summaries of methods used and references for data sources such that the reported estimates are transparent and steps included in their calculations may be traced and verified. Some countries use different categorisations for waste at local or regional levels. In such instances, the inventory compiler should pay special attention to the consistency with the IPCC categorisation and explain how the data were manipulated to fit the IPCC categories. Inventory compilers should also include information on how they obtained the dry matter content, the carbon content, the fossil carbon fraction and the N2O and CH4 emission factors or any other relevant information. In some countries, incineration plants are used to produce both heat and electricity. In such cases, emissions from incineration of waste for energy purposes should be reported under Energy Sector (fossil CO2, N2O and CH4 from Stationary Combustion, and biogenic CO2 as an information item). Resulting emissions should not be reported in the Waste Sector in order to avoid double counting. In cases where gas, oil or other fuels are used as support fuel to start the incineration process or to maintain the required temperature, consumption of this fuel should not be reported under waste incineration but under the Energy Sector (see Chapter 2, Stationary Combustion, in Volume 2, Energy ). Such fuels, normally, account for less than 3 percent of total calorific input of MSW incineration but could be more important with the incineration of hazardous waste.

References Anderl, M., Halper, D., Kurzweil, A., Poupa S., Wappel, D., Weiss, P. and Wieser M. (2004). Austria’s National Inventory Report 2004: Submission under the United Nations Framework Convention on Climate Change. BREF (2005). European IPPC Bureau. Reference Document on the Best Available Technology for Waste Incineration. Seville, July 2005. Chandler, A.J, Eghmy, T.T., Hartlén, J., Jhelmar, O., Kosson, D.S, Sawell, S.E., van der Sloot, H.A. and Vehlow J. (1997). Municipal Solid Waste Incinerator Residues. The International Ash Working Group, Studies in Environmental Science 67, Elsevier Amsterdam. Crutzen, P.J. and Andreae, M.O. (1990). ‘Biomass burning in the tropics: Impact on atmospheric chemistry and biogeochemical cycles’, Science 250: 1669-1678. EMEP. (2004). EMEP/CORINAIR Guidebook, Update September 2004. http://reports.eea.eu.int/EMEPCORINAIR4/en/group_09.pdf GIO (2004). National Greenhouse Gas Inventory Report of JAPAN. Ministry of the Environment/ Japan Greenhouse Gas Inventory Office of Japan (GIO) / Center for Global Environmental Research (CGER) / National Institute for Environmental Studies (NIES). October 2004. GIO (2005). National Greenhouse Gas Inventory Report of JAPAN. Ministry of the Environment/ Japan Greenhouse Gas Inventory Office of Japan (GIO) / Center for Global Environmental Research (CGER) / National Institute for Environmental Studies (NIES). Guendehou, G.H.S. and Ahlonsou E.D. (2002). Contribution to non-CO2 greenhouse gases inventory for Cotonou (Republic of Benin): waste sector, In: Proceedings of the Third International Symposium on Non-CO2 Greenhouse Gases: Scientific Understanding, Control Options and Policy Aspects, Maastricht, The Netherlands, Jan 2002, pp. 79-81. Guendehou, G.H.S. (2004). Personal communication. Cotonou 2004.

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Volume 5: Waste

IPCC (1997a). Revised 1996 IPCC Guidelines for National Greenhouse Inventories, Volume 3 Reference Manual. Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (1997b). Revised 1996 IPCC Guidelines for National Greenhouse Inventories, Volume 2 Workbook. Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. IPCC (2000). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, Penman, J., Kruger, D., Galbally, I., Hiraishi, T., Nyenzi, B., Emmanuel, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K. and Tanabe, K. (Eds). Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA/IGES, Hayama, Japan. IPCC (2003). Good Practice Guidance for Land Use, land-Use Change and Forestry, Penman, J., Gytarsky, M., Hiraishi, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., and Wagner, F. (Eds), Intergovernmental Panel on Climate Change (IPCC), IPCC/IGES, Hayama, Japan. Johnke, B. (2003). Emissionsberichterstattung / Inventarerstellung für das Jahr 2002 [Emission reporting / preparation of the inventory for the year 2002]. Umweltbundesamt, Berlin 2003 [In German]. Kilpinen, P. (2002). Formation and decomposition of nitrogen oxides. In: Raiko, R., Saastamoinen, J., Hupa, M. and Kurki-Suonio, I. 2002. Poltto ja palaminen. International Flame Research Foundation - Suomen kansallinen osasto. Gummerus Oy, Jyväskylä, Finland. [In Finnish]. Korhonen, S., Fabritius, M. and Hoffren, H. (2001). Methane and nitrous oxide emissions in the Finnish energy production. Vantaa: Fortum Power and Heat Oy.36 p. (TECH-4615). Löffler, G., Vargadalem, V. and Winter, F. (2002). Catalytic effect of biomass ash on CO, CH4 and HCN oxidation under fluidised bed bombustor conditions. Fuel 81, 711-717. PrEN. (2001). Characterization of waste: Calculation of dry matter by determination of dry residue and water content. PrEN 14346. Spakman, J., van Loon, M.M.J., van der Auweraert, R.J.K., Gielen, D.J., Olivier, J.G.J. and Zonneveld, E.A. (2004). Method for calculating greenhouse gas emissions. Emission Registration Series/Environmental Monitor No. 37b, MinVROM. The Hague 2003. Tsupari, E., Monni, S., and Pipatti, R. (2005). Non-CO2 greenhouse gas emissions from boilers and industrial processes - evaluation and update of emission factors for the Finnish National Greenhouse Gas Inventory. VTT Research Notes 2321. Espoo, Finland. 82 p. + app. 24 p. UN (2002). United Nations Population Division: World Population Prospects – The 2002 Revision Population Database. http://esa.un.org/unpp/index.asp?panel=3 USEPA (1995). US EPA's Compilation of Air Pollutant Emissions Factors, AP-42, Edition 5, United States Environmental Protection Agency (USEPA). http://www.epa.gov/ttn/chief/ap42/ USEPA (1997). Control Technology Center. Evaluation of Emissions from the Open Burning of Household Waste in Barrels. Volume1. Technical Report. United States Environmental Protection Agency (USEPA). USEPA (1998). Paul M. Lemieux. Evaluation of Emissions from the Open Burning of Household Waste in Barrels : Project Summary. United States Environmental Protection Agency (USEPA). USEPA (2001). US-EPA Emission Inventory Improvement Program. Volume III Chapter 16 Open Burning. United States Environmental Protection Agency (USEPA). http://www.epa.gov/ttn/chief/eiip/techreport/volume03/iii16_apr2001.pdf

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Wastewater Treatment and Discharge

CHAPTER 6

WASTEWATER TREATMENT AND DISCHARGE

2006 IPCC Guidelines for National Greenhouse Gas Inventories

6.1

Volume 5: Waste

Authors Michiel R. J. Doorn (Netherlands), Sirintornthep Towprayoon (Thailand), Sonia Maria Manso Vieira (Brazil), William Irving (USA), Craig Palmer (Canada), Riitta Pipatti (Finland), and Can Wang (China)

6.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Wastewater Treatment and Discharge

Contents 6

Wastewater Treatment and Discharge 6.1

Introduction ......................................................................................................................................... 6.6

6.1.1 6.2

Changes compared to 1996 Guidelines and Good Practice Guidance ......................................... 6.9

Methane emissions from wastewater ................................................................................................... 6.9

6.2.1

Methodological issues ................................................................................................................. 6.9

6.2.2

Domestic wastewater ................................................................................................................. 6.10

6.2.2.1

Choice of method ................................................................................................................. 6.10

6.2.2.2

Choice of emission factors ................................................................................................... 6.12

6.2.2.3

Choice of activity data ......................................................................................................... 6.13

6.2.2.4

Time series consistency ....................................................................................................... 6.16

6.2.2.5

Uncertainties ........................................................................................................................ 6.16

6.2.2.6

QA/QC, Completeness, Reporting and Documentation ....................................................... 6.17

6.2.3

6.3

Industrial wastewater ................................................................................................................. 6.18

6.2.3.1

Choice of method ................................................................................................................. 6.19

6.2.3.2

Choice of emission factors ................................................................................................... 6.20

6.2.3.3

Choice of activity data ......................................................................................................... 6.21

6.2.3.4

Time series consistency ....................................................................................................... 6.22

6.2.3.5

Uncertainties ........................................................................................................................ 6.23

6.2.3.6

QA/QC, Completeness, Reporting and Documentation ....................................................... 6.23

Nitrous oxide emissions from wastewater ......................................................................................... 6.24

6.3.1

Methodological issues ............................................................................................................... 6.24

6.3.1.1

Choice of method ................................................................................................................. 6.24

6.3.1.2

Choice of emission factors ................................................................................................... 6.25

6.3.1.3

Choice of activity data ......................................................................................................... 6.25

6.3.2

Time series consistency ............................................................................................................. 6.26

6.3.3

Uncertainties .............................................................................................................................. 6.26

6.3.4

QA/QC, Completeness, Reporting and Documentation ............................................................ 6.27

References ......................................................................................................................................................... 6.28

2006 IPCC Guidelines for National Greenhouse Gas Inventories

6.3

Volume 5: Waste

Equations Equation 6.1

Total CH4 emissions from domestic wastewater ............................................................... 6.11

Equation 6.2

CH4 emission factor for each domestic wastewater treatment/discharge pathway or system ............................................................................. 6.12

Equation 6.3

Total organically degradable material in domestic wastewater ......................................... 6.13

Equation 6.4

Total CH4 emissions from industrial wastewater ............................................................... 6.20

Equation 6.5

CH4 emission factor for industrial wastewater .................................................................. 6.21

Equation 6.6

Organically degradable material in industrial wastewater ................................................. 6.22

Equation 6.7

N2O emissions from wastewater effluent .......................................................................... 6.25

Equation 6.8

Total nitrogen in the effluent ............................................................................................. 6.25

Equation 6.9

N2O emission from centralized wastewater treatment processes ....................................... 6.25

Figures

6.4

Figure 6.1

Wastewater treatment systems and discharge pathways ...................................................... 6.7

Figure 6.2

Decision Tree for CH4 emissions from domestic wastewater ............................................ 6.10

Figure 6.3

Decision Tree for CH4 emissions from industrial wastewater treatment ........................... 6.19

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Wastewater Treatment and Discharge

Tables Table 6.1

CH4 and N2O emission potentials for wastewater and sludge treatment and discharge systems ......................................................................................................... 6.8

Table 6.2

Default maximum CH4 producing capacity (Bo) for domestic wastewater ....................... 6.12

Table 6.3

Default MCF values for domestic wastewater ................................................................... 6.13

Table 6.4

Estimated BOD5 values in domestic wastewater for selected regions and countries ......... 6.14

Table 6.5

Suggested values for urbanisation (U) and degree of utilisation of treatment, discharge pathway or method (Ti,j) for each income group for selected countries ............ 6.15

Table 6.6

Example of the application of default values for degrees of treatment utilization (T) by income groups .............................................................................................................. 6.16

Table 6.7

Default uncertainty ranges for domestic wastewater ......................................................... 6.17

Table 6.8

Default MCF values for industrial wastewater .................................................................. 6.21

Table 6.9

Examples of industrial wastewater data ............................................................................. 6.22

Table 6.10

Default uncertainty ranges for industrial wastewater ........................................................ 6.23

Table 6.11

N2O methodology default data .......................................................................................... 6.27

Boxes Box 6.1

Subcategory - Emissions from advanced centralised wastewater treatment plants ........... 6.26

2006 IPCC Guidelines for National Greenhouse Gas Inventories

6.5

Volume 5: Waste

6 WASTEWATER TREATMENT AND DISCHARGE 6.1

INTRODUCTION

Wastewater can be a source of methane (CH4) when treated or disposed anaerobically. It can also be a source of nitrous oxide (N2O) emissions. Carbon dioxide (CO2) emissions from wastewater are not considered in the IPCC Guidelines because these are of biogenic origin and should not be included in national total emissions. Wastewater originates from a variety of domestic, commercial and industrial sources and may be treated on site (uncollected), sewered to a centralized plant (collected) or disposed untreated nearby or via an outfall. Domestic wastewater is defined as wastewater from household water use, while industrial wastewater is from industrial practices only. 1 Treatment and discharge systems can sharply differ between countries. Also, treatment and discharge systems can differ for rural and urban users, and for urban high income and urban low-income users. Sewers may be open or closed. In urban areas in developing countries and some developed countries, sewer systems may consist of networks of open canals, gutters, and ditches, which are referred to as open sewers. In most developed countries and in high-income urban areas in other countries, sewers are usually closed and underground. Wastewater in closed underground sewers is not believed to be a significant source of CH4. The situation is different for wastewater in open sewers, because it is subject to heating from the sun and the sewers may be stagnant allowing for anaerobic conditions to emit CH4. (Doorn et al., 1997). The most common wastewater treatment methods in developed countries are centralized aerobic wastewater treatment plants and lagoons for both domestic and industrial wastewater. To avoid high discharge fees or to meet regulatory standards, many large industrial facilities pre-treat their wastewater before releasing it into the sewage system. Domestic wastewater may also be treated in on-site septic systems. These are advanced systems that may treat wastewater from one or several households. They consist of an anaerobic underground tank and a drainage field for the treatment of effluent from the tank. Some developed countries continue to dispose of untreated domestic wastewater via an outfall or pipeline into a water body, such as the ocean. The degree of wastewater treatment varies in most developing countries. In some cases industrial wastewater is discharged directly into bodies of water, while major industrial facilities may have comprehensive in-plant treatment. Domestic wastewater is treated in centralized plants, pit latrines, septic systems or disposed of in unmanaged lagoons or waterways, via open or closed sewers. In some coastal cities domestic wastewater is discharged directly into the ocean. Pit latrines are lined or unlined holes of up to several meters deep, which may be fitted with a toilet for convenience. Figure 6.1 shows different pathways for wastewater treatment and discharge. Centralized wastewater treatment methods can be classified as primary, secondary, and tertiary treatment. In primary treatment, physical barriers remove larger solids from the wastewater. Remaining particulates are then allowed to settle. Secondary treatment consists of a combination of biological processes that promote biodegradation by micro-organisms. These may include aerobic stabilisation ponds, trickling filters, and activated sludge processes, as well as anaerobic reactors and lagoons. Tertiary treatment processes are used to further purify the wastewater of pathogens, contaminants, and remaining nutrients such as nitrogen and phosphorus compounds. This is achieved using one or a combination of processes that can include maturation/polishing ponds, biological processes, advanced filtration, carbon adsorption, ion exchange, and disinfection. Sludge is produced in all of the primary, secondary and tertiary stages of treatment. Sludge that is produced in primary treatment consists of solids that are removed from the wastewater and is not accounted for in this category. Sludge produced in secondary and tertiary treatment results from biological growth in the biomass, as well as the collection of small particles. This sludge must be treated further before it can be safely disposed of. Methods of sludge treatment include aerobic and anaerobic stabilisation (digestion), conditioning, centrifugation, composting, and drying. Land disposal, composting, and incineration of sludge is considered in Volume 5, Section 2.3.2 in Chapter 2, Waste Generation, Composition, and Management Data, Section 3.2 in Chapter 3, Solid Waste Disposal, Section 4.1 in Chapter 4, Biological Treatment and Disposal, and Chapter 5, Incineration and Open Burning of Waste, respectively. Some sludge is incinerated before land disposal. N2O emissions from sludge and wastewater spread on agricultural land are considered in Section 11.2, N2O emissions from managed

1

Because the methodology is on a per person basis, emissions from commercial wastewater are estimated as part of domestic wastewater. To avoid confusion, the term municipal wastewater is not used in this text. Municipal wastewater is a mix of household, commercial and non-hazardous industrial wastewater, treated at wastewater treatment plants.

6.6

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Wastewater Treatment and Discharge

soils, in Chapter 11, N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application, in Volume 4 of the Agriculture, Forestry, and Other Land Use (AFOLU) Sector. Figure 6.1

Wastewater treatment systems and discharge pathways

Domestic/industrial wastewater

Collected

Uncollected

Untreated

Rivers, lakes, estuaries, sea

Treated

Sewered to plant

Stagnant sewer

Treated on site Domestic: latrine, septic tank Industrial: on site plant

Wetland Aerobic treatment

Sludge

Anaerobic Digestion

Land Disposal

Untreated

Rivers, lakes, estuaries, sea

To ground

Anaerobic treatment

Reactor

Lagoon

Landfill or Incineration

Note: Emissions from boxes with bold frames are accounted for in this chapter.

Methane(CH 4 ) Wastewater as well as its sludge components can produce CH4 if it degrades anaerobically. The extent of CH4 production depends primarily on the quantity of degradable organic material in the wastewater, the temperature, and the type of treatment system. With increases in temperature, the rate of CH4 production increases. This is especially important in uncontrolled systems and in warm climates. Below 15°C, significant CH4 production is unlikely because methanogens are not active and the lagoon will serve principally as a sedimentation tank. However, when the temperature rises above 15°C, CH4 production is likely to resume. The principal factor in determining the CH4 generation potential of wastewater is the amount of degradable organic material in the wastewater. Common parameters used to measure the organic component of the wastewater are the Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD). Under the same conditions, wastewater with higher COD, or BOD concentrations will generally yield more CH4 than wastewater with lower COD (or BOD) concentrations. The BOD concentration indicates only the amount of carbon that is aerobically biodegradable. The standard measurement for BOD is a 5-day test, denoted as BOD5. The term ‘BOD’ in this chapter refers to BOD5. The COD measures the total material available for chemical oxidation (both biodegradable and non-biodegradable). 2 Since the BOD is an aerobic parameter, it may be less appropriate for determining the organic components in anaerobic environments. Also, both the type of wastewater and the type of bacteria present in the wastewater influence the BOD concentration of the wastewater. Usually, BOD is more frequently reported for domestic wastewater, while COD is predominantly used for industrial wastewater.

2

In these guidelines, COD refers to chemical oxygen demand measured using the dichromate method. (American Public Health Association, American Water Works Association and Water Environment Federation, 1998)

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

Nitrous Oxide (N 2 O) Nitrous oxide (N2O) is associated with the degradation of nitrogen components in the wastewater, e.g., urea, nitrate and protein. Domestic wastewater includes human sewage mixed with other household wastewater, which can include effluent from shower drains, sink drains, washing machines, etc. Centralized wastewater treatment systems may include a variety of processes, ranging from lagooning to advanced tertiary treatment technology for removing nitrogen compounds. After being processed, treated effluent is typically discharged to a receiving water environment (e.g., river, lake, estuary, etc.). Direct emissions of N2O may be generated during both nitrification and denitrification of the nitrogen present. Both processes can occur in the plant and in the water body that is receiving the effluent. Nitrification is an aerobic process converting ammonia and other nitrogen compounds into nitrate (NO3-), while denitrification occurs under anoxic conditions (without free oxygen), and involves the biological conversion of nitrate into dinitrogen gas (N2). Nitrous oxide can be an intermediate product of both processes, but is more often associated with denitrification.

Treatment and Discharge Systems and CH 4 and N 2 O Generation Potential Treatment systems or discharge pathways that provide anaerobic environments will generally produce CH4 whereas systems that provide aerobic environments will normally produce little or no CH4. For example, for lagoons without mixing or aeration, their depth is a critical factor in CH4 production. Shallow lagoons, less than 1 metre in depth, generally provide aerobic conditions and little or no CH4 is likely to be produced. Lagoons deeper than about 2-3 metres will generally provide anaerobic environments and significant CH4 production can be expected. Table 6.1 presents the main wastewater treatment and discharge systems in developed and developing countries, and their potentials to emit CH4 and N2O.

TABLE 6.1 CH4 AND N2O EMISSION POTENTIALS FOR WASTEWATER AND Types of treatment and disposal

Untreated

River discharge

Stagnant, oxygen-deficient rivers and lakes may allow for anaerobic decomposition to produce CH4.

Sewers (closed and under ground)

Not a source of CH4/N2O.

Sewers (open)

Stagnant, overloaded open collection sewers or ditches/canals are likely significant sources of CH4.

Aerobic treatment Anaerobic treatment

Treated

Collected Uncollected

CH4 and N2O emission potentials

Rivers, lakes and estuaries are likely sources of N2O.

Centralized aerobic wastewater treatment plants

6.8

SLUDGE TREATMENT AND DISCHARGE SYSTEMS

May produce limited CH4 from anaerobic pockets. Poorly designed or managed aerobic treatment systems produce CH4. Advanced plants with nutrient removal (nitrification and denitrification) are small but distinct sources of N2O.

Sludge anaerobic treatment in centralized aerobic wastewater treatment plant

Sludge may be a significant source of CH4 if emitted CH4 is not recovered and flared.

Aerobic shallow ponds

Unlikely source of CH4/N2O. Poorly designed or managed aerobic systems produce CH4.

Anaerobic lagoons

Likely source of CH4. Not a source of N2O.

Anaerobic reactors

May be a significant source of CH4 if emitted CH4 is not recovered and flared.

Septic tanks

Frequent solids removal reduces CH4 production.

Open pits/Latrines

Pits/latrines are likely to produce CH4 when temperature and retention time are favourable.

River discharge

See above.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Wastewater Treatment and Discharge

6.1.1

Changes compared to 1996 Guidelines and Good Practice Guidance

The Revised 1996 IPCC Guidelines (1996 Guidelines, IPCC, 1997) included separate equations to estimate emissions from wastewater and from sludge removed from the wastewater. The distinction has been removed because the CH4 generation capacities for sludge and wastewater with dissolved organics are generally the same, and separated equations are not necessary. The 2006 Guidelines include a new section to estimate CH4 emissions from uncollected wastewater. Also, guidance has been included to estimate N2O emissions from advanced wastewater treatment plants. Furthermore, the industrial wastewater section has been simplified by suggesting that only the most significant industrial sources need to be addressed. See Section 6.2.3.

6.2

METHANE EMISSIONS FROM WASTEWATER

6.2.1

Methodological issues

Emissions are a function of the amount of organic waste generated and an emission factor that characterises the extent to which this waste generates CH4. Three tier methods for CH4 from this category are summarised below: The Tier 1 method applies default values for the emission factor and activity parameters. This method is considered good practice for countries with limited data. The Tier 2 method follows the same method as Tier 1 but allows for incorporation of a country specific emission factor and country specific activity data. For example, a specific emission factor for a prominent treatment system based on field measurements could be incorporated under this method. The amount of sludge removed for incineration, landfills, and agricultural land should be taken into consideration. For a country with good data and advanced methodologies, a country specific method could be applied as a Tier 3 method. A more advanced country-specific method could be based on plant-specific data from large wastewater treatment facilities. Wastewater treatment facilities can include anaerobic process steps. CH4 generated at such facilities can be recovered and combusted in a flare or energy device. The amount of CH4 that is flared or recovered for energy use should be subtracted from total emissions through the use of a separate CH4 recovery parameter. The amount of CH4 which is recovered is expressed as R in Equation 6.1. Note that only a few countries may have sludge removal data and CH4 recovery data. The default for sludge removal is zero. The default for CH4 recovery is zero. If a country selects to report CH4 recovery, it is good practice to distinguish between flaring and CH4 recovery for energy generation, which should be reported in the Energy Sector taking into account the avoidance of double counting emissions from flaring and energy used. Emissions from flaring are not significant, as the CO2 emissions are of biogenic origin, and the CH4 and N2O emissions are very small so good practice in the Waste Sector does not require their estimation. However, if it is wished to do so these emissions should be reported under the Waste Sector. A discussion of emissions from flares and more detailed information are given in Volume 2, Energy, Chapter 4.2. Emission from flaring is not treated at Tier 1.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

6.9

Volume 5: Waste

6.2.2

Domestic wastewater

6.2.2.1

C HOICE

OF METHOD

A decision tree for domestic wastewater is included in Figure 6.2. Figure 6.2 Decision Tree for CH 4 emissions from domestic wastewater Start

Are wastewater treatment pathways characterised?

No

Collect data on the share of wastewater treatment in each pathway.

Yes

Are measurements or other bottom-up data available from the most important pathways?

Yes

Is a countryspecific method available?

Yes

Estimate emissions using bottom-up data. Box 3: Tier 3

No No

Are country-specific emission factors available for the key pathways?

Yes

Estimate emissions using country-specific emission factors. (Bo, MCF, etc.) Box 2: Tier 2

No

Is this a key category1?

Yes

No

Estimate country-specific Bo and MCFs for the key pathways.

Estimate emissions using default emission factors. (Bo, MCF, etc.) Box 1: Tier 1

1. See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

6.10

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Wastewater Treatment and Discharge

The steps for good practice in inventory preparation for CH4 from domestic wastewater are as follows: Step 1:

Use Equation 6.3 to estimate total organically degradable carbon in wastewater (TOW).

Step 2:

Select the pathway and systems (See Figure 6.1) according to country activity data. Use Equation 6.2 to obtain the emission factor for each domestic wastewater treatment/discharge pathway or system.

Step 3:

Use Equation 6.1 to estimate emissions, adjust for possible sludge removal and/or CH4 recovery and sum the results for each pathway/system.

As described earlier, the wastewater characterisation will determine the fraction of wastewater treated or disposed of by a particular system. To determine the use of each type of treatment or discharge system, it is good practice to refer to national statistics (e.g., from regulatory authorities). If these data are not available, wastewater associations or international organisations such as the World Health Organization (WHO) may have data on the system usage. Otherwise, consultation with sanitation experts can help, and expert judgement can also be applied (see Chapter 2, Approaches to Data Collection, in Volume 1). Urbanisation statistics may provide a useful tool, e.g., city sizes and income distribution. If sludge separation is practised and appropriate statistics are available, then this category should be separated out as a subcategory. If default factors are being used, emissions from wastewater and sludge should be estimated together. Regardless of how sludge is treated, it is important that CH4 emissions from sludge sent to landfills, incinerated or used in agriculture are not included in the wastewater treatment and discharge category. If sludge removal data are available, the data should be consistent across the sectors, and categories, amount disposed at SWDS, applied to agricultural land, incinerated or used elsewhere should be equal to the amount organic component removed as sludge in Equation 6.1. Wastewater and sludge that is applied on agricultural land should be considered in Volume 4 for AFOLU Sector, Section 11.2, N2O emissions from managed soils, in Chapter 11, N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application. Wastewater treatment system/pathway usage often differs for rural and urban residents. Also, in developing countries, there are likely to be differences between urban high-income and urban low-income residents. Hence, a factor U is introduced to express each income group fraction. It is good practice to treat the three categories: rural population, urban high income population, and urban low income population separately. It is suggested to use a spreadsheet, as shown in Table 6.5 below.

The general equation to estimate CH4 emissions from domestic wastewater is as follows: EQUATION 6.1 TOTAL CH4 EMISSIONS FROM DOMESTIC WASTEWATER

(

⎡ CH 4 Emissions = ⎢∑ U i • Ti. j • EF j ⎢⎣ i , j

)⎤⎥ ( TOW − S ) − R ⎥⎦

Where: CH4 Emissions = TOW

CH4 emissions in inventory year, kg CH4/yr

= total organics in wastewater in inventory year, kg BOD/yr

S

=

organic component removed as sludge in inventory year, kg BOD/yr

Ui

=

fraction of population in income group i in inventory year, See Table 6.5.

Ti,j

=

degree of utilisation of treatment/discharge pathway or system, j, for each income group fraction i in inventory year, See Table 6.5.

i

=

income group: rural, urban high income and urban low income

j

=

each treatment/discharge pathway or system

EFj =

emission factor, kg CH4 / kg BOD

R

amount of CH4 recovered in inventory year, kg CH4/yr

=

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6.2.2.2

C HOICE

OF EMISSION FACTORS

The emission factor for a wastewater treatment and discharge pathway and system (terminal blocks with bold frames in Figure 6.1) is a function of the maximum CH4 producing potential (Bo) and the methane correction factor (MCF) for the wastewater treatment and discharge system, as shown in Equation 6.2. The Bo is the maximum amount of CH4 that can be produced from a given quantity of organics (as expressed in BOD or COD) in the wastewater. The MCF indicates the extent to which the CH4 producing capacity (Bo) is realised in each type of treatment and discharge pathway and system. Thus, it is an indication of the degree to which the system is anaerobic.

EQUATION 6.2 CH4 EMISSION FACTOR FOR EACH DOMESTIC WASTEWATER TREATMENT/DISCHARGE PATHWAY OR SYSTEM EF j = Bo • MCF j

Where: EFj =

emission factor, kg CH4/kg BOD

j

=

each treatment/discharge pathway or system

Bo

=

maximum CH4 producing capacity, kg CH4/kg BOD

MCFj

= methane correction factor (fraction), See Table 6.3.

Good practice is to use country-specific data for Bo, where available, expressed in terms of kg CH4/kg BOD removed to be consistent with the activity data. If country-specific data are not available, a default value, 0.6 kg CH4/kg BOD can be used. For domestic wastewater, a COD-based value of Bo can be converted into a BODbased value by multiplying with a factor of 2.4. Table 6.2 includes default maximum CH4 producing capacity (Bo) for domestic wastewater.

TABLE 6.2 DEFAULT MAXIMUM CH4 PRODUCING CAPACITY (BO) FOR DOMESTIC WASTEWATER 0.6 kg CH4/kg BOD 0.25 kg CH4/kg COD Based on expert judgment by lead authors and on Doorn et al., (1997)

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Chapter 6: Wastewater Treatment and Discharge

Table 6.3 includes default MCF values. TABLE 6.3 DEFAULT MCF VALUES FOR DOMESTIC WASTEWATER Type of treatment and discharge pathway or system

Comments

MCF 1

Range

Untreated system Sea, river and lake discharge

Rivers with high organics loadings can turn anaerobic.

0.1

0 – 0.2

Stagnant sewer

Open and warm

0.5

0.4 – 0.8

Flowing sewer (open or closed)

Fast moving, clean. (Insignificant amounts of CH4 from pump stations, etc)

0

0

Centralized, aerobic treatment plant

Must be well managed. Some CH4 can be emitted from settling basins and other pockets.

0

0 – 0.1

Centralized, aerobic treatment plant

Not well managed. Overloaded.

0.3

0.2 – 0.4

Anaerobic digester for sludge

CH4 recovery is not considered here.

0.8

0.8 – 1.0

Anaerobic reactor

CH4 recovery is not considered here.

0.8

0.8 – 1.0

Anaerobic shallow lagoon

Depth less than 2 metres, use expert judgment.

0.2

0 – 0.3

Anaerobic deep lagoon

Depth more than 2 metres

0.8

0.8 – 1.0

Septic system

Half of BOD settles in anaerobic tank.

0.5

0.5

Latrine

Dry climate, ground water table lower than latrine, small family (3-5 persons)

0.1

0.05 – 0.15

Latrine

Dry climate, ground water table lower than latrine, communal (many users)

0.5

0.4 – 0.6

Latrine

Wet climate/flush water use, ground water table higher than latrine

0.7

0.7 – 1.0

Latrine

Regular sediment removal for fertilizer

0.1

0.1

Treated system

1

Based on expert judgment by lead authors of this section.

6.2.2.3

C HOICE

OF ACTIVITY DATA

The activity data for this source category is the total amount of organically degradable material in the wastewater (TOW). This parameter is a function of human population and BOD generation per person. It is expressed in terms of biochemical oxygen demand (kg BOD/year). The equation for TOW is:

EQUATION 6.3 TOTAL ORGANICALLY DEGRADABLE MATERIAL IN DOMESTIC WASTEWATER TOW = P • BOD • 0.001 • I • 365

Where: TOW

=

total organics in wastewater in inventory year, kg BOD/yr

P

=

country population in inventory year, (person)

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BOD

=

country-specific per capita BOD in inventory year, g/person/day, See Table 6.4.

0.001

=

conversion from grams BOD to kg BOD

I

=

correction factor for additional industrial BOD discharged into sewers (for collected the default is 1.25, for uncollected the default is 1.00.)

The factor I values in Equation 6.3 are based on expert judgment by the authors. It expresses the BOD from industries and establishments (e.g., restaurants, butchers or grocery stores) that is co-discharged with domestic wastewater. In some countries, information from industrial discharge permits may be available to improve I. Otherwise, expert judgment is recommended. Total population statistics should be readily available from national statistics agencies or international agencies (e.g., United Nations Statistics, see http://esa.un.org/unpp/). Table 6.4 includes BOD default values for selected countries. It is good practice to select a BOD default value from a nearby comparable country when country-specific data are not available. The degree of urbanization for a country can be retrieved from various sources, (e.g., Global Environment Outlook, United Nations Environment Programme and World Development Indicators, World Health Organization). The urban high-income and urbanlow income fractions can be determined by expert judgment when statistical or other comparable information is not available. Table 6.5 includes default values of Ui and Ti,j for selected countries.

TABLE 6.4 ESTIMATED BOD5 VALUES IN DOMESTIC WASTEWATER FOR SELECTED REGIONS AND COUNTRIES BOD5 (g/person/day)

Range

Reference

Africa

37

35 – 45

1

Egypt

34

27 – 41

1

Asia, Middle East, Latin America

40

35 – 45

1

India

34

27 – 41

1

West Bank and Gaza Strip (Palestine)

50

32 – 68

1

Japan

42

40 – 45

1

Brazil

50

45 – 55

2

Canada, Europe, Russia, Oceania

60

50 – 70

1

Denmark

62

55 – 68

1

Germany

62

55 – 68

1

Greece

57

55 – 60

1

Italy

60

49 – 60

3

Sweden

75

68 – 82

1

Turkey

38

27 – 50

1

United States

85

50 – 120

4

Country/Region

Note: These values are based on an assessment of the literature. Please use national values, if available. Reference: 1. Doorn and Liles (1999). 2. Feachem et al. (1983). 3. Masotti (1996). 4. Metcalf and Eddy (2003).

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0.10 0.09 0.08 0.12

0.12 0.06 0.12 0.07 0.06 0.80

0.73 0.94 0.90 0.76 0.68

0.78 0.80

0.25 0.19

0.92

0.52 0.57 0.62 0.39

0.59 0.71 0.54 0.65 0.72 0.20

0.27 0.06 0.10 0.24 0.32

0.22 0.20

0.16 0.25

0.08

0.00

0.59 0.56

0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.29 0.23 0.34 0.28 0.22 0.00

0.38 0.34 0.30 0.49

Rural urban-high2 urban-low2

0.90

0.00 0.00

0.90 0.90

0.30 0.20 0.11 0.37 0.42

0.00 0.00 0.00 0.00 0.00 0.20

0.02 0.02 0.02 0.10

Septic Tank

0.02

0.45 0.45

0.02 0.02

0.10 0.00 0.00 0.00 0.00

0.47 0.47 0.47 0.47 0.47 0.00

0.28 0.28 0.28 0.28

0.00

0.00 0.00

0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.50 0.10 0.00 0.00 0.00 0.50

0.04 0.04 0.04 0.04

0.08

0.10 0.10

0.08 0.08

0.60 0.80 0.89 0.63 0.58

0.00 0.10 0.10 0.10 0.10 0.30

0.10 0.10 0.10 0.10

0.00

0.45 0.45

0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.3 0.33 0.43 0.43 0.43 0.00

0.56 0.56 0.56 0.48

0.05

0.00 0.00

0.05 0.05

0.10 0.05 0.00 0.00 0.04

0.18 0.18 0.18 0.18 0.18 0.00

0.32 0.15 0.32 0.15

0.00

0.20 0.20

0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.08 0.08 0.08 0.08 0.08 0.00

0.31 0.05 0.31 0.15

2006 IPCC Guidelines for National Greenhouse Gas Inventories

0.00

0.00 0.00

0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.07 0.07 0.00 0.00 0.00 0.10

0.00 0.10 0.00 0.00

0.95

0.80 0.80

0.95 0.95

0.90 0.95 1.00 1.00 0.96

0.67 0.67 0.74 0.74 0.74 0.90

0.37 0.70 0.37 0.70

0.00

0.00 0.00

0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00

NA

0.00 0.00

NA NA

NA NA NA NA NA

0.14 0.14 0.14 0.14 0.14 0.10

0.17 0.17 0.17 0.17

NA

0.40 0.40

NA NA

NA NA NA NA NA

0.10 0.10 0.10 0.10 0.10 0

0.24 0.24 0.24 0.24

NA

0.00 0.00

NA NA

NA NA NA NA NA

0.03 0.03 0.03 0.03 0.03 0

0.05 0.05 0.05 0.05

NA

0.40 0.40

NA NA

NA NA NA NA NA

0.68 0.53 0.53 0.53 0.53 0.90

0.34 0.34 0.34 0.34

6.15

NA

0.20 0.20

NA NA

NA NA NA NA NA

0.05 0.20 0.20 0.20 0.20 0

0.20 0.20 0.20 0.20

Degree of utilisation of treatment or discharge pathway or method for each income group (Ti,j )3 U=rural U= urban high income U=urban low income Septic Septic 4 4 Latrine Other Sewer4 None Latrine Other Sewer None Latrine Other Sewer None Tank Tank

Notes: 1. Urbanization projections for 2005 (United Nations, 2002). 2. Suggested urban-high income and urban low income division. Countries are encouraged to use their own data or best judgment. 3. Ti.j values based on expert judgment, (Doorn and Liles, 1999). 4. Sewers may be open or closed, which will govern the choice of MCF, see Table 3.3 5. Destatis, 2001. Note: These values are from the literature or based on expert judgment. Please use national values, if available.

Africa Nigeria Egypt Kenya South Africa Asia China India Indonesia Pakistan Bangladesh Japan Europe Russia Germany5 United Kingdom France Italy North America United States Canada Latin America and Caribbean Brazil Mexico Oceania Australia and New Zealand

Country

Urbanization(U) 1 Fraction of Population

TABLE 6.5 SUGGESTED VALUES FOR URBANISATION (U) AND DEGREE OF UTILISATION OF TREATMENT, DISCHARGE PATHWAY OR METHOD (Ti,j) FOR EACH INCOME GROUP FOR SELECTED COUNTRIES

Chapter 6: Wastewater Treatment and Discharge

Volume 5: Waste

Example Table 6.6 includes an example. Categories with negligible contributions are not shown. Note that the table can easily be expanded with a column for MCF for each category. The degree of urbanization for this country is 65 percent. TABLE 6.6 EXAMPLE OF THE APPLICATION OF DEFAULT VALUES FOR DEGREES OF TREATMENT UTILIZATION (T) BY INCOME GROUPS Treatment or discharge system or pathway Urban high-income

Urban low-income

Rural

T (%)

Notes

To sea

10

No CH4

To aerobic plant

20

Add industrial component

To septic systems

10

Uncollected

To sea

10

Collected

To pit latrines

15

Uncollected

To rivers, lakes, sea

15

To pit latrines

15

To septic tanks

5

Total

100%

Uncollected

Must add up to 100 %

Reference: Doorn and Liles (1999)

6.2.2.4

T IME

SERIES CONSISTENCY

The same method and data sets should be used for estimating CH4 emissions from wastewater for each year. The MCF for different treatment systems should not change from year to year, unless such a change is justifiable and documented. If the share of wastewater treated in different treatment systems changes over the time period, the reasons for these changes should be documented. Sludge removal and CH4 recovery should be estimated consistently across years in the time series. Methane recovery should be included only if there are sufficient facility-specific data. The quantity of recovered methane should be subtracted from the methane produced as shown in Equation 6.1. Because activity data are derived from population data, which is available for all countries and all years, countries should be able to construct an entire time series for uncollected and collected wastewater. If data on the share of uncollected wastewater treated onsite vs. untreated are missing for one or more years, the surrogate data and extrapolation/interpolation splicing techniques described in Chapter 5, Time Series Consistency, of Volume 1, General Guidance and Reporting, can be used to estimate emissions. Emissions from wastewater typically do not fluctuate significantly from year to year.

6.2.2.5

U NCERTAINTIES

Chapter 3, Uncertainties, in Volume 1 provides advice on quantifying uncertainties in practice. It includes guidance on eliciting and using expert judgements which in combination with empirical data can provide overall uncertainty estimates. Table 6.7 provides default uncertainty ranges for emission factor and activity data of domestic wastewater. The following parameters are believed to be very uncertain: •

•

•

6.16

The degrees to which wastewater in developing countries is treated in latrines, septic tanks, or removed by sewer, for urban high, urban low income groups and rural population (Ti,,j). The fraction of sewers that are ‘open’, as well as the degree to which open sewers in developing countries are anaerobic and will emit CH4. This will depend on retention time and temperature, and on other factors including the presence of a facultative layer and possibly components that are toxic to anaerobic bacteria (e.g., certain industrial wastewater discharges). The amount of industrial TOW that is discharged into open or closed domestic sewers for each country is very difficult to quantify.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Wastewater Treatment and Discharge

TABLE 6.7 DEFAULT UNCERTAINTY RANGES FOR DOMESTIC WASTEWATER Parameter

Uncertainty Range

Emission Factor Maximum CH4 producing capacity (Bo)

± 30%

Fraction treated anaerobically (MCF)

The MCF is technology dependent. See Table 6.3. Thus the uncertainty range is also technology dependent. The uncertainty range should be determined by expert judgement, bearing in mind that MCF is a fraction and must be between 0 and 1. Suggested ranges are provided below. Untreated systems and latrines, ± 50% Lagoons, poorly managed treatment plants± 30% Centralized well managed plant, digester, reactor, ± 10%

Activity Data Human population (P)

± 5%

BOD per person

± 30%

Fraction of population income group (U)

Good data on urbanization are available, however, the distinction between urban high income and urban low income may have to be based on expert judgment. ± 15%

Degree of utilization of treatment/ discharge pathway or system for each income group (Ti,j)

Can be as low as ± 3% for countries that have good records and only one or two systems. Can be ± 50% for an individual method/pathway. Verify that total Ti,j = 100%

Correction factor for additional industrial BOD discharged into sewers (I)

For uncollected, the uncertainty is zero %. For collected the uncertainty is ± 20%

Source: Judgement by Expert Group (Authors of this section).

6.2.2.6

QA/QC, C OMPLETENESS , R EPORTING D OCUMENTATION

AND

It is good practice to conduct quality control checks and quality assurance procedures as outlined in Chapter 6, Volume 1. Below, some fundamental QA/QC procedures are included. •

Activity Data

•

•

Characterize all wastewater according to the percentages flowing to different treatment systems (aerobic and anaerobic), and the percentage of untreated wastewater. Make sure that all wastewater is characterized so that the wastewater flows sum to 100 percent of the wastewater generated in the country. Inventory compilers should compare country-specific data on BOD in domestic wastewater to IPCC default values. If inventory compilers use country-specific values they should provide documented justification why their country-specific values are more appropriate for their national circumstances.

Emission Factors

•

For domestic wastewater, inventory compilers can compare country-specific values for Bo with the IPCC default value (0.25 kg CH4/kg COD or 0.6 kg CH4/kg BOD). Although there are no IPCC default values for the fraction of wastewater treated anaerobically, inventory compilers are encouraged to compare values for MCFs against those from other countries with similar wastewater handling practices. Inventory compilers should confirm the agreement between the units used for degradable carbon in the waste (TOW) with the units for Bo. Both parameters should be based on the same units (either BOD or COD) in order to calculate emissions. This same consideration should be taken into account when comparing the emissions.

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•

CH 4 Recovery and Sludge Removal • •

A carbon balance check can be used to ensure that the carbon contained in the inflow and outflow (effluent BOD, methane emission and methane recovery) are comparable. If sludge removal is reported in the wastewater inventory, check for consistency with the estimates for sludge applied to agriculture soils, sludge incinerated, and sludge deposited in solid waste disposal.

Comparison of emissions estimates using different approaches For countries that use country-specific parameters, or Tier 2 or higher methods, inventory compilers can cross-check the national estimate with emissions using the IPCC default method and parameters.

COMPLETENESS Completeness can be verified on the basis of the degree of utilization of a treatment or discharge system or pathway (T). The sum of T should equal 100 percent. It is a good practice to draw a diagram similar to Figure 6.1 for the country to consider all potential anaerobic treatment and discharge systems and pathways, including collected and uncollected, as well as treated and untreated. Any industrial wastewater treated in domestic wastewater treatment facilities should be included in the collected category. If sludge is removed for the purpose of incineration, disposal in landfills or as fertilizer on agricultural lands, the amount of organic material removed as sludge should be consistent with data used in the relevant sectors (see text under Section 6.2.2).

REPORTING AND DOCUMENTATION It is good practice to document and report a summary of the methods used, activity data and emission factors. Worksheets are provided at the end of this volume. When country-specific methods and/or emission factors are used, the reasoning for the choices as well as references to how the country-specific data (measurements, literature, expert judgement, etc.) have been derived (measurements, literature, expert judgement, etc.) should be documented and included in the reporting. If sludge is incinerated, landfilled, or spread on agricultural lands, the quantities of sludge, and associated emissions, should be reported in the waste incineration, SWDS, or agricultural categories, respectively. Where CH4 is recovered for energy use, then the resulting greenhouse gas emissions should be reported under Energy Sector. As discussed in Section 6.2.1, good practice in the Waste Sector does not require the estimation of CH4 and N2O from CH4 recovery and flaring. However, if it is wished to do so emissions from flaring should be reported under the Waste Sector. More information on reporting and documentation can be found in Volume 1, Chapter 6, Section 6.11 Documentation, archiving and reporting.

6.2.3

Industrial wastewater

Industrial wastewater may be treated on site or released into domestic sewer systems. If it is released into the domestic sewer system, the emissions are to be included with the domestic wastewater emissions. This section deals with estimating CH4 emissions from on-site industrial wastewater treatment. Only industrial wastewater with significant carbon loading that is treated under intended or unintended anaerobic conditions will produce CH4. Organics in industrial wastewater are often expressed in terms of COD, which is used here.

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6.2.3.1

C HOICE

OF METHOD

A decision tree for industrial wastewater is included in Figure 6.3. Figure 6.3

Decision Tree for CH 4 emissions from industrial wastewater treatment Start

Identify major industrial sectors with large potentials for CH4 emission.

For these industrial sectors, is a country-specific method from individual facilities or companies available?

Estimate emissions using bottom-up data.

Yes

Box 3: Tier 3

No

For these industrial sectors, are COD and wastewater outflow data available?

Yes

Are country-specific emission factors for selected industrial sectors available?

No

Estimate emission factors using a review of industry wastewater treatment practices.

No

Is industrial wastewater a key category1?

Yes

Collect COD and outflow for each industrial sector.

Yes

Estimate CH4 emissions using country-specific emission factors. Box 2: Tier 2

No Estimate outflow using industrial production data.

Estimate emissions using default data. Box 1: Tier 1

1. See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for discussion of key categories and use of decision trees.

Assessment of CH4 production potential from industrial wastewater streams is based on the concentration of degradable organic matter in the wastewater, the volume of wastewater, and the propensity of the industrial sector to treat their wastewater in anaerobic systems. Using these criteria, major industrial wastewater sources with high CH4 gas production potential can be identified as follows: •

•

pulp and paper manufacture, meat and poultry processing (slaughterhouses),

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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•

•

alcohol, beer, starch production, organic chemicals production, other food and drink processing (dairy products, vegetable oil, fruits and vegetables, canneries, juice making, etc.).

Both the pulp and paper industry and the meat and poultry processing industries produce large volumes of wastewater that contain high levels of degradable organics. The meat and poultry processing facilities typically employ anaerobic lagoons to treat their wastewater, while the paper and pulp industry also use lagoons and anaerobic reactors. The non-animal food and beverage industries produce considerable amounts of wastewater with significant organic carbon levels and are also known to use anaerobic processes such as lagoons and anaerobic reactors. Anaerobic reactors treating industrial effluents with biogas facilities are usually linked with recovery of the generated CH4 for energy. Emissions from the combustion process for energy should be reported in the Energy Sector. The method for estimating emissions from industrial wastewater is similar to the one used for domestic wastewater. See the decision tree in Figure 6.3. The development of emission factors and activity data is more complex because there are many types of wastewater, and many different industries to track. The most accurate estimates of emissions for this source category would be based on measured data from point sources. Due to the high costs of measurements and the potentially large number of point sources, collecting comprehensive measurement data is very difficult. It is suggested that inventory compilers use a top-down approach that includes the following general steps: Step 1:

Use Equation 6.6 to estimate total organically degradable carbon in wastewater (TOW) for industrial sector i

Step 2:

Select the pathway and systems (Figure 6.1) according to country activity data. Use Equation 6.5 to obtain emission factor. For each industrial sector estimate the emission factor using maximum methane producing capacity and the average industry-specific methane correction factor.

Step 3:

Use Equation 6.4 to estimate emissions, adjust for possible sludge removal and or CH4 recovery and sum the results.

The general equation to estimate CH4 emissions from industrial wastewater is as follows: EQUATION 6.4 TOTAL CH4 EMISSIONS FROM INDUSTRIAL WASTEWATER

CH 4 Emissions = ∑ [( TOWi − S i ) EFi − Ri ] i

Where: CH4 Emissions =

CH4 emissions in inventory year, kg CH4/yr

TOWi =

total organically degradable material in wastewater from industry i in inventory year, kg COD/yr

i

=

industrial sector

Si

=

organic component removed as sludge in inventory year, kg COD/yr

EFi

=

emission factor for industry i, kg CH4/kg COD for treatment/discharge pathway or system(s) used in inventory year If more than one treatment practice is used in an industry this factor would need to be a weighted average.

Ri

=

amount of CH4 recovered in inventory year, kg CH4/yr

The amount of CH4 which is recovered is expressed as R in Equation 6.4. The recovered gas should be treated as described in Section 6.2.1.

6.2.3.2

C HOICE

OF EMISSION FACTORS

There are significant differences in the CH4 emitting potential of different types of industrial wastewater. To the extent possible, data should be collected to determine the maximum CH4 producing capacity (Bo) in each industry. As mentioned before, the MCF indicates the extent to which the CH4 producing potential (Bo) is

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Chapter 6: Wastewater Treatment and Discharge

realised in each type of treatment method. Thus, it is an indication of the degree to which the system is anaerobic. See Equation 6.5. EQUATION 6.5 CH4 EMISSION FACTOR FOR INDUSTRIAL WASTEWATER EF j = Bo • MCF j

Where: EFj

=

emission factor for each treatment/discharge pathway or system, kg CH4/kg COD, (See Table 6.8.)

j

=

each treatment/discharge pathway or system

Bo

=

maximum CH4 producing capacity, kg CH4/kg COD

MCFj

=

methane correction factor (fraction) (See Table 6.8.)

Good practice is to use country and industry sector specific data that may be available from government authorities, industrial organisations, or industrial experts. However, most inventory compilers will find detailed industry sector-specific data unavailable or incomplete. If no country-specific data are available, it is good practice to use the IPCC COD-default factor for Bo (0.25 kg CH4/kg COD). In determining the Methane correction factor (MCF), which is the fraction of waste treated anaerobically, expert judgement is recommended. A peer-reviewed survey of industry wastewater treatment practices is one useful technique for estimating these data. Surveys should be conducted frequently enough to account for major trends in industry practices (i.e., every 3-5 years). Chapter 2, Approaches to Data Collection, in Volume 1, describes how to elicit expert judgement for uncertainty ranges. Similar expert elicitation protocols can be used to obtain the necessary information for other types of data if published data and statistics are not available. Table 6.8 includes default MCF values, which are based on expert judgment. TABLE 6.8 DEFAULT MCF VALUES FOR INDUSTRIAL WASTEWATER Type of treatment and discharge pathway or system

Comments

MCF 1

Range

Rivers with high organics loadings may turn anaerobic, however this is not considered here.

0.1

0 – 0.2

Aerobic treatment plant

Must be well managed. Some CH4 can be emitted from settling basins and other pockets.

0

0 – 0.1

Aerobic treatment plant

Not well managed. Overloaded

0.3

0.2 – 0.4

Anaerobic digester for sludge

CH4 recovery not considered here

0.8

0.8 – 1.0

Anaerobic reactor (e.g., UASB, Fixed Film Reactor)

CH4 recovery not considered here

0.8

0.8 – 1.0

Anaerobic shallow lagoon

Depth less than 2 metres, use expert judgment

0.2

0 – 0.3

Anaerobic deep lagoon

Depth more than 2 metres

0.8

0.8 – 1.0

Untreated Sea, river and lake discharge Treated

1

Based on expert judgment by lead authors of this section

6.2.3.3

C HOICE

OF ACTIVITY DATA

The activity data for this source category is the amount of organically degradable material in the wastewater (TOW). This parameter is a function of industrial output (product) P (tons/yr), wastewater generation W (m3/ton of product), and degradable organics concentration in the wastewater COD (kg COD/m3). See Equation 6.6. The following steps are required for determination of TOW: (i)

Identify the industrial sectors that generate wastewater with large quantities of organic carbon, by evaluating total industrial product, degradable organics in the wastewater, and wastewater produced.

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(ii)

Identify industrial sectors that use anaerobic treatment. Include those that may have unintended anaerobic treatment as a result of overloading of the treatment system. Experience has shown that usually three or four industrial sectors are key.

For each selected sector estimate total organically degradable carbon (TOW). EQUATION 6.6 ORGANICALLY DEGRADABLE MATERIAL IN INDUSTRIAL WASTEWATER TOWi = Pi • Wi • CODi

Where: TOWi

=

total organically degradable material in wastewater for industry i, kg COD/yr

i

=

industrial sector

Pi

=

total industrial product for industrial sector i, t/yr

Wi

=

wastewater generated, m3/t product

CODi

=

chemical oxygen demand (industrial degradable organic component in wastewater), kg COD/m3

Industrial production data and wastewater outflows may be obtained from national statistics, regulatory agencies, wastewater treatment associations or industry associations. In some cases quantification of the COD loading in the wastewater may require expert judgement. In some countries, COD and total water usage per sector data may be available directly from a regulatory agency. An alternative is to obtain data on industrial output and tonnes COD produced per tonne of product from the literature. Table 6.9 provides examples that could be used as default values. These should be used with caution, because they are industry-, process- and country-specific. TABLE 6.9 EXAMPLES OF INDUSTRIAL WASTEWATER DATA Industry Type

Wastewater Generation W

Range for W

3

Alcohol Refining

3

COD 3

COD Range (kg/m3)

(m /ton)

(m /ton)

(kg/m )

24

16 – 32

11 2.9

2 – 7

9

3 – 15 1.5 – 5.2

Beer & Malt

6.3

5.0 – 9.0

Coffee

NA

NA –

5 – 22

Dairy Products

7

3 – 10

2.7

Fish Processing

NA

8 – 18

2.5

Meat & Poultry

13

8 – 18

4.1

Organic Chemicals

67

0 – 400

3

Petroleum Refineries

0.6

0.3 – 1.2

1.0

0.4 – 1.6

Plastics & Resins

0.6

0.3 – 1.2

3.7

0.8 – 5

Pulp & Paper (combined)

162

85 – 240

9

1 – 15

Soap & Detergents

NA

1.0 – 5.0

NA

0.5 – 1.2

Starch Production

9

4 – 18

10

1.5 – 42

Sugar Refining

NA

4 – 18

3.2

1 – 6

Vegetable Oils Vegetables, Fruits & Juices Wine & Vinegar

3.1

1.0 – 5.0

NA

0.5 – 1.2

20

7 – 35

5.0

2 – 10

23

11 – 46

1.5

0.7 – 3.0

2 – 7 0.8 – 5

Notes: NA = Not Available. Source: Doorn et al. (1997).

6.2.3.4

T IME

SERIES CONSISTENCY

Once an industrial sector is included in the inventory calculation, it should be included for each subsequent year. If the inventory compiler adds a new industrial sector to the calculation, then he or she should re-calculate the

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Chapter 6: Wastewater Treatment and Discharge

entire time series so that the method is consistent from year to year. General guidance on recalculation of estimates through time series is provided in Volume 1, Chapter 5, Time Series Consistency. As with domestic wastewater, sludge removal and CH4 recovery should be treated consistently across years in the time series. CH4 recovery should be included only if there are facility-specific data. The quantity of recovered CH4 should be subtracted from the CH4 produced as shown in Equation 6.4.

6.2.3.5

U NCERTAINTIES

Uncertainty estimates for Bo, MCF, P, W and COD are provided in Table 6.10. The estimates are based on expert judgement. TABLE 6.10 DEFAULT UNCERTAINTY RANGES FOR INDUSTRIAL WASTEWATER Parameter

Uncertainty Range

Emission Factor Maximum CH4 producing capacity (Bo)

± 30%

Methane correction factor (MCF)

The uncertainty range should be determined by expert judgement, bearing in mind that this is a fraction and uncertainties cannot take it outside the range of 0 to 1.

Activity Data Industrial production (P)

± 25% Use expert judgement regarding the quality of data source to assign more accurate uncertainty range.

Wastewater/unit production (W)

These data can be very uncertain as the same sector might use different waste handling procedures at different plants and in different countries. The product of the parameters (W•COD) is expected to have less uncertainty. An uncertainty value can be attributed directly to kg COD/tonne of product. –50 %, +100% is suggested (i.e., a factor of 2).

COD/unit wastewater (COD)

Source: Judgement by Expert Group (Co-chairs, Editors and Authors of this sector).

6.2.3.6

QA/QC, C OMPLETENESS , R EPORTING D OCUMENTATION

AND

It is good practice to conduct quality control checks and quality assurance procedures as outlined in Chapter 6, QA/QC and Verification, of Volume 1. Below, some fundamental QA/QC procedures include:

•

• • • • •

For industrial wastewater, inventory compilers may review the secondary data sets (e.g., from national statistics, regulatory agencies, wastewater treatment associations or industry associations) , that are used to estimate and rank industrial COD waste output. Some countries may have regulatory control over industrial discharges, in which cases significant QA/QC protocols may already be in place for the development of the wastewater characteristics on an industry basis. For industrial wastewater, inventory compilers should cross-check values for MCFs against those from other national inventories with similar wastewater characteristics. The inventory compilers should review facility-specific data on CH4 recovery to ensure that it was reported according to criteria on measurements outlined in Chapter 2, Approaches to Data Collection, in Volume 1. Use a carbon balance check to ensure that the carbon contained in CH4 recovery is less than the carbon contained in BOD entering the facility that reports CH4 recovery. If sludge removal is reported in the wastewater inventory, check for consistency with the estimates for sludge applied to agriculture soils, sludge incinerated, and sludge deposited in solid waste disposal. For countries that use country-specific parameters or higher tier methods, inventory compilers should crosscheck the national estimates with emissions using the IPCC default method and parameters.

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COMPLETENESS Completeness for estimating emissions from industrial wastewater depends on an accurate characterization of industrial sectors that produce organic wastewater. In most countries, approximately 3-4 industrial sectors will account for the majority of the organic wastewater volume, so the inventory compilers should ensure that these sectors are covered. Periodically, the inventory compilers should re-survey industrial sources, particularly if some industries are growing rapidly. This category should only cover industrial wastewater treated onsite. Emissions from industrial wastewater released into domestic sewer systems should be addressed and included with domestic wastewater. Some sludge from industrial wastewater treatment may be incinerated or deposited in landfills or on agricultural lands. This constitutes an amount of organic waste that should be subtracted from available TOW. It is good practice to be consistent across sectors: the amount of sludge that is removed from TOW should be equal to the amount of sludge disposed at landfills, applied to agricultural soils, incinerated or treated elsewhere.

REPORTING AND DOCUMENTATION It is good practice to document and report a summary of the methods used, activity data and emission factors. Worksheets are provided at the end of this volume. When country-specific methods and/or emission factors are used, the reasoning for the choices as well as references to how the country-specific data (measurements, literature, expert judgement, etc.) have been derived (measurements, literature, expert judgement, etc.) should be documented and included in the reporting. If sludge is incinerated, landfilled, or spread on agricultural lands, the quantities of sludge and associated emissions should be reported in the waste incineration, SWDS, or agricultural categories, respectively. If CH4 recovery data are available for industrial wastewater treatment, these should be documented for flaring and energy recovery separately. The treatment of recovered CH4 and how to report emissions from flaring should be the same as the guidance for domestic wastewater in Section 6.2.2.6. More information on reporting and documentation can be found in Volume 1, Chapter 6, Section 6.11 Documentation, archiving and reporting.

6.3

NITROUS OXIDE EMISSIONS FROM WASTEWATER

6.3.1

Methodological issues

6.3.1.1

C HOICE

OF METHOD

Nitrous oxide (N2O) emissions can occur as direct emissions from treatment plants or from indirect emissions from wastewater after disposal of effluent into waterways, lakes or the sea. Direct emissions from nitrification and denitrification at wastewater treatment plants may be considered as a minor source and guidance is offered in Box 6.1 to estimate these emissions. Typically, these emissions are much smaller than those from effluent and may only be of interest to countries that predominantly have advanced centralized wastewater treatment plants with nitrification and denitrification steps. No higher tiers are given, so it is Good practice to estimate N2O from domestic wastewater effluent using the method given here, No decision tree is provided. Direct emissions need to be estimated only for countries that have predominantly advanced centralized wastewater treatment plants with nitrification and denitrification steps. Accordingly, this section addresses indirect N2O emissions from wastewater treatment effluent that is discharged into aquatic environments. The methodology for emissions from effluent is similar to that of indirect N2O emissions explained in Volume 4, Section 11.2.2, in Chapter 11, N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application. The simplified general equation is as follows:

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Chapter 6: Wastewater Treatment and Discharge

EQUATION 6.7 N2O EMISSIONS FROM WASTEWATER EFFLUENT

N 2 O Emissions = N EFFLUENT • EFEFFLUENT • 44 / 28

Where: N2O emissions

= N2O emissions in inventory year, kg N2O/yr

N EFFLUENT

=

nitrogen in the effluent discharged to aquatic environments, kg N/yr

EFEFFLUENT

=

emission factor for N2O emissions from discharged to wastewater, kg N2O-N/kg N

The factor 44/28 is the conversion of kg N2O-N into kg N2O.

6.3.1.2

C HOICE

OF EMISSION FACTORS

The default IPCC emission factor for N2O emissions from domestic wastewater nitrogen effluent is 0.005 (0.0005 - 0.25) kg N2O-N/kg N. This emission factor is based on limited field data and on specific assumptions regarding the occurrence of nitrification and denitrification in rivers and in estuaries. The first assumption is that all nitrogen is discharged with the effluent. The second assumption is that N2O production in rivers and estuaries is directly related to nitrification and denitrification and, thus, to the nitrogen that is discharged into the river. (See Volume 4, Table 11.3 of Section 11.2.2 in Chapter 11, N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application.)

6.3.1.3

C HOICE

OF ACTIVITY DATA

The activity data that are needed for estimating N2O emissions are nitrogen content in the wastewater effluent, country population and average annual per capita protein generation (kg/person/yr). Per capita protein generation consists of intake (consumption) which is available from the Food and Agriculture Organization (FAO, 2004), multiplied by factors to account for additional ‘non-consumed’ protein and for industrial protein discharged into the sewer system. Food (waste) that is not consumed may be washed down the drain (e.g., as result of the use of garbage disposals in some developed countries) and also, bath and laundry water can be expected to contribute to nitrogen loadings. For developed countries using garbage disposals, the default for non-consumed protein discharged to wastewater pathways is 1.4, while for developing countries this fraction is 1.1. Wastewater from industrial or commercial sources that is discharged into the sewer may contain protein (e.g., from grocery stores and butchers). The default for this fraction is 1.25. The total nitrogen in the effluent is estimated as follows:

EQUATION 6.8 TOTAL NITROGEN IN THE EFFLUENT

N EFFLUENT = ( P • Protein • FNPR • FNON − CON • FIND − COM ) − N SLUDGE

Where: NEFFLUENT =

total annual amount of nitrogen in the wastewater effluent, kg N/yr

P

=

human population

Protein

=

annual per capita protein consumption, kg/person/yr

FNPR

=

fraction of nitrogen in protein, default = 0.16, kg N/kg protein

FNON-CON =

factor for non-consumed protein added to the wastewater

FIND-COM =

factor for industrial and commercial co-discharged protein into the sewer system

NSLUDGE =

nitrogen removed with sludge (default = zero), kg N/yr

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BOX 6.1 SUBCATEGORY - EMISSIONS FROM ADVANCED CENTRALISED WASTEWATER TREATMENT PLANTS

Emissions from advanced centralised wastewater treatment plants are typically much smaller than those from effluent and may only be of interest for countries that have predominantly advanced centralized wastewater treatment plants with controlled nitrification and denitrification steps. The overall emission factor to estimate N2O emissions from such plants is 3.2 g N2O/person/year. This emission factor was determined during field testing at a domestic wastewater treatment plant in the Northern United States (Czepiel et al., 1995). The emission data were obtained at a plant that received only domestic wastewater. This wastewater already included non-consumption protein, but did not include any co-discharged industrial and commercial wastewater. No other countryspecific emission factors are available. The emissions from N2O from centralized wastewater treatment processes are calculated as follows:

EQUATION 6.9 N2O EMISION FROM CENTRALIZED WASTEWATER TREATMENT PROCESSES

N 2 O PLANTS = P • T PLANT • FIND −COM • EFPLANT

Where: N2OPLANTS = total N2O emissions from plants in inventory year, kg N2O/yr P

= human population

TPLANT

= degree of utilization of modern, centralized WWT plants, %

FIND-COMM

= fraction of industrial and commercial co-discharged protein (default = 1.25, based on data in Metcalf & Eddy (2003) and expert judgment)

EFPLANT

= emission factor, 3.2 g N2O/person/year

Note: When a country chooses to include N2O emissions from plants, the amount of nitrogen associated with these emissions (NWWT) must be back calculated and subtracted from the NEFFLUENT. The NWWT can be calculated by multiplying N2OPLANTS by 28/44, using the molecular weights.

6.3.2

Time series consistency

If a country decides to incorporate plant emissions into the estimate, this change must be made for the entire time series. Potential sludge removal should be treated consistently across years in the time series.

6.3.3

Uncertainties

Large uncertainties are associated with the IPCC default emission factors for N2O from effluent. Currently insufficient field data exist to improve this factor. Also, the N2O emission factor for plants is uncertain, because it is based on one field test. Table 6.11 below includes uncertainty ranges based on expert judgment.

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Chapter 6: Wastewater Treatment and Discharge

TABLE 6.11 N2O METHODOLOGY DEFAULT DATA Definition

Default Value

Range

Emission Factor EFEFFLUENT

Emission factor, (kg N2O-N/kg –N)

EFPLANTS

Emission factor, (g N2O/person/year)

0.005

0.0005 – 0.25

3.2

2–8

Activity Data P

Number of people in country

Country-specific

± 10 %

Protein

Annual per capita protein consumption Fraction of nitrogen in protein (kg N/kg protein) Degree of utilization of large WWT plants

Country-specific

± 10 %

0.16

0.15 – 0.17

FNPR Tplant FNON-CON

Factor to adjust for non-consumed protein

FIND-COM

Factor to allow for co-discharge of industrial nitrogen into sewers. For countries with significant fish processing plants, this factor may be higher. Expert judgment is recommended.

6.3.4

Country-specific

± 20 %

1.1 for countries with no garbage disposals, 1.4 for countries with garbage disposals

1.0 – 1.5

1.25

1.0 – 1.5

QA/QC, Completeness, Reporting and Documentation

This method makes use of several default parameters. It is recommended to solicit experts’ advice in evaluating the appropriateness of the proposed default factors.

COMPLETENESS Unless sludge removal data are available, the methodology for estimating emissions from effluent is based on population and on the assumption that all nitrogen associated with consumption and domestic use, as well as nitrogen from co-discharged industrial wastewater, will eventually enter a waterway. As such, this estimate can be seen as conservative estimate and covers the entire source associated with domestic wastewater use. The methodology does not include N2O emissions from industrial sources, except for industrial wastewater that is co-discharged with domestic wastewater into the sewer system. The N2O emissions from industrial sources are believed to be insignificant compared to emissions from domestic wastewater. Very few countries collect data on wastewater sludge handling. If these data exist, it is suggested to make them available to the appropriate inventory teams. The emission factor used for N2O emissions from effluent is the same as the emission factor used for indirect N2O emissions in the AFOLU Sector.

REPORTING AND DOCUMENTATION It is good practice to document and report a summary of the methods used, activity data and emission factors. Worksheets are provided at the end of this volume. When country-specific methods and/or emission factors are used, the reasoning for the choices as well as references to how the country-specific data (measurements, literature, expert judgement, etc.) have been derived (measurements, literature, expert judgement, etc.) should be documented and included in the reporting. If sludge is incinerated, landfilled, or spread on agricultural lands, the associated quantities of sludge should be reported in the waste incineration, SWDS, or agricultural categories, respectively. More information on reporting and documentation can be found in Volume 1, Chapter 6, Section 6.11 Documentation, archiving and reporting.

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References American Public Health Association and American Water Works Association (1998). Standard Methods for the Examination of Water and Wastewater, 20th edition, Water Environment Federation, ISBN 0-87553-2357. Czepiel, P., Crill, P. and Harriss, R. (1995). ‘Nitrous oxide emissions from domestic wastewater treatment’ Environmental Science and Technology, vol. 29, no. 9, pp. 2352-2356. Destatis (2001). "Öffentliche Wasserversorgung und Abwasserbeseitigung 2001, Tabelle 1 "Übersichtstabelle Anschlussgrade" (Statistical Office Germany (http://www.destatis.de/) Doorn, M.R.J., Strait, R., Barnard, W. and Eklund, B. (1997). Estimate of Global Greenhouse Gas Emissions from Industrial and Domestic Wastewater Treatment, Final Report, EPA-600/R-97-091, Prepared for United States Environmental Protection Agency, Research Triangle Park, NC, USA. Doorn, M.R.J. and Liles, D. (1999). Global Methane, Quantification of Methane Emissions and Discussion of Nitrous Oxide, and Ammonia Emissions from Septic Tanks, Latrines, and Stagnant Open Sewers in the World. EPA-600/R-99-089, Prepared for U.S. EPA, Research Triangle Park, NC, USA. FAO (2004). FAOSTAT Statistical Database, United Nations Food and Agriculture Organization. Available on the Internet at Feachem, R.G., Bradley, D.J., Gareleck H. and Mara D.D. (1983). Sanitation and Disease – Health Aspects of Excreta and Wastewater Management, World Bank, John Wiley & Sons, USA. IPCC (1997). Houghton, J.T., Meira Filho, L.G., Lim, B., Tréanton, K., Mamaty, I., Bonduki, Y., Griggs, D.J. and Callander, B.A. (Eds). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Intergovernmental Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France. Masotti, L. (1996). "Depurazione delle acque. Tecniche ed impianti per il tratatmento delle acque di rifiuto". Eds Calderini. pp. 29-30 Metcalf & Eddy, Inc. (2003) Wastewater Engineering: Treatment, Disposal, Reuse. McGraw-Hill: New York, ISBN 0-07-041878-0. United Nations (2002). World Urbanization Prospects, The 2001 Revision Data Tables and Highlights. Population Division, Department of Economic and Social Affairs, United Nations Secretariat. ESA/P/WP.173. March 2002.

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Annex 1: Worksheets

ANNEX 1

WORKSHEETS

2006 IPCC Guidelines for National Greenhouse Gas Inventories

A1.1

Volume 5: Waste

Contents Annex 1 4B

Worksheets Biological Treatment of Solid Waste ............................................................................................... A1.3 CH4 emissions from Biological Treatment of Solid Waste ...................................................... A1.3 N2O emissions from Biological Treatment of Solid Waste ...................................................... A1.4

4C

Incineration and Open-Burning of Waste ......................................................................................... A1.5 CO2 emissions from Incineration of Waste .............................................................................. A1.5 Total amount of waste open-burned ......................................................................................... A1.6 CO2 emissions from Open Burning of Waste ........................................................................... A1.7 CO2 emissions from incineration of fossil liquid waste ........................................................... A1.8 CH4 emissions from Incineration of Waste .............................................................................. A1.9 CH4 emissions from Open Burning of Waste ......................................................................... A1.10 N2O emissions from Incineration of Waste ............................................................................ A1.11 N2O emissions from Open Burning of Waste ......................................................................... A1.12

4D

Wastewater Treatment and Discharge ............................................................................................ A1.13 Organically Degradable Material in Domestic Wastewater ................................................... A1.13 CH4 emission factor for Domestic Wastewater ...................................................................... A1.14 CH4 emissions from Domestic Wastewater ............................................................................ A1.15 Total Organic Degradable Material in wastewater for each industry sector ........................... A1.16 CH4 emission factor for Industrial Wastewater ...................................................................... A1.17 CH4 emissions from Industrial Wastewater ............................................................................ A1.18 Estimation of nitrogen in effluent ........................................................................................... A1.19 Estimation of emission factor and emissions of indirect N2O from Wastewater .................... A1.20

Note: For 4A Category Solid Waste Disposal, see spreadsheet IPCC Waste Model.

A1.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Annex 1: Worksheets

Sector Category Category Code Sheet

Biological Treatment System

Waste Category/ Types of Waste1

Waste Biological Treatment of Solid Waste 4B 1 of 1 Estimation of CH4 emissions from Biological Treatment of Solid Waste STEP 1 STEP 2

STEP 3

A

B

C

D

E

Total Annual amount treated by biological treatment facilities3

Emission Factor

Gross Annual Methane Generation

Recovered/flared Methane per Year

Net Annual Methane Emissions

(Gg CH4)

(Gg CH4)

(Gg CH4)

(Gg)

(g CH4/kg waste treated)

C= (A x B) x10-3

E = (C - D)

Composting

Anaerobic digestion at biogas facilities2

Total 1 2

Information on the waste category should include information of the origin of the waste (MSW, Industrial, Sludge or Other) and type of waste (Food waste or Garden and Park Waste). If anaerobic digestion involves recovery and energy use of the gas, the emissions should be reported in the Energy Sector.

3

Information on whether the amount treated is given as wet or dry weight should be given.

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Volume 5: Waste

Sector Category Category Code Sheet

Waste Biological Treatment of Solid Waste 4B 1 of 1 Estimation of N2O emissions from Biological Treatment of Solid Waste STEP 1

Biological Treatment System

Waste Category /Types of Waste1

STEP 2

A

B

C

Total Annual amount treated by biological treatment facilities3

Emission Factor

Net Annual Nitrous Oxide Emissions

(Gg)

(g N2O/kg waste treated)

(Gg N2O) E = (C - D) x10- 3

Composting

Anaerobic digestion at biogas facilities2

Total 1 Information on the waste category should include information of the origin of the waste (MSW, Industrial, Sludge or Other) and type of waste (Food waste or Garden and Park Waste). 2 If anaerobic digestion involves recovery and energy use of the gas, the emissions should be reported in the Energy Sector. 3 Information on whether the amount treated is given as wet or dry weight should be given.

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Annex 1: Worksheets

Sector Category Category Code Sheet

Type of Waste

Waste Incineration and Open Burning of Waste 4C1 I of I Estimation of CO2 emissions from Incineration of Waste A Total Amount of Waste Incinerated (Wet Weight)

B Dry Matter Content 1

(Gg Waste)

dm (fraction)

C D Fraction of Fraction of Fossil Carbon in Dry Carbon in Total Matter 2 Carbon3 CF FCF (fraction) (fraction)

E Oxidation Factor

F Conversion Factor

G Fossil CO2 Emissions

OF (fraction)

44/12

(Gg CO2) G= A x B x C x D x E x F

Municipal Solid Waste (MSW) 4, 5 Composition 4,5 Plastics Textiles Rubber Nappies

Industrial solid waste Hazardous waste Clinical waste Sewage sludge Other (specify) Total 1 2 3 4 5

For default data and relevant equations on the dry matter content in MSW and other types of waste, see Section 5.3.3 in Chapter 5. For default data and relevant equations on the fraction of carbon, see Section 5.4.1.1 in Chapter 5. For default data and relevant equations on the fraction of fossil carbon, see Section 5.4.1.2 in Chapter 5. Users may either enter all MSW incinerated in the MSW row or the amount of waste by composition by adding the appropriate rows. All relevant fractions of fossil C should be included. For consistency with the CH4 and N2O sheets, the total amount incinerated should be reported here. However the fossil CO2 emissions from MSW should be reported only once (either for total MSW or the components).

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Volume 5: Waste

Sector Category Category Code Sheet

Region, city, etc.

Waste Incineration and Open Burning of Waste 4C1 1 of 1 Estimation of total amount of waste open-burned STEP 1 A B C Population Fraction of Per Capita Waste Population Burning Generation Waste

P (Capita)

P frac (fraction)

D E Fraction of the Number of days waste amount by year 365 burned relative to the total amount of waste treated MSWP Bfrac 1 (kg waste/capita/day) (fraction) (day)

F Total Amount of MSW Open-burned

MSWB (Gg/yr) F=AxBxCxDxE

Sum of regions, cities, etc. (Total amount of MSW open-burned in the country) Total 1 When all the amount of waste is burned Bfrac could be considered equal 1. When a substantial quantity of waste in open dumps is burned, a relatively large part of waste is left unburned. In this situation, Bfrac should be estimated using survey or research data available or expert judgement.

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Annex 1: Worksheets Sector Category Category Code Sheet STEP 1 Type of Waste

Municipal Solid Waste (MSW) 5,6 Plastics Textiles Rubber Composition Nappies 5,6 etc

Waste Incineration and Open Burning of Waste 4C2 1 of 1 Estimation of CO2 emissions from Open Burning of Waste STEP 2 F G H I Total Amount of Waste Dry Matter Fraction of Fossil Fraction of Carbon open-burned Content 1 Carbon (Wet Weight) in Dry Matter 2 in Total Carbon 3 dm CF FCF (Gg Waste) (fraction) (fraction) (fraction) 4 F = (A x B x C x D) This comes from previous table

J Oxidation Factor

K Conversion Factor

L Fossil CO2 Emissions

OF (fraction)

44/12

(Gg CO2) L= F x G x H x I x J x K

add as needed Other (specify) Total 1 2 3 4 5 6

For default data and relevant equations on the dry matter content in MSW and other types of waste, see Section 5.3.3 in Chapter 5. For default data and relevant equations on the fraction of carbon, see Section 5.4.1.1 in Chapter 5. For default data and relevant equations on the fraction of fossil carbon, see Section 5.4.1.2 in Chapter 5. The amount MSW can be calculated in the previous sheet “Estimation of Total Amount of Waste Open-burned”. See also Equation 5.7. Users may either enter all MSW incinerated in the MSW row or the amount of waste by composition by adding the appropriate rows. All relevant fractions of fossil C should be included. For consistency with the CH4 and N2O sheets, the total amount open-burned should be reported here. However, the fossil CO2 emissions from MSW should be reported only once (either for total MSW or the components).

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Volume 5: Waste

Sector Category Category Code Sheet

Type of Waste

Waste Incineration and Open Burning of Waste 4C1 I of I Estimation of CO2 emissions from incineration of fossil liquid waste A Total Amount of Fossil Liquid Waste Incinerated (Weight)

B Fossil Carbon Content of Fossil Liquid Waste

C Oxidation Factor for Fossil Liquid Waste of type i

CL (fraction)

OF (fraction)

Gg Waste

D Conversion Factor

E Fossil CO2 Emissions

44/12

(Gg CO2) E= A x B x C x D

Lubricants Solvents Waste oil Other (specify) Total

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Annex 1: Worksheets

Sector Category Category Code Sheet

Type of Waste

Waste Incineration and Open Burning of Waste 4C1 I of I Estimation of CH4 emissions from Incineration of Waste A

B

C

Amount of Waste Incinerated (Wet Weight) 1

Methane Emission Factor

Methane Emissions

(Gg Waste)

(kg CH4/Gg Wet Waste) 1

(Gg CH4) C= A x B x 10-6

2

Municipal Solid Waste Industrial solid waste Hazardous waste Clinical waste Sewage sludge Other (specify) Total 1 2

If the total amount of waste is expressed in terms of dry waste, the CH4 emission factor needs to refer to dry weight instead. -6 Factor of 10 as emission factor is given in kg /Gg waste incinerated on a wet weight basis.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

Sector Category Category Code Sheet

Type of Waste

Waste Incineration and Open Burning of Waste 4C2 I of I Estimation of CH4 emissions from Open Burning of Waste F

G

H

Total Amount of Waste Open-burned (Wet Weight) 1 ,2 (Gg Waste)

Methane Emission Factor

Methane Emissions

(kg CH4/Gg Wet Waste) 2

(Gg CH4) H= F x G x 10-6

3

Municipal Solid Waste Other (specify) Total 1 2 3

Total amount of MSW open-burned is obtained by estimates in the Worksheet “Total amount of waste open-burned”. If the total amount of waste is expressed in term of dry waste, the CH4 emission factor needs to refer to dry weight instead. -6 Factor of 10 as emission factor is given in kg /Gg waste incinerated on a wet weight basis.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Annex 1: Worksheets

Sector Category Category Code Sheet

Type of Waste

Waste Incineration and Open Burning of Waste 4C1 I of I Estimation of N2O emissions from Incineration of Waste A Total Amount of Waste Incinerated (Wet Weight 1) (Gg Waste)

B Nitrous Oxide Emission Factor (kg N2O/Gg Wet Waste) 1

C Nitrous Oxide Emissions

(Gg N2O) C= A x B x 10-6

2

Municipal Solid Waste Industrial solid waste Hazardous waste Clinical waste Sewage sludge Other (specify) Total 1 If the total amount of waste is expressed in terms of dry waste, the CH4 emission factor needs to refer to dry weight instead. -6

2 Factor of 10 as emission factor is given in kg /Gg waste incinerated on a wet weight basis.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

Sector Category Category Code Sheet

Type of Waste

Waste Incineration and Open Burning of Waste 4C2 I of I Estimation of N2O emissions from Open Burning of Waste F Total Amount of Waste Openburned (Wet Weight) 1,2 (Gg Waste)

G Nitrous Oxide Emission Factor (kg N2O/Gg Dry Waste) 2

H Nitrous Oxide Emissions

(Gg N2O) H= F x G x 10-6

3

Municipal Solid Waste Other (specify) Total 1 Total amount of MSW open-burned is obtained by estimates in the Worksheet “Total amount of waste open-burned”. 2 If the total amount of waste is expressed in terms of dry waste, a fraction of dry matter should not be applied. -6 3 Factor of 10 as emission factor is given in kg /Gg waste incinerated on a wet weight basis.

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Annex 1: Worksheets

Sector Category Category Code Sheet

Region or City

Waste Domestic Wastewater Treatment and Discharge 4D1 1 of 3 Estimation of Organically Degradable Material in Domestic Wastewater STEP 1 A

B

Population

Degradable organic component

(P) cap

(BOD) (kg BOD/cap.yr) 1

C D Correction factor for industrial BOD Organically degradable material in discharged in sewers wastewater (I) 2 (TOW) (kg BOD/yr) D=AxBxC

Total 1 g BOD/cap.day x 0.001 x 365 = kg BOD/cap.yr 2 Correction factor for additional industrial BOD discharged into sewers, (for collected the default is 1.25, for uncollected the default is 1.00).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

Sector Category Category Code Sheet

Type of treatment or discharge

Waste Domestic Wastewater Treatment and Discharge 4D1 2 of 3 Estimation of CH4 emission factor for Domestic Wastewater STEP 2 A Maximum methane producing capacity (B0) (kg CH4/kgBOD)

B Methane correction factor for each treatment system (MCFj)

C Emission factor (EFj) (kg CH4/kg BOD) C=AxB

add as needed

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Annex 1: Worksheets

Sector Category Category Code Sheet

Income group

Waste Domestic Wastewater Treatment and Discharge 4D1 3 of 3 Estimation of CH4 emissions from Domestic Wastewater STEP 3 A B C D E Fraction of Degree of Emission Organically Sludge Type of utilization Factor degradable material removed treatment or population income group in wastewater discharge pathway (U i) (T i j) (EF j) (TOW) (S) (kg CH4/kg (fraction) (fraction) (kg BOD/yr) (kg BOD/yr) BOD) Sheet 2 of 3

F Methane recovered and flared (R)

G Net methane emissions

(kg CH4/yr)

(kg CH4/yr)

Sheet 1 of 3

(CH4)

G = [(A x B x C) x ( D -E)] - F

Rural

Urban high income Urban low income Total

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Volume 5: Waste

Sector Category Category Code Sheet

Industry Sectors

Waste Industrial Wastewater Treatment and Discharge 4D2 1 of 3 Total Organic Degradable Material in wastewater for each industry sector STEP 1 A B C D Total industry product Wastewater Chemical Oxygen Total organic degradable material in generated Demand wastewater for each industry sector (Pi) (Wi) (CODi) (TOWi) 3 3 (t product/yr) (m /t product) (kgCOD/m ) (kgCOD/yr) D=AxBxC

Industrial sector 1 Industrial sector 2 Industrial sector 3

add as needed Total

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Annex 1: Worksheets

Sector Category Category Code Sheet

Type of treatment or discharge

Waste Industrial Wastewater Treatment and Discharge 4D2 2 of 3 Estimation of CH4 emission factor for Industrial Wastewater STEP 2 A Maximum Methane Producing Capacity (B0) (kg CH4/kg COD)

B Methane Correction Factor for the Treatment System (MCFj) (-)

C Emission Factor (EFj) (kg CH4/kg BOD) C=AxB

add as needed

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

Sector Category Category Code Sheet

Industrial sector

Units

Waste Industrial Wastewater Treatment and Discharge 4D2 3 of 3 Estimation of CH4 emissions from Industrial Wastewater STEP 3 Type of treatment or discharge pathway

A Total organic degradable material in wastewater for each industry sector (TOWi) (kg COD/yr) Sheet 1 of 3

B Sludge removed in each industry sector

C Emission factor for each treatment system

D Recovered CH4 in each industry sector

E Net methane emissions

(Si) (kg COD/yr)

(EFi) (kg CH4/kgBOD) Sheet 2 of 3

(R i) (kg CH4/yr)

(CH4) (kg CH4/yr) E = [(A – B) x C] – D

Industrial sector 1 Industrial sector 2 Industrial sector 3

add as needed Total

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Annex 1: Worksheets

Sector Category Category Code Sheet

units

Waste Domestic Wastewater Treatment and Discharge 4D1 1 of 2 Estimation of nitrogen in effluent A Population

B Per capita protein consumption

C Fraction of nitrogen in protein

(P)

(Protein) (kg/person/ year)

(FNPR) (kg N/kg protein)

(people)

D E F Fraction of nonFraction of Nitrogen consumption industrial and removed with protein commercial cosludge discharged (default is zero) protein (FNON-CON) (FIND-COM) (NSLUDGE) (-)

(-)

(kg)

H Total nitrogen in effluent

(NEFFLUENT) kg N/year) H = (A x B x C x D x E) – F

Total

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 5: Waste

Sector Category Category Code Sheet

Waste Domestic Wastewater Treatment and Discharge 4D1 2 of 2 Estimation of emission factor and emissions of indirect N2O from Wastewater A Nitrogen in effluent (NEFFLUENT)

B Emission factor

C Conversion factor of kg N2O-N into kg N2O

(kg N/year)

(kg N2O-N/kg N)

44/28

D Emissions from Wastewater plants (default = zero) (kg N2O-N/year)

E Total N2O emissions

(kg N2O-N/year) E= A x B x C – D

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