ISSN 1881-6436

Discussion Paper SeriesNo. 19-01

Conjoint analysis of Japanese households’ preferences for renewableenergy and the conditions for its diffusion
Shin Kinoshita
September 2019

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

1

Conjoint analysis of Japanese households’ preferences for renewable energy andthe conditions for its diffusion
Shin Kinoshita
Associate Professor, Ryukoku University
E-mail: skinoshita@econ.ryukoku.ac.jp
ABSTRACT
Recently, in Japan and other countries, renewable energy sources such as solar power are being
promoted as alternative energy sources instead of nuclear power and fossil fuels. Renewableenergy is “green energy,” in that it does not emit greenhouse gases that contribute to global
warming. Following the Great East Japan Earthquake, nuclear power plants ceased operations
and have not yet resumed. Although these plants are planned to resume operations, Japan cannot
rely on nuclear power as these plants will be decommissioned in the future.
This research utilizes conjoint analysis to investigate the common conditions and use ofrenewable energy in Japanese households. The researcher presents hypothetical renewableenergy programs to households, analyzes the conditions under which they participate in the
programs, and examines which renewable energy sources they prefer. Four types of renewableenergy sources are considered: solar power, wind power, biomass, and fuel cells/private electric
generators.
Other conditions are monthly bills, management suppliers (major existing suppliers, major new
suppliers, or small/medium new local suppliers), new local employment, benefits forparticipants (tax credits, coupon tickets in a local area, awards from local public organizations,name listed on a local government website, and free tickets for green parks and environmental
plants). Many studies about energy are focused on social norms, which are defined as social
behavior, considering sociality and social issues such as climate change. Sometimes, individuals
accommodate themselves to neighbors. The current study examines whether individuals choose
renewable energy programs based on non-monetary incentives such as social norms or monetary
incentives such as monthly bills. A random parameter logit model is used for this estimation.
From the estimation results, households clearly prefer lower bills. Households evaluate solar
power above biomass and do not highly evaluate wind power and fuel cells. Though householdswith children below the age of thirteen are supposed to be altruistic and interested in climatechange issues and renewable energy for future generations, significant results to this effect were
not obtained. However, in the subsample of households interested in advanced appliances, suchas solar panels, micro-home wind generators, storage batteries, and fuel cells, the coefficient of
the renewable energy ratio was positive and significant. A higher renewable energy ratio ispreferred among such households. The coefficient of local new employment was generally
positive and significant. Households support renewable energy electric power plants if theseplants contribute to local new employment. They prefer major new suppliers to major existing
suppliers and do not prefer new small/medium local suppliers. Regarding benefits, they did notevaluate tax credits compared with awards or names listed on the website of the local
government, and negatively evaluated local coupon tickets and free tickets of environmental
plants and national parks. Households do not respond to monetary incentives and prefer nonmonetary incentives. The results conclude that monetary incentives are not always necessary forthe common use of renewable energy.
Key words: Renewable energy, conjoint analysis, random parameter logit model
Category Number: 10
JEL Classification Code: C25, L51, L94, L95, Q28

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Conjoint analysis of Japanese households’ preferences for renewable energy andthe conditions for its diffusion1
1. Introduction
Recently, in Japan and world countries, renewable energy such as solar energy and wind powerhas been promoted as an alternative energy source to replace nuclear power and fossil fuels.
Renewable energy also called green energy does not emit green-house gases such as CO2, andthus does not contribute to global warming. Japan after the 2011 earthquake, suspended theoperation of nuclear power plants and did not resume its operation yet. Nuclear power plants areplanned to resume; however, Japan cannot largely rely on nuclear power since almost all
nuclear power plants will be decommissioned in the future. For this reason, renewable energy
should be promoted as early as possible. Some studies have shown that Japanese people
negatively evaluate nuclear power after the recent earthquakes. 2
Since July 2012, the Japanese government has started the feed-in-tariff system to promoterenewable energy. Households and companies can sell electricity generated by renewableenergy such as solar and wind power, and major electric power companies are obliged topurchase electricity. Moreover, the Japanese government released the energy composition in thefiscal year 2030 as an energy plan. The composition is as follows: fossil fuels are 56%[liquefied natural gas (LNG) 27%, coal 26%, oil 3%], nuclear power is 20–22%, and renewableenergy is 22–24% (solar 7%, hydraulic power 8.8–9.2%, wind 1.7%, biomass 3.7–4.6%,
geothermal 1–1.1%). In the fiscal year 2016, fossil fuels were 83%, nuclear power was 2% andrenewable energy was 15%, the Japanese government plans to reduce the use of fossil fuels,
resume nuclear power plants, and implement renewable energy.
This study examines the conditions in which Japanese households is using renewable energy.
The study presents hypothetical renewable energy programs to households, analyzes theconditions in which they participate in the programs and examine renewable energy sources
they prefer.
A conjoint analysis, which is one of the stated preference methods, is used to analyze
individual decision to participate in hypothetical renewable energy programs. Four types ofrenewable energy sources are considered such as solar energy, wind power, biomass, and fuelcells/private electric generator. Fuel cells are not renewable energy but do not emit any greenhouse gases. Fuel cells are a similar to other renewable energy sources. Fuel cells are essential
to save energy and should be considered as one of renewable energy sources.
Other conditions are monthly electricity bills, suppliers that provide electricity, local new
employment in new electric power plants, benefits for participants. Social norm is discussed inmany studies on renewable energy sources and is energy saving3. It is defined as social behavior,considering sociality and social issues such as global warming and neighbors. This study
examines whether individuals choose renewable energy, caused by non-monetary incentives
such as social norm as well as by monetary incentives such as electricity bills. For example,
individuals who support local new employment rather than lower electricity bills are motivatedby non-monetary incentives and social norm. Also, benefits related with monetary and nonmonetary incentives are considered.
1This study was funded by grants-in-aid for scientific research (C) (Kakenhi) of Japan Society for The
Promotion of Science (No. 16K03679). The questionnaire was assisted by the service of the Rakuten
Insight Company and got many useful comments about the survey strategy.
2
Morita and Managi (2015) and Murakami et al. (2015) concluded that individuals negatively evaluatednuclear power and positively renewable energy from willingness to pay (WTP) using a conjoint analysis.
3
Allcott (2011) is one of the famous energy studies about social norm. Households were received reportsof electricity consumption and compared with neighbors to promote energy saving by a field
experiment.

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Many studies have reported on the importance of renewable energy as an energy source. Since
big earthquakes often occur in Japan, nuclear power plants cannot be resumed. Under these
conditions, it is essential to promote renewable energy. The contribution of this study is toinvestigate the preferences for renewable energy in Japanese individuals who experience a great
and urgent change in the energy environment.
This article consists of the following sections. Section 2 describes the literature. In Section 3,
the contents of questionnaire and the results are explained. Section 4 describes the research
design of the conjoint analysis. In Section 5, a random parameter logit model as an econometricmethod is illustrated. In Section 6, the estimation results are considered. Lastly, in Section 7, theconclusions and policy implications are considered.
2. Literature review
Worldwide, many studies on renewable energy sources have been published. Some studies
examined individual preferences for general renewable energy and others for individual
renewable energy such as solar and wind power. Some studies use the stated preference
methods (SPMs) and others use the revealed preference methods to estimate individual
preferences. The conjoint analysis, an experiment of choice, and Contingent Valuation Method(CVM) are popular as SPMs.
This paper examines households’ preferences for individual renewable energy using the
conjoint analysis and the future conditions in which renewable energy can be used. Especially,
Bae and Rishi (2018) referred to conjoint profiles. They analyzed the conditions in which
individuals participate in renewable energy programs in South Korea using the conjoint analysis.
Three alternatives were presented to individuals. Two alternatives were renewable energy
programs, and one was an opt-out option, where individuals did not participate in any renewableenergy programs. Solar energy, wind power, fuel cells, and biomass energy were considered as
renewable energy sources. Other attributes were renewable energy ratio in electricity generation,
additional electricity bills, distance from electric power plants to individual residence, thenumber of workers employed in electric power plants, incentives to participate in renewableenergy programs. The conditional logit model, random parameter logit model, and latent clustermodel were used for estimation, and the distribution of parameters or variations of preferencesamong individuals were derived.
Yoo and Ready (2014) also used the conjoint analysis to estimate individual preferences forrenewable energy such as solar power, wind power, and biomass. The variations of individual
preferences were analyzed at Pennsylvania, United States. Willingness to pay (WTP) was
calculated using the random parameter logit model and hybrid-typed random parameter latentclass model which expressed the variations of individual preferences. The results foundvariations in individual preferences, especially a large variation in solar power. Other attributes
were new employment in Pennsylvania and electricity bills. One alternative was status-quo.
Gracia et al (2012) estimated individual preferences for renewable energy in Spain using the
conjoint analysis. Individual expressed high WTP for higher solar power ratio and low WTP for
higher wind power and biomass ratio. They expressed high WTP for locally used renewableenergy. Yang et al. (2016) estimated individual preferences for renewable energy in Denmark
using the conjoint analysis. When the ratio of all energy sources became higher, especially wind
power, it causes temporal energy shortages. Consumers highly evaluated renewable energy but
they even more highly evaluated the mix of renewable energy sources. They preferred thecurrent supplier than other famous suppliers. Our study also takes in account the suppliers fromwhom households purchase electricity. Kaenzig et al. (2013) estimated German individual
preferences for suppliers in a deregulated electricity market using the conjoint analysis. The
renewable energy ratio was one of the attributes of electric power suppliers. German individuals
gave 16% additional payment for electricity generated from renewable energy by WTP. They4

also found that individuals preferred local suppliers. Our study also takes local suppliers into
consideration. Caedella et al. (2017) analyzed that households chose electricity generated byconventional fossil fuels or renewable energy such as solar and wind power when eachelectricity bill was volatile using the conjoint analysis. Risk-averse households would avoid
volatile electricity bills generated by renewable energy even if they preferred renewable energy.
The probability of choosing renewable energy plans would decrease if the electricity bills
generated by renewable energy were volatile. The probability that households chose renewableenergy plans would increase if the electricity bills generated by fossil fuels were volatile.
Electric power generation by renewable energy such as solar and wind power depends on
weather conditions; however, electricity supply is unstable. The prices are volatile and spike incase of increasing demand and decreasing supply. Individual energy choices when the prices arevolatile needs to be analyzed. Shin et el. (2014) estimated the individual preferences forrenewable energy portfolio in Korea using the conjoint analysis. People thought new
employment was the most important rather than higher renewable energy ratio and higher bills.
They accepted 1.39% higher bills from WTP. Bordhers et al. (2007) estimated the individual
WTP for green energy using the conjoint analysis. This study analyzed that individuals decided
whether to participate in green energy programs or remain in the status-quo at the first stage andwhich green energy program they choose at the next stage by the nested logit model. Bergman
et al. (2006) estimated the individual preferences for renewable energy in Scotland using the
conjoint analysis. The study analyzed the external cost and benefit caused by investment of
renewable energy such as wind power. Wind power generation plants create new local
employment but ruin the scenery around the area. The study suggested the investment of
renewable energy considering local total benefits. Sundt and Rehdanz (2015) estimatedindividual WTP for renewable energy and analyzed the differences in individual evaluation for
the type of renewable energy using the meta-regression analysis in which WTP is a dependent
variable. Hydraulic power got the worst evaluation. Roe et al. (2001) were the first to analyze
individual preferences for renewable energy. This study analyzed consumers’ WTP for green
energy in the United States by a hedonic analysis in which the price premium was a dependent
variable. Consumers highly evaluated green energy to reduce the global heating gases.
Some studies estimated the preferences for generation techniques using renewable energy suchas solar panels and home micro-wind generators. Scarpa and Willis (2010) estimated that
British households’ preferred generation techniques using renewable energy using the conjoint
analysis. From WTP, households positively evaluated such generation techniques, but they did
not reveal high monetary evaluation to cover expensive installation cost. Willis et al. (2011)
also estimated British households’ preferences for generation techniques using renewableenergy using the conjoint analysis. The study found that elder households did not prefer suchgeneration techniques. In near future, aging society will be going. If elder households do notneed such generation techniques, generation techniques using renewable energy will not be
widely used even though the techniques should be promoted.
Various approaches about individual preferences for renewable energy have been proposed.
Herbes et al. (2015) estimated WTP for renewable energy by using a novel neuro-science
method. According to some studies, strategic bias is caused in the CVM and conjoint analysis.
A neuro-science method revealed 15% higher WTP for renewable energy compared with otherenergy sources. Boeri and Longo (2017) estimated the preferences by the regret-minimization
approach as well as the utility-maximization approach. The study used the conjoint analysis andestimated parameters by the random parameter logit model and hybrid-latent cluster model.
Electricity generated by renewable energy is highly priced and unstable in electricity supply
even though it reduces the global heating gases. Considering these negative features ofrenewable energy, after households choose renewable energy, they will regret. The regretminimization approach is suitable to analyze the choice behavior of renewable energy. Bartczak
et al. (2017) analyzed risk-preferences and loss-aversion to avoid negative externality of5renewable energy using conjoint analysis. Gracia et al. (2016) estimated individual willingness
to accept in Norway using the conjoint analysis when wind power plants were built.
Nudges, which are used in behavior economics, are also commonly used in energy research.
Nudges, which are non-monetary incentives, affect individual behavior. For example, nudges
induce households to save energy. Momsen and Stoerk (2014) noted the effects of nudges when
individuals choose renewable energy. From the results of simple experiment, default nudge
raised the probability to choose renewable energy by 44.6%.
There are some studies for Japanese households. Morita and Managi (2015) estimatedconsumers’ preferences for energy sources after the great east earthquake in 2011 by a conjoint
analysis. Especially, considering higher consumers’ interest in renewable energy, thepreferences for renewable energy were estimated. The WTPs for energy sources were used forthe policy suggestion of energy-mix. Consumers revealed negative WTP for nuclear power, andpositive WTP for renewable energy such as solar and wind power. Murakami et al. (2015) also
found that consumers in Japan and United States showed negative WTP for nuclear power, andpositive WTP for renewable energy which is the mix of solar, wind power, biomass, andgeothermal power using the conjoint analysis. But WTP for each renewable energy source was
not estimated. Rehdanz et al. (2017) estimated consumers’ preferences for energy sources after
the accidents in nuclear power plants in 2011 in Fukushima prefecture in Japan using the
conjoint analysis. Consumers revealed positive WTP for renewable energy and negative WTP
for nuclear power. The WTPs were different by the distance from nuclear power plants andFukushima prefecture and the values in absolute were greater as the distance was nearer.
Some studies use a CVM to estimate individual preferences for renewable energy. CVM isalso a SPM. Lee and Heo (2016) estimated WTP for renewable energy in Korea and foundconsumers would pay additional 3.21 USD per month for electricity generated by renewableenergy. Guo et al. (2014) estimated WTP for renewable energy in Beijing and found consumers
would pay additional 2.7 to 3.3 USD per month for electricity generated by renewable energy.
Kim et al. (2013) estimated consumers’ WTP for renewable energy in Korea. Electricity
generated by renewable energy was considered as a differentiated good. The differentiation wasobserved between renewable energy and other energy sources but was not observed between
renewable energy sources, and perfect substitution was observed between renewable energy
sources.
Some studies about renewable energy did not use SPMs. Conte and Jacobsen (2016) usedconsumer data of all electric utility companies in the United States. and analyzed the attributesof households who purchased green electricity generated by renewable energy through local
utilities. The results showed that highly educated households tended to purchase green
electricity. Inhoffen et al. (2019) found that the investment in renewable energy power plants
such as solar panels allocation inefficiencies would be caused when minimum prices andindividual decision were affected by individual social environment through the household data
in Germany.
3. Questionnaire and its results
The data were collected via a web-based questionnaire, using the services of the Rakuten
Insight Company. Monitors that registered Rakuten services answered the questions. The
Rakuten Insight Company randomly sent the questionnaire to the registers that are the potential
respondents by E-mail, and they decided to answer or not. The data were collected in February
2019. The sample size is 1,000 households in total, with 668 from the Kanto region aroundTokyo and 332 from the Kansai region around Osaka. These two areas are the biggest two
major urban areas in Japan. The sample is weighted by each area’s population. In thequestionnaire, households were asked individual socio-demographic attributes such as6

occupation, annual income, and perception about energy problems. Table 1 is sociodemographic attributes of sample households. The sample does not seem to be extremely biased.
Table 1 Socio-demographic attributes of sample households.
Total
Occupation

Household annual
income (million JPY)

Education

Family composition(including multiple
answers)

Dwelling type

GenderAge (years old)Age (years old)

Residential area

Employed and own-employed
Unemployed (including students, housewives,
and retirees)
Other
Less than 2
2–3.99
4–5.99
6–7.998–9.99More than 10
Junior high school and high school
Technical school and junior college
University and graduate school
Other
SingleCouple (including in-house and not in-house)
In-house with parents or grand parents
In-house with brothers and sisters
With less than six years old children (including
in-house and not in-house)
With six-thirteen years old children (including
in-house and not in-house)
With thirteen-nineteen years old children(including in-house and not in-house)
With more than nineteen years old children(including in-house and not in-house)
Other
Detached house (including two households
house)
Collective housing (condominium, apartment,
housing complex, etc.)
Company housing, dormitory housing, etc.
Other
Male
FemaleAverage
Minimum
Maximum
20–29
30–39
40–49
50–59
Kanto
KansaiNumber
1000
691
192

%
100
69.1
19.2

117
239
218
231
134
85
93
224
236
530
10
224
430
180
53
157

11.7
23.9
21.8
23.1
13.4
8.5
9.3
22.4
23.6
53
1
22.4
43
18
5.3
15.7

118

11.8

96

9.6

115

11.5

13
413

1.3
41.3

545

54.5

30
12
510
490
40.6
20
59
212
241
307
240
668
332

3
1.2
51
49

21.2
24.1
30.7
24
66.8
33.2

7

Households were asked about their interest in solar panels, home micro-wind generator,
storage battery, and home generation fuel cells (gas cogeneration) related to their interest in
renewable energy. More than half of the households were not interested in any appliances. Only
few (less than 5%) households have already owned renewable energy appliance. About 20% ofhouseholds, especially 29.3% in solar panels, are interested in these appliances but cannot
purchase due to the limitations in their residence. Some solutions are needed so that households
purchase these appliances. Households were asked about their most desirable energy source.
About 40.4%of households agree that solar power is the most desirable. Next, 19% ofhouseholds find nuclear power as the most desirable. About 21.4% of households think wind
power is the second desirable energy source. About 10% of households think geothermal powerand biomass are the third desirable energy source. On the other hand, very few households thinkfossil fuels are the most desirable energy source. Overall, households are interested inrenewable energy.
About 44.5% of households think that the realization of lower electricity and gas bills is themost important for future energy problem. About 26.4% of households think that stableelectricity and gas supply is the most important and 35.8% households think it is the second
important energy problem. About 17.5% of households think energy saving is the second
important and 30% of households think energy saving is the third important energy problem.
Only 13.2% of households think that the promotion of renewable energy is the second and17.9% households think it is the third important energy problem. Less than 5% households think
the promotion and consumption of local energy sources, suspension of nuclear power, andresumption and promotion of nuclear power are important.
About 47.2% households think electricity bills become higher after the Great East Japan
Earthquake. About 77.6% of households are more aware of energy savings after the earthquake.
About 90.5% of households think CO2 should be reduced. About 23.5% of households thinkthat the future suitable dependence on nuclear power in Japan is 0%, which is the most popular.
About 20.7% of households think it is from 40 to 59% and 20.3% households think it is from 20
to 39%. On the other hand, 27.7% of households think that the future suitable dependence on
renewable energy in Japan is from 40 to 59%, which is the most popular. About 25.2% ofhouseholds think it is from 20 to 39% and 19.1% households think it is from 60 to 79%. About
44.8% of households worry about unstable electricity supply due to weather conditions as themost serious problem in the promotion of renewable energy. About 24.3% of households worry
about the higher prices due to expensive installment cost of solar panels and home wind
generator. About 21% of households worry about the destruction of landscape by solar panels
and wind generators. Only 9.4% of households do not have any serious problems about
renewable energy.
4. Conjoint analysis
The conjoint analysis is used to examine the future condition in which renewable energy will
be used4. It is one of the SPMs, where researchers present some hypothetical alternatives toindividuals and individuals choose the most preferred one. This study analyzes the individual
choice under change of energy sources, monthly electricity bills, suppliers etc. Alternatives
express renewable energy programs with various attributes. This study examines households’
preferred attributes of renewable energy programs.
The CVM is another popular SPM, but it is not a choice experiment and can be used toevaluate users’ valuation of non-marketable targets such as forests and beaches. CVM does not

48

Louviere et al. (2000), Hensher et al. (2005), Kuriyama and Shoji (2005), Tsuge et al. (2011), andKuriyama et al. (2013) are referred for a conjoint analysis.

evaluate each attribute. The conjoint analysis is adopted because this study adopts a choice
experiment.
Next, the researcher decides on the number of attributes, keeping in mind that a small numberof attributes will not fully reveal consumer preferences, while many attributes make it difficultfor participants to choose among options. This study adopts six attributes—energy sources,monthly electricity bills, renewable energy ratio, local new employment, suppliers that provide
electricity and benefits that household’s gain from renewable energy programs. After theselection of attributes and their levels, their profiles are compiled. However, if all combinationsof attributes and levels are used, the patterns are numerous, which can cause strong correlation
between some attributes, i.e., multicollinearity. To avoid these problems, profiles are created by
the orthogonal planning method. Various option cards are obtained, and profiles are made by
selecting cards and their combinations, after deleting unrealistic and dominant cards. An
example of unrealistic cards is when monthly electricity bill is lower, despite higher renewableenergy ratio. This example is also one of the dominant cases. Table 2 represents an example ofprofile. Excel conjoint analysis version 2.0 (Esumi) was used for the orthogonal planning.
Table 2 An example of profile.
Attributes
Alternative 1:
Conventionalenergyprogram
Energy sources
Fossil fuels
Monthly electricity bill0 (unchanged)
Renewable energy ratio 15%
Local new employment 0 employee
Suppliers
Major existing
supplier
Benefits
Nothing

Alternative 2:
Renewable energy
program

Alternative 3:
Renewable energy
programBiomass+1000 JPY
25%
70 employees
Small-medium local new
supplier
Tax credit

Solar power+4000 JPY
25%
30 employees
Major new supplierFree tickets

The following three alternatives are presented to households. They choose the most preferred
one.
Alternative 1: Conventional energy program (status-quo)
Alternative 2: Renewable energy program
Alternative 3: Renewable energy program
In Alternative 1, households use conventional energy sources such as fossil fuels (LNG, coal,and oil). The composition is assumed such that nuclear power is 2%, fossil fuels are 83% andrenewable energy is 15%, which is the composition for the whole of Japan in the fiscal year
2016. The composition in Alternative 1 is the current energy composition5. In Alternative 2 and3, households mainly use renewable energy. The renewable energy ratio is much higher than in
Alternative 1.
These alternatives have several attributes that are energy sources, monthly electricity bills,
renewable energy ratio, local new employment, suppliers that provide electricity, and benefits
that households obtain from renewable energy programs. A detailed explanation about attributes
is as follows:5

Bae and Rishi (2018) adopted an opt-out option, where households do not choose any renewable energy
options. The current study considers the more prevalence of renewable energy compared with thecurrent one and assumes that Alternative 1 consists of the current composition. Yang et al. (2016)
adopted higher renewable energy ratio in all options to avoid the status-quo bias.

91. Energy sources
In Alternative 1, households use conventional energy sources such as fossil fuels (LNG, coal,and oil). The composition is assumed such that nuclear power is 2%, fossil fuels are 83%, andrenewable energy is 15%, which is the whole Japanese composition in the fiscal year 2016. In
Alternative 2 and 3, renewable energy (green energy) ratio is much higher. Either solar or wind
power or biomass or fuel cells is used. Respondents gain information about the pros and cons ofeach renewable energy to facilitate the energy sources. The following statements are used for
respondents. In econometric analysis, dummy variables are used for each renewable energy
source, where biomass is the base category.
Solar power: Solar panels are settled on the sunlight spaces and electricity is generated by solar
power. Only with solar power, electricity is generated. Solar power does not emit any noises
and green-house gases such as CO2. Solar panels are durable and can be used for many years.
On the other hand, solar power has several problems. The installation of solar panels needs large
spaces. The power depends on weather conditions such as sunlight-hours and is unstable. The
investment cost is rather expensive. The panels may break due to typhoons and earthquakes.
The panels are reflected by sunlight.
Wind power: Wind power generators are built on land or ocean and electricity is generated bywind power. With wind, electricity is generated even during night. Wind power does not emit
the green-house gases and generate electricity highly efficiently. Large-scale production cangreatly reduce its cost. On the other hand, wind power has several problems. Since wind power
generators are huge, large spaces are needed. Wind power generators emit noises and vibrations
during electric power generation. The power depends on air volume and is unstable.
Fuel cells: Chemical reaction between hydrogen and oxygen generates electricity. Fuel cells do
not emit the green-house gases and noises. Fuel cells can generate electricity with higher energy
availability. On the other hand, investment costs are high, and its durability is low, usually lessthan 10 years. Fuel cells are not renewable energy but green energy that does not emit the greenhouse gases.
Biomass energy: Electricity is generated by renewable and organic resources from animals andplants such as woodchips and raw garbage. Biomass energy is a carbon-neutral energy sourcethat does not increase CO2 levels and leads to reuse of garbage. On the other hand, it utilizes
edible fuels and has risk to cause increase in food prices.
2. Monthly electricity bills
In Alternative 2 and 3, when renewable energy is used, monthly electricity bills will be higher
compared with Alternative 1. Bills depend on electricity-generation costs and investment costsof electric power plants and managing status of suppliers. The levels are +1000 JPY, +2000 JPY,
+3000 JPY, +4000 JPY, and +5000 JPY. In Alternative 1, the bills never change.
3. Renewable energy ratio
Renewable energy ratio is considered. The levels are 25%, 50%, 75%, and 100%. Fossil fuels
are used for other energy sources. In Alternative 1, the ratio is 15% which is the ratio in thefiscal year 2016.
4. Local new employment
When electric power plants are built, residents are employed. Local new employment is
considered as economic effects. The levels are 10, 30, 50, 70, and 100 employees. New local
employment means a positive externality for local economy. Positive coefficient associated withthis variable means individuals are altruistic and respond to non-monetary incentives.
5.Suppliers that provide electricity
Households are concerned about renewable energy programs and the suppliers of electricity. In
Japan, since April 2016, the deregulation of electricity and gas retail sales for generalhouseholds has begun. Various suppliers start to provide electricity and gas. Major existing
suppliers, major new suppliers, or small and medium local new suppliers provide electricity. In
Alternative 1, conventional major existing suppliers provide electricity. The examples of major

10existing suppliers are major electric power companies such as the Kansai Electric Power
Corporation and the Tokyo Electric Power Corporation and major urban gas companies such as
the Osaka Gas Corporation and the Tokyo Gas Corporation. These companies have provided
electricity or gas till now. Major new suppliers have not provided either electricity or gas so far.
Telecommunication companies have started to provide electricity after the deregulation in April
2016. Local suppliers manage operation in the region where the respondents live. If households
choose local suppliers, they support local economy. In econometric analysis, dummy variables
for each supplier are used, where major existing suppliers are the base category.
6. Benefits from renewable energy programs
When households choose renewable energy programs, they receive some benefits. For
example, they obtain tax credit, coupon tickets which are used in local areas where households
live, awards from local public organization or name listed on the local government website andfree tickets to green parks and environmental power plants6. When households choose
Alternative 1, they do not receive any benefit. Some benefits are monetary incentives, and someare non-monetary ones. Tax credit and coupon tickets are monetary incentives, while namelisted on the local government website and free tickets to green parks are non-monetary
incentives. However, coupon tickets are limited to the local area where households live. The
preferences for coupon tickets can also express the individual interests in contributions to local
communities. In econometrics analysis, dummy variables are used for each benefit, where namelisted on the website is the base category.
The levels of each attributes are listed in Table 3.
Table 3 The levels of attributes.
Attributes
LevelsRenewable energy sources Solar power, wind power, fuel cells, biomass
Monthly electricity bills+1000 JPY, +2000 JPY, +3000 JPY, +4000 JPY, +5000 JPY
Renewable energy ratio
25%, 50%, 75%, 100%
Local new employment
10, 30, 50, 70, 100 employees
Suppliers
Major existing, major new, small-medium local new suppliers
Benefits
Tax credit, coupon tickets, award from local public organization
or name listed on a local government web site, free tickets forgreen parks and environmental plants
Households are presented profiles in Table 3 and choose their most preferred alternative
among 10 choice scenarios. Each question has various levels of attributes. Respondents’ age islimited under 59. In the questionnaire, householders were asked whether they have children. It
is found that households which have small children have interests in future energy sources andenvironmental problems. They are altruistic. They are against nuclear power and fossil fuels
which causes global warming and express their support for renewable energy. Households withelderly people do not have small children at the time of questionnaire.
5. Random parameter logit model (RPLM)
In a choice experiment, the dependent variable is discrete. Therefore, a discrete choiceeconometric model should be used. A conditional logit model is a popular option in this context.
However, it assumes an Independent and Identical Distribution (IID), which is derived from the
Independence of Irrelevant Alternatives (IIA). Since the IIA is restricted and easily violated inmany cases, a RPLM (mixed logit model) is often used as a general discrete choice econometric6

Bae and Rishi (2018) took tax credit, green mileage, eco-leveling, free tickets for green parks into
consideration. The current study uses benefits that are easier to understand for Japanese households.

11

model7. It allows for random variation of individual preferences, unrestricted substitution
patterns, and correlation among unobserved factors over time.
A RPLM assumes that each parameter has a specific distribution. The utility is specified asU=α x +β z +εThis function specifies that individual n chooses alternative j, where α is a non-randomparameter and β is a random parameter that represents an individual’s preference and therefore
varies across individuals. In this paper, constant terms and the parameter of a monthly electricity
bill, which is a price parameter, are non-random parameters, and x is a variable vector that
includes a monthly bill. On the other hand, the parameters of each renewable energy, renewableenergy ratio, local new employment, suppliers and benefits are random parameters; z is avariable vector that includes these factors. 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 βover all β .
The distribution of β must be assumed. Usually, a normal, log-normal, or triangular
distribution, etc., can be assumed. In this paper, a normal distribution is assumed because it is a
general distribution and easy to estimate.
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 (SLL) is
SLL =

#$ #$

d lnP (

where dnj is an indicator. It equals 1 if an individual n chooses Alternative j, or 0 otherwise. The
SLL was maximized to capture the maximum simulated likelihood estimator. In addition, 100
Halton draws were used for simulation. For estimation, Limdep NLOGIT 5 was used.
WTP for each attribute is calculated using estimated coefficient parameters8. If the utility
function is linear, it is expressed as+,- = . /,- + 0, 1,-

7
8

The explanation of a random parameter logit model derives from Train (2003).
Kuriyama et al. (2005) and Murakami et al. (2015).

12

where V is the deterministic term of the utility function. The total differentiation of the V
formula is3+,- =

4+,4+,3/,- +31
4/,41,- ,-Now the WTP of z1 which is one of the attributes, such as each renewable energy is obtained.
If the utility level does not change (dV = 0) and other variables except z1 are not changed, themarginal WTP (MWTP) is obtained as follows:
4+,416789 = − ,-$<4+,4/,-;

where xm is a monetary variable such as a monthly electricity bill. MWTP is also written byusing parameters as follows:
06789 = − =>0;

where β is the coefficient of each attribute and β? denotes a monetary coefficient such as amonthly electricity bill. WTP is obtained by dividing the coefficient of each attribute by amonetary coefficient.
6. Estimation results
6.1 Estimation results in full sample
In this section, the estimation results are discussed. Table 4 illustrates the choice number andratio for each alternative.
Table 4 Choice number and ratio for each alternative.
Alternative 1
Alternative 2
Number
3769
3264
Ratio
37.69
32.64

Alternative 3
2967
29.67

Total
10000
100

Alternative 1 is the most popular. There are many households that prefer the current andconventional energy usage. However, the total ratio of Alternatives 2 and 3 is about 60%. Morethan half of the households chose renewable energy programs. Table 5 shows the estimation
results in full sample.
Table 5 Estimation results in full sample.
Variables
Coefficient
Random parameter (mean)
Solar power
0.16542Wind power−0.15045
Fuel cells−0.5871
Renewable energy ratio−0.00107
Local new employment
0.00326

Z valueP-value

2.11
−1.46
−6.81
−0.6
2.68

0.03470.143800.55130.0073

**
***
***

13

Major new supplier
0.153812.47
0.0134
Small-medium local new supplier−0.01984−0.22
0.8273
Tax credit−0.06797−0.64
0.5252
Local coupon ticket−0.38621−3.62
0.0003
Free ticket for green parks−0.22147
−1.83
0.067
Non-Random parameter
Monthly electricity bills−0.00056−20.970Constant 1−1.57691−9.52
0Constant 2−0.0463−0.62
0.5357
Standard deviation
Solar power
1.424721.49
0Wind power
1.3529613.74
0Fuel cells
0.379442.42
0.0157
Renewable energy ratio
0.04123
20.81
0Local new employment
0.0231222.8
0Major new supplier
0.62559
8.31
0Small-medium local new supplier
0.48912
4.34
0
Tax credit
0.96063
15.17
0Local coupon ticket
0.40086
2.81
0.0049
Free ticket for green parks
0.04368
0.38
0.7053
2
McFadden R
0.2255611
Log likelihood−8508.0814
Sample size
10000
Note: *** 1% significant level, ** 5% significant level, * 10% significant level, no *insignificant

**

***
*
***
***

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

The coefficient for the monthly electricity bills is negative and significant at the 1%
significance level. Households clearly prefer lower electricity bills. To establish renewableenergy throughout Japan, electricity generated by renewable energy should be lower.
Next, the results of each renewable energy source are discussed. Dummy variables are used foreach renewable energy source, where biomass energy is the base category. Thus, positivecoefficient means that certain renewable energy is preferred to biomass and negative means that
it is not preferred to biomass. The coefficient of solar power is positive and significant at the 5%
significance level. Solar power is preferred to biomass. Wind power has a negative coefficient,
but it is not significant. Significant differences in preferences are not observed between wind
power and biomass. The coefficient of fuel cells is negative and significant at 1% significancelevel. Fuel cells are not significantly preferred to biomass. Bae and Rishi (2018) obtained
positive and significant coefficients for every renewable energy source in South Korea. The
results were different in Japan and South Korea. Fuel cells are not popular in Japan and are
expensive for households. Therefore, fuel cells are not preferred to biomass. On the other hand,biomass is widely known to households as renewable energy though only a small percent of thehouseholds prefer biomass as the most desirable future energy source in the questionnaire.
Renewable energy ratio is not significant. Households do not choose alternatives with higher
renewable energy ratio9.
9

Bae and Rishi (2018) used the quadratic term of renewable energy ratio. If the coefficient of thequadratic term is negative, the quadratic function is concave down and has renewable energy ratio
where the choice probability reaches maximum. Individuals do not prefer higher and higher renewableenergy ratio and they have the best renewable energy ratio. They also do not prefer much higher ratio.
The current study tried it. However, positive and significant coefficient of the quadratic term wereobtained, which is the reverse result of Bae and Rishi (2018). In Japan, renewable energy ratio where

14

The coefficient associated with local new employment is positive and significant at the 1%
significance level. Households hope new employment will increase in the region where they
live after renewable energy electric power plants are built. They are altruistic and hope positive
economic effects in the region. The results are consistent with Bae and Rishi (2018) 10.
The estimation results about electric power suppliers that provide renewable energy programs
are discussed. Three types of suppliers are considered—major local existing suppliers, major
new suppliers, and small and medium local new suppliers. Dummy variables are used for eachsupplier. Major existing suppliers are the base category. The coefficient of major new suppliers
is positive and significant at the 5% significance level. Households prefer major new suppliers
to major local existing suppliers. Major local existing suppliers have long-time experience inproviding electricity and have been securing constant electricity supply. But households may
expect new services and cheaper bill plans from major new suppliers. After the deregulation in
April 2016, new suppliers have provided electricity and gas. Japanese households have becomefavorable for new suppliers. In Alternative 1, major local existing suppliers provided electricity
generated by fossil fuels. If households do not support fossil fuels and support renewable energy,
they prefer new suppliers who provide electricity generated by renewable energy than major
local existing suppliers who provided electricity generated by fossil fuels. The coefficient ofsmall-medium local new suppliers is negative and not significant. The significant differences in
preferences between small-medium local new suppliers and major local existing suppliers are
not observed. The results imply that small-medium local new suppliers can acquire customers ifthey use renewable energy instead of fossil fuels in electricity generation.
Lastly, the estimation results regarding benefits from renewable energy programs are discussed.
Households receive benefits such as tax credit, coupon tickets which are used in only local areas
where households live, award from local public organization or name listed on a local
government website and free tickets to green parks and environmental power plants. Dummy
variables are used for each benefit, where name listed on a local government website is the basecategory. Tax credit is a monetary incentive. Local coupon tickets are also a monetary incentive,
but households can contribute to their local area. Award from local public organization or namelisted on a local government website is a non-monetary incentive. Free tickets to green parksand environmental plants are a kind of monetary incentive but for households that are notinterested in free tickets to green parks and environmental power plants is non-monetary
incentive. From the estimation results, tax credit is not significant. Households do not respondto monetary incentives. The coefficient of coupon tickets is negative and significant at the 1%
significance level. Households prefer awards or name listed on the local government website tocoupon tickets. The coefficient of free tickets is negative and significant at the 10% significancelevel. Households prefer to award or name listed to free tickets. These results imply thathouseholds do not respond to monetary incentives11.
The random parameter logit model estimates the variation of individual preferences. Estimatedstandard deviation of coefficient parameter expresses the variation of individual preferences andthe choice probability reaches minimum exists. The ratio is 50%. Japanese individuals prefer more than
50% ratio with quadratic proportion.
10
Bae and Rishi (2018) considered the distance from households’ residence to electric power plants as an
attribute. Sometimes electric power plants cause negative externalities. Wind power generators emitnoises and vibrations, and solar panels reflect sunlight. These electric power plants cause some troubles
to neighbors. Households hope local economic effects but oppose electric power plants which are built
near their house to avoid these troubles. Bae and Rishi (2018) analyzed individual behavior to avoid
negative externalities. From the results, households did not hope that electric power plants were built
near their house. The current study adopted other attributes and did not adopt the distance fromhouseholds’ residence due to the restriction of the number of attributes.
11
Bae and Rishi (2018) obtained the results that the coefficient of green mileage is positive andsignificant but other benefits are not significant.

15

distribution. From the results, variables except free tickets have significant standard deviation.
The variation of individual preferences and distribution is observed in variables except freetickets.
Table 6 shows WTP for each attribute. WTP is obtained after dividing each parameter of
attributes by a monthly electricity bill that is monetary parameter. WTP expresses households’
evaluation or monthly additional payment for each attribute.
Table 6 WTP.
Solar power
Wind power
Fuel cells
Renewable energy ratio
Local new employment
Major new supplier
Small-medium local new supplierTax credit
Local coupon tickets
Free tickets for green parks

295.3929−268.661−1048.39−1.91071
5.821429
274.6607−35.4286−121.375−689.661−395.482

Households pay additional 295 JPY per month for solar power. On the other hand, they show
large negative evaluation for wind power and fuel cells. Especially, households do not choose
fuel cells unless the price is discounted by 1048 JPY. Households express highly positive WTP
for major new suppliers and they pay additional 275 JPY per month. Households have negative
WTP for every benefit. They do not evaluate benefits except award or name listed. WTP for
local new employment is very small value of only 5.8 JPY.
6.2 Estimation results in subsamples
In this sub-section, the differences of preferences across individual socio-demographic
attributes and perceptions for renewable energy and energy problems are analyzed. WTP in eachsubsample is compared. The random parameter logit model is used for estimation.
6.2.1 Children
Table 7 illustrates WTP of households that have children less than 13 years old and households
that do not have children. Households that have infant children are altruistic and are interested
in renewable energy and environmental problems for future generations.
Table 7 WTP: households with and without less than 13 years old children.
Attributes
With less than thirteen
Without less than thirteen
years old children
years old children
Solar power
520.073*370.444**Wind power−350.09−358.17
Fuel cells−988.8***−1089.6***
Renewable energy ratio
7.12727
1.64815
Local new employment
7.418184.24074*
Major new supplier47.8909387.611***
Small-medium local new supplier 348.964−62.981
Tax credit
201.709−172.61
Local coupon ticket−473.25−821.78***
Free ticket−93.109−428.39*
Sample size
2350
7650

16

Note: *** 1% significant level, ** 5% significant level, * 10% significant level, no *insignificant
In both subsamples, WTP of solar power is positive and that of fuel cells is negative. Bothhouseholds prefer solar power and do not prefer fuel cells to biomass. Wind power is not
significant. Renewable energy ratio is not significant. Difference in preferences for renewableenergy between households with children less than 13 years old and households without infantchildren are not observed. WTP of households without children less than 13 years old showed apositive and significant correlation for local new employment; however, WTP of householdswith children less than 13 years old is not significant. Households with children less than 13year-old are not interested in local new employment when they decide to participate inrenewable energy programs. They are not altruistic. WTP of households with children less than
13 years old children for local coupon tickets and free tickets to green parks and environmental
plants are not significant. They do not decide to participate in renewable energy programs to get
coupon tickets or free tickets compared with award from local public organization or namelisted on a local government web site. On the other hand, WTP of households without less than
thirteen years old children for these benefits are negative and significant. They do not preferthese benefits.
6.2.2 Age
Table 8 expresses WTP of younger and older households. The average age is 40.6 years old.
Younger households are under 39 years old and older households are over 40 years old.
Table 8 WTP: younger and older households.
Attributes
Younger
Older
Solar power
465.224*
349.095**
Wind power−597.878*−169
Fuel cells−958.082***−1172.7***
Renewable energy ratio
6.32653
4.15873
Local new employment
8.5102**
5.06349*
Major new supplier109.898
540.857***
Small-medium local new supplier −47.1837
58.127
Tax credit
396.245−425.51*
Local coupon ticket−281.02−1072.7***
Free ticket−202.98−616.86**
Sample size
4530
5470
Note: *** 1% significant level, ** 5% significant level, * 10% significant level, no *insignificant
WTP of both subsamples for solar power is positive and significant, and WTP of bothsubsamples for fuel cells is negative and significant. On the other hand, WTP of younger
households for wind power is negative and significant at 10% significant level. Younger
households do not prefer wind power compared with biomass. WTP of both subsamples for
local new employment is positive and significant. WTP of younger households for tax credit,
local coupon tickets, and free tickets to nature parks and environmental plants are not significant,but WTP of older households for these benefits are negative and significant. Older households
do not prefer these benefits compared with award from local public organization or name listed
on a local government web site. Or they prefer the award.
6.2.3 Education

17

Table 9 illustrates WTP of higher and lower education households. Highly educatedhouseholders graduated from university and graduate school, while lower education households
are not university graduates.
Table 9 WTP: higher and lower education households.
Attributes
Higher education
Lower education
Solar power
90.6875
566.1385***Wind power−1040.4***−17.9077
Fuel cells−1107.5***−1025.52***
Renewable energy ratio
4.66667
2.061538
Local new employment
5.14583
3.661538
Major new supplier
165.958460.5077***
Small-medium local new supplier−27.52113.03077
Tax credit
130.729−263.2
Local coupon ticket−649.65**−777.615***
Free ticket−328.31−483.585*
Sample size
5300
4700
Note: *** 1% significant level, ** 5% significant level, * 10% significant level, no *insignificant
WTP of highly educated households for solar energy is not significant, while WTP of lower
education households is positive and significant. Higher education households do not prefersolar energy; however, lower education households prefer that compared with biomass. WTP ofhigher education households for wind power and fuel cells is negative and significant. Higher
education households do not prefer wind power and fuel cells. In both subsamples, local new
employment is not significant. The result is different from the results of full sample. Lower
education households prefer new major suppliers.
6.2.4 Area
Table 10 expresses WTP of households in Kanto area and households in Kansai area.
Table 10 WTP: households who live in Kanto area and Kansai area.
Attributes
Kanto
KansaiSolar power
460.1481***
374.7966Wind power−351.278−580.22*
Fuel cells−1170.43***−936.339***
Renewable energy ratio
0.185185
8.779661*
Local new employment
6**
3.508475
Major new supplier311.8148**
389.3559**
Small-medium local new supplier
312.2778−449.559*
Tax credit−264.333
150.2542
Local coupon ticket−872.648***−628.831**
Free ticket−421.148−441.712
Sample size
6680
3320
Note: *** 1% significant level, ** 5% significant level, * 10% significant level, no *insignificant
WTP of households in Kanto area for solar power is positive and significant, but it is not
significant in Kansai area. Households in Kanto area prefer solar power but households in
Kansai area do not prefer it. In both areas, fuel cells are not preferred. WTP of households in18

Kansai area for renewable energy ratio is positive and significant at 10% significant level.
Households in Kansai area prefer the higher renewable energy ratio. Households in Kanto area
positively evaluate local new employment but households in Kansai area do not evaluate local
new employment when they participate in renewable energy programs. Households in bothareas positively evaluate major new suppliers and negatively evaluate local coupon tickets.
6.2.5 Annual income
Table 11 expresses WTP of higher and lower income households. Higher income households
are those whose annual income is over 6 million JPY, while lower income households are those
whose annual income is under 6 million JPY.
Table 11 WTP: higher and lower income households.
Attributes
Higher income
Lower income
Solar power
139.86496.5263***
Wind power−523.7−308.842
Fuel cells−1311.02***−1073.18***
Renewable energy ratio
5.2
6.140351*
Local new employment
4.52
4.070175
Major new supplier
263.2
379.193***
Small-medium local new supplier 317.92−49.4737
Tax credit
206.78−239.175
Local coupon ticket−913.06**−730.895***
Free ticket
45.92−571.053**
Sample size
3120
6880
Note: *** 1% significant level, ** 5% significant level, * 10% significant level, no *insignificant
WTP of higher income households for solar energy is not significant, while WTP of lower
income households is positive and significant. Higher income households do not prefer solar
energy, while lower income households prefer it. In both higher and lower income households,
WTP for wind power is not significant and WTP for fuel cells are negative and significant.
Wind power and fuel cells are not preferred. Local new employment is not significant in bothhouseholds. Lower income households prefer major new suppliers.
6.2.6 The perception of energy saving
Households were also asked about their perception of energy saving. Energy saving household
is defined as a household conscious about energy saving after the earthquake, while non-energy
saving household is defined as a household not conscious about energy saving after theearthquake. Table 12 expresses WTP.
Table 12 WTP: households that are conscious about energy saving and non-energy saving
households that are not conscious about energy saving.
Attributes
Energy saving
Non-energy saving
Solar power
279.1379**
886.9318**
Wind power−523.276***
232.2045
Fuel cells−1071.57***−879.864**
Renewable energy ratio
2.655172−16.5455
Local new employment
3.844828
2.409091
Major new supplier
293.3448*
300.5455
Small-medium local new supplier−191.655
617.5909
Tax credit−82.6207−294.614

19

Local coupon ticket−622.517***−904.227
Free ticket−351.517−487.273
Sample size
7760
2240
Note: *** 1% significant level, ** 5% significant level, * 10% significant level, no *insignificant*

Both household groups revealed positive and significant WTP for solar energy. Households
who are conscious about energy saving had revealed negative and significant WTP for wind
power. Both household groups revealed negative and significant WTP for fuel cells and local
coupons.
6.2.7 Interest in appliances associated with renewable energy
Households were asked about their interest in appliances associated with renewable energy
such as solar panels, home micro-wind generator, storage battery and fuel cells. Table 13 and 14
illustrates WTP of households which already have appliances, have an interest in appliances andhave a plan to purchase, have an interest in appliances but do not have a plan to purchase andhave an interest in appliances but cannot purchase due to the limitation of their residence. The
sample excludes households which are not interested in these appliances. Different results fromother analysis are obtained, in which WTP for renewable energy ratio is positive and significant.
Households who are interested in these appliances support higher renewable energy ratio.
Table 13 WTP: households who are interested in solar panels and home micro-wind generator.
Attributes
Solar panels
Home micro-wind
generator
Solar power
357.9848**
120.1754
Wind power−412.47*−671.211**
Fuel cells−1144.85***−1105.23***
Renewable energy ratio
14.21212***
14.38596***
Local new employment
8***
10.54386***
Major new supplier348.6364***
175.193
Small-medium local new supplier
105.0909
153
Tax credit−164.439−142.912
Local coupon ticket−832.212***−769.193**
Free ticket−534.773**−355.175
Sample size
5270
3400
Note: *** 1% significant level, ** 5% significant level, * 10% significant level, no *insignificant
Table 14 WTP: Households who are interested in storage battery and fuel cells.
Attributes
Storage battery
Fuel cells
Solar power
246.1667
138.1667
Wind power−345.75**−499.9Fuel cells−921.783***−971.467
Renewable energy ratio
16.16667***
12.46667
Local new employment
9.716667***
9.316667
Major new supplier384.0167331.3833
Small-medium local new supplier
207.95−25.6
Tax credit−107.783−361.033
Local coupon ticket−759.45**−980.883
Free ticket−292.267−515.933

20

*
***
***
***
*

***

Sample size
4610
3720
Note: *** 1% significant level, ** 5% significant level, * 10% significant level, no *insignificant
6.3 Discussion
Various estimations have been tested. All estimation results, including subsample analysis
gave negative significant coefficient of monthly electricity bills. Regardless of individual social
attributes and perception of energy problems, households prefer cheaper electricity. Generally,
to promote renewable energy, electricity generated by renewable energy is used.
Regarding the estimation results of renewable energy sources, in general, the coefficient ofsolar energy is positive and significant. A higher positive WTP was observed. Households
prefer solar energy than biomass. However, in some household groups such as those living in
the Kansai area, solar power is not preferred. The coefficient of fuel cells was negative andsignificant among all household groups. Any households with social attributes and perception ofenergy problems do not choose electricity generated by fuel cells. Results of wind power wereobtained using subsample analysis. But results could not gain positive and significant
coefficient. In general, wind power is not significantly preferred than biomass. Solar energy isfamiliar to Japanese households, but fuel cells and wind power are not familiar to them and are
expensive to households. This explains why different results are obtained. Generally, renewableenergy ratio was not significant. Electricity with higher renewable energy ratio is not preferred
for households. However, households which are interested in appliances related with renewableenergy prefer higher renewable energy ratio since the coefficient was positive and significant.
Households who have less than thirteen years old children were expected to be altruistic and to
be interested in future environmental and energy problems and positively highly evaluate
renewable energy, but expected results were not obtained.
Many households are favorable for local new employment after renewable energy electric
power plants are built. Regarding the preference for suppliers who provide electricity generated
by renewable energy, in general, households tend to prefer new major suppliers, but do notprefer small-medium local new suppliers. In Alternative 1, major existing suppliers use fossil
fuels. On the other hand, in Alternatives 2 and 3, major new suppliers use renewable energy.
Households who prefer renewable energy will choose suppliers that use renewable energy even
if the suppliers are newcomers.
To analyze households’ response to monetary and non-monetary incentives, benefits thathouseholds receive by participating in renewable energy programs were considered. All
households prefer awards from local public organization or name listed on a local government
web site to tax credit, which is a monetary incentive. Households do not prefer local couponsand free tickets of nature parks and environmental institutions. Households totally do notrespond to monetary incentives.
7. Conclusions and policy implications
This article examined the conditions in which renewable energy would be widely used andwhat kind of renewable energy sources would be preferred by Japanese households using the
conjoint analysis. Since the earthquake in March 2011, nuclear power plants have been shut
down; however, operations are planned to resume. However, in the future, nuclear power plants
would be reduced due to decommissioning of plants. The use of fossil fuels should also be
reduced to avoid the global warming, although Japan has relied on fossil fuels such as LNG
after the earthquake. Based on these reasons, the use of renewable energy such as solar powershould be encouraged in Japan than in other countries. Many studies have estimated individual
preferences for renewable energy. Some studies estimated preference for higher total renewableenergy ratio, but not for individual renewable energy. Some estimated preferences for individual21renewable energy such as solar and wind power. Some studies propose government energy
policy such as energy-mix and other studies examined individual decision to participate inrenewable energy programs. Our study is linked to latter studies. Alternative 1 expresses thecurrent energy usage, in which households use electricity generated by fossil fuels. In
Alternative 2 and 3, they use electricity generated by renewable energy. The choice experimentfinds out the kind of renewable energy source, bill plans, suppliers, and benefits persuading
individuals to participate in renewable energy programs.
From the estimation results, Japanese households highly evaluate solar power and do notevaluate wind power and fuel cells. Especially, they negatively evaluated fuel cells. Solar poweris familiar to people, while other renewable energy sources are not so familiar. That is why
households do have clear idea to use wind power and fuel cells. Fuel cells that do not emit thegreen-house gases are classified as renewable energy source. However, fuel cells are too
expensive, and households do not have any idea how to use fuel cells as an energy source.
Hence, households express negative evaluation. Similarly, Bae and Rishi (2018) in their study
on Korean households found that Koreans did not prefer wind power and biomass but positively
evaluated solar power and fuel cells. In contrast, our study showed a different result in fuel cells.
Renewable energy ratio was not significant. Even though households who have children under
the age of thirteen are supposed to be altruistic and interested in global warming and renewableenergy, the significant results were not obtained.
The coefficient of local new employment was generally positive and significant. Households
hoped that renewable energy power plants would contribute to new employment and economy.
Regarding the estimation results of suppliers of renewable energy programs, households prefermajor new suppliers to major existing suppliers and do not prefer small-medium local new
suppliers. Kaenzig et al. (2013) showed that German households highly evaluated local
suppliers. The results showed that households preferred major suppliers to local ones. Since
Alternative 1 suppliers were major existing suppliers of fossil fuels, many households choose
new suppliers who use renewable energy to avoid fossil fuels.
Lastly, regarding benefits, households did not evaluate tax credit, local coupon tickets, and freetickets obtained from environmental institutes and national parks in many estimation resultscompared with award or their names listed on the of the local government web site. Households
do not respond to monetary incentives such as tax credit, and they prefer non-monetary
incentives such as awards or names listed on the local government website. The results conclude
that monetary incentives are not so needed to promote renewable energy. This study analyzed
the Japanese households’ preferences for renewable energy and the conditions in which they use
renewable energy. Since Japan has experienced the Great East Japan Earthquake in the recent
past, it is difficult to resume nuclear power plants for energy generation. Under these situations,the use of renewable energy is the only solution.
Acknowledgements
This study was funded by grants-in-aid for scientific research (C) (Kakenhi) of Japan Society
for The Promotion of Science (No. 16K03679). The questionnaire was assisted by the service of
the Rakuten Insight Company and got many useful comments and advice about the strategy ofsurvey. All remaining errors are mine.
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