The Rebound Effect in Residential Electricity Use:
Evidence from a Propensity Score Matching Estimator

Kenichi Mizobuchi
Kenji Takeuchi

October 2016
Discussion Paper No.1639

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANThe Rebound Effect in Residential Electricity Use:
Evidence from a Propensity Score Matching Estimator

Kenichi Mizobuchi*
Department of Economics, Matsuyama University
4-2, Bunkyo, Matsuyama, Ehime 790-8578 Japan

Kenji Takeuchi
Graduate School of Economics, Kobe University
2-1, Rokkodai, Nada, Hyogo 657-8501 Japan

Abstract
By combining the propensity score matching with the difference-in-differences method, we
examine the change in household electricity consumption that might be caused by the
replacement of air-conditioners. The result suggests that the replacement to energy-efficient
air-conditioners might decrease power consumption, especially in spring and summer.
Furthermore, based on our estimation result, we calculate the size of the rebound effect monthly.
The size of the rebound varies considerably with the seasons. We found positive rebound insummer (8% to 22% in August) and winter (134% to 192% in December and January). On theother hand, negative rebound, implying that the actual power-saving effect is greater than the
expected saving effect, was found in mild-climate seasons (–3% to –129%). The average size ofthe rebound is positive and ranges between 45% and 58%.
Keywords: space cooling; space heating; rebound effect; propensity score matching;difference-in-differences
JEL classification codes: C23; D12; Q41*

Corresponding author.
Tel: +81 89 925 7111; e-mail address: kmizobuc@cc.matsuyama-u.ac.jp

11. Introduction
Energy efficiency of home appliances has improved considerably in the past few decades. For
example, there is a significant improvement (44.1%) in the energy efficiency of air-conditioners inJapan between 1995 and 2015, as Figure 1 shows. Even though the change in the last decade wasmodest (9.2% between 2005 and 2015), the technology is improving steadily towards lower
consumption of electricity.

// Figure 1 //When households replace their electric appliances with new ones, one might expect power savings
as a result. Based on this expectation, policymakers often encourage the replacement of household
electric appliances with more energy-efficient ones by using policy instruments such as a subsidy
program. However, scholars have asserted that replacement to energy-efficient equipment may
induce additional energy consumption by the rebound effect: the gap between the expected saving
effect from the technological improvement and the actual saving after the energy-efficientinvestment (Sorrell and Dimitropoulos, 2008). Allcott and Greenstone (2012) emphasized that theresults of many engineering or observable analyses of energy-efficient investment had been plagued
with this well-known bias.
Some studies examined the existence of the rebound effect based on the household energy
consumption (Dubin et al., 1986; Metcalf and Hassett, 1999; Davis, 2008; Davis et al., 2014). For
example, Dubin et al. (1986) analyzed the effect of an improvement in insulation on electricity
conservation by using data from 504 households in Florida. They found that the actual conservation
by insulation is 13% lower than engineering estimates for cooling and 8–12% lower for heating.
Metcalf and Hassett (1999) also analyzed the effect of improvement in insulation based on the2monthly electricity consumption data of the Residential Energy Conservation Survey. They also
pointed out the large gap between the estimated saving effect and the prediction based ontechnological progress. Davis (2008) investigated the change in energy consumption of households
who received energy-efficient washing machines based on a field experiment. He compared the daily
power consumption of each household before and after the intervention. The results show asignificant power-saving effect after the households received a new washing machine. Moreover, theestimated rebound effect of introducing an energy-efficient washing machine was negligible, since
the price elasticity was small. Davis et al. (2014) evaluated the effect of a large-scale program forelectric appliance replacement in Mexico, using data on 1.9 million households. They found that
replacement of refrigerators reduced electricity consumption by 8%, although this reduction was
only one-quarter of what they had expected ex ante. On the other hand, they found that households
that had replaced air-conditioners increased electricity consumption.
The methodology of the above-mentioned studies is based on the randomized control trial(hereafter, RCT). However, social psychologists have pointed out that the RCT methodology can bemisleading, because it might invoke the Hawthorne effect. In particular, under the RCT, people
participating experiment may be conscious of being observed and this could affect their behavior.
For example, in an RCT for evaluating a given educational program, the stakeholders, namely theschool management representatives, teaching staff, parents, or guardians of the children, etc., willvisit the classroom under the experiment. The presence of these visitors may make the students ofthe class conscious and thus motivate them to increase their efforts to study than usual. In this case, asignificant effect of the educational program may be erroneously found due to the Hawthorne effect,
even if there is no direct effect by the program.
This study examines the existence of the causality effect of power saving by replacement toenergy-efficient air-conditioners. We compare the monthly electricity consumption between two

3

household groups: the one that replaced their air-conditioners in the previous two years and the otherthat did not do so. Since the allocation between the treatment group and the control group is not
random in our study, several socio-economic characteristics may affect the replacement behavior.
Thus, we employ the propensity score matching method to adjust the covariates (i.e., socioeconomic
characteristics) of treatment and control groups. Moreover, to control the effects of unobservablefactors, we combine the difference-in-differences method with the propensity score matching andestimate the causality effect more rigorously.
This paper is organized as follows. Section 2 explains analytical methods of the propensity scorematching and the difference-in-differences method. In Section 3, the data used in empirical analysis
is described. Our study is based on a web-based questionnaire survey for Japanese households wholive in Kansai area. We use their monthly electricity consumption data for two years. Section 4
presents the results of our empirical analysis and discusses them. Section 5 is conclusion.

2. Empirical Methodology
This paper investigates the power-saving effect by replacing an air-conditioner with anenergy-efficient one. Here, a binary treatment indicator Di,t equals 1 if household i replaced the
air-conditioner with the energy-efficient one at time t and 0 otherwise. Letting Yi,t+1 (Di,t) be theamount of electricity usage of household i at time t+1, the treatment effect of household i may thenbe written as i  Yi,t 1 (1)  Yi,t 1 (0)Here, for each i, Yi,t+1(1) and Yi,t+1(0) are counterfactual, and only either of them is observable(fundamental problem of causal inference). To resolve this problem, an average treatment effect on
the treated (hereafter, ATT) uses aggregate-level information, instead of individual behavioral data.

4

The ATT with regard to power-saving effect of replacing air-conditioners can be expressed as

 ATT  E[ i / Di ,t  1]  E[Yi ,t 1 (1) / Di ,t  1]  E[Yi ,t 1 (0) / Di ,t  1] .

(1)Here, if the assignment of the replacement of air-conditioners was random, we can replace thesecond term of equation (1) E[Y(0)/Di,t 1] with E [Yi , t 1i,t1

( 0 ) / D i ,t  0 ]. That is, the mean

power consumption of households who did not replace would serve as the counterfactual outcome of
replacement households. In this case, the average treatment effect  ATT would be identified.
However, if the assignment was not random, the estimates of  ATT may suffer from a selection
bias. That is, observable and unobservable household characteristics, which affect the decision to
replace air-conditioners, also affect the electricity demand.
In the case of non-random assignment, the identification of  ATT relies on two standard
assumptions. First is the conditional independence assumption (hereafter, CIA), which means thatconditional on the set of relevant covariates, the assignment of the treatment is independent of thepotential outcome.1 Second is the assumption of the common support. This assumption means thathouseholds with the same covariates have a positive probability of being both treated and untreated.
In other words, each household has a positive probability of being in the treatment (replacement)group and the control (non-replacement) group.
To establish a methodology that satisfies the above requirement for identification of  ATT ,Rosenbaum and Rubin (1983) proposed the propensity score matching methods (Heckman et al.,1997, 1998b) and Heckman et al. (1998a) further developed this methodology. It employs ahousehold from the control group who has similar covariates with the household of the replacement

1

That is, although the assignment of treatment might be dependent on the observable covariates, if we control forthese covariates, we can think that the treatment was assigned almost randomly.

5group as the counterfactual. The difference between the power consumption of the household in thereplacement group and that in the control group may then be attributed to the replacement of
air-conditioners. That is, matching mimics “randomization” by balancing the distributions of therelevant covariates in the replacement group and the control group. Rosenbaum and Rubin (1983)
defined the probability of the treatment indicator variable that is conditional on the observablecovariates

P(Di Xi ) as the propensity score. By matching each household between replacement

and control group based on the propensity score, we can maintain independence between thedecision of replacement (assignment) and the decision of power saving (potential outcome). The
ATT is estimated by calculating the difference of the power consumption between matched treatment
and control groups as follows:
PSM
 ATT
 ECP {E[Yi ,t 1 (1) / Di ,t  1, P( Di ,t / X i ,t )]  E[Yi ,t 1 (0) / Di ,t  0, P( Di ,t / X i ,t )] .

(2)

Here, CP indicates the common support, which means an overlapping interval between thepropensity scores of replacement households and those of control group households.
Moreover, in a panel data setting, Heckman et al. (1997, 1998b) proposed combining thepropensity score matching with the difference-in-differences method (hereafter, DD). Their
DD-PSM estimator is defined as follows:DD  PSM  ECP{E[Yi ,t 1 (1) / Di,t  1, P( Di ,t / X i ,t )]  E[Yi,t 1 (0) / Di ,t  0, P( Di,t / X i ,t )]}(3)
where  Yi , t 1  Yi ,t  1  Yi , t 1

is the change in power consumption before and after the replacement.

The DD-PSM estimator can exclude the time-independent fixed effect. Heckman et al. (1997,
1998b) and Smith and Todd (2005) showed that the performance of the DD-PSM estimator is better
than that of the PSM estimator without the DD.

6

3. Data
By using an online survey, we asked households who live in Kansai area, which is comprised of
Osaka, Kyoto, Hyogo, Nara, Shiga, and Wakayama prefectures, on their status of electricity usage.2
Since the Japanese electricity retail market for households was not deregulated until April 2016,
most households in Kansai area purchased electricity from the Kansai Electric Power Co., Inc.
(KEPCO) in February and April 2015, when our survey was implemented. KEPCO provides theircustomers online accessible data on their monthly electricity consumption for the past two-year
period. We requested the households to download and submit this data to us.3 We collected 733
households’ monthly electricity consumption and their responses to the survey questionnaire. Table 1
summarizes the descriptive statistics of the data.

// Table 1 //This study regards households who switched to energy-efficient air-conditioners as the treatmentgroup and households who did not do so as the control group. Variables from April to January inTable 1 are indicators of the treatment.4 The treatment is defined in terms of replacement status of
air-conditioners for the same month between 2013 and 2014. For example, April variable takes thevalue of one if a household replace its air conditioner between May 2013 and March 2014.5 Thus,

2

The data collection was conducted by an online survey company. This company has their own registeredhouseholds and asked those who are living in Kansai area to participate to the survey. The participating households
are selected by first come first served basis, until the number reaches 800.
3This data is provided in Microsoft Excel file format and includes not only the monthly electricity consumption but
also the date of meter reading, number of days of utilization, and the monthly electricity bill.
4
Since electricity consumption data is available only for two years and our survey has been implemented for twomonths, data for February and March is available only for limited samples. Thus, we omitted these two months.
5
For example, a household who replaced air-conditioner in July 2013 is in the treatment group for April, May, andJune. The household is not in the treatment group for the months after July, since it uses new air-conditioner after July
2013.

7

the household recognized as the treatment by this variable uses old air conditioner in April 2013 andnew air conditioner in April 2014. Here, we also need to consider other factors than replacement thatmight affect the household’s electricity consumption. For example, socio-economic variables such as
household income, number of household members, ownership and size of the house, and ownershipof the electric appliances. Temperature might be the most important factor that influences the usageof the air-conditioners. Figure 2 shows monthly average outdoor temperature in FY2013 andFY2014.6 There are considerable variations across months; the average temperature in the summerof FY2013 is slightly higher than that of FY2014.

// Figure 2 //Figure 3 and Figure 4 show the average electricity consumption (kWh/day) for each fiscal year
(FY2013 and FY2014) and each month (from April to January). The monthly electricity
consumption data provided by KEPCO does not include the amount of electricity consumption
generated by the home photovoltaic system. Thus, electricity consumption of households who havephotovoltaic system is underestimated in this data. Therefore, we exclude the households who install
the photovoltaic system with their houses from our dataset in Figure 3 and Figure 4, and also fromthe analysis hereafter. For the treatment households, the FY2013 data is before the replacement andthe FY2014 data is after the replacement. Therefore, we can compare the monthly household
electricity consumption between two time points. There are two important findings from Figure 3
and Figure 4. First, power consumptions of treatment households are larger than those of control
group’s households in all months. This suggests that household characteristics significantly differ

6

The daily outdoor temperature data of many location of Japan is available from the Japan Meteorological Agency(http://www.jma.go.jp/jma/indexe.html). Average temperature of nearest observatory is calculated based on
information of participants’ address and their days of electricity consumption for each month.

8between the two groups. Second, the differences of power consumptions between the treatment andcontrol groups are smaller in FY2014 than that in FY2013. By testing the differences between two
years, we can confirm that the differences are statistically significant for April, July, August, andSeptember in 2013. On the other hand, the difference is statistically significant only for August in
2014. Although this might result from the replacement of the energy-efficient air-conditioners, more
rigorous statistical methodology is needed to investigate the causality effect.

// Figure 3 //// Figure 4 //4. Estimation results
4.1. Propensity score estimator
We begin by estimating the propensity score by using the probit model. We use a dummy variable
that takes one if the households replace the air conditioner and zero otherwise as the dependent
variable. The independent variables are variables related to home appliances (number of
air-conditioners, number of TV sets, number of refrigerators, frequency of dishwasher use, frequencyof cloth washer use, and frequency of electric kettle use), household characteristics (age of the
respondent, number of family members, number of children in elementary school age, number ofhousehold member whose age is 65 and over, household income, marital status, whether they are
dual income family, home ownership, and whether they live in detached houses), and outdoor
temperature in 2013. Here, the outdoor temperature differs across months, and thus, we estimate thepropensity score month by month. Based on the monthly estimated propensity score, we conducted
three types of matching methods: NN matching, radius matching, and kernel matching. Generally,9there is a trade-off between bias and variance in each matching method. That is, if we reduce thevariance, the bias of estimate would increase (and vice versa). In the NN matching, the household in
the control group who has the value of propensity score nearest to that of the household in thetreatment group is selected as the partner of matching. However, in this method, if the value of thepropensity score of the control group household is far from that of the treatment group household,the quality of matching decreases. To avoid this, the radius matching sets an upper limit on the valueof propensity score and targets all control households whose propensity scores fall within the certain
range. Kernel matching uses the kernel function and constructs the counterfactual dependent variable.
As this method targets almost all control group households, the size of variance is smaller than eitherof the other two matching methods (in compensation, kernel matching results in the largest bias).
Table 2 shows the estimation results of the probit model for August 2013 and 2014.7 Before thematching, there are many variables that have statistically significant coefficients: the numbers of
air-conditioners, TVs, and refrigerators; the frequency of using the washing machine; age;
singlehood; home ownership; size of the house; and detached house. On the other hand, nocovariates have significant effect for the replacement after the matching. When we look at the resultof balance check of covariates, the sizes of mean bias are significantly diminished (Table 3). For
example, the size of mean bias of kernel matching for August is decreased from 29.9% to 4.8% by
the matching.

// Table 2 //// Table 3 //7

After matching, there are no statistical differences in the independent variables between the treatment and thecontrol groups in other months. Full estimation results are available upon requests.

10

4.2. Estimation results by DD with matching
Table 4-1, 4-2, and 4-3 show the estimated power-saving effect based on the DD-PSM estimator
before and after propensity score matching for each matching method. The DD-PSM estimators
represent the difference in the change of average household electricity consumption (kWh/day) from2013 to 2014 between treatment group and control group. Before matching, the estimators arenegative and statistically significant for April, May, July, August, and September. This result is
consistent with our expectation, since the household who replaced its air-conditioner with anenergy-efficient one can decrease its power consumption compared to the households who did not do
so. However, as we have seen in Table 2, some covariates had significant difference between
replacement and control group before matching, and these might affect the differences in electricity
consumption. To exclude the effect of these covariates, we conduct the propensity score matching.
The DD-PSM estimators after matching are shown in the right-hand side of Table 4. For NN
matching (Table 4-1), the DD-PSM estimators of April, August, September and October are negativeand significant. DD-PSM estimators of April, May, July, August and September are negative andsignificant for radius and kernel matching methods. These results suggest that power-saving effects
of replacement in these months are statistically significant even after matching. Moreover, in mostcases, the sizes of DD-PSM estimators after matching are smaller than those before matching. Thismeans that the observable covariates have an effect on the results, and in such cases, the DD-PSM
estimator without matching would not adequately capture the treatment effect. From these estimation
results, we can confirm that the replacement of air-conditioners to energy-efficient ones would
contribute to the decrease of household power consumption especially in spring and summer
seasons.
// Table 4 //11

4.3. The rebound effect
The analysis in subsection 4.2 suggests that the replacement of air-conditioners can decrease thepower consumption in spring and summer seasons. However, the power saving might be smaller
than the technologically expected level because of the influence of the rebound effect. On a monthly
average, our respondents had used their air-conditioners from 11.77 to 13.16 years. By using thesefigures and the data on average electricity consumption by air-conditioners (Japanese Agency for
Natural Resources and Energy, 2015), we calculate the technological power-saving rate by replacing
the old air-conditioner with the new one.8 From this figure and the estimated ATT, we can calculate
the size of the rebound effect caused by replacing the air-conditioner.
Table 5 shows the sizes of the monthly estimated rebound effect. The values in the first column
are the estimated amount of electricity consumption from the air-conditioner usage by the
replacement household. Assuming that most households do not use air-conditioners in June, thesevalues are calculated by taking the difference between electricity consumption (kWh/day) in June2013 and other months. There is great variability between seasons. The power consumption fromair-conditioner usage is large in August (6.50 kWh/day) and January (7.71 kWh/day), while thesevalues become small in the mild-climate seasons, such as October (1.88 kWh/day) and November(1.11 kWh/day).
The monthly calculated size of the rebound effect is shown from the second to fourth columns inTable 5. The rebound effect is calculated as follows:8

The technological saving rate varies depending on the old air-conditioner and the new one that replaces it.
Therefore, we asked replacement households about the years in usage of their respective previous air-conditioners.
From this information, we identified the respective years of manufacture Based on the average power consumption
values of the air-conditioners that were manufactured in a given year, we estimated the power consumptions of theprevious air-conditioners. We also estimated the power consumptions of new air-conditioners by using the averageamount of power consumption of the air-conditioners that were manufactured in each year from the time of
replacement. From this information, we calculated the technological saving rate for each household that replaced its
air-conditioner.

12

(𝑅𝑒𝑏𝑜𝑢𝑛𝑑 𝐸𝑓𝑓𝑒𝑐𝑡) = {1 −(𝐴𝑐𝑡𝑢𝑎𝑙 𝑠𝑎𝑣𝑖𝑛𝑔 𝑟𝑎𝑡𝑒)} × 100,(𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑠𝑎𝑣𝑖𝑛𝑔 𝑟𝑎𝑡𝑒)

where the actual saving rate is calculated from the PSM-DD estimator (see Table 4) and the value ofelectricity consumption of column 1 of Table 5, and the technological saving rate is calculated basedon the above method (see footnote 8).
Our results suggest that there are large variations in the rebound effect between seasons. The
rebound effect becomes positive in August, October, November, December, and January. On theother hand, the rebound effect becomes negative in April, May, July, and September.
The negative rebound means that the electricity consumption is reduced more thantechnologically expected. The result might be explained by the following reasons, the first of which
is climate. The seasons in which we found the negative rebound effect are of relatively mild climate,such as April, May, July, and September. April and May are the spring season, and the outdoor
temperature of July and September is lower than that of August. Therefore, households might find it
easier to adopt electricity-saving measures. As Dubin et al. (1986) pointed out, the price elasticitiesof the demand of cooling and heating vary according to the seasons.
The second reason is the additional function of new energy-efficient air-conditioners.
Manufacturers have introduced an additional function in recent models of air conditioners todecrease their electricity consumption. For example, the auto power-saving mode can cut down
excess energy use automatically when the sensor perceives that there are no people in the room.
Moreover, the auto turnoff option stops the operation of air-conditioners automatically if the time ofpower-saving operation continues for a given length of time. Furthermore, the auto-clean function
can clean dust on the filter automatically and makes energy-efficient operation possible. Since thepotential effect of these functions is not fully reflected in our calculation of technologically expected13electricity consumption, it is possible that the actual saving rate is greater than the technologicalsaving rate.
The third is the possibility of behavioral change of users by the provision of energy-saving
advices. Most remote control devices for the recent models of air-conditioners in Japan have afunction that shows the power-saving advices depend on the usage condition of the air-conditioner.
For example, if the room is receiving strong sunshine when an air-conditioner is working, themessage of recommendation to close a curtain is shown on the screen of remote control device.
Moreover, the amount of power consumption and estimated electricity bill based on the current
usage is indicated on the screen of remote control device and it may make the user aware of theenergy cost and encourage electricity-saving behavior. This information provision might lead to thepower-saving effect more than the theoretically expected levels.

// Table 5 //On the other hand, we can consider two reasons for the positive rebound effect. The first is the
economic incentive. The replacement of air-conditioners might reduce the price of energy service
and lead to the increase in demand for this service (e.g., longer usage time, lower preset temperaturein summer, or higher preset temperature in winter). These additional demands may cancel out theamount of power saving that was expected based on the technological progress (Sorrell andDimitropoulos, 2008). Again, the second reason is the climate. Dubin et al. (1986) showed that theprice elasticity of energy demand in winter is larger than that of summer. This means that therebound effect can be larger in winter. The results of our study also show that the size of the reboundeffect is large in winter season. Moreover, in winter season, the power consumption after the
replacement increases beyond the level before the replacement. This phenomenon is called the14

“backfire effect.”
In summary, the results of our study suggest that the size of the rebound effect varies by theseason. We found that the weighted average of the rebound effect from April to January ranges from
45.1% to 58.5%. Based on the review of previous studies, Sorrell et al. (2009) summarized that thesize of the rebound effect of households in OECD countries was less than 30%. Therefore, the sizeof rebound effect estimated in our study is larger than that in the previous studies.9

5. Conclusion
This study examined the causality effect from the replacement with energy-efficient
air-conditioners to decrease power consumption by comparing the replacement and non-replacement
household groups. Based on a questionnaire survey and monthly electricity consumption data in2013 and 2014 of 733 participant households who live in Kansai area, we estimated the ATT of thereplacing air conditioners. We assumed the replacement households who replaced their
air-conditioners with the energy-efficient ones for the previous two years as the treatment group, andthe non-replacement households as the control group. From our empirical analyses, we confirmed
the significant ATT effect of the replacement, especially in summer and winter. We also showed that
the ATT with only the DD method would overestimate the ATT estimator. Moreover, ATT with only
the matching method might result in a biased ATT estimator because of the low robustness against
the unobservable factors. We estimated the rebound effect of replacing air-conditioners based on
both estimated ATT and the technological saving rate. The annual rebound effect estimated ranges
from 45.1% to 58.5%, which is slightly larger than the results of previous studies. We also confirmed

9

The rebound effect of our study targeted only the air-conditioner, while the target of Sorrell and Dimitropoulos(2009) was households’ whole energy consumption. The difference of the size might come from this reason.

15

that the rebound effects vary seasonally. The negative rebound effects were found in the
mild-climate seasons and the positive rebound effects were found in hot and cold seasons. In
particular, the backfire effects were observed in severely cold seasons (December and January).
When we look at the annual average of rebound effect in our estimation, the issue may notappear very serious. However, there were large variations in the size of the rebound across seasons,and thus, we can say that it is important to consider the countermeasure for the rebound effect,
especially in winter. Improvement of household thermal insulation performance is one of theeffective countermeasures to decrease the high rebound effect in summer and winter seasons, which
has not been sufficiently focused in the Japanese energy policy until now. Therefore, encouraging theincrease of well-insulated houses may be the next agenda to improve energy efficiency of theJapanese household sector.

Acknowledgements
This research is supported by the Japan Society for the Promotion of Science (Grant-in-Aid forYoung Scientists (B) #26780164; Grant-in-Aid for Scientific Research (B) #16H03006).

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Note: The data is from Japanese Agency for Natural Resources and Energy (2015). Expected electricity consumption is calculated
for representative energy-efficient air-conditioner models in the reference year that have both cooling and heating functions, with acooling capacity of 2.8 kW. Assumed usage is 18 hours from 6 AM to 12 AM of summer (from 2 June to 21 September) and winter(from 28 October to 14 April) in conventional wooden-framed houses in Tokyo area.

Fig. 1. Seasonal power consumption of air-conditioner in Japan19

Fig. 2. Monthly Average Temperatures20

Fig. 3. Average Power Consumption in FY2013

21

Fig. 4. Average Power Consumption in FY2014

22

Table 1. Descriptive statistics
Variable

Obs.

Mean

Std.Dev

Min

Max

April

733

0.082

0.274

0

1

May

733

0.076

0.266

0

1

June

733

0.083

0.276

0

1

July

733

0.09

0.286

0

1

August

733

0.087

0.282

0

1

September

733

0.083

0.276

0

1

October

733

0.08

0.272

0

1

November

733

0.075

0.264

0

1

December

733

0.074

0.261

0

1

January

733

0.078

0.268

0

1

# Air-conditioner

733

2.894

1.737

0

9

# TV

733

3.022

1.195

1

8

# Refrigerator

733

2.171

0.47

1

5

Dishwasher 1)

Treatment dummy

733

0.375

0.653

0

2

2)

733

0.345

0.605

0

2

Electric Kettle 3)

733

0.500

0.734

0

2

Age

Cloth washer

733

50.327

10.559

20

69

)

733

3.557

1.741

1

8

# Family member

733

2.724

1.382

1

9

Children (dummy)

733

0.108

0.31

0

1

Elderly (dummy)

733

0.263

0.441

0

1

Singlehood (dummy)

733

0.28

0.449

0

1

Double income (dummy)

733

0.307

0.462

0

1

Home ownership (dummy)

733

0.75

0.433

0

1

Floor size 5)

733

3.375

1.372

1

6

Detached (dummy)

733

0.546

0.498

0

1

PV (dummy)

733

0.1

0.3

0

1

Income 41) 0: no dishwasher, 1: low usage frequency, 2: high usage frequency2) 0: no cloth washer, 1: low usage frequency, 2: high usage frequency
3) 0: no electric Kettle, 1: low usage frequency, 2: high usage frequency4) 1: under 200 million yen, 2: 200-399 million yen, 3: 400-599 million yen, 4: 600-799 million yen,
5: 800-999 million yen, 6: 1,000-1,199 million yen, 7: 1,200-1,399 million yen, 8: over 1,400 million yen5) 1: under 30 m2, 2: 31-60 m2, 3: 61-90 m2, 4: 91-120 m2, 5: 121-150 m2, 6: over 151 m2

23

Table 2. Means of covariates for August 2013 data
Before Matching# Air-conditioner

After Matching

Treatment

Control

Difference

Treatment

Control

Difference

3.667

2.724

0.943 ***

3.667

3.554

0.113

***

3.579

3.455

0.124

# TV

3.579

2.952

0.627

# Refrigerator

2.263

2.147

0.116 *

2.263

2.212

0.052

Dishwasher 1)

0.404

0.339

0.065

0.404

0.354

0.05

Clothwasher 2)

0.456

0.315

0.141 *

0.456

0.449

0.008

Electric Kettle 3)

0.614

0.496

0.118

0.614

0.603

0.011

Age

55.19350.28

4.913 ***

55.19354.236

0.957

Income 3)

3.772

3.475

0.297

3.772

3.937

-0.165# Family member

2.93

2.658

0.272

2.93

2.944

-0.014

Children (dummy)

0.088

0.097

-0.009

0.088

0.088

-0.001

Elderly (dummy)

0.298

0.256

0.042

0.298

0.281

0.017

Singlehood (dummy)

0.105

0.305

-0.199 ***

0.105

0.132

-0.027

Double income (dummy)

0.228

0.301

-0.073

0.228

0.235

-0.007

Home ownership (dummy)

0.947

0.72

0.227 ***

0.947

0.91

0.037

***

3.825

3.759

0.065

Floor size 4)

3.825

3.26

0.565

Detached (dummy)

0.702

0.487

0.214 ***

0.702

0.658

0.044

Temperature

28.22528.452-0.22728.22528.292-0.0671) 0: no dishwasher, 1: low usage frequency, 2: high usage frequency2) 0: no cloth washer, 1: low usage frequency, 2: high usage frequency
3) 0: no electric Kettle, 1: low usage frequency, 2: high usage frequency4) 1: under 200 million yen, 2: 200-399 million yen, 3: 400-599 million yen, 4: 600-799 million yen,
5: 800-999 million yen, 6: 1,000-1,199 million yen, 7: 1,200-1,399 million yen, 8: over 1,400 million yen5) 1: under 30 m2, 2: 31-60 m2, 3: 61-90 m2, 4: 91-120 m2, 5: 121-150 m2, 6: over 151 m2

24

Table 3. Mean biases of covariates before and after matching (%)
Before matching

NN matching

Radius matching

Kernel matching

April

31.0

9.9

5.1

4.7

May

26.7

9.7

3.3

3.2

June

27.5

9.5

3.2

3.1

July

27.5

11.1

5.4

5.0

August

29.9

11.1

5.3

4.8

September

29.9

5.4

4.9

4.7

October

28.5

10.9

4.7

4.3

November

26.4

12.9

5.1

4.6

December

24.8

11.7

5.2

4.7

January

22.1

11.9

6.9

5.3

25

Table 4-1. Power-saving effects of replacement (DD after Nearest Neighbor Matching)Replacement

Control (U)Control (M)DD (U)DD (M)

April-0.992-0.177-0.085-0.815**-0.907May-1.470-0.558-0.759-0.912***-0.711June-0.485-0.394-0.453-0.091July-1.631-0.926-1.178-0.705**-0.453

August-2.483-1.206-1.360-1.278***-1.123*September-2.371-1.355-1.581-1.016***-0.790*

October-1.040-0.676-0.312-0.364-0.728*

November-0.531-0.516-0.248-0.015-0.283

December-0.322-0.519-0.612

0.197

0.289

January

1.046

0.084

-0.327

0.962

1.373

*

-0.031

Note: ***, **, * indicates statistical significance at 1%, 5%, and 10% respectively. Control (U) and Control (M) are thepower-saving effect of control group without matching and that of control group with matching, respectively. DD (U) and DD(M) are the difference between the treatment and control groups with matching and that without matching.

Table 4-2. Power-saving effects of replacement (DD after Radius Matching)Replacement

Control (U)Control (M)DD (U)DD (M)

April-0.992-0.177-0.193-0.815**-0.799*May-1.470-0.558-0.762-0.912***-0.708*June-0.485-0.394-0.480-0.091July-1.631-0.926-0.971-0.705**-0.660**August-2.483-1.206-1.516-1.278***-0.967**

September-2.371-1.355-1.532-1.016***-0.839**October-1.040-0.676-0.833-0.364-0.206

November-0.531-0.516-0.762-0.0150.231

December-0.322-0.519-0.818

0.197

0.498

January

1.046

0.084

0.053

0.962

-0.005*1.001

Note: ***, **, * indicates statistical significance at 1%, 5%, and 10% respectively. Control (U) and Control (M) are thepower-saving effect of control group without matching and that of control group with matching, respectively. DD (U) and DD (M)are the difference between the treatment and control groups with matching and that without matching.

26

Table 4-3. Power-saving effects of replacement (DD after Kernel Matching)Replacement

Control (U)Control (M)DD (U)DD (M)

April-0.992-0.177-0.206-0.815**-0.786*May-1.470-0.558-0.768-0.912***-0.702*June-0.485-0.394-0.470-0.091July-1.631-0.926-0.991-0.705**-0.640**August-2.483-1.206-1.533-1.278***-0.951**

September-2.371-1.355-1.526-1.016***-0.845**October-1.040-0.676-0.843-0.364-0.197

November-0.531-0.516-0.784-0.0150.253

December-0.322-0.519-0.808

0.197

0.487

January

1.046

0.084

0.102

0.962

-0.014*0.952

Note: ***, **, * indicates statistical significance at the 1%, 5%, and 10%, respectively. Control (U) and Control (M) are thepower-saving effects of control group without matching and that of control group with matching, respectively. DD (U) and DD (M)are the differences between the treatment and control groups with matching and that without matching.

27

Table 5. Rebound effects of replacement of energy efficient air-conditioner

Rebound effect (%)

Electricity use byMonthair-conditioner in2013 (kWh/day)NN

Radius

Kernel

April

4.04

-32.33-16.58-14.80May

2.52-70.72-70.01-68.45

June

-

-

-

-

July

1.61

-57.33-129.22-122.30

August

6.50

8.38

21.08

22.44

September

4.24

2.92

-3.10

-3.84

October

1.88-93.68

45.06

47.69

November

1.11-28.61204.91215.16

December

4.45

133.64157.85156.64January

7.71

192.47167.43164.09

45.13

58.08

58.46

Annual(April-January)28