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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 appropriate-
ness 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
Butch, 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
9
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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 relation-
ships 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 infor-
mat~on 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 disaggre-
gated 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--manufaC-
turers, 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 imple-
mentation. Surveys are particularly good for assessing qualitative
variables such as awareness and trust of information or the attractive-
ness of particular qualities of a new technology or incentive program.
They are also valuable for interpreting observational data. Data on
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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 pro-
tecting 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 some-
times 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.,
19797. 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.
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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 revela-
tion 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 under-
standing of the issues becomes clearer, research can move from ethno-
graphic 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
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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
tr ials . 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 r 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 imple-
mentation 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 compar ison 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
exper imenting 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 par-
ticipants 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 Wis-
consin 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 assign-'
ing 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
.
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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 parti-
cipation 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
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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 BE=VIO~L 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 r isk 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 e
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.
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17
Data collection moves from open-ended to more tightly
methods--from ethnographic interviews and discussions
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 pro-
grams 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.
controlled
to surveys and
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.
US ING BEHAVIORAL METHODS TO ANALYZE POLICY I SSUES
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.
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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 complete-
ness 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 infor-
mation 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.
OCR for page 19
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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 differ-
ences 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 pro-
grams 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: nonpar-
ticipants 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 com-
parisons 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
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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 tech-
niques are applied to the same programs (e.g., Bonneville Power Adminis-
tration'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
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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 availabil-
ity of technical assistance, consumer protection features, the credibil-
ity 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
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24
(e.ge, 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 infor-
mation (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 '~he the r a label or rating would move
energy efficiency information to a nigher position in the decision
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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 f ield trials that use exper imental 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
~ -- ,
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TABLE 5 Appropriateness of Six Analytic Methods for Addressing Behav-
ioral Issues Related to Research and Development of Energy Efficient
Technology
Issue
Estimating
What Factors Behavioral
Enhance Component of
Method Acceptance? 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 Especially Especially
Experimentation valuable valuable
Evaluation Research
Outcome Evaluation Not appropriate Not appropriate
Process Evaluation Valuable in technology
transfer programs Not appropriate
a function of its consumer features; the marketing efforts of manufac-
turers and dealers; and so forth.
Surveys and ethnographic methods are valuable components of a
behavioral strategy for developing energy-efficient 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.
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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 avail-
able 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 ranae of
behavioral issues that arise in implementing energy efficiency in
buildingse 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.
_
Representative terms from entire chapter:
energy information
