ISSN 1881-6436

Discussion Paper SeriesNo. 16-01

Estimation of household's preference for energy sources by
conjoint analysis in Japan

Shin Kinoshita
October 2016

Faculty of Economics,
Ryukoku University
67 Tsukamoto-cho, Fukakusa, Fushimi-ku,
Kyoto, Japan1

Estimation of household's preference for energy sources by
conjoint analysis in Japan1

Shin Kinoshita2

Abstract
Aftermath the great East Japan earthquake on March 11th, 2011, nuclear power plants
ceased operations. We need other energy sources instead of nuclear power. Thermal
power sources such as oil and coal increase emissions of greenhouse gases which
compromises their viability and acceptability. Thus, here, we specifically focus on
renewable energy sources such as solar and wind power. We estimate household
preferences for energy sources by conjoint analysis, and calculate willingness to pay(WTP) after estimation by a random parameter logit model. We find that households
negatively value nuclear power, while positively value renewable energy sources.
Further, the stability of electricity supply is valued highly. We posit a future,
post-earthquake, post-deregulation electricity supply system.

Keywords
conjoint analysis, random parameter logit model, WTP, renewables, deregulation
JEL classification
C25, L51, L94, L95, Q42
1
2This study was aided by research funding from Osaka Gas Co. Ltd.
Faculty of Economics, Ryukoku University

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

2

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 also stopped operation and were subjected to inspections. Though the
Kyushu electric power company’s Sendai nuclear power plant recommenced operationsin 2015, the bulk of the country’s nuclear power plants remain out of action. We
urgently need alternative energy sources instead of nuclear power. Since the earthquake,Japan has relied on thermal power generated by natural gas (LNG), importing more
LNG as a consequence. However, the price of LNG is too volatile and too vulnerable toexchange rate dynamics. Higher costs thereof are shifted to households via tariff rates.
After the earthquake, people who live in the Kanto area around Tokyo experienced
planned outages because nuclear power plants had ceased operations. Electricity
shortages are particularly pertinent in the context of peak demand in the summer andwinter. We have been requested to save electricity use in case of a sudden outage.
Climate change is another serious problem which is clearly relevant in this context.
Relying on thermal power begets emissions of greenhouse gases such as CO2. Japan
faces difficulties in reducing CO2 emissions in spite of the promise of the Kyoto
Protocol to other countries in the world. Renewable energy sources such as solar andwind power are needed instead of nuclear and thermal power. However, renewableenergy sources have not prevailed as principal energy sources even though many solar
panels have been constructed and fitted. In July 2012, the Japanese government
introduced a feed-in-tariff system to promote renewable energy sources. Manycompanies construct solar panels and sell electricity. However, tariffs offered by theexisting major electric power companies are relatively highly priced, particularly solar
power tariffs.
The deregulation of the electric power industry, which started in 2000, is also an
important and pertinent topic. Consumers can purchase electricity from all electric
power companies including newcomers and electric power companies based in otherareas, as well as the electric power company in their area. At the beginning, the target
was only large demand consumers. After 2003, the target was gradually expanded toencompass smaller demand consumers. At last, from April 2016, all households can
now purchase electricity from any company including new companies from otherindustries such as telecommunication. Deregulation of the gas industry will start in
April 2017.
3

The environment around energy markets and energy sources has been changing
dramatically in Japan, not least since the Fukushima disaster. To explore and quantify
this it is pertinent to estimate households' preferences for energy sources and discussenergy policies which accord with those preferences. In order to estimate households'
preferences, we adopt conjoint analysis. We focus on preferences for nuclear power andrenewable energy sources such as solar and wind power. We evaluate preferences interms of willingness to pay (WTP). If WTP for renewable energy sources is positive andsubstantial and WTP for nuclear power is small or negative, this provides support forpolicy interventions that reduce the share of nuclear power and promote renewableenergy sources. Some households would purchase electricity generated by renewableenergy sources even if the electricity tariff rate was higher. Given that deregulation was
fully completed in April 2016, there is now good scope for providers to generate
electricity using renewable energy sources. Accordingly, this study could provide useful
insights into future energy policies from the viewpoint of consumer preferences.
This paper consists of the following sections. In section 2, energy and environmental
problems in the Japanese context are presented. In section 3, related studies are
introduced. In section 4, the conjoint analysis methodology is explained. In section 5,
econometric methods are elaborated upon. In section 6, estimation results are delineated.
Finally, in section 7, we conclude, with a focus on the policy implications emanatingfrom this study.
2. Energy and environmental problems in Japan
We have many energy problems in Japan, including supply-demand mismatch issues.
In terms of climate change, through the 1997 Kyoto Protocol, Japan agreed to reduce its
emissions of greenhouse gases by 6% from 2008 to 2012, compared to a 1990 baseline.
However the total amount of greenhouse gases had increased to 10 billion 343 million
tons; this represents a 6.9% increase compared with levels at the 2010 fiscal year3. This
increase was caused by electricity generated using fossil fuels such as natural gas, coal,and oil.
Since July 2012, the feed-in-tariff system has started to promote renewable energy
sources such as solar and wind power. The prevalence of renewable energy sources has
the potential to reduce emissions of greenhouse gases. All companies which generate
3"The white paper on energy usage in 2013"(2014) from the Agency for Natural Resources andEnergy in the Ministry of Economy, Trade and Industry
4electricity by renewable energy sources can sell electricity to nine major electric powercompanies, including the Kansai electric power company4. The major electric powercompanies are required to purchase electricity from these companies. However, the bid
price associated with these nine major electric power companies is relatively high.
Importantly, these high prices are then shifted to households via electricity tariff rates.
The bid price was over 650 JPY (6.5 US$) per month in the 2016 fiscal year. This priceis ten times the price in the 2012 fiscal year5.
In July 2015, the Japanese government tabled an energy plan in which it put forward
what it considered to be an optimal composition of energy sources –the so-called“best-mix” –for the fiscal year 2030. The share of nuclear power will be raised toaround 20%-22% assuming nuclear power plants recommence operations. This share
was only 1% in the fiscal year 2013. However the share was 30% in December 2010
before the earthquake. While the share of renewable energy sources such as solar andwind power will be raised to around 22%–24%, from 2% in the fiscal year 2013. The
notable point of this plan is to expand the share of renewable energy sources. The shareof coal will be reduced to 26%, from 30% in 2013, and the share of LNG will be
reduced to 27%, from 43% in 2013.
Lastly, we mention the deregulation of the electric power industry vis-à-vis retail sales.
Deregulation started in 2000 for large scale consumers such as factories, officebuildings, and commercial facilities. These consumers can purchase electricity from allelectric power companies including Power Producer and Supplier(PPS) which arenewcomers in the electric industry, as well as the existing electric power company intheir area. The target consumers of deregulation have been expanded to smaller scaleconsumers since 2000. Since April 2016 general households have become the target
consumers of deregulation. Households can purchase electricity freely from all electric
power companies including newcomer companies and major electric power companies
in other areas. Some electric power companies do not have any nuclear power plants
and provide electricity generated only by renewable energy resources. Some households
might object to nuclear power and support renewable energy resources; such households
could thus purchase electricity from a company which does not have any nuclear power
4

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 respective regions.
5This calculation is from the Agency for Natural Resources and Energy in the Ministry of Economy,
Trade and Industry (2016), "Concerning the determination of bid prices and charges for renewableenergy sources in the fiscal year 2016".
http://www.meti.go.jp/press/2015/03/20160318003/20160318003.html
5

plants and generates electricity only by renewable energy sources. Sometelecommunication companies now provide electricity; discounted prices are offered if
households purchase electricity along with smartphones, internet, or CATV.
3. Related literature
In this section, certain studies focusing on household energy choices are introduced.
Nakajima, Ida, and Kinoshita (2006) estimate household preferences for electricity andgas using conjoint analysis. They estimate relevant parameters using a conditional logit
model. In 2006, the competition between electric power companies and gas companies
was intense in urban areas. This study estimated preferences for an all-electric service,gas cogeneration, and fuel batteries. Morita and Managi (2013) estimated preferences
for energy sources, particularly renewables, after the earthquake in 2011, by conjoint
analysis. They estimate WTP 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 sources.
Murakami et al. (2015) estimate consumers' WTP for renewable energy and nuclear
energy in the US and Japan. They use a choice experiment methodology and consumers
in both countries showed negative preferences for nuclear power and positive
preferences for renewable energy in both countries. Ida, Takemura, and Sato (2015)
explore conflicts between nuclear power and electricity rates in Japan wherein a
trade-off can occur between low rates with nuclear or high rates without it.
In terms of preferences for renewable energy sources, Roe et al. (2001) estimate WTP
for green energies among consumers in the US. This study uses hedonic analysis and thedependent variable is the price premium. Bordhers, Duke, and Parsons (2007) also
estimate WTP for green energies among consumers. Those authors adopted a choice
experiment approach with a nested logit model for estimation purposes; 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. Importantly, they evaluate only
renewable energy sources whereas this study evaluates renewable energy sources
compared with other energy sources including nuclear power. Scarpa and Willis (2010)
estimate preferences of UK households for renewable energy sources, again using a
choice experiment approach. Banfi et al. (2008) estimate WTP for energy saving
methods using a choice experiment approach in the context of Swiss households.
The novelty of this study compared with previous studies is the estimation ofhousehold preferences for energy sources in Japan after households have experienced an
6unprecedented disaster. As such, this study could provide useful insights vis-à-vis
energy policies in countries which frequently experience substantive natural disasters.
Moreover, this study also focuses on the deregulation of the electric power industry as
already discussed.
4. Conjoint analysis
In this paper preferences for energy sources among Japanese households are estimatedby conjoint analysis. Conjoint analysis is a stated preference methodology; more
specifically it is a type of choice experiment which estimates the preferences ofindividuals for hypothetical goods or services that have several attributes, each of which
can be evaluated by WTP. Households choose an option out of a set of alternatives
which may exist or may be hypothetical, representing possible future options.
Contingent Valuation Method (CVM) is another popular stated preference method, but it
is not a choice experiment. In conjoint analysis, profiles of goods or services whichhave several attributes are presented to respondents. A profile which has only few
attributes may not be realistic, whereas a profile which has too many attributes canplace undue cognitive burdens on respondents. In general, five or six attributes tend tobe adopted. Profiles were created using the orthogonal planning method to avoid
multicollinearity between attributes (Louviere, Hensher, and Swait, 2000; Kuriyama andShoji , 2005; Tsuge, Kuriyama, and Mitani, 2011; Kuriyama, Tsuge, and Shoji, 2013).
In this choice experiment, households choose one electric power company which has
several attributes and generates electricity by particular energy sources, as attributes.
This choice construct is consistent with household options, post-deregulation, in Japan.
Goett, Hudson, and Train (2000) examined customers' choice for retail energy suppliers
by conjoint analysis; they included the ratio of renewable energy sources as an attributeof energy suppliers as well as other attributes such as a fixed price. From the analysis
herein we can simulate the behavior of households and potentially avail of useful
information to promote renewable energy sources in the post-deregulation era. We
suppose the following alternatives:
Alternative 1: an electric power company which has nuclear power plants and generates
electricity by nuclear and thermal power such as LNG and coal.
Alternative 2: an electric power company which has zero nuclear power plants andgenerates electricity by thermal power such as LNG and coal. This electric powercompany purchases electricity from other electric power companies which generate7electricity by renewable energy sources such as solar and wind power through afeed-in-tariff system.
Alternative 3: an electric power company including newcomer companies which has
zero nuclear power plants and generates electricity by renewable energy sources such as
solar and wind power or thermal power such as LNG and coal.
Alternative 1 assumes that households purchased electricity from electric powercompanies before the earthquake in March 2011. Alternative 2 assumes that households
purchase electricity from the current electric power companies after the earthquake.
Alternative 3 assumes that households purchase electricity from electric powercompanies after deregulation was completed in April 2016.
The attributes and the levels of each alternative are as follows.
1. Electricity rate (per month):
Electricity rate per month increases or decreases compared with the current rate. There
are several assumptions. If households purchase electricity from nine major electric
power companies such as Kansai electric power company, sometimes the electricity rate
might be cheaper if nuclear power plants start operation again. Sometimes the electricity
rate might be higher if it is thermal power because the prices of oil and LNG are volatile.
The electricity rate might be higher in the context of renewable energy sources because
the electric power companies purchase electricity through the feed-in-tariff system. Ifhouseholds purchase electricity from a new electric power company, such as atelecommunications company, they may purchase electricity at discounted prices when
they make simultaneous telephone, internet, or CATV purchases.
2. CO2 emissions:
CO2 emissions will increase or decrease in 2030 compared to 2014. There are someassumptions. When nuclear power plants start operation again, CO2 emissions will
decrease. When thermal power is used to a greater extent, CO2 emissions will increase;
while increasing the use of renewable energy sources, CO2 emissions will decrease.
3. The stability of electricity supply or the possibility of outages:
If nuclear power plants start operation again, electricity will be supplied constantly. Ifhouseholds purchase electricity from a power company which generated electricity bythermal power, sometimes, electricity will not be supplied constantly due to planned
outages stemming from electricity shortages. If households purchase electricity from a
power company which generated electricity by renewable energy sources, sometimes
electricity supply might not be stable due to weather conditions. When electricity is
8constantly supplied, there are no outages in a year. When electricity is not constantly
supplied, short-term outages may occur a few times a year or the lights in the houses
become dimmer. A dummy variable is used which equals 1 where there is stableelectricity, else 0.
4. The main energy source:
We suppose an energy supply source set consisting of nuclear power, thermal power(LNG), solar power, and wind power. A dummy variable is used for each energy source
where thermal power is the base category.
In Table 1, the levels of each variable are summarized.
[ Table 1 near here ]We construct profiles using the orthogonal planning method in SPSS conjoint version
17.0, to avoid multicollinearity between attributes. From the various cards which were
produced, cards and combinations were selected to construct profiles; unrealistic anddominant cards and combinations were removed. In alternative 1, only nuclear power
and thermal power (LNG) are included. In alternative 2 and 3, only thermal power(LNG), solar power, and wind power are included. The levels of other attributes (ex.
electricity rate) depend on these energy sources. However, we use all levels of attributesin each alternative. Table 2 is an example profile; households answer with respect toeight choice questions like in this profile. Through a pretest, we identify issues with thequestionnaire and correct profiles to maximize understanding and minimize ambiguity
for respondents.
[ Table 2 near here ]The data were collected via a web-based questionnaire, utilizing the services of the
Rakuten Research company. The sample size is 250 households in each (Kanto6 andKansai7) area, thus 500 households in total. Data were collected in August 2014 before
the deregulation for general households in April 2016. Table 3 presents the attributes ofhouseholds in the sample. The percentages of not employed in occupation and less than
2,000 thousand JPY (20 thousand US$) in household income are larger, caused by
6

Kanto area is in East Japan around Tokyo. It includes Saitama, Chiba, Tokyo and Kanagawa
prefectures.
7
Kansai area is in West Japan around Osaka. It includes Shiga, Kyoto, Osaka, Nara, Hyogo andWakayama prefectures.
9housewives and retirees.
[ Table 3 near here ]5. Econometric analysis
In a choice experiment the dependent variable is discrete. In order to estimate thischoice model, 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 fromthe Independence of Irrelevant Alternatives(IIA). This assumption is restricted andeasily violated in many cases. Consequently, we adopt 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:
U = α′ x + β′ z + εThis 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 rate which is a price parameter, are non-random parameters. x is a variable

vector which includes electricity rate. On the other hand, the parameters of CO2
emissions, the stability of electricity supply and the main energy sources are randomparameters. z is a variable vector which includes CO2 emissions, the stability ofelectricity supply, and the main energy sources. ε is a random error term and has an
IID extreme value.
The probability conditional on β isL

β=exp(β′ x )Ȃ exp(β′ x )The random parameter logit probability is
10exp β′ xP =Ȃ exp β′ xf(β)dβThis 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
P =

1
R

L (β )

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

&

%

d lnP $

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, 138-154; Hensher, Rose, and Greene, 2005, 605-694).
For estimation we use Limdep NLOGIT 5.
After estimation, the WTP for each attribute including each energy source, iscalculated. If the utility function is linear, it is expressed as:V

V = α′ x + β′ z

is the deterministic term of the utility function. The total differentiation of the V

formula is:

dV =

∂V
∂V
dx +
dz
∂x
∂z
11

Now we obtain the WTP of z1 which is one of the attributes, such as solar power. If theutility level does not change (dV = 0) and other variables, except z1, are unchanged,
we can obtain the marginal WTP (MWTP) as follows:

MWTP = −

∂V
∂z

/∂V
∂x .

xm is a monetary variable such as a price. We can also write MWTP by invoking
parameters as follows:β
MWTP = − 0β.
β is the coefficient of each attribute and β. denotes a monetary coefficient such as anelectricity rate. We can obtain WTP by dividing the coefficient of each attribute by amonetary coefficient.
6. Estimation results
First, descriptive statistics are presented followed by the estimation results from therandom parameter logit model.
6.1 Descriptive statistics
Table 4 shows the number of choices and the ratio of choices.
[ Table 4 near here ]Alternative 3 is the most popular. Households prefer new electric power companies
which do not have any nuclear power plants and generate electricity by renewableenergy sources and LNG. Table 5 presents descriptive statistics.
[ Table 5 near here ]Electricity rate in alternative 2 is the highest and it in alternative 1 is the lowest.
12

6.2 Random parameter logit model estimation results
Table 6 illustrates the estimation results from the inferential model.
[ Table 6 near here ]The electricity rate coefficient has a negative sign and is significant at the 1% level. If
the electricity rate is lower, the probability of choosing that alternative increases. The
CO2 coefficient has a negative sign and is significant at the 1% level. If CO2 emissions
decrease, the probability of choosing that alternative increases. The coefficientassociated with the stability of the electricity supply has a positive sign and issignificant at the 1% significance level. If the possibility of outages is zero, theprobability of choosing that alternative increases. Next, the estimation results pertinent
to the main energy sources are explained. Dummy variables for each energy source havebeen used, where thermal power is the base category. The nuclear power coefficient has
a negative sign and is significant at the 1% level. If the main energy source is nuclear
power instead of thermal power, the probability of choosing that alternative decreases.
On the other hand, the coefficient associated with renewable energy sources such as
solar and wind power has a positive sign and is significant at the 1% level. If the mainenergy source is renewable instead of thermal power, the probability of choosing thatalternative increases.
6.3 WTPWTP for each attribute is calculated. WTP is obtained by dividing the parameter of
each attribute by the parameter of electricity rate which is a price parameter. Table 7
shows the WTP for each attribute.
[ Table 7 near here ]WTP for stable electricity supply is 731.04. Thus, households will pay an additional
731.04 JPY (7.3104 US$) per month for a stable supply of electricity. Households
positively value a stable supply of electricity. WTP for CO2 emissions is −57.84. Thus,
if the electricity rate is cheaper by 57.84 JPY (-0.5784 US$), households will allow anincrease in CO2 emissions. WTP for nuclear, solar and wind power is −5239.06,
13

1414.01 and 610.79 respectively. Households will, thus, pay an additional 1414.01 JPY(14.1401 US$) per month for solar power and an additional 610.79 JPY (6.1079 US$)per month for wind power instead of thermal power. Households therefore positively
value renewable energy sources. On the other hand, households negatively evaluate
nuclear power. Specifically, households will only satisfice with nuclear power if theelectricity rate is cheaper by 5239.06 JPY (-52.3906 US$) per month.
6.4 Elasticities
We calculate the elasticities of price and other attributes. Table 8 shows priceelasticities. Price elasticities indicate how the choice probability for each alternative
increases or decreases when the price changes by 1%. Own-elasticities measure thedirect effect of percentage changes on the same alternative. By contrast,
cross-elasticities measure the effect of percentage changes of an alternative on the otheralternatives. Own-elasticities of alternatives 1, 2 and 3 are −0.04795, −0.29334 and
0.00096 respectively. Clearly, the own-elasticities of alternatives 1 and 2 are negative;
thus, if the price decreases by 1%, the choice probability of alternatives 1 and 2
increases. For alternative 1 the choice probability increases by 0.04795%; for alternative
2 the choice probability increases by 0.29334% Thus, households are relatively more
responsive with respect to alternative 2. Own-elasticities of alternatives 3 is not
significant. Some cross-elasticities are positive, while others are negative. When theprice increases with respect to alternative 1, the choice probability of alternatives 2
decreases and that of alternatives 3 increases. When the price increases with respect toalternative 2, the choice probability of alternatives 1 and 3 increase. When the priceincreases with respect to alternative 3, the choice probability of alternatives 1 and 2
decrease.
[ Table 8 near here ]Next, we discuss elasticities of other attributes (Tables 9–13). Almost all
own-elasticities are positive. Regarding the stability of the electricity supply, if there areno outages the choice probabilities in all alternatives increase. In terms of energy
resources, if the main energy source is solar or wind power the choice probabilities
increase in the alternatives. On the other hand, the own-elasticity of nuclear power isnegative. Thus, if the main energy source is nuclear power, the choice probability
decreases in the alternative. Cross-elasticities are negative. If there are no outages in an
14

alternative, the choice probabilities in the other alternatives decreases. If the mainenergy source is solar or wind power in an alternative, the choice probabilities in theother alternatives decrease.
[ Table 9 near here ][ Table 10 near here ][ Table 11 near here ][ Table 12 near here ][ Table 13 near here ]6.5 Comparison of households' preferences between Kanto and Kansai areas
After the great earthquake, households who live in the Kanto are suffered fromdamages. Moreover they experienced planned outages. We suppose that there will be adifference in households' preferences over nuclear power, renewable energy sources,
and the stability of electricity supply. Households' WTP for nuclear power will be morenegative and their WTP for renewable energy sources and stability of electricity supply
will be more positive in Kanto, compared to Kansai. Tables 14 and 15 present relevant
estimation results and Table 16 shows WTP of households in the Kanto and Kansai
areas.
[ Table 14 near here ][ Table 15 near here ][ Table 16 near here ]WTP for stability in Kansai is larger than in Kanto, which is converse to expectation(663.18 JPY in Kanto and 928.51 JPY in Kansai). However this research was carriedout in 2014, three years after the earthquake and this result therefore testifies to theeffects of time on diminishing collective memory and issue importance. Moreover, WTP
for renewable energy sources in Kanto is greater than in Kasai (1405.86 JPY in Kanto
15and 1272.8 JPY in Kansai for solar power).
Lastly, we test for parameter differences in order to quantify the extent to which
preferences are heterogeneous between Kanto and Kansai. If preferences are indeed
different, households in Kanto and Kansai make contrasting choices vis-à-vis electric
power companies. We test for differences by the likelihood test and use the following
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 from
Kansai and Kanto. LL(A) is the log likelihood for Kansai and LL(B) is the log
likelihood for Kanto. The null hypothesis is that preferences or parameters between
Kanto and Kansai area 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 calculated statistic is 0.392 and thus the null is not rejected because the criticalvalue for the 5% significance level is 22.362 for 13 degrees of freedom. Preferences forelectric power companies are, thus, not significantly different between the Kanto andKansai areas. From these results, households in both Kanto and Kansai areas have thesame preferences for electric power companies. They may prefer new electric powercompanies which do not have any nuclear power plants and generate electricity byrenewable energy sources.
7. Conclusions and Policy Implications
We estimate preferences of Japanese households for energy sources by conjoint
analysis and calculate WTP for each energy source and its attributes. WTP for nuclear
power is negative. On the other hand, WTP for renewable energy sources such as solar
and wind power, is positive. Further, WTP for a stable supply of electricity is positive.
Japanese households positively value renewable energy sources and stability ofelectricity supply but do not positively value nuclear power. From this study we can
support policy interventions to reduce the share of nuclear power and promoterenewable energy sources. Households will pay higher electricity rates for renewableenergy sources. Households will accept nuclear power if the electricity rate is lower.
This study may support the feed-in-tariff system for promotion of renewable energy
sources. If the stability of electricity supply associated with renewable energy sources16

improves, renewable energy sources will become widely used.
After the great East Japan earthquake in March 2011, nuclear power operations werelargely suspended. Thermal power which has been the main energy source following theearthquake is also difficult to enlarge because of climate change concerns and rising fuelcosts. Renewable energy sources such as solar and wind power are expected to prevail
as future energy sources.
Various companies have started to provide electricity since the onset of deregulation,
which was completed in April 2016. Some providers do not possess nuclear power
plants and provide electricity generated only by renewable energy sources. Somehouseholds who object to nuclear power, thus, have the option of purchasing electricity
from such providers. This study offers a post-earthquake, post-deregulation blueprint.
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.
References
Banfi, Silvia, Mehdi Farsi, Massimo Filippini, and Martin Jakob (2008), “Willingness topay for energy-saving measures in residential buildings”, Energy Economics, 30,
p503-516.
Borchers, Allison M., Joshua M. Duke, and George R. Parsons (2007), “Doeswillingness to pay for green energy differ by source?,” Energy Policy, 35, p3327-3334.
Goett, Andrew, Kathleen Hudson, and Kenneth Train (2000), “Customers' choice among
retail energy suppliers: The willingness-to-pay for service attributes,” The Energy
Journal, 21, p1-28.
Hensher, David A., John M. Rose, and William H. Greene (2005), Applied Choice
Analysis: A primer, Cambridge University Press.
Ida, Takanori, Kousuke Takemura, and Masayuki Sato (2015), “Inner conflict between
nuclear power generation and electricity rates: A Japanese case study,” Energy
17Economics, 48, p61-69.
Kuriyama, Koichi and Yasushi Shoji edited (2005) Economic evaluation of theenvironment and tourism, (in Japanese), Keisou shobou.
Kuriyama, Koichi, Takahiro Tsuge, and Yasushi Shoji (2012) Introduction to economic
evaluation of the environment for beginners, (in Japanese), Keisou shobou.
Louviere, Jordan J., David A. Hensher, and Joffre D. Swait (2000), Stated Choice
Methods Analysis and Application, Cambridge University Press.
McFadden (1974), “Conditional logit analysis of qualitative choice behavior,” in P.
Zarembka, ed., Frontiers in Econometrics, Academic Press, New York, p.105-142.
Morita, Takamki and Shunsuke Managi (2013) "The energy mix after the great East
Japan earthquake - demand analysis considering characteristics of each energy source”,
RIETI Discussion Paper Series 13-J-066, (in Japanese), Research Institute of Economy,
Trade and Industry.
Murakami, Kayo, Takanori Ida, Makoto Tanaka, and Lee Friedman (2015), “Consumers'
willingness to pay for renewable and nuclear energy: A comparative analysis between
the US and Japan,” Energy Economics, 50, p178-189.
Nakajima, Miki, Takanori Ida, and Shin Kihoshita (2006) “The conjoint analysis forenergy demand of households” Journal of Public Utility Economics (in Japanese) 58.2,
p23-33.
Roe Brian, Mario F. Teisl, Alan Levy, and Matthew Russel (2001), “US consumers'
willingness to pay for green electricity,” Energy Policy, 29, p917-925.
Scarpa, Riccardo and Ken Willis (2010), “Willingness-to-pay for renewable energy:
Primary and discretionary choice of British households' for micro-generation
technologies,” Energy Economics, 32, p129-136.
Takahiro, Tsuge, Koichi Kuriyama, and Yohei Mitani(2011), New techniques ofenvironmental evaluation, (in Japanese), Keisou shobou.
The Agency for Natural Resources and Energy in the Ministry of Economy, Trade andIndustry(2014) "The white paper on energy usage in 2013" (in Japanese).
Train, Kenneth E. (2002), Discrete Choice Methods with Simulation, Cambridge

18

Table 1 The levels of each variable
Attribute

Level

Electricity rate

−2000, −1500, −1000, −500, 0 (unchanged)(month, JPY)
CO2+500, +1000, +1500, and +2000−20%, −10%, 0% (unchanged), +10%, and +20%

Outages (year)

Yes(0), No(1)nuclear power, thermal power (LNG), solar power, and wind

Energy source

power

Table 2 An example profile
Attribute
Electricity rate(month, JPY)
CO2 emissions
Outages (year)
Main energy source

Alternative 1

Alternative 2

Alternative 3

−2000

+1000

−1000

−10%
No
nuclear power

+10%
Yes
thermal power

−20%
Yes
solar power

Table 3 Attributes of households
number

Occupation

Householdincome(thousand
JPY)

Educational
background

%

Total

500

100

company worker

243

48.6

public worker

28

5.6

student2

0.4

not employed （including housewives and retirees)

135

27

self-employed

45

9less than 2,000

159

31.8

2,000–3,990

116

23.2

4,000–5,990

87

17.4

6,000–7,990

63

12.6

8,000–9,990

36

7.2

more than 10,000

39

7.8

junior high school, high school

122

24.4

technical school, junior college

119

23.8

university, graduate school

255

51

19

single

91

18.2

131

26.2

225

45

two households

26

5.2

detached house (including two household houses)

248

49.6

collective housing (condominium, apartment,

241

48.2

company housing, dormitory housing etc.

11

2.2

Kanto

250

50

Kansai

250

50

male

296

59.2

female204

40.8

Average

47.45

two people
Familycomposition husband and wife (parents) and children

Dwelling
type

housing complex etc.)

Area
Sex

Age (years)

Min

21

Max

69

Table 4 Choice probability
number
Alternative 1
Alternative 2
Alternative 3

ratio

1187
1167
1646

0.297
0.292
0.412

Table 5 Descriptive statistics
electricity
CO2
rate
mean

stability−589.72 −5.712

nuclear thermal solar wind
power power power power

1

0.663

0.337

0

0

median

−1000

−10

1

1

0

0

0

mode

−2000

−20

1

1

0

0

0

Alternative 1 standard
deviation

1327.71

14.04

0

0.473

0.473

0

0

minimum

−2000

−20

1

0

0

0

0

maximum

2000

20

1

1

1

0

0

1074.55 −11.87

0.626

0

0.167

0.66

0.172

mean
Alternative 2 median
mode

1000

−10

1

0

0

1

0

1500

−20

1

0

0

1

0

20

standard
deviation

658.442

9.867

0.484

0

0.373

0.47

0.378

minimum

−500

−20

0

0

0

0

0

maximum

2000

10

1

0

1

1

1

−388.52 −10.04

0.437

0

0.175

0.33

0.492

mean
median

−1000

−10

0

0

0

0

0

mode

−1000

−10

0

0

0

0

0

Alternative 3 standard
deviation

784.653

8.394

0.496

0

0.38

0.47

0.5

minimum

−1000

−20

0

0

0

0

0

maximum

2000

20

1

0

1

1

1

Standarderror

z valuep value

Table 6 Estimation results
Variable

Coefficient

random parameters (mean)
CO2
Stability−0.03875
0.48980.00569
0.13376

−6.81
3.66

0
0.0003

Nuclear
Solar
Wind−3.51017
0.947390.40923

0.30989
0.15276
0.09601−11.33
6.2
4.26

0
0
0

−13.78

0

7.24
0.5

0
0.6181

8.81
13.28
13.52
11.1
1.56

0
0
0
0
0.1187

non-random parameters
Electricity
rate
Constant 1
Constant 2−0.00067 0.0000487
0.85603
0.032230.118240.06465

standard deviation
CO2
Stability
Nuclear
Solar
Wind

0.05174
1.848274.878
1.476810.285130.00587
0.139210.36084
0.13304
0.18274

Log likelihood −3297.20787
McFadden R2 0.24969

21

Table 7 WTP
Variable
CO2

WTP (JPY)−57.84

Stability
Nuclear731.04−5239.06

Solar
Wind

1414.01610.79

Table 8 Price elasticities
Choice probability
Alternative 1

Alternative 2

Alternative 3−0.04795 ***−0.01752 ***0.00276

0.10269 ***−0.29334 ***0.14535 ***−0.02597 ***−0.08449 ***0.00096

Alternative 1
Alternative 2
Alternative 3*** denotes significance at the 1% level.
Table 9 CO2 elasticities
Choice probability
Alternative 1
Alternative 1
Alternative 2
Alternative 3

Alternative 2−0.04064 ***0.01794 ***0.00457 ***0.06137 ***−0.1441 ***0.12688 ***Alternative 3
0.06079 ***0.08206 ***−0.1124 ****** denotes significance at the 1% level.
Table 10 Stability elasticities
Choice probability
Alternative 1
Alternative 2
Alternative 3

Alternative 1

Alternative 2

Alternative 3

0.29601 ***−0.10195 ***−0.13815 ***−0.11446 ***0.17177 ***−0.05992 ***−0.06498 ***−0.08162 ***0.14915 ****** denotes significance at the 1% level.
Table 11 Nuclear power elasticities
Choice probability
Alternative 1

Alternative 2

22

Alternative 3

Alternative 1
Alternative 2−0.15977 ***0.10019 ***

0
0

0
0

Alternative 3

0.07034 ***00*** denotes significance at the 1% level.
0 means a fixed parameter.
Table 12 Solar power elasticities
Choice probability
Alternative 1

Alternative 2

Alternative 3

Alternative 1

0−0.10665 ***−0.04427 ***Alternative 2
Alternative 3000.19813 ***−0.12898 ***−0.03985 ***0.038 ****** denotes significance at the 1% level.
0 means a fixed parameter.
Table 13 Wind power elasticities
Choice probability
Alternative 1
Alternative 1
Alternative 2
Alternative 3000Alternative 2

Alternative 3−0.01102 ***0.02377 ***−0.02328 ***−0.04326 ***−0.06419 ***0.08178 ****** denotes significance at the 1% level.
0 means a fixed parameter.
Table 14 Estimation results for Kanto
variablecoefficient

standarderror

z valuep value

random parameters (mean)
CO2stability
nuclearsolar
wind−0.0430.43107−3.33788
0.913810.41630.008410.184960.55787
0.208770.13729

−5.11
2.33
−5.98
4.38
3.03

0
0.0198000.0024−9.38

0

non-random parameters
electricity
rate−0.00065

0.00006923

constant 1
constant 20.75610.026430.167470.09077

4.51
0.29

0
0.7709standard deviation
CO2

0.057130.00926

6.17

0stability
nuclear1.69868
4.529630.1834
0.46275

9.26
9.79

0
0

solar
wind

1.119310.071610.18096
0.29834

6.19
0.24

0
0.8103log likelihood−1650.38

2

0.24888

McFadden R

Table 15 Estimation results for Kansai
variablecoefficient

standarderror

z valuep value

random parameters (mean)
CO2stability
nuclearsolar
wind−0.03394
0.64996−3.71425
0.890960.41686

0.00790.1883
0.48689
0.215290.13445

−4.3
3.45
−7.63
4.14
3.1

0
0.0006000.0019non-random parameters
electricity
rate
constant 1
constant 2−0.00070.00006888−10.1400.867640.03168

0.166140.09152

5.22
0.35

0
0.7293

6.26
8.84
9.49
8.86
1.51

0
0
0
0
0.1299standard deviation
CO2
stability
nuclearsolar
wind

0.04851
1.76304
5.12631.519210.33028

log likelihood
McFadden R2−1646.63
0.25059

0.00775
0.199390.54034
0.17154
0.21809

Table 16 WTP (JPY)
variable

Kanto

Kansai24

CO2
stability−66.15663.18−48.49928.51

nuclearsolar−5135.2 −5306.07
1405.86
1272.8wind

640.46595.5125