Willingness to participate in community-based renewable energy projects: A contingent valuation study in South Korea

Renewable and Sustainable Energy Reviews 112 (2019) 643–652

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Willingness to participate in community-based renewable energy projects: A contingent valuation study in South Korea

T

JongRoul Wooa,1, Sungsam Chungb,1, Chul-Yong Leec,∗∗, Sung-Yoon Huhd,∗ a

Institute for Data, Systems, and Society, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139-4307, United States Korea Energy Economics Institute (KEEI), 405-11 Jongga-ro, Jung-gu, Ulsan, 44543, South Korea c School of Business, Pusan National University, 2 Busandaehak-ro 63 beon-gil, Geumjeong-gu, Busan, 46241, South Korea d Department of Energy Policy, Seoul National University of Science & Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811, South Korea b

ARTICLE INFO

ABSTRACT

Keywords: Renewable energy Public participation Benefit-sharing Willingness to accept Social acceptance Local acceptance

Several countries have recently adopted models of community-based renewable energy projects, mainly in order to improve the local community’s acceptance of renewable energy facilities. This study analyzes the South Korea’s general public and local residents’ acceptance of community-based renewable energy projects using the contingent valuation method. Respondents’ willingness to participate in a renewable energy project are measured by the expected return on investment in the project. Analysis results indicate that the average annual rate of return expected by the general public is 3.1% for solar photovoltaic power plants, 5.4% for wind power, and 7.1% for biomass power plants. For local residents, the average annual rate of return expected by local residents is 12.3% for solar photovoltaic power plants, 9.1% for wind power, and 10.8% for biomass power plants. These results show that acceptance is markedly lower among local residents in comparison with the general public. However, simulation results prove that it is possible to provide local residents with returns beyond their expectation if the entirety of the additional incentives under the current system are given to them. Relevant policy implications and recommendations are provided based on the analysis results.

1. Introduction

In order to meet this ambitious goal, it is necessary to address the task of improving the currently prevailing low levels of acceptance of renewable energy. Factors impeding the diffusion of renewable energy are technological, financial, institutional, and acceptance-related [1–3]. Of these, renewable energy technology has seen rapid advancement and in turn, rapid decreases in cost [4–6]. In keeping with improvements in economic viability, financing for renewable energy is becoming easier, while even short-term institutional improvements have become possible due to the policy stances of governments. However, because acceptance is a matter linked to the general perception held by a majority of people, it represents a challenging issue in that it cannot be overcome within a short period of time [3,7,8]. Acceptance of renewable energy may further be distinguished between the general public acceptance and the local acceptance among the residents within the vicinity of the power plant. The latter has proven to be the more challenging matter [3,9,10]. An individual’s degree of support for renewable energy is an entirely separate matter from whether that individual would be willing to accept having a large-

Since climate change has emerged as an issue of worldwide concern, renewable energy has attracted attention as a means of pursuing various goals, including the reduction of greenhouse gases [39,40], environmental protection [41], easing the energy sector’s dependence on imports [42], diversification of national energy sources [43], and economic growth [44]. Numerous nations are currently pursuing the expansion of renewable energy, with a range of policies including targets, regulations, public financing and fiscal incentives, and combination of them [45]. In 2017, the South Korean government announced ambitious plans to generate up to 20% of all electricity via renewable energy by 2030 [46]. However, meeting this goal would necessitate the installation of an estimated 48.7 GW of capacity in equipment for renewable energy power generation [46]. In terms of annual averages, this amounts to 4 GW per year – more than twice the current capacity increase trend (approximately 1.7 GW per year).

Corresponding author. Corresponding author. E-mail addresses: [email protected] (J. Woo), [email protected] (S. Chung), [email protected] (C.-Y. Lee), [email protected] (S.-Y. Huh). 1 These authors contributed equally to this work. ∗

∗∗

https://doi.org/10.1016/j.rser.2019.06.010 Received 30 May 2018; Received in revised form 27 November 2018; Accepted 7 June 2019 Available online 15 June 2019 1364-0321/ © 2019 Published by Elsevier Ltd.

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scale renewable energy power plant nearby or not. This is well-attested to by numerous examples in most countries where disputes have frequently arisen regarding the selection of an actual location for renewable energy power plants, in spite of broad social support for renewable energy compared to nuclear or fossil-fuel generation [11–14]. This problem is particularly relevant in South Korea, where roughly a third (37.5%) of the cancellations and postponements of solar photovoltaic power and wind power plants that were approved in 2016, were due to opposition from local residents. The reasons why local residents oppose the installation of renewable energy power plants in their vicinity are diverse, and environmental externalities are often pointed out as the cause of strong opposition [12,15–17,47]. However, a more fundamental reason for their opposition is that renewable energy projects offer no substantial gain to the residents, and are operated in such a manner that the economic profits accrue to an external project developer [18–20]. As a result of this, nations such as Germany, the U.S., and Denmark have adopted models of community-based renewable energy projects, such as local ownership, in order to directly add to local income and, in turn, resolve issues of local acceptance [21–23]. Accordingly, the South Korean government has also looked into measures for introducing incentive policies for community-based renewable energy projects. In 2017, it enacted a policy where additional weights of 10–20% are assigned to renewable energy certificates (RECs) depending on the extent of local participation in solar photovoltaic and wind energy projects. Community involvement in all phases of energy projects is essential to make such projects sustainable [24,25]. As the importance of public participation in energy projects has been emphasized, research on community-based renewable energy have also been published since the late 2000s. The majority of these studies have concluded that the existence of benefit sharing with local residents contributes positively to improving the local community’s acceptance of renewable energy projects [11,18,22,23,26,27]. For these studies, the areas covered by empirical analysis were the UK [18,22,23,27], Belgium [55], Tunisia [11], and Australia [26] while there seems to be no research on community-based renewable energy focusing on Asian countries. In terms of energy sources, most of the studies are focused on wind power generation [11,18,23,26]. The majority of these studies have employed qualitative research methodologies such as case studies, surveys, and in-depth interviews. On the other hand, it is difficult to find studies that posit a specific form of community-based renewable energy projects and quantitatively assess the willingness of local residents to participate and to invest in the project. Furthermore, it is even more difficult to find studies that have carried out quantitative analysis on the policy effects of government incentive programs for renewable energy projects. Against this backdrop, the main objective of this study is to quantitatively estimate the local residents’ willingness to participate in a community-based renewable energy project. This study represents three marginal contributions to the research on acceptance of renewable energy. First, we quantitatively analyze the level of acceptance of renewable energy among the general public and local residents. Specifically, we examine the general public’s and local residents’ willingness to participate in a project to build a new renewable energy power plant, using the contingent valuation method (CVM) to estimate their willingness to accept (WTA) for expected rates of return depending on their participation. Second, we quantitatively analyze differences in the levels of acceptance by type of renewable energy. The types of renewable energy considered in this study are those that have most commonly been built, namely, solar photovoltaic, wind, and biomass energy power plants. Through such analysis, we are able to capture varying degrees of aversion that the general public and local residents have toward different types of renewable energy. Third, we analyze whether the South Korean government’s incentives aiming to

encourage local participation in renewable energy projects could actually incentivize residents to improve their WTA. The subject of analysis in this study is South Korea, as it has recently introduced incentive programs for local participation. Furthermore, the findings of this study may be able to provide policy implications for other countries that have recently adopted, or are considering the adoption of, similar programs. For example, by applying the methodologies presented in this study to other countries, it would be possible to estimate, for each country, the adequate levels of compensation for local residents living near renewable energy power plants. 2. Methodology 2.1. Overview In this study, we firstly analyze the Korean general public’s willingness to participate in projects to build new renewable energy power plants (solar photovoltaic, wind, and biomass energy power plants), using the CVM to estimate their WTA for expected rates of return depending on their participation. We then conduct the same analysis for local residents in areas where renewable power plants are to be built. Secondly, we conduct a simulation analysis based on the results of CVM to investigate whether the South Korean government’s incentive program aiming to encourage local participation in renewable energy projects could actually incentivize residents to meet the average expected rate of return. 2.2. Contingent valuation method Here, we use the CVM to quantitatively derive the average WTA of residents toward a community-based renewable energy project. While it is relatively easy to measure the economic value of goods and services that are traded in markets since they have market prices, different methods must be used for the valuation of non-market goods as they have no market price. The CVM can be used to measure the monetary value of non-market goods by using state preference data [28,29]. The validity and reliability of this method have led to its broad application in the quantitative valuation of various non-market goods in a wide range of fields, including environmental policy [30,31], climate policy [32,33], and energy policy [34–36]. In the CVM, respondents are administered questionnaires to evaluate how much they would be willing to pay in order to get the utility of some non-market good. Conversely, they may be asked about how much they are willing to accept in order to cope with the loss of the utility provided by some non-market good. Specifically, the CVM employs various methods for eliciting willingness to pay (WTP) and WTA, such as open-ended questions, bidding, payment cards, and dichotomous choice (DC). Of these, the most widely adopted method is DC, where the researcher first presents the respondent with a certain sum, prompting a yes/no response as to whether the respondent is willing to accept or pay that amount [37]. The DC method is advantageous in that it can lower non-response rates because it prompts the simplest form of answers. It can also lessen the starting point bias because the researcher can present the respondent with various predetermined sums. Furthermore, it is associated with a low likelihood that the respondent will over (or under)report his/her WTP or WTA. Depending on the number of questions asked, the DC method may take the form of single bounded (SB), one and one-half bounded (OOHB), or double bounded (DB). The DB method asks the respondent intention to accept the suggested status with the given price. If the respondent answers “Yes” (or “No”), the same question is asked using the higher bid price (or the lower bid price). The OOHB method is similar to the DBDC, but if the response is “No”, no further questions are asked. The SB method asks the

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respondent only one WTP or WTA question. Compared to the SB and OOHB methods, the DB method yields point estimates that are much better, lower variance, and better statistical efficiency [38]. Thanks to these strengths, double bounded dichotomous choice (DBDC) is currently the most widely-used elicitation method. In this study as well, we employ the DBDC method to collect information on WTA.

GYN = Pr(AiL < zi + zi

=

G NN = Pr(zi +

= Pr(WTAi >

i

AiL

zi

=

zi

AiH

zi

i

>

AiH

zi

=1

zi

AiH

AiL

AiH

zi

+ IiYN zi

AiH

zi zi

Ai zi

Ai

AiH (4)

2.4. Survey design The survey categorizes respondents as either 1) general public or 2) local residents depending on whether they reside within the vicinity of the planned renewable energy project grounds or not. Separate questionnaires were administered to each category. The purpose of this was to compare the overall acceptance among the general public with the acceptance of the local residents who actually live close to the renewable energy power plant. Furthermore, in order to examine differences in acceptance across various types of renewable energy, acceptance levels were collected separately for solar photovoltaic, wind, and biomass energy power plants. The characteristics of the survey design are summarized in Table 1. Table 2 summarizes the characteristics of the 508 respondents in the general public survey and the 306 respondents in the local resident survey. The local resident survey covered only household heads and spouses between the ages of 20 and 65 who reside

Here, WTAi is the WTA of respondent i, z i is a vector of explanatory variables including respondent i’s individual characteristics and the surrounding environment, is a set of the coefficients to be estimated with regards to the explanatory variables, and i is the error term. In this study, we have assumed that the error term follows a normal distribution with mean zero and variance 2 . Meanwhile, it is possible to express the probabilities of the four possible response patterns as in equation (3) below, where represents the standard normal cumulative distribution:

AiL ) = Pr

i

Ai

> AiH ) = Pr

zi

+ IiNN 1

(2)

i

<

zi

{IiYY lnGYY + IiYN lnGYN + IiNY lnG NY + IiNN lnG NN }

+ IiNY

(1)

i

GYY = Pr(zi +

zi

AiH )

The WTA of each respondent can be expressed as a function of a wide range of variables, including individual characteristics, preferences, and the surrounding environment. Assuming a linear WTA function, the WTA of each respondent i within the sample can be expressed as in equation (2) below:

WTAi = zi +

Ai

i=1

Ai )

AiH )

zi

i

N

ln(L) =

ln

G NN

Ai

<

The log-likelihood of the DBDC model for the given sample can be expressed as equation (4). Here, IiYY , IiYN , IiNY , and IiNN are binary indicator variables that represent the combinations of the four response patterns as given by each respondent. Finally, we estimate parameters and by maximizing equation (4):

= IiYY ln

GYN = Pr(AiL < WTAi

zi

AiL

AiH ) = Pr

i

AiL

(3)

AiL )

G NY = Pr(Ai < WTAi

i

Ai ) = Pr zi

AiH

zi

=

In the DBDC method, WTA bids are offered over two rounds. In this study, respondents were presented with a situation where they would receive certain sums as dividends, depending on the size of their investment, should they choose to participate in a renewable energy plant project. If the respondent said “Yes” to the first presented return sum Ai – that is, if the respondent is willing to invest in the project – the respondent is presented with a second sum, AiL , which is equal to half of the initial sum. If the respondent says “No” to the first presented sum, the respondent is presented with a second sum, AiH , which is equal to twice the initial sum. The probabilities of each of the four possible outcomes of the response pattern, “Yes”-“Yes”, “Yes”-“No”, “No”-“Yes”, “No”-“No”, (GYY , GYN , G NY , G NN ) can be expressed as in equation (1) below:

GYY = Pr(WTAi

Ai

G NY = Pr(Ai < zi +

2.3. Double bounded dichotomous choice method

i

AiL

Table 1 Summary of survey design.

Population Sample size Sampling method Survey method Survey period Fieldwork provider

1) General nationwide public questionnaire

2) Questionnaire for local residents in the vicinity of renewable power plant grounds

Head of household (and spouse), aged 20 to 65, nationwide 508 persons Sampled at random from proportional quotas based on age and region Web survey May 22 to May 29, 2017 Hankook Research

Head of household (and spouse), aged 20 to 65, residing in administrative areas within 1 km of renewable power plant grounds 306 persons Purposive quota sampling method Face-to-face interview May 19 to May 30, 2017

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Table 2 Descriptive statistics of the respondents.

Total number of respondents Sex Age

Level of education Average monthly household income

Type of renewable power plant in vicinity

Male Female 19-29 30-39 40-49 50-59 60 or higher Less than high school More than university/college Less than KRW 2 million (USD 1880) KRW 2–3 million (USD 18802820) KRW 3–4 million (USD 28203760) KRW 4–5 million (USD 37604700) More than KRW 5 million (USD 4700) Wind power Solar photovoltaic Biomass

1) General nationwide public questionnaire

2) Questionnaire for local residents in the vicinity of renewable power plant grounds

No. respondents (%)

No. respondents (%)

508 (100%) 244 (52%) 264 (8.5%) 43 (8.5%) 103 (20.3%) 147 (28.9%) 151 (29.7%) 64 (12.6%) 88 (17.3%) 420 (82.7%) 41 (8.1%)

306 (100%) 155 (50.7%) 151 (49.3%) 12 (3.9%) 48 (15.7%) 80 (26.1%) 107 (35.0%) 59 (19.3%) 220 (71.9%) 86 (28.1%) 68 (22.2%)

80 (15.7%)

76 (24.8%)

91 (17.9%)

72 (23.5%)

102 (20.1%)

35 (11.4%)

194 (38.2%)

55 (18.0%)

– – –

101 (33.0%) 103 (33.7%) 102 (33.3%)

in areas within 1 km of renewable power plant sites. Note: KRW and USD denote South Korean won and United States dollar. We take the USD equivalent as of March 2018 (USD 1 = KRW 1,063.67) (Bank of Korea; www.bok.or.kr/). It is necessary to discuss about the payment vehicle in the CVM. As noted in Section 1, there are diverse reasons for social opposition to the installation of a renewable facility. Community-based renewable energy project is based on the assumption that economic incentives in the form of benefit-sharing provided to the local residents can offset some of the social opposition resulting from these various reasons. As the definition of a community renewable energy project is flexible and the form of benefit-sharing mechanisms also varies (e.g.: local ownership, taxation and community funds, encouraging local contracting, benefits-in-kind), the willingness of people to participate in the project can be measured by various indicators. However, considering the fundamental characteristic of the community renewable energy scheme is that input from members of that community is used for the installation of a renewable energy facility and the scheme must benefit the community [22], this study measured the people's willingness to participate in and their acceptance of a project by the expected return on investment on that project. This is in accordance with previous studies that pointed out the usefulness of financial benefits to promote acceptance and/or ameliorate public resistance to the renewable technology [23,48–50].2 In the survey, both the rate of return and the corresponding amount of returns were provided to the respondents for a clear understanding. Under these settings, the smaller the estimated average expected rate of return of a project, the higher the local acceptance. This is because people are willing to accept the community-based renewable energy project around their residence despite the lower return on investment. The method of measuring the public acceptance of renewable energy projects as a monetary-related term has been widely used in previous studies [36,51,52].

Specifically, the survey contained questions investigating basic perceptions of energy and the environment, questions investigating demographic characteristics, and questions under the CVM on acceptance for community-based renewable energy projects.3 The latter provided the respondent with an overall explanation of communitybased renewable energy projects, along with detailed descriptions of the project timeline, implementation methods, and project characteristics in order to facilitate comprehension for the respondent. When respondents had additional questions, the interviewer answered them in detail. Respondents were also directed to fully understand the nature of the three types of renewable energy – solar, wind, and biomass – through pictures and other visual aids. For accurate implementation of CVM, the survey assumed that the respondents could directly participate in a project involving the construction of a renewable energy power plant within their vicinity (within a 1 km radius of the respondent’s place of residence), with questions investigating the respondents’ willingness to participate and the amount in return that would be necessary to secure their participation. As described above, the CVM questions followed the DBDC method, with five different initial bid values (annual rates of return): 1.0%, 3.0%, 4.5%, 5.5%, and 6.0%. 3. Results and discussion 3.1. Estimation results 3.1.1. Nationwide general public In order to gauge the general public’s overall willingness to 3 Prior to the main survey, we provided the detailed information about renewable energy sources (such as picture, basic principle, installation size & area, advantage & disadvantage of solar photovoltaic, wind energy, and biomass power plants) to the respondents. Then, we asked respondents how well they knew about the renewable energy sources with using a one (I know nothing) to five (I know them very well) point scale questionnaire. The average responses of the general nationwide public and the local residents near renewable power plants were 3.04 and 3.33, respectively.

2 In addition, Bauwens [55] also pointed out that the volume of financial investment made is one of the two main consideration when defining the level of engagement among members of community-based renewable energy projects.

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Table 3 Responses regarding the acceptance for community-based renewable energy project: nationwide general public. Solar photovoltaic

Wind

Table 4 Distribution of responses by bid amount: nationwide general public.

Biomass Solar photovoltaic

Q. Are you willing to participate in a community-based project for the construction and operation of a renewable energy plant? Yes 341 (67.1%) 242 (47.6%) 213 (41.9%) No 167 (32.9%) 266 (52.4%) 295 (58.1%) Q. Which is your preferred form of participation in the community-based project for the construction and operation of the renewable energy plant? Stock investment 81 (15.9%) 59 (11.6%) 54 (10.6%) Bond investment 351 (69.1%) 305 (60.0%) 281 (55.3%) No particular 76 (15.0%) 144 (28.3%) 17 (34.1%) preference

Wind

Biomass

participate in and acceptance of the types of renewable energy power plants – solar photovoltaic, wind, and biomass energy – that are most likely to be widely adopted in South Korea in the future, a total of 508 respondents from all over the country were divided into five groups, after which they were administered the CVM questions with the five initial bid values given above. Table 3 reports the willingness of respondents to participate in solar photovoltaic, wind, and biomass power plant construction projects as well as their preferred methods of participation.4 The general public was most willing to participate in a solar photovoltaic (67.1%) energy project, followed by wind (47.6%) and biomass (41.9%). Thus, in terms of public acceptance, we expect that solar photovoltaic energy projects would be relatively easier to pursue when compared to wind or biomass energy projects. The top reason given by respondents for their unwillingness to invest in wind energy was "worried about noise annoyance," (42.67%) indicating that the public may respond sensitively to noise issues associated with wind turbines nearby. In the case of biomass energy, the top reason for unwillingness to invest was "worried about environmental pollution and destruction of the ecosystem (45.0%)," indicating that the general public perceived biomass power plants as having adverse environmental effects. In terms of the form of participation, respondents chose bond investment overwhelmingly, indicating that they preferred a project that could give them steady, albeit possibly smaller, returns. Table 4 shows the distribution of responses by bid amounts and type of renewable energy (solar, wind, and biomass) and Table 5 reports the results of the WTA analysis via the CVM model. The respondents’ average expected rates of return from a community-based solar photovoltaic, wind, or biomass power plant project was derived using the constant terms of models 1-1, 2-1, and 3-1. In all cases, the estimates of the constant terms were positive and significant, thus indicating that the respondents had a positive baseline level of acceptance for these community-based projects. The positive effects of the projects as perceived by the respondents and their willingness to invest in them may vary depending on their demographic characteristics. Models 1-2, 2-2, and 3-2, which broke down the constant term by the effects of various demographic variables, show the coefficient estimates for the explanatory variables affecting the respondents’ acceptance. The explanatory variables used here include the respondents’

Initial bid

No. samples

“Yes”“Yes”

“Yes”“No”

“No”“Yes”

“No”“No”

1.0% 3.0% 4.5% 5.5% 6.0% 1.0% 3.0% 4.5% 5.5% 6.0% 1.0% 3.0% 4.5% 5.5% 6.0%

106 100 102 100 100 106 100 102 100 100 106 100 102 100 100

28 35 33 49 55 23 22 28 42 44 18 20 19 30 36

16 23 33 28 25 10 19 23 23 18 8 15 24 22 20

5 17 13 11 8 5 15 12 9 12 6 14 14 13 10

57 25 23 12 12 68 44 39 26 26 74 51 45 35 34

demographic characteristics, attitudes toward renewable energy, and willingness to participate in the projects. According to the estimation results of models 1-1, 2-1, and 3-1, the general public’s average expected rates of return on investment from solar photovoltaic, wind, and biomass power plant projects were, approximately 3.1%, 5.4%, and 7.1% respectively. Since lower estimates of the average annual rates of return imply that respondents were still willing to invest in them despite the low expected returns, we may interpret these results as indicating that solar photovoltaic energy had the highest relative level of acceptance, followed by wind and biomass energy. Next, looking at the estimation results of models 1-2, 2-2, and 3-2, no statistically significant differences in the average expected return were found across variations in gender and income. On the other hand, higher education levels were associated with lower expected returns. Unlike in the case of solar photovoltaic energy, older respondents associated wind and biomass energy with lower expected returns. Further, for all types of renewable energy, respondents who were willing to participate in the project had formed relatively lower expectations of returns (and thus, higher acceptance). The marginal expected returns for respondents’ participation in the projects were 4.6%, 8.0%, and 8.5% for solar photovoltaic, wind, and biomass energy, respectively. This indicates that the respondents’ willingness to participate in the projects had an increasingly strong effect on expected returns (acceptance). Respondents who expressed no particular preference for the form of participation had the highest expected returns (i.e., lowest acceptance), and those who preferred bond investments had higher acceptance compared to those who preferred stock investments. Based on these results, we may infer that respondents who were relatively more adventurous, innovative, and risk-tolerant were likely to be more willing to accept and invest in community-based renewable energy projects. 3.1.2. Local residents in the Plant’s vicinity Next, we report the responses to the questionnaire administered to the local residents who lived in the vicinity of a renewable energy power plant. As described earlier, because we limited the subjects of this questionnaire to “people living in an administrative area located within a 1 km radius of the renewable power plant grounds at the time of the survey,” it allows us to measure the attitudes and levels of acceptance among local residents with regard to the actual solar photovoltaic, wind, and biomass power plants. Since these respondents already resided near or in the vicinity of a renewable energy power plant, we may interpret this questionnaire as dealing with the acceptance among those who have already learned about renewable energy power plants. In Table 6, we first present the willingness to participate and preferred form of participation among local residents living in the

4 Respondents were informed that community-based renewable energy projects could take the form of stock-type investment or bond-type investment, after which they were asked which of the two they preferred. Stock investment refers to a form of participation where residents gain direct ownership of a certain portion of the project’s shares, thus taking part in both the gains and risks incurred by the project as per the sum of their investments and shares. Bond investment refers to a form of participation where residents make investments in bonds issued by the renewable energy project, through which they are guaranteed to receive a fixed amount of returns in monthly payments.

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Table 5 Estimations results of the models: nationwide general public. Solar photovoltaic Variable Constant Sex Age Income Level of education Need for more renewable energy Willingness to participate in the project Preference for stock investment Preference for bond investment Sigma (variance) Mean WTA

Model 1-1 3.0899***

Wind Model 1-2 13.8221*** 0.0209 0.0064 −0.0728 −0.2557** −0.4760 −4.6145*** −2.5493*** −1.4705** 4.6387***

5.6093*** 3.09 %/year

Model 2-1 5.3720***

7.9073*** 5.37 %/year

Biomass Model 2-2 17.3698*** 0.6990 −0.0772*** −0.0113 −0.3345** 0.4678 −7.9902*** −3.8385*** −2.3398*** 5.5213***

Model 3-1 7.0540***

8.1257*** 7.05 %/year

Model 3-2 17.4581 0.4614 −0.0601** −0.0251 −0.3066** 0.5468 −8.5056*** −4.7874*** −2.6550*** 5.2687***

Note: *significant at the 0.10 level; ** significant at the 0.05 level; *** significant at the 0.01 level.

perceptions of such facilities in places where they actually existed. Table 7 shows the distribution of responses by bid amounts and Table 8 reports the results of the WTA analysis via the CVM model for the local residents living near renewable energy power plants. Owing to the small sample size, all 306 responses were pooled for CVM analysis while the type of renewable energy (solar, wind, and biomass) was indicated through dummy variables. First, with model 3-1, we analyzed the average level of acceptance – that is the respondents’ average expected rates of return – via the constant term. Model 3-2 shows the estimation results after including the explanatory variables influencing WTA. The overall expected return (10.7%) derived from model 3-2 reflects the average WTA, which is calculated by evaluating the explanatory variables (age, gender, income, etc.) at their full sample mean values. The level of acceptance in the vicinity of solar photovoltaic energy power plants, for instance, is the level of acceptance that is calculated by assigning fixed values to the location dummies – for example, "vicinity of the solar plant = 1, vicinity of the wind plant = 0" while leaving the values of all other explanatory variables (such as age, gender, income, etc.) unchanged. Similarly, the acceptance in the vicinity of wind energy power plants is derived by assigning "vicinity of solar plant = 0, vicinity of wind plant = 1," while the acceptance in the vicinity of biomass energy plants is the average level of acceptance derived by assigning "vicinity of solar plant = 0, vicinity of wind plant = 0." According to the estimation results of model 3-2, expected returns from community-based renewable power plants were lowest (thus, implying highest acceptance) in the case of wind energy (9.1%), followed by biomass (10.8%) and solar photovoltaic energy (12.3%). This is in contrast to the earlier result based on the general public’s responses, where WTA was highest for solar photovoltaic energy, followed by wind and biomass energy. Meanwhile, the difference in the WTA of biomass and solar photovoltaic energy may not hold much statistical meaning, since the relevant coefficient estimates from model 3-2 were not significant. According to our results, while wind energy was generally perceived as having the lowest acceptance levels due to noise and other concerns, residents living near actual wind energy power plants were found to have higher acceptance compared to those living near other types of renewable energy power plants. Conversely, while solar photovoltaic energy was generally perceived as having the highest acceptance level, residents living near actual solar photovoltaic energy power plants were found to have very low acceptance levels. This may be due to the fact that solar photovoltaic power plants are often owned by outsiders, and that the benefits from the solar photovoltaic energy generated do not accrue to local residents. Since conflicts between investors and local residents have intensified along with the diffusion of solar energy in South Korea, there is a need to promote community-based ways of implementing such projects. The relatively higher local acceptance for wind and biomass power plants may be

Table 6 Responses regarding acceptance for community-based renewable energy projects: local residents. Solar photovoltaic

Wind

Biomass

Q. Are you willing to participate in a community-based project for the construction and operation of a renewable energy plant? Yes 24 (23.3%) 29 (29.0%) 14 (13.7%) No 79 (76.7%) 71 (71.0%) 88 (86.3%) Q. Which is your preferred form of participation in the community-based project for the construction and operation of the renewable energy plant? Stock investment 4 (3.9%) 11 (11.0%) 10 (9.8%) Bond investment 30 (29.1%) 28 (28.0%) 21 (20.6%) No particular preference 69 (67.0%) 61 (61.0%) 71 (69.6%)

Table 7 Distribution of responses by bid amount: local residents.

Solar photovoltaic

Wind

Biomass

Initial bid

No. samples

“Yes”“Yes”

“Yes”“No”

“No”“Yes”

“No”“No”

1.0% 3.0% 4.5% 5.5% 6.0% 1.0% 3.0% 4.5% 5.5% 6.0% 1.0% 3.0% 4.5% 5.5% 6.0%

21 21 20 21 20 18 21 19 21 22 22 21 20 21 18

2 0 5 4 2 1 0 1 1 0 0 0 2 0 1

1 2 1 1 4 0 0 5 9 12 0 0 4 5 3

0 2 0 1 1 1 1 5 2 2 0 7 2 5 0

18 17 14 15 13 16 11 8 9 8 22 14 12 11 14

vicinity of a community-based solar photovoltaic, wind, or biomass power plant. Looking at the responses in Table 6, we found that local residents had markedly lower acceptance levels compared to the general public. The percentage of respondents who expressed willingness to participate was 67.1% of the general public and 23.3% of the local residents surveyed for solar photovoltaic energy; 47.6% of the general public and 29.0% of the local residents surveyed for wind energy, and 41.9% of the general public and 13.7% of the local residents surveyed for biomass energy. In terms of the preferred form of participation, unlike the general public that preferred the stable form of participation through bond investment, local residents expressed no particular preferences. They also expressed little interest in the project itself, as well as exhibiting low willingness to participate. Therefore, we can infer that in the case of South Korea, local residents hold predominantly negative 648

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First, the returns that may be realistically anticipated from a community-based renewable energy power plant are calculated based on the general conditions under which these projects are implemented. However, the residents’ in-cash participation share was set at 20%, which is the minimum level that meets the requirement for the 20% REC weight under the government’s current incentive policy. Further, the respective capacities of the biomass and wind power plants were set at 1 MW and 3 MW, which are the minimum levels that meet the requirements of the incentive policy. Additionally, we included biomass energy in our simulation for the purpose of comparing expected policy effects despite the fact that under the current policy, it is excluded from the application of preferential REC weights. The preconditions and assumptions for the simulations of expected returns from the implementation of community-based solar photovoltaic, wind, and biomass energy projects are summarized in Table 9 below. Under the assumptions given in Table 9, the returns that may be anticipated from community-based renewable energy projects, both with and without the government provision of preferential REC 20% weights, are reported in Table 10 below. Analysis results indicate that the application of additional REC 20% weights would bring in broad increases ranging from 1.44 to 3.65%p in investors’ returns on community-based renewable energy projects. Specifically, the increases in the annual rates of return for solar photovoltaic, wind, and biomass energy projects were 1.76%p (4.12% → 5.88%), 1.44%p (2.52% → 3.96%), and 3.65%p (2.77% → 6.42%), respectively. In the presence of additional REC 20% weights, we find that the annual rates of return of solar photovoltaic and wind energy projects are 5.88% and 3.96%, respectively, which contrast considerably with the WTA expected rates of return (solar photovoltaic 12.3%, wind 9.1%) as estimated earlier via CVM analysis. However, if the project operator offers all of the additional gains from the additional REC 20% weight to the residents whose in-cash participation share is 20%, this would result in increases of up to 18%p in the participating

Table 8 Estimation results of the models: local residents. Variable

Model 3-1

Model 3-2

Constant Vicinity of solar energy plant Vicinity of wind energy plant Sex Age Income Level of education Need for more renewable energy Willingness to participate in the project Preference for stock investments Preference for bond investments Sigma (variance) Mean WTA

11.0743

19.5360*** 1.5171 −1.6806** −0.3533 0.0750* 0.3316** −0.2717* −2.0029*** −4.1961*** −4.4776*** −5.9514*** 4.2791*** 10.71%/year

7.0041 11.07%/year

Note: *significant at the 0.10 level; ** significant at the 0.05 level; *** significant at the 0.01 level.

interpreted as being attributable to the fact that they are required, due to their larger scale, to recompense residents within a 5-km radius. 3.2. Simulation analysis Amid the currently low levels of local acceptance for renewable energy power plants, there will be a need to implement effective policies for encouraging the voluntary participation of local residents in order for community-based power plants to successfully secure a foothold in South Korea.5 In view of this, in this section, we conduct a simulation where the residents’ expected rates of return (i.e., WTA) are compared with the rates of return that may be realistically anticipated under the incentive policies for community-based renewable energy projects, as currently implemented by the South Korean government, thus assessing the effects of the policy.

Table 9 Preconditions and assumptions for simulations of community-based renewable energy projects. Solar photovoltaic

Wind

Biomass

Capacity Capacity factor SMP (assuming a 20-year duration) REC (assuming a 20-year duration) Interest on PF loans Investment costs O&M

1 MW 14.75% As of 2016, KRW 77/kWh (USD 0.072/kWh)

3 MW 23% As of 2016, KRW 77/kWh (USD 0.072/kWh)

100 kW 75% As of 2016, KRW 77/kWh (USD 0.072/kWh)

As of 2016, KRW 86/kWh (USD 0.081/kWh) with or without +20% additional weight 4.5%/year KRW 1.4bil./MW (USD 1.32mil./MW) KRW 16mil./MW∙year (USD 15,040/MW∙year)

Insurance payments

KRW 14mil./MW∙year (USD 13,160/MW∙year)

Fuel costs Discount rate Corporate tax Inflation Duration Debt ratio Residents’ in-cash participation share Degradation Rate

0 7% 24.2% 2.5% 20 years 50% 20%

As of 2016, KRW 86/kWh (USD 0.081/kWh) with or without +20% additional weight 4.5%/year KRW 2.5bil./MW (USD 2.35mil./MW) KRW 30mil./MW∙year (USD 28,200/ MW∙year) KRW 17.5mil./MW∙year (USD 16,450/ MW∙year) 0 7% 24.2% 2.5% 20 years 50% 20%

As of 2016, KRW 86/kWh (USD 0.081/kWh) with or without +20% additional weight 4.5%/year KRW 3.5bil./MW (USD 3.29mil./MW) KRW 3.5bil./MW∙year (USD 3.29mil./ MW∙year) KRW 280mil./MW∙year (USD 263,200/ MW∙year) KRW 42/kWh (USD 0.039/kWh) 7% 24.2% 2.5% 20 years 50% 20%

0.8%

0.3%

0%

residents’ annual returns. That is, the annual rates of return for participating residents in solar photovoltaic, wind, and biomass energy projects would increase by up to 8.80%p (4.12% → 12.92%), 7.20%p (2.52% → 9.72%), and 18.25%p (2.77% → 21.02%), respectively. Comparing these results alongside the results reported in Table 8 from the previous section, the findings may be summarized as in Table 11 below. In conclusion, we have found that the incentive program

5 Korean government implemented an incentive scheme for resident-participatory renewable power plant projects in January of 2017. The incentive scheme includes awarding an REC weight value up to 20% according to the participation rate of local residents in renewable energy generation projects, granting additional points in bidding on the SMP+REC fixed price, and priority loans for the required funds.

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KRW 103.72mil. (USD 97,511) 1.317 11.42% 7.05 years

implemented by the government with regards to community-based renewable energy projects is capable of providing sufficient returns to meet the residents’ expected returns when incentives are given to them entirely. 4. Conclusions and policy implications

KRW 1,049.29mil. (USD 986,481) 1.150 8.96% 8.83 years

KRW 17.02mil. (USD 16,001) 1.052 7.77% 8.90 years

In order to provide relevant policy implications from the results, it is necessary to briefly introduce the community-based renewable energy project currently being implemented in South Korea. In a situation where a large number of renewable energy projects are being delayed or cancelled due to the strong opposition from the residents, the South Korean government is implementing various incentive policies for community-based renewable energy projects. Such incentives include allowing an additional REC weight, supporting sales of produced electricity, and supporting low cost loans for the required funds. With regard to the form of residents’ participation, in principle, the residents should contribute directly and participate as shareholders. However, the method of providing financial resources can be determined by consultation between developer and residents according to business conditions (e.g.: the direct contribution of residents, use of compensation to the residents, and utilization of loans), so that various business models can be constructed. In the case of the REC weight, which was the simulation target of this study, the main incentive is to provide up to 20% of additional REC weights to the renewable energy project with local residents' participation. More specifically, it is targeted at solar and wind power plants of which generation capacity are larger than 1 MW and 3 MW, respectively, and more than 5 residents who live within 1 km radius of the power plant should participate in the project. The level of additional REC weighting for individual projects is determined by considering several aspects of the project, such as the generation capacity, location of the plants, share ratio of resident participation, financial contribution ratio. The detailed REC calculation method is presented in MOTIE [53]. In this study, we examined the willingness of the general public and local residents in the vicinity of renewable energy facility to participate in community-based renewable energy projects, by quantitatively estimating the WTA for the expected returns from project participation. We also assessed whether the South Korean government’s incentive program for community-based renewable energy projects could meet the WTA of local residents. CVM estimation results indicate that the average expected rates of return among South Korea’s general public for community-based renewable energy projects were 3.1% for solar, 5.4% for wind, and 7.1% for biomass energy. On the other hand, the average expected rates of return among local residents were 12.3% for solar, 9.1% for wind, and 10.8% for biomass energy. Two noteworthy observations may be made here. First, even though people have a preference for renewable energy, they may be opposed to the construction of renewable energy power plants within their own communities. The government can resolve such a NIMBY phenomenon by providing local residents with adequate levels of incentives. Second, while public acceptance was highest for solar energy, followed by wind and biomass energy, acceptance among local residents was highest for wind energy, followed by biomass and solar energy. We find that this result is also associated with the matter of recompensing the local residents. That is, in South Korea, current wind and biomass energy projects are mandatorily required to provide local residents within a 5-km radius of the plant grounds because they are larger scale projects. On the other hand, because solar energy projects are usually implemented in capacities under 100 kW with no requirements for recompensing local residents, they are prone to elicit resistance from nearby residents. Therefore, the provision of adequate levels of compensation seems to be an important factor in boosting acceptance. According to our simulation results, it is possible to provide local residents with returns beyond the levels expected by residents if the

B/C ratio Internal rate of return (IRR) Time to recovery of principal

KRW 39.01mil. (USD 36,675) 1.298 10.88% 7.66 years

Average annual expected rate of return Maximum possible expected rate of return for participating residents due to REC preferential treatment Net returns (in present value)

KRW 20.74mil. (USD 19,499) 1.159 9.12% 8.62 years

KRW 272.89mil. (USD 256,555) 1.039 7.52% 9.81 years

With no REC 20% preferential weight 2.77% With no REC 20% preferential weight 2.52% With REC 20% preferential weight 5.88% 12.92% With no REC 20% preferential weight 4.12%

With REC 20% preferential weight 3.96% 9.72% Wind Solar photovoltaic

Table 10 Analysis of changes in investors’ expected rates of return on community-based renewable energy projects, with or without REC 20% preferential weight.

Biomass

With REC 20% preferential weight 6.42% 21.02%

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Table 11 Comparison of average WTA and anticipated returns from community-based renewable energy plant project.

Solar photovoltaic Wind Biomass

Nationwide general public: average WTA calculated by CVM

Local residents: average WTA calculated by CVM

3.1% per year 5.4% per year 7.1% per year

12.3% per year 9.1% per year 10.8% per year

Maximum possible expected rate of return for participating residents after REC 20% preferential treatment < < <

entirety of the incentives under the current system was given to them. As of June 2017, half a year after the implementation of the incentive system, however, there were no cases of application for a communitybased renewable energy project in South Korea [54]. This is mainly because people tend to prefer compensatory forms with lower uncertainty. As suggested by the survey results, respondents overwhelmingly preferred bond-type investments with fixed benefits over stock-type investments. However, the current incentive concerning the “additional provision of REC 20% weights” tied to the residents’ participation share is difficult for a layperson to understand, and is associated with substantial uncertainty. Therefore, it is necessary to consider a form of compensation that can be more clearly and easily understood by ordinary people. In any case, setting reasonable standards for compensation in the infant stage of a project is the most pressing necessity. It is difficult to cut back from the level of compensation that was guaranteed beforehand while the planned operation spans of renewable energy projects are 10–20 years or more. Therefore, the government must pursue communication between local governments, developers, and residents during the earliest stages of renewable energy projects in order to establish adequate compensatory schemes. Although this study has examined renewable energy projects in South Korea, we anticipate that its findings will be able to contribute to the energy policies of other nations where acceptance levels need to be improved. By applying the methodologies presented in this study to other countries, it would be possible to conduct quantitative international comparisons of acceptance levels for renewable energy. Furthermore, it would be possible to estimate, for each country, the adequate levels of compensation for local residents living near renewable energy facilities.

12.92% per year 9.72% per year 21.02% per year

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