Replacement or Additional Purchase:
The Impact of Energy-Efficient Appliances on
Household Electricity Saving
Kenichi Mizobuchi
Kenji Takeuchi
May 2015
Discussion Paper No.1520

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANReplacement or Additional Purchase:
The Impact of Energy-Efficient Appliances on Household Electricity Saving

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
This study examined the influence of additional and replacement purchases ofenergy-efficient air-conditioners on power savings. We used a questionnaire survey andmeasured electricity use data from 339 Japanese households, collected from two city areas
with different level of government-requested electricity-saving rates, namely, Osaka (10%)
and Matsuyama (5%). The main findings of our study are as follows: 1) Households thatpurchased energy-efficient air-conditioners saved more electricity than those that did not. 2)“Additional-purchase households” showed significant energy savings, whereas “replacement
households” did not. The rebound effect may negate the energy-saving effects of a new
air-conditioner. 3) Altruistic attitude is associated with more active participation in powersaving. 4) Households in Osaka saved more electricity than those in Matsuyama, probably
because the government call to save electricity was more forceful.
Keywords: electricity demand; energy efficiency; home appliances; electricity conservation
directives; carbon dioxide emissions*

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

e-mail: takeuchi@econ.kobe-u.ac.jp

11. Introduction
Unlike in Europe and the United States, where central heating and air-conditioning
are predominant, houses and apartments in Japan usually use unit cooling and heating ineach room. 1 Therefore, Japanese households make frequent purchase and replacement
decisions for air-conditioners. Consequently, people in Japan have more energy-efficientinvestment opportunities (i.e., chances to purchase energy-efficient air-conditioners for
replacement or additional use) than those in European countries and United States. These
investments have important implications not only for cooling and heating costs but also for
total energy demand and the risk of climate change. In Japan, air-conditioners account for
60% of household electricity consumption during peak summer and winter hours (Ministryof Economy, Trade and Industry, 2013).
Several scholars have investigated the relationship between energy-efficientinvestment and actual energy saving. Halvorsen and Larsen (2001), for example, analyzed
the influence of electricity appliance possession on power demand, using Norwegian
household panel data. Berkhout et al. (2004) analyzed electricity and gas demand in the
Netherland based on possession of several electric appliances. Davis et al. (2014) evaluatedthe effect of a large-scale electric appliance replacement program in Mexico, using data on
1.9 million households. Rehdanz (2007) analyzed the factors that contributed to1

According to EDMC (2014), room air-conditioners have a diffusion rate of 90% in Japan and their average number per
household is 2.64 based on 2012 data.

2

space-heating expenditure, using panel data from 12,000 German households. The results ofthese studies suggest that high-income, owner-occupier households invest in energy-efficientequipment but tend to not engage in energy-saving behavior.
Extending the scope of previous work, this study compares two modes ofenergy-efficient equipment investment: replacement and additional purchases. While bothinvestment types seem to contribute to energy saving, we need to perform an empiricalanalysis to understand how these different appliance-purchase motives actually affect energy
saving behavior. For example, an energy-efficient appliance that replaced an old appliancemight not contribute to power saving when a rebound effect occurs (Sorrell andDimitropoulos, 2008). On the other hand, households that purchase additional appliancesmight be able to reduce their electricity consumption with relative ease because the need of
the additional appliances might be low.
Because of its electricity shortage, Japan offers an interesting environment for theinvestigation of the impact of home appliances on power demand. The shutdown of nuclear
power plants after the meltdown at the Fukushima Daiichi Nuclear Power Plant in 2011
urged electricity saving, particularly during the peak summer season. We use a questionnaire
survey and electricity usage data from 339 Japanese households in the summers of 2011 and2012, collected from two city areas with different government-requested electricity-saving
rates: Osaka city area (10%) and Matsuyama city (5%).
Approximately 25% of the power supply in Japan was generated by nuclear power3

(EDMC, 2014) until the closure of nuclear power plants in Japan after the Great East Japan
Earthquake of March 2011 due to safety concerns.2 The shutdown of the nuclear plants
caused power shortage, especially in the summer and winter seasons when electricity
demand is high. Therefore, power saving was required in all sectors after 2011. In summer
2012, the Japanese government issued an “electricity conservation directive” for seven of
the ten regions of Japan, depending on the expected power shortage3. Figure 1 shows theservice areas of the ten major electric power companies in Japan.4[Figure 1 here]

This study targeted households in two city areas (Osaka city area in Kansai regionand Matsuyama city in Shikoku region) with different electricity-saving goals: a reduction of
10% for Osaka and 5% for Matsuyama from the 2010 level. Therefore, our study caninvestigate the effect of this policy on household electricity-saving behavior. Moreover, if
power demand exceeds power supply, large-scale power failure would occur. Therefore,
people might cooperate with each other, saving electricity for themselves and for others.
This altruism mirrors the power-saving behavior that aims to mitigate climate change. Thus,
we can consider an altruistic attitude as an important determinant of household decisions on
saving electricity. We measure the extent of altruism from the responses to five questions2

All 54 nuclear power reactors in Japan were shut down by May 2012 and remain closed as of March 2015.
Since many nuclear power plants in Japan were still working in summer 2011, no electricity-saving request was issued
during the season, except for the Kanto and Tohoku regions, where electricity demand is tight.
4
The Japanese electricity market is dominated by ten regional monopolies.
34

and investigate their relationship to electricity-saving behavior (Clark et al. 2003).
The main purpose of this study can be summarized as follows. It aims to compare theimpact on electricity demand from two investment modes for energy-efficient equipment:
replacement and additional purchase. Our focus is on the electricity conservation directive
during the summer of 2012. The econometric analysis of this study would reveal how
energy-efficient appliances helped household responses to power conservation directive.
While analyzing power-saving behaviors, we also look at the effect of the strength ofelectricity conservation directive and the role of the altruistic motive.
This paper is organized as follows: Section 2 discusses previous studies onhousehold energy-saving behavior. In Section 3, the data and the method of empiricalanalysis are described. Section 4 presents the results of our empirical analysis. Section 5
discusses our findings with a focus on energy-efficient air-conditioner purchase and its
effects on the level of power savings. Section 6 concludes the paper and notes some
limitations of our study.

2. Literature Review
2.1

Previous Studies
Many scholars have studied energy-saving behaviors of households (for literature

review, see Madlener [1996] and Espey and Espey [2004]). These studies focus on two
subjects: energy-efficient investment and energy-saving behavior. Energy-efficient5

investment involves introducing energy-efficient equipment such as household electric
appliances, thermal insulation material, and solar panels. When these equipment are installed,households can reduce energy consumption without changing their daily behavior. On theother hand, energy-saving behavior involves decreasing energy consumption withoutchanging equipment. Energy-saving behavior can be analyzed on a discrete-continuous orconditional demand approach. The discrete-continuous approach to estimating energy
demand is based on a discrete or continuous choice analysis of energy equipment (Dubin
and McFadden, 1984; Nesbakken, 2001; Halvorsen and Larsen, 2001). The conditional
demand approach is based on an energy demand analysis given the features of energy
equipment (Leth-Petersen and Togeby, 2001; Meier and Rehdanz, 2010).

2-2.

Energy-efficient investments
Studies on energy-efficient investments have analyzed the factors (e.g.,socioeconomic characteristics and government policy) that influence household investment
decisions. Krumm (1983) conducted a pioneering study in this field that used micro datafrom 1,520 American households. A multinomial logit model was estimated, which
distinguished between purchase of room units and installation of central air-conditioning.
Household income, housing unit characteristics, and climate conditions significantly
affected investment. Cameron (1985) analyzed, also using U.S. household data, the adoptionof insulated windows and double-glazed windows with a nested logit model. Cost of6

investment, energy price, and household income significantly influenced adoption decisions.
Scott (1997) studied 1,200 Irish households analyzing three types of investments. Householdincome, ownership form of house, amount of potential energy savings, and time and effort tofind optimal equipment significantly affected energy-efficient investment. Targeting 305
households in Switzerland, Banfi et al. (2008) evaluated the willingness to pay forenergy-efficient investment by conducting a choice experiment. Households placed
significant value on investment benefits. Grosche and Vance (2009) also found, for Germanhouseholds, that the costs and benefits of energy-saving investments have a significant
influence on their willingness-to-pays. Furthermore, Nair et al. (2010) showed, targeting
3,000 households in Switzerland, that households with higher energy consumption tended tomake energy-efficient investments. While these studies used discrete dependent variables,
other studies used a Tobit model with investment expenditure as the dependent variable(Mendelsohn, 1977; Montgomery, 1992; Mahapatra and Gustavsson, 2008; Charlier, 2013).
Using expenditure data, they examined not only the influencing factors but also their impact
on investment. Results of these studies suggest that energy-efficient equipment are generallypurchased by high-income, high-energy-spending, owner-occupier households. However,the form of purchase of energy-efficient equipment, which may affect the level of energy
saving, has received relatively little attention.

2-3.

Energy-saving behavior

7

Most empirical studies on energy-saving behavior have focused on energy priceelasticity (Espey and Espey, 2004). For example, Dubin and McFadden (1984) analyzed
household electricity demand using micro data on 3,249 U.S. households. A number ofstudies have examined household electricity demand using micro data or micro panel data
across several countries (Garbacz, 1985, 1987; Branch, 1993; Jung, 1993; Nesbakken, 1999;
Vaage, 2000; Halvorsen and Larsen, 2001; Wu et al., 2004; Zhang and Kotani, 2012). Meierand Rehdanz (2010) analyzed the determinants of space-heating expenditure using panel
data on 5,000 U.K. households. They investigated the effects of many socioeconomic
characteristics, types of house, and energy price. Homeowners tended to use more energy
than those who rented their houses. This was likely due to poor energy efficiency ofdetached houses (homeowners) compared to apartments (in many cases, rental households).

2-4.

The aim of present study
This study analyzes the factors influencing electricity demand in Japanesehouseholds, focusing on their investment in energy-efficient air-conditioners. Several studies
have considered past energy-efficient investments. Halvorsen and Larsen (2001) estimatedthe short- and long-term price elasticity of electricity using Norwegian household panel data.
They analyzed the influence of electricity appliance possession on power demand. Usinghousehold panel data in the Netherlands, Berkhout et al. (2004) analyzed electricity and gas
demand considering possession of several electric appliances. Reiss and White (2005)

8

estimated price and income elasticities of electricity demand based on 1,307 California
residents, considering the possession of home electric appliances. Davis et al. (2014)
evaluated the effect of a large-scale electric appliance replacement program in Mexico, using
data on 1.9 million households. Under this program, households replaced their old
refrigerators and air-conditioners with energy-efficient models. They found that households
that had replaced refrigerators reduced electricity by 8%, although this reduction was only
one-quarter of what they had expected ex ante. Rehdanz (2007) analyzed the factors that
contributed to space-heating expenditure, using panel data from 12,000 German households.
Energy-efficient heating systems and insulated windows significantly decreased use ofheating energy. Moreover, owner-occupiers tended to invest in energy saving more thanrental households did. On the other hand, owner-occupiers were more insensitive to changes
in energy price than were renters. She reported that owner-occupier households tended toprefer energy-efficient investments to energy-saving behavior.
In summary, previous studies on energy-efficient investments and energy-saving
behavior suggest that high-income, owner-occupied households invest in energy-efficientequipment. These households tend to not engage in energy-saving behavior. Thus, we test
the hypothesis that households investing in energy-efficient equipment would be morereluctant to practice energy-saving behavior.
This study estimates the electricity demand function, investigating household
electricity demand in summer (space cooling). Space cooling is almost always fueled by9electricity. Therefore, we do not need to consider possible choices among other energy
sources. As discussed previously, most Japanese households use space cooling/heating, andnot central air-conditioning but room air-conditioning. Therefore, insulation of
air-conditioners is relatively easy. These conditions allow us to examine the effect ofenergy-efficient air-conditioners on household energy-saving levels. Furthermore, we
distinguish between “replacement” and “additional-purchase” households to analyze
between-group differences on power saving. We expect a decrease in electricity usage withthe replacement of old appliances by new, efficient models. On the other hand, since
additional units would be added to existing ones, electricity usage seems to increase. These
expectations, nevertheless, might not hold if the rebound effect is invoked byenergy-efficient equipment.

3.
3-1.

Data and ModelData
We administered a questionnaire survey to households in the Osaka city area andMatsuyama city in the summer of 2012. We requested the Nippon Research Center andMatsuyama Chamber of Commerce and Industry to conduct the survey among Osaka andMatsuyama households, respectively. The surveyors sent the questionnaire to 700 randomly
selected households in both cities. The households answered the questionnaire online on the
Nippon Research Center’s webpage (Osaka) or on paper (Matsuyama). The queries related

10

to the households’ socioeconomic characteristics, the nature and extent of theirelectricity-saving behaviors, air-conditioner usage and number of units, presence or absenceof energy-efficient air-conditioners, and form of purchase (replacement or additionalpurchase).
Aside from the questionnaire, we asked households to send a specification of theirelectricity usage in August 2012. The specification was dropped in the mailbox by the
person in charge of meter reading, hired by the electricity company of each region.5 The
amount of electricity usage and fees for the current month and same month a year ago were
included in this specification to calculate the saving rate from the previous year. Table 1
presents the descriptive statistics. The average electricity saving from 2011 to 2012 was
2.5%, while the difference was not statistically significant.

[Table 1 here]

In the questionnaire survey, we asked households whether they purchased a new
air-conditioner between May 2009 and March 2011. If yes, the respondent was asked
whether it was a replacement or additional purchase and whether it was targeted by the
Eco-Points Program (hereafter EPP), a subsidy program for energy-efficient home
appliances 6 . Percentage of households that purchased energy-efficient air-conditioners

5

There are ten regional-monopoly electricity companies in Japan. The Osaka city area is serviced by the Kansai
Electricity Company, and Matsuyama city by the Shikoku Electricity Company.
6
The Eco-Points Program was implemented by the Japanese government from May 2009 to March 2011 to increase
acceptance of energy-efficient solutions. Specifically, those who purchased home electronic appliances with a highenergy-saving level (the target products of EPP were air-conditioners, refrigerators, and TVs) received “eco points,” which
could then be exchanged for money (1 point = 1 yen, where €1 = 128.6 yen as on April 2, 2015) on a prepaid transportationpass, electronic money, gift certificates, regional specialty products, and so forth. According to the Ministry of Economy,
Trade and Industry et al. (2010), about 639.1 billion eco points were issued in May 2011.

11

targeted by the EPP were almost the same between replacement and additional units (9.1 %and 6.8%, respectively, as shown in Table 1). Moreover, several households purchased
replacement and additional units that were not targeted by the EPP (4.1% and 3.2%,
respectively). The energy-saving performance of these less-efficient air-conditioners areslightly lower than those targeted by the EPP. However, a significant proportion of
consumers purchased air-conditioners not targeted by the EPP, even while the program was
active, because their prices were in many cases lower than those of the EPP-targeted ones. In
our analysis, we consider the combined number of EPP and non-EPP air-conditioners.7
Actually, modern air-conditioners, even if not targeted for EPP, are highly energy efficient.
Due to the Top Runner Program by the Japanese government8, companies of air-conditioner
must continue to upgrade the energy efficiency of their air-conditioners on a yearly basis.
Osaka households comprised about 37% of our sample. The Japanese government
requested households in Kansai region, which includes Osaka, to decrease electricity by
10% from the 2010 level.9 A number of measures were put in place toward this goal. For
example, the local government published a booklet and created website on power-saving
measures, and the media promoted power-saving behaviors. Electricity companies, too,
conducted power-saving promotion measures. For instance, the Kansai Electricity Company
7

Only nine households made both replacement and additional purchases of energy-efficient air-conditioners. We excluded
these households from our data set.
8
The purpose of this program is to promote energy-efficient electric appliances and automotive vehicles. Under thisprogram, energy efficiency targets are set based on the value of the most energy-efficient products on the market at thetime of the value-setting process, and all machinery and equipment products covered by the program should exceed
these targets. If a company continues to sell products that fall below the targets, the company name will be published andpenalty charges imposed.
(http://www.enecho.meti.go.jp/policy/saveenergy/toprunner2011.03en-1103.pdf)
9
A 15% electricity-saving request was issued in July 2012. The request was reduced to 10% after the Ohi nuclear power
plant of the Kansai Electricity Company resumed operations. The Ohi nuclear facility was again shut down in September
2013. It remains closed as of March 2015.

12

implemented an “electricity-saving trial,” a voluntary-based program in which participantsthat achieved a certain electricity-saving level were given a money coupon. Households in
Shikoku region, which includes Matsuyama, were asked to decrease power usage by 5%;
however, the power-saving measures implemented were more modest than those of Kansai
region.
We use two subjective measures on social preferences of households. One is the
New Ecological Paradigm (NEP) scale, representing the environmental attitude of
respondents (Dunlap et al., 2000; Kotchen and Moore, 2007). Respondents were asked torate on a 5-point Likert scale the extent to which they agreed or disagreed with several
statements on environmental issues. Our survey included five statements from the NEP scaleand combined them into a summated scale that provides a measure of general environmental
attitude (that is, the NEP scale can take a value between 5 and 25). Another measure ofsocial preferences is the altruism scale (ALT). Respondents indicated on a 5-point Likert
scale the extent to which they agreed or disagreed with five different statements on altruistic
motivation (Clark et al., 2003). Then, they were combined into a summated scale thatprovides a measure of general altruistic attitude (that is, the ALT scale can take a value
between 5 and 25).

3-2.

Regression model
We estimate a household electricity demand function. The dependent variable is the13electricity usage in August 2012. We examine the influence of energy-efficient investments,
considering the two types of air-conditioner purchases (replacement and additional units).
The regression model for our study is as follows:

Y2012 ,i    1OSAKA i   replace REPLACE i   add ADD i   2 X i   i ,(1)where Y2012,i indicates the logarithmic value of the electricity consumption for August 2012
( ELEC _ 2012 ); OSAKA represents the dummy variable for household in Osaka city area;

REPLACEi and ADDi indicate the number of efficient air-conditioners owned, theformer by replacement and the latter by additional purchase;Xi

represents other variables.

Electricity consumption decreases with replacement if household behavior isunchanged, since the replaced unit is generally less efficient. Therefore, the coefficient of
REPLACE is expected to be negative. However, as described in Section 3, households thatinvest in energy-efficient equipment engage in less energy-saving behavior than households
that do not. If they increase the usage of air conditioner than before, the coefficient might beinsignificant or even positive.
On the other hand, we might expect that electricity consumption of households thatpurchased a new energy-efficient air-conditioner will increase. The coefficient of ADD is
expected to be positive. However, households that purchase an additional unit might reduce
the usage of their existing air-conditioner, in which case total electricity consumption might14

decrease or remain the same. In that case, the coefficient of ADD can become negative or
non-significant.

4.

Estimation Results
Table 2 shows the estimation results. Columns (1) and (2) provide the results basedon logged electricity consumption in 2012 as the dependent variable, columns (3) and (4)indicate the results based on logged electricity consumption in 2011 as the dependent
variable, and columns (5) and (6) show the results based on the electricity-saving rate
between 2011 and 2012 as the dependent variable. The result shows that the number of
air-conditioners (AIR_N) and the room area (AIR_WIDE) are positive and statisticallysignificant. Thus, household electricity consumption in summer is surely affected byair-conditioner use.

[Table 2 here]

We examine the effect of energy-efficient air-conditioners using different models.
First, we examine if the energy-efficient appliances contribute to power saving. The models
shown in columns (1), (3), and (5) include the EFFICIENT_AIR variable, which combines
REPLACE and ADD. Because the estimation coefficient of EFFICIENT_AIR for 2012 isnegative and statistically significant, we can conclude that households which purchased
energy-efficient air-conditioners saved more electricity than households that did not. This15

might demonstrate the effect of the diffusion policy initiated by the EPP, which promoted
energy-efficient appliances.
We note different forms of purchase. Columns (2), (4), and (6) show results based on
REPLACE and ADD variables. The estimated coefficients of ADD are negative andstatistically significant. In contrast, those of REPLACE are not significant. That is, we did
not find power-saving effects for the replacement of old air-conditioners. Our result supports
Davis et al.’s (2014) finding that households which shifted to energy-efficient
air-conditioners increased electricity demand. On the other hand, households that purchased
additional energy-efficient air-conditioners tend to save electricity. Results on purchasemode differences will be discussed in more detail in the next section.
The coefficients of other variables are almost in line with the expectation. The 2012
electricity-saving request for the Kansai region (10%), which includes Osaka, is greater than
the request for Shikoku region (5%), which includes Matsuyama. Therefore, we canconclude that the social pressure was stronger in Osaka than in Matsuyama. The coefficientsof the OSAKA dummy are negative and statistically significant (except for 2011).10 We caninterpret this result to mean that households in Osaka saved more electricity in response to
the request. Since the meltdown at the Fukushima Daiichi Nuclear Power Plant, Japanesecitizens have become increasingly conscious of power saving. This result may indicate thatelectricity-saving requests could further encourage households to save power. Our results
10

In the summer of 2011, the main target of electricity saving was not the household sector, but the industrial and service
sectors. In fact, most of the measures targeting households were initiated after 2012. Moreover, since only Kanto andTohoku regions showed a tight electricity demand in summer 2011, the electricity saving request for Kansai region, wheresome of the nuclear power plants were still working at that time, was not very serious.

16

support Reiss and White’s (2008) finding that public pressure on households in California
could encourage sustained reductions in electricity demand.
One might imagine that electricity prices between the two areas are different.
Actually, as shown in Table 3, the electric rate structures of both Kansai and Shikoku regions
are almost same. The price structure is stepwise, increasing with electricity usage. Therefore,
the current unit price may be difficult to perceive, and consumers may be insensitive to unit
price changes.11 Furthermore, the average electricity price becomes higher when households
use more electricity. Since the price variable in our regression is calculated by dividing total
electricity expenditure by total power usage, the reverse causality might lead to a positive
estimate of the coefficient.

[Table 3 here]

Environmental awareness (NEP) was not significant in many estimated models.
Households with strong environmental preferences might use less electricity even before theearthquake and have less room for additional electricity savings. The estimated coefficientsfor altruism (ALT) are positive and statistically significant for 2012. Thus, we found thathouseholds with higher altruistic index save more electricity. Households facing thepossibility of a large-scale power outage in summer 2012 saved electricity, possibly not only

11
The power charge did not change either in 2011 or in 2012. However, with the increase in thermal power generation
following the shutdown of nuclear power plants, fuel costs rose sharply. Therefore, electric companies have been increasing
prices since 2013. For example, the Kansai Electric Power Co., Inc., increased household power charges by about 9.75% on
May 1, 2013. The company increased the price by about 10.23% again on April 1, 2015. Therefore, consumers may be
more sensitive to price changes in recent years than before.

17for their own sake but also for others’ benefit.

5.

Discussion
This section further discusses the results of the regression analysis. First, why did

households that replaced old air-conditioners not save more electricity than otherhouseholds? Davis et al. (2014) suggest some reasons. The first is the change in appliancesize and features: the new air-conditioner might be larger than the replaced unit.
Furthermore, recent models provide various functions such as rapidity cooling, thermal
management, and automatic cleaning. Thus, power consumption might increase even if the
new air-conditioner has energy-efficient features. The second reason is the rebound effect:
although technological progress can reduce energy usage, reduction in the cost of energy
services may lead to an increase in the demand for energy services (Sorrell andDimitropoulos, 2008). For example, let us consider the replacement of an old air-conditioner
with a new energy-efficient one. If the hours of operation and the preset temperature are
identical, energy consumption would decrease by the technological improvement. However,improvement of energy efficiency means reduction of operating costs, resulting in additional
demand for air-conditioning. Mizobuchi (2008) also found a substantial rebound effectamong Japanese households. Last but not least, findings in previous research suggest thathouseholds that prefer power-saving investment might not be active in energy-saving
behavior but might be insensitive to electricity-saving policy initiatives (Rehdanz, 2007).

18

Our results show that additional-purchase households are more likely to saveelectricity. This can be explained by two reasons. The first is the possibility of greater powersavings from an additional air-conditioner. Most additional-purchase households, especiallythose targeted by the EPP, might have purchased air-conditioners for rooms that scarcely
need air-conditioning. Therefore, when required, they could save a significant amount ofelectricity by reducing the use of these rooms in their residence. Compared to householdswith only the minimum number of air-conditioners, additional-purchase households shouldbe able to readily save power. The second reason is the difference in power-saving
consciousness. Households that purchased new air-conditioners in addition to existing units
might be well aware of the possibility of an increase in electricity consumption during
summer, raising their power-saving consciousness and encouraging their power saving
behaviors.
To understand the factors that determine whether to replace an old unit or make an
additional purchase, we estimate a model that explains the choice of purchasing mode. Table
4 shows the results of probit models that take REPLACE, ADD, and EFFICIENT_AIR as
dependent variables. Regarding the decision to replace a unit, the coefficients of age andaltruistic attitudes are positive and statistically significant. On the other hand,additional-purchase households tend to own their house, have a large family, and havehigher environmental awareness. Interestingly, households with higher environmental
awareness might make additional purchases. This may be one of the reasons that households

19making additional purchases tend to save electricity, as shown in Table 2.

[Table 4 here]

6.

Conclusion
This study investigated the effects of energy-efficient air-conditioners under public

electricity conservation directive. Many energy-efficient air-conditioners were purchased
during the implementation of the EPP in Japan (May 2009 to March 2011). We divided
households that purchased energy-efficient air-conditioners into “replacement” and“additional-purchase” households, and examined the power-saving difference between thetwo groups. Additional-purchase households attained a significant power-saving, whereas
replacement households did not. As discussed in Section 5, (1) a higher potential for
power-saving and (2) higher consciousness could possibly neutralize the effects of increased
number of appliances. Our results suggest that the degree of power saving might depend on
the purchasing form. These differences of purchase mode should be considered for effective
policy making.
We should note some limitations of this study. First, whether our analysis
appropriately captures the power-saving effect of replacement households is not clear, given
the situation in the summer of 2012. The study period is characterized by restricted powerusage in most parts of Japan. Thus, most households were probably more conscious of their20

electricity use during this time. Although we found that replacement households showedstatistically insignificant power saving, this might not be true under the usual circumstances.
Similarly, the power-saving effect of additional-purchase households might be overestimated.
Electricity conservation directive have not been issued since 2013 with a fall in electricity
demand and the increase in electricity supply from natural-gas-fired power plants. Therefore,
power-saving consciousness might gradually decrease.
The second limitation of this study is the relatively small sample size. Recent studies
on energy demand have used large sample sizes (in the thousands or tens of thousands). Our
study required actual current and past electricity consumption data (August 2011 and 2012),
and respondents were asked to directly send in their power usage data rather than indicate
the information on a questionnaire. This limited our sample size raises questions about thegeneralization of our findings. These limitations remain to be addressed in future research.

Acknowledgements
This research is supported by the Japan Society for the Promotion of Science(Grant-in-Aid for Young Scientists (B) #22730217), which is acknowledged by K.
Mizobuchi. K. Takeuchi would like to thank the Department of Economics at the University
of Gothenburg for the opportunity provided to him to work as a visiting researcher.

21

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26

Table 1. Descriptive statistics of the variables included in the analysis
Variable

Definition

Mean

ELEC_2012

Electricity consumption of August 2012 （kWh）

438.673

ELEC_2011

Electricity consumption of August 2011 （kWh）

REDUCTION

Std.

No. ofMinMax

245.66282.0001724.000

339

437.369

253.888

76.0001807.000

339

Power-saving rate of August 2012 (%, year-on-year)

2.473

19.425-66.500

100.530339

P_ELEC_2012

Electricity price per kWh in 2012 (yen/ kWh)22.9241.937

16.42626.503339

P_ELEC_2011

Electricity price per kWh in 2011 (yen/kWh)23.0921.850

15.10826.236339

EFFICIENT_AIR

Purchased energy efficient air conditioning (yes=1, no=0)

0.233

0.423

0.000

1.000

339

0.133

0.340

0.000

1.000

339

Dev

Obs

Replaced with energy efficient air conditioning (yes = 1, no
REPLACE= 0)
REPLACE_EPP

Replacement Targeted by the EPP

0.091

0.289

0.000

1.000

339

REPLACE_NO EPP

Replacement Not targeted by the EPP

0.041

0.199

0.000

1.000

339

0.100

0.301

0.000

1.000

339

Purchased additional energy efficient air conditioning (yes =ADD
1, no = 0)
ADD_EPP

Additional Purchase Targeted by the EPP

0.068

0.252

0.000

1.000

339

ADD_NO EPP

Additional Purchase Not targeted by the EPP

0.032

0.177

0.000

1.000

339

OSAKA

Household in Osaka prefecture (yes = 1, no = 0)

0.372

0.484

0.000

1.000

339

AGE

Age of respondent

44.69410.82722.00075.000330

INCOME *1

Income level

3.139

1.469

1.000

8.000

317

DETACHED

Single-family house

0.524

0.500

0.000

1.000

336

OWN

Own home (yes = 1, no = 0)

0.664

0.473

0.000

1.000

336

FAM_SIZE *2

Family size

2.868

1.320

1.000

7.000

334

CHILD *3

Number of children under elementary school age

0.198

0.517

0.000

3.000

334

OLD*4

Number of elderly people

0.240

0.556

0.000

2.000

334

NEP

Environmental awareness16.097

3.129

7.000

25.000339

ALT

Altruistic character

16.589

2.578

7.000

23.000339

AIR_N

Number of air-conditions owned

3.525

1.508

1.000

9.000

339

AIR_WIDE

Size of room where main air conditioner installed

10.509

4.406

1.000

25.000327

TEMP_2012

Average outside air temperature of August 2012 （℃）

28.5090.528

27.68029.473339

TEMP_2011

Average outside air temperature of August 2011 （℃）

27.8300.689

26.49429.038339*1: Under 2 million yen = 1, 2–4 million yen = 2, 4– 6 million yen = 3, 6–8 million yen = 4, 8–10 million yen = 5, 10–12 million yen = 6, 12–14
million yen = 7, over 14 million yen = 8.
*2: one = 1, two = 2, three = 3, four = 4, five = 5, six = 6, seven = 7, eight = 8, nine = 9, over ten = 10.
*3: zero = 0, one = 1, two = 2, three = 3, four = 4, over five = 5.
*4: zero = 0, one = 1, two = 2, three = 3, four = 4, over five = 5.

27

Table 2. Estimation resultslog(ELEC_2012)
(1)

OSAKA

-0.129(2)**(0.052)

EFFICIENT_AIR-0.089log(ELEC_2011)-0.135***(0.052)*(0.049)REPLACE

ADD

(3)

(4)

(5)

-0.068-0.072-10.039(0.057)(0.057)(3.390)-0.051-3.811(0.049)(2.372)

INCOMEOWN

DETACHED*-1.904(0.057)(0.055)(2.820)-0.146

***

NEP

ALT

-6.681**(0.072)

*

0.006

**

0.005

**

-0.099-0.107(0.106)(0.002)(0.106)

0.020

0.018

0.002

0.001

1.480

(0.017)(0.016)(0.019)(0.019)(0.858)(0.859)

0.089

0.096

0.121

-4.327-4.150(0.058)(0.058)(0.065)(3.166)(3.178)-3.868-4.034(3.106)(3.106)-0.047-0.036-0.160***0.132-0.0960.129-0.166***(0.058)
***

0.133

***

-0.092***(0.047)
***

0.131

**

0.135

0.127

*

(0.065)**(0.063)(0.024)**-0.140*-0.145**(0.062)
***

0.136

***

*

1.436

(0.026)(0.026)(1.089)(1.082)-0.067-0.064-3.400-3.306(0.048)(0.048)(2.513)(2.491)-0.096-0.058(1.818)

0.114

***

0.115

***

(0.042)(0.042)(0.044)(0.043)(1.833)

0.000

0.003

0.005

0.007

-0.627(0.007)(0.007)(0.007)(0.007)(0.357)(0.365)-0.010-0.012-0.709-0.763(0.009)(0.009)(0.504)(0.509)-0.016(0.008)*-0.018(0.009)**28*(3.426)(0.002)(0.047)
OLD

0.004

***

(3.412)(0.002)(0.024)CHILD-10.257(0.002)(0.059)FAM_SIZE

***

0.013

-0.2020.004(6)-0.012(0.071)AGE

REDUCTION*-0.570*Table 2. (Cont'd)
(1)

AIR_N

0.137

(2)

***

(0.020)
AIR_WIDE

0.018

0.138

(3)

***

(0.020)***(0.005)

0.018

0.128

(4)

***

(0.020)***(0.006)

0.019

0.043

***

0.059

***

(0.014)

0.058

0.019

***

(0.006)**(0.017)
P_ELEC_2012***(0.019)(0.006)

P_ELEC_2011

0.129

0.042

1.113

1.122

(0.859)(0.854)-0.034-0.038(0.239)(0.240)(0.017)***(0.015)-3.050

TEMP_2011

0.052

0.047

(0.044)(0.044)-0.014-0.016(0.0370)(0.037)

DIFF_TEMP

Constant(6)**

DIFF_P_ELECTEMP_2012

(5)

2.153

*

2.350

*

4.142

***

4.249

***

*

-3.071(1.646)(1.648)

2.257

2.217

(2.876)(2.902)28.537***29.069(1.289)(1.284)(1.117)(1.114)(10.791)(10.778)

Observations

310

310

310

310

310

310

R-squared

0.552

0.558

0.514

0.519

0.100

0.103

1. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively.
2. Standard errors are in parentheses.
3. DIFF_P_ELEC = P_ELEC_2012 - P_ELEC_2011
4. DIFF_TEMP = TEMP_2012 - TEMP_2011

29

*

***

Table 3. Electric rate structure for the household sector
Kansai Electric Power CO. INC. *1
Minimum charge

Electric power charge (per kWh)First 14 kWh

333.00 yen

15 to 120 kWh

19.38 yen

121 to 300 kWh

24.54 yen

Above 301 kWh

25.88 yenFirst 11 kWh

392.70 yen

12 to 120 kWh19.45 yen

121 to 300 kWh

25.77 yen

Above 301 kWh

29.12 yen

Shikoku Electric Power CO. INC. *2
Minimum charge

Electric power charge (per kWh)

*1. http://www.kepco.co.jp/*2. http://www.yonden.co.jp/

30

Table 4. Probit model for the purchase decision on energy efficient air conditioners
EFFICIENT_AIR
VARIABLESAGE

INCOMEOWN

FAM_SIZE

(i)

(ii)

(iii)

REPLACEADD

EFFICIENT_AIR

0.016 *

-0.0050.010(0.009)(0.009)(0.008)0.096-0.0160.061(0.061)(0.054)(0.053)0.128

0.421 *

0.274

(0.235)(0.256)(0.201)0.018

0.182 **

0.095

(0.082)NEP

ALT

log(ELEC_2011)

Constant(0.0907)(0.072)-0.0400.089 ***

0.021

(0.033)(0.033)(0.028)0.067 *

-0.0530.010(0.037)(0.040)(0.033)0.208-0.407 *-0.040(0.181)(0.219)(0.167)-4.000 ***-0.085-2.129 **(1.158)(1.3601)(1.047)

Observations

314

314

314

Log likelihood-119.558-89.400-164.193

1. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively.
2. Standard errors are in parentheses.

31

Figure 1. Service Areas of the Ten Major Electric Power Companies in Japan

32