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Energy Efficiency on an Urban Scale Global Institute of Sustainability27

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for the financial “bottom line” play an important role in

motivating energy-efficiency upgrades. However, greater

focus on saving money as a motivation for conserving

energy was associated with higher baseline electricity

consumption, rather than lower consumption.

• Pro-Environment Attitudes/Beliefs Have Little Impact on Behavior: In the Residential study, intention to

conserve energy in the future was associated with higher

likelihood of obtaining an energy-efficiency checkup.

However, other pro-environment attitudes were associated

with lower checkup likelihood. Attitudes and beliefs did

not predict pursuit of residential upgrades, business

participation in EP, or baseline energy use in any study.

• Motives Matter More: Unlike attitudes and beliefs,

self-reported motivations for conserving energy were

consistently associated with decisions to participate in

EP, and with baseline electricity consumption. Some of

these effects were striking and counterintuitive. Among

residential homeowners, high social motives (e.g., to be

seen as an eco-friendly person) were associated with

lower likelihood of pursuing en energy-efficiency checkup.

Among commercial organizations, higher business-related

motivation (e.g., save money, making the business more

competitive; market the business as environmentally

responsible) strongly predicted greater likelihood of

participation. However, relative intensity of the motivation

to save money, as a reason for conserving energy, was

consistently associated with higher levels of electricity

consumption. It may be that households and businesses

that face higher electrical bills each month find the

possibility of saving money especially compelling. But

this consistent finding does suggest that motivation to

save money had not previously been effective in driving

energy-efficient behavior. In contrast, relative intensity of

motivation to preserve the environment and act responsibly

in ecological matters was associated with lower levels of

consumption, at least in residential settings.

• Get the Word Out: Eligible homeowners who had heard

about the EP program through a greater number of

channels were more likely to seek out an energy-efficiency

checkup (although a similar effect was not observed for

actually getting an upgrade). Among businesses, those

who heard about the EP program from a contractor were

more likely to participate; other channels did not appear to

have the same impact.

APPENDIX EBEHAVIORAL ELEMENTS OF ENERGY USE AND PARTICIPATION IN ENERGIZE PHOENIX

ENERGIZE PHOENIX SUMMATIVE REPORT: BEHAVIOR TEAM

EXECUTIVE SUMMARY

The Behavior Change team’s mandate is to analyze the roles of

psychosocial variables such as demographics, household and

business characteristics, beliefs, attitudes, and motivations in

predicting participation in the Energize Phoenix (EP) program,

as well as patterns of energy use. The research presented in

this report addresses three core aims:

1) Assess possible predictors of participation in EP upgrade

programs among residential homeowners and businesses,

including survey respondent demographics, household/

business characteristics, attitudes and beliefs about

conservation, motivations to conserve energy, and ways

respondents had learned about EP.

2) Assess possible predictors of baseline energy usage by

residential homeowners, businesses, and residents of

two multi-unit housing complexes, with an emphasis on

attitudes and motivations.

3) Examine the impact of “TED” – in-home, real-time

feedback devices – on electricity usage among residents

of two multi-unit housing complexes: Arizona State

University’s Taylor Place undergraduate residence hall and

the Sidney P. Osborn subsidized apartment complex.

The following key points are suggested by the overall pattern

of findings presented in this report:

• Kids Matter: The presence of children in the home offers

some motivation for homeowners to consider energy-

efficiency upgrades; single family households were more

likely to receive a checkup than households consisting of

roommates – the reference group. However, children also

present significant constraints. Households with a greater

number of children in the home were less likely to obtain a

checkup, and also tended to consume more electricity than

households with no or few children.

• Money is Complicated: As a resource, money may support

pursuit of energy-efficiency upgrades. Households in which

the respondent worked full-time, and those with higher

household incomes, were more likely to receive an upgrade

through the EP program. For businesses, implications



EP commercial program did not have an independently defined checkup stage comparable to that in the residential program). We had access to energy use data from the local utility for a subset of these businesses. One set of analyses was aimed at identifying predictors of participation in the EP program – i.e. receiving an upgrade. As with the Residential project, another set of analyses sought to identify key predictors of baseline energy use.

3. Dashboard Project. The central aim of the dashboard project was to examine the effects of “The Energy Detective” or “TED” – an in-home, real electricity use feedback device (also known as a Home Energy Information (HEI) device) – on actual electricity consumption among residents of two multi-unit housing complexes: (a) the Taylor Place residence hall on the Downtown campus of Arizona State University, and (b) the Sidney P. Osborn (SPO ) assisted public housing development, also in downtown Phoenix. In both complexes, a randomly selected subset of participating rooms/apartments received a TED display device, as well as an energy Measuring Transmitting Unit (MTU) that measured energy use for the duration of the project. Data for this project were gathered by way of surveys and energy use information, collected either directly from TED data logging devices (Taylor Place) or from the complex management (SPO). In primary analyses, effects of the Dashboard devices were assessed by comparing the experimental versus control groups’ electricity usage. In additional analyses, the extent to which various psychosocial variables predicted baseline energy use and/or Dashboard-related savings was examined as well.

Across projects, survey questions focused on five specific categories of predictors, each of which may play an important role in explaining decision-making about energy consumption and investment in energy efficiency:

• Demographics. Demographic variables such as gender, ethnicity, age, education level, employment status, and political orientation are commonly used as the “first line” of predictors in behavioral research. These predictors sometimes account for substantial amounts of variability in behavioral outcomes (i.e., why some people behave differently from others). Demographic factors may not actually cause behaviors, in the sense that being one ethnicity or another directly causes one to use more or less electricity. However, demographics are associated

• Limited Effect of Real-Time Electricity Use Feedback Dashboards: In two studies, one with an undergraduate residence hall and one in a low-income, publicly subsidized apartment complex, we found little evidence that the presence of a real-time electricity use feedback device led to reduced consumption. No effect was found in the residence hall; a small effect was observed in the apartment complex, but during the winter months when demand for electricity is typically low. However, findings suggest that use of the real-time, immediate feedback display option on these devices may lead to greater savings than alternative settings (e.g., month-to-date usage).

GENERAL INTRODUCTION: AIMS AND APPROACH

The Energize Phoenix Behavior Change team’s mandate is to analyze the roles of psychosocial variables such as demographics, household and business characteristics, beliefs, attitudes, and motivations in predicting participation in the EP program, as well as patterns of energy use and savings. Our approach was founded on existing theory and empirical work from the domains of social psychology, health psychology, and sustainability/conservation psychology, all of which seek both an understanding of the causes of human behavior and the pursuit of effective tools for socially meaningful behavioral intervention.

Findings in this report reflect data from three projects within the Energize Phoenix program:

1. Residential Project. Behavioral data for the Residential Project were gathered through surveys completed by residential homeowners in the target geographical area who (a) received upgrades through the EP program, (b) received a home energy checkup but had not gone on to complete an upgrade as of March 31, 2013, or (c) did not receive a checkup, but were eligible for the program and willing to take the survey. We had access to energy use data from the local utility for a subset of these households. One set of analyses was aimed at identifying predictors of participation in the EP program, both at the checkup and the upgrade level. Another set of analyses sought to identify key predictors of baseline energy usage.

2. Commercial Project. Behavioral data for the Commercial Project were gathered through surveys completed by decision-makers of businesses in the target geographical area that either (a) received an upgrade through the EP program, or (b) had not received an upgrade as of March 31, 2013 but were still willing to complete a survey (the



with elaborate profiles of social roles, resources (social,

economic, and informational), and constraints that are

likely to have causal effects. As a result, demographic

effects can provide important clues to the underlying

causes of behavior. In addition, demographic effects can

help policy-makers make informed decisions about how to

approach various populations in behavioral interventions.

• Household and Business Characteristics. Household

characteristics such as income and household composition,

and business characteristics such as corporate status

and hours of operation, often present incentives and/or

constraints around energy use and investment in energy

efficiency. We also assessed features of the physical plant,

such as lighting and equipment, associated with patterns

of energy use likely to predict interest in upgrades.

• Attitudes/Beliefs. Social Psychological models of behavior

have traditionally emphasized attitudes (i.e., positive

versus negative affective feelings about a behavior) and

beliefs about the need for, control over, and consequences

of behavior as important predictors of behavior itself.

Unfortunately, the actual evidence regarding impact of

attitudes/beliefs on behavioral outcomes in Social and

Health Psychology is mixed at best. However, we felt it

important to examine the predictive power of explicitly

held attitudes and beliefs about the environment, global

warming, and energy conservation as potential predictors

of EP participation and energy use outcomes.

• Motives for Conserving Energy. A newer approach to

predicting behavior asks what motivations people have for

engaging in that behavior – what they believe they stand

to gain. This is of particular interest in the psychology of

sustainable behavior because people may have a wide

variety of reasons for conservation, including saving

money, preserving the environment, protecting one’s

offspring and future kin, and improving one’s social status.

Importantly, these varying motives may have intersecting

or even conflicting implications for a given behavior,

with one motive promoting the behavior but another

discouraging it. Also, the relative importance of certain

kinds of motivations may tell us something about which

motivations actually predict behavioral follow-through, and

which do not.

• How Respondents Heard About the EP Program. The

Energize Phoenix program was marketed to participants

through a wide variety of channels, including news

articles and radio/TV programs, websites, social media,

contractors, community organizations, and word-of-mouth.

We conducted analyses to see whether any particular

channel(s) appeared especially effective in reaching

participants and predicting participation in the EP program.

Findings presented in this report represent collaborative

efforts with the EP ASU Geography team, Economics team and

Dashboard team, based upon data collected in collaboration

with City of Phoenix and Arizona Public Service (APS) EP

program staff. In addition to these empirical findings, we offer

a brief summary of key points in previous research on the

psychology of energy efficiency and sustainable behavior (see

D.1 below), and summaries of implications of the Behavior

team analyses.

RESIDENTIAL PROJECT

DATA COLLECTION: SAMPLE AND PROCEDURES

The Residential Project survey was accompanied by a

voluntary waiver which, with a respondent’s permission, would

permit APS to release a respondent’s energy usage data to

ASU. Survey respondents were approached using a number

of methods. First, undergraduate research assistants were

trained and ASU-approved per City of Phoenix requirements

for door-to-door survey administration, and then assigned

a set of residential addresses within the program target

area. In this way, surveyors attempted to obtain a completed

English- or Spanish-language survey from every home in the

target area before program launch or early in the program.

If an adult resident of the household was home, the surveyor

asked him or her to complete the survey. If no one answered

the door, or if the resident was unable to complete the

survey at that time, the surveyor left a stamped, addressed

envelope with the respondent so they could return the survey

via mail. Additionally, surveyors set up booths at a variety

of local events (e.g., Willo Neighborhood Historic Home Tour,

Phoenix 5K) and invited attendees to complete the survey at

those events. Finally, surveys were included as an optional

component of the participant program application package

administered by contractors. Per federal Better Buildings

grant program reporting requirements, the utility data waiver

was mandatory for program participants.

Survey Respondents. Through these procedures, residential

respondents completed 929 surveys. From this initial set of

surveys, 61 were removed as duplicates (same address and/

or email), and 172 were removed because the respondent’s

stated address was not within the program target area.



In addition, surveys from the 168 respondents who self-identified as renters were removed from analyses, as only four renters participated in the EP program and we anticipated that the process for these four households was likely very different from the process for homeowners. Thus, the analyses below predicting EP program participation reflect data from the remaining 566 surveys, completed by homeowners giving an address in the EP target area.

Of the total sample, 96.5% of respondents completed the survey in English, and the remainder in Spanish. Approximately half of respondents were female. With respect to ethnicity, 58% self-identified as Non-Hispanic White/Caucasian, and 13% as Hispanic/Latino; the remainder indicated another ethnicity or declined to provide this information. Approximately a third of respondents self-identified as Democrats, 13% as Republicans, and 14% as Independents; notably, nearly a third of respondents declined to provide information about political party. Approximately 60% of respondents had a college degree (including 2-year degrees), and about 40% of respondents’ partners had a college degree. Approximately 60% of respondents stated that they worked full-time.

With regard to household composition, 35% of respondents reported living as a couple with no children, and 22% as single adults with no children. Twenty-nine percent (29%) reported living in a single-family household with children (with either one adult or a couple). The remaining respondents reported living with roommates or with extended family. The average respondent had lived just under 30 years in Arizona, and about 14 years in their current home. Mean household income was $94,525 per year (median = $75,000). It is important to note that this income level is considerably higher than the known level for the area, likely reflecting the removal of renters from the survey as well as a general tendency for higher-SES individuals to complete surveys and participate in studies. Thus, caution should be used in considering whether the present findings might generalize to low-income and lower-middle-income populations.

Data Cleaning and Reduction: Demographics and Household Characteristics. Demographic and household characteristic data were examined to assess validity of responses and address problems with outlier values likely to have an undue influence on analyses. Where possible and conceptually valid, categorical responses (e.g., political party) were collapsed into a smaller number of categories, in order to increase statistical power in logistic regression (“logit”) analyses. The

following changes were made based on this examination:

• Ethnicity: Responses were collapsed into three categories – Non-Hispanic White/Caucasian, Hispanic/Latino, and Other/Declined.

• Political Affiliation: Responses were condensed into three categories - Democrat/Green, Republican, and Other/Declined.

• Education (participant and partner): The eight response options for level of education were collapsed into two - No College Degree (including “some college” but without any degree) vs. College Degree (including two-year degrees and professional schools). For partner education, “No Partner” was a third category.

• Income: 12 of the 598 respondents indicated an income greater than $300,000, in some cases in the several millions. In order to avoid the problems commonly associated with outliers in statistical analyses, the income for these respondents was entered as $300,000, effectively capping income at this level.

• Children in Household: Answers to the three questions about number of children of different ages were summed to create a “Total Number of Children” variable.

Preliminary Analyses and Subscale Development: Attitudes

Items. The 21 initial, seven-point scale Likert-type items

assessing attitudes toward the environment and conservation

were subjected to a Principal Components Analysis (PCA),

with the aim of identifying conceptually meaningful sets

of items that could be treated as subscales in this sample.

Unfortunately the PCA failed to identify statistical patterns

among all original items that would point the way to

meaningful composites. Based on analysis of the conceptual

meaning of the individual items, we created six attitude

subscales used to predict participation in EP as well as

patterns of energy use and savings. Cronbach’s alpha was

calculated for each subscale as evidence of the coherence/

internal consistency among items within that subscale; values

near or greater than .70 are typically considered acceptable

for three-item scales and all met this criterion, so all were

retained as potential predictors.

• “Global Warming”: Three items assessing agreement that

the world is experiencing an environmental crisis caused

by human behavior (Mean = 5.80, SD = 1.58, Cronbach’s

alpha = .92).



• “Valuing Conservation”: Three items assessing personal effort to conserve energy and natural resources (Mean = 5.51, SD = 1.26, Cronbach’s alpha = .75).

• “Personal and Societal Efficacy”: Three items assessing belief that humans (including the self) can change energy habits and solve environmental problems (Mean= 5.75, SD = 1.24, Cronbach’s alpha = .75).

• “Human Supremacy”: Three items assessing belief that modern science will solve environmental problems, nature exists for human use, and economic development is more important than preserving the environment (Mean = 3.24, SD = 1.49, Cronbach’s alpha = .62).

• “Negative Emotions”: Three items assessing the experience of guilt, anger, and sadness when thinking about the effects of energy use on the environment. (Mean = 4.22, SD = 1.79, Cronbach’s alpha = .86).

• “Intent to Conserve”: Two items assessing intended effort to conserve energy in the next few months (Mean = 5.36, SD = 1.54, Cronbach’s alpha = .71).

The remaining four items did not load adequately on any subscale, and were excluded from further analyses.

Preliminary Analyses and Subscale Development: Motivation Items. The nine initial items assessing the respondent’s motivations for conserving energy were subjected to a Principal Components Analysis (PCA), again with the aim of identifying conceptually meaningful sets of items that could be treated as subscales. As with the attitude items, the PCA was conducted using data from all available questionnaires. In a preliminary PCA, examination of the scree plot of eigenvalues clearly indicated a two-factor solution. We therefore conducted a follow-up PCA forcing two factors and using direct oblimin rotation to maximize simple structure (i.e., tight fit of individual items to one factor or the other). Based on the PCA, combined with analysis of the conceptual meaning of the items, we created two motivation subscales.

• “Responsibility Motivation”: Six items assessing motivations to save money, preserve the environment, do the “right thing,” protect future generations, preserve national security, and protect his/her family’s future (Cronbach’s alpha = .83).

• “Social Motivation”: Three items assessing motivation to keep up with what others are doing, to self-present as an environmentally responsible person, and because

respected others say energy conservation is important (Cronbach’s alpha = .80).

Self-report measures of motivation often show ceiling effects, such that most people give a very high rating of every motive. In anticipation of this problem, we conducted an additional logit analysis in which ipsatized single item scores (i.e., standardized to the individual’s own mean and standard deviation across the nine motivation items) for all nine original items were entered as predictors.

Data Reduction: How Respondents Heard About the Program. Responses to the question about whether and how respondents had heard about the Energize Phoenix program were combined into five non-mutually-exclusive binary (yes/no) variables: Program Marketing (e.g., newspaper/newsletter ads, door hangers, or EP websites); Contractor; Community (e.g., school, church, friend or neighbor); Other; and None.

RESULTS

The Behavior team had a series of research questions it sought to shed light on through Energize Phoenix research. Each question is designated with a letter code indicating to which program activities it pertained; specifically, R=Residential, C=Commercial and D=Dashboards.

R.2: WHAT PERSON-LEVEL VARIABLES PREDICT HOW LIKELY SOMEONE IS TO PARTICIPATE IN THE ENERGIZE PHOENIX PROGRAM, AND AT WHAT LEVEL (HOME ENERGY CHECKUP VS. UPGRADE)?

Of the 566 respondents who completed surveys, 205 had received or would later receive a home energy checkup through the Energize Phoenix program, as of March 31, 2013. Of those who received a checkup, 111 went on to complete at least one home upgrade by the same cut-off date.

Two sets of simultaneous logistic regression (“logit”) analyses were conducted to identify significant predictors of each level of participation, relative to the preceding level. In these analyses several possible predictors of a dichotomous outcome are entered into the statistical model simultaneously; for each predictor, the analysis provides a test of the statistical significance of the effect, as well as a “weight” estimating the magnitude of the effect in terms of change in log-odds of the positive (i.e., getting the checkup vs. not) outcome. While logit produces fairly accurate estimates of significance (the p-value, or probability that the observed effect reflects sampling error rather than a “real” effect), the weights should be interpreted with caution as they depend greatly on the set of predictors and sample in the current model.



FIGURE 1: ANALYSIS STEPS PREDICTING RESIDENTIAL AND COMMERCIAL PARTICIPATION

a final logit model including all significant predictors from the more constrained models, suggesting that the effects of Ethnicity could be explained in terms of differences in household characteristics, attitudes, and/or channels of program exposure among those of different ethnicities. Also, having a partner with a college degree was associated with significantly greater probability of checkup level participation, compared with not having a partner (Wald = 5.51, p = .019, B = 0.80); the same effect was not observed for respondents whose partners did not have a college degree.

FIGURE 2: HOME ENERGY CHECKUP-LEVEL PARTICIPATION BY ETHNICITY

Source: ASU Global Institute of Sustainability

In the interest of maximizing statistical power, separate logit

analyses were conducted for each of the major categories

of predictors. This raises the possibility that predictors from

different categories, each identified as significant, will account

for overlapping effects in participation outcomes. Thus, as

an additional step, we entered only those predictors that

had emerged as significant in the more constrained models

(including models examining how respondents had heard

about the program, see below) into a final logit analysis, to see

whether one or more predictors would drop out of the model.

Effect size statistics and p-values in the text below are taken

from the initial, more constrained models, but results of the

final model are always summarized as well.

Demographics

Checkup-Level Participation. Demographics logit analyses

included seven categorical predictors (Sex, Ethnicity, Political

Affiliation, Respondent Education, Partner Education,

Respondent Employment Status, and Partner Employment

Status) as well as one continuous predictor – Age. In terms

of predicting home energy checkup-level participation, the

model including these predictors fit the data significantly

better than an “empty” model with only an intercept. However,

it accounted for less than 10% of variability in checkup-level

participation vs. non-participation (Cox & Snell R 2 = .090).

Of the predictors, only two significantly predicted checkup-

level participation (relative to no checkup). With respect to

respondent Ethnicity, Non-Hispanic Whites/Caucasians were

significantly more likely to participate at the checkup level

than both Hispanics/Latinos (Wald = 11.16, p = .001, B =

-1.45) and those reporting another ethnicity or declining

to report (Wald = 4.06, p = .044, B = -0.58). Importantly,

however, the effect of Ethnicity was no longer significant in

Upgrade-Level Participation. Demographic variables were

less useful in predicting the step from getting a checkup to

completing an upgrade. The demographics-focused model

did not fit the data significantly better than an empty model,

although it accounted for about 10% of variability in upgrade

outcomes (Cox & Snell R2 = .092). Note that statistical power

decreases sharply from predicting checkup-level participation

to predicting upgrade-level participation, so comparable

effect sizes in predicting the two levels of participation may

still differ greatly in terms of statistical p-vales. The only

effect that even approached significance was for respondent

employment status. Specifically, respondents who did not

work full time were marginally less likely to obtain an upgrade

than those who did work full time (Wald = 3.44, p = .064, B

= -0.71) – an effect that held up in the final analysis with

significant predictors from the separate categories.

Household Characteristics

Checkup-Level Participation. Household characteristics

analyses included three categorical predictors (landscape

type, pool ownership, and household composition) as well


**p < 01, *p < .05. Denotes a significant difference between percentage of respondents in this Ethnicity category that received a home energy checkup, and the percentage among non-Hispanic Whites.



as six continuous predictors (household income (in $1000s), number of adults in the home, number of children in the home, years in home, years residing the state of Arizona, and years expecting to live in Arizona). In terms of predicting checkup-level participation, the model including these predictors fit the data significantly better than an “empty” model with only an intercept. However, it only accounted for approximately 15% of variability in checkup outcomes (Cox & Snell R 2 = .152).

One important finding from this analysis is that household income was not at all associated with probability of getting a checkup (Wald = .060, p = .807, B <.001). However, various aspects of household composition did emerge as predictors of getting a checkup. First, longer duration of residence in the current home was associated with lower probability of checkup-level participation (Wald = 3.92, p = .048, B = -0.03), although this effect did not remain significant in a final model with all significant predictors from previous models.

Household composition broadly speaking appeared to be an important predictor of checkup-level participation. Rates were lower among households with more adults (Wald = 7.16, p = .007, B = -0.61) as well as those with more children (Wald = 5.12, p = .024, B = -0.41). Household Composition as a categorical variable also emerged as an overall significant predictor (Wald = 13.58, p = .009). Specifically, with “Roommates” as the reference group, households consisting of adult couples (Wald = 4.53, p = .033, B = 2.28) or single families (Wald = 5.03, p = .025, B = 2.47) were significantly more likely to have received a checkup. Although the effect of number of children was no longer significant in the final logit across significant predictors, the other effects remained at least marginally significant. Taken as a whole, these findings suggest that those in smaller and more stable family unit arrangements (i.e., couples or single parents with few children) may be most likely to seek an energy-efficiency checkup.

In addition, two outdoor properties of the home were significantly associated with getting a checkup. First, those in homes without pools were less likely to receive a checkup than those with a pool (Wald = 15.45, p < .001, B = -0.99). Second, landscape type had a strong and significant overall effect in predicting checkup-level participation (Wald = 8.40, p = .039). Relative to no landscaping (“Neither”), no specific type of landscaping was significantly different. However, examination of the means for each landscape type suggests that those whose homes had primarily grass-based landscaping were more likely to receive a checkup than those with a landscape that demanded less water. In the final, more comprehensive

logit analysis, the Pool effect remained significant, although the landscape effect did not. Taken together, these effects suggest that those who were already “doing the right thing” from a conservation standpoint (i.e., no pool, water-efficient landscaping) may have seen less need to seek out additional information on how they could conserve energy further.

FIGURE 3: HOME ENERGY CHECKUP-LEVEL PARTICIPATION BY HOUSEHOLD COMPOSITION


*p < .05. Denotes a significant difference between these two Household Composition categories in terms of percentage of respondents that received a home energy checkup.

FIGURE 4: HOME ENERGY CHECKUP-LEVEL PARTICIPATION BY LANDSCAPE TYPE


Upgrade-Level Participation. Some household characteristics did predict the transition from checkup-level participation to upgrade, with this model fitting the data significantly better than an empty model without predictors, and accounting for about 15% of variability in upgrade outcomes (Cox & Snell R 2 = .154). Two predictors emerged as significant; both remained significant or marginally significant in the final logit analysis as well. First, whereas income failed to predict checkup-level participation, greater income was significantly associated with greater likelihood of upgrade once a checkup had been completed (Wald = 6.34, p = .012, B = .008). Presumably the importance of income increases when making a decision



about actually carrying out the upgrade, which could involve

significant costs to homeowners despite the EP subsidy. Also,

not having a pool was significantly associated with greater

likelihood of upgrade (Wald = 4.75, p = .029, B = 0.83). It

is important to note that while traditional pool motors are

high energy consumers, they were not part of the whole home

performance program, and pool motor upgrades were not

eligible for EP rebates (although the local utility provides

incentives). As a result, homeowners with pools (and the

associated energy costs) may have had greater incentive to

have a home energy checkup, but the contractors performing

the checkups may not have emphasized pool motor upgrades

in their proposed solutions, and even if they did, homeowners

would have less incentive to receive such an upgrade through

the EP program.

Attitudes

Checkup-Level Participation. Attitudes emerged as significant

predictors of receiving a checkup through the Energize Phoenix

program; the model including these predictors fit the data

significantly better than an “empty” model with only an

intercept, although it predicted less than 10% of variability

in checkup-level participation (Cox & Snell R 2 = .090). Higher

intention to conserve was significantly and strongly associated

with greater probability of checkup-level participation

(Wald = 31.78, p <.001, B = 0.49).

Remarkably, other pro-environment attitudes actually predicted

lower probability of checkup-level participation. Specifically,

higher belief in human supremacy over the environment was

associated with higher likelihood of checkup (Wald = 9.37,

p = .002, B = 0.22), and higher scores on valuing conservation

were associated with lower likelihood of checkup (Wald = 3.98,

p = .046, B = -0.20). Moreover, both greater belief in global

warming (Wald = 3.02, p = .082, B = -0.14) and greater

negative emotion when thinking about the effects of human

energy use (Wald = 3.60, p = .058, B = -0.13) were associated

with lower likelihood of getting a checkup at the marginal level

of significance. It is important to note that only two of these

effects remained significant in the final logit model (intention

to conserve and human supremacy). However, one reasonable

possibility, consistent with the landscaping and pool effects

above, is that individuals who identify strongly with pro-

environmental attitudes may believe (rightly or wrongly) that

they have already taken major steps to conserve resources,

and do not see a need for a checkup to identify more potential

improvements. The one positive effect is for the more future-

oriented intention to increase one’s efforts to conserve energy.

FIGURE 5: MEAN ATTITUDES BY RESIDENTIAL CHECKUP-LEVEL PARTICIPATION STATUS


**p < 01, *p < .05, + p < .10. Denotes a significant or marginally significant effect of this attitude in predicting checkup-level participation in the logistic regression model with all attitude scales entered as predictors.

Upgrade-Level Participation. Notably, attitudes did not predict making the jump from checkup-level participation to actually getting an upgrade. The logit model including the attitude variables in predicting the difference between checkup-only and upgrade participants did not fit the data significantly better than an intercept-only model, nor did any single attitude subscale emerge as significant, or even marginally significant.

Motives for Conserving Energy

Checkup-Level Participation. Examined as composites, motives emerged as significant predictors of receiving a checkup through the Energize Phoenix program; the model including these predictors fit the data significantly better than an “empty” model with only an intercept, although it predicted less than 2% of variability in checkup-level participation (Cox & Snell R 2 = .019). Specifically, higher scores on social (i.e., to keep up with what others are doing; to be seen as an environmentally responsible person) were associated with lower likelihood of checkup (Wald = 9.51, p = .002, B = -0.18). Notably, higher scores on the social motives composite were significantly associated with more pro-environmental ratings on the attitudes that emerged as significant predictors of checkup in the analysis above, and the effect of social motives was no longer significant in the final logit analysis that included these attitude measures. This likely reflects an impact of self-presentation on the attitudes items, suggesting that these items were influenced by desire to be seen as highly pro-environment, in a way that may not translate to pro-environmental behavior. No effect was observed for the responsibility motives composite. The logit model with ipsatized motives as predictors did not fit the data significantly better than an empty model, and no individual item emerged as a significant predictor.



Upgrade-Level Participation. In general, motivations to conserve (both raw score composites and ipsatized individual motive items) were not strong predictors the transition from checkup-level participation to upgrade-level participation. Neither model fit the data better than an empty model. However, the ipsatized motivation to “keep up” with what others are doing emerged as significant (Wald = 5.53, p = .019, B = 0.82), and the motive of promoting national security (Wald = 3.27, p = .071, B = 0.47) as marginally significant when only motives were entered as predictors, and these remained marginally significant in the final model with all significant predictors from the more constrained models.

R.5: WHAT PERSON-LEVEL VARIABLES ARE STATISTICALLY ASSOCIATED WITH ENERGY USE AND ENERGY SAVINGS?

In order to examine the predictors of baseline energy usage, we examined the statistical association of person- and household-level variables derived from the residential survey with energy usage during the year of 2010 – prior to the implementation of any residential upgrades subsidized by Energize Phoenix. Of the 566 survey respondents (205 participants and 361 non-participants) whose data were used in analyses predicting program participation, it proved possible to obtain complete 2010 electricity billing data from the local utility for 167 households (108 participants and 59 non-participants). One household was removed due to consistent, implausibly low usage levels (< 20 kWh/month), leaving a total of 166 households.

Because the utility’s monthly billing cycles vary from household to household, and daily usage data were not available, it was necessary to transform the raw billing data to create energy usage estimates for actual calendar months that could be compared across households. For each household, this was done by (a) calculating mean daily usage in kWh for each billing cycle, and then (2) forming an estimate of each month’s use by multiplying the daily mean from each of the two relevant bills by the number of days in that month covered by each bill, and then summing these two figures. While this approach is not error-free, it substantially reduced the noise caused by variable billing cycles without introducing any systematic bias.

Temperatures in Phoenix fluctuate dramatically over the course of a calendar year, driving comparable fluctuations in demand for electricity. Because we anticipated that the predictors of baseline energy use might differ across seasons, we formed three different energy use composites for each household: cold months (January, February, November,

and December); neutral months (March, April, May, and

October); and hot months (June, July, August, and September).

When a household’s monthly usage was below 100 kWh, that

month was treated as missing; no more than one missing

month was allowed per seasonal composite, or the entire

composite was treated as missing.

Five multiple regression models were used to examine

respondent- and household-level predictors of each of the

three seasonal baseline electricity use composites. As with the

analyses predicting Energize Phoenix program participation,

one model included demographic characteristics of the

respondent as predictor variables; one included household

characteristics; one included the respondent’s attitudes and

beliefs about conservation; and two included the respondent’s

motivations for conserving energy. Analyses in each model

used only those households for which all necessary survey

data were available.

Demographics

These models included seven categorical predictors (respondent

sex, ethnicity, and political affiliation, respondent education,

partner education, respondent employment status, and partner

employment status) as well as one continuous predictor –

respondent age. Analyses predicting the cold month usage

composite included 113 households; those predicting the

neutral and hot month composites included 110 households.

None of the three overall models predicting seasonal energy

usage was significant: for cold months F(11, 101) = 1.56,

R 2 = .146, n.s.; for neutral months F(11, 98) = 1.48,

R 2 = .142, n.s.; and for hot months F(11, 98) = 1.28,

R 2 = .125, n.s.. However, individual predictors occasionally

emerged as significant. Households in which the survey

respondent identified as neither White nor Hispanic used

less electricity than those in which the respondent was White

(the reference group) in both the cold month (B = -343.93,

p = .026) and Neutral Month (B = -314.12, p = .037) analyses.

Also, households in which the respondent identified as

republican used more electricity during cold months than

households in which the respondent was libertarian,

independent, or another political affiliation (the reference

group; B = 396.02, p = .031). This effect was not observed

during the neutral months. No demographic variable

significantly predicted energy use during the hot months.

Household Characteristics

These models included three categorical predictors (landscape



type, pool ownership, and household composition) as well as six continuous predictors (household income (in $1000s), number of adults in the home, number of children in the home, years in home, years residing the state of Arizona, and years expecting to live in Arizona). Analyses predicting the cold month usage composite included 101 households; those predicting the neutral and hot month composites included 99 households.

All three overall models predicting seasonal energy usage were significant: for cold months F (14, 86) = 2.32, R 2 = .274, p = .009; for neutral months F (14, 84) = 3.05, R 2 = .337, p = .001; and for hot months F (14, 84) = 3.40, R 2 = .361, p < 001. For all three seasonal composites, households with greater numbers of children (but not numbers of adults) consumed more electricity: for cold months B = 170.11, p = .029; for neutral months B = 168.72, p = .019; and for hot months B = 227.41, p = .072.

Greater number of years’ residence in the current home also predicted greater baseline electricity use in all three seasonal composites: for cold months B = 12.00, p = .081; for neutral months B = 13.51, p = .037; and for hot months B = 22.04, p = .054. However, greater number of years’ residence in the state of Arizona was associated with lower household electricity usage during the neutral (B = -6.60, p = .036) and hot months (B = -11.24, p = .043), likely reflecting acclimation and/or adaptation of lifestyle to the late-spring and summer heat of this region.

Finally, households with primarily desert-based landscaping used less electricity than households with no landscaping (the reference group), but only during the hot months (B = -1588.78, p = .024). Household income was not a significant predictor of electricity usage in any of the three seasonal composites.

Attitudes

Overall, explicit conservation-related attitudes and beliefs failed to predict actual electricity usage. Analyses predicting the cold month usage composite included 137 households; those predicting the neutral and hot month composites included 134 households. These analyses included composites assessing: belief in global warming; personal value of conservation; belief in personal and societal efficacy to change energy use; belief in human supremacy over environmental issues and problems; negative emotions when thinking about the effects of energy use on the environment; and intention to conserve energy in the next few months. None of the three

overall models using attitudes to predict seasonal energy

usage was significant: for cold months F(6, 130) = 0.98, R 2

= .043, n.s.; for neutral months F(6, 127) = 1.45, R 2 = .064,

n.s.; and for hot months F(6, 127) = 1.19, R 2 = .053, n.s..

Within these models, no individual attitude composite was

found to have a significant effect.


Two separate models examined the extent to which energy-conservation motives predicted each of the three seasonal energy use composites. For each season, one model included only the two motivation composites created from individual motivation items: the Responsibility Motives composite and the social motives composite. Analyses predicting cold month usage included 134 households; those predicting neutral and hot month usage included 131 households. None of these three overall models was significant: for cold months F(2, 131) = 0.17, R 2 = .003, n.s.; for neutral months F(2, 128) = 0.80, R 2 = .001, n.s.; and for hot months F(2, 128) = 1.20, R 2 = .018, n.s.. In these models, neither of the motivation composites proved to be a significant predictor.

However, all three models using the individual ipsatized (i.e., relative) motivation item ratings as predictors were significant: for cold months F(9, 112) = 2.33, R 2 = .158, p = .019; for neutral months F(9, 109) = 2.26, R 2 = .157, p = .023; and for hot months F(9, 109) = 2.34, R 2 = .162, p = 019. A number of consistent patterns emerged in terms of specific motivations significantly associated with energy use. The relative importance of preserving the environment was associated with lower energy use in all three models: for cold months B = -364.26, p = .005; for neutral months B = -281.19, p = .023; and for hot months B = -441.71, p = .044.

Remarkably, higher relative importance of saving money as a motive for conserving energy was associated with higher baseline energy usage in all three models: for cold months B = 234.10, p = .008; for neutral months B = 270.23, p = .002; and for hot months B = 441.55, p = .004.

Finally, higher relative importance of making sure future generations do not inherit our environmental problems was also associated with higher baseline use in all three models: for cold months B = 251.45, p = .007; for neutral months B = 235.19, p = .009; and for hot months B = 379.16, p = .017. One possibility is that this motivation is more salient for adults with children, and as noted above, households with more children were found to consume more electricity

in these analyses.



R.6: HOW DID PARTICIPANTS FIND OUT ABOUT THE PROGRAM? DOES THIS PREDICT LEVEL OF PARTICIPATION (CHECKUP ONLY, UPGRADE, NEITHER) AND/OR ENERGY SAVINGS?

The majority of survey respondents (66.8%) had heard about the Energize Phoenix program from at least one source by the time they completed the survey. Respondents were equally likely to have heard about the program from members of their communities as from formal marketing efforts, consistent with prior evidence that word-of-mouth and community-based social marketing are crucial components in behavioral intervention campaigns. A smaller proportion of respondents had heard about the program from a contractor.

FIGURE 6: HOW RESIDENTIAL SURVEY RESPONDENTS HAD HEARD ABOUT EP

effects remained significant in the final logit model including

significant predictors from the five more constrained models.

However, the decision to upgrade was not significantly

predicted by how participants had heard about the EP

program, either in terms of total number of channels

(Spearman’s r = -.016, p = .827) or in terms of individual

channels in the logit analysis. The implication is that multi-

channel marketing is important for getting energy efficiency

program prospects “in the door” and that other factors

influence their decision to follow through on an upgrade.

R.7: IS THERE A REBOUND EFFECT, SUCH THAT GETTING THE UPGRADES INCENTIVIZES PARTICIPANTS TO USE MORE ENERGY IN OTHER WAYS?

Addressing this question will require follow-up data, not yet

available, with participants who received upgrades. Thus we

are unable to address this question at this time.

R.8: IS THERE A SPILLOVER EFFECT, SUCH THAT GETTING UPGRADES IS CORRELATED WITH A CHANGE IN PARTICIPANTS’ SAVING/CONSERVING IN OTHER WAYS?

Addressing this question will require a follow-up survey, not

yet complete, with participants who received upgrades. Thus

we are unable to provide data on this question at this time.

SUMMARY

Program Participation: Although the correlational nature

of this study necessitates great caution in inferences about

causal direction, the findings presented above suggest that

psychosocial factors can be useful predictors of people’s

decisions to seek a home energy checkup and/or pursue

an upgrade, information which may be useful in shaping

programs and their marketing. The specific predictors of these

two levels of participation differed in very important ways.

This is consistent with research from other areas of behavior

change; for example, the well-known “Stages of Change”

model in health psychology states that individuals at different

levels of the change process (e.g., thinking about quitting

smoking versus planning a quit attempt) are motivated and

supported in different ways. The present results suggest a

similar approach may be needed for promoting residential

energy-efficiency upgrades and other investments.

At the checkup level, demographic and household factors

proved to be important predictors of participation. Adult

couples without children and single families with children

were most likely to receive checkups. Households with

fewer children, and those in which the respondent was a


***p < 001. Denotes a significant effect of having heard about EP through this channel (versus not having heard about EP through this channel) on probability of receiving a home energy checkup.

On average, respondents had heard about the EP program

through 0.68 channels (SD = 0.61; includes participants

who had not heard about the program at all). The number of

ways respondents had heard about the program was a strong

and significant predictor of receiving a checkup (Spearman’s

r = .43, p < .001). A simultaneous logit analysis with the four

channels as well as total number of channels fit the data

significantly better than an empty model, and predicted more

than 20% of variability in checkup vs. non-participation

outcomes (Cox & Snell R 2 = .194). This effect was not specific

to any particular channel of information. In a simultaneous

logit analysis, all four channels were strongly, independently,

and significantly associated with greater likelihood of

checkup-level participation: marketing (Wald = 20.55,

p < .001, B = 1.14), contractor (Wald = 52.53, p < .001,

B = 2.99), community (Wald = 54.23, p < .001, B = 1.98) and

other (Wald = 12.58, p < .001, B = 1.02). Notably, all four



non-Hispanic White, were also more likely to receive a checkup.

Taken together, these factors may reflect a combination of

household stability with available time, such that established

single-family households with no or few children had a

combination of motivation and opportunity that facilitated

seeking out a checkup.

Attitudes also had strong, yet surprising, implications for

probability of checkup-level participation. Although higher

intention to conserve was associated with higher likelihood of

getting a checkup, several other pro-environment attitudes

were associated with lower likelihood of getting a checkup.

Higher scores on social motivation to conserve energy (e.g.,

to be like other people; to be seen as eco-friendly), as well as

certain household characteristics suggesting pro-environment

decisions (e.g., water-conserving landscaping; not having

a pool) were associated with lower checkup rates. Taken

together, these effects may suggest a kind of person who

strongly self-identifies as eco-friendly, but believes (rightly or

wrongly) that he/she has already taken sufficient steps in this

regard, and is not motivated to seek out additional ways to

improve energy efficiency in the home.

Program marketing also had a clear impact at the checkup

stage. The more ways respondents had heard about the

program, the more likely they were to pursue a checkup. It is

worth noting that most surveys were conducted quite early in

the program period, leaving ample time for households to seek

a checkup after completing the survey itself if they chose to

do so. Importantly, household income was not a significant

predictor of checkup level participation.

Baseline Electricity Use: A different set of factors predicted

actually getting an energy-efficiency upgrade, once the checkup

was complete. Resources appeared to play a much more

important role at this stage – both higher household income

and respondent full-time employment were associated with

greater likelihood of upgrade-level participation. In contrast,

demographics, attitudes and motivations, and Energize Phoenix

program exposure were less important predictor.

The predictors of baseline (i.e., prior to any upgrades) energy

usage also differed in important ways from the predictors of

program participation. Demographic variables were of little

use in predicting electricity consumption, especially during the

hot months. Households with more children, and those that

had lived in the current home for a longer duration, tended

to use more electricity. However, those who lived in Arizona

longer used less energy in neutral and hot months. Beyond

these effects, however, household characteristics also had surprisingly little predictive utility. Attitudes also failed to predict historical patterns of energy use.

Of the four types of predictors examined in these analyses, motivations for conserving energy appeared to have the most powerful effects. Higher motivation to preserve the environment, relative to other motives, was strongly associated with lower baseline energy usage. In contrast, higher relative motivation to save money was associated with higher baseline energy usage. Of course, it may be that those who are currently spending more on their monthly electricity bills are more motivated by the promise of financial savings. However, this finding also suggests that primarily financial motives are not, in and of themselves, very effective in promoting energy conservation behavior. This effect is consistent with a rich body of social psychology literature suggesting that external motivations for a behavior (such as the promise of material reward) may actually begin to replace and inhibit internal motivations (such as enjoyment), eventually reducing the motivation to engage in that behavior once the reward is no longer on the table. Finally, higher motivation to make the world better for future generations was also associated with higher baseline energy use – a point discussed under “caveats,” below.

Caveats: Two caveats are especially important for interpreting these findings. First, as noted above, these data are not based on an experimental design, and thus inferences about the direction of causality are not warranted. One reasonable possibility, which we were unable to explore, is that some observed associations were actually accounted for by structural variables not included in this data set. For example, it may be that households in which the respondent placed greater value on protecting future generations from current damage to the environment were more likely to be households with children, and that households with more children simply live in larger homes. Thus, the effects of the “future generations” motivation and of number of children in the household on electricity usage may actually reflect size of the home.

Second, although most Energize Phoenix program participants completed the survey, only a small proportion of eligible non-participants did so. As a result, some of the observed effects in predicting participation may reflect biases in our control group (i.e., non-participants) rather than “real” effects. It is possible, for example, that individuals with strong pro-environment attitudes were more likely to fill out a survey even



if they had no intention of participating, whereas less eco-friendly non-participants did not bother. It is still noteworthy that these individuals did not then go on to request a checkup after completing the survey, as the residential surveys were distributed early in the project period and most who completed one would have had ample time to do so. However, the non-participants in the current sample likely differ from the larger population of non-participants in important ways, and this may have influenced our findings.

COMMERCIAL PROJECT


As with the Residential Surveys, Commercial Survey respondents were approached by a number of methods. First, research assistants were trained and ASU-approved per City requirements for door-to-door survey administration, and then assigned a set of commercial addresses within the program target area. In this way, surveyors attempted to obtain a completed survey and waiver from every business in the target area. If no employee was willing to complete the survey at that time, the surveyor left a stamped, addressed envelope so they could return the survey via mail. Surveys were also included as an optional component of the participant program application package administered by contractors. Per federal Better Buildings grant program reporting requirements, the utility data waiver was mandatory for program participants. Surveyors went out again at the end of the second year and beginning of the third year specifically to organizations that had completed upgrades but had not returned a survey in order to make a final attempt to secure one.

After removing redundant surveys from the same business (the more complete survey was retained, or if both were complete, the first one completed was retained) and surveys from businesses that both did not own the building and were not allowed to improve it, 318 usable surveys were available. Of these, 226 were completed by respondents from businesses that received an upgrade by March 31, 2013, and 92 were completed by respondents from businesses that did not participate as of this cut-off date. It is important to note that this indicates an extremely low compliance rate among non-participant businesses, raising the possibility that the non-participant comparison group in these survey analyses is biased relative to all possible non-participant businesses in important ways. We discuss implications of this possible bias in the Commercial Project analysis summary, and cross-reference our findings with those of the Geography Team, which conducted more limited but similar analyses using a

non-participant comparison group drawn from the National

Establishment Time Series (NETS) industry database (see

Appendix F: Commercial Participation Factors).

Moreover, because many survey respondents skipped

questions in completing the surveys, and logit analysis uses

listwise deletion (i.e., if even one data point needed for an

analysis is missing for a given business, that business is

removed from the analysis entirely), the analyses below are

based on much smaller sample sizes. This raises further

questions about the representativeness of the sample, and

indicates that considerable caution is needed in interpreting

these results.

Preliminary Analyses and Subscale Development: Attitudes

Items. The ten initial items assessing attitudes toward the

environment and conservation were subjected to a Principal

Components Analysis (PCA), with the aim of identifying

conceptually meaningful sets of items that could be treated

as subscales in this sample. Unfortunately the PCA failed

to identify statistical patterns among items that would

point the way to meaningful composites. Based on analysis

of the conceptual meaning of the items, we created three

attitude subscales and two individual items used to predict

participation in EP, as well as patterns of energy use and

savings.

• “Climate Change Beliefs”: Two items assessing agreement

that the world is experiencing an environmental crisis

caused by human behavior (inter-item r = .64).

• “Valuing Conservation”: Two items assessing agreement

that it is important to conserve energy and natural

resources (inter-item r = .61).

• “Personal and Societal Efficacy”: Two items assessing

belief that humans are capable of changing the effects

they have on the environment (inter-item r = .52).

• “Company Efficacy”: One item assessing belief that the

company is able to change its energy use.

• “Prioritization of Economic Growth,” One item assessing

belief that economic growth is more important than

environmental protection.

The remaining two items were not included in subsequent

analyses.

Preliminary Analyses and Subscale Development: Motivation

Items. The nine initial items assessing the business’s



motivations for conserving energy were subjected to a

Principal Components Analysis (PCA), again with the aim of

identifying conceptually meaningful sets of items that could

be treated as subscales. As with the attitude items, the PCA

was conducted using data from all available Commercial

questionnaires. In a preliminary PCA, examination of the scree

plot of eigenvalues clearly indicated a two-factor solution.

We therefore conducted a follow-up PCA forcing two factors

and using direct oblimin rotation to maximize simple structure

(i.e., tight fit of individual items to one factor or the other).

Notably, this PCA analysis indicated a quite different factor

structure for motivation than was observed among residential

participants. Based on the PCA analysis, and considering the

conceptual meaning of the items, we created two motivation

subscales, entered into one binary logistic regression analysis

predicting participation:

• “Environmental Motivation”: Three items assessing

motivations to preserve the environment, to protect

future generations, and to do the “right thing” (Cronbach’s

alpha = .89).

• “Business Motivation”: Three items assessing motivation

to keep up with other businesses, to be more competitive,

and to follow industry leaders (Cronbach’s alpha = .79).

As noted in the section on the Residential Program, above,

self-report measures of motivation often show ceiling effects,

such that most people give a very high rating of every motive.

In anticipation of this problem, we conducted an additional

logit analysis in which ipsatized (i.e., standardized to the

individual’s own mean and standard deviation across the nine

motivation items) individual item scores for all nine original

items were entered as predictors.

RESULTS

C.3: WHAT BUSINESS-LEVEL VARIABLES AND BUILDING CHARACTERISTICS PREDICT HOW LIKELY AN ORGANIZATION IS TO PARTICIPATE IN THE ENERGIZE PHOENIX PROGRAM?

Implications of business-level variables for EP participation

were examined in three logistic regression (“logit”)

analyses. In these analyses, several possible predictors

of a dichotomous outcome are entered into the statistical

model; for each predictor, the analysis provides a test of the

statistical significance of the effect, as well as a “weight”

estimating the magnitude of the effect in terms of change in

log-odds of participation. While logit produces fairly accurate

estimates of significance (i.e., p-value or the probability that

the observed effect reflects sampling error rather than a

“real” effect), the weights should be interpreted with caution,

as they depend greatly on the specific predictors and sample

in a particular analysis.

In the first logit analysis, variables reflecting features of the

business that involve decision-making structure, nature of the

business per se, or demographics of the owners were entered

simultaneously. Predictor variables included: legal status of

business (corporation vs. other); property status (own vs.

lease), only place of business, small business, woman-owned

business, minority-owned business, veteran-owned business,

whether or not the business has a formal policy promoting

alternative transportation, whether or not the business has

a formal sustainability policy, whether or not the business

currently takes specific measures to improve electricity

efficiency, whether or not the business had received any offers

for other energy incentives/rebates in the preceding two years,

and whether or not the business had taken advantage of any

other offers in the past two years (all Y/N). This analysis

included 139 businesses (98 participants and 41 non-

participants) for which all necessary data were available.

This model accounted for approximately 20% of variability in

participation outcomes (Cox & Snell R 2 = .194), classifying

78% of businesses correctly, and a number of individual

predictors emerged as significant. Specifically:

• Legal Status was one such predictor, with corporations

showing greater likelihood of participation than other types

of entities (e.g., sole proprietorships, partnerships, LLCs;

Wald = 9.34, p = .002, B = 1.51).

• Businesses that owned their building, rather than leasing

it, were more likely to participate in the program (Wald =

8.33, p = .004, B = 1.35).

• Businesses that reported already taking specific

electricity efficiency steps were somewhat more likely to

participate than those that did not, although this effect

only approached significance (Wald = 2.63, p = .105,

B = 0.81).

• Woman-Owned businesses appeared less likely to

participate than other businesses (Wald = 5.09, p = .024,

B = -1.48).

In a second logit analysis these same predictors were entered

along with features of the physical plant expected to influence

energy demand. Specifically, the second logit added: lighting



type (Y/N for each – incandescent, fluorescent, compact

fluorescent/CFL, high pressure sodium, LED, Halogen) and

major equipment (Y/N for each – pumps/motors, ovens/

kilns, refrigerators, computers/TVs, compressed air, process

equipment, cooling towers), each as a binary Y/N variable. The

analysis was performed using backward stepwise regression.

In this version of logistic regression, all predictors are included

in the model on the first “step.” In subsequent steps, one

non-significant predictor at a time is removed from the model,

simplifying it and increasing statistical power (by increasing

the case-to-predictor ratio as much as possible). This

process continues until all predictors have a p-value below a

given threshold (in this analysis, p = .10) indicating that all

remaining predictors are significant.

This type of analysis has both strengths and limitations. On

one hand it can accommodate a larger number of predictors

while still retaining reasonable statistical power; on the

other hand, it risks “overfitting” the model, capitalizing on

chance characteristics in a particular sample that may not

generalize to other populations. For these reasons we chose

to run both the more constrained (above) and more inclusive

versions of the model, and compare findings. Predictors that

were significant in the more constrained model, but lost

in the larger model, may indicate apparent business-level

characteristics that were actually explained by features of

energy demands in the physical plant.

The second logit analysis included 139 businesses (97

participants and 40 non-participants) for which all necessary

data were available. The model converged in the 27th step.

At this point, four predictors were significant – legal status

(corporation vs. other), property status (own vs. lease),

women-owned status, and use of incandescent lighting – and

a fifth predictor, use of processing equipment, was marginally

significant.

In the third logit analysis, these five predictors were

entered into a single, simultaneous logit model predicting

participation. This analysis used a larger sample, as only data

on these five predictors were needed for inclusion, making

it possible to obtain a better estimate of the effect sizes

associated with these predictors. The analysis included 198

businesses (141 participants and 57 non-participants) for

which the necessary data were available.

The model accounted for approximately 15% of variability in

participation outcomes (Cox & Snell R 2 = .146), classifying

77% of businesses correctly. With the exception of use

of processing equipment, all of the predictors that were significant in the preceding analysis remained significant. Legal status of corporation was associated with significantly higher probability of participating (Wald = 8.17, p = .004, B = 1.09), as was building ownership relative to leasing (Wald = 6.98, p = .008, B = 0.92). Again, women-owned businesses appeared less likely to participate (Wald = 4.16, p = .041, B = -1.06). Finally, use of incandescent lighting was still associated with higher likelihood of participation (Wald = 6.91, p = .009, B = 1.04).

C.7: ARE ORGANIZATIONS THAT ALREADY ENCOURAGE GREEN BEHAVIOR MORE LIKELY TO UPGRADE? ARE THEY MORE SUCCESSFUL OVERALL IN REDUCING THEIR ENERGY CONSUMPTION?

As noted above (see C.6), analyses based on our survey data did not find that businesses with formal sustainability or alternative transportation policies were more likely to participate in the Energize Phoenix program than businesses without such policies. Businesses that reported having previously taken concrete steps to improve their energy efficiency were somewhat more likely to participate, although this effect did not reach conventional levels of significance.

C.8: DO THE ENVIRONMENTAL ATTITUDES AND BELIEFS OF THE BUSINESS’S DECISION-MAKERS RELATE TO THE DECISION TO UPGRADE?

Attitudes

Analysis of attitudes was based on a subsample of 169 businesses (117 participants, 52 non-participants) for which the necessary data were available. In this subsample, the logit model including attitude variables did not improve significantly upon an empty model with no predictors, and none of the attitude variables significantly predicted participation.


Analysis of motivations for conserving energy as predictors of participation was based upon a subsample of 196 businesses (140 participants, 56 non-participants) for which the necessary data were available. First, a simultaneous logit model including only the two motivations composites as predictors was significantly better than a model with no predictors, but accounted for only a small amount of variability in participation outcomes (Cox & Snell R 2 = .037). Specifically, the business motivation composite emerged as a significant predictor of participation (Wald = 7.14, p = .008, B = 0.28). The effect of environmental motivation was not significant.



FIGURE 7: RELATIVE MOTIVATIONS TO CONSERVE BY COMMERCIAL PARTICIPATION STATUS

and motivations as measured through the commercial

survey with energy usage during the year of 2010 – prior to

the implementation of any upgrades subsidized by Energize

Phoenix. Analyses included 152 businesses for which survey

and 2010 energy use data (provided by the local utility) were

both available.

Because the utility’s monthly billing cycles vary from

business to business, and daily usage data were not

available, it was necessary to transform the raw billing data

to create energy usage estimates for actual calendar months

that could be compared across businesses. This was done

in the same way as in the residential energy use analyses

above. For each business (a) mean daily usage in kWh was

calculated for each billing cycle, and (2) an estimate of each

month’s use was calculated by multiplying the daily mean

from each of the two relevant bills by the number of days in

that month covered by each bill, and then summing these

two figures. Also as in the residential analyses, we formed

three different energy use composites for each business:

cold months (January, February, November, and December);

neutral months (March, April, May, and October); and hot

months (June, July, August, and September).

Attitudes

Analyses uncovered no evidence that respondent attitudes

toward conservation were associated with 2010 energy

consumption by his/her business. None of the three overall

models predicting energy use from attitudes was significant:

for cold months F(5, 116) = 0.48, R 2 = .020, n.s.; for neutral

months F(5, 115) = 0.46, R 2 = .020, n.s.; and for hot months

F(5, 115) = 0.44, R 2 = .019, n.s.. None of the individual

attitude composites had a significant effect in any model.


In contrast, the respondent’s motives for conserving energy

emerged as powerful predictors of 2010 electricity use.

The three models in which the business and environmental

motivation composites were entered as predictors were all

significant: for cold months F(2, 149) = 6.93, R 2 = .085,

p = .001; for neutral months F(2, 148) = 7.15, R 2 = .088,

p = .001; and for hot months F(2, 148) = 7.63, R 2 = .093,

p = 001. For all three seasonal composites, higher scores

on the business motivation composite were associated

with higher 2010 electricity usage: for cold months B =

28290.79, p = .003; for neutral months B = 32478.79, p

= .002; and for hot months B = 39498.18, p = .001. In

contrast, environmental motivation composite scores did


*p < 05, +p < .10. Denotes a significant or marginally significant effect of this relative motivation in predicting commercial participation status, in the logistic regression model with all relative motivations entered as predictors.

Second, all ipsatized individual item scores are entered as predictors in a separate logit model (186 businesses; 134 participants, 52 non-participants). This model is a significant improvement on a model with only an intercept and accounts for a greater amount of variability in participation outcomes – approximately 10% (Cox & Snell R 2 = .098). In this analysis, relative intensities of four motivations were associated with greater likelihood of participating: promoting the business as environmentally responsible (Wald = 5.85, p = .016, B = 0.83); saving money on electricity bills (Wald = 4.64, p = .031, B = 0.60); making the business more competitive (Wald = 4.51, p = .034, B = 0.65); and keeping up with what other businesses are doing (Wald = 2.85, p = .091, B = 0.47).

Taken together, these findings suggest that businesses may be motivated to take advantage of energy efficiency subsidy programs such as Energize Phoenix primarily in order to save money and promote their business, rather than by pro-environmental motives per se. Importantly, these motivations remained at least marginally significant in a follow-up logit model including the business characteristics identified in section C.3 above, indicating that the motivational effects do not overlap with effects of more objective business characteristics.

C.9: DO MORE PRO-ENVIRONMENTAL ATTITUDES AND BELIEFS PREDICT LOWER HISTORICAL ENERGY CONSUMPTION, AND/OR HIGHER POST-CHECKUP AND POST-UPGRADE ENERGY SAVINGS?

In order to examine the predictors of baseline energy usage, we examined the statistical association of respondent attitudes



not significantly predict electricity usage in any of the three seasonal models.

C.10: DOES AN ORGANIZATION’S DESIRE TO LOOK “GREEN” INFLUENCE UPGRADE DECISIONS?

As noted above (see C.8), greater intensity of motivation to market the business as environmentally responsible, relative to the other motives measured in the survey, was significantly and uniquely associated with participation in Energize Phoenix.

C.13: HOW DID ORGANIZATIONS FIND OUT ABOUT THE PROGRAM? IS THERE A CORRELATION WITH THEIR LEVEL OF PARTICIPATION (CHECKUP OR UPGRADES)? WITH HOW MUCH ENERGY THEY SAVE?

Data were available from 267 businesses regarding how they had heard about the Energize Phoenix program. Of these, 56 (21%) reported not having heard about the program at all prior to taking the survey; 49 (18%) had heard about EP through formal marketing channels (newspaper ads, web sites, etc.); 129 (49%) had heard about the program from a contractor; 25 (9%) from family, friends, or a community member; and 28 (10%) from some other source.

FIGURE 8: PARTICIPATION RATES BY HOW BUSINESSES HAD HEARD ABOUT EP PROGRAM

27% of participation outcomes (Cox & Snell R 2 = .273).

Having heard about the program from a contractor emerged

as a marginally significant predictor of participation (Wald =

3.63, p = .057, B = 1.34). Not surprisingly, not having heard

about the program at all (Wald = 5.14, p = .023, B = -1.62)

was negatively associated with probability of participation.

SUMMARY

Program Participation: Analyses of the predictors of

business participation in Energize Phoenix offered some new

insights into the key factors that drive such investments. At

least in these analyses, features of the physical plant (with

the exception of incandescent lighting) had little impact on

probability of participation. Demographic variables (with one

exception under “caveats” below) and attitudes also generally

failed to predict participation. Key predictors included aspects

of the business that involved legal structure sophistication

(legal status as a corporation) and decision-making autonomy

(ownership of the building), as well as motivations involving

promotion and enhancement of the business itself (e.g.,

saving money, making the business more competitive,

ability to market the business as “green”). Pro-environment

attitudes, motivations, and policies had little to do with the

decision to pursue an energy-efficiency upgrade. Rather, this

decision appears to rely on pragmatic, bottom-line issues –

a finding with clear implications for marketing future such

programs to local businesses.

Having heard about the program from a contractor was also

associated with higher likelihood of participation, whereas

other forms of marketing and exposure did not appear to

have this effect. This diverges from the findings with the

residential study, in which all channels of program promotion

and an increase in the number of channels of exposure

were associated with higher probability of checkup level

participation. Business decision-makers may be more swayed

by endorsements of the program by actual contractors, along

with the detailed information that contractors are able to

provide. The results also likely reflect the contrasting program

marketing approaches to the two sectors.

Baseline Energy Use: As observed for program participation,

explicit pro-environmental attitudes and beliefs attitudes

failed to predict baseline (2010) energy use by businesses

included in these analyses. Motivations were much more

powerful in this regard. Strikingly, however, higher business-

focused motivation was associated with higher baseline

electricity usage, not lower usage. Again, this suggests that


+p < .10. Denotes a marginally significant effect of having heard about EP through this channel on probability of participating in EP.

In a logit analysis with a single predictor of participation

status (178 businesses included; 122 participants, 56

non-participants), total number of ways participants had

heard about the program was not significantly related to the

decision to participate (Wald = 0.10, p = .749). Implications

of specific channels for participation were examined using a

separate, follow-up logit analysis in which the five response

options above (four channels plus “had not heard”) were

entered simultaneously as predictors (101 business included;

55 participants, 46 non-participants). This model improved

significantly upon the empty model and accounted for over



energy-efficiency attempts by businesses are fundamentally

about the bottom line. Whether financially-driven motivations

are effective in promoting successful energy conservation

is unclear.

Caveats: Similar cautionary notes are needed here as in the

summary of the residential study. The correlational study

design makes it difficult to infer causal direction, even when

a significant predictor of program participation or baseline

energy use is observed. Also, the present sample of non-

participants is probably quite biased compared to the total

population of non-participating but eligible businesses. For

example, although these analyses suggest that women-owned

businesses were less likely to participate, it is highly probable

that women business owners who chose not to participate in

the program were more likely than men to agree to complete

the survey anyway. It is useful to compare the findings

from these analyses with those of the Geography Team (see

Appendix F: Commercial Participation Factors), who conducted

similar analyses using the NETS system to provide information

about non-participating businesses. To the extent that the

two analyses offer converging findings, those findings should

be quite reliable. Where the findings differ, the NETS-based

findings may be more trustworthy.

DASHBOARD PROJECT

The Energy Detective (TED) device, is an in-home electricity

monitoring system that allows residents to track their real-

time usage in kilowatt-hours (kWh) and/or dollars spent,

among other tracking functions. The TED unit consists of

three distinct but linkable components: a set of clamps and a

transmitter that installed within the home’s electrical panel to

wirelessly transmit electricity use information; a “Gateway”

wall plug that receives and records this information; and a

“Display” that makes the information visually available to

residents. Thus, the full system allows people to view the

amount of electricity they are currently using, and to track

changes in electricity consumption that are linked to use of

specific appliances.

An extensive body of research has documented the helpful role

that real-time feedback can play in behavior modification (see

Appendix XX-3 for a brief review of this literature). Especially

when the consequences of a behavior are otherwise fairly

distal and aggregated over long periods of time, as is the case

with energy use (i.e., electricity bills aggregate a month’s

usage, and reflect consumption from weeks earlier), real-time

feedback gives people the chance to map their behavior to

its consequences more directly and precisely. The feedback

principle has been applied to enhance performance and

improve behavior in a variety of domains, including education

and health promotion. Increasingly, researchers are interested

in whether real-time feedback on energy consumption helps

people to alter their behavior in the energy-efficiency domain

as well.

Most studies so far have examined the impact of feedback

devices on electricity usage by single-family, middle- and

upper-middle class homes. In two randomized, controlled

studies executed in coordination with the Dashboard team as

well as City of Phoenix and ASU partners, the behavior team

examined the impact of TED device feedback on electricity

consumption in quite different settings: a college residence

hall and a low-income subsidized housing apartment complex,

both in the light rail corridor that served as the target area for

Energize Phoenix.


Taylor Place University Residence Hall

The Taylor Place residence hall is located on the Downtown

Phoenix campus of Arizona State University. In total, 121 dorm

rooms on four floors of the residence hall were included in

the present study. Floors were loosely segregated by major;

most residents on two of the floors were journalism/mass

communication majors, and most residents of the other

two floors were public programs or criminal justice majors,

although there was some mixing across floors. Approximately

two-thirds of residents in these rooms were women, and

one-third were men. Residents ranged in age from 17 to

21 years, with a mean just over 18 years. Although many

residents declined to provide information regarding their

ethnicity, approximately half of those who did self-identified

as non-Hispanic White, approximately a third as mixed-

ethnicity, just over 10% as Hispanic/Latino, and the rest as

another ethnicity. Similarly, many residents declined to provide

information about their political affiliation. Among those

who did, approximately a third identified as Democrats, and

another third as Republicans, with the remainder identifying

as Independent, Green, or having another affiliation.

Prior to the start of the semester, TED Gateways were installed

in all 121 rooms and set to record energy use from lighting

and plug-in devices. TED real-time displays were also installed

in half of the rooms on each floor using a “checkerboard”

approach, such that alternating rooms on each side of a

hallway received a device, and either one’s room or the one



across the hall had a display. In addition, each floor was assigned quasi-experimentally to one of two conditions: designation as a “community floor” with discussions of energy conservation to be organized by the floor’s residential assistant; or as a non-community floor (see sections D.5 and D.6 under “Results,” below for more detail on this aspect of the intervention). One floor representing each academic major was assigned to each condition, in order to avoid confounding these two factors. Of the 121 rooms, 28 had a TED display and were located on a community floor, 30 had a TED display but were not on a community floor, 31 did not have a TED display but were on a community floor, and 32 did not have a TED display and were not on a community floor.

FIGURE 9: TAYLOR PLACE DASHBOARD STUDY EXPERIMENTAL DESIGN

replacement survey in early October 2012, with an explanation of the error and a request to complete it again.

Surveys were identified by room and resident’s date of birth, allowing us to identify particular individuals and match their surveys to other data. Both residents of a given room had the opportunity to complete a survey, and in many cases both did so. However, data analyses in this study were conducted using room as level of analysis, because the key dependent variable (energy use) and independent variable (TED Display Presence) were both room-level variables. As a result, it was necessary to choose a single survey to represent each room. If only one resident had completed the survey, that one was used. If both residents had completed the survey, but only one resident completed a follow-up survey in December (not used in the present analyses), that resident’s survey was used to represent the room. If both residents had completed the main survey, and neither or both had completed the follow-up survey, then the survey representing the room was selected using a random number generator. Finally, if both an initial (August 2012) and replacement (October 2012) survey were available from the same individual, the initial survey was retained to represent the room. In this manner, 82 surveys were chosen for inclusion (no survey was available from 39 rooms). Of these, 56 were from the original administration in August, and 26 from the follow-up in October.

A variety of energy-savings resources were provided to residents in all participating rooms, and thus served as constants for the study. First, each room was provided with a “smart” power strip that included “master” and “slave” outlets, such that power to any devices plugged into the “slave” outlets was cut automatically when the “master” device was turned off. Second, residents were informed that their residential assistants had “Kill-A-Watt” devices available for check-out. These are small electricity load monitors that plug into a wall outlet; any electrical device can beplugged into the Kill-A-Watt, which then shows how much electricity the device consumes. Third, all residents received five emails from the behavior team over the course of the Fall Semester, each containing two energy-saving tips (e.g., turning up the temperature on mini-refrigerators; exchanging traditional holiday light strings for energy-efficient LED lights).

At the end of the semester, after residents had vacated the residence hall for the holiday break, daily electricity use data were downloaded from the TED Gateways in each room. Mean daily usage from the months of September, October,


Residents moved into their rooms in the middle of August,

2012. The Taylor Place Resident Survey (see Appendix XX-4)

awaited each resident in his or her room at that time, with a

request to complete it and return it to the floor’s residential

assistant. The surveys included questions on resident

demographics, number of hours per day the room is typically

occupied, presence and use of eight specific electrical devices

in the room (laptop and desktop computers, mini-refrigerator,

microwave, electric kettle, television, fan, and hair dryer),

two questions on frequency of talking with others about

energy usage, two items on thinking about energy usage when

turning on/off electrical devices, nine items on motives for

conserving energy, and eight items assessing conservation-

related attitudes. Unfortunately, many of the initial, completed

surveys were discarded by a residential assistant from a

non-participating floor of the residence hall, who was unaware

of the study, and these were not retrievable. In order to

maximize available survey data, we provided residents with a



and November 2012 was averaged for each room to create

a Fall semester consumption index. These months were

selected because residents were in the room for the entire

month (avoiding move-in and move-out months of August

and December), and because they represent a range of

weather conditions in the Phoenix area, from hot and humid

(September) to temperate and dry (November). This three-

month index was used as the dependent/outcome measure in

all analyses of energy use.

Sidney P. Osborn Public Housing Development

The Sidney P. Osborn complex is a publicly subsidized, low-

rise housing development located in downtown Phoenix. The

complex has 146 units with 2-5 bedrooms each across 26

buildings, available for rent by families with annual incomes

below a threshold determined by household size (e.g.,

$56,600 for a 4-person household). Resident households

receive “allowances” of between $22 and $111 per month

for electricity, depending upon month of year and number

of bedrooms, but are required to pay any electricity charges

above this amount. Because the complex is owned and

managed by the city of Phoenix, it was possible to obtain

electricity usage records for all units.

Participation in the study was limited to those in 2-4 bedroom

units, those speaking English or Spanish as the primary

language, and to those who had lived in their unit for at least

12 months and who intended to remain in the unit for at least

six more months. This latter criterion was needed to ensure

that pre-post intervention comparisons of energy use could be

made for the same household.

In May 2012, participants were recruited through evening

presentations at the complex, as well as door-to-door visits

by members of the research team (all visits were completed

in English or Spanish, as needed). By these means, 82

households were recruited for participation. Household was

treated as the unit of analysis, and these 82 households

were randomly assigned to three experimental conditions:

(1) Education Session Only Control (“EDOnly,” n = 26); (2)

Education Session + TED Device (“EdTED,” n = 28); and (3)

Education + TED + household-specific Budgeting Information

(“EdTEDBud,” n = 28) conditions. In this final condition,

households received tailored electricity use budgeting

recommendations in addition to the TED display, with the aim

of helping households manage their usage more effectively.

Five households in the EdTED condition were unreachable or

withdrew from the study at the time of TED device installation

(June-July 2012), leaving 23 households in this condition. Similarly, 11 households in the EdTEDBud condition were unreachable or withdrew from the study at this time, leaving 17 households in this condition.

TED feedback “dashboards” were installed in the units of remaining households in the EdTED and EdTEDBud conditions, and Gateways installed in all participating households, between June 14 and July 12 of 2012. Electrical contractors made the necessary adjustments to the unit’s electrical panel, and installed the units.

All participating households were offered personalized, in-home energy conservation education sessions between June 27 and August 8. During these visits the research team administered an initial survey, performed an inventory of major electrical appliances and devices, measuring the energy used by each item and sharing that information with the participant, and provided information about steps a resident could take to reduce their energy consumption (e.g. closing curtains during peak sunlight hours). Not all households took advantage of this opportunity: 13 (50%) of the households in the EdOnly condition, 19 (83%) of those still in the EdTED condition, and 13 (76%) of those still in the EdTEDBud condition received such visits. Only data from these 45 households were ultimately included in data analyses.

All remaining households were visited again between September 19 and October 17 to administer a second survey, to verify that the TED displays were working (when applicable), and to answer any questions the household might have about energy use. The devices were removed and an exit survey administered between January 15 and February 15, 2013. Households that completed the study and returned the TED device at this time were given a gift basket containing $75 worth of energy-saving devices and supplies.

Adjusting usage data for weather and building characteristics. The initial step was to recode as missing any months where the total kWh used was implausibly low for an occupied apartment. We used the rationale that minimum usage for an occupied apartment would include at least one refrigerator running at all times, plus one CFL light for 8 hours a day. Using the energy usage calculator at (http://visualization.geblogs.com/visualization/appliances/) we calculated that this would amount to approximately 140 kWh/month. All values below that 140 recoded as missing (-99). Additionally one apartment had a usage in February 2012 that was approximately 10 times the expected value for an apartment of that size during that month

and was also recoded as missing (-98).



Next, to account for building characteristics we computed two

separate regression models, one for hot months (70 degrees

and above) and one for cold months (below 70 degrees; there

were no months with a mean of 70 degrees). Each data point

(per month and per apartment) was treated as an independent

observation. For each model, we estimated a regression line

based on mean monthly temperature, Floor level (first or

second floor), Orientation (N, S, E or W), Unit location within

building (outside, or middle), Number of occupants, and

Square footage of a given apartment and saved the residuals.

Because the models accounted for the intercept (constant) as

well as the effects of each predictor, some degree of baseline

usage was partialed out of the residuals. Because baseline

usage is of conceptual importance, we re-adjusted for this

by adding back in the mean of the predicted usage values for

the cold (M = 364.20) and hot (M = 1132.69) models to the

residuals for each month. This process resulted in a mean

cold-month usage of 364.20 (sd = 195.07), and a mean hot-

month usage of 1132.69 (sd = 346.95; because the sum of

the residuals is, by definition, zero the mean of the adjusted

scores was necessarily equal to the mean of the predicted

scores that had been added back in).

RESULTS

D.1: WHAT ARE THE DOCUMENTED KNOWNS AND UNKNOWNS REGARDING HUMAN BEHAVIOR LINKS TO RESIDENTIAL ENERGY EFFICIENCY?

Appendix XX-3, “Residential Energy Efficiency and the

Psychology of Sustainable Behavior,” offers a brief summary of

key approaches to the promotion of sustainable behavior that

are well-documented in empirical research. These approaches

commonly build on the rich literature on behavior change

more broadly speaking, in the social psychology and health

psychology fields. The review emphasizes evidence regarding

in-home, real-time feedback devices, as these are the focus

of the present study. However, several other empirically-

supported approaches are addressed as well.

D.2: WHAT PERSON-LEVEL VARIABLES PREDICT ENERGY USE LEVELS AMONG RESIDENTS IN MULTI-UNIT RESIDENTIAL COMPLEXES?

Taylor Place

Mean daily electricity usage in the Taylor Place rooms was 1.91

kWh (SD = .76) across the three target months (September-

November 2012). In preparation for predicting energy use,

three indices were created to reflect pro-environment attitudes

and motivations for conserving energy.

• Pro-Conservation Attitude: Principal components analysis

was used to examine the factor structure of the eight

conservation attitudes items. Examination of the scree

plot strongly indicated a single-factor solution. However,

the two reverse-scored items loaded negatively on this

factor, even after reversing the scores so that higher

values indicated more pro-conservation attitudes.

Anticipating that this indicated a response set on the part

of participants completing the survey, we excluded these

items, and created an index by averaging ratings of the

remaining six items (Cronbach’s alpha = .83).

• Responsibility Motives: Based upon the principal

components analysis conducted with the Residential

Program survey participants (a much larger sample),

the nine motives for conserving energy items were

aggregated into two indices of Responsibility Motives

and Social Motives. The Responsibility Motives index

assesses responsibility-focused motives for conserving

energy, such as saving money, preserving the environment,

doing the “right thing,” and protecting future generations

(Cronbach’s alpha = .83). See the “Data Collection” sub-

section of the Residential Project section of this report for

details on the items included in this index.

• Social Motives: Also based on preliminary analysis

from the Residential Survey, three items were averaged

to create an index of social motives for conservation

(Cronbach’s alpha = .85). These items assessed desire

to keep up with what others are doing, desire to be seen

as environmentally responsible, and belief that people

the respondent respects say conservation is important,

as motives for saving energy. See the Residential Project

section of this report for further details.

A single simultaneous regression analysis was used to

examine the implications of non-manipulated person- and

room-level variables on energy use in Taylor Place rooms in

the study. This analysis was limited to 68 rooms for which

survey data were available, and all necessary questions had

been answered (SPSS regression procedures use listwise

deletion). Predictors in this analysis included participant sex,

total number of electrical devices, number of hours per day

the room was typically occupied, the index of pro-conservation

attitudes, and the two motivation indices. The overall model

was marginally significant, accounting for approximately 17%

of variability in energy use (R = .417, p = .057).



FIGURE 10: SCATTERPLOT PREDICTING ENERGY USAGE BY RESPONSIBILITY MOTIVATION

These regression models included the following predictors: respondent Gender, respondent Age, Duration of Residence in the unit, typical Thermostat Setting (on hot days), number of hours per day the unit was typically empty, perceived frequency of electrical bill overages, difficulty paying the electricity bill, how well respondents understand how much energy their electrical appliances/devices use, how well participants understand what they can do to save energy, and rank ordering of three motives for conserving energy (saving money, preserving nature, for children’s future). This last set of predictors was taken from a single task in which participants were shown pictures (e.g., dollar bills, children) illustrating each of these three reasons plus a fourth – because leaders of the community say it is important – and asked to rank their importance as reasons for conserving energy. Rank importance of “leaders” was not included in the analyses, as it is perfectly predicted by the other three “rank” items causing a multicollinearity problem for regression, and because it showed the least variability (nearly all respondents rated it as the least important motivation). Data for these variables were drawn from the first (demographics, duration of residence in unit, typical thermostat settings, hours per day unit is empty) and second (remaining predictors) surveys completed by participants during in-home visits. Thus, it is important to remember that the “predictors” were actually measured after the periods of energy use that they are being used to predict when interpreting the results – causal inferences are not easily justified.

The regression model predicting Cold-month electricity usage was not significant, F(12, 22) = 1.73, p = .128), and no individual predictors emerged as significant, although the effect of difficulty paying the electrical bill was marginally significant (β = 0.38, p = .051). Not surprisingly, respondents from households that used more electricity tended to report greater difficulty paying the bill.

The regression model predicting Neutral-month electricity usage was significant, F(12, 22) = 2.33, p = .041. Typical hot-day thermostat setting emerged as a significant predictor of energy usage (β = -0.38, p = .030), with those setting the thermostat to a higher temperature using less electricity, even though these were moderate-temperature months by Arizona standards. In addition, households in which the respondent was older used significantly less electricity (β = -0.43, p = .022), and those that had lived in the unit for a longer period of time used significantly more electricity (β = 0.36, p = .035). No other predictors were significantly associated with Neutral-month energy use.


The only significant predictor of energy use was number of

electronic devices in the room (β = .249, p = .046). However,

higher scores on the Responsibility Motives index were

associated with lower electricity consumption at the marginal

level of significance (β = -.364, p = .084), suggesting that

respondents’ pro-conservation motives did indeed carry over

into energy-conserving behavior. No other predictors in the

model were significant.

Sidney P. Osborn

Most participant surveys were completed by the female

head of household. Analyses were performed using only the

45 households that were reachable for the second in-home

visit, during which the second survey was administered.

Simultaneous regressions were used to examine the predictors

of baseline (i.e., pre-intervention) electricity use. Because we

anticipated that, in Phoenix, the predictors of energy use may

differ in important ways between the hot summer months and

more temperate months, we ran three separate analyses: one

predicting mean monthly electricity usage during January,

February, November, and December of 2011 (“Cold” months),

one predicting mean monthly electricity usage during March,

April, May, and October of 2011 (“Neutral” months), and one

predicting mean monthly usage during June-September of

2011 (“Hot” months).



The regression model predicting Hot-month electricity usage was also significant, F(12, 22) = 2.44, p = .033. Not surprisingly, typical hot-day thermostat setting emerged as a significant predictor of energy usage (β = -0.42, p = .016), with those setting the thermostat to a higher temperature using less electricity. Number of hours per day in which the unit was typically empty also predicted usage (β = -0.36, p = .046), suggesting that participants may have remembered to turn the air conditioning off or set it to a higher temperature when they were outside the home. Finally, the rank orders of saving money (β = -1.09, p = .042) and of preserving nature (β = -0.82, p = .079) as motivations for conserving energy were significant predictors of energy use. Remarkably, in both cases, respondents who rated these motivations as more important (i.e., rank closer to 1) tended to come from households that used more electricity, resulting in a negative regression weight.

D.3: DOES HAVING A REAL-TIME ENERGY USE FEEDBACK “DASHBOARD” HELP USERS SAVE ENERGY? DOES THIS EFFECT DEPEND UPON USER DEMOGRAPHICS?

Taylor Place

The impact of the TED feedback dashboards was assessed in terms of two outcome variables – one proximal and one ultimate. The proximal outcome was an index formed by averaging the five survey items assessing talking with others about energy usage, and thinking about energy use when actually using electrical devices (Cronbach’s alpha = .82) – an index of the practical salience of energy use during the course of the study. The second outcome was the three-month index of actual electricity use. In each case, analyses were three-way Analyses of Variance (ANOVAs) with room as the unit of analysis and TED Display Presence, Community Floor Status, and Sex entered as predictors.

The analysis predicting Energy Salience was limited to 79 rooms for which the necessary survey data were available. TED Display Presence did not significantly predict Energy Salience, nor was the effect of TED Display Presence significantly moderated by any other variable. In this analysis, resident Sex was the only variable to have a significant effect, F(1, 71) = 5.26, p = .025, partial eta squared - .069, with women (M = 2.62) reporting higher salience than men (M = 1.86).

The analysis predicting actual electricity usage included 110 rooms for which the necessary data were available (including resident sex). The predictors entered into the ANOVA model were TED Display Presence, Community Floor Status, and Sex. The main effect of having a TED display on electricity usage

was not significant, nor did TED Display Presence interact significantly with any other predictors in the model. However, the interaction of Display Presence with Sex approached significance, F(1, 102) = 2.64, p = .108. Specifically, men who had a TED Display in their rooms appeared to use somewhat less energy then men without TED Displays, whereas this was not the case for women.

FIGURE 11: ENERGY USAGE BY TED DISPLAY PRESENCE AND RESPONDENT SEX


In order to assess whether the effect of the TED displays on

actual electricity usage might be detected after accounting for

other resident characteristics, we repeated the above analysis

entering Hours per Day the room was occupied, the Pro-

Conservation Attitudes index, the Responsibility Motivation

index, and the Social Motivation index as covariates.

Importantly, this analysis was restricted in statistical power

as it was limited to the 68 rooms for which both survey and

energy use data were available. In this analysis, the main

effect of having a TED Display on energy usage was still not

significant, nor was this effect significantly moderated by

any other variable. However, Responsibility Motivation again

emerged as a significant predictor in this analysis, with

higher Responsibility Motivation associated with lower energy

usage, F(1, 56) = 4.38, p = .041, partial eta-squared = .072.

No other predictors, or interactions among factors, were

significant.

Sidney P. Osborn

The impact of the TED devices on electricity usage was

examined using mixed-model Analyses of Variance (ANOVAs)

in which Time was treated as a within-subjects variable

and Condition (EdOnly vs. EdTED vs. EdTEDBud) as a

between-subjects variable, with respondent Sex, Age, and

duration of residence in unit entered as covariates. Two sets

of analyses were conducted – one with average monthly



electricity usage during August and September (“Cooling Months”) as the dependent variable, and one with average usage during November and December (“Heating Months”) as the dependent variable, in case the effects of having a dashboard differed across these seasons. In each case, the Time variable contrasted the relevant period in 2011, before the intervention, with the same period in 2012, when the intervention was underway.

FIGURE 12: MONTHLY ELECTRICITY USE DURING HEATING MONTHS, ESTIMATED MARGINAL MEANS BY CONDITION

dramatically. This discrepancy is all the more surprising as the units do not have electric heating, although residents may own portable space heaters. We are hesitant to over-interpret this effect in the absence of further information. However, one reasonable possibility is that feedback on energy use has less impact during the intense heat of Phoenix summers, when air conditioning accounts for the bulk of energy consumption but is necessary for physical comfort. In contrast, energy use feedback may have greater impact on use of other kinds of electrical devices, which are seen as luxuries rather than necessities and which account for a greater proportion of variability in energy use during seasons with more moderate temperatures.

D.4: WHAT TYPE OF FEEDBACK MESSAGES INCREASES ENERGY CONSERVATION (ENVIRONMENTAL VS. FINANCIAL)? IS THIS CORRELATED WITH USER DEMOGRAPHICS?

Sidney P. Osborn

TED feedback devices can be set to provide feedback in a number of formats, and study participants were encouraged to try different formats and select the one they found most helpful. At the end of the study, study staff checked which setting each device had been on and recorded it for analysis.

FIGURE 13: TED FEEDBACK DISPLAY SETTING OPTIONS


Note: The significant Time x Condition interaction indicates that the effect of time on electricity use (i.e., pre-intervention vs. during the intervention period) differed significantly across the three experimental conditions.

For the “Cooling Months” of August and September, the TED

devices did not appear to have an effect on electricity usage;

the Time x Condition interaction was non-significant and

extremely small, F(2, 38) = 0.19, p = .826. However, the TED

feedback devices did appear to have an effect on electricity

usage during the “Heating Months” of November and

December. During this period the Time x Condition interaction

was significant, F(2, 37) = 3.42, p = .043, indicating

differences among the experimental conditions in pre-post

intervention change in electricity use. Although the pairwise

pre-to-during-intervention contrasts were not significant for

any single condition, likely due to the very small cell sizes,

examination of the estimated marginal means indicates that

electricity usage in the EdOnly condition increased during this

period from 2011 to 2012, whereas usage in the EdTED and

EdTEDBud conditions decreased from 2011 to 2012.

It is surprising that an effect of the TED devices was

observed during the “Heating” months rather than the

“Cooling” months, when air conditioning is necessary for

physical comfort in Phoenix and electricity use typically rises


The most commonly used setting was “Real-Time,” which

shows dollars per hour being used as well as current watts in

use. Alternative, but less frequently used, settings aggregate

various indices of energy usage (dollars, kWh, or CO2 output)

over a longer period of time, or give feedback on voltage

rather than kilowatt-hours. Because more immediate, precise

feedback should have a greater impact on behavior change

than more aggregate feedback, we wished to compare the

effects of TED devices set to Real-Time display versus other

possible settings.

In order to address this question, we used two mixed-

model ANOVAs similar to those used in D.3 above, this time



combining the two TED Conditions but conducting separate

analyses for households whose TEDs were set to Real-Time

feedback, versus households whose TEDs were set to another

feedback setting. In each case, the TED condition was

contrasted with the EdOnly control condition. Because effects

of the TED devices were only observed during the winter

Heating months, analyses were limited to those months.

The effect of TED devices set to give Real-Time feedback was

pronounced; the Time x Condition interaction for this test

was highly significant, F (1, 27) = 11.50, p = .002. Estimated

marginal means for electricity use among the EdOnly control

condition rose from 309.85 kWh in 2011 to 341.66 in 2012;

means among TED users who had set their devices to give

Real-Time feedback dropped from 443.22 in 2011 to 367.54 in

2012. In contrast, TED devices set to other feedback settings

had no significant effect, F (1, 19) = 0.21, p = .652 for the

Time x Condition interaction. In this case, estimated marginal

means for electricity usage increased from pre-test to the

intervention period in both the Education Only and the TED

conditions. These analyses suggest that the TED devices were

only effective at promoting electricity conservation among our

participants when set to display Real-Time energy usage.

D.5: TO WHAT EXTENT DOES SPECIFIC TRAINING/INFO ON HOUSEHOLD ENERGY USE AND CONSERVATION AFFECT ENERGY SAVINGS?

Taylor Place

The Taylor Place Dashboard study included two floors

assigned to receive a community-focused intervention,

aimed at increasing discussion about energy conservation

among residents. The resident assistants on this floor

agreed to facilitate these discussions as part of their regular

floor meetings with residents, and received a document

outlining specific content for the discussions: the energy

conservation tips emailed by the study team; experience with

and observations about the TED devices; and ways in which

residents attempted to reduce their energy usage. The intent

of this manipulation was twofold: (1) to establish a social

norm around energy conservation, and (2) to build a sense of

community in the endeavor to conserve energy. Both social

norms and community-based social movements have been

the target of successful conservation-focused interventions

in previous research (see Appendix XX for more detail).

This aspect of the intervention sought to implement these

strategies, to see whether they had an independent effect on

energy use and/or enhanced the impact of the TED displays.

FIGURE 14: ELECTRICITY USE DURING HEATING MONTHS, ESTIMATED MARGINAL MEANS BY DISPLAY SETTING


The significant Time x Condition interaction indicates that the effect of Time (i.e., pre-intervention vs. during the intervention period) on Heating Month electricity usage differed significantly between the Education Only households and the subset of TED Display households who had the display set to the “real-time” setting. This interaction was not significant when comparing the Education Only households to households whose TED devices were set to other feedback settings.

Unfortunately, security concerns prevented supervision of the

community-focused aspect of the intervention by the Behavior

team, and there is considerable evidence that this aspect of

the intervention was not implemented as designed. At a mid-

semester check-in, one Community Floor resident assistant

indicated that he was not holding floor meetings at all, but

simply emailing important information to residents or speaking

to them individually and informally; the other resident

assistant indicated that she had mentioned the energy

conservation issues at the first floor meeting, but forgotten to

do so in subsequent meetings. It quickly became clear that the

resident assistants were not motivated to carry out this aspect

of the intervention.

To help compensate for these problems, members of the

behavior team attended floor meetings on the two Community

Floors in November 2012, which had been convened

specifically for that purpose. During the meetings team staff

covered the content initially planned for this aspect of the

intervention. However, it was likely too late in the semester

to achieve the intended goals of the floor meetings, i.e.

establishing norms and developing a sense of community

around the issue. As noted above (see D.3), analyses did not

uncover evidence that the “Community Floor” intervention had

any effect on energy savings. The main effect of Community

Floor Status was not significant, in any analysis, in predicting

either Energy Salience or Electricity Usage.



Sidney P. Osborn

A personalized, in-home, energy conservation training was provided to all households participating in the Sidney P. Osborn study, and thus served as a constant. In the EdOnly condition, energy use increased somewhat from pre- to post-intervention. However, this increase is comparable to the mean change for the corresponding period of time among households in the same complex that did not participate in the study at all (74 households), and thus did not receive the brief education intervention. Thus, this study did not find evidence that the in-home energy conservation instructional visits had any impact on energy usage.

FIGURE 15: MEAN ELECTRICITY USE, EDONLY HOUSEHOLDS VS. NON-PARTICIPANTS

pay any electricity bill at all. The intent of this condition was

to provide a specific goal for electricity usage, potentially

enhancing the effect of the real-time feedback provided by

the TED display. However, analyses described above (see

section D3 above, Figure XX) did not find that the impact of

the TED device differed between the EdTED and EdTEDBud

conditions. Thus, adding budgeting information (EdTedBud)

did not appear to enhance the energy-saving effects of the

TED devices (EdTed).

SUMMARY

Person Level-Predictors of Electricity Use: Two key

findings emerged from the analyses predicting baseline

electricity usage. First, human behavior matters. In the Taylor

Place study, number of electrical devices used in the room

was a strong predictor of electricity consumption. While this

may seem obvious, it is important to remember that the

decision to own an electricity-consuming device is, in fact,

a choice, and potentially an important target of intervention.

In the Sidney P. Osborn study, thermostat setting was an

important predictor of energy use, at least during the seasons

of the year when air conditioning is necessary in the Phoenix

area. Again, this behavioral effect offers a clear target for

intervention, and is already the focus of many attempts.

Second, as with the Residential and Commercial studies,

motivations for energy conservation proved much more

important in predicting electricity consumption than pro-

environment attitudes and beliefs. In the Sidney P. Osborn

sample, higher rank order of saving money as a motive for

conserving was associated with higher baseline electricity

use, consistent with the findings of the Residential study

discussed earlier. A somewhat different pattern emerged

with the college students in the Taylor Place residence

hall, however. For these students, higher “responsibility

motives” were associated with lower electricity use. This

motivation index included the motivation to save money, but

also more explicitly pro-environment motivations; these two

categories of motivation covaried more closely in the college

student sample than in homeowner adults. One speculative

possibility, important to explore in future research, is

that young adults do not yet see the responsibility to be

careful with money and the responsibility to take care of

the environment as distinct and unrelated. This global “do

the right thing” motivation may be a more potent driver of

moderate energy consumption than the explicitly financial

motivation found to be problematic in our other samples.


D.6: DO SPECIFIC ADDITIONAL FEEDBACK/INFORMATION MESSAGES AND THE PRESENCE/ABSENCE OF A REAL-TIME ENERGY USE FEEDBACK DASHBOARD INTERACT?

Taylor Place

The design of the intervention in the Taylor Place study

systematically crossed the TED Display variable with the

“Community Floor” variable, facilitating analyses asking

whether the community floor intervention enhanced the impact

of having a TED display (i.e., a Display Condition x Community

Floor interaction). As noted above (see D.3), analyses

did not uncover any evidence that the “Community Floor”

intervention interacted with TED Dashboard Status in this way,

in predicting either Energy Salience or three-month Energy

Usage.

Sidney P. Osborn

Households in the “EdTEDBud” condition of this study received

tailored electricity budgeting information in addition to their

TED display. This budget indicated how many kilowatt-hours

participants could consume per month and stay within the

electricity allotment for their unit, thereby avoiding having to



Effects of Real-Time Feedback Devices: Unfortunately, these analyses offer limited support for the effectiveness of the TED devices in reducing electricity consumption among residents of multi-unit housing complexes. The two kinds of complexes included in the Dashboard study present different challenges and motivational structures than typical, single-family homes. In Taylor Place, students do not have to pay an electricity bill that is tied to their use, so the feedback system is drastically different. In the publicly subsidized Sidney P. Osborn project, residents are also somewhat buffered from the financial consequences of energy use. Whatever the causes, there was no main effect of TED device presence in Taylor Place. There was a hint that men were more likely to benefit from the devices than women; whether this was due to a ceiling effect for salience of energy usage among the women, or the greater appeal of these technology “toys” to the male students, cannot be determined with the present data. There was an effect of TED device presence in the SPO study, but only during the coolest months of the year. One important finding was that the real-time, immediate feedback settings were more effective at promoting electricity savings than other settings; a pattern quite consistent with earlier research.

Caveat: The primary limitation of these two studies is their very small sample size. Because the SPO study, in particular, had so few households in each condition, null findings may reflect a lack of statistical power rather than the true absence of an effect. Also, it is important to recognize the limitations of the “Community Floor” intervention in the Taylor Place study. This aspect of the intervention essentially failed to take place, so it cannot be inferred that educational- and community-based interventions would not have enhanced the impact of TED device presence, or even had independent effects on dorm residents’ energy consumption.

appendix e behavioral elements of energy ......of predictors in behavioral research. these...

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