Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency [1 ed.] 9781614705154, 9781614705031

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Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena, Nova

ENERGY SCIENCE, ENGINEERING AND TECHNOLOGY

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HOUSEHOLD ENERGY: ECONOMICS, CONSUMPTION AND EFFICIENCY No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

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Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

ENERGY SCIENCE, ENGINEERING AND TECHNOLOGY

HOUSEHOLD ENERGY: ECONOMICS, CONSUMPTION AND EFFICIENCY

Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved.

BARTOLI MENDEZ AND

JOSIAH PENA EDITORS

Nova Science Publishers, Inc. New York

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

Copyright © 2012 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Household energy : economics, consumption, and efficiency / editors, Bartoli Mendez and Josiah Pena. p. cm. Includes Index. ISBN  ((%RRN) 1. Dwellings--Energy conservation. 2. Dwellings--Energy consumption. I. Mendez, Bartoli. II. Pena, Josiah. TJ163.5.D86H67 2011 333.79'63--dc23 2011023223

Published by Nova Science Publishers, Inc.  New York

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

CONTENTS Preface Chapter 1

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Chapter 2

v Renovation Activities in Canadian Single and Multi-Family Dwellings Lucie Maruejols and Denise Young

1

Household Energy: Application of Zero Energy Buildings and Town Sora Yi, Jongho Yoon and Min Kyeong Kim

41

Chapter 3

Traveler Response to the 2005 Gas Price Spike Matthew S. Bomberg and Kara M. Kockelman

Chapter 4

The Welfare Implications of Carbon Taxes and Carbon Caps: A Look at U.S. Households Sumala Tirumalachetty and Kara M. Kockelman

Chapter 5

Chapter 6

Development of an Environmental Sustainability Model for New Residential Development in South Australia: An Ecological Footprint Approach J. Morrissey, A. Carre and J. Boland Promoting Sustainable Development through Integrated Wood Stove Technology, Aforestation and Agro-Aquaculture A. C. Achudume

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115

143

167

vi Chapter 7

Contents How to Promote Energy Savings among Households: Theoretical and Practical Approaches Linda Steg and Wokje Abrahamse

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Index

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179 207

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PREFACE This book presents topical research in the study of the economics, consumption and efficiency of household energy. Topics discussed include renovation solutions for improving the energy efficiency of existing buildings; features and technology of the zero-energy home; traveler response to the gas price spike; the economic impacts of energy taxes versus household-level emissions caps; promoting sustainable development through integrated wood stove technology, aforestation and agro-aquaculture; and the contribution of social and environmental psychology for understanding and promoting household energy conservation. Chapter 1 - In this chapter we examine several factors that influence the decision to renovate in the residential sector and provide an overview of renovation activities undertaken in Canadian single- and multi-family dwellings. Renovation activity is generally advertised as an effective solution for improving the energy efficiency of existing buildings. Some barriers to undertaking renovations, such as those related to costs and incomes apply to all types of housing. Others tend to be specific to multi-family dwellings which form an important segment of the Canadian residential sector. The composition of this portion of the housing sector is such that it includes many rental units, where the landlord-tenant relationship alters the agents’ set of incentives regarding energy use and the demand for various types of renovations. Our case study of the Canadian residential sector outlines the general issues associated with the renovation decision and examines the prevalence of various types of renovations in Canada, with attention paid to the roles played by building type, ownership status, and income. The analysis is based on household-level micro data from the 2003 Survey of Household

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Bartoli Mendez and Josiah Pena

Energy Use (SHEU-2003), conducted by Statistics Canada, and aggregate elements of the SHEU-2007 survey. Chapter 2 - Energy security is one of the most significant pending issues around the world. Ecoplation (a term made up from a word of “ecological” and a word of “inflation” means the status of growing price of consumption goods caused by the increase of production cost due to climate changes such as global warming) is inflation caused by environmental factors. The 2008 report of World Research Institute (WRI) estimated that, within a decade, the rise in the prices of oil, natural gas, and power supply by 20%, 40%, and 45% respectively, will bring about severe Ecoplation to human beings. To address such an issue, various nations in the world have been committing itself to focus on two ways: “new and renewable energy” and “energy efficiency and saving”. Among them, the energy efficiency called the fifth fuel (the first fuel is fire, the second one oil, the third one nuclear energy, and the fourth one new and renewable energy) has become a key alternative to ensure energy security and cut greenhouse gas emissions. Alliance to Save Energy (ASE, 2010) analyzed that the United States would have consumed its energy 50% more than the current level had the Oil shock not occurred in 1973 (Figure 1). Mckinsey reported in 2009 that the increase of energy efficiency and save will be expected to reduce the energy demand of the world by over 20% until 2020. Unlike coals or oils, energy saving does not cause any environmental pollution or greenhouse gas emissions, unlike wind energy or solar energy, it is not affected by weather, and unlike bio-energy, it does not compete with foodstuffs. Chapter 3 - Knowledge of travelers’ response to spikes in gas prices is key to planning for future instabilities in gas prices and offers insight into possible pricing strategies as mechanisms for reducing vehicle travel or improving efficiency of fuel use. A survey of over 500 residents in Austin, Texas capitalized on a severe spike in gas prices that transpired in September of 2005. This work examines how respondents’ travel behavior changed during and following the spike. This paper describes the findings using basic descriptive statistics, before/after behavioral correlations, and ordered probit and binary logit models to determine which factors are responsible for behavioral changes in response to gas price spikes. Chapter 4 - Climate change has emerged as a leading environmental concern in recent years. The two widely discussed and debated options for abatement of greenhouse gas (GHG) emissions are a cap-and-trade system, at the level of producers, and an emissions tax. More interesting is the question of capping (and trading) at the level of individual households. Regardless of

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Preface

vii

policy pursued, a key concern in implementing such policies relates to equity: stakeholders wish to understand the distributional or effects, whereby poorer households may be disproportionally impacted. In this paper, household expenditure data from the U.S. Consumer Expenditure Survey are used to anticipate the economic impacts of energy taxes versus household-level emissions caps (with buy-out permitted, for those who exceed their budget) across different income classes and different types of expenditures, including those on transport. A translog utility model was calibrated to estimate demand quantities under two different tax rates and four different cap-and-trade scenarios. While the 9-category demand system does not allow for likely consumption shifts (toward less energy-intensive items) within each demand category, the model still provided a series of meaningful results. For example, the $100-per-ton case was estimated to yield the same total carbon reductions (just over 12 percent) as a cap of 15 tons per person (per year). The majority of the emissions reductions under a cap-and-trade policy are estimated to come from higher income groups, while reductions are expected to be much more uniformly distributed under a tax policy. Welfare loss (in terms of equivalent variation) as a share of income is found to be higher for lower income households when carbon taxes are implemented. In the end, a cap-and-trade policy seems most effective in reducing emissions without negatively impacting lower-income households, and without worrying whether taxes will engender enough thoughtful consumption shifts to ensure steep reductions. Chapter 5 - The inter-related issues of climate change and energy use have brought renewed focus on the importance of renewable energy generation and energy efficiency. In the residential sector, these strategies have the potential to contribute to significant energy reduction while adding to social security and long term economic well-being. In Australia, a lack of data has impeded the policy debate on residential energy use. In particular debates about environmental performance, standards and targets are contested and lack an empirical basis. A key problem is the complexity and multi-scaled nature of the challenges faced by decision makers. Policy measures need to account for complexity on the one hand, and to communicate to diverse audiences in simple terms on the other. To address these challenges for the residential sector, reliable evidence is required to inform design and planning stages, while the rationale for decision-making needs to be transparent. In this context, this paper presents a method for the assessment and benchmarking of new residential developments with reference to global ecological limits. Research is conducted in conjunction with the Land Management Corporation

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(LMC), the State Government land development agency in South Australia (SA), as part of wider initiatives to reduce energy use and emissions in the residential sector in SA. Informed by sustainability indicators theory, the research aims to link state of the art quantification at the component scale with succinct communication of global level implications. An Ecological Footprint model, incorporating detailed life cycle assessment, is developed to assess parameters of housing construction, housing design and performance and development configuration in terms of global ecological impacts. The Lochiel Park green development in SA is incorporated as a benchmark of current ‘good practice’ housing development. The developed model enables the comparison of alternative development scenarios and policy interventions for new residential development, allowing decision makers to simultaneously assess and communicate the implications of alternative policy pathways to diverse stakeholders. The approach incorporates scientifically robust and credible quantification while facilitating communication via a readily understandable global index. The integration of ecological footprint methodology with detailed life cycle assessment data for specific targeted outcomes, as reported here, represents an exemplar of best practice scientific analysis for policy making. Chapter 6 - It is recognized that many issues in respect of fuel wood, air pollution, deforestation and aforestation are addressed international agreement as the 1985 Vienna Convention for the Protection of the ozone layer, the 1987 Montreal Protocol on Substances that Deplete the Ozone layer as amended, the 1992 United Nations Framework Convention on Climate Change and 2009 United Nations Conference on Climate change. Inconclusive as the deliberations and negotiations were on certain critical issues, the Conference drew global attention to the emergency proportions of temperature rise as well as the diminishing capacity of natural mechanisms for absorption of sink carbon dioxide (CO2 ). The imperative of reasonable remedial measures is all too obvious. The use of fuel-wood stove is becoming subject of global discussion. The role and importance of wood stove in developing nations affecting rural population have been discussed in various platforms. For instance, the status of women in provision of food and socio-economic structure tend to produce gradually diminishing time. Most useful time are spent gathering biomass especially fuel wood. In urban areas up to 55% of household income is spent to purchase cooking wood. Most often, woods are burnt in open fire and inefficient fuel stove in kitchens with little or no ventilation device. The necessity of promoting better life quality to rural women who are the end users of wood stove, thus affecting socio-economic,

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poverty, deforestation, climate change and health are the common aim of discussion. The basic and ultimately objective of this study is to reduce adverse effects on the atmosphere from the energy sector by promoting policies as appropriate, to increase the contribution of environmentally sound and cost-effective energy systems, particularly , through wood stove, less polluting and more efficient energy production, and use. In addition, to use wood as fuel for which countries have serious difficulties in switching to alternatives, and the situations of countries highly vulnerable to adverse effects of climate change. The impact on agriculture and other areas of the economic importance has been devastating. Soil infertility is one consequence, as the unprotected earth surface is repeatedly battered by the rains and soil nutrients like nitrogen are washed away. Areas to regenerate woody perennials in landuse systems. Common forms of agroforestry in the wood and wood products lead to deforestation. Soil infertility is one consequence of deforestation, increased temperature; severe weather events and greenhouse gases (GHG) are on increase. Global population is also increasing, so is increased in carbon dioxide. There is utmost need for sustainable exploitation and management of low cost woodstove. The development of simple technically integrated woodstove adaptable to local needs would provide energy content per unit mass. Selective cutting schemes, reforestation and management of woody perennials could therefore, offset the release of CO2. Finally, government at the appropriate level and institutions should cooperate in the provision of expertise and other supports including the promotion of international research efforts, in particular, with a view to enhancing the technology of integrated woodstove. Integrated irrigation-aquaculture (IIA) is only beginning in Nigeria. Projects are now in place to encourage integrated agriculture enterprises, offering hope for development of a more dynamic agriculture in Nigeria. Chapter 7 - Households are responsible for a large part of total energy requirements and CO2 emissions. We review the contribution of social and environmental psychology for understanding and promoting household energy conservation. A general framework is proposed, comprising: (1) identification and measurement of the behavior to be changed, (2) examination of the main factors underlying this behavior, (3) design and implementation of interventions to change behavior to reduce household energy use, and (4) evaluation of the effects of interventions. We discuss how psychologists empirically studied these four topics.

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Chapter 1

RENOVATION ACTIVITIES IN CANADIAN SINGLE - AND MULTI-FAMILY DWELLINGS Lucie Maruejols and Denise Young CBEEDAC, and Department of Economics University of Alberta, Edmonton, Alberta, Canada

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ABSTRACT In this chapter we examine several factors that influence the decision to renovate in the residential sector and provide an overview of renovation activities undertaken in Canadian single- and multi-family dwellings. Renovation activity is generally advertised as an effective solution for improving the energy efficiency of existing buildings. Some barriers to undertaking renovations, such as those related to costs and incomes apply to all types of housing. Others tend to be specific to multifamily dwellings which form an important segment of the Canadian residential sector. The composition of this portion of the housing sector is such that it includes many rental units, where the landlord-tenant relationship alters the agents’ set of incentives regarding energy use and the demand for various types of renovations. Our case study of the Canadian residential sector outlines the general issues associated with the renovation decision and examines the prevalence of various types of renovations in Canada, with attention paid to the roles played by building type, ownership status, and income. The analysis is based on householdlevel micro data from the 2003 Survey of Household Energy Use (SHEU-

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Lucie Maruejols and Denise Young 2003), conducted by Statistics Canada, and aggregate elements of the SHEU-2007 survey.

Keywords: Energy-saving renovations; household analysis; multi-family dwellings; economic barriers

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1. INTRODUCTION Energy-use patterns in residential dwellings, especially in terms of heating and cooling requirements, depend on many factors. Some factors, such as thermostat settings at various times of day and the extent to which technologies such as air-conditioning are used, are related to the day-to-day behavior of residents. Others are related to a building’s physical attributes. These physical attributes, which include the building’s thermal envelope (insulation of wall, roof, and foundation, window glazing, weather-sealing of doors and windows) and the choice of HVAC (heating, ventilation and airconditioning) systems, will vary depending on when a dwelling was constructed and the extent to which it has since been renovated. Major renovations to the various elements of a building’s structure can play an important role in decreasing the amount of energy used for heating and ventilation purposes, but they require an upfront financial commitment and may cause disruptions in the lives of occupants. That is, whether to renovate an older dwelling is an economic behavioral decision; albeit one that is made on a less frequent basis than many other energy-related decisions. Several previous studies have examined renovations to buildings in the residential sector. These include (i) engineering-based studies of the most effective types of renovations in terms of energy savings for various geographical areas / climates; and (ii) economic studies of the incentives and behavioral agency issues that affect decisions to undertake renovations. Engineering studies generally point to the importance of ensuring that there is a high-quality thermal envelope in order to make effective use of current or yet to be upgraded heating and/or cooling technologies. Economic studies include discussions of the roles of monetary factors (installation costs, expected energy savings, household income) and agency issues that arise in rental dwellings where the building owner usually pays for any major renovations but tenants benefit from any resulting improvements in thermal comfort and, depending on who is paying for heat, possibly lower energy bills.

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In this chapter, we examine data on renovations made to Canadian single and multi-family dwellings in the context of the engineering and economic issues identified above. This case study is based on two recent national household-level surveys that include detailed sets of questions on renovation activities. The 2003 Survey of Household Energy Use (SHEU-2003) includes information on several types of renovations undertaken in 2003 and those planned for 2004 for single-detached and multi-family dwellings (excluding high-rise apartment buildings). Renovation data were gathered for both owneroccupied and rental units, with landlords providing renovation information pertaining to rental units. The most recent Survey of Household Energy Use (SHEU-2007) takes a different, more retrospective, approach to examining a slightly broader set of renovation categories by gathering information on any previous renovations made by the current owner and the timing of these renovations. Unlike the SHEU-2003 survey, however, response rates from landlords were low. As a result reliable renovation data are not available for rental properties, leading to the exclusion of low-rise apartment units from any analysis based on the most recent SHEU survey. The remainder of this chapter is structured as follows. In Section 2 we outline the basic economics of the decision to renovate, including barriers that are faced by low income households and agency effects that occur in many (primarily multi-family) dwellings that are not owner-occupied. Issues related to the order in which renovations should be made in order to maximize energy efficiency gains are discussed in Section 3. Data on energy-saving renovations from the two most recent Canadian Surveys of Household Energy Use are analyzed next, starting with a general overview of the renovations data in Section 4 and then proceeding to an examination in Section 5 of policy issues in the context of survey findings related to building types, building occupancy (owner-occupied vs. tenants), and income levels. Section 6 concludes.

2. THE ECONOMICS OF ENERGY-SAVING RENOVATIONS Older residences, unless they have been retrofitted with newer technologies, tend to be less energy efficient than those which have been constructed more recently.1 Since these older dwellings make up the bulk of 1

The relationship between building age and energy inefficiency is not necessarily monotonic as the extent to which energy-saving technologies are adopted in new dwellings may depend on factors such as the electricity price at the time of construction. See, for example, Costa and Kahn (2011).

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the housing stock in most areas, they provide a prime target for policies aimed at reducing energy use in the residential sector. The design of effective policies requires an understanding of when and why the owners and/or occupants decide to renovate. Renovations in the residential sector may be undertaken for a variety of reasons that can range from aesthetics (as is the case with many kitchen, bathroom, or basement renovations) to regular (or irregular) upkeep maintenance to ensure that the building’s shell and equipment are in satisfactory working order. Renovations related to aesthetics are often undertaken around the time at which there is a change in occupancy. Landlords may make improvements in order to attract tenants. Owners who are planning to sell may undertake renovations that improve the attractiveness of the property to potential buyers. Those in the market for a new house may look for a dwelling that provides good redesign potential for work to be undertaken when they move in. Maintenance on a building’s structural components (roof, windows, foundation, and walls) or heating and cooling technology may be made on a regular basis or as major repairs are needed. A desire for enhanced safety and comfort can also be contributing factors to the decision to renovate a dwelling (Martinaitis et al, 2007). Although households may undertake renovations that will improve energy-efficiency with energy costs or environmental concerns as their primary motivational factors, energy-saving retrofits are often undertaken in conjunction with needed repairs or with the redesign of portions of the dwelling’s living space (Harvey, 2009; Jakob, 2006). When energy-saving retrofits can be performed in conjunction with other dwelling renovations, the resulting economies of scale and scope can serve to reduce the associated monetary and time costs. For example, upgrades to a dwelling’s insulation that coincide with roof or floor repairs cause less disruption than in the case where the repairs and insulation work are performed at different times. If undertaken in isolation from other work, changes to insulation would entail both the purchase of the new insulating materials and the labor costs associated with the work needed to access the attic, wall or floor areas where the materials are to be installed. If undertaken along with planned roof repair or living space redesign activities, the bulk of the costs associated with the energy-saving portion of the renovation work would be primarily due to the purchase of insulating materials. In the first scenario, the costs associated with the energy savings will be substantial compared to the second scenario where energysaving renovations are timed so as to overlap with other work in order to minimize the overall costs.

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The timing of and extent to which energy-saving renovations will be undertaken, either on their own or in conjunction with other renovations or repairs, will vary with a variety of factors. High income households will have better access to credit and are more likely to have available funds to cover the upfront costs of renovations. Moreover, the likely larger energy intensity (energy used per unit of space or per household) of higher income households may make a renovation more profitable for these households as larger gains in terms of reduced energy consumption can be expected. On the other hand, since energy expenses form a smaller portion of the overall budget for high income households, they may not consider energy cost savings as a strong motivational factor. For lower income households, renovations may be encouraged by policy action in order, for example, to reduce fuel poverty, where a reduction in the heating costs may be used to increase thermal comfort. Whether or not a dwelling is occupied by the owner will affect accessibility to the building. While an owner-occupant may be willing to temporarily adjust to inconveniences such as reduced living space or the presence of trades-people during prolonged renovations, knowing that they will soon benefit from the resulting improvements, a tenant may be more likely to opt for a change of residence, leading to a loss of rental income for the landlord / owner. Split incentive complications also arise when a dwelling’s occupant is not the one who is responsible for paying for heat and/or electricity use in the dwelling. A landlord who pays for heating or cooling, and therefore benefits from improvements that lower energy costs, is expected to be more likely to pay the upfront costs of providing good insulation and energy-efficient equipment than one who relegates the responsibility for heating bills to individual tenants. Rental market conditions may also affect the renovation decision of landlords, as landlords may be more inclined to undertake retrofit activities, thus increasing the attractiveness of their rental properties, in case of high vacancy rates. Inversely, in a market characterized by a short supply of rental dwellings, tenants may be hesitant to relocate in case of poor thermal comfort, resulting in landlords not being induced to undertake costly retrofits (Maruejols and Young, 2010). A different set of complications can arise in the context of owned condominiums and other multi-family dwellings where the management of a property involves several agents. In most cases, the approval of a home/condominium owner association will be a necessary pre-condition for modifications that will affect the structure of the building, such as work to the exterior surface, replacement of a roof or the installation of a new heating

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system. The preferences of the various owners can be diverse and all may not support the same energy saving options. Although the details of the calculations will vary depending on whether or not there are complications that arise due to split incentives or to a joint undertaking of other repair or redesign projects along with the energy efficiency improvements, there are a common set of considerations that are assumed to influence all decisions regarding the selection of energy-efficiency options when renovations are undertaken. These relate primarily to the upfront purchase and installation costs and expected changes in purchased energy costs. As an example, consider an older building whose insulation is poor compared to the current standards for newer buildings. The amount of energy needed to heat / cool the dwelling can be reduced by upgrading the insulation to current standards. A standard tool for the evaluation of the energy-saving (portion of the) renovation(s) would be to use a life-cycle cost (LCC) approach. The life-cycle costs associated with a given energy-saving renovation option consist of the initial upfront costs of making the renovation (that is the purchase and installation costs) plus the present value of future energy costs associated with the option over its expected lifetime. It is assumed that the option with the lowest LCC will be chosen. For example, three insulation options for a poorly insulated dwelling may be under consideration:   

retain the current insulation (do nothing); improve insulation to meet current minimum building code standards; improve insulation as much as possible, surpassing current building code standards.

Assuming the same expected lifetime for each option2, each of these will have a corresponding LCC of the form

That is, each of options a, b, and c entails upfront purchase and installation costs as well as the resulting stream of heating and cooling costs 2

Complications arise if different lifetimes are associated with the options under consideration. In such cases, a common timeline for the operating costs should be selected, with additional replacement installation / purchase costs added into the formula at appropriate intervals for shorter-lived options.

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associated with maintaining thermal comfort over the useful lifetime of the renovation. In the scenarios considered in this example, option a will have zero initial installation and purchase costs, as this insulation is already in place. The operating costs, which will accrue over the current period and the next T years and are therefore discounted based on the household’s rate of discount (r), are those associated with providing thermal comfort given the current conditions of the dwelling. These operating costs will depend on the particular heating and cooling equipment that is in place, the expected future prices of the energy source(s) used by this equipment, and the temperatures that are set by the household at various times of the day which will in turn be affected by many additional factors such as local weather conditions and household characteristics such as whether or not somebody is at home during the day. Given all of these factors, even for the simplest do nothing option, LCCa may vary substantially across households, especially in a geographically large country with widely varying climatic conditions and energy markets that vary across regions. Finally, depending on whether or not the agent deciding on which renovation option to choose is also the agent who pays for heating and cooling, the second component of the LCC may or may not be as pertinent to the renovation decision. In a situation where the owner makes the renovation decision but the occupant pays for heating and cooling, as is a common practice in many rental markets, the owner will not place much weight on the operating cost term, except to the extent that poor insulation may affect the landlord’s ability to find stable tenants and/or the market rent. The situation becomes even more complicated for options b and c. Each of these options involves the purchase and installation of the insulation materials, with these costs being higher for the better quality insulation used in option c. The operating costs, however, are expected to be lower for option c with its higher quality insulation than for option b. And both of these will be lower than for option a. In practice, operating costs are commonly estimated based on the assumption that the occupant will select the same levels of thermal comfort before and after renovations. Such an assumption will generally lead to an underestimate of operating costs in comparison to the do nothing option due to rebound effects. That is, as it becomes less expensive to heat or cool a dwelling, the occupants may use some of these energy cost savings to purchase additional thermal comfort (increasing dwellings temperature and/or heating the dwellings for longer periods). These effects have been shown to exist but to remain limited (Bell and Lowe, 2000; Martinaitis et al, 2007; Sorrell, 2009). The remaining caveats regarding operating costs discussed above in the context of option a also apply to options b and c. LCC and other

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approaches to evaluating the costs of installing new technologies are summarized in Ryan and Young (2009). While the purchase cost of the insulation, aside from regional price variations, will be similar for a given dwelling size and configuration, the installation costs will depend on the particular circumstances faced by the agent making the renovation decision. Household wealth and the cost of credit may determine whether or not an owner-occupant is able to afford the upfront costs of making improvements to insulation. If the timing of the insulation decision coincides with structural repairs or maintenance, the labor costs associated with accessing the areas to be insulated will be shared with those associated with the insulation renovation. Therefore only a portion of the installation costs will appear in the LCC related to the energy-saving renovation, as in the case for the cost estimations done by Amstalden et al (2007). If the accommodation is a rental dwelling, costs associated with compensating the tenant for the inconvenience or foregone rent if renovations are made while the dwelling is unoccupied will need to be added to the first component of the corresponding LCC calculation. The simple insulation example illustrates many of the complications involved in even a seemingly simple decision regarding whether or not to upgrade the insulation in an older dwelling. Although the same basic decision is made for each dwelling (select the option with the lowest associated LCC), the results of this decision may vary greatly depending on the particular circumstances. As a result rates of renovation may vary greatly across income groups and across owner-occupied and rental accommodations (which are often multi-family dwellings such as duplexes or apartment buildings). Further complications arise when other realistic scenarios are considered. Instead of undertaking a single energy-saving renovation, agents often consider packages of retrofits where changes may be made to insulation levels, windows, heating and cooling equipment, and other aspects of a dwelling. As the situation becomes more complex, other assessment methods become necessary. Examples of more complex appraisal methods that take into consideration a variety of factors that may include both energy efficiency criteria and general living space improvements can be found in Martinaitis et al (2007) and Verbeeck and Hens (2005).

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3. EFFECTIVENESS OF ENERGY SAVING RENOVATIONS While new housing should certainly be constructed according to high standards of energy efficiency, the renewal rate of the housing stock is so low in most countries that energy efficiency gains from newer construction methods and materials will not impact the sector significantly before several decades (Amstalden et al, 2007). Given that the bulk of the housing stock in the foreseeable future will continue to consist primarily of buildings that have already been erected, efforts to improve the energy efficiency of the residential sector need to target existing buildings. The most drastic approach to increasing the energy efficiency of the stock of older and relatively inefficient buildings in the residential sector would be a strategy of demolition and reconstruction. This would lead to lower energy requirements for the occupants than even the most thorough set of renovations, as house and window orientations can be altered along with the installation of a better thermal envelope and highly-efficient heating and cooling technologies. Such a strategy, however, is neither feasible on a large scale nor would it be necessarily beneficial given the energy requirements and environmental impacts associated with the production and transportation of new building materials and the disposal of old materials (Power, 2008). The most common approach to increasing energy efficiency in the existing housing stock has been, and will continue to be, one of renovating or retrofitting older buildings. Although new energy-saving devices, such as energy efficient light bulbs or programmable thermostats, have been promoted as a solution for improving the energy efficiency of the residential and commercial sector, Harvey (2009) points out that improvements to building structures and HVAC systems are able to yield larger energy use gains than improvements at the device level. In terms of the expected gains that can be achieved by any particular individual renovation or combination of energy conservation measures, quantitative information that would apply equally to all buildings in general is impossible to derive. It is generally found that improvements to a building’s thermal envelope and HVAC systems can significantly reduce the energy requirements of a dwelling. The extent of the benefits, however, varies with other building characteristics which can affect the performance of upgraded technologies. The structure of the building, the technology present before the renovation, the climate, and the type of fuel used for domestic purposes, are all elements that affect how much energy savings a given new technology can achieve. Generally, the poorer the condition of the dwelling before renovation the larger the potential for energy savings, as is the case with older houses

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without insulation (Anastaselos et al, 2009). The incremental energy savings from the renovation of newer, more efficient, dwellings is smaller. The extent of the renovation work and the choice of new technologies also affect the new energy performance. From a strictly technical point of view, a wide range of energy savings can be realized with currently available technologies: from a small, one or two figure, percentage reduction in energy requirements to the achievement of a net zero house that relies on an improved thermal envelope and renewable energy produced on-site to meet all of its energy use requirements.3 As outlined in Section 2, economic and financial considerations will determine the feasibility of various renovation options and hence the energy efficiency gains that are realistically achievable. When selecting a type of insulation or technology to replace existing elements, it is found that the most effective options, those that generate the greatest reductions in energy use, are often the most expensive options. Comparing the heating energy savings with the costs of installing new insulation, Galvin (2010) analyzes the monetary investment required per unit of energy saved for various degrees of energy efficiency gains. He finds that renovating to a higher standard saves more energy for the renovated dwelling than renovating to a lower standard, but that the cost per unit of energy saved is higher. As the effectiveness of a retrofit option increases, the cost per energy saved increases at a faster rate. Renovating to high standards is thus affordable by only a few households. For other households, the chosen energy-use effectiveness of the new installations is determined by their available budget. Most building owners in the residential sector face budget constraints that limit their ability to undertake complete packages of energy-saving renovations at once. As a result, they must choose the order in which to make investments in the building envelope, heating / cooling technologies, and other possible options such as the installation of solar panels. If it is not feasible to simultaneously improve a dwelling’s insulation, upgrade to double or triple glazed windows, and install a high-efficiency furnace in a dwelling, which should be undertaken first in order to maximize the resulting energy savings and environmental benefits? Questions such as this have been addressed in a variety of economic and engineering evaluations and case studies. The general consensus indicates that in locations where heating is an important consideration, a building’s thermal 3

See, for example, http://solaralberta.ca/video/approaching-net-zero-energy-peter-amerongen (last accessed Feb. 24, 2011).

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envelope should take priority since it can play a major role in reducing the demand for the use of a building’s heating technology. This thermal envelope consists primarly of insulation (of the roof, walls and other building components), windows and doors, and exchange of inside and outside air (Harvey, 2009). Jakob (2006) reports that improved insulation, by reducing the temperature differential between a room’s outside walls and its interior living space, increases thermal comfort. This in turn allows residents to use lower thermostat settings without sacrificing comfort. Additional studies that examine energy efficiency options in a variety of European countries and emphasize the importance of insulation include Amstalden et al (2006), Balaras et al (2007), for example. Verbeeck and Hens (2005) use a simulation model for five reference buildings in conjunction with net present value (NPV) calculations (similar to the LCC calculations discussed in Section 2) to analyze the energy consumption, CO2 emissions, and financial implications of a variety of packages of energy saving measures. In the Belgian context, they recommend that insulation upgrades be undertaken before the replacement of inefficient heating systems. Their rankings of potential energy-saving improvements in order of decreasing priority are: roof insulation, floor insulation (if accessible), glazed windows, efficient heating system, and the installation of renewable energy systems. Similar rankings are provided by Balaras et al (2007) for Greece. Bell and Lowe (2000) point out that contractors in Britain who were accustomed to installing furnaces in poorly insulated buildings tended to recommend oversized models compared to what is needed to provided satisfactory thermal comfort in a well-insulated dwelling. This suggests that if a furnace is upgraded before a building’s thermal envelope, larger and more expensive furnace models than will ultimately be needed may be installed, leading to higher overall costs and heating overcapacity. Overall, studies that have examined the feasibility of various renovations have found that a number of options are economically and environmentally sound. Table 1 provides an overview of the energy gains associated with specific renovation measures from a sample of case studies found in the existing literature. Results are difficult to generalize as each performance is closely linked with local conditions and the choice of new technology. Another consideration that can affect the effectiveness of a specific renovation, such as upgrading the current heating system, relates to the ease of use of the technology. A set of demonstration case studies of rental units in England found that users found some technologies more difficult to use than

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their previous systems and that certain design features should be avoided if energy savings are desired. Table 1. Examples of Energy Savings from Renovations Study Harvey (2009)

Context Existing buildings

Type of Renovation Comprehensive renovation.

Holton (2002), Gamble et al. (2004) and Rudd et al. (2004); in Harvey (2009) Francisco et al. (1998); in Harvey (2009) Viriden et al. (2003); in Harvey (2009)

Wide variety of US climates

Modest insulation and window improvements.

US houses

Sealing of ductwork.

Old apartment building in Zurich, Switzerland. Apartment block in Switzerland Multi-unit housing in Germany 4003 homes in Louisiana

Comprehensive retrofit, including replacement of roof.

Humm (2000); in Harvey (2009)

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Harvey (2009)

Hughes and Schonder (1998); in Harvey (2009)

Bell and Lowe (2000)

UK low rise housing

Power (2008)

German homes and rented apartments, mostly built before 1978.

Power (2008)

British tenement flats

Envelope upgrade.

Among other measures, external insulation and finishing systems. Retrofits including switch from natural gas to a ground source heat pump for space and water heating. Insulation and ventilation improvements, heating system replacement. High quality insulation including external and internal cladding, efficient heating and energy systems, solar collector for hot water, heat recovery mechanisms and, where possible, the addition of south-facing balconies. Insulation to roofs, external walls, windows, replacement of central heating and hot water

Energy Savings Reductions in the energy intensity by factors of 2 to 3 relative to current local practice. Energy savings of 30 75 %.

15 - 20 % of annual heating and air conditioning energy use. 88 % savings in heating energy use.

Reduce heating requirement by almost factor of 3. Reduction in heating energy use by a factor of 8. Eliminate natural gas use; reduce electricity use by 1/3.

35 % to 70 % reduction of heating energy use. 80 % of energy consumption.

60 % reduction in energy use.

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Renovation Activities ... Study

Context

Power (2008)

British individual houses Swiss residential building stock Hellenic building stock

Amstalden et al (2007) Balaras et al. (2007),

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Anastaselos et al. (2009)

Typical threestorey apartment building in Greece

Type of Renovation systems, draft sealing of doors. Basic insulation measures.

13 Energy Savings

60 % reduction in energy use.

Various packages of floor, façade and roof insulation.

40 - 65 % reduction in heat demand.

Insulation of exterior wall. Weather proofing of openings. Installation of doubled glazed windows. Solar collector for sanitary hot water. External thermal insulation composite systems (ETICS).

33-60 % energy savings. 16-21 % energy savings. 14-20 % energy savings. 50-80 % energy savings. 17.21 % less total energy consumption than typical insulation practice (case of new construction).

In fact, in one case, even though temperature settings were the same as those used previously, one occupant made more use than expected of a secondary decorative heating source and less use than expected of the highefficiency main heating source. This led to energy use that exceeded prerenovation engineering-based predictions by about 40 % (Bell and Lowe, 2000). To the extent that there is a higher changeover rate of occupants, such problems are of more concern in rental accommodations than in owned dwellings as each new tenant must become familiarized with the relative efficiencies of multiple heating sources and the operation of complex control systems in order to maximize the energy saving benefits accruing from the upgrade. Finally, as mentioned above in Section 2, the anticipated or potential energy-saving benefits from renovations to a dwelling’s thermal envelope and heating / cooling systems may be partially offset by the fact that thermal comfort becomes more affordable once these changes are made. As the energy costs associated with keeping the dwelling at a given temperature fall, residents may decide to set higher (lower) temperatures during the heating (cooling) season. These rebound effects can lead to energy savings that are less than those predicted by engineering estimates. Bell and Lowe (2000), although they note several instances where energy savings were not as large as had been anticipated, find little evidence of changes in temperature-setting

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Lucie Maruejols and Denise Young

behavior among the occupants of the renovated dwellings in their sample of case studies. This is in contrast to results reported in Sorrell (2009) of temperature take-backs of 0.4⁰C to 0.8⁰C and moderate (less than 30%) rebound effects. Martinaitis et al (2007) mention findings from previous studies where rebound effects are found to be between 10 % and 30 % for space heating.

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4. RESIDENTIAL RENOVATIONS IN CANADA: AN OVERVIEW OF GENERAL PATTERNS The two most recent editions of the Survey of Household Energy Use (SHEU), conducted by Statistics Canada on behalf of Natural Resources Canada for 2003 and 2007, offer a view of the renovation practices of Canadian households (Office of Energy Efficiency, 2006 and 2010).4 The SHEU-2003 survey, which was conducted via computer-assisted personal inhome interviews, collected information on energy-reducing renovations undertaken in 2003 as well as those planned for 2004. The sampling design of this survey was constructed in order to provide a random sample representative of all Canadian owned and rented household dwellings, with the exception of high-rise apartments (more than 4 storeys). For rental dwellings, landlords were contacted in order to obtain information that could not be provided by the tenants. As a result, renovation data along with information on other building characteristics and socio-economic household characteristics are available for a variety of owned and rented dwelling types. In particular, single detached (SD) houses, low-rise apartments (LRAs) and other types of multi-family dwellings, namely double, duplex, row and terrace houses (DDRTs), are included in the survey. The SHEU-2007 survey data are more comprehensive along some dimensions, and less along others. For the 2007 survey, data collection was expanded to include both energy-saving retrofits undertaken recently and those undertaken as long ago as 20 or more years. While the earlier SHEU-2003 provides us with a one-year snapshot of actual and planned renovations, the subsequent survey provides an opportunity to look at the evolution of renovation practices over the past two decades, allowing for a better 4

Additional data on household renovations in Canada are available from the Energuide for Houses database. These data, however, only include information from program participants. The SHEU data sampling design provides a random sample covering a broader range of households and dwelling types.

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appreciation of the extent of residential renovation activities. However, the SHEU-2007 survey was conducted via mail-in paper questionnaires, where energy-related renovation questions were answered by occupants who owned their dwellings and landlords of rental properties. Landlord response rates, however, were especially low. As a result, much of the data collected pertaining to rental dwellings are not deemed to be reliable. Therefore only renovation data corresponding to SD houses and DDRT styles dwellings that were owner-occupied in 2007 are publicly available. Apartments, primarily occupied by tenants, and other types of rental properties are thus excluded from the longer term overview of Canadian residential renovation activity available from SHEU-2007. For SD and DDRT owner-occupied residences, there are some minor differences in terms of categories of renovations covered in the two surveys. While the SHEU-2003 survey asks detailed questions about programmable thermostats (PTs) and window types, they are not treated as renovation categories, and therefore no information regarding planned changes for 2004 was gathered. In SHEU-2007, however, these are treated as renovation categories, with information requested regarding the year(s) in which PTs and new windows were installed. In addition, the 2007 survey added insulation around hot water pipes and improvements to exterior doors as renovation categories. In the analysis below, we focus primarily on renovation rates.5 That is, we examine the proportions of dwellings that have undergone particular types of renovations relative to the total number of dwellings in the category. It should be noted that only renovations undertaken by households in their current dwellings are generally reported in the surveys, and not those undertaken by a previous owner before a change in occupancy. Thus, reported renovation rates are likely to be underreported than the actual proportions of the housing stock that have been renovated. This will be more important for the 2007 data that provide a retrospective view of renovations than for the 2003 data that only look at one-year snapshots of current and planned renovations.

5

All statistics reported in our tables and figures make use of the sample weights provided in the surveys.

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SHEU-2003 Snapshot The statistics presented in Table 2 indicate that, overall, about 15 % of the Canadian households covered in the SHEU-2003 survey undertook at least one energy-related dwelling improvement in 2003. A similar percentage planned to do so in 2004. Larger households were slightly more likely to renovate than smaller ones, with the highest rates associated with moderately-sized (3 member) households. Consistent with expectations, owner-occupied residences, where there are no agency problems, had higher actual and planned renovation rates. Higher income households, who are more likely to be able to afford extensive renovations and have better access to credit, had the highest renovation rates across income categories.6 Single-detached homes and, somewhat surprisingly, low-rise apartments are more likely to have been renovated in 2003 than other dwellings. In terms of planned renovations, the rates were highest for single family dwellings and lowest for all types of multifamily dwellings. As discussed in Section 2, when households contemplate dwelling renovations, they may find it optimal to make several changes at once in order to minimize costs and disturbance and/or to maximize energy efficiency gains. From SHEU-2003, the average numbers of renovations per dwelling for households who undertook (planned) at least one energy-improving retrofit in 2003 (2004) are between 1 and 2.7 This may be seen as relatively low compared to the expectation that households would tend to integrate energy renovations as part of larger scale modifications to their residences. However, it is important to consider the fact that large scale renovations are expensive and only undertaken occasionally. It should also be kept in mind that energyrelated retrofits are often performed in conjunction with general renovations that may include such aspects as painting, plumbing or general remodeling that are not recorded in the survey. So, even when a household reports only one energy-related renovation, it could have been undertaken as part of a wider package of home improvements. There are many additional interesting aspects of renovation behavior not reported in Table 2. For example, there is evidence that dwellings that would be expected to benefit most from renovation activity are those most likely to 6

7

Even if households take advantage of government subsidies available for energy-related renovations, in many programs the household is responsible for the initial cash outlay, with rebates paid after retrofits are completed The average number of renovations across all households, including those who undertook (planned) no energy-improving retrofits in 2003 (2004) is slightly less than 0.4.

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be retrofitted. That is, those living in less energy efficient dwellings were more likely to renovate. On average, households who renovated in 2003 displayed a 6% higher energy intensity (amount of energy used per square foot of dwelling area) over the year than their counterparts who did not, while those who planned to renovate in 2004 had energy intensities that were 13% higher than their counterparts with no renovation plans. Renovated dwellings were slightly (2 to 4%) smaller than those of the non-renovating households. The number of years spent in a dwelling does not appear to be related to the renovation decision. Renovating households moved into their dwellings in 1992 on average, and non-renovating households in 1991. Renovation rates differ according to the location of the dwelling, with 19% of rural dwellings undergoing at least one renovation in 2003, against only 15% for urban dwellings. Table 2. Planned and Actual Renovation Rates: 2003 and 2004 (SHEU-2003)

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Renovation rates and scale of renovations (SHEU 2003)

Total By household size 1 member household 2 members household 3 members household 4 members household 5 or more members household By dwelling type Single Detached Double, Duplex, Row, Terrace Low-Rise Apartments By occupancy Rental Owner-occupied By income group > $20,000 $20,000 to $40,000 $40,000 to $60,000 $60,000 to $80,000 $80,000 to $100,000 > $100,000

One or more improvement s made to dwelling in 2003 15%

One or more improvements for dwelling planned for 2004 16%

Total number of improvement s in 2003* 1.40

Total number of planned improvement s for 2004* 1.27

15% 14% 20% 16% 14%

14% 15% 18% 19% 17%

1.49 1.37 1.46 1.33 1.34

1.41 1.27 1.27 1.17 1.19

16% 14%

18% 14%

1.40 1.37

1.25 1.34

16%

13%

1.44

1.25

14% 16%

14% 17%

1.39 1.41

1.26 1.27

12% 16% 17% 16% 19% 15%

13% 14% 17% 19% 21% 20%

1.30 1.49 1.33 1.25 1.39 1.49

1.31 1.27 1.30 1.22 1.13 1.27

*Values presented are based on the subset of households who undertook (planned) at least one renovation for the corresponding year.

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In general, renovations to relatively less energy-efficient older buildings will have a greater impact on energy use than those undertaken in newer buildings, which typically already incorporate energy-efficient construction materials and technologies. In Figure 1, we present a breakdown of renovations by building type and age. Actual renovation rates are highest for the oldest cohort of buildings, and tend to fall along with building age. For the oldest cohort of buildings, that were over 50 years old at the time of the survey, more than 25% of the DDRT dwellings underwent at least one renovation, followed by 23% of SD residences, and under 20% of LRA units. For almost all other age cohorts, renovation rates are highest for single family dwellings and low rise apartments and lowest for other types of multi-family housing.

Figure 1. Renovated Dwellings by Dwelling Type and Age Cohort (SHEU-2003).

SHEU-2007 Retrospective An overview of energy-related household renovations over a longer time horizon, albeit restricted to owned SD and DDRT dwellings, is available from the 2007 SHEU data. Although (as seen in the discussion of the SHEU-2003 data above) only a small percentage of homes are renovated in any given year, over the course of time, a high percentage of owner-occupied dwellings are eventually renovated. While the 2003 snapshot indicates that less than a quarter of dwellings built before 1970 were renovated in 2003, we see from Figure 2 that approximately 4 out of 5 owner-occupied SD and DDRT

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dwellings of this vintage have been renovated at some point8. Renovation rates are almost as high for dwellings constructed in the 1970s and 1980s. Even among newer buildings, renovation rates remain quite high, dropping to about 50% for those built in the 1990s and 40% for the most recently constructed buildings.

Figure 2. Renovated SD and DDRT Dwellings by Age Cohort (SHEU-2007).

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Types of Renovations There are several types of energy-related improvements that can be selected by dwelling occupants and owners. Since the set of SHEU-2007 renovation categories is broader than that of SHEU-2003, we focus on the results from the 2007 edition of the survey. For the combined set of owned SD and DDRT style dwellings, we see from Table 3 that the most commonly undertaken renovation has been the replacement of windows, with an overall renovation rate of more than 40% of dwellings. Improvements to the roof structure or surface, heating equipment, caulking or weatherstripping, and the installation of a programmable thermostat (PTs) have each been implemented by about 33% of households living in these types of dwellings. These renovations are likely especially popular due to the fact that they constitute a necessary aspect of general maintenance (roof structures or surfaces and HVAC equipment must be replaced periodically) and/or are inexpensive and easily installed by the occupants (caulking, PTs9). Changes or improvement to exterior doors have been made by approximately a quarter of the households. 8

Due to peculiarities of the reporting of the ranges of construction dates in the two surveys, it is not possible to match pre-1970 age cohorts across the two editions of the survey. 9 Although the installation of PTs is quite common, their effectiveness in terms of saving energy is debatable as those who install and program them are often the same households who would otherwise manually adjust their thermostats (Ryan and Cherniwchan, 2007).

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Surprisingly, improvements to individual types of insulation are among the less popular activities, undertaken in 11% to 21% of all dwellings over the years. Given the costs associated with obtaining access to specific components of a dwelling that would benefit from improved insulation, this is not surprising. However, looking at whether households have undertaken any renovation work involving insulation of the building structure, we find that 36% of households have implemented at least one major insulation upgrade over the years. This rate makes general insulation the second most popular activity after the replacement of windows. Renovation rates of a dwelling foundation are especially low, likely due to the difficulty and costs associated with accessing this part of a building. Foundation repairs impact energy efficiency to the extent that reduction of moisture is often necessary to insure that basement insulation is effective10, and are especially prevalent in certain types of households, as described in Section 5. Table 3. Renovation Rates by Type and Building Age Cohort (SHEU-2007)

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Renovation Category Roof structure or surface Heating equipment Caulking or weatherstripping Insulation of basement or crawl space Exterior wall siding Insulation around hot water pipes Insulation of roof or attic Insulation of exterior wall Exterior door Ventilation or AC equipment

10

All Owned SD and DDRT

Building construction period* Before 1946

1946 to 1969

1970 to 1979

1980 to 1989

1990 to 1999

2000 to 2007

32%

51%

41%

36%

34%

11%

n.a.

36%

52%

47%

43%

32%

18%

8%

33%

51%

41%

38%

30%

17%

10%

18%

25%

25%

18%

13%

10%

9%

13%

30%

18%

16%

5%

3%

n.a.

17%

26%

21%

17%

15%

8%

10%

21%

39%

33%

24%

10%

4%

n.a.

11%

33%

14%

10%

5%

n.a.

n.a.

24%

34%

34%

30%

21%

7%

n.a.

19%

22%

24%

20%

21%

13%

11%

See, for example, http://www.weatherization.org/floorandfoundationinsulation.htm (last accessed Feb. 25, 2011).

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Foundation

5%

n.a.

4%

2%

n.a.

n.a.

n.a.

Window Installed a programmable thermostat (PT) Any insulation**

43%

61%

59%

55%

43%

12%

n.a.

34%

29%

37%

37%

37%

31%

30%

36%

58%

51%

42%

24%

14%

n.a.

*

n.a indicates cases where data are not available or not deemed to be reliable by Statistics Canada. ** Improvements made to at least one of: exterior wall siding, or insulation of basement, roof, attic or exterior walls.

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Figure 3. Vintage Distribution of Renovations in SD Dwellings (SHEU-2007).

Figure 3 depicts the timing of various renovation types for owneroccupied SD dwellings according to four renovation vintages.11 Most renovations undertaken in these dwellings are clearly recent. Depending on the category, from 40% to 76% of renovations have been undertaken within the past 10 years (sum of the percentages for the first two vintage categories), with most of these having been performed within 5 years of the survey date. In general, this bodes well for energy use related to the housing stock, as the technologies put into place should be relatively energy efficient. The situation varies depending on the renovation category, however, with some types of renovations being older on average than other. Notably, about one quarter of the renovations involving insulation of the roof or attic dates back to more than 20 years prior to the survey. Between 15% to 18% of changes made to (i) the insulation of the basement, crawl space; (ii) the insulation of exterior walls; and (iii) renovations made to exterior wall siding also date back at least 20 years. Renovation categories with large proportions dating back to more 11

Most SHEU-2007 data breakdowns by renovation vintage for DDRT dwellings are deemed to be unreliable by Statistics Canada and are therefore not reported.

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Lucie Maruejols and Denise Young

than two decades ago correspond primarily to upgrades of the thermal envelope of the buildings, the most crucial improvements from an energy efficiency perspective. The data presented in Figure 3 include renovations made to SD dwellings from all age cohorts. They are thus likely to be biased toward more recent renovations as improvements made to newer buildings could only have been completed recently. Of particular interest in terms of energy efficiency gains are relatively recent renovations made to relatively older buildings whose initial construction materials and installed technologies could be highly inefficient in comparison to those used in newer dwellings. Breakdowns of renovations in owner-occupied SD and DDRT dwellings by building age are provided in Table 3 and by date of renovation in Figures 4a and 4b. From Table 3 we see that, for most types of renovations, the oldest cohorts of buildings clearly have the highest renovation rates. More than half of dwellings in the oldest cohort, made up of dwellings which were at least 60 years old at the time of the survey, have had windows, roofs, and/or heating equipment replaced by the current owner. And nearly 60% have undergone at least one type of insulation improvement to their thermal envelope. In terms of specific areas that have been retrofitted with improved insulation though, the renovation rates are much lower, with rates ranging from a low of only 25% for basements / crawl spaces to a high of about 40% for roofs and attics. The higher rate of roof and attic insulation is not unexpected, given that over one half of dwellings in this age cohort had structural or surface roof repairs. For the next oldest cohort, that includes many buildings that were over 40 (or 50) years old at the time of the survey, renovation rates remain high in many categories. However there are noticeable drops, especially in roof renovations and in the insulation of exterior walls. In Figures 4.a and 4.b, which examine the subset of dwellings to which energy-saving improvements were made, information is provided on when renovations were made in the two oldest cohorts of dwellings. For example, while almost 80% of roof structure repairs for the oldest cohort were undertaken within the past 10 years, over one half of all roof insulation upgrades were undertaken before that, with an approximate even split between upgrades undertaken in the past 11 to 20 years and those undertaken more than 20 years ago. At the exception of renovations related to roof structure, windows, heating equipment, caulking, or water pipe insulation, over 20% of all other renovations date back to at least 20 years prior to the survey. Only about half of insulation improvements, of particular importance for energy efficiency, were undertaken within the 10 year period preceding the survey.

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Patterns are similar for the next oldest cohort (Figure 4.b) in cases where reliable data are available for both cohorts for a given renovation category. A snapshot of renovations made in 2003 from the SHEU-2003 survey is provided in Figure 5 and indicates generally similar patterns to those found in SHEU-2007. Dwellings built before 1970 have higher 2003 renovation rates than those in the next oldest cohort, except for roof structure and surface renovation rates (which are both around 7%), and window replacements (where the renovation rates were higher in newer buildings). a)

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b)

Note: renovation types not depicted correspond to data deemed to be unreliable by Statistics Canada. Figure 4.a: Vintage Distribution of Renovations for Dwellings Built before 1946 (SHEU-2007). b: Vintage Distribution of Renovations for Dwellings Built between 1946 and 1969 (SHEU-2007).

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Figure 5. Comparison of Renovation Rates by Vintage (SHEU-2003).

Despite the low proportion of the housing stock undergoing renovations in a given year, as illustrated by the SHEU-2003 data, the SHEU-2007 results show that over the years most dwellings have undergone at least one renovation. A high percentage of older dwellings, that is those with the greatest potential for improvement over their original features, have been renovated at some point. However, a non-negligible proportion of these renovations took place some time ago. As a result, the equipment and technologies used in these renovations may not be particularly efficient compared to current standards. Hence potential energy efficiency gains are likely attainable through renovation of currently non-renovated older dwellings and through upgrades of renovations completed decades ago. That is many older dwellings continue to offer the potential for continued energy efficiency gains in the Canadian residential sector. Of particular interest for the design of policy is the distribution of renovations across building types as well as the relationship between renovation rates and socioeconomic factors such as tenancy arrangements and household income. These aspects are addressed in Section 5.

5. OPPORTUNITIES FOR POLICY ACTION While energy efficiency gains, and financial benefits from reduced energy consumption, can be achieved through renovations, the significant upfront costs of many renovation projects can provide a disincentive to owners and residents. A variety of incentive programs have been put in place in many jurisdictions in order to encourage renovations in the residential sector. See, for example, International Energy Agency (2008) for an overview of policy strategies and a number of national and regional case studies. In Canada,

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several programs have been put into place and have contributed to the observed high rates of renovation in the housing sector in recent years. These include the ecoENERGY Retrofit – Homes program (whose predecessor was the Energuide for Houses program)12, the Residential and Rental Residential Rehabilitation Assistance Programs13, the Canada Mortgage and Housing Corporation’s Mortgage Loan Insurance Refund program which is available, among other qualifying situations, on refinanced mortgages for renovations of energy inefficient homes14, and a number of provincial, municipal, and utilitysponsored programs and tax incentives. A searchable Directory of Energy Efficiency and Alternative Energy Programs in Canada is maintained by Natural Resources Canada’s Office of Energy Efficiency.15 The focus of a particular program is often a specific target group or type of renovation, frequently with an aim to deal with barriers or disincentives that limit renovation rates among the targeted group(s). The Home Energy Assistance Toronto (HEAT) program, for example, offers grants to assist in the affordability of insulation upgrades in low-rise residential dwellings (SD, DDRT and LRAs).16 Toronto’s Better Building Partnership provides assistance for a broad spectrum of energy-related renovations made to multifamily dwellings through its Multifamily Energy Efficiency Rebates (MEER) program.17 Other programs, such as the national-level Residential and Rental Residential Rehabilitation Assistance Programs target low-income owners or the accommodations of low-income renters. Some of these programs have short time windows within which renovations must be completed. In order to gauge where policy adjustments might be made in order to address shortfalls in particular renovation rates, we focus here on portions of the SHEU survey data pertaining to cases where barriers and disincentives are likely to play an important role. Namely, we break down the renovation data by: dwelling type, dwelling ownership (owned vs. rented) and income groups. Given the differences in the SHEU data sets that we are examining, especially the lack of renovation data for apartment buildings and renter-occupied 12

http://oee.nrcan.gc.ca/residential/energuide-for-houses.cfm (last accessed January 24, 2011). While these programs are primarily aimed at bringing housing for low income households to minimum health and safety standards, some of the allowable repairs (heating and structural) have implications for energy efficiency. http://www.cmhc-schl.gc.ca/en/co/prfinas/ prfinas_001.cfm and http://www.cmhc-schl.gc.ca/en/co/prfinas/prfinas_008.cfm (last accessed January 24, 2011). 14 http://www.cmhc-schl.gc.ca/en/co/moloin/moloin_008.cfm (last accessed January 28, 2011). 15 http://www.oee.nrcan.gc.ca/corporate/statistics (last accessed January 28, 2011). 16 http://www.toronto.ca/livegreen/greenlife_saveenergy_rebates_heat.htm (last accessed January 28, 2011). 17 http://bbptoronto.ca/get-started-2/multi-family/ (last accessed January 28, 2011). 13

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dwellings in the SHEU-2007 survey, we focus primarily on the SHEU-2003 data when looking at differences across building types and ownership-related issues. The impact of income on renovation activity is assessed based on information from both surveys.18

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Renovation Rates by Dwelling Type Renovations undertaken in multi-family dwellings are likely to benefit a larger number of households than those undertaken in single-detached houses, as one improved building can provide benefits for several households. In multi-family dwellings however, the renovation decision and execution process is more complex, as it can involve several parties (landlords, tenants, building managers, etc.) who may have dissimilar preferences and constraints. These circumstances can slow down the process of deciding on the timing and extent of renovations, possibly preventing beneficial renovations from being undertaken. While, overall, 15% of the residential dwellings covered in SHEU-2003 were renovated over the course of that year, the rate was slightly lower in DDRTs (14%) than in SD houses (16%) and LRAs (16%). (See Table 2, Section 4). Given the coordination problems associated with making renovations in multi-family dwellings (DDRTs and LRAs), these rates are consistent with what might be expected for DDRTs but somewhat surprising for LRAs. Planned renovations for the following year, however, are more in line with expectations as planned rates were basically the same as the previous year’s actual rates for DDRTs, higher for the SDs and much lower for LRAs. In particular, planned renovations were 4 to 5 percentage points higher for SD than for multi-family dwellings. Among those who undertook (planned) renovations in 2003 (2004), the numbers of improvements per dwelling are similar across dwelling types. Renovating DDRT households/owners chose to make 1.37 different types of renovation on average, compared to 1.4 for SDs and 1.44 for LRAs in 2003. Numbers of planned renovations for 2004 were 1.34 on average for DDRTs and 1.25 for both SDs and LRAs. The lower numbers of planned, compared to actual, renovations across all dwelling types may well reflect the fact that in any given year unexpected repairs are needed, and these unexpected repairs may either have direct energy efficiency impacts 18

As in Section 4, all statistics incorporate the sample weights provided in the SHEU survey datasets.

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(such as the case of the replacement of a furnace with a newer more energy efficient model) or indirectly lead to energy efficiency improvements that are undertaken at the same time in order to take advantage of economies of scale or scope. When looking at renovation rates by age cohort (Figure 1, Section 4), there are no discernable patterns in terms of relative actual and planned renovations rates across building types. In the oldest cohort, both planned and actual rates are highest in DDRTs and lowest in LRAs. In the next oldest cohort, actual (planned) renovation rates are highest in LRAs (DDRTs) and lowest in DDRTs (LRAs). The ordering changes once more in the next cohort. The lack of a pattern across building types for actual renovations may be due to the sensitivity of renovation rates to unexpected repairs, especially those related to the building’s structure or HVAC system.

Figure 6. Renovation choices by dwelling type, % of renovated dwellings (SHEU2003).

In terms of types of renovations, Figure 6 depicts results from SHEU-2003 that suggest that twice as many LRAs as DDRTS have undergone renovations to the roof structure or surface and exterior wall siding, possibly due at least in part to differences in design for the two types of dwellings . Upgrades to the heating and ventilation or AC equipment appear to be slightly more frequent in LRAs than in the other types of dwellings. This is likely due to increased usage pressures on these systems in multi-family dwellings, with many individual dwelling units in a given building. In terms of the thermal envelope, the insulation of exterior walls, roof and attic and the replacement of windows were slightly higher in SDs in 2003, while the insulation of basements or crawl spaces was more common among DDRTs.

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From the 2007 data, which exclude LRAs and any rental units, there are also differences evident in the choices of renovation types across different types of dwellings over the years. Contrary to the 2003 findings, renovations involving insulation of basements/crawl spaces are more prevalent in SDs than in DDRTs, likely due to the fact that a SD dwelling is more likely to have a basement or crawl space than a DDRT. Renovation rates for at least one insulation upgrade also tend to be higher for SDs than for DDRTs, at least for dwellings built between 1946 and 198019. Improvements to insulation around water pipes are more popular in DDRTs, as are improvements to ventilation or AC equipment (19% of SD owners have undertaken ventilation or AC system upgrades versus 23 % for DDRTs). Window replacements have been more popular in SDs, as have been improvements to heating equipment. Improvements to the thermal envelope, namely windows and insulation, are thus mostly popular among SD owners and tenants, who seem more likely to renovate at all. This observation could explain the large proportion of the Canadian housing stock that has undergone at least one insulation upgrade, as noticed in Section 4. These renovation options may be more difficult to implement in multi-family dwellings due to the effects of split incentives and agency issues. Given that there are no discernable patterns in general renovation rates across building types, at least for those building types covered in the survey, but possibly important differences in the types of renovations undertaken, program design should take into consideration the promotion of specific types of renovations that are underrepresented for particular building types, such as retrofits of the thermal envelope for multi-family dwellings.

Renovation Rates by Occupancy Status Table 4 shows that, overall, about 75% of the SD, DDRT and LRA housing stock covered in the SHEU-2003 survey is owner-occupied, while about 25% is made up of rental units. The highest rate of ownership is in the SD segment of the market (96%), followed by the DDRT market (61%) and the LRA market (16%). Returning to Table 2 (Section 4), we see that the renovation rate was higher in 2003 in owner-occupied dwellings (16%) than in rental properties (14 %), despite the latter dwellings being on average older than their owner-occupied counterparts. The same is true for planned 19

Data are not deemed to be sufficiently reliable to investigate the patterns for other construction vintages or specific insulation options.

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renovation rates, with a rate of 17% for owner-occupied dwellings and 14% for rental accommodations. The numbers of (planned) renovations per renovated dwelling are very similar across occupancy status, albeit slightly higher for the owner-occupied group. This is the opposite of what might be expected under a scenario whereby landlords would attempt to minimize periods of vacancy by undertaking more than one retrofit at a time. On the other hand, it might be consistent with attempts to disrupt the lives of longterm tenants as little as possible while in the process of performing renovations.

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Figure 7. Renovated dwellings (in %), by occupancy (SHEU-2003).

In Figure 7, actual 2003 and planned 2004 renovation rates for rented and owned dwellings are broken down by building age cohorts. Actual renovation rates are consistently higher for owned dwellings. In terms of planned renovations, the only case for which rates are higher for rental than owneroccupied units is found within the 1990 to 1999 construction date cohort. The same general patterns persist if we examine renovation rates by age cohorts for the three dwelling types separately, with only occasional exceptions (rental LRAs built before 1949 or between 1980 and 1999, and rental SDs built between 1990 and 1999 were renovated more often than their owner-occupied counterparts in 2003).20 The general pattern of older buildings being renovated more often also persists in most cases, regardless of the dwelling type or occupancy status.

20

Renovations rates by dwelling type and occupancy for various construction periods can be obtained from the authors upon request.

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Types of Renovation There were pronounced differences between the types of renovations undertaken in rental and owner-occupied properties in the various types of dwellings covered in the SHEU 2003 survey. Table 4 presents information regarding various renovations by dwelling type for rented vs. owned dwellings. For policy design purposes, it is noteworthy that two of the three insulation category renovations in DDRTs were undertaken primarily in owned units. Results are similar, but not quite as uneven for heating equipment improvements. In particular, none of the attic insulation renovations, only 23% of the exterior wall insulation renovations, and only 28% of the heating equipment renovations that were undertaken in DDRTs were in rental units. This occurred even though 39% of DDRTs were occupied by tenants. Results are similar for two of the three insulation types in LRAs. Although renters constitute over 80% of the market, more than 40% of all wall and basement insulation renovations were undertaken in ‘condo’ buildings with owneroccupants. For SD units, where owner-occupancy predominates, there are only minor differences across ownership status, with splits of specific renovation types across rented vs. owned dwellings closely mirroring the split of the housing market across owned and rented houses.

Basement insulation

Heating equipment

0.05 0.95 0.56 0.44 0.95 0.05 0.33 0.67

0.03 0.97 0.42 0.58 0.97 0.03 0.32 0.68

0.01 0.99 0.00 1.00 0.94 0.06 0.16 0.84

0.05 0.95 0.23 0.77 0.56 0.44 0.14 0.86

0.02 0.98 0.33 0.67 0.52 0.48 0.14 0.86

0.05 0.95 0.28 0.72 0.94 0.06 0.27 0.73

Ventilation or AC equipment

Exterior walls insulation

0.04 0.96 0.45 0.55 0.84 0.16 0.22 0.78

Roof insulation

Total *

0.04 0.96 0.35 0.65 0.87 0.13 0.24 0.76

Exterior wall siding

LRA

0.06 0.94 0.39 0.61 0.84 0.16 0.26 0.74

Roof structure or surface

DDRT

Rental Owned Rental Owned Rental Owned Rental Owned

One or more upgrade planned for 2004

SD

One or more upgrade in 2003

Split of renovations by rental and owner-occupied properties, by dwelling type (SHEU 2003)

All Respondents SHEU03

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Table 4. Distribution of renovations made in 2003, by dwelling type and occupancy (SHEU-2003)

0.00 1.00 0.06 0.94 0.91 0.09 0.23 0.77

Renovations made to the foundation are excluded.

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The results for multi-family dwellings are the most interesting from a policy perspective as they are the least likely to be renovated in the first place, and among those living in multi-family dwellings tenants have a smaller likelihood of benefiting from basic improvements, such as improved insulation. DDRTs (LRAs) constituted about 16% (18%)21 of the Canadian housing market according to the 2006 Canadian census, with nearly 40% (85%) of these dwellings being occupied by tenants, according to the SHEU2003 data. The low rates of renovation for this combination of dwelling type and occupancy status, along with their prevalence in the housing market, make rental multi-family units a target with a high potential for reducing energy use through higher renovation rates.22 The main barriers to be addressed in these instances reside in the tenant-landlord relationship where tenants may not be able and/or allowed to perform renovations themselves, while landlords may not be willing to invest in improvements in their dwellings. This is especially important in cases where tenants pay directly for their energy usage, and landlords do not benefit either from the improved thermal comfort or the reduced energy charges that result from renovations. For further details on the issues involved with improving energy efficiency in rental dwellings see Maruejols and Young (2010)

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Renovation Rates by Income Not surprisingly, according to both the SHEU-2003 and SHEU-2007 data, dwellings that house higher income households are more likely to be renovated than for the lowest income cohort. Higher income households are often those who also undertake somewhat more extensive renovations than their counterparts. Of interest for policy decisions are not only the renovation rates but the types of renovations undertaken. We first examine the SHEU2003 data, based on 6 income cohorts, and then, due to data limitations, look at the 2007 data for three broader income groups.

21

Source: Statistics Canada. cumulative profile, 2006 - Canada, Provinces and Territories (table), 2006 Census of Population, 3 char. postal code (Forward Sortation Areas) (database), Using E-STAT (distributor). http://estat.statcan.gc.ca/cgi-win/cnsmcgi.exe?Lang=EandESTFi=EStat\English\SC_RR-eng.htm(last accessed: February 12, 2011). 22 However, it should be noted that multi-family dwellings often exhibit better enery efficiency performance, measured in terms of energy use per household, than SD dwellings due in part to the presence of shared walls that reduce heating requirements.

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Types of Renovation In the SHEU 2003 data, the two lowest income groups (< $20,000 Cdn per annum and $20,000 to $40,000 Cdn per annum) represent approximately 15% and 25% of households in the types of dwellings covered in the survey, respectively. These households are under-represented in terms of many types of renovations. However, improvements to exterior wall siding, foundations, and to some extent roof structures or surfaces, take place relatively more often among these income cohorts. Although rarely chosen by renovating households overall, 15% of foundation improvements took place among households in the lowest income grouping, and another 42% of foundation repairs were undertaken by households in the next income cohort. Also noteworthy is that 39% of renovations of exterior wall siding and 30% of roof upgrades were undertaken in dwellings occupied by households in this second lowest income grouping. These high rates of foundation and siding renovations among low income households may be indicative of a generally lower state of repair of these dwellings. These types of repairs are pre-requisite to other types of energy-efficiency improvements, as quality moisture barriers are needed to protect insulation. And good insulation is needed for the effectiveness of (high-efficiency) HVAC systems. The set of households with income between $40,000 to $60,000 Cdn per annum, constitute about 21% of the sample. These households are underrepresented in terms of renovations made to foundations (which likely corresponds to dwellings that are in a better general state of repair than those of lower income households) and ventilation but constitute near 23% of the renovations made to exterior wall siding and near 25% of the renovations made to insulation of basement and of exterior walls. Again, this pattern is generally consistent with ensuring that basics are in place first. A quality thermal envelope is a pre-requisite to obtaining the desired performance from high efficiency HVAC systems. Households of the next income cohort, between $60,000 and $80,000 per annum, represent just above 16% of the survey respondents, but comprise most of the renovations made to the heating equipment (22%) and ventilation or AC system (24%). Wealthier households, from $80,000 to $100, 000 Cdn per annum, represent 8% of the survey population and account for over 17% of renovations made to insulation of exterior walls, and over 20% of renovations made to the insulation of basement and crawl space. The pattern is similar for households in the highest income category, who are also more likely to improve their roof insulation than other income groups. These high rates of insulation upgrades could be due to a higher propensity of high income

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households to engage in (often expensive) redesign activities that allow for relatively inexpensive access to areas that would benefit from improved insulation if these upgrades are performed at the same time. Data breakdowns for only a smaller number of income cohorts (low: less than $40,000 Cdn per annum; middle: $40,000 to $80,000 Cdn per annum; high: above $80,000 Cdn per annum), and these only for owned SD dwellings, are feasible for the SHEU-2007 survery. The rates for SDs dwellings can be further broken down by construction vintage, but this is only possible for dwellings built prior to 1979 (which covers the vintages of primary interest for policies aimed at residential sector renovations). Figure 8 depicts the prevalence of specific renovation choices for owneroccupied SDs of all vintages combined. Somewhat surprisingly, the renovation rates for windows, roofs, or undertaking at least one insulation upgrade, decrease as household incomes rise. This is the inverse of what is observed regarding the installation of a PT or the retrofits of a ventilation or air conditioning system. The other renovation types, namely caulking, exterior door, heating equipment and insulation of water pipes, are undertaken at somewhat similar rates for owners for SDs at all income levels. These patterns may be due, at least in part, to the fact that energy expenses constitute a smaller proportion of the household budget for high-income households. If saving money on energy bills is not a major concern for a high income household, it may not be surprising that they opt for ‘gadgets’ such as PTs and improvements to relatively ‘luxury’ goods such as air conditioning systems.

Figure 8. Renovation rates by income group and type of renovation for all SDs (SHEU-2007).

These observations, that do not take construction date into considerations, do not always continue to hold when we focus on the older building cohorts.

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Lucie Maruejols and Denise Young

For dwellings built before 1979 (Table 5), renovation rates are always higher for the high income group, with the exception of window replacements in houses built in the 1970s. Patterns for low and middle income groups however are not readily discernable. Across all pre-1979 dwelling vintages, improvements rates for PT, caulking and heating equipment exhibit pronounced differences across income groups, with renovation rates increasing with income. The prevalence of making at least one insulation upgrade, as well as that of improving the roof structure or surface, are similar for low and middle income owners of dwellings constructed before 1946, and are higher for low income than for middle income owners of dwellings built between 1947 and 197923. Similarly, renovations to windows and exterior doors are more frequently undertaken by low income households than their middle income counterparts in the oldest age cohort. For other pre-1979 dwellings, however, the renovation rates associated with these upgrades increase with income. Among dwellings built during the 1970s, differences across income groups are not as pronounced as those found for other vintages, with renovation rates for windows, exterior doors, roofs and insulation (of any part of the structure) for high income households comparable to those observed in the low or middle income groups. Improvements to caulking and heating equipment do not seem to be sensitive to income when looking at all SD houses in general, but they are clearly more frequent among high income owners of older (pre-1979) dwellings. Upgrades to the thermal envelope (namely insulation, windows and to some extent roof structure) seem to be more prevalent in lower income households at the aggregate level, but are visibly more popular among high income households than their lower income counterparts when considering only older dwellings. The differences in observed patterns regarding the income sensitivity of renovation rates for all dwellings and for older dwellings could be explained by a possible tendency for low and middle income households to renovate more than high income households in relatively newer dwellings. It should be kept in mind that the numbers presented reflect only renovations undertaken by current owners, and not those that may have been made prior to acquisition. Hence, a possible factor underlying the survey results may be a tendency for higher income households to buy previously renovated dwellings while low and middle income households may opt to purchase dwellings in poorer 23

Specifically, among owners of dwellings built between 1947 and 1969, 49.1% within the low income group and 48.7% within the middle income group performed at least one insulation upgrade to their dwellings.

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condition, at a lower purchase price, with an intention to undertake renovations themselves.

Heating equipment

Roof

Ventilation or AC system

Window

n/a

54%

37%

30%

44%

16%

44%

n/a

61%

32%

55%

45%

23%

47%

18%

45%

n/a

50%

46%

60%

55%

33%

60%

25%

49%

n/a

64%

24%

49%

34%

29%

44%

19%

41%

22%

54%

35%

49%

41%

34%

49%

18%

38%

22%

59%

51%

59%

50%

39%

53%

n/a

43%

31%

62%

n/a

47%

n/a

n/a

38%

n/a

41%

n/a

56%

37%

44%

37%

34%

48%

17%

34%

n/a

59%

50%

46%

43%

35%

50%

n/a

40%

25%

56%

Insulation around hot water pipes

Exterior doors

Dwellings built between 1970 and 1979

Caulking

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Dwellings built between 1947 and 1969

Less than $40k $40k to $80k $80k to $150k Less than $40k $40k to $80k $80k to $150k Less than $40k $40k to $80k $80k to $150k

Any Insulation

Dwellings built before 1946

PT

Vintage

Annual Income

Table 5. Renovation rates for SD houses by vintage and household income (SHEU-2007)

Timing of Renovations Although only limited information is publicly available regarding the timing of renovations for the three income groups (and therefore detailed tables are not provided), a clear pattern emerges from the SHEU-2007 data for older (pre-1979) dwellings. Income affects the timing, or frequency, of renovations undertaken by individual households. As income rises, the proportion of renovations performed recently (within the 5 years prior to the survey) increases in most cases. For the few cases where data are available, the proportions of renovations that were undertaken more than 20 years prior to

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the survey are substantially larger for the low income group than for the high income group.

Summary Income is predictably an important factor when it comes to whether/when a dwelling is renovated, as well as the extent and the types of renovations observed. Therefore income is an important consideration for policy purposes. The 2003 snapshot showed that households in the lowest income ranges have a disproportionate tendency to choose upgrades needed to maintain the integrity of the structure, such as preventing moisture problems through foundation and exterior wall repairs, and that the households in middle and higher income ranges are those who undertake most of the insulation improvements. The retrospective provided through SHEU-2007 demonstrates that some types of renovation are more sensitive to income than others, especially for low and middle income households. 24 Leaving aside the highest income households, who are likely to be less sensitive to monetary incentives provided through government programs, it is found that in older dwellings, both relatively expensive heating system upgrades and, somewhat surprisingly, relatively inexpensive steps such as installing a PT or applying caulking are sensitive to income and are adopted more often by relatively wealthier households.25 An increased focus on low income households may be worth considering in terms of policy design related to home heating technologies. For older vintage (pre1979) dwellings, insulation, roof structure and exterior door renovations are in many cases as frequent among low as among middle income households, and in some cases are actually more frequent in the lower income category. Consequently, policy programs to encourage these renovation options could be aimed more generally, without necessarily targeting those with low incomes.

CONCLUSION Renovations made to older dwellings are especially relevant for achieving sector-wide energy efficiency goals, as these buildings constitute a large portion of the housing stock and, unless extensively renovated, tend to embody 24

More subtle income effects could possibly be captured if it were feasible to look at narrower income ranges with the SHEU-2007 data. 25 A possible reason for the income sensitivity of low-priced renovation options may be that low income households are less likely to pay for energy audits, and may be less aware of the benefits from these types of inexpensive renovations.

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relatively less efficient technologies than newer homes. In this chapter, we have examined the state of renovations in various types of Canadian dwellings based on two recent surveys and found that most of the older housing stock has been renovated at some point during the past 20 years, but many of those renovations were not recent. Of particular importance for reducing energy use in the residential sector are upgrades to the thermal envelope, especially insulation, as they have been noted in the literature as an extremely important factor in terms of energysaving renovations. That is, the quality of a dwelling’s thermal envelope has important implications for the effectiveness of other upgrades such as the installation of high efficiency HVAC systems. Canadian renovation data show that, relative to other renovation choices, the proportion of households that have made at least one upgrade to their thermal envelope is fairly high, but the renovation rates for the insulation of specific dwelling components (roof, basement, exterior wall) are often low and a large proportion of households who completed insulation upgrades did so long ago. Further analysis of renovation rates by dwelling type, occupancy status and household income groupings showed that barriers to renovation exist within specific household and dwelling categories. These barriers can to some extent be reduced by policy actions targeted so as to encourage renovations where they are most needed and least likely to take place. Low-income households are also the households most likely to live in older, rental, and multi-family dwellings. It is these households who face the greatest difficulties in terms of living in the least energy efficient dwellings if unaided through policy instruments, in part because of affordability barriers and in part because of agency problems related to the multi-unit nature of many of their residences and rental agreements. Incentives that aim only at the alleviation of income barriers may not be sufficient to trigger retrofits as many of these households will face other institutional, agency or split incentive barriers. Successful targeting of policies could lead to significant energy use reductions given the relatively poor conditions of older dwellings. The timing and extent of programs are important elements to the success of policy actions. Energy efficiency improvements are often undertaken sporadically, in conjunction with other needed repairs or structural maintenance of a dwelling so to reduce fixed costs and disturbance to the occupants. Households will likely not consider disruptive changes to their dwellings for the sole reason of improving the energy efficiency of their home. Also, due to financial constraints, households tend to not undertake all planned renovations at once, but instead work on specific areas at any given time,

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Lucie Maruejols and Denise Young

leaving other improvements for later, as financing becomes available. Longterm programs are therefore generally preferable, as short-lived programs may only be of practical use to a small set of households. In summary, a large number of issues need to be addressed in the design of programs provided by governments and utility providers. The extent to which particular barriers affect renovation rates is likely to vary across time and location. The Canadian data provide an illustration of many of the issues in the context of one particular cold-climate country.

ACKNOWLEDGMENTS The authors would like to thank Glen Ewaschuk and Dominic Demers of Natural Resources Canada for their assistance in organizing and compiling renovation data from the 2007 SHEU survey.

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REFERENCES Amstalden, R., M. Kost, C. Nathani and D. Imboden (2007) “Economic potential of energy-efficient retrofitting in the Swiss residential building sector: The effects of policy instruments and energy price expectations,” Energy Policy 35: 1819-1829. Anastaselos, D., E. Giama and A. Papadopoulos (2009) “An assessment tool for the energy, economic and environmental evaluation of thermal insulation solutions,” Energy and Buildings 41: 1165-1171. Balaras, C., A. Gaglia, E. Georgopoulo, S. Mirasgedis, Y. Sarafidis and D. Lalas (2007) “European residential buildings and empirical assessment of the Hellenic building stock, energy consumption, emissions and potential energy savings,” Building and Environment 42: 1298-1314. Bell, M. and R. Lowe (2000) “Energy efficient modernization of housing: a UK case study,” Energy and Buildings 32: 267-280. Costa, D. and M. Kahn (2011) “Electricity Consumption and Durable Housing: Understanding Cohort Effects” NBER Working Paper 16732, Cambridge MA. Galvin, R. (2010) “Thermal upgrades of existing homes in Germany: The building code, subsidies, and economic efficiency,” Energy and Buildings 42: 834-844.

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Harvey, D. (2009) “Reducing energy use in the buildings sector: measures, costs, and examples,” Energy Efficiency 2: 139-163. International Energy Agency (2008) “Promoting Energy Efficiency Investments: Case studies in the residential sector” Paris: International Energy Agency. Jakob, M. (2006) “Marginal costs and co-benefits of energy efficiency investments: the case of the Swiss residential sector,” Energy Policy 34: 172-187. Martinaitis, V., E. Kazakevičius and A. Vitsaukas (2007) “A two-factor method for appraising building renovation and energy efficiency improvement projects,” Energy Policy 35: 192-201. Maruejols, L. and D. Young (2010) “Split Incentives and Energy Efficiency in Canadian Multi-Family Dwellings,” University of Alberta Department of Economics Working Paper Series WP 2010-18, Edmonton, AB. Office of Energy Efficiency (2006) 2003 Survey of Household Energy Use (SHEU) Detailed Statistical Report. Natural Resources Canada, Ottawa. _____ . (2010). Survey of Household Energy Use 2007: Detailed Statistical Report. Natural Resources Canada, Ottawa. Power, A. (2008) “Does demolition or refurbishment of old and inefficient homes help to increase our environmental, social and economic viability?” Energy Policy 36: 4487-4501. Ryan, D.L. and J. Cherniwchan (2007) "Ownership and Use of Programmable Thermostats in Canada in 2003”, Canadian Building Energy End-Use Data and Analysis Centre Research Report CBEEDAC 2007-RP-9, Edmonton. Ryan, D.L. and D. Young (2009) "Modelling Energy Savings and Environmental Benefits from Energy Policies and New Technologies," in J. Evans and L. Hunt (eds.) International Handbook On The Economics Of Energy, Edward Elgar Publishers, 234-256. Sorrell, S. (2009) "The rebound effect: definition and estimation," in J. Evans and L. Hunt (eds.) International Handbook On The Economics Of Energy, Edward Elgar Publishers, 199-233. Verbeeck, G. and H. Hens (2005) “Energy savings in retrofitted dwellings: economically viable?” Energy and Buildings 37: 747-754.

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In: Household Energy Eds: Bartoli Mendez and Josiah Pena

ISBN 978-1-61470-503-1 © 2012 Nova Science Publishers, Inc.

Chapter 2

HOUSEHOLD ENERGY: APPLICATION OF ZERO ENERGY BUILDINGS AND TOWN Sora Yi,1, Jongho Yoon2,† and Min Kyeong Kim3,‡ 1

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Department of Urban Planning Research, Daejeon Development Institute, Korea 2 Department of Architectural Engineering, Hanbat National University, Korea 3 Department of Environment and Safety Research, Seoul Development Institute, Korea

1. INTRODUCTION Energy security is one of the most significant pending issues around the world. Ecoplation (a term made up from a word of “ecological” and a word of “inflation” means the status of growing price of consumption goods caused by the increase of production cost due to climate changes such as global warming) is inflation caused by environmental factors. The 2008 report of World Research Institute (WRI) estimated that, within a decade, the rise in the 

Research Fellow, Department of Urban Planning Research, Daejeon Development Institute, Korea. Email: [email protected], Tel: +82-42-530-3524. Professor, Department of Architectural Engineering, Hanbat National University, Korea. Email: [email protected], Tel: +82-42-821-1126. ‡ Research Fellow, Department of Environment and Safety Research, Seoul Development Institute, Korea. Email: [email protected], Tel: +82-2-2149-1046. †

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prices of oil, natural gas, and power supply by 20%, 40%, and 45% respectively, will bring about severe Ecoplation to human beings. To address such an issue, various nations in the world have been committing itself to focus on two ways: “new and renewable energy” and “energy efficiency and saving”. Among them, the energy efficiency called the fifth fuel (the first fuel is fire, the second one oil, the third one nuclear energy, and the fourth one new and renewable energy) has become a key alternative to ensure energy security and cut greenhouse gas emissions. Alliance to Save Energy (ASE, 2010) analyzed that the United States would have consumed its energy 50% more than the current level had the Oil shock not occurred in 1973 (Figure 1). Mckinsey reported in 2009 that the increase of energy efficiency and save will be expected to reduce the energy demand of the world by over 20% until 2020. Unlike coals or oils, energy saving does not cause any environmental pollution or greenhouse gas emissions, unlike wind energy or solar energy, it is not affected by weather, and unlike bio-energy, it does not compete with foodstuffs.

Figure 1. The US energy demand (ASE, 2010).

This chapter describes energy supply, consumption, and characteristics in the household sector, and then introduces the features and technology of zeroenergy home. With respect to zero-energy home, this chapter introduces the various applications for efficient energy consumptions, particularly in Korea and Japan. Given the large amount of energy used from heating and hot water systems, thermal insulation and energy recovery and use from the solar thermal and PV system should be mentioned. Following this, zero-energy

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town equipped with “Building Integrated various renewable energy” should be mentioned.

2. ENERGY SUPPLY, CONSUMPTION, AND CHARACTERISTICS IN HOUSEHOLD SECTOR

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2.1. Energy Supply and Consumption by Fuel, Region, and Sector World primary energy supply has increased almost twofold from 6,115 Mtoe in 1973 to 12,267 Mtoe in 2008 (the left in Figure 2, IEA 2010). While the share of oil supply has decreased to 33.2% in 2008 from 46.1% in 1973, those of other fuels supply have grown. Taking a look at the primary energy supply in accordance with regional shares, the share of OECD member nations has reduced to 44.2% in 2008 from 61.0% in 1973, whereas those of China and other Asian countries have increased more than twofold, respectively, from 7.0% and 5.6% to 17.4% and 11.5%. World final energy consumption has doubled from 4,676 Mtoe in 1973 to 8,428 Mtoe in 2008. Particularly, the rate of electricity generation has remarkably increased to 17.2% from 9.4% (the right in Figure 2, IEA 2010). Given the final energy consumption in accordance with regional shares, the share of OECD member nations has decreased to 43.8% in 2008 from 60.1% in 1973, whereas that of China and other Asian countries have almost doubled, respectively, from 7.9% and 6.4% to 16.4% and 11.6%.

Figure 2. Shares of total primary energy by fuel.

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Figure 3. Total final consumption by sector.

Given the final energy consumption in accordance with fuel shares, gas consumption has increased more than two times to 1,313 Mtoe from 671 Mtoe, and electricity consumption more than three times to 1,446 Mtoe from 439 Mtoe. In terms of consumption by sector, the share of coals and oils has highly decreased in household, commercial, public, agriculture, and other sectors, whereas those of gas and electricity have increased in the sectors (Figure 3, IEA 2010).

2.2. Shares of Energy Consumption and Electric Appliances in the Household Sector of Korea and Japan The Japanese final energy consumption in 2006 stood at 15.3 EJ, 12% (2.3 EJ) of which went to the household sector. The change of the final energy consumption by use in the Japanese household sector over the last two decades (1987-2006) indicated that the consumption for heating, hot water, and cooking, respectively, increased 36%, 25% and 20% with the total increase of 31%. Particularly, the uses of household energy for air-conditioning and miscellaneous items including lights and television sharply increased 132% and 88%, respectively. Over the last two decades, the number of inhabitants per household has dropped 0.61 persons from 3.08 to 2.47, whereas the final energy consumption by household, during the same period, has increased 11%.

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The energy consumption in the item of others, in particular, has increased 43% (IEE, 2008). Table 1. Shares of final energy consumption and number of electric appliances per household (100%: 46.7GJ/household/year, rate of electricity use 44 %) Item

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Heating

*

Rate of final energy consumption (%) Japan Tokyo (2005) (2006) 27 16

Rate of electricity use (%)

13

2

3

100

Cooling Hot water Cooking Lighting

30 8 8

33 9 39

9 22 100

Refrigeration Television Others

7 5 13

100 100 100

Number of electric appliances(unit)

Japan (2006) (Oil heater 1.3)* Air conditioner 2.56

Detached house Incandescent light 15 Fluorescent light 22 Apartment building Incandescent light 10 Fluorescent light 16 1.26 2.48 DVD player/video recorder 0.98 Bidet 0.87 Computer 1.07

Korea (2009) Electric mattress/blanket 0.55 Air conditioner 0.60 Electric fan 1.76

Incandescent light 2.68 Fluorescent light 5.30 Others 1.08

1.78 1.43 Washing machine 0.98 Electric rice cooker 0.90 Microwave oven 0.76 Bidet 0.31 Computer/laptop 0.83 Hair drier 1.01 Electric iron 0.93 Vacuum cleaner 0.79

No electric loads.

Table 1 presents the shares of final energy consumption and the number of electric appliances per household (Nakano, 2009; Tokyo City, 2008; KPX, 2009). The annual final energy consumption per household accounts for 46.7 GJ, 44% of which is consumed in the form of electricity. The heating and hot

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water, which have a large final energy consumption share, showed the relatively small rates in terms of the electricity use, recording 13% and 9%, respectively.

3. ENERGY EFFICIENT HOME DESIGNS AND EQUIPMENTS 3.1. Energy Efficient Home Designs The two pillars to facilitate energy saving not only in the household sector, but also in the commercial sector are as follows:  

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

Extension of energy saving houses and buildings. Improvement of energy efficient appliances and extension of the devices with high energy efficiency

The paths through which heat comes outside are windows, walls·floors·ceilings, and ventilators. 1/3 of the entire heat in houses comes out of the paths. Houses with high thermal insulation (windows and walls·floors·ceilings) and airtightness (ventilation) can save heating energy, which accounts for 1/4 of energy consumption (Figure 4 on the left, Tokyo, 2011). According to the research of Tokyo in Japan, houses built on the basis of energy saving standards for next generation(enacted after 1999) reduced 27% of heating energy and 16% of cooling energy compared to those built on the existing energy standards (enacted after 1980).

Figure 4. Places necessary for thermal insulation installation (left) and solar thermal system installed in veranda (right).

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Table 2. Example of estimation on household energy saving potential (100: 80.9GJ/household/year, primary energy basis)

Energy saving measure

Heating

Cooling

Hot water

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Cooking

Lighting

Refrigeration Others Total

Gas stove => Heat pump air conditioner with high efficiency Energy (Saving rate 57%) saving house Traditional (10 years (Saving rate ago) heat pump air 7%) conditioner => High efficient heat pump air conditioner (Saving rate 35%) Traditional (10 years Energy ago) heat pump air saving conditioner => house High efficient heat pump (Saving rate air conditioner 6%) (Saving rate 42%) Traditional gas hot water heater => Heat pump hot water heater (Saving rate 30%) Traditional gas range => High efficient gas range (Saving rate 21%) Incandescent light bulb 60/54W (Durability 1,000 hrs) =>Fluorescent light bulb 12W (Durability 2,000 hrs) (Saving rate 78%) General typed fluorescent lights are not replaced (Assumption of 50% of the entire consumption) Traditional (10 years ago) freezer => High efficient freezer (Saving rate 65%) -

Existing energy consumption

Energy consumption after renovation

13

4

9

4

3

2

16

11

4

3

11

7

11

4

33 100

33 68

Table 2 (Adapted from Nakano, 2009; Tokyo City 2008) shows an example of estimation about energy saving potential when a house is built with the following elements: thermal insulation, airtightness, heat pump air conditioner with high efficiency, heat pump water heater, high efficient gas range, high efficient freezer, and high efficient lights. Only with such arrangements, a house can save almost 32% (primary energy basis) of the entire energy consumption.

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In addition, by making use of Task and Ambient Lighting (TAL: reduces light levels except at key task areas, saving rate 7.5%) or a high efficient lighting apparatus (saving rate 4.1%), a house can be given additional saving effects. Using high efficient lighting apparatus and appliances can reduce not only power consumption, but also heats coming out of the devices. The reduction in the heat generation can prevent indoor temperature from going up, thereby help to decrease cooling energy during summer.

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3.2. Energy Efficient Equipments: Types and Features of Photovoltaic Cell Depending on the type of semiconductor used for Photovoltaic modules (PV modules), the modules are divided into three types: silicon type, compound type, and organic compound type (Table 3, Adapted from NEDO, 2009). If a module is chosen, less cost and high conversion efficiency (efficiency in converting from sunlight to electricity) should be put in priority. Up until now, crystalline silicon modules are mainly chosen, but thin film silicon modules are drawing attention from people because they can be made in the form of large–scale production for super-size modules. The conversion efficiency of amorphous modules tends to drop 10% after the use of the modules (Initial thermal degradation), and the dropped efficiency is called “stabilized efficiency”. Thin film silicon multi-junction PV modules whose initial thermal degradation does not occur, have high efficiency, and can be supplied at a low price. Since the thin film silicon modules can perform badly as a temperature goes up, a lot of studies on the improvement of the issue have been in progress. Although CIS (Copper-Indium-Selenide) which is being distributed in the U.S. and Europe is highly efficient, there is a controversy over collecting indium minerals. Because CIS combined with the concentrating system can track sunlight, its entire power generation is larger than that of fixed modules. Organic compound modules whose development is still in progress have also many issues, such as high efficiency, durability, and others. Generally, the durability of photovoltaic cell modules lasts 20-30 years and the durability of power container lasts 10-15 years. However, amorphous modules can easily cause thermal degradation immediately after they are exposed to a strong direct ray of light, so their durability can probably shorten

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compared to other modules. On the contrary, pigment sensitizing modules are expected to last their durability more than silicon modules. Table 3. Types and features of photovoltaic cell modules Feature

Compound module Organic compound module

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Thin film Silicon

Silicon module

Crystalline

Type

Mono-crystalline » High conversion efficiency and long durability » The oldest module using thin mono-crystalline with the thickness of 200um~300um. Module panels cost high due to the use of a lot of silicon, but it holds high conversion efficiency. Thin film silicon » High conversion efficiency and long durability » It uses multi-crystalline module panel with relatively small crystal. Because it is less expensive than Mono-crystalline module, and easy to be manufactured, it is mostly used today. Its conversion efficiency rate is lower than that of mono-crystalline module. Amorphous » No effect by silicon material and strong endurance against high temperature. » It uses non-crystalline amorphous, silicon thin film. Although it can generate super-size, large-scale production, it has lower conversion efficiency rate than crystalline silicon. Multi-junction » Available for large-scale production » It joins amorphous silicon and non-crystalline silicon. It can be available for super-size, large-scale production as it uses less amount of silicon than amorphous module. It has higher efficiency than amorphous module. CIS » Black color and no effect by silicon material. » It is a thin film module that uses compound semiconductor such as CIS(Copper-Indium-Selenide), instead of using silicon. Studies on its high performance as well as possibility of super-size, large-scale production are in progress. Pigment sensitized » Possibility of selecting colors and low price » It is a module using titanium oxide in the way that pigment absorbs lights and releases electrons. It is expected to be supplied at low price due to easy manufacturing and cheap material. But studies on its high efficiency and endurance are needed. Organic thin film » In progress of development and low price. » It uses organic semiconductor. It is possible to be supplied at low price as it uses light and flexible plastic films. But, for practical use, studies on its high efficiency and endurance are needed.

Module conversion efficiency

~15%

~14%

~6%

~10%

~11%

11% (in research stage)

5% (in research stage)

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The formula to calculate the annual electric power generation of photovoltaic cell is presented as follows: Ep=H×K×P×365/1

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Ep: Annual expected electric power generation (kWh/year) H: Annual average solar radiation per installation surface per day (kWH/m2/day) K: Loss factor, about 73%, depending on a type of modules and a degree of pollution on light receiving surface - Annual average loss by the rise in temperature of cell: about 15% - Loss by power container: about 8% - Loss by pipes and pollution of light receiving surface: about 7% P: System capacity (kW) 365: Annual days 1: Solar light intensity by standard status (kW/m2) Since the real solar radiation is greatly variable compared to annual average value by region, it is very important to determine loss factor by installation environments (effects of shadow or shade) and installed equipment. As the electric power generation of photovoltaic cell is proportional to the solar light intensity, it reduces to 1/2-1/10 during a cloudy day and 1/5-1/20 during a rainy day once it is compared to a fine day. Particularly, when snow piles up, solar light can not reach PV panels so the power generation of PV nearly stands at zero. When snow does not pile much, heat stored in part of PV cells can melt the snow. But if it snows a lot, it does not work well. Therefore, in a region with heavy snow, the panels should be installed with the consideration of their angles, in order for snow not to pile up on the panels. The power generation of PV modules are influenced by installation conditions, including latitude, weather, installation direction and angles, and ventilation. The best orientation should be with a right southern exposure, and the best angle of inclination should be 20-30 degrees (the difference of power generation between 20 and 30 degrees is 1-2%). In principle, PV modules should be installed at the best orientation and degrees. However it is impossible to do that because external appearance, structure (e.g., wind pressure), and economic conditions should be taken into account. When the modules are installed on the roof of the existing building, generally they

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should be installed with the consideration of orientation and degrees which are suitable to the installation position. In the case of the installation of crystalline PV modules, if they are open to right east or right west, their power generation reduces about 15% compared to the installation of the modules facing right south (Figure 5, Kankyo-business, 2009; Tokyo, 2010).

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Figure 5. Efficiency of power generation depending on installation direction and an example of installation.

Another important condition when a module is installed is to avoid shade or shadow. When hills, buildings, trees, electric poles, and so on cast their bright shadows over PV panels, the power generation drops, yet it does not go to zero. Parts of the panels shadowed can generate powers by 10-40% thanks to their surrounding scattering light. When opaque objects such as leaves are put on the PV panels, the power generation does not decrease by as much amount of lights as the objects blocked, but more than that. If such a phenomenon lasts long, the temperature of the blocked cell goes up and a hot spot phenomenon that undermines its characteristics can occur. Usually, bypass diodes are built into PV cells, so such the phenomenon can be prevented. The PV cell has a temperature factor which means that the power generation efficiency is undermined by rise in temperature. In a high summer, the surface temperature of PV modules stands at around 70 degrees. Under this situation, the power generation efficiency rate of crystalline silicon cells and of amorphous silicon PV cells drops 20% and 10%, respectively. The lower a temperature coefficient of a PV cell is, the stronger high temperature of the PV cell is. In terms of seasons, spring and summer see large power generation whereas fall and winter relatively lower power generation because of solar

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radiation, temperature change, and heavy rains. The generation voltage of a PV cell is also affected by seasons. In winter, the voltage slightly goes up compared to summer because of the characteristics of crystalline PV cell. Since it is affected by solar radiation, it should be monitored and follow-up controlled by a power container in order to generate the maximum voltage of the PV cell. In addition, the power generation can drop due to dirt and dusts left on the modules. In city areas, the rate of decreased power generation reaches lower than 5% on average. Rain and wind mostly wipe out the dirt and dust, so the function of the modules returns to normal. However, in regions near heavy traffic roads, because oily contaminants are attached to the module, they, sometimes, are not wiped out by a rain. Currently, economically, it is better to sell electricity through power line connected with a power company rather than to save the remaining power generation into a storage battery for its use at night. In the case of household, it is more efficient to use a PV cell in the daytime and the electricity bought from a power company at night. However, the disaster prevention system necessary to store electricity usually makes use of a storage battery. When a type of PV cell is chosen, installation cost as well as efficiency should be taken into account. For example, in the case of an amorphous PV cell that uses small amount of silicon and consumes small energy during its production, the price of the cell is inexpensive, yet its efficiency is low. To make the same power generation as other PV cells, it requires larger installation size. So, total cost of equipment and installation ends up growing. Therefore, a design should be made with the consideration of the entire system. Since the module’s durability lasts 20-30 years, recycling it can cause the reduction in the cost. As of the years between 2010 and 2020, the price of a used 4kW module is expected to be 15% of that of a new module. If used modules are utilized for installation, the installation cost including power container and other fees can be reduced by 50% more than new modules are used (Kankyo-business, 2009; NEDO, 2009). Households are usually reluctant to install the modules due to the initial cost of the installation, so the government's subsidies should be fully utilized. In Tokyo, Japan, the government supports 1,000 USD/kW for PV generation system, 90 USD/m2 for solar hot water heater, and 165-330 USD/m2 for solar thermal system (Tokyo City, 2010). As an example of public and private sectors combined, there is a solar system complex in Amersfoort near Amsterdam, the Netherlands. 500 households participate in the solar power generation house project called ‘Megawatt project’, the Netherland

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government and the EU supports half of the purchase cost of the house, and an owner pays half of it. Out of the roofs of 500 houses, the roofs of 250 houses will be owned by tenants, and others will be owned by power companies over the next decade. During 10 years, 20% of annual power generation made by a PV cell-installed roof is offset from the utility bill of the inhabitant living under the roof. The rest 80% of it is owned by a power company. If a contract period expires, inhabitants can purchase the PV facility with inexpensive price (Kankyo-business, 2009).

4. OPTIMIZING PASSIVE HOUSE DESIGN WITH MICROCLIMATE SIMULATION

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4.1. Energy Simulation in Passive House Design With the rise of energy-saving’s importance in building design, passive house design has become popular method as a means of improving heating/cooling performance of buildings. A lot of countries are promoting the construction and distribution of low-energy buildings since the largest savings in energy-use can be obtained in new buildings through building design and operating plan. Among numerous elements of passive house design process, energy simulation is very important since it heavily affects the form, use of building material, and cost. This paper proposes coupling of conventional multi-zone method and CFD to calibrate the energy simulation results. The coupling of two simulation methods enables analysis of microclimate modifications in building design.

4.2. Microclimate Modification with a Coupling of Multi-Zone and CFD Energy Simulations 4.2.1. Microclimate Elements in Passive House Design Microclimate is strongly related to energy balance which is a systematic presentation of energy flows and transformations. When energy source is concentrated at a particular spot, the energy can be continuously moved from an area of high concentration to an area of low concentration in a given volume. Similarly, microclimate around the building, i.e., building

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microclimate, can be modified by proper building designs and the geometry, e.g. building surface, density, barrier, terrain, 3D objects and huge plant etc. The geometry introduces a pathway of airflow, a windbreak and a non-uniform solar access, etc. For example, a protected courtyard design against cold air can easily make the building warmer than in exposed situations. The deviation in climate plays an important role in architectural planning. In this paper, we focus the thermo- and aero-dynamic effects that can be changed by the aspect proportion or height-to-width ratio, the orientations and the form of buildings, and the mixture of materials, the density or the rate of mixture. Table 4 shows several factors of the thermo- and aero-dynamic effects around a building. In site selection, favorable locations should be considered with every elevation difference and character of land cover, which induce variations in a local climate. A less favorable site can be improved by windbreaks and surrounding surfaces that induce an advantageous reaction to temperature and radiation impacts. Table 4. Building design elements and the thermo and aero-dynamic effects

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Arrangement

Form

Facade

*****

Microclimate in a building A. Building orientation B. Topography C. Courtyard D. Courtyard roof E. Gable roof F. Curved roof G. Fence design H. Windbreak I. Huge plant J. Building over pilotis K. Uniform window shape L. Non-uniform window shape M. Horizontal window shape N. Vertical projection O. Horizontal projection

Thermodynamic ***** ***** * *** **** *** * ** * *

Aerodynamic ***** **** * ** * **** ***** *** ***** ** ***** *** *** ***

: very strong, **** : strong, *** : efficient, ** : effective, * : expectable.

A good passive design which gives some shade in summer and allows the sun to penetrate as much as possible in winter consider the positioning, orientation and height of buildings. Deciduous trees aid to achieve the windbreak and seasonal irradiation impacts related to the albedo of walls and

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other structures facing the sun. These make a substantial effect on the microclimate of intervening spaces as well as the heating of the buildings themselves. The energy balance in form and the mixture of material is also related with the irradiation of floor and walls. Exposure versus shadow patterns affects the surface temperatures and consequently the amount of heat transferred to air as the sensible heat flux and consecutively the air temperature. The potential of airflow at low level also depends on these factors. The building materials of the surfaces were also found to be decisive in the heat storage rate as well as in the nocturnal cooling rate.

4.2.2. Multi-Zone and CFD Coupling for the Analysis of Microclimate Modifications A naturally ventilated and thermally inhomogeneous building modifies the building microclimate due to the evolution of outside airflow and inside buoyancy, stack effects and thermal flows with a fluctuating direction and magnitude, turbulence, temperature, humidity, shortwave and long wave radiations fluxes. For Simulation of microclimate modification, a large number of influencing thermo- and aero-dynamic factors can be chosen for different design cases. The microclimate modification can be defined by the fundamental laws of dynamics and thermo-dynamics of fluids, i.e., conservation equations of mass, momentum and heat. Topological and geometric building designs also affect the microclimate changes. Terrains and surrounding buildings definitely have strong impacts on the natural ventilation performance, particularly on the wind-driven ventilation. The internal sources and sinks modify the distributions of temperature and specific humidity. An advection-diffusion equation that describes the loss of flow speed in internal source/sink terms can define the microclimate modification induced by the topological and geometric factors. However, typical multi-zone models such as EnergyPlus (EP) cannot accurately predict the effects of microclimate modifications. For example, Pedestrian wind comfort depends on the types of activity, dressing, specific weather e.g. air temperature, relative humidity, solar radiation and mostly determined by the wind speed and air temperature. CFD methods can estimate accurately local airflows and the condition distribution. The variations in the airflow, being affected by site-specific design, building geometry and topography etc., easily show the interconnections of temperature, humidity and velocity in the space. Simulation results under different periods, e.g. round a year, season, week and day etc., can show different indoor conditions.

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Figure 6. Energy simulation method using EP-CFD coupling.

The proposed method combines advantages of EP and CFD shown in Figure 6 using a multi-scale approach that analyzes the flows using the multiscales between macro- and microclimates. The multi-scale approach is attractive to mathematicians due to the convergence performance to the accurate solution. It can accelerate the solution by computing only corrections of a series of coarse grid levels. The use of several scales for analysis units, i.e., grids with nodes, can greatly reduce the number of iterations and the processing time, particularly when the model contains a large number of control volumes. Macroclimate data is used to calculate the average value which can combine values of small units. The EP program estimates the local average value in a volume by using climate data observed at some stations in Korea. However, the results derived from climate data are not exact in some zones due to microclimate modifications with thermo-, aero-dynamics. A CFD method iteratively updates the results in small unit scales.

4.3. Application of Microclimate Simulation to Passive House Design in South Korea 4.3.1. Description of the Project Although a lot of studies on energy simulation have been conducted by researchers of physics, climatology and architecture, they did not try to apply their method to a real house design. Single EP simulation cannot obtain the detail of energy flow in the design and single CFD simulation is too complex to make the problem to converge into the real solution in the whole house design. Several efficient design factors for energy-saving are applied to estimate energy gain in real designs. The application of these factors in real

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house models is important to prove energy performance in real house design. Note that the purpose of this section is not to analyze house designs, but to test the energy efficiency of the microclimate factors in these designs. Pine Tree House which is the 6th design of Min-Maru series by GAWA Design Group1 is located in the highest area in the Min-Maru house complex. The main feature of the house is two masses with different kinds of space and the topography preservation by pilotis. Pile foundations with treated timbers are used to float the living room and the kitchen on the slope of the mountain and the room space forms a skip floor over the topography. The skip-up-floors form separated spaces for different uses as guest room, living room and bedroom by the building levels. The house has the size of 12600×14700×7600 and two stories. Figure 7 shows the layouts and pictures of the house. The reasons for choosing Pine Tree House are following: 

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 

This house has several topographical features to be able to observe microclimate effects. This house has high capacity to control several microclimate effects. This house employs several Korean traditional designs e.g. Maru (Korean wooden floor), Jungja (Korean pavilion) and Ondol (a Korean floor heater) etc. which is attractive to the Korean people.

Some limitation of this test in the real house model is that the actual measurement of microclimate was not available and the microclimate factors are estimated by computer simulation based on macro climate and the house design data. Thus, the test result may be not exactly equal to the real energy consumption however it is not exceed the allowable margin of error.

4.3.2. CAD Model Conversion for CFD Simulation The main construction using concrete with reinforcing rod and lightweight woods are represented in Table 5. The zones in the house are set up with microclimate design elements, geometrical features and materials. Figure 8-a and b represent the CAD model of the zones which are classified with layers and a plane cut of the CAD model. The sun-path diagram shown in Figure 8-c is used to estimate the solar and shadow range for EP energy simulation. The CAD model is converted to a 3D solid model since the CAD model with 2-D meshes cannot be directly used for CFD simulation. Figure 8-d shows the 3D solid model that is input model for Fluent software. 1

GAWA Design Group, http://www.kawadesign.co.kr.

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Figure 7. Pine tree House by S.Y. Choi, (a) drawings, (b) views.

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Table 5. Construction materials and outline of pine tree house Structures

Materials Reinforced concrete

Factor House style

Lightweight woods T. 12CFRC boards

Area of site Area of building Gross floor area Building-toland ratio Floor area ratio

Main structure

Exterior materials

Interior materials Heating

T. 18 APITONs (pinetree) T.22 pair glasses App. Wallpaper on T.9.5 plaster board T.16 Pine-tree louver (for open type) Ondol Maru

Year of completion Designer

Scale 2 stories residence 389.00 m2 126.72 m2 164.55 m2 35.44% 42.30% 2003 Gawa company

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Figure 8. CAD model of pine tree house.

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4.3.3. Microclimate Data Simulation Thermo- and aerodynamic microclimate design method including passive design is quantitatively evaluated by EP-CFD simulation. First, EP simulation results show the average values of zones which are estimated from site and zones. The resolution of the values is one node per volume of zone, thus the EP simulation cannot estimate the streamline of real airflow. However, the method is not complex and easy to evaluate the energy performance of building zones with passive and active set-ups. The calculation of the heating and cooling loads on a building or zone is the most important step in determining the size and type of cooling and heating equipment required to maintain comfortable indoor air conditions. Building heat and moisture transfer mechanisms are complex and as unpredictable as the weather and human behavior, both of which strongly influence load calculation results. Table 6. Some of the factors that influence results The factors Weather conditions Heating effects

Thermal properties Thermal mass Construction quality Heat added/lost Acceptable comfort

Influenced results Temperature, moisture, wind speed, latitude, elevation and solar radiation etc. Condiction/convection: walls, roofs, floors, doors and windows Radiation: wall and roof surface temperatures Insulation, glass transmittance, surface absorption The delay of indoor to outdoor temperature change Air, heat, and moisture leakage Ventilation, distribution systems Quality levels of occupants

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If the factors shown in Table 6 are once used for calculation of a complex equation, the heating and cooling loads can be obtained as the result of the equation. They are always used by active design researchers, developers of building envelop, architects, etc. Furthermore, they can be useful factors to evaluate the thermal and comfort of the passive designs and the set-ups. Then, a comparison between the passive design and active method e.g. electronic air conditioning concludes the needs of additional energy consumption of the passive designs, if the passive designs do not have sufficient condition of zero energy. The evaluation of microclimate design methods will perform such a comparison and concludes the needs of the amount of additional energy input. Although zero energy building with passive design methods is the best case, such a zero energy performance using only passive methods is too ideal and difficult to achieve.

4.3.4. Results By EP simulation, changes of topographic condition, window ratio, and insulation thickness are evaluated. Figure 9 represents the heating and cooling loads which occur through the changes of different conditions. Since EP does not have CFD calculation, the method uses a simple parametric estimation based on analytical data related to the pressure coefficients. The differences of slope angle increases the heating load and decrease the cooling load as Figure 9 (a) and (b) show. The additional heating gain is caused by the solar radiation since building can be warmer and drain earlier in hillside. The cooling gain i.e., decrease of cooling load is due to the large amount of microclimate which is produced by the different thermal condition by solar radiation. Heating by day causes anabatic airflows since the heated air moves up and pressure decreases. The large amount of airflows makes a cooling gain of about 3kW m-2 per 10 degree. The changes of window ratio make smaller heating and cooling loads due to the large effects of the air movements. The average heating and cooling loads are respectively about 114kW m-2 and 72kW m-2 and the values are much smaller than the results of topography. Figure 9 (b) represents the graphs heating and cooling loads. EP simulation shows that the larger window is not helpful for cooling since the increase of radiation through the window causes overheating and the cooling load is increased in summer. Although the windows are a cause of the heat loss in winter, the increase of heating gain through the window increases the heating gain. The modification of insulation thickness in Figure 9 (c) yields very interesting results. The insulation is a very important for passive solar design,

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ventilation and cooling methods. For the passive solar, the increase of insulation thickness results in the heating gain, since the radiation heat can be preserved. However, the thick insulation is not efficient for cooling since the thick material disturbs the passing air through the wall. The zone with a thick insulation needs about 2kW m-2 additional cooling loads. Although the EP simulation could calculate the energy performances of different designs, it cannot calculate the real flow streamline with partial changes of physical conditions. Hence, CFD simulation method is coupled to the EP simulation. A great advantage of coupling is to obtain the better convergence to the solution since the initial values can be given by EP simulation results. After calculating EP method, the results’ values and the state of parameters are put in Fluent software. CFD calculation updates the average node with a single value from EP simulation into large amount of nodes with the values. The geometrical features which derive the thermo- and aerodynamic states can be analyzed by CFD method.

(a)

a. Heating

b. Cooling

(b)

a. Heating

b. Cooling

(C)

a. Heating

b. Cooling

Figure 9. Heating and cooling loads, (a) by change of slope angle, (b) by change of window ratios, (c) by change of insulation thickness.

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We tested 4 energy-saving models, i.e., 2 for heating and 2 for cooling, and compared the results to the energy performance of the original model. Table 7 shows the 4 test models. The original model has a virtual house condition based on the Pine-tree house. Table 7. Heating and cooling models based on the simulation results of microclimate design elements 2 Category

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Orientation Site condition Windsheltering

Heating model 1 South Topography

Heating model 2 South Topography

Cooling model 1 South Topography

Cooling model 2 South Topography

-

Medium density windsheltering using trees Normal shape gable roof with insulation

Artifical wall and large horizontal projection Gable roof with insulation with projection

-

-

Normal shape gable roof with insulation

Very large size gable roof with insulation

Double glazed window with wooden frame

Insulation HTC: 1.8W m 2 K-1

Double glazed with insulation

Double glazed with insulation

-

-

Horizontal projection

Normal insulation HTC 040W m -2K-1

-

Inter-zone thermodynamic air balancing

Superinsulation HTC: 0.059W m-2K-1 Inter-zone thermodynamic air balancing

Building using pilotis -

Crossventilation

Crossventilation

-

-

Uniform shape

Nonuniform shape

Roof design

Normal shape gable roof

Window

Double glazed window HTC 130: 3.3W m -2 K-1 Building using pilotis -

Geometry

Insulation

Airflow

Window shape

2

Original model Southwest Topography

Natural ventilation and airconditioner Mixture of uniform and nonuniform shapes

HTC: Heat Transmission Coefficient.

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Figure 10. Comparison of heating and cooling loads EP-CFD method using microclimate design models.

The house is located on a slope to the southwest with a gable roof and double glazed window. For the heating model, wind-sheltering and insulation are added. For the cooling model, geometry and window shape are changed. The heating and cooling gains using modifications of Pine Tree House using the 4 models are compared to the results of the full application in the original model, i.e., blue dashed lines. The heating and cooling models can be jointly used for thermal condition for both winter and summer. Figure 10-a shows the comparison of heating loads between original model and 2 heating models and Figure 10-b represents the cooling load comparison between original model and 2 cooling models. The original model of Pine Tree House has a lot of Korean traditional design elements that enable to derive microclimate effects. However, the model does not consider passive solar design with strong insulation. Hence, the heating model 1 slightly modifies the original house for a better passive heating performance. Energy performance using design elements of Pine Tree House is analyzed by streamline of thermo- and aerodynamics showing physical distribution of temperature, pressure and kinetic energy etc. Figure 10 shows the heating and cooling load from CFD method. Only 12 times of the simulations are performed due to the complexity of calculation.3 The dashed lines indicate the results of the full application by using all design elements. Some design elements are efficient only for one between heating and cooling. For example, design elements for a high ventilation performance are good for summer but not efficient for winter. Hence, the main aim of EP-CFD

3

Note that CFD simulation generally needs about 1000~1000000 times more computation costs than EP method.

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simulation is to find some good combinations which accomplish positive microclimate effects.

4.4. Design Recommendations EP-CFD simulation is applied to the simulation of energy-saving using microclimate. If architects have rich information about the microclimate factors related to heating and cooling, the Pine Tree House can be modified for the better energy performance. The microclimate modification for heating is closely related to the common knowledge of passive house standard, green building and sustainable architecture as follows:     

Maximizing the opportunities to use solar energy Compact plan forms reduce infiltration losses Optimized glazing ratios for heat gains and lighting Using thermal mass to reduce fluctuations in room temperatures Sheltering the building from strong cold wind.

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The cooling energy can be reduced by careful considerations of the site, building geometry and design elements:     

Maximizing the potential use of natural wind for natural ventilation Using thermo- and aerodynamic flow to avoid overheating Controlling the streamline of airflow using design elements Reducing the internal loads by distributing and balancing Shield windows from unwanted solar gain in the hot season.

The original house model can be slightly modified for a better passive heating performance using the streamlines of thermo- and aero-dynamics showing physical distribution of temperature, pressure and kinetic energy etc. Possible air leakages can be minimized for winter season. Following alterations will help improve energy efficiency of the house.    

The orientation shift to the south Wind-sheltering with medium density trees. More insulation Use of Inter zone thermodynamic for air balancing between zones

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Enforcement of density in wind-sheltering for better performance in blocking direct and small cold wind Insulation of the roof and the window Large gable roof and the non-uniform windows to make the smooth streamline and efficient solar control

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The comparison between the 1-year performance of simple passive modeling and a passive modeling combined with microclimate design method shows that microclimate method reduces the thermal variance. Figure 11 represents the 1 year temperature by EP simulation. CFD cannot be utilized for 1 year calculation due to the complexity hence; the calculation utilizes the flow net method in EP after setting of the microclimate design elements. In the result, thermal variances in simple passive method are larger than 10 but the combination with microclimate has smaller variances of 5 to 10 . The main advantage of microclimate design elements improve the airflow between outdoor and indoor in summer and enable to utilize inter-zone air current in winter. Consequently, higher indoor temperatures in winter and lower temperatures in summer are obtained. The features make it possible to save building energy costs by reducing heating and cooling loads.

Figure 11. 1-year temperature comparison between a passive method and a combination of passive method and flow net of microclimate design.

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5. ENERGY SAVING POTENTIAL OF SOLAR THERMAL AND PV SYSTEM FOR HIGH-RISE APARTMENT COMPLEX

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5.1. Introduction According to the Korean mid- and long-term national energy basic plan over the next two decades announced in the mid 2008, the portion of new and renewable energy on the basis of low carbon green growth is expected to grow to 11% 4.6 times more than the current 2.4% by 2020. Although many measures are proposed to pursue the plan, the most key strategy related to residential buildings is a project of supplying nearly zero energy “Green Home” or “Green Village” with one million houses. With the project, new and renewable energy such as solar thermal, photovoltaic, geothermal is provided to one million houses to foster nearly zero energy home by 2020. The energy sources include PV energy, solar thermal energy, geothermal energy, fuel cells for household, bio wood pellets, small-sized wind power and others. PV and solar thermal energy account for the most of the sources, so solar energy is planned to be supplied to 650,000 houses. Both single-detached houses and apartments are included in the project. Particularly, since apartments account for 70% of the domestic sales market for newly residential buildings, applying the solar energy system plan to the apartments can play a vital role in the project. However, unlike single-attached houses, the Korean apartments are lofty and very close to their adjoining buildings, so applying the solar energy system fundamentally causes restrictions. In an apartment complex, span-wise distance (distance between buildings), a type of layout, and shading of each household depending on orientation can be greatly variable, so they give an enormous impact on the appliance of PV and solar thermal systems. Accordingly, in this section, It will be described whether the solar energy system with the consideration of the features of the domestic high-rise apartment complex is feasible as follows. 



Comprehensive solar access evaluation of an apartment complex through the analysis of annual accumulated solar radiation of the building envelope depending on a type of apartment layout and classification based on a location and its pattern analysis Supply / Demand load matching analysis through the analysis of power and thermal load of apartment complex and applied renewable

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energy systems Prediction of appropriate installation scale of PV and solar thermal systems available in a high-rise apartment complex and thereby solar energy contributions to total energy.

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5.2. Solar Access Evaluation Depending on a Type of Building Layout 5.2.1. A Type of Apartment Layout and Dimensions Available for System Installation The positions in which solar thermal and PV systems can be installed are roofs, outer walls of a building and communal lands in a complex. When we apply the system to the positions, the annual expected energy potential is related to a type of building layout and orientation. In this study, a total of 12 combination conditions is evaluated including 3 types of orientation : a due southern exposure, an east southern exposure with 30 degrees, an east southern exposure with 60 degrees, and 4 types of building layout: Type-01, Type-02, Type-03, and Type-04 (Refer to Figure 13) Figure 12 presents an illustration of the dimensions of unit and roofs being necessary to apply the solar energy system, based on the most usual type of an apartment layout described in this study through the analysis of the domestic apartment drawings. This study assumes that a complex has 9 buildings, and one building consists of 20 floors, each of which has two households. It also assumes rake-shaped roof with an incline of 30 degrees. The size of floor per each unit is 186 ㎡, and the size of a valid floor except for common area is 130㎡. So those are considered a heating area. The proportion of the window size in the front balcony to the frontal outer wall is 71%. In the case of north window, its size is assumed to be a half of that of south window. In the meantime, in the case of calculation about the available dimensions, this study takes real conditions and applies the idea of the available valid dimensions. By doing so, it assumes that the system is applied to any given dimensions out of real calculated dimensions. On the basis of those, the results of the dimensions available for the solar energy system in a unit, a building and a complex are illustrated on the left of the Figure 12 The options of dimensions available for system installation are classified into 4 conditions: Roof installation (IO-R), opaque wall installation (IO-1), and installation combination of opaque wall and window area (IO-2, IO-3).

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.

Figure 12. Illustration of dimensions available for solar energy system.

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5.2.2. Evaluation of Solar Access and Classification of Annual Shading Ratio This study evaluated the solar access of the places where the solar energy system can be installed by evaluating annual solar radiation of a building envelope depending on the building layout. It classified each household into four specific grids, and generated annual accumulated solar radiation per grid in the form of kWh/㎡.yr value. In the meantime, if solar radiation values 5.2.3. Evaluation of Solar Access and Classification of Annual Shading Ratio This study evaluated the solar access of the places where the solar energy system can be installed by evaluating annual solar radiation of a building envelope depending on the building layout. It classified each household into four specific grids, and generated annual accumulated solar radiation per grid in the form of kWh/㎡.yr value. In the meantime, if solar radiation values of some places not affected by annual shading of adjoining buildings are normalized with a relative ratio value, for example, if the solar radiation of a roof becomes 100 as a basis, and each position from the roof is normalized with a relative value, it is easy to figure out the degree of shading effect depending on the location of each household. In particular, if the places affected by shading of adjoining buildings are grouped at a given level, they can be used as data, such as available classes and classification areas depending on both economic conditions and the conditions of dimensions for installation when PV or solar thermal system is applied later.

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Figure 13. Results of solar access evaluation of a type of layout.

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This study defined Annual Shading Ratio (ASR (%) = {1 - (annual accumulated solar radiation of a certain position)/(annual accumulated solar radiation of a roof)}* 100) that presents the reduction rate of annual accumulated solar radiation caused by the shading effect of adjoining buildings. Based on the ASR, there are 5 classes: Class A is the section between 0 and 10%, Class B the section between 10 and 20%, Class C the section between 20 and 30%, Class D the section between 30 and 40%, and Class E the section between 40 and 50%. Figure 13 illustrates the results of solar access evaluation of 4 types of the building layout. The bold-lines with the depth of color present borderlines of each class. For example, in the event that a solar thermal system is applied to the entire building, if you want to apply the system, with the consideration of the system’s efficiency, to the region whose rate of the minimum annual shading effect is within 10%, you may make a design plan within the border region A. The results help to comprehensively distinguish the level of annual solar radiation for three buildings and the class of shading affected by adjoining buildings. And with the data, it is easy to determine a proper position in which the solar thermal or PV system is installed. In this part, the result is proposed by the type of layout and orientation, and applied them to a method of considering shading effect in the next step, a step of load matching analysis. In the case of applying as a shading factor to calculate the reduction of heat and power generation caused by shading, the factor is applied as the medium value of each class, and thereby class A applies 100%, class B 85%, class C 75%, class D 65% and class E 55%. In addition, the results of solar access evaluation effectively help to not only determine the proper location to install solar thermal and PV systems, but also make a comprehensive decision on some situations, including capability of securing annual solar radiation depending on the location of each unit, a relative superior location for a right of light, and the proper type of layout for apartments from the solar access standpoint.

5.3. Evaluation of Available Supply of Solar Energy System in a Complex 5.3.1. Load Pattern and Weather Condition New and renewable energy in a residential complex is divided into thermal energy necessary for DHW(domestic hot water) and space heating, and electric energy necessary for lighting, cooling and other electric

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appliances. The thermal energy can be generated by using solar energy system, geothermal and fuel cell generation and cogeneration, while the electric energy can be supplied through PV panels, wind power, fuel cells, and cogeneration. Such the systems can be offered as either a single system or several systems combined. Considering the actual circumstances of domestic apartments, this study assumes the thermal and electric loads as follows: In the case of thermal load, the solar thermal system is applied to DHW only or DHW + space heating; In the case of electrical load, just crystalline PV system is applied. In earlier chapter, the results of solar access evaluation showed available places, dimensions, and factors of shading effect. By applying the solar thermal and PV systems to the various results described in the previous chapter, It will be evaluated how much thermal and electric generation is made, and what percentage of new and renewable energy among the total load of the entire complex can be supplied. The TRY type standard hourly weather data of Daejeon, Korea is applied for building energy simulation. To interpret load matching through the new and renewable production of the total load, this study used MERIT program developed by ESRU in the UK in order for electric load through PV modules, and, in the case of thermal load, it used TRNSYS of University of Wisconsin in the US and undertook spreadsheet work. Figure 14 illustrates the process and results of load matching, including weather data by hour for this study, the total load of apartments, and a demand / supply load response through the renewable energy systems.

5.3.2. Load Matching Modeling for the Solar Thermal System In this step, the study designed the load matching modeling to be expected when the solar thermal and PV systems are applied in accordance with dimension options. The results of the study show predictable solar saving fraction when new and renewable energy system was applied through various combinations of the followings: a type of a building layout, orientation, a location of a building in a complex, a position of solar thermal/PV systems, available dimensions for the system, shading effect, and available scope of shading class. Through the hourly simulation with TRNSYS, the study brought about the results of the production that was made when vacuum-typed solar thermal system was applied to the available dimensions of roof and three outer walls together. Base on the results, the shading effect depending on a building location was applied by using shading factors described in the earlier step of solar access evaluation. At this time, with the consideration of reality, solar

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thermal system is applied to a building only when annual shading factors are categorized in Class A and Class B, or are more than 80%. The types of load to which the solar thermal system should cover are divided into DHW or DHW + heating. In fact, when the system is installed in the entire roof of a building, its efficiency can greatly be changed according to the number of households receiving the produced heat. Therefore, assuming that the solar thermal system installed in a building’s roof supplies its energy to, respectively, 10, 20, 30, and 40 households in the building, this study showed the results of its analysis in accordance with the different number of households. In the case of a system installed in a vertical outer wall, it defines that all the produced heat energy by the solar collector is just utilized in that unit household.

Figure 14. Process and results of load matching.

5.3.3. Load Matching Modeling for PV System In the case of PV system, its production is very intuitive compared to the solar thermal system. The power generation is influenced by the efficiency of PV modules, temperature compensation, and an angle of its installation. In addition, the change of shading caused by adjoining buildings can give significant impact on it. This study assumes the precision of feasibility of the system to be installed in the initial step, and comprehensively takes into account the effects of the angle of installation, orientation, and temperature compensation. By doing so, it produced the power generation by hour, and, through the MERIT program that helps to analyze load matching of the total

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electric load, evaluated the electric production for which the PV modules are applied. In addition, as for the shading effect, based on the results of the MERIT program, it reflected the shading factors described in the previous chapter to produce the final PV power generation and the electric load sharing ratio of the total load. The options of available dimensions and shading classes are evaluated in the same conditions as that of the solar thermal system. Various conditions were investigated with the dimension options varied ranging from IO-R to IO1, IO-2, and IO-3, and the scope of available households depending on shading class varied from Class A to Class C.

5.3.4. Evaluation of Solar Saving Fraction according to Scenarios To place the PV and solar thermal systems effectively in the restricted installation conditions in a complex of apartments, the features of each system and its reduction potential should be taken into account. When a solar thermal system is installed in a roof of a building, it should be considered that how many households in a building receive the energy generated by the system. In fact, it is unreasonable and impossible for each household to receive the energy generated by the system in the roof at the same time by the system in its frontal side, in order to meet on the load. If the system is placed on the frontal vertical wall of a building, it can be applied to the extent of shading group A or shading group A and B due to the system’s efficiency and economical investment. According to the earlier results of shading analysis, the borderline of group B is formed near the upper 10 floors. So the solar thermal system on the frontal side of a building can be applied to about 20 households on the upper floors per building. As a result, the solar thermal system installed on a roof can supply its thermal production to households on the lower floors, and at this time, the number of the households ranges from 10 up to 20. In the case of households on the upper 10 floors, it is reasonable for the system to be installed on the outer wall side of a building per household in order to take on the load of each household for the use of DHW and heating. For solar DHW system or solar DHW + heating system installed on a roof-top area, 10 households or 20 households per building were chosen, and the dimension options were restricted to IO-1 (opaque wall on the frontal side) and IO-2 (1/4 of the dimensions of the opaque wall and the window on a balcony), and the shading classes were restricted to Class A or Class A and B. Therefore total 16 scenarios for the solar thermal system configuration were evaluated.

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The evaluation results revealed that if the solar thermal system is applied only for the purpose of DHW, according to the dimension options, the solar saving fraction achieves 10-20%, and that if the system is applied for DHW + heating, the fraction achieves 12-22%. On the other hand, this study intends for solar thermal and PV system to be applied at the same time in order to balance the sharing of thermal and electric loads in apartment complex. After a practical and optimal combination of PV and solar thermal system configuration which can maximize the efficiency of both systems was established, annual thermal and electric energy production and solar saving fraction in the level of whole building complex were evaluated. It is desirable to install a PV system on the inclined surface of a roof rather than vertical wall surface, because it should work for whole year-round. In particular, since the PV system is highly vulnerable to partial shading, it should take restrictions and low efficiency if it is installed on the frontal side of a building in a complex. In the case of solar thermal system, it mostly takes load during winter season when solar altitude is low, and it has the system overheated during summer season. Therefore, when the solar thermal system is attached to the frontal side of a building with an incline of 90 degrees, it can not only get solar radiation better during winter season, but also solve the problem of overheating due to the high incidence angle. As a result, in terms of system efficiency, it is desirable for the PV system to be installed on the roof-top side, and for the solar thermal system to be placed on the frontal outer wall of a building. Figure 15 presents the results of the total thermal and electric production in a complex in the case that the PV systems are installed on all available roofs in a complex, and that it is installed on the frontal side of a building with the separation of DHW and DHW + space heating under the conditions of IO1(dimension option-1) and IO-2(dimension option-2). The evaluation results showed that the PV system installed on 9 building roofs was able to supply 11.2% of the total electric energy consumption in the entire complex. It shows that, in the case that DHW energy is supplied through vacuum-typed solar collector installed on the frontal side of a building, the solar thermal system can meet the 18-46% of the total DHW consumption according to the dimension options and shading classes. In addition, it indicated that, in the case that the thermal loads of DHW + space heating are taken into account, the solar thermal system can cover the 4-12% of the total thermal energy consumption according to the dimension options and shading classes.

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Figure 15. Results of the total thermal production and electric production in a complex.

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5.4. Conclusion Solar thermal system and PV system have similarities in generating heat and electricity through solar energy. But the PV system requires annual generation, while the solar thermal energy can only be utilized during mainly winter season. In particular, the solar thermal system requires a measure to deal with the overheating issue. Because of high density, apartments are one of objects having difficulty in securing available dimensions to install the solar thermal and PV systems. In particular, since there are many variables, such as, restricted available dimensions, a type of system, the location to be installed, and the scale of the system, installing the system is very complicated. Under this background, in this section, the study evaluated the total available dimensions for the solar thermal and PV systems, considering a type of layout of apartments and orientation. It also evaluated the solar access, including annual shading ratio depending on the location of each household and each building. Based on those evaluations, it evaluated the thermal and electric production when the solar thermal and PV systems are applied, and the solar saving fraction of the total loads. The results of the study suggested predictable solar saving fractions in the case that the new and renewable energy system is applied through various combinations of the followings: a type of building layout, orientation, a building's location in a complex, a position of solar thermal energy and PV system, available dimensions, shading

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effect, and shading classes. And after selecting the actual combinations of the elements, the study constituted scenarios, and thereby evaluated the new and renewable energy contribution ratio of the total load in a complex. In the case of economic restriction for renewable energy system, the option of the least system capacity with the best efficiency would be desirable to install PV system on the whole roof-top and solar DHW system on the opaque outer wall with the IO-1 area option and the shading class option of ASR-A. Evaluation results revealed that the system can supply its energy for about 5.6% of the total energy consumption (heat and electricity), If the scope of the appliance of the solar thermal system extends to IO-2 dimension option or households with ASR-B shading class, the system’s energy contribution can range between 7 and 10.7%. A scenario that a system responds to the total thermal loads of DHW + heating is not different from a scenario that it responds to the load of DHW only. Therefore, for the purposes of system simplicity and its use, it is desirable for the solar thermal system to be used only for the purpose of DHW. This study assumed four general types of layout for an apartment complex, and thereby it calculated available dimensions for solar thermal and PV system, and evaluated solar access depending on shading effect and the thermal and electric production through the PV and solar thermal system based on installation locations. Considering these evaluations, the study evaluated the maximum solar saving fraction to be expected by applying the solar energy system to an apartment complex by scenario. As shown above, in the event that the solar thermal and PV system is applied to apartments, this study can be effectively utilized for assessing feasibility review and usual capacity, selecting an appropriate system and locations, and evaluating economic conditions.

6. CONCEPT OF ESTABLISHING ZERO ENERGY TOWN AND MEASURE OF FACILITATING ITS EXTENSION 6.1. Concept of Establishing Zero Energy Towns Since people have recognized the limitation of energy saving technology, and the needs of developing and facilitating new and renewable energy technology as an active measure, they have actively established a 100% energy self-supply town (energy self-reliance which supply some and 100% of new

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and renewable energy on its own) where fossil fuels are not used (Figure 16). By generating various eco-friendly energy, including solar thermal energy, PV energy, geothermal energy, fuel cell, landfill gas, micro hydro power, wind energy, and biomass, the energy self-supply town supplies heating/cooling, hot water, and electric energy to a town with tens of or hundreds of houses on its own. The design of energy self-supply town only includes the concept of energy, so by changing or extending the design considering regional characteristics, more applications, such as zero energy town, solar city, ecology town, and eco-friendly town, can be achieved.

Figure 16. Massive application of energy self-supply system.

6.2. Advantages and Disadvantages of Central Supply System of New and Renewable Energy To address the issue of the total load of heat and electricity in a complex with a certain size, various new and renewable energy, such as solar thermal energy, PV energy, geothermal energy and wind energy, should be supplied. Along with that, energy saving and energy efficiency technologies, in particular, should be prepared.

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When various new and renewable energy systems are provided for a certain scaled complex in a large-scale and mixed way, there are some advantages as follows: 



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





Complement of continuous supply limitations to sources of new and renewable energy: Since nature energy, such as solar energy and wind energy, is influenced by weather and climate, it is hard to supply them continuously. For example, when it rains or snows for several days, it is hard to difficult utilizing solar energy, instead wind energy can play a complementary role. Mutual sharing of supply system and improvement of system efficiency: For example, when a solar thermal system is used in a single house, the loads (consumption) that do not occur are thrown away. But when a supply system is shared with many houses whose loads occur sporadically, loads are less thrown away and thereby the system can efficiently be operated. In addition, because the loads include public buildings and public facilities as well as each household, the system’s efficiency will be enhanced. On balance, it is expected to improve system efficiency due to load continuity and central supply. Extension of installation dimensions: In the case of a single house, the installation dimensions for solar thermal and PV system are limited to roofs and walls with a southern exposure. In a case of a complex, it is possible to secure larder installation dimensions as it has public space as well as dimensions of each household. For example, if a solar thermal system or PV system is installed on the upper part of a public parking, it is possible to not only secure additional dimensions, but also use the equipment as a sunshade of the parking. Easy maintenance and management: One of big obstacles to distribute new and renewable energy is the maintenance and management of the system. For example, solar thermal system consists of complicated elements, such as pipe circulating thermal medium and heat accumulators, and system control by season is very significant. Therefore, maintaining and managing a system is important. In fact, it is not easy for each household to manage the system well, so managing a single central supply system by an expert can bring out more profits as well as system efficiency. Economical cost: Central, large-scale system can cut initial investment money, and improve efficient operation fee.

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PR effect through a large-scale system: If a large-scale system is successfully installed on a complex, such success can bring out promotion effect, and thereby it can affects the wide use of individual system.

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Energy loads occurring in a complex are divided into thermal load and electric load. Since a complex largely consists of houses, the loads are mostly heating, cooling, hot water, electricity, and cooking which are necessary for households. To establish successful energy self-reliance town, it is necessary to apply energy saving and high efficiency technologies to most areas, and then new and renewable energy technology is applied to the rest. In other words, through technical priorities from the economical standpoint and the optimized combination of systems, it is possible to implement 100% energy self-reliance communities which do not use fossil fuels. As shown in Figure 17, for example, simply gathering many zero energy solar houses is not appropriate to implement an energy self-reliance community. In other words, to prepare a measure for common load and load continuity, more enhanced system combination is necessary.

Figure 17. Concept of zero energy community.

Various types of communities exist depending on community size, population density, living standard, climate conditions, building type, culture pattern, available resources and features of energy system. But given the goal of integrating sources of new and renewable energy, representative elements are displayed as follows: (100% renewable energy self-reliance “100 Community” establishment project by EU, COM(97) 599 of 26.11.1997

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Energy for the Future: Renewable Sources of Energy - White Paper for a Community Strategy and Action Plan)    

Energy consumption density per unit dimension of available new and renewable energy sources Type of energy infrastructure and its availability Consumption pattern of electric energy Community size

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6.3. Process of Establishing a Zero Energy Town Figure 18 shows the details of systematic process of establishing a zero energy town. Prior to the process, comprehensive works, such as the entire overview of the project, budge scope, and a project schedule should be planned. First, an appropriate area should be chosen. To do that, it is desirable to previously make evaluation standards about several items, including regional characteristics, the size of the town, PR, installation conditions, consumer support, maintenance, land conditions, building conditions, and economical conditions, and then to make detail schedules. Next, load size and an energy use pattern in the chosen region should be analyzed. The fundamental condition to evaluate the type of new and renewable energy sources and the amount of their availability is to understand a pattern of energy consumption of the selected region. Therefore, a load pattern and use energy sources by each building should be analyzed in advance. After the analysis of energy pattern, planning for energy self-reliance on the target area should be made. In other words, by considering the total load, analysis results of load by element, the total budget size of the project, a goal of energy self-supply ratio should be configured. Once the available budget size and self-supply ratio are determined, it is possible to potentially decide optimized new and renewable system type and size. If the basic planning is evaluated and basic data is analyzed completely, specific data should be collected, and feasibility test be examined through field research. In addition, with the field research, buildings and households available for the system will be determined. At this time, given the field conditions, specific research about installation position, installation conditions, in particular, should be conducted.

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In the next step, through the field research, specific information should be gathered together to carry out basic design about the entire new and renewable energy system. For the basic design, the climate of the target area should be analyzed. So a variety of climate information, including available solar energy sources, wind energy sources, and temperature/humidity, should be analyzed. Particularly, annual average climate data for system design or climate data by hour should be analyzed.

Figure 18. Process of establishing a zero energy town.

Figure 19 illustrates the results of system basic design and alternative configuration. After the completion of basic design, economical conditions should be evaluated. In this step, it is necessary to obtain evaluation results about energy saving rate and new/renewable energy self-supply ratio through applicable systems. And through the calculation of initial investment cost of each system, it is also possible to receive the outline of payback period. Based on the comprehensive results above, it is desirable to prepare multi-step scenarios depending on the change of the budget size subsidized by the central government each year.

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Figure 19. Example of results of basic system design and results of alternative configuration.

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6.4. Conclusion For energy saving of each town unit, a systematic access to analyze energy consumption pattern in the chosen town is very important. In the past, Korean zero energy village (or Green village) project haven’t brought any remarkable success as it reached to the extent that the government with its assigned restricted budget evenly distributed and applied systems only to the households applying for the project. Therefore, it is necessary to investigate the amount of thermal load and electric load in a town, and then to determine the optimized type and size of a system to meet the self-reliance of the town. As a result, to achieve a successful project, local governments and architects should work together to push forward comprehensive, systematic design and engineering, and at the same time all residents in a town should put the public interests rather than individual interests in priority and voluntarily participate in the project.

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REFERENCES ASE, 2010, Alliance to Save Energy . Bourbia, F., Awbi, H.B., “Building cluster and shading in urban canyon for hot-dry Climate”, Renewable Energy, 2004. http://www.esru.strath.ac.uk/ Programs/Merit.htm. http://www.trnsys.com/. IEA, 2010, “Key world energy statistics 2010”, http://www.worldenergy outlook.org/. IEE, 2008, The Institute of Energy Economics, Japan, Section of gauge analysis unit: survey on EDMC/energy·economic statistics. Jongho Yoon, Jaisung Park, kyungil Jin, Ucheol Shin, 2008, "Solar Access Evaluation of Roof and Facad in Apartment Buildings Depending on the Types of Building Layout and Orientation” Journal of the architectural institute of Korea planning and design 24(10): 219-226. Jongho Yoon, Jaisung Park, Ucheol Shin, “A Study on the Power Saving Fraction of Site Electrical Load depending on the installation area of PV system in Apartment Complex, ISSN 1598-6411. Journal of the Korean Solar Energy Society. Vol.28, No.3, Jun. 2008. Kankyo-business, 2009, “Value of PV generation”, April 2009. KPX, 2009, Korea Power Exchange, “Research on distribution rate of home electric appliances and pattern of power consumption for household 2009”. Mills, G., “An Urban Canopy-Layer Climate Model”, Theor. Appl. Climatol., 1997. Nakamura, Y., Oke, T., “Wind, temperature and stability conditions in an eastwest oriented urban canyon”, Atmos. Envir., 1988. NEDO, 2009, New Energy and Industrial Technology Development Organization, “The Japanese PV roadmap towards 2030”. Santamouris, M., Papanikolaou, N., Koronakis, I., Livada, I., Asimakopoulos, D., “Thermal and airflow characteristics in a deep pedestrian canyon under hot weather conditions”, Atmos. Envir., 1999. The US department of energy, “Getting Started with EnergyPlus”, University of Illinois, 2007. Tokyo City, 2008, “Execution program for Tokyo after 10 years 2008”. Tokyo City, 2010, “Execution program for Tokyo after 10 years 2010”. Tokyo City, 2011, “Execution program for Tokyo after 10 years 2011”.

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Yoshida, A., Tominaga, K., Watani, S., “Field measurements on energy balance of an urban canyon in the summer season”, Energy and Buildings, 1990/91. Yukio Nakano, 2009, “End-use Energy Demand and Energy-saving Technologies in Residential and Commercial Sectors”.

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Chapter 3

TRAVELER RESPONSE TO THE 2005 GAS PRICE SPIKE Matthew S. Bomberg1, and Kara M. Kockelman2,† 1

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Department of Civil and Environmental Engineering The University of California at Berkeley, 210 McLaughlin Hall, Berkeley, California, US 2 The University of Texas at Austin ECJ 6.9, Austin, Texas, US

ABSTRACT Knowledge of travelers’ response to spikes in gas prices is key to planning for future instabilities in gas prices and offers insight into possible pricing strategies as mechanisms for reducing vehicle travel or improving efficiency of fuel use. A survey of over 500 residents in Austin, Texas capitalized on a severe spike in gas prices that transpired in September of 2005. This work examines how respondents’ travel behavior changed during and following the spike. This paper describes the findings using basic descriptive statistics, before/after behavioral correlations, and ordered probit and binary logit models to determine 

Graduate Student Researcher. Department of Civil and Environmental Engineering, The University of California at Berkeley, 210 McLaughlin Hall, Berkeley, CA 97420, E-mail: [email protected]. † Corresponding Author: Professor and William J. Murray Jr. Fellow, The University of Texas at Austin, ECJ 6.9, Austin, Texas 78712, Tel: (512) 471-4379. FAX: (512) 475-8744. Email: [email protected].

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Matthew S. Bomberg and Kara M. Kockelman which factors are responsible for behavioral changes in response to gas price spikes.

Keywords: Gas prices, gas price spike, Hurricane Katrina, travel behavior

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INTRODUCTION Gas prices are of undeniable importance to both consumers and economies at the local, state, and national levels in the US and abroad. Oil imports provide a significant percentage of North America and Europe’s refined gasoline, however, the global oil market is notoriously unstable, and can produce sudden spikes in prices. These spikes can generate everything from consumer outrage to a damaging ripple throughout a nation’s economy. An understanding of how consumers respond to spikes in gas prices is crucial to developing contingency plans for future spikes. Moreover, spikes in gas prices provide a brief but meaningful glimpse into how consumers tend to operate at higher-than average prices. Such observation can yield insight into how pricing strategies encourage both reductions in driving and more efficient use of fuel. September of 2005 was such a period in the US. The confluence of a number of factors such as the emergence of new, major oil-consuming nations, aging US refining infrastructure, and increased US demand were pushed over the top by the Hurricane Katrina disaster. Prices reached the $3.00/gallon level, comparable in real terms to the Iranian crisis of the early 1980s. In an effort to quantify and illuminate the behavioral responses of travelers to this spike, a survey of 563 residents was undertaken in Austin, Texas in February 2006. The survey asked respondents to rank the degree to which they changed a series of travel behaviors in response to heightened fuel prices during and following the spike. The survey also investigated opinions on a variety of issues of gas pricing and energy policies. This paper presents descriptive statistics and findings of simple before-after correlation analyses and ordered probit model and binary logit model estimation.

LITERATURE REVIEW Economics suggests that a price change will affect consumption decisions, with each marginal change in price yielding a corresponding marginal change

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in demand. In the case of gasoline, travelers might react by decreasing overall travel either through eliminating trips, utilizing substitute modes, or by more efficiently using fuel. Elasticities of demand provide an easily understood metric of consumer price sensitivities. Though calculated on an aggregate basis, these elasticities can prove good predictors of what individual consumers might do, given a certain price change. Literature on gasoline demand elasticities generally separates these into short-run and long-run elasticities. Gasoline is regarded as a commodity which is highly inelastic in the short term. In the 1970s and 1980s, a number of different econometric studies of gasoline demand were conducted, inspired by concerns over high fuel prices and energy conservation (Espey, 1998). In the 1990s, several studies endeavored to review large bodies of these studies and reaffirmed gasoline’s short run inelastic demand. Goodwin (1992) reviewed a variety of studies performed in a number of different locations from the 1980’s and 1990’s and found a mean elasticity of -0.27 for gas consumption as well as a mean elasticity of -0.16 for traffic levels (both with respect to price)1. Goodwin also noted that more recent work generally revised elasticity estimates upwards. Dahl (1995) reviewed a number of previous gasoline demand surveys conducted since 1977 in a wide range of locations around the world and updated this work with 18 more recent studies conducted in the US. She found a short run elasticity of demand of -0.19 from the more recent studies, and speculates that elasticities have become less over time based on her relatively small set of more recent studies. Espey (1998) identified a wide discrepancy in methods used to compute demand elasticities for gasoline, and attempted to identify the effects of different types of models. Performing an international meta-analysis of 363 estimates of short-run demand elasticity with respect to price from between 1966 and 1997, Espey found that model structure influences price effects, short-run elasticities vary across countries, and short-run price elasticity estimates have fallen over time, which she attributes to improvements in fuel economy. Espey (1998) also concluded that US demand for gasoline is generally less elastic in the short run than in Europe, and that models that allow for lags in demand produce higher elasticities than those not accounting for such lags. More recently, Goodwin et al. (2004) updated work on gas price effects. Reviewing empirical studies since 1990 from around the world, they find that a 10% increase in the real price of fuel will produce a 1% reduction in vehicle 1

Elasticity is defined as the percent change in consumption of a good caused by a one-percent change in price. Thus, an elasticity of -0.27 indicates a 2.7 percent decrease in consumption given a 10 percent increase in price.

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miles traveled and a 2.5% reduction in fuel consumption, as well as a 1.5% increase in fuel efficiency of vehicles and a less than 1% decrease in net vehicle ownership. The authors speculate that fuel consumption falls more than volume of traffic because price increases trigger a more efficient use of fuel through technical improvements to vehicles and more fuel-conserving driving styles. Despite much evidence that gasoline is highly inelastic in the short run, two studies suggest that its inelasticity may be over-estimated in the case of sudden spikes in price. Dargay and Gately (1997) question an assumption of most demand models of perfect price reversibility (price increases and decreases have equal and opposite effects), noting that in the 1980s as gas prices fell after rising from 1974-81, only a fraction of demand for gas was recovered. They hypothesized the existence of “hysteresis” or path dependency in gasoline demand, pointing out that consumers may respond more strongly to price spikes than reductions which generally result from inflation and that below a certain threshold the costs of adjustment to a price increase may outweigh benefits attained. They developed a model which allowed for imperfect price reversibility, and found that this model better explained changes in price which exceed some historical maximum. Perfectly reversible models, they argue, would overestimate the effects of price cuts and price recoveries which do not totally undo the demand reductions of the initial price rise, and would underestimate the effects of surpassing previous price levels. Thus, gasoline may be less inelastic in cases of sudden and large spikes, especially those which exceed historical maximums. Dargay and Gately note that statistical evidence demonstrates that certain price rises provoke stronger consumer response, especially those of the 1970s which were sudden and large. These increases also occurred amid a climate of uncertainty about energy security (Espey, 1998). It is possible that uncertainty about future gas prices also caused people to “overreact” in an attempt to buy or insure themselves out of future costs. Puller and Greening (1999) studied the short-term behavior of US households, and attempted to improve upon preceding gas demand models which account for price lags only on an annual basis. They imposed four different quarterly lag structures on gas prices, and selected as optimal a “snap back” lag structure in which demand has a large negative effect followed by a return to previous levels as consumers adjust to durable stock. In this way, they computed an impact elasticity of -.8 while still obtaining a short-run elasticity of -.35, which is consistent with previous literature. This model suggests that within the first quarter after a price change, consumers respond

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to a price rise with a much larger decrease in consumption than is indicated by total short run elasticity, but subsequently increase consumption as they develop more efficient ways of using existing vehicles. A household’s demand for gasoline has been decomposed in several ways. Eltony (1993) recognized three behavioral changes that households make in response to fuel price changes: driving fewer miles, purchasing fewer cars, and buying more fuel efficient vehicles. Running a model on data from Canadian households between 1969-1988, the author found these behaviors account for 75%, 15%, and 10% of household’s short term response to increased fuel prices. Puller and Greening (1999) disaggregated gasoline demand to demand for vehicle miles traveled and demand for miles per gallon, and found an elasticity of -.69 for a household’s vehicle miles traveled (VMT) and -.22 for fuel economy (miles per gallon [MPG]). They attributed these to households making large adjustments in the form of decreasing overall driving, but in the process reducing their demand for efficient driving by foregoing high efficiency miles such as vacations.

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STUDY AREA The study was conducted in the Austin, Texas region. Respondents primarily lived within Austin city limits, and the vast majority lived within Travis County. When compared to the U.S. at large as well as similarly sized cities and regions (e.g., Las Vegas, San Antonio, Milwaukee and Nashville), Austin enjoys an extremely well educated and younger population, as well as relatively high incomes. Using Census 2000 statistics, Austin is comparable to most other U.S. regions in terms of commute mode choice (74.9% drive alone and just 5.9% take transit or walk/bike). While Austin tends to be one of the most congested regions of its size (Lomax and Shrank 2005), it is quite possible that Austinites’ responses to high gas prices are reasonable estimates for other locations throughout the U.S. Figure 1 shows a plot of the price of regular unleaded gasoline between December 2003 and June 2006. The spike during the summer of 2005 capped a period beginning in January 2005 in which the price rose almost 75%, from $1.80/gal to nearly $3.10/gal. Though this rise also coincided with the normal rise in gas prices that occurs during summer months as people drive more (Schoen 2006), the peak price far exceeded that of the summer of 2004 ($2.04/gal). The most severe spike occurred at the beginning of September

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2005, as prices rose from $2.60/gal to their peak of nearly $3.10/gal in about two weeks, a 19.2 % increase. Two previous spikes in gas prices in the US offer a historical precedent for the spike of 2005. Figure 2 shows a plot of the consumer price index of motor fuel with respect to time in the US during the periods surrounding three major spikes in gas prices. In 1973, US support for Israel in the Yom Kippur War caused OPEC nations to stop exporting crude oil to the US. Prices rose from 38.5¢/gal in May 1973 to 55.1¢/gal in June 1974, a 43.1 percent increase. In terms of 2005 dollars, the peak price was around $1.80/gal, far lower than today’s price. The spike in prices was largely a result of shortages in the supply of crude oil; in the US, strategies like rationing of gas and the institution of a 55 mph speed limit were undertaken to reduce consumption.

Figure 1. Gas Prices During Spike and Data Collection Period.

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Notes: In Figure 2, CPI-U is the consumer price index for all urban consumers. The base periods are the third quarter of 1973 for the 1973-1974 price shock, the first quarter of 1979 for the 1979-1980 price shock, and the fourth quarter of 2003 for the 2004-2005 price shock.

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Figure 2. Comparison of 2005 Spike to Historical Spikes (Source: Peterson 2006).

The wake of the 1973 spike saw the formation of the Department of Energy and Strategic Petroleum Reserve and downsizing of automobiles by the “Big Three” US automobile manufacturers. Prices failed to drop significantly, however, and in 1979, the Iranian revolution led to a decrease in Iran’s oil production. Despite increased OPEC output, panic and speculation caused the price of oil to skyrocket. Real prices rose from around $1.60/gal to $2.73/gal (in 2005 dollars) between March 1980 and March 1981, a 64 percent increase. Prices then declined slightly, followed by another, smaller spike in 1982, as the Iran-Iraq war began, crippling both countries’ output of crude oil and driving US prices to $3.13/gal. The initial spike between 1979 and 1980 proved far more harmful to the US economy than the later spike, despite the fact that the latter achieved a higher price. Compared to historical spikes, then, the spike of 2005 was much more abrupt, which likely explains the lack of government response. The percentage increase in price was smaller in 2005 than in prior shocks, but the rate of increase far exceeded previous spikes. The 2005 spike also set a new

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maximum for gas prices (in real and in nominal terms). Unlike previous spikes, the 2005 spike resulted not from a shortage in crude oil, but growing demand (in the US and worldwide) combined with a shortage in refined gasoline. Its severity makes it an interesting case study. Moreover, forecasts of future oil supply-demand imbalances makes understanding behavioral responses rather critical to anticipating future traffic patterns. Indeed, many market analysts predict that because high prices have been driven not just by decreased supply but also by growing demand, both for crude oil and refined gasoline, prices at the pump will remain high. Crude oil prices were around $70 a barrel in July 2006 (compared with $30 a barrel as recently as late 2003), and these are not expected to fall in any significant way over the next 10 years (Peterson 2006). To be fair, crude oil supply and refining capacity have both been crippled recently by a variety of factors such as instability in major oil producing nations like Iraq, Iran, Venezuela, and Nigeria and natural disasters (especially in the US). However, even if global supply of both crude oil and refined gasoline increases, it is questionable whether it will be able to keep pace with growing global demand. High prices may well be here to stay, and it is important to understand how traffic will respond.

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DATA AND ANALYSIS The study roughly consisted of three phases. In the first, data was collected from Austin residents via a survey. In the second phase, descriptive statistics and simple measures of correlation were used to analyze behavioral adjustments resulting from gas price spikes and attitudes towards energy policy in the wake of the spike. In the final phase, ordered probit and logit models were used to analyze the most noticeably affected behaviors, as functions of demographic and neighborhood variables.

Data Acquisition The primary data source for this study was a survey of Austin residents. Two versions of the survey, a general version and student version, were used. A hard copy version of the survey (with postage-paid return envelopes) was distributed to approximately 400 households. The survey also was made available on the Internet and advertised to hundreds of Austinites via emails to their neighborhood associations. Hard-copy distribution was achieved by

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going door to door among 10 distinct neighborhoods, as recommended via an informal survey of several realtors2. The student version was only available electronically, and was distributed by solicitations sent out through various University of Texas colleges and academic departments, primarily the College of Engineering. Data was collected between February and April 20063. After removing incomplete responses, the data set included 563 observations. 44% of these respondents were students, 38% of respondents were non-students responding electronically, and 18% of respondents were non-students responding via mail. The door-to-door distribution thus achieved a response rate of approximately 25%. As Table 1 indicates, the sample overrepresents young males without college educations and middle-age women with college educations, while under-representing middle-aged males and females without college educations. Accordingly, the sample was adjusted to represent Travis County 2000 demographics on the basis of age, educational attainment, and gender. Table 1. Sample Breakdown by Demographic Attributes

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Demographic Young Male, No College Degree4 Young Male, College Graduate Middle Age Male, No College Degree Middle Age Male, College Graduate Older Male, No College Degree Older Male, College Graduate Young Female, No College Degree Young Female, College Graduate Middle Age Female, No College Degree Middle Age Female, College Graduate Older Female

Number of Respondents 169 27 3 87 2 14 77 36 8 132 8

Survey Percentage 30.02 4.80 0.53 15.45 0.36 2.49 13.68 6.39 1.42 23.45 1.42

Census Percentage 16.29 7.74 11.66 11.65 2.08 1.53 13.58 7.46 12.44 10.39 5.18

2

The realtors were asked to compile a list of neighborhoods which they felt represented all neighborhood types in Austin with respect to demographics, density, and distance to the Central Business District (CBD). The most frequently recommended neighborhoods were visited, with Census Block Group data used to ensure that no neighborhood type was overlooked. 3 During the majority of the data collection period, gas prices hovered around $2.30/gal (regular, unleaded). Towards the end of April 2006, prices began to rise again, ultimately reaching about $2.95/gal again in May. 4 No College Degree includes currently enrolled college students. College Graduate includes those individuals with a two-year Associates Degree. The age breakdowns are: Young = 1834 years of age, Middle Aged = 35-64 years, Older = 65 years and older.

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Students and non-students initially were combined into one sample. For “full-time student” respondents (i.e., those enrolled in 9 or more credit hours and working fewer than 35 hours/week), information about commutes to campus was substituted for work-commute information. Students residing on campus were considered to be working at home. All results reported here are on the basis of a population-weighted (bias-corrected) sample. One irremovable bias in the sample may well be respondents’ concern for the environment. Judging from the responses to the questions about energy policy in section 3 of the survey, the sample largely consisted of people with strong concerns about the environmental implications of energy consumption who may be more inclined to adopt conservation-oriented behaviors in the face of high prices. However, because the study examines responses to a spike in gas prices, this bias may be moderated: people concerned about the environment probably already travel in more fuel-efficient ways, and thus may not be able to easily exhibit as much of an increase in fuel-saving behaviors. In addition, the non-student portion of the sample consisted exclusively of persons residing in homes, and thus excluded one third of US households (who live in apartments). Finally, the sample is constrained by the fact that people were asked to report their travel behavior months after the fact, and may have had problems remembering exactly how they responded to the price spike. The survey covered respondent transportation needs, respondent vehicle ownership and usage, energy policy, and demographics. The surveys can be viewed online at http://www.ce.utexas.edu/prof/kockelman/public_html/ GasPriceResponseSurvey.pdf. The core of the survey consisted of two multipart questions in which participants were asked to consider a set of behaviors which reduce fuel consumption and rank the degree to which they exhibited a change in such behaviors in response to higher gas prices. This question was asked about behavior during the summer of 2005 (in the period surrounding Hurricane Katrina, when gas prices spiked) and after the summer of 2005 (but before prices rose again in 2006). Additional data was obtained from other sources. The fuel economies of respondents’ vehicles were obtained from a database maintained by the US Department of Energy and the US Environmental Protection Agency5. GIS software was used to geocode respondents’ home locations and match these to neighborhood form and accessibility variables by Traffic Serial Zone (TSZ).6 5 6

The database can be found at www.fueleconomy.gov. EPA fuel economies were used. Respondents’ residences were located in TransCAD (Caliper, 2002), using the two nearest cross-streets respondents identified in the survey. Respondents often reported major streets that served as boundaries between TSZs and made fine-grained location of their residence

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Table 2. Explanatory Variables Used Explanatory Variable

Description

Mean

SOV

Drive alone to work 2+ times/week Take bus to work 2+ times/week Walk to work 2+ times/week Bike to work 2+ times/week Walk or bike to work 2+ times/week Carpool to work 2+ times/week Work at home 2+ times/week Commute to work using different modes 2+ times/week each Take children to school or daycare Home-to-work travel time (minutes) Number of non-work related driving trips/week Money ($) spent on gas/week Vehicle Miles Traveled/week Average fuel economy of all households vehicles used 2+ times/week (mpg) Fuel economy less than 20 mpg Fuel economy greater than 30 mpg Respondent’s age (years) Indicator variable for males Household income before taxes ($/year)

Bus Walk Bicycle Bike/Ped Carpool Work at Home Multiple Modes

Children HBW Travel Time HBNW Trips/Week7 Gas Expenditures

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VMT/Week Fuel Economy

Low MPG High MPG Age Gender Income

Min

Max

0.552

Std. Dev. 0.498

0

1

0.206

0.405

0

1

0.146 0.073 0.202

0.353 0.260 0.407

0 0 0

1 1 1

0.057

0.232

0

1

0.147

0.423

0

1

0.249

0.433

0

1

0.121

0.326

0

1

13.86

13.00

0

90

6.04

4.17

2.5

20

78.11

53.58

25

250

81.97

66.24

25

250

23.55

7.53

0

55

0.220

0.415

0

1

0.123

0.328

0

1

35.44 0.533 52,420

15.79 0.499 58,371

18 0 0

83 1 200,000

difficult. In instances when a cross-street reported by a respondent formed the boundary between two or more TSZs (43.5% of cases), the location and accessibility variables for all TSZs touched by the street were averaged. Satellite imagery from Google Earth was used to identify instances in which it was highly unlikely that one of the TSZs included the participant’s home (for instance, a cross-street demarcating a park and a residential neighborhood or a cemetery and a residential neighborhood). 7 “Trips” came directly from the survey where they were reported by respondents. Respondents were asked to report “On average, how many round-trip NON-WORK related trips do you make each week by car?”

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96

Matthew S. Bomberg and Kara M. Kockelman Table 2. Continued

Explanatory Variable

Description

Mean

Full-Time Student

Enrolled in 9 or more credit hours and working fewer than 35 hours/week Employed Part- or FullTime Attained at least Bachelor’s degree Number of persons in household Vehicles used by household 2+ times/week per driver in household Population within 1 mile radius of TSZ Residential Area in 1 mile radius of TSZ (in square miles) Commercial Area in 1 mile radius of TSZ (in square miles) Basic jobs in 1 mile radius of TSZ Retail jobs in 1 mile radius of TSZ Service industry jobs in 1 mile radius of TSZ Total jobs in 1mile radius of TSZ Euclidean Distance from TSZ to CBD (mi.) Bus stops/square mile in TSZ (Jobs + households)/square mile in TSZ

Employed College Educated Household Size Vehicles/Driver

Local Population8 Residential Area

Commercial Area

Basic Employment Retail Employment

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Service Employment Total Employment CBD Distance Bus Stop Density Zone Density

Min

Max

0.416

Std. Dev. 0.493

0

1

0.680

0.467

0

1

0.488

0.500

0

1

2.574

1.279

1

6

0.533

0.516

0

2

60276

6367

30791

72393

5.50

0.39

3.49

6.38

0.43

0.03

0.27

0.51

13952

3185

5860

25652

5874

943.53

2618

7775

16703

3647

7124

27325

75.06

2.61

65.07

86.05

4.37

3.74

0.685

17.78

46.97

52.42

0

212

6248

5103

293.86

67108

The neighborhood form variables include density of bus stops in the TSZ (stops/mi2), Euclidean distance to the CBD, and total zonal density, defined as:

8

Population, employment, and area statistics were computed by Gupta et al. (2004) for each of Austin’s Traffic Serial Zones (TSZs) using 1 mile radii. Each index reflects the total population, area (in square miles) or number of jobs within a 1 mile radius of the respondent’s home TSZ centroid.

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Traveler Response to the 2005 Gas Price Spike

 Zonal 

Households  Jobs Area

97 (1)

where Households and Jobs are the total number of households and jobs in the TSZ in 1997 and Area is the area of the TSZ (in square miles). In addition, a series of regional Accessibility Indices (AIs) from Gupta et al’s (2004) travel demand models of credit-based congestion pricing for the Austin region were added to the data set. Table 2 summarizes all the explanatory variables used.

Analysis of Behavioral Changes with Descriptive Statistics Summary statistics offer a general picture of the behavioral responses to high gas prices. Tables 3 and 4 summarize the (population-weighted) response during and after the spike. During the spike, behaviors seeing the most increase were “shopping around for gas” (67.4%), reducing overall driving (61.6%), and chaining activities (58.6%). Those experiencing the lowest increases were increased driving of the household’s most fuel efficient vehicle (22.1%) (implying less driving of the less efficient vehicles), carpooling (21.5%), transit use (17.9%), and bicycle trips (15.6%).

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Table 3. Descriptive Statistics of Behavior During Spike Behavior

Mean

Standard Percentage No Deviation Change

Percentage Increase

Percentage Significant Increase 24.70 10.70 13.90 7.90

Shopping around for gas 3.38 1.23 32.11 67.44 Reducing overall driving 3.05 1.12 35.30 61.60 Trip chaining 3.07 1.15 40.20 58.60 Greater attention to vehicle 2.76 1.04 56.53 43.47 maintenance Driving at steadier speeds 2.61 0.98 64.41 35.22 6.30 Driving at slower speeds 2.53 0.95 66.40 32.40 5.90 Walking trips 2.42 0.84 69.40 29.00 4.20 Buying partial tanks of gas 2.46 1.02 72.13 24.83 8.00 Driving most fuel efficient vehicle 2.37 0.90 74.68 22.13 4.50 Carpooling 2.30 0.85 73.69 21.45 3.90 Transit use 2.26 0.79 78.40 17.90 3.60 Bicycle trips 2.23 0.75 81.60 15.60 3.30 Key: 1 = Decrease (in behavior), 2 = No change, 3 = Slight increase, 4 = Moderate increase, 5 = Significant increase

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Matthew S. Bomberg and Kara M. Kockelman Table 4. Descriptive Statistics of Behavior Post-Spike

Behavior

Mean

Standard Deviation 0.75 0.75 0.84 0.62

Percentage Decrease 9.20 9.28 11.73 6.10

Percentage No Change 56.30 59.15 57.10 71.50

Percentage Increase 34.50 31.57 31.16 22.40

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Shopping around for gas 4.22 Chaining activities 4.18 Reducing overall driving 4.13 Greater attention to vehicle 4.14 maintenance Walking trips 4.10 0.64 6.30 73.90 19.80 Buying partial tanks of gas 3.98 0.74 9.20 75.60 15.10 Transit use 3.99 0.76 7.50 77.60 14.80 Driving at steadier speeds 4.04 0.59 6.80 78.50 14.60 Driving at slower speeds 4.02 0.61 8.00 77.90 14.20 Driving most fuel efficient vehicle 4.01 0.67 5.70 81.00 13.30 Carpooling 3.95 0.75 8.75 78.99 12.26 Bicycle trips 3.88 0.76 9.90 81.60 8.50 Key: 1 = Significant decrease (in behavior), 2 = Moderate decrease, 3 = Slight decrease, 4 = No change, 5 = Increase

In general, behaviors exhibiting the greatest increase were easy adjustments to make, while those with lesser response may have been simply impossible (for instance, a shift toward the household’s more fuel efficient vehicle, in households owning a single vehicle) or required comparatively significant lifestyle adjustments (for instance, carpooling). After the spike, fewer people reported increases in gas-saving behaviors relative to their behavior during the spike, and some even reported decreases. More respondents (as weighted by population percentages, to correct for sample biases) reported no change in behavior, suggesting that spikes in gas prices did little to change these people’s behavior and/or that more efficient behavior was retained even after the price spike. The ranking of behaviors eliciting the greatest behavioral shifts before and after the spike were the same, which may imply a hierarchy of gas-saving behaviors people are most likely to accept. Moreover, the after-spike averages were all close to 4 (the ranking for no change), as people who intensified their gas-saving behavior after the spike were “canceled out” by people whose behaviors reverted to pre-spike tendencies. The overall effect of an isolated spike on an areas’ population may thus be to produce two classes of residents: one class that drives and travels in much more fuel efficient ways, and whose adoption of more fuel efficient tendencies happens gradually (possibly as they are able to make more longterm adjustments), and another class whose travel behavior differs little from before the spike.

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A not-insignificant percentage of respondents (about 6%) reported that they had moved or changed jobs in response to high gas prices, and almost 3 times as many reported that they had considered doing this. In the longer term, far more may make such location adjustments, affecting urban form and moderating travel needs. The average responses suggest some important behavioral reactions to moderate gas price spikes, however, many of these behaviors can occur simultaneously. For instance, in choosing to bike somewhere, a person typically is reducing his/her overall driving. To study this overlap, simple correlations of responses during the spike were computed. Responses were coded as a one if there was an increase in the behavior. Table 5 shows the results, with almost all pairs positively correlated. Only two pairs of behaviors were not correlated in a statistically significant way (driving the most efficient vehicle and carpooling, and driving the most efficient vehicle while increasing transit use). The strongest correlations were seen between driving slower and driving at steadier speeds (p =.734) as well as reducing overall driving and chaining activities (p = .555), likely because these behaviors can be easily accomplished simultaneously. There were also strong correlations between non-response during and following the spike (i.e., no changes in gas-saving behaviors at either time, as one might expect) and, to a lesser extent, increasing a behavior during the spike and then further increasing following the spike - suggesting a lagged intensification of the behavior. This pattern was similar for all behaviors studied.

Ordered Probit Models of Trip Chaining and Reductions in Overall Driving Ordered probit models were used to examine the likelihood of respondents increasing trip chaining or reducing their driving, in response to the gas price spike. The model was constructed as follows: Let y signify a traveler’s reported change in behavior and y* signify the latent level of continuous response underlying the reported change. Additionally, let μj (j=1, 2, 3) be the thresholds for behavioral change such that:   

y = 0 (Decrease in behavior) if y*  0 y = 1 (No change in behavior) if 0  y*  μ1 y = 2 (Slight increase in behavior) if μ1  y*  μ2

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Table 5. Correlations of Behavioral Increases Reported During Spike

Driving Most Efficient Vehicle Carpooling Chaining Activities Reducing Overall Driving Shopping Around for Gas Greater Attention to Maintenance Driving Slower Driving at Steadier Speeds Buying Partial Tanks Transit Use Walking Trips Bicycle Trips

Driving Most Efficient Vehicle 1.000

Carpooling

-0.012 0.197 0.200

Chaining Activities

Reducing Overall Driving

1.000 0.234 0.257

1.000 0.555

1.000

0.112

0.107

0.253

0.286

1.000

0.130

0.177

0.286

0.302

0.322

1.000

0.169 0.133

0.155 0.142

0.299 0.277

0.304 0.309

0.240 0.283

0.382 0.400

1.000 0.734

1.000

0.041

0.071

0.213

0.262

0.254

0.316

0.226

0.223

1.000

-0.099 0.077 0.100

0.412 0.335 0.141

0.136 0.242 0.149

0.279 0.320 0.196

0.062 0.140 0.003

0.207 0.234 0.165

0.189 0.293 0.180

0.234 0.260 0.120

0.135 0.156 0.086

Shopping Around for Gas

Attention to Maintenance

Driving Slower

Driv-ing at Steadier Speeds

Buying Partial Tanks

Transit Use

Walkin g Trips

Bicycle Trips

1.000 0.392 0.166

1.000 0.427

1.000

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101

Table 6. Explanatory Variables Excluding Students from Sample

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Explanatory Variable SOV Bus Walk Bicycle Bike/Ped Carpool Work at Home Multiple Modes

Description Drive alone to work 2+ times/week Take bus to work 2+ times/week Walk to work 2+ times/week Bike to work 2+ times/week Walk or bike to work 2+ times/week Carpool to work 2+ times/week Work at home 2+ times/week Commute to work using different modes 2+ times/week each Children Take children to school or daycare HBW Travel Time Home-to-work travel time (minutes) HBNW Trips/Week Number of non-work related driving trips/week Gas Expenditures Money ($) spent on gas/week VMT/Week Vehicle Miles Traveled/week Fuel Economy Average fuel economy of all households vehicles used 2+ times/week (mpg) Low MPG Fuel economy less than 20 mpg High MPG Fuel economy greater than 30 mpg Age Respondent’s age (years) Gender Indicator variable for males Income Household income before taxes ($/year) Employed Employed Part- or Full-Time College Educated Attained at least Bachelor’s degree Household Size Number of persons in household Vehicles/Driver Vehicles used by household 2+ times/week per driver in household Local Population Population in 1 mile radius from TSZ Local Residential Area Residential Area in 1 mile radius from TSZ (in square miles) Local Commercial Commercial Area in 1 mile radius from Area TSZ (in square miles) Local Basic Basic jobs in 1 mile radius from TSZ Employment Local Retail Retail jobs in 1 mile radius from TSZ Employment Local Service Service industry jobs in 1 mile radius Employment from TSZ Local Employment Total jobs in 1mile radius from TSZ Distance to CBD Distance from TSZ to CBD (mi.) Bus Stop Density Bus stops/square mile in TSZ Zone Density (Jobs + households)/square mile in TSZ

Mean 0.705 0.058 0.018 0.055 0.073 0.049 0.131 0.134

Std. Dev. Min 0.457 0 0.234 0 0.134 0 0.228 0 0.272 0 0.215 0 0.453 0 0.134 0

Max 1 1 1 1 1 1 1 1

0.207 13.80 7.15

0.406 14.07 4.39

0 0 2.5

1 90 20

90.17 96.66 23.85

56.67 68.13 6.46

25 25 0

250 250 55

0.210 0.109 45.53 0.422 89,688 0.857 0.836 2.286 0.912

0.408 0.313 13.15 0.495 49849 0.350 0.371 1.109 0.331

0 0 21 0 0 0 0 1 0

1 1 83 1 200,000 1 1 6 2

58457 5.57

6136 0.41

30791 3.49

66079 6.37

0.42

0.04

0.27

0.51

12924

2785

5860

16876

5742

1028

2618

7775

15857

3581

7124

24330

75.32 5.10 28.16 4241

2.57 3.72 23.25 2877

65.07 0.86 0 293.9

84.87 16.20 118 16417

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Matthew S. Bomberg and Kara M. Kockelman Table 7. Ordered Probits for Trip Chaining and Reducing Overall Driving

Variable

Transportation Needs

Demographics

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Neighborhood Characteristics

Thresholds

Constant Bus Bike/Ped Work at Home Multiple Modes HBNW Trips/Week VMT/Week Fuel Economy Gender Employed Age College Educated Income ($) Vehicles/Driver Resid. Area Comm. Area Basic Empl. Retail Empl Service Empl. Total Empl. Zone Density Distance to CBD Bus Stop Density μ0 μ1 μ2 μ3

Log-Likelihood Constants Only Log-Likelihood Pseudo R-Squared

Trip Chaining Final Estimates β t-stat p 9.372 11.663 0.000 1.248 8.908 0.000 -0.380 -4.653 0.000 -0.634 -0.044

-4.113 -6.595

0.000 0.000

-0.464 -0.630

-8.352 0.000 -10.771 0.000

-3.38E-06 -0.422 -9.59E-01 6.797

-3.286 -4.520 -14.692 5.468

0.001 0.000 0.000 0.000

3.87E-04 -1.87E-04

4.014 -4.735

0.000 0.000

-3.42E-05 -1.984 -1.49E-02 0.000 2.417 3.204 4.240 -394.837 -455.367 0.133

-4.536 -9.216 -9.856 0.000 25.570 33.164 35.632

0.000 0.000 0.000 0.000 0.000 0.000 0.000

Reducing Overall Driving Final Estimates β t-stat p 14.780 16.057 0.000 0.826 6.914 0.000 0.573 -0.754 -0.046 -0.004 -3.22E-02

8.267 -6.392 -6.548 -11.996 -8.946

0.000 0.000 0.000 0.000 0.000

-0.500 -0.019 0.304 -5.01E-06 -0.468

-5.278 -6.308 3.228 -4.280 -3.877

0.000 0.000 0.001 0.000 0.000

8.47E-05 5.03E-04 -2.47E-04 -0.126

4.058 6.125 -7.690 -10.834

0.000 0.000 0.000 0.000

0.000 1.81 2.572 3.599 -420.673 -468.034 0.101

0.000 16.934 24.116 24.600

0.000 0.000 0.000 0.000

Note: Final model specifications include only variables significant at the 0.01 level.

 

y = 3 (Moderate increase in behavior) if μ2  y*  μ3 y = 4 (Significant increase in behavior) if y* > μ3

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The latent response y* is specified as a linear function of explanatory



variables (x’), unknown parameters (  ) and a standard normal random error term (ε):

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 y*  x '   

(2)

This specification was estimated using a set of explanatory variables that included each respondent’s transportation needs, demographic attributes, and neighborhood/location characteristics. Table 2 provides summary statistics for all explanatory variables. Variables were eliminated in a stepwise manner on the basis of statistical insignificance (p-value > .01), and occasionally combined into more inclusive variables or redefined as binary (indicator) variables. The models were initially calibrated for the complete data set; however, these models had low goodness of fit (pseudo r-squared values), with full-time students highly unlikely to adopt either behavior. Accordingly, the models were re-calibrated excluding full-time students, which improved the parameter interpretability markedly. Table 6 thus displays summary statistics for explanatory variables after removing full-time students from the data set, and Table 7 offers the results of these ordered probit models. The models were all calibrated using a sample weighted to reflect actual Austin demographics, using PUMS data for Travis County controlling for gender, college education, and 3 categories of age. The results suggest that, all else equal, individuals were most likely to increase trip chaining in response to the price spike if they lived in or near the CBD. The distance-to-CBD was the most practically significant explanatory variable, with a change of one standard deviation in this variable (3.72 mi) reducing E(y*) by 0.738. In general, the most practically significant explanatory variables were those describing the respondents’ neighborhoods. Those residing further from the CBD and in areas with a higher fraction of residential land use were less likely to turn to trip chaining, while those enjoying higher levels of retail jobs near their home were more likely to chain trips1. These results suggest that individuals adopting trip chaining to cope with high gas prices may have done so largely because it was an easily available strategy (e.g., with many retail opportunities near their home). Interestingly, neither the respondent’s amount of driving (in miles per week) 1

It should be noted that retail and service job levels had coefficient estimates of opposing signs in this model. These two variables are highly collinear (p = +.917), which is the likely explanation for the odd sign on the service employment variable in this and other models.

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nor level of gas expenditures proved to be statistically significant predictors of trip chaining tendencies (in response to gas prices). Finally, both females and individuals not working for pay were more likely to increase their trip chaining than males and wage-earners, which may imply that stay-at-home mothers were responsible for a significant portion of the reported increases in trip chaining. According to the models, reductions in overall driving occurred most among those living near high levels of retail jobs. Surprisingly, both total driving and total number of home based non-work trips per week variables had negative effects on driving reductions, suggesting that such persons may already be “locked in” to less efficient and longer-distance travel situations. As expected, higher incomes also were associated with less inclination toward travel reduction, as these individuals are likely to be less price responsive. Few of the explanatory variables in the final model were practically significant; however, a change in retail job intensities was associated with a .517 shift in E(y*), which is reasonably significant, and a change in service job intensities was associated with a -.885 shift in E(y*), though the negative sign is likely misleading (see footnote 6). Both models had reasonable but somewhat low pseudo R-squared values (0.133 and 0.101), suggesting that these behavioral responses are more random than can be accounted for through regression models of standard demographic and urban form inputs.

Binary Logit Models of Attention to Vehicle Maintenance, Driving Slower, and Driving at Steadier Speeds Binary logit models were used to analyze behaviors that a high percentage of respondents reported increasing, but which lacked adequate variation across levels of increase for ordered probit analysis. These include giving more attention to one’s vehicle’s maintenance, driving slower, and driving at steadier speeds – all presumably to enhance fuel economy (and thus moderate increases in one’s gasoline costs). Respondents chose to either increase (modify) or not increase (not modify) each behavior, in response to the price spike. Again, removing full-time students from the sample improved the models. Elasticities (for response probability) were computed with respect to all explanatory variables included in the final models. These set all variables to their mean values and relied on standard methods (see, e.g., Greene 2002). Table 8 summarizes the model results. The most statistically significant factors influencing vehicle maintenance were the number of basic, retail, and service industry jobs near respondents’

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homes, as well as respondents’ weekly gas expenditures. The elasticities suggest that urban form features are most practically significant; on balance, respondents reported an increased attention to maintenance if they lived near high levels of basic and retail employment. All four of these variables were of at least moderate statistical significance. Interestingly, neither education nor gas expenditures factored significantly into increased attention to maintenance in the final model. Respondents’ vehicles’ fuel economy was directly related to a reported increase in attention to maintenance; this is unsurprising, since those most concerned about the fuel economy of their vehicle may also be concerned about keeping it functioning at peak performance. Many of the elasticities in this model seemed too high to be reasonable (e.g., several were over 10). The model of driving slower predicts that individuals living in highly commercially developed neighborhoods were somehow most likely to reduce speeds during the spike. This was the most statistically and practically significant variable in the set. Perhaps those who live near large concentrations of commercial development also live near highways. (Large commercial developments, like power centers and shopping malls, are frequently sited near highways.) These respondents may find themselves driving on highways more frequently than others, with more opportunities to slow down and impact their vehicles’ fuel economy. Such a hypothesis would also explain the inverse relationship between zoned density and reported speed reductions. Income and fuel economy were both inversely related to reduced speed. High income respondents are less sensitive to price and also may have higher values of travel time, which would decrease their incentive to slow down. Indeed, simple calculations involving a gas price of $3 per gallon reveal that only if a vehicles occupants have a combined value of travel time (VOTT) less than $10 per hour and are driving a relatively fuel inefficient vehicle (20 mpg or less) will the savings on gas overcome the cost of time losses from traveling slower.2

2

Calculations were done using fuel economy loss estimates of 9.7% and 17.1% for 55 mph to 65 mph and 55 mph to 70 mph speed increases, respectively. These fuel economy reductions come from West et al.’s (1997) 9-vehicle averages. For a vehicle getting 35 mpg (e.g. the Honda Civic Hybrid), going 65 mph with a VOTT of $5/hr achieves the lowest total travel cost (gas cost plus time cost). At VOTTs of $10/hr or more, going 70 mph or more achieves the lowest travel cost. For a vehicle getting 20 mpg (e.g., an efficiently driven Ford Explorer), going 55 mph with a VOTT of $5/hr achieves the lowest travel cost, while 65 mph nets the lowest cost at a VOTT of $10/hr, and 70+ mph at VOTTs of $15/hr or more.

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Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and

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Table 8. Binary Logit Models for Attention to Vehicle Maintenance, Driving Slower, and Driving at Steadier Speeds Responses

Variable

Attention to Vehicle Maintenance Final Estimates β t-stat p Elasticity 2.769 1.342 0.180 1.845 1.937 2.895 0.004 0.024

Constant Walk HBW Time Transportation HBNW Trips/Week Needs VMT/Week Gas Expenditures 1.30E-02 Fuel Economy 5.80E-02 College Educated -0.762 Demographics Income ($) Household Size -3.92E-01 Population -2.57E-04 Resid. Area Comm. Area 8.28E-04 Neighborhood Basic Empl. Characteristics Retail Empl. 2.49E-03 Service Empl. -9.14E-04 Zone Density Bus Stop Density Log-Likelihood -177.425 Constants Only Log-Likelihood -226.701 Rho-Squared 0.2174

β -6.416 -2.882 -0.033 -0.102

Driving Slower Final Estimates t-stat p -2.805 0.005 -2.556 0.011 -2.526 0.012 -2.570 0.010

Elasticity -4.559 -0.037 -0.324 -0.518

6.77E-03 -0.083 0.775 -1.29E-05

2.325 -3.002 2.452 -4.087

0.434 -1.407 0.460 -0.822

Driving at Steadier Speeds Final Estimates β t-stat p Elasticity -3.571 -1.387 0.166 -2.303 -0.034 -2.553 0.011 -4.00E-04

8.34E-03 3.661 0.000 0.520 4.347 0.000 0.781 2.706 0.007 0.922 -2.812 0.005 -0.424

0.020 0.003 0.014 0.000

-2.879 0.004 -0.597 -3.311 0.001 -10.011 21.577

4.174

0.000 6.482

-1.15E-04

-2.336 0.020 -0.347

5.876 0.000 7.131 3.647 0.000 9.527 -5.638 0.000 -9.658

-166.256 -205.077 0.1893

-5.60E-02 -2.442 0.015 -0.862 -1.19E-05 -3.964 0.000 -0.688 -3.43E-04 1.72E+00 17.922 5.31E-04

-4.024 2.562 2.959 4.079

0.000 0.010 0.000 0.000

-12.933 6.169 4.887 4.427

0.022 -175.502 -211.027 0.1683

3.172 0.002 0.400

Note: Final model specifications include only explanatory variables significant at the 0.05 level. Elasticities are calculated assuming explanatory variables to be at means.

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Driving at steadier speeds is, according to elasticities in the final model, most strongly influenced by population density. Individuals living at low population densities were more likely to report driving at steadier speeds. To a lesser extent, respondents living near high levels of residential and commercial land use and high levels of basic jobs reported this behavior. These factors seem to suggest that respondents in suburban areas were most likely to drive at steadier speeds, possibly because suburban roads generally require less stop and go driving. In any case, it seems clear that urban form and land use patterns may be key drivers of human response to gas prices. To some extent, these proxy for other attributes (e.g., a respondent’s frequency of highway driving frequencies and his/her automobile “captivity”). Nevertheless, it is interesting to witness how urban form may shape more than simply mode choice and VMT.

Comparison of Models of Reported Behavior Table 9 compares the effects of the most statistically significant explanatory variables from the final models of reported behavior. There is clear consistency across models, since all regularly significant variables had similar effects across models. The most practically significant variables were those having to do with urban form; high levels of basic and retail jobs near respondents’ homes were of high practical significance and directly related to reported increases in gas-saving behaviors in two models, while the presence of service industry jobs appeared to reduce gas-saving behaviors in all three. In general, variables related to transportation needs and demographics were statistically significant in models related to the amount of driving respondents did (trip chaining and overall reduction in driving), but not the style of driving (attention to maintenance, driving slower, and driving at steadier speeds). Transportation needs and demographics, however, were not practically significant in any of the final models. Opinions on Energy Policy and Gas Pricing US energy policy may be critical to moderating future gas price spikes. Respondents were asked to consider scenarios of permanently higher gas prices and provide their opinions on policy and pricing. All responses were weighted to reflect actual Austin demographics. As a point of reference, the survey stated that gas prices in Europe range from $4/gallon to $8/gallon, and asked respondents to consider prices in this range. Given an adjustment period of two years, 16.2% of respondents stated that they felt that the US economy would adapt fine with prices in this range. 34.6% of respondents felt the

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economy would experience a slight downturn in economic growth, but no recession. 30.8% reported that they felt there would be a slight recession, and 18.4% thought there would be a severe recession. Clearly, there is a fair level of disagreement among Austinites. Table 9. Comparison of Final Models

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Explanatory Variables

Increased Trip Chaining

Reduced Overall Driving

Greater Attention to Vehicle Maintenance

Driving Slower

Driving at Steadier Speeds

Bus + + Bike/Ped Work at Home + Multiple Modes HBNW Trips/Week Gas Expenditures + VMT/Week + Fuel Economy Gender Employed Age Income Vehicles/Driver Population AI – Residential Area AI Commercial Area AI + ++ Base Employment AI + ++ ++ Retail Employment AI + ++ ++ Service Employment AI ---Total Employment AI CBD Distance -Bus Stop Density Key: ++ = Strong practical significance, xi directly related to y; + =Weak practical significance, xi directly related to y; - = Weak practical significance, xi inversely related to y; -- = Strong practical significance, xi inversely related to y. Note: The Trip Chaining and Reduced Overall Driving models used ordered probit specifications, while the remaining three were estimated as binary logit models.

Notes: This table includes variables from final models having t-stats > 3.5. Strong practical significance was considered to be an absolute elasticity value greater

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than 1 for attention to vehicle maintenance, driving slower, and driving at steadier speeds, or a marginal effect y for Δxi = one standard deviation of xi

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xi

 .5

Of those who currently commute by driving alone 66.8% reported that they would continue to do so if prices were $4/gallon or less, while 27.8% reported that they would continue driving alone to work even if prices were to exceed $8/gallon. Using LimDep, a grouped data regression model was estimated in order to discern what factors influence a respondent’s “break price” (i.e., the price at which he/she would stop driving alone to work). This model uses the survey-stated thresholds (of $4, $5, $6, $7, and $8 per gallon) to predict the unobserved, actual break price. It was found that the strongest factor inclining people to stop driving alone to work (or school) was full-time student status. For each standard deviation increase in this indicator variable (0.49), the expected break price falls by $1.80. In addition, for each standard deviation increase in one’s distance to the region’s CBD (3.74 mi) and in population density (6368 people/mi2), the expected break price is predicted to rise by $1.45 and $1.25 per gallon, respectively. The survey also asked for opinions on Hybrid Electric Vehicles (HEVs). 36.7% of respondents feel that the long-term savings on gasoline justifies the initial investment in an HEV, while 31.4% feel that the initial investment is too high, and 10.4% simply feel that HEVs do not pay for themselves. 12.2% said they would consider owning (or do own) an HEV for reasons other than gas savings, with most of these mentioning environmental reasons or dependence on foreign oil as their justification. 9.2% of respondents selected “other,” and common responses here included concerns about the long-term durability and maintenance costs of HEVs, support for alternative fuels instead of HEVs, problems finding desired types of vehicles in hybrid models, and concerns about the newness of the technology. In response to a question about HEVs at European gas price levels, 41.1% of respondents reported that they would consider an HEV a worthwhile investment if prices reached $4/gallon or more, 61.3% of respondents reported this at $5/gallon or more, and 77.9% at $8/gallon or more. 9.5% of respondents felt that an HEV would not be a worthwhile investment at any price. At a gas price of $3/gallon, the annual gas-related savings per year to fuel a hybrid car (using a conservative estimate of 35 mpg for its fuel economy, but a somewhat liberal 15,000 miles/year driving assumption) instead of a typical compact car (26 mpg) would be $445/year. At $5 per gallon, the annual savings would be $742. To fuel a hybrid SUV (assuming a fuel economy of 30 mpg) instead of an average SUV (20 mpg), one could

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expect to save $750 annually at $3 per gallon, and $1250 at $5 per gallon. Over the course of 10 years, such vehicles’ sticker-price differences (and battery replacement costs) may only make simple economic sense if gas prices reach $5/gallon. Of course, the global-warming, emissions/air quality and other (social and environmental) costs associated with less fuel efficient vehicles may be argued to tip the balance at lower gas prices. In addition, several questions concerning energy policy were used to gauge how the severe price spike may have affected opinion. In response to a question in which respondents were asked to select three or more factors which were responsible for high gas prices during the summer of 2005, the most common factor selected was instability in the Middle East and other oil producing region (70.8% of respondents chose this). 54.3% of respondents indicated natural disasters, 53.1% selected OPEC’s monopoly power, 46.5% selected the emergence of other major oil consuming nations (e.g., China and India), 38.8% selected the lack of recent oil reserve discoveries, 36.0% selected “other”, and 33.3% indicated oil company mergers. Common “other” (fill-in) responses included oil company greed, collusion, and price gouging, refining capacity shortages, and price speculation. The frequency of respondents blaming the oil and gas industry for high prices is somewhat unsurprising, since many major oil corporations have reported record-breaking profits during the past two years (Quinn, 2006). However, a congressionally mandated Federal Trade Commission (FTC) investigation of post-Katrina gas prices found no instances of illegal market manipulation (though it did find 15 instances of pricing that fit Congress’ definition of “price gouging”). Respondents were asked which of a series of measures to address fuel supply shortages they would support. The most popular measure was incentives for fuel-efficient vehicles, supported by 71.0% of respondents. 68.2% supported incentives for alternative fuel use, 45.3% supported incentives for non-solo driving, 31.6% supported an increased gas tax 30.5% supported increased fuel efficiency standards, and 20.8% supported increased exploitation of domestic reserves (including, perhaps, the Artic National Wildlife Reserve [ANWR] area’s holdings). Mandatory limitations on driving were extremely unpopular, supported by only 5.9% of respondents. 13.2% of respondents selected “other”, and common suggestions included improved mass transit, subsidizing alternative fuel research, and better land-use planning. Several (10) respondents also reported that there were no fuel supply shortages and that high gas prices were a result of speculation and/or price gouging, giving answers such as “knee jerk reaction to world events which had no actual influence on oil prices.” Finally, respondents were also asked how

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much of a gas tax increase they would support if the revenues went to renewable energy research or mass transit. 34.3% indicated that they would support an increase of anywhere between $0.10 and $0.49, while 23.3% said they would not support any increase. Interestingly, 18.6% reported they would support an increase of $0.50-$0.99, and 10.8% said they would support an increase of more than $2.00.

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CONCLUSIONS The results suggest that travelers respond in a variety of ways to gas price spikes. Travelers seem most likely to respond by reducing their overall driving, a finding which seems consistent with Eltony’s (1993) study, which attributed 75% of short-term reductions in gasoline demand to a reduction in vehicle miles traveled. Studies of correlation suggest that much of this reduction may be achieved through increased use of other modes or trip chaining (as opposed to merely decreasing out of home activities). Adjustments in style of driving also appear to be a viable strategy of coping with high gas prices, as significant percentages reported increased attention to vehicle maintenance (presumably to ensure peak fuel efficiency), driving slower, and driving at steadier speeds. The impact of land use patterns in one’s residential environment on response to high gas prices is striking; the observed response to gas prices transcends even factors like income, education, average gas expenditures, and average amount of driving. Instead, respondents appeared to adopt strategies for coping with high gas prices based on what was easily enabled by their neighborhood type. Behavioral changes based on reduced driving seem linked to respondents living in more central neighborhoods (closer to CBD) with more amenities. These respondents likely found it easier to use alternate modes like walking or biking, or trip chain because they lived in denser neighborhoods with less functionally segregated land use. Respondents reporting that they drove slower and at steadier speeds, meanwhile, lived near high levels of commercial area and low population densities, features which suggest more suburban neighborhoods. These respondents likely do more highway driving and were able to more easily drive in an efficient manner than those respondents who utilize urban streets. However, despite the apparent split between the types of responses of urban and suburban dwelling respondents, there is still a reasonable level of correlation between reduced

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overall driving and driving slower and at steadier speeds, suggesting that some respondents reacted to the 2005 spike by employing a series of strategies. While the observed responses indicate that many respondents altered behavior following the September 2005 spike, the more interesting ramifications of this study concern how respondents would operate and think under scenarios where prices stayed at this level (or higher). Indeed, during the summer of 2006, gas prices again hit $3/gallon across the US, suggesting that high gas prices may be here to stay, or at least a regular summertime occurrence. The survey questions on energy policy seem to indicate that many respondents are ready to move away from transportation options and policies that depend on fossil fuels, or use them inefficiently. Majorities supported incentives for more fuel-efficient vehicle usage and alternative fuel use, and more than 30% of respondents supported non-SOV travel, an increased gas tax, and increased fuel economy standards. $4 per gallon appears to be a significant breakpoint for many respondents, both in terms of curbing SOV commuting and making HEV ownership a popular investment. In addition, the apparent link between reduced and more efficient trip planning and more residency in Austin’s more urban neighborhoods (with better land use balance) suggests that higher gas prices may cause people to select better planned neighborhoods, with more mixed land uses and more transit- and pedestrianfriendly travel options. Permanently high gas prices may mean a painful period of adjustment for individual drivers as well as the US economy; however, they also could prove a priceless remedy to a variety of problems, including foreign oil dependence, deteriorating air quality, and auto-centric urban form.

REFERENCES Behrens, C. E. and C. Glover (2006). Gasoline Prices: New Legislation and Proposals. CRS Issue Brief IB10134. Congressional Research Service, The Library of Congress. Dahl, C. (1995). Demand for Transportation Fuels: A Survey of Demand Elasticities and their Components. The Journal of Energy Literature. Vol. 1, 3-27. Dargay, J. and D. Gately (1997). Demand for Transportation Fuels: Imperfect Price-Reversibility? Transportation Research Part B: Methodological, Vol. 31, 71-82. Eltony, M. N. (1993). Transport Gasoline Demand in Canada. Journal of Transport Economics and Policy, Vol. 27, 193-208.

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Espey, M. (1998). Gasoline Demand Revisited: An International MetaAnalysis of Elasticities. Energy Economics, Vol. 20, 273-296. Goodwin, P. B., J. Dargay and M. Hanly (2004). Elasticities of Road Traffic and Fuel Consumption With Respect to Price and Income: A Review. Transport Reviews, Vol. 24, 275-292. Graham, D. and S. Glaister (2002). The Demand for Automobile Fuel: A Survey of Elasticities. Journal of Transport Economics and Policy, Vol. 36, 1-25. Greene, W. H. (2002). Econometric Analysis 5th Edition. Prentice Hall, Inc, Upper Saddle River, New Jersey. Gupta, S., S. Kalmanje, and K. Kockelman (2004). Credit Based Congestion Pricing: Travel, Land-Value, and Welfare Impacts. In Transportation Research Record: Journal of the Transportation Research Board, No. 186, TRB, National Research Council, Washington, D.C., 45-53. FTC (2006) Investigation of Gasoline Price Manipulation and Post-Katrina Gasoline Price Increases. Federal Trade Commission, Accessed on July 31, 2006 at http://www.ftc.gov/reports/060518PublicGasolinePrices InvestigationReportFinal.pdf. Puller, S. L. and L. A. Greening (1999). Household Adjustment to Gasoline Price Change: An Analysis Using 9 Years of U.S. Survey Data. Energy Economics, Vol. 21, 37-52. Quinn, S. (2006) Exxon Mobil Profit Hits More than $10 Billion. WashingtonPost.com. http://www.washingtonpost.com/wp-dyn/content/ article/2006/07/27/AR2006072701264.html. Accessed July 30, 2006. Schoen, J.W. (2006) Further surge in gas prices expected. MSNBC News. http://www.msnbc.msn.com/id/12265103/. Accessed July 30, 2006. Schrank, D. and Lomax, T. (2005) “The 2003 Urban Mobility Report.” Texas Transportation Institute, The Texas AandM University. Peterson, J. et al. (2006) The Economic Effects of Recent Increases in Energy Prices. Publication No. 2835. Congressional Budget Office. Victoria Transport Policy Institute (2005).Transportation Elasticities: How Prices and Other Factors Affect Travel Behavior. http://www.vtpi.org/ tdm/tdm11.htm. Accessed December 15, 2005. West, B.H. et al. (1999). Development and Verification of Light-Duty Modal Emissions and Fuel Consumption Values for Traffic Models. FHWA-RD99-068. Federal Highway Administration. US Department of Energy and US Environmental Protection Agency (2006). www.fueleconomy.gov. http://www.fueleconomy.gov. Accessed March 15, 2006.

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Chapter 4

THE WELFARE IMPLICATIONS OF CARBON TAXES AND CARBON CAPS: A LOOK AT U.S. HOUSEHOLDS Sumala Tirumalachetty1, and Kara M. Kockelman2,‡ 1

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2

Delcan Corporation, Vienna, Virginia Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, Texas, US

ABSTRACT Climate change has emerged as a leading environmental concern in recent years. The two widely discussed and debated options for abatement of greenhouse gas (GHG) emissions are a cap-and-trade system, at the level of producers, and an emissions tax. More interesting is the question of capping (and trading) at the level of individual households. Regardless of policy pursued, a key concern in implementing such policies relates to equity: stakeholders wish to understand the distributional or effects, whereby poorer households may be disproportionally impacted.

 ‡

Transportation Analyst. Delcan Corporation, Vienna, Virginia. E-mail: [email protected]. Corresponding author: Professor and William J. Murray Jr. Fellow, Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, 6.9 E. Cockrell Jr. Hall. Austin, TX 78712. E-mail: [email protected].

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Sumala Tirumalachetty and Kara M. Kockelman In this paper, household expenditure data from the U.S. Consumer Expenditure Survey are used to anticipate the economic impacts of energy taxes versus household-level emissions caps (with buy-out permitted, for those who exceed their budget) across different income classes and different types of expenditures, including those on transport. A translog utility model was calibrated to estimate demand quantities under two different tax rates and four different cap-and-trade scenarios. While the 9-category demand system does not allow for likely consumption shifts (toward less energy-intensive items) within each demand category, the model still provided a series of meaningful results. For example, the $100-per-ton case was estimated to yield the same total carbon reductions (just over 12 percent) as a cap of 15 tons per person (per year). The majority of the emissions reductions under a cap-andtrade policy are estimated to come from higher income groups, while reductions are expected to be much more uniformly distributed under a tax policy. Welfare loss (in terms of equivalent variation) as a share of income is found to be higher for lower income households when carbon taxes are implemented. In the end, a cap-and-trade policy seems most effective in reducing emissions without negatively impacting lowerincome households, and without worrying whether taxes will engender enough thoughtful consumption shifts to ensure steep reductions.

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Keywords: Carbon emissions, Carbon trading, Carbon credits, Cap and trade, Welfare effects

BACKGROUND Climate change has emerged as a leading environmental concern in recent years. Nations all over are debating policies to reduce emissions of carbon 1 dioxide (CO2) and other greenhouse gases . Per capita emissions in the United States were estimated to average 23.4 tons of CO2 equivalents (CO2e) in 2005, more than twice European Union levels (10.7 tons per capita) and more than four times the world average of 5.8 tons (WRI 2009). The higher U.S. levels stem from greater per-capita consumption of transport, built space, and consumer items along with lower levels of efficiency, within multiple sectors, including transport (Quadrelli and Peterson 2007).

1

For background on the science of climate change and the consequences of inaction see, for example, the Intergovernmental Panel on Climate Change (2007a) and Stern (2007).

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Transportation sector’s GHG emissions account for28% of all U.S. GHG 2 emissions, and these continue to grow at a higher rate than overall emissions. Many studies have examined how shifts to more efficient vehicles, greater use of less energy-intensive modes, and lower overall travel might achieve certain levels of emissions reductions (Kockelman et. al 2009, Bomberg et al. 2008). However, at the scale of national policy, the focus has been on the introduction of carbon taxes or implementation of a cap and trade strategy. Such policies affect not only transport costs and associated demands, but also imply increased prices of food, electricity and natural gas. Unfortunately, there has been little comprehensive work examining household expenditures and related GHG emissions across the entire range of goods and services that will be affected by such policies. This paper presents a framework for studying household trade-offs, impacts on travel demands, and overall emissions savings under the two policies. The next section provides more details on these policies.

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CARBON TAXES AND CARBON CAPS The U.S. Congress has been debating proposals to address greenhouse gas targets and climate change policies for several years now (e.g., McCain and Lieberman’s 2005 Climate Stewardship and Innovation Act, Bingaman and Specter’s 2007 Low Carbon Economy Act, and Waxman and Markey’s 2009 American Clean Energy and Security Act). In 2005 the European Union (EU) established the world’s first cap-and-trade system for greenhouse gas, and Canada’s British Columbia and Quebec provinces have introduced carbon taxes to try and reduce emissions.The prevailing options for abatement of carbon emissions are a (upstream) cap-and-trade system and a carbon emissions tax. The "cap" refers to an upper limit on the amount of CO2e that may be emitted from the use of electricity, oil, natural gas and food production. And "trade" refers to the system in which households or firms can buy or sell the rights to emit, called credits. A market would be established so that high-level GHG producers who use need credits (beyond their allowed credits) would have to pay for these. Those who lead less energy intensive lives and/or who invest in energy efficiency are unlikely to use all their allowances and can add to their income by spending surplus units in the 2

Total U.S. emissions rose 13% between 1990 and 2003, while those from the transportation sector rose 24% (Brown et al. 2005).

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market. Market clearance would results in a price per ton of CO2e so that supply matches demand. The increased cost of production would be largely passed along to consumers, depending on demand elasticities. A tax, by contrast, is a less complex option that requires emitters to pay a tax for every ton of CO2e produced. The government would set a price per ton on carbon, which would translate to an implicit tax on gasoline, diesel, natural gas, electricity and other sources. Higher prices would induce households and firms to reduce consumption and move towards more carbon efficient lifestyles (for instance, shifting to more fuel efficient vehicles). How quickly consumers move away from higher priced goods, however, is not always clear. (For example, price elasticities on gasoline can be quite low: just -0.09 in the short-run and -0.38 in the long run, according to Small and Dender’s (2007) analysis of 1966-2001 U.S. data.) A budget (or cap) on each households’ GHG emissions may well serve as a much clearer target signal, engendering faster and less welfare-impacting change. While administration of a carbon tax is relatively straightforward, a capand-trade policy requires more implementation effort. Taxes provide incentives (via price signals) for consumers to reduce their emissions as well as investors to move toward cleaner technologies. While the price of carbon is fixed under this strategy, total emissions are uncertain and depend on the response behavior of households, firms, investors and others. In contrast, caps mostly ensure pre-defined emissions reductions, but the price of carbon will vary with the carbon market’s trading activity and levels of initial allowances provided. Moreover, more data generally are required for cap and trade policies: a key issue in the EU’s 2005-2007 (upstream) carbon-permit experience was lack of data on nations’ emissions inventories, resulting in over-allocation of credits (Ellerman et al. 2007). With a comprehensive emissions reporting system now in place, this and other issues are expected to be addressed in the second phase of the EU’s trading scheme. Under a cap-and-trade program, the government can issue permits for free to regulated firms (upstream approach), households (downstream), and/or other entities; auction the permits; or use some combination of free distribution and auctions. While an upstream policy is simpler to implement, it is likely to appear much like a carbon tax to consumers, in the form of higher prices, and may not have as much impact on behavior. Roberts and Thumin (2006) discuss this and other issues involved in downstream versus upstream cap-and-trade systems. The focus in this paper is on the former, to see what economic (and econometric) techniques may suggest for behavioral

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adaptation, welfare, and emissions reductions under the downstream cap-andtrade versus emissions tax scenarios.

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POLICY IMPACTS In choosing between policy instruments, several criteria are relevant. These are cost effectiveness (to achieve target reductions), uncertainty (of outcomes), and incidence (i.e., distributional equity across households and/or other stakeholders) (Aldy et al. 2008). The last of these is often referred to as the regressivity effect. While taxes create revenues that can address regressivity to some extent, incidence and impact really depend on policy specifics and consumer flexibility. Though downstream cap-and-trade policies -- at the level of households -are rarely discussed in the literature, the U.K.’s Department of Environment, Food and Rural Affairs (DEFRA) has sponsored some investigation into their feasibility and distributional impacts. As a result of such work, Thumin and White (2008) report that 71% of U.K. households in the lowest three income deciles would have surplus allowances to sell, while 55% of households in the highest three income deciles would either have to buy allowances or reduce their emissions. In other words, lower income households may well benefit from a (downstream) cap-and-trade policy. Moreover, the cost at which the market for credits will clear could be substantially lower than tax applied up top, or the implicit tax of a cap applied at the level of energy producers. Thoughtful research is needed in these areas. A number of studies have investigated the impacts of energy and carbon taxes on household income distribution. For example, Brannlund and Nordstrom (2004) assumed a doubling of Sweden’s carbon tax and compared the outcomes of two alternative recycling options: a lower overall value-added tax (VAT) and a lower VAT on public transport (equivalent to a transit subsidy). They found that both reforms are regressive, with the second one also resulting in a higher burden on households living in less populated areas. Wier et al. (2005) assessed the distributional impact of Denmark’s carbon tax by combining an input-output model and national consumer survey. They found the tax to be regressive, particularly for rural households. For the Netherlands, Kerkhof et al. (2008) also found that a carbon tax is regressive. In some contrast, Tiezzi (2001) concluded that Italy’s carbon tax is not regressive, but this may be because the tax lies mainly on transport fuels.

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A few such studies have been conducted for the U.S. context. Lasky (2004) observed that regardless of how credits are distributed (i.e., upstream to energy producers or downstream to final consumers), most of the costs of meeting a nationwide cap on carbon emissions will be borne by consumers facing persistently higher prices for power, fuels and other high-energy products. Dinan and Rogers (2002) examined the effects of a 15% reduction in US carbon emissions, under different mechanisms for allocating emissions permits. When all costs are passed on to consumers, they estimated that a 15percent cut in CO2 emissions would cost the average U.S. household in the lowest income quintile (i.e., lowest 20-percent) about 3.3 percent of its average income. By comparison, a household in the top quintile was estimated to pay about 1.7 percent of its average income.3 Here, the economic impacts of such policies across different classes of households are estimated and then compared using Consumer Expenditure Survey (CEX, 2002) data for choice behavior model calibrations. The following section provides details on all data sets used.

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DATA The Consumer Expenditure (CEX) Survey is a national level survey conducted by the US Census Bureau for the Bureau of Labor Statistics (BLS) every five years. This survey collects information on household incomes and expenditures, thereby reflecting buying habits of US consumers (BLS, 2001). In addition, information on individual and household, demographics, employment status and vehicle characteristics is collected. The diary portion of the survey is a self-administered instrument that captures information on all purchases made by a consumer over a two-week period. The interview survey is conducted on a rotating panel basis, administered over five quarters, and collects data on quarterly expenditures higher cost items, in addition to soliciting information on regular purchases. Each component of the CEX survey queries an independent and strategically sampled set of U.S. households. For this analysis, the 2002 interview survey data made available at the National Bureau of Economic Research (NBER, 2003) archive of microdata extracts was used (along with household-level expansion factors, to better match the U.S. population). 3

Although the lowest quintile would bear the cost as a higher share of household income, it would pay the least in absolute terms.

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NBER processes the original data to consolidate hundreds of expenditure, income, and wealth items into 109 distinct categories. Only households with complete information in all four quarters were selected for analysis. An annual household savings variable was computed by subtracting total annual expenditures from a household’s net annual income. If savings were negative (which is possible when households spend more than they take home), the savings variable was set to zero. A new income variable was then computed, equal to the sum of expenditures plus savings.

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Table 1. Descriptive Statistics of the 2002 U.S. Consumer Expenditure Survey Data Variable Expenditures ($) Savings ($) Other ($) Gas ($) Electricity ($) Air travel ($) Public transport ($) Gasoline ($) Food at Home ($) Food away from Home ($) % of Total Household Expenditure Savings Other Gas Electricity Air travel Public transport Gasoline Food at Home Food away from Home

Mean 45,705 15,224 22,150 345.1 1,011 258.4 144.9 1,299 3,880 1,389

Std. Dev. 38436 28780 17049 453.2 654.3 679.8 583.8 980.9 2166 1796

Min 3359 0 772 0 0 0 0 0 0 0

Max 604,931 530,042 333,674 3,984 7,092 11,600 24,955 10,704 21,515 51,983

23.33 53.79 1.02 3.09 0.52 0.36 3.33 11.39 3.18

24.91 21.36 1.65 2.74 1.40 1.19 2.46 7.42 2.94

0.000 0.019 0.000 0.000 0.000 0.000 0.000 0.000 0.000

96.77 99.90 18.01 32.89 27.40 33.87 22.66 60.74 27.38

The final data set has expenditure data from 4,472 households across the 109 categories, which were then aggregated into 9 expenditure categories most meaningful for this analysis. These constitute household Savings, along with household expenditures on Natural Gas, Electricity, Air Travel, Public

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Transport, Gasoline4, Food Consumed in the Home, Food Consumed Outside the Home (dining out), and a category for Other expenditures (such as consumer goods, vehicle purchase and maintenance, and health care expenses). Table 1 provides (population-weighted) descriptive statistics for annual expenditures across these categories (as absolute values and as shares of total household expenditure). The average 2002 income of households in the sample is $47,312. And transport expenditures (from Air Travel, Public Transport, and Gasoline – but not personal-vehicle purchase and maintenance, for example) are found to constitute 4.21% of a household’s total expenditures, on average, with Gasoline accounting for nearly 80% of this share (since personal-vehicle travel is so much more common than air and transit use, in most households). It is interesting to contrast the relatively high variability (across households) in all three transportation expenditure categories versus the relatively low standard deviation in (and coefficient of variation for) Natural Gas and Electricity expenditures. Some households travel a great deal, while others do not; some take long vacations from time to time, while others stay local. Nearly all must heat and/or cool their home all year long, while maintaining householdsustaining appliances often non-stop. Price data are not collected in the CEX survey data, and had to be obtained from other sources. Unit prices ($1 per unit) were assumed for Savings and Other expenditure categories, and Table 2 shows the mean and standard deviation for all other price assumptions, across the U.S.’s Northeast, Midwest, Southeast and Western regions. Consumer Price Indices (CPIs) were taken as a proxy for regional pricing for both at-home and away-from-home food-consumption categories. These BLS-provided values are normalized with respect to 1982/1984 values. Prices for air travel (per seat-mile) were obtained from quarterly airfare data released by the U.S. Department of Transportation (DOT 2003), and public transport prices come from the National Transit Database (NTD 20035). Of course, airlines (and other providers) tend to offer a wide variety of prices in any market, and it is unlikely that the average fares from these reports will match any particular fare offered to respondent households. Nevertheless, such information is useful in gauging per-mile travel cost variations across U.S. regions

4 5

This category includes diesel fuel. The NTD (2003) relies on the average number, length and fare of transit trips from over 600 transit agencies, across the nation; these are then used to determine the average cost per mile of using public transportation.

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Table 2. Price Assumptions Region Northeast Midwest Southeast West Northeast Midwest Southeast West Northeast Midwest Southeast West Northeast Midwest Southeast West Northeast Midwest

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Southeast West Northeast Midwest Southeast West Northeast Midwest Southeast West

Category Electricity Electricity Electricity Electricity Gas Gas Gas Gas Gasoline Gasoline Gasoline Gasoline Food at Home Food at Home Food at Home Food at Home Food away from Home Food away from Home Food away from Home Food away from Home Air Travel Air Travel Air Travel Air Travel Public Transport Public Transport Public Transport Public Transport

Mean 0.114 0.082 0.079 0.111 9.496 6.796 8.299 7.852 1.454 1.423 1.371 1.502 177.1 170.1 171.3 185.4 181.4

Std. Dev. 0.003 0.005 0.003 0.001 0.429 0.395 0.319 0.214 0.117 0.123 0.123 0.131 0.673 0.714 0.512 0.884 1.402

175.7

1.100

180.0

1.116

175.4

1.413

0.160 0.183 0.184 0.152 0.0452 0.0398 0.0211 0.0227

0.549 0.415 0.463 0.327 0.1262 0.1594 0.0314 0.0424

Units $/kWh

Notes Average of all monthly data for 2002

$/1000 cuft

Average of all monthly data for 2002

$/mile

Average of all monthly data for 2002

CPI (100 in 1982)

Average of all monthly data for 2002

CPI (100 in 1982)

Average of all monthly data for 2002

$/mile

Average of quarterly data for 2002

$/mile

Computed as (fare/trip)/(mil es/trip) for each state and region

Note: Price data for electricity, gas, gasoline and food categories come from www.bls.gov. Airfare data were obtained from http://ostpxweb.dot.gov/, and public transit prices come from http://www.ntdprogram.gov.

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METHODOLOGY

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Consumer demand theory assumes that individuals choose demand quantities that maximize a (latent) utility function subject to a budget constraint. Flexible functional forms are sought to offer reasonably behavioral approximation subject to theory restrictions, such as homogeneity (to accommodate the notion of pure inflation, without impacting demand levels), summability (so that expenditures equal one’s budget), and symmetry (so that compensated demands’ price derivatives are symmetric). Such functions include Christensen et al.’s (1975) transcendental logarithmic (translog) (for direct and indirect utility) and Deaton and Muellbauer’s (1980) Almost Ideal Demand System (AIDS) (typically used with firms’ cost functions). Obtaining standard Marshallian (uncompensated) demand functions by maximizing the direct utility function subject to budget constraints can be quite cumbersome for complex functions. By beginning from a specification of indirect utility, one can rely on a relationship called Roy’s Identity (Roy 1943) to quickly arrive at individual demand equations (using the ratio of price and income derivatives). Carbon taxes increase prices according to the intensity of each goods’ carbon emissions. The demand quantities in this case can be obtained by changing prices in standard demand equations. In contrast, under a (downstream) cap-and-trade policy, households have to meet an additional carbon budget, resulting in the following utility maximization problem: max

subject to

(1)

where is a differentiable direct utility function, is a vector of n consumption goods (including electricity, gasoline and so on), is a vector of unit prices, is a vector of carbon emission rates, M represents the household’s annual income constraint, and B is the carbon budget (in metric tons per year per household, for example). Utility maximization under twin budgets has been applied in the case where an individual faces time and money budgets. Kockelman (2001) modeled households’ consumption of various discretionary “activities” as a function of access travel times (to activity sites) and both income and time budgets. Shaikh and Larson (2003) developed a demand system for recreational activities based on the AIDS specification, with choices constrained by both money and time.

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Depending on human psychology and the penalty (both monetary and non-monetary) for exceeding a cap (and the benefits of staying under a cap), the behavioral effects of such a policy may differ quite a bit from a welfareequivalent drop in money budget. Another complexity is the fact that existence of a second budget (on carbon emissions in this case) generally adds parameters to the preference specification. Without actual data points on such budget contexts, and their associated demand levels, one cannot statistically identify these added parameters. However, here households are permitted to buy their way out of their carbon budget, by paying a pre-determined carbon emissions penalty (or price, effectively) – and they benefit from consuming below their carbon budget (at the same rate). This single-price penalty translates emissions directly into dollars, so the carbon budget effectively merges with the income budget and the parameter identification question disappears. Nevertheless, the question of how people would really respond to the presence of a second, explicit budget (even when emissions are exchangeable, at a known price) remains; actual testing of such budgets, in a thoughtfully designed lab setting or in practice would be required to tackle this largely psychological question. It is a question that can have profound implications for economic inference, but is beyond the scope of this work. (Intuitively, one might expect most households to view the second budget as a rather strict budget and strive to hit it, even if buying out is simple. As a result the emissions savings of such a policy may be much greater than this work suggests.) In order to estimate preference functions, demanded quantities and welfare impacts under both policy settings, equation (1) is used here. Thus, this work starts from a direct utility function. Christensen et al.’s (1975) translog specification enables rather flexible examination of substitution patterns among the expenditure categories (along with non-constant expenditure shares) and so was selected for model estimation. More details on this specification can be found in Deaton and Muellauer (1980).

Direct Translog Utility Function The translog form for (direct) utility is as follows: ∑

0.5 ∑ ∑

(2)

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Maximizing utility subject to the budget constraint ∑ following expenditure share equations: ∑

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∑

), one has the

(3)

∑ ∑ where and . Since budget shares must sum to 1.0 (i.e., households use all their income, for consumption and/or savings), additional normalization is required for unique parameter identification. The standard 1 (Jorgenson and Lau 1979). normalization is ∑ All parameters characterizing this system of demand equations (3) were estimated using a simultaneous equations system (SES) to ensure that parameter values were consistent across equations. Since the associated indirect utility expression cannot be obtained (as described earlier), numerical methods were used to estimate demanded quantities under the carbon-cap scenarios. These numerical methods include calculating the Hessian for the Lagrangian from a quasi-Newton approximation. Before turning to a discussion of methods for obtaining welfare results, it merits mention that the data aggregation process used here, and the associated functional specification, can be quite limiting for certain emissions-savings (and other) behaviors that exist. Such aggregation implies that all dollars expended within a single category are equivalent. Substitution among alternatives (e.g., those of different carbon intensity) within a category will not result in an estimate of lower carbon emissions. Of course, the Gasoline category is very homogeneous (though different prices exist within that category, thanks to different grades of automotive fuel). But categories like Air Travel and Public Transport offer different options that may be more or less efficient (e.g., large jets flying moderate distances full, or large train cars running corridors mostly empty, versus nearly full buses). And the Other category includes a tremendous diversity of energy implications (from one car to the next, one refrigerator to the next, and so forth). Households therefore have more flexibility in consumption (and emissions decisions) than the model allows for. Thus, the welfare implications of either policy (cap or tax) may well be much gentler than model results will indicate. To address such issues, greater disaggregation from the start and/or nested utilities and demand equations, within each category (with sub-nest demands conditioned on category expenditure), would allow analysts to able to appreciate likely substitution behaviors better (e.g., from one vehicle type to another). Nevertheless, estimation of such complicated functional forms,

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subject to twin budgets, is far from straightforward. More microeconomic research in this area would be very useful.

Welfare Calculations The net benefits or welfare implications of an economic policy can be rather rigorously assessed using the notion of equivalent variation (EV) (see, e.g., Varian 1992, and Small and Rosen, 1981), which represents the increase (or reduction) in income that would be equivalent to the policy change (either a carbon emissions tax or cap). In other words, it is the income change that results in the same (post-policy) level of (maximized) utility. Since, the indirect utility function associated with the system of demand equations used here (3) and its associated expenditure function cannot be directly evaluated, EV values for each household in the CEX sample were arrived at by iteratively evaluating the maximized utility expression (effectively the indirect utility), subject to different money-budget constraints. The income constraint (M) was modified until correspondence was achieved in utility values (pre- and postpolicy implementation). In this way, the equivalent variation in expenditure was obtained, for each household.

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SETTING CARBON TAXES AND CARBON BUDGETS In theory, the same emissions outcomes and policy responses should be achievable via a carbon tax or a cap-and-trade system (Metcalf 2008). But carbon tax rates and carbon caps or credit limits must be designed carefully. Low tax rates may not motivate any shifts in behavior, whereas high tax rates may excessively burden low income households. One can argue that the tax should be set equal to the social cost of added GHG emissions, but such costs can be very difficult to determine, particularly with a long-term problem like climate change, fraught with uncertainty and complexity. Even marginal sequestration or GHG-avoidance costs can prove difficult to evaluate, and prices found in existing emissions trading systems may bear the marks of a political compromise. Nevertheless, Tol (2005) assessed 103 published estimates of marginal GHG costs and arrived at an average of $13.64 per metric ton of CO2e. The IPCC’s Working Group II survey of 100 estimates finds a range of just $3 to $95 per ton (IPCC 2007). Metcalf (2005) recommended a carbon tax just under $17/ton of CO2, with an annual increase

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of 2%. And Nordhaus (2007) has concluded that a carbon tax starting at $7.40/ton of CO2 would be optimal, so long as it increases by 2 to 3% a year in real terms (after inflation), until 2050. Of course, taxes like these are quite low and may have no behavioral impacts in many sectors of the economy for many if not all households. (For example, $10 per ton translates to less than 1¢/gallon, which will have no effect on gasoline sales. [Kockelman et al. 2009]) Several EU countries have already implemented carbon taxes6, and different taxes have been proposed in the United States7. In order to stabilize carbon emissions prices on GHG emissions are expected to be $25 to $70 per ton CO2e by 2020, rising to $127 to $230/ton by 2050. Here, tax rates of $50/ton and $100/ton of CO2 are imposed, to study the welfare implications across household classes (Clarke et. al 2007). New prices on each of the nine demand categories are calculated by simply 8 adding each category’s existing price (as shown in Table 1) to the product of that category’s associated carbon intensity (CO2e per unit consumed) and the carbon tax rate used ($50 or $100 per ton). Energy intensity coefficients for several expenditure categories were obtained from EIA and EPA documents (EIA 2002, EIA 2005, EPA 2005), and all values used are shown in Table 3. CO2 emissions by air travel are estimated to vary from 1.21 lbs CO2 per passenger mile (for short flights) to 0.849 lb CO2 per passenger mile for long flights9, so an average value of 0.934 lbs/mile was used here. Here the carbon cap is set at either 10 or 15 tons, per person per year, to roughly approximate the resulting carbon emissions (per capita) that the $50 and $100 tax scenarios yield. Households with excess credits (typically estimated to be low-income and/or larger households in the CEX data set) can sell these and increase their income, while households with a binding carbon 6

For example, Sweden enacted such a tax in 1991. Currently, the tax is $150 per ton of carbon, but no tax is applied to fuels used for electricity generation, and industries are required to pay only 50% of the tax (Johansson 2000). In Finland, the current tax is €18.05 per ton of CO2 (€66.2 per ton of carbon) or $24.39 per ton of CO2. 7 For example, Boulder, Colorado implemented the nation’s first tax on gas and electricity bills (Kelley, 2006). And California regulators have been studying fee structures (see, e.g., Young 2009). 8 The pre-determined price on GHG emissions (of $50 or $100 per ton) provides some guarantee on price for households and the governing agency, while mimic a penalty system and simplifying calculations here. Simulation of the entire market and credit-price clearance (using all CEX households, for example) would also ensure the cap is met. 9 These estimates come from 3Degrees Group, at http://www.3degreesinc.com/calc3/ methodology/.

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budget constraint can increase their carbon cap limit by buying credits at the same rate (either $50 or $100/ton). Though the credit cost is pre-determined (rather than market-determined) in these scenarios, the solution mechanisms used still ensure that the emissions-per-capita target is met.

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Table 3. Price Changes under Energy Taxes Base Prices ($ per unit)

Carbon Emission Assumptions (lbs per unit)

Taxed Prices ($ per unit)

% Change in Price

$10.83

33.56%

1.3 0.934 0.3

1000 cuft kWh mile mile

Tax ($ per unit, if GHG = $50/ton) $2.72

Gas

$8.11

120

Electricity Air Travel Public Transport Gasoline Food at Home Food outside Home

0.096 0.17 0.03

1000 cuft kWh Mile Mile

0.03 0.02 0.01

0.13 0.19 0.04

30.72 12.2 21.14

1.51 1

Gallon Unit

19.56 1

gallon unit

0.44 0.02

1.95 1.02

29.39 2.27

1

Unit

1

unit

0.02

1.02

2.27

ESTIMATION Parameters for the direct translog utility function and the expenditure share equations were estimated using STATA software’s nonlinear seemingly unrelated regressions (SUR) routine, but constrained to ensure parameter consistency (and thus implying an SES specification). The parameters were estimated using budget share equations for 8 of the 9 categories (since summability [of all expenditures - including savings, to equal income] implies the final equation’s results), and results of the estimation are shown in Table 4. Transit and air travel expenditures are less reliably estimated, exhibiting lower goodness of fit statistics; yet gasoline expenditures were quite stable. Utility function parameter estimates thus obtained were used to estimate demand quantities under the carbon caps and tax rates described earlier.

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Table 4. Estimation Results for Translog Demand Equations

Values Natural Gas Electricity Air Travel Public Transport Gasoline Food at Home Food-away from Home Savings Other Expenses R2 Values

Natural Gas -0.122

Electricity

Public Transport -0.044

Gasoline

-0.092

Air Travel -0.132

Food Outside Home -0.130

Savings

Other

-0.175

Food Home 0.233

-1.752

1.213

-92.7 -24.7 -29.1 5.28 -20.7 54.6

-24.7 -95.5 17.8 14.9 -0.041 43.5

-29.1 17.8 -102.8 -24.7 22.5 41.6

5.28 14.9 -24.7 -76.7 -48.3 -31.0

-20.7 -4.13E-02 22.5 -48.3 -12.5 -10.6

54.6 43.5 41.6 -31.0 -10.6 253.2

20.8 -37.0 29.9 30.6 15.2 -50.2

46.9 -28.0 46.2 74.6 35.6 -62.6

39.6 109.0 -1.34 55.2 18.8 -238.8

20.8 46.9 39.6 0.554

-37.0 -28.0 109 0.656

29.9 46.2 -1.343 0.473

30.6 74.6 55.2 0.294

15.2 35.6 18.8 0.723

-50.2 -62.6 -238.8 0.754

10.4 35.9 -55.7 0.637

35.9 -279.7 130.8 0.762

-55.7 130.8 -58.4 n/a

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For each household, the direct utility equation (2) was maximized using MATLAB, subject to the governing constraint(s) and associated prices. Under the tax policy, there was the one, money budget constraint and the set of increased prices (as per Table 2). Under the cap-and-trade policy, the demanded quantities (and thus GHG emissions) were estimated subject to both strict money and carbon budgets (with many households emitting fewer GHGs than their carbon budget allowed), and then trading was introduced, with households allowed to sell or buy carbon credits, thus effectively increasing and decreasing their monetary budgets – along with their consumption levels (and thus their carbon footprints). The process iterates until each household has improved its utility, with no household facing a reduction in its implied utility level, and none of the households who started below their carbon budget actually exceeding their budget. In this way, the carbon cap is met by most households, but with a pre-determined cost of credits1. In the end, the assumptions on cap limits and credit prices lead to more households selling credits than buying them when the cap is set at 15 ton/person, and the reverse at 10 ton/person. It should be noted that numerical estimation of maximum utility values for each household (in MATLAB) is time consuming and can lead to local optima in certain cases (roughly 5 percent of cases). To avoid this, the initial seed vector for demanded quantities was randomized to 10 values and the maximum of the resulting ten values was taken. To determine the associated welfare (EV) implications, one needs to obtain the dual of the utility maximization problem. Since minimizing monetary expenditures subject to a non-linear constraint on utility (eq. 2) is complex, line search methods were used (Fox, 1984), and this primal problem was solved for just 10% of the sample (in order to reduce estimation time, which was around 5 hours on a standard desktop computer, with 2GB memory and 3.2 GHz Processor) The results of these calculations are presented in the next section.

RESULTS The estimation of household carbon emissions under caps versus taxes provides several interesting results. Figure 1 shows expected utility levels 1

As noted earlier, in most cap-and-trade policies the price of credits is market determined. Such flexibility adds some complication, however, in simulation of market outcomes and for households trying to optimize their consumption patterns (without knowing market price ahead of time).

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against household expenditures, in the base case. The utility function is nondecreasing and concave in expenditures, as economic theory suggests (see, e.g., Deaton and Muellauer 1980). Carbon emissions in all other scenarios were compared against this base scenario’s results. 12000

Utilty Function

10000 8000 6000 4000 2000 0 0

20,000

40,000

60,000

80,000 100,000 120,000 140,000 160,000 180,000 200,000

Expenditure in $

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Figure 1. Household Utility versus Annual Household Expenditures.

Figure 2 shows CO2e emissions under the different policies tested here. As shown in Figure 2a, model-predicted emissions per household appear linear with respect to expenditures under the base case and the tax scenarios (but with lesser slope in the two tax scenarios). In the all the cap-and-trade combinations (shown in Figures 2b, 2c and 2d), there is a clear dispersion in predicted emissions. This dispersion is mainly due to the differences in carbon caps across household sizes, pushing 1-person households toward 10 and 15 tons of emissions (depending on the policy scenario), 2-person households towards 20 and 30 tons, and so forth. Larger households are more likely to have unused carbon credits, as household demand for shared energy services such as heating and lighting does not increase linearly with the number of occupants, while the carbon credit allocation (as modeled here) follows a linear pattern with household size.A tax of $100 per ton is predicted to reduce average carbon emissions per capita by over 12%. Introducing a carbon cap of 10 tons (per person per year) yields the greatest GHG reduction: 19% and 23% when credits are sold/bought at $50 and $100, respectively. Thus, it seems that combining a cap with a market for credits can have substantially greater impacts. The question then becomes whether the welfare implications will favor such policy? To investigate the distributional effects, households were

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120

C a r b o n E m is s io n s ( to n s /h o u s e h o ld /y e a r )

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100

100

80 60 40 Base Tax50 Tax100

20 0

0

50,000

10,000

150,000

200,000

Expenditure

120

80 60 40 20 0

0

50,000

10,000

Base Tax 100 Cap-Trade 10 50 150,000 200,000

Expenditure

120

100

100

80 60 40 Base Cap-Trade 15 50 Cap-Trade 15 100

20 0

C a r b o n E m is s io n s ( to n s /h o u s e h o ld /y e a r )

120

C a r b o n E m is s io n s ( to n s /h o u s e h o ld /y e a r )

C a r b o n E m is s io n s ( to n s /h o u s e h o ld /y e a r )

sorted by income, and Table 5 shows average emissions by class. The majority of GHG savings under a cap-and-trade policy is predicted to come from the highest income groups. In contrast, emissions reductions appear rather uniformly distributed (across household classes) under taxes. Under the capand-trade policy, lower income households are estimated to be responsible for more GHG emissions than under a carbon tax policy and the base case, thanks to the additional income these households enjoy via sales of their extra carbon allowances. Of course, as noted early in this paper, expenditures in a category of consumption do not really translate linearly to GHG emissions in that category: higher-income households may be buying more expensive clothes, more expensive cars and pricier airplane tickets than others, which would not result in proportionally higher carbon emissions. The model developed here is primarily for illustration of the evaluation methods and some basic sense of policy implications; it is not finely specified enough to detect such changes.

0

50,000

10,000

Expenditure

150,000

200,000

80 60 40 Base Cap-Trade 10 50 Cap-Trade 10 100

20 0

0

50,000

10,000

150,000

200,000

Expenditure

Figure 2. Comparison of Carbon Emissions (tons/household/year) under Different Scenarios.

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Table 6 provides the welfare implications (in terms of equivalent variation, EV) across the household groups, both in absolute terms and as a percentage of income. As one might expect, most households can expect to bear a cost when GHGs come under regulation. And the cap-and-trade leads to substantially higher welfare losses for higher income households than a tax policy; it thus results in higher overall welfare loss to the set of CEX households (largely because higher income households have more income to “play with”, in making an equivalency to the policy’s utility implications). Average EV is positive for the lowest income group in three of the four capand-trade cases, which is important to note. Not so surprisingly, carbon taxes appear regressive overall, with EV as a percentage of income higher for lower income households. Model predictions suggest that even at $50 and $100 per ton of CO2e, taxes have very little impact on the behavior of higher income households.

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CONCLUSIONS AND EXTENSIONS While taxation is commonly pursued as a policy for impacting the demand of goods carrying external costs, carbon emissions remain largely uncharted territory, with target reductions having major implications for most households and (upstream) cap-and-trade policy gathering significant support from policymakers. This paper developed a framework to estimate carbon emissions under carbon taxes as well as a downstream (household-level) form of capand-trade. A direct translog utility model was calibrated to provide demand quantities under various policy scenarios. Results suggest that carbon taxes will be somewhat regressive, penalizing lower income households at a higher rate than others, and cap-and-trade policies offer an opportunity for welfare gain by many households at the lower end of the economic spectrum. However, tax revenues can address disparities while helping households save energy (via, for example, income tax deductions, subsidies for alternative modes and smarter urban design, and investments in energy efficiency at the household level). Thus, a tax policy may offset much of the impact on lower-income and/or other households. In either approach, the level of the tax or price of carbon credits must be set carefully, to be most effective.

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Table 5. Average Household CO2e Emissions (tons per year) across Household Classes Overall No. of households Avg. Income Base Tax 50* Tax 100* Cap-and-trade 10-50** Cap-and-trade 10-100** Cap-and-trade 15-50** Cap-and-trade 15-100** *

444 $47,619 31.9 30.0 27.9 25.8 24.7 30.5 29.5

Class 1 ($100 k) 28 $147,569 79.6 77.2 72.7 38.1 37.5 42.2 40.8

Tax X refers to scenarios with a carbon tax of $X/ton. Cap-and-Trade X-Y refers to cases where household emissions are capped at X tons/person/year and can be traded at a fixed rate of $Y/ton.

**

Table 6. Annual Welfare Implications of Policies across Household Classes

EV No. of households Avg. Income Tax 50* Tax 100* Cap-and-trade 10-50**

All Households 444 $47,619 -$1,,457 -$2812 -$13,381

Class1

Class2

Class3

($100k) 28 $147,569 -7,,859 -7463 -60,536

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Table 6. Continued

Cap-and-trade-10-100** Cap-and-trade 15-50** Cap-and-trade 15-100** EV as a % of income Tax 50* Tax 100* Cap-and-trade 10-50** Cap-and-trade-10-100** Cap-and-trade 15-50** Cap-and-trade 15-100** *

-$13,369 -$11,006 -$11,101

Class1 380 466 345

Class2 -564 -2,548 -2,469

Class3 -5,502 -6,446 -5,313

Class4 -16,946 -10,464 -11,592

Class5 -31,183 -21,256 -22,715

Class6 -67,596 -50,708 -54,565

-2.9% -6.5% -11.4% -20.8% -18.4% -14.7%

-3.2% -7.5 2.1 2.7 6.8 8.1

-3.7% -6.7 -12.7 -2.1 -10.8 -8.4

-2.6% -6.5 -16.6 -14.3 -17.7 -13.8

-2.4% -6.1 -24.8 -31.6 -19.8 -22.0

-2.1% -5.7 -36.0 -40.7 -28.2 -30.0

-3.7% -5.5 -44.3 -49.1 -36.4 -38.8

Tax X refers to scenarios with a carbon tax of $X/ton. Cap-and-Trade X-Y refers to cases where household emissions are capped at X tons/person/year and can be traded at a fixed rate of $Y/ton.

**

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While this work highlights several useful methods for anticipating household consumption, optimizing consumption under various policies, and anticipating welfare impacts of such policy, it lacks several useful features and presents only a partial picture of the distributional impacts of such policy. For example, controls for household characteristics (such as household size, presence of children, and age and education of household head(s)) in the demand equations should enhance prediction. In the cap-and-trade policy, the cost of carbon credits is assumed known, whereas in most policies under consideration, market forces would decide it. In addition, the translog preference specification assumes non-zero expenditures, in contrast to several of the data points. And the Other category should have some level of carbon emissions associated with it. Perhaps the most limiting issue is that the 9-cateogory model does not allow for substitution within categories (e.g., different categories of airline travel [which then impacts air travel emissions per dollar spent], different types of vehicles owned [which then impacts fuel expenditures] and different appliances [which can affect electricity and natural gas emissions]). It and thus neglects many opportunities that households have to reduce emissions more flexibly than moving dollars across coarse categories. As different households will have different opportunities at different costs to curb their emissions, it is likely that the distributional effects will change. More consumption flexibility will also mean steeper cuts at lower welfare loss. Though the work presented here does not provide precise estimates of transportation mode shifts or vehicles owned, it provides a valuable introduction to the issues involved in modeling household responses to policy changes, along with useful methods for estimating emission savings and evaluating policy impacts under different settings. It also provides what may be a lower bound on emissions reductions and an upper bound on welfare losses under such policies. More details will be useful for policymakers and other stakeholders, as nations and communities seek optimal policy for reaching carbon targets.

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Bomberg, M., K. Kockelman and M. Thompson. 2009. GHG Emissions Control Options: Assessing Transportation and Electricity Generation Technologies and Policies to Stabilize Climate Change. Proceedings of the 88th Annual Meeting of the Transportation Research Board. Brannlund, R. and J. Nordstrom. 2004. Carbon tax simulations using a household demand model. European Economic Review 48, 211–233. Brown M. A., F. Southworth, and T. K. Stovall. 2005. Towards a built climate friendly environment, Pew Center. http://www.pewclimate.org/ docUploads/Buildings_FINAL.pdf. Accessed 25th October 2007. Bureau of Labor Statistics (BLS). 2003. 2002 Consumer Expenditure Interview Survey Public Use Microdata Documentation. U.S. Department of Labor Bureau of Labor Statistics, Washington, D.C. http://www.bls. gov/cex/csxmicrodoc.htm#2002. Bureau of Labor Statistics (BLS). 2004. Consumer Expenditures in 2002. U.S. Department of Labor, Bureau of Labor Statistics, Report 974. Washington, D.C. Christensen, L., D. Jorgenson, and L. J. Lau. 1975. Transcendental Logarithmic Utility Functions, American Economic Review, 53, 367-383. Clarke, L. E., J. A. Edmonds, H. D. Jacoby, H.M. Pitcher, J. M. Reilly, R. G. Richels. 2007. Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations, U.S. Climate Change Science Program. Available at http://globalchange.mit.edu/files/document/CCSP_SAP2-1a-FullReport. pdf. Accessed June 10, 2009. Deaton, A., J. Muellauer. 1980. Economics and Consumer Behavior. Cambridge University Press. Department of Transportation (DoT). 2003. United States Department of Transportation, Office of Aviation Analysis Domestic Airline Fares Consumer Report. Available at http://ostpxweb.dot.gov/aviation/x50%20role_files/consumerairfarereport.htm. Accessed May 25, 2009. Dinan, T, and Rogers, D. L. 2002. Distributional Effects of Carbon Allowance Trading: How Government Decisions Determine Winners and Losers. National Tax Journal, 55(2): 199–221. Ellerman, A. Denny, Barbara Buchner, and Carlo Carraro. 2007. Allocation in the European Emissions Trading Scheme. Cambridge: Cambridge University Press. Energy Information Administration (EIA). 2002. Updated State-level Greenhouse Gas Emission Coefficients for Electricity Generation. 19982000. http://www.eia.doe.gov/pub/oiaf/1605/cdrom/pdf/e-supdoc.pdf. Accessed June 14th, 2008.

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Energy Information Administration (EIA). 2005. Voluntary Reporting of Greenhouse Gases Program (Fuel and Energy Source Codes and Emission Coefficients). http://www.eia.doe.gov/oiaf/1605/coefficients. html. Environmental Protection Agency (EPA). 2005. Emission Facts Average Carbon Dioxide Emissions Resulting from Gasoline and Diesel Fuel http://www.epa.gov/OMS/climate/420f05001.pdf. Fox, J. Linear Statistical Models and Related Methods. New York: John Wiley and Sons, 1984. Intergovernmental Panel on Climate Change (IPCC). 2007a. Climate Change 2007—The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the IPCC. Cambridge University Press. Intergovernmental Panel on Climate Change (IPCC). 2007b. Summary for policymakers. In Climate change 2007: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, eds. Parry, M.L., O.F. Canziani, J.P. Palutikof, P.J. van der Linden, and C.E. Hanson. Cambridge: Cambridge University Press, 7–22. Johansson, B. 2000. Economic Instruments in Practice 1: Carbon Tax in Sweden. OECD. Available at: http://www.oecd.org/dataoecd/25/0/2108273.pdf. Date Accessed: 15th June 2009. Jorgenson, D. W., and L.J. Lau. 1975. The Structure of Consumer Preferences. Annals of Social and Economic Measures. 4 (1), 49-101. Kelley, K. 2006. City Approves ‘Carbon Tax’ in Effort to Reduce Gas Emissions. New York Times, Nov. 18. Kerkhof, A. C., H.C. Moll, E. Drissen and H.C. Wilting. 2008. ‘Taxation of multiple greenhouse gases and the effects on income distribution: A case study of the Netherlands. Ecological Economics, 318–326. Kockelman, K., M. Thompson and C. Whitehead. 2009. Americans' Travel Choices and their Relative Contributions to Climate Change: What NearTerm Behavioral Shifts Will Buy Us, and Opportunities for Meeting Carbon Targets. Proceedings of the 48th Annual Meeting of the Southern Regional Science Assocation, in San Antonio, and under review for publication in J of Urban Planning and Development. Kockelman, K., M. Bomberg, M. Thompson and C. Whitehead. 2008. GHG Emissions Control Options: Opportunities for Conservation Report Commissioned by the National Academy of Sciences for the Committee for

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the Study on the Relationships Among Development Patterns, VMT, and Energy Conservation. Lasky, M. 2003. The Economic Costs of Reducing Emissions of Greenhouse Gases: A Survey of Economic Models, U.S. Congressional Budget Office (CBO) Technical Paper 2004-4. Available at http://www.cbo.gov/ftpdocs/ 41xx/doc4198/2003-3.pdf. Metcalf, G.E. 2005. Tax Reform and Environmental Taxation. National Bureau of Economic Research (NBER) Working Paper No. 11665. Cambridge, Massachusetts. Available at: www.nber.org/papers/w11665. pdf. Metcalf, G. E. 2008. Designing a Carbon Tax to Reduce U.S. Greenhouse Gas Emissions. National Bureau of Economic Research (NBER) Working Paper No. W14375. Available at: www.nber.org/papers/w14375.pdf. National Bureau of Economic Research (NBER). 2003. Archive of Consumer Expenditure Survey micro data extracts. Available at: http://www.nber. org/data/ces_cbo.html. NTD 2003. National Transit Database. http://www.ntdprogram.gov. Accessed 25th April 2009. Quadrelli, R, and S. Petersona. 2007. The energy–climate challenge: Recent trends in CO2 emissions from fuel combustion. Energy Policy 35, 59385952. Roberts, S. and J. Thumim. 2006. A Rough Guide to Individual Carbon Trading: The Ideas, the Issues and the Next Steps. Centre for Sustainable Energy, Report to the U.K.’s Department for Environment, Food and Rural Affairs. Available at www.defra.gov. Roy, René. 1943. De l’Utilité: Contribution à la Théorie des Choix. Hermann, Paris. Small, K, A., and K. Dender. 2006. “Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect.” Energy Journal, 28(1): 25–52. Small, K., and H. Rosen, 1981. Applied welfare economics with discrete choice models. Econometrica 49, 105–130. Stern, N. 2007. The Economics of Climate Change. Cambridge: Cambridge University Press. Tiezzi, S. 2001. The welfare effects of carbon taxation on Italian households. Working Paper 337, University of Siena, Department of Economics. Available at http://ssrn.com/abstract=314726 Thumim, J and V.White. 2008. Centre for Sustainable Energy. Distributional Impacts of Personal Carbon Trading: A Report to the Department for

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Environment, Food and Rural Affairs (Defra), London. Available at: www.defra.gov. Tol R.S.J. 2005. The Marginal Damage Costs of Carbon-dioxide Emissions: An Assessment of the Uncertainties. Energy Policy 33, 2064–2074. Varian, H., 1992. Microeconomic Analysis, third ed. W.W. Norton and Company, New York. Wier, M., K. Birr-Pedersen, H.K. Jacobsen and J. Klok. 2005. Are CO2 taxes regressive? Evidence from the Danish experience, Ecological Economics 52, 239–251. WRI. World Research Institute 2009. Climate Analysis Indicators Tool (CAIT) version 6.0. Washington, DC: World Resources Institute.. Available at http://cait.wri.org. Young, S. 2009. California Weighs Nation's First Statewide Carbon Tax On Polluting Industries. Huffington Post. Available at http://www.huffington post.com/2009/06/25/california-weighs-nations_n_220798.html. Accessed November 12, 2009.

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Chapter 5

DEVELOPMENT OF AN ENVIRONMENTAL SUSTAINABILITY MODEL FOR NEW RESIDENTIAL DEVELOPMENT IN SOUTH AUSTRALIA: AN ECOLOGICAL FOOTPRINT APPROACH J. Morrissey,1, A. Carre2,† and J. Boland3,‡ Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved.

1,2

Centre for Design, RMIT University, Melbourne VIC 3001, Australia 3 Division of Information Technology, Engineering and the Environment, School of Mathematics and Statistics, University of South Australia, Mawson Lakes, Adelaide SA 5095, Australia

ABSTRACT The inter-related issues of climate change and energy use have brought renewed focus on the importance of renewable energy generation and energy efficiency. In the residential sector, these strategies have the  † ‡

E-mail: [email protected]. E-mail: [email protected]. Corresponding Author: Division of Information Technology, Engineering and the Environment, School of Mathematics and Statistics, University of South Australia, Mawson Lakes Boulevard, MawsonLakes.Adelaide SA 5095, Australia. Fax: +61 8 830 23449. E-mail: [email protected].

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J. Morrissey, A. Carre and J. Boland potential to contribute to significant energy reduction while adding to social security and long term economic well-being. In Australia, a lack of data has impeded the policy debate on residential energy use. In particular debates about environmental performance, standards and targets are contested and lack an empirical basis. A key problem is the complexity and multi-scaled nature of the challenges faced by decision makers. Policy measures need to account for complexity on the one hand, and to communicate to diverse audiences in simple terms on the other. To address these challenges for the residential sector, reliable evidence is required to inform design and planning stages, while the rationale for decision-making needs to be transparent. In this context, this paper presents a method for the assessment and benchmarking of new residential developments with reference to global ecological limits. Research is conducted in conjunction with the Land Management Corporation (LMC), the State Government land development agency in South Australia (SA), as part of wider initiatives to reduce energy use and emissions in the residential sector in SA. Informed by sustainability indicators theory, the research aims to link state of the art quantification at the component scale with succinct communication of global level implications. An Ecological Footprint model, incorporating detailed life cycle assessment, is developed to assess parameters of housing construction, housing design and performance and development configuration in terms of global ecological impacts. The Lochiel Park green development in SA is incorporated as a benchmark of current ‘good practice’ housing development. The developed model enables the comparison of alternative development scenarios and policy interventions for new residential development, allowing decision makers to simultaneously assess and communicate the implications of alternative policy pathways to diverse stakeholders. The approach incorporates scientifically robust and credible quantification while facilitating communication via a readily understandable global index. The integration of ecological footprint methodology with detailed life cycle assessment data for specific targeted outcomes, as reported here, represents an exemplar of best practice scientific analysis for policy making.

Keywords:Ecological Footprint; Life Cycle Assessment; Evidence-based; Policy; Residential

1. INTRODUCTION The inter-related issues of climate change and energy use have brought renewed focus on the importance of renewable energy generation and energy

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Development of an Environmental Sustainability Model …

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efficiency. In the residential sector, these strategies have the potential to contribute to significant energy reduction while adding to social security and long term economic well-being. In particular, the construction of new buildings offers large potential with comparatively low marginal costs(Jakob, 2006). In Australia, a lack of data has impeded the policy debate, and in particular debates about environmental performance standards and targets are contested and lack an empirical basis. The research described in this paper was conducted in conjunction with the Land Management Corporation (LMC), the State Government land development agency in South Australia (SA). LMC has been involved with the design and planning stages of SA’s exemplar green development ‘Lochiel Park Green Village’, a residential development of 106 houses, located 8 kms from Adelaide CBD(S.A. Government, 2010). The development incorporates a range of sustainable technology and design solutions, and has been planned and constructed as anexemplar for other urban developments throughout SA. Initiatives include: all domestic dwellings to be built to higher building envelope energy efficiency rating1, all dwellings to incorporate rooftop photovoltaic cells for electricity generation, to a capacity of 1 kW per 100 M2 of habitable floor area; all dwellings to have gas boosted solar hot water systems; waste generation to be reduced by 35%.This paper describes an integrated assessment model developed by the University of South Australia and the Centre for Design at RMIT University for the Land Management Corporation. The developed model facilitates comprehensive assessments of new residential developments, taking account of ecological sustainability at local and global levels and considering whole of life impacts.Lochiel Park is incorporated as a benchmark of currently achievable, ‘good practice’2. The purpose of this paper is to describe the approach applied, to outline the rationale and underlying logic of the model and to discuss the potential to expand the method to enable a continued improvement of ‘good practice’ to ‘sustainable practice’.

1

7.5 stars building envelop energy standard compared with the mandatory 6 stars standard, as assessed under the Australian NatHERS scheme (See Section 4.3.1 for more details). 2 The use of the terminology ‘good practice’ as opposed to ‘best practice’ is discussed further in Section 5.

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2. INDICATORS FOR DECISION MAKING 2.1. The Need for Indicators Along with the pressure to utilise scientifically based, robust and credible evidence on which to base policy decisions (Head, 2010), decision makers also have to accommodate participatory approaches and stakeholder involvement through transparency and clearer communication mechanisms, as discussed by Santos et al. (2006). In fundamental terms, there is a dual need to account for complexity, on the one hand, and to communicate in simple terms on the other. However, a key problem remains the complexity and multi-scaled nature of the challenges faced by decision makers.To navigate these challenges, reliable evidence, focused at the appropriate scale is required to inform design and planning stages. Decision makers are therefore increasingly seeking accounting metrics based on empirical and scientifically credible data(Liu et al., 2008) and tools to inform sustainable solutions and to provide clear guidance for action (Stoeglehner and Narodoslawsky, 2008; Xing et al., 2009). In this context, the Ecological Footprint is an indicator which has become a widely applied ecological sustainability assessment metric (Scotti et al., 2009).

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2.2. Ecological Footprint The Ecological Footprint (EF) represents the critical natural capital requirements of a defined economy or population in terms of the corresponding biologically productive areas (Wackernagel et al., 1999). EF translates the consumption patterns of a given population into productive land requirement according to the regenerative capacity of the planet, as a whole (Pulselli et al., 2008). EF allows decision makers to document resource levels, set goals, identify options for action, and track progress toward desired outcomes (Wackernagel, 2009). As previously discussed, empirically based indicators to inform sustainable solutions are presently needed for the residential sector in Australia. The intuitive meaning and ease of computation of the EF, as discussed by (Scotti et al., 2009), are key strengths which present a strong case for use of EF in this context. However, while the literature points to a growing number of applications of the EF concept in planning decision making, to assess infrastructure choices or to assess the ecological impacts of various urban forms (Amekudzi et al., 2009), a number of authors highlight issues with the metric and its application for this end:

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

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The area demand of material and energy flows of different qualities and environmental effects are just summed up, thus not distinguishing between sustainable and non-sustainable resource consumption (Stoeglehner and Narodoslawsky, 2008). Aggregate results used in isolation can create an overly simplistic view of complex systems and give the impression that improvements in one area always compensate for deteriorations in others (Kitzes and Wackernagel, 2009).

Recognising these limitations of EF, the authors incorporate an additional assessment technique into the EF modelling conducted for the purposes of this paper. Life Cycle Assessment (LCA), as discussed in Section 2.3, is selected for unique advantages which compensate for particular weaknesses of EF.

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2.3. Life Cycle Assessment (LCA) LCA, described by Tukker(2000) as ‘a specific elaboration of a generic environmental evaluation framework’, was primarily developed for designing low environmental impact products.The application of LCA methodology allows questions regarding the costs and benefits of intervening at various stages of the life-cycle and the interactions between these stages to be assessed(Zabalza Bribián et al., 2009). There are many examples of the application of LCA analysis for appraisal of building projects in the literature. Of relevance to this study, international case studies of LCA of residential buildings have been reported in the literature by various authors (Adalberth, 1997; Erlandsson and Borg, 2003; Mithraratne and Vale, 2004; Ortiz et al., 2009a; Ortiz et al., 2009b; Peuportier, 2001; Utama and Gheewala, 2008). Ortiz et al.(2009b) in particular, provide a useful overview of published LCA studies, applied within the building sector. Like other assessment techniques, LCA has a number of limitations for application at the building scale.Buildings are complex products, composed of a multitude of materials and components each constituting various design variables (Pushkar et al., 2005). LCA applied at a buildings scale may identify satisfactory levels of specified component performance, for example,but not address overall consumption and environmental loadings over a buildings life cycle (Burnett, 2007). Buildings are also integrated with urban infrastructure systems, making system boundaries for analysis unclear (Zabalza Bribián et

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al., 2009). However, the incorporation of LCA within an EF framework provides one means for addressing these issues.

2.4. Rationale for EF Model Development Following from the review of EF and LCA assessment techniques in Sections 2.2 and 2.3, Table 1 presents a summary overview of key characteristics of EF and LCA assessment methods.

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Table 1.Characteristics of EF and LCA assessment techniques, as related to residential impact assessment Assessment technique

Detailed component level assessment

EF LCA Integrated EF Model

No Yes Yes

Accounts for complex system boundary issues Yes No Yes

Overly simplistic view of complex systems

Facilitates ready communication of whole building impacts

Yes No No

Yes No Yes

The rationale for combining these approaches for the purposes of this study is clear. The integrated EF model combines the detailed component scale advantages presented by LCA analysis with the more global macro assessment provided by EF analysis. The result is a model which decision makers can use to inform design and planning decisions, and a ready means of communicating the implications of those decisions. Section 3 describes how these techniques were applied and integrated to enable development of the final EF model.

3. DEVELOPMENTOF AN EF MODEL TO INFORM DECISION MAKING 3.1. Approach An ecological sustainability assessment model was developed by a research team comprised of University of South Australia and RMIT

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University academics, in conjunction with LMC. The goal was to inform housing policy interventions in SA targeted to the earliest stages of design and planning approval. Housing data were collated across appropriate scales, including State level data and component level data. A number of stakeholder consultations, held with key Government department decision makers, identified and established the most critical parameters in terms of housing and planning policy which an assessment model could help to inform and influence. The priority areas identified focus upon those aspects where policy measures would have most impact on the environmental performance of individual housing units and on combined impacts of these units at the development / precinct scale; that is, where planning influence and authority could most effectively be brought to bear upon sustainable outcomes in terms of housing. Table 2 details identified parameters for inclusion in the model.

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Table 2. Priority housing parameters identified through stakeholder consultation Lifecycle stage New construction Building lifespan Construction type Block size House size Maintenance Construction type Residential energy use Construction type Envelope energy efficiency House size Green energy Photovoltaic installation

Details Years Terrace, semi-detached, detached, mews, apartment Area in sqm Area in sqm Terrace, semi-detached, detached, mews, apartment Terrace, semi-detached, detached, mews, apartment Mandatory rating (‘6 stars’) or high efficiency rating (‘7.5 stars’) MJ/sqm.annum Area in sqm Percentage of energy provided from a green energy source kW capacity installed

From this starting point, the EF model was developed by the following process: 

Relative footprint impacts associated with identified housing parameters are estimated by modelling expected changes and interventions.

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By incorporating housing data specific to individual developments, the relative difference to the average EF for SA is arrived at. Output from the model is presented on a per capita basis, relative to baseline SA and Australian data. A ‘good practice’ EF benchmark is developed as a target for sustainable performance, based on expected per capita impacts of Lochiel Park Green village residents.

These steps are described in more detail in Sections 3.2 – 3.5

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3.2. Life Cycle Assessment of Housing Archetypes Life Cycle Assessments were conducted for 4 typical housing designs, selected to be typical of current building practice in SA. These baseline archetypes included a terrace house, detached house, a mews unit and an apartment unit. An LCA model for each archetype was developed using the SimaPro software. SimaPro is a design support program for processes and material systems, which wasfirst developed by Leiden University’s CML (Institute of Environmental Sciences). SimaPro has been designed to compare products and services on a lifecycle basis on the levels of inventory data, impact indicators and single score valuation methods. SimaPro is comprised of main data process categories of material, energy, transport, processing use, waste scenario, waste treatment, etc., with each category further divided into subcategories (Lee et al., 2009). The software has been designed for the purpose of Inventory Analysis Impact Assessment. The comparison of material alternatives for this analysis was carried out using SimaPro 7.0, incorporating input from ‘AusLCI’ (CSIRO, 2010)and ‘ecoinvent’(Swiss Centre for Life Cycle Inventories, 2010) databases, which applied data on typical construction materials and inclusions, adjusted for South Australian conditions. Final LCA results account for the total amount of all materials throughout the building lifecycle. The Nationwide House Energy Rating Scheme (NatHERS) approved software AccuRate, was applied to calculate housing unit specific ongoing heating and cooling loads for the selected house designs3. The AccuRate software has been validated through BESTEST (Delsante, 2004). Simulations for this study were conducted for NatHERS climate zone 16, the default 3

In the NatHERS rating system, unique starbands are set for each climate zone in Australia, taking into account the extremes of the local weather conditions (Delsante, 2004).

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climate zone for Adelaide, SA. These data were combined with typical and representative energy consumption patterns for Australian households, obtained from Australian Commonwealth Government sources (ABARE, 2009; DEWHA, 2008), to establish typical operating energy loads. Final LCA results provide a comprehensive measure of impacts of the analysed house designs across lifecycle stages4.LCA baseline archetypes were adjusted based on the sustainable design principles applied for Lochiel Park houses. All output data were incorporated into the reference EF baseline.

3.3. Ecological Footprint Analysis A short overview of the EF methodology applied is provided here. Monfredaet al.(2004), Wackernagelet al.(1999) and Wiedmannet al.(2006) provide in-depth description and discussion of EF methodology. Readers are referred to these authors for detail expositions of underlying EF calculations and assumptions. The land requirement for calculation of total EF for specified impacts is comprised of 5 principal components, as consistent with the methodology described in the Living Planet Report(WWF, 2010). These land components consist of:

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    

Built-up land area (area occupied by infrastructures for transportation and housing activities) Forest land area (area allocated for use of timber /paper resources) Grazing land area (area allocated for production of agricultural resources) Cropland area (area allocated for production of food-stuffs or fuels), Energy land area (area allocated for forest land required to sequester CO2 emissions from energy production processes)

Equation 1, after Scottiet al.(2009), describes the general calculation for an individual land area, of type i, whereby Yield is the number of tonnes per hectare required to obtain Consumption (t) from that land type.

4

LCA results are contingent on assumptions inherent in SimaPro and AccuRate software.

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(1) After land areas for the five land types specified are calculated, the total EF is obtained by the sum total of these. To enable global comparison of EF calculations, and to ensure global consistency in results, values obtained for eachland type are converted into global hectares (gha), a standardized unit of biologically productive area, characterized by an idealproductivity equal to the average of the 11.4 billion bio-productive hectares (ha) on earth. This is achieved by applying an Equivalence Factor adjustment, as described in Equation 2.

(2) For this study, data from the Australia’s National Footprint and Bio-capacity Accounts for 2001 were scaled on the basis of proportional population impacts to provide a SA specific per capita EF. This represents the baseline case for the developed model.

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3.4. Integration of LCA Data into Final EF Model The final EF model was obtained by adjusting per capita SA EF data in three ways. Firstly, calculated SA Footprint baseline data were integrated with LCA outputs, including data for calculated housing archetypes and Lochiel Park green village designs. Specifically, housing archetype LCA data were integrated into the housing component of the EF. This enabled detailed component specific impacts to be identified within the framework of overall EF outputs. Secondly, where not already completed in the first step of data integration, algorithms were developed which established the empirical relationship of the parameters in Table 2 with average per capita EF. This step linked policy relevant parameters (Table 2), with the overall impact as assessed by EF. Finally, an adjusted EF was calculated for the Lochiel Park development, and this was incorporated in the model as a ‘good practice’ ecological sustainability exemplar, serving as a benchmark for both future developments and future performance targets. Figure 1 provides an overview of the process.

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Figure 1.Overview of the process of developing the model.

3.5. Developing the Lochiel Park ‘Good Practice’ Benchmark EF EnergyEfficiency Under the National House Energy Rating Scheme(NatHERS), unique starbands are set for each climate zone in Australia (ABCB, 2006). These starbands equate to strictly defined standards of thermal performance and take into account the extremes of the local weather conditions. Table 3 presents maximum energy consumption loads for selected star ratings for Adelaide climatic conditions.

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J. Morrissey, A. Carre and J. Boland Table 3. Maximum energy per unit area (MJ/m2) loads for selected star ratings

Climate Zone 16: Adelaide Star rating Thermal energy requirement MJ/m2

0.5 584

2.5 270

5 125

7.5 58

10 3

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The planning guidelines for Lochiel Park mandatea 7.5 star performance on all buildings in the development (58 MJ/m2). According to calculations by the Land Management Corporation(Cohen and Oliphant, 2008), this improvement in building energy efficiency to a 7.5 stars standard will result in a 43% reduction in energy necessary for space heating and cooling, reducing the baseline of 1054.53 MJ.annum-1 to a figure of 706.54 MJ.annum-1 for Lochiel Park units, including both electricity and gas requirements.

PhotovoltaicElectricity Many of the interventions planned by the Land Management Corporation (LMC) can be classified as being essentially independent of one another. An exception is made in the case of photovoltaic (PV) cells for generating electricity. These are considered only after the impacts of energy efficiency measures have been accounted for. This sequencing is critical in developing an effective approach to total energy consumption reduction. The rationale applied is that reduced energy demand through energy efficiency measures allows greater flexibility in satisfying remaining energy. In order to include the effects of PV generated electricity in the final EF, the total electricity that could be expected to be generated by 1.5 kW of cells in Adelaide over the year is calculated. For this, it is assumed that all PV cells are oriented towards the equator at an angle of the latitude minus 10, the assumed optimum tilt.The estimated solar radiation is 2005.5 kWh/m2. This figure is converted to the number of kWh per day per kW of installed capacity. Matching the output for various system efficiencies with real output figures from an operating system, it is estimated that the operating efficiency over the year averages at 9%. Based on an installation target of 1 kW of installed capacity per 100m2of living space, the estimated energy generated per occupantis 1,133 kWhper annum. Transport For the purposes of this analysis, an assumption of an increase in public transport use of 10% magnitude is made. This is based on the LMC target of

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increased public transport usage in Lochiel Parkthrough design and behavioural change initiatives. A baseline of 8000.5 km per person is assumed (Agrawal et al., 2005). Table 4 provides data on road and rail km per person travelled, showing the baseline and adjusted Lochiel Park figures. The increase in public transport by residents of Lochiel Park is assumed in the ‘buses’ category, as Lochiel Park is not within close proximity to a rail line. Table 4. Road/Rail km per person travelled in SA, baseline and adjusted LP figures

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Mode of transport Passenger vehicles Motorcycles Buses Rail

Baseline 7,542 42 89 327

Lochiel Park 7,168 42 473 327

CarbonOffset The ‘Urban forest’ isa significant component of the Lochiel Park development and is an attempt to recreate the native habitat of the area from a section of the existing brownfield site. Because the Urban forest enhances biodiversity and overall global bio-capacity as an explicit development strategy on the part of LMC, it is assumed that this contributes carbon offsets to the Green Village component. However, the methodology to quantify these credits is not straightforward within standard EF calculations. The following approach was applied. According to(Cohen and Oliphant, 2008), the carbon bio-sequestration potential of the Lochiel Park area of 7 hectares of forest is 10,600 t CO2-e over 80 years5. Divided by the number of years and residents, a figure of 0.516 t CO2-e per resident per year is obtained. This figure was then applied to the total figure for car travel, which was reduced by 1669 km car travel per resident per year6.

5

Bio-sequestration potential of woodlands of the Pre-European vegetation communities of the Campbelltown area. 6 The figure of 3.1 x 10-4 t CO2-e per km for petrol car travel is applied (Greenfleet, 2010).

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4. RESULTS Adjustments of the Lochiel Park per capita EF assume a reduction in overall waste production of 35%, and a 50% reduction in land use for housing due to the higher density of development at Lochiel Park (Net density of 24.9 dwelling units/ha or 106 dwellings over 4.25 hectares), in addition to the interventions described in Section 3.5. The changes instituted in the charter for Lochiel Park make a substantial difference of 9.7% reduction in the EF when compared with the baseline footprint for South Australia, resulting in a final total footprint of 6.32 ghaper capita.

4.1. Data and Model Application A summary of housing archetype parameters as integrated to the final EF model are provided in Table 5. Data in Table 5 incorporate LCA outcomes as discussedas well as applying ABS data on average occupancy and energy consumption rates(ABS, 2008). For initial baseline analysis, a 50 year building life-span was assumed, however this parameter is variable in the final developed model for new development assessment.

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Table 5. LCA output: Summary of housing archetypes and assumptions Key parameter Average occupancy Building life-span Construction impact Energy Consumption Baseline Total Electricity (consumed) Total Gas (consumed) Total greenhouse gas p.a.

Unit Persons Years gha/dwelling

Detached 2.70 50 0.16

Terrace 2.70 50 0.12

Apartment 2.10 50 0.07

Mews 1.20 50 0.05

kWh/yr MJ/yr

5682.00 1604.17

5682.00 1604.17

5171.00 1379.72

4167.00 1043.33

kgCO2e

5680.49

5680.49

5164.02

4156.59

Increasing efficiency in material use is apparent across the housing archetypes, due to the increasing prevalence of shared components and smaller gross floor areas. This is reflected in the respective construction impacts of the developed archetypes. Figure 2illustrates this, showing the relative impacts of construction, expressed in gha / dwelling, which decreases with unit size.

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Figure 2.Construction EF of housing archetypes.

These data form the reference baseline of the developed model. This reference baseline is critical as it demonstrates the level of impact from current building forms and construction practices. Improvements in design and practice can then be compared with this reference baseline. Figure 3 shows the per capital EF which is expected from occupants of a typical new detached house in SA, for example, compared with the per capita EF of an occupant of a Lochiel Park house and the average Australian per capita EF.

Figure 3.Benchmark against Australian EF/capita and Lochiel Park EF/ capita.

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Table 6. Comparison of the impacts of two 100 unit development proposals Development

DEVELOPMENT A Terrace (SA baseline) Detached (SA baseline) Mews (SA baseline) Apartment (SA baseline) DEVELOPMENT B Terrace (SA baseline) Detached (SA baseline) Mews (SA baseline) Apartment (8% solar energy generation)

Houses/ Units (No.)

Occupancy (persons)

Elec. Consumed (kWh p.a.)

Elec. Generated (kWh p.a.)

Net Elec. (kWh p.a.)

Gas Consumed (MJ p.a.)

Greenhouse emissions (kg CO2e p.a.)

Construction impact (gha)

10

2.7

5682

0

5682

1604.17

5680.49

0.12

60

2.7

5682

0

5682

1604.17

5680.49

0.16

10 10

1.2 2.1

4167 5171

0 0

4167 5171

1043.33 1379.72

4156.59 5164.02

0.05 0.07

10

2.7

5682

0

5682

1604.17

5680.49

0.12

10

2.7

5682

0

5682

1604.17

5680.49

0.16

10 60

1.2 2.1

4167 3366

0 1362.84

4167 2003.16

1043.33 672.78

4156.59 2010.13

0.05 0.07

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The model is applicable at the individual housing unit level and also at precinct or development scales. Table 6 provides data on two hypothetical housing development proposals, each of 100 units, for example. ‘Development A’ contains units which apply typical baseline archetypes, including 10 mews units, 10 terraced units, 10 apartments and 60 detached dwellings. ‘Development B’, presented as a ‘green development’, contains 10 mews units, 10 terraced units and 10 detached units, based on baseline archetypes, as well as 60 apartment units. The apartments are each modelled with the capacity to provide 8% of its annual energy needs through Solar PV energy generation. Figure 4 presents the calculated per capita impacts of a hypothetical future resident of each of these developments, benchmarked against the Lochiel Park per capita footprint and presented with the Australian average per capita footprint. In this way, it is possible to explicitly compare current Business as Usual practice with current ‘good practice’, or with future improvement targets.

Figure 4. Benchmark of the impacts of two 100 unit development proposals.

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5. DISCUSSION Stoeglehner and Narodoslawsky(2008) and Huijbregtset al.(2008) suggest that EF could best be applied in the decision-making process as a ‘filter’ or ‘screening indicator’, to identify feasible development alternatives in line with ecological sustainability principles. Options which pass predefined EF criteria may then be subject to detailed assessments and may enter the final decision weighing environmental, social and economic impacts, thus increasing the efficiency of the decision-making processes. Outcomes from this study support and lend weight to this argument. The developed model synthesises many of the benefits of the constituent LCA and EF metrics. From a decision making perspective, the integrated EF model can allow prioritisation of policy initiatives in terms of effectiveness, cost and ease of implementation against a reference EF for both SA baseline and the current ‘good practice’ benchmark presented by Lochiel Park. In turn, there is a clear, numerical and evidenced based approach for the ranking of different policy options. The EF model, applied at early conceptual stages, particularly design and planning stages, can assess and quantify theenvironmental impacts of alternative construction types, lot sizes and energy efficiency inclusions. As discussed by Ortiz et al.(2009a), the proper design and choice of building materials during the pre-construction phases can improve the energy efficiency during the operation phase and the final distribution of buildings’ consumption for heating and cooling. By design, LCA is a comparative analytical tool specifically designed to assess the environmental impacts of one product in relation to the impacts of other products providing the same functions (Andersson et al., 1998; Olsen et al., 2001). The role of LCA in decision making for the built environment is therefore limited, due to its lack of consideration of environmental aspects of a broader range of activities (Tukker, 2000). Ecological footprint on the other hand, is ideally suited to communication of a more holistic consideration of impacts. The EF method excels in many features that are mandatory for integral indicators;it presents a meaningful value that can be easily imagined and represented (Medved, 2006), for example. The output of results as a final condensed index, linked to global carrying capacity facilitates the ease of communication and ease of understanding across groups of stakeholders in the regard. The incorporation of detailed LCA data into higher level EF outputs demonstrates clear links between housing impacts with other policy areas. There is potential for final EF indicator outputs to be shared and communicated across government departmentswith associated governance

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benefits. In the case of the ‘good practice’ benchmark developed for Lochiel Park for example, proximity to a public transport hub would undoubtedly have reduced the calculated per capita EF further. This highlights the co-operation required between housing and transport decision makers for future sustainability gains. The application of the integrated EF model could facilitate this future co-operation, for example by helping to address difficulties associated with consensus building in urban planning environments is discussed by Amekudziet al.(2009). In addition to this, Wiedmannet al.(2006) discuss the application of EF to formulate tailor-made policies and to ensure that strategies towards achieving sustainable consumption are coherent. The integrated EF model allows for the benchmarking of new housing developments in an empirical and rational manner, as opposed to a more normative basis. The 2010 Living Planet Report states that the ‘fair earth share’, or benchmark for sustainable consumption currently rests at 1.8 gha per person for every human individual on the planet, based on current levels of biocapacity(WWF, 2010). This figure can be viewed as the benchmark for ecological sustainability, against a current EF of 2.7 gha per person for all of human activity. In this respect, the Lochiel Park benchmark is referred to as ‘good practice’ as opposed to ‘best practice’ throughout this paper, in view of the substantial progress still required before ‘good practice’ becomes sustainable practice. The developed model could be useful in this regard, to help decision makers to highlight where current consumption is accruing and to facilitate discussion and target setting for both current business as usual practice and current ‘good practice’, to engender a transition to sustainable practice. While the advantages of LCA and EF are incorporated within the developed model and a number of synergies manifest, the developed model is limited in a number of ways, which the authors duly acknowledge. From an EF perspective, in addition to the issues already discussed, the policy force of EF may be limited if detached from conventional economic cost–benefit analysis and social evaluations. The simple value of EF may not tell the entire story about the environmental performance of a settlement or community for example, as discussed byScottiet al.(2009). On a more metaphysical level, the reductionist approachto scientific enquiry has been criticised on the basis that complex systems cannot be accurately assessed by dividing a system into constituent parts which are studied individually at first, and then collectively(Bell and Morse, 2003). Indicators may not fully illustrate the complexity of the systems or processes they represent, for example, or may reflect only a sub-set of the possible representations of it(Munda, 2006). More

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detailed overview perspectives of this debate are available from the literature (Andersen, 2001; Gasparatos et al., 2008; Pesce, 1999).

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6. CONCLUSIONS AND OUTLOOK The importance of the built environment for sustainable development is widely recognised in the literature (Burnett, 2007; Ding, 2008; Jones et al., 2007; Ortiz et al., 2009b; UNEP, 2007), as is the need for appropriate tools and assessment methods to inform decision making (Brugmann, 1996; Russell and Thomson, 2009; Stoeglehner and Narodoslawsky, 2008; Xing et al., 2009). The goal of the applied approach is to establish and communicate the significance of impacts of various policy alternatives, established through robust scientific method, to decision makers and the public without obscuring specific details of data or method at the component level. Simultaneously, the approach provides a means for decision makers to engage with complexity. The developed model allowsan assessment of detailed life cycle data for housing to be placed in the context of overall global impact, enabling decision makers to assess the impact of decision making alternatives at local and global scales. From a policy implementation perspective, such integrated assessments are a necessity (Pulselli et al., 2007; Wiedmann et al., 2006). The developed model is shown to be appropriate for application in the SA context and could readily be extended to relate to other regions in Australia and internationally. The methodological framework could in fact be applied to a myriad of other policy areas and need not be limited to housing policy uses. The reported approach would be appropriate for assessments where detailed specific component level assessment needs to be combined with macro or global level indicators.

ACKNOWLEDGMENTS This research was conducted at the University of South Australia and the Centre for Design, RMIT University as part of work commissioned by the Land Management Corporation, SA. The views expressed herein are those of the authors and are not necessarily those of the Land Management Corporation. The authors wish to acknowledge the Land Management

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Corporation and to express their gratitude to individuals within LMC for their contribution to this work.

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Ding, G.K.C., 2008. Sustainable construction--The role of environmental assessment tools. Journal of Environmental Management 86, 451-464. Erlandsson, M., Borg, M., 2003. Generic LCA-methodology applicable for buildings, constructions and operation services--today practice and development needs. Building and Environment 38, 919-938. Gasparatos, A., El-Haram, M., Horner, M., 2008. A critical review of reductionist approaches for assessing the progress towards sustainability. Environmental Impact Assessment Review 28, 286-311. Greenfleet, 2010. Head, B.W., 2010. Reconsidering evidence-based policy: Key issues and challenges. Policy and Society 29, 77-94. Huijbregts, M.A.J., Hellweg, S., Frischknecht, R., Hungerbühler, K., Hendriks, A.J., 2008. Ecological footprint accounting in the life cycle assessment of products. Ecological Economics 64, 798-807. Jakob, M., 2006. Marginal costs and co-benefits of energy efficiency investments: The case of the Swiss residential sector. Energy Policy 34, 172-187. Jones, P., Patterson, J., Lannon, S., 2007. Modelling the built environment at an urban scale--Energy and health impacts in relation to housing. Landscape and Urban Planning 83, 39-49. Kitzes, J., Wackernagel, M., 2009. Answers to common questions in Ecological Footprint accounting. Ecological Indicators 9, 812-817. Lee, K., Tae, S., Shin, S., 2009. Development of a Life Cycle Assessment Program for building (SUSB-LCA) in South Korea. Renewable and Sustainable Energy Reviews 13, 1994-2002. Liu, Y., Gupta, H., Springer, E., Wagener, T., 2008. Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management. Environmental Modelling and Software 23, 846-858. Medved, S., 2006. Present and future ecological footprint of Slovenia--The influence of energy demand scenarios. Ecological Modelling 192, 25-36. Mithraratne, N., Vale, B., 2004. Life cycle analysis model for New Zealand houses. Building and Environment 39, 483-492. Monfreda, C., Wackernagel, M., Deumling, D., 2004. Establishing national natural capital accounts based on detailed Ecological Footprint and biological capacity assessments. Land Use Policy 21, 231-246. Munda, G., 2006. Social multi-criteria evaluation for urban sustainability policies. Land Use Policy 23, 86-94.

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Olsen, S.I., Christensen, F.M., Hauschild, M., Pedersen, F., Larsen, H.F., Tørsløv, J., 2001. Life cycle impact assessment and risk assessment of chemicals -- a methodological comparison. Environmental Impact Assessment Review 21, 385-404. Ortiz, O., Bonnet, C., Bruno, J.C., Castells, F., 2009a. Sustainability based on LCM of residential dwellings: A case study in Catalonia, Spain. Building and Environment 44, 584-594. Ortiz, O., Castells, F., Sonnemann, G., 2009b. Sustainability in the construction industry: A review of recent developments based on LCA. Construction and Building Materials 23, 28-39. Pesce, M., 1999. Reductionism versus Holism: multiple models of the spiritual quest. Technology in Society 21, 457-470. Peuportier, B.L.P., 2001. Life cycle assessment applied to the comparative evaluation of single family houses in the French context. Energy and Buildings 33, 443-450. Pulselli, F.M., Ciampalini, F., Leipert, C., Tiezzi, E., 2008. Integrating methods for the environmental sustainability: The SPIn-Eco Project in the Province of Siena (Italy). Journal of Environmental Management 86, 332341. Pulselli, R.M., Simoncini, E., Pulselli, F.M., Bastianoni, S., 2007. Emergy analysis of building manufacturing, maintenance and use: Em-building indices to evaluate housing sustainability. Energy and Buildings 39, 620628. Pushkar, S., Becker, R., Katz, A., 2005. A methodology for design of environmentally optimal buildings by variable grouping. Building and Environment 40, 1126-1139. Russell, S.L., Thomson, I., 2009. Analysing the role of sustainable development indicators in accounting for and constructing a Sustainable Scotland. Accounting Forum 33, 225-244. S.A. Government, 2010. Adelaide. Santos, R., Antunes, P., Baptista, G., Mateus, P., Madruga, L., 2006. Stakeholder participation in the design of environmental policy mixes. Ecological Economics 60, 100-110. Scotti, M., Bondavalli, C., Bodini, A., 2009. Ecological Footprint as a tool for local sustainability: The municipality of Piacenza (Italy) as a case study. Environmental Impact Assessment Review 29, 39-50. Stoeglehner, G., Narodoslawsky, M., 2008. Implementing ecological footprinting in decision-making processes. Land Use Policy 25, 421-431. Swiss Centre for Life Cycle Inventories, 2010. Zurich.

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Tukker, A., 2000. Life cycle assessment as a tool in environmental impact assessment. Environmental Impact Assessment Review 20, 435-456. UNEP, 2007. Buildings and Climate Change - Status, Challenges, and Opportunities. United Nations Environment Program, Geneva. Utama, A., Gheewala, S.H., 2008. Life cycle energy of single landed houses in Indonesia. Energy and Buildings 40, 1911-1916. Wackernagel, M., 2009. Methodological advancements in footprint analysis. Ecological Economics 68, 1925-1927. Wackernagel, M., Onisto, L., Bello, P., Callejas Linares, A., Susana López Falfán, I., Méndez García, J., Isabel Suárez Guerrero, A., Guadalupe Suárez Guerrero, M., 1999. National natural capital accounting with the ecological footprint concept. Ecological Economics 29, 375-390. Wiedmann, T., Minx, J., Barrett, J., Wackernagel, M., 2006. Allocating ecological footprints to final consumption categories with input-output analysis. Ecological Economics 56, 28-48. WWF, 2010. The Living Planet Report. WWF, Zoological Society of London, Global Footprint Network, Gland, Switzerland. Xing, Y., Horner, R.M.W., El-Haram, M.A., Bebbington, J., 2009. A framework model for assessing sustainability impacts of urban development. Accounting Forum 33, 209-224. Zabalza Bribián, I., Aranda Usón, A., Scarpellini, S., 2009. Life cycle assessment in buildings: State-of-the-art and simplified LCA methodology as a complement for building certification. Building and Environment 44, 2510-2520.

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Chapter 6

PROMOTING SUSTAINABLE DEVELOPMENT THROUGH INTEGRATED WOOD STOVE TECHNOLOGY, AFORESTATION AND AGRO-AQUACULTURE A. C. Achudume

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Institute of Ecology and Environmental Studies Obafemi Awolowo University, Ile-Ife, 220001 Nigeria

ABSTRACT It is recognized that many issues in respect of fuel wood, air pollution, deforestation and aforestation are addressed international agreement as the 1985 Vienna Convention for the Protection of the ozone layer, the 1987 Montreal Protocol on Substances that Deplete the Ozone layer as amended, the 1992 United Nations Framework Convention on Climate Change and 2009 United Nations Conference on Climate change. Inconclusive as the deliberations and negotiations were on certain critical issues, the Conference drew global attention to the emergency proportions of temperature rise as well as the diminishing capacity of natural mechanisms for absorption of sink carbon dioxide (CO2 ). The imperative of reasonable remedial measures is all too obvious. The use of fuel-wood stove is becoming subject of global discussion. The role and importance of wood stove in developing nations affecting rural population have been discussed in various platforms. For instance, the status of women in provision of food and socio-economic structure tend

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A. C. Achudume to produce gradually diminishing time. Most useful time are spent gathering biomass especially fuel wood. In urban areas up to 55% of household income is spent to purchase cooking wood. Most often, woods are burnt in open fire and inefficient fuel stove in kitchens with little or no ventilation device. The necessity of promoting better life quality to rural women who are the end users of wood stove, thus affecting socioeconomic, poverty, deforestation, climate change and health are the common aim of discussion. The basic and ultimately objective of this study is to reduce adverse effects on the atmosphere from the energy sector by promoting policies as appropriate, to increase the contribution of environmentally sound and cost-effective energy systems, particularly , through wood stove, less polluting and more efficient energy production, and use. In addition, to use wood as fuel for which countries have serious difficulties in switching to alternatives, and the situations of countries highly vulnerable to adverse effects of climate change. The impact on agriculture and other areas of the economic importance has been devastating. Soil infertility is one consequence, as the unprotected earth surface is repeatedly battered by the rains and soil nutrients like nitrogen are washed away. Areas to regenerate woody perennials in land-use systems. Common forms of agroforestry in the wood and wood products lead to deforestation. Soil infertility is one consequence of deforestation, increased temperature; severe weather events and greenhouse gases (GHG) are on increase. Global population is also increasing, so is increased in carbon dioxide. There is utmost need for sustainable exploitation and management of low cost woodstove. The development of simple technically integrated woodstove adaptable to local needs would provide energy content per unit mass. Selective cutting schemes, reforestation and management of woody perennials could therefore, offset the release of CO2. Finally, government at the appropriate level and institutions should cooperate in the provision of expertise and other supports including the promotion of international research efforts, in particular, with a view to enhancing the technology of integrated woodstove. Integrated irrigation-aquaculture (IIA) is only beginning in Nigeria. Projects are now in place to encourage integrated agriculture enterprises, offering hope for development of a more dynamic agriculture in Nigeria.

Keywords: Sustainable development, fuel wood, woodstove, deforestation, afforestation, aquaculture

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INTRODUCTION In recognition of the activities that may be discussed in pursuance of the above subject matter, the vision of conservative development cannot be appropriate without discussion on many challenges confronting countries with large rural population in terms of natural resources, poverty, a high disease burden, food security and limited water access as well as climate change. There are major weaknesses in the methods and mechanisms adapted to support the development of the ecological, economic, social and cultural roles of woodstove vis-à-vis forest resources (UNCED 1992). While trying to manage resources sustainably, an environmental policy that focuses mainly on the conservation and protection of resources must take cognizance of those who depend on the resources for their livelihoods. Otherwise, it could have an adverse impact on poverty, and other chances for long-term success in resource control and environmental conservation. Equally, a development policy that focuses mainly on protocol on substances without addressing the sustainability of the forest resources and correlation of demographic, socioeconomic information at the Micro- and Macro levels, and undertaking periodic analyses of forest programmers will sooner or later run into unsustainable biomass production and the anti-poor bias (1987 Montreal Convention). Poverty and the actions of the poor were seen as one of the main causes of non-sustainable development, rather than recognizing that poverty and environmental degradation are both consequences of existing development pattern (IUCN, 1980). In most developing countries, rural population is gradually diminishing, notwithstanding the agricultural lands that are losing productivity are increasing (Akpinar et al 2005). While this situation primarily results in increasing impoverishment of rural society, it also causes problems such as deforestation, erosion and productivity loss with the misuse of the resources. A fair and equitable use of present resources is clearly necessary and is enshrine in the principle of social justice. But first, damaging the natural resources emerges other problems of migration, poverty and hunger. Most affected ones by these problems are women and children. Overcoming these problems would be possible by development of technical engineering and multidisciplinary skills and quality of human resources; public education, administrative structures and mechanisms including intersectional coordination and employment. In recent times, the use of fuel-wood stove is becoming subject of global discussion. The role and importance of wood stove in developing nations

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affecting rural population have been discussed in various platforms (Smith 2006). For instance, the status of women in provision of food and socioeconomic structure tend to produce gradually diminishing time. Most useful time are spent gathering biomass especially fuel wood (Achudume 2007). In urban areas up to 55% of household income is spent to purchase cooking wood (Smith 2005). Wood and wood products are used routinely as furniture’s, globally as energy source for warmth, in developing countries for cooking and heating. Most often, woods are burnt in open fire and inefficient fuel stove in kitchens with little or no ventilation device. These problems can be mitigated by education and simple mechanical and technical devices of making clean fuel burning stove that could be channeled to making huge profit economically i.e. cooking large quantities of food for various communities (e.g. schools, entertainments, festivals and hospital services) (Achudume 2007). The necessity of promoting better life quality to rural women who are the end users of wood stove, thus affecting socio-economic, poverty, deforestation, climate change and health are the common aim of discussion. The basic and ultimately objective of this study is to reduce adverse effects on the atmosphere from the energy sector by promoting policies as appropriate, to increase the contribution of environmentally sound and cost-effective energy systems, particularly , through wood stove, less polluting and more efficient energy production, and use. In addition, to use wood as fuel for which countries have serious difficulties in switching to alternatives, and the situations of countries highly vulnerable to adverse effects of climate change.

FUEL WOOD AND DEFORESTATION The imperative of reasonable remedial measures of deforestation is all too obvious. Wood and wood products lead to deforestation. In the decades past the world had witnessed uncontrolled massive felling of trees for timbering especially in Africa, Asia and South America. The impact on agriculture and other areas of the economic importance has been devastating. Soil infertility is one consequence, as the unprotected earth surface is repeatedly battered by the rains and soil nutrients like nitrogen are washed away. Plants that depend on trees for their survival, growth and fruiting also become stunted. Soil erosion is depleted, with the result that building an entire village and farmlands are lost, as in the case in many parts of eastern Nigeria (Jotoafrica 2009). And as with other parts of the world, deforestation means more heat as the loss of shade provided by trees exposes humans to direct rays of the sun.

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These are trends that must be checked and with the resources at the disposal of government. At the global level, government responsibility is to ensure that its forests are in a state that can moderate the carbon cycle through absorptive capacity. Species of trees discovered in the forests of Gabon and elsewhere are known to be particularly valuable in absorption of carbon dioxide. Governments at various levels, non-governmental organizations, groups and individuals have to be actively involved. At least, Arbor Day, observed annually worldwide for tree planting, should be an occasion for mass participation (1992 Convention).

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AFFORESTATION AND REFORESTATION There are forestry practices in which selected trees species are planted as means of offsetting or reducing a part of anthropogenic activities e.g. selective cutting schemes, lengthened rotations, reduced-impact logging to increase the carbon storage on site through adoption of sustainable harvesting methods. Afforestation and reforestation of no-forest areas can increase, and prevention of deforestation can maintain, the amount of carbon held in forests. Adopted tree species of Gmelina arborea and Testona grandis are suitable (Dayal, 2000). Agroforestation is a common forms used in rural areas to regenerate woody perennials in a land-use systems. Common forms of agroforestry in the country are Taungya system, non-timber tree farms, and inter-cropping of leguminous trees with food crops (Nair, 1993, ICRAF 1997, Babalola and Amonum 2008). Sustainable production of wood for fuel from forest can displace fossil fuels. The regrowth of a sustainably managed forest offsets the release of CO2 . Thus woodstove can supply energy virtually without net contribution to GHG levels (Babalola, 2000). Properly cut and dried fuel wood and well-designed, well-built and well-used improved stoves with chimneys and hoods reduce kitchen pollution substantially (Smith, 2006). Cultural constraints to adoption of improve stoves are important while cooking traditions are deeply rooted in many cultures; the fire is the centre of the home and has much cultural and spiritual significance. Consequently, the following suggestions are worth considering: 

There is dare need for sustainable exploitation and management of forest resources. More forests are needed to provide stove wood, while the existing one should be protected from scavengers.

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Policies that encourage the use of wood fibers in ways that keep carbon sequestered for durable period should be enunciated There should be waste reduction planning in wood industries. Steps should be geared toward sustainable wood and timber for fuel wood. Rather than burning sawdust and wood waste that increase emission of GHG, these should be converted to compact stove wood fuel for rural population.

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RICE-FISH FARMING IN NIGERIA The Nigerian Special Programme for Food Security (NSPFS) was launched in 109 sites in all 36 States and the Federal Capital Territory (FCT) in January 2002. The NSPFS programme is also strengthened with assistance to be provided by a total of 524 Chinese technicians (including 70 in aquaculture) in the framework of the South-South Cooperation Programme ($US22°million). At the time the pivotal Food Security Project and includes smallholder irrigation, production and marketing of field crops and horticulture, livestock production, breeding and nutrition, aquaculture, farm mechanization and agroprocessing. Integrated aquaculture is included as a diversification strategy to assist rural farmers to increase income and improve household food security. With poor agricultural extension services in the country, there has been little effort at increasing public awareness for viable integration of agricultural activities, even though the benefits to rural farmers have been well documented during the past twenty years. Benefits of IIA include increased yield, improved water management with multiple-use of water, heightened synergies, increased revenues and poverty reduction. However, this situation is changing with the paradigm shift towards a private sector-driven economy. More than 70 percent of Nigerians live in rural areas and over 65 percent of the labour force is occupied in the agricultural sector. Thus the programme’s focus on expanding farm activities through integrated activities and increasing on-farm revenues can diversify livelihoods and improve rural economies in areas where poverty is heavily concentrated (Ayeni 1995). The NSPFS Programme empowers communities and farmers through its “bottomup” approach. Farmers benefit from “packages” of their own choosing and all activities are implemented on a cost recovery basis, with farmers repaying the cost of their package to the programme. The long-term perspective of this programme is to eliminate rural poverty (FAO 1999).

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Towards this end, the Aquaculture and Inland Fisheries Project (AIFP) has been designated as the most appropriate vehicle for providing assistance to private fish farmers for increasing domestic fish production and creating a sustainable aquaculture industry. Aquaculture development is expected to take a similar path as that taken by the poultry industry, which today is completely privatized and successful in Nigeria (AIFP 2003). At present, rice-fish farming is primarily “captural” in practice whereby wild fish that enter the flooded rice fields from irrigation canals and streams are trapped in the fields, and allowed to grow along with the rice. When the rice is harvested, fish are captured for consumption or sales. Visits with rice farmers in Cross River State, revealed up to 92°kg of fish were harvested per ha of rice under such conditions (NSPFS, 2003). Most fish harvested in such conditions are catfish (Clarias or Heterobranchus species), which are much sought after by consumers who pay 300°Naira or more per kilogram (1.00 US$ = 126 Naira; 1993 official exchange rate). Tilapia and other species sell for only one third to one half this amount. It is obvious that the sale of fish caught in rice paddies contributes significantly to farmer’s incomes as 92 kg of mostly catfish could have a market value of 25°000 Naira (US$190) or more. In considering an average rice production of 2.1°tonnes per ha with a value of US$777 (US$370 per tonne), income from wild fish adds more than 20percent to the value of the rice to the farmer’s revenues (Ezewan 1991). On-farm adaptive research trials (OFAR) in rice-fish farming have been undertaken with favourable results in Lagos, Niger and Imo States as well as in the Federal Capital Territory (FCT) near Abuja through the Agricultural Development Programme (ADP) extension staff. Rice-fish trials in Niger State used a local swamp/lowland rice variety (FARO15) integrated with the tilapia Oreochromis niloticus over a four-month production period(Gomma and Lamai 2000). Results were compared with a non-integrated rice field of the same area as the integrated field. The results indicate significant advantage in rice-fish farming. Results could actually be more interesting if catfish were included as they sell for a higher price.

CONCLUSION Efforts on recycling waste wood e.g. unusable wood furniture’s preferentially remodeled to compact stove wood should be strengthened. Governments at the appropriate levels, with the cooperation of the relevant

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United Nations bodies and as appropriate, intergovernmental and nongovernmental organizations, and the private sector, should: 









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

Cooperate in developing economically viable, and environmentally friendly wood stove to sustain development efforts, in particular in developing countries; Promote the development at the national level of appropriate forest trees for making compact integrated fuel wood and economic policy decisions for sustainable development, inter alia, through environmental impact assessments Promote the research and development of improved stove designs that give adequate attention to the cultural and social significance of how fuel wood should be used in households. That such stove may have social, ecological and economic benefits. Promote the research, development, transfer and use of technologies and practices for environmentally sound biomass ‘gasifer’ stoves, including endogenous technologies in all relevant sectors that when such stoves are operating well they have emission levels rivaling those of liquefied petroleum gas (LPG). Promote the research and development of inexpensive stoves that shows promise not only for high energy efficiency but also for the potential to reduce air pollution exposure substantially. Promote the development of institutional, scientific, planning and management capacities, particularly for rural areas with a long-term strategy of sustainable development.

The NSPFS is making efforts to sensitize and create awareness of the ricefish culture technology through farmer participatory demonstrations. These are directed to convince farmers of the technical and economic viability of ricefish farming in lowland/swampy areas, fadamas (floodplains) and irrigation schemes. So far, on-going demonstration plots have been established in different locations. In Nigeria we are excited about the many challenges and opportunities that lie ahead for IIA to create rural employment and increase on farm production and revenues.

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REFERENCES Achudume, AC 2009. Environmental Health, Development and Economic Empowerment of Rural Women in Nigeria. Environ. Dev. Sustain. 11;459-469 AIFP. 2003. Inventory of fish farms in Nigeria. AIFP Project Document. Abuja, Aquaculture and Inland Fisheries Project. Akeredolu, FA and Oyawale, OC 2000a. Monitoring of Sawdust “Burning Process in a Laboratory-Scale Combustion Chamber” NSCHE Journal. 19;(1and2),9-16. Akeredolu, FA and Oyawale, OC 2000b. Flaming versus Smouldring Combustion of Sawdust. NSCHE Jurnal 19(1and2), 1-8. Akpinar, N Talay, I Ceylan, C Gündüz, S 2004. Rural Women and Agrotourism in the Context of Sustainable Rural Development: A case study from Turkey. Environ. Dev. Sustain. 6;473-486. Ayeni, J.S.O. (Ed.) 1995. Report of national aquaculture diagnostic survey. New Bussa, Niger State (Nigeria), National Institute for Freshwater Fisheries Research, 106 pp. Babalola, FD 2008. Impact of Climate Change on Biodiversity: Sustainable Forest management as a mitigation options: In Towards a low Carbon Economy. Preceeding of the 1st Annual Conference of the Institute of Ecology and Environmental Sutdies, OAU., Ile-Ife. Babalola,FD 2006. Sustainable management Strategies for Recuitment of Economic Timber Species in a degraded Natural Tropical Lowland Rainforest. Nigeria Journal of Forestry. 36(2);79-90. Babalola, FD and Amonum, JI. 2008. The Potential and Significance of Agroforestry Systems in Nigeria: Review of concepts and practices and Natural Resource Management (eds. Onyekwelu, JC., Adekunle, VAJ., Oke, DO.). Proceedings of the First National Conference of Forests and Forest Product Society. Federal University of Technology, Akure, Nigeria, 16-18 April, 2008, pp 11-17. ISBN 978-37981-4-6. Department for International Development (DFID) 2009. Adapting to climate change in Africa; Joto Africa 1;1-8. Ezenwa, ZI 1991. Fish production through exploitation of aquaculture potentials of the estuaries and floodplains of Nigeria. In Proceedings of the 4th Annual Seminar of Committee of Directors of Research Institutes (CODRI), Dec. 1991, pp. 49–60. Lagos, CODRI.

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FAO. 1999. Africa Regional Aquaculture Review. Proceedings of a workshop held in Accra, Ghana, 22–24 September 1999. CIFA Occasional Paper No. 24. Accra, FAO, 50 pp. Fine, PM Cass, GR Simoneit, BRT. 2002. Chemical Characterization of Fine Particle Emissions from the Fireplace Combustion of Woods grown in the Southern United States. Environ. Sci. and Technol. 36;1442-1451. Gomna, AK Yaro, I Lamai, SL 2000. Evaluation of the growth performance yield and survival of Oreochromis niloticus at different stocking densities in rice-cum-fish culture system. Journal of Science, Technology and Mathematics Education (JOSTMED) 3(1): 149–155. International Centre for Research in Agroforestry (ICRAF) Medium Term Plan 1998-2000, pp 1-5. International Union for the Conservation of Nature (IUCN); United Nations Environmental Protection 1980. World Conservation Strategy. Gland Switzerland. Miller, KA Siscovik, DS Sheppard, L Shepherd, K Sullivan, JH Anderson, GL Kaufman, JD 2007. Long-term exposure to Air Pollution and Incidence of Cardiovascular Events in Women. N. Engl. J. Med. 356(5): 447-458. International Union for the Conservation of Nature (IUCN) United Nations Environ Protection 1980 World Conservation Strategy. Gland Switzerland. Nair, PKR 1993. An Introduction to Agroforestry. The Netherlands Kluwer Academic Publishers, 499pp. NSPFS. 2003. Mission Report/Fisheries, 21 September – 6 October 2003. Imo, Akwa Ibom, Cross River, Rivers, Bayelsa, Delta, Edo, Ondo and Kogi States. Fisheries Team. Lagos, Special Programme for Food Security, 19 pp. Smith, KR Bruce, N Arana, B 2006. The Guatemala Air Pollution Intervention Trial (RESPIRE) Presented at the Annual Conference of the International Society for Environment Epidemiology, Paris, France 2-6 September 2006. Smith, KR Roger, J Cowlin, SC 2005. Household fuels and ill-health in developing Countries: What improvement can be brought by LP Gas (LPG), Paris, France, World LP Gas Association and Intermediate Technology Development Group. Straif, K IARC Monograph Working Group 2006. Carcinogenicity of some indoor Pollutants: Emissions from Household combustion of Biomass fuel, and high-temperature frying. Lancet Oncology.

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United Nations Development Protection (UNDP) 1992. Combating Deforestation. Agenda 21,Chapter 3 pp 1-7.New York. UN Publication Service United Nations Development Protection (UNDP) 1992. Protection of the Atmosphere Earth Summit. Agenda 21, Chapter 9 pp 2-13. New York Publication Service

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Chapter 7

HOW TO PROMOTE ENERGY SAVINGS AMONG HOUSEHOLDS: THEORETICAL * AND PRACTICAL APPROACHES Linda Steg† and Wokje Abrahamse

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Rijksuniversiteit Groningen, Netherlands University of Surrey, United Kingdom

ABSTRACT Households are responsible for a large part of total energy requirements and CO2 emissions. We review the contribution of social and environmental psychology for understanding and promoting household energy conservation. A general framework is proposed, comprising: (1) identification and measurement of the behavior to be changed, (2) examination of the main factors underlying this behavior, (3) design and implementation of interventions to change behavior to reduce household energy use, and (4) evaluation of the effects of interventions. We discuss how psychologists empirically studied these four topics. *

†

A version of this chapter also appears in Psychological Approaches to Sustainability: Current Trends in Theory, Research and Applications, edited by Victor Corral-Verdugo, Cirilo H. Garcia-Cadena and Martha Frias-Armenta, published by Nova Science Publishers, Inc. It was submitted for appropriate modifications in an effort to encourage wider dissemination of research. Correspondence: Linda Steg, Department of Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, the Netherlands. E-mail: [email protected].

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INTRODUCTION Greenhouse gas emissions have steadily increased by about 1% per year during the last decade (e.g., EPA, 2004; RIVM, 2004). CO2 is the most important greenhouse gas, responsible for about 84% of the total emissions of greenhouse gases (EPA, 2004). Household energy use significantly contributes to greenhouse gas emissions. For example, households are responsible for approximately 15 to 20% of total energy requirements in OECD countries (OECD, 2001) by using electricity, natural gas and fuels; in the Netherlands, this percentage is 23% (Ministerie van EZ, 1999). Many governments aim to reduce household energy use, and consequently, greenhouse gas emissions. However, despite their efforts, household energy consumption is still increasing. In the Netherlands, electricity and fuel use have increased steadily from 1990, due to increases in possession and use of electric appliances, and increases in car use (Steg, 1999). More effective energy policies seem to be warranted to reduce the emission of greenhouse gases by households. This chapter discusses factors influencing household energy use, and ways to promote household energy savings. Attempts to promote household energy conservation will be more effective when one (1) selects high impact behaviors, that involve relatively large amounts of energy use and CO2 emissions, and monitors behavior over longer periods of time, (2) examines which factors are related to those behaviors, (3) implements interventions that change these antecedents and the behavior, and (4) systematically evaluates the effects of these interventions in terms of behavior changes, changes in behavioral antecedents, changes in energy use, environmental quality and human quality of life (Geller, 2002; Steg and Vlek, 2008). This chapter provides a brief overview of how psychologists have addressed these issues.

SELECTING AND ASSESSING ENERGY USE BEHAVIORS If psychologists aim to significantly contribute to the reduction of energy problems, they should study behaviors associated with high energy consumption. Households use energy in a direct and in an indirect way (e.g. Vringer and Blok, 1995). Direct energy use is the use of electricity, natural gas and other fossil fuels, and indirect energy use refers to the energy used in the production, transportation and disposal of goods and services. In European countries, about half of total household energy use is direct energy use, the

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other half is indirect energy use (Kok, Falkena, Benders, Moll, and Noorman, 2003; Reinders, Vringer, and Blok, 2003). Although a substantial part of total household energy use involves indirect energy use, only few studies examined the indirect use of energy (Abrahamse, Steg, Vlek, and Rothengatter, 2007; Gatersleben, Steg, and Vlek, 2002; Poortinga, Steg, Vlek and Wiersma, 2003). Household activities vary widely in the amount of energy they use. In 2005, in the UK, about 53% of domestic direct energy use related to space heating, 20% to water heating, 16% to the use of household appliances, 6% to lighting and 5% to cooking (Maslin, Austin, Dickson, Murlis, Owen, and Panizzo, 2007). Environmental scientists have developed various tools for assessing direct and indirect energy use, such as life-cycle analysis, or inputoutput analysis (e.g., Kok, Benders, and Moll, 2006) that are useful for identifying behaviors associated with relatively high levels of energy use. These data can help practitioners decide which type of conservation behavior would be most worthwhile. Besides impact, psychologists should also consider the feasibility of behaviour changes. Of course, the feasibility can be facilitated via various intervention strategies (see Section Interventions to promote energy conservation). After a specific energy-related behavior is identified, it needs to be measured properly. That is, valid behavioral measures are needed. Based on this, one can decide which (groups of) individuals should be targeted. Moreover, by monitoring (changes in) environmental behavior over time, one can assess whether interventions have been successful. Most studies on household energy use rely on self-reported data. Some studies revealed that self-reports are adequate indicators of actual behavior (e.g., Fujii, Hennesy, and Mak, 1985; Warriner, McDougall, and Claxton, 1984), but others reported weak correlations between self-reported and observed behavior (e.g., Corral-Verdugo, 1997). Whenever possible, one should try to measure actual behavior; smart meters and GPS devices could yield useful insights in this respect. When the measurement of people’s actual behavior is not feasible, it is important to consider how to collect valid and reliable measures of self-reported behavior (see also Vining and Ebreo, 2002), and to check the accuracy of the employed measures. Psychologists have studied separate energy conservation behaviors, such as reducing car use, or switching off lights, but they have also tried to assess total household energy use. Meter readings reflect how much electricity, gas, fuel or water has been used by a particular household. However, meter readings do not reveal which specific behaviors contributed most to total energy, fuel or water use. From an educational point of view this is

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problematic, for people generally do not know which and whose behaviors significantly affect resource use, and people cannot receive specific feedback on the results of their behavioral changes (see also Gatersleben et al., 2002). Therefore, composite behavioral measures of energy use have been proposed based on a well-defined set of specific behaviors (see Abrahamse et al., 2007; Gatersleben et al., 2002). This approach implies that respondents first indicate which goods they possess (e.g., fridges, cars) and how often they use these. Subsequently, environmental scientists asses the direct as well as indirect ‘energy contents’ of these behaviors. Next, the energy contents of various behaviors are summed, yielding a measure of total energy use involved in a given household behavior pattern. On the basis of this approach, households can be provided with information on specific ways to reduce their energy use. Also, feedback can be provided about the specific behavior changes that have been most effective in realizing energy savings, and those that have not been effective (see Abrahamse et al., 2007). This is important, as households may gain insight into the relative impact of the various energysaving options they consider implementing.

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FACTORS INFLUENCING HOUSEHOLD ENERGY USE Behavioral interventions are generally more effective when they target important antecedents of the relevant behavior and remove barriers to change. Therefore, it is important to understand which factors promote or inhibit energy conservation. Various factors influence household energy use and energy savings. Individuals need to be aware of the need for and possible ways to reduce household energy use, they need to be motivated to conserve energy, and they should be able to adopt the relevant behaviors. Each of these factors will be discussed briefly below.

Knowledge In general, people are well aware of the problems related to household energy use, and are concerned about these problems (Abrahamse, 2007), although there is still confusion about the causal processes involved (e.g. Bord, O’Connor, and Fischer, 2000; Gorsira, Steg, Bolderdijk, and Keizer, in preparation). For example, many people think global warming is caused by the depletion of ozone in the upper atmosphere (which is incorrect), while only a

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limited number of people think global warming is caused by heating and cooling homes, which is correct (Bord et al., 2000). In 2008, only 21% of a representative sample of the Dutch population knew that global warming does not result in ozone depletion, and only 35% knew that acid rain is not caused by global warming (Gorsira et al., in preparation). Moreover, people know relatively little about the energy use associated with their daily behaviors. For example, when assessing the energy use of household appliances, people tend to rely on a simple heuristic: the size of appliances. The larger the appliance, the more energy it is believed to use (Baird and Brier, 1981; Schuitema and Steg, 2005). Obviously, this heuristic is not always accurate. Moreover, people have a tendency to underestimate the energy use involved in heating water, which suggests that people are not well aware of the fact that energy sources are needed to do this (Schuitema and Steg, 2005). Assessing indirect energy use is even more complicated, as, typically, no information of the ‘embedded’ energy use of products and services is provided. People know relatively little about the energy use associated with the production, transportation, and disposal of products (Tobler, Visschers, and Siegrist, 2009). For example, people think the term ‘organic’ means that these products are more environmentally-friendly than non-organic products, while they do not necessarily consider the energy use involved in transporting organic products to the supermarket (e.g., flown in from overseas).

Motivations Various studies have examined relationships between motivational factors and energy behaviors. Below, we first discuss relationships between motivations and household energy use, and next relationships between motivational factors and specific energy behaviors.

Household Energy Use Household energy use is most strongly related to socio demographics, in particular income and household size, while motivational factors do not play an important role (Gatersleben et al., 2002). Not surprisingly, wealthier and larger households use more energy. This is true for direct as well as indirect energy use (Abrahamse, 2007), and for energy use in home as well as for transport (Poortinga, Steg, and Vlek, 2004). This implies that households use more energy as soon as they need to (household size) or when they have the opportunity to do so (income). However, larger households use less energy per

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person than smaller households, because they share appliances, space, cars, etc. In contrast, intention to reduce household energy use is most strongly related to motivational factors, in particular attitudes towards energy conservation and perceived behavioral control, while socio demographics do not play a significant role. This is true for direct as well as indirect energy use (Abrahamse, 2007). Environmental considerations are less strongly related to intention to reduce household energy use. So, even though concern with environmental and energy problems is generally high in Western countries (Abrahamse, 2007; Poortinga, Steg, and Vlek, 2002; Schultz and Zelezny, 1999), people often do not act in line with their concerns. As well as lacking knowledge of the energy use related to various behaviors (see above), many people attach only a low priority to saving energy. Energy use and energy conservation are not only driven by concerns about environmental and energy problems. Many other factors play a role, such as status, comfort and effort (Stern, 2000). People are less likely to reduce their energy use when saving energy involves high behavioral costs in terms of money, effort or convenience. For example, people are far more likely to carry out proenvironment activities such as recycling, which has a low cost in money and effort, than others such as reducing car use which have higher financial and lifestyle costs (see Lindenberg and Steg, 2007, for a review). This does not imply that environmental and normative concerns do not affect high-cost behavior. Some people do reduce their energy use even at the cost of personal disadvantage. Normative and environmental concerns are important in promoting energy conservation, because they provide the most solid basis for it (Lindenberg and Steg, 2007). If people only conserve energy for hedonic or cost reasons, they will stop doing so as soon as the behavior is no longer attractive or cost-effective. When energy conservation results from normative concerns as well, it is more robust against such changes.

Energy Behaviors Motivational factors proved to be related to specific energy behaviors. Energy behaviors have been studied from different theoretical perspectives. Below, we elaborate on three types of motivations that are the focus of different theoretical perspectives: perceived cost and benefits, affect, and moral and normative concerns. We also indicate how these different perspectives may be integrated into a coherent framework, and elaborate on the role of habits.

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Cost-Benefit Considerations The Theory of Planned Behavior (TPB; Ajzen, 1991) assumes that individuals make reasoned choices and choose alternatives with highest benefits against lowest costs (e.g., in terms of money, effort and/or social approval). The TPB proposes that behavior follows from an individual’s intention, which reflects how much effort one is willing to take to engage in a specific behavior. Intentions depend on attitudes towards the behavior (that is, the degree to which engagement in behavior is positively valued), social norms (that is, social pressure from important others to engage in a particular behavior), and perceived behavioral control (that is, beliefs on whether one is capable of performing the behavior). All other factors, such as socio demographics or values, are believed to affect intentions indirectly, via attitudes, social norms and perceived behavioral control. The TPB has proven to be successful in explaining various types of behavior associated with direct or indirect energy use, including travel mode choice (e.g., Bamberg and Schmidt, 2003; Heath and Gifford, 2002), the purchase of energy-saving light bulbs, use of unbleached paper, and meat consumption (Harland, Staats, and Wilke, 1999). Affect Various studies demonstrated that affect is related to behavior, in particular energy use related to car use (see Gatersleben, 2007, for a review). Most studies were exploratory and not theory-driven. Dittmar’s (1992) theory on the meaning of material possessions provides a promising perspective to study the role of affective and symbolic factors in more detail. This theory proposes that the use of material goods fulfils three functions: instrumental, symbolic, and affective. Interestingly, Steg (2005) showed that commuting car use is most strongly related to symbolic and affective motives, while instrumental motives are less important, indicating that it is important to consider affective and symbolic aspects of energy use. Moral and Normative Concerns Many scholars studied the role of moral and normative concerns in relation to energy conservation. This is not surprising, because energy conservation is often costly, e.g., in terms of money, time, or effort. In this case, people probably only conserve energy if they think this helps to benefit the environment, taking the additional behavioral costs for granted. Various theoretical perspectives have been employed to study the role of moral and normative concerns. First, scholars have examined the value-basis of

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environmental beliefs and behavior (De Groot and Steg, 2007; 2008b; Nordlund and Garvill, 2002; Stern and Dietz, 1994; Stern, Dietz, Kalof, and Guagnano, 1995). These studies revealed that people are more likely to engage in pro-environmental actions such as energy conservation when they endorse values beyond their immediate own interests, that is, self-transcendent, prosocial, altruistic or biospheric values, while egoistic or self-enhancement values are negatively related to pro-environmental behavior. Second, scholars have studied the role of environmental concern. The most influential perspective here is the New Environmental Paradigm (NEP), which reflects people’s beliefs about humanity’s ability to upset the balance of nature, the existence of limits to growth for human societies, and humanity’s right to rule over the rest of nature (Dunlap and Van Liere, 1978; Dunlap, Van Liere, Mertig, and Jones, 2000). It appeared that a higher environmental concern is associated with acting more pro-environmentally, although these relationships are generally not strong (e.g., Poortinga, Steg, and Vlek, 2004; Schultz and Zelezny, 1998). A recent study revealed that environmental concern is less predictive of behavior-specific beliefs than are values (Steg, De Groot, Dreijerink, Abrahamse, and Siero, in press), probably because values reflect a wider range of motivations than does NEP. A third line of research is based on the norm-activation model (NAM; Schwartz, 1977; Schwartz and Howard, 1981) and the value-belief-norm theory of environmentalism (VBN theory; Stern, 2000; Stern, Dietz, Abel, Guagnano, and Kalof, 1999). These theories assume that people conserve energy when they feel a moral obligation to do so, which depends on the extent to which people are aware of the problems caused by their behavior, and feel responsible for these problems and their solution. The VBN-theory further proposes that awareness of the problems is rooted in environmental concern (NEP) and values. The NAM and VBN theory appeared to be successful in explaining low-cost environmental behavior and “good intentions” such as willingness to change behavior (e.g., Nordlund and Garvill, 2003; Stern et al., 1999), political behavior (e.g., Gärling, Fujii, Gärling, and Jakobsson, 2003), environmental citizenship (e.g., Stern et al., 1999), or acceptability of energy policies (e.g., De Groot and Steg, 2008a; Steg, Dreijerink, and Abrahamse, 2005), but their explanatory power is generally low in situations characterized by high behavioral costs or strong constraints on behavior, such as reducing car use (e.g., Bamberg and Schmidt, 2003; Hunecke, Blöbaum, Matthies, and Höger, 2001). In such settings, the TPB appears to be more powerful in explaining behavior (Bamberg and Schmidt,

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2003), probably because the TPB considers a wider range of factors, notably non-environmental motivations and perceived behavioral control. A fourth line of research focuses on the influence of social norms on behavior. The theory of normative conduct (Cialdini, Kallgren, and Reno, 1991; Cialdini, Reno, and Kallgren, 1990) distinguishes two types of social norms. Injunctive norms refer to the extent to which behavior is supposed to be commonly approved or disapproved of. Descriptive norms reflect the extent to which behavior is perceived as common. When injunctive and descriptive norms are in conflict, behavior will be most strongly influenced by the norm that is most salient. This theory has been validated in a series of experimental studies about littering in public places and energy use (Cialdini et al., 1990; 1991). It appeared that people are more likely to violate a particular social norm when they see that others violate that specific norm as well. Recent studies revealed that norm violations spread, that is, when people see that a particular norm is being violated, they are more likely to violate other norms as well, suggesting that people are not merely copying the behavior of others, but that perceptions of norm violations reduce the likelihood of normative behavior in general (Keizer, Lindenberg and Steg, 2008).

Multiple Motivations Various scholars have integrated concepts and variables from different theoretical frameworks, showing that behavior results from multiple motivations (e.g., Harland et al., 1999; Heath and Gifford, 2002; Stern et al., 1995). Goal-framing theory (Lindenberg, 2006) explicitly acknowledges that behavior results from multiple motivations. This theory postulates that goals govern or “frame” the way people process information and act upon it. Three general goals are distinguished: a hedonic goal-frame “to feel better right now”, a gain goal-frame “to guard and improve one’s resources”, and a normative goal-frame “to act appropriately”. In a given situation, one of these goals is focal and influences information processing the most (that is, it is the goal-frame), while other goals are in the background and increase or decrease the strength of the focal goal. The hedonic goal-frame is a priori strongest, while in particular the normative goal-frame needs external social and institutional support in order to become focal. The three goal-frames coincide with the three types of motivational factors discussed above (Lindenberg and Steg, 2007). That is, theories and models on affect focus on hedonic goals, the TPB focuses on gain goals, while theories on norms, values and environmental concern (e.g. the NAM, VBN theory) focus on normative goals. As such, goal-

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framing theory provides an integrative framework for understanding proenvironmental behavior.

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Habits The theoretical frameworks discussed above generally imply that individuals make reasoned choices. However, in many cases, behavior is habitual and guided by automated cognitive processes, rather than being preceded by elaborate reasoning. When people frequently act in the same way in a particular situation, that situation will be mentally associated to the relevant goal-directed behavior. The more frequently this occurs, the stronger and more accessible the association becomes, and the more likely it is that an individual acts accordingly. Thus, habitual behavior is triggered by a cognitive structure that is learned, stored, and retrieved from memory when individuals perceive a particular situation. Various studies revealed that environmental behavior, and more particularly car use, is habitual (e.g., Aarts and Dijksterhuis, 2000; Aarts, Verplanken, and Van Knippenberg, 1998; Klöckner, Matthies, and Hunecke, 2003). Fujii and colleagues found that temporarily forcing car drivers to use alternative travel modes induced long-term reductions in car use (Fujii and Gärling, 2003; Fujii, Gärling, and Kitamura, 2001). The impacts of such temporary changes were particularly strong for habitual car drivers. This suggests that habitual drivers have inaccurate and modifiable perceptions of the pros and cons of different transport modes.

Ability to Engage in Energy Conservation Obviously, human behavior does not depend on motivations alone. Many contextual factors may facilitate or constrain energy conservation and influence individual motivations (Ölander and Thøgersen, 1995; Stern, 1999; Thøgersen, 2005), such as the availability of energy efficient appliances, the quality of public transport, or pricing regimes (e.g., Santos, 2008; Vining and Ebreo, 1992). In some cases, constraints may even be so strong that motivations make little difference in the environmental outcome (see, e.g., Corraliza and Berenguer, 2000). Only few scholars included contextual factors in their studies (Black, Stern, and Elworth, 1985; Hunecke et al., 2001), and surprisingly, contextual factors are not included in theories to explain energy use. The TPB only considers individuals’ perceptions of contextual factors, as expressed in perceived behavioral control. It is highly important to understand

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how contextual factors influence energy use in order to design intervention strategies that remove important barriers for energy conservation.

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INTERVENTIONS TO PROMOTE ENERGY CONSERVATION The question of how to encourage household energy conservation has been a topic of interest within social and environmental psychological research for a number of decades. Behavioral interventions may be aimed at changing an individual’s perceptions, preferences, motivations, and norms via informational strategies. Alternatively, interventions may be aimed at changing the context in which decisions are being made, for instance, through financial rewards, laws, or the provision of energy-efficient equipment. The latter strategy is aimed at changing the pay-off structure, so as to make energysaving activities relatively more attractive. When energy saving is rather costly or difficult because of external barriers to energy conservation, changes in the circumstances under which behavioral choices are made may be needed so as to increase individual opportunities to conserve energy and to make energy saving behavior choices more attractive (cf. Ölander and Thøgersen, 1995; Stern, 1999; Thøgersen, 2005). The costs and benefits of behavioral alternatives may be changed in various ways. First, the availability and quality of products and services may be altered via changes in physical, technical, and/or organizational systems. Environmentally harmful behavioral options can be made less feasible or even impossible, or new and/or better-quality (energy-saving) behavior options may be provided (e.g., recycling bins, energy efficient technology). Second, legal regulations can be implemented (e.g., prohibiting the use of specific appliances). Legal measures of course require that the relevant laws and regulations are enforced, and that violations are penalized. Third, pricing policies are aimed at decreasing prices of energy saving behavior options and/or increasing prices of less energy-saving alternatives. Behavioral interventions are typically classified according to the taxonomy for behavior change interventions as proposed by Geller, Berry, Ludwig, Evans, Gilmore and Clark (1990), which distinguishes between antecedent and consequence intervention strategies (see also Lehman and Geller, 2004). Antecedent interventions are assumed to influence one or more determinants prior to the performance of energy behaviors. Examples of such interventions are information, commitment, and goal setting. For instance, providing households with information about energy-saving options in the

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office may result in energy savings, because people have acquired (more) knowledge. Consequence strategies on the other hand are based on the assumption that positive or negative consequences will influence behavioral choices. To illustrate, pro-environmental behavior will become a more attractive alternative when positive consequences are attached to it, e.g., by the provision of a monetary incentive. Feedback, punishments and rewards are well-known examples of this type of interventions. A browse through the social and environmental psychology literature reveals an abundance of intervention studies with an aim to encourage consumers to conserve energy – with varying degrees of success (for reviews see Abrahamse, Steg, Vlek, and Rothengatter, 2005; Dwyer, Leeming, Cobern, and Porter, 1993; Schultz, Oskamp, and Mainieri, 1995).

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Antecedent Strategies Information is a widely used intervention to encourage household energy savings – its success, however, is rather debatable. This may be general information about energy-related problems, or specific information about possible solutions, such as information about various energy-saving measures households can adopt. Providing information serves to increase households’ awareness of energy problems and their knowledge about possibilities to reduce these problems. It appears that information provision about energy conservation or environmental issues does indeed generally lead to an increase in knowledge, or awareness, but it does not necessarily translate into behavior changes (Gardner and Stern, 2002; Geller, 1981; Staats, Wit, and Midden, 1996). The provision of personalized, tailored, information tends to be more effective. An advantage of this approach is that households receive relevant information only, rather than getting an overload of general information, which may not always apply to their specific situation. Examples of such approaches are home energy audits, which have proven to be quite effective in encourage energy savings among households (e.g. Winett, Love, and Kidd, 1982-1983), or personalized information about energy saving options via the Internet, which proved to be effective as well (Abrahamse, Steg, Vlek, and Rothengatter, 2007). It has been suggested that, the provision of information may be more effective when it makes salient social norms in favor of energy conservation. One particular study found that towels were re-used more frequently by hotel

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guests when they were provided with information that emphasized descriptive social norms in favor of re-use (e.g. ‘did you know 75% of our guests help save the environment by reusing their towels’) compared to when they were given environmental information only (e.g. ‘help save the environment by reusing your towels’) (Goldstein, Cialdini, and Griskevicius, 2008). Alternatively, information provision can also be more effective when it is given in a certain social context. Neighborhood interactions may be important in this respect, as this may lead to the diffusion of information, and it may help people to develop and establish social norms (see Weenig and Midden, 1991). Commitments are potentially powerful and cost-effective interventions. Commitments essentially entail making a promise to try and engage in a certain pro-environmental behavior (e.g. try driving less often), and in doing so, eliciting a moral obligation to stick to the promise made. In terms of large scale implementation, commitments do not necessarily have to cost a lot of money (in contrast to for instance financial incentives), but they may be difficult to implement when they rely on personal contact. Various studies have found commitment to be effective in encouraging energy conservation (e.g. Pallak and Cummings, 1976) and recycling (e.g. DeLeon and Fuqua, 1995; Wang and Katzev, 1990). Especially in view of the long-term effects found in several studies (Katzev and Johnson, 1983; Pallak and Cummings, 1976), commitment may be a successful strategy for reducing household energy use. Goal setting entails giving households a reference point, for instance to save 5% or 15% energy. A goal can be set by the experimenters, or by the households themselves. A study by Becker (1978) found that a relatively difficult goal (20%) was more effective when it was combined with feedback, as compared to a relatively easy goal (2%). This indicates that in order for a (difficult) goal to work, households need feedback on how they are performing in relation to the goal. Also, eliciting implementation intentions, in which people are not only asked whether they intend to change their behavior, but also to indicate how they plan to do so (i.e. reach that goal), appeared to be effective (e.g., Bamberg, 2002; Jakobsson, Fujii, and Gärling, 2002).

Consequent Strategies Feedback is often applied to promote energy conservation. Feedback consists of giving households information about their energy consumption, or energy savings. It can influence behavior, because households can associate

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certain outcomes (e.g., energy savings) with their behavior. Ideally, feedback is given immediately after the behavior occurs, because households need to understand the relationship between the feedback and their behavior (Geller, 2002). Feedback appears to be an effective strategy for reducing household energy use (e.g., Seligman and Darley, 1977), although some exceptions exist (e.g., Katzev, Cooper, and Fisher, 1980-1981). Results of studies using feedback seem to suggest that the more frequent the feedback is given, the more effective it is. Positive effects have for instance been found for continuous feedback (e.g., McClelland and Cook, 1979-1980). A study by Kantola, Syme and Campbell (1984) showed that high frequency is not necessarily the key to success: by giving feedback one single time, evoking dissonance between people’s reported attitudes (i.e. favorable towards energy conservation) and their behavior (i.e. high energy usage), households significantly reduced their energy use. Feedback about individual performance relative to the performance of others may be helpful in encouraging energy conservation. By providing people with feedback on how they are doing as a group, social norms in favor of a certain pro-environmental behavior may become salient. Similarly, by giving comparative feedback about how a group of individuals is doing relative to other groups may evoke feelings of social comparison, which may be especially effective when important or relevant others are used as a reference group. The results of the use of comparative feedback are mixed, and seem to be dependent on the target group that is studied. In the area of household energy conservation, group feedback is generally no more effective than individual feedback (e.g. Abrahamse et al., 2007; Midden, Meter, Weenig, and Zieverink, 1983). Group feedback has also been implemented to encourage energy conservation in organizational settings, with promising results (e.g. Siero, Bakker, Dekker, and Van den Burg, 1996). Another line of research suggests that the effects of comparative feedback may depend on whether people already behave according to the group norm (see for instance, Schultz, Nolan, Cialdini, Goldstein, and Griskevicius, 2007). In fact, differential effects of feedback have been found for relatively high and low consumers of energy, the latter group (who already behaved in a proenvironmental way) actually increased their energy use as a result of feedback (e.g. Brandon and Lewis, 1999). Apparently, the behavior of others is taken as a reference point to strive for. Interestingly, this boomerang effect was neutralized by adding an injunctive message (in this case a smiley), which probably conveyed social approval (Schultz et al., 2007).

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Monetary rewards may serve as an extrinsic motivator to conserve energy. Rewards can either be contingent on the amount of energy saved, or a fixed amount (e.g., when a certain percentage is attained). Overall, rewards seem to have a positive effect on energy savings. Results of several studies (e.g. Slavin, Wodarski, and Blackburn, 1981) do however suggest that the effect of rewards is rather short-lived.

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Combining Interventions Combinations of interventions are generally more effective than single interventions. This makes sense to the extent that different people may have different barriers to change (Gardner and Stern, 2002). A combination of antecedent (e.g., information) and consequence strategies (e.g., feedback) is generally more effective than the individual interventions. Interventions within the realm of social and environmental psychology typically focus on informational strategies, rather than changing contextual factors which may more strongly steer households’ behavioral decisions (see Abrahamse et al., 2005; Dwyer et al., 1993; Schultz et al., 1995, for reviews). This is regrettable, because to the extent that contextual factors strongly influence energy use, structural strategies are probably very effective in promoting household energy savings. Moreover, informational and structural strategies could complement one another. For instance, informational strategies may be an important element in the implementation of structural strategies that force individuals to change their behavior (Gärling and Schuitema, 2007). For example, public support for structural strategies may be increased by informing individuals about the need for and the possible consequences of such strategies.

Evaluation of the Effectiveness of Interventions Studies aimed at evaluating an intervention’s effectiveness should follow rigorous experimental research designs that reveal the effectiveness of single as well as combinations of interventions for one or more ‘treatment’ groups and a comparable control group. In addition, it is important to also study longterm effects as well, as interventions may lose their effectiveness as soon as they are discontinued (e.g. as is often the case with rewards; see Abrahamse et al., 2005). Effect measurements should not only focus on (changes in) energy behaviors. First, it is important to monitor (changes in) determinants of energy

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use and energy savings as this enhances our understanding of why intervention programs were successful or not. This allows change agents to adapt interventions in order to increase their effectiveness. For instance, failure of group feedback to encourage pro-environmental behaviors may well be attributable to the fact that social norms did not change as a result of the intervention. Second, it is important to monitor (changes in) total energy use and environmental quality, since this is the ultimate goal of behavioral interventions. Here, collaboration with environmental scientists is needed. Based on this, feedback can be provided to the target population so as to inform them about the effectiveness of their efforts to conserve energy and to reduce energy problems. This may strengthen people’s commitment to change their behavior, and to maintain the changes already implemented. Third, one would need to know changes in people’s quality of life, which is an important component of the more general notion of sustainable development. Studies revealed that people generally expect that environmental policies will not seriously threaten their quality of life (De Groot and Steg, 2006; see also Steg and Gifford, 2005). As yet, most studies (see Steg and Gifford, 2005, for a review) examined expected changes in quality of life, while actual changes resulting from environmental policies or conditions have hardly been monitored over time. Expected changes may differ from actual changes in perceived quality of life, for example, because people do not fully understand or imagine how interventions will affect their life. Also, they may underestimate the positive consequences of energy policies on, for example, environmental quality. Next to studying actual effects of interventions, psychologists studied the perceived effectiveness and acceptability of environmental policies before policies have been implemented, particularly in the travel domain (e.g., Bamberg and Rölle, 2003; Eriksson, Garvill, and Nordlund, 2006; 2008; Jakobsson, Fujii, and Gärling, 2000; Schade and Schlag, 2003; Schuitema and Steg, 2008; see Steg and Schuitema, 2007, for a review), but also regarding energy use (Nilsson, Von Borgstede and Biel, 2004; Steg et al., 2005). Most studies examined individual factors related to perceived effectiveness or acceptability judgments. These studies revealed, among other things, that policies are more acceptable when they are believed to be more fair, when they are effective in reducing relevant problems, and when they do not seriously affect individual freedom. Moreover, policies are more acceptable to people who have strong environmental values, who are highly aware of the problem, and who feel a strong moral obligation to reduce the problems (De Groot and Steg, 2007; Steg et al., 2005). Thus, normative and environmental concerns are

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important for the acceptability of policies. Acceptability may increase after policies have been implemented. For example, acceptability of the congestion charge in Stockholm increased after the implementation of the charge, probably because the charge had more positive effects on congestion, environmental problems, and parking problems than expected beforehand, while the additional travel costs for households were lower than expected (Schuitema, Steg, and Forward, 2009). A few studies examined the extent to which perceived effectiveness and acceptability depends on specific policy features, such as rewards versus penalties, or the type of behaviour being targeted (e.g., Poortinga, Steg, Vlek, and Wiersma, 2003; Steg, Dreijerink, and Abrahamse, 2006). It appeared that policies that increase the attractiveness of pro-environmental behavior (that is, energy savings) are evaluated as more effective and acceptable than policies aimed at decreasing the attractiveness of environmentally harmful behavior (that is, not saving energy; Steg et al., 2006). Moreover, people prefer policies aimed at promoting the adoption of energy-efficient equipment above policies aimed at reducing the use of existing equipment (Poortinga et al., 2003; Steg et al., 2006), and energy savings in home above energy savings in transport (Poortinga et al., 2003). Interestingly, people high in environmental concern evaluate governmental regulations and behavioral strategies as more acceptable, while people with a low environmental concern prefer marketoriented and technological strategies (Poortinga, Steg, and Vlek, 2002).

CONCLUSION Psychologists have an important role to play in promoting energy conservation via behavioral changes. Behavioral interventions are generally more effective when they are systematically planned, implemented and evaluated. Four key issues to be addressed are: (1) identification of the behavior to be changed, (2) examination of the main factors underlying this behavior, (3) application of interventions to change the relevant behaviors and their determinants, and (4) evaluation of intervention effects on the behavior itself, its main determinants, energy use, environmental quality, and human quality of life. Individuals can contribute significantly to achieving long-term environmental sustainability by reducing household energy use. The challenge for psychologists is to understand the individual and structural factors and processes that threaten environmental sustainability, so that energy saving could be facilitated and emerge worldwide.

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Studies on household energy conservation typically have a monodisciplinary focus. However, multidisciplinary approaches can have clear added value. For instance, sociologists can provide valuable insight into the meanings individuals attach to sustainable practices, with regards to existing institutional and contextual arrangements (e.g. Spaargaren, 2003). Also, as has been mentioned earlier, input from environmental scientists can be of valuable importance to further improve intervention studies. Environmental scientists can help translate energy-related behaviors into energy use and environmental impact, e.g., in terms of CO2 emissions, and help select high-impact behaviors. It is therefore important to consider energy conservation from an interdisciplinary perspective. Equally well, close collaboration between academia and the policy arena is essential in order to develop and evaluate effective interventions to encourage household energy conservation.

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INDEX

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A abatement, vii, 129, 132 access, 5, 18, 22, 37, 60, 75, 77, 78, 79, 80, 81, 85, 92, 140, 191 accessibility, 5, 106 accommodation, 9 accommodations, 9, 15, 29, 32 accounting, 98, 137, 164, 185, 186, 187 accuracy, 206 acid, 208 activation, 212, 224, 226 adaptation, 133, 157 adaptations, 226 adjustment, 98, 122, 127, 171 ADP, 197 advancements, 188 adverse effects, x, 190, 193 aesthetics, 4 Africa, 193, 199, 200 age, 4, 20, 21, 25, 30, 33, 38, 104, 105, 107, 114, 116, 155 agencies, 138 agents, 221, 236 aggregation, 141 aggregation process, 142 agricultural sector, 196 agriculture, x, 48, 190, 191, 193 AIDS, 139, 140

air quality, 124, 127 air temperature, 61, 62 all-electric, 231 alternative, 214, 217 alternatives, 210, 216 alters, v, 1 altruism, 233 Amsterdam, 228, 230, 234 antecedents, 205, 207, 234 application, 223 aquaculture, v, x, 191, 195, 196, 199, 200 architects, 67, 72, 93 Asia, 193 Asian countries, 47 assessment, viii, 9, 43, 162, 164, 165, 166, 167, 168, 176, 183, 185, 186, 187, 188, 235 assessment techniques, 166, 167 assessment tools, 185 atmosphere, x, 190, 193, 208 attitudes, 209, 210, 219, 233, 235 attractiveness, 223 audits, 41, 217 authority, 168 Automobile, 127 automobiles, 102 availability, 215, 216 avoidance, 143

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Index

awareness, 198, 212, 217, 225, 226, 227, 231

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B barriers, v, 1, 2, 3, 28, 29, 35, 36, 42, 207, 215, 216, 220 base, 102, 149, 150, 164 behavior, xi, 203, 204, 206, 207, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 233, 234, 235 behavioral change, vii, 96, 99, 111, 207, 223, 225 behaviors, 96, 99, 103, 105, 106, 109, 110, 111, 118, 121, 142, 204, 205, 206, 207, 208, 209, 210, 216, 221, 223, 224, 225 behaviours, 225 beliefs, 211, 212, 226, 227, 235 benchmarking, viii, 162, 182 benefits, 6, 10, 12, 15, 16, 28, 29, 41, 43, 98, 140, 142, 166, 181, 182, 186, 196, 198, 210, 216 bias, 105, 192 biodiversity, 175 biomass, ix, 86, 190, 192, 198 Boston, 228 breakdown, 20 breeding, 195 Britain, 12 building code, 7, 43 bulbs, 211 Bureau of Labor Statistics, 135, 156 buyers, 4 C CAD, 64, 66 Canadian residential sector, v, 1, 27 canals, 196 cap-and-trade system, vii, 129, 132, 133, 143 capital account, 186, 188

carbon, vii, ix, x, 74, 130, 131, 132, 133, 134, 139, 140, 141, 142, 143, 144, 145, 148, 149, 150, 151, 152, 153, 154, 155, 159, 175, 189, 190, 194, 195 carbon dioxide, ix, x, 130, 189, 190, 194 carbon emissions, 132, 134, 139, 140, 142, 144, 149, 150, 152, 155 case studies, 12, 13, 16, 28, 166 case study, vi, 1, 3, 43, 103, 158, 187, 199 cash, 18 category a, 142 category d, vii, 130 catfish, 196, 197 causal model, 225 Census, 34, 100, 104, 135 certification, 188 challenges, viii, 162, 164, 185, 191, 198 chemicals, 186 children, 107, 114, 155, 192 chimneys, 194 China, 47, 124 CIS, 53, 55 cities, 100, 230 citizenship, 212 City, 50, 53, 59, 94, 158, 185 cladding, 14 classes, vii, 77, 79, 82, 83, 84, 85, 110, 130, 135, 144, 151 classification, 75, 77 climate, vi, viii, x, 11, 42, 45, 60, 63, 64, 87, 89, 91, 99, 130, 131, 143, 156, 157, 159, 161, 163, 170, 173, 190, 191, 193, 199, 226, 231 climate change, vi, viii, x, 45, 130, 131, 143, 161, 163, 190, 191, 193, 199, 226, 231 climates, 2, 13 closure, 228 Co, 231 CO2, ix, x, xi, 12, 130, 135, 143, 144, 159, 171, 176, 185, 189, 190, 194, 203, 204, 224 coefficient of variation, 137

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

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Index cogeneration, 80 cognitive process, 214 collaboration, 222, 224 college students, 105 colleges, 104 collusion, 124 color, iv, 55, 79 combustion, 159, 201 commercial, 10, 48, 51, 118, 121, 126 commodity, 97 communication, viii, 162, 164, 167, 181 communities, 89, 155, 176, 193, 196 community, 89, 183 compensation, 82 complement, 188, 221 complexity, viii, 71, 73, 140, 143, 162, 164, 183 complications, 6, 9 composition, v, 1 computation, 71, 165 computer, 16, 64, 148 computing, 63 conditioning, 2, 13, 37, 49, 67 configuration, viii, 8, 83, 92, 162 conflict, 213 confusion, 208 CONGRESS, iv, 124, 127, 131 Congressional Budget Office, 128, 158 consensus, 12, 182 conservation, v, xi, 10, 62, 97, 105, 184, 191, 203, 204, 206, 207, 209, 211, 215, 217, 218, 219, 223, 224, 225, 226, 228, 229, 231, 233, 235, 236 constraints, 213, 215 construction, viii, 4, 10, 15, 20, 21, 23, 25, 31, 33, 37, 38, 59, 64, 162, 163, 168, 170, 177, 181, 185, 187 consumer goods, 137 consumer price index, 100, 102 consumers, 96, 97, 98, 99, 102, 132, 133, 134, 135, 196, 217, 220, 235 consumption patterns, 148, 165 contingency, 96, 233 control, 209, 211, 213, 215, 221, 229 control group, 221

209 convergence, 62, 68 cooking, ix, 49, 88, 190, 192, 194, 205 cooling, 2, 3, 4, 6, 7, 9, 10, 12, 16, 51, 53, 59, 61, 66, 67, 68, 69, 71, 72, 73, 80, 86, 88, 170, 174, 181, 208 cooperation, 197, 228 coordination, 30, 192 copyright, iv Copyright, iv correlation, 96, 103, 125, 126, 191 correlations, vii, 95, 111, 206 cost, vi, x, 5, 7, 8, 11, 45, 53, 54, 58, 59, 88, 92, 119, 132, 133, 134, 135, 138, 139, 143, 144, 148, 151, 155, 181, 183, 190, 193, 196, 210, 212, 218 cost effectiveness, 133 cost saving, 5, 8 cost-effective, 210, 218 costs, 210, 211, 213, 216, 223 covering, 16 CPI, 102, 138 credible quantification, ix, 162 crops, 194 crude oil, 100, 102, 103 crystalline, 53, 54, 55, 56, 57, 58, 80 culture, 89, 198, 200 D damages, iv data collection, 17, 104 data set, 29, 104, 109, 116, 135, 136, 144 database, 16, 34, 106 decision makers, viii, 162, 164, 165, 167, 168, 182, 183 decision making, 225 decision-making process, 181, 187 decisions, 215, 221 deforestation, ix, x, 189, 190, 191, 192, 193, 194 degradation, 53 Delta, 200 demographics, 209, 211 demonstrations, 198

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Index

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Denmark, 134, 235 Department of Energy, 102, 106, 128 Department of Transportation, 137, 156 depth, 79, 170 derivatives, 139 developing countries, 192, 197 developing nations, ix, 190, 192 development policy, 191 deviation, 60, 123 diesel fuel, 137 diffusion, 62, 218, 236 diodes, 57 disaster, 58, 96 disorder, 230 dispersion, 150 dissonance, 219, 229 distribution, 27, 59, 62, 67, 71, 72, 93, 104, 133, 134, 181 diversification, 195 diversity, 142 DOT, 137 downsizing, 102 draft, 14 drawing, 53 durability, 54, 58, 123 dusts, 58 dynamic factors, 61 E ecological, 229 ecology, 86 economic efficiency, 43 economic growth, 122 economic policy, 142, 197 economic theory, 149 economic well-being, viii, 162, 163 economics, v, 3 economies of scale, 5, 30 education, 116, 118, 126, 155, 193 Education, 227, 229 educational attainment, 104 elaboration, 166 elasticity of demand, 97

electricity, 4, 6, 14, 47, 48, 49, 50, 53, 58, 84, 85, 87, 88, 131, 132, 139, 140, 144, 155, 163, 174, 204, 205, 206, 228, 229, 233 electrons, 55 emergency, ix, 189 emission, 140, 155, 195, 198, 204 emitters, 132 empirical studies, 98 employment, 108, 117, 118, 135, 192, 198 employment status, 135 endurance, 54, 55 energy audit, 217 energy consumption, 5, 12, 14, 28, 43, 47, 48, 49, 50, 51, 52, 53, 64, 67, 84, 90, 92, 105, 170, 173, 176, 184, 204, 205, 219, 226, 231, 233, 235 energy efficiency, v, vi, viii, 1, 3, 6, 9, 10, 11, 12, 19, 25, 26, 27, 28, 30, 35, 41, 42, 43, 44, 46, 51, 63, 72, 87, 132, 152, 162, 163, 168, 174, 181, 186, 198 energy input, 67 energy recovery, 47 energy supply, 47 engagement, 210 engineering, 2, 3, 12, 15, 16, 93, 192 England, 13 environment, 105, 125, 156, 181, 183, 186, 210, 211, 218, 227, 229 environmental aspects, 181 environmental degradation, 192 environmental effects, 165 environmental factors, vi, 46 environmental impact, 10, 166, 181, 188, 197, 224 environmental issues, 217 environmental policy, 187, 191, 233, 235 environmental protection, 230, 231 Environmental Protection Agency, 106, 128, 157, 227 environmental quality, 205, 222, 224 environmental sustainability, 187, 224

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

Index

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environmentalism, 212, 235 EPA, 106, 144, 157, 204, 227 equipment, 4, 6, 7, 9, 22, 23, 25, 27, 31, 33, 34, 36, 37, 38, 39, 56, 58, 66, 88, 216, 223 equity, vii, 129, 133 erosion, 192, 193 EU, 59, 89, 132, 133, 144 Europe, 53, 96, 98, 122 European Union, 131, 132, 232 evidence, viii, 16, 19, 98, 99, 162, 164, 185, 233 evidence-based policy, 185 evolution, 17, 61 exchange rate, 196 exclusion, 3 execution, 29 expenditures, vii, 117, 118, 126, 130, 131, 135, 136, 137, 139, 145, 148, 149, 150, 155 expertise, x, 191 exploitation, x, 125, 190, 195, 200 exposure, 56, 76, 88, 198, 200 external costs, 152 extracts, 135, 158 F failure, 221 fairness, 227 family units, 35 farmers, 195, 196, 198 farms, 194, 199 Federal Highway Administration, 128 feedback, 207, 219, 220, 221, 225, 227, 229, 231, 233 feelings, 220 FHWA, 128 fibers, 195 field crops, 195 films, 55 financial, 2, 11, 12, 28, 42, 210, 215, 218 financial incentives, 218 Finland, 144

211 fish, 196, 197, 198, 199, 200 fixed costs, 42 fixed rate, 153, 154 flexibility, 134, 142, 148, 155, 174 flights, 144 fluctuations, 72 food, ix, 131, 132, 137, 139, 171, 190, 191, 192, 194, 195, 235 food production, 132 food security, 191, 195 force, 183, 221 Ford, 119 forest resources, 191, 195 formation, 102, 235 formula, 7, 55 fossil, 205 fossil fuel, 205 fossil fuels, 205 foundations, 36, 63 Framework Convention on Climate Change, ix, 189 framing, 213, 230 France, 201 freedom, 222, 227 fuel, 204, 206 fuel cell, 74, 80, 86 fuel consumption, 98, 106 fuel efficiency, 98, 125 fuel prices, 96, 97, 99 fuel stove, ix, 190, 192 fuel-wood stove, ix, 190, 192 funds, 5 G Gabon, 194 gas, 204, 205, 206, 227, 232 gases, 204 geometry, 60, 62, 71, 72 Germany, 14, 43 glasses, 65 global climate change, 226 global demand, 103 global scale, 183 global warming, vi, 45, 208

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212

Index

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goal setting, 216, 225 goal-directed, 214, 224 goal-directed behavior, 214 goals, 213 goods and services, 131, 205 governance, 182 government, 204 governments, 42, 204 GPS, 206 grades, 142 grants, 28 Greece, 12, 15 greed, 124 greenhouse, vi, vii, x, 46, 129, 130, 131, 158, 177, 190, 204, 227, 233 Greenhouse, 204, 230, 232 greenhouse gas, 204, 227 greenhouse gas (GHG) emissions, vii, 129 greenhouse gases, x, 130, 158, 190, 204 grids, 63, 77 grouping, 36, 187 groups, 206, 219, 221 growth, 74, 193, 200, 212 Guatemala, 200 guidance, 164 guidelines, 174 H habitat, 175 harvesting, 194 health, x, 28, 137, 186, 190, 193, 201 health care, 137 heat loss, 68 heat transfer, 61 heating, 205, 208 hedonic, 210, 213, 230 height, 60, 61 heuristic, 208 highways, 118 history, 185 home activities, 125 homes, 14, 18, 21, 28, 41, 43, 44, 105, 118, 121, 208, 231

homogeneity, 139 hotel, 218 hotels, 229 House, 59, 60, 63, 64, 65, 71, 72, 168, 170, 173 household, xi, 203, 204, 205, 206, 207, 208, 209, 215, 217, 218, 219, 220, 221, 224, 225, 226, 228, 230, 231 household income, ix, 3, 27, 37, 41, 134, 135, 190, 192 household sector, 47, 49, 51 household-level micro, vi, 2 households, 204, 207, 209, 216, 217, 218, 219, 221, 223, 231, 232, 235 housing, v, viii, 1, 4, 10, 14, 18, 20, 24, 27, 28, 32, 34, 35, 41, 43, 162, 168, 169, 170, 172, 176, 177, 180, 182, 183, 186, 187 hub, 182 human, vi, 46, 66, 121, 140, 182, 192, 205, 212, 215, 224, 226, 228 human activity, 182 human behavior, 66, 215, 226, 228 human psychology, 140 human resources, 192 humanity, 212 humidity, 61, 62, 91 Hurricane Katrina, 96, 106 hybrid, 123, 124 hypothesis, 119 hysteresis, 98 I id, 221 ideal, 67 identification, xi, 140, 141, 203, 223 imagery, 106 imbalances, 103 impact assessment, 167, 186 Impact Assessment, 169, 185, 186, 187, 188 impact energy, 23 implementation, xi, 203, 218, 219, 221, 223, 225, 232

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Index imports, 96 improvements, 3, 4, 6, 9, 10, 12, 13, 14, 17, 19, 20, 22, 25, 30, 31, 33, 34, 36, 37, 38, 40, 42, 98, 165 in situ, 213 incentive, 217 incentives, 218, 234 incidence, 83, 133 income distribution, 158 income effects, 40 income tax, 152 India, 124 indicators, 206 indium, 54 individual development, 169 individuals, 105, 116, 117, 118, 139, 184, 194, 206, 210, 214, 215, 219, 221, 224 Indonesia, 188 industries, 144, 195 industry, 108, 114, 118, 121, 124, 184, 187, 196 inefficiency, 4 infertility, x, 190, 193 inflation, vi, 45, 98, 139, 143 information processing, 214 infrastructure, 90, 96, 165, 166 injury, iv, 229 insight, 207, 224 institutions, x, 191 insulation, 2, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 22, 24, 25, 28, 31, 32, 33, 34, 36, 37, 38, 40, 41, 43, 47, 51, 53, 67, 68, 69, 70, 71, 73 integration, ix, 162, 172, 196, 230 integrity, 40 intentions, 211, 212, 219, 225, 226, 234 interactions, 218 interdisciplinary, 224 Internet, 217 interpretability, 116 intervention, 206, 215, 216, 217, 221, 224, 225 intervention strategies, 206, 215, 216 investment, 11, 82, 88, 92, 123, 126

213 investments, 11, 43, 152, 186 investors, 133 Iran, 102, 103 Iraq, 102, 103 iron, 50 irradiation, 61 irrigation, x, 191, 195, 196, 198 isolation, 5, 165 Israel, 100 issues, vi, viii, ix, 1, 2, 3, 29, 32, 35, 42, 45, 54, 96, 133, 142, 155, 161, 163, 165, 166, 167, 183, 184, 185, 189, 205, 217, 223 Italy, 134, 187 J Japan, 47, 49, 51, 58, 93 justification, 123, 229 K Korea, 45, 47, 49, 63, 80, 93 L labour force, 196 Land Management Corporation (LMC), viii, 162, 163, 174 Land Use Policy, 186, 187 landlord-tenant relationship, v, 1 laptop, 50 laws, 61, 215 laws and regulations, 216 lead, x, 8, 10, 16, 30, 42, 132, 148, 190, 193, 217, 218 leakage, 67 life cycle, viii, 162, 166, 183, 184, 186 life quality, ix, 190, 193 life-cycle, 206 lifestyle, 210 lifestyles, 235 lifetime, 7

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

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Index

light, 10, 50, 52, 53, 54, 55, 56, 57, 79, 211 likelihood, 213 livestock, 195 local conditions, 13 local government, 93, 185 logging, 194 London, 230, 232 Louisiana, 14 LPG, 198, 201

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M magnitude, 61, 175 majority, vii, 100, 104, 130, 151 management, x, 6, 88, 186, 190, 195, 196, 198, 199, 227, 228 manipulation, 124 manufacturing, 55, 187 marginal costs, 163 market, 223 marketing, 195, 232 mass, x, 62, 66, 72, 125, 190, 194, 233 mass media, 233 materials, 5, 8, 10, 20, 25, 60, 61, 64, 65, 166, 170, 181 matter, iv, 191, 225 meanings, 224 measurement, xi, 64, 203, 206 measurements, 94, 221 measures, 206, 207, 216, 217, 225, 227, 231, 232 meat, 211 media, 233 melt, 56 memory, 148, 214 mergers, 124 meta-analysis, 97 metabolism, 230 meter, 206 methodology, ix, 144, 162, 166, 170, 175, 185, 187, 188 microclimate, 59, 60, 61, 62, 63, 64, 66, 67, 69, 71, 72, 73, 74 Middle East, 124

migration, 192 missions, xi, 43, 203, 204, 224, 227, 230, 232 misuse, 192 model specification, 116, 120 modelling, 165, 169 models, vii, 12, 62, 63, 69, 71, 96, 97, 98, 99, 103, 109, 116, 117, 118, 121, 122, 123, 159, 187, 214, 225, 230 modernization, 43 modifications, 6, 19, 59, 62, 63, 71 modules, 53, 54, 55, 56, 57, 58, 80, 82 moisture, 23, 36, 40, 66, 67 momentum, 62 money, 210, 211, 218 monopoly, 124 monopoly power, 124 morality, 225 motives, 211, 234 multidisciplinary, 224 multi-family dwellings, v, 1, 2, 3, 6, 9, 17, 18, 29, 30, 31, 32, 34, 35, 42 N NAM, 212, 214 national policy, 131 National Research Council, 127 natural, 204, 205 natural disaster, 103, 124 natural disasters, 103, 124 natural gas, vi, 14, 46, 131, 132, 155, 204, 205 natural resources, 191, 192 negative consequences, 216 negative effects, 117 Netherlands, 59, 134, 158, 200, 203, 204, 225, 230, 232, 234, 235 network, 236 New York, v, 227, 228, 233, 235 New Zealand, 186 next generation, 51 Nigeria, x, 103, 189, 191, 193, 195, 196, 198, 199, 200 nitrogen, x, 190, 193

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

Index nodes, 63, 68 normative behavior, 213 norms, 210, 213, 214, 215, 218, 219, 221, 226, 229, 231, 232, 234 North America, 96 Norway, 230 nuclear energy, vi, 46 nutrients, x, 190, 193 nutrition, 195

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O obligation, 212, 218, 222 observations, 226 observed behavior, 206 obstacles, 88 OECD, 204, 231 oil, vi, 46, 47, 96, 100, 102, 103, 123, 124, 125, 127, 132 oil production, 102 operating costs, 7, 8 operating system, 174 opportunities, 72, 117, 119, 155, 198, 216, 235 organic, 208 Organisation for Economic Co-operation and Development, 231 orientation, 228, 229 overlap, 5, 111 overload, 217 ownership, vi, 2, 29, 32, 34, 98, 106, 126 ozone, ix, 189, 208 P PA, 204, 227 paradigm shift, 196 parameter estimates, 145 Paris, 231 participants, 16, 106 pathways, ix, 162 payback period, 92 penalties, 223 perceptions, 213, 215, 228 permission, iv

215 permit, 133 personal contact, 218 personal norms, 229, 234 personality, 233 persuasion, 236 petroleum, 198 photovoltaic cells, 163 physics, 63 planning decisions, 167 play, 209, 223 PM, 200 policy initiative, 181 policy instruments, 42, 43, 133 policy issues, 4 policy making, ix, 162, 235 policy options, 181 policy responses, 143 policymakers, 152, 155, 157 pollution, vi, ix, 46, 55, 56, 189, 194, 198 population, x, 36, 89, 100, 105, 108, 109, 110, 121, 123, 126, 135, 137, 165, 171, 190, 208, 222 population density, 89, 121, 123 poultry, 196 poverty, x, 5, 190, 191, 192, 193, 196 poverty reduction, 196 power, 212, 232 power generation, 54, 55, 56, 57, 58, 59, 79, 82 precedent, 100 predictors, 233 preparation, iv, 208, 229 present value, 7, 12 preservation, 63 press, 212, 226, 234 pressure, 211 prevention, 58, 194 price changes, 99 price effect, 98 price elasticity, 98 price gouging, 124, 125 price signals, 133 prices, 216 pricing policies, 216, 232

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

216

Index

primary data, 104 principles, 170, 181 private, 228, 229 probability, 118 probit models, 111, 116 producers, vii, 129, 132, 134 production, 205, 208 profit, 193 project, 59, 74, 89, 90, 92 prosocial behavior, 226, 230 protection, 191, 230, 231 psychology, v, xi, 203, 217, 220, 227, 228, 233, 235 public, 213, 215, 221, 226, 227, 228, 229, 233 public awareness, 195 public education, 192 public interest, 93 public support, 221 Q quality of life, 205, 222, 224, 226, 234 quantification, viii, 162

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R radiation, 55, 56, 57, 60, 62, 66, 67, 68, 75, 77, 79, 83, 174 Radiation, 66 radius, 108, 114, 115 rain, 208 range, 212, 213 reactions, 111 real terms, 96, 143 reality, 81 reasoning, 214 recession, 122 recognition, 191 recommendations, iv reconstruction, 10 recovery, 14, 196 recreational, 140 recycling, 58, 134, 197, 210, 216, 218, 226, 227, 235

reductionism, 185 Reform, 158 reforms, 134 regenerate, x, 190, 194 regenerative capacity, 165 regression, 117, 123 regression model, 117, 123 regrowth, 194 regulations, 216, 223 reinforcement, 229, 231 relationship, 219 relationships, 209, 212 relevance, 166 reliability, 228 renewable energy, vi, viii, 11, 12, 46, 47, 74, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 125, 161, 163 Renovation activity, v, 1 rent, 8, 9 repair, 5, 6, 36 replication, 225 requirements, xi, 2, 10, 35, 165, 174, 203, 204, 225, 235 research design, 221 researchers, 63, 67 reserves, 125 residential, 225, 229, 233, 235 residential neighborhood, 106 residential sector, v, viii, 1, 2, 4, 10, 11, 28, 37, 41, 43, 44, 162, 163, 165, 185, 186 resolution, 66 resources, 89, 93, 171, 191, 192, 194, 214 response, v, vii, 3, 17, 80, 95, 96, 99, 103, 104, 106, 109, 110, 111, 116, 118, 121, 123, 124, 125, 133, 228 restrictions, 75, 83, 139 retail, 117, 118, 121 revenue, 232 rewards, 215, 217, 220, 221, 223 rice field, 196, 197 rights, iv, 132 risk, 186, 231 risk assessment, 186

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

Index room temperature, 72 rotations, 194 routes, 225 rural areas, 194, 196, 198 rural population, ix, 190, 191, 192, 195 rural poverty, 196 rural women, ix, 190, 193

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S safety, 4, 28 sample, 208 savings, 2, 5, 11, 12, 13, 16, 43, 44, 59, 119, 123, 124, 131, 136, 141, 142, 145, 151, 155, 204, 207, 216, 217, 219, 220, 221, 223 sawdust, 195 scale system, 88 scattering, 57 scavengers, 195 school, 107, 114, 123, 193 science, 130, 186 scientific method, 183 scope, 5, 30, 81, 82, 85, 90, 140 SCP, 234 security, vi, 45, 46, 99 seed, 148 self-enhancement, 212 self-report, 206, 226, 235 self-reports, 206, 226, 235 semiconductor, 53, 55 sensitivity, 30, 39, 41 sequencing, 174 series, 213 services, iv, 150, 169, 185, 193, 195, 205, 208, 216 SES, 141, 145 shade, 56, 57, 61, 193 shape, 61, 69, 70, 71, 121 shock, vi, 46, 102 short supply, 6 shortage, 103 showing, 71, 72, 175, 177, 213 signs, 117 silicon, 53, 54, 55, 57, 58

217 simulation, 12, 59, 62, 63, 64, 66, 67, 68, 69, 71, 72, 73, 80, 81, 148 simulations, 71, 156 social comparison, 220 social context, 218 social dilemma, 228, 233 social evaluation, 183 social justice, 192 social movements, 234 social norms, 210, 213, 218, 219, 221, 229, 232 social psychology, 227, 233 social security, viii, 162, 163 society, 192 sociological, 230 sociologists, 224 software, 64, 68, 106, 145, 169, 170, 184 solar system, 59 solidarity, 230 solution, v, 1, 10, 63, 68, 145, 212 South America, 193 South Korea, 63, 186 Spain, 187 species, 194, 196 specifications, 122 speculation, 102, 124, 125 spending, 132 stability, 94 stakeholders, vii, ix, 130, 133, 155, 162, 182 standard deviation, 116, 123, 137 stars, 163, 168, 174 state, viii, 36, 41, 68, 96, 139, 162, 194 State Government land development agency in South Australia, viii, 162, 163 states, 68, 182 statistics, vii, 18, 28, 29, 93, 95, 96, 100, 103, 108, 109, 116, 137, 145 storage, 58, 61, 194 stoves, 194, 198 Strategic Petroleum Reserve, 102 strategies, 206, 215, 216, 220, 223, 225, 231, 232 strength, 214

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

218

Index

structure, ix, 2, 6, 10, 22, 23, 25, 26, 30, 31, 34, 38, 40, 56, 65, 98, 99, 190, 192, 214, 216 students, 225 style, 22, 121, 125 subsidy, 134 substitution, 141, 142, 155 suburban neighborhoods, 126 summability, 139, 145 summer, 236 surplus, 132, 134 Survey of Household Energy Use, vi, 2, 3, 16, 44 survival, 193, 200 sustainability, viii, 162, 164, 165, 167, 172, 181, 182, 184, 185, 186, 187, 188, 191, 224, 235 sustainable development, v, 183, 187, 191, 192, 197, 198, 222 Sweden, 134, 144, 157, 230 switching, 206 Switzerland, 13, 14, 188, 200 symbolic, 211, 228, 234 symmetry, 139

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T tanks, 109, 110 target, 4, 10, 28, 35, 90, 91, 132, 133, 145, 152, 169, 175, 182, 207, 220, 222 target population, 222 tax incentive, 28 tax increase, 125 tax policy, viii, 130, 148, 151, 152 tax rates, vii, 130, 143, 144, 145 taxation, 152, 159 taxes, v, vii, 107, 114, 130, 131, 132, 133, 134, 139, 143, 144, 149, 151, 152, 159 taxonomy, 216 techniques, 133, 167, 230 technologies, 2, 4, 8, 11, 13, 24, 25, 27, 41, 89, 198

technology, v, x, 4, 11, 12, 13, 47, 86, 89, 123, 163, 191, 198, 216 temperature, ix, x, 8, 12, 15, 16, 53, 54, 56, 57, 60, 61, 62, 66, 71, 72, 73, 74, 82, 91, 94, 189, 190, 201 tenants, 3, 4, 6, 8, 17, 29, 32, 34, 59 territory, 152 testing, 140, 234 Theory of Planned Behavior, 210 thermal degradation, 53, 54 thermal energy, 74, 80, 84, 85, 86, 87 titanium, 55 total energy, xi, 15, 75, 85, 174, 203, 204, 206, 207, 222 trade, vii, 129, 130, 131, 132, 133, 134, 139, 143, 148, 150, 151, 152, 153, 154, 155 trade policy, vii, 130, 132, 134, 139, 148, 151, 152, 155 trade-off, 131 traditions, 194 traffic, 228, 234 transformations, 60 transparency, 164 transport, vii, 130, 131, 134, 136, 137, 169, 175, 182, 209, 215, 223, 225, 226, 227, 228, 232, 234 transport costs, 131 transportation, 10, 106, 116, 121, 126, 131, 137, 138, 155, 170, 184, 205, 208, 229 travel, 211, 214, 222, 224, 227, 229 treatment, 221 turbulence, 61 Turkey, 199 U U.S. Department of Labor, 156 UK, 14, 43, 80, 205, 227, 230 UN, 201 uniform, 60, 61, 70, 73 United, vi, ix, 46, 131, 144, 156, 188, 189, 197, 200, 201, 203 United Kingdom, 203

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,

Index United Nations, ix, 188, 189, 197, 200, 201 United States, vi, 46, 131, 144, 156, 200 urban, ix, 19, 93, 94, 102, 111, 117, 118, 121, 126, 152, 163, 165, 166, 182, 186, 188, 190, 192, 228, 232, 234 urban areas, ix, 190, 192 urban life, 228, 234 USA, 95

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V vacuum, 81, 84 validity, 228 valuation, 169, 233 values, 211, 212, 214, 222, 226, 231, 234 variables, 85, 103, 106, 108, 109, 116, 117, 118, 120, 121, 166, 213 variations, 8, 60, 62, 138 VAT, 134 vector, 140, 148 vegetation, 176 vehicles, 98, 99, 106, 107, 109, 114, 118, 119, 123, 124, 125, 131, 132, 155, 175 velocity, 62 Venezuela, 103 ventilation, ix, 2, 14, 31, 36, 37, 51, 56, 62, 68, 70, 71, 72, 190, 192 vision, 191 vulnerability, 157 W

219 war, 102 Washington, 127, 156, 159, 227 waste, 163, 169, 176, 195, 197 waste treatment, 169 water, 14, 15, 17, 23, 25, 31, 37, 39, 47, 49, 50, 52, 53, 58, 80, 86, 88, 163, 186, 191, 196, 205, 206, 208 water heater, 52, 53, 58 water resources, 186 wealth, 9, 135 welfare, 132, 133, 140, 141, 142, 144, 148, 150, 151, 152, 155, 159 welfare economics, 159 welfare loss, 151, 155 Welfare loss, viii, 130 Western countries, 209 windows, 2, 4, 9, 12, 14, 15, 17, 22, 25, 29, 31, 32, 37, 38, 51, 66, 68, 72, 73 Wisconsin, 80 wood, v, ix, x, 74, 189, 190, 191, 192, 193, 194, 195, 197, 198 wood products, x, 190, 192, 193 wood waste, 195 World Research Institute (WRI), vi, 46 worldwide, 103, 194, 224 Y yield, vii, 10, 96, 130, 144, 196, 200, 206 Z Zone 1, 174

walking, 126

Household Energy: Economics, Consumption and Efficiency : Economics, Consumption and Efficiency, edited by Bartoli Mendez, and Josiah Pena,