Making homes more energy efficient is widely viewed as a low cost option for reducing the negative externalities arising from energy use. (1) As a result, residential energy efficiency programs have become a major component of U.S. energy policy. Chief among these policies is the Department of Energy's Weatherization Assistance Program (WAP). (2) The WAP provides heavily subsidized efficiency upgrades (e.g., improved insulation, duct-sealing, etc.) to low-income households. To date, over 7 million homes have been upgraded through the program. It is important to note, however, that participating households receive more than just the physical upgrades. Recipient households generally receive instructions on how to interpret their energy bills as well as coaching on how they can alter their behavior to conserve energy. (3) While an existing literature reveals that the WAP provides sizable energy savings (Hirst and Goeltz (1984, 1985), Sebold and Fox (1985), Hirst (1986, 1987), Schweitzer (2005)), the previous studies estimate the program's composite effect--that is, the combined impacts of the efficiency improvements and the behavioral interventions. (4) To provide a more nuanced understanding of the impacts of the weatherization program, this paper presents the first estimates which disentangle the energy savings provided by the engineering and behavioral treatments.
Isolating the effect of the physical upgrades allows us to make two key contributions. First, we are able to directly compare the actual electricity savings achieved by the efficiency upgrades to ex-ante, engineering predictions. While several older studies attempt a similar exercise (Sebold and Fox (1985), Hirst (1986), Dubin, Miedema and Chandran (1986)), none have isolated the savings provided solely by the efficiency upgrades--which is the value engineering models attempt to predict. In a recent study, Fowlie, Greenstone and Wolfram (2015) provide evidence that engineering predictions overstate the natural gas savings provided by residential weatherizations. In line with these results, we find that the electricity savings are similarly overstated. Second, we are able to examine whether combining behavioral treatments with the efficiency upgrades can achieve additional savings. It is important to note that the impact of the behavioral treatments is theoretically ambiguous. If providing information on conservation strategies induces behavioral changes, then additional energy savings may be achieved. Alternatively, informing households how to interpret their energy bills may make the reduction in the costs of energy services (e.g., space cooling) more salient, potentially exaggerating the rebound effect caused by the efficiency upgrades. Given that the WAP specifically targets low-income households--which are potentially the most responsive to energy price changes--the resulting rebound effect may be quite pronounced. (5)
To study the effects of a weatherization program, we focus on 275 low-income households in San Diego, California that received free energy efficiency retrofits. In addition to the retrofits, a random subset of the households were offered a behavioral treatment that consisted of three components: 1) households were educated on how to interpret their energy bills, 2) households were provided with information on specific energy conservation practices, and 3) households were asked to make non-binding "commitments" to specific energy-saving behavioral changes.
To determine how the retrofits and behavioral treatments affect electricity consumption, we identify the change in monthly electricity consumption that occurs following each treatment. Our results suggest that the retrofits and behavioral treatments have heterogeneous effects across households. Among the homes with air conditioning units, the retrofits reduce electricity use by an average of 7% and the behavioral interventions reduce electricity use by an additional 24%. We find that these reductions occur almost exclusively during the summer months--suggesting that the retrofitted households use less energy to cool their homes. In contrast, among households without air conditioning units, neither treatment is found to significantly affect consumption.
To examine how accurately an engineering model predicts the savings provided by the upgrades, we compare our ex-post estimates to ex-ante predictions from the Database for Energy Efficient Resources (DEER). The DEER model is used by the California Public Utility Commission (CPUC) and California Energy Commission to predict the energy savings provided by building upgrades. Moreover, the CPUC uses the DEER predictions when determining how large of an incentive payment each utility should receive for their energy efficiency programs. (6) Our results reveal that the DEER predictions overstate the energy savings. For example, among the homes with air conditioning units, only 79% of the predicted savings are realized. Echoing the conclusions from previous studies (Blumstein (2010), Kaufman and Palmer (2012), Fowlie, Greenstone and Wolfram (2015)), our findings suggest that if ex-ante estimates continue to play a role in subsidizing energy efficiency programs, more accurate predictions are needed. (7)
In addition to exploring the effect of the retrofits, our results provide evidence that the behavioral treatments can cause additional energy savings. While several related studies reveal that behavioral treatments used in isolation can provide modest reductions in consumption (Allcott (2011), Allcott and Rogers (2012), Harding and Hsiaw (2014)), none have explored how the treatments interact with energy efficiency improvements. This interaction is particularly important because federal and state efficiency programs increasingly bundle the interventions together and, as outlined earlier, the impacts of information in this bundled context may well undermine the goals of the efficiency upgrades. Despite this theoretical possibility, our results suggest that not only can the simple behavioral interventions reduce energy use, the reductions can exceed the savings resulting from the much more costly efficiency upgrades. (8)
The remainder of this paper proceeds as follows. Section 2 describes the retrofit program. Section 3 discusses our empirical strategy. The estimates of the average energy savings are presented in Section 4. Section 5 presents a comparison of our ex-post estimates of the energy savings and ex-ante, engineering predictions. Section 6 concludes.
ENERGY EFFICIENCY PROGRAM AND DATA
2.1 Retrofit Program
The Energy Savings Assistance Program (ESAP) provides free energy efficiency upgrades to low income households in San Diego Gas and Electric's (SDG&E) service territory. Program eligibility is determined by household size and income. For example, a four person household must have a combined annual income below $47,700. (9) The program is advertised through monthly bill inserts as well as on SDG&E's and the CPUC's websites. Eligible households must self-select into the program. After enrolling online or by phone, the households receive an energy audit which identifies the set of upgrades that will be performed at each household. The specific improvements vary by household, but in general, the upgrades include installing energy efficient lighting and windows, as well as improving existing insulation, ventilation, and duct-sealing.
Our analysis examines the 275 low income households that received free upgrades between November 2011 and September 2012. (10) In addition to the home upgrades, 144 of the retrofitted households were randomly assigned to a behavioral treatment group. After receiving the energy efficiency upgrades, the households assigned to the behavioral treatment group were contacted by telephone and offered a free, in-home visit to learn about energy saving actions. Of the 144 treatment group households, 38 selected to receive the treatment. (11) These households were visited by a trained educator who explained how to interpret their SDG&E bill (e.g., clarifying the tiered pricing system) and walked through the home demonstrating ways to save energy. (12) At the end of the informational session, the educator asked each participant to make a non-binding " commitment" to three energy-saving actions of their choice. Each treated household chose to make the non-binding commitments. The most common energy-saving actions pledged by the households included unplugging unused appliances, turning off lights, and drying clothes on lines. (13) Only one household pledged to reduce their use of air conditioning (AC). Despite this fact, our subsequent results suggest that energy savings as a result of the in-home treatments were largely confined to homes with AC units--suggesting that the "commitments" had limited effectiveness.
To estimate the impact of the retrofits and the behavioral treatments on electricity consumption, we use billing data from SDG&E. The billing data provides the monthly electricity consumption and the monthly expenditure on electricity for each household. It is important to note that the billing cycle start and end dates vary across households. For each household, we observe the monthly bills with start-dates later than July 31, 2011 and end-dates earlier than October 1, 2012. Therefore, we typically observe 12 bills per household. (14) Given that the retrofits occur between November 2011 and September 2012, the number of pre and post-retrofit observations varies by household.
Table 1 reveals that 237 households receive only a retrofit and 38 households receive both a retrofit and the behavioral treatment. Table 1 also summarizes the demographic and building characteristics of the households in our sample. It is important to note that only 27% of the households have AC units. (15) Given that many of the upgrades are designed to insulate the homes, the retrofits likely...
Upgrading Efficiency and Behavior: Electricity Savings from Residential Weatherization Programs.
|Author:||Zivin, Joshua Graff|
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