Energy Efficiency in Buildings: Behavioral Issues

Table of contents :
ENERGY EFFICIENCY IN BUILDINGS: BEHAVIORAL ISSUES
Copyright
CONTENTS
PREFACE
CHAPTER 1 ENERGY CONSERVATION POLICY AND BEHAVIOR
THE HUMAN DIMENSION OF ENERGY USE
ABOUT THIS REPORT
BEHAVIORAL QUESTIONS RELATED TO CONSERVATION PROGRAMS FOR BUILDINGS
Information Programs
Incentive Programs
Standards
Technological Research and Development
CHAPTER 2 METHODS FOR ANSWERING BEHAVIORAL QUESTIONS
SIX ANALYTIC METHODS
Traditional Energy Demand Models
Analysis of Existing Data
Surveys
Ethnographic Methods
Small-Scale Controlled Experiments
Evaluation Research
A STRATEGY FOR ASSESSING BEHAVIORAL ISSUES
USING BEHAVIORAL METHODS TO ANALYZE POLICY ISSUES
Information and Information Programs
Incentive Programs
Standards
Technological Research and Development
CHAPTER 3 THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES
CRITERIA OF EFFECTIVENESS
EFFECTS OF THE SIZE AND TYPE OF INCENTIVE
Surveys of Preference
Participation in Incentive Programs
Conclusions
NONFINANCIAL FEATURES OF INCENTIVE PROGRAMS
EFFECTIVENESS OF INCENTIVES IN THE LOW-INCOME HOUSING SECTOR
Low-Income Participation in Incentive Programs
Incentive Programs for Low-Income Households
CONCLUSIONS
CHAPTER 4 INFORMATION-BASED HOME RETROFIT PROGRAMS
HOW CAN A PROGRAM BE DESIGNED SO THAT THE INFORMATION IT OFFERS IS USED?
HOW CAN A PROGRAM BE DESIGNED TO SPREAD INFORMATION WIDELY?
HOW CAN THE EFFECTS OF A PROGRAM BE FORECAST?
HOW CAN THE EFFECTS OF A PROGRAM BE ASSESSED ACCURATELY?
TO WHAT CAN PROGRAM EFFECTS BE ATTRIBUTED?
RECOMMENDATIONS
CHAPTER 5 HOME ENERGY RATINGS
CHARACTERISTICS OF AN IDEAL HOME ENERGY RATING SYSTEM
QUESTIONS ABOUT DESIGNING RATINGS
In What Units Should a Rating be Presented?
Energy Units
Dollars
Arbitrary Scales
How Much Precision Should a Home Energy Rating Offer?
Should a Rating Explicitly Estimate the Effects of Retrofits?
What Energy Uses Should a Rating Reflect?
QUESTIONS ABOUT IMPLEMENTING RATING PROGRAMS
Who Should Rate Homes?
What are the Key Institutions for Getting a Rating System Accepted?
What Other Institutional Arrangements Might Strengthen a Rating System?
DEVELOPING EFFECTIVE HOME ENERGY RATING SYSTEMS
What to Test
How to Conduct a Test
How to Assess Outcomes
Early Indicators
Effects on the Sale of Energy-Efficient Homes
Effects on the Energy Efficiency of Homes
CONCLUSIONS AND RECOMMENDATIONS
CHAPTER 6 PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS
RESEARCH STRATEGY
THE FIRST STAGE OF RESEARCH
Measuring Retrofits
Assessing Engineering Models in Practice
Other Direct Measures
Self-Reported Behavior
GUIDELINES FOR RESEARCH DESIGN
REFERENCES

Citation preview

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

i

ENERGY EFFICIENCY IN BUILDINGS: BEHAVIORAL ISSUES

Paul C.Stern, Editor

Committee on Behavioral and Social Aspects of Energy Consumption and Production Commission on Behavioral and Social Sciences and Education National Research Council

NATIONAL ACADEMY PRESS Washington, D.C., 1985

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

ii

NOTICE: The project that is the subject of this report was approved by the Governing Board of the National Research Council, whose members are drawn from the councils of the National Academy of Sciences, the National Academy of Engineering, and the Institute of Medicine. The members of the committee responsible for the report were chosen for their special competences and with regard for appropriate balance. This report has been reviewed by a group other than the authors according to procedures approved by a Report Review Committee consisting of members of the National Academy of Sciences, the National Academy of Engineering, and the Institute of Medicine. The National Research Council was established by the National Academy of Sciences in 1916 to associate the broad community of science and technology with the Academy's purposes of furthering knowledge and of advising the federal government. The Council operates in accordance with general policies determined by the Academy under the authority of its congressional charter of 1863, which establishes the Academy as a private, nonprofit, self-governing membership corporation. The Council has become the principal operating agency of both the National Academy of Sciences and the National Academy of Engineering in the conduct of their services to the government, the public, and the scientific and engineering communities. It is administered jointly by both Academies and the Institute of Medicine. The National Academy of Engineering and the Institute of Medicine were established in 1964 and 1970, respectively, under the charter of the National Academy of Sciences. Available from: Commission on Behavioral and Social Sciences and Education National Research Council 2101 Constitution Ave., N.W. Washington, D.C. 20418 Printed in the United States of America

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

iii

COMMITTEE ON BEHAVIORAL AND SOCIAL ASPECTS OF ENERGY CONSUMPTION AND PRODUCTION ELLIOT ARONSON (Chair), Stevenson College, University of California, Santa Cruz JOHN M.DARLEY, Department of Psychology, Princeton University DANIEL H.HILL, Institute for Social Research, University of Michigan ERIC HIRST, Energy Division, Oak Ridge National Laboratory WILLETT KEMPTON, Institute of Family and Child Study, Michigan State University THOMAS J.WILBANKS, Energy Division, Oak Ridge National Laboratory PAUL C.STERN, Study Director

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

iv

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

CONTENTS

v

CONTENTS

PREFACE

vii

1

ENERGY CONSERVATION POLICY AND BEHAVIOR The Human Dimension of Energy Use, About This Report, Behavioral Questions Related to Conservation Programs for Buildings,

1 2 3 4

2

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS Six Analytic Methods, A Strategy for Assessing Behavioral Issues, Using Behavioral Methods to Analyze Policy Issues,

9 9 16 17

3

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES Criteria of Effectiveness, Effects of the Size and Type of Incentive, Nonfinancial Features of Incentive Programs, Effectiveness of Incentives in the Low-Income Housing Sector, Conclusions,

29 30 33 41 46 49

4

INFORMATION-BASED HOME RETROFIT PROGRAMS How Can a Program be Designed so that the Information it Offers is Used? How Can a Program be Designed to Spread Information Widely? How Can the Effects of a Program be Forecast? How Can the Effects of a Program be Assessed Accurately? To What Can Program Effects be Attributed? Recommendations,

53 53 56 56 57 59 62

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

CONTENTS vi

5 HOME ENERGY RATINGS Characteristics of an Ideal Home Energy Rating System, Questions About Designing Ratings, Questions About Implementing Rating Programs, Developing Effective Home Energy Rating Systems, Conclusions and Recommendations, 64 65 66 69 73 77

6 PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS Research Strategy, The First Stage of Research, Guidelines for Research Design, 79 81 83 90

REFERENCES 92

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREFACE

vii

PREFACE

In 1980, with funds from the U.S. Department of Energy (DOE), the National Research Council established a Committee on Behavioral and Social Aspects of Energy Consumption and Production to draw on knowledge from the social and behavioral sciences in order to improve understanding of energy consumption and production in the United States. In its first report (Stern and Aronson, 1984), the committee developed a new perspective on energy issues and applied it to three areas of energy policy: energy use and conservation, energy emergencies, and energy activity at the local level. In a second report (Stern, 1984), a panel of the committee applied this perspective to some problems of forecasting and policy analysis most often addressed with formal mathematical models based on engineering data and economic concepts. The committee developed its perspective on energy use in response to a request from DOE for advice on how knowledge from the behavioral and social sciences other than economics could illuminate energy policy issues. Thus, the committee did not begin from economic assumptions; we did not, for instance, assume that it is adequate to characterize energy users as rational economic actors making choices in a market. We took a broader view. For example, we identified five different views of energy users that that may have great value for policy analysis: (1) energy users as investors who seek to maximize net financial value over the long term; (2) energy users as consumers whose choices reflect desires for personal benefits that are not financial; (3) energy users as people who express personal attitudes and social values; (4) energy users as members of social groups who reflect the influence of friends and associates; and (5) finally, energy users as people who want to minimize effort and avoid future problems and inconveniences. The recognition of this broad range of motives affecting energy users' behavior has numerous implications for energy conservation policy, which the committee explored in its previous reports. Moreover, we tried to establish the conditions under which each of these motives would be more or less powerful. In 1984 DOE expressed the need for a more detailed understanding of some of these implications. Accordingly, the committee was asked to apply its perspective to selected policy questions and to develop ways to help the staff of DOE's Office of Buildings Energy Research and Development to improve its analysis of behavioral issues in the area of

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREFACE

viii

consumer choice. This report directly addresses three issues: (1) consumer adoption of energy-efficient retrofits of existing buildings; (2) the role of energy efficiency in purchase decisions regarding new and used homes; and (3) consumer response to financial incentives for energy conservation. In addition, our analysis offers a framework for identifying behavioral issues regarding energy efficiency in buildings and for choosing methods for analyzing those issues. The report reflects the deliberations and contributions of the full committee as well as our discussions with DOE staff. The actual writing and editing of the report was the primary responsibility of Paul Stern, the committee's study director. Helpful comments on sections of the report were received from Gautam Dutt, Michael Rothkopf, and reviewers for the Commission on Behavioral and Social Sciences and Education. In addition, it is a pleasure to express appreciation to other people who made important contributions to the committee's work on this report: John Millhone, Diane Pirkey, Lynda Connor, Fred Abel, and Barry McNutt of DOE; David Goslin and Brett Hammond, executive director and associate executive director of the Commission on Behavioral and Social Sciences and Education; and Eugenia Grohman, the Commission's associate director for reports. ELLIOT ARONSON, Chair Committee on Behavioral and Social Aspects of Energy Consumption and Production

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

ENERGY CONSERVATION POLICY AND BEHAVIOR

1

CHAPTER 1 ENERGY CONSERVATION POLICY AND BEHAVIOR

Although energy policy is not high on the 1985 agendas of federal officials, energy use remains important to the nation. Energy use still affects air and water quality, and it might, in the long term, bring about major changes in climate. Inefficient energy use also makes the economy less efficient and less competitive internationally. And the high price of energy is a constant burden on households, businesses, and municipalities—especially those that lack the capital to purchase energy-efficient technology. Thus, even if energy prices do not increase in the future as they did in the past decade, there is still reason for a national effort to improve the efficiency of energy use. Households are an important focus for that effort. Until now, residential energy conservation forced by energy price increases in the United States has mainly taken the form of reduced standards of living rather than increased efficiency of energy use (see Frieden and Baker, 1983; King et al., 1982; Morlan, 1981, cited in Hirst, Marlay, et al., 1983; Stern, Black, and Elworth, 1983). Moreover, the reduced living standards have occurred primarily among low-income households: investments in improved energy efficiency have mainly been made by the wellto-do (Dillman, Rosa, and Dillman, 1984; Energy Information Administration, 1980), while low-income households have cut back on health care, education, and other household expenses to pay for energy (Dillman, Rosa, and Dillman, 1984). Because low-income households have made few investments in energy-efficient technology, energy costs have been rising fastest for those households (Energy Information Administration, 1982), millions of which now pay more than one-third of their incomes directly for energy (Cooper et al., 1983). Because of such developments, many state, municipal, and private decision makers consider energy efficiency important even though it is not high on the list of federal priorities. Many state energy offices are working to relieve the hardship of energy costs and to stem the flow of energy dollars out of state economies. Electric utilities in several parts of the country are looking to residential energy efficiency as a cost-effective alternative to power plant construction for meeting demand. And even the federal government continues to implement conservation policies enacted over the last decade, such as the low-income home weatherization program and the appliance standards and

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

ENERGY CONSERVATION POLICY AND BEHAVIOR

2

labeling programs. The continuing interest in policies and programs to improve household energy efficiency is reflected in a series of well-attended research and policy conferences over the last several years (see American Council for an Energy-Efficient Economy, 1984; Ester et al., 1984; Harris and Blumstein, 1984; Harris and Hollander, 1982; Morrison and Kempton, 1984). THE HUMAN DIMENSION OF ENERGY USE This report focuses on what we have called the human dimension of energy use—the way factors such as habit, trust, personal values, word-of-mouth communication, and recent personal experience affect the energy choices of individuals, households, and organizations. In earlier reports (Stern, 1984; Stern and Aronson, 1984), we have shown the importance for energy policy and policy analysis of an understanding of current knowledge about consumer behavior, including human thinking, motivation, information processing, and decision making; of the rules that govern the behavior of organizations and individuals in the face of change and uncertainty; and of other social and psychological processes. This report extends the earlier work, applying it to some unsolved problems regarding energy efficiency in buildings. It may be useful at the outset to suggest the kinds of policy problems that have been exacerbated by inattention to the human dimension of energy use. Efforts to implement energy efficiency by exhorting consumers to save or by sending them information about how to do it have had little effect (see Ester and Winett, 1982). People often fail to notice, understand, or trust the information. Tax incentives have not reached all the people who could benefit, partly because many low-income people do not routinely pay attention to details of the tax code or keep the necessary records. Thus, tax credits have mainly subsidized affluent consumers (Energy Information Administration, 1980) and people who would have invested in energy efficiency even without the incentives (Berry, 1982). And new energy-saving technologies are not readily adopted even when they offer a high return on investment (see, e.g., Office of Technology Assessment, 1982), in part because energy bills give confusing information that cannot readily be used to confirm energy savings (see Kempton and Montgomery, 1982; Kempton et al., 1984). When policy makers fail to recognize energy's human dimension, policy initiatives have often faltered. When the Residential Conservation Service program offered homeowners individualized energy information at low cost or even for free, the response was decidedly underwhelming (U.S. Department of Energy, 1984; Hirst, 1984; Hirst, Berry, and Soderstrom, 1981; Rosenberg, 1980). When conservation programs offer loan subsidies for home weatherization, few people take out loans, and the rate of participation has had much less to do with the size of the subsidy than with the way the programs are marketed and managed (Stern, Berry, and Hirst, 1985). Other disappointments in energy conservation may also be due to unanticipated behavior. When weatherization programs seem successful—

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

ENERGY CONSERVATION POLICY AND BEHAVIOR

3

when they lead to the insulation of walls, the caulking of doors and windows, and the installation of energyefficient furnaces—energy savings have not matched predictions. On the average, savings are somewhat less than predicted, but the variability is a bigger surprise: while some buildings save double what was predicted, others show substantial increases in energy use (Goldman, 1984; Goldman and Wagner, 1984; Hirst, White, and Goeltz, 1983; Hirst and Goeltz, 1984). Many plausible explanations have been offered for these outcomes, but none has been proved and the reason or reasons remain unknown. Perhaps people take some of their energy savings back as increased comfort by altering thermostat settings; perhaps installers of energy-efficient equipment do not install it the way the predictive models expect; and perhaps even the carefully constructed computer models used to predict energy savings are not correct or not precise, since different engineers using the same model of the same building often vary by 100 percent in their estimates of how much energy a building uses (K. Teichman, U.S. Department of Energy, personal communication). ABOUT THIS REPORT The committee's past reports show that conservation policies and programs have been built on an inadequate understanding of how people respond to prices, information, incentives, and other stimuli. Some of the needed knowledge exists, but much still has to be developed in the process of designing and implementing energy policies and programs. This report offers guidance for policy makers who need to understand the human dimension to make policies and programs in the building sector work. It addresses a small selection of policy issues, applying the relevant behavioral knowledge that exists and suggesting ways to develop the needed knowledge that does not yet exist. This report follows from the past work of the committee and draws heavily on evidence reviewed in our previous reports (Stern and Aronson, 1984; Stern 1984). Following those reports, it emphasizes contributions from the noneconomic behavioral and social sciences, particularly research on topics that have not been the primary foci for economists, such as word-of-mouth communication, program implementation, and the effects on consumer behavior of the sources of information rather than its content. We draw on some work by economists, but have not attempted to systematically review recent research in economics that, like the work reported here, could help illuminate the role of behavior in energy use. Such research includes modeling efforts that allow different determination of behavior as a function of climate, income, housing and appliance stock, and time of year (e.g., Dubin, 1985; Ruderman, Levine, and McMahon, 1984); efforts to model appliance choice separately from appliance utilization (e.g., Dubin and McFadden, 1984; Goett and McFadden, 1982); efforts to test psychological hypotheses in econometric models (e.g., Hill, 1985, 1986); analyses of consumer expectations of the behavior of markets (e.g., Mishkin, 1983); and economic models of information search by consumers (e.g., Hirshleifer and Riley, 1979; Salop and Stiglitz, 1977; Wilde and Schwartz, 1979). Such work by

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

ENERGY CONSERVATION POLICY AND BEHAVIOR

4

economists is moving on a parallel track with the kinds of behavioral research that form the primary basis for this report. The remainder of this chapter distinguishes four approaches to conservation policy—information programs, financial incentives, standards, and technological research and development—and identifies the major kinds of behavioral questions that arise in considering each kind of policy. Chapter 2 offers a framework that policy analysts can use to get answers to these types of behavioral questions. It discusses the methods available for answering such questions and outlines the strengths and weaknesses of each. Chapters 3 through 6 examine selected policy issues in more detail. Chapter 3 examines the effectiveness of incentive programs for residential energy efficiency, reviewing available data and drawing conclusions about what makes such programs attractive to energy users. Chapter 4 focuses on the role of information in home retrofit programs, showing how the key behavioral questions that arise in that context could be addressed more comprehensively in the future. Chapter 5 addresses another information-based policy option, the development of home energy rating systems. Drawing on available knowledge, it offers suggestions for designing and evaluating future rating systems. Finally, Chapter 6 examines a current issue in the implementation of energy-efficient technology: the discrepancy between observed and expected energy savings from home retrofits. It sets forth a research program for determining the major causes of the gap between prediction and reality that considers both the behavioral and technical factors involved. We believe this report will be of value to policy makers considering and implementing policies for residential energy efficiency. It is especially pertinent to the policy issues specifically raised in chapters 3 through 6, but it can also help in considering other policy and program options in the residential building sector. Thus, the report speaks to concerns of several offices in the U.S. Department of Energy (DOE), state energy offices, utility companies, municipal governments, and nonprofit organizations. BEHAVIORAL QUESTIONS RELATED TO CONSERVATION PROGRAMS FOR BUILDINGS Information Programs Many conservation programs for buildings are based largely on information. For example, Residential Conservation Service programs have offered better information to energy users through home energy audits, and DOE's Home Energy Rating System (HERS) and appliance labeling programs aim to save energy by providing more accurate information to purchasers about the energy efficiency of buildings and appliances. The success of these programs depends on the effect of new or improved information on major expenditures by energy users, for home purchases and retrofits and for major appliances. To design and implement such programs effectively, several types of behavioral questions must be addressed.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

ENERGY CONSERVATION POLICY AND BEHAVIOR

5

How can a program be designed so that the information it offers is used? Experience shows that information from energy programs often goes unnoticed, fails to be understood or correctly interpreted, or is ignored because of mistrust of its source. How can a program be designed to spread information widely? Energy programs reporting the most widespread success within a target audience are usually those that are advertised by word of mouth or through highly credible local institutions. Community groups are being used increasingly to implement energy programs because of a belief, supported by case studies, that such groups spread information more successfully (e.g., Gaskell and Pike, 1983; also see Chapter 3). How can the effects of a program be forecast? Formal energy demand models rarely contain terms for information, so they can only forecast its effects by making assumptions about how information affects the variables that the models represent. Empirical knowledge about how people respond to information is essential for forecasting. How can the effects of a program be assessed accurately? For measuring the effects of information on energy use, surveys asking if people received the information or what they did after receiving it are less reliable than direct measurement. But metering energy use gives an incomplete picture of the effects of a program if people choose to improve comfort rather than cut energy use. Thus, some program outcomes (e.g., comfort) that are essentially behavioral influence the energy effects of programs. To what can program effects be attributed? For improving programs, it may be more important to know what produced a program's effect than to know how large it was. Few program evaluation studies to date have considered this question. Incentive Programs Federal and state governments now offer tax incentives for energy efficiency, and utility companies offer energy loan and rebate programs. Such incentives are effective if they encourage investments that would not otherwise have been made, and their success in turn affects the need for other conservation programs. Some major behavioral questions about financial incentives have been examined (Stern, 1984: Chapter 3; Stern, Berry, and Hirst, 1985), and we update that material in Chapter 3. In general, there are five behavioral questions about incentives that should be considered. How does investment change as a function of the size of an incentive? Evidence suggests that incentives work not only by changing the economic calculus for people who are considering investments, but also by attracting other people's attention to energy efficiency. If further study confirms this hypothesis, analyses of investments only in terms of their size would have to be broadened.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

ENERGY CONSERVATION POLICY AND BEHAVIOR

6

How does investment depend on the type of incentive offered? Households may have definite, though nonuniform, preferences for different types of incentive. For example, some studies show that rebates are more attractive to many households than loan subsidies of the same economic value: they are preferred by low-income households and households that foresee bleak economic times for themselves (see Chapter 3). Policy analyses have rarely examined the possibility that different types of incentive attract different types of consumers. What programmatic factors affect consumers' use of incentives? The marketing, organization, and management of an incentive program makes a tremendous difference in program success. These effects can even overwhelm that of the size of the incentive (see Chapter 3). How much investment would have occurred without the stimulus of an incentive? Most people who use incentives say they would have invested anyway. Self-reports, however, are not a reliable way to study people's motives, and more needs to be known to answer this important policy question. To what extent does an incentive increase the pace of an investment? The limited evidence suggests that incentives speed investment, but more needs to be known to see if the effect is large enough to justify particular incentive programs. Standards Although energy-efficiency standards for buildings are not currently being pursued in the federal government, there is federal legislation regarding appliance standards, and some state and local governments set energy efficiency standards for appliances and in building codes. Under the appliance standards legislation, DOE has been analyzing the behavior of appliance manufacturers and purchasers to determine whether standards would produce energy savings in addition to what can be expected as a result of market pressures (rising prices, foreign competition, etc.). Four major behavioral questions are implicit in such analyses. Under what conditions does energy efficiency influence consumers' purchases? If energy efficiency is an explicit consideration when consumers choose buildings or appliances, better information will make their decisions more economically rational in terms of energy. If energy is not being considered, however, a national goal of increased efficiency may require setting standards. How might alternatives to standards, such as appliance labels or energy ratings for buildings, make energy efficiency a prominent consideration in purchase decisions? Well-designed information may attract attention to energy efficiency and make standards less necessary. To implement informational alternatives to standards,

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

ENERGY CONSERVATION POLICY AND BEHAVIOR

7

informational strategies that attract attention to energy efficiency must be developed. How is the importance of energy efficiency in purchase decisions affected by the circumstances and purposes surrounding the purchase? People probably do not consider energy efficiency when replacing a furnace or water heater that suddenly breaks down, but there may be more time for comparison shopping for refrigerators or dishwashers purchased in nonemergency situations. Knowledge about the circumstances of purchase may show that standards are more needed for some appliances than others. In the absence of standards, how do manufacturers, builders, and others make choices? To evaluate the need for standards one must know about the production of equipment in the absence of standards. Purchasers are not the only ones whose choices are relevant. There is a need to know more about how appliance manufacturers use information about competition, expected energy prices, and market characteristics in deciding whether to develop new product lines. There is also a need to know more about how the choices of builders, distributors, contractors, and retailers affect the decisions of purchasers. Technological Research and Development New technologies are constantly being developed for building construction and retrofits and for use in appliances. Behavioral questions arise because the practical effect of any new technology depends on human choices about its purchase and use. Adoption decisions, in turn, depend on whether estimates of energy savings from the new technology are reliable, and it is hard to make estimates when the energy savings depend not only on the operation of the technology but on the behavior of the people who use it. For example, superinsulated houses save energy, but if people open windows frequently to freshen the air, savings will be much less than expected. Technological research and development raise at least two such behavioral questions. Which energy-efficient building technologies are most likely to be accepted readily in the market? This question is essentially in the market research field. For example, a heat reclaimer for flue gases may be easier to build as a new product than to include in a redesigned furnace or water heater, but the market for heat reclaimers to retrofit on flues may be very small compared with the market for energy-efficient furnaces or water heaters with built-in heat reclaimers. How can reliable estimates of energy savings be developed for new technologies? Energy use in a building can change by 100 percent when the occupant changes (see, e.g., Sonderegger, 1978). This fact is a warning against estimating energy savings from a new technology without observing how it works in field conditions when operated by people like

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

ENERGY CONSERVATION POLICY AND BEHAVIOR

8

the intended users. Even holding the user constant, engineering estimates are imperfect because decisions about the purchase and the intensity of use of a technology influence each other (Dubin and McFadden, 1984). In sum, no conservation policy or program can be evaluated realistically without examining a broad range of behavioral issues. However, policy analysts have not yet given high priority to studying the processes of choice among consumers, manufacturers, builders, and other important actors. More such study can be done, even within existing resource limits. The next chapter offers a framework for answering behavioral questions about energy efficiency in buildings.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

9

CHAPTER 2 METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

This chapter gives an overview of the behavioral questions that are relevant for policy about energy efficiency in buildings, it describes the available methods for answering such questions; presents a general strategy for approaching the questions; and discusses the appropriateness of each method, given present knowledge, for addressing behavioral questions about energy information, incentives, standards, and new technology in the buildings sector. SIX ANALYTIC METHODS Traditional Energy Demand Models Energy demand models are analytic tools in which mathematical equations are used to estimate how demand might respond to various policy choices. Such models have considerable appeal as a method of energy policy analysis. They are broad, multipurpose tools that can address a wide range of policy questions and call attention to unanticipated effects of policies on other parts of the energy or economic system. They can give the sort of quantitative answers decision makers want to their questions, and they can often do this quickly. When correctly formulated, models can provide necessary checks of consistency with physical and economic constraints that might otherwise be overlooked in policy analysis. Table 1 briefly describes the major types of energy demand models. But the models usually used for energy policy analysis have many limitations. A number of general and serious criticisms have been raised by modelers and others (see Ascher, 1978; Brewer, 1983; Freedman, 1981; Freedman, Rothenberg, and Sutch, 1983; Greenberger, Crenson, and Crissey, 1976). In policy analysis, models are most appropriate for anticipating effects of interventions that are quantitative and that operate by processes that are well understood or that have been successfully modeled in similar situations. Often, however, not enough is known to defensibly quantify the variables, or the path of implementation is less straightforward. In such cases, the use of existing models cannot be easily justified. For example, available energy models lack data on variables related to information that consumers receive or act on. To

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS 10

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

11

estimate the effectiveness of an information program, a modeler might adjust the price elasticity or a lag coefficient as a proxy for the program's effect, but to do this is to assume the program's effect rather than to estimate it. Models based on the economic theory of information and consumer search (e.g., Hirshleifer and Riley, 1979; Salop and Stiglitz, 1977; Wilde and Schwartz, 1979) can improve the situation as an empirical basis is developed for choosing among the search strategies consumers may plausibly use. Analysis of Existing Data The Energy Information Administration and the utility companies have extensive data on residential and commercial energy use. Such data are useful for relatively quick and low-cost analysis of relationships that are represented in the data set, such as responses to fuel price changes or to incentives offered in different conservation programs. Analyses of existing data are limited, of course, by the data available. For studies of appliance efficiency, data can be found on purchases and list prices, but information on costs of production is held by manufacturers as proprietary. Utility data, which accurately report energy use, have limited value because they usually lack information on consumers' incomes, demographic variables, or behavior. And in disaggregated data sets that include information on energy use, data on demographic variables and local weather conditions are not often included. There have also been problems getting access to existing data at the individual level because of concern about privacy. Better data exist for analyzing energy use in the residential sector than in the commercial or industrial sectors; aggregate data are generally more available than disaggregated data; and energy use data are better than data on equipment stocks, with data on attitudinal factors even less adequate. The value of existing data also depends on its level of aggregation in relation to the question at hand. Data sets that include disaggregated data on residential consumption can be aggregated to compare utility service areas or states in which different programs, incentives, or regulations are in effect. Such comparisons can be valuable if interpretations are made with sufficient care. Surveys Surveys of energy users and other relevant populations—manufacturers, lenders, architects, building owners, and so forth—can give information about their initial reactions to new technologies, planned programs and policies, and about responses to programs during implementation. Surveys are particularly good for assessing qualitative variables such as awareness and trust of information or the attractiveness of particular qualities of a new technology or incentive program. They are also valuable for interpreting observational data. Data on

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

12

miles driven in the family car or money spent on a new energy-efficient home may reflect a variety of behaviors or decision processes, and surveys can help reduce ambiguity. And surveys can ask such questions of a sample that is representative of a population of interest. But surveys suffer from some generic limitations. Respondents may give socially acceptable rather than accurate responses. Surveys may fail to predict behavior because respondents' answers are based on faulty memories of what they have done or because they are unable to predict what they will do. Unreliability increases when surveys are used to assess responses to a hypothetical situation (e.g., a planned information program) or to predict behaviors that involve many steps before completion (e.g., expensive investments in energy efficiency). In the federal government, surveys present a practical problem because of the difficulty and delay involved in getting approval from the Office of Management and Budget (OMB) for survey instruments. The requirement for OMB approval, which rests on the rationale of reducing the burden on respondents, has stimulated researchers to develop various alternatives to the usual survey approaches: respondents have been paid, which satisfies concerns about undue burden; surveys have been funded by the National Science Foundation, whose procedures for protecting human subjects satisfy concerns about burden; and data have been collected by utility companies, state governments, or other groups independently of OMB. The Department of Energy (DOE) has also sometimes sponsored analysis and interpretation of such data without needing clearance. DOE can perform a useful function by sponsoring such analysis when there is a need for understanding of national trends or to explain differing success in programs that are superficially similar. The OMB clearance rule has delayed some surveys, halted others, and promoted creativity among researchers seeking timely answers for policy questions. The net effect of OMB regulation on respondent burden remains unknown. Research has continued under the rule, but it has sometimes been distorted. For example, having research conducted by different organizations in different parts of the country, which can be done without OMB clearance, is likely to result in the collection of noncomparable data. This problem plagued interpretation of the time-of-use electricity pricing experiments of the 1970s (Hill et al., 1979). To the extent that OMB clearance is perceived as an obstacle to be avoided, it becomes more difficult for policy analysts to achieve the careful design and standardization of survey questions that is needed to draw generalizable conclusions from research. A practical approach to standardization within the existing system is for researchers to use or modify survey items that have been laboriously developed by the Energy Information Administration for its Residential Energy Consumption Survey (RECS) and other surveys. A longer-term approach is to get key questions included in ongoing panel surveys such as the RECS. However, this approach is not appropriate for answering questions about particular local programs.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

13

Ethnographic Methods Detailed, open-ended interviews such as anthropologists conduct when trying to understand foreign cultures are sometimes useful for gaining an initial understanding of behavior when it is not yet clear which behaviors or beliefs are most important to understand. For example, ethnographic interviews revealed that many people think of energy in budget-based units, such as dollars per month, rather than in energy units (Kempton and Montgomery, 1982). This finding was a revelation to some analysts, who were designing information programs on the assumption that physical units would be meaningful to people. The ethnographic approach is also useful for getting a first approximation to the decision processes of individuals or organizations. As understanding of the issues becomes clearer, research can move from ethnographic approaches to more quantitative methods, such as surveys or small-scale experiments. Focused group discussion is a technique developed by marketers that combines features of both survey and ethnographic methods. A trained leader directs a discussion among ten or so members of a population whose response to a program element or product design is of interest. The participants' comments are used as a rough gauge of the reactions of the group they are presumed to represent. Like ethnography, focused group discussion does not involve representative sampling, and like ethnography, it can give early qualitative indication of people's reactions. Focused group discussion is not as systematic as survey research, and it is not always less expensive, but it can usually collect data faster. Small-Scale Controlled Experiments The experimental approach has been generally neglected in energy policy analysis. The best-known exception has been the time-of-use pricing experiments conducted during the 1970s, some of which involved random assignment of households to experimental electricity rates. Experimentation was the method of choice in those studies because there was no empirical basis for modeling the effect of prices based on time of use and because the experimental rates were so far from most energy users' past experience that self-reported intentions could not be relied upon. The same rationale suggests that experiments could provide the most valid answers to many questions about the design of energy information programs and about the marketing and implementation of conservation programs. The greatest advantage of experiments over other research techniques is their ability to control for large numbers of extraneous variables whose effects make the interpretation of nonexperimental data difficult. This is the situation with most conservation programs; the Residential Conservation Service (RCS) is a good example. Most evaluations have treated RCS as a single, uniform program and have attempted to make summary judgments about the RCS concept. But the variation among nominally identical programs is more striking than the averages (see

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

14

Chapter 3), and policy success depends on understanding and replicating program success where it occurs. Many factors that may be responsible for success could easily be the subject of low-cost experimental field trials. For example, a utility company could randomly assign some of its customers to receive telephone marketing of RCS audits, to be contacted as a follow-up to the audits, or to receive audits from community groups. Such procedures are being used in an evaluation of audit follow-up techniques that DOE is sponsoring in collaboration with the Florida Power and Light Company. Or, the effects of marketing efforts by a private company can be compared with identical efforts sponsored by a government agency (Miller and Ford, 1985). Strong inferences can be drawn from such trials if they compare new and existing approaches to program management. The experimental approach is inexpensive relative to full implementation of a program or policy. In the context of an already planned pilot program, an experiment requires only normal evaluation efforts and the addition of special care in assigning participants to programs and in making data on program participants comparable with data on suitable comparison group. The experimental method has had difficulties as a policy tool. Some researchers, unfamiliar with practical policy concerns, have experimented with unrealistic treatments, such as price rebates greater than 100 percent, and produced impractical recommendations as a result (see Stern and Oskamp, 1985). Experimental studies often meet practical opposition from program managers who are eager to get on with their programs and who feel they know enough to act without awaiting the results of formal research. Experiments also face political opposition on the ground that if the policy is a good one, it should be made available to all, not just a small experimental group (for a discussion of such issues, see Mosteller and Mosteller, 1979). Moreover, if experimental subjects believe an experiment to be temporary rather than a permanent change in policy, it may affect their behavior. An ethical question is sometimes raised about the propriety of experimenting with human populations because participants in some experiments will benefit relative to participants in others. There are often ways to avoid such problems. For some policies (such as utility rate reforms), it is possible to use crossover designs in which participants take turns living with each experimental rate so that all are subject to the same set of incentives. Or a program can be offered to the control group after a delay to minimize the differential benefit. When it is not possible to equalize incentives, it becomes necessary to judge what the public and prospective participants will consider fair. Intuition is not always a reliable guide, and empirical methods can help. An illustration is the approach used successfully in the Wisconsin time-of-use electricity pricing experiment. The state public utility commission, which sponsored the experiment, wanted the rigor of true experimentation, which in this instance required randomly assigning households to different electric rates. To see if it was possible to do this in a way that was ethically acceptable to the public, the research team convened random samples of people to judge the fairness of alternative rate structures for the experiment. The juries, and

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

15

eventually the participants themselves, agreed that it would be fair to set rates so that the average household in each group would experience no change in bills if it did not change its times of using electricity. While this meant that households that normally used most of their electricity in peak times would pay more if they did not change their behavior and that other households would pay less, the approach was considered fair (Black, 1979). This jury approach may be applicable to determining the fairness of potentially controversial experimental approaches before conducting the experiment. Evaluation Research Evaluations of past and present energy programs are a great untapped source of knowledge—not only about what works but about the reasons for successes and failures. Outcome evaluations quantitatively estimate overall program effects. For example, they may measure rates of participation in a program, sales of a new technology, improvement in the energy efficiency of building shells, or the net energy savings from a policy or program. Careful outcome studies can quantify a program's success and can be used for cost-benefit analysis. Process evaluations examine the way a policy or program is implemented rather than focusing on its final effect. They usually involve surveys, close observations, and interviews of program staff and clients and can offer insight into why a program succeeded or failed that cannot come from an outcome evaluation. When process and outcome evaluations are used together, they can tell which features of a program were responsible for its outcome. By identifying the important factors and relationships in the implementation process, evaluation studies can suggest promising revisions for programs. Evaluation research can use any of the methods outlined above. The most reliable information comes from explicitly treating programs and policies as experiments from their beginning. To do this, an evaluation plan would include creation of a suitable comparison group, randomly assigned if possible, and careful measurement of effects in all groups. (Full accounts of issues in evaluation research design can be found in texts such as Cook and Campbell, 1979.) When random assignment is not feasible, some quasi-experimental research designs retain many of the advantages of controlled experiments. Whatever the type of research design, however, more can be learned from the experience of a program if an evaluation plan is developed as a program is developed; an evaluation plan tacked on after a program has been operated inevitably produces weaker research because of the inability to measure preprogram conditions and because important questions must be answered from memory or by reference to incomplete archives rather than by observation. Examples of evaluations begun at an early stage include the DOE-sponsored evaluation of the Alliance to Save Energy's low-income mechanical retrofit program and the evaluation of a shared-savings home retrofit program by the government of Hennepin County, Minnesota. Evaluation studies can often be strengthened by using several research methods in concert. For example, surveys are ideal for

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

16

getting participants' reactions to a program, even one that includes experimental controls. Surveys directly measure responses that can only be inferred from “hard” data on energy savings or participation rates. In process evaluations, open-ended interviews can identify critical features of a program that both researchers and program operators have failed to anticipate. And small-scale experiments with program elements can be very informative as part of an evaluation study even if the overall evaluation does not use an experimental design. A problem with most of the evaluation studies of incentive and information programs is that they do not illuminate the reasons for a program's success or failure. There has been an emphasis on assessing outcomes but relatively little attention given to qualitative factors in a program's marketing and implementation that can mean the difference between success and failure. Even in the few instances in which process and outcome evaluations have been done of the same program, there has been little effort to tie the two approaches together. A STRATEGY FOR ASSESSING BEHAVIORAL ISSUES The success of efforts to conserve energy depends on the decisions of numerous individuals and organizations to produce, market, and adopt energy-efficient technologies. A policy or program that is designed without taking into account all the relevant actors and choices runs a high risk of failure. The risk can be reduced by a strategy that takes the various actors into account from the start and molds the policy or program to increase its acceptability to them. The strategy requires repeated and structured interaction between the developers of the program or policy and those who are its targets. It is best described by an example. In designing a home energy rating system, one would begin by interviewing potential users to learn what they would like to learn from a rating (see Ackerman et al., 1983, for an example of the approach). The process could begin with relatively open-ended discussions involving groups of bankers, builders, realtors, homeowners, and so forth, to generate a few ideas for types of ratings that might prove acceptable. Then the potential users could be asked to respond to proposed ratings in a focused group discussion or survey format. The purpose at this stage would be to rule out some rating systems as unacceptable so that more careful attention can be given to the remaining candidates. Ratings that pass the screening could be tried in a more realistic setting on a few houses, and user reactions could be reassessed by open-ended interviews or surveys. Potentially attractive ratings can then be tried in the field with experimental controls, using different versions on different homes or in different communities, with follow-up surveys used to assess the reactions of the relevant populations. When a rating system is formally instituted, the same procedure of surveying can be used as part of the process evaluation. Note that the procedure involves changing research methods as the policy or program moves toward implementation. At each stage, the list of options is narrowed and their presentation is made more realistic.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

17

Data collection moves from open-ended to more tightly controlled methods—from ethnographic interviews and discussions to surveys and then to experiments. At each stage, however, more than one method of research may be appropriate. Surveys and ethnographic methods are useful at the start for learning what issues concern people, but because people cannot reliably predict their behavior in situations they have not experienced, self-report methods have only limited value for predicting program effects. Surveys are useful as a measurement technique in pilot studies for assessing reactions to alternative versions of a program. However, the experimental method offers the most definitive knowledge of what specific versions of a policy or program work best. The above strategy is appropriate not only for informational programs such as home energy rating systems, but also for incentive and regulatory programs and for the development of new technologies. Manufacturing firms are well aware that a product's success depends on the reactions of distributors and customers, which is why they have market research departments. Government, however, has sometimes failed to look carefully enough at what is acceptable before promoting policies and technologies. The failure of federal building energy performance standards is traceable to insufficient communication between the federal government and the building industry, and the resulting view in the industry that the standards did not address its legitimate concerns. Similarly, a screw-in fluorescent bulb developed with DOE funds in the 1970s met initial market resistance because DOE had focused on issues of engineering and life-cycle cost and had not given enough attention to the problems of introducing a 7-dollar product into a 50-cent market. Designing programs and technologies by involving representatives of the potential users has an added advantage. It gives the users early information about the existence of the innovation, simplifying the marketing task later on. Participation also tends to commit people to the version that they helped choose. It follows that it is important to involve individuals or groups that are influential with other members of the target population for the new program, policy, or technology. USING BEHAVIORAL METHODS TO ANALYZE POLICY ISSUES This section discusses the role of the different research methods for addressing behavioral questions that arise in policy analyses of energy information, incentives, standards, and technologies. This fourfold classification of policies and programs is somewhat artificial: many incentive programs have informational aspects, standards can affect the use of information, the adoption of new technologies depends on incentives and information, and so forth. Furthermore, there are often synergisms between policy types that make it advantageous for policy makers to deliberately combine them in a single program. Thus, the important behavioral questions for any one policy or program may be found under more than one of the following headings.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

18

The rest of this chapter presents our judgments about methods to use for answering the behavioral questions we identified in Chapter 1 as important in each type of policy or program. The judgments, which are summarized in Tables 2 through 5 below, are conditioned on the present state of knowledge and the current adequacy of analytic tools. Information and Information Programs Table 2 summarizes the appropriateness of the six analytic methods identified above for addressing the five key behavioral issues related to information. How Can a Program be Designed so that the Information it Offers is Used? The effects of energy information depend not only on its completeness but on its credibility, specificity, comprehensibility, vividness, and other qualities (Stern and Aronson, 1984). For analyzing the effects of such factors, existing data sets are irrelevant and existing quantitative models are almost useless. Currently available models tend to assume information to be complete or at least constant or to subsume its effects under other explanatory concepts, such as elasticity, discount rate, or time lag. To gain understanding for the purpose of designing information, it makes more sense to address the behavioral questions directly, using nonmodeling approaches. Surveys and ethnographic methods are more promising. Ethnographic interviews can uncover fruitful hypotheses about the way people understand energy use (e.g., Kempton and Montgomery, 1982), and surveys can refine those hypotheses and determine the generality of the responses revealed by ethnographic studies. For example, survey research can identify householders' misconceptions about energy used in their homes and can also estimate the prevalence and magnitude of the misconceptions (Kempton et al., 1984). Experiments can offer even more definitive knowledge about the role of qualitative factors in energy information. For example, experiments on the importance of sources of information in which people receive the same information from different sources (e.g., Craig and McCann, 1978; Miller and Ford, 1985) quantifies the effect of the source of information on a particular set of behaviors. Such knowledge provides important guidance for program design that cannot come from models and would not be as convincing if it came from surveys of what people believe they would do. Evaluations of information programs can offer uniquely valuable knowledge from field settings if interviews or surveys are used to determine how information about a program reached people and how they responded to that information. Even more convincing information can come from program evaluations in which experimental controls are used to study some aspect of the information offered.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS 19

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

20

How Can a Program be Designed to Spread Information Widely? There is a body of literature on the diffusion of innovation that is relevant to the spread of energy information (for applications to energy conservation, see Darley and Beniger, 1981; Stern and Aronson, 1984:Chapter 4). To learn more about the spread of information in a particular context, two strategies are appropriate. One is to ask people, using surveys or ethnographic methods, how and from what sources they get their information. The other is to try out different methods of spreading information in a field setting and measure the results. The second strategy gives more reliable results but can involve much more effort. It is easier to collect data in the context of a program evaluation. If an ongoing program uses different ways of spreading information, an evaluation study can readily assess the success of the different methods. An example is an evaluation of the Minnesota Residential Conservation Service program, in which the choice of having energy audits performed either by utility personnel, private contractors, or community groups produced very different rates of requests for audits (Polich, 1984). How Can the Effects of a Program be Forecast? Forecasting the effects of information cannot at present be done on the basis of any well-developed theory; the only reasonable approach is to rely on data from past programs and to make judgments about differences and similarities between those programs and the one whose effects are to be predicted. Most government energy information programs have had small effects or none, and the same can be expected from new programs unless they adopt some of the more effective techniques that have been demonstrated in various studies (see Stern and Aronson, 1984: Chapter 4). How Can the Effects of a Program be Assessed Accurately? The most effective outcome evaluation is one based on comparison of participants in a program with two kinds of comparison groups: nonparticipants in the program and similar consumers who are not served by the program. Comparison with eligible nonparticipants gives an index of direct effects of the program, although the possibility of self-selection complicates interpretation of the results in most research designs; comparison with consumers not served allows a researcher to identify contagion effects in which a program affects nonparticipants through their indirect knowledge of it. Although each of these comparisons offers valuable information, such quasi-experimental studies are not definitive. (More detailed discussion of evaluation design is presented in Chapter 4; for a more technical and complete discussion of quasi-experimental research methods, see Cook and Campbell, 1979.) It is useful to build some experimental control into a program, for example, by offering information to different clients in different

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

21

forms, but evaluation researchers usually arrive on the scene too late to use this approach. To What Can Program Effects be Attributed? To answer this question adequately requires a process evaluation in combination with an outcome evaluation. Process evaluations can help explain the results of outcome evaluations, especially when both techniques are applied to the same programs (e.g., Bonneville Power Administration's residential incentive programs, which have been administered in somewhat different ways by the participating utilities). After-the-fact questions to participants can give valuable insight into the reasons for a program's success or failure, but because participation can change the ways people make sense of their experience, self-reports must be interpreted cautiously. The way to be sure of conclusions from a process evaluation is to alter the program based on those conclusions and observe the effects. Incentive Programs Table 3 summarizes the appropriateness of the six analytic methods for addressing five key behavioral issues related to incentives for conservation. How Does Investment Change as a Function of the Size of an Incentive? Existing models can be useful for estimating the effect of any given size of incentive, but the usual assumption that a smooth curve relates the two variables is open to question. There is evidence to suggest that response may be a nonlinear function of the size of an incentive (Hill and Stern, 1985; Stern, Berry, and Hirst, 1985) and also that size itself may be a less important factor than awareness of the existence of an incentive (Heberlein and Warriner, 1983; see also Chapter 3). Evaluation of these possibilities using existing data is needed to make models more reliable. Surveys offer only weak data on the effect of incentive sizes because people can only compare incentives in hypothetical situations. Experimental methods are a better alternative. How Does Investment Depend on the Type of Incentive Offered? The available energy models tend to equate different types of incentive (e.g., loan, rebate, tax credit) on net present value criteria, implicitly assuming that only the size of an incentive matters. But consumers may respond differently as a function of other financial features of incentives: a grant reduces first costs while a long-term loan can prevent negative cash flow. Also, different kinds

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS 22

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

23

of consumers probably have different preferences between types of incentive (see Chapter 3). It is possible to address questions about incentive type by asking consumers directly about their preferences but, the question being hypothetical, responses are only suggestive. A more effective way to address the question is through the comparative analysis of data on consumer responses to programs offering different types of incentives (Hirst, 1984; also see Chapter 3). The most reliable knowledge would come from experiments that offer consumers a choice of incentives of different types but of equal value. This could be readily done in the context of ongoing incentive programs, with the results coming in the form of an ordinary evaluation study. What Programmatic Factors Affect Consumers' Use of Incentives? Nonfinancial features of incentive programs, such as the availability of technical assistance, consumer protection features, the credibility of a program's sponsor, or the quality of interaction between clients and program personnel may be critically important to a program's success (Miller and Ford, 1985; Stern, Berry, and Hirst, 1985; see also Chapter 3). Surveys and open-ended ethnographic approaches are useful for understanding the role of these factors. After an incentive has been offered, surveys of users and nonusers can help illuminate the reasons for their responses. Valuable insights about nonfinancial features of programs have also come from evaluation studies that analyze programs offering a single incentive but administering it in different ways (e.g., Lerman, Bronfman, and Tonn, 1983; Lerman and Bronfman, 1984; Polich, 1984). The experimental approach can often yield quite precise assessments of nonfinancial factors by manipulating them in the course of conducting a program. For example, a program can give special training to some energy auditors and not others, follow up energy audits with personal contacts for some customers and not others, offer additional promotional services on a random basis, or experiment with other marketing or implementation innovations. This is probably the most practical use of the experimental method in developing incentive programs. How Much Investment Would Have Occurred Without the Stimulus of an Incentive? Program evaluators sometimes use surveys to ask people who have taken advantage of an incentive if they would have made the same investment in the absence of the incentive. Answers to such questions must be interpreted with extreme caution. A more reliable approach is to compare people to whom an incentive was made available with people who did not have the incentive available but who were otherwise similar. This can be done by adding a comparison group to a program evaluation design. Because of self-selection of program participants, a comparison of eligible nonparticipants is less than satisfactory. A comparison group of people who took advantage of the incentive later

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

24

(e.g., Newcomb, 1984) is an improvement, but there remain problems of comparability (see Chapter 2 and Cook and Campbell, 1979 for fuller discussion of methodological issues). Realistically, “triangulation” on the answer through different methods is probably the best approach. To What Extent Does an Incentive Increase the Pace of Investment? Quantitative models are sometimes used for addressing this question, but to be reliable for the purpose they need a stronger empirical base, which requires using other research methods. The best approach is probably through evaluation research, using appropriate comparison groups. Following carefully chosen comparison groups on a yearly basis will indicate when program participants might have made the changes they made if the program had not been available. Standards Table 4 summarizes the appropriateness of the six analytic methods for addressing the four behavioral issues related to standards for the energy efficiency of buildings or appliances. Under What Conditions Does Energy Efficiency Influence Consumers' Purchases? The direct way to address this question is to ask consumers, using surveys or interviews. Although the results would not be definitive, they would give useful information. Surveys of salespersons, dealers, and manufacturers may also give useful information. The question can be approached differently by calculating implicit discount rates from data on purchases of appliances or other technologies for which standards might be set. High implicit discount rates indicate that energy efficiency is not a major influence on purchases; they do not, however, provide information on the conditions under which efficiency may become more influential. How Might Alternatives to Standards, Such as Appliance Labels or Energy Ratings for Buildings, Make Energy Efficiency a Prominent Consideration in Purchase Decisions? The assessment of informational alternatives to standards should use the same methods used for assessing other kinds of energy information (see above). A laboratory approach can also help assess the effects of information on appliance purchases. Consumers could be confronted with a hypothetical purchase decision and be asked to request information one piece at a time until they have enough to make a decision. The question would be whether a label or rating would move energy efficiency information to a higher position in the decision

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS 25

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

26

process. Being hypothetical, this approach has limits: it is better for ruling out alternatives than for deciding on a final label or rating. The effects of ratings and labels are most accurately assessed through field trials that use experimental methods in realistic situations and through evaluations of ongoing programs. How is the Importance of Energy Efficiency in Purchase Decisions Affected by the Circumstances and Purposes Surrounding the Purchase? The direct approach to this question is, again, a survey. Useful knowledge can be gained simply by asking homeowners, builders, building owners, or other purchasers what factors they consider in purchasing particular appliances or other technologies. The implicit discount rate approach can also be used to address the question. If the implicit discount rate for air conditioners is about 20 percent and that for water heaters is about 150 percent (Ruderman, Levine, and McMahon, 1984), the difference may be due to circumstances of the purchase: one appliance may be purchased mainly by homeowners for their use and the other mainly by contractors for resale. Combining data from surveys with analysis of existing data provides a check on the results of each method. In the Absence of Standards, How do Manufacturers, Builders, and Others Make Choices? For aggregate forecasts, quantitative modeling is the method of choice. However, existing models need a stronger empirical basis for their assumptions about behavior, particularly the behavior of purchasers: it is clear for appliance purchases that a simple assumption of cost-minimization does not do justice to the complexity of the phenomena (Stern, 1984: Chapter 5). The needed empirical knowledge can come from research on the three previous questions. Technological Research and Development Table 5 summarizes the appropriateness of the six analytic methods for addressing the two behavioral issues related to research and development of energy-efficient technologies. Which Energy-Efficient Building Technologies Are Most Likely to be Readily Accepted in the Market? Available models are appropriate for estimating the economic costs of producing technologies and the energy saved by adopting them. But acceptance is also influenced by many other factors those models do not address: the prices manufacturers charge for a piece of equipment with a given production cost; the rates of adoption of the new technology as

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

27

TABLE 5 Appropriateness of Six Analytic Methods for Addressing Behavioral Issues Related to Research and Development of Energy Efficient Technology Issue Method What Factors Enhance Acceptance? Estimating Behavioral Component of Energy Savings Demand Models Somewhat valuable Potentially valuable Analysis of Existing Data Not useful Not useful Surveys Especially valuable Somewhat valuable Ethnographic Methods Especially valuable Valuable Small-Scale Experimentation Especially valuable Especially valuable Evaluation Research Outcome Evaluation Not appropriate Not appropriate Valuable in technology transfer programs Not appropriate Process Evaluation

a function of its consumer features; the marketing efforts of manufacturers and dealers; and so forth. Surveys and ethnographic methods are valuable components of a behavioral strategy for developing energyefficient technology (see above). They are especially useful for identifying design features that would be attractive to potential manufacturers or purchasers. Reactions of those groups to designs or prototypes can help guide choices of design modifications, which can be market tested while still in the prototype phase. As a new technology moves toward implementation, surveys and small-scale experiments become more useful for refining the design, just as they do for policies and programs. Design options can be subjected to experimental trial by users to assess public acceptance in the same way they are subjected to engineering tests of their costs and efficiency of operation, when new technologies are being introduced in conjunction with specific technology transfer efforts, evaluation research is appropriate for assessing those efforts.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

METHODS FOR ANSWERING BEHAVIORAL QUESTIONS

28

How Can Reliable Estimates of Energy Savings be Developed for New Technologies? New energy-efficient technologies do more than save energy—they also free income that can be spent on other things, some of which also use energy. This issue is amenable to modeling (e.g., Dubin, 1985; Dubin and McFadden, 1984), though data needs sometimes are serious limitations (see Stern, 1984: Chapter 5 for a discussion in the context of energy-efficient appliances). Doubts about the basis for the behavioral assumptions of models leave room for nonmodeling approaches to the problem (discussed in Chapter 6 in the context of home retrofits). To assess the effect of a new technology on behavior, it is useful to give some consumers a chance to use the technology. Since only a few consumers can be involved in trying prototypes, ethnographic approaches, which gain the deepest insight from the fewest consumers, may be the method of choice for understanding reactions to prototypes. An experimental approach, comparing relevant behaviors before and after adoption of a new technology with behavior of comparable energy users without the technology, becomes useful as more prototypes become available for trial. Data collected in a few small experiments may be enough to validate or refine the assumptions of models, which may then become fully appropriate for forecasting the effects of new technology on behavior. The framework outlined above can guide research on a wide range of behavioral issues that arise in implementing energy efficiency in buildings. The following chapters look more closely at a few areas of conservation policy, identifying the relevant behavioral issues, reviewing available evidence, and outlining how the issues can be addressed more completely in the future.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

29

CHAPTER 3 THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES Federal, state, and local governments, utility companies, and community organizations have offered a variety of financial incentives to induce homeowners and occupants to invest in insulation, weather-stripping, furnace improvements, and other energy-efficient technology. At least in terms of cost, these have been among the largest energy conservation programs in U.S. history. The federal energy tax credit, for example, may cost the treasury $2.5 billion between 1981 and 1986 (Hirst, Goeltz, and Manning, 1982), and state conservation tax credits have amounted to over $50 million per year in California alone (Randolph, 1984) . The Tennessee Valley Authority has provided $250 million in interest-free loans to its customers (Berry, 1982), and the Bonneville Power Administration is spending at least that much to hold down residential electricity demand and thus avoid even higher expenses for new power plants. This chapter reviews recent data on the effectiveness of residential incentive programs. It focuses on three of the important behavioral issues affecting incentive programs: the effects of the size of an incentive, the type of incentive, and the nonfinancial features of an incentive. Less evidence is available on the other two issues: the incremental effect of incentives on investment and the effect on the pace of investment. The chapter focuses on the first three issues, concluding with a discussion of them in the context of programs for low-income housing. It examines the available data, draws some conclusions, and points some specific directions for research. A behavioral perspective on incentive programs emphasizes a program's effects on consumers' decision processes. It leads to a focus not only on the size of financial incentives, but also on their form. It also leads to an interest in how programs get the attention of their intended audiences, communicate with them, address energy users' concerns, seek and use credible sources for communication, and minimize the effort and risks of investing in energy efficiency. To address these issues, evaluation studies must take into account a wide range of program and client characteristics and outcome variables. The major ones are listed below:

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

30

Program Variables Type of incentive Size of incentive Size of target population Type of target population Period studied (months) Qualitative features: credibility of sponsor motivation of sponsor marketing effort consumer protection features other services (audits, bidding) restrictions on participation Client Characteristics Household income Number of household members Education Size of home Type of structure Appliance holdings Type of heating and cooling system Fuels used Home ownership Energy-related attitudes and beliefs Outcome Variables Percent of target population attracted to program (e.g., requesting energy audits) Percent of those attracted who use the incentive Investment per household using incentive Incremental energy savings by households using incentive Incremental savings by those attracted to program Administrative cost

Unfortunately, very few evaluations contain the full range of information listed above. Therefore, our analysis is limited to a restricted part of the phenomenon of response to incentive programs, with this limitation, we give a central place to qualitative factors, such as the form of an incentive and the nonfinancial features of program design, marketing, and implementation. CRITERIA OF EFFECTIVENESS Two classes of criteria can be used to judge a program's effectiveness at improving energy efficiency: the level of induced investment in energy efficiency and measured energy savings. Although energy savings is sometimes considered the only true test of effectiveness, both criteria must be examined for technical and policy reasons. Technically, it is not yet possible to estimate energy savings accurately

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

31

from given efficiency improvements: estimates are often wrong in the aggregate and for individual homes, and savings usually vary by 50 percent or more from estimates (Goldman, 1984; Hirst and Goeltz, 1984). Thus, one cannot infer energy savings from physical changes in a building; one must measure them. For policy reasons, it is important to pay attention to increased efficiency as well as energy savings because different conservation programs have different goals. For an electric utility sponsoring conservation as an alternative to building new power plants, forestalling growth in energy demand is the paramount goal. For such a utility company, economic calculations are not affected by whether the goal is achieved by improving efficiency or in other ways, although it does matter whether the restraint of demand will be robust under short-term changes in the regional economy. For a city's or community group's conservation program, however, increased comfort and health may count as important benefits of energy efficiency even if there is no reduction of total energy use. Thus, both energy efficiency and energy use should be measured to assess the effectiveness of conservation programs. Comfort, health, and other policy goals should also be measured where relevant. Data on measured energy savings from conservation programs are quite limited. Hirst (1984) reviewed such data from studies of Residential Conservation Service (RCS) programs and loan subsidy (mainly zero-interest loan) programs. Participants in six residential conservation service programs saved between 3 and 9 million Btu per household per year (in the median program, 5 million) and participants in six loan programs saved between 10 and 20 million (median, 12 million), compared with savings by nonparticipants in the same programs. Hirst concluded that incentive programs save about three times as much energy as RCS programs. Hirst's conclusion should be qualified, however, considering the different ways participation is defined in the two types of programs. For the RCS programs, all households requesting energy audits are counted as participants, regardless of subsequent actions to improve energy efficiency. For the incentive programs, however, participants are defined as only those households that took advantage of the incentives. In most instances, a minority of the households that request energy audits take advantage of incentives, and they are the ones that made the most extensive investments. For the programs Hirst cited for which data are available, the costs of retrofit per participant ranged from $1,500 to $2,500 (Hirst, Bronfman, et al., 1983; Hirst, Goeltz, et al., 1983; Puget Sound Power and Light Company, 1984; Weiss et al., 1983). Thus, a comparison of “participants” exaggerates differences in the effects of the two types of program: if only the households that made major investments were compared, the difference in energy savings between the two types of program might be very small.1

1In RCS programs, capital investment per “participant” probably averaged about $600 (Centaur, 1983). This average might represent an

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

32

Only limited data are available for comparing energy savings in programs offering different types of incentives, and no clear differences are evident. Hirst (1984) has reported the findings from loan programs, and we have been able to find only three comparable reports of measured energy savings from grant programs. Participants in the Canadian Home Insulation Program, which offers a grant of 60 percent of the cost of recommended conservation measures, cut energy use 12.8 percent per household compared with an econometric estimate of what the same households would have done in the absence of the program (Energy, Mines, and Resources Canada, 1983). Participants in the Weatherization Assistance Program offered free to low-income homes in the United States saved 14 million Btu, also compared with an econometric estimate (Peabody, 1984). Seattle City Light's Low-Income Electric Program, which offers free weatherization for households earning less than 90 percent of the median income in the region, saved its average participant about 12 million Btu in the first year (Newcomb and Weiss, 1983a). These outcomes are within the range of savings reported by Hirst (1984) for loan programs. The lack of differences is not meaningful because households counted as participants in these major incentive programs are almost always the ones that made large investments in recommended conservation measures. When programs offer large incentives for major investments, differences in effect are more likely to be due to some programs' success at getting more people to follow audit recommendations than to differences in savings among households who follow the recommendations. The best test of effectiveness is what a program does for its population of potential participants, not what it does for the households that accept its recommendations. Consequently, the rate of participation in conservation incentive programs is a useful index of effectiveness. When programs save about the same average amount of energy per participant, as in the case of the major loan and grant programs for which data are available, rate of participation is a good proxy for total energy saved. Rate of participation is also an important variable in its own right because it indicates a program's effectiveness at marketing; when combined with an index of the intensity of participation, such as spending per household on retrofits, it becomes a rough index of improved energy efficiency. (It is only a rough index because expenditures on different retrofits can have very different effects on energy use.) Since more data are

investment of $1,500 each by 40 percent of those requesting audits and no investment by the rest or an investment of $2,500 each by 25 percent and no investment by the rest. If one assumes that the most active RCS participants invested $1,500 and saved 13 million Btu, as did households investing the same amount in the Puget Sound Power and Light program (Hirst, 1984) or that they invested $2,500 and saved 20 million Btu, as in Northern States Power's loan program (Hirst, Goeltz, et al., 1983), and if the other households saved nothing, the average savings for all households defined as RCS participants would have been 5 million Btu— the median savings for RCS reported by Hirst.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

33

available on participation than on measured energy savings, the discussion below focuses mainly on participation, measured by the number or percentage of eligible households completing retrofits or by the cost of the retrofits. EFFECTS OF THE SIZE AND TYPE OF INCENTIVE Financial incentives obviously vary in their economic value, but they also vary on several other dimensions. They vary in immediacy: people can collect grants and rebates quickly when they make a retrofit, while tax credits can take a year or more to receive. They vary in their effects on cash flow: a partial rebate requires an immediate expenditure, while some loans can be structured so that the stream of energy savings balances the stream of loan payments. They vary in the requirement to assume debt: loan subsidies require a household to go into debt, while grants and rebates make borrowing optional. They vary in the effort needed to use them: tax incentives require keeping records and filing forms, while some rebates are available at the point of decision without any additional effort by the energy user. They vary in eligibility requirements: screening processes deter some applicants who fear rejection. No doubt incentives vary in other ways as well. To limit the number of dimensions to be considered, we loosely classify incentives as reduced-interest loan subsidies, interest-free loans, or grants or rebates. A few programs combine loan and grant features or include other incentives, such as reduced cost through group bidding. Evidence on the relative attractiveness of different types of incentives comes from surveys of the expressed preferences of actual or potential program participants and from levels of participation in programs offering different kinds of incentives. Reliable evidence on the effect of incentive size comes only from actual participation in programs. Surveys of Preference Surveys of populations eligible for incentive programs measure expressed preferences for different types of subsidy. A few surveys that have asked people to choose among hypothetical incentive packages have had rather mixed results. The Southern California Gas Company found about equal preference for an interest-free loan and a credit of about 50 percent (Berry, 1982). A survey for Seattle City Light found about equal preference for an interest-free loan with payment deferred 10 years and for a combination of a 10 percent grant and a 6 percent loan; however, one-quarter of the respondents did not rank the interest-free loan option among the five choices given (Berry, 1982). A national study in Canada found no difference in preference between loan subsidies and grants, but a preference for tax credits over grants when the size of the subsidy was held constant at 50 or 100 percent (Hickling-Partners, 1983). And in a survey of customers of the Pacific

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

34

Gas and Electric Company, preference for an interest-free loan increased with income while the opposite occurred for a 50 percent rebate (Berry, 1982). A survey of people eligible for audit and loan subsidy programs administered by the Northern States Power Company found that over 70 percent of the eligible population preferred a 20 percent rebate to a 7 percent loan with payment deferred until the home is sold. Only those respondents who had already taken advantage of the company's low-interest loan program preferred the loan (Hirst, Goeltz, et al., 1983). These surveys show no clear overall preference for type of incentive, but they suggest that preference may be segmented by income or other customer characteristics. More consistent results come from surveys of people who have accepted or declined an incentive actually offered them. According to people who accept loan subsidies, they are often essential to action. Only 29 percent of those using zero-interest loans in Pacific Gas and Electric's CAL/NEVA program said they would have made the same retrofits if the loan program had not been available (Moulton, 1984). The comparable figure in the Bonneville Power Administration's Weatherization Pilot Program was 45 percent (Hirst, Bronfman, et al., 1983) and for the Northern States Power loan program, 29 percent (Hirst, Goeltz, et al., 1983). However, many people simply will not borrow for energy conservation. This was the case for 77 percent of people eligible for the Michigan RCS program (Katz and Morgan, 1983) and 52 percent of the people who did not take out a loan even though they were eligible for a Northern States Power loan subsidy (Hirst, Goeltz, et al., 1983). Among people who received home energy audits in the CAL-NEVA low-income program but who declined the program's interest-free loans, fear of indebtedness was given as the major reason (Moulton, 1984). Concern about indebtedness was also a major factor in response to the Washington Water Power Company's zero-interest loan program. Four factors predicted 25 percent of the variance in householders' decisions to accept or decline the company's loan offer after receiving an energy audit: how convinced they were by the auditor's description of the program, their willingness to accept debt for weatherization, their willingness to use one's home to guarantee a loan, and the proportion of one's home mortgage left to be paid (Olsen, 1984). Data for grant programs show a similar pattern: many households that accept grants say they would not have retrofitted without the assistance, but other households find grants an unattractive form of incentive. In the Low Income Electric Program sponsored by Seattle City Light, 68 percent of the participants said that if the free weatherization program were not available, they would not have taken advantage of a loan program (Newcomb and Weiss, 1983a). But many households decline large grants when offered. According to a study of the Eugene (Oregon) Water and Electric Board's weatherization program, one reason is lack of capital. The program's average grant required a household to spend $400 of its own, and many households, especially those declining the subsidy, did not believe they could afford that much. The evaluation concluded that, independent of income, households

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

35

that were better able to manage their budgets were more likely to take advantage of the grant program (Olsen and Fonseca, 1984). The most likely interpretation of the survey data is that different types of households have different preferences with respect to incentives, partly as a function of income (see below), but also of other factors. Renters, for example, are ineligible for loan programs that require a lien on the home. And other characteristics of a household, such as ability to manage a budget, can also affect the attractiveness of loans and grants. The possibility that different kinds of households prefer different kinds of incentives should be investigated further so that incentives may be designed for their target populations. The best approach to increasing participation may turn out to involve a choice of types of incentive. Participation in Incentive Programs Table 6 summarizes available data on participation in residential incentive programs available to households at all income levels. It should be interpreted with several qualifications in mind. Programs of the same type vary in the terms of the incentives they offer, the maximum level of investment they subsidize, their target populations, and the restrictions or added attractions they offer, and many programs change over time on the above dimensions. Programs also vary greatly in their marketing efforts, which obviously affect participation rates, and participation rates probably also change over time. Evidence from several programs (Moulton, 1984; Newcomb and Weiss, 1983b; Olsen and Fonseca, 1984; Weiss, et al., 1983; Wickman, 1984) suggests a learning curve, with rates of participation increasing over the first two to three years. Thus, participation rates equalized for time, as in Table 6, may not be fully comparable across programs. Although Table 6 shows a range of participation rates within each program type for which several reports were found, there is a clear trend. Grant or rebate programs generate the highest rate of retrofit activity (mean and median, 7 percent per year); interest-free loan programs generate a lower rate of retrofit activity (mean and median, 3 percent per year); and partial loan subsidies have the lowest rate of participation (mean, 1 percent per year; median, less than 1 percent). Assuming that other variations between programs are random, the difference in participation rates is statistically reliable.2

2Comparison of the three program types (excluding the mixed program) by analysis of variance gives F=9.62, p=.002 when all programs are considered, and F=3.32, p=.08 when only programs in the United States are included in the analysis. The nonparametric Kruskal-Wallis test gives comparable values of H=13.97, p=.0009 for all programs and H=4.64, p=.10 for United States programs. The success of grant programs outside the United States is apparently responsible for much of the observed difference between types of incentives.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

TABLE 6 Retrofit Activity in Response to Broadly Based Residential Incentive Programs Loan or Grant Rate (%) Length of Programa Program (source) Partial Loan Subsidies Northern States Power (Hirst, Goeltz, et 7 9 mo. al., 1983) Rhode Islanders Saving Energy (Stern, 11 2 yr. Black, and Elworth, 1981) 19th Ward, Rochester, N.Y. (Katz and 12 1 yr. Morgan, 1983) New York Home Insulation and 9–11 7 yr. Conservation (New York State Public Service Commission, 1985) Mean Zero-Interest Loans Seattle City Light (Newcomb and Weiss, 0 first year 1983a; Weiss and Newcomb, 1981; Weiss third year et al., 1983) Bonneville Pilot (Lerman, Bronfman, and 0 2.5 yr. Tonn, 1983) Portland General Electric (Burnett, 1982) 0 9 mo. Pacific Power and Light (Hannigan and 0 1 yr. King, 1982) TVA Zero-Interest (Moulton, 1984) 0 4 yr. Puget Sound Power Loan (Puget Sound 0 7 yr. Power and Light Company (1983, 1984) Mean Mixed Loans and Grants __c 9 yr.d Swedish loan and grant (Klingberg and Warkov, 1983; Wickman, 1984)

36

Rate of Retro-fittingb (%) 3 0.2 1 0.1

1 3 6 4 about 1 about 3 3 2 3 3

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

Program (source) Partial Grants or Rebates Bonneville Interim (Lerman and Bronfman, 1984) Eugene (Oregon) Buyback (Olsen and Fonseca, 1984) Puget Sound Power Grant (Puget Sound Power and Light Company, 1983, 1984) Canadian Home Insulation (Energy, Mines, and Resources Canada, 1983) Danish Grant (Petersen, 1984) Canadian Oil Substitutione (Anderson, 1984) British Home Insulation (Gaskell and Pike, 1983) Netherlands Grant (de Haan, 1985) Mean a

37

Loan or Grant Rate (%)

Length of Programa

Rate of Retro-fittingb (%)

93

20 mo.

5

77

17 mo.

4

72

3 yr.

2

60

5 yr.

7

20 50

34 mo. 3 yr.

11 8

66

2 yr.

8

22–33

3 yr.

7f 7

period covered in evaluation study. Households using the program's subsidy per eligible household per year. c 35 percent grant up to about $400 and 12 percent loan for additional costs. About three-quarters of government payments have been in the form of loans. d For single-family homes. e This program supported residential fuel switching. f All households eligible throughout 1979–1981; program restricted to renters in 1982 and had a 5 percent rate of retrofitting in 1983. b

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

38

However, the warnings above about comparability must be borne in mind in judging the meaning of the statistical differences. The differences seem to be partly a function of the size of the incentives. Interest-free loans are worth more than partial loan subsidies, and they appear to be worth less than the large grants offered by U.S. programs. The strength of the latter conclusion, however, depends on the discount rate used to calculate the economic value of loan subsidies. Nominal discount rates in the range of 10 to 15 percent are consistent both with data on consumers' expressed preferences for energy conservation incentives (Stern, Berry, and Hirst, 1985) and with the performance of U.S. investment markets over the past few years. At a 10 percent nominal discount rate, the median net present value of the loan subsidy is 57 percent for the four zero-interest loan programs for which data are available; at a 15 percent rate, this value is 71 percent.3 Since these values are close to the 63 percent median subsidy in the eight partial grant or rebate programs in Table 6, the apparent preference for grants over loans may not be due to a difference in their financial value. Reluctance to assume debt for energy conservation may explain the preference, but there is no conclusive evidence on this point. Assumptions about discount rate do not affect the analysis when considering the effect of different sizes of grants or rebates. Among the eight grant and rebate programs in Table 6, the highest participation rates seem to come in programs offering the smallest grants. The explanation for this surprising result can probably be found in the details of how the programs are designed and operated. Grant programs have had much greater success in countries other than the United States. The three U.S. grant programs, which offer a median subsidy of 77 percent, have a median participation rate of 4 percent per year; the five foreign programs, with a median subsidy of 50 percent, have a median participation rate of 8 percent a year. One possible explanation for this finding is a difference in the energy situations of the nations. Denmark has long made energy conservation a national priority, in part because of its great dependence on imported oil. However, that dependence does not exist for Canada, and the Canadian grant programs have also been highly effective. A second possible explanation is climatic difference between the countries for which program evaluations are available and the areas served by the U. S. grant programs. The former serve homes with much greater needs for heating. However, the only available direct evidence shows that in the

3These calculations assume that loans payable on the resale of a house will come due, on average, in 10 years. Thus, the value of the subsidy in the Bonneville Pilot or Puget Sound Power and Light zero-interest loan programs is 61 percent at a 10 percent discount rate or 75 percent at a 15 percent discount rate. At the same discount rates, the subsidy value of a partially deferred 10-year Seattle City Light loan is 53 percent or 67 percent and that of a Tennessee Valley Authority 7-year loan is 30 percent or 41 percent.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

39

Canadian Home Insulation Program, heating load in degree-days was unrelated to the likelihood of household participation (Hickling-Partners, 1983). A third possible explanation is the smaller size of the investments made as a result of the foreign grant programs, almost all of which led to a smaller average investment than any of the three U.S. programs cited in Table 6. However, because of the lower rates of subsidy offered in the foreign programs, expenditures per household were not so different in the U.S. and foreign programs.4 A fourth possible explanation for the differences between the United States and other countries is in the procedure for obtaining subsidies. All three U.S. programs require householders to request and receive free energy audits from the sponsoring utility; the subsidy then applies only to those investments the utility determines to be economically justified. In the foreign programs, there is no requirement for an energy audit (although audits are available privately in Denmark, and the grants can be applied to their cost): a householder purchases goods and services covered by program guidelines and claims the subsidy directly from the government. The U.S. procedure involves an additional step for the household and entails direct intrusion and regulation by an outside entity. Households that will not devote much effort to conservation decisions or that do not trust the local utility company may not request energy audits. The simplicity of the foreign grant programs and their willingness to entrust choices to households may make the programs more attractive. This argument is bolstered by the success of one grant program run on the foreign model in the United States: the Pacific Gas and Electric Company's grants for small commercial consumers. The company mails applications to commercial customers that use between 10,000 and 100,000 kwh/yr. of electricity, offering rebates amounting to 50 percent of the estimated cost of items listed on the application. Customers simply submit the one-page application with a receipt for purchase and are mailed a check. In its first 14 months of operation, the participation rate averaged nine percent per year (D. Wilcox, Pacific Gas and Electric Company, personal communication), a value very similar to those observed in similarly structured programs in other countries and higher than that reported in any U.S. incentive program (see Table 6). An audit-based system, despite lower participation rates, has potential advantages. Basing grants on expert audits may better protect against fraud and may save more energy, but the value of these advantages cannot be accurately estimated. The foreign program

4The value of investments induced by the eight partial grant or rebate programs listed in Table 6 were, respectively, $1,700, $1,760, $1,960, $650Can., 8,870Dkr, $l,180Can., 60 pounds sterling, and 2,780 guilders; the costs to households were, respectively, $129, $400, $550, $260Can., 7,200Dkr, $590Can., 20 pounds sterling, and about 700 guilders.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

40

evaluations provide no data on fraud and few on energy savings.5 Only one foreign program, the Canadian Home Insulation Program, has yet produced data on measured energy savings. Those savings are near the average for U.S. incentive programs, even though the average investment per household in the Canadian program was only about $520 (Energy, Mines, and Resources Canada, 1983), compared with more than $1,500 in the U.S. programs for which data are available. However, differences in measurement methods are great enough to deter strong comparisons. It is worth noting a direct comparison in Table 6 between grant and zero-interest loan programs offered by the same sponsor at the same time. The Puget Sound Power and Light Company in Washington has offered zerointerest loans since 1978 and grants of 72 percent of retrofit costs since 1982. Both programs have yielded retrofits at the rate of two percent per year. However, the loan program is available only to homeowners in electrically heated homes while the grant program, which is not restricted to homeowners, is available to a population almost twice as large. Since renters rarely take advantage of home retrofit programs, expansion of the program could have been expected to decrease participation rates; thus, the comparison is consistent with the idea that grants are more attractive than loans. Conclusions Despite the multifaceted differences between incentive programs, the data support a few general conclusions about the attractiveness of different incentives. The size of incentives is correlated with retrofit activity for loan subsidies, although data are insufficient to determine the shape of the relationship between subsidy and response. For grant and rebate programs, however, no systematic relationship is evident between retrofit activity and the size of a subsidy. The international comparisons emphasize that the size of the incentive is not the most important determinant of effectiveness in grant programs. Comparisons of types of incentive offer some evidence that households prefer grants over loans. That conclusion, however, depends on the value assumed for the discount rate in comparing the value of grants to loans. More significant is the evidence suggesting that different kinds of households prefer different kinds of incentives. Survey data indicate that grants tend to be preferred by low-income households, those that feel they cannot afford to spend large sums, and those that are averse to indebtedness for other reasons. Preferences

5Pacific Gas and Electric Company strongly suspected, however, that customers in its commercial rebate program were taking advantage of the subsidies by installing inferior quality reflective roof coating and claiming full rebates. After roof coating was dropped from the list of subsidized retrofits, the rate of participation dropped from 16 to 5 percent on an annual basis (D. Wilcox, Pacific Gas and Electric Company, personal communication).

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

41

are probably governed by other factors as well, and each type of incentive is probably attractive to some households that would not consider using the other type. Future research should address the question of market segmentation of preferences for incentives. As a first step, data can be collected from participants in programs offering a choice of incentive types. An experimental approach could also be used to see if a group of households offered a choice of incentives has a higher response rate than groups of households offered only one type of incentive program. NONFINANCIAL FEATURES OF INCENTIVE PROGRAMS Nonfinancial features of incentive programs are important to their success because nonfinancial motives affect energy user' decisions (Stern, 1984; Stern and Aronson, 1984; Stern, Berry, and Hirst, 1985). The nonfinancial aspects likely to matter most in incentive programs can be considered under four categories— promotion, simplification, reliability, and trust. Promotion involves gaining consumers' attention through marketing and communication techniques and selling a program with effective communication techniques and extensive contact with potential clients. Simplification involves combining separate choices involved in retrofitting into a single decision. Reliability concerns the common fear of homeowners that contractors' work will fail to live up to its promise. And trust involves potential clients' attitudes toward program sponsors. The importance of nonfinancial factors was suggested by the data in the previous section, and it is illustrated more forcefully by the wide variation in consumer response to programs that are the same in financial terms. Tables 7, 8, and 9 present data on consumer response to incentive programs that offered the same incentive packages through several different utility companies. Table 7 presents results from nine utility companies participating in a New York State-mandated loan subsidy program; Tables 8 and 9 present similar results from zero-interest loan and grant programs administered by the Bonneville Power Administration. In all three programs, there was more than a tenfold variation in program participation rates. Such variation may be due to differences at two points of consumer decision: the initial response to a program, as indicated by requests for energy audits, or the decision to make recommended retrofits after receiving an energy auditor's recommendations. The major source of variation in the New York program was the decision to take loans after receiving audits (Table 7); in Bonneville's grant program, however, the major source of variation was in rates of requests for audits (Table 8); and in Bonneville's zero-interest loan program, there was wide variation at both points of decision (Table 9). The differences may be due in part to the size of the incentives offered. As incentives become more attractive (moving from Table 7 to Table 8 to Table 9), people who request audits become increasingly certain to take advantage of the incentive. But even with a very large incentive, households' initial interest in a program, and therefore the overall rate of

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

42

participation, is still highly dependent on nonfinancial factors. This finding suggests that the stronger the financial incentives are, the more important the nonfinancial factors, especially marketing, become to a program's success. TABLE 7 Participation in the New York State Home Insulation and Conservation Program Eligible Residential Audits per Eligible Loans per Audit Utility Company Customers Home (%/yr.) (%) Brooklyn Union Gas Co. 451,000 2.5 6.0 Central Hudson Gas and 155,000 1.4 3.5 Elec. Consolidated Edison 300,000 2.6 6.1 Long Island Lighting Co. 690,000 1.3 1.4 National Fuel Gas Co. 357,000 1.0 12.6 N.Y. State Elec. and Gas 533,000 1.2 11.0 Niagara-Mohawk Power 850,000 1.0 11.6 Orange and Rockland 105,000 2.0 0.7 Elec. Co. Rochester Gas & Electric 184,000 1.8 28.1 Total (omitting double 3,500,000 1.6 8.2 counting) or Average 2.6:1 40.1:1 Ratio (highest/lowest)

Loans per Eligible Home (%/yr.) 0.14 0.05 0.16 0.02 0.12 0.14 0.12 0.01 0.51 0.13 51.0:1

NOTE: The New York Home Insulation and Conservation Program required regulated utilities in the state to offer reduced-rate loans (9–11 percent) to qualified customers receiving energy audits; the data are for 1978–1984 from New York State Public Service Commission (1985) .

This point is strikingly illustrated by a marketing experiment recently conducted as part of a shared savings program in Hennepin County, Minnesota (Miller and Ford, 1985). The county government contracted with a private company that agreed to install energy-saving equipment in homes in return for payment from the homeowners of a percentage of the value of energy saved over a five-year period. The program was marketed only by direct mail, with addressees randomly assigned to receive three forms of solicitation letters. One letter was sent on the company's letterhead with no mention of cooperation with the county; the second went out on company letterhead and mentioned the county's role; the third went out on county letterhead and was signed by the chairman of the county Board of Commissioners. The source of information had a remarkable effect on consumer interest:

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

43

requests for energy audits came from 6, 11, and 26 percent, respectively, of households receiving the three types of letters. After people had received audits, however, the form of the original solicitation had no further effect; about 29 percent of all the people who actually met company representatives signed contracts with the company. TABLE 8 Participation in the Bonneville Power Administration's Residential Weatherization Pilot Program Eligible Residential Audits per Eligible Homes Weatherized per Homes Weatherized per Utility Company Eligible Home (%/yr.) Audit (%) Eligible Home (%/yr.) A 3,164 7.0 45.0 2.4 B 1,933 8.2 88.5 7.3 C 1,900 10.8 88.1 9.5 D 1,875 10.5 33.3 3.5 E 5,000 4.0 60.0 2.4 F 944 23.2 28.1 6.5 9.6a 7.8 0.8a G 2,520a H 13,351 5.1 56.6 3.3 I 6,840 5.5 77.0 4.4 J 517 11.6 66.7 7.7 K 1,073 17.0 66.7 10.4 7.3a 55.4 4.0a Total or Average 39,317a Range (highest/lowest) 5.8:1 11.3/1 13.0/1 NOTE: The Weatherization Pilot Program, which operated for 2.5 years between 1981 and 1983, offered zero-interest loans, repayable when the house was sold, to single-family electric heating customers. Households receiving energy audits were offered free water heater insulation wraps, shower flow restrictors, and electric outlet gaskets. Data from Lerman, Bronfman, and Tonn (1983). aUtility G could not estimate the number of its 5,040 residential customers who used electric heat. The figures noted were calculated on the assumption that 50 percent heated electrically, a figure lower than that for any of the other utilities in the sample. The effect of this assumption is probably to overestimate the success of Utility G's program and to narrow the range of participation rates in the last column of the table.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

44

TABLE 9 Participation in the Bonneville Power Administration's Interim Residential Weatherization Program Eligible Residential Audits per Eligible Homes Weatherized per Homes Weatherized per Utility Company Customers Home (%/yr.) Audit (%) Eligible Home (%/yr.) A 62,047 11.8 61.0 7.3 B 5,056 14.2 82.9 11.8 C 99,994 23.2 57.9 13.3 D 3,500 23.1 83.4 19.3 90.9 1.4a E 2,853 1.6a F 10,865 12.1 83.7 10.2 G 267,000 2.4a 77.2 1.9a Total or Average 433,115 (audit) 9.1a 59.6 5.3a 445,479 (retrofit) Range (highest/lowest) 14.5:1 1.6:1 13.8:1 NOTE: The Bonneville Power Administration Interim Residential Weatherization Program offers a grant to participating homes based on expected energy savings and amounting , on the average, to 93 percent of the cost of installed Weatherization measures. The data cover 20 months in 1982–1983, from Lerman and Bronfman (1984). aUnder previous programs, Utilities E and G had audited 400 and 17,800 homes, respectively, and had weatherized 354 and 5,482, respectively. The noted calculations are based on appropriately reduced figures for the eligible populations.

Another recent study adds information about what may be responsible for initial responses to incentive programs. Mark Polich (1984) presented data collected by the state of Minnesota on energy audits in that state's RCS program. Although the program does not provide financial incentives, it is like most incentive programs in the United States in that its effectiveness depends on requests for energy audits. Minnesota had promulgated administrative rules for RCS that had directed utilities to subcontract with local auditors whenever possible, with the result that energy audits in Minnesota have been conducted in three different ways: using utility company employees, subcontracting with a private energy firm, and subcontracting with an existing community group. Table 10 summarizes the findings from the program. Utilities that subcontracted with a private firm cut costs and increased the rate of

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

45

requests for audits, while maintaining about the same quality of audits as utilities using their own personnel. Utilities that subcontracted with a community group cut costs further, and both the rate of requests and the quality of the audits improved. Not reflected in the table are differences in marketing strategy in the three types of programs. Despite a market research project that reportedly showed that response rates to audits in Minnesota might be increased tenfold by including shower-flow restrictors with audit offers, door-to-door marketing, and other aggressive promotional practices, only the community groups adopted such practices. And only the community groups contacted households after completing energy audits to encourage them to take the recommended conservation actions. TABLE 10 Characteristics of Energy Audits in the Minnesota Residential Conservation Service Program Audit Cost ($) Time Spent (hrs.) Auditor Performancea Response Rate (%) Organization Performing Audit 1 2 3 Utility Company 148 2–2.5 fair-good fair fair 3.6 Private Subcontractor 73 2 poor-good good fair 5.7 54 3 fair-exc. exc. good 14.7 Community Group as Subcontractor aRatings

of field observers of audits on quality of: (1) inspection of house and heating system, (2) interaction with homeowner, and (3) presentation of audit results. Data from Polich (1984).

Polich (1984) interpreted the results in terms of the incentives facing the programs' sponsors. He argued that most of the major utilities in Minnesota were aggressively marketing energy at the time and had little incentive other than customer relations and regulatory requirement to pursue conservation programs. Polich sees the community groups as motivated to enhance the welfare of community residents and to benefit the local economy by keeping energy dollars in the community. Whatever the groups' motives, the higher rate of audit requests in programs operated by community groups was probably due to some combination of aggressive marketing, a reputation gained by doing high-quality audits, and residents' trust in the groups.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

46

EFFECTIVENESS OF INCENTIVES IN THE LOW-INCOME HOUSING SECTOR Distributional effects are a major concern in evaluating conservation programs. Low-income households pay a much greater share of their income for energy than higher-income households (Cooper et al., 1983) and that share has been rising rapidly with increasing energy prices (Energy Information Administration, 1982). Moreover, lowincome housing has great potential for improvement because it is of below-average energy efficiency (see Table 11). Despite concern about weatherizing low-income housing, most residential conservation programs in the United States have disproportionately attracted higher-income households. This result is consistently reported in evaluations of home energy audit programs (Hirst, Berry, and Soderstrom, 1981) and of the more comprehensive programs offered through the Residential Conservation Service (U.S. Department of Energy, 1984; Hirst, 1984). However, these programs are not incentive programs. They primarily offer accurate and specific information to those who might invest in energy-efficient equipment; they do little to alleviate the shortage of investment capital among the owners and occupants of low-cost housing. Thus, there is reason to hope that incentive programs may be more successful at reaching the low-income households. In this section we examine data relevant to that hope. We review reports of participation by low-income and higher-income households in TABLE 11 Percentage of Homes Having Weatherization Features in 1980, by 1979 Family Income Family Income Weatherization Characteristic Poverty Level National Average All windows have storm windows 25 38 All doors have storm doors 23 32 All of ceiling is insulated 47 69 Ceiling is completely uninsulated 34 15 All walls are insulated 37 53 All walls are uninsulated 37 20 Some or all storm windows some or all storm doors, and ceiling 25 49 insulation 27 10 None of the above types of insulation Data from Energy Information Administration (1982) .

$35,000 or more 40 33 82 6 67 11 56 4

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

47

incentive programs available to both groups and reports of incentive programs aimed specifically at low-income or rental housing or at people in low-income communities. We give particular attention to the role of nonfinancial features of incentives, which may be critical for reaching low-income populations (see Gaskell and Pike, 1983; Hirst, Berry, and Soderstrom, 1981; Stern, Black, and Elworth, 1981). Low-Income Participation in Incentive Programs The largest financial incentive program in the United States, the federal conservation tax credit, has been used predominantly by upper-income households (Hirst et al., 1985). The limited use of tax credits by low-income households may be due to the fact that many of them do not pay enough tax to be able to claim a credit. In addition, most low-income households have no need to keep detailed tax records except to claim an energy credit and so are unlikely to do so. There are reasons to expect zero-interest loan programs to be more attractive than tax credits for low-income households. They offer the capital that low-income people especially lack, and they do not require recordkeeping. They often defer payment until resale of the home, when cash becomes available, or stabilize household cash flow by keeping the payments small enough to be covered by energy savings. Yet they have not been very attractive to low-income households. For example, the Pacific Gas and Electric Company found that only 2 percent of the households taking zero-interest loans in 1983 had incomes below 125 percent of federal poverty guidelines (Moulton, 1984). The Tennessee Valley Authority, which offers zero-interest loans to a population of which it defines 32 percent as low-income, gave only six percent of its loans to low-income customers in 1979. Through special efforts to reach that population, TVA substantially increased audit requests from low-income households from 7 percent of requests in 1979 to 21 percent in 1982 (Moulton, 1984), and the proportion of loans to lowincome households also increased, to 12 percent in 1982. However, the drop in participation from requesting audits to using loans indicates that low-income households are less attracted to loans than higher-income households, even when they know of the program, similarly, higher-income households were overrepresented among households taking out zero-interest loans under the Bonneville Power Administration's Weatherization Pilot Program (Hirst, Bronfman, et al., 1983) and Puget Sound Power and Light's loan program (McCutcheon, 1983). Some grant programs have had better results in the low-income housing sector, possibly because of the success of grants at encouraging investment among people who receive energy audits. The limited data show that low-income people are less likely than higher-income people to hear of a grant program, but that once aware of it, they are at least as likely to use it. In the Canadian Home Insulation Program, which offered a grant of up to 500 Canadian dollars to pay 60 percent of the cost of materials and labor for home weatherization, higher-income people were more likely to be aware of the program: the range was from 51 percent to 78 percent with increasing income

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

48

(Hickling-Partners, 1983). But unlike the experience of the TVA loan program, low-income people did not drop out disproportionately before deciding to invest. Among the 72 percent of the national sample who knew of the program, those who participated were lower on socioeconomic indicators than those who did not. Participation among people with a high school education was around 50 percent, compared with about 27 percent for people with college and university educations. Lower-to middle-income households participated at rates of 35 to 42 percent, compared with an average of 33 percent for the whole sample. Similarly, among households that requested energy audits in the Eugene (Oregon) Water and Electric Board's buy back program, those that used the program's grants did not have higher incomes, as is the usual experience in loan programs (Olsen and Fonseca, 1984).6 Incentive Programs for Low-Income Households The failure of many incentive programs to penetrate the low-income housing market has led some of the programs to increase their efforts. For example, after the Tennessee Valley Authority realized that only seven percent of the energy audits and six percent of the loans in its zero-interest loan program were going to lowincome households, it began using local community groups as outreach agents, distributing specially written promotional material by hand, and promoting energy audits with an offer of three free loaves of bread (Moulton, 1984). As noted above, the promotion effort raised the proportion of audit requests from low-income people to 21 percent after three years. The outreach efforts attracted the attention of low-income people, although their rate of investment still lagged, possibly because of their aversion to indebtedness. Table 12 summarizes the features and experiences of ten incentive programs aimed specifically at lowincome populations for which recent and reasonably detailed reports are available. The sample is unrepresentative of all such programs, but it includes many programs operated by groups that are making serious and explicit efforts to reach low-income populations and that are aware of what similar groups have done. Thus, the sample probably represents the “state of the art” in low-income incentive programs. A few characteristics stand out. All of them are consistent with other available evidence on how to reach low-income households. First, the programs offer very strong incentives: five of the programs offer free weatherization, while none of the programs available to a cross-section of the population does. Second, the programs rely heavily on community groups, usually in a cosponsorship role and, where details on marketing are available, always in a central marketing role. Third, the marketing efforts are labor-intensive, often including door-to-door

6Households

that used the grants had a median income of $22,400 compared with $25,000 for households that did not.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

49

canvasses, presentations at public meetings, and workshops held off the sponsors' premises. This practice is consistent with evidence that a strong outreach component is important for all residential conservation programs (e.g., U.S. General Accounting Office, 1981), especially those serving low-income households. Fourth, where the programs have had sufficient resources to target a full community, participation is often strong. Four programs, in Rochester, Memphis, Seattle, and a community in the Netherlands, had a mean participation rate of eight percent per year and a median rate of five percent per year even though they were aimed at segments of the housing market that have been consistently unresponsive in the past.7 The studies of these programs indicate that incentive programs can be made to reach the low-income population with at least the level of success that incentives have shown with the general population. Success seems to require strong incentives, a credible local sponsor, and a stable communication network in the community or intense marketing efforts relying on personal contact with potential clients. CONCLUSIONS The evidence on seemingly obvious facts about incentives is surprisingly inconclusive. Even the size of an incentive does not have a clear and strong relationship to consumers' willingness to use it: other sources of variation in the data are much larger. When the size and type of incentive are held constant, participation rates typically vary by a factor of ten or more. With so much money being spent on incentive programs, the need for further research is clear. On the basis of our previous work and the recent evidence, we believe that such research should emphasize issues of program design, marketing, and implementation, particularly: the credibility and motivation of the sponsoring organization; the use of credible local organizations in the marketing effort; emphasis in marketing on stimulating word-of-mouth communication; and efforts to make successes widely known to past and future clients. Chapter 4 outlines ways to assess the roles of such variables. Below, we summarize the substantive conclusions that can be drawn from available data. The evidence that larger incentives increase participation in conservation programs is surprisingly weak. The data on loan subsidies

7The highest rate of participation by far was in the one program outside the United States, which is consistent with findings about programs for the general public. The phenomenon in this case probably has much to do with the community, where two small, demographically stable developments were chosen for the program and where a consortium of public and private local institutions developed the program for its benefits to local employment as well as for energy savings (van der Linden and van Eijk, 1985).

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES 50

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES 51

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

THE EFFECTIVENESS OF RESIDENTIAL CONSERVATION INCENTIVES

52

of different sizes supports this conclusion, but not the data on grants and rebates of different sizes. With larger incentives, more households that have made contact with an incentive program (e.g., through an energy audit) seem to make investments. However, larger incentives do not seem to induce households to make initial contact with a program. Among programs offering very large incentives, differences in reaching their clienteles can account for tenfold variations in participation rates. The type of incentive makes a difference. There is some evidence that households prefer grants or rebates to loan subsidies of equal value. This conclusion depends, however, on the discount rate assumed when equating loans and grants. Preference for loans over grants varies across households. Low-income homeowners and households that are pessimistic about their financial futures tend to prefer grants and rebates, while higher-income households and people skilled in managing budgets tend to prefer loans. Programs that offer a choice of loans or grants may be able to attract people who would reject incentives if only one type were offered. Differences in program marketing and implementation are probably responsible for the widely disparate rates of participation in programs offering identical financial incentives. Potential clients are attracted to programs that have energy audits conducted by local community groups or other organizations that they trust and that have strong motives to make the program work. Aggressive marketing through word-of-mouth and other attentiongetting media increases participation. Low-income households can be reached by financial incentive programs. To do so requires a strong incentive, preferably a grant or rebate rather than a loan; implementation by a trusted organization; and a strong marketing effort that aims to reach people by word-of-mouth and through local social networks. Existing community groups have become the organizations of choice for marketing and often for managing low-income programs both in the United States and elsewhere.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS

53

CHAPTER 4 INFORMATION-BASED HOME RETROFIT PROGRAMS

In Chapter 1 we identified five general behavioral questions whose answers are important to the design and implementation of information-based conservation programs; in Chapter 2 we outlined the roles of six analytic methods in answering those questions. This chapter considers each of those questions in a more specific context, that of home retrofit programs that rely heavily on information, such as Residential Conservation Service (RCS) programs and financial incentive programs that begin with a home energy audit. We show how each question about information programs has been addressed in the past and outline ways each question could be addressed more comprehensively in the future development of such programs. HOW CAN A PROGRAM BE DESIGNED SO THAT THE INFORMATION IT OFFERS IS USED? In the United States, the most prominent conservation programs that rely on information use home energy audits to convey that information. For such programs, getting the information used has two aspects: increasing the proportion of eligible households that request energy audits and increasing the rate of retrofit activity among households that receive audits. Most audit-based conservation programs have been based on clear, if implicit, beliefs about what leads households to request energy audits and then to retrofit their homes. The original RCS regulations assumed that if consumers could get low-cost information that was accurate, specific to their own residences, and expressed in terms of economic payback, they would request that information. The regulations further assumed that people would retrofit their homes if an energy audit showed that a retrofit would have a net benefit. Thus, program design followed from some fairly straightforward assumptions about consumer behavior. However, some empirical knowledge about consumer behavior existed when RCS began, and more has been gained since. The available evidence shows the importance to energy use of principles of behavior in addition to long-term cost minimization (for a review, see Stern and Aronson, 1984). Consumer values such as comfort and esthetics influence people's choices. The opinions and

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS

54

experiences of even ill-informed friends and associates can affect choices more than the statistics of “experts.” Consumers sometimes act to express personal values in ways that have significant, if incidental, effects on their levels of energy consumption. And people often act as problem-avoiders, doing nothing to change their habits until a sudden change in energy prices or availability or some other external factor forces them to pay attention to energy use, and then acting without thorough consideration, in the hope that the energy problem will then stop interfering with the more important things in their lives. These influences on action help explain the ways people respond to energy information. People often do not seek information actively; rather, they use it when it is conveniently available and when something about it attracts their attention. People listen more closely to friends and neighbors than to mass media, and they are more likely to act on information that comes from an organization they trust. People can become committed to a major course of action if they begin with a small step, taken voluntarily. And because people's homes, motives, and life situations vary, they respond differently to the same information. Knowledge about how people use information suggests a strategy for designing information programs. In order to appeal to potential clients, a program should involve from the outset representatives of groups that understand the client populations, that are trusted, and that communicate regularly with the potential clients, preferably by word-of-mouth. This strategy has been used, explicitly at times, and with apparent success. For example, when the Bonneville Power Administration initiated its Hood River Conservation Project, a team of social scientists first identified the major social groups that would have to be represented if the program was to get needed community support (Keating and Flynn, 1984). The program also early established a community advisory group that would ensure that each of the major social groups was involved. Such local groups are valuable for planning programs so that they serve their clients well, for communicating dissatisfactions to program managers, for publicizing a program, and for increasing public confidence in it by making it more responsive to its clients. Marketing through community groups has had remarkable success. In Minnesota, the RCS program audited only 4 percent of the eligible homes in localities where energy audits were conducted by utility company personnel, but 15 percent in places where local community groups conducted the audits (Polich, 1984). Similarly, by using local groups and direct personal contact, the Tennessee Valley Authority increased the proportion of its audits going to low-income households from 6 to 21 percent (Moulton, 1984). Knowledge about communication processes has implications for the tactics of energy audit programs. As we detailed in a previous work (Stern and Aronson, 1984: Chapter 4), an energy auditor is more effective when he or she presents information in clear, understandable, and attention-getting ways. Such presentation implies, among other things, interacting with the householder; presenting information in the form of case studies, preferably of nearby homes; demonstrating energy loss

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS

55

vividly with “smoke sticks,” infrared scanners, or other techniques that show how and where heat is lost around windows or through walls; and involving the householder in hands-on activity. A conservation program could use before-and-after thermograms or energy-use feedback information to make energy savings visible to their participants and thus increase the program's credibility. It could offer free low-cost energy-saving devices, such as flow restrictors for shower heads or insulating wraps for water heaters, to induce households to take further action, following the principle of behavioral momentum. The one-stop shopping and consumer protection features that have at times been part of the RCS concept are also consistent with behavioral principles because they make conservation attractive to householders who wish to avoid problems associated with the home improvement industry. A number of local conservation programs have used combinations of the above tactics with great success, in local government programs (e.g., City of Santa Monica, 1985; Fitchburg Office of the Planning Coordinator, 1980), in utility programs (e.g., Moulton, 1984; Olsen and Cluett, 1979), and in programs operated by community groups (e.g., Freedberg and Schumm, 1984; Katz and Morgan, 1983). Several of the above programs have claimed much greater success reaching low-income populations than is usually found in information-based programs. In the most ambitious of these programs, in Hood River, Oregon (see Keating and Flynn, 1984; Peach et al., 1984) and in Santa Monica, California (see City of Santa Monica, 1985), behavioral principles and free installation of energy-conserving equipment are being combined an an effort to reach 100 percent of the local population. Santa Monica canvasses the city door-to-door, offering a brief energy audit and free installation of up to three low-cost energy-saving technologies. Over the first nine months, about one-third of all homes contacted, representing a socioeconomic cross-section of the community, accepted the program's audits, and energy-saving devices were installed in 97 percent of the homes. In the Hood River project, major conservation retrofits are offered free under a detailed marketing plan designed to overcome barriers of mistrust that exist even for a giveaway program. A community advisory council helps relay community reactions to the program managers. The response to these vigorous efforts offers lessons for the managers of other programs, especially because both the Hood River and Santa Monica programs include serious evaluation plans. Evaluations are an important element of the systematic analysis that is needed for program managers to learn what behavioral strategies and tactics will be useful in their particular conditions. Evaluations should emphasize process issues in order to identify the features of a program's implementation that are responsible for its outcomes (see below). Small field experiments can provide conclusive knowledge about how specific program elements work in home retrofit programs. In any large utility service area, a recommended program element can be made a part of the program in selected neighborhoods or towns, with other neighborhoods or towns serving as comparison groups (for more discussion of the problem of comparison groups, see below). A program element that looks promising in field trials in one area can be assessed for

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS

56

generalizability by trying it in other parts of the country where there may be reason to expect a different level of effectiveness. Such field trials should assess effects by recording the level of requests for energy audits and the rate of retrofit activity, by measuring energy efficiency more directly, and by assessing actual energy savings. (Energy savings are, in effect, the product of improved energy efficiency and occupant behavior; we discuss ways to separate these components in Chapter 6.) Small field trials need involve only a few hundred households, including control groups. Their results can be communicated to the operators of other home retrofit programs for their information and use. HOW CAN A PROGRAM BE DESIGNED TO SPREAD INFORMATION WIDELY? This question was not explicitly asked when RCS was designed. Rather, it was assumed that program clients get information one by one, from official sources (in RCS, from energy auditors). But it has been repeatedly shown that the adoption of innovations (which is what RCS encourages) is also influenced by word-of-mouth communication from friends and associates and by relevant personal experience, and evidence has been accumulating that adoption of energy-saving home retrofits fits the usual pattern. With word-of-mouth communication, information from an energy program can spread faster than energy audits can be accomplished. This possibility of contagion of information raises issues for program evaluation (see below). Behavioral research offers general guidance for spreading the information from home retrofit programs. It suggests, for example, that delivering energy audits to a group of neighbors can spread the word about retrofits (see Olsen and Cluett [1979] for an account of this practice in the Seattle City Light program). It suggests that information will spread faster if a retrofit is installed in the home of a family with many personal contacts in the community and if a convincing measure of the energy saved is made available to that family and to others. And it suggests that mentioning the experience of a neighbor in an energy audit can promote retrofit by encouraging program participants to speak to neighbors who have such experience. Small experimental trials conducted in the context of existing retrofit programs could readily evaluate such hypotheses. The results would be assessed by techniques of program evaluation (see below). HOW CAN THE EFFECTS OF A PROGRAM BE FORECAST? The initial forecasts of the effects of RCS were based on very limited data or theory. Initially, President Carter set an arbitrary expectation that the program would reach 90 percent of all residential buildings within five years. In its 1979 regulatory analysis of the program, DOE estimated 35 percent penetration within five years, based on an extrapolation of the one-year response rate to a few existing free energy audit programs. It has been argued (Glazer, 1984) that a

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS

57

more accurate forecast could have been made at that time, by using the annual response rate to existing audit programs that charged $15 (the legal maximum charge under RCS). However, forecasting participation rates is more difficult than this implies. Even when the costs of energy audits and other financial features are the same in different programs, rates of audit requests often vary by a factor of five or more (see Chapter 3). Forecasting energy savings from a retrofit program is even more difficult than forecasting participation. The simplest approach involves multiplying an estimate of participation by an estimate of the average energy savings for each participating residence. But this method of estimation does not take into account that the existence of a retrofit program may have effects on nonparticipants: such an effect might occur by direct communication between participants and nonparticipants, through heightened publicity in local mass media about the importance of retrofit, or in other ways. And program participants vary tremendously in what they do with audit information. The experience of loan and grant programs, which keep records of incentive payments, give an indication of the proportion of audit recipients who follow the auditors' recommendations. The percentages may vary by a factor of ten or more between utilities offering the same incentives to similar customers (see Chapter 3). Moreover, it is not safe to infer energy savings from records of actions taken. Such predictions have been in error by at least 50 percent more than half the time (e.g., Goldman, 1984; Hirst and Goeltz, 1984). Much of the variance is due to the behavior of installers and building occupants, though it is not clear how much. Some methods for estimating overall energy savings from programs are discussed in the next section, and the problem of assessing the relationship between retrofit activity and energy savings is addressed in Chapter 6. HOW CAN THE EFFECTS OF A PROGRAM BE ASSESSED ACCURATELY? In order to assess program effects accurately, it is necessary to systematically evaluate the outcomes of conservation programs to see what changes they produce in energy efficiency and the energy consumption of the program participants' buildings. Several evaluations of individual RCS programs, collections of programs at the state level, and conservation incentive programs relying on energy audits have calculated participation rates and estimated the energy saved, its economic value, and the cost/benefit ratios of the programs (for reviews, see U.S. Department of Energy, 1984; Hirst, 1984). DOE has also offered national conclusions about RCS from a set of evaluations within states. Because of the uneven quality and noncomparability of the available analyses, it is premature to draw national conclusions. This caveat is particularly true for the national evaluation of RCS (U.S. Department of Energy, 1984) because of problems in the component statewide analyses and because of the shakiness of the assumptions used to draw national conclusions from them. The national evaluation is based on completed

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS

58

evaluations in only a few states and, because the methodologies and assumptions used in those state evaluations are different in important ways, the evaluations are nearly impossible to compare. For example, some evaluations use utility bills as the measure of energy savings, while others impute energy savings from engineering calculations based on consumers' self-reports of the energy-saving measures they have taken. Still other evaluations merely report the claimed cost of the energy-saving improvements. Some evaluations estimate energy savings by subtracting postaudit consumption from preaudit consumption, while other evaluations match respondents or use various statistical techniques to attempt to control for such variables as conservation by nonparticipants, changes in weather, the ages and educations of household members, energy attitudes, differences in the houses, and household incomes. It is not always clear from the reports which factors were controlled, and there has not been a systematic attempt to learn from the evaluations which of the factors make an important difference. In addition to these noncomparable factors, the DOE evaluation is based on the following assumptions: that the state evaluations are equally valid; that a system of weighting the results reported from a few states accurately represents a national total; that increased comfort in retrofitted homes has zero benefit in cost/benefit calculations; that the existence of RCS has no effect on energy use except among households receiving audits; and that RCS has zero benefit to utility companies or governments. Some of these assumptions are untrue; the rest are implausible. The DOE evaluation also uses a variety of assumptions to impute values for energy savings in states for which no data exist. The first step to improved program evaluations is adequate data. Given the present state of knowledge, data should be collected on both actual energy use and on measures adopted because there is as yet no clear basis for choosing which of these is the better index of total program effects. To tell whether the differences between these indices is due to faulty installation of retrofits, overoptimistic engineering estimates, or occupants' choosing increased comfort levels, data on indoor temperature settings and occupant comfort should also be collected. For such data to be accurate, longitudinal studies and actual measurement of temperature will be necessary. (These measurement problems are discussed in more detail in Chapter 6.) Data on climate, house size, and household sociodemographic variables are also important for assessing the degree to which participant and nonparticipant groups are different. Econometric modeling techniques have sometimes been used to hold constant the relationships of such variables to energy use and estimate the net effect of a retrofit program (e.g., Peat, Marwick, and Partners, 1983). In addition to adequate data, better methodology is also necessary for improved program evaluation. Two key unanswered questions illustrate this need. First, are energy audits used mainly by people who have already decided to retrofit their homes? Even among households matched on education, income, general energy attitudes, and so forth, those that have just decided to retrofit are probably more likely to

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS

59

use energy audit programs than others. If so, comparing participants and nonparticipants would overestimate the effect of a program by giving it credit for decisions to retrofit that were made independently of the program. Second, does the presence of a retrofit program induce energy savings among nonparticipants, possibly by increasing energy consciousness or through word-of-mouth influence of participants on nonparticipants (contagion)? If so, comparisons between participants and nonparticipants would underestimate a program's effect, especially when a local group of nonparticipants, who may have heard of the program, is used for comparison purposes in an evaluation study. There is no sure way to answer either question by the research methods of surveying participants and nonparticipants, checking utility bills, and modeling household decisions. More sophisticated approaches are needed. To hold constant the decision to retrofit, one must find a group of households similar in motivation to those that have decided to retrofit but that have not participated in the program. One such comparison group would consist of households that participate in the program in the future. Another would consist of households that have requested but not yet received audits. A program that has a waiting list for energy audits can randomly assign some participants to get audits immediately and use those on the waiting list as a comparison group. To answer the question about contagion, it would be necessary to study a comparison group for whom the program in question is not available, such as households in areas where the program has not been implemented or where a different program exists. It may be easier to study contagion effects when a program aims to promote adoption of a particular piece of equipment, such as a clock thermostat or a water-heater wrap, for which sales can be monitored before and after the program. None of these methods offers perfect control for all the plausible alternative explanations of observed results. Therefore, the best way to improve understanding probably involves a combination of methods. If different methods yield similar results under similar conditions, each gains credibility. Table 13 summarizes some alternative comparison groups for evaluations of conservation programs and the advantages and disadvantages of each. TO WHAT CAN PROGRAM EFFECTS BE ATTRIBUTED? The question of attributing effects is the province of evaluation research that focuses on the processes by which programs are marketed and implemented. Few evaluations have paid close attention to the effects of particular features of conservation programs, their administration, or their participants, so few evaluations have been useful for attributing the effects of programs to their elements. Most evaluations have aimed to judge programs as a whole against criteria of energy savings or cost-effectiveness, an approach that implicitly and incorrectly assumes that program implementation does not matter. What is needed is an understanding of what makes some versions of retrofit programs effective so that the successes can be duplicated.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS 60

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS 61

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS

62

Some evaluations have looked within programs to identify the causes of their success or failure. Among the factors that have been identified are marketing efforts, strength of consumer protection guarantees, the credibility of the sponsoring organization and its commitment to the program, and efforts to simplify consumer decision making (see Chapter 3). These variables have been identified by asking people for their opinions or judgments— sometimes with open-ended interviews of program participants or managers, sometimes with carefully constructed survey instruments. Surveys are a useful way to gauge the effects of program features, especially in the absence of clear expectations about which program features are most important. But for definitive knowledge about the effects of particular program elements or combinations of elements, it is not sufficient to rely on the impressions of program officials or the self-reports of participants. It is necessary to treat the program elements explicitly as experiments. Ideally, promising program features would be identified from surveys, previous process evaluations, or other exploratory methods. They could then be offered as alternatives to existing practice in ongoing programs. Participants could be randomly selected to receive either the experimental or control program element, and the effects could be assessed in the program's ongoing evaluation research. Two recent examples of the use of controlled experiments involve a test of an enhanced informational component of a time-of-use electricity pricing program (Heberlein and Baumgartner, 1985) and a comparison of marketing techniques for a shared-savings program for residential retrofits (Miller and Ford, 1985). Surveys of participants and program officials can give useful supplementary information about reactions to the program element in question, and the outcome portion of the evaluation would assess the effects on retrofit activity and energy consumption. RECOMMENDATIONS Behavioral research has identified promising strategies and program elements for information-based conservation programs, and available evidence supports the validity of the behavioral approach. However, from the viewpoint of managers who want to improve participation in their programs, available knowledge is too scanty to offer reliable and specific advice. This situation can be remedied by more thorough efforts to assess behavioral factors in program evaluation research and to measure their effects in field trials. We offer the following specific recommendations for research on information-based home retrofit programs: 1. Continue to implement and carefully evaluate ambitious programs that aim to install all economically justifiable residential retrofits in particular areas (e.g., Hood River and Santa Monica). Such programs are laboratories for learning about the effects of various methods of overcoming resistance to home retrofits. These ambitious programs are in particular need of careful process evaluation to assess the effects

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

INFORMATION-BASED HOME RETROFIT PROGRAMS

63

of particular marketing and dissemination techniques. We emphasize that resources for collecting and analyzing data on process variables must be reserved for that purpose: too often, past programs that could have provided important lessons for other program managers have not done so because funds intended for evaluation have been used for other purposes as the program neared its end. 2. Conduct and carefully evaluate small-scale programs aimed at low-income housing. Existing information-based programs have failed to reach proportionally as many low-income households as other households, and indications are that more aggressive marketing efforts are needed to achieve success. Programs that have claimed success in the low-income housing sector have usually applied behavioral principles, but because of insufficient funds for evaluating the programs, the effects of particular program elements cannot be separated, so the lessons of their experience are not clear. Evaluation of these efforts is important because what works in a general populations has not worked for the low-income population in the past and because low-income programs are among those that have made the most extensive use of behavioral approaches. 3. Conduct small-scale experimental field trials of promising program elements within ongoing conservation programs. A number of promising program elements can be tested rigorously under field conditions simply by making them available at random to a portion of a program's clients. Such experiments are quite inexpensive when included in an ongoing program evaluation. They can provide strong evidence about the effectiveness of an intervention that may generalize to other similar programs. Experiments should be conducted with program elements in three important areas: Marketing. Experiments can determine, for example, how much participation increases when potential clients are contacted door-to-door or when different kinds of organizations perform the outreach tasks. b. Audit techniques. Programs that train auditors in communication skills and in delivering information in a vivid and personalized manner should be tested experimentally to quantify the effects of these approaches and determine which ones best justify their costs. c. Postaudit follow-up. Small-scale experiments can test the effects on participation by telephoning households that have received energy audits to ask if they plan to retrofit and by sending participants information documenting their and their neighbors' energy savings and the improved comfort reported by people in the community as a result of having participated in the program. a.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

64

CHAPTER 5 HOME ENERGY RATINGS

Building technology is too complex for the average home purchaser to be able to make an informed decision about the energy costs of operating different homes. Therefore, home energy ratings have been advocated to help consumers, as well as builders and lenders, take energy costs more fully into account in their decisions. This committee agrees. After careful study of the factors affecting energy-related decisions by individuals and organizations, we recommended that “[f]ederal or private agencies…develop simple, understandable indices of energy efficiency, comparable to miles-per-gallon, for…building shells” and that “labeling, rating, and certification programs should be supported to ensure that indices of energy efficiency come into common use” (Stern and Aronson, 1984: 197, 198). This chapter addresses some of the problems involved in developing such rating programs and getting them used. Research on energy ratings has so far focused mainly on developing ratings that meet three major technical criteria: reliability, validity, and accuracy. A reliable rating system is one that gives the same building the same rating regardless of who does the rating. A valid rating system is one in which the higher a building's rating for energy efficiency, the less energy it in fact uses to provide given levels of heat and cooling (holding constant climate, occupant behavior, and other external factors). An accurate rating system is one in which a building's rating is borne out by measured energy use: if a building's energy use is rated to be double that of another building or double what it would be after retrofit, that predicted use will be the same as the measured energy use (when external factors are held constant). However, reliability, validity, and accuracy do not necessarily add up to success for an energy rating system. In fact, the chief barriers to success at present are not technical. According to a recent study of six existing rating systems by the Consumer Energy Council of America (McCarty and Willner, 1985), the most significant barriers they face are funding, inadequate involvement among some sponsor groups, and low levels of consumer awareness. It is essential to keep in mind that the goal of a rating is to affect the decisions of home purchasers, builders, real estate agents, primary and secondary mortgage lenders, and other intended audiences so that energy efficiency is reflected in

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

65

a home's market value or so that new and retrofitted homes reach higher standards of energy efficiency. Technical adequacy is important, but it is equally important that a rating system be understandable to its intended users, available when needed for decision making, and credible. This chapter addresses three of the five major behavioral questions that arise in informational programs (see Chapter 1), focusing primarily on issues of designing programs to diffuse information and get it used and also on the issue of program evaluation. It is premature to address the remaining questions—about forecasting the effects of programs and about attributing these effects to one cause or another—until careful evaluations have been done. There is little direct knowledge about how design and implementation aspects of home energy rating systems affect whether a system is used. Over 40 home energy rating or certification systems of various types were in operation in the United States by 1982 (Hendrickson, Garrett-Price, and Williams, 1982); however, few if any of these were designed on the basis of systematic analysis, and there has been little or no careful evaluation of their effects. Since 1982 the situation has improved only a little, but enough is known about the context of energy ratings to identify the critical questions in designing and implementing energy ratings, make some educated guesses about possible answers, and offer ways to check the accuracy of the guesses. This chapter begins by discussing the characteristics of a home energy rating system that would make it ideal for its various users. It then discusses some major issues in designing and implementing home energy ratings and offers suggestions for research and programs that can move home energy ratings toward wider use. Design questions concern characteristics of the ratings themselves that can make them more or less understandable, interesting, meaningful, or relevant to potential users. Implementation questions concern ways of delivering ratings to their potential users. Although it may seem to make sense to think about implementing a program only after its design is clear, it makes as much sense to reverse the order: a rating system designed so that it is irrelevant to the organizations that will implement it is destined for failure. CHARACTERISTICS OF AN IDEAL HOME ENERGY RATING SYSTEM Home energy rating systems can have many users and many uses. Thus, an ideal rating system must be intelligible to home builders, primary and secondary mortgage lenders, appraisers, real estate agents, retrofit contractors, and the buyers, sellers, and occupants of homes. It must be relevant to decisions about building design, financing, retrofitting, advertising, and purchase. And it must be applicable to both new and existing homes. Although it is probably impossible to design and implement a system with all these characteristics, we offer as a guide to what may be sacrificed in terms of a program's acceptance and usefulness as a result of technical decisions. Based on our earlier work on the way individuals and organizations use energy information, we offer the

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

66

following list of characteristics of a home energy rating system that would be ideal from the users' perspectives: (1) it is simple; (2) it is expressed in familiar units; (3) it it easy for the user to verify; (4) it is readily translatable into cost; (5) it can provide meaningful regular (e.g., monthly) feedback to indicate changes in the energy intensity of the home; and (6) it allows the user to examine either the efficiency of the building's construction, the effects of occupant behavior, or both, as desired. The last two characteristics are relevant only if the system will be used by building occupants; they are not necessary in a rating designed only to influence purchases. The obverse of this point is that a rating system that is not designed to give feedback or take behavior into account is much less useful as a stimulus to retrofit existing buildings. This list of ideal characteristics sets the context for our discussion. QUESTIONS ABOUT DESIGNING RATINGS In What Units Should a Rating be Presented? Home energy ratings have been expressed in energy units, in dollars, and on various arbitrary scales. Energy Units From a technical standpoint, energy units have a clear advantage: energy use is what is to be rated. They also have a disadvantage: a difficult decision must be made because different fuels are measured in different units and because there might be debate about what constant should be used to compare electricity with fossil fuels. For example, if all homes are rated for energy use in kilowatt-hour equivalents, the ratings will confuse many purchasers of gas- or oil-heated homes. Behaviorally, there is no advantage to using energy units: people are not likely to understand them. People understand the meaning of miles-per-gallon because both miles and gallons are familiar units and because a mile of travel is an understandable index of what gasoline produces for consumers. But the units of measure for household energy are not so intuitively meaningful. As a result, people generally think of household energy use in dollars per month, weeks between oil deliveries, or other budget-based units (Kempton and Montgomery, 1982). There is no evidence that householders readily understand information presented in Btus per degree-day, therms per winter, or other physical units, and little reason to believe that bankers or builders are much different. Home energy ratings presented in energy units are more likely to confuse people than enlighten them.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

67

Dollars Rating homes in dollars of energy cost, or dollars per square foot, has the advantages of familiarity, relevance to purchase decisions, and comparability across homes. A representative of the Federal National Mortgage Association (Fannie Mae) told Pennsylvania researchers that for the needs of lenders, it was important to have estimates of costs of energy conservation investments and savings in terms of a home's operating costs (Gallagher and Desmond, 1984). Such information is relevant to decisions about whether to raise a borrower's debt limit to allow a mortgage on an energy-efficient home. Unfortunately, however, there are technical problems with ratings in dollars. A determination must be made of whether a rating should use current energy prices or expected future prices, and if the latter, what estimates should be used. For example, if a mortgage lender takes future energy costs into account in setting debt limits, the viability of the mortgages will be affected by how well the energy rating system anticipates the price of fuel. Also, a meaningful dollar rating must assume constant climate and occupant behavior. Such ratings may mislead householders who expect to pay the rated energy cost in the first winter of occupancy. Users would have to be educated about what they can and cannot expect from a dollar rating. Arbitrary Scales Ratings can be given on a binary scale (e.g., pass-fail) or in other arbitrary scales of varying sophistication. An advantage of arbitrary ratings is that, unlike dollar ratings, they are unaffected by changes in fuel prices, weather, and occupant behavior. But their arbitrariness can be a disadvantage unless an effort is made to give them meaning, possibly by “anchoring” them to certain meaningful values. We know of only one empirical effort to examine user response to alternative units for use in home energy ratings. In a pilot project in Massachusetts, a home rating system was developed for use in the RCS energy audit program (Ackerman et al., 1983). The researchers discussed the concept of ratings with selected lenders, appraisers, and real estate agents, both before and after developing eight possible formats for a rating system. The formats were also the subject of two focused group discussions among local homeowners. The system chosen used a 0–10 scale in which zero represented a home with no energy-saving features and 10 represented a home with no energy bills (Ackerman et al., 1983). All the groups, as well as the organization doing the rating, found the “anchored” 0–10 scale, combined with estimated annual energy costs, understandable and acceptable. The Massachusetts 0–10 system was constructed with users in mind and was accepted by users in early field testing. For this reason, it is worthy of further test and adaptation. Other rating scales may prove equally acceptable. One of these is a five-star rating system developed by Western Resources Institute (Luboff, 1983), which also arose from an effort to seek consensus among segments of the building

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

68

industry. Both these approaches, which have the advantage of simplicity, have had good user response in field trials, and both deserve systematic evaluation under field conditions. Groups interested in developing new rating systems can learn from the methods used to develop them even if they do not choose to use either of them. How Much Precision Should a Home Energy Rating Offer? Is it better to use a binary (certification) scale or one with more levels presented? If the latter, how fine should the detail be? One principle to keep in mind is that a rating should not offer precision that exceeds its accuracy. If a rating is only accurate within 10 percent, presenting it to two significant figures can undermine its credibility; if it is accurate to within 20 percent, it should not be presented as anything more precise than a five-point scale. A binary scale (a pass/fail or certification approach), such as utilities sometimes use when they certify “energy-efficient homes,” has the advantage of simplicity. Certificates are meaningful to builders, realtors, and others who can use the rating as an advertising point. They may be more useful in the new home industry than in influencing the home resale market. The ability of users to take advantage of a more detailed rating system depends on whether they can relate different meanings to different ratings. In a category system (e.g., poor, good, very good, excellent) the number of categories should be kept small, probably not more than five. This sort of rating probably has meaning for comparing homes, but it is likely to be less useful for deciding whether to invest in a retrofit because the categories are so coarse. Numerical ratings add further detail, but can be confusing if the meaning is unclear. Ratings in dollars are easy for people to interpret, although they can lead to unrealistic expectations of predictive accuracy because homeowners will compare ratings with actual energy bills, which are affected by weather and occupant behavior. Arbitrary scales promise less, but tend to be less meaningful. The notion of anchoring them to some understandable values, as done in the Massachusetts project, seems wise. Decisions about the precision of a rating system should depend on its goals and on what is meaningful to the intended users. Binary scales may be appropriate for certifying new buildings because the dividing line can be set slightly above ordinary building practice to provide an incentive for improving energy efficiency. More detail is appropriate, however, when a program aims to influence retrofits of existing houses before resale. There are usually many effective retrofits that can be made in an old home that would still leave it below an energyefficiency criterion set for new buildings. Arbitrary numerical scales should probably be anchored to points that have some meaning for all the intended users. But these conclusions are all tentative. Prototype scales should be presented to samples of the user populations to get their reactions before a rating system in put into use.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

69

Should a Rating Explicitly Estimate the Effects of Retrofits? The Massachusetts home energy rating system experiment gave each home two ratings: as audited and as they would be after taking the energy efficiency measures recommended by RCS. It also offered estimates of average annual energy costs with and without the retrofits. Because this approach makes it easy for the seller or buyer of an existing home to judge the likely cost and savings from recommended retrofits, it makes good sense when a rating system is intended to encourage retrofits of existing housing before resale and to increase the market value of energy-efficient homes. To remain credible, though, such estimates must be accurate, which requires a full energy audit of the home. Although such an audit normally costs $100–150, an estimate of the effect of retrofit can be done for an additional $15 per home when a rating is attached to an RCS energy audit (McCarty and willner, 1985), and for $25–35 when included in a mortgage lender's appraisal process (Hoskin, 1983). Accurate estimates from ratings can be useful, but the additional information may confuse people who have no interest in undertaking retrofits. This possibility should be addressed empirically in pilot projects with rating systems. What Energy Uses Should a Rating Reflect? Ratings may attempt to estimate only energy use for heating, or energy for heating and cooling, or a larger proportion of home energy use. It is reasonable to expect that the interest of potential users in one or another package of information depends on climate, but there are no relevant data on this point. As more energy uses and more discretionary appliances are subsumed in an energy rating, it becomes more difficult to produce an accurate rating. The potential for confusion among users probably also increases, especially when ratings are used to compare homes with different appliances included. For example, a home with a central heat pump air conditioner may use more energy than a home that is only partly cooled by room air conditioners, even though it uses the energy more efficiently. It will probably be difficult to express these differences simply and clearly in a rating. If an energy rating is to encompass more than space and water heating, careful thought, technical research, and assessment of user reactions should precede introduction of the rating. QUESTIONS ABOUT IMPLEMENTING RATING PROGRAMS Technically adequate ratings have no effect unless they are delivered to users in a way that encourages their use in decision making. Careful design of ratings is necessary, but some institutional questions must also be addressed.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

70

Who Should Rate Homes? Experience with other energy programs suggest that a rating system will be implemented best when the organization responsible can do the job at low cost; has the appropriate knowledge, sufficient resources, and the motivation to do the job well; and is credible to the user communities. These considerations do not point unequivocally to a single most appropriate institution, but they do suggest some approaches to try in pilot projects. Cost considerations suggest that ratings be implemented as part of other activities, such as those of home energy auditors, real estate appraisers, or “house doctors.” Since the groups that carry out such activities vary in knowledge and resources, some could provide good energy raters only if a reliable, valid, accurate, and sufficiently simple rating system were developed by an expert group for use in the routine of energy auditing or appraisal. For example, in the Western Resources Institute's program (Luboff, 1983), trained real estate appraisers use a heat loss methodology already developed by the Bonneville Power Administration. To ensure credibility, two strategies seem promising: to have the rating conducted by a credible institution or to get the rating system accepted by a credible institution. It is possible to tell which organizations would be credible sponsors by asking for the reactions of potential users to a list of possible rating organizations. But a more promising strategy involves building credibility into the system. The Western Resources Institute's rating system seems to have accomplished this by convincing local mortgage lenders to accept Bonneville Power's heat loss methodology. The banks pay their appraisers to calculate energy ratings using the methodology, and they offer larger mortgage loans for purchasers of homes that receive high enough ratings. The banks' acceptance creates instant credibility for the system among home purchasers, and people considering selling their homes are likely to feel an incentive to retrofit because an energy-efficient home will be financially attractive to a larger number of buyers. A sponsor's motivation should also be considered. House doctors or other purveyors of retrofit services have a vested interest in making a house look more energy efficient after they perform their service than before, so their ratings may be suspect. Real estate appraisers are motivated to accurately assess market prices, so may be good candidates if a rating system can be made convenient for them. However, unless energy ratings are used by banks in their lending decisions or appraisers conclude that energy-efficient homes are worth more, appraisers may be reluctant to conduct energy ratings. Home energy auditors can rate homes conveniently. Their motivation to rate accurately depends on the context of the audit. Auditors for a utility company that is motivated to conserve energy, whether because of a prospect of supply shortage or because of regulatory pressure, would have a motive to produce accurate ratings. Utilities that are not motivated to conserve energy would not be motivated to document the need for it. An electric utility with a large overcapacity would be unlikely to offer ratings, and if it did, its ratings might not be

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

71

credible. Local nonprofit groups have sometimes produced more thorough energy audits than utilities at lower cost (Polich, 1984), probably reflecting in part a stronger motivation to cut energy costs for consumers. Such groups might be effective raters if a rating system were designed to be attached to energy audits. The problem of who should rate homes becomes most difficult when the organizations that have the motivation and credibility lack resources or expertise. In some areas, utilities have low motivation or low credibility, but more credible and motivated groups, such as community organizations or local government, lack resources and expertise. The RCS experience shows that collaborations that link the resources of utilities or state government with the motivation and credibility of local organizations can be effective (Cowell and Rebitzer, 1984; Polich, 1984; Stern and Aronson, 1984). What are the Key Institutions for Getting a Rating System Accepted? Home energy rating systems can become effective in various ways: they can be used directly by home purchasers in making decisions; homeowners, developers, and real estate agents can use them to help advertise energy-efficient homes; they can add value to energy-efficient homes if used by appraisers; they can make homes salable at higher prices if bankers use them in calculating total cost of ownership and in setting mortgage limits; and operators of home retrofit programs might use them to market their services. In short, rating systems operate in a complex institutional environment, in which it is not immediately clear whether any particular institution's acceptance is the key. Institutional acceptance, however is a central question and a major barrier to the acceptance of rating systems (McCarty and Willner, 1985). We are aware of only two studies that have directly asked for reactions to energy ratings from major institutions in the building industry. A study in Massachusetts involved discussions with primary and secondary mortgage lenders, appraisers, real estate agents, utility representatives, and homeowners before and after implementation of a pilot home energy rating system (Ackerman et al., 1983). A more recent study in Pennsylvania surveyed homeowners and had discussions with the major industry groups before implementing a “home energy scorecard” (Gallagher and Desmond, 1984). This limited evidence and the experience of the Western Resources Institute rating system suggest that secondary mortgage lenders are a keystone of the complex institutional arrangement. Several banks were willing to use ratings in mortgage decisions only if such action would not jeopardize the sale of the mortgage to a secondary lender. Thus, some would raise debt limits to qualify buyers for mortgages on energy-efficient homes only if secondary lenders would accept that judgment in repurchasing the mortgages. It proved important in Massachusetts that secondary lenders were willing to change a previous policy and allow retrofit loans to be included in first mortgages before a retrofit is complete. In the Western Resources Institute program, acceptance of a

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

72

particular rating system by secondary lenders was essential to primary lenders' acceptance of the ratings. Once the primary lenders accepted the rating system it was a fairly easy step to include a rating in the appraisal process. The involvement of secondary mortgage lenders can create incentives for other actors in the housing market and can make home energy ratings work without any effort by home buyers. The Massachusetts project was approached by a nonprofit building corporation to rate some new homes being constructed for low-income buyers; the builder had learned that the local bank would allow higher debt-to-income ratios for borrowers if the homes had high ratings. Thus, an energy-efficient home would become affordable for people who otherwise would have remained in less-efficient, older housing. In this way the energy efficiency of the housing stock improves without the home purchaser needing to consider energy costs explicitly. Real estate agents have been reluctant to take the lead on home energy ratings. In Pennsylvania, where a state survey found that 85 percent of residents believed that energy efficiency would be an important consideration in selecting their next home and 90 percent felt a home energy labeling program would encourage people to select energy efficient homes, real estate brokers believed that the customers did not want ratings (Gallagher and Desmond, 1984). In Massachusetts, realtors raised concerns about their possible liability for information in the ratings. The Massachusetts realtors wanted to follow the lenders. Even after the pilot program began, they wanted assurances that the banks were committed to it. Real estate appraisers often follow the market as they see it. In Massachusetts, some appraisers did not believe that energy efficiency affected home values (Ackerman et al., 1983). Even those who supported ratings did not want to do the rating themselves because they believed they could not recoup the cost. In Pennsylvania two years later, appraisers were very positive about rating systems: many reported that they were being asked to appraise the value of energy conservation features of homes and were glad to be offered a ready tool for doing the job (Gallagher and Desmond, 1984). The experience of Western Resources Institute shows that appraisers follow the desires of their customers, the banks, as well as the housing market. Less information is available on the reactions of utility representatives, trade associations, and builders. In both Massachusetts and Pennsylvania, the utilities were concerned mostly with technical points in the rating calculations. The Pennsylvania report concluded that it was important to involve these groups early because it improved the rating system and gained the acceptance of groups whose opposition might be influential. The importance of possible utility company opposition depends on the implementation of the rating system: opposition is critical if a program is to be implemented by utility-run RCS programs but not if it is part of the home appraisal process.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

73

What Other Institutional Arrangements Might Strengthen a Rating System? We have noted that home energy ratings exist in a complex institutional environment: this suggests that yet other parts of that environment may make the difference between success and failure for rating systems. For example, a rating system can make local building codes or standards for energy-efficient construction more feasible. If a rating system is incorporated in building codes, it could be implemented and funded by local governments as part of the process of granting building permits. Or, if the accuracy of a rating system is backed by insurance, the organizations responsible for conducting or using the system could stand very firmly behind their ratings. Energy service companies now take out insurance to back their guarantees of low levels of energy use; builders can do the same. The cost of the insurance would depend on the record of the energy service company or the builder in achieving promised savings. A rating system with proven accuracy in the aggregate might lower the cost of the insurance and thereby improve the position of energy service companies in the home retrofit market and of energy-efficient builders in the new home market. DEVELOPING EFFECTIVE HOME ENERGY RATING SYSTEMS It is not possible to offer precise prescriptions for a home energy rating system. Just like a new technology, an energy rating system requires research, development, and demonstration before an effective model is widely accepted. Rating systems can be based on relevant technical and behavioral knowledge and refined by testing successive approaches in the physical and social reality in which they will operate. This point was discussed generically in Chapter 2; this section offers suggestions on which approaches are now ready for testing and on how the testing might be conducted. What to Test Our suggestions are confined to issues of rating design and implementation. In choosing this focus, however, we do not wish to suggest that further technical research and development on ratings is unnecessary nor to overlook the fact that design and implementation issues are intertwined with the technical quality of ratings. Our comments assume that rating procedures exist or will soon be developed that can produce ratings accurate enough to justify a numerical rating of considerably more detail than a five-point category system. The five-star system developed by Western Resources Institute deserves careful and quantitative evaluation as it is now operating and as it might be adopted in additional communities. This system has been among the most successful of existing rating systems and has already had a measurable effect on the approval rates of mortgage loans by

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

74

banks (Hoskin, 1983). An evaluation should assess its effects on home sales and home retrofits—even though the program is aimed only at the former—in order to make comparisons possible with other rating systems. Evaluation studies should also be conducted on programs modeled on the Department of Energy's Massachusetts Home Energy Rating System Project (Ackerman et al., 1983). Evaluation of the original program was not carried far enough to assess the effects on retrofits or home sales, and a thorough evaluation of such a project is needed. We believe that the screening of design alternatives that was done in Massachusetts, which selected a 0–10 scale anchored by “no energy improvements” and “no energy bills,” has enough validity to serve as a basis for future pilot studies. It is also worth expressing ratings in annual heating costs and these ratings should be tested both with and without a 0–10 rating scale. It is worth testing both the dollar-based scale and the arbitrary scale in home energy audits, with and without estimates of the costs and savings from recommended retrofits. It is advisable to be gin with pilot studies in which householders are randomly offered different packages of information from the above alternatives. When the field of possible rating systems is narrowed, field tests should use only one rating design per housing market because using several designs at once will confuse real estate agents, lenders, and others who will see ratings of many different homes. In ratings delivered with energy audits, the sponsor of the audit will make a difference. The choice of sponsor should be made to suit the location of the study and should take into account the credibility, motivation, resources, and expertise of candidate groups. Marketing of rating systems is also critical to implementation. Thus, it is desirable to fit a rating process into the routine of a local organization that already has a market for its services. Real estate appraisers are one example; a successful energy auditing program is another. How to Conduct a Test One clear lesson from the experience with rating systems and other energy programs to date is that all the relevant actors should be involved from the start. For a home rating system, this may include local officials, bankers, builders, appraisers, real estate agents, utility companies, and home buyers, sellers, and occupants. Even if it seems clear that a rating should have a particular design, it is important to get the reactions of the target groups, which may bring to light important local conditions that escaped the analysts who drafted the project plan. For example, information in a rating system that is valued by people in a cold climate may be seen as incomplete or misleading in a climate where cooling or water heating is the major household energy use. Of course, the most important reason for involving target groups is that the process makes them aware of and committed to the rating system that is chosen. Such involvement is the first step in marketing, which has so far been a serious problem for rating systems (McCarty and Willner, 1985).

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

75

Tests of rating systems should be considered part of a learning process. Thus, a rating project should be monitored from the start to assess the reactions of users to the rating design, the conduct of the rating, its marketing, and other details of program implementation. This information can help rating system operators improve their programs and inform the designers of future rating systems. Such process evaluations focus on implementation issues and assess both quantitative and qualitative factors. User surveys can determine whether users believe the ratings are done professionally and reliably, whether raters are available on short notice, and so forth. Information from the organizations that operate rating programs can quantify the training given to raters, the amount of time they spend conducting ratings, and the operator's level of marketing effort. Qualitative judgments must also be made about the sponsor's commitment to the program, the rating system's emerging reputation for reliability in the community, the kind and degree of communication about the rating system between key institutions, and so forth. How to Assess Outcomes A successful home energy rating system will take time to show effects on energy use, and the effects will not all be of one kind. Evaluation, therefore, should carefully look for effects that can be expected at the time of assessment. Below we note three kinds of effects to examine, and some examples of each. Early Indicators Initial outcome evaluations might look for such indicators as: (1) Adoption of ratings by mortgage lenders as criteria to qualify buyers for mortgages; (2) Requests for ratings, including information as to whether the requests are coming from developers of new buildings, homeowners planning to sell or to retrofit, real estate agents, appraisers, or others; (3) Mention of ratings in advertising by real estate agents or mortgage lenders; (4) Appearance of ratings on real estate agents' summaries of house characteristics; (5) Reports from real estate agents that home buyers are asking about ratings of homes on the market; (6) Percentage of houses sold that have ratings. Effects on the Sale of Energy-Efficient Homes Once rated homes begin to sell, it becomes possible to assess effects of the rating more directly. One possible effect is that

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

76

highly rated homes sell sooner, reducing the costs to the seller. Another is the debt/income ratios that banks use to qualify purchasers for mortgage loans. A more important effect, though it is harder to measure, is on sale prices of homes. One way to estimate this effect is to match recently sold rated homes with nearby unrated homes that were also sold recently. This method should include comparisons of house size and other factors affecting value and control these statistically through regression or other techniques, matching should be used to control directly for the value of location. Another method for estimating the price effect is econometric modeling of sale prices in a housing market, with the presence of a rating treated as a dummy variable in regression analysis. These methods would not yield definitive results because they do not assess the possibility that ratings might encourage retrofits or energy-efficient building practices in homes that are not rated. We have discussed the evaluation of this contagion effect in Chapter 4. Time-series data on a given housing market and a comparison market are needed to evaluate that possibility. The most definitive research design is experimental: ratings would be included in an RCS audit or an appraisal process, but would be made available to only a randomly selected half of the relevant homeowners and lenders. This approach to analysis was tried (but not completed) in the Massachusetts pilot project. Effects on the Energy Efficiency of Homes The new home market may be more easily influenced by ratings because it is easier to achieve impressive savings for new homes than for older homes that are retrofitted and because very simple rating systems can be effective in setting goals for home construction. To assess effects in either the new or existing housing market requires a full energy audit of a sample of rated and unrated homes. To assess contagion to builders and homeowners who do not use ratings, it would be useful to evaluate improvements in energy efficiency in comparison with another area. The creative experimental design tried in the Massachusetts project is especially appropriate for assessing effects on home retrofits. Retrofit activity after the RCS audits in the two sets of homes would give a good index of the effect of the rating on retrofits. It would be a stringent test, since both groups received detailed retrofit recommendations and the only difference was the addition of a rating. A comparison of rated and unrated homes in the absence of the information from a RCS audit would probably show a greater effect. It is also possible to assess the effects of ratings on home energy use by collecting energy bills rather than measuring retrofit activity. The two variables, energy use and energy efficiency, have quite different meanings for policy. If ratings cause builders to produce more energy-efficient homes and lead buyers to pay a higher price for them, this may increase or decrease energy use within a housing market. If people purchase energy-efficient homes that are larger or more appliance-intensive than they could otherwise afford, a rating system

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

77

that is a success in terms of efficiency may not produce any net energy savings. If some people purchase detached houses, which most people prefer, rather than less energy-intensive and less costly attached houses, there might be the same effect. If some people move from older rental units to new energy-efficient homes, there may be either an increase or a decrease in their energy use. Although some of these outcomes do not save energy, all of them represent an increased standard of living for the purchasers of new energy-efficient homes. Thus, the choice of a measure of effect depends on whether the policy goal is to reduce energy consumption, to improve energy efficiency, or some combination of the two. CONCLUSIONS AND RECOMMENDATIONS Research on home energy rating systems has focused mainly on improving their accuracy. Relatively less attention has been given to the design and implementation of ratings, and it is in these areas that rating systems have hit their most stubborn barriers (McCarty and Willner, 1985). Experience and behavioral research show that implementation is the first consideration. The best strategy for implementation is to gain the cooperation of the range of actors that will be affected with a rating system: mortgage lenders, builders, real estate agents, appraisers, homeowners and buyers, retrofit contractors, and the sponsors of home energy audit programs. Which of these groups is most critical depends on who will be conducting ratings and on whether the primary target of the system is the new home market, the home resale market, or home retrofits. In ratings intended to affect home sales, the participation of mortgage lenders has been essential. We offer four recommendations for implementing home energy rating systems and for research to identify effective rating systems and improve their delivery. 1. Advice and cooperation should be solicited from all interested groups in a housing market well before a rating system is put into place. 2. Several rating designs should be evaluated in experimental field trials. Three ways of presenting ratings have so far shown promise in pilot programs: a five-star rating derived from a heat loss methodology, a 0–10 rating with anchor points at “no energy-saving features” and “no energy bills,” and an annual cost estimate derived from an RCS energy audit. The first two of these should be given experimental field trials alone and in combination with cost estimates. Before using other possible formats under field conditions, potential users should be asked for their reactions. 3. Experimental tests of ratings should be conducted only in the context of ongoing activities within which ratings might become routine, such as energy audits, appraisals, or the approval of building permits. Only in such settings are energy ratings likely to be funded and institutionalized.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

HOME ENERGY RATINGS

78

4. Resources should be made available for controlled, quantitative evaluation of the effect of energy ratings on rated homes and housing markets. To date, no home energy rating system has been carefully evaluated to determine whether it produces the results it is designed for: increasing the salability and market price of energy-efficient homes and increasing the energy efficiency of new and existing housing stock. The ideal research design for assessing effects on retrofits and sale prices is an experiment in which a number of homes are rated but the ratings are made available for only a random sample of those homes. Whole housing markets should also be studied to see if a rating system affects the energy efficiency of the population of homes offered for sale; econometric approaches are useful for this purpose. Studies should assess both effects on energy efficiency and on measured energy use.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

79

CHAPTER 6 PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS Actual energy savings achieved from home retrofits in the United States have varied considerably from predicted values. Engineering models have usually overestimated energy savings on the average, and predictions for individual homes have varied widely on both sides of observed energy use (Goldman, 1984; Goldman and Wagner, 1984; Harris, 1985; Hirst and Goeltz, 1984; Hirst, White, and Goeltz, 1983). Figure 1 shows a typical scatterplot of predicted energy savings compared with actual savings in a single retrofit program. The scatterplot represents a correlation of only 0.33; in only 45 percent of the homes was the actual energy savings within 50 percent of the predicted value (Hirst and Goeltz, 1984). These discrepancies are of more than theoretical concern. They are important both to homeowners who wish to make wise decisions about retrofit and to both public and private organizations that have an interest in retrofit activity. For example, an electric utility company that is counting on home retrofits to obviate the need for a new power plant must be able to tell 10 years in advance whether its conservation programs will save enough energy to meet that goal and whether programs that meet that goal will cost less than building generating capacity. Regulatory agencies that want utilities to invest in conservation rather than constructing new plants have the same needs. For these purposes, savings estimates that are accurate in the aggregate are sufficient. But for other purposes, accurate estimates for individual homes are important. Companies in the home retrofit industry, for example, will only be credible if they can give their customers accurate predictions of energy savings—and the companies could guarantee energy savings if they could make good enough predictions. The credibility of savings estimates can also determine whether a utility conservation program will achieve its goals because a program whose predictions are not achieved by its initial customers is unlikely to reach all the customers its sponsors expect. There are many possible reasons that predicted and actual energy savings might not match. The estimates may be unreliable because real homes do not fit the categories used in the estimation models and because operators of the models do not all make the needed approximations in the same way. The estimation procedures may unrealistically assume perfect retrofits when there are only imperfect ones. Or, it

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

80

may be possible to install retrofits of the assumed quality, but installers may be less than thorough or highly variable in their work. Contractors who use estimating techniques may make overly optimistic assumptions to support their sales efforts. And even if contractors, installers, and materials perform up to expectations, home occupants may become heavier energy users as a result: they may reset thermostats for more comfort or use money saved through energy efficiency to buy new appliances. Or, occupants who are induced to change habits when the retrofit is made (for example, by operating shades in a sunspace) may revert to old habits over time. Finally, the accuracy of an estimate may deteriorate over time as wall insulation settles, other materials break down, or occupant behavior changes.

FIGURE 1 Actual and predicted natural gas savings for 346 homes audited by Northern States Power, Minnesota. Six of the homes had values outside the bounds of the graph. SOURCE: Hirst and Goeltz (1984:17).

It makes a difference which of these hypotheses accounts for most of the discrepancy between actual and predicted energy savings, because they have different implications about how to improve prediction. One hypothesis implies that better estimation can be achieved by refining models, another calls for better training of model operators, another for better quality control in installation, another for better materials, another for regulating deceptive advertising, and yet others for

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

81

taking occupant behavior into account. Some of the hypotheses imply that, overall, predicted energy savings can be attained while others imply that they are unattainable. Some hypotheses have implications for the variability in estimation: of these, some imply that predictions for single homes can be improved by simple measures such as better training of the people doing the estimating; others imply that variability will be very hard to reduce because it depends on occupant behavior. The hypotheses also have different implications for how interested parties should act. For a utility company investing in conservation, one hypothesis suggests that an investment in inspecting contractors' work could bring energy savings into line with expectations; another hypothesis implies that savings predictions should be lowered because they have not taken into account people's behavioral responses to improved energy efficiency. That implication would lead utilities to decrease support for residential conservation if that support was based on expected relief from demand growth. For a homeowner, support for one hypothesis would underline the importance of choosing a contractor carefully; support for another would mean that all contractors' estimates are probably too optimistic. For a home retrofit contractor, support for one hypothesis would argue for training energy auditors very carefully before making promises about energy savings; support for another would give a warning to tell customers that expected savings can be achieved only if they do not change their behavior. At present, the relative accuracy of the various hypotheses cannot be determined empirically. This chapter outlines an approach to making such a determination. In the language of Chapter 1, the chapter addresses an issue of technological research and development, focusing on the problem of estimating energy savings while taking behavior into account. RESEARCH STRATEGY The research problem of determining why predicted and actual energy savings are different is difficult because many factors may prove important, because data do not yet exist on some of them, and because some of them are difficult to measure accurately. For these reasons, we believe the problem calls for a two-stage approach that relies heavily on data collection under field conditions. The first stage would use exploratory studies that are very detailed but small in scale to clarify the dimensions of the problem. The aim would be to gauge the approximate range of variation due to each plausible hypothesis, narrow the list of potentially important variables, evaluate possible measures for these, and thus make larger-scale research feasible. The second stage would address a narrower range of questions using low-cost measurement techniques for those variables when those techniques had been demonstrated in the first stage to be sufficiently accurate. A large, representative sample of home retrofits would be used in the second stage to make it possible to generalize quantitative findings.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

82

In the first stage of research, the primary operating principle should be to assess the full range of possibilities, using a sample of home retrofits chosen for variety rather than representativeness. TO assess hypotheses about installation quality, the sample must include retrofits done by contractors with varying levels of experience and expertise, as well as retrofits believed to be of the highest achievable quality. First-stage research should be conducted in housing markets where such a wide range is available for sampling. To assess hypotheses about occupant behavior, the sample should include homes whose occupants vary widely in age, income, education, and household composition. To test hypotheses about the adequacy of predictive models, the sample should include houses that vary in age, type of construction, and conformity or nonconformity to typical design and construction methods. In addition, as part of the research, particular retrofits should be modeled by different experts to clarify the size and sources of discrepancies between model outputs. Another principle for the first stage of research is to use multiple measures when it may be possible to validate low-cost indices for use in further research. For example, a regression model of a home's use of heating fuel may prove as accurate for some purposes as more expensive methods involving direct monitoring of furnaces (Fels, 1983; Fels, Rachlin, and Socolow, 1984). If both direct measurement and regression models are used in careful first-stage research, it will be possible to tell whether some version of the less expensive method is adequate for use in a definitive, large-scale study. The first stage of research should involve small studies in one to three housing markets in climates with a significant heating load. In the cold climates of the northeastern and north central states there is significant potential for cutting energy use in samples of homes that include many of older construction. The Pacific Northwest is another good site for research because, although heating loads are not as great, its history of low energy prices has left a large potential for weatherization, and considerable research skills have been built through the efforts of the Bonneville Power Administration. There is also need to study energy savings in homes in hot climates, but less is known at present about modeling cooling loads. Initial research in colder climates should advance knowledge, making future studies feasible in warmer climates. If studies proceed one at a time, research should probably begin in an area with heavy heating loads because it may be possible to get reliable determinations of effects with a small sample and even with rather rough measurement techniques. Within each climate zone, a housing market should be chosen for its variety of housing types, contractor types, socioeconomic status among homeowners, and the availability of a competent research team. We emphasize that studies in a few markets will not be generalizable nationally: the purpose of the first stage is to determine the major causes of gaps between actual and predicted energy savings so that minor causes can safely be ignored in more comprehensive research. Measurements for each home in the sample should be made for at least a year before and a year or more after retrofitting, with somewhat

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

83

different schedules for different measures. Comparison of energy use for the year before and the year after retrofit is essential to verify estimation models. Occupant behavior should be assessed before the retrofit and for a year or more afterward so that behavior can be held constant statistically to check the models. Particular behaviors, including thermostat settings, should be monitored over the first month after a retrofit to identify short-term behavior changes that may mitigate energy efficiency. All the above information can help tell whether gaps between actual and predicted energy savings are due mainly to estimation models or to actual changes in buildings and their occupants. Reassessment after two and three years is necessary in at least a subsample of homes to examine hypotheses involving deterioration of retrofit materials, slow reversion to old habits of energy use, or behavioral changes that have the effect of using the money the retrofit saved to increase energy consumption in other ways. There is need to improve understanding of both aggregate predictions and predictions for individual homes. To make estimates credible to building operators, there is a need to improve the accuracy of predictions for individual buildings or to separate the behavioral and structural components of estimates. If much of the inaccuracy in prediction is due to variation in behavior, it will be critical to communicate that information to building owners and occupants. THE FIRST STAGE OF RESEARCH This section discusses possible measurement techniques for the first stage of research. In the second stage, not discussed in this report, the list will be shorter because some measurements will prove to be unnecessary and because some hypotheses will be eliminated from consideration. Measuring Retrofits Air infiltration accounts for approximately one-third of heat loss in conventional housing (Malik, 1978). Thus, it is a major object of retrofits, especially low-cost ones that emphasize caulking and weather-stripping. Air infiltration rates may be estimated from data on air leakage collected with an instrumented blower door (Dutt, Jacobson, and Socolow, 1985). This measurement takes about half an hour and costs about $100 per home (G.Dutt, Princeton University, personal communication). Infiltration can also be measured with a tracer gas (Malik, 1978), but these measurements are sensitive to momentary weather conditions and so are of questionable reliability. A new and promising technique involving continuous sampling of a tracer gas has been developed at Brookhaven National Laboratory (Dietz et al., 1985). Measurement should be made before and after retrofitting and, to test for breakdown of air barriers, after a period of two or three years. Direct measurement of air infiltration is important in the

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

84

first-stage research because some retrofit activities focus almost entirely on limiting infiltration, and data on the direct effects of such efforts are lacking. An added benefit would be the collection of field data relevant for analyzing issues of indoor air quality. Gaps and “bypasses” in insulation in attics can account for 35 percent of a home's heat loss in winter (Beyea, Dutt, and Woteki, 1978). They are a likely explanation for disappointing results in retrofits because small, hardto-reach attic spaces may easily be passed over by an insulation contractor who is in a hurry or who is not careful. It is possible to locate bypasses by examining the insulation with an infrared scanner. The research team can make such an examination before and after installation, recording the location and size of all gaps in wall and attic insulation. A return visit in a year can detect setting of insulation installed in walls. Such infrared scanning and recording of results for an average home takes roughly an hour and costs about $250, depending on the amount of detail required in the report (Dutt, personal communication). At present, precise relationships between the size and location of gaps and insulation and heat loss cannot be specified. Therefore, field observations in the first stage can provide only rough indications, not precise estimates, of the size of the problem. This may be enough to tell whether the issue is worthy of more detailed study. Steady-state furnace combustion efficiency is a target of retrofits, especially in older gas- and oil-heated homes that do not have efficient furnaces. Combustion efficiency can also be affected by replacement of furnace filters, replacement of oil burner nozzles, and other maintenance activities. Maintenance—or the lack of it—is a possible source of errors in estimating energy savings from furnace improvements. A technician with the proper equipment can measure combustion efficiency in 10 or 15 minutes with equipment costing about $150 (T.Vineyard, Oak Ridge National Laboratory, personal communication). This measurement should be made before and after furnace retrofits and again after a full winter to check on maintenance. Note that measuring a furnace's efficiency tells nothing about how much of the heat reaches the living area: methods to directly measure heat losses in a distribution system are not yet well validated. Energy use by furnaces readily converts into a measure of total heat loss, including distribution losses and losses through the building shell. It can be measured directly by submetering a fuel line or indirectly by metering a furnace's “on time” at the thermostat and multiplying by the rate of fuel feed to the furnace. Metering at the thermostat involves very little incremental cost if the thermostat is also being monitored for other purposes (see below). In at least some homes, the instruments should record the specific times the furnace runs, to compare furnace operation against occupants' reports of their behavior (see below). The timed measure of energy use has several advantages over other indices. Unlike meter readings or the observations described above, it can give continuous data. It can be used as a direct observation against which to assess the adequacy of regression models of furnace operation based on meter readings. It can reflect and be checked against a range of behaviors, including thermostat

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

85

settings, which can be independently monitored at low cost, and the opening of windows, which cannot. A disadvantage of recording specific times compared to cumulative time is the extra cost of data collection and analysis. For this reason, complete data should be collected only in homes where it will be used to compare against other time-specific data, such as observed or reported behavior. The measure of total energy use reflects the combination of distributional losses in a heating system and inefficiencies due to an oversize furnace or oil burner nozzle. Because the measure does not distinguish among these inefficiencies, it has limited value for evaluating retrofits aimed at correcting them. But in combination with other measures, a measure of total furnace operation can clarify the importance of some factors in retrofit that are not easy to measure directly. Assessing Engineering Models in Practice Discrepancies between actual and predicted energy savings may be due to errors or omissions in engineering models or inconsistencies in their application. One issue is how much accuracy can be improved by making models more detailed. In stage-one research, models offering different degrees of detail should be tested on the same data. To the extent that simpler models are adequate for addressing the questions determined to be important, the burden of data collection can be reduced in stage-two research. Another question concerns the variance in estimates as a function of judgments by auditors and model operators. It would be useful to have some homes examined by a second auditor and to have some audit reports entered into models by a second operator to estimate the magnitude of these two possible sources of variability. If differences are large, it may be worthwhile to experiment with training to standardize the experts' judgments. A third question concerns the modeling of interactions between retrofits. The effect of insulation on energy use is smaller when a home has an efficient furnace, but engineering models of this sort of interaction have not been tested against data on actual retrofits. If the stage-one research includes a variety of retrofit packages, its data can help improve engineering models of these interactions. Other Direct Measures Indoor temperature regulation is the short-term behavioral adjustment with the greatest effect on a home's energy use. Indoor temperature must be monitored to test the frequently asserted hypothesis that people, especially those in low-income households, who have been sacrificing comfort to pay energy bills will respond to improved energy efficiency by resetting thermostats. It is also critical to a less often-stated reverse hypothesis: since drafts raise the temperature level needed for comfort in winter (Nishi, 1978), reducing air infiltra

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

86

tion may induce people to lower indoor temperatures because they need less heat to compensate for drafts. Indoor temperature can be monitored at thermostats along with thermostat settings, at a cost of at least $150 per home (Dutt, personal communication). It would be advisable to keep a running record of temperatures in some homes to assess the extent and timing of any behavioral changes that occur and as a check on the accuracy of occupants' self-reports of temperatures. Thermostat settings are closely related to indoor temperatures during the heating and cooling seasons when the systems function properly. They are not the same, however, and may sometimes be what people report when asked for the temperature in the home. Thus, both thermostat settings and indoor temperatures should be monitored. Hot water use is a major energy variable in homes that is behaviorally controlled: Kempton (1984) has observed a threefold variation in hot water used per day per person among seven households. Hot water use should be monitored in some homes to see if it changes after a retrofit. It may increase if people feel they can afford to use more, or it may decrease, for example, if baths and hot drinks were being used to warm people in a cold house. Accurate measurement would require a flow meter on the outlet of the hot water tank, an investment of about $100 per home (Dutt, personal communication). Additional metering of energy use by the water heater would give useful data on the average operating efficiency of water heaters and on the actual energy savings from retrofits or replacements of water heaters. The operation of appliances can be assessed by submetering appliances whose use is likely to be affected by home retrofits. The most obvious of these are room air conditioners and space heaters; stoves may also be affected when they double as space heaters. These appliances should be monitored in a subsample of homes. Fuel and electricity bills are, of course, essential for studying the effects of retrofits and for building and validating single-home energy models that may greatly decrease the cost of the second stage of research. The importance of billing data is that they are regularly available without adding instruments to a home. They give less perfect accounts of fuel use than complex data recorders, but promising techniques are being developed for assessing energy savings with models based only on billing and weather data. One such method has been developed by Margaret Fels and her associates at Princeton University (see Fels, 1983). In this method, consumption of fuels used for heating is estimated for each home by an equation: Fi=a+bHi,

where Fi is the average daily fuel consumption in time interval i, and Hi is the heating degree-days per day during time interval i below a reference temperature, Tref. Ordinary least-squares regression equations are calculated for several values of the Tref, and the equation giving the best fit is selected as the model of energy use in that home. Tref is interpreted as the average daily temperature below which the home uses heat; a is interpreted as the base load for

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

87

the fuel in question, used for cooking, lighting, and other functions that do not change with the ambient temperature; and b is interpreted as the dependence of fuel use on temperature. For assessing energy savings from retrofitting, normalized annual consumption (NAC) equations are calculated for a year before and a year after the retrofit, using the equation: NAC =365a+bHo(Tref),

where Ho(Tref) is the number of heating degree days (base Tref) in a “typical” year. Thus, the NAC formula can be used to partition energy savings into those due to base-load effects, to the weather sensitivity of energy use, and to changes in the reference temperature, the last two of which can be expected to fall after retrofitting the building shell. The above system is very inexpensive compared to instrumenting homes, but two important operational problems must be recognized. First, the system is sensitive to the quality of billing and weather data. Careful checking of billing data to eliminate estimated bills, changes in occupancy, and other sources of error is essential in developing NAC models (Berry and Vineyard, 1985). Second, the need to calculate Tref for each home puts the procedure beyond the capability of inexperienced analysts using standard statistical programs. A simplified system that calculates NAC based on an assumed uniform value of Tref has been developed at Oak Ridge National Laboratory (Berry and Vineyard, 1985). In analyses of gas-heated homes in Minnesota and electrically heated homes in the Pacific northwest, the simplified system, setting the reference temperature at 60°F, has yielded estimates of NAC very close to those obtained with the Princeton method, although estimates of the heating and base-load components of energy use diverge farther from those calculated when the reference temperature is determined empirically. Further testing of both calculation procedures should be a part of stage-one research because of the value that can be gained in later research from an acceptable estimation technique that does not require instrumentation. Self-Reported Behavior Temperature and thermostat settings should be measured directly, but the first-stage research should also collect self-reports to check their validity. If it proves possible to get reliable data from self-reports, it will greatly decrease the cost of the second-stage research. Many researchers mistrust the accuracy of self-reports because of the possibility that they will be influenced by “social desirability effects:” householders reporting something between the truth and what they think the researcher would like to hear or the neighbors expect. It is possible, however, that careful survey techniques can yield reliable estimates. For example, in one study of 779 households in the Pittsburgh area (Beck, Doctors, and Hammond, 1980), 71 percent of households reported their thermostat settings

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

88

correctly to within 2°F. Furthermore, the errors fell about equally on each side, suggesting random rather than systematic reporting errors. Comfort is a critical subjective variable in most hypotheses about changes in occupant behavior after retrofits. It is also important in interpreting data from evaluations of retrofit programs. When a retrofit program saves less energy than anticipated, it may not matter to a utility whether the reason was an inadequacy in an estimation model or an increase in occupants' comfort levels—in either case, the program does less than expected to obviate the need for new power plants. But for a state or local government, a retrofit contractor, or a homeowner, human comfort may be as important a reason for retrofitting as lowered energy costs. It is therefore essential to measure it, which can only be done by self-report. Survey questions should can assess relative comfort on a rough (e.g., five-point) subjective rating scale. They should focus on not only the degree of discomfort but on its frequency (“only on the coldest or hottest days,” “often,” or “almost always,” during the heating or cooling season). They should assess which members of the household have discomfort, recognizing that the most uncomfortable person may not be one who responds to the survey. And questions about comfort should be asked during the same period of the year for pretests and posttests (preferably when discomfort is expected to be great) because of the unreliability of people's memories. Draftiness is a subjective comfort-related variable that should be assessed directly. Air infiltration and convection near cold windows and exterior walls can produce sensations of draft to which people may respond by adding clothing, by resetting thermostats, by using space heaters, or by feeling discomfort. Direct measurement of air infiltration can be compared with reported feelings of draftiness and discomfort and reports of behavioral adjustments to those feelings to determine how much the reduction of air infiltration does to change the experience and behavior of occupants of retrofitted homes. Use of supplementary heating and cooling equipment should be assessed by instrumenting space heaters in some homes and also by self-reports because it is impractical to meter all space heaters in all homes in a sample. Direct measures and self-reports can be checked against ongoing measures of furnace operation and against regression models of energy used for heating. According to recent research with regression models, non-furnace energy use in winter increases in northern climates due to some combination of the use of space heaters, increased energy use to heat water, and increased cooking and lighting (Fels, Rachlin, and Socolow, 1984). Where space heaters are not metered, survey questions should inventory space heating equipment, get specifications for the equipment if feasible, and ask about the frequency of use for each piece of equipment. To validate regression models of the impact of this equipment when direct measurements cannot be made, it will be useful to conduct surveys during the heating or cooling season and to ask about the use of supplementary heating and cooling during the previous day. People can report this more accurately than they can estimate average use over a season, and if total household energy use is being continuously

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

89

monitored, the reports can be checked against those measurements to directly assess the effects of supplementary heating and cooling on total energy use. Closing off rooms to save on heating is a common expedient in cold climates, and it is at sometimes a predictor of participation in retrofit programs (Tonn and Berry, 1984). It is a fairly simple behavioral adaptation that may have important effects on energy bills. It is also easily reversible if energy costs become a less salient problem for households. Thus, opening closed rooms is a likely behavioral reversion after retrofits. It is a simple matter to ask, in each administration of a household survey, whether rooms are closed off in winter, how many rooms are involved, and for how much of the winter. Since the effects of this adaptation on energy use will depend on the heat distribution system, data on that system should be collected for the homes surveyed. Ventilation is an important behavioral variable in European studies of residential energy use (e.g., van Raaij and Verhallen, 1983) and may become important in the U.S. as homes are retrofitted to higher standards of air tightness. Surveys can assess how often windows and doors are opened to decrease stuffiness, to vent smoke or odors, or to air out rooms. Such information may help make sense of apparent anomalies in data on furnace operation and might also be an indicator of behavioral responsiveness to changes in indoor air quality after retrofits. Adding or removing clothing is a simple behavioral adaptation to indoor temperature. However, it is difficult for researchers to get accurate measures of the insulating value of clothing over the course of several winters. An approximation can be made with interviewers' observations, which are useful when cumulated over many homes. Survey questions about the usual behavior of household members would also have some value. But in the early phase of research, the best way to explore the importance of clothing as a behavioral adaptation may be through ethnographic interviews of members of a few households before and after retrofits. A good example of this technique applied to hot water use is presented by Kempton (1984). Economic adjustments related to energy use include depressed living standards before retrofitting because of energy costs and increases in living standards because of energy savings from the retrofits. Retrofitting may also affect a household's ability to make timely payments of utility or other bills. Preretrofit adjustments must be assessed and may include less expenditures on food, clothing, entertainment, transportation, or other expenditures the household considers normal. Self-reports cannot give a reliable dollar value for such sacrifices, but they can tell which households perceived sacrifice and what items in the household budget were affected (one study that used such survey items is by Dillman, Rosa, and Dillman, 1983). After retrofitting, surveys should repeat the questions, along with questions about whether the retrofitting has enabled the household to afford things and activities it could not previously afford. Surveys might also inquire about appliance purchases as an indication of whether the retrofits have affected major household purchases or their energy intensity.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

90

GUIDELINES FOR RESEARCH DESIGN The costs of the first stage of research are influenced primarily by the number of homes sampled, the extent of instrumentation, the effort needed to simplify data collected by continuous monitoring, and the number and type of occupant surveys conducted. Additional survey questions involve smaller incremental expense. The first stage of research can obviously be done with different degress of thoroughness, at different cost. Because we cannot predict the level of resources that will be available for the research or the number of sources from which the resources will come, we have not outlined a detailed research program. We can, however, offer some guidelines for setting priorities. 1. Research should ensure that all data collected are cleaned, documented, and analyzed. Too often, research projects collect valuable data but exhaust their funds before the data become usable. Research sponsors should consider holding back a portion of funds to prevent that eventuality. 2. A single research project should not collect more data than it can clean, document, and analyze. If resources are limited, it is better to conduct a small, focused study than to collect large amounts of data in the hope they will be analyzed later. 3. Resources can be conserved by narrowing the research question or by reducing sample sizes. To address the full range of technical and behavioral questions about the effects of retrofits and their interactions requires full instrumentation of homes and repeated and detailed surveys of occupants. However, a detailed study of even 25 homes would give valuable information if they spanned the range of a housing market and were followed for two to three years after retrofitting. In addition, less than complete instrumentation can generate useful knowledge about some important questions. For example, a study of behavioral changes after retrofitting requires repeated and detailed surveys, but valuable information can be gained with limited instrumentation to assess thermostat settings, indoor temperature, furnace use, and perhaps use of space heaters and hot water. Variability in estimates due to expert judgment can be estimated by using additional energy auditors or operators of engineering models, without any survey research or instrumentation of homes. And the validity of regression models of retrofit effects can be assessed by comparing different models using data from the same homes. Instrumentation of fuel use at the furnace would be valuable for determining if the temperature coefficient in a model corresponds to furnace use. This chapter illustrates the importance of understanding both technology and human behavior for developing policies affecting the energy efficiency of buildings. Physical models are unreliable predictors of the effects of physical changes on a building's energy use in part because those models do not account for systematic variation in the behavior of builders, retrofit contractors, and building occupants. To

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

PREDICTED AND ACTUAL ENERGY SAVINGS FROM HOME RETROFITS

91

understand the causes of the unsatisfying performance of the models and to improve the ability to predict the effect of retrofits requires simultaneous assessment of equipment and human behavior; to make policies that could improve the effectiveness of retrofits will also require attention to both technology and behavior. In this way, the issue of predicted versus actual energy savings underlines the value of the kinds of analysis considered in this report: energy use is a human activity that occurs through technology; to understand it, one must comprehend not only the relevant technologies but also the people who use them.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

92

REFERENCES

Ackerman, A.D., Cox, M.K., Schuck, L.J., and Tarini, E.J. 1983 The Massachusetts Home Energy Rating System. PNL-4763. Richland, Wash.: Pacific Northwest Laboratory. American Council for an Energy-Efficient Economy 1984 Doing Better; Setting the Agenda for the Second Decade. 12 volumes. Washington, D.C.: American Council for an Energy-Efficient Economy. Anderson, C.D. 1984 Evaluation of Canada's oil substitution program (COSP). Pp. 3–18 in Vol. J, Doing Better; Setting an Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Andrews, E.R. 1984 Residential weatherization through zero-interest financing: the PG&E experience. Pp. 5–16 in Vol. I, Doing Better: Setting an Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Ascher, W. 1978 Forecasting; An Appraisal for Policy-Makers and Planners. Baltimore, Md.: Johns Hopkins University Press. Beck, P., Doctors, S.I., and Hammond, P.Y. 1980 Individual Energy Conservation Behaviors. Cambridge, Mass.: Oelgeschlager, Gunn, & Hain. Berry, L.G. 1982 The Role of Financial Incentives in Utility-Sponsored Residential Conservation Programs; A Review of Customer Surveys. ORNL/CON-102. Oak Ridge, Tenn.: Oak Ridge National Laboratory. Berry, L., and Vineyard, T. 1985 Evaluation Plan for State Gas Heating System Retrofit pilot Programs. ORNL/CON-171. Oak Ridge, Tenn.: Oak Ridge National Laboratory. Beyea, J., Dutt, G., and Woteki, T. 1978 Critical significance of attics and basements in the energy balance of Twin Rivers townhouses. Pp. 103–120 in R.H. Socolow, ed., Saving Energy in the Home. Cambridge, Mass.: Ballinger.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

93

Black, J.S. 1979 The Role of Social Scientists in Field Experiments on Energy. Paper presented at the meeting of the American Psychological Association, September, New York. Brewer, G.D. 1983 Some costs and consequences of large-scale social systems modeling. Behavioral Science 28:166–185. Burnett, T. 1982 Measuring weatherization effectiveness: Portland General Electric Company's experience. In E.Hirst, ed., Proceedings of the EPRI Workshop on Measuring the Effects of Utility Conservation Programs. EPRI EA-2496. Palo Alto, Calif.: Electric Power Research Institute. Centaur Associates, Inc. 1983 1983 RCS Evaluation Highlights: Cost-Benefit Evaluation of the Residential Conservation Service Program. Report to the Office of Conservation and Renewable Energy, U.S. Department of Energy. City of Santa Monica 1985 Santa Monica Energy Fitness Program Third Quarter Progress Report. Santa Monica, Calif. Cook, T.D., and Campbell, D.T. 1979 Quasi-Experimentation: Design and Analysis Issues for Field Settings. Boston: Houghton-Mifflin. Cooper, M.N., Sullivan, T.L., Punnett, S., and Berman, E. 1983 Equity and Energy: Rising Energy Prices and the Living Standards of Lower Income Americans. Boulder, Colo.: Westview. Cowell, S.L., and Rebitzer, R. 1984 Beyond technology: energy conservation delivery systems that work. Pp. 23–24 in Vol. H, Doing Better: Setting the Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Craig, C.S., and McCann, J.M. 1978 Assessing communication effects on energy conservation. Journal of Consumer Research 5:82–88. Darley, J.M., and Beniger, J.R. 1981 Diffusion of energy-conserving innovations. Journal of Social Issues 37(2):150–171. de Haan, R.M. 1985 The National Insulation Programme. Paper presented at the International Conference on Consumer Behaviour and Energy Policy, April, Versailles, France. Dietz, R., Goodrich, R., Cote, E., and Wieser, R. 1985 Detailed description and performance of a passive perfluorocarbon tracer system for building ventilation and air exchange measurements. In Proceedings of the ASTM Symposium on the Measurement of Air Leakage Performance in Buildings. Philadelphia, Pa.: American Society of Testing and Materials.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

94

Dillman, D.A., Rosa, E.A., and Dillman, J.J. 1984 Lifestyle and home energy conservation in the United States: the poor accept lifestyle cutbacks while the wealthy invest in conservation. Journal of Economic Psychology 3:299–315. Dubin, J.A. 1985 Consumer Durable Choice and the Demand for Electricity. Amsterdam, The Netherlands: North-Holland. Dubin, J.A., and McFadden, D.L. 1984 An econometric analysis of residential electric appliance holdings and consumption. Econometrica 52:345–362. Dutt, G.S., Jacobson, D., and Socolow, R.H. 1985 Pressurization testing, infiltration reduction and energy savings. In Proceedings of the ASTM Symposium on the Measurement of Air Leakage Performance in Buildings. Philadelphia, Pa.: American Society of Testing and Materials. Energy Information Administration 1980 Residential Energy Consumption Survey: Conservation. DOE/EIA-0207/3. Washington, D.C.: U.S. Department of Energy. 1982 Residential Energy Consumption Survey: Consumption and Expenditures April 1980 through March 1981. Part 1. National Data. DOE/ EIA-0321/1. Washington, D.C.: U.S. Department of Energy. Energy, Mines, and Resources Canada 1983 Evaluation of the Canadian Home Insulation Program (CHIP). Report No. PE 50/1983, Program Evaluation Branch. Ottawa, Ont., Canada: Energy, Mines, and Resources Canada. Ester, P., Gaskell, G., Joerges, B., Midden, C.J.H., van Raaij, W. F., and de Vries, T., eds. 1984 Consumer Behavior and Energy Policy. Selected/edited proceedings of the international conference held at Noordwijkerhout, The Netherlands, September 1982. Amsterdam: North-Holland. Ester, P., and Winett, R.A. 1982 Toward more effective antecedent strategies for environmental programs. Journal of Environmental Systems 11:201–221. Fels, M.F. 1983 The Princeton Scorekeeping Method: An Introduction. Center for Energy and Environmental Studies, Report No. 163. Princeton University, New Jersey. Fels, M., Rachlin, J., and Socolow, R.H. 1984 Seasonality of non-heating consumption: a study based on submeter data. Pp. 42–60 in Vol. B, Doing Better: Setting the Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Fitchburg Office of the Planning Coordinator 1980 Fitchburg Action to Conserve Energy (FACE) Final Report. Fitchburg, Mass.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

95

Freedberg, M., and Schumm, D. 1984 Financing multifamily energy conservation private funding for energy loans. Pp. 194–209 in Vol. J, Doing Better: Setting an Agenda for the Second Decade. Washington, D.C.: AmericanCouncil for an Energy-Efficient Economy . Freedman, D. 1981 Some pitfalls in large econometric models: a case study. Journal of Business 54:479–500. Freedman, D., Rothenberg, T., and Sutch, R. 1983 On energy policy models. Journal of Business and Economic Statistics 1:24–32. Frieden, B.J., and Baker, K. 1983 The market needs help: the disappointing record of home energy conservation. Journal of Policy Analysis and Management 2:432–448. Gallagher, J.T., and Desmond, D.J. 1984 Pennsylvania's home energy scorecard: development and implementation. Pp. 82–93 in Vol. B, Doing better: Setting the Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Gaskell, G., and Pike, R. 1983 Consumer Energy Conservation Policies and Programmes in Britain. CECP Technical Reports Vol. VI, Science Center Berlin, International Institute for Environment and Society. Berlin, Federal Republic of Germany: Science Center. Glazer, S. 1984 The Residential Conservation Service: expectations, performance, and potential for the future. Energy Conservation Bulletin 4(1):1, 3–6. Goett, A., and McFadden, D.L. 1982 Residential End-Use Energy Planning System (REEPS). Prepared for the Electric Power Research Institute. EPRI EA-2512. Cambridge Systematics, Inc., Cambridge, Mass. Goldman, C. 1984 Measured energy savings from residential retrofits: Updated results from the BECA-B project. Pp. 107–121 in Vol. B., Doing Better: Setting the Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Goldman, C., and Wagner, B.S. 1984 Saving energy in occupied buildings: results from the Lawrence Berkeley Laboratory residential data bases. In B.M. Morrison and W.Kempton, eds., Families and Energy: Coping with Uncertainty. Conference proceedings. Institute for Family and Child Study. East Lansing, Mich.: Michigan State University. Greenberger, M., Crenson, M.A., and Crissey, B.L. 1976 Models in the Policy Process; Public Decision Making in the Computer Age. New York: Russell Sage Foundation.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

96

Hannigan, R., and King, P. 1982 Residential conservation programs at Pacific Power and Light Company: models, forecasts, and assessments. In E.Hirst, ed., Proceedings of the EPRI Workshop on Measuring the Effects of Utility Conservation Programs. EPRI EA-2496. Palo Alto, Calif.: Electric Power Research Institute. Harris, J. 1985 Monitored Energy Performance of New and Retrofitted Residential Buildings: Results from the “BECA” Data Base. Paper presented at the International Conference on Consumer Behaviour and Energy Policy, April, Versailles, France. Harris, J., and Blumstein, C., eds. 1984 What Works: Documenting Energy Conservation in Buildings. Washington, D.C.: American Council for an Energy-Efficient Economy. Harris, J.P., and Hollander, J.M., eds. 1982 Improving Energy Efficiency in Buildings: Progress and Problems. Proceedings of the 1980 summer study at University of California, Santa'Cruz. Washington, D.C.: American Council for an Energy-Efficient Economy. Heberlein, T.A., and Baumgartner, R.M. 1985 Changing Attitudes and Electricity Consumption in a Time-of-Use Experiment. Paper presented at the International Conference on Consumer Behaviour and Energy Policy, April, Versailles, France. Heberlein, T.A., and Warriner, G.K. 1983 The influence of price and attitude on shifting residential electricity consumption from on- to offpeak periods. Journal of Economic Psychology 4:107–130. Hendrickson, P.L., Garrett-Price, B.A., and Williams, T.A. 1982 Overview of Existing Residential Energy-Efficiency Rating Systems and Measuring Tools. PNL-4359. Richland, Wash.: Pacific Northwest Laboratory. Hickling-Partners 1983 Analysis methodology for marketing analysis. In CHIP Analysis Methodologies. Ottawa, Ont., Canada: Energy, Mines, and Resources Canada: Hill, D.H. 1985 Consumer Response to Gasoline Prices. Paper prepared for the Committee on Behavioral and Social Aspects of Energy Consumption and Production, National Research Council, Washington, D.C. 1986 The dynamics of household driving demand. Review of Economics and Statistics 68(1). Hill, D.H., Groves, R.M., Howrey, E.P., Kline, A.C., Kohler, D.F., Lepkowski, J.M., and Smith, M.A. 1979 Evaluation of the Federal Energy Administration's Load Management and Rate Design Demonstration Projects. EA-1152. Palo Alto, Calif.: Electric Power Research Institute.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

97

Hill, D.H., and Stern, P.C. 1985 Assessing Nonlinearity in the Behavior of Gasoline Consumers. Paper prepared for the Committee on Behavioral and Social Aspects of Energy Consumption and Production, National Research Council, Washington, D.C. Hirshleifer, J., and Riley, J.G. 1979 The analytics of uncertainty and information—an expository survey. Journal of Economic Literature 17:1375–1421. Hirst, E. 1984 Household energy conservation: a review of the federal Residential Conservation Service. Public Administration Review 44:421–430. Hirst, E., 1981 Berry, L.G., and Soderstrom, J. Review of utility home energy audit programs. Energy 6:621–630. Hirst, E., Bronfman, B., Goeltz, R., Trimble, J., and Lerman, D. 1983 Evaluation of the BPA Residential Weatherization Pilot Program, ORNL/CON-124. Oak Ridge, Tenn.: Oak Ridge National Laboratory. Hirst, E., Clinton, J., Geller, H., and Kroner, W. 1985 Energy Efficiency in Buildings: Current Status and Future Directions. Washington, D.C.: American Council for an Energy-Efficient Economy. Hirst, E., and Goeltz, R. 1984 Comparison of Actual and Predicted Energy Savings in Minnesota Gas-Heated Single-Family Homes. ORNL/ CON-147. Oak Ridge, Tenn.: Oak Ridge National Laboratory. Hirst, E., Goeltz, R., and Manning, H. 1982 Household Retrofit Expenditures and the Federal Residential Energy Conservation Tax Credit. ORNL/CON-95. Oak Ridge, Tenn.: Oak Ridge National Laboratory. Hirst, E., Goeltz, R., Thornsjo, M., and Sundin, D. 1983 Evaluation of Home Energy Audit and Retrofit Loan Programs in Minnesota: The Northern States Power Experience. ORNL/CON-136. Oak Ridge, Tenn.: Oak Ridge National Laboratory. Hirst, E., Marlay, R., Greene, D., and Barnes, R. 1983 Recent Changes in U.S. Energy Consumption; What Happened andWhy . Oak Ridge, Tenn.: Oak Ridge National Laboratory. Hirst, E., White, D., and Goeltz, R. 1983 Comparison of Actual Electricity Savings with Audit Predictions in the Bonneville Power Administration Residential Weatherization Pilot Program. ORNL/CON-142. Oak Ridge, Tenn.: Oak Ridge National Laboratory. Hoskin, B. 1983 FHLB of Seattle supports shelter industry program. Bank Notes Autumn:6–7. Seattle, Wash.: Federal Home Loan Bank of Seattle.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

98

Katz, E., and Morgan, S. 1983 The financing of energy conservation segices to low income households: Alternatives to grants. Pp. 223–240 in B.M. Morrison and W.Kempton, eds., Families and Energy: Coping With Uncertainty. Institute for Family and Child Study. East Lansing, Mich.: Michigan State University. Keating, K.M., and Flynn, C.B. 1984 Researching the human factor in Hood River: “buildings don't use energy, people do.” Pp. 250–259 in Vol. I, Doing Better; Setting the Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Kempton, W. 1984 Residential hot water: a behaviorally driven system. pp. 127–138 in Vol. F, Doing Better: Setting the Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Kempton, W., and Montgomery, L. 1982 Folk quantification of energy. Energy 7:817–827. Kempton, W., Harris, C.K., Keith, J.G., and Weihl, J.S. 1984 Do consumers know “what works” in energy conservation? In J. Harris and C.Blumstein, eds., What Works: Documenting Energy Conservation in Buildings. Washington, D.C.: American Council for an Energy-Efficient Economy. King, M.J., Belzer, D.B., Callaway, J.M., and Adams, R.C. 1982 An Analysis of Changes in Residential Energy Consumption, 1973–1980. Richland, Wash.: Pacific Northwest Laboratory. Klingberg, T., and Warkov, S. 1983 Loans for energy conservation in Sweden and Connecticut: some comparisons. Journal of Economic Psychology 3:367–377. Krumholz, N., and McDermott, M. 1984 Poor people, neighborhood groups, and energy conservation. Pp. 63–75 in Vol. H, Doing Better: Setting an Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Lerman, D.I., and Bronfman, B.H. 1984 Process Evaluation of the Bonneville Power Administration Interim Residential Weatherization Program. ORNL/CON-158. Oak Ridge, Tenn.: Oak Ridge National Laboratory. Lerman, D.I., Bronfman, B.H., and Tonn, B. 1983 Process Evaluation of the Bonneville Power Administration Residential Weatherization Pilot Program. ORNL/CON-138. Oak Ridge, Tenn.: Oak Ridge National Laboratory. Luboff, J. 1983 Energy efficiency ratings—new factor in home loans: the Shelter Industry Program. Washington Public Policy Notes 11(4) (Published by Institute for Public Policy and Management, University of Washington). Malik, N. 1978 Field studies of dependence of air infiltration on outside temperature and wind. Pp. 143–166 in R.H.Socolow, ed., Saving Energy in the Home. Cambridge, Mass.: Ballinger.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

99

McCarty, K.S., and Willner, A. 1985 Home Energy Rating Systems: Purposes, Operations, Barriers, and Future Research Needs, Washington, D.C.: Consumer Energy Council of America. McCutcheon, L. 1983 Characteristics of Participants in Puget Power's Conservation Program: Single Family Residential Homes. Interim Report No. 1. Puget Sound Power and Light Company, Bellevue, Wash. Miller, R.D., and Ford, J.M. 1985 Shared Savings in the Residential Market: A Public/Private Partnership for Energy Conservation. Baltimore, Md.: Energy Task Force, Urban Consortium for Technology Initiatives. Mishkin, F. 1983 A Rational Expectations Approach to Macroeconomics. Chicago: University of Chicago Press. Morrison, B.M., and Kempton, W., eds. 1984 Families and Energy: Coping with Uncertainty. Conference proceedings. Institute for Family and Child Study. East Lansing, Mich.: Michigan State University. Mosteller, F., and Mosteller, G. 1979 New statistical methods in public policy Part I: experimentation. Journal of Contemporary Business 8:79–92. Moulton, D.H. 1984 The impact of utility-sponsored energy conservation loan programs on low-income households. Pp. 104–116 in Vol. H., Doing Better; Setting an Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Newcomb, T.M. 1984 Conservation program evaluations: the control of self-selection bias. Evaluation Review 8:425–440. Newcomb, T.M., and Weiss, C.S. 1983a Evaluation of the Low-Income Electric Program. Conservation and Solar Division, Seattle City Light Department, Seattle, Wash. 1983b Follow-up Evaluation of Home Energy Loan Program Workflow. Conservation and Solar Division, Seattle City Light Department, Seattle, Wash. New York State Public Service Commission 1985 Home Insulation and Energy Conservation Program, Preliminary Data for 1984 Annual Report. New York State Public Service Commission, Albany, N.Y. Nishi, Y. 1978 The role of air velocity in promoting comfort andacceptability of thermal environments 65–85F (18–30C). In J. J.J.Stolwijk, ed., Energy Conservation Strategies in Buildings: Comfort, Acceptability, and Health. New Haven, Conn.: John B. Pierce Foundation of Connecticut, Inc. Office of Technology Assessment 1982 Energy Efficiency of Buildings in Cities. Washington, D.C.: U.S. Government Printing Office.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

100

Olsen, M.E. 1984 Participation in Household Weatherization Programs. Unpublished manuscript. Department of Sociology, Michigan State University. Olsen, M.E., and Cluett, C. 1979 Evaluation of the Seattle City Light Neighborhood Energy Conservation Program. Battelle Human Affairs Research Center, Seattle, Wash. Olsen, M.E., and Fonseca, V.A. 1984 Consumer Responses to Community Energy Conservation Programs in the United States. Unpublished manuscript. Department of Sociology, Michigan State University. Peabody, G.E. 1984 Weatherization Program Evaluation. SR-EEUD-84–1, Energy Information Administration. Washington, D.C.: U.S. Department of Energy. Peach, H.G., Peters, D., Oliver, T.V., and Goldstein, D.B. 1984 Cooperation and diversity in a large-scale conservation research project. Pp. 286–293 in Vol. I, Doing Better: Setting an Agenda for the Second Decade. Washington, D.C.: American Council for an EnergyEfficient Economy. Peat, Marwick, and Partners 1983 Analysis Methodology: CHIP Incrementality Models and Economic Analysis. Ottawa, Ont., Canada: Energy, Mines, and Resources Canada. Petersen, B. 1984 The Danish energy-conservation program in buildings. Pp. 92–108 in Vol. J, Doing Better: Setting an Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Polich, M.D. 1984 Minnesota RCS: the myths and .the realities. Pp. 141–151 in Vol. G, Doing Better: Setting the Agenda for the Second Decade. Washington, D.C.: American Council for an Energy-Efficient Economy. Puget Sound Power and Light Company 1983 A Review of Puget Sound Power & Light Company's Conservation Programs. SRC Report No. 7135-R4. Bellevue, Wash.: Puget Sound Power and Light Company. 1984 Background Data for Conservation Update Dec. 31, 1984. Unpublished. Conservation and Divisions Services, Puget Sound Power and Light Company, Bellevue, Wash. Randolph, J. 1984 Energy conservation programmes: a review of state initiatives in the USA. Energy Policy 12:425–438. Rosenberg, M. 1980 The RCS Project—Preliminary Findings and Recommendations. Boston: Technical Development Corporation.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

101

Ruderman, H., Levine, M.D., and McMahon, J.E. 1984 The Behavior of the Market for Energy Efficiency in ResidentialAppliances Including Heating and Cooling Equipment . Draft paper, Lawrence Berkeley Laboratory, Berkeley, Calif. Salop, S., and Stiglitz, J. 1977 Bargains and ripoffs: a model of monopolistically competitive price dispersion. Review of Economic Studies 44:493–510. Sonderegger, R.C. 1978 Movers and stayers: the resident's contribution to variation across houses in energy consumption for space heating. In R. H.Socolow, ed., Saving Energy in the Home. Cambridge, Mass.: Ballinger. Stern, P.C., ed. 1984 Improving Energy Demand Analysis. Panel on Energy Demand Analysis, Committee on Behavioral and Social Aspects of Energy Consumption and Production, National Research Council. Washington, D.C.: National Academy Press. Stern, P.C., and Aronson, E., eds. 1984 Energy Use: The Human Dimension. Committee on Behavioral and Social Aspects of Energy Consumption and Production, National Research Council. New York: Freeman. Stern, P.C., Berry, L.G., and Hirst, E. 1985 Residential conservation incentives. Energy Policy 13:133–142. Stern, P.C., Black, J.S., and Elworth, J.T. 1981 Home Energy Conservation: Issues and Programs for the 1980s. Mount Vernon, N.Y.: Consumers Union Foundation. 1983 Responses to changing energy conditions among Massachusettshouseholds . Energy 8:515–523. Stern, P.C., and Oskamp, S. 1985 Managing scarce environmental resources. In D.Stokols and I. Altman, eds., Handbook of Environmental Psychology. New York: Wiley. Tonn, B., and Berry, L. 1984 Conservation Potentials, Participation, and Retrofit Choices in the Connecticut Residential Conservation Service (CONN SAVE) Program. ORNL/CON-161. Oak Ridge, Tenn.: Oak Ridge National Laboratory. U.S. Department of Energy 1984 Residential Conservation Service Evaluation Report. Washington, D.C.: U.S. Department of Energy. U.S. General Accounting Office 1981 Residential Energy Conservation Outreach Activities—A New Federal Approach Needed. Washington, D.C.: U.S. Government Printing Office. van der Linden, J., and van Eijk, T. 1985 Adoption of insulation measures: a case study of a community conservation programme for tenants. In G.Gaskell and B. Joerges, eds., Public Policy and Private Actions: A Multinational Study of Local Energy Schemes. London, England: Gower Press.

About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution.

REFERENCES

102

van Raaij, W.F., and Verhallen, T.M.M. 1983 Patterns of residential energy behavior. Journal of EconomicPsychology 4:85–106. Weiss, C.S., and Newcomb, T.M. 1981 Evaluation of the Home Energy Check Program. Conservation and Solar Division, Seattle City Light, November. Weiss, C.S., Bradley, R., Shaffer, J.C., and Coates, T. 1983 Home Energy Loan Program Cost-Effectiveness Evaluation. Conservation and Solar Division, Seattle City Light, Seattle, Wash. Wickman, K. 1984 Some effects of economic policy measures to stimulate conservation of energy in the building sector—a discussion. Pp. 19–32 in T.Klingberg, ed., Effects of Energy Conservation Programs. M84:2. Gävle, Sweden: National Swedish Institute for Building Research. Wilde, L.L., and Schwartz, A. 1979 Equilibrium comparison shopping. Review of Economic Studies 46:543–553.