The Society for Economic Studies
The University of Kitakyushu
Working Paper Series
No.2022-1(accepted in May 31,2022)

Social acceptance of wind turbines in Japan: An empirical study using choice experiments
byKengo IWATA, Ph.D. Student, Graduate School of Global Environmental Studies,
Kyoto University(Phone: +81-75-753-9167 /Email: iwata.kengo.66r@st.kyoto-u.ac.jp)
Shinsuke KYOI, Ph.D. Student, Graduate School of Agriculture, Kyoto University
Yue CAO, Graduates, Graduate School of Social System Studies,
The University of Kitakyushu
Yoshiaki USHIFUSA, Professor, Faculty of Economics, The University of Kitakyushu

Abstract
In Japan, the interest in renewable energies, such as offshore wind power, is growing to achieve zero
greenhouse gas emissions by 2050. However, coastal communities have concerns about the negative impact
of offshore wind turbines on the landscapes, health, economic, and environmental aspects and often oppose
the construction of offshore wind turbines. Therefore, understanding the people’s perceptions about offshore
wind turbines is essential to build the consensus among local people and promote offshore wind farms inJapan. This study conducted a nationwide online survey and a choice experiment for 900 valid respondents.
A mixed logit model reveals that the marginal willingness to pay (MWTP) for the distance to the windturbines was JPY 98.2, which indicates that people prefer far away wind farms to neighboring turbines.
Additionally, the model estimated JPY 36.6 for the number of wind turbines and JPY 74.2 JPY for CO2
reduction, which means that people demand more wind turbines and reduce CO2 emissions.
Keywords: Offshore wind farm, Social acceptance, Choice experiments, Mixed logit model1 Introduction
This study investigates the conflicts between decarbonization via renewable energy use and local
environments preservation, often referred to as the “green vs. green debate,” (Warren et al. 2005;
Groothuis et al. 2008), and propose corresponding policy implications regarding the introduction of
offshore wind turbines.
As noted in the 2015 Paris Agreement, "energy decarbonization" has been a pressing issue on a global
scale. Consequently, in recent years, the development of wind-power generation is expanding worldwide.
However, in Europe, the construction of onshore wind power is saturated; instead, the development ofoffshore wind power is booming (Figure 1). Currently, the United Kingdom has the largest installedcapacity for offshore wind power in Europe, accounting for 45% of the total, followed by Germany(34%) and Denmark (8%) (WindEurope 2020).

Figure 1. Annual offshore wind installations by country and cumulative capacity
Source: WindEurope (2020)

In October 2020, Prime Minister Suga announced Japan's goal of reducing greenhouse gas emissions tovirtually zero, or carbon neutral, by 2050. Subsequently, in December 2020, the government released its"Green Growth Strategy for Carbon Neutrality by 2050.” Under this strategy, the government
established action plans for 14 key areas where growth is expected in terms of both industrial and energy
policies. One of these 14 areas is offshore wind power, with the goal of introducing 10 million kW of
offshore wind power by 2030 and 30–45 million kW by 2040.
2

However, the introduction of extensive offshore wind power in Japan's waters may affect the landscapeand marine ecosystems. Therefore, concerns of local stakeholders (residents and fishermen) and
opposition to offshore wind power may increase in the future. For example, in the United Kingdom,
although 80% of the general public supported wind power, 75% of all wind power projects werecancelled owing to a lack of local acceptance (Bell et al. 2005).
Thus, a detailed analysis of social acceptability and the development of a local consensus are urgent
issues in the promotion of offshore wind power. To understand social acceptability in detail, we need tounderstand people's preference for offshore wind power. Here, we conducted an internet survey of thegeneral public in Japan along with choice experiments. In the choice experiments, we evaluated people's
preferences for the introduction of offshore wind power in terms of monetary value (willingness to pay;
WTP) for six different attributes. To include the heterogeneity of people's preferences in the model, a
mixed logit model was used to estimate the preferences.

2 Literature Review
2.1 International Trends on Social Acceptance
Quantitative assessments of social acceptance and preferences for offshore wind power are often basedon surveys of local residents and tourists (e.g., Voke et al. 2013; Westerberg et al. 2015; Kim et al. 2019;
Ladenburg et al. 2020). Studies have identified the characteristics of local residents and tourists who are
receptive to offshore wind power facilities; prioritizing these characteristics/factors may enhance social
acceptance. Haggett (2011) discussed the adaptability of offshore wind based on a literature review ononshore wind, and identified five common factors that influence people's responses. Klain et al. (2020)
conducted choice experiments with residents of an area along the New England coast in the UnitedStates, where a utility-scale offshore wind farm was being planned. The authors’ found that local
residents showed a strong preference for offshore wind turbines that provide high-quality artificial reefs.
Similarly, Kim et al. (2019) conducted choice experiments on the impact of offshore wind-energy
development projects on Korean citizens. The experiments’ results were added to a cost-benefit analysis,
which suggested that the projects’ benefits were unlikely to outweigh their costs. Finally, Westerberg etal. (2013) investigated the impact of offshore wind farms on coastal tourism in Languedoc Rousillon,
Southern France. The authors conducted choice experiments to elicit tourists' preferences for offshore
wind turbines at various distances from the coast. The results showed that age, nationality, vacation
activities, and loyalty to the destination influenced attitudes toward the compensation policy.
2.2 Japanese Trends on Social Acceptance3

In Japan, research has examined the social acceptance of renewable energy, in general, and wind power,in particular. Maruyama et al. (2007) analyzed the socioeconomic dynamics of renewable energy
technologies. The authors examined community wind power generation, where the initial costs arefinanced by citizens, and examined how citizen initiatives can influence the social acceptance of
renewable energy and social change. Meanwhile, Motosu and Maruyama (2016) focused on theacceptance of people who are not objecting to a wind farm in their backyard and clarifies the implicationsof the silent situation. The authors’ survey revealed that most respondents were receptive to existing
local wind turbines but had a negative attitude towards new wind turbines.
Meanwhile, the most recent studies have been Nakano et al. (2018) and Keeley et al. (2021). Nakano etal. (2018) focused on understanding the differences between eastern and western Japan in terms of
citizens' preferences for renewable energy. The authors found that the social acceptance of renewableenergy and WTP in eastern Japan are influenced by citizens' strong concern for the global environment
and willingness to participate in policymaking. Meanwhile, in western Japan, it is related to support forliberalization of the electricity retail market and development of distributed power systems. Keeley etal. (2021) identified the key factors influencing the social acceptance of renewable energy in Japan,
using WTP as a measure, by incorporating spatial data on renewable and non-renewable power plants,
natural and productive capital, and renewable energy potential.
Studies also suggest that region-specific factors which influence the social acceptance of offshore windshould not be underestimated. The unique conditions of each region, such as social networks and spatial
factors, strongly influence residents’ social acceptance. Hence, research should incorporate these factors
in the analytical framework and evaluate social acceptance in the context of each region. Here, we
include Japan’s nationals conditions to incorporate region-specific factors into our analysis of social
acceptance.
2.3 Green versus green debate
Energy generated by wind is classified in the green energy category. Green energy has the externalbenefit of mitigating climate change and air pollution because it is fossil fuel-independent energy. Windpower development is also typically viewed as emblematic of such climate and pollution mitigation
efforts (Ellis et al. 2007; Jones et al. 2011). However, wind power is highly visible in nature andsignificantly affects the landscape. Several studies have argued that the landscape is the most important
determinant of attitudes toward wind power planning and development (Pasqualetti 2001; Groothuis etal. 2008; Jones and Eiser 2009; Wolsink 2010). Besides negative impacts on landscapes, concerns areoften expressed about noise and ecological impacts.

4Thus, on the one hand, the pro-energy group argues that renewable energy facilities should be promoted
as an energy source that does not increase the environmental burden on the earth. On the other hand, theopposition group argues that renewable energy facilities should not be promoted because they may
destroy the natural landscape and have an inevitable impact on the landscape, although they share thesame concern for the environment. One of the objectives of this study is to investigate this "green vs.
green debate," the conflict between decarbonization and local environmental protection regarding theuse of renewable energy. We assess the public's preferences for the various environmental impacts ofoffshore wind and identify the various relationships between these impacts. Furthermore, by assessing
the diversity of preferences, we identify potential conflicts in the implementation of offshore wind power.
Specifically, we use a mixed logit model to analyze the public's preferences for five attributes of offshore
wind power: distance from the shore, number of wind turbines, number of species, carbon dioxide (CO2)
reduction, and new job creation.

3 Methodology
3.1 Choice experiments
The conjoint analysis used in this study is a method to understand the goods to be evaluated as consistingof multiple attributes, represent multiple types of goods by the differences in attribute levels, and clarify
the evaluation of marginal changes in each attribute. Various question formats have been proposed forconjoint analysis. However, in the field of environmental economics, "choice experiments" arecommonly used. In these experiments, participants are asked to choose the most preferable option among
multiple alternatives. This study also employs choice experiments.
3.2 Analytical Model
We use a mixed logit model as our analytical model. This model was proposed by Revelt and Train(1998) as one that relaxes the restrictive assumptions in the conditional logit model. The mixed logit
model simultaneously resolves the relaxation of the independence of irrelevant alternatives (IIA)
assumption and homogeneity of preferences. Further, this model is a flexible model that can approximate
any random utility model (McFadden and Train 2000). First, let 𝑈𝑛𝑖 be the utility of respondent n when
they choose option i from the choice set C, and consider the random utility function as in equation (1)below.
𝑈

𝑉

𝛽

𝛽′ 𝑥

𝜀
𝜀
(1)

5

where the error terms 𝜀𝑛𝑖 are assumed to follow the first kind extreme value distribution independently
and identically. In the conditional logit model, the utility parameter is uniform across individuals.
However, as indicated by the subscript n in the utility parameter, our mixed logit model incorporates theassumption that different individuals have different preferences.
Furthermore, we assume that the distribution of 𝛽𝑛 is the normal distribution 𝜙(𝛽|𝑏,𝑊) with mean b andvariance-covariance W. Then, the selection probabilities for the mixed logit model are as follows(McFadden 1974; Train 2009).

𝑃

exp 𝑉 𝛽∑ ∈ 𝑒𝑥𝑝 𝑉 𝛽𝑏, 𝑊∙ 𝜙 𝛽|𝑏, 𝑊 𝑑𝛽
(2)

In general, the integral calculations here are not algebraically solvable and the estimation requires theuse of approximate calculations by simulation. Here, we compute the selection probability using thefollowing procedure:
1. Extract 𝛽 from 𝜙(𝛽|𝑏,𝑊) R times,
2. Substitute the extracted 𝛽 into the conditional selection probability formula (equation (3)) andcalculate R selection probabilities,

𝐿

𝑒𝑥𝑝 𝑉 𝛽∑ ∈ 𝑒𝑥𝑝 𝑉 𝛽

𝛽

(3)
and
3. Find the mean value of the result using equation (4). where 𝛽r is the rth extracted 𝛽.

𝑆𝑃

1
𝑅

𝐿

𝛽
(4)

Note that SPni (simulated probability) is an unbiased estimator of Pni, that is, E(SPni) = Pni (Train 2009).
6

If δ

is a dummy variable that equals one when individual n chooses option i, the simulated log-

likelihood (SLL) function can be expressed as in equation (5).

𝑆𝐿𝐿 𝑏, 𝑊𝛿 𝑙𝑛𝑆𝑃𝑏, 𝑊(5)The parameters to be estimated (b,W) are the maximum simulated likelihood estimator (MSLE) thatmaximizes SLL (McFadden and Train 2000).
Thus, the mixed logit model allows us to obtain not only the mean parameter b but also information W
on the variance of the utility parameter. Note that equation (2) shows, the ratio of choice probabilities
Pni/Pnj in the mixed logit model includes the denominator Lni(𝛽) in the integral; this denominator depends
on all alternatives. This shows that the ratio of choice probabilities also depends on all alternatives
except i and j, completely relaxing the IIA assumption.
The marginal WTP (MWTP) can be obtained from the estimation of the parameter vector 𝛽 by Equation
(6).

𝑀𝑊𝑇𝑃

𝜕𝑉
∗
𝜕𝑥
−

𝜕𝑉
𝜕𝑃𝑅𝐼𝐶𝐸

−

𝛽∗
𝛽

(6)
where 𝛽∗ denotes the estimated parameters other than the amount of the levy.
3.3 Overview of the Survey
The survey was conducted online by Rakuten Insight Inc from December 22 to 23, 2020. 900
questionnaires were returned by 900 monitors registered with Rakuten Insight Inc., in the national range,
aged between 20 and 69 years. The number of samples is acceptable because it is generally considereddesirable to have approximately 1,000 samples to conduct choice experiments.
Table 1 shows the means and standard deviations of the respondents' attributes, such as gender and age.
The gender ratio of the respondents was approximately 50% for both men and women, indicating almost
no bias in sample collection. The age of respondents varied, but not substantially, and the sample was7drawn from all age groups.

Table 1.

Respondents' Attributes

Variablesex

1: male2: femaleage Age (20-69)
nf

Number of family members 1: 1 person, ..., 7: 7 or
more

wj

Offshore wind power in Japan (Pros or Cons) 1:
Pro, ... 5: Con

hiHousehold income 1: less than 2 million yen, ..., 8:
20 million yen or more

Mean

Std. Dev.

Min

Max

1.497

0.500

1

2

45.81713.481

20

69

2.749

1.307

1

7

2.419

0.875

1

5

3.468

1.614

1

8

3.4 Attributes and Levels
The attributes and levels pertaining to the choice experiments were set as shown in Table 2. The
following is a description of each attribute and an overview of how the levels were set.
"Distance to shore" and "number of wind turbines" are attributes that are mainly considered as “not in
my backyard” (NIMBY) issues arising from the landscape impact of wind power generation facilities.
The levels were set after referring to current performance data and the literature.
For "levy on renewable energy," three levels were set as 1 yen/kWh, 3 yen/kWh, and 5 yen/kWh basedon the Ministry of Economy, Trade and Industry's "levy to promote renewable energy generation," which
was in place from May 2019 to April 2020 and set at 2 yen 98 sen per kWh. This attribute is necessaryfor economic evaluation (WTP estimation).
"Species" represents the number of species that may be affected by the construction and operation of
offshore wind power facilities. Data from the New Energy and Industrial Technology Development
Organization (NEDO) was used to set baseline values for the levels. The number of species of marineorganisms and birds was 44 each, for a total of 88 species. As all species may not be affected at the sametime, the levels were set at 30, 60, and 90 species, with 60 species serving as the reference value.
The standard values for "CO2 reduction" and "new job creation" were calculated based on the estimateddata of the proposal for the promotion of offshore wind power generation by the Japan Wind Power
8

Association (JWPA). Three levels were set with reference to the calculation results (CO2 reduction of
7.1 t/kW, and job creation of 27 people per wind turbine).
Table 2.

Attributes and Levels

Attributes

Level 1

Level 2

Level 3Distance from the shore (km)

10

15

30

Number of the turbines

20

30

40

Levy on renewable energy (yen/kW)

1

3

5

Number of species that may be affected

30

60

90

CO2 reduction (t/kW)

5

7

10

New job creation (worker/turbine)

20

30

50

3.5 Profile Design
After establishing the attributes and levels, three profiles of the choice experiments were developed.
These three profiles consist of two offshore wind power generation plans created by orthogonal planning,and one "status quo" profile with no offshore wind power generation plan and conventional power
generation continues. The questions were designed as shown in Table 3 and each respondent was asked
six times.

Table 3. Sample questions for the choice experiment
Given the following offshore wind farm project plans, which plan do you think is preferable? Pleasechoose one from plan numbers (1), (2), and (3).
Project Number①②

Distance from the shore

10km

10km

Number of the turbines

40

30

Levy on renewable energy

5yen/kWh

3yen/kWhNo windmills

Number of species that may be affected

30

60(Status quo)

CO2 reduction

7t/kw

10t/kw

New job creation per turbine

30

20

9

③

4 Results
The statistical software Stata16 was used to estimate the parameters. Table 4 defines the variables usedin the estimation and Table 5 shows the estimation results obtained using the mixed logit model. As thez-values of the coefficients shows, the coefficients for each attribute (explanatory variable) are
statistically significant at the 1% level. The MTWP is 98.2 yen for distance (98.2 yen WTP for every 1
km distance of offshore wind turbines from the coast), 36.6 yen for number of wind turbines (36.6 yen
WTP for every additional offshore wind turbine), -8.1 yen for species (-8.1 yen WTP for each species
affected by offshore wind power construction), 74.2 yen for CO2 reduction (74.2 yen WTP for each tonof CO2 reduced by the offshore wind project), and -10.9 yen for job creation (-10.9 yen WTP for eachnew job created by the offshore wind project).
Next, we examined the estimation results for the heterogeneity of preferences. In the estimation of themixed logit model, all parameters except the levy (cost) are assumed to be random parameters following
a normal distribution. The parameter “cost” is assumed to be a fixed parameter to account for theestimation of MWTP. The results show that the standard deviations of the random variables for all
attributes, β, are significant at the 1% level. Thus, heterogeneity in preferences was observed for all
attributes. The variation in preferences was particularly large for "distance" and "CO2 reduction,"suggesting a variety of preferences among respondents. For example, the 95% confidence interval of
the MWTP for "CO2 reduction" shows that the lower limit MWTP is 17.6 yen, while the upper limit
MWTP is 130.8 yen. We discuss the estimation results in detail in the next section.
Table 4. Definition of Variables
Variable
Name

Variable Definition

distance

Distance from the shore

nturbin

Number of offshore wind turbines

speciesNumber of species that might be affectedco2

Amount of CO2 reduction

labor

Number of new jobs createdcost

Levy on renewable energy

10Table 5. Estimated results
Mixed logit model
distance
nturbin
species
co2
labor
cost

Coef: Mean

Coef: S.D.

MWTP (yen)

0.122

0.224

98.21

(10.67)(17.47)[50.89, 145.53]

0.045

0.044

36.63

(10.74)(8.22)[18.31, 54.95]-0.010

0.034

-8.11

(-4.33)(14.32)[-14.82, -1.39]

0.092

0.163

74.18

(5.78)(7.95)[17.56, 130.81]-0.013-0.031-10.86(-4.48)(-5.21)[-19.19, -2.53]-0.124(-3.66)

Numberofobservations
Log likelihood

16200-4198.56

Note: Z-values are in parentheses. 95% confidence intervals are in square brackets.

5 Discussion and Conclusion
The purpose of this study was to determine the peoples’ preferences for and the economic value ofoffshore wind power generation through an experimental questionnaire survey including choice
experiments. A mixed logit model was used to quantitatively determine the diversity of preferences. The
following discussion is based on the estimation results.
First, we evaluated the public's preferences for offshore wind power through the choice experiments.
We found that the MWTP was 98.2 yen for “distance,” 36.6 yen for “number of wind turbines,” -8.1
yen for “species,” 74.2 yen for “CO2 reduction,” and -10.9 yen for “job creation. The general public
highly values offshore wind power for its "distance" effect on the landscape and its "CO2 reduction"effect on climate change mitigation. This is line with previous evidence (e.g., Westerberg et al. 2013;
Klain et al. 2020). Our results provide supporting evidence that distance is directly related to landscapeand that "how" highly visible wind turbines are located/constructed significantly affects their social
acceptability. Notably, the public highly evaluated "CO2 reduction" as an external benefit.
11

In contrast, the response (WTP) to effect on species due to offshore wind construction was relatively
low. Although the scenario design of the choice experiments may affect participant’s responses, thegeneral public may not be very interested in the modification of the ecosystems (onshore and offshore)in areas where offshore wind turbines will be constructed. However, this assertion may not be readily
generalizable. Biases may have arisen in the responses because the scenarios and levels were vague.
Further research is required on the importance of ecosystems affected by offshore wind turbines.
Second, the estimation results of the mixed logit model revealed that the general public has diverse
preferences for all attributes. In particular, "distance" and "CO2 reduction" showed a large variation in
preferences and high MWTP. Importantly, we provide empirical evidence in a Japanese context that"distance," which is related to the landscape, is an important determinant in the social acceptability ofoffshore wind power, in line with previous evidence (e.g., Westerberg et al. 2013; Kim et al. 2019).
Meanwhile, the standard deviation for distance is the largest, with a 95% confidence interval of 50.9 to145.5 yen. This suggests that the evaluation of this attribute varies greatly from person to person. One
potential variable which may affect this is the respondents’ place of residence. For example, the WTP
may be very high in the planned construction site or area where offshore wind turbines will be
constructed.
Furthermore, the preferences for "CO2 reduction" varied substantially and had the second highest
standard deviation with a 95% confidence interval of 17.6 to 130.8 yen. This may be due to the highlevel of environmental awareness of the respondents and the difference in their values for the public
interest. Respondents who are usually environmentally conscious may naturally respond sensitively to
climate change issues. Contrarily, some may be more interested in their daily lives than in preserving
the global environment and ecosystems. In line with NIMBYism, the corresponding WTP may berelatively low in areas where offshore wind power could be constructed.
The results of these two attributes (“distance” and “CO2 reduction”) suggest that there may be a “green
vs green debate” among people. However, the mixed logit model cannot perform detailed analyses, suchas a factor analysis of preference heterogeneity. Future studies should further investigate whether the
WTP of the "distance" (“CO2 reduction) coefficient is relatively high (low) in a particular area.
Finally, our study has the following policy implications. First, when planning and zoning theconstruction of offshore wind projects, the distance of offshore wind turbines from the landscape,particularly from the coast, should be considered. This is especially important for fixed-foundation
turbines (not floating wind turbines), where there is a trade-off between distance and construction cost.
12

As we show, the general public may have a strong reaction to changes in their landscape.
Second, policymakers and practitioners should emphasize that promoting offshore wind power willcontribute to climate change mitigation. This is important as in recent years, the number of Japanesecitizens interested in global environmental issues has been increasing rapidly, as in developed countries
in Europe and the United States. Appealing to such people may significant affect the social acceptabilityof offshore wind among the Japanese public.
In summary, planners will need to understand people's preferences for offshore wind power to overcomethe "green vs. green debate."

13

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