ISSN 1881-6436

Discussion Paper SeriesNo. 18-04

Conjoint analysis of Japanese households’ energy-saving behavior after theearthquake: the role of the preferences for renewable energy
Shin Kinoshita
September 2018

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

1

Conjoint analysis of Japanese households’ energy-saving behavior after theearthquake: the role of the preferences for renewable energy
Shin Kinoshita1

Abstract
Energy savings among households are the important energy problems in Japan. After the Great
East Japan earthquake in March 2011, nuclear power plants were forced to cease operations. We
Japanese have worried about electricity shortages and has been requested to save electricity usage
in case of a sudden outage.
This paper analyzes the conditions that households save electricity usage by a conjoint analysis.
I presented three energy-saving plans with several attributes to households. As the conditions, anannual electricity bill, CO2 emissions, a stable electricity supply and energy sources which
generate electricity are considered. Especially, I focus on the relation between preferences forenergy sources and energy-saving behavior. If renewable energy sources such as solar and wind
power are used in the electricity generation, households who support renewable energy mightsave electricity usage. I used a random parameter logit model and a nested logit model for
estimation.
The promotion of renewable energy and energy savings should be solved as a Japan’s official
energy policy. If households that prefer renewable energy tend to reduce their electricity usage,
the promotion of renewable energy and energy savings could be solved simultaneously.
The estimation results indicated that households largely respond to an annual electricity bill. In
addition, they also respond to CO2 emissions and a stable electricity supply to save electricity
usage. If nuclear power is used in the electricity generation, they don’t save electricity usage. If
renewable energy is provided as a main energy source, they tend to save electricity usage. Thus,
the use of renewable energy gives incentives for households to save electricity usage. It is possible
to promote energy savings and renewable energy simultaneously by utilizing their interest in
renewable energy.

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

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

2

Conjoint analysis of Japanese households’ energy-saving behavior after theearthquake: the role of the preferences for renewable energy 2
1. Introduction
Energy savings among households are the important energy problems in Japan. After the Great
East Japan earthquake in March 2011, due to the serious accidents in nuclear power plants inFukushima prefecture, nuclear power plants were forced to cease operations. People who live in
the Kanto-area around Tokyo experienced planned power outages because nuclear power plants
stopped operation. We Japanese have worried about electricity shortages especially at times ofpeak of demand in summer and winter and has been requested to save electricity usage in case of
sudden outages. Electricity shortages will continue until nuclear power plants resume operation.
To avoid electricity shortages, energy savings are needed. Climate change is also one of thereasons of energy saving. If we reduce electricity usage, we reduce global greenhouse gases(GHG) such as CO2.
I present three energy-saving plans with several attributes to households and analyze theconditions that households save electricity usage by a conjoint analysis. Households choose themost desirable energy-saving plan. As the conditions, an annual electricity bill, CO2 emissions, astable electricity supply and energy sources are considered. An annual electricity bill is a monetaryfactor, while CO2 emissions, a stable electricity supply and energy sources are non-monetaryfactors. If an annual electricity bill is reduced, households may choose the energy-saving plan
which saves more electricity usage. Households might save more electricity usage to reduce CO2
emissions. If a stable electricity supply is secured, they will save more electricity usage.
Especially, I focus on the role of energy sources which are used in generation of electricity andthe relation with their preferences for renewable energy and energy-saving behavior. If renewableenergy sources such as solar and wind power are used as a main energy source, households mightsave more electricity usage. Households who support renewable energy might save more
electricity usage.
To promote renewable energy is another important energy problem. After the earthquake, Japan
has relied on fossil fuels such as oil, natural gas (LNG) and coal. However, fossil fuels emit GHG
such as CO2. Instead of nuclear power and fossil fuels, renewable energy should be promoted asalternatives. To promote renewable energy, the Japanese government introduced a feed-in-tariff
system in July 2012. Moreover, the Japanese government published the desirable composition ofenergy sources called the “best-mix” in fiscal year 2030 as an energy plan3. This plan states thatthe share of renewable energy will be raised to around 22–24%. However, despite the feed-intariff system, the share of renewable energy except water power is only 3.2%4.
Accordingly, the promotion of renewable energy and energy savings should be Japan’s official
energy policy. To promote renewable energy, the penetration of photovoltaic panels and wind
generators among households is essential. If we find that households who support renewableenergy tend to save more electricity usage, these two problems could be solved simultaneously.
Kinoshita (2016) used a conjoint analysis to reveal a high willingness to pay (WTP) for
renewable energy sources such as solar and wind power. Kinoshita (2017) then clarified thefactors that lead households to reduce electricity usage. One of the factors was energy sources
used in electricity generation. However, I couldn’t find the clear relation between energy sources
and saving behavior. The present paper revises the previous work and makes clear the relation
between households’ preference for renewable energy sources and their energy-saving behavior
as an extension of those studies.
2

This study was aided by grants-in-aid for scientific research (C) (kakenhi) of Japan Society for The
Promotion of Science, No. 16K03679, Shin Kinoshita.
3
The Agency for Natural Resources and Energy in the Ministry of Economy, Trade and Industry
4
The Federation of Electric Power Companies of Japan3

In the short-term, energy savings can help avoid the tightness of electricity demand and supply,
whereas in the long-term energy savings can help alleviate global warming and long-term energyshortage problems. In this paper the long-term energy savings are discussed.
This paper is consisted of the following section. In section 2, related literature is introduced. In
section 3, a conjoint analysis and profiles are illustrated. In section 4, econometric methods are
explained. In section 5, estimation results are presented. In section 6, conclusions and policy
implications are discussed.
2. Related literature
I analyze energy-saving behavior of Japanese households after the earthquake. Specifically, I
focus on the relation with their preferences for renewable energy. Households who support
renewable energy might save more electricity usage. We find several studies about energy savingsin Japan after the earthquake.
Tanaka and Ida (2013) is the first study which analyzed Japanese households’ electricity-saving
behavior after the earthquake by a conjoint analysis. They asked households the settings of air
conditioners, refrigerator, and standby power of electrical appliances in the areas of the Tokyo
Electric Power Company (TEPCO) and the Kansai Electric Power Company (KEPCO). They
found that in both areas households tend to save electricity usage after the earthquake and in
TEPCO area households save more electricity usage because they experienced the planned
outages. However, they don’t mention non-monetary factors such as CO2 emissions andespecially the relation between energy savings and renewable energy.
Some studies examined energy-saving behavior of Japanese households by a field experiment.
Mizobuchi and Takeuchi (2012) analyzed the effects of economic incentives on energy-saving
behavior in Matsuyama. They found that only 34 % of participants reduced their electricity usage.
Mizobuchi and Takeuchi (2013) considered non-monetary factors as well as monetary factors.
They found that monetary factors have more effects on households’ energy savings than nonmonetary factors. Ito et al. (2015) also noted non-monetary factors which they called moral
suasion. They also found that households in the economic incentive group saved more electricity
usage than those in the moral suasion group. These studies note non-monetary factors, but don’t
note the effects of energy sources on energy-saving behavior.
These studies were conducted after the earthquake in Japan. Some studies emphasize the social
norms in energy-saving analysis. The social norms are like non-monetary factors. Poortinga et al.
(2003) found the effects of social and psychological factors on energy savings by a conjoint
analysis. Allcot (2011) is also one of the famous studies about the social norms in energy savings.
Non-price intervention has the same impact as raised prices on electricity savings of households
from a field experiment.
Lastly, I introduce some studies about households’ preferences for renewable energy. Morita andManagi (2013) used a conjoint analysis to estimate preferences for energy sources, particularly
renewables, after the earthquake. They estimated the Willingness to Pay (WTP) for each energy
source and suggested policy implications vis-à-vis the Japanese government’s energy mix. Theyobtained negative WTP for nuclear power, but positive WTP for renewable energy sources suchas solar and wind power. Murakami et al. (2015) estimated consumers’ WTP for renewable energy
and nuclear power in the US and Japan. They used a conjoint analysis and found that consumers
in both countries showed negative preferences for nuclear power and positive preferences forrenewable energy.
I analyze the conditions that households save electricity usage. Especially, I focus on the role ofrenewable energy as an energy source used in electricity generation. Households who support
renewable energy might save more electricity usage. There are many studies about energy savingsand preferences for renewable energy. However, they don’t mention the relation though these two
problems are essential in Japan.

43. Conjoint analysis
I use a conjoint analysis to analyze households’ energy-saving behavior.5 A conjoint analysis is
one of the stated preference methods (SPM) to analyze the individual choice for several
alternatives under future and hypothetical conditions. Individual preferences can be estimatedfor hypothetical goods or services which have several attributes. We present several alternatives
and respondents choose the most desirable alternative of the hypothetical goods or services. A
conjoint analysis is one of the choice experiments.
In this paper, three alternatives are presented to households and they choose the most desirable
one. Sometimes, the goods or services have not yet prevailed, and this method is often used inmarketing research. I analyze households’ energy-saving behavior under hypothetical conditions
wherein an annual electricity bill changes as an example. In a conjoint analysis, households arepresented with goods or services, each of which has several attributes. The researcher decides
the number of attributes and their levels to make profiles. A profile that has few attributes is notenough to describe a good object of study, but a profile with too many attributes makes it
difficult for respondents to choose among options. In general, five or six attributes are suitable.
After attributes and their levels are decided, their profiles are compiled. However, if all the
combinations of attributes and levels are adopted, the patterns are too many and strong
correlation between some attributes is caused, which is called multicollinearity. To avoid these
problems, profiles are created by the orthogonal planning method. From various cards that we
obtain through the orthogonal planning method, selecting cards and their combinations, profiles
are made after deleting unrealistic and dominant cards. I used SPSS conjoint version 17.0 for theorthogonal planning.
Contingent Valuation Method (CVM) is another popular stated preference method, but it is nota choice experiment. CVM can be used to evaluate users’ valuation of non-marketable targets
such as forests and beaches. CVM evaluates the value of one target and doesn’t evaluate thevalue of each attribute.
I presented the following three alternatives to households in the questionnaire.
Alternative 1: Energy-Saving Plan A: keep temperature at 26℃ in summer and 18℃ in winter(Households don’t save electricity usage).
Alternative 2: Energy-Saving Plan B: keep temperature at 28℃ in summer and 16℃ in winter(Households slightly save electricity usage.).
Alternative 3: Energy-Saving Plan C: keep temperature at 30℃ in summer and 14℃ in winter(Households substantially save electricity usage.).
Households choose the most desirable alternative. They use an air conditioner. Temperatures intheir room is used as examples to allow respondents to visualize clearly the trade-off inherent ineach scenario. In alternative 1, they might feel more comfortable, but they don’t save electricity
usage. In alternative 2, they slightly save electricity usage, but they feel less comfortable at thetrade-off of experiencing less moderate temperatures. In alternative 3, they save a great deal ofelectricity usage at the cost of increased discomfort. These alternatives have several attributes andconditions whose levels are changeable. As the conditions, an annual electricity bill, CO2
emissions, a stable electricity supply, and energy sources which are used in electricity generation
are included. These attributes and their levels will now be discussed.
1. Annual electricity bill
When households save electricity usage, they can save money for electricity compared with
5

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

5current payments. The levels are −30%, −20%, −10%, 0 (unchanged), +10%, and +20%. The
levels will change under the degrees of saving and some conditions. If households save more
electricity usage, they can save more money. If households don’t save electricity usage in the casethat alternative 1 should be chosen, an annual electricity bill may rise under some conditions. If
nuclear power is used as a main energy source, an annual electricity bill might be reduced even if
households don’t save electricity usage. If fossil fuels are used, sometimes an annual electricity
bill might be higher through rising fuel prices. If renewable energy is used, an annual electricity
bill might be higher through the feed-in-tariff system. An annual electricity bill is a monetaryfactor. “Annual” electricity bill is adopted so that households can imagine long-term electricity
savings.
2. CO2 emissions
Some households save electricity usage to reduce CO2 emissions. If households are interested inhelping combat global warming, they might save more electricity usage to reduce CO2 emissions.
CO2 emissions are non-monetary factors. The reduction levels are −20%, −10%, 0% (unchanged),and +10%. If households don’t save electricity usage or fossil fuels are used as a main energy
source, CO2 emissions may increase.
3. Stable electricity supply
When electricity is supplied without interruption, no outages occur in a year. When electricity
isn’t stable, short-term outages may occur a few times a year, or lights in their house may become
dimmer. A dummy variable is used which equals 1 for no outages and is 0 otherwise. If renewableenergy is used, electricity-supply interruptions may arise due to weather conditions. If households
don’t save electricity usage, it may cause regular interruptions due to planned outages by electric
power companies stemming from electricity shortages.
4. Main energy sources
Households use electricity generated by a main energy source. The main energy sources which
are used in electricity generation is following: nuclear power, fossil fuel such as LNG, solar power,
and wind power. The main energy source has the highest share in the composition of energy
sources. The share is assumed more than 50%. Households use electricity generated by eachenergy source from an electric power and gas company. A dummy variable is used for each energy
source where fossil fuels are the base category. Households might change their electricity-saving
behavior by energy sources.
Table 1 summarizes the levels of each variable.
Table 1 Levels of each variable
Variables
Annual Electricity bill
CO2 emissions
Stable electricity supply
Energy sources

Level-30%, -20%, -10%, 0 (unchanged), +10%, and +20%
-20%, -10%, 0% (unchanged) and +10%
Yes (1), no (0)nuclear power, fossil fuel (LNG), solar power, and wind
power

Respondents were informed of the questionnaire’s purposes to facilitate data collection. Nuclear
power plants ceased operation after the earthquake but the plans to start them again are difficult
to implement. More use of fossil fuels such as LNG, coal and oil, which emit CO2, makes it
difficult to avoid global warming. In these situations, renewable energy sources such as solar andwind power should be promoted urgently. At the same time, households need to reduce electricity
usage.
Through the orthogonal planning method, I made profiles after deleting unrealistic and dominant
cards. One of the examples of unrealistic cards is that CO2 emissions increase even though nuclear
power or renewable energy is used. One of the examples of dominant cards is that an electricity
bill is reduced even if households don’t save electricity usage and the main energy source is6renewable energy. Table 2 presents an example of profile.
Table 2 Example of profile
Alternative 1
Attribute（don’t save）
Set temperature
at 26℃ insummer and
18℃ in winterAnnual electricity bill
-10%
(JPY)
CO2 emissions
unchanged
Stability
stableEnergy source
nuclear

Alternative 2（save a little)
Set temperature
at 28℃ insummer and
16℃ in winter

Alternative 3（save a lot)
Set temperature
at 30℃ insummer and
14℃ in winter

-30%

-10%

-10%
stableLNG-20%stablewindHouseholds choose the most desirable alternative. They answer with respect to 10 choicequestions. Each question has various levels of attributes. The data were collected via a web-basedquestionnaire, utilizing the services of the Rakuten Research Company. The sample size is 750
households in Kanto6, Kansai7, and Chukyo8 areas which are three major urban areas in Japan. In
Kanto area households purchase electricity from the Tokyo electric power company (TEPCO).
TEPCO has some nuclear power plants. Households in Kanto area experienced planned outages
after the earthquake. In Kansai area households purchase electricity from the Kansai electric
power company (KEPCO). KEPCO also has some nuclear power plants in Fukui prefecture.
Households in Kansai area didn’t experience planned outages but were requested to saveelectricity usage because the nuclear power plants stopped operation. In Chukyo area households
purchase electricity from the Chubu electric power company (CEPCO). CEPCO has the Hamaoka
nuclear power plant. CEPCO doesn’t largely depend on nuclear power compared with TEPCO
and KEPCO. The three areas are different in the dependence on nuclear power and the experiencesof planned outages, thus are also different in households’ behavior toward energy savings. The
sample is weighted by each area’s population. Respondents’ age is under 59 because we areinterested in long-term energy savings. Data were collected in February 2017. Table 3 shows thesocio-demographic attributes of sample households. Respondents who are unemployed andwhose income is less than 2 million JPY are more observed than the population.
Table 3 Socio-demographic attributes

Occupation
Household income(thousand JPY)

Educational background

Total
Employed
UnemployedLess than 2,000
2,000-3,990
4,000-5,990
6,000-7,990
8,000-9,990
More than 10,000
Junior high school, high school

Number
750
578
172
205
162
168
91
64
60
199

%
100
77.1
22.9
27.3
21.6
22.4
12.1
8.5
8
26.5

6

Tokyo, Chiba, Kanagawa and Saitama prefecture
Osaka, Kyoto, Hyogo, Shiga and Nara prefecture
8
Aichi, Gifu and Mie prefecture
7

7

Family composition

Dwelling type

Living area

Sex
Age (years old)

Technical school, junior college
University, graduate school
SingleCouple
Three
Four
Five
More than six
Detached
house(including
twohouseholds house)
Collective
housing(condominium,
apartment, housing complex etc.)
company housing, dormitory housing
etc.
Kanto
ChukyoKansai
Male
Female
20-29
30-39
40-49
50-59
Average
Minimum
Maximum

177
374
163
160
189
164
51
23
348

23.6
49.9
21.7
21.3
25.2
21.9
6.8
3.1
46.4

374

49.9

28

3.7

435
114
201
382
368
154
192
227
177
40.57
20
59

58
15.2
26.8
50.9
49.1
20.5
25.6
30.3
23.6

In the questionnaire, I surveyed households’ opinions and perceptions about energy problems.
54.7% households feel that an electricity bill has become higher after the earthquake. 66.1% saveelectricity usage after the earthquake. 90.8% think they should reduce global greenhouse gases.
On the most desirable future energy source, 39.2% think solar power is the most desirable energy
source. Only 7.5% think wind power is the most desirable one, but 20.9% think it is second
desirable one. On the other hand, only 17.2% think nuclear power is the most desirable energy
source. 11.9% think LNG is the most desirable one. I also surveyed households’ opinions andperceptions of energy-saving appliances. 38.9% have energy-saving air conditioners and 32.5%
have energy-saving refrigerators. 50.7% aren’t interested in solar panels and 67.7% aren’tinterested in a wind power generator.
4. Econometric analysis
4.1 Random parameter logit model
In a choice experiment, the dependent variable is discrete. To estimate the choice model, therefore,a discrete choice econometric model should be used. A conditional logit model is a popular modelin this context. However, this model assumes an Independent and Identical Distribution (IID), andthis assumption derives from the Independence of Irrelevant Alternatives (IIA). This assumption
is restricted and easily violated in many cases. Consequently, a random parameter logit model(mixed logit model) is used as a general discrete choice econometric model. This model allows
the random variation of individual preferences, unrestricted substitution patterns, and correlation

8

among unobserved factors over time.9
A random parameter logit model assumes that each parameter has a specific distribution. The
utility is specified as
U =α x +β z +εThis function specifies that individual n chooses alternative j, where α is a non-random parameterand β is random parameters that represent the preference of each individual and varies amongindividuals. In this paper, a constant term and the parameter of an annual electricity bill, which isa price parameter, are non-random parameters. x is a variable vector that includes an annual
electricity bill. On the other hand, the parameters of CO2 emissions, a stable electricity supply,and energy sources are random parameters. z is a variable vector that includes CO2 emissions,a stable electricity supply, and energy sources. ε is a random error term and has an IID extreme
value.
The probability conditional on β is
exp β xL β =Ȃ exp β x
The random parameter logit probability isP =exp β xȂ exp β xf β dβThis probability is the unconditional choice probability calculated as the integral of L β overall β .
The distribution of β must be assumed. Usually, a normal, lognormal, or triangular distribution,etc., can be assumed. In this paper, a normal distribution is assumed.
Simulation methods were used for estimation. The simulated probability is
1
P =
R

"
!#$

L

β!

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

*

)

#$ #$

d lnP (

where dnj is an indicator. It equals 1 if individual n chooses alternative j or is 0 otherwise. SSL
was maximized to capture the maximum simulated likelihood estimator. In addition, 100 Haltondraws were used for simulation. For estimation, Limdep NLOGIT 5 was used.
4.2 Nested logit model
In alternative 1, households don’t save electricity usage, while in alternatives 2 and 3, they saveelectricity usage. When respondents face these alternatives, they first choose to save or not to saveelectricity usage. Then, if they decide to save, they choose the degrees of savings. Alternatives 2
9Train (2003) and Louviere et al. (2000) are referred for the explanation of a random parameter logit
model.

9and 3 are included in the same category or nest. A nested logit model is applicable. In a nested
logit model, the cumulative distribution of error term ε is assumed in following formula:
exp −

5

6#$ ∈42e,-./ /12

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

12 ,$

1;

where Vni is a deterministic term in utility function. The maximum likelihood method was used
for estimation. Using this choice probability, a likelihood function is formed, a log-likelihood
function is maximized, and estimates are obtained. The choice probability can be written using
this formula:P = P |42 P 42

where P |42 is a conditional probability that individual n chooses alternative i under thecondition that individual n chooses an alternative within a nest and individual n chooses a nest k.
P 42 is the probability that individual n chooses an alternative within a nest k. This means that anindividual chooses a nest and then chooses an alternative within the nest. P is the product of as are written in the following
formula:eA.2 B12 C.2
P 42 = 5 A B1 C
Ȃ<#$ e .; ; .;eD.: /12
P |42 =
Ȃ ∈42 eD.: /12
where W is a set of variables representing the attributes of a nest and Y is a set of variables
representing the attributes of each alternative. I is called an inclusive value (IV) in a nest and iswritten as follows:
I 6 = ln∈42eD.: /12

In a nested logit model, IV parameters are estimated. IV is also called a log-sum variable or an
expected maximum utility. IV parameters lie between 0 and 1. When all IV parameters are 1, themodel is a conditional logit model. When an IV parameter is above 1, the structure of nest isn’t
suitable. This means that the correlation between alternatives outside the nest is stronger thaninside the nest.
In a nested logit model, scale parameters are normalized in upper or elemental nest level to 1.
The upper nest level is “save” or “don’t save”. The elemental nest level is each alternative.
Random utility model 1 (RU1) is the model where scale parameters in an elemental nest level arenormalized to 1. Random utility model 2 (RU2) is the model where scale parameters in upper nest

10

level are normalized to 1 and scale parameters in an elemental nest level are free. RU2 is used inthis paper.
5. Estimation results
In this section, I explain the estimation results and discuss households’ energy-saving behavior.
Firstly, I show the results of a random parameter model and a nested logit model. Next, I show
the results of differences in households’ socio-demographic attributes.
5.1 Estimation results
Table 4 expresses the choice number and choice probability for each alternative.
Table 4 Choice probability
Alternative 1
Alternative 2
Alternative 3
total
Number
1513
4254
1733
7500
Choice
0.202
0.567
0.231
1
probability
The choice probability for alternative 2 (save a little) is the highest, whereas the choiceprobability for alternative 1 (don’t save) is the lowest. Households tend to save electricity usage.
In this subsection, the estimation results of a random parameter logit model are explained. Table
5 illustrates the estimation results of a random parameter logit model.
Table 5 Estimation results (random parameter logit model)Standard
Variable
Coefficient
Z value
P value
Error
Random parameters(mean)
CO2 emissions-0.00954
0.00429-2.22
0.026
Stability
0.76946
0.07451
10.33
0.000
Nuclear-0.66369
0.09301
-7.14
0.000
Solar
0.56977
0.09425
6.05
0.000
Wind
0.26894
0.08795
3.06
0.002
Non-random parameters
Annual electricity bill-0.01197
0.00176-6.8
0.000
Constant for alt 1
0.649260.101056.43
0.000
Constant for alt 2
1.82583
0.0712625.62
0.000Standard deviation
CO2 emissions
0.07935
0.0037121.38
0.000
Stability
0.16572
0.256120.65
0.518
Nuclear1.889150.10476
18.03
0.000
Solar
1.257900.09619
13.08
0.000
Wind
0.58279
0.147643.95
0.000
Log likelihood-6297.451
McFadden R2
0.236
The annual electricity bill coefficient has a negative sign and is significant at the 1% level. If
an annual electricity bill is reduced, households might choose the energy-saving plan which

11saves more electricity usage. This finding implies that under the condition that their annual
electricity bill can be reduced they might exert their efforts to save more electricity usage. An
annual electricity bill is a monetary factor. Households respond significantly to a monetaryfactor, and it induces their incentives to save electricity usage. The CO2 emissions coefficient
has a negative sign and is significant at the 5% level. To reduce CO2 emissions, households
might save more electricity usage. The coefficient associated with a stable electricity supply has
a positive sign and is significant at the 1% significance level. Households value heavily a stableelectricity supply in energy saving. If a stable electricity supply is secured, households mightsave more electricity usage. CO2 emissions and a stable electricity supply are non-monetaryfactors. Households also respond to non-monetary factors.
Next is the estimation results of energy sources. Dummy variables for each energy source areused, with fossil fuels as 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 fossil
fuels, households don’t save electricity usage. On the other hand, the coefficient associated withrenewable energy, which is both solar and wind power, has a positive sign and is significant at
the 1% level. If the main energy source is renewable energy instead of fossil fuels, households
might choose the energy-saving plan which saves more electricity usage. This finding implies
that households might save more electricity usage if renewable energy is used as a main energy
source and households who support renewable energy might save more electricity usage. This
result indicates that renewable energy and energy savings can be promoted simultaneously.
A random parameter logit model represents the variations in individuals’ preferences. The
standard deviations of random parameters except a stable electricity supply are significant.
Almost all the households need a stable electricity supply. In terms of other variables, somehouseholds need them whereas others do need not.
I tried the estimation by a nested logit model because the alternatives have a nest structure.
Alternative 1 is “don’t save” and alternative 2 and 3 are “save”. This choice set has a nest structure
where alternative 2 and 3 are within the same category as “save”. Households choose “save” or“don’t save” at the first stage, and then they choose the degrees of savings at the next stage if theychoose “save.” Random utility model 2 (RU2) is used where a scale parameter in the upper level
whose alternative is “save” or “don’t save” is normalized to 1, while a scale parameter in thelower level which is each alternative is free. Table 6 illustrates the estimation results of a nested
logit model.
Table 6 Estimation results (nested logit model)Standard
Z value
P value
Variable
Coefficient
Error
Annual electricity bill
-0.00900.0017-5.19
0.000
CO2 emissions-0.0095
0.0027-3.47
0.001
Stability
0.40650.06795.99
0.000
Nuclear-0.4038
0.0676
-5.97
0.000
Solar
0.41490.1070
3.88
0.000
Wind
0.08630.06951.24
0.214
Constant for alt 1
0.53950.37961.42
0.155
Constant for alt 2
1.29350.26614.86
0.000
IV parameter
Don’t save
1
Save
1.03880.23164.49
0.000
Log likelihood-7285.98
McFadden R2
0.221

12

The estimation results are almost the same as the results of a random parameter logit model. The
IV parameter is significant at 1% level. However, it exceeds 1. It should be between 0 and 1. This
result indicates that the nest structure isn’t suitable. This means that substitution between
alternatives 1 and 2, or alternatives 1 and 3 is larger than that occurring between alternatives 2
and 3. Households don’t choose between whether to save or not to save, and don’t consider
alternative 2 and 3 to be similar even if these alternatives are in the same category.
5.2 Differences in households’ socio-demographic attributes
I asked households about their socio-demographic attributes and perceptions of energy problems
in the questionnaire. I examine the differences of energy-saving behavior across households’
socio-demographic attributes and perceptions. I divided the sample into two subsamples tobalance the sample size in two subsamples. Table 7 is the list of subsamples. I use a dummy
variable for each subsample. I assign 1 or 0 for each subsample. I show the assignment in table 7.
Table 7 List of subsamples
Attributes
Subsamples(dummy variable)
HouseholdLow income (0)incomeHigh income (1)FamilySmall family (0)composition
Big family (1)Definition

Under 4 million JPY
More than 4 million JPY
Single and couple family
Married parents and unmarried children
family, and more than two adult generations
familyResidential type Detached house Detached house (two household houses are(1)
included)Collective house Condominium, apartment, housing complex(0)
and a company and dormitory housing
Living area
Kanto (1)Households who live in Kanto area
Chukyo (0)Households who live in Chukyo area
Kansai (0)Households who live in Kansai area
Age
Younger age (1)Less than 39 years old (average)
Older age (0)More than 40 years old
Perceptionof High bill (1)Households who think electricity bill iselectricity billhigher after the earthquake
Low bill (0)Households who don't think electricity bill ishigher after the earthquake
Perceptionof Save (1)Households who save electricity usage after
energy-saving
the earthquake
Households who don't save electricity usage
Don’t save (0)after the earthquake
Desirable energy Renewable energy Households who prefer renewable energy
source(1)
Non-renewable
Households who prefer non-renewableenergy (0)energySample size
367
383
323
427

348
402
435
114
201
346
404
410
340
496
254
512
238

I use the cross terms of each independent variable and dummy variable to examine thedifferences. Table 8 expresses the estimation results. A random parameter logit model is used for
estimation.

13

Table 8 Estimation results with cross terms
HouseholdincomeCO2 emissions-0.009Stability
0.792
Nuclear
-0.66
Solar
0.599
Wind
0.36
Annual electricity bill-0.014
Cross term
Annual electricity bill
0.004
CO2 emissions-0.001
Stability-0.046
Nuclear-0.009Solar-0.058Wind-0.181
Constant for alt 1
0.648
Constant for alt 2
1.826
McFadden R2
0.236
Age

CO2 emissions-0.006

Stability
Nuclear
Solar
Wind
Annual electricity bill

0.785
-0.7860.719
0.416-0.012cross term
Annual electricity bill

-0.0004

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

***
***

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

CO2 emissions-0.008
Stability-0.03
Nuclear0.278
Solar-0.308**Wind-0.308**Constant for alt 1
0.657
***
Constant for alt 2
1.832
***
McFadden R2
0.237
Note) *** 1%, ** 5%, *10% significant level

Familycomposition-0.016
***
0.557
***
-0.539
***
0.623
***
0.192
-0.01
***

Residential
type-0.015 **0.712***-0.607 ***
0.623
***
0.241
**
-0.014 ***-0.0020.618-0.5310.449
0.155-0.011-0.003
0.012
0.373
***
-0.22
-0.096
0.136
0.645
***
1.828
***
0.236
Perception ofelectricity bill

0.003
0.011
0.127
-0.123-0.104
0.064
0.652
***
1.829
***
0.236
Perception ofenergysaving-0.012 *-0.001-0.0130.26**-0.239
0.23
0.191
0.659
***
1.836
***
0.237
Desirableenergysource-0.0070.438-0.3440.126-0.109-0.005

***
**

**

0.305
-0.143-0.16-0.434-0.004-0.009***-0.012

***

0.007
0.53
-0.514
0.614
0.577
0.6
1.794
0.238

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

-0.0020.667-0.744
1.065
1.014
0.644
1.843
0.243

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

-0.013**0.629-0.5860.329
0.154-0.011

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

-0.0020.007
0.266-0.144
0.455
0.222
0.654
1.831
0.237

**
***
***
***

Living area

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

**

***

There is no significant cross term in household income and residential type. We don’t observe
any differences in household income and residential type. Households who have more family
members positively evaluate a stable electricity supply in savings. Households who live in Kanto
area also positively evaluate a stable electricity supply. This is because they suffered from outages
after the earthquake. They save more electricity usage if a stable electricity supply is secured.

14

Younger households don’t prefer renewable energy of both solar and wind power compared witholder households. Younger households don’t save electricity usage compared with older
households even if the main energy source is renewable energy. Households who think anelectricity bill is higher after the earthquake positively evaluate a stable electricity supply andsolar power in savings. Households who save more electricity usage after the earthquake evaluate
a lower annual electricity bill, a stable electricity supply and solar and wind power in savings.
They negatively evaluate nuclear power. They might save more electricity usage if the mainenergy source is renewable energy, a stable electricity supply is secured, and an annual electricity
bill is reduced.
Next, I examine the differences between households who support renewable energy as the mostdesirable one and who support non-renewable energy. I asked households the most desirable
future energy source in the questionnaire. Renewable energy sources include solar, wind,geothermal, biomass and hydraulic power, while non-renewable energy sources include nuclear,coal and natural gas. The aim of this paper is to examine the relation between households’
preferences for renewable energy and energy-saving behavior. Households who prefer renewableenergy may save more electricity usage. From the estimation results, households who support
renewable energy positively evaluate a lower annual electricity bill, a stable electricity supply andrenewable energy of both solar and wind power. If the main energy source is renewable energy,
households who support renewable energy save more electricity usage. They negatively evaluate
nuclear power.
Parameter differences are tested to examine that energy-saving behavior is different or not
between two subsamples. For the parameter differences test, the likelihood test and the following
test statistic are used.
−2[LL(A+B) − (LL(A)+LL(B))]
LL(A+B) is the log likelihood which is obtained after estimation by pooling data of two
subsamples. LL(A) and LL(B) are the log likelihoods for each subsample. The null hypothesis isthat parameters or behavior between two subsamples are equal. The alternative hypothesis is thatparameters or behavior are not equal. The test statistic is chi-squared distributed with degrees of
freedom equal to the number of parameters. The critical values for 1%, 5% and 10% significancelevel are respectively 27.69, 22.36 and 19.81 for 13 degrees of freedom. Table 9 expresses theresults of the parameter differences test. If the calculated statistic is inside the rejection area, thenull hypothesis is rejected. Parameters between two subsamples are, thus, significantly different
and households in two subsamples show different energy-saving behavior.
Table 9 Results of the parameter differences test
test statistics
Household income
50.136 ***Family composition
22.339 *Residential type34.652 ***Living area
50.105 ***Age57.963 ***Perception of electricity bill
22.050 *Perception of energy-saving
92.126 ***Desirable energy source
190.150 ***Note) *** 1%, ** 5%, *10% significant level

different
different
different
different
different
different
different
different

In all the socio-demographic attributes and perceptions, households’ energy-saving behavior
between two subsamples is different. For example, in the questionnaire, three major city areas in

15

Japancompanies in each area have their different power source composition. Especially, the ratio of
nuclear power is different. The Tokyo electric power company in Kanto area has several nuclear
power plants in Fukushima and Niigata prefecture. The Kansai electric power company in Kansai
area has also several nuclear power plants in Fukui prefecture. But the Chubu electric powercompany in Chukyo area has only one Hamaoka nuclear power plant and doesn’t depend on
nuclear power. Moreover, households in Kanto area experienced planned outages after theearthquake. Households in Kansai area didn’t experience planned outages but were requested tosave electricity usage because the nuclear power plants stopped operation. From these different
situations, households’ energy-saving behavior might be different.
6. Conclusions and policy implications
I analyzed households’ energy-saving behavior in Japan after the earthquake. To avoid energy
shortages and the global heating, energy savings among households are urgent and essential.
The prevalence of renewable energy such as solar and wind power is also urgent and essential. I
analyzed the conditions that households save electricity usage through a conjoint analysis andused a random parameter logit model and a nested logit model for estimation. I included anannual electricity bill as a monetary factor, and CO2 emissions, a stable electricity supply andmain energy sources as non-monetary factors. The estimation results indicated that households
largely respond to an annual electricity bill. If an annual electricity bill is reduced, households
might save more electricity usage. In addition, regarding non-monetary factors, if CO2
emissions are reduced and a stable electricity supply is secured, households have incentives tosave more electricity usage.
Especially, I focus on the role of energy sources used in the electricity generation whenhouseholds save electricity usage. I analyze the relation between households’ preferences forrenewable energy and energy-saving behavior. If the main energy source is renewable energy,
households may save more electricity usage. From the estimation results, if the main energy
source is nuclear power, they don’t choose to save electricity usage, and if the main energy sourceis renewable energy, they tend to save more electricity usage. If households support renewableenergy, they choose an energy-saving plan which uses renewable energy as a main energy sourcein electricity generation and saves more electricity usage. Thus, renewable energy gives incentives
for households to save electricity usage.
The promotion of energy savings and renewable energy are urgent in Japan’s energy policy.
Households have been shown to highly evaluate renewable energy. It is possible to promoteenergy savings by appealing to their interests in renewable energy. Our findings indicate that
renewable energy and energy savings can be promoted simultaneously. However, electricity
generated by solar and wind power depends on weather conditions. Storage batteries are needed
to ensure a stable electricity supply.
Acknowledgements
This study was aided grants-in-aid for scientific research (C) (kakenhi) of Japan Society for
The Promotion of Science. I presented this paper at the academic meetings of the Summer
Workshop of Economic Theory, the Japanese Economic Association and the International
Conference of Japan Economic Policy Association in 2017. We thank the members of themeetings and the following discussants at the academic meetings: Prof. Qingfeng Liu, Prof.
Tomoyuki Sakamoto and Prof. Masayuki Sato. The authors would like to thank Enago(www.enago.jp) for the English language review. All remaining errors are ours.
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