ISSN 1881-6436

Discussion Paper SeriesNo. 18-05

Conjoint analysis of Japanese households’ purchase behavior of energy-saving
appliances: the role of renewable energy

Shin Kinoshita

September 2018

Faculty of Economics,
Ryukoku University
67 Tsukamoto-cho, Fukakusa, Fushimi-ku,
Kyoto, Japan
612-8577

1

Conjoint analysis of Japanese households’ purchase behavior of energy-saving
appliances: the role of renewable energy
Shin Kinoshita1
Abstract
Energy savings among households are important energy problems in Japan. After the Great East
Japan earthquake in March 2011, nuclear power plants were forced to cease operations. We
Japanese have worried about electricity shortages and have been requested to save electricity
usage in case of a sudden outage. To avoid electricity shortages, energy savings are needed.
For energy savings, energy-saving appliances such as air conditioners and refrigerators should
be promoted among households. I analyzed the conditions that households purchase energysaving appliances by a conjoint analysis. As the conditions, an annual electricity bill, CO2
emissions, a stable electricity supply, subsidies, the initial cost of purchasing and energy sources
which generate electricity are considered. A random parameter logit model and a nested logit
model were used for estimation.
To promote renewable energy such as solar and wind power is another important energy problem
to solve electricity shortages and to reduce greenhouse warming-effect gases such as CO2.
I focus on the relation between the preferences for renewable energy and the purchasing behavior
of energy-saving appliances. If households purchase energy-saving appliances by using
renewable energy, the problem of promoting renewable energy and energy savings could be
solved simultaneously.
The estimation results indicated that households largely respond to an annual electricity bill, an
initial cost and subsidies, which are monetary factors. In addition, non-monetary factors such as
CO2 emissions and a stable electricity supply also might give incentives for households topurchase energy-saving appliances. Regarding to energy sources, if renewable energy is used asa main energy source in the electricity generation, they tend to purchase energy-saving appliances.

Key words: energy saving, conjoint analysis, renewable energy
JEL Classification: C25, L51, L94, L95, Q28

1Faculty of Economics, Ryukoku University
67 Tsukamoto-cho, Fukakusa, Fushimi-ku, Kyoto, Japan 612-8577
skinoshita@econ.ryukoku.ac.jp, 81 (075) 645-8517

2

Conjoint analysis of Japanese households’ purchase behavior of energy-saving
appliances: the role of renewable energy2
1. Introduction
Energy savings are the important energy problems in Japan. After the Great East Japan
earthquake in March 2011, due to the serious accidents of nuclear power plants in Fukushima
prefecture, nuclear power plants were forced to cease operations. People who live in Kanto areaaround Tokyo experienced planned power outages because of electricity shortages from the stopof nuclear power plants. We Japanese have worried about electricity shortages especially at timesof peak of demand in summer and winter and have been requested to save electricity usage incase of sudden outages. Electricity shortages will continue until nuclear power plants start
operation. To avoid electricity shortages, energy savings are needed. Climate change is also oneof the reasons to save energy. If we reduce electricity usage, we reduce global greenhouse gases(GHG) such as CO2.
As one of the solutions of energy savings, energy-saving appliances air conditioners andrefrigerators should be promoted among households. I analyze the conditions that households
decide to purchase energy-saving appliances. Sometimes, the governments give subsidies tohouseholds when they purchase energy-saving appliances.
As the conditions, an annual electricity bill, an initial cost in purchasing, subsidies, CO2
emissions, a stable electricity supply, and energy sources which generate electricity are considered.
An annual electricity bill, an initial cost and subsidies are monetary factors, while CO2 emissions,a stable electricity supply and energy sources are non-monetary factors. I focus on non-monetaryfactors as well as monetary factors. If an annual electricity bill and an initial cost are reduced,
households may purchase energy-saving appliances. More subsidies give incentives forhouseholds to purchase them. If CO2 emissions are reduced, they might purchase such appliances.
They will choose energy-saving appliances when a stable electricity supply is secured.
Especially, I focus on the role of energy sources which generate electricity. I examine the relation
with the preferences for renewable energy and the purchasing of energy-saving appliances.
Households might purchase energy-saving appliances if renewable energy such as solar and wind
power is used in the electricity generation.
To promote renewable energy is another important energy problem. After the earthquake, Japan
has relied on fossil fuels such as oil, natural gas (LNG) and coal. However, fossil fuels emit GHG
such as CO2. Instead of nuclear power and fossil fuels, renewable energy should be promoted asalternatives. To promote renewable energy, the Japanese government introduced a feed-in-tariff
system in July 2012. Moreover, the Japanese government published the desirable composition ofenergy sources called the “best-mix” in fiscal year 2030 as an energy plan3. This plan states thatthe share of renewable energy will be raised to around 22–24%. However, despite the feed-intariff system, the share of renewable energy except water power is only 3.2%4.
Accordingly, to promote renewable energy and energy saving should be Japan’s official energy
policy. To promote renewable energy, the penetration of photovoltaic panels and wind generators
among households is essential. If households that support renewable energy tend to purchaseenergy-saving appliances, these two problems could be solved simultaneously.
Kinoshita (2016) used a conjoint analysis to reveal a high willingness to pay (WTP) for
renewable energy sources such as solar and wind power. Kinoshita (2017) then clarified thefactors that lead households to reduce electricity usage. One of the factors was energy sources2This study was aided by grants-in-aid for scientific research (C) (kakenhi) of Japan Society for The
Promotion of Science, No. 16K03679, Shin Kinoshita.
3
The Agency for Natural Resources and Energy in the Ministry of Economy, Trade and Industry
4
The Federation of Electric Power Companies of Japan3

used in electricity generation. However, I couldn’t find the clear relation between energy sources
and saving behavior. The present paper revises the previous work and makes clear the relation
between households’ preference for renewable energy sources and their energy-saving behavior
as an extension of those studies.
In the short-term, energy saving can help avoid the tightness of electricity demand and supply,
whereas in the long-term energy saving can help alleviate global warming and long-term energy
shortage problems. I consider the long-term energy saving.
This paper is consisted of the following section. In section 2, related literature is introduced. In
section 3, conjoint analysis and profiles are illustrated. In section 4, econometric methods are
explained. In section 5, estimation results are presented. In section 6, conclusions and policy
implications are suggested.
2. Related literature
There are many studies that households purchase energy-saving appliances. I introduce some ofthem. Nakagawa et al. (2013) analyzed the preferences for energy-saving appliances amonghouseholds in Japan by a conjoint analysis. They noted the liquidity constraint problem as thereason households don’t purchase energy-saving appliances, i.e., these appliances are very
expensive. They analyzed that subsidies and payment frequency (split payments) helpedhouseholds to purchase such appliances. They found that subsidies and split payments havesignificant effects on the purchase of such expensive appliances. They treat with only monetary
reasons and don’t treat with non-monetary reasons such as CO2 emissions and energy sources.
Mizobuchi and Takeuchi (2016) examined repurchases and additional purchases of energy-saving
appliances among Japanese households through a field experiment. Households who purchase anenergy-saving air conditioner can save more electricity than households who don’t purchase thistype of appliance. Households who purchase an additional energy-saving air conditioner can savemore electricity, whereas households who repurchase an energy-saving air conditioner don’t save
additional electricity. Revelt and Train (1998) estimated the households’ preferences for highefficiency appliances using a random parameter model. Revealed preference method was used,
not stated preference method. They found that households need rebates and a loan whenpurchasing high efficiency appliances. Ida et al. (2014) estimated the preferences for smart meters,
solar panels, and electric cars among Japanese households via conjoint analysis. They calculated
WTP for such equipment. Scarpa and Willis (2010) referred to appliances related with renewableenergy. They estimated the preferences of UK households for renewable energy technologies
using conjoint analysis. They found that households represented high WTP for micro generation
technologies such as solar photovoltaic, micro-wind and so on. However, the value was not solarge to cover the higher initial costs.
In the investment of energy-saving appliances, an energy efficiency gap is often discussed5. Dueto externalities, the investment inefficiency is caused. To avoid this problem, some policies suchas subsides are needed. However, I suggest that this problem will be solved by their preferences
for renewable energy.
Next, I introduce some studies about households’ preferences for renewable energy sources.
Morita and Managi (2013) used conjoint analysis to estimate preferences for energy sources,
particularly renewables, after the earthquake in 2011. They estimated the WTP for each energy
source and suggested policy implications vis-à-vis the Japanese government’s energy mix. Theyobtained negative WTP for nuclear power, but positive WTP for renewable energy sources suchas solar and wind power. Murakami et al. (2015) estimated consumers’ WTP for renewable energy
and nuclear energy in the US and Japan. They used conjoint analysis, and consumers in bothcountries showed negative preferences for nuclear power and positive preferences for renewable54

Allcot and Greenstone (2012) estimates an energy efficiency gap and suggests some policy implications.

energy.
I analyze the conditions that households purchase energy-saving appliances. Especially, I focus
on the role of energy sources used when electricity is generated. If the energy source is renewableenergy, households who support renewable energy might purchase energy-saving appliances. In
other words, they need energy-saving appliances with solar panels or wind generators. There aremany studies about purchasing behavior of energy-saving appliances and preferences forrenewable energy. However, they don’t mention the relation though these two problems arenecessary in Japan.
3. Conjoint analysis
I use a conjoint analysis to analyze households’ preferences for energy-saving appliances6.
Conjoint analysis is one of the stated preference methods (SPM) to analyze the individual
choice for several alternatives under future and hypothetical conditions. Individual preferences
can be estimated for hypothetical goods or services which have several attributes. We present
some alternatives and respondents choose one alternative of the hypothetical goods or services.
In this paper, three alternatives are presented to households and they choose the most preferred
one. Sometimes, the goods or services have not yet prevailed, and this method is often used inmarketing research. I analyze households’ preferences for energy-saving appliances which have
hypothetical attributes and conditions. The energy-saving appliances have several attributes
such as initial costs and the levels of these attributes change. The researcher decides the numberof attributes and their levels to make profiles. A profile that has few attributes is not enough todescribe a good object of study, but a profile with too many attributes makes it difficult for
respondents to choose among options. In general, five or six attributes are suitable. After
attributes and their levels are selected, their profiles are completed. However, if all the
combinations of attributes and levels are adopted, the patterns are too large and cause strong
correlation between some attributes, which is called multicollinearity. To avoid these problems,
profiles are created by the orthogonal planning method. From various cards that we obtainthrough the orthogonal planning method, selecting cards and their combinations, profiles are
made after deleting unrealistic and dominant cards. I used SPSS conjoint version 17.0 for theorthogonal planning.
Contingent Valuation Method (CVM) is another popular stated preference method, but it is nota choice experiment. CVM can be used to evaluate users’ valuation of non-marketable targets
such as forests and beaches. CVM evaluates the value of one target and doesn’t evaluate the valueof each attribute.
I presented the following three alternatives to households in the questionnaire.
Alternative 1: Status Quo: Households don’t purchase any energy-saving appliances.
Alternative 2: Energy-Saving Program A: Households purchase energy-saving appliances.
Alternative 3: Energy-Saving Program B: Households purchase energy-saving appliances.
When households choose alternative 1, they don’t purchase any energy-saving appliances. Whenhouseholds choose alternative 2 or 3, they purchase energy-saving appliances such as air
conditioners and refrigerators. They also purchase solar panels and a wind generator. They choose
the most desirable alternative with some attributes. These appliances have several attributes
whose levels are changeable. As attributes, purchase prices (an initial cost), an annual electricity
bill, subsidies, CO2 emissions, stable electricity supply, and energy sources which generate
electricity are adopted. These attributes and their levels will now be discussed.
6

I refer to Louviere, Hensher and Swait (2000), Kuriyama and Shoji (2005), Tsuge, Kuriyama and Mitani(2011), and Kuriyama, Tsuge and Shoji (2013) for a conjoint analysis.

5

1. Purchase prices of energy-saving appliances (an initial cost)
Households purchase energy-saving appliances such as air conditioners and refrigerators, forexample. In addition, they can purchase solar panels and a home micro-wind power generator.
The purchase prices are 1, 1.5, or 2 million Japanese yen (JPY). The prices seem too expensive ifhouseholds purchase only one air conditioner or refrigerator. But I assume that they purchaseseveral appliances when they join an energy-saving program. They use these appliances for 10
years because in the feed-in-tariff system the procurement period of renewable energy for electric
power companies is 10 years. When households don’t purchase any energy-saving appliances, theinitial cost is 0 JPY.
2. Annual electricity bill
If households purchase energy-saving appliances, they can save their annual electricity billcompared with current one. The levels are −50%, −30%, −10%, and 0% (unchanged). When they
don’t purchase any energy-saving appliances, their annual electricity bill is unchanged. Whenthey purchase more expensive energy-saving appliances, they can reduce more annual electricity
bill. Sometimes an annual electricity bill is unchanged even if households purchase energy-saving
appliances. But CO2 emissions are more reduced instead. When households generate electricity
by solar and/or wind power, they can sell surplus electricity that they don’t use to electric powercompanies and thereby gain income. The income from selling electricity depends on their surplus
electricity and their electricity saving. This income is included in the reduction of their annual
electricity expenditure. If households gain income by selling electricity, their annual electricity
bill is reduced. The procurement period of solar power in fiscal year 2016 for residence and lessthan 10kW is 10 years.7 Households can imagine long-term electricity saving by designating theperiod as “per year”.
3. Subsidies
Sometimes, households receive subsidies from the governments when they purchase energysaving appliances, solar panels, and wind generators. Households are supposed to earn somepercentage of its purchase prices as a subsidy. The levels in the questionnaire are 0%, 10%, and30%. 0% means that households don’t get subsidies. Sometimes, households don’t get subsidies
even if they purchase energy-saving appliances. Such households may have a strong interest inthese appliances. Of course, if households don’t purchase these appliances, they don’t get
subsidies.
4. CO2 emissions
If we use energy-saving appliances, we may reduce CO2 emissions. If households have aninterest in curbing global warming, they might purchase energy-saving appliances to reduce CO2
emissions. CO2 emissions are non-monetary factors. The levels are −20%, −10%, 0% (unchanged),and +10%. If fossil fuels are used in generation of electricity, sometimes CO2 emissions may beunchanged or increase.
5. Stable electricity supply
When electricity is constantly supplied, no outages occur in a year. When electricity isn’t
constantly supplied, short-term outages may occur a few times in a year, or lights in houses may
become dimmer. A dummy variable is used. It is assigned 1 for no outages and is assigned 0 for
outages. When renewable energy is used, its electricity supply sometimes might not be stable due
to weather conditions. When renewable energy isn’t used, the electricity supply is always stable.
6. Main energy sources
Households use electricity when they use energy-saving appliances. The main energy source
which is used in electricity generation is following: nuclear power, fossil fuels such as LNG, oiland coal, solar power, and wind power. The main energy source has the highest share in thecomposition of energy sources. A dummy variable is used for each energy source where fossil
7

6

The procurement period for wind power is 20 years (the Agency for Natural Resources and Energy in
the Ministry of Economy, Trade and Industry). However, I assume 10 years for simplicity.

fuels is the base category. Households purchase electricity generated by nuclear power and fossil
fuels from an existing electric power company or generate electricity by their own solar panels
and a wind generator. When households don’t purchase any energy-saving appliances, they use
the energy source used by electric power companies in their area. In 2016, the main provided
energy source was fossil fuels (LNG).
Respondents were informed of the questionnaire’s purposes to facilitate data collection. Nuclear
power plants ceased operation after the earthquake but plans to start them again are difficult toimplement. More use of fossil fuels such as coal and oil, which emit CO2, makes it difficult to
avoid global warming. In these situations, renewable energy such as solar and wind power should
be promoted urgently. At the same time, households need to reduce electricity usage. Table 1
summarized the levels of each variable.
Table 1 Levels of each variable
Variable
Initial cost (million JPY)
Annual electricity bill
Subsidies
CO2 emissions
Stability
Energy sources

Level
1, 1.5, 2-50%, -30%, -10% and 0% (unchanged)
0%, 10%, 30%-20%, -10%, 0% (unchanged) and +10%
Yes (1), no (0)nuclear power, fossil fuels, solar power, and wind power

Through the orthogonal planning method, I made profiles after deleting unrealistic and dominant
cards. When households use nuclear power and renewable energy, CO2 emissions are reduced.
When households use nuclear power and fossil fuels, electricity is constantly supplied. Whenhouseholds purchase solar panels and a wind generator, the initial costs are 2 million JPY, butsometimes they can purchase them at 1 or 1.5 million JPY. One of the examples of unrealistic

cards is that CO2 emissions increase even though nuclear power or renewable energy isused. One of the examples of dominant cards is that an electricity bill is reduced, and an
initial cost is very cheap even though renewable energy is used. Table 2 presents an example
of profile.
Table 2 Example of profile
Initial cost (million yen)
Annual electricity bill
Subsidies
CO2 emissions
Stability
Energy source

Status quo
0unchanged
0unchanged
stablefossil fuels

Program A
1.5
-30%
10%
-20%
stablenuclear

Program B
2
-30%
0
-30%
stablesolar

Households choose the most desirable alternative. They answer with respect to 10 choicequestions. Each question has various levels of attributes. The data were collected via a web-basedquestionnaire, utilizing the services of the Rakuten Research Company. The sample size is 750
households in Kanto8, Kansai9, and Chukyo10 areas. In Kanto area households purchase electricity
from the Tokyo electric power company (TEPCO) which has several nuclear power plants. They
8

Tokyo, Chiba, Kanagawa and Saitama prefecture
Osaka, Kyoto, Hyogo, Shiga and Nara prefecture
10
Aichi, Gifu and Mie prefecture
97

experienced the planned outages after the earthquake. In Kansai area households purchaseelectricity from the Kansai electric power company (KEPCO) which also has several nuclear
power plants. They didn’t experience any planned outages but were requested to save electricity
usage because the nuclear power plants stopped operation. In Chukyo area households purchaseelectricity from the Chubu electric power company (CEPCO) which has the Hamaoka nuclear
power plant. CEPCO doesn’t largely depend on nuclear power compared with TEPCO andKEPCO. The three areas are different in the dependence on nuclear power and are also different
in households’ preferences for energy-saving appliances. The sample is weighted by each area’s
population. Respondents’ age is under 59 because these appliances are used for 10 years. Data
were collected in February 2017. Table 3 shows the attributes of sample households. Respondents
who are unemployed and whose income is less than 2,000 are more observed than the population.
Table 3 Socio-demographic attributes

Occupation
Household income(thousand JPY)

Educational background

Family composition

Dwelling typeSex
Age (years old)

Number
Total
750
Employed
568
Unemployed
182
Less than 2,000
209
2,000-3,990
153
4,000-5,990
173
6,000-7,990
97
8,000-9,990
48
More than 10,000
70
Junior high school, high school
203
Technical school, junior college
173
University, graduate school
374
Single151
Couple
160
Three
197
Four
159
Five
62
More than six
21
Detached house (including two households 363
house)
Collective
housing(condominium, 355
apartment, housing complex etc.)
Company housing, dormitory housing etc.
32
Male
382
Female
368
20-29
154
30-39
192
40-49
227
50-59
177
Average
40.50
Minimum
20
Maximum
59

%
100
75.7
24.3
27.9
20.4
23.1
12.9
6.4
9.3
27.1
23.1
49.9
20.1
21.3
26.3
21.2
8.3
2.8
48.4
47.3
4.3
50.9
49.1
20.5
25.6
30.3
23.6

In the questionnaire, I surveyed households’ opinions and perceptions of energy problems.

854.3% households feel that electricity bill has become higher after the earthquake. 67.4% saveelectricity usage after the earthquake. 87.9% think they should reduce global greenhouse gases
and only 12.2% think they don’t need to reduce global greenhouse gases. On the most desirable
future energy source, 37.7% think solar power is the most desirable energy source. On the other
hand, only 19.5% think nuclear power is the most desirable energy source. 12.7% think LNG is
the most desirable energy source. I also surveyed households’ opinions and perceptions of energysaving appliances. 38.8% have energy-saving air conditioners and 33.3% have energy-saving
refrigerators. 53.6% don’t have an interest in solar panels and 71.2% don’t have an interest in awind power generator.
4. Econometric analysis
4.1 Random parameter logit model
In a choice experiment, the dependent variable is discrete. To estimate the choice model,
therefore, a discrete choice econometric model should be used. A conditional logit model is a
popular model in this context. However, this model assumes an Independent and Identical
Distribution (IID), and this assumption derives from the Independence of Irrelevant Alternatives(IIA). This assumption is restricted and easily violated in many cases. Consequently, a randomparameter logit model (mixed logit model) is used as a general discrete choice econometric model.
This model allows the random variation of individual preferences, unrestricted substitution
patterns, and correlation among unobserved factors over time.11
A random parameter logit model assumes that each parameter has a specific distribution. The
utility is specified as
U =α x +β z +εThis function specifies that individual n chooses alternative j, where α is non-random parametersand β is random parameters that represents the preference of each individual and varies amongindividuals. In this paper, constant terms and the parameter of annual electricity bill, which is a
price parameter, are non-random parameters. x is a variable vector that includes an annual
electricity bill. On the other hand, the parameters of CO2 emissions, a stable electricity supply,and energy sources are random parameters. The parameters of an initial cost and subsidies, which
are monetary factors, are also random parameters. z is a variable vector that includes the initialcost, subsidies, CO2 emissions, a stable electricity supply, and energy sources. ε is a random
error term and has an IID extreme value.
The probability conditional on β is
exp β xL β =Ȃ exp β x
The random parameter logit probability isP =exp β xȂ exp β xf β dβThis probability is the unconditional choice probability calculated as the integral of L β overall β .
The distribution of β must be assumed. Usually, a normal, lognormal, or triangular distribution,
11

Train (2003) and Louviere et al. (2000) are referred for the explanation of a random parameter logit
model.

9etc., can be assumed. In this paper, a normal distribution is assumed.
Simulation methods were used for estimation. The simulated probability is
1
P =
R

"
!#$

L

β!

where R is the number of draws. This simulated probability is an unbiased estimator of P . The
simulated log likelihood is
SSL =

*

)

#$ #$

d lnP (

where dnj is an indicator. It equals 1 if individual n chooses alternative j, or is 0 otherwise. SSL
was maximized to capture the maximum simulated likelihood estimator. In addition, 100 Haltondraws were used for simulation. For estimation, Limdep NLOGIT 5 was used.
4.2 Nested logit model
In the alternative 1, households don’t purchase any energy-saving appliances, while in thealternatives 2 and 3, they purchase energy-saving appliances. When respondents face thesealternatives, they first choose to purchase energy-saving appliances or not. Then, if they decideto purchase, they choose better energy-saving appliances. Alternatives 2 and 3 are included in thesame category or nest. A nested logit model is applicable. In a nested logit model, the cumulative
distribution of error term ε is assumed in following formula:exp −5
6#$ ∈42e,-./ /12

12This distribution is a kind of generalized extreme value (GEV) distribution, where K is thenumber of nests and k is a nest number. Unobservable error term ε is correlated amongalternatives within a nest and isn’t correlated outside a nest. λ6 is a scale parameter that measuresthe correlation between error terms within a nest k. The higher the value, the lower the correlation
is. When λ6 = 1, error terms aren’t correlated within the same nest. In a conditional logit model,
all scale parameters between alternatives have the same value. The probability that individual nchooses alternative I isP =e9.: /12 Ȃ ∈42 e9./ /12
9./ /12Ȃ5<#$ Ȃ ∈4; e

12 ,$

1;

where Vni is a deterministic term in a utility function. The maximum likelihood method is used
for estimation. Using this choice probability, a likelihood function is formed, a log-likelihood
function is maximized, and estimates are obtained. This choice probability can be written using
this formula:P = P |42 P 42
where P |42 is a conditional probability that individual n chooses alternative i under thecondition that individual nn chooses a nest k.

10

P 42 is the probability that individual n chooses an alternative within a nest k. This means that anindividual chooses a nest and then chooses an alternative within the nest. P is the product of aformula:eA.2 B12 C.2A.; B1; C.;Ȃ5<#$ eD.: /12e
P |42 =
Ȃ ∈42 eD.: /12

P 42 =

where W is a set of variables representing the attributes of a nest and Y is a set of variables
representing the attributes of each alternative. I is called an inclusive value (IV) in a nest and iswritten as follows:
I 6 = ln∈42eD.: /12

In a nested logit model, IV parameters are estimated. IV is also called a log-sum variable or an
expected maximum utility. IV parameters lie between 0 and 1. When all IV parameters are 1, themodel is a conditional logit model. When an IV parameter is above 1, the structure of nest is notsuitable. This means that the correlation between alternatives outside the nest is stronger thaninside the nest.
In a nested logit model, scale parameters are normalized in upper or elemental nest level to 1.
The upper nest level is “purchase” or “don’t purchase.” The elemental nest level is eachalternative. Random utility model 1 (RU1) is the model where scale parameters in an elemental
nest level are normalized to 1. Random utility model 2 (RU2) is the model where scale parameters
in upper nest level are normalized to 1 and scale parameters in elemental nest level are free. RU2
is used in this paper.
5. The estimation results
In this section, I show the estimation results and discuss the preferences for energy-saving
appliances. Firstly, I show the results of a random parameter model and a nested logit model fromfull sample. Next, I show the results from subsamples.
5.1 The estimation results
Table 4 expresses the choice number and choice probability for each alternative.
Table 4 Choice probability
Alternative 1
Alternative 2
Alternative 3
total
Number
3488
1996
2016
7500
Choice0.46510.26610.26881
probability
The choice probability for Alternative 1 is the highest. However, more than 50 % households
choose Alternative 2 or 3, which means that more than 50 % households are interested in energysaving appliances.
Table 5 illustrates the estimation results of a random parameter logit model.
Table 5 Estimation results (random parameter logit model)

11

Variable
Random parameter
Initial cost
Subsidies
CO2 emissions
Stability
Nuclear
Solar
Wind
Non- random parameter
Electricity bill
Constant for alt 1
Constant for alt 2
Standard deviation
Initial cost
Subsidies
CO2 emissions
Stability
Nuclear
Solar
Wind
Log likelihood
McFadden R2

CoefficientStandard
Error-0.01949
0.0087-0.00719
0.69653-1.45308
1.41504
1.05380.002240.00290.00509
0.153480.194940.120360.11644

-8.71
3
-1.41
4.54
-7.45
11.76
9.05

0.000
0.003
0.158
0.000
0.000
0.000
0.000

-0.02263
0.30977
0.91692

0.002540.42365
0.19293

-8.92
0.73
4.75

0.000
0.465
0.000

0.03026
0.00230.05223
1.53834
3.376020.44555
0.26066-4688.76
0.431

0.00134
0.00598
0.008170.128420.22782
0.177780.1707

22.67
0.38
6.39
11.98
14.82
2.51
1.53

0.000
0.701
0.000
0.000
0.000
0.012
0.127

Z value

P value

Both the initial cost and annual electricity bill coefficients have a negative sign and are
significant at the 1% level. If the initial cost and annual electricity bill are lower, households
tend to purchase energy-saving appliances. The subsidies’ coefficient has a positive sign and issignificant at the 1% level. If subsidies are higher, households tend to purchase energy-saving
appliances. These are monetary attributes. We find that households respond significantly to themonetary factors. On the other hand, the CO2 emissions coefficient has a negative sign but isn’t
significant. Households don’t respond to CO2 emissions. Even if CO2 emissions are reduced byenergy-saving appliances, households don’t purchase energy-saving appliances. The coefficientassociated with stable electricity supply has a positive sign and is significant at the 1% level.
Households value heavily stable electricity-supply in energy saving.
Next is the estimation results about energy sources. Dummy variables for each energy source
are used, with fossil fuels as the base category. The nuclear power coefficient has a negative
sign and is significant at the 1% level. If the electricity is generated by nuclear power instead offossil fuels, households don’t purchase energy-saving appliances. On the other hand, thecoefficient associated with renewable energy, both solar and wind power, has a positive sign andis significant at the 1% level. If the electricity is generated by renewable energy sources insteadof fossil fuels, households might purchase energy-saving appliances. Or households have apotential to invest solar panels and a wind generator.
From the results, households respond to the monetary factors. For the promotion of energysaving appliances, the initial cost and annual electricity bill should be lower, and subsidies
should be higher. Moreover, the electricity should be supplied constantly. By using electricity
generated by renewable energy, households might purchase energy-saving appliances. These
results indicate that renewable energy and energy-saving appliances can be promoted
simultaneously.

12

The constant term for Alternative 2 is positive and significant at the 1% level. Households tend
to purchase energy-saving appliances regardless of their attribute levels. The standard deviationof random parameters expresses the variations in households’ preferences. Almost all attributes
except subsidies are significant. The preferences vary among households whereas thepreferences for subsidies are similar.
I tried the estimation by a nested logit model because the alternatives have a nest structure.
Alternative 1 is “don’t purchase”, while Alternatives 2 and 3 are “purchase”. This choice set has
a nest structure where Alternatives 2 and 3 are within the same category as “purchase”.
Households choose “purchase” or “don’t purchase” at the first stage, and then they choose
Program A or Program B at the next stage if they choose “purchase.” Random utility model 2(RU2) is used where a scale parameter in the upper level whose alternative is “purchase” or “don’tpurchase” is normalized to 1, while a scale parameter in the lower level which is each alternative
is free. Table 6 shows the estimation results.
Table 6 Estimation results (nested logit model)Standard
Variable
Coefficient
Z value
P value
Error
Initial cost
-0.00220.0009-2.54
0.011
Annual electricity bill
-0.00560.0012-4.64
0.000
Subsidies
0.00420.00133.32
0.001
CO2 emissions-0.0027
0.0020-1.36
0.172
Stability
0.17140.05363.2
0.001
Nuclear-0.1530
0.0488
-3.13
0.002
Solar
0.40880.0919
4.45
0.000
Wind
0.35940.08484.24
0.000
Constant for alt 10.24010.20911.15
0.251
Constant for alt 2
0.03690.0761
0.48
0.628
IV parameter
Don’t purchase
1
Purchase
0.4476
0.09554.69
0.000
Log likelihood-7600.53
McFadden R２
0.048
The estimation results are almost the same as the results of a random parameter logit model. The
IV parameter is significant at 1% level. Moreover, the value is between 0 and 1, indicating thatthe nest structure is suitable. Alternative 1, which is “don’t purchase,” and Alternatives 2 and 3,
which are “purchase,” don’t have substitution. Alternatives 2 and 3 have a strong substitution.
Households choose “purchase” or “don’t purchase” in the first stage, and then they choose
Program A or Program B in the next stage. McFadden R２ is much smaller than that of a randomparameter logit model. To explain the households’ purchase of energy-saving appliances, arandom parameter logit model is superior to a nested logit model.
5.2 Differences across households’ socio-demographic attributes
I asked households about their socio-demographic attributes and perceptions of energy problems
in the questionnaire. I examine the differences of purchase behavior for energy-saving appliancesacross households’ socio-demographic attributes and perceptions. I divided the sample into two
subsamples to balance the sample size in two subsamples. Table 7 is the list of subsamples. I use
a dummy variable for each subsample. I assign 1 or 0 for each subsample. I show the assignment
in table 7.

13

Table 7 List of subsamples
Attributes
Subsamples(dummy variable)
HouseholdLow income (0)incomeHigh income (1)FamilySmall family (0)composition
Big family (1)Residential type

Living area

Detached(1)
Collective(0)

Definition

Sample size

Under 4 million JPY
More than 4 million JPY
Single and couple family
Married parents and unmarried children
family, and more than two adult generations
familyhouse Detached house (two household houses areincluded)house Condominium, apartment, housing complex
and a company and dormitory housing

Kanto (1)Chukyo (0)Kansai (0)Young age (1)Old age (0)of High bill (1)Households who live in Kanto area
Households who live in Chukyo area
Households who live in Kansai area
Age
Less than 39 years old (average)
More than 40 years old
Perception
Households who think electricity bill iselectricity billhigher after the earthquake
Low bill (0)Households who don't think electricity bill ishigher after the earthquake
Perception of Save (1)Households who save electricity usage after
energy-saving
the earthquake
Don’t save (0)Households who don't save electricity usage
after the earthquake
Desirable
Renewable energy Households who prefer renewable energy(1)energy source
Non-renewable
Households who prefer non- renewableenergy (0)energy

362
388
311
439

363
387
435
114
201
346
404
407
343
506
244
483
267

I use the cross terms of each independent variable and each dummy variable to examine thedifferences. Table 8 expresses the estimation results. A random parameter logit model is used for
estimation.
Table 8 The estimation results with cross terms
Household
Familyincomecomposition
Initial cost
-0.016***-0.019***Subsidies
0.009
**
0.007
CO2 emissions-0.006-0.010Stability
0.686
***
0.684
***
Nuclear-1.955***-1.658
***
Solar
1.350
***
1.317
***
Wind
1.044
***
1.103
***
Annual electricity -0.025***-0.019***14

Residential type

Living area

-0.0190.010-0.0090.612-1.5761.285
0.959-0.020-0.0200.005-0.0040.603-2.1861.172
0.840-0.024

***
***
***
***
***
***
***

***

**
***
***
***
***

bill
Cross term
Initial cost
Annual electricity
bill
Subsidies
CO2 emissions
Stability
Nuclear
Solar
Wind
Constant for alt 1
Constant for alt 2
McFadden R2

0.002
0.002

0.000
-0.006-0.004-0.007
0.134
0.682
0.029
0.034
0.463
1.037
0.433
Age

0.003
-0.003
0.003
0.005
0.004
-0.008
0.030
0.160
0.211
0.272
0.232
1.016
***
0.157
0.260
0.291
-0.086
0.192
0.331
0.314
0.305
0.396
0.922
***
0.919
***
1.016
***
0.431
0.431
0.432
Perception of Perceptionof Desirable energy
electricity billenergy-saving
source-0.026***-0.022***-0.023
***
0.012
***
0.006
0.001
-0.014*-0.020**-0.011
1.013
***
1.210
***
0.864
***
-1.702***-1.794***-0.384
1.749
***
1.210
***
0.722
***
1.205
***
0.947
***
0.454
**
-0.018***-0.021***-0.015
***

**

***

Initial cost
-0.015***
Subsidies
0.007*
CO2 emissions-0.008
Stability
0.826***
Nuclear-1.770
***
Solar
1.653
***
Wind
1.419
***
Annual electricity -0.023***
bill
Cross term
Initial cost
0.002
0.009
Annual electricity 0.001-0.009bill
Subsidies-0.001-0.007CO2 emissions-0.0040.009
Stability-0.255-0.548
Nuclear
0.302
0.251
Solar
-0.508**-0.587
Wind-0.780***-0.265Constant for alt 1
0.411
0.310
Constant for alt 2
1.000
***
0.937
McFadden R2
0.435
0.432
Note) *** 1%, ** 5%, *10% significant level-0.001-0.006

***
**

*
**

***

0.007
-0.0040.002
0.015-0.646
0.407
0.208
0.134
0.528
1.077
0.433

0.001
0.002

***

**

***

0.010
-0.015

***
***

0.009
0.004
-0.238-1.857
1.011
0.865
0.429
1.028
0.441

*

***
***
***
***

There is no significant cross term in family composition and residential type. We don’t observe
any differences in family composition and residential type. Higher income households positively
evaluate nuclear power. Households who live in Kanto area also positively evaluate nuclear power
even though they suffered from serious accidents of nuclear power plants in Fukushima. Younger
households don’t evaluate renewable energy than older ones. Households who think electricity
bill is higher after the earthquake give less evaluation for a stable electricity supply and solar
power. They accept higher initial cost and prefer a lower annual electricity bill. Households who
save electricity usage after the earthquake also accept higher initial cost and give less evaluation

15

for a stable electricity supply. Only older households purchase energy-saving appliances when
renewable energy is used in electricity generation.
Next, I examine the differences between households who support renewable energy as the mostdesirable one and ones who support non-renewable energy. I asked households the most desirable
energy source in the future in the questionnaire. Renewable energy sources include solar, wind,geothermal, biomass and hydraulic power, while non-renewable energy sources include nuclear,coal and natural gas. One of the aims of this paper is to examine the relation between preferences
for renewable energy and purchase of energy-saving appliances. Households who preferrenewable energy may purchase energy-saving appliances. From the estimation results,
households who support renewable energy positively evaluate a lower annual electricity bill, astable electricity supply and solar and wind power. They accept higher initial cost and negatively
evaluate nuclear power. When renewable energy is used in electricity generation, households who
support renewable energy may purchase energy-saving appliances.
Parameter differences are tested to examine that purchase behavior of energy-saving appliances
is different or not between two subsamples. For the parameter differences test, the likelihood test
and the following test statistic are used.
−2[LL(A+B) − (LL(A)+LL(B))]
LL(A+B) is the log likelihood which is obtained after estimation by pooling data from two
subsamples. LL(A) and LL(B) are the log likelihoods for each subsample. The null hypothesis isthat parameters or behavior between two subsamples are equal. The alternative hypothesis is thatparameters or behavior are not equal. The test statistic is chi-squared distributed with degrees of
freedom equal to the number of parameters. The critical values for 1%, 5% and 10% significancelevel are respectively 33.41, 27.59 and 24.77 for 17 degrees of freedom. Table 9 expresses theresults of the parameter differences test. If calculated statistic is inside the rejection area, the null
hypothesis is rejected. Parameters between two subsamples are, thus, significantly different andhouseholds in two subsamples show different behavior.
Table 9 Results of the parameter differences test
test statistics
Household income
14.431
Family composition
16.588Residential type21.370
Living area
19.517Age
67.242***
Perception of electricity 47.724***
billPerception of energy- 89.814***
savingDesirable energy source
182.060***Note) *** 1%, ** 5%, *10% significant level

same
same
same
same
different
different
different
different

In household income, family composition, residential type and living area, we don’t observe thesignificant differences between two subsamples. However, we observe the differences between
younger and older generation. In all items about perceptions, we observe the differences between
two subsamples.
6. Conclusions and policy implications

16

In Japan after the earthquake, the prevalence of energy-saving appliances including solar
panels and wind generators and renewable energy such as solar and wind power is urgent andessential topic. I analyzed the conditions that households purchase energy-saving appliances
through a conjoint analysis. I included an annual electricity bill, an initial cost and subsidies asmonetary factors, and CO2 emissions, stability of electricity supply and main energy sources as
non-monetary factors. The estimation results indicated that households largely respond tomonetary factors. If an annual electricity bill is reduced, initial cost is cheaper, and subsidies
increase, households might purchase energy-saving appliances. To promote energy-saving
appliances, it is necessary to reduce an electricity bill and an initial cost and to raise subsidies.
Households also need stable electricity supply, but they don’t show any interests to reduce CO2
emissions.
Especially, I focus on the role of renewable energy as an energy source in generation ofelectricity when households purchase energy-saving appliances. We found that if the mainenergy source is renewable energy households might purchase energy-saving appliances. The
findings also say that households have their strong interest in solar panels and wind generators.
By utilizing households’ preferences for renewable energy, energy-saving appliances will bepromoted. These results indicate that renewable energy and energy-saving appliances can bepromoted simultaneously.
If households show high evaluation for renewable energy, it is possible to promote energy-saving
appliances by appealing to their interest in renewable energy. However, electricity generated bysolar and wind power depends on the weather conditions. Storage batteries is needed therefore toensure a stable electricity supply.
Free-riding is another serious problem arising in long-term energy efficiency. For example, CO2
emissions can be solved by other people’s efforts, so an individual might not have an incentive topurchase energy-saving appliances. To promote energy-efficient appliances, the governments
should offer subsidies to each household. But, households have a strong interest in renewableenergy and they might decide to purchase energy efficient appliances without subsidies.
Acknowledgements
This study was aided grants-in-aid for scientific research (C) (kakenhi) of Japan Society for
The Promotion of Science. I presented this paper at the academic meeting of the Summer
Workshop of Economic Theory, the Japanese Economic Association and the International
Conference of Japan Economic Policy Association in 2017. We thank the members of themeetings and the following discussants at the academic meeting: Prof. Qingfeng Liu, Prof.
Tomoyuki Sakamoto and Prof. Masayuki Sato. The authors would like to thank Enago(www.enago.jp) for the English language review. All remaining errors are ours.
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