Wells to Wire: Life Cycle Assessment of Natural Gas-Fired Electricity 3030719707, 9783030719708

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Sarah Marie Jordaan

Wells to Wire Life Cycle Assessment of Natural Gas-Fired Electricity

Wells to Wire

Sarah Marie Jordaan

Wells to Wire Life Cycle Assessment of Natural Gas-Fired Electricity

Sarah Marie Jordaan Advanced and International Studies The Johns Hopkins University Washington, DC, USA

ISBN 978-3-030-71970-8 ISBN 978-3-030-71971-5 (eBook) https://doi.org/10.1007/978-3-030-71971-5 © Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Wells to Wire presents an unbiased, comprehensive examination of the state of knowledge for life cycle assessments (LCAs) of natural gas-fired electricity, covering a suite of environmental impact categories. An exploration of the life cycle environmental impacts of gas-fired electricity is used to introduce the field of LCA, advancements in methods and data, and the limitations thereof. Natural gas, particularly as a fuel for electricity generation, serves as a dichotomy within energy and environmental systems analysis. While the cleanest burning fossil fuel, it is not without impacts, making it an excellent case study for introducing life cycle assessment. With an introduction to the field of LCA and a focus on natural gas-fired electricity, the reader is presented with the state of the art in life cycle data and scientific debate related to this product system. The author elucidates data and methodological challenges inherent to the field of LCA, exemplified using published research and with new analyses. The text explores how to conduct LCA, describing the analysis from the simplified perspective of a numerator and denominator.

With each chapter, the complexity of undertaking a LCA of gas-fired power is unravelled beyond a simple fraction to the expansive network of infrastructure examined in this type of research. Students, instructors, LCA practitioners, and energy professionals will benefit from not only the introduction to data and methods, but also this useful summary of the state of the art in the field. Policymakers and the interested public can learn more about the implications of LCA results for decision-support and the commentary about the economics of natural gas and its role as a bridge fuel. This book provides a useful reference and a springboard for researchers and experts interested in specializing in LCA, natural gas, or both.

To my loved ones and to those who helped me along the way.

Foreword

The USA has seen unprecedented and long-needed change in the power sector over the last decade, with natural gas surpassing coal as the dominant generation source and now rapidly succumbing to renewable energy sources as least-cost, highreliability sources for the energy transition. With such immense changes to the sector, the need for solid, quantitative, analyses of impacts and trade-offs are more important than ever, especially as the sun continues to rise on renewable power, energy storage, and social justice. Dr. Jordaan’s Wells to Wire expertly demonstrates the extent to which the now well-established field of life-cycle assessment continues to hold immense potential to resolve these issues across multiple dimensions—spanning the environment to economic to social aspects of our energy infrastructure. This book emerges presciently at a time when understanding the full dimension of impacts associated with natural gas is something that is vital to explore allay concerns for the environment and society expand in tandem. Life cycle assessment has been around for around half a century and has methodologically anchored our understanding of the environmental impacts of the electric sector. It is not unusual for analyses to greatly simplify the infrastructure effects of energy development. Wells to Wire expertly asks the right questions and cleverly wields large datasets to engender more accurate, much-needed analyses embodying the innate complexity of natural gas systems. For example, Jordaan presents methods and analysis of land-use for different energy sources with a focus on reclamation and fuel supply infrastructure, elements that have not previously been considered. In this book, life-cycle assessment has been skillfully used for some much-needed analyses for consistent comparisons across energy types. This is not surprising given Jordaan’s deep scholarship in land-based comparisons of energy systems, in part, which have quantified the immense land requirements of fossil fuels compared to renewables. Beyond land, the importance of understanding climate impacts of energy choices has never been more important. Natural gas has been called a bridge fuel to a more sustainable energy future. While true in the short-term, the quantitative assessments embodied in Wells to Wire bring to question the environmental and economic sustainability of natural gas serving as a bridge fuel on the longer term. A bridge should span a physical obstacle without blocking passage underneath. While natural gas ix

x

Foreword

may serve as a baseload of reliability in the short term, it may ultimately fail in facilitating a rapid passage to a sustainable future over the long term. Decision-makers and developers can confidently turn to these pages for an empirical justification of their choices and a reconciliation of outcomes. The transition from fossil fuel to renewable energy is both critically needed, and needs tools to be developed and put into effect so that we can examine unbiased assessments that can guide policy decisions as this transition occurs. While natural gas continues to play a role in the power sector, it must be scaled-back and ultimately replaced with clean energy altogether. This book gives insight into how to balance challenging decisions in the face of natural gas as an increasingly dominant fuel for electricity generation. It is only occasionally that a book like this one comes on the scene to provide an end-to-end combination of scientific and economic analyses to inform policy decisions about the future of an energy technology. The challenges the world now faces in climate change are real. It is a new world where natural gas must be fully accounted for. Dr. Jordaan’s Wells to Wire ends with a salient recognition of the actors, entities, and industries who increasingly bear the onus to prove the holistic sustainability of natural gas and, if unable, to decide what role natural gas will serve. Dan Kammen James and Catherine Lau Distinguished Professor of Sustainability University of California Berkely, USA Former Science Envoy, US State Department Coordinating Lead Author, Intergovernmental Panel on Climate Change

Acknowledgements

The completion of this book would not have been possible without the support from two Johns Hopkins University awards. A Discovery Award funded several years of research on emissions from the life cycle of natural gas-fired electricity in the USA. My collaborator and co-Principal Investigator Scot Miller and his doctoral student Leyang Feng provided valuable input into the life cycle assessment completed by Sakineh Tavakkoli under my supervision. While the results of that study were not published in this book, valuable insights from our collaboration informed this research. Several students in my research group—Energy Technology and Policy Assessment (ETAPA)—provided data support in early stages of the work that was funded by a Johns Hopkins Catalyst Award. In addition to Sakineh’s efforts on that publication, Shreya Rangarajan and Mike Yu collected data on natural gas prices. Andrew Ruttinger has been a big help on global research related to topics discussed in this book, and while that analysis will be published elsewhere, the collaboration also informed the content and vice versa. The work was improved by collaborations with the National Renewable Energy Laboratory on land use (namely Garvin Heath, Jordan Macknick and others) as well as with Hendrix College (Matt Moran and Maureen McClung). The research on the greenhouse gas outcomes of liquified natural gas was completed with colleagues from my prior collaborations at the University of Calgary (Adebola Kasumu, Vivian Li, James Coleman, and Jeanne Liendo). My collaborations on the water use of electricity generation with Lauren Patterson and Laura Diaz Anadon provided a solid basis for the related discussion in this book. I was fortunate to receive well-informed, constructive criticism that improved my research during numerous invited seminars (namely Harvard, Princeton, George Washington University, and the University of Maryland). Daniel Kammen provided the foreword, for which I am very grateful and honored. My friend and collaborator Rebecca Hernandez provided a very needed helping hand during the last stretch, as did my family (especially my mother). Finally, the American Center for Life Cycle Assessment provides an annual forum through which I gain insights from other experts (for example, from my colleagues from the National Energy Technology Laboratory). While I note these organizations and collaborators, the research results and analysis presented here are my own and do not represent their views (except where they are directly cited). xi

Contents

1 An Introduction to Life Cycle Assessment of Natural Gas-Fired Electricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Global Evolution of Natural Gas Production and Use in Electricity Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life Cycle Assessment as a Framework to Understand Environmental Impacts of Gas-Fired Power . . . . . . . . . . . . . . . . . . . . . . . . . LCA Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Will Be Covered in This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 4 7 8 10

2 LCA Framework, Methods, and Application . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Goal and Scope Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inventory Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13 13 14 17 20 24 26 28 28

3 The Denominator: Natural Gas Production, Throughput, and Electricity Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Infrastructure Lifetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Natural Gas Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Facility Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Power Plant Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31 31 32 33 37 39 41 42

1 1 3

xiii

xiv

Contents

4 Life Cycle Impact Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact Categories and Characterization Models . . . . . . . . . . . . . . . . . . . . . A Focus on Improving Individual Impact Categories . . . . . . . . . . . . . . . . . Greenhouse Gas Emissions and Other Air Pollution . . . . . . . . . . . . . . . Water Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quantification of Life Cycle Land Use Impacts . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 45 46 51 51 54 57 61 62

5 Global Markets and Competitiveness of Gas-Fired Power . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Levelized Cost of Electricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Economics of Natural Gas Production . . . . . . . . . . . . . . . . . . . . . . . . . . Production Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Natural Gas Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Additional Costs Associated with LNG . . . . . . . . . . . . . . . . . . . . . . . . . . Implications of LNG for Life Cycle Assessment of Gas-Fired Power . . . . Liquefaction to Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electricity Generation to End Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67 67 68 72 72 72 74 75 75 80 81 83

6 Tackling Uncertainty Across the Life Cycle of Gas-Fired Power . . . . 85 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Uncertainty in LCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Data Sources, Precision, and Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Approaching Uncertainty in LCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Monte Carlo Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Break-Even (Parametric) Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Scenario Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 7 Natural Gas as a Bridge Fuel? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Compatibility with Paris Agreement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Political, Economic, and Technological Uncertainties . . . . . . . . . . . . . . . . . Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

103 103 104 105 107 108 110

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Chapter 1

An Introduction to Life Cycle Assessment of Natural Gas-Fired Electricity

Abstract The electric sector is undergoing a transition to a more environmentally sustainable energy system, and electricity generated from natural gas has played a role to date as a lower carbon alternative to coal. While it combusts the cleanest burning fossil fuel, natural gas-fired power is not without environmental impacts. Life cycle assessment offers an internationally standardized methodology to quantify the environmental burdens from materials extraction to waste disposal. Environmental impacts of gas-fired power can thus be robustly examined using this approach. The primary goal of the book is to introduce the reader to the field of life cycle assessment with an unbiased, comprehensive examination of the state of knowledge of natural gas-fired electricity. Due to the irreducible variability of infrastructure types across an expansive network associated with this product system, the focus of the analyses laid out in this book is on the fuel cycle and electricity generation. Wider systems boundaries that include upstream material extraction and waste disposal are considered as applicable. This chapter introduces the premise of the book and lays out the subsequent chapters for the readers. Insights are developed on not only how to undertake a life cycle assessment of gas-fired power, but also how to assess uncertainty, emerging markets and their implications and the role of natural gas as a bridge fuel.

Introduction Natural gas is the cleanest burning fossil fuel, but the full scope of its environmental implications for the electric sector remains unclear. Displacing coal with natural gas can result in reduced greenhouse gas emissions from the power sector, yet the fugitive methane emissions from the natural gas supply chain may reverse such benefits if left unmanaged. Similarly, switching from coal to gas in the electric sector results in a net reduction of water consumption. This reduction does not capture the full picture: environmental benefits are not experienced by all regions impacted by such a transition—the spatial patterns of water used for hydraulic fracturing over time have not yet been thoroughly investigated across shale gas producing regions. These two examples shed light on how the overall environmental impacts of the sector remain unresolved, amongst scientists, policymakers, and the broader public. © Springer Nature Switzerland AG 2021 S. M. Jordaan, Wells to Wire, https://doi.org/10.1007/978-3-030-71971-5_1

1

2

1 An Introduction to Life Cycle Assessment of Natural Gas …

Life cycle assessment (LCA) is an analytical tool that can be used to examine the environmental impacts of a product or process from cradle to grave. New research has been initiated in recent years to understand the life cycle environmental implications of changes in the natural gas and power sectors; yet results remain disputed in many cases due to challenges with uncertain data, rapidly evolving operations and technologies, and assumptions. The most prominent example in the past decade is the influence of methane leaks from natural gas production systems on the life cycle of natural gas-fired electricity. One notable study in 2011 suggested these upstream emissions may reverse the environmental benefits of electricity generated from coal compared to natural gas [1]. These results were quickly disputed [2] with subsequent studies noting the uncertainty in upstream emissions and how this reversal would only hold true if emissions were substantially underestimated [3]. More recently, Alvarez et al. [4] found that 2015 emissions may indeed have been underestimated, suggesting environmental benefits may be overestimated (or even not realized) if the global warming potential is considered over 20-year time horizons. Tanaka et al. [5] suggest that—even if that were the case under some conditions—the metrics presently used for comparing different greenhouse gases are limited and using more robust metrics affirms the environmental benefits of natural gas relative to coal. The goal of this book is to introduce the field of LCA using an unbiased, comprehensive examination of the state of knowledge of natural gas-fired electricity, covering a suite of environmental impact categories. To meet this goal, the book has three objectives: (1) to introduce readers to the field of LCA using natural gasfired electricity as a case study; (2) to delineate the state-of-the-art in life cycle data, research, and scientific debate related to natural gas-fired electricity, and (3) to elucidate data and methodological challenges inherent to the field of LCA, exemplified through the case of natural gas-fired power. We will explore how to conduct LCA, describing the analysis from the perspective of a numerator and denominator. Natural gas, particularly as a fuel for electricity generation, creates a dichotomy within energy and environmental systems analysis. While the cleanest burning fossil fuel, it is not without impacts across supply chains, making it an excellent case study for introducing life cycle assessment. While many books address either natural gas, natural gas-fired electricity, LCA of natural gas vehicles, or LCA as a field in and of itself [6–10], this book combines an introduction to the field of LCA using natural gas-fired electricity as an example. In this first chapter, the evolution and growth of the natural gas sector will be explored to illuminate the significance of this book. Basic concepts and terms associated with LCA will be introduced, including the four methodological stages (goal and scope definition, inventory analysis, impact assessment, and interpretation). The natural gas supply chain through use in power generation will be explained, following a natural gas molecule from extraction from the ground through combustion in a power plant and the delivery of electricity to end use. Finally, each chapter of the book will be summarized to provide the reader with the big picture of the book.

Global Evolution of Natural Gas Production and Use …

3

Global Evolution of Natural Gas Production and Use in Electricity Generation The natural gas sector is undergoing global changes, with the recent expansion of liquefied natural gas trade (LNG) and the unlocking of substantial shale gas reserves. The combination of hydraulic fracturing and vertical drilling represents an innovation that has unlocked enormous shale reserves, leading to the 34 trilling cubic feet of natural gas produced in 2019—the highest annual production recorded in the United States and 9% greater than domestic consumption [11]. Global demand for natural gas in the electric sector has been growing in recent years, exemplified by the coal-togas transition in the sector in the United States. The share of U.S. electricity generated from natural gas increased from 19% in 2005 to 24% and 33% in 2010 and 2015 respectively [12] initiated by the shale boom and the subsequent shift from coal to gas in the electric sector. Up until the 2019–2020 pandemic, natural gas was expanding globally via liquefied natural gas (LNG) trade and the oversupply had created global glut. The market had already experienced a slowdown in 2019, which was confounded by an additional demand shock with COVID-19 [13]. The International Energy Agency (IEA) expects a progressive recovery to pre-crisis levels in 2021 and, despite 75 billion m3 of lost growth from 2019 to 2025, foresees an average growth rate of 1.5%. While industrial growth is the primary contributor, even the lowest emitting scenario anticipates medium-term growth in natural gas demand from the power sector up until 2030. This IEA scenario—the Sustainable Development Scenario (SDS)—is fully aligned with the Paris agreements goals [14]. Natural gas consumption in this scenario increases to an annual average rate of 0.9% until the end of the 2020s. After 2030, however, declines are anticipated in the SDS, in contrast to the other scenarios which all anticipate steady growth. Although natural gas-fired electricity generation declines, capacity grows compared with today as gas expands its role as a provider of power system flexibility.

Life Cycle Assessment as a Framework to Understand Environmental Impacts of Gas-Fired Power Both renewables and natural gas are set to play a substantial role in the future electric grid. Coal without carbon capture and storage (CCS) comes under fire due to impacts to the environment. Although natural gas is expected to grow in even the lowest carbon scenarios because it is the cleanest burning fossil fuel, it remains a fossil fuel (if extracted from oil and gas reservoirs). As a result, overall impacts of gas-fired power are especially scrutinized and require balanced and unbiased technical analysis. LCA provides a standardized, broadly accepted method to complete balanced, yet rigorous examinations of the impacts. Several chapters of this book—notably Chap. 4—will include such comparisons to illustrate useful application of the method.

4

1 An Introduction to Life Cycle Assessment of Natural Gas …

LCA methodology has two key strengths that serve to better understand and mitigate the environmental impacts of natural gas in the electric sector, providing a method to produce unbiased results [9]. First, the method is internationally standardized through ISO, providing a robust and comprehensive way to understand the cradle-to-grave impacts of products and processes. LCA strives to deliver superior environmental outcomes: once the impacts have been quantified, mitigation options can be identified and implemented. Second, radically different product systems that deliver similar services can be compared. Importantly for this book, electricity generated from natural gas can be compared to that generated from coal, nuclear, or renewables. Like a LCA of any other product or process, analyses of gas-fired power must grapple with the field’s limitations [9]. Concerns about “hired guns” modifying results to endorse their products has been an ongoing issue and certainly true for the fossil fuel sector. Similarly, LCA results can be used inappropriately in product marketing; for example, results promoting a product may be showcased while hiding uncertainties or focusing on favorable results. The limitations of LCA are rooted in inconsistencies in system boundaries, inputs, functional units. Recent harmonization studies found large differences in system boundaries and inputs in LCAs of gas-fired power [15, 16]. Additionally, even changes in the functional unit from electricity generated to electricity delivered (the latter including the transmission and distribution (T&D)) can result in substantial changes in results if inefficiency T&D is high enough [17]. These criticisms are truly exemplified by the case of natural gas since assumptions are often disputed between industry and other stakeholders. Methane leaks from natural gas production systems provide a good example—their magnitude is often downplayed by industry, questioned by scholars, and disputed by environmentalists [3, 18–20]. The analysis of natural gas production and electricity generation using LCA is particularly challenged in this regard, as it involves a complex network of expansive infrastructures. Leaks may occur across hundreds of thousands of different components, ranging from production sites, through pipelines, midstream sites and infrastructures, with further inefficiencies throughout the electric grid. Even the most detailed studies to date report different populations of infrastructure types for the fuel cycle of natural gas power (Table 1.1). The fuel cycle encompasses the part of the life cycle of gas-fired electricity that is directly related to the extraction and transport of natural gas up to the power plant.

LCA Methodology Due to the complexity of the infrastructure involved with the life cycle of gas-fired electricity in addition to vested interests, the standardized LCA methodology holds importance in developing robust results. The method involves four stages, as outlined below:

LCA Methodology

5

Table 1.1 Example infrastructure counts published for the natural gas supply chain, referred to here as the fuel cycle Fuel cycle stage

Heath et al. [21]

EPA GHGI [22]

Tavakkoli et al. [23] Unit

Production wells

500,000 (natural gas)

251,076 (hydraulic fracturing)

84,155 (hydraulic fracturing)

Well

Gathering pipelines

200,000

403,863

160,126

Mile

Gathering stations

10,000

5276

2550

Station

Storage facilities

400

349 (compressor stations)

461

Underground facility unless noted

Processing plants

580 plants

667 plants

801 plants

Plant

Transmission stations

10,000 compressors

1844 stations

1720 stations

Station unless noted

Transmission pipelines

320,000

301,164

229,296

Mile

1.

2.

3. 4.

Goal and Scope definition. The product and purpose of the LCA are determined in this LCA stage. Practitioners select the system boundaries and the functional unit, the latter serving as the basis of comparison for the analysis (the denominator). Inventory analysis. The second stage of a LCA involves the compilation of relevant data inputs and outputs. For example, resource inputs and environmental releases will be quantified for each part of a product’s life cycle. Impact assessment. The environmental impacts associated with the inputs, outputs, and emissions are calculated and, where possible, aggregated to indices. Interpretation. The interpretation stage is geared towards informing decisions. The results of the LCA are evaluated relative to the goal and scope definition. Uncertainty assessment is a critical component of the interpretation, to inform decision-makers of how results may change under different conditions.

A life cycle model is developed while implementing the methodology. It is important to distinguish the LCA model (Fig. 1.1a) from the LCA method (Fig. 1.1b). The LCA model provides guidance on how practitioners complete the inventory analysis, primarily in the form of a process flow diagram. A process flow diagram is a schematic that describes the key processes involved in the life cycle. Figure 1.1a presents an example of a generic process flow diagram, while a detailed schematic developed specifically for gas-fired power will be presented in Chap. 2. The LCA model is developed iteratively by implementing the four stages of the LCA method (Fig. 1.1b), with a detailed process flow diagram being developed in the Goal and Scope Definition stage. A critical component of this first stage of LCA is the selection of a functional unit. The functional unit is defined by the International Organization for Standardization (ISO) as: the quantified performance of a

6

1 An Introduction to Life Cycle Assessment of Natural Gas …

product system, process, or organization for use as a reference unit. As the product of focus for this book is LCA of gas-fired electricity, the functional unit is typically selected as a unit of electricity generated, such as kilowatt-hour (kWh) or Megawatthour (MWh). Care must be taken as the analysis proceeds in appropriate selection and characterization of the functional unit. While it may appear straightforward, substantial differences have been noted depending on whether the functional unit is electricity generated or delivered [17].

Materials aquisition

Products

Materials Formulation, processing, and manufacturing Transportation and distribution

Co-products Water pollution

Energy Use of product Recycling, if applicable Natural resources (water, land, etc) Waste management

Air pollution Solid waste Other environmental impacts (e.g. biodiversity)

(a)

1. Goal and scope

Data

2. Inventory analysis

4. Interpretation of results

3. Impact assessment

Performance improvements • Product development and improvement • Strategic planning • Policy implementation • Marketing

(b) Fig. 1.1 a, b LCA models and methods. a Life cycle model (adapted from Fava et al. [24]). b LCA procedure, including the four phases of the LCA methodology (adapted from International Organization for Standardization (ISO) 2006 [25])

What Will Be Covered in This Book

7

What Will Be Covered in This Book This book centers on the part of the life cycle that includes the natural gas fuel cycle up through electricity generation. The analyses may consider additional liquefaction stages, depending on the scope of the analyses being discussed. Electricity generated from LNG imports will be examined in Chap. 5 (Fig. 1.2). To envision the life cycle of natural gas-fired electricity, the reader is invited to imagine the path of a natural gas extracted from the ground. When extracted, natural gas is typically a mixture, the majority of which is methane (Table 1.2). It may be extracted through a well that produces primarily gas or primarily oil; the latter is referred to as associated gas and is viewed as a co-product. Depending on the composition of the hydrocarbons produced from the well site, some purification and separation may be required via processing. Even the heating value of the natural gas— representing the amount of energy that is released upon combustion—is variable, ranging from 42–55 Megajoules per kilogram (MJ/kg) [26] (Table 1.2).

Gas production and supply chain

Processing

Extraction

Midstream infrastructure (e.g. storage, compressor stations, liquefaction)

End use

Ocean transport (if applicable)

Fig. 1.2 Simplified representation of the life cycle of natural gas products

Table 1.2 Typical compositions of natural gas [27]. Note that some estimates may be based on volume; for example, Heath et al. used an aggregate U.S. national estimate of 78.8% methane by volume Components

Chemical composition

Range (mole %)

Methane

CH4

70–90

Ethane

C2 H6

0–20

Propane

C3 H8

0–20

Butane

C4 H10

0–20

Carbon dioxide

CO2

0–8

Oxygen

O2

0–0.2

Nitrogen

N2

0–5

Hydrogen sulfide

H2S

0–5

Rare gases

A, He, Ne, Xe

Trace

8

1 An Introduction to Life Cycle Assessment of Natural Gas …

Once the natural gas has been extracted from the ground, it is transported through pipelines to a processing facility (if required) where impurities are removed and/or products are separated. The rest of the infrastructure through which the gas travels may include underground storage, one or more compressor stations (which increase the pressure of the gas and enable its flow through pipelines), and other midstream infrastructure such as liquefaction for peak shaving (storage to regulate demand). If the natural gas is to be exported to an international market, then it may be liquefied for ocean transport, then regasified and transported once it reaches the target destination. The target destination depends on the end use, and here we examine electricity generation. As a result, the natural gas would end up in a power plant in the region of interest to the practitioner (defined in the goal and scope definition). In this book, several examples of how the choice of region can influence LCA results of gas-fired electricity will be examined. The analyses presented here focus specifically on the quantification of the fuel cycle and electricity generation of gas-fired power, unless noted otherwise (such as in Chap. 4, where impacts are harmonized and compared to other electricity generation sources). For greenhouse gas emissions, for example, ignoring the material extraction stage for renewables like solar and wind would falsely result in zero emissions [15]. For natural gas, these emissions contribute less than 1% of emissions; however, consistency should be applied in comparative analyses. While the focus of this book is on the operations of the fuel cycle and electricity generation, to help readers better characterize a highly complex system more accurately, practitioners are encouraged to develop the scope of their analyses comprehensively. In particular, consistency and transparency is crucial component of credible, comparative LCAs.

Summary With each chapter, the complexity of undertaking a LCA of gas-fired power will be unraveled beyond a simple fraction to the expansive network of infrastructure that must be considered. Readers of this book may be graduate students or specialized researchers interested in either LCA or the natural gas sector. LCA practitioners and professionals in the energy sector will benefit from not only the data and methods presented, but also the summary of the state-of-the-art research in the field. As readers may find particular chapters more useful than others depending on their interests, content of each chapter is described in the following summary. After the present introductory chapter, Chap. 2 comprehensively covers the four methodological stages of LCA in depth (goal and scope definition, inventory analysis, impact assessment, and interpretation), relating the method to the example of natural gas. Readers interested in learning the basics of LCA will strongly benefit from this chapter, or those who are interested in understanding how LCA methods are applied to natural gas specifically. While broad LCA concepts are explored, additional details regarding applying these concepts to natural gas are discussed. The chapter has specialized sections related to the choice of functional unit, allocation methods, and

Summary

9

available impact assessment methods (as well as a discussion about how to choose from the available methods) [28–31]. Chapter 3 examines how to quantify the energy flows through the supply chain (also referred to as throughput), from how much natural gas can be extracted from a well, the natural gas throughput that travels through each infrastructure type, leading up to the amount of natural gas combusted at the power plant and the amount of electricity generated. Critical to the implementation of LCA is the choice of functional unit, which serves as the denominator in the final calculations. For the case of gas-fired power, the functional unit is the unit of electricity generated (e.g. Megawatthour or MWh). Methods to quantify estimated ultimate recovery from natural gas wells will be reviewed, including a compilation of published data. Different infrastructure assets are examined along with the throughput of natural gas through each facility type. Last, data specific natural gas-fired generation is used as an example to demonstrate the calculations used to convert each life cycle phase to the basis of a functional unit (unit of electricity generated) for the final LCA results. The variability in efficiency across electricity generation technologies and heat rates across generating units are reviewed, using real-life data examples from power plants in the U.S.A and aggregate country-level statistics internationally. Life cycle inventories and impact assessment methods used in LCAs of gasfired power will be covered in Chap. 4. Across impact categories, natural gas faces many of the same challenges as other product systems in LCA: data. Due to the high levels of variability and uncertainty inherent to the natural gas sector, impact results remain disputed amongst scientists and policymakers alike. The typical life cycle approach of characterizing results into a suite of aggregate impact categories is therefore less valuable for decision-makers in this particular sector, due to high levels of controversy surrounding particular assumptions employed in LCA models. As a result, deep dives into specific life cycle impacts and their inventories will be taken for greenhouse gas emissions, air pollution, water consumption, and land use. Recent analyses for each impact will be reviewed, along with a critical analysis of the state of data and impact methods. Specific attention will be paid to highlight where additional methodological and data improvements are required. Chapter 5 will cover the evolution of natural gas economics and global markets, and how these factors influence LCA results. Shale gas reserves have been unlocked across the planet with the combination of horizontal drilling and hydraulic fracturing. At the same time, the global trade of natural gas has been increasing with a burgeoning liquefied natural gas (LNG) industry. These twin developments have resulted in new political, economic, and environmental dynamics across the supply chain from the exporting to importing nations [32]. The addition of new processes in the natural gas supply chain (e.g. liquefaction, ocean transport, and regasification) can have substantial impacts on life cycle results. The economics of natural gas will be reviewed followed by a discussion of present and emerging markets for natural gas (with emphasis on LNG) and their implications for LCA. Uncertainty is a critical component of LCA methodology for practitioners because it enables decision-makers to understand how impacts may change under different conditions. Chapter 6 will include an overview of the importance of uncertainty

10

1 An Introduction to Life Cycle Assessment of Natural Gas …

assessment in LCA, followed by a review of methods frequently employed by LCA practitioners, and ending with examples specific to natural gas-fired electricity. The chapter will provide an introduction to uncertainty assessment, starting with sensitivity analysis followed by a detailed discussion of the most commonly used uncertainty assessment tools in LCA [32]. The methods will be introduced along with a review of how they are employed in LCAs of gas-fired power [8]. The final chapter will address the overall sustainability of natural gas and its potential role as a bridge fuel, using LCA as a framework for the discussion. The conclusion will not only summarize the book’s overall findings in the context of natural gas as a bridge fuel, but it will also revisit remaining research areas that readers may want to pursue. Only one end use will be covered in this manuscript, for example, but results for the fuel cycle may be useful for analyses that focus on other end uses. Demand for natural gas by the electric sector is presently anticipated to grow over the coming decades, pointing to a need to understand the environmental implications. This book presents a comprehensive overview of LCA as a systematic tool to build this understanding, illuminating present findings and areas of inquiry.

References 1. Howarth, R.W., Santoro, R., Ingraffea, A.: Methane and the greenhouse-gas footprint of natural gas from shale formations. Clim. Change 106, 679–690 (2011) 2. O’Sullivan, F., Paltsev, S.: Shale gas production: potential versus actual GHG emissions. MIT Joint Program on the Science and Policy of Global Change, p. 234 (2012) 3. Brandt, A.R., Heath, G.A., Kort, E.A., O’Sullivan, F., Ptron, G., Jordaan, S.M., Tans, P., Wilcox, J., Gopstein, A.M., Arent, D.: Methane leaks from North American natural gas systems. Science 343, 733–735 (2014) 4. Alvarez, R.A., Zavala-Araiza, D., Lyon, D.R., Allen, D.T., Barkley, Z.R., Brandt, A.R., Davis, K.J., Herndon, S.C., Jacob, D.J., Karion, A.: Assessment of methane emissions from the US oil and gas supply chain. Science eaar7204 (2018) 5. Tanaka, K., Cavalett, O., Collins, W.J., Cherubini, F.: Asserting the climate benefits of the coal-to-gas shift across temporal and spatial scales. Nat. Clim. Change 9, 389–396 (2019) 6. Lyons, W.C., Plisga, G.J.: Standard Handbook of Petroleum and Natural Gas Engineering. Elsevier (2011) 7. Nigge, K.: Life cycle assessment of natural gas vehicles: development and application of site-dependent impact indicators, vol. 6. Springer Science & Business Media (2012) 8. Matthews, H.S., Hendrickson, C.T., Matthews, D.H.: Life cycle assessment: quantitative approaches for decisions that matter. Retrieved June 2018, 1 (2018) 9. Tillman, A.M., Baumann, H.: The Hitchhikers guide to LCA. Studentlitteratur AB, Lund, Sweden (2004) 10. Schenck, R., White, P.: Environmental Life Cycle Assessment: Measuring the Environmental Performance of Products. American Center for Life Cycle Assessment Vashon, Washingto (2014) 11. EIA Natural gas explained: Where our natural gas comes from. Energy Informainistration (EIA). https://www.eia.gov/energyexplained/natural-gas/where-our-natural-gas-comesfrom.php. Accessed on 14 Jan 2021 12. Energy Information Administration: Form EIA-923 detailed data with previous form data. Accessed on Jan 08 2019

References

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13. IEA Gas 2020. International Energy Agency (IEA) (2020) 14. International Energy Agency, (Sustainable Development Scenario, World Energy Outlook. https://www.iea.org/reports/world-energy-model/sustainable-development-sce nario. Accessed on 3 Mar 2020 15. O’Donoughue, P.R., Heath, G.A., Dolan, S.L., Vorum, M.: Life cycle greenhouse gas emissions of electricity generated from conventionally produced natural gas: systematic review and harmonization. J. Ind. Ecol. 18, 125–144 (2014) 16. Heath, G.A., O’Donoughue, P., Arent, D.J., Bazilian, M.: Harmonization of initial estimates of shale gas life cycle greenhouse gas emissions for electric power generation. Proc. Natl. Acad. Sci. USA 111, 3167 (2014) 17. Surana, K., Jordaan, S.M.: The climate mitigation opportunity behind global power transmission and distribution. Nat. Clim. Change 9, 660–665 (2019) 18. Hauck, M., Steinmann, Z., Laurenzi, I.J., Karuppiah, R., Huijbregts, M.: How to quantify uncertainty and variability in life cycle assessment: the case of greenhouse gas emissions of gas power generation in the US. Environ. Res. Lett. 9, 074005 (2014) 19. Tollefson, J.: Methane leaks erode green credentials of natural gas. Nature 493, 12 (2013) 20. Atherton, E., Risk, D., Fougere, C., Lavoie, M., Marshall, A., Werring, J., Williams, J.P., Minions, C.: Mobile measurement of methane emissions from natural gas developments in northeastern British Columbia, Canada. Atmos. Chem. Phys. 17 (2017) 21. Heath, G., Warner, E., Steinberg, D., Brandt, A.: Estimating US Methane Emissions from the Natural Gas Supply Chain. Approaches, Uncertainties, Current Estimates, and Future Studies (2015) 30. EPA.: U. S. Inventory of US greenhouse gas emissions and sinks: 1990–2017 (2019) 23. Tavakkoli, S., Feng, L., Miller, S., Jordaan, S.M.: The implications of generation efficiencies and supply chain leaks for the life cycle greenhouse gas emissions of natural gas-fired electricity in the United States (under review) 24. Fava, J.A., Denison, R., Jones, B., Curran, M.A., Vignon, B., Selke, S.: A technical framework for life cycle assessment: workshop report; August 18–23, 1990; Society of Environmental Toxicology and Chemistry (SETAC): (1991) 25. International Organization for Standardization, (ISO). 14040/44 International standard: Environmental Management–Life Cycle Assessment. International Organisation for Standardization, Geneva, Switzerland (2006) 26. Hauschild, M.Z., Rosenbaum, R. K., Olsen, S.I.: Life Cycle Assessment. Springer, Berlin (2018) 27. Jolliet, O., Saadé-Sbeih, M., Shaked, S., Jolliet, A., Crettaz, P.: Environmental Life Cycle Assessment. CRC Press (2015) 28. Klöpffer, W., Grahl, B.: Life Cycle Assessment (LCA): A Guide to Best Practice. Wiley (2014) 29. Curran, M.A.: Life Cycle Assessment Handbook: a Guide for Environmentally Sustainable Products. Wiley (2012) 30. World Nuclear Association Heat Values of Various Fuels. https://www.world-nuclear.org/inf ormation-library/facts-and-figures/heat-values-of-various-fuels.aspx. Accessed on 14 Jan 2021 31. Siažik, J., Malcho, M.: Accumulation of primary energy into natural gas hydrates. Proc. Eng. 192, 782–787 (2017) 32. Kasumu, A., Li, V., Coleman J.W., Liendo J., Jordaan, S.M.: Country-level life cycle assessment of liquefied natural gas trade for electricity generation. Environ. Sci Tech. 52(4), 1735–1746 (2018)

Chapter 2

LCA Framework, Methods, and Application

Abstract Life cycle assessment (LCA) offers a robust, internationally standardized method for determining environmental costs and benefits of product systems. For credible and consistent results, the broadly accepted method should be applied with transparent reporting of data, assumptions, and results. In this chapter, the four stages of the LCA method—goal and scope definition, inventory analysis, impact assessment, and interpretation—are explained in detail, using gas-fired power to exemplify how to develop a process-based LCA.

Introduction Life cycle findings can result in reversals of environmental conclusions about products or the discovery of potential environmental improvements [1]. For example, potential negative consequences of early lead batteries for use in electric vehicles (EV) were discovered using a life cycle approach before widespread commercialization in the 1990s [2]. With powerful economic forces at play in the sectors involved across the life cycle of products, the concern over the misuse of LCA for corporate gain has been an ongoing challenge for the field [3]. Ever since the development and broader use of LCA—with the first being completed by Coca-Cola company in the form of a Resource and Environmental Profile Analysis (REPA)—the methods have been a contentious point [3]. The contention was not because of Coca-Cola specifically, but more so because the choice of methods (and assumptions) can yield biased results that can be used incorrectly for marketing purposes by corporate and other interests. Growing concerns about spin doctors and hired guns (trained experts capable of manipulating the data to promote specific products and outcomes) led to the need for standardizing methods, particularly as LCAs were applied more broadly to powerful sectors such as energy and fossil fuels. As a result, the methodological approach has undergone a rigorous standardization process through the International Organization for Standardization (ISO) [4]. LCA follows a four-step methodology that includes (1) goal and scope definition, (2) inventory analysis, (3) impact assessment and (4) interpretation. The International Organization for Standardization (ISO) describes each of these four iterative steps, © Springer Nature Switzerland AG 2021 S. M. Jordaan, Wells to Wire, https://doi.org/10.1007/978-3-030-71971-5_2

13

14

2 LCA Framework, Methods, and Application

which will be discussed in-depth in this chapter [3, 4]. The standardization process led to a robust methodology, a specific cookbook which holds practitioners accountable by requiring comprehensive analysis and transparency to certify their LCAs. While there are several types of life cycle approaches, such as economic input output LCAs, we focus on process based LCAs which examine specific processes within a defined system boundary. Such a scope permits a comprehensive examination of a complex fuel cycle and life cycle throughout this book. This chapter will provide an overview of the ingredients for each life cycle phase, combined into a recipe for each, to simplify the process for those who are new to the field. Each stage will be explained relative to gas-fired electricity.

General Framework The central theme of this chapter will be to cover each stage of the LCA approach, as internationally accepted via the International Organization for Standardization (ISO), an organization that seeks broad consensus to develop standards (Fig. 2.1). The first ISO standard for LCA was published in 1996 with a later revision in 2006. While clearly there was a dire need for consensus, the standardization process received early criticisms, importantly that it was premature and risked stagnating the development of LCA since it was early in its adoption [5, 6]. Such criticism holds true in some regards and are yet to be fully resolved. For example, data availability and science has progressed in leaps and bounds since 1996 (and even 2006) so many methods currently being developed were not even conceived when the standards were initially formulated. That said, the ISO framework was developed for flexibility (with ongoing updates and amendments) and met important goals of ensuring the accountability of companies in publishing their LCAs. Regardless, many organizations do not complete certification and practitioners should always keep a critical eye on the methods and assumptions being employed.

1. Goal and scope

Data

2. Inventory analysis

4. Interpretation of results

3. Impact assessment (b)

Fig. 2.1 LCA methodology (developed from ISO 2006)

Performance improvements • Product development and improvement • Strategic planning • Policy implementation • Marketing

General Framework

15

The four stages of methodology will be covered in depth, including [3, 4, 7–10]: 1.

2.

3.

4.

Goal and Scope Definition. The product and purpose of the LCA are determined in this stage. Practitioners select the product system, delineate the system boundaries and determine the functional unit, the latter serving as the basis of comparison for the analysis. The functional unit in LCA is the factor used to normalize the environmental impacts. For example, one kilowatt-hour (kWh) or one Megawatt-hour (MWh) may be used as the functional unit for a life cycle study of greenhouse gas emissions from natural gas-fired electricity generation, where results will be presented in grams of CO2 equivalent released for each unit of electricity generated (e.g., 1 kWh). Alternatively, if the study were focused on natural gas vehicles, a more appropriate functional unit would be a passenger-mile travelled. Inventory Analysis. The second stage of LCA involves the compilation of relevant data inputs and outputs and quantifying them on the basis of a functional unit. For example, resource inputs and environmental releases will be quantified for each part of a product’s life cycle. An inventory analysis would require the inputs of each process within the system boundaries and relating them to the functional unit by considering the material and energy flows across the system. For example, the emissions from the natural gas supply chain are related to the functional unit by the amount of natural gas required by the power plant for each unit of electricity generated (also known as a heat rate of a power plant, which may be expressed as mmBTU of gas consumed for MWh generated, for instance). Impact Assessment. The goal of the third stage, impact assessment, is to understand and evaluate the significance and magnitude of environmental impacts caused by each of the life cycle stages within the system boundaries for the product in question. The environmental impacts associated with the inputs, outputs, and emissions are calculated and, where possible, aggregated to indices. For example, greenhouse gas emissions released throughout the life cycle of gas-fired electricity involve not only carbon dioxide (CO2 ) but non-combustion sources like methane (CH4 ) leaks. Impact assessment will involve characterizing these emissions as carbon dioxide equivalents (CO2 e) so that they can be considered in aggregate. Interpretation. The interpretation stage is geared towards informing decisions. Interpretation involves an evaluation of results relative to the goal and scope of the LCA in order to generate conclusions and/or recommendations for decisionmakers (e.g., report commissioners). Uncertainty assessment is a critical component of the interpretation, to inform decision-makers of how results may change under different conditions. Studies examining the life cycle impacts of gas-fired electricity are subject to high levels of variability and uncertainty due to an expansive network of infrastructure that occurs across heterogeneous landscapes, watersheds, and geologies. Emphasis on quantifying and communicating the implications of this variability and uncertainty is highly important for impactful results.

16

2 LCA Framework, Methods, and Application

Before a more comprehensive look at LCA methodology, it is important to distinguish LCA from other types of analyses; after all, there is a degree of overlap. Table 2.1 presents a comparison of LCA to other tools, outlining the differentiating factors. Note that there are distinct differences between methods but also clear links. Understanding the similarities and differences between LCA and other environmental assessment tools underscores the importance of clearly delineating and documenting decisions about the goal and scope of the LCA. For example, LCAs may Table 2.1 Comparison of LCA to other environmental assessment tools. This table is adapted from Jolliet et al. [9], with slight adjustments to acknowledge that the scope of LCA and carbon footprinting are defined according to the respective methodologies; for example, only a portion of the life cycle may be examined and will depend on the system boundaries selected Tool

Object of study

Scale and scope

Substances and Basis for impacts comparison

Basic elements

Life cycle assessment

Product or service

Global or regional; life cycle (as defined)

Many substances (as defined)

Function of the product or service

Mass balance; multimedia model* ; effects assessment

Carbon footprint

Product activity or company

Global, life Greenhouse cycle (as gases; climate defined) change

Product function, activity, or company

Mass balance; global warming potential

Water footprint

Product activity or company

Local or regional; most important life cycle stages

Product function, activity, or company

Water balance; consumption; competition; adaptation

Material flow analysis

Raw material Regional Single or or compound or national; multiple material materials life cycle

Given time and region

Mass balance; material flow tracking

Environmental impact assessment

New localized activity

Local Highly scale; local variable activity

Local carrying capacity

Highly variable

Risk assessment Installation or chemical substance

Local or regional; selected stage

Relevant substances; toxicity

Maximum level of risk

Multimedia model* ; effects assessment

Substance flow analysis

Regional or global; substance cycle

Single substance

Given time and region

Mass balance; multimedia model*

Polluting substance

Water consumption; water related exposure

* Multimedia models refer to mathematical models that describe the rate and transport of pollutants

under specific environmental conditions (e.g., assuming environmental media such as air and water as uniform and steady state)

General Framework

17

focus on one impact category (e.g. greenhouse gas emissions), resulting in overlap with another assessment tool (e.g., carbon footprinting). The ISO standard promotes the examination of multiple impacts to discourage practitioners from cloaking of hidden negative impacts. As a result, limitations should be acknowledged appropriately so that the audience can appropriately interpret results vis-à-vis the goal and scope.

Goal and Scope Definition Any LCA should commence with a clearly defined goal and scope. What is the intent of the analysis? Who is the audience? Indeed, the goal and scope definition stage of LCA provides critical direction for the subsequent stages of the LCA: the product system, the system boundaries, and the functional unit are all determined during this stage. The recipe for this stage is threefold, involving defining the product system, the functional unit, as well as the inventory items and impacts to be analyzed. The steps included in the recipe are summarized in this section. (1)

Define the product system that is being studied

Before commencing to undertake a LCA of gas-fired electricity, or any other for that matter, the product system must be defined. A product system comprises of all processes within the boundaries of the analysis. It can be considered the collection of unit processes (the smallest processes that can be disaggregated) connected by material and energy flows resulting in the product or service under study [11]. LCA examines a product system as the combination of all sub-processes required to produce one functional unit. The challenge is that the number of these processes could become quite large and their delineation could be interpreted as arbitrary. For example, a natural gas production site requires a drilling rig to drill the well and that rig requires metals and other materials in its construction. Those manufacturing facilities also require electricity, which may come from a variety of sources depending on the location. Then one must look to the construction of those power plants, and so on. A robust LCA thus necessitates defining the system boundaries clearly and diligently, delineating what processes and options are modeled. In our case, we are examining the collective products and processes that result in electricity generated from natural gas. A process diagram, or initial flow chart, serves as a necessary and useful guide for the inventory analysis and subsequent steps of the LCA methodology. Figure 2.2 details the systems boundaries that are generally considered as a guide for the analyses examined in this book, noting that they may be modified to discuss specific applications. As is true for any LCA, the system boundaries may be revisited throughout the LCA process, refining them as the analysis is completed.

Production Production

Well drilling Completion Workover Liquids unloadings Pneumatic devices Production Production compressor

Storage

Processing • Processing plants • Acid gas removal • Routine Maintenance

Waste management, decommissioning, end-of-life, etc.

• Wells • Pipelines • Compressor stations • Compressor stations • Gathering and boosting espisodic events

Gathering

Other materials extraction, transportation of materials, construction, etc.

Fig. 2.2 Illustrative system boundaries of the LCAs examined in this book

• • • • • • •

System boundaries

Transmission • Pipelines • Compressor stations • Compressor exhaust • Flaring at stations • Venting at stations • Pneumatic devices

Combustion

• Pipelines and distribution

• Liquefaction • Tanker Berthing & Deberthing • LNG Ocean Transport

LNG transport/receipt

• Combustion emissions

• Transmission and distribution (T&D) losses

18 2 LCA Framework, Methods, and Application

Goal and Scope Definition

(2)

19

Determine the functions and the functional unit

The functional unit is an important decision, as it serves as the basis for comparisons to be made to other systems that can result in a substitutable product [12]. For travel using different modes (e.g., car, plane, and bike), a useful functional unit could be passenger-mile traveled. Impacts, such as global warming, can then be quantified on the basis of a common functional unit across different products with the same function. For example, greenhouse gas emissions in terms of carbon dioxide equivalents (CO2 e) can thus be quantified for each travel option per passenger-mile traveled. In our case of gas-fired power, it makes sense to use a unit of electricity as the basis for comparison—the impacts can then be compared to other types of electricity generation. Figure 2.3 illustrates the position of the functional unit in life cycle results; it is essentially the denominator in the analysis. Specifically, the impact can be viewed as the numerator in a life cycle result (e.g., kilogram of emissions) with the denominator as the functional unit (e.g., Megawatt-hour of electricity generated) (Table 2.2). Functions can be slightly different but lead to substantially different results. (3)

Select the inventory items and/or impacts studied

The inventory items (i.e., the processes to be analyzed) are determined by leveraging the process flow diagram. A full determination of the data and results to be quantified in a LCA involves initial decisions surrounding the impact categories that will be analyzed (e.g., resource use, ecological consequences, and human health) and the impact assessment methods to be implemented (see the section on impact assessment for more information).

Life cycle result

Impact

Funconal unit

Fig. 2.3 Illustrative diagram explaining the role of the functional unit in the life cycle result. The calculation of a life cycle result hinges on the choice of functional unit

Table 2.2 Examples of functional units and their use in life cycle results Product system

Impact of interest

Example functional unit

Life cycle result

Natural gas-fired electricity

Global warming, estimated using carbon dioxide equivalent (CO2 e)

Unit of electricity generated; for e.g., Megawatt-hour (MWh)

CO2 e MWh

Natural gas fuel supply

Water consumption, Thousand cubic feet of measured in cubic metres natural gas (Mcf) (m3 )

m3 o f water Mc f o f naturalgas

20

2 LCA Framework, Methods, and Application

Inventory Analysis Life cycle inventory (LCI) is the life cycle phase where the consumption of resources and the quantities of waste flows and environmental pollutants (e.g. emissions) are estimated and attributed to each process within the defined system boundary of a product’s life cycle [12]. This stage can be tricky due regional variability (e.g., in operations, impacts, the relative amount of co-products to which one can allocate the impacts), but also temporal variability (e.g., times during which processes and impacts occur as well as their duration). In the inventory analysis, specific terms are required to understand the procedure. Importantly, a unit process is a key concept for LCA; it is the smallest element for which the inputs and outputs are quantified [13]. The inventory analysis is considered one of the last objective steps of LCA [14], since the process hinges on data compilation and calculations. Specifically, it involves compiling inputs and outputs across the life cycle of a product; for example, the tons of steel required for construction and facilities equipment, the cubic feet of natural gas produced over the life of a facility, or the lifetime Megawatt-hours of electricity generated by a power plant. Like the other phases of LCA, there is a recipe for the inventory analysis; however, it is more detailed than the goal and scope definition, involving eight rather than three steps. (1)

Preparing for data collection

Preparation for data collection should be based on goal and scope definition, with the process flow diagram as a helpful guide to show which data are to be the focus of efforts. The system boundary delineates the processes to be examined, and the quantitative data to be collected for each unit process (e.g., power plant). The “gold standard” would be primary data, or data measured specific to the location and duration of the study [14]. Secondary data is often used, with analysts leveraging credible data, such as peer-reviewed literature, governmental and intergovernmental reports. There are often data gaps and uncertainties, so experts and industry may be consulted where data are limited. In doing so, analysts should be well prepared and researched regarding available information on the processes to be discussed [3]. In consulting experts, analysts are cautioned to ensure that ethical standards are met in terms of involvement of human subjects but also issues of confidentiality and anonymity are considered before stakeholder engagement. (2)

Data collection

While it might seem daunting for the first-time LCA practitioner, data collection can be unpacked into easily managed chunks: the unit processes. Using the process flow diagram as a guide, the product system can be broken into unit processes (see Fig. 2.4 for an example of gas-fired power). The LCA process is iterative, so practitioners should iterate across stages and ensure they are aligned. Here, the goal and scope definition must align with the inventory analysis. The goal and scope may contract due to low data availability or even expand if relevant, interesting data are readily available. In one study of LNG use in electricity generation, the system boundaries

Inventory Analysis

21

Input flows, e.g.: • •

Unit process: natural gas producon

Metals for equipment Energy use

Intermediate flow: natural gas produced

Input flows, e.g.: • •

Unit process: gathering sites

Metals for equipment Energy use

Intermediate flow: natural gas compressed

Input flows, e.g.: • •

Unit process: gathering pipelines

Metals for equipment Energy use

Intermediate flow: natural gas transported

Input flows, e.g.: • •

Unit process: processing

Metals for equipment Energy use

Intermediate flow: processed natural gas

Input flows, e.g.: • •

Unit process: transmission sites

Metals for equipment Energy use

Intermediate flow: natural gas compressed

Input flows, e.g.: • •

Unit process: transmission pipelines

Metals for equipment Energy use

Output flows, e.g.: • • •

Emissions to air Emissions to water Waste

Output flows, e.g.: • • •

Emissions to air Emissions to water Waste

Output flows, e.g.: • • •

Emissions to air Emissions to water Waste

Output flows, e.g.: • • •

Emissions to air Emissions to water Waste

Output flows, e.g.: • • •

Emissions to air Emissions to water Waste

Output flows, e.g.: • • •

Emissions to air Emissions to water Waste

Intermediate flow: natural gas delivered

Input flows, e.g.: • •

Metals for equipment Energy use

Unit process: power plants

Output flows, e.g.: • • •

Emissions to air Emissions to water Waste

Fig. 2.4 Conceptual illustration of the inventory analysis for the life cycle of natural gas-fired electricity (without LNG). Note that the intermediate flows in this example are natural gas; however, natural gas may be leaked so the value will decrease through each stage

were expanded as the influence of transmission and distribution losses along power lines were found to be an important factor [15]. The process flow diagram must be revised to ensure that any changes are reflected. (3)

Data Validation

Questions about data uncertainty in LCA are real, not to mention the need for careful calculation, leading to the importance of data validation. Matthews et al. [14] correctly recommend starting with common sense order of magnitude checks. Practitioners can and should compare to published LCA results and other secondary data to ensure that the results make sense. Finding a major difference may or may not be due to a mistake: it is the responsibility of the analyst to determine the root cause of the discrepancy. Could it, for example, be a technological improvement relative to older operations? It is important to document and note factors that may affect data quality and relevance, such as the vintage of the data, and certainly to triple-check

22

2 LCA Framework, Methods, and Application

calculations as the analysis proceeds. Practitioners should always check data inputs and results with existing knowledge: are the orders of magnitude consistent? If they are not, then there may be either an error or even a potentially important reason. (4)

Co-product allocation

Quite often, multiple products are produced from one unit process, leaving questions regarding which product is responsible for the environmental impacts. Allocation in LCA is a technique that can be used to manage such questions by proportional assignment of quantities of inputs and outputs to various co-products. Allocation can be based on a variety of quantities associated with the process; for example, it could be volumetric or based on mass. Allocation procedures are particularly relevant for natural gas since wells often produce co-products along with natural gas, such as natural gas liquids and oil. Additionally, natural gas is often used for combined heat and power (CHP). For the case of CHP, a portion of the inputs and outputs would be associated to the electricity while the rest would be associated with the useful heat. If there were two products from a well (e.g., oil and natural gas) then the impacts could be allocated based on mass, energy, or volume of liquid produced. Using any of these methods, the impact allocated to a product should be the fraction of the total amount produced. Physical quantities (like mass or energy) are generally recommended; however, others can be used in the absence of accepted physical quantities (e.g., price of the product). Equations (2.1) and (2.2) provide examples of how impacts may be allocated based on energy. MJ 0.17kgoil × 45 kg oil    = 0.17 MJ MJ + 0.76kg × 45 kg naturalgas × 50 kgnaturalgas oil

(2.1)

MJ 0.76kgnaturalgas × 50 kgnaturalgas     = 0.83 = MJ MJ 0.17kgoil × 45 kg + 0.76kg × 50 naturalgas kgnaturalgas oil

(2.2)

λoil =  0.17kgoil λgas

The allocation factor calculated in Eq. (2.1) means that 0.17 times the inputs and outputs should be allocated to oil using this method. Similarly, the allocation factor calculated in Eq. (2.2) means that 0.83 times the inputs and outputs should be allocated to oil using this method. Analysts should be clear when differentiating co-products and waste. It would be incorrect to allocate impacts to a waste stream; however, it is correct to allocate impacts to useful co-products. For example, heat from electricity generation can be either waste heat or used as a product. For the former, the cooling methods (e.g., air cooling, ponds, or towers) used in thermoelectric power generation generally result in what is considered waste heat since it remains unused for any purpose. In this case, allocation procedures are not applicable to a waste stream, and all impacts are assigned to the electricity generation. If recovered rather than wasted, heat may be used for space heating (i.e., combined heat and power), or industrial processes. If the

Inventory Analysis

Input flows

23

Unit process

Output flows

Emissions and waste

Intermediate flows

Input flows

Unit process

Output flows

Input flows from technosphere

Unit process

Intermediate flow (products and funcons)

Intermediate flows

Input flows

Unit process

Output flows

(a)

Input flows from nature

(b)

Fig. 2.5 Unit processes and quantifying inputs and outputs. a conceptualizes unit processes within the process flow diagram, and b illustrates what types of data are required for each unit process. Figures adapted from Matthews et al. [14] and ISO 14040 [4]

heat is used as a product, allocation procedures should be applied, and impacts will be proportionally assigned to each product. (5)

Relating data to the unit process

Data must then be scaled to each of the unit processes described in the process flow diagram (Fig. 2.5). First, input and output data for all the activities (unit processes) within the system boundary must be normalized to each intermediate flow. Second, the flows that link the activities of different unit processes are calculated based on the requirements to produce one functional unit. (6)

Relating data to the functional unit

Each unit process then needs to be related to the functional unit in order to obtain the final results. This means that the intermediate flows (e.g., the amount of natural gas combusted in a power plant to generate a unit of electricity) can be used to scale the inputs and outputs for each unit process that are required to produce one functional unit. For each unit process, the impact (numerator) should now be on the basis of a unit of electricity, such as kWh or MWh (denominator). The resource use and /or emissions to the environment can then be summed up for the whole system. For gas-fired power, heat rates and efficiencies are useful for relating the data from the fuel cycle to the functional unit. (7)

Data aggregation

Once the inputs and outputs for each unit process have been related to the functional unit, data can then be aggregated in preparation for the impact assessment stage. An example is presented in Table 2.3 where impacts for three (albeit broad) life cycle stages have been related to the functional unit of electricity generated (MWh) and can therefore be summed to determine the life cycle result.

24

2 LCA Framework, Methods, and Application

Table 2.3 An example inventory of natural gas-fired power, including raw materials acquisition, materials transport, and power plant operations (without carbon capture and storage). Assumptions for both the fuel cycle and electricity generation have drawn from Skone et al. [16] assuming a plant efficiency of 51% HHV. The units for the fuel cycle are in kilograms (kg) of the noted substance emitted per MWh Flow

Natural gas fuel cycle Processing

Transmission

Total

Electricity generation

Unit

Extraction CO2

7.18

16.41

3.93

27.57

350

kg/MWh

CH4

0.95

0.11

0.70

1.78

0.00856

kg/MWh

Impact Assessment Life cycle impact assessment (LCIA) is the process of quantifying life cycle results on the basis of the potential contributions of resource extraction and environmental releases in terms of a subset of their potential impacts [17]. The results of the LCIA are presented in specific impact categories (e.g., climate change, eco-toxicity, ozone depletion, resource scarcity) that are often aggregated indices. A wide variety of environmental impact categories are examined in LCA (Table 2.4), each being related to varying spatial scales from local to global. With spatially aggregated and undifferentiated inventories, determining impacts at scales lower than global will remain technically unfeasible. For some cases where impact categories are determined at global scales, additional spatiotemporal analyses may not be required even when more granular data are available. However, if spatiotemporal differentiation is determined to be important during the Goal and Scope Definition and inventories permit, then methods should be developed accordingly. The recipe for LCIA comes with mandatory and optional elements. The mandatory part of LCIA includes selection, classification, and characterization. During the first step, selection, relevant impact categories are chosen, along with the relevant impact indicators and the methods of aggregation (or characterization). The second step, classification, involves assigning inventory results to specific impact categories. For example, carbon dioxide and other greenhouse gases would be assigned to climate change. The last mandatory step, characterization, involves using the inventory results to calculate the category indicator result. For climate change, for example, the use of global warming potentials (GWPs) enables the representation of greenhouse gas emissions on an equivalent basis to carbon dioxide (generally referred to as carbon dioxide equivalents (CO2 e)). To convert other greenhouse gases on an equivalent basis as carbon dioxide, the IPCC publishes GWP that can be applied with simple multiplication. To convert methane (CH4 ) to CO2 e, one must simply multiply the mass emitted by the most recently published GWP. The IPCC presently recommends 36 and 87 for 20-year time horizons for 100-year and 20-year time horizons respectively. Tables 2.5 and 2.6 present simple characterization results using these GWPs. Similar methods should be applied for characterization in other impact categories.

Impact Assessment

25

Table 2.4 Summary of life cycle impact categories, their scales and examples of LCA data Impact category

Scale

Examples of LCI data

Climate change

Global

Carbon dioxide (CO2 ), methane (CH4 )

Stratospheric ozone depletion Global

Chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HFCs), Methyl Bromide (CH3 Br)

Acidification

Sulfur Oxides (SOx ), Nitrogen oxides (NOx ), hydrochloric acid (HCl)

Regional, local

Eutrophication

Local

Phosphate (PO4 ), Ammonia (NH4 )

Photochemical smog

Local

Non-methane hydrocarbon (NMHC)

Terrestrial toxicity

Local

Toxic chemicals reported with lethal or harmful concentrations to terrestrial animals and ecosystems

Aquatic toxicity

Local

Toxic chemicals reported with lethal or harmful concentrations to fish and aquatic ecosystems

Human health

Global, regional, local Total releases to water, air, soil; exposure and impacts to humans

Resource depletion

Global, regional, local Quantity of mineral or fossil fuel used, can be relative to availability

Land use

Global, regional, local Quantity disposed of in a landfill or land modifications such as transformation or occupation impacts

Water use

Regional, local

Water consumed or withdrawn

Adapted from prior work (e.g., U.S. EPA 2006; Matthews et al. [14]) Table 2.5 An example of characterization for a combined cycle natural gas-fired power plant using a 100-year GWP Flow

Natural gas fuel cycle Extraction

Processing

Transmission

Electricity generation

Total

Unit kgCO2 e/MWh

CO2

7.18

16.41

3.93

350

377.57

CH4

34.38

4.14

25.28

0.31

64.22

kgCO2 e/MWh

Total

41.56

20.55

29.20

350.31

441.80

kgCO2 e/MWh

Assumptions are based on but adapted from Skone et al. [16] Table 2.6 An example of characterization for a combined cycle natural gas-fired power plant using a 20-year GWP. Assumptions are based on Skone et al. [16] Flow

Natural gas fuel cycle Extraction

Processing

Transmission

Electricity generation

Total

Unit

CO2

7.18

16.41

3.93

350

377.57

kgCO2 e/MWh

CH4

83.08

10.00

61.08

0.74

155.20

kgCO2 e/MWh

Total

90.26

26.41

65.01

350.74

532.78

kgCO2 e/MWh

26

2 LCA Framework, Methods, and Application

The optional steps (normalization, grouping, and weighting) are also dependent on the study and methodology applied. Normalization involves calculating the magnitude of the category indicator results relative to the total in a locality, region or globally, for example. Grouping involves assigning impact categories to groups of similar impacts. Finally, weighting impacts means that the LCA practitioner applies a relative value to the importance of the impacts, allowing integration across all impact categories. There are several published impact assessment methods that can be applicable during the LCIA phase [7–10]. Such pre-developed impact assessment methods often apply some form of weighting impacts. LCIA is a cornerstone stage of LCA, but like the other stages, it is not without challenges [18]. First, it is considered subjective relative to the inventory analysis stage, based on science but combined with values. Second, a comprehensive focus and boundary is often lacking, making the LCIA generic rather than providing meaningful results specific to the environment. Third, related to the subjectivity of this LCA stage, indicators are not equally important or precise. This limitation typically leads to subjective methods, such as weighting. Fourth, data and methods are typically emerging and not completely mature, making it challenging to characterize impacts accurately (this will be more comprehensively addressed in Chap. 4). Finally, impact results are subject to high levels of uncertainty, particularly where methods/data are lacking. Such uncertainty is exemplified in the assumed linearity of many of the impact characterization and other methods.

Interpretation The interpretation stage of a LCA requires the practitioner to make clear what the results mean for the decision-maker. Interpretation involves a subset or all the following: • Studying results in relation to Goal and Scope Definition to determine whether conclusions can be made that are consistent with study scope. • Undertaking the appropriate sensitivity or uncertainty analyses. • Checking back to ensure your system boundaries are appropriate. While a practitioner may be discouraged if there appears to be no major results (e.g., two projects have virtually the same impact), I encourage two interpretations. First, no major finding is a finding: if one can conclusively state that the life cycle results of two projects are comparatively similar, this finding may be meaningful and should be interpreted with this in mind. Second, interpretation also involves uncertainty assessment. Practitioners should ensure to view with a critical eye both data quality and potential uncertainties in terms of how they may influence results. Uncertainty analysis such as breakeven analysis can provide insight into more valuable interpretation (such concepts are explored in depth in Chap. 6).

Interpretation

27

LCA often stops at the inventory stage, skips over impacts and goes right to the interpretation stage due to the aforementioned limitations in LCIA [14]. While this may appear to be a limitation, such inventory results can be quite conclusive and informative; for example, if one alternative consumes a greater number of resources. Regardless, the interpretation in LCA should occur at every stage [12]. The recipe for interpretation involves the following steps. • Identifying significant issues, for example, determining important findings and identifying critical methodological choices. • Evaluation of these issues considering the results. • Establishing confidence in results and testing their robustness. • Conclusions, recommendations, and reporting. There are numerous quantitative methods for establishing confidence in results and testing their robustness, ensuring both completeness and consistency [3, 14]. First, a dominance (or gravity) analysis identifies the heaviest hitting life cycle activities; for example, the generation stage for gas-fired electricity generation results in the dominant contribution to greenhouse gases. Similarly, a contribution analysis determines the largest contributing process or intervention to each impact category or environmental load to the total impact. There are many well-established methods for testing the robustness of the results of LCA models. Completeness checks involve identifying data gaps and determining the completeness of impact assessment. Consistency checks involve evaluating whether the methodological choices were appropriate relative to goal and scope definition. Uncertainty assessment is a useful tool to check for the influence of uncertain data on the results and can capture the effects of multiple uncertain inputs (to be covered in Chap. 6). Sensitivity analyses—often considered an option for uncertainty assessment—can be used to identify individual inputs that are critical to the robustness of the results. Variation analysis determines the influence of alternative scenarios. Related, breakeven analysis can parametrically determine the input assumptions required for results of two alternatives to break even or to be equivalent. And finally, data quality assessment examines data gaps as well as approximates data, and appropriates data as required. Quantitative uncertainty assessment is a critical component of the interpretation stage; however, the purpose is to interpret results for decision-makers. A decisionmaker analysis involves determining who has power in relation to each part of the results since LCA results often impact a variety of different sectors; specifically, who has the power to reduce impacts. Sometimes, it is clear for whom the LCA is being interpreted; however, the report commissioners may be interested in how to leverage results.

28

2 LCA Framework, Methods, and Application

Conclusion Environmental analyses of gas-fired power, due to the controversy over being the cleanest burning fossil fuel and substantial economic interests, reap enormous benefits from the robust, standardized methods that LCA brings to the table. At the same time, simplified or streamlined LCA approaches simply are less applicable for the product system of gas-fired power due to the irreducible variability of the expansive network of infrastructure and known uncertainties in some impact categories (to be elucidated in subsequent chapters). While known LCA models and databases may include simplified representations of gas-fired power, they will not represent the full scope of variability and uncertainty in both inputs and life cycle results. For better representation both variability and uncertainty, process based LCA provides a robust approach that can incorporate comprehensive datasets towards a more realistic examination of the natural gas fuel cycle and the power plants where the combustion of the gas occurs.

References 1. Hellweg, S., Mila i Canals, L.: Emerging approaches, challenges and opportunities in life cycle assessment. Science 344, 1109–1113 2. Lave, L.B., Hendrickson, C.T., McMichael, F.C.: Environmental implications of electric cars. Science 268, 993–995 (1995) 3. Tillman, A.M., Baumann, H.: The Hitchhikers Guide to LCA. Studentlitteratur AB, Lund, Sweden (2004) 4. International Organization for Standardization (ISO).: 14040 International standard: Environmental Management–Life Cycle Assessment–Principles and Framework. International Organisation for Standardization, Geneva, Switzerland (2006) 5. Pryshlakivsky, J., Searcy, C.: Fifteen years of ISO 14040: a review. J. Clean. Prod. 57, 115–123 (2013) 6. Hertwich, E.G., Pease, W.S.: ISO 14042 restricts use and development of impact assessment. Int. J. Llfe Cycle Assess. 3, 180 (1998) 7. Curran, M.: A. Life Cycle Assessment Handbook: a Guide for Environmentally Sustainable Products. Wiley (2012) 8. Klöpffer, W., Grahl, B.: Life Cycle Assessment (LCA): a Guide to Best Practice. Wiley (2014) 9. Jolliet, O., Saadé-Sbeih, M., Shaked, S., Jolliet, A., Crettaz, P. Environmental Life Cycle Assessment. CRC Press (2015) 10. Hauschild, M.Z., Rosenbaum, R.K., Olsen, S.I.: Life Cycle Assessment. Springer, Berlin (2018) 11. Life Cycle Initiative Glossary of Life Cycle Terms.: https://www.lifecycleinitiative.org/resour ces/life-cycle-terminology-2/#p. Accessed on 7 Jan 2021 12. Rebitzer, G., Ekvall, T., Frischknecht, R., Hunkeler, D., Norris, G., Rydberg, T., Schmidt, W., Suh, S., Weidema, B.P., Pennington, D.W.: Life cycle assessment: part 1: framework, goal and scope definition, inventory analysis, and applications. Environ. Int. 30, 701–720 (2004) 13. Life Cycle Initiative; United Nations Environmental Programme Glossary of Life Cycle Terms. Accessed on 28 Mar 2020 14. Matthews, H.S., Hendrickson, C.T., Matthews, D.H.: Life Cycle Assessment: Quantitative Approaches for Decisions that Matter. Retrieved June 2018, 1 15. Kasumu, A.S., Li, V., Coleman, J.W., Liendo, J., Jordaan, S.M.: Country-level life cycle assessment of greenhouse gas emissions from liquefied natural gas trade for electricity generation. Environ. Sci. Technol. 52(4), 1735–1746 (2018)

References

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16. Skone, T.J., Schivley, G., Jamieson, M., Marriott, J., Cooney, G., Littlefield, J., Mutchek, M., Krynock, M., Shih, C.Y.: Life Cycle Analysis: Natural Gas Combined Cycle (NGCC) Power Plants. Keystone Logic and the National Energy Technology Laboratory (2018) 17. Pennington, D.W., Potting, J., Finnveden, G., Lindeijer, E., Jolliet, O., Rydberg, T., Rebitzer, G.: Life cycle assessment part 2: current impact assessment practice. Environ. Int. 30, 721–739 (2004) 18. Sala, S., Pant, R., Hauschild, M., Pennington, D.: Research needs and challenges from science to decision support. Lesson learnt from the development of the international reference life cycle data system (ILCD) recommendations for life cycle impact assessment. Sustainability 4, 1412–1425 (2012)

Chapter 3

The Denominator: Natural Gas Production, Throughput, and Electricity Generation

Abstract To arrive at a life cycle result, not only impacts must be calculated, but the denominator (or functional unit) must be estimated. For each unit process within the life cycle boundaries, the amount of material that passes through must be estimated; for natural gas production systems, we quantify the natural gas extracted by production sites and the gas that travels through each facility in the fuel cycle (i.e., the throughput). The results for each unit process in the fuel cycle is then related to the functional unit—a unit of electricity generated—using heat rates or efficiencies of natural gas-fired power plants. In this chapter, methods used to estimate the denominator of life cycle results for gas-fired power are explained, from estimated ultimate recover of natural gas from a well to facility throughput, to electricity generation. The extensive network of natural gas infrastructure that is present in the fuel cycle of gas-fired power results in an additional layer of variability in LCA, which should be considered by practitioners interested in this product system.

Introduction The selection of the denominator—the functional unit—in life cycle assessment (LCA) is a crucial decision that can be highly influential on the outcome of the results. LCAs are completed for a function that is fulfilled by a product system, where the functional unit is the unit of output to which all environmental impacts across the life cycle are related [1–5]. It is the very factor used to normalize the environmental impacts. For our case of gas-fired power, the functional unit is generally one unit of electricity generated; for example, one kilowatt-hour (kWh) or one Megawatt-hour or MWh. The function that our system provides is thus the generation of electricity. We note that the inclusion of power transmission and distribution (T&D) implies a very subtle difference in the functional unit: the unit of electricity delivered (rather than generated). This chapter involves a comprehensive examination of the quantification of the functional unit and how it is related to intermediate flows, from the supply chain of natural gas through electricity generation at a power plant. Recall that in the inventory analysis stage of LCA (described in Chap. 2), data are first analyzed at the scale of each of the unit processes described in the process flow diagram. Input/output data © Springer Nature Switzerland AG 2021 S. M. Jordaan, Wells to Wire, https://doi.org/10.1007/978-3-030-71971-5_3

31

32

3 The Denominator: Natural Gas Production, Throughput …

for all unit processes are then normalized to each intermediate flow. The flows that link the activities of different unit processes are calculated based on the requirements to produce one functional unit. For simplicity, we will commence by separating the unit processes associated with the natural gas supply chain from those associated with electricity. Impacts of the natural gas supply chain are first defined based on a unit of natural gas, which can then be related to the functional unit using the amount of natural gas consumed at the power plant for each unit electricity generated; for example, using the heat rate of the power plant or the efficiency. For production facilities, the amount of natural gas produced (cubic feet, for example) can be used to relate the impacts to the functional unit (Megawatt-hour, for example) using either metric. The lifetime throughput—or the total flow of natural gas that has passed through a facility over its life—is the measure that can be related to the functional unit for each unit process in the natural gas supply chain. This chapter will examine the relevant energy throughputs for typical unit processes of interest to our system boundaries. Data specific natural gas-fired generation will be used as an example to demonstrate the calculations used to relate each life cycle phase to the basis of a functional unit (unit of electricity generated) for the final LCA results. The variability in efficiencies across technologies and heat rates across generating units will be discussed, using real-life data examples from power plants in the U.S.A and aggregate country-level statistics internationally.

Infrastructure Lifetime In the absence of a complete primary dataset that details the lifetime throughput of each facility (a real challenge for many processes that are still ongoing or that are under development), one of the most important parameters is the producing lifetime of the infrastructure. Annual or monthly throughput can then serve as the basis for the lifetime generation. Such estimates for lifetime throughput for each facility are highly sensitive to the assumption about the operational life of the facility and the month or year chosen to be representative. Of course, it is a major challenge to predict the exact duration of time that a facility will be in operation or exactly when it will be decommissioned. While methods to evaluate uncertainty will be examined in detail in Chap. 6, it is critical to acknowledge and investigate the influence of uncertainty for facility throughput and ultimately the functional unit. In consultation with experts, we compiled assumptions for an analysis of gas-fired power in the Barnett Shale region of Texas that can serve as a useful baseline [6] (Table 3.1). Data collection enabled a range of estimates to be determined (Table 3.1), which can serve as inputs for an uncertainty analysis (see Chap. 6 for more details). Note that a regionally aggregated value represented transmission pipeline throughput. In the absence of site-level data, such aggregate assumptions can inform LCAs; however, practitioners should seek to quantify variability over the population in the region and timeframe under study whenever possible to improve the accuracy of the results.

Natural Gas Extraction

33

Table 3.1 Sample sizes (n), population sizes (N), and infrastructure lifetime assumptions used to estimate the functional unit of lifetime throughput for each life cycle phase in the seven-county study area of the Barnett Shale in Texas, United States Life cycle stage Lifetime throughput estimator

Throughput unita

Lifetime (years)b

Range (years)c

Production sites EUR

mcf

25

10–30

Gathering sitesd EUR

mcf

25

10–30

Gathering pipelinese

mcf

25

10–30

Processing sites Facility throughput

mcf

30

10–50

Transmission sites

Total production (study area)

mcf

30

10–50

Transmission pipelinesf

Total production (study area)

mcf

30

10–50

Power plants

Lifetime generation

MWh

30

10–50

EUR

a Mcf

refers to thousand cubic feet and MWh refers to Megawatt hour lifetime of facilities was estimated assuming a 30 year economic lifetime [5, 7, 8] except for wells (and gathering infrastructure connected to wells) which were assumed to have 25 years of production [9, 10]. Note that to estimate EUR, well-level production curves were fit to real data rather than multiplying an average production rate by the estimated lifetime; therefore, the production decline over the lifetime of the well is represented in our estimates. c Facility lifetimes were assumed to range from the minimum of 10 years to a maximum of 50 years, except for wells (and gathering infrastructure connected to wells), which have lifetimes assumed to range from 10 as a conservative estimate to 30 years [8–12] d Gathering sites typically service more than one well. To account for this, the average number of wells per gathering site was calculated for the study area (=88). Each well was assumed to produce for 25 years. The lifetime throughput was then calculated by multiplying the average wells per site by the average EUR per well. e Each production site has an associated gathering pipeline, connecting the production site to transmission pipelines. The EUR for gathering pipelines is taken to be the cumulative EUR of the associated wells f Transmission pipelines were considered collectively for the analysis with production from the seven-county study area used as the estimate of throughput b The

Natural Gas Extraction For wells and infrastructure that is directly related to wells (production sites, gathering pipelines, and gathering sites), Estimated Ultimate Recovery (EUR) can be used to relate the facility to the functional unit. EUR provides estimates of well-level natural gas production over the well’s producing life. If a well is no longer producing, the lifetime is established. More frequently than not, wells are still producing at the time of an analysis. Further complicating the matter, wells may appear to have

34

3 The Denominator: Natural Gas Production, Throughput …

completed production but may be temporarily shut-in or recompleted, so they may continue to produce natural gas. Like other infrastructure elements, this means that the full lifetime is unknown as the end of production will be in the future and subject to technological and economic conditions. That said, informed assumptions can support the analysis along with uncertainty assessments. In general, there are two options used to define the EUR: the use of prior estimates or the analysis of specific wells relevant to the study for the time at hand. The former has generally been the convention [13]; however, more recent studies have commenced estimating EUR for wells within the geographic scope of the analysis [6]. In the past, EUR has been estimated using initial production rates and decline curve characteristics [14]. Such methods have yielded useful results (Table 3.2) that can serve as data inputs for LCAs. Decline curve analysis is the most common approach to evaluating the performance of reservoir and for calculating EUR. A variety of methods have been developed, such as the fitting of exponential and hyperbolic curves to existing production datasets. Such methods may result in over- or under-estimation of well-level lifetime recovery. For example, exponential decline curves will underestimate production for wells producing in geologic formations where natural gas does not flow as easily as those where conventional production is economically viable (e.g. shale gas) [60]. Further confounding the challenge, studies often rely on only one value for EUR—a practice that overlooks how well-level variability may influence results and other assumptions that are points in a probability distribution of which we cannot be certain. More recently, new methods provide a path to more accurate results for shale gas wells [19], which we will use as an example for EUR calculations. This curve-fitting method that has been found to more accurately predict gas production from shale gas wells has been more recently used in LCA studies. While this method has been selected as most applicable to horizontal wells, we note that other methods may be more suitable for vertical and directional wells. The amount of gas recovered from a shale gas well (EUR) is calculated by:  EUR = M × RF = Mk t/τ

(3.1)

Table 3.2 A sample of published Estimated Ultimate Recoveries (EURs) for natural gas wells in the United States. EUR is a prediction of the natural gas produced over the life of a well EUR (Bcf)

INTEK [15]

Baihly [16]

Warlick [17]

EIA-AEO [18]

Lifetime

30

30

–

30

Barnett

1.42

2.989

2–2.5

0.255–2.174

Marcellus

1.18

–

4.2–4.4

2.045–13.574

Haynesville

3.57

5.915

>6.5

5.261–10.162

Fayetteville

2.07

1.390

2.1–3.5

–

Woodford

2.98

1.696

2–5

0.0055–2.524

Eagle Ford

–

3.793

–

0.0010–1.530

Natural Gas Extraction

35

where M is the original mass of gas contained in the reservoir (converted to a volumetric basis), RF is the dimensionless recovery factor (a number between 0 and unity that describes the proportion of gas that can be recovered), t is the number of months the well has been producing (or the well lifetime in months), τ is the interference time (months), and k is a constant depending on the gas composition, temperature, and initial reservoir and well flowing pressures pi and pf . The interference time τ is the point in time where the gas flow between fractures drops below the original reservoir pressure. For a given well in any given producing basin, the history of monthly volumetric production in thousand cubic feet (Mcf) can be estimated for an assumed well lifetime. As an example, Patzek [19] found that for the Barnett shale the values of pi and pf are 3500 psi and 500 psi, respectively, resulting in k = 0.645. Using these values, monthly production data can be used to fit a production curve, estimating τ and M simultaneously for every well in the sample. As previously noted, the lifetime remains an uncertain value. Various lifetimes (e.g.,10, 15, 20, 25, and 30 years) may be assumed to represent potential economic lifetimes for the wells in the dataset but also to account for uncertainty. Testing the results against these assumptions about production lifetimes is an example of a sensitivity analysis (for more information on this approach to understanding uncertainty, refer to Chap. 6). To estimate the parameters used for fitting the curve (M and τ ), the Levenberg– Marquardt algorithm [61] was used to minimizes the error between real data and the fitted curve, as defined in the following objective function:  m(t) − Mk t/τ

(3.2)

where m(t) is an array of the monthly cumulative natural gas production for each well and t is the corresponding month. M and τ are then optimized so that the absolute value of objective function is minimized. In order to complete such an analysis, actual production data for the length of each well’s producing life is required. Such data may be acquired from public data, such as from the Energy Information Administration’s Annual Energy Outlook [18] or commercial databases that may sell license to the data. Enverus Drilling Info [20] is an example of the latter; it is a database that retains historical well production data and characteristics provided by state agencies, with quality screens applied at the state-level to remove anomalies and provide consistency across jurisdictions. EUR may serve to define the intermediate flow for numerous unit processes. In the aforementioned study of the Barnett shale of Texas, site-level EUR (including co-location of wells) was directly employed as the denominator to calculate the life cycle land use intensity for production sites and gathering pipelines (Fig. 3.1). There may be some infrastructure that has less than ideal throughput data. For example, throughput data for compressor stations have been scarce – horsepower is more readily available than estimates for the actual natural gas that passes through the

Fig. 3.1 Distribution of estimated ultimate recovery (EUR, in billion cubic feet (bcf)) for the 400 sampled sites from the seven-county study area in the Barnett using methods from Patzek [19]

36 3 The Denominator: Natural Gas Production, Throughput …

Natural Gas Extraction

37

infrastructure. For infrastructure that services numerous production sites, like gathering infrastructure, the average number of wells per unit of infrastructure can be used to relate EUR to the throughput of the infrastructure. It must be noted that drilling multiple producing wells at one site may not only result in a higher amount of product, but it may also diminish impacts through reduced infrastructure, emissions, and land use (for example). EUR can be used to estimate throughput for the case of single or multiple wells on the same site, using monthly production data for an individual well or as the sum of the individual EURs for all co-located wells. The temporal lifetime of a producing well is uncertain and can influence the results. To test sensitivity of our results to differing assumptions of average well lifetime, we calculated well-level EUR across a range of assumed well lifetimes. The sensitivity of the EUR estimate to assumption about well lifetime is demonstrated. The “single wells” portion of the plot assumes sites that have only one well whereas the “co-located wells” portion of the plot accounts for observed occurrences where multiple wells were drilled within a given site’s boundaries. Whether the single well or co-located well approach is most applicable depends on the impact of interest. For land area requirements, considering the co-location of wells is important since the efficiency of land use is higher. Emissions analysis is another story, however, since the drilling and completion of wells may add to the results. Additionally, even for land, the area required for sites with multiple wells may be higher than that with single wells; analyses of the number of wells per site is encouraged. Data showed that 291 sites from our sample had one well, while the remainder ranged from 2 to 11 wells per site in the study area for the year 2009. The boxes in the figure represent the interquartile range, the line in the box represents the median, the whiskers represent the maximum and minimum, and the blue diamond represents the mean. Because there is indeed uncertainty in well lifetime for regions like the Barnett where wells may have been producing for more than a decade. The influence of uncertain lifetimes on results is further investigated in Chap. 6. It is important to note that the example here is specific to horizontally drilled shale gas in the Barnett shale of Texas; however, the approach is broadly applicable across regions. For example, Tavakkoli et al. (in review) expanded the analysis to all horizontal, hydraulically fractured natural gas wells that were producing in the United States in 2005, 2010, and 2015. In the future, such analyses may be expanded to conventional, as well as other vertical and directional wells.

Facility Throughput For unit processes other than wells with throughput that cannot be defined using EUR, there are three primary ways that facility throughput can be estimated: • Facility: an individual facility is used to estimate throughput. • Aggregated to a region: numerous facilities within a defined region. • Product system: all facilities that fall within the scope of a full product system.

38

3 The Denominator: Natural Gas Production, Throughput …

Other infrastructure associated with domestic consumption of natural gas include processing facilities, transmission lines and the associated compressor stations, liquefaction, ocean transport and regasification (as required), and power plants. For each of these infrastructure types, methods to estimate throughput are described below. While lifetime throughput can be calculated as total throughput for the operational lifetime, working capacity can also be leveraged in the determination of throughput. For facilities where periodic data are available, estimates for the lifetime throughput can be estimated. Taking annual data as an example, the lifetime throughput of natural gas that travels through the infrastructure can be approximated as the product of annual throughput for the year of question and the infrastructure element’s assumed lifetime. Where throughput data are not available, analyses may rely on regional production. For example, in Jordaan et al. [6] transmission sites were treated differently than processing facilities because site-level throughput and disaggregated data were not publicly available. The total natural gas production in 2009 from the 7-county study area in the Barnett shale of Texas was divided by the number of transmission sites in the study area to estimate an average annual throughput for each site. The lifetime throughput was then multiplied by the assumed life of the facility. Similarly, the annual throughput for all transmission pipelines in the study area was assumed to be the study area’s annual production of natural gas. Transmission compressor stations are located every 50–60 miles to boost and maintain the pressure of natural gas [21], as a result, a unit of natural gas might be compressed multiple times between the source of generation and power plants. Determining the throughput of natural gas compression facilities is less straightforward than for other infrastructure since the relationship is not one-on-one between the installed capacity or horsepower (HP) and the amount of natural gas that flows through that station [22]. Specifically, natural gas may flow through many compressor stations before it reaches the end use. Data on actual throughput may be lacking, as is the case for the United States. Installed horsepower and capacity may serve as substitutes. In 2007, for example, a total of 16.9 million installed horsepower as well as a total throughput capability of 881 Bcf per day was reported by the Energy Information Administration [23], translating to 19,027 mcf HP−1 y−1 . In addition to transmission compressor stations, there are three other compression infrastructure elements in natural supply chain: production, gathering, and storage compressor stations. Since natural gas may be compressed several times in different stations, this approach is recommended for using relevant throughput reported at the same facility or location. Such data can provide useful placemarks, but the lack of data availability points to a need to improve datasets that characterize less studied infrastructure types which are becoming increasingly important in different aspects of LCA, such as compressor stations. For example, Tavakkoli et al. [22] made the reasonable assumption that the throughput of the storage sites is representative of the throughput of compressor stations located at the storage sites.

Power Plant Operation

39

Power Plant Operation Arguably the most important part of a LCA of natural gas-fired electricity is the efficiency of combustion at the power plant, which can be used to convert each life cycle phase to the basis of a functional unit (unit of electricity generated) for the final LCA results. The functional unit for power plants can be calculated as electricity generation in a baseline year—selected in the goal and scope definition stage of the LCA—multiplied by the assumed lifetime. All upstream impacts associated with the natural gas fuel supply depend on the amount of natural gas required to generate this electricity. Efficiencies are generally expressed as a percentage, represented by the useful energy that comes out of a system (in this case, a power plant) divided by the energy consumed by the system. Thermodynamically, they cannot be greater than 100% and the highest efficiency natural gas boilers hover around 85% and the highest net efficiency natural gas power plants reaching just over 60% [24]. Heat rates are another expression of efficiency, specifically for electrical generators or power plants [25], that measure the efficiency at which fuel can be converted into electricity. Specifically, for gas-fired power, the value would be a measure of the amount of natural gas (e.g., in cubic feet or British Thermal Units (BTUs)) used to generate a unit of electricity (e.g., kWh). Efficiencies expressed as a heat rate can easily be converted into percentages by dividing the equivalent BTU content for a kWh (3412 BTU). A heat rate of 10,500 Btu/kWh would be equivalent to an efficiency of 33%. Heat rates provide a simple conversion by which unit processes associated with the fuel supply can be related to the functional unit. LCA practitioners should be consistent regarding whether the data they use in their analyses is higher or lower heating value (HHV or LHV) and report which type of is used. HHV is the amount of heat released from combustion of a fuel (initially at 25 °C) relative to when products have returned to the initial temperature, accounting for the latent heat of vaporization. LHV, on the other hand, does not consider the heat of vaporization of water and considers a final temperature of 150C. Using the latter artificially inflates the efficiency of the process and may be misleading if not clearly reported or used consistently. The efficiencies of the full power plant fleet, including those specific to natural gas, are not static over time. For natural gas, it will depend on the type of generation technology. The two most common categories can be categorized as simple cycle and combined cycle (Fig. 3.2) [26]. Simple cycle involves a gas combustion turbine that exhausts the gas into the atmosphere. The exhaust gas for can be used in an exchanger to pre-heat the air that passes from the compressor to the combustor, thus creating a more efficient system (often referred to as regenerative cycle). Combined cycle involves using a heat recovery steam generator to re-use waste energy in a steam turbine. The latter has been recognized as among the highest efficiency options for thermal power generation, with operation efficiencies reaching into the 60% range in recent years. In addition to these options, steam generation and internal combustion engines are also used for electricity generation.

Compression

(a)

Turbine Exhaust gas

Gas turbine

Fig. 3.2 Illustrative examples of gas-fired electricity generation technologies ( adapted from Fadok [27])

Gas

Inlet air

Fuel

(b)

Steam turbine Condenser

Heat recovery steam generator

40 3 The Denominator: Natural Gas Production, Throughput …

Power Plant Operation

41

The technology employed in gas-fired generation becomes a critical factor in understanding all life cycle impacts, as all upstream impacts associated with the fuel supply are dependent on the amount of gas combusted in the generation of electricity. If the LCA is conducted for a region, for example, there may be a mix of these technologies employed. Further, the generation fleet in the region may evolve over time. The United States exemplifies the case, as well as many subregions in the country since the 2009 shale boom has initiated a transition from coal-fired power to gas generation. Increasing efficiencies combined low natural gas prices have made gasfired power competitive as baseload power. Combined cycle gas turbines (CCGT) were generally not used as baseload prior to the shale boom due to the volatility in natural gas prices. CCGT became among the capacity additions of choice, along with increasingly competitive renewable options (i.e., wind and solar). For example, the gas-fired generation fleet has been transitioning to a more efficient baseline as combined cycle power plants increased from 75 to 85% between 2005 and 2015 [28], increasing the average operating efficiencies from 33 to 45% [29]. Over time, efficiencies can be expected to generally improve as technologies mature, making the overall trends in life cycle environmental impacts a moving target with new technological innovation. The Energy Information Administration publishes information about heat rates in of the power generation types in the United states as well as data reported by individual power plants that can characterize efficiencies [28]. Decisions about power plant heat rates or efficiencies are extremely influential in the estimation of life cycle results, since they are used to relate the upstream impacts associated with the fuel supply to the functional unit [30, 31]. Importantly, the goal and scope definition should bound the geographic scope under study. Individual power plants may be characterized [22, 23], or regional averages may be employed [30, 31], depending on the question at hand. Overall, the heat rates employed should reflect the variability of power plants within the scope of the study. Studies should seek to reflect the differences in efficiencies across regions and particularly to characterize the variability across individual power plants (where data permits). Investigations of the variability of efficiency across regions yield large differences (Table 3.3).

Conclusions After providing context around the complexity of the fuel cycle of gas-fired power, the need for detailed examinations of the expansive and evolving network of infrastructure should be clearer. Natural gas throughput is challenged by large populations of infrastructure that require substantial data collection and analysis of large datasets. Each life cycle phase requires tailored approaches and assumptions to model lifetime throughput as either the production, consumption, or flow through the infrastructure and facilities. On top of the fuel cycle, understanding the life cycle impacts of natural gas-fired electricity may involve the examination of an individual power plant or a region with more than

42

3 The Denominator: Natural Gas Production, Throughput …

Table 3.3 Select power generation efficiencies with potential upgrades for global fleet and the top 10 countries in terms of CO2 reduction potential for improving efficiency (estimates published by General Electric in 2016) [32] Country

2015 gas generation

average efficiency Potential (%) efficiency with Upgrades (%)

CO2 reductions (million metric tonnes)

World

5,713,194

39

43

203

564,068

26

30

45

1,316,652

45

48

34

Russian Federation United States Japan

427,836

45

48

12

Saudi Arabia

160,229

32

36

11

Iran

184,498

43

46

6

United Arab Emirates

107,746

34

37

4

China

134,041

39

42

4

Korea

163,691

44

46

4

Egypt

152,635

45

48

4

India

153,579

45

48

4

one (or many) power plants. As a result, understanding the heat rates or efficiencies of the power plants under study is critical. The required upstream infrastructure, material requirements across the life cycle, through each life cycle stage up to and including waste management provide a more complete picture and require that the practitioner relate impacts to both fuel cycle and power generation. Practitioners should include specific details to be accurate: the technologies considered, the time horizon, and the geographic area under study for each supply chain segment. Like any other product system, data challenges should be expected. Data are not always available or may not be reliable, however, particularly at the global scale [31]. For example, while heat rates are published annually for the United States and can even be calculated for each power plant, high resolution global data was not available in the form of time series data at the time that this book was written. A robust LCA will include a detailed report of data used in the analysis but also data gaps with relevant uncertainty analyses to ensure the results can be correctly interpreted.

References 1. Matthews, H.S., Hendrickson, C.T., Matthews, D.H.: Life Cycle Assessment: Quantitative Approaches For Decisions That Matter. Retrieved June 2018, 1 2. International Organization for Standardization, (ISO).: 14040 International Standard: Environmental Management–Life Cycle Assessment–Principles and Framework. International Organisation for Standardization, Geneva, Switzerland (2006)

References

43

3. Rebitzer, G., Ekvall, T., Frischknecht, R., Hunkeler, D., Norris, G., Rydberg, T., Schmidt, W., Suh, S., Weidema, B.P., Pennington, D.W.: Life cycle assessment: part 1: framework, goal and scope definition, inventory analysis, and applications. Environ. Int. 30, 701–720 (2004) 4. Hauschild, M.Z., Rosenbaum, R.K., Olsen, S.I.: Life Cycle Assessment. Springer, Berlin (2018) 5. Schenck, R., White, P.: Environmental Life Cycle Assessment: Measuring the Environmental Performance of Products. American Center for Life Cycle Assessment Vashon, Washington (2014) 6. Jordaan, S.M., Heath, G.A., Macknick, J., Bush, B.W., Mohammadi, E., Ben-Horin, D., Urrea, V., Marceau, D.: Understanding the life cycle surface land requirements of natural gas-fired electricity. Nat. Energy 1 (2017) 7. Texas Gas Transmission, L.: Frequently Asked Questions. www.txgt.com/Safety.aspx?id= 1447. Accessed on 12 Dec 2016 8. Clark, C.E.; Han, J.; Burnham, A.; Dunn, J.B.; Wang, M.: Life-Cycle Analysis of Shale Gas and Natural Gas. Argonne Nat Lab (2012) 9. Texas Railroad Commission. Oil and Gas Well Records—Online. https://www.rrc.state.tx. us/oil-gas/research-and-statistics/obtaining-commission-records/oil-and-gas-well-records-onl ine/. Accessed on 2 June 2017 10. Logan, J., Heath, G.A., Macknick, J., Paranhos, E., Boyd, W., Carlson, K.: Natural Gas and the Transformation of the U.S. Energy Sector: Electricity, NREL/TP-6A50–55538. Joint Institute for Strategic Energy Analysis (JISEA) (2012) 11. Williams Transco Central Penn Line South Pipeline Lifetime. Accessed on 29 2015 12. Texas Railroad Commission Memorandum: change in determination administrative policy for gas well classification (2016) 13. Heath, G.A., O’Donoughue, P., Arent, D.J., Bazilian, M.: Harmonization of initial estimates of shale gas life cycle greenhouse gas emissions for electric power generation. Proc. Natl. Acad. Sci. 111, E3167–E3176 (2014) 14. Lee, W.J., Sidle, R.: Gas-reserves estimation in resource plays. SPE Econ. Manage. 2, 86–91 (2010) 15. INTEK Review of Emerging Resources: U.S. Shale Gas and Shale Oil Plays. Prepared for the Energy Information Administration: Washington, D.C. (2011) 16. Baihly, J., Altman, R., Malpani, R., Luo, F.: Study assesses shale decline rates. The American Oil & Gas Reporter, 5 (2011) 17. Warlick, D.: A current view of the top 5 US gas shales. Oil & Gas Financial Journal, 1 (2010) 18. EIA Annual Energy Outlook 2020. Department of Energy (2020) 19. Patzek, T.W., Male, F., Marder, M.: Gas production in the Barnett Shale obeys a simple scaling theory. Proc. Natl. Acad. Sci. 110, 19731–19736 (2013) 20. Enverus Drilling Info Oil and gas production data (2020) 21. AGA How Does the Natural Gas Delivery System Work? https://www.aga.org/natural-gas/del ivery/how-does-the-natural-gas-delivery-system-work-/. Accessed on 3 July 2020 22. Tavakkoli, S., Feng, L., Miller, S., Jordaan, S.M.: The implications of generation efficiencies and supply chain leaks for the life cycle greenhouse gas emissions of natural gas-fired electricity in the United States (under review) 23. EIA Natural gas compressor stations on the interstate pipeline network: developments since 1996. Office of Oil and Gas (2007) 24. Larson, A.: World’s most–efficient combined cycle plant: EDF Bouchain. Power (Magazine) 161, 22–23 (2017) 25. EIA What is the efficiency of different types of power plants? https://www.eia.gov/tools/faqs/ faq.php?id=107&t=3 26. Forsthoffer, W.E.: Forsthoffer’s best practice handbook for rotating machinery. Elsevier (2011) 27. Fadok, J.: Advanced gas turbine materials, design and technology. In: Advanced Power Plant Materials, Design and Technology. Elsevier, pp. 3–31 (2010) 28. EIA Form EIA-923 detailed data. https://www.eia.gov/electricity/data/eia923/. Accessed on 8 Jan 2019

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29. EIA Table 8.2. Average Tested Heat Rates by Prime Mover and Energy Source, 2009–2019. https://www.eia.gov/electricity/annual/html/epa_08_02.html. Accessed on 14 Apr 2020 30. Kasumu, A.S., Li, V., Coleman, J.W., Liendo, J., Jordaan, S.M.: Country-level life cycle assessment of greenhouse gas emissions from Liquefied natural gas trade for electricity generation. Environ. Sci. Technol. 52(4), 1735–1746 (2018) 31. Surana, K., Jordaan, S.M.: The climate mitigation opportunity behind global power transmission and distribution. Nat. Clim. Chang. 9, 660–665 (2019) 32. GE GE Global Power Plant Efficiency Analysis. General Electric (GE) (2016)

Chapter 4

Life Cycle Impact Assessment

Abstract Across impact categories, electricity generated from natural gas faces many of the same challenges as other product systems in life cycle assessment (LCA): data. Due to the high levels of variability and uncertainty inherent to the natural gas sector, life cycle impact results remain under investigation amongst scientists and policymakers. The typical life cycle approach of characterizing results into aggregate impact categories therefore can be less valuable for decision-makers in this sector. The limitations to the assumptions and the depth of the analysis as well as the impacts and processes of importance may not be obvious using such aggregate indices. Consequently, specific life cycle impacts and their inventories will be discussed after the presentation of aggregate results, including deep dives into air emissions (including greenhouse gases), water consumption and withdrawal, and land use. State-of-the-art methods for these example mid-point impact categories will be reviewed, along with a critical analysis of the state of data and important challenges for future research. Where relevant, results for gas-fired power are presented as comparisons to other electricity generation sources for context. A dilemma remains for gas-fired power. Studies that include comprehensive suites of impact categories provide an important picture of trade-offs thereby reducing the potential to overlook negative consequences. Alternatively, focusing on one impact category using an indepth investigation that considers site-level information enables analysts to account better for the inherent complexity of the product system.

Introduction Environmental impacts can be defined as either positive or negative impacts to the environment resulting from human intervention. LCA is a powerful tool to understanding the environmental impacts of products and processes. As discussed in Chap. 2, a life cycle result is presented in terms of a numerator and a denominator. The numerator and denominator are quantitative representations of an environmental impact and the functional unit, respectively. The life cycle inventory (LCI) involves establishing all elementary flows that are relevant to a product system, in this case, natural gas [1]. The life cycle impact assessment (LCIA) aims to assess the © Springer Nature Switzerland AG 2021 S. M. Jordaan, Wells to Wire, https://doi.org/10.1007/978-3-030-71971-5_4

45

46

4 Life Cycle Impact Assessment

contribution of each elementary flow (e.g., emission or resource use) to their respective environment impacts. To do so, impacts are placed in impact categories, which often serve as indices of total impact. In fact, this is one of the mandatory steps of LCIA—the selection of impact categories, category indicators and characterization methods. LCIA and LCI for comprehensive LCAs are thus closely tied. An important distinction must be made for the LCIA between midpoint and endpoint indicators. Endpoint indicators relate to what is called “areas of protection” which refer to what society may seek to protect, namely: the natural environment, human health, and natural resources [2, 3]. An endpoint assessment will calculate the magnitude of harm caused to these areas of protection by the life cycle of a product. Alternatively, midpoint indicators characterize impacts between the emission and the endpoint; for example, using global warming potential to quantify the emissions of greenhouse gases in terms of carbon dioxide equivalents. Natural gas-fired electricity involves a highly diffuse upstream natural gas supply chain spread out over vast geographies, with a similarly expansive set of power plants that use different technologies and are both influenced by and influencing their operating environments. As a result, the spatiotemporal disconnect between inventories and impacts have created largest challenges for LCA in providing meaningful results across impact categories. Many environmental impacts examined within LCAs are fundamentally local, yet LCA often ignores spatial differentiation in inputs and results [4]. Even for global challenges like climate change, the local context matters for natural gas due to the site- and facility-level variability of elementary flows such as atmospheric emissions. In this chapter, a detailed overview of LCIA as it relates to gas-fired electricity as well as outstanding questions that practitioners face in its implementation are provided. Specific examples will be discussed to illuminate methodological gaps and advancements in LCIA of relevance to natural gas-fired power. After introducing popular impact assessment models, three key life cycle impact categories are reviewed in depth regarding their relationship with spatiotemporal assessment: greenhouse gas implications of gas-fired power, water consumption implications of the coal-to-gas transition of the electric sector, and the spatial land requirements and impacts of gas-fired power and its alternatives. While many valuable impact assessment models exist, such as the tool for the reduction and assessment of chemical and other environmental impacts (TRACI) [5], the limits of knowledge are sought in this chapter. The goal is to cover how present methods may be employed but also to identify areas of future research for practitioners to improve upon present practice.

Impact Categories and Characterization Models The steps in LCIA—the selection of impact categories, their classification, and characterization as well as non-mandatory components—require a firm understanding of how well developed the impact categories under question are. The International

Impact Categories and Characterization Models

47

Organization for Standardization (ISO) has LCA standards that recommend the selection should be comprehensive and span the environmental issues of importance to the study in question. While such a case is optimal, challenges remain in many aspects of methodological development—particularly for the case of gas-fired power—where facility-level variability is high, and the development has immense coverage over different regions and ecosystems. These challenges are manifested in how accurately LCA practitioners can express the elementary flows (e.g., release of pollutants) in terms of the implications to the endpoint impact categories (e.g., harm to human health, the natural environment, or to natural resources). While many impact categories are mature, many continue to develop, or remain in need of development Sala et al. [6], leaving a lot of room for practitioners to improve both data and methods (Fig. 4.1). Many of these challenges take root in not only facility-level variability, but also in the fact that the different unit processes in LCA can occur across many different regions and environments. The local environment may, for example, already be experiencing either high or low concentrations of specific pollutants which may mean that different ecological or human health thresholds may be closer to their limits. Specifically, the incremental impact from product development may have a high or low impact on the baseline for where the processes are occurring. The baseline is also not static. The amount of nitrogen in a local area, for example, can vary with rainfall. Pre-existing local pollution loading influences how harmful any new releases can be to the environment. These challenges are true for all products examined in LCA and are exemplified by natural gas due to its diffuse geographical dispersion and evolution in operational practices and technologies over time. In fact, these very questions make the impact assessment stage of LCA particularly tricky—LCA often stops at the inventory stage and goes right to the interpretation. Regardless of these challenges, there have been powerful arguments for using LCA to examine a comprehensive set of environmental impact categories. Most importantly, companies may choose to focus on only one impact that makes their product look environmentally superior while (perhaps purposely) overlooking another impact category with very negative environmental consequences [1, 7]. In fact, studies are often commissioned to examine only one impact. As a result, more comprehensive coverage is correctly encouraged (even required) to be certified as compliant to the ISO standard. Perhaps most referenced is the Intergovernmental Panel on Climate Change (IPCC) method to characterize greenhouse gas emissions on a common basis (carbon dioxide equivalents) using Global Warming Potential (GWP). Climate impacts of fossil fuels have been increasingly disputed with growth of their use and broader acceptance of climate science. As a result, such standardization is an enormous asset to the field of LCA. The IPCC model, however, only examines greenhouse gases; many other impact assessment models exist. Popular impact assessment models and their impact categories are presented in Table 4.1 [7]. As recommended by ISO, LCAs of natural gas often examine environmental impacts using the wide-reaching characterization models outlined in Table 4.1 but are similarly challenged by questions of accuracy and broad applicability. And even

Environmental cause and

Eutrophication, terrestrial Ecotoxicity, freshwater Ecotoxicity, marine Ecotoxicity, terrestrial Land use Resource depletion: water Fossil and mineral depletion Renewable resources

Ecotoxicity, freshwater

Ecotoxicity, marine

Ecotoxicity, terrestrial

Land use

Resource depletion: water

Fossil and mineral depletion

Renewable resources

Natural resources

Natural environment

Human health

Methods in need of development

Methods in development

Mature methods

Fig. 4.1 Impact categories are constantly improving, and many have yet to reach maturity. An illustrative depiction of this progress is reflected, with impact assessment methods qualitatively determined to be mature, in development, or in need of development (adapted from Sala et al. [6])

Eutrophication, aquatic

Eutrophication, terrestrial

Photochemical ozone formation

Photochemical ozone formation

Eutrophication, aquatic

Ionising radiation (ecosystems)

PM/respiratory inorganics

PM/respiratory inorganics Ionising radiation (human health)

Human toxicity, non-cancer

Human toxicity, non-cancer

Ionising radiation (ecosystems)

Human toxicity, cancer

Human toxicity, cancer

Ionising radiation (human health)

Ozone depletion

Ozone depletion

Endpoint impacts Climate change

Midpoint impacts

Climate change

48 4 Life Cycle Impact Assessment

X

X

MEEuP

ReCiPe

Swiss X Ecoscarcity 07

TRACI

X

X

X

LUCAS

USEtox

X

X

LIME

X

X

X

X

IPCC

X

X

X

Impact 2002+

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

EPS 2000

X

X

X

X

X

X

EDIP X 2003/EDIP976

X

X

X

X

X

X

X

X

X

Eco-indicator 99

X

X X

CML2002

X

X

CED

Climate Ozone Respiratory Human Ionizing Ecotoxicity Ozone Acidification Terrest. Aquatic Land Resource change depletion inorganics toxicity radiation formation eutrophication eutrophication use consumption

Table 4.1 Popular impact assessment models Matthews et al. [7]

Impact Categories and Characterization Models 49

50

4 Life Cycle Impact Assessment

these impact assessment models generally focus on midpoint categories rather than endpoint. Generalized assessments can be useful in broad technological comparisons, however. Table 4.2 presents LCIA results from a 2015 study, where electricity generated from natural gas can be broadly compared against other technologies [8]. With some exceptions, natural gas-fired power ranks firmly second to coal but higher than other alternatives for nearly all impact categories. Exceptions include a higher ranking of particulate matter for natural gas relative to coal and lower land occupation relative to specific renewable sources. Studies that include broader, more comprehensive suites of impact categories are unlikely to fully capture the full variability across regions and sites. Table 4.2 Results for midpoint categories for different electricity generation technologies (adapted from Hertwich et al. [8]). Results include waste disposal, well pre-production, and materials used in the construction of infrastructure and are reported on the basis of a kilowatt-hour of electricity generated. The geographic scope of the study is global Impact category

Greenhouse gases

Particulate matter

Ecotoxicity

Eutrophication

Land occupation

Unit

kg CO2 e.

kg PM10 eq.

kg 1,4-DCB eq.

kg P eq.

ma

Subcritical wo CCS

0.933

0.0003

0.0096

0.0005

0.0204

2

IGCC wo CCS

0.791

0.0002

0.008

0.0004

0.0177

SCPC wo CCS

0.871

0.0003

0.0089

0.0005

0.0191

Subcritical w CCS

0.263

0.0004

0.0149

0.0007

0.0291

IGCC w CCS

0.201

0.0002

0.0109

0.0006

0.0238

SCPC w CCS

0.236

0.0004

0.0136

0.0006

0.0268

NGCC wo CCS

0.527

0.0008

0.0063

0

0.0005

NGCC w CCS

0.247

0.0009

0.0081

0

0.0007

Poly-Si ground

0.057

0.0001

0.0032

0

0.0099

Poly-Si roof

0.0575

0.0001

0.0037

0

0.0018

CIGS ground

0.0195

0

0.0005

0

0.0097

CIGS roof

0.0243

0

0.0007

0

0.0006

CdTe ground

0.0161

0

0.0005

0

0.01

CdTe roof

0.0206

0

0.0007

0

0.0006

CSP–trough

0.0227

0

0.0001

0

0.009

CSP–tower

0.033

0.0001

0.0004

0

0.014

Reservoir 1

0.0788

0.0002

0.0002

0

0.0044

Reservoir 2

0.0056

0

0

0

0.0262

Wind onshore

0.0084

0

0.0003

0

0.0003

Wind offshore steel

0.0114

0

0.0004

0

0.0003

Wind offshore gravitybased

0.0111

0

0.0004

0

0.0003

Impact Categories and Characterization Models

51

While some ranges are presented in the study, the wide-ranging variability has yet to be captured in a full assessment with hundreds of thousands of wells producing in the United States alone. The spatiotemporal heterogeneity combined with the uncertainty in impacts give rise to questions about the methodology and data inputs specific to individual impact categories.

A Focus on Improving Individual Impact Categories When both data inputs and life cycle results are highly variable and influenced by region, spatial differentiation becomes even more important for the impact assessment stage of LCA. Impacts related to air pollution, water consumption and ecosystems require the integration of spatiotemporal considerations to provide context for decision-makers. States may import natural gas for electricity generation, resulting in impacts due to hydraulic fracturing in other regions. Even within a water rich state, highly localized water demand may be incurred for drilling during a time when water is scarce. While LCA was not conventionally a spatially explicit method [4], new research has been proving not only the need but also the possibility of improving both spatial and temporal dimensions of LCA Jordaan et al. [9]. These impact categories can then have contextual relevance for different regions, which is critical for the LCA practitioner to communicate during the interpretation stage. Potting and Hauschild [10] presented three categories of special LCA: site-generic, site-dependent, and sitespecific. Site-generic LCAs are the most common —they lack any spatial information and assume homogeneity. Site-dependent LCAs can involve different scales of spatial resolution, resulting in questions regarding what resolution should be applied (e.g., informed landscape type or resolution of the data [11]). Finally, site-specific LCAs model individual sources and local responses. There is a trade-off between the amount of site-specific information that is used in a LCA and the ability of decision-makers to apply LCA results more broadly to a product system—the results may only apply to that site or region. Temporal accounting presents similar challenges for decisions related to the impact assessment stage of LCA of natural gas-fired electricity. LCA quantifies environmental impacts at a baseline year and according to time horizons selected during the initial goal and scope definition stage [12], and results generally provide a snapshot of impacts and resource consumption. Often data inputs were published or acquired at different years and may be outdated.

Greenhouse Gas Emissions and Other Air Pollution Recent questions about the life cycle greenhouse gas implications of natural gas-fired electricity have centered upon whether upstream fugitive emissions may reverse conclusions about the climate benefits compared to coal-fired power. The origins

52

4 Life Cycle Impact Assessment

of the debate around fugitive emissions take root in U.S. Environmental Protection Agency’s early estimates. They were used in several published LCAs. Studies increasingly found that the emissions inventories were outdated, underestimating actual emissions [13]. The search to uncover the magnitude of these emissions ensued, with major updates by way of independent, published work and subsequent revisions to many of the outdated EPA estimates. Such research has clearly had substantial implications for decision-making. Another critical assumption in these analyses is the time horizon used to calculate the carbon dioxide equivalence of methane through Global Warming Potentials, which are based on 20-year and 100year time horizons. Since methane has higher warming potential relative to carbon dioxide and an atmospheric lifetime of approximately 12 years relative to carbon dioxide’s lifetime (the latter is mostly absorbed on the scale of centuries, but a portion remains for 1000s of years) [13, 14]. The trade-offs between 20- and 100year time horizons remain under investigation and are discussed further in Chap. 6 on uncertainty analysis. LCAs focused on energy technologies have contributed to uncovering relatively hidden and uncertain contributors to emissions along energy supply chain and highlighted their importance for further investigation [15–18]. Emissions across the life cycle are not always characterized with the same level of precision and accuracy [8]. More recently, the potential for uncertain fugitive methane emissions to evaporate the benefits of natural gas-fired electricity when compared to coal has resulted in international debate [16, 18–20]. Among related issues, the question of what time horizon should be explored when estimating the relative global warming potential between methane emissions and carbon dioxide remains unresolved. The greenhouse gas emissions associated with gas-fired electricity have been variable, with results in this section representing domestic production (the import of liquefied natural gas by tanker is examined in Chap. 5). Life cycle phases characterized in process based LCAs include production, processing, transportation, and storage, through end use (Fig. 4.2). Greenhouse gas emissions from each of the life cycle stages of natural gasfired electricity are subject to substantial site- and project-level variability by region and depending on many factors, such operational practices [21]. Local operational practices inform whether mitigation measures are implemented. Petroleum geology also plays into the equation. Site-level variability for facilities becomes even more pronounced for oil and gas production due to the heterogeneity of geologic reservoirs [22–24]. Locational variability extends much farther than just regional differences in oil and gas reservoirs: greenhouse gas emissions from life cycle stages are also influenced by national infrastructure differences, but these differences are not well understood and may be measured differently in different jurisdictions [25]. For natural gas production, even primary data from measurements are not always directly comparable due to different reporting thresholds for facility-level emissions (e.g., 25 kilotonnes (kt) CO2 e a year for the U.S.; 10 kt for British Columbia (BC); 50 kt for Alberta) [17]. Certainly, the methane leaks quantified in different LCAs have been shown to be variable [26]. To address inconsistencies, the National Renewable Energy Laboratory developed an analytical procedure that they applied specifically to electricity generation technologies called harmonization [26–29]. The procedure

A Focus on Improving Individual Impact Categories

53

Fig. 4.2 Life cycle stages generally considered in process based LCAs of gas-fired power, both fuel cycle and electricity generation [18]

involved modifying models, assumptions, and boundaries for consistency. Even once key parameters and systems boundaries are harmonized, variability is reflected in life cycle results (Fig. 4.3). The results in Fig. 4.3 show little difference between life cycle emissions of electricity generated from the combustion of shale gas compared to that from conventional natural gas. At the same time, the uncertainty of methane emissions remains. Most notably, recent LCAs highlight the need to improve data and estimates related to fugitive methane emissions [17, 20, 22, 30]. One of the most important contributions of LCA to energy research is the identification of data gaps and uncertainties and their relative contributions to life cycle emissions estimates. Another reason to undertake a LCA is to promote stakeholder dialogue, which has been particularly effective for the energy sector regarding improving data reporting by both industry and government alike. Such initiatives have been ongoing to improve not only data but also mitigation. The variability noted in greenhouse gas emissions transcends to other air pollutants. One of the largest benefits of natural gas-fired electricity if used instead of coal is the reduction of air pollutants at the stack. While the reductions are clear, natural gas-fired electricity is not without impacts [31] and the air quality implications of high-density hydraulic fracturing have yet to be resolved (particularly with diesel trucks used during drilling and volatile organic compounds released during operations). Air pollutants such as ozone precursors, for example, have already been shown to have air quality impacts and their impacts to human health are heavily dependent on ambient air quality [32, 33].

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4 Life Cycle Impact Assessment

Convenonal Unconvenonal

Shale

US domesc

Fig. 4.3 Harmonized life cycle estimates for different types of natural gas, compiled by Heath et al. [27]. The study focused on comparing existing LCAs that consider shale gas and conventional, with point estimates for unconventional and domestic for comparison. Results include waste disposal, well pre-production, and materials used in the construction of infrastructure. The boxes in the figure represent the interquartile range (IQR), which is the difference between the third quartile (Q3) and the first quartile (Q1). The line in the box represents the median, the whiskers represent the maximum and minimum. The x represents the mean

Even deploying higher efficiency combined cycle natural gas options may not result in environmental benefits across all impact categories relative to coal [8, 33]. Combined cycle natural gas plants (see Chap. 3) have been reported to result in higher NOx emissions than their coal counterparts, resulting in higher terrestrial acidification potential (Table 4.2). Further, applying carbon capture and storage to natural gas may increase all other impact categories by 20–80%, in part due to the parasitic load and need for increased natural gas. It is important to note, however, that many studies have reported close alignment between the NOx emissions from natural gas and coal-fired power (0.2–3.9 kg NOx/MWh for coal options versus 0.2– 3.8 kg NOx/MWh for natural gas alternatives) [8], suggesting close attention should be paid to regional differences in technologies and fuel quality in such comparisons.

Water Consumption Typical LCAs focus on how the net life cycle water consumption of different types of electricity generation may compare. Examining product systems can be informative; however, water implications of the spatiotemporal patterns of energy decisions cannot

A Focus on Improving Individual Impact Categories

55

be captured by examining product systems alone. Importantly for gas-fired power, hydraulic fracturing requires more water in comparison to conventional gas production. Most studies point to the fact that water requirements for hydraulic fracturing are relatively insignificant when compared to that for power generation based on each Megawatt-hour generated [34, 35]. But LCAs of water consumption for energy are also spatiotemporally dependent but often presented in spatially aggregate bar-charts, without mapping spatially differentiated maps. Water resource requirements can face highly localized impacts, which may result in scarcity constraints. These impacts are typically characterized without connection to spatially explicit inventories and ignore the temporal variability in streamflow. Even those studies that attempt to relate water consumption with availability typically rely upon administrative boundaries that represent political jurisdiction rather than ecological systems [35–37]. The spatiotemporal dynamics of the water consumed by energy decisions are exemplified by a case study of the coal-to-gas transition of the electric sector of Pennsylvania [9, 38]. Results demonstrated that watershed-scale results are highly variable when using spatially differentiated inventories of water consumption for two life cycle stages of coal and gas-fired electricity generation. The life cycle stages included fuel extraction and electricity generation in Pennsylvania from 2009 to 2012. We found that watershed-scale water consumption may be reversed from a net increase to decrease (and vice versa) when the total water consumption is compared to the water consumed specifically for life cycle stages of electricity generation. The finding reinforces the importance of further developing spatially resolved inventories for LCAs of energy technologies (Fig. 4.4). Life cycle studies have generally focused on a specific region using national political boundaries or subnational political boundaries [38–40]; however, water impacts from energy technologies can be highly variable across and within each of these regions (as shown by our example). An important emerging question for LCA practitioners is: what spatial resolution should be employed to quantify water use? Ecological scales, such as watersheds, are most relevant for understanding local impacts to availability and ecosystems [41]. Yet, most water consumption data are aggregated at political scales such as counties, states, provinces, or a country. More recently, research has moved towards developing site-level inventories that can be aggregated at the watershed scale [9, 38]. Another topic critical to developing a better understanding for water impacts in LCA relates to the temporal variation of water use (both consumption and withdrawal) compared to the availability of water. Water stress metrics have only recently been connected to consumption in LCA with the “available water remaining” or AWARE method, which quantifies the amount of fresh water available in each region as the freshwater supply minus consumption (both human and environmental demand) [41– 43]. More remaining water means the burdens from new consumption are smaller, as the potential for water deprivation caused by new consumption can be contextualized with a consistent method [41–43]. With the emergence of life cycle inventories of water consumption at the spatial resolution of watersheds and at the temporal resolution of months (Fig. 4.5), future research may better connect inventories and

56

4 Life Cycle Impact Assessment

Fig. 4.4 Watershed-level results for the coal-to-gas transition in Pennsylvania [9]. a presents the total water consumption changes associated with the coal to gas transition (Mgal) from 2009 to 2012 including fossil extraction and power generation (updated from Patterson et al. [38]); b power end product water consumption changes (Mgal) for the coal-to-gas transition in the power sector where the fuel assessment is limited to the fuel consumed by the sector from 2009 to 2012. The bars for each watershed represent the magnitude of the increase or decrease in water consumption for each industrial activity included within the scope of the study: gas extraction (Hydraulic Fracturing—HF Extract), coal extraction (Coal Mine), natural gas power (NG Power), and coal power (Coal Power). The color of each watershed represents the total increase or decrease in water consumption between 2009 and 2012

A Focus on Improving Individual Impact Categories

57

Fig. 4.5 Monthly water demand for two life cycle stages of coal and gas-fired electricity generation in Pennsylvania for the coal to gas transition from 2009 to 2012 (fuel extraction and electricity generation) for four watersheds. Water consumption across watersheds in Pennsylvania can found using our online tool: https://nicholasinstitute.duke.edu/hydraulic-fracturing/

impacts. Such methods would provide a more robust decision-making context for water managers in the face of stream-flow variability and its confluence with spikes in water demand from energy development [38].

Quantification of Life Cycle Land Use Impacts While many studies have examined the land use of energy technologies [22, 44– 50], recent research has noted the need for more robust comparative analyses of the land use of electricity generation due to the inconsistent application of methods and assumptions. It is therefore unsurprising that the spatial requirements of natural gasfired electricity have been less examined from a life cycle perspective in comparison to other impact categories. In fact, land use provides a useful example of an impact category that is less vetted in comparison to others. The methodology employed to quantify land use in LCA can be complex: there are many different impacts that can be considered and quantifying their value to society can be challenging [45, 46, 51, 52]. In prior literature that builds upon the framework for LCIA of land use developed by the United Nations Environment Programme

58

4 Life Cycle Impact Assessment

(UNEP)/Society of Environmental Toxicology and Chemistry (SETAC) Life Cycle Initiative working group [11, 53–56], three types of land use impacts have been identified: transformation, occupation, and permanent impacts. Land transformation (LT ) arises from the change of land to a new type, typically for an intended human activity such as energy developent, quantified in terms of area (e.g. meters squared or m2 ). Land occupation (LO ) results from the human use of land over a duration of time with original land type being a potential outcome post-reclamation, quantified in terms of area-time (e.g., m2 -year). Permanent land use refers to irreversible impacts to ecosystems when an ecosystem will not fully recover after use. Land transformation and occupation are defined in Eqs. (4.1) and (4.2). LT = A × Q

(4.1)

Lo = A × t × Q

(4.2)

where A is the area that is transformed or occupied, Q is land quality, and t is the amount of time a piece of land is occupied. The difficulties in quantifying these indicators are two-fold: the highly subjective concept of quality and the reliance of results on the location of the land disturbance. A developer may look at a landscape with many projects and admire the multi-use while a conservationist may look at the same landscape with disdain. The quality aspect will differ for different biogeographical regions, impacts, and perspectives, thus requiring a more robust and specific definition when used in practice (e.g., biodiversity or ecosystem services). Much LCA research has focused on the concept of quality; however, researchers may never agree on what it should encompass until values are made explicit within the assessments. Specific challenges to the methodology relate directly to the fact that ecosystem impacts are generally more acute locally and that there are many different impacts that may result from the conversion of land. LCA thus has faced a challenging dilemma as a field: practitioners often argue that the quantification of land area alone is insufficient in understanding impacts (or the “quality” of land preand post-transformation), so the investigation of land has often stagnated as even understanding land transformation requires substantial analysis. Recent methodological developments, including machine learning of aerial and satellite images, have enabled improved quantification of the life cycle land requirements of gas-fired electricity but results generally remain focused on better quantifying the amount of land required (i.e., transformation) [22]. The system boundaries using such novel methods only capture the fuel cycle and electricity generation to date. Unlike greenhouse gas emissions, upstream materials extraction has been found to only have negligible impacts for renewable electricity; for example, upstream land area associated with solar photovoltaic (PV) is estimated to be less than 1% of the area related to electricity generation [57]. While specific to the Barnet Shale of Texas, the applicability of new methods to other gas producing regions may in the future illuminate variability associated with the fuel supply. When including the fuel supply of natural gas—which is not applicable for renewable electricity—results

A Focus on Improving Individual Impact Categories

59

Table 4.3 Land use intensity of renewable electricity generation facilities reported by the National Renewable Energy Laboratory [22, 47–49]. The annual results (m2 /(MWh/year)) and results for an assumed a 30-year lifetime (m2 /MWh) for the average land use intensity of U.S. renewable electricity generation facilities are reported. The number of projects sampled in the study are represented by sample size n. Note that rooftop solar would not require additional land for the use phase of the life cycle Type of electricity generation

Annual direct areaa (m2 /(MWh/year))

Direct area (m2 /MWh)

Natural gas-fired power

–

0.62

Small Photovoltaicb

12

0.40

Large Photovoltaicc

13

0.43

Concentrating solar power

11

0.36

Geothermal

0.57

0.019

Wind

0.77

0.026

a Direct

area refers to land permanently or temporarily disturbed due to physical infrastructure development b Small is defined as greater than 1 MW, but smaller than 20 MW c Large is defined as greater than 20 MW, all ground mounted

suggest that the gas-fired power offers no real advantage over renewable sources in this region. Our recent research determined that the total life cycle land requirements of natural gas-fired electricity for the Barnett shale of Texas was 0.62 m2 /MWh (95% confidence intervals ±0.01 m2 /MWh). We found that total land use was dominated by midstream infrastructure, particularly pipelines (74%). More surprisingly, we demonstrated that renewable energy may require less land when time horizons representative of the economic life of the technologies are applied (Table 4.3). Though renewable energy is often considered as more destructive to landscapes when compared to gas-fired power, the application of new methods may reveal more evidence that confirms that this may not always hold true when upstream impacts— notably the fuel cycle—are considered. While some of the present set of results sometimes appear contradictory, different methods have been applied to compare renewable energy with non-renewable that treat time differently. The impacts of the different temporal assumptions on results are exemplified in Table 4.3. There is a clear need to robustly examine the amount of land required to maintain energy infrastructure across the life cycle of energy technologies using clear and transparent spatiotemporal data and consistent assumptions. More specifically, the timescales at which different types of renewable energy becomes equivalent to fossil energy warrant investigation. Going beyond simply the land area required for electricity generation, the “quality” aspect can represent many valued aspects of land, from carbon mitigation to biodiversity to cultural values. This impact category remains nascent relative to many others, which can be exemplified by ecosystem services. Even beyond LCA, it is well-recognized that there is no consensus for valuing ecosystem services [58]. Rugani et al. [59] noted the diversity of methods that can be used to accomplish this goal. Among the many approaches that can be used to monetize ecosystem services

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4 Life Cycle Impact Assessment

in LCA, the Economics of Ecosystems and Biodiversity (TEEB) framework has been broadly applied. This well-established framework [60, 61] assigns values to four major categories: provisioning (e.g. food production), regulating (e.g. carbon storage), habitat (e.g. gene pool protection), and cultural services (e.g. recreation and aesthetic). Ecosystem values for the Chihuahuan Desert region were recently estimated by Taylor et al. [62, 63] and applied within a comparative LCA of electricity generated from natural gas, solar, and wind [64]. Particularly when considering time within the calculations, the results illuminate interesting trends in how each technology compares over time (Fig. 4.6). The average land use intensity of gas-fired generation for the region was estimated to be 0.31 m2 /MWh with a standard deviation of 0.30 m2 /MWh (Fig. 4.6). Results for the region were just under half of those for the recent study in the Barnett shale of Texas, suggesting that regional variability can be influential in life cycle quantifications of land use for this pathway. The average land use intensity of solar was determined to be 0.68 m2 /MWh (standard deviation of 0.31 m2 /MWh), but the breakeven with natural gas is under 10 years. The average ecosystem services cost per MWh for gas-fired generation is estimated to be 0.63 $/MWh with a standard

Fig. 4.6 a–d. Land use and ecosystem services costs of energy projects in the Chihuahuan Desert Region. a, b present results for the land use intensity and c, d focus on ecosystem services. Figures republished with permission from Jordaan et al. (in press) [64]

A Focus on Improving Individual Impact Categories Table 4.4 Estimated time of restoration for select ecosystem types [65]

61

Ecosystems Agricultural land,

Time (years) pioneera

vegetation