ISSN 1881-6436

Discussion Paper SeriesNo. 18-03

What is the most desirable energy source in Japan after theearthquake and the deregulation: suggestions through conjoint
analysisShin Kinoshita

September 2018

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

1

What is the most desirable energy source in Japan after theearthquake and the deregulation: suggestions through conjoint
analysisShin Kinoshita1

Abstract
Aftermath the great East Japan earthquake on March 11th, 2011, nuclear power plants
ceased operations. We depended on fossil fuels after the earthquake. However, fossil
fuels increase greenhouse gases emissions. We need other energy sources in place of
nuclear power and fossil fuels. Thus, we specifically focus on renewable energy such as
solar and wind power as an alternative energy source. We estimate households’
preferences for energy sources by conjoint analysis and calculate willingness to pay(WTP) after estimation by random parameter logit model. We find that households
negatively evaluate nuclear power, while positively evaluate renewable energy bothsolar and wind power. Further, the stability of electricity supply is highly evaluated.
Their preferences are different across their socio-demographic attributes andperceptions for energy problems. We proposed our future energy supply after theearthquake and the deregulation from our results.

Keywords
conjoint analysis, renewable energy, deregulation of electricity retail sales
JEL classification
C25, L51, L94, L95, Q42

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

What is the most desirable energy source in Japan after theearthquake and the deregulation: suggestions through conjoint
analysis2
1. Introduction
Aftermath the great East Japan earthquake in March 2011, Japan has faced drastic
energy and environmental changes and many problems. After the earthquake, theFukushima nuclear facility ceased operation due to serious accidents. Other nuclear
power plants in Japan also stopped operation and were subjected to inspections. Though
the Sendai nuclear power plant of Kyushu electric power company recommenced
operations in 2015, other nuclear power plants haven’t been operated yet. We urgently
should promote alternative energy sources in place of nuclear power. Since theearthquake, we Japanese have relied on fossil fuels such as natural gas (LNG), oil andcoal, importing more fossil fuels. However, fuel prices are too volatile and too
vulnerable to exchange rate dynamics. Higher costs are shifted to electricity bill thathouseholds pay. Moreover, fossil fuels emit global greenhouse gases (GHG) such as
CO2.
After the earthquake, people who live in Kanto area around Tokyo experienced
planned outages because nuclear power plants in Fukushima had ceased operations.
Electricity shortage is another serious problem. We have been requested to saveelectricity usage when electricity demand hits its peak in the summer and winter in caseof a sudden outage.
Climate change is also one of the serious problems. After the earthquake, we haverelied on fossil fuels which emit CO2. It is difficult to reduce CO2 emissions despite thepromise of the 1997 Kyoto Protocol to other countries in the world. Japan agreed toreduce the emissions of greenhouse gases by 6% from 2008 to 2012, compared to a1990 baseline.
Renewable energy such as solar and wind power should be promoted as an alternative
energy in place of nuclear and fossil fuels to reduce CO2 emissions. In July 2012, theJapanese government introduced a feed-in-tariff system to promote renewable energy.
All companies which generate electricity by renewable energy can sell their electricity

2This study was aided by research funding from Osaka Gas Co. Ltd.
3

to nine major electric power companies3. These major electric power companies are
required to purchase electricity from these companies. However, the bid prices are
relatively high, particularly solar power tariffs. These high bid prices are then shifted tohouseholds via electricity bill. The bid price of solar power as an example is around 20
Japanese Yen (JPY) per 1kWh in 2018 fiscal year4.
The Japanese government designs the energy plan about the desirable composition ofenergy sources that we call “best-mix” for the fiscal year 2030. The share of nuclear
power will be raised to around 20-22% assuming nuclear power plants recommence
operations. This share was only 1%5 in the fiscal year 2013. The share was 30% in
December 2010 before the earthquake. While the share of renewable energy such as
solar and wind power will be raised to around 22–24%, from 11% in the fiscal year
2013. The share of fossil fuels such as coal, LNG, and oil will be reduced to 56%, from
87% in 2013.
The deregulation of electricity and gas retail sales is also an important topic. The
deregulation started in 2000. In April 2016, the deregulation of electricity retail sales forgeneral households started. In April 2017, the deregulation of gas retail sales for generalhouseholds started. Now, all consumers can purchase electricity and gas from all
suppliers including new entrants. Some suppliers provide electricity and gas bydiscounted prices if households purchase electricity and gas or telecommunication
services at the same supplier.
The environment surrounding energy in Japan has been changing dramatically since
the Fukushima disaster. We should discuss our future energy sources. I estimate
Japanese households' preferences for energy sources by conjoint analysis. From thehouseholds' preferences in terms of willingness to pay (WTP), we discuss the mostdesirable energy sources and composition as a Japanese energy policy. Especially, I
focus on the preferences for renewable energy sources such as solar and wind power
and nuclear power. If their WTP for renewable energy sources is positive andsubstantially large, the policy to promote renewable energy is supported and this meansthat households accept a little higher priced electricity if it is generated by renewableenergy sources. If their WTP for nuclear power is negative and substantially large, weshould abolish nuclear power plants. After the deregulation, new suppliers with their
3

In Japan there are nine major electric power companies; Hokkaido, Tohoku, Tokyo, Chubu,
Hokuriku, Kansai, Chugoku, Shikoku and Kyushu electric power companies. These companies havemonopoly power in their own operation area.
4
The Agency for Natural Resources and Energy in the Ministry of Economy, Trade and Industry
5 These shares are from the Agency for Natural Resources and Energy in the Ministry of
Economy, Trade and Industry.
4

own services and energy sources start their business. If households highly evaluate
renewable energy, suppliers will start to provide electricity generated by renewableenergy, and such households might purchase electricity from such suppliers. Ifhouseholds show negative evaluation for nuclear power, they won’t purchase electricity
from suppliers which use nuclear power for electricity generation, and suppliers will beforced to cease to use nuclear power. The questionnaire was surveyed in August 2014
when three years had passed and before the deregulation for general households.
Accordingly, this study could provide useful suggestions for future energy policies fromthe viewpoint of consumer preferences.
This paper consists of the following sections. In section 2, related studies are
introduced. In section 3, conjoint analysis is explained. In section 4, econometricmethods are elaborated upon. In section 5, estimation results are delineated. Finally, in
section 6, we conclude, with a focus on the policy implications emanating from thisstudy.
2. Related literature
I estimate Japanese households’ preferences for energy sources with some attributes by
conjoint analysis. Especially, I focus on the preferences for renewable energy sources
such as solar and wind power which are the promising future energy sources after theearthquake. In this section, I survey related studies about this topic. Firstly, I introduce
studies after the earthquake in Japan.
Morita and Managi (2015) estimated the preferences for energy sources, particularly
renewable energy sources, after the earthquake by conjoint analysis. They estimatedWTP for each energy source and suggest policy implications vis-à-vis the energy mix of
the Japanese government. They obtained negative WTP for nuclear power, while
positive WTP for renewable energy which includes solar and wind power. Murakami etal. (2015) estimated the WTP for renewable and nuclear energy among US and Japanese
consumers. They used conjoint analysis. They found that consumers in both countries
showed negative WTP for nuclear power and positive WTP for renewable energy. They
included solar, wind, biomass and geothermal as renewable energy sources and used theratio of renewable energy. Ida, Takemura, and Sato (2015) found conflicts between
nuclear power and electricity prices in Japan where a trade-off can occur between low
prices with nuclear or high prices without it by conjoint analysis. However, these studies
didn’t mention the energy market after the deregulation.
Roe et al. (2001) is one of the first studies about preferences for renewable energy.
5They estimated WTP for renewable energy sources such as solar and wind power
among consumers in the US. They used hedonic analysis and the price premium as its
dependent variable. They found that consumers were willing to pay for renewableenergy and reduction of global greenhouse gases such as CO2. Bordhers, Duke, andParsons (2007) also estimated WTP for renewable energy among consumers. They
adopted conjoint analysis and nested logit model for estimation. At the first stage
consumers choose to join a green program or not. At the second stage consumers choose
the best program among several green options. They also obtained positive WTP for
renewable energy and consumers preferred solar power than other sources. Scarpa andWillis (2010) referred to appliances related with renewable energy. They estimated thepreferences 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 so large to cover the higher initial costs. Sundt and Rehdanz (2015) estimated the
WTP for renewable energy and analyzed the factors which determined consumers’ WTP
for renewable energy by a meta-regression analysis where the WTP is the dependent
variable. They found many factors which explained the differences in evaluation among
each renewable energy. Yoo and Ready (2014) estimated variations in preferences foreach renewable energy by using random parameter logit model. They found variationsin preferences for solar power.
Sometimes electricity generated by renewable energy is unstable. For households thestability of electricity supply is necessary even though they prefer renewable energy. I
mention the stability of electricity supply and also estimate the preferences for thestability. Some papers estimated the preferences for the stability and the cost of outages.
Ozbafli and Jenkins (2015) estimated households’ WTP for an improved electricity
service by contingent valuation (CV) in North Cyprus. On the other hand, Ozbafli andJenkins (2016) used conjoint analysis. Both papers found that households would accept
higher monthly electricity bill to avoid the cost of outages. Kim, Nam, and Cho (2015)
estimated households’ WTP by contingent valuation (CV) in South Korea. Theyobtained positive cost of outages from both an unannounced and announced outages.
However, they didn’t mention the relation with renewable energy.
Compared with previous studies, I estimate households’ preferences for energy sources
in Japan after they have experienced an unprecedented disaster. As such, this study
could provide useful insights vis-à-vis energy policies in countries which frequently
experience massive natural disasters. Moreover, I focus on the deregulation ofelectricity and gas retail sales for general households. After the earthquake and6deregulation, we Japanese should discuss our energy sources and its supply system.
3. Conjoint analysis
In this paper, Japanese households’ preferences for energy sources are estimated byconjoint analysis6. Conjoint analysis is one of the stated preference methods (SPM) to
analyze the individual choice for several alternatives under future and hypotheticalconditions. Individual preferences can be estimated for hypothetical goods or services
which have several attributes, and we can evaluate each attribute by WTP. We present
some alternatives and respondents choose one alternative of the hypothetical goods orservices. In this paper, three alternatives are presented to households and they choose
the most preferred one. Sometimes, the goods or services haven’t yet prevailed, and thismethod is often used in marketing research. I analyze households’ preferences forelectricity or suppliers which have hypothetical attributes such as monthly electricity
bill, and the levels of these attributes change. In conjoint analysis, profiles of goods orservices which have several attributes are presented to respondents. The researcher
decides the number of attributes and their levels to make profiles. A profile which has
only few attributes may not be realistic, whereas a profile which has too many attributes
makes it difficult for participants to choose among options. In general, five or
six attributes are suitable. After attributes and their levels are selected, their profiles arecompleted. However, if all the combinations of attributes and levels are adopted, thepatterns are too large and cause strong correlation between some attributes, what we call,multicollinearity. To avoid these problems, profiles are created by the orthogonalplanning method. From various cards that we obtain through 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 the orthogonalplanning.
Contingent Valuation Method (CVM) is another popular stated preference method, but
it isn’t a 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 value of each attribute.
Households choose one electricity provided by an electric power company. The
6

Louviere, Hensher, and Swait (2000), Kuriyama and Shoji (2005), Tsuge, Kuriyama, and Mitani(2011), and Kuriyama, Tsuge, and Shoji (2013) are referred for conjoint analysis. Nakajima, Ida, andKinoshita (2006) is one of the studies about energy market which use conjoint analysis. They
estimate Japanese households’ preferences for electricity and gas under their intense competition.
7

electricity has several attributes such as supply-stability and CO2 emissions and isgenerated by some energy sources. Goett, Hudson, and Train (2000) examinedcustomers' choice for retail energy suppliers by conjoint analysis. They included theratio of renewable energy as an attribute of energy suppliers as well as other attributes
such as a fixed price. The following three alternatives were presented to respondents.
Alternative 1: Electricity provided by an electric power company which has nuclear
power plants. The electricity is generated by nuclear power and fossil fuels such as
LNG, oil and coal.
Alternative 2: Electricity is generated by fossil fuels such as LNG, oil and coal andrenewable energy through a feed-in-tariff system. The electric power companies don’t
use nuclear power because all nuclear power plants aren’t operated. The electric powercompanies purchase electricity from other suppliers which generate electricity byrenewable energy through a feed-in-tariff system and sell the electricity to households.
Alternative 3: Electricity provided by an electric power company including new entrants
which doesn’t have any nuclear power plants. The electricity is generated by renewableenergy or fossil fuels.
Alternative 1 assumes that households purchased electricity from an electric powercompany before the earthquake in March 2011. This means that nuclear power plants
resume operations and electricity generated by nuclear power and fossil fuels isprovided. Alternative 2 assumes that households purchase electricity from a current7
electric power companies after the earthquake. Nuclear power plants don’t resume
operations, and electricity generated by fossil fuels and renewable energy through afeed-in-tariff system is provided. Alternative 3 assumes that households purchaseelectricity from an electric power company in the future after the deregulation. Nuclear
power plants don’t resume operations, and electricity generated by fossil fuels andrenewable energy is provided.
The attributes and the levels of each alternative are as follows.
1. Monthly electricity bill:
Monthly electricity bill will change compared with the current bill. The levels are−2000, −1500, −1000, −500, 0 (unchanged), +500, +1000, +1500, and +2000 JPY. The
electricity bill might be cheaper when households purchase electricity from the nine7

The questionnaire was surveyed in August 2014.
8

major electric power companies since nuclear power plants resume operation.
Sometimes the electricity bill might be higher when the electricity is generated by fossil
fuels through rising fuel prices. The electricity bill might be higher when the electricity
is generated by renewable energy due to the feed-in-tariff system. When households
purchase electricity from a new coming supplier such as a telecommunications company,
they might purchase electricity at discounted prices through simultaneous purchaseswith telephone, internet, or CATV.
2. CO2 emissions:
CO2 emissions will change in 2030 compared to 2014. The levels are −20%, −10%,0% (unchanged), +10%, and +20%. When nuclear power plants resume operation, CO2
emissions will decrease. When fossil fuels are used, CO2 emissions usually increase.
When renewable energy is used, CO2 emissions will decrease.
3. Electricity-supply stability:
When nuclear power is used, electricity will be supplied constantly without
interruptions and outages. When the electricity is generated by fossil fuels, theelectricity might not be supplied constantly due to planned outages from electricity
shortages. In that case, nuclear power plants don’t operate. When renewable energy isused, sometimes electricity supply might not be stable due to weather conditions. Whenelectricity is constantly supplied, outages will not happen in a year. When electricity is
not constantly supplied, short-term outages may happen a few times a year or the lights
in the houses become dimmer. A dummy variable is used. It equals 1 when electricity isconstantly supplied, and 0 otherwise.
4. Energy sources:
When the electric power companies generate electricity, they use nuclear power, fossil
fuels, solar power, or wind power as energy sources. Each energy source is just the mainenergy source which has the highest ratio in electricity generation. A dummy variable isused for each energy source where fossil fuels are the base category.
In Table 1, the levels of each attribute are summarized.
Table 1 The levels of each attribute
Attribute

Level

Monthly electricity −2000, −1500, −1000, −500, 0 (unchanged), +500, +1000, +1500,
bill (JPY)and +2000
CO2 emissions−20%, −10%, 0% (unchanged), +10%, and +20%

Stability

Yes (1), No (0)Energy source

nuclear power, fossil fuels, solar power, and wind power
9Through the orthogonal planning method, I made profiles after deleting unrealistic anddominant cards. One of the examples of unrealistic cards is that CO2 emissions increase
even though nuclear power or renewable energy is used. One of the examples ofdominant cards is that electricity bill is very cheap even though renewable energy is
used.
Table 2 is an example of profile. Households answer with respect to eight choicequestions like this profile where the levels change. Through a pretest, we identify
problems with the questionnaire and correct profiles to maximize understanding andminimize ambiguity for respondents. In the questionnaire, I gave some information
about energy problems for respondents to understand the purpose of the questionnaire.
The information is about energy shortage after the earthquake, the global warming, thefeed-in-tariff system and the deregulation of electricity retail sales.
Table 2 Example of profile
Attribute
Monthly Electricity bill (JPY)

Alternative 1

Alternative 2

Alternative 3

−2000

1000

−1000

CO2 emissions

−10%

10%

−20%

Stability

Yes

No

No

Nuclear power

Fossil fuels

Solar power

Energy source

The sample was collected via a web-based questionnaire, utilizing the services of the
Rakuten Research company. The sample size is 250 households in Kanto8 and Kansai9area, thus 500 households in total. These two areas are the two biggest city areas inJapan. Households in Kanto area purchase electricity from Tokyo electric powercompany (TEPCO) which has some nuclear power plants. The Fukushima nuclear
facility is one of them. They experienced planned outages after the earthquake.
Households in Kansai area purchase electricity from Kansai electric power company(KEPCO) which has also some nuclear power plants. They didn’t experience planned
outages but were requested to save electricity usage because the nuclear power plants
stopped operation. Before the earthquake, TEPCO and KEPCO heavily depended on
8

Kanto area is in East Japan around Tokyo. It includes Saitama, Chiba, Tokyo and Kanagawa
prefectures.
9
Kansai area is in West Japan around Osaka. It includes Shiga, Kyoto, Osaka, Nara, Hyogo andWakayama prefectures.
10

nuclear power. The ratio of nuclear power was 27% in TEPCO and 44% in KEPCO in
2010. The perceptions of households in Kanto and Kansai area for energy sources might
have changed after the earthquake. The sample was collected in August 2014. Table 3
presents the socio-demographic attributes of households in the sample. The percentagesof unemployed in occupation and less than 2 million JPY in household income arelarger, caused by housewives and retirees. In other attributes, the percentages arereasonable in urban area.
Table 3 Socio-demographic attributes

Occupation

Annualhouseholdincome(million
JPY)

Education

Familycomposition

Residential
type

Area

Number

%

Total

500

100

Employed

243

63.2

Unemployed （ including housewives andretirees)

137

27.4

other

47

9.4

Less than 2

159

31.8

2–3.99116

23.2

4–5.9987

17.4

6–7.9963

12.6

8–9.9936

7.2

More than 10

39

7.8

Junior high school and high school

122

24.4

Technical school and junior college

119

23.8

University and graduate school

255

51

Single91

18.2

Couple131

26.2

Husband and wife (parents) and children

225

45

Two households

26

5.2

Detached house (including two householdhouses)248

49.6

Collective house (condominium, apartment,
housing complex etc.)

241

48.2

Company housing, dormitory housing etc.

11

2.2

Kanto

250

50

Kansai

250

50

11

Sex
Age (years)

Male

296

59.2

Female204

40.8

Average

47.45

Min

21

Max

69

In the questionnaire, I surveyed households’ opinions and perceptions of energy
problems. 77.8% households feel that electricity bill has become higher after theearthquake. 74.6% save electricity usage after the earthquake. 78.4% think they should
reduce global greenhouse gases and only 6% think they don’t need to reduce these gases.
35.4% think nuclear power plants should be abolished. 33.6% think Japanese
government should operate nuclear power plants. 18.2% allow current electric powercompanies to operate nuclear power plants. About the feed-in-tariff system for thepromotion of renewable energy, 25.4% think the tariff is higher and 20.8% think it isfair. However, 44.4% don’t have proper knowledge and information to evaluate thetariff. On the best future energy source, 34.4% think solar power is the best energy
source. On the other hand, only 16.2% think nuclear power is the best energy source.
13.2% think LNG is the best energy source. I also surveyed households’ opinions about
the deregulation of electricity retail sales. 77.6% answered that they had a little
information about the deregulation. They think lower electricity bill, the stability ofelectric power supply, and suppliers which don’t have nuclear power plants are essential
when they choose suppliers.
4. Econometric analysis
In a choice experiment the dependent variable is discrete. To estimate this choicemodel, we, thus, need to employ a discrete choice econometric model. The conditional
logit model is a popular choice model in this context. However, this model assumes
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, we use a random parameter logit model (mixed
logit model). This model allows the random variation of individual preferences,unrestricted substitution patterns and correlation in unobserved factors over time.
The random parameter logit model assumes that each parameter has a distribution. The
utility is specified as:

12

=

+

+

This function specifies that individual n chooses alternative j. α is a non-randomparameter and β is a random parameter which represents the preference of eachindividual and varies over individuals. In this paper constant terms and the parameter ofelectricity bill which is a price parameter are non-random parameters. x is a variable

vector which includes monthly electricity bill. On the other hand, the parameters of CO2

emissions, the stability of electricity supply and energy sources are random parameters.
z is a variable vector which includes CO2 emissions, electricity-supply stability, andenergy sources. ε is a random error term and has an IID extreme value.
The probability conditional on β is=ȂThe random parameter logit probability is=ȂThis probability is the unconditional choice probability calculated as the integral of
L ! β over all β .
We should assume the distribution of β . Usually we assume normal, lognormal
triangular distribution etc. In this paper, the normal distribution is assumed.
We use simulation methods for estimation. The simulated probability is(1#" = &%')*'

R is the number of draws. This simulated probability is an unbiased estimator of P ! .
The simulated log likelihood is10,, = & &
)* )*

13

-. #/

dnj is an indicator. It equals 1 if individual n chooses alternative j, else 0. We maximize
SSL to capture the maximum simulated likelihood estimator. We use 100 times Haltondraws for simulation (Train, 2003, p138-154; Hensher, Rose, and Greene, 2005,
p605-694). For estimation we use Limdep NLOGIT 5.
After estimation, the WTP for each attribute including each energy source is calculated.
If the utility function is linear, it is expressed as:2=+

is the deterministic term of the utility function. The total differentiation of theformula is:
2

=

42
4

+

42
4

Now we obtain the WTP of z1 which is one of the attributes, such as solar power. If theutility level does not change (= ) and other variables, except z1, are unchanged,
we can obtain the marginal WTP (MWTP) as follows:42
4 *789 = :
<424 ;xm is a monetary variable such as a price. We can also write MWTP by invoking
parameters as follows:
789 = : =;β! is the coefficient of each attribute and β> denotes a monetary coefficient such as anelectricity bill. We can obtain WTP by dividing the coefficient of each attribute by amonetary coefficient.
5. Estimation results

145.1 Estimation results of random parameter logit model
In this section, I analyze the estimation results of random parameter logit model. Table
4 shows the number of choices and the ratio of choices.
Table 4 Choice probability
Number Ratio
Alternative 1

1187

0.297

Alternative 2

1167

0.292

Alternative 3

1646

0.412

Alternative 3 is the most popular. Households prefer electricity provided by electric
power companies which don’t use nuclear power and generate electricity by renewableenergy and fossil fuels.
Table 5 illustrates the estimation results of random parameter logit model.
Table 5 Estimation results
Variable

Coefficient Standard
Error

z valuep value

Random parameters (mean)
CO2−0.03875

0.00569−6.81

0Stability

0.48980.133763.66

0.0003

Nuclear−3.51017

0.30989−11.330Solar

0.947390.15276

6.2

0

Wind

0.40923

0.09601

4.26

0

Non-random parameters
Electricity bill −0.00067

4.87E-05 −13.78

0Constant 1

0.856030.11824

7.24

0Constant 2

0.03223

0.064650.5

0.6181

Standard deviation
CO2

0.05174

0.00587

8.81

0Stability

1.848270.1392113.28

0Nuclear

4.878

0.36084

13.52

0

Solar

1.476810.13304

11.1

0

Wind

0.285130.18274

1.56

0.118715

Log likelihood −3297.20787
McFadden R2 0.24969
The coefficient of electricity bill has a negative sign and is significant at the 1% level.
If the electricity bill is lower, households choose the alternative. The coefficient of CO2
emissions has a negative sign and is significant at the 1% level. If CO2 emissions
decrease, households choose the alternative. The coefficient associated with theelectricity-supply stability has a positive sign and is significant at the 1% significancelevel. If electricity is constantly supplied without outages, households choose thealternative. Next, the estimation results of energy sources are explained. Dummy
variables for each energy source have been used, where fossil fuels are the base category.
The coefficient of nuclear power has a negative sign and is significant at the 1% level. If
the electricity is generated by nuclear power in place of fossil fuels, households don’tchoose the alternative. On the other hand, the coefficient of renewable energy both solar
and wind power has a positive sign and is significant at the 1% level. If the electricity is
generated by renewable energy in place of fossil fuels, households choose thealternative. From the estimation results, households prefer the electricity which is lower
monthly electricity bill, reduces CO2 emissions, is constantly supplied, and is generated
by renewable energy, not by nuclear power.
5.2 WTPWTP for each attribute is calculated. WTP is obtained by dividing the parameter of
each attribute by the parameter of monthly electricity bill which is a price parameter.
Table 6 shows the WTP for each attribute.
Table 6 WTP
Variable WTP (JPY)CO2−57.84

Stability 731.04
Nuclear−5239.06

Solar

1414.01

Wind

610.79WTP for a stable electricity supply is 731.04. Thus, households will pay an additional
731.04 JPY per month for a stable electricity supply. Households highly evaluate a16stable electricity supply. WTP for CO2 emissions is −57.84. Thus, if the electricity bill ischeaper by 57.84 JPY, households will allow to increase CO2 emissions. WTP for
nuclear, solar and wind power is −5239.06, 1414.01 and 610.79 respectively.
Households will, thus, pay an additional 1414.01 JPY per month for solar power and an
additional 610.79 JPY per month for wind power. Households therefore highly evaluate
renewable energy sources. On the other hand, households negatively evaluate nuclear
power. Households will only satisfice with nuclear power if the electricity bill ischeaper by 5239.06 JPY per month.
5.3 Subsample analysis
I divide the sample into subsamples by households’ socio-demographic attributes andtheir perceptions of energy problems. I compare their WTPs between the subsamples
and test the differences of their preferences for electricity or suppliers by the test forparameter differences to quantify the extent to which preferences are heterogeneous. If
preferences are indeed different, households in the two groups make contrasting
preferences for electricity. We test the differences by the likelihood test and use thefollowing test statistic.
−2[LL(A+B)-(LL(A)+LL(B))]
LL(A+B) is the log likelihood which is obtained after estimation by pooling data of
two subsamples. LL(A) and LL(B) are the log likelihood which is obtained after
estimation by each subsample. The null hypothesis is that preferences or parameters
between two subsamples are equal. The alternative hypothesis is that preferences orparameters are not equal. The test statistic is chi-squared distributed with degrees of
freedom equal to the number of parameters. The critical value for the 5% significancelevel is 22.362 and for 1% it is 27.688 for 13 degrees of freedom.
Table 7 is the list of subsamples. I divided the sample into two subsamples to balance
the sample size in two subsamples.
Table 7 List of subsamples
Attributes

Subsamples

Definition

Samplesize

HouseholdincomeLow income

Under 4 million JPY

275

High income

More than 4 million JPY

225

17

Family
composition

Small family

Single and couple family

222

Big family

Married parents and unmarried children278

family, and moregenerations family

than

two

adult

Residential

Detached

Detached house (two household houses

type

house

are included)Collective
house

Condominium, apartment, housing
complex and a company and dormitory

248
252

housing
Living area
Age
Perception ofelectricity bill

Perception ofenergy-saving

Knowledgeofthederegulation

Kanto

Households who live in Kanto area

250

KansaiHouseholds who live in Kansai area

250

Young ageLess than 46 years old (average)

255

Old age

More than 47 years old

245High bill

Households who think electricity bill ishigher after the earthquake389

Low billHouseholds who don't think electricity
bill is higher after the earthquake

111

Save

Households who save electricity usage
after the earthquake

373

Not saveHouseholds who don't save electricity
usage after the earthquake

127

Knowledge

Households who have knowledge andinterest of the deregulation

218

No
knowledge

Households who don't have knowledge
and interest of the deregulation

282

Table 8 illustrates the WTP and the test statistics of the parameter differences test.
Table 8 WTP and the likelihood test
Variables

High income

Low income

Big family

Small family

CO2-51.71-58.60-44.32-71.52

Stability

790.94819.28727.29867.91

Nuclear-5942.52-4796.29-4618.35-5701.27

Solar

1310.06

1513.80

1656.24

1170.55Wind

512.36737.20646.57554.0218

Test statistic

6.25

same

10.76

same

Variables

Detached house

Collective house

Kanto

KansaiCO2-43.76-73.88-66.15-48.49Stability

920.31

608.18663.18928.51

Nuclear-4414.05-5643.20-5135.20-5306.70

Solar

1494.221433.491405.86

1272.80Wind

759.33486.02640.46595.51

Test statistic

16.38

same

0.40

same

Variables

Young age

Old ageHigh bill

Low bill

CO2-47.73-71.89-53.39-88.76

Stability

613.13939.95807.641825.33

Nuclear-4312.97-7522.63-4846.89-7881.19

Solar

1336.69

1527.531136.154020.86Wind

562.11649.73430.523346.86

Test statistic

49.43

different

110.83different

Variables

Save

Not save

Knowledge

No knowledge

CO2-55.52-56.73-45.59-62.60Stability

766.891053.15753.37841.83

Nuclear-4899.21-4334.95-5320.68-5416.95

Solar

1701.07599.351764.471095.84Wind

645.29636.18711.08539.44

Test statistic

40.02

different

34.17

differentHouseholds of lower income, bigger family, detached house, Kanto, and older age
more highly evaluate renewable energy. And, households who don’t think electricity bill
become higher after the earthquake, save more electricity usage, and have knowledge
and interest of the deregulation also more highly evaluate renewable energy. In the
future, renewable energy will become popular among them. Households of higher
income, smaller family, collective house, Kansai, and older age more negatively
evaluate nuclear power. Households who don’t think electricity bill become higher after
the earthquake, save more electricity usage, and don’t have knowledge of the
deregulation also more negatively evaluate nuclear power. In all subsamples,19households show negative WTP, and it will be difficult to resume operations in the
future.
From the test statistics, we don’t observe the differences of preferences for electricity
or suppliers between two subsamples in household income, family composition,
residential type, and living area. However, we observe the differences between younger
and older generation. When households’ perception of electricity bill andelectricity-saving and knowledge of the deregulation are different, the differences ofpreferences for electricity or suppliers are also different.
6. Conclusions and Policy Implications
I estimate preferences of Japanese households for energy sources by conjoint analysis
and calculate WTP for each energy source and other attributes of electricity. Japanesehouseholds show negative WTP for nuclear power. On the other hand, their WTP for
renewable energy sources such as solar and wind power is substantially positive. Further,
their WTP for a stable electricity supply is positive. Japanese households positively
evaluate renewable energy and electricity-supply stability but negatively evaluate
nuclear power. From this study, we can support policy interventions to reduce the shareof nuclear power and promote renewable energy. Households will pay higher electricity
bill if the electricity is generated by renewable energy. Households will accept nuclear
power if the electricity bill is much lower. This study may support the feed-in-tariff
system to promote renewable energy because households accept higher priced
electricity if the electricity is generated by renewable energy. If the stability ofelectricity supply associated with renewable energy is improved, renewable energy will
become widely used. Households’ attributes and perception are also important for
suppliers to sell electricity and for our future energy policy.
After the great East Japan earthquake in March 2011, nuclear power operations havebeen largely suspended. Fossil fuels are also difficult to be used because of climatechange concerns and rising fuel costs. Renewable energy such as solar and wind poweris expected to prevail as future energy sources.
Various suppliers have started to provide electricity after the deregulation in April
2016. Some suppliers don’t possess any nuclear power plants and will provide
electricity generated by renewable energy as a main energy source. Some households
who object to nuclear power will purchase electricity from such suppliers. This study
provides some useful energy policies in Japan and countries where big earthquakes
often happen.
20

Acknowledgments
This study was aided by research funding from Osaka Gas Co. Ltd. We presented anearlier version of this paper at the academic meeting of the Japanese Economic
Association and the Japan Economic Policy Association in May 2015. We thankmembers of the workshop hosted by Osaka Gas Co. Ltd and the following discussants at
the academic meeting: Prof. Takahiro Tsuge, Prof. Shinichi Hanada and Prof. Chiharu
Kobayashi. The authors would like to thank Enago (www.enago.jp) for the English
language review. All remaining errors are ours.
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