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Social acceptance of offshore wind energy development in South Korea: Results from a choice experiment survey
Renewable and Sustainable Energy Reviews 113 (2019) 109253
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Social acceptance of offshore wind energy development in South Korea: Results from a choice experiment survey
T
Hyo-Jin Kim, Ju-Hee Kim, Seung-Hoon Yoo∗ Department of Energy Policy, Graduate School of Energy & Environment, Seoul National University of Science & Technology, 232 Gongreung-Ro, Nowon-Gu, Seoul, 01811, Republic of Korea
A R T I C LE I N FO
A B S T R A C T
Keywords: Offshore wind power development Choice experiment Environmental costs Willingness to pay Cost-benefit analysis
According to the South Korean Government, new offshore wind power generators with a capacity of 12–13 GW will be installed by 2030. This article attempts to investigate the social acceptance of an offshore wind energy development project by evaluating its environmental costs. For this purpose, 1,000 households throughout the country were randomly selected, and a choice experiment (CE) survey was undertaken with them in August 2018. The 4 attributes considered here are distance from the land, offshore wind farm size, offshore wind turbine height, and decrease in marine life. An increase in the yearly income tax is also considered as an attribute. The results of applying the CE approach reveal that people place significant value on reducing the environmental impacts of the offshore wind energy development project. If the results are added to the cost-benefit analysis of the proposed offshore wind energy development project, the benefits of the project are not likely to exceed the costs of the project. The implementation of the project should be thoroughly re-considered.
1. Introduction South Korea’s renewable energy generation accounts for 7.2% of the total as of 2017, while about 70% comes from coal and nuclear power [1]. President Park, the head of the government that actively promoted the expansion of coal-fired generation and nuclear power, was impeached by the National Assembly for various reasons, and the impeachment was confirmed by the Constitutional Court in April of 2017. After she resigned as president, a candidate who pledged to reduce coalfired generation and nuclear power was elected and inaugurated as the new president. The new government is pushing for an energy transition policy to reduce the share of coal and nuclear power generation and increase the share of renewable energy generation by changing its policy stance. In other words, the so-called ‘Renewable Energy Plan 3020’ will increase the portion of renewable energy generation from 7.2% in 2017 to 20.0% by 2030 [2]. As a result, the Government is actively promoting an expansion of solar energy and wind power. However, it is difficult to install wind turbines on South Korea’s flat land due to the high population density. Because about 63.2% of the nation’s land is forest, the installation of wind turbines in the mountains can be considered as a powerful means of expanding renewable energy. It is not desirable to install wind
turbines in the mountains as the installation will inevitably damage the forests, absorbing carbon dioxide in large quantities. To make matters worse, the mountainous area with a substantial amount of wind is mostly designated a protected area; thus, legally, wind turbines cannot be installed there. To sum up, large-scale land wind power generation is not feasible in reality, and it is difficult to expand even small-scale land wind power generation due to the opposition of local residents on the grounds of noise and vibration. Consequently, the development of offshore wind power (OWP) is becoming a realistic alternative. OWP generation, like solar and onshore wind power generation, has the advantages of not demanding fuel and not emitting greenhouse gas (GHG). In addition, the quality of wind at sea is superior to that on the land, and the utilization rate of OWP is much higher than that of onshore wind power or solar power [3]. According to the Renewable Energy Plan 3020, new OWP generators with a capacity of 12–13 GW will be installed by 2030. Although compensation for fishing rights and systematic connection to the land will incur considerable costs, the Government is ambitious in its pursuit of the plan. However, OWP generation can affect the marine ecosystem negatively due to noise and vibration. It is known that the noise level from the current 1 MW offshore wind generator is 103–106 dB. This is more
Abbreviations: CE, choice experiment; OWP, offshore wind power; GHG, greenhouse gas; WTP, willingness to pay; MWTP, marginal WTP; ASC, alternative-specific constant; MNL, multinomial logit; EIA, environmental impact assessment; MOMAF, Ministry of Maritime Affairs & Fisheries; PV, present value ∗ Corresponding author. E-mail addresses: [email protected] (H.-J. Kim), [email protected] (J.-H. Kim), [email protected] (S.-H. Yoo). https://doi.org/10.1016/j.rser.2019.109253 Received 16 October 2018; Received in revised form 8 June 2019; Accepted 27 June 2019 1364-0321/ © 2019 Published by Elsevier Ltd.
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be examined in this article is Cheongsapo OWP farm, which is planned on the coast of Pusan, the second-largest city in South Korea. Cheongsapo is located about 3 km away from Haeundae Beach, which receives more than 10 million tourists a year. Cheongsapo OWP farm is designed to be 1.2 km away from the land. Its generation capacity is 40 MW. If Cheongsapo OWP farm is built as scheduled, 6 additional OWP farms with a total capacity of 500 MW will also be built along the beach. Thus, the Korea Ministry of Oceans and Fisheries desperately requires information on the environmental costs of building Cheongsapo OWP farm to determine whether to allow it from the point of view of marine environmental impact assessment. This is because the construction of Cheongsapo OWP farm can be justified only when the benefits arising from the project are greater than its costs. Estimating environmental costs is the key to assessing the feasibility of the construction, as environmental costs must be added to the overall costs.
than 90 dB more than the noise from subways or motorcycles and 100 dB more than that from an electric saw. In particular, noise from OWP generators can have a fatal impact on nearby marine plants and animals, since it can be transmitted to the atmosphere and sometimes into the ocean along vertical structures. Vibration from turbines can also directly affect marine mammals. Antifouling paint is usually used to protect the surface of OWP plants from rusting or the attachment of life forms. However, an organic tin component (TriButylTin), which is known to be quite harmful material, is included in the paint. When the antifouling paint flows into the sea along the current, it can have a detrimental effect not only on life in the sea but also on the marine ecosystem [4–6]. Furthermore, an OWP farm could have a visually detrimental effect on residents who live in housing complexes along the coast. People who enjoy recreation and leisure on the beach, such as fishing tourists and beachgoers, especially can notice a severe visual effect [7–11]. Of course, the OWP farm itself is a landmark, which could exert a positive effect on the local economy as more visitors from other regions are attracted. However, from the perspective of the marine ecosystem and the visual aspect of residents in areas with maritime wind power, it is judged that the negative impact is greater than the positive one. In sum, OWP generation is eco-friendly in that it does not emit GHGs, but it is also eco-harmful, causing visual damage and marine ecosystem damage. Therefore, if a specific OWP development project is proposed, it is necessary for the Government to compare its eco-friendliness with its eco-harmfulness objectively and decide whether to approve the project based on which is larger. In other words, if the eco-friendliness is larger than the eco-harmfulness, it would be desirable to move ahead with the construction of an OWP complex; if not, it would be better not to do so. There are two difficulties. First, we need to be able to compare ecofriendliness with eco-harmfulness, but this comparison is difficult because they have different units. The former is evaluated as a reduction in GHG emissions, while the latter is evaluated as the distance from the land and a decrease in the amount of marine life. Therefore, it is necessary to assess them in monetary values for unit unification to facilitate the comparison. Second, it is very difficult to assess eco-harmfulness as a monetary unit, while the effect of reducing GHG emissions can be determined relatively easily in monetary units using the price of a GHG emission permit. This is because eco-harmfulness has a variety of attributes and is a kind of non-market goods. Moreover, valuing it is difficult because it is not traded in the market [12,13]. Thus, the application of specially designed economic techniques is required to assess the non-market value of a multi-attribute good [14–18]. A typical way to approach this is to conduct a choice experiment (CE) [19–22]. The CE approach is the most prevalent methodology for multi-attribute goods and is applied in almost all of the few previous studies that have assessed the environmental costs of wind power [4,23–28]. As will be explained in more detail below, this study randomly selected 1,000 households from all over the country under the supervision of a professional survey firm to apply the CE method. The main purpose of this article is investigate the social acceptance of the OWP development project by evaluating its environmental costs using the CE approach. The subsequent composition of this article is as follows. Section 2 describes in detail the methodology and application procedures used in this study. Section 3 explains the economic and statistical models for analyzing the data collected through the CE survey. Section 4 presents a discussion of the results after reporting them. The last section is devoted to the conclusion.
2.2. CE approach The CE approach is theoretically grounded in the random utility maximization model [29]. The model implies that, if an individual chooses one alternative among several, the utility resulting from selecting that alternative is always greater than the utility resulting from choosing another. Therefore, the application of the approach requires a survey of people. The CE method is useful for estimating the relative values of different attributes of environmental and non-market goods or new products. The environmental costs of an OWP farm can be assessed effectively through the CE approach. In general, respondents are requested to choose their preferred alternative among three or four alternatives, which include a baseline state alternative, presented to them in the CE survey. Each alternative comprises several attributes of concern, including the price attribute. CE is a useful for estimating the relative importance of several attributes of a good or service [30]. The economic value or the cost of increasing or decreasing the level of each attribute can be obtained by analyzing the data on respondents’ choices and then interpreting or utilizing the results. The economic value or cost is expressed as willingness to pay (WTP) and is usually measured as marginal WTP (MWTP) for a one-unit increase or decrease in an attribute. 2.3. Attributes In designing a CE, the first important task is to determine the appropriate attributes and define their levels. An extensive literature review and consultation with experts enabled us to identify a preliminary list of attributes of an OWP farm. Most previous studies have revealed that the distance from the land [8,9,25], the size of the OWP farm [7,31–34], the height of the offshore wind turbine [7,32,35–37], and marine life reduction [4,11] have important implications for the environmental impacts of OWP farms. The final set of attributes was chosen through discussion with experts, such as policy makers, stakeholders, and environmental activists. As reported in Table 1, the finally determined attributes are distance from the land, offshore wind farm size, offshore wind turbine height, decrease in marine life, and price. A focus group interview with 30 people was conducted to check whether they were fully meaningful, understandable, and persuasive to the respondents. Their responses were affirmative. Their descriptions and levels are also explained in Table 1. They are assumed to be orthogonal in terms of valuation function rather than production function. Furthermore, all the other attributes of Cheongsapo OWP farm are assumed to be the same in the course of the value judgments required in the CE survey. The shorter the distance from the offshore wind turbine to the land, the greater the number of offshore wind turbines, and the higher the offshore wind turbine height, the greater the detrimental effect visually. The baseline state of distance from the land, offshore wind farm size,
2. Methodology 2.1. Object to be examined In this article, the CE is applied to evaluate the environmental costs of an offshore wind farm development. More specifically, the object to 2
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Table 1 Descriptions and levels of the four chosen attributes and the price attribute used in this study. Attributes
Descriptions
Levels
Distance from the land
Distance from the offshore wind turbine to the land (unit: km)
Offshore wind farm size
Number of offshore wind turbines
Offshore wind turbine height
Length of the offshore wind turbine above sea level (unit: m)
Decrease in marine life
Percentage decrease in the populations of benthos, fish, mammals, and birds from the current state because of the installation and operation of offshore wind turbines in the area (unit: %)
Price
Willingness to pay for reducing the environmental costs of offshore wind energy development through an increase in the yearly income tax per household (unit: Korean won)
Level Level Level Level Level Level Level Level Level Level Level Level Level Level Level Level Level Level
Notes:
#
1: 2: 3: 1: 2: 3: 1: 2: 3: 1: 2: 3: 4: 1: 2: 3: 4: 5:
1.2 km# 15 km 30 km 20# 10 5 150 m# 100 m 60 m 50%# 30% 15% 10% 0# 1,000 2,000 4,000 7,000
indicates the baseline state of each attribute. USD 1.0 was approximately equal to KRW 1,121.39 at the time of the survey.
where Wjl and εjl are the deterministic and stochastic parts of the utility function, respectively; Xjl is a vector containing the levels of the attributes for alternative l given to respondent j ; Tj is respondent j ’s characteristics, such as ASC ; and the β ’s are the coefficients that correspond to each attribute, Xs . Omitting jl for simplicity, we can apply Roy’s identity, which is well-known in economics, to Equation (1) and derive the MWTP estimate, MWTPXs , as:
and offshore wind turbine height is the level with the most negative visual effect. The level of the attribute for decrease in marine life is explained as the percentage decrease from the current state in the populations of benthos, fish, mammals, and birds because of the installation and operation of offshore wind turbines in the area. The baseline state of this attribute means a 50% decrease in the marine life populations than the previous state because of the installation and operation of offshore wind turbines. The price attribute is defined as WTP for reducing the environmental costs of offshore wind energy development through an increase in the yearly income tax per household.
MWTPXs = −(∂W / ∂Xs )/(∂W / ∂Xp ) = −βs /βp for s = 1,2,3,4
MWTPXs = −(∂W / ∂Xs )/(∂W / ∂Xp ) = −βs /βp is the derivative of W with respect to Xs and βs is the coefficient corresponding to Xs . MWTP for a particular attribute means how much the respondent’s WTP increases (decreases) when one unit of the attribute is increased (decreased). If an increase in an attribute results in an increase in utility, the sign for MWTP is positive, and if an increase in an attribute results in a decrease in utility, that for MWTP is negative.
2.4. Choice sets Since a number of alternatives can be created from Table 1, several alternatives with possible combinations of attributes should be derived. To this end, the orthogonal main-effect design was employed and sixteen alternatives were obtained. From these, eight choice sets were generated. Each choice set was made up of two alternatives and the baseline state alternative. Four choice sets were randomly selected among the eight choice sets and allocated to the first group, and then the remaining four choice sets were assigned to the second group. Thus, each group included four choice sets. One of the two groups was randomly presented to each interviewee. In other words, each interviewee was presented with four choice sets and reported four responses to the questions posed that indicated which alternatives he or she preferred among the three alternatives in each choice set.
3.2. Obtaining the utility function Looking at Equation (1), one can see that estimating the utility function implies estimating β ’s. McFadden [38] developed the multinomial logit (MNL) model that can be applied when estimating β ’s, which is the most widely employed in the literature. Although the MNL model inevitably assumes independence from irrelevant alternatives, it has a good advantage of enabling us to specify the log-likelihood function as a closed form [39–42]. Thus, the MNL model is adopted in this paper. Let J be the number of interviewees and I jl be a dummy variable that is defined as one if interviewee j selects alternative l ; otherwise, I jl is zero. The log-likelihood function for our MNL model is derived as:
3. Model 3.1. Utility function and MWTP
J
According to the practices adopted in most CE literature, the utility function is assumed to have a linear functional form. Let the levels of the distance from the land, offshore wind farm size, offshore wind turbine height, decrease in marine life, and price be Xs for s = 1,2,3,4, p, respectively. In addition, an alternative-specific constant (ASC) is introduced to capture the effect of any other factors not contained in the model. The ASC is one if the respondent selects the third alternative (baseline state) and zero otherwise. Let the utility that respondent j obtains when s/he chooses alternative l be Vjl . Vjl is specified as:
ln L =
∑ j=1
3
I
(exp (Wjk )) jk ⎤ ⎡∏ ln ⎢ k =31 ⎥. ⎣ ∑n = 1 exp (Wjn ) ⎦
(3)
The parameters of the log-likelihood function in this study are estimated by applying the maximum likelihood (ML) estimation method. In other words, we can obtain the values for parameter β ’s that maximize the objective function, Equation (3). The ML estimators for β ’s are known to be both consistent and asymptotically efficient [43]. In principle, W in Equation (3) should be ∂W where ∂ is scale factor. Because ∂ cannot be identified in the MNL estimation, the scale factor is almost always normalized to one in the literature dealing with real estimation of the MNL model [44]. Thus, the scale factor is set equal to
Vjl = Wjl (Xjl , Tj ) + εjl = ASCj + β1 X1, jl + β2 X2, jl + β3 X3, jl + β4 X 4, jl + βp Xp, jl + εjl
(2)
(1) 3
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estimates, which are calculated using the procedures provided by Krinsky and Robb [46].
Table 2 Estimation results of the multinomial logit model. Variablesa
Multinomial logit coefficient estimatesc
ASCb Distance from the land (unit: km) Offshore wind farm size (unit: number) Offshore wind turbine height (unit: m) Decrease in marine life (unit: %) Price (unit: Korean won) Number of observations Scaled R2 Log-likelihood Wald statisticd (p-value)
−0.3764 0.0072 −0.0089 −0.0030 −0.0111 −0.4935 4,000 0.0202 −3,913.39 835.16 (0.000)#
4.3. Discussion of the results (-5.92)# (2.22)** (-2.06)** (-3.65)# (-9.66)# (-4.40)#
Using the results presented in Table 3, we can estimate the environmental costs, which are combinations of these attributes using the MWTP estimates for an increase or decrease in the attributes. In other words, multiplying the figures reported in Table 3 by the levels of attributes gives us the environmental costs of the alternative for the hypothetical state of the offshore wind energy development. As an illustration, the results of calculating the environmental costs at which households assess several alternatives for the hypothetical state of the offshore wind energy development are shown in Table 4. For example, the environmental costs of the third alternative, which means the hypothetical state with a 28.8 km increase in the distance from the land, a decrease of 15 offshore wind turbines, a 90 m decrease in the offshore wind turbine height, and a 40%p decrease in marine life from the baseline state, are computed as KRW 21,309 (USD 19.00). Table 4 also provides the annual national value for each alternative by using the number of total households. There were in total 19,751,807 households in South Korea in 2018 [47]. We find that the annual national value of the third alternative is KRW 420.88 billion (USD 375.32 million) when we multiply the number of total households in 2018 by the environmental cost estimates obtained from Table 4. The above analysis results have a variety of potential uses. First, by using these findings, one can identify which attributes people value and thus the environmental impacts that should be reduced first. According to the estimated utility function, the absolute value of the coefficient estimate for a decrease in marine life is the greatest among the four environmental impact attributes. However, that for the offshore wind turbine height is the smallest. The former is 3.7 times the latter. Therefore, if the cost of reducing the environmental impact is the same, it would be better to concentrate on reducing the level of the decrease in marine life than to reduce the offshore wind turbine height. Second, using Tables 3 and 4, we can not only calculate the environmental costs for a variety of alternatives but also identify alternatives that result in specific environmental costs. Alternatives may be proposed that satisfy acceptable levels of environmental costs within the scope that the total costs do not exceed the total benefits. Third, the analysis results can be used to estimate the environmental costs arising from other OWP projects, because various OWP farm projects are underway, as mentioned earlier.
Notes: a The variables are defined in Table 1 b ASC refers to the alternativespecific constants that represent dummies for the respondent’s choice of the baseline state alternative. c # and ** indicate statistical significance at the 1% and 5% levels, respectively, and the t-values are reported in parentheses beside the estimates. d The null hypothesis is that all the parameters are zero, and the corresponding p-value is reported in parentheses beside the statistic.
one in this study. 4. Results and discussion 4.1. Estimation results A nationwide CE survey of 1,000 randomly chosen households was implemented by a professional polling company through person-toperson interviews during August 2018. Each household gave us 4 observations. Thus, we gained a data set of 4,000 (= 1,000 respondents × 4 choice sets). Table 2 reports the results of the MNL model estimation. All the coefficients’ estimates are statistically distinguishable from 0 at the 5% level. The expected signs for the coefficient estimates for the 5 attributes except for distance from the land are all negative. The coefficient estimate for the distance from the land has a positive sign. As the distance from the offshore wind turbine to the land rises, the utility of the South Korean public increases. However, the coefficient estimates for the offshore wind farm size, offshore wind turbine height, and decrease in marine life have negative signs. Thus, a one-unit decrease in the level of these three attributes increases the public utility. The coefficient for price also has a negative sign. This implies that, as the price increases, the utility decreases. This result is quite reasonable given that the price contributes negatively to the utility. The signs of all the estimated coefficients are consistent with our prior expectations. The scaled R2 for the estimated MNL model is computed to be 0.0202. It is a measure of goodness-of-fit relative to a model with just ASC term and has somewhat better properties for discrete dependent variable problems [45]. Moreover, the log-likelihood ratio test statistic for the null hypothesis that all the parameters except for ASC term are zero is calculated to be 176.16 and its p-value is 0.000. Consequently, the null hypothesis is rejected at the 1% level. The estimated MNL model is statistically significant at the 1% level.
4.4. Preliminary cost-benefit analysis of the proposed OWP farm project As of October 2018, the project has been approved launch, but environmental impact assessment (EIA) of the project has not been carried out yet. In the future, the Korea Ministry of Environment, the Korea Ministry of Maritime Affairs & Fisheries (MOMAF), and local governments will closely review the EIA report and decide whether the project should be implemented or the report should be revised. Moreover, the OWP farm company should consult with MOMAF about the use of sea areas and the Ministry will determine whether to approve the project or not after careful investigation of the impacts of the project on marine environment. The stakeholders also should examine the economic feasibility of the project considering environmental costs arising from the project in the process of reviewing the EIA report. Thus, a preliminary cost-benefit analysis of the proposed Cheongsapo OWP farm project is tried as a final exercise. Korea Ministry of Strategy and Finance, the financial authority of South Korea, provides a guideline for conducting the cost-benefit analysis: the use of a 4.5% social discount rate. Since the construction is expected to begin in mid-2019 and end at the end of 2020, and the life expectancy of the offshore wind generator is 20 years, it is assumed that the benefits occur for 20 years from 2021 to 2040. Therefore, the costs of the
4.2. MWTP estimates Finally, the MWTP estimates for an increase or a decrease in the level of each attribute can be derived through Eq. (2). The results of the MWTP value estimation are provided in Table 3. The household MWTP estimates for a 1 km increase in the distance from the land, a decrease in the offshore wind farm size, a 1 m decrease in the offshore wind turbine height, and a 1%p decrease in marine life are obtained as KRW 147 (USD 0.13), 181 (0.16), 60 (0.05), and 224 (0.20), respectively, in the yearly income tax. These values are interpreted as the environmental costs of Cheongsapo OWP farm’s development in South Korea. Table 3 also presents the 95% confidence intervals for the MWTP 4
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Table 3 Estimation results of the marginal willingness to pay (MWTP) values. MWTP per household per year
Increase in distance from the land (unit: km) Decrease in offshore wind farm size (unit: number) Decrease in offshore wind turbine height (unit: m) Decrease in marine life (unit: %p)
Estimates
t-values
95% confidence intervals
KRW KRW KRW KRW
2.25 1.81 2.49 4.01
KRW KRW KRW KRW
147** (USD 0.13) 181* (USD 0.16) 60** (USD 0.05) 224# (USD 0.20)
24 to 315 (USD 0.02 to 0.28) 128 to 443 (USD 0.11 to 0.40) 22 to 135 (USD 0.02 to 0.12) 147 to 409 (USD 0.13 to 0.36)
Notes: #, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. USD 1.0 was approximately equal to KRW 1,121.39 at the time of the survey. The confidence intervals are computed using the procedures given by Krinsky and Robb [46]. Table 4 The environmental costs of alternatives for the hypothetical state of the offshore wind energy development. Attributes
Alternative A
Alternative B
Alternative C
Distance from the land Offshore wind farm size Offshore wind turbine height Decrease in marine life Environmental costs per household per year Annual national environmental costs
15 km 10 100 m 30% KRW 11,319 (USD 10.09) KRW 223.56 billion (USD 199.36 million)
30 km 10 60 m 15% KRW 16,884 (USD 15.06) KRW 333.48 billion (USD 297.38 million)
30 km 5 60 m 10% KRW 21,309 (USD 19.00) KRW 420.88 billion (USD 375.32 million)
Notes: USD 1.0 was approximately equal to KRW 1,121.39 at the time of the survey. There were 19,751,807 households in South Korea in 2018 [47].
zero and one, respectively, and thus portrays that the project is not socially profitable. In summary, Cheongsapo OWP farm project does not pass a test of the cost-benefit analysis. It does not seem that the project possesses the economic feasibility. It is recommended to abandon the project or redesign it in a way that will reduce the environmental impacts.
project consist of the OWP facilities installation and operating costs, and environmental costs, and the benefits of the project arise from power supply and carbon dioxide emissions reduction. The process of obtaining the environmental costs of the project is made up of two steps. First, the annual environmental costs per household are computed considering the properties of the proposed Cheongsapo OWP project. The distance from the land, the offshore wind farm size, the offshore wind turbine height, and the decrease in marine life are estimated to 1.2 km, 8, 80 m, and 5%. Thus, the annual environmental costs per household amount to KRW 7,544 (USD 6.73). In the second step, annual environmental costs are calculated by multiplying the environmental costs per household by the number of households in the appropriate population. In this study, the households of Pusan where Cheongsapo OWP farm is located are defined as the relevant population. The number of households in Pusan is 1,368,360 [47]. The annual environmental costs are KRW 10.32 billion (USD 9.20 million). The present value (PV) of the environmental costs, which occur for twenty years from 2021 to 2040, is calculated as KRW 122.97 billion (USD 109.66 million). The PV of the OWP facilities installation and operating costs is computed as KRW 257.36 billion (USD 229.59 million). This value corresponds to the private cost. Thus, the PV of the social costs of the project amounts to KRW 380.33 billion (USD 339.16 million). The benefits of producing renewable power, which are currently being assessed in South Korea as the sum of the system marginal price and price for renewable energy certificate, are approximately KRW 211.62 thousand (USD 188.71) per MWh. As the total generation capacity of the project is 40 MW, assuming a 34% utilization rate, the annual power generation is expected to be 119,136 MWh. The figure, 34%, is based on a performance that has already been achieved by OWP farm operating in South Korea. The total annual benefits of the project are calculated to be KRW 25.21 billion (USD 22.48 million). Thus, the PV of the total benefits of the project is worth KRW 300.31 billion (USD 267.80 million). The net PV and the benefit-cost ratio of the project from a private perspective is computed to be KRW 54.03 billion (USD 48.18) and 1.22, respectively, which are greater than zero and one, respectively. On the surface, the project seems to secure economic feasibility. However, from a social perspective, when cost-benefit analysis in consideration of environmental costs is performed they are calculated to be KRW -80.12 billion (USD 71.45 million) and 0.79, respectively, which are less than
5. Conclusions This article aimed to measure the environmental costs of Cheongsapo offshore wind farm development in South Korea. To this end, the CE technique was adopted, employing the data from a nationwide survey of 1,000 interviewees. The 4 attributes of environmental impacts caused by the offshore wind farms considered in this study were: a) the distance from the offshore wind turbine to the land; b) the number of offshore wind turbines; c) the length of the offshore wind turbine above sea level; and d) the percentage decrease in the populations of benthos, fish, mammals, and birds because of the installation and operation of offshore wind turbines in the area from the current state. The coefficients for the utility function were statistically significant when estimated by applying the MNL model. The results showed that an increase in the distance from the land and a decrease in the offshore wind farm size, offshore wind turbine height, and marine life increase the utility. More specifically, the environmental costs for a 1 km increase in the distance from the land, a decrease in the offshore wind farm size, a 1 m decrease in the offshore wind turbine height, and a 1%p decrease in marine life are reflected as increases of KRW 147 (USD 0.13), 181 (0.16), 60 (0.05), 224 (0.20), respectively, per household per year. This article described some useful policy implications that can contribute to the decision making regarding the development of OWP. In particular, information about the environmental costs is essential when analyzing the economic feasibility of a new OWP development project. For instance, these findings can help to determine whether a new project should be implemented by comparing the benefit of implementing the project with the value of the damage that it will cause. In this regard, if the environmental costs obtained above are added to the cost-benefit analysis of the proposed offshore wind energy development project, the benefits of the project are not likely to exceed the costs of the project. The implementation of the project should be 5
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thoroughly re-considered. Additionally, from a research perspective, this article contributes to the literature by applying a CE technique, one of the economic valuation techniques, to investigating the environmental costs of the attributes of OWP development. The authors found that the application was successful because the estimation results were statistically meaningful and the respondents actively participated in the CE survey. In particular, the implications of this study will be even more beneficial in that it is the first study in South Korea that requires information on the environmental costs of OWP development. Of course the framework of this study needs to be expanded further. For example, although the object to be investigated in the study is Cheongsapo OWP farm, it is necessary to compare the results with each other after applying the framework of the paper to other candidate sites for OWP farm in South Korea. As the government plans to drastically increase OWP farm in the near future, this work will be especially valuable in revising or consolidating the plan. It is also interesting to compare our results with the environmental costs of other ocean energy developments such as tidal power generation and tidal stream power generation. This is because the government is investing a lot of money in the development of other marine energy. A comparison of the results of our work with those of future works that will be undertaken in other countries will yield new implications.
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Declarations of interest
[28]
None. [29]
Acknowledgement
[30]
This research was a part of the project titled ‘Marine ecosystembased analysis and decision-making support system development for marine spatial planning’, funded by the Korean Ministry of Oceans and Fisheries (Grant number 20170325).
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Social acceptance of offshore wind energy development in South Korea: Results from a choice experiment survey
T
Hyo-Jin Kim, Ju-Hee Kim, Seung-Hoon Yoo∗ Department of Energy Policy, Graduate School of Energy & Environment, Seoul National University of Science & Technology, 232 Gongreung-Ro, Nowon-Gu, Seoul, 01811, Republic of Korea
A R T I C LE I N FO
A B S T R A C T
Keywords: Offshore wind power development Choice experiment Environmental costs Willingness to pay Cost-benefit analysis
According to the South Korean Government, new offshore wind power generators with a capacity of 12–13 GW will be installed by 2030. This article attempts to investigate the social acceptance of an offshore wind energy development project by evaluating its environmental costs. For this purpose, 1,000 households throughout the country were randomly selected, and a choice experiment (CE) survey was undertaken with them in August 2018. The 4 attributes considered here are distance from the land, offshore wind farm size, offshore wind turbine height, and decrease in marine life. An increase in the yearly income tax is also considered as an attribute. The results of applying the CE approach reveal that people place significant value on reducing the environmental impacts of the offshore wind energy development project. If the results are added to the cost-benefit analysis of the proposed offshore wind energy development project, the benefits of the project are not likely to exceed the costs of the project. The implementation of the project should be thoroughly re-considered.
1. Introduction South Korea’s renewable energy generation accounts for 7.2% of the total as of 2017, while about 70% comes from coal and nuclear power [1]. President Park, the head of the government that actively promoted the expansion of coal-fired generation and nuclear power, was impeached by the National Assembly for various reasons, and the impeachment was confirmed by the Constitutional Court in April of 2017. After she resigned as president, a candidate who pledged to reduce coalfired generation and nuclear power was elected and inaugurated as the new president. The new government is pushing for an energy transition policy to reduce the share of coal and nuclear power generation and increase the share of renewable energy generation by changing its policy stance. In other words, the so-called ‘Renewable Energy Plan 3020’ will increase the portion of renewable energy generation from 7.2% in 2017 to 20.0% by 2030 [2]. As a result, the Government is actively promoting an expansion of solar energy and wind power. However, it is difficult to install wind turbines on South Korea’s flat land due to the high population density. Because about 63.2% of the nation’s land is forest, the installation of wind turbines in the mountains can be considered as a powerful means of expanding renewable energy. It is not desirable to install wind
turbines in the mountains as the installation will inevitably damage the forests, absorbing carbon dioxide in large quantities. To make matters worse, the mountainous area with a substantial amount of wind is mostly designated a protected area; thus, legally, wind turbines cannot be installed there. To sum up, large-scale land wind power generation is not feasible in reality, and it is difficult to expand even small-scale land wind power generation due to the opposition of local residents on the grounds of noise and vibration. Consequently, the development of offshore wind power (OWP) is becoming a realistic alternative. OWP generation, like solar and onshore wind power generation, has the advantages of not demanding fuel and not emitting greenhouse gas (GHG). In addition, the quality of wind at sea is superior to that on the land, and the utilization rate of OWP is much higher than that of onshore wind power or solar power [3]. According to the Renewable Energy Plan 3020, new OWP generators with a capacity of 12–13 GW will be installed by 2030. Although compensation for fishing rights and systematic connection to the land will incur considerable costs, the Government is ambitious in its pursuit of the plan. However, OWP generation can affect the marine ecosystem negatively due to noise and vibration. It is known that the noise level from the current 1 MW offshore wind generator is 103–106 dB. This is more
Abbreviations: CE, choice experiment; OWP, offshore wind power; GHG, greenhouse gas; WTP, willingness to pay; MWTP, marginal WTP; ASC, alternative-specific constant; MNL, multinomial logit; EIA, environmental impact assessment; MOMAF, Ministry of Maritime Affairs & Fisheries; PV, present value ∗ Corresponding author. E-mail addresses: [email protected] (H.-J. Kim), [email protected] (J.-H. Kim), [email protected] (S.-H. Yoo). https://doi.org/10.1016/j.rser.2019.109253 Received 16 October 2018; Received in revised form 8 June 2019; Accepted 27 June 2019 1364-0321/ © 2019 Published by Elsevier Ltd.
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be examined in this article is Cheongsapo OWP farm, which is planned on the coast of Pusan, the second-largest city in South Korea. Cheongsapo is located about 3 km away from Haeundae Beach, which receives more than 10 million tourists a year. Cheongsapo OWP farm is designed to be 1.2 km away from the land. Its generation capacity is 40 MW. If Cheongsapo OWP farm is built as scheduled, 6 additional OWP farms with a total capacity of 500 MW will also be built along the beach. Thus, the Korea Ministry of Oceans and Fisheries desperately requires information on the environmental costs of building Cheongsapo OWP farm to determine whether to allow it from the point of view of marine environmental impact assessment. This is because the construction of Cheongsapo OWP farm can be justified only when the benefits arising from the project are greater than its costs. Estimating environmental costs is the key to assessing the feasibility of the construction, as environmental costs must be added to the overall costs.
than 90 dB more than the noise from subways or motorcycles and 100 dB more than that from an electric saw. In particular, noise from OWP generators can have a fatal impact on nearby marine plants and animals, since it can be transmitted to the atmosphere and sometimes into the ocean along vertical structures. Vibration from turbines can also directly affect marine mammals. Antifouling paint is usually used to protect the surface of OWP plants from rusting or the attachment of life forms. However, an organic tin component (TriButylTin), which is known to be quite harmful material, is included in the paint. When the antifouling paint flows into the sea along the current, it can have a detrimental effect not only on life in the sea but also on the marine ecosystem [4–6]. Furthermore, an OWP farm could have a visually detrimental effect on residents who live in housing complexes along the coast. People who enjoy recreation and leisure on the beach, such as fishing tourists and beachgoers, especially can notice a severe visual effect [7–11]. Of course, the OWP farm itself is a landmark, which could exert a positive effect on the local economy as more visitors from other regions are attracted. However, from the perspective of the marine ecosystem and the visual aspect of residents in areas with maritime wind power, it is judged that the negative impact is greater than the positive one. In sum, OWP generation is eco-friendly in that it does not emit GHGs, but it is also eco-harmful, causing visual damage and marine ecosystem damage. Therefore, if a specific OWP development project is proposed, it is necessary for the Government to compare its eco-friendliness with its eco-harmfulness objectively and decide whether to approve the project based on which is larger. In other words, if the eco-friendliness is larger than the eco-harmfulness, it would be desirable to move ahead with the construction of an OWP complex; if not, it would be better not to do so. There are two difficulties. First, we need to be able to compare ecofriendliness with eco-harmfulness, but this comparison is difficult because they have different units. The former is evaluated as a reduction in GHG emissions, while the latter is evaluated as the distance from the land and a decrease in the amount of marine life. Therefore, it is necessary to assess them in monetary values for unit unification to facilitate the comparison. Second, it is very difficult to assess eco-harmfulness as a monetary unit, while the effect of reducing GHG emissions can be determined relatively easily in monetary units using the price of a GHG emission permit. This is because eco-harmfulness has a variety of attributes and is a kind of non-market goods. Moreover, valuing it is difficult because it is not traded in the market [12,13]. Thus, the application of specially designed economic techniques is required to assess the non-market value of a multi-attribute good [14–18]. A typical way to approach this is to conduct a choice experiment (CE) [19–22]. The CE approach is the most prevalent methodology for multi-attribute goods and is applied in almost all of the few previous studies that have assessed the environmental costs of wind power [4,23–28]. As will be explained in more detail below, this study randomly selected 1,000 households from all over the country under the supervision of a professional survey firm to apply the CE method. The main purpose of this article is investigate the social acceptance of the OWP development project by evaluating its environmental costs using the CE approach. The subsequent composition of this article is as follows. Section 2 describes in detail the methodology and application procedures used in this study. Section 3 explains the economic and statistical models for analyzing the data collected through the CE survey. Section 4 presents a discussion of the results after reporting them. The last section is devoted to the conclusion.
2.2. CE approach The CE approach is theoretically grounded in the random utility maximization model [29]. The model implies that, if an individual chooses one alternative among several, the utility resulting from selecting that alternative is always greater than the utility resulting from choosing another. Therefore, the application of the approach requires a survey of people. The CE method is useful for estimating the relative values of different attributes of environmental and non-market goods or new products. The environmental costs of an OWP farm can be assessed effectively through the CE approach. In general, respondents are requested to choose their preferred alternative among three or four alternatives, which include a baseline state alternative, presented to them in the CE survey. Each alternative comprises several attributes of concern, including the price attribute. CE is a useful for estimating the relative importance of several attributes of a good or service [30]. The economic value or the cost of increasing or decreasing the level of each attribute can be obtained by analyzing the data on respondents’ choices and then interpreting or utilizing the results. The economic value or cost is expressed as willingness to pay (WTP) and is usually measured as marginal WTP (MWTP) for a one-unit increase or decrease in an attribute. 2.3. Attributes In designing a CE, the first important task is to determine the appropriate attributes and define their levels. An extensive literature review and consultation with experts enabled us to identify a preliminary list of attributes of an OWP farm. Most previous studies have revealed that the distance from the land [8,9,25], the size of the OWP farm [7,31–34], the height of the offshore wind turbine [7,32,35–37], and marine life reduction [4,11] have important implications for the environmental impacts of OWP farms. The final set of attributes was chosen through discussion with experts, such as policy makers, stakeholders, and environmental activists. As reported in Table 1, the finally determined attributes are distance from the land, offshore wind farm size, offshore wind turbine height, decrease in marine life, and price. A focus group interview with 30 people was conducted to check whether they were fully meaningful, understandable, and persuasive to the respondents. Their responses were affirmative. Their descriptions and levels are also explained in Table 1. They are assumed to be orthogonal in terms of valuation function rather than production function. Furthermore, all the other attributes of Cheongsapo OWP farm are assumed to be the same in the course of the value judgments required in the CE survey. The shorter the distance from the offshore wind turbine to the land, the greater the number of offshore wind turbines, and the higher the offshore wind turbine height, the greater the detrimental effect visually. The baseline state of distance from the land, offshore wind farm size,
2. Methodology 2.1. Object to be examined In this article, the CE is applied to evaluate the environmental costs of an offshore wind farm development. More specifically, the object to 2
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Table 1 Descriptions and levels of the four chosen attributes and the price attribute used in this study. Attributes
Descriptions
Levels
Distance from the land
Distance from the offshore wind turbine to the land (unit: km)
Offshore wind farm size
Number of offshore wind turbines
Offshore wind turbine height
Length of the offshore wind turbine above sea level (unit: m)
Decrease in marine life
Percentage decrease in the populations of benthos, fish, mammals, and birds from the current state because of the installation and operation of offshore wind turbines in the area (unit: %)
Price
Willingness to pay for reducing the environmental costs of offshore wind energy development through an increase in the yearly income tax per household (unit: Korean won)
Level Level Level Level Level Level Level Level Level Level Level Level Level Level Level Level Level Level
Notes:
#
1: 2: 3: 1: 2: 3: 1: 2: 3: 1: 2: 3: 4: 1: 2: 3: 4: 5:
1.2 km# 15 km 30 km 20# 10 5 150 m# 100 m 60 m 50%# 30% 15% 10% 0# 1,000 2,000 4,000 7,000
indicates the baseline state of each attribute. USD 1.0 was approximately equal to KRW 1,121.39 at the time of the survey.
where Wjl and εjl are the deterministic and stochastic parts of the utility function, respectively; Xjl is a vector containing the levels of the attributes for alternative l given to respondent j ; Tj is respondent j ’s characteristics, such as ASC ; and the β ’s are the coefficients that correspond to each attribute, Xs . Omitting jl for simplicity, we can apply Roy’s identity, which is well-known in economics, to Equation (1) and derive the MWTP estimate, MWTPXs , as:
and offshore wind turbine height is the level with the most negative visual effect. The level of the attribute for decrease in marine life is explained as the percentage decrease from the current state in the populations of benthos, fish, mammals, and birds because of the installation and operation of offshore wind turbines in the area. The baseline state of this attribute means a 50% decrease in the marine life populations than the previous state because of the installation and operation of offshore wind turbines. The price attribute is defined as WTP for reducing the environmental costs of offshore wind energy development through an increase in the yearly income tax per household.
MWTPXs = −(∂W / ∂Xs )/(∂W / ∂Xp ) = −βs /βp for s = 1,2,3,4
MWTPXs = −(∂W / ∂Xs )/(∂W / ∂Xp ) = −βs /βp is the derivative of W with respect to Xs and βs is the coefficient corresponding to Xs . MWTP for a particular attribute means how much the respondent’s WTP increases (decreases) when one unit of the attribute is increased (decreased). If an increase in an attribute results in an increase in utility, the sign for MWTP is positive, and if an increase in an attribute results in a decrease in utility, that for MWTP is negative.
2.4. Choice sets Since a number of alternatives can be created from Table 1, several alternatives with possible combinations of attributes should be derived. To this end, the orthogonal main-effect design was employed and sixteen alternatives were obtained. From these, eight choice sets were generated. Each choice set was made up of two alternatives and the baseline state alternative. Four choice sets were randomly selected among the eight choice sets and allocated to the first group, and then the remaining four choice sets were assigned to the second group. Thus, each group included four choice sets. One of the two groups was randomly presented to each interviewee. In other words, each interviewee was presented with four choice sets and reported four responses to the questions posed that indicated which alternatives he or she preferred among the three alternatives in each choice set.
3.2. Obtaining the utility function Looking at Equation (1), one can see that estimating the utility function implies estimating β ’s. McFadden [38] developed the multinomial logit (MNL) model that can be applied when estimating β ’s, which is the most widely employed in the literature. Although the MNL model inevitably assumes independence from irrelevant alternatives, it has a good advantage of enabling us to specify the log-likelihood function as a closed form [39–42]. Thus, the MNL model is adopted in this paper. Let J be the number of interviewees and I jl be a dummy variable that is defined as one if interviewee j selects alternative l ; otherwise, I jl is zero. The log-likelihood function for our MNL model is derived as:
3. Model 3.1. Utility function and MWTP
J
According to the practices adopted in most CE literature, the utility function is assumed to have a linear functional form. Let the levels of the distance from the land, offshore wind farm size, offshore wind turbine height, decrease in marine life, and price be Xs for s = 1,2,3,4, p, respectively. In addition, an alternative-specific constant (ASC) is introduced to capture the effect of any other factors not contained in the model. The ASC is one if the respondent selects the third alternative (baseline state) and zero otherwise. Let the utility that respondent j obtains when s/he chooses alternative l be Vjl . Vjl is specified as:
ln L =
∑ j=1
3
I
(exp (Wjk )) jk ⎤ ⎡∏ ln ⎢ k =31 ⎥. ⎣ ∑n = 1 exp (Wjn ) ⎦
(3)
The parameters of the log-likelihood function in this study are estimated by applying the maximum likelihood (ML) estimation method. In other words, we can obtain the values for parameter β ’s that maximize the objective function, Equation (3). The ML estimators for β ’s are known to be both consistent and asymptotically efficient [43]. In principle, W in Equation (3) should be ∂W where ∂ is scale factor. Because ∂ cannot be identified in the MNL estimation, the scale factor is almost always normalized to one in the literature dealing with real estimation of the MNL model [44]. Thus, the scale factor is set equal to
Vjl = Wjl (Xjl , Tj ) + εjl = ASCj + β1 X1, jl + β2 X2, jl + β3 X3, jl + β4 X 4, jl + βp Xp, jl + εjl
(2)
(1) 3
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estimates, which are calculated using the procedures provided by Krinsky and Robb [46].
Table 2 Estimation results of the multinomial logit model. Variablesa
Multinomial logit coefficient estimatesc
ASCb Distance from the land (unit: km) Offshore wind farm size (unit: number) Offshore wind turbine height (unit: m) Decrease in marine life (unit: %) Price (unit: Korean won) Number of observations Scaled R2 Log-likelihood Wald statisticd (p-value)
−0.3764 0.0072 −0.0089 −0.0030 −0.0111 −0.4935 4,000 0.0202 −3,913.39 835.16 (0.000)#
4.3. Discussion of the results (-5.92)# (2.22)** (-2.06)** (-3.65)# (-9.66)# (-4.40)#
Using the results presented in Table 3, we can estimate the environmental costs, which are combinations of these attributes using the MWTP estimates for an increase or decrease in the attributes. In other words, multiplying the figures reported in Table 3 by the levels of attributes gives us the environmental costs of the alternative for the hypothetical state of the offshore wind energy development. As an illustration, the results of calculating the environmental costs at which households assess several alternatives for the hypothetical state of the offshore wind energy development are shown in Table 4. For example, the environmental costs of the third alternative, which means the hypothetical state with a 28.8 km increase in the distance from the land, a decrease of 15 offshore wind turbines, a 90 m decrease in the offshore wind turbine height, and a 40%p decrease in marine life from the baseline state, are computed as KRW 21,309 (USD 19.00). Table 4 also provides the annual national value for each alternative by using the number of total households. There were in total 19,751,807 households in South Korea in 2018 [47]. We find that the annual national value of the third alternative is KRW 420.88 billion (USD 375.32 million) when we multiply the number of total households in 2018 by the environmental cost estimates obtained from Table 4. The above analysis results have a variety of potential uses. First, by using these findings, one can identify which attributes people value and thus the environmental impacts that should be reduced first. According to the estimated utility function, the absolute value of the coefficient estimate for a decrease in marine life is the greatest among the four environmental impact attributes. However, that for the offshore wind turbine height is the smallest. The former is 3.7 times the latter. Therefore, if the cost of reducing the environmental impact is the same, it would be better to concentrate on reducing the level of the decrease in marine life than to reduce the offshore wind turbine height. Second, using Tables 3 and 4, we can not only calculate the environmental costs for a variety of alternatives but also identify alternatives that result in specific environmental costs. Alternatives may be proposed that satisfy acceptable levels of environmental costs within the scope that the total costs do not exceed the total benefits. Third, the analysis results can be used to estimate the environmental costs arising from other OWP projects, because various OWP farm projects are underway, as mentioned earlier.
Notes: a The variables are defined in Table 1 b ASC refers to the alternativespecific constants that represent dummies for the respondent’s choice of the baseline state alternative. c # and ** indicate statistical significance at the 1% and 5% levels, respectively, and the t-values are reported in parentheses beside the estimates. d The null hypothesis is that all the parameters are zero, and the corresponding p-value is reported in parentheses beside the statistic.
one in this study. 4. Results and discussion 4.1. Estimation results A nationwide CE survey of 1,000 randomly chosen households was implemented by a professional polling company through person-toperson interviews during August 2018. Each household gave us 4 observations. Thus, we gained a data set of 4,000 (= 1,000 respondents × 4 choice sets). Table 2 reports the results of the MNL model estimation. All the coefficients’ estimates are statistically distinguishable from 0 at the 5% level. The expected signs for the coefficient estimates for the 5 attributes except for distance from the land are all negative. The coefficient estimate for the distance from the land has a positive sign. As the distance from the offshore wind turbine to the land rises, the utility of the South Korean public increases. However, the coefficient estimates for the offshore wind farm size, offshore wind turbine height, and decrease in marine life have negative signs. Thus, a one-unit decrease in the level of these three attributes increases the public utility. The coefficient for price also has a negative sign. This implies that, as the price increases, the utility decreases. This result is quite reasonable given that the price contributes negatively to the utility. The signs of all the estimated coefficients are consistent with our prior expectations. The scaled R2 for the estimated MNL model is computed to be 0.0202. It is a measure of goodness-of-fit relative to a model with just ASC term and has somewhat better properties for discrete dependent variable problems [45]. Moreover, the log-likelihood ratio test statistic for the null hypothesis that all the parameters except for ASC term are zero is calculated to be 176.16 and its p-value is 0.000. Consequently, the null hypothesis is rejected at the 1% level. The estimated MNL model is statistically significant at the 1% level.
4.4. Preliminary cost-benefit analysis of the proposed OWP farm project As of October 2018, the project has been approved launch, but environmental impact assessment (EIA) of the project has not been carried out yet. In the future, the Korea Ministry of Environment, the Korea Ministry of Maritime Affairs & Fisheries (MOMAF), and local governments will closely review the EIA report and decide whether the project should be implemented or the report should be revised. Moreover, the OWP farm company should consult with MOMAF about the use of sea areas and the Ministry will determine whether to approve the project or not after careful investigation of the impacts of the project on marine environment. The stakeholders also should examine the economic feasibility of the project considering environmental costs arising from the project in the process of reviewing the EIA report. Thus, a preliminary cost-benefit analysis of the proposed Cheongsapo OWP farm project is tried as a final exercise. Korea Ministry of Strategy and Finance, the financial authority of South Korea, provides a guideline for conducting the cost-benefit analysis: the use of a 4.5% social discount rate. Since the construction is expected to begin in mid-2019 and end at the end of 2020, and the life expectancy of the offshore wind generator is 20 years, it is assumed that the benefits occur for 20 years from 2021 to 2040. Therefore, the costs of the
4.2. MWTP estimates Finally, the MWTP estimates for an increase or a decrease in the level of each attribute can be derived through Eq. (2). The results of the MWTP value estimation are provided in Table 3. The household MWTP estimates for a 1 km increase in the distance from the land, a decrease in the offshore wind farm size, a 1 m decrease in the offshore wind turbine height, and a 1%p decrease in marine life are obtained as KRW 147 (USD 0.13), 181 (0.16), 60 (0.05), and 224 (0.20), respectively, in the yearly income tax. These values are interpreted as the environmental costs of Cheongsapo OWP farm’s development in South Korea. Table 3 also presents the 95% confidence intervals for the MWTP 4
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Table 3 Estimation results of the marginal willingness to pay (MWTP) values. MWTP per household per year
Increase in distance from the land (unit: km) Decrease in offshore wind farm size (unit: number) Decrease in offshore wind turbine height (unit: m) Decrease in marine life (unit: %p)
Estimates
t-values
95% confidence intervals
KRW KRW KRW KRW
2.25 1.81 2.49 4.01
KRW KRW KRW KRW
147** (USD 0.13) 181* (USD 0.16) 60** (USD 0.05) 224# (USD 0.20)
24 to 315 (USD 0.02 to 0.28) 128 to 443 (USD 0.11 to 0.40) 22 to 135 (USD 0.02 to 0.12) 147 to 409 (USD 0.13 to 0.36)
Notes: #, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. USD 1.0 was approximately equal to KRW 1,121.39 at the time of the survey. The confidence intervals are computed using the procedures given by Krinsky and Robb [46]. Table 4 The environmental costs of alternatives for the hypothetical state of the offshore wind energy development. Attributes
Alternative A
Alternative B
Alternative C
Distance from the land Offshore wind farm size Offshore wind turbine height Decrease in marine life Environmental costs per household per year Annual national environmental costs
15 km 10 100 m 30% KRW 11,319 (USD 10.09) KRW 223.56 billion (USD 199.36 million)
30 km 10 60 m 15% KRW 16,884 (USD 15.06) KRW 333.48 billion (USD 297.38 million)
30 km 5 60 m 10% KRW 21,309 (USD 19.00) KRW 420.88 billion (USD 375.32 million)
Notes: USD 1.0 was approximately equal to KRW 1,121.39 at the time of the survey. There were 19,751,807 households in South Korea in 2018 [47].
zero and one, respectively, and thus portrays that the project is not socially profitable. In summary, Cheongsapo OWP farm project does not pass a test of the cost-benefit analysis. It does not seem that the project possesses the economic feasibility. It is recommended to abandon the project or redesign it in a way that will reduce the environmental impacts.
project consist of the OWP facilities installation and operating costs, and environmental costs, and the benefits of the project arise from power supply and carbon dioxide emissions reduction. The process of obtaining the environmental costs of the project is made up of two steps. First, the annual environmental costs per household are computed considering the properties of the proposed Cheongsapo OWP project. The distance from the land, the offshore wind farm size, the offshore wind turbine height, and the decrease in marine life are estimated to 1.2 km, 8, 80 m, and 5%. Thus, the annual environmental costs per household amount to KRW 7,544 (USD 6.73). In the second step, annual environmental costs are calculated by multiplying the environmental costs per household by the number of households in the appropriate population. In this study, the households of Pusan where Cheongsapo OWP farm is located are defined as the relevant population. The number of households in Pusan is 1,368,360 [47]. The annual environmental costs are KRW 10.32 billion (USD 9.20 million). The present value (PV) of the environmental costs, which occur for twenty years from 2021 to 2040, is calculated as KRW 122.97 billion (USD 109.66 million). The PV of the OWP facilities installation and operating costs is computed as KRW 257.36 billion (USD 229.59 million). This value corresponds to the private cost. Thus, the PV of the social costs of the project amounts to KRW 380.33 billion (USD 339.16 million). The benefits of producing renewable power, which are currently being assessed in South Korea as the sum of the system marginal price and price for renewable energy certificate, are approximately KRW 211.62 thousand (USD 188.71) per MWh. As the total generation capacity of the project is 40 MW, assuming a 34% utilization rate, the annual power generation is expected to be 119,136 MWh. The figure, 34%, is based on a performance that has already been achieved by OWP farm operating in South Korea. The total annual benefits of the project are calculated to be KRW 25.21 billion (USD 22.48 million). Thus, the PV of the total benefits of the project is worth KRW 300.31 billion (USD 267.80 million). The net PV and the benefit-cost ratio of the project from a private perspective is computed to be KRW 54.03 billion (USD 48.18) and 1.22, respectively, which are greater than zero and one, respectively. On the surface, the project seems to secure economic feasibility. However, from a social perspective, when cost-benefit analysis in consideration of environmental costs is performed they are calculated to be KRW -80.12 billion (USD 71.45 million) and 0.79, respectively, which are less than
5. Conclusions This article aimed to measure the environmental costs of Cheongsapo offshore wind farm development in South Korea. To this end, the CE technique was adopted, employing the data from a nationwide survey of 1,000 interviewees. The 4 attributes of environmental impacts caused by the offshore wind farms considered in this study were: a) the distance from the offshore wind turbine to the land; b) the number of offshore wind turbines; c) the length of the offshore wind turbine above sea level; and d) the percentage decrease in the populations of benthos, fish, mammals, and birds because of the installation and operation of offshore wind turbines in the area from the current state. The coefficients for the utility function were statistically significant when estimated by applying the MNL model. The results showed that an increase in the distance from the land and a decrease in the offshore wind farm size, offshore wind turbine height, and marine life increase the utility. More specifically, the environmental costs for a 1 km increase in the distance from the land, a decrease in the offshore wind farm size, a 1 m decrease in the offshore wind turbine height, and a 1%p decrease in marine life are reflected as increases of KRW 147 (USD 0.13), 181 (0.16), 60 (0.05), 224 (0.20), respectively, per household per year. This article described some useful policy implications that can contribute to the decision making regarding the development of OWP. In particular, information about the environmental costs is essential when analyzing the economic feasibility of a new OWP development project. For instance, these findings can help to determine whether a new project should be implemented by comparing the benefit of implementing the project with the value of the damage that it will cause. In this regard, if the environmental costs obtained above are added to the cost-benefit analysis of the proposed offshore wind energy development project, the benefits of the project are not likely to exceed the costs of the project. The implementation of the project should be 5
Renewable and Sustainable Energy Reviews 113 (2019) 109253
H.-J. Kim, et al.
thoroughly re-considered. Additionally, from a research perspective, this article contributes to the literature by applying a CE technique, one of the economic valuation techniques, to investigating the environmental costs of the attributes of OWP development. The authors found that the application was successful because the estimation results were statistically meaningful and the respondents actively participated in the CE survey. In particular, the implications of this study will be even more beneficial in that it is the first study in South Korea that requires information on the environmental costs of OWP development. Of course the framework of this study needs to be expanded further. For example, although the object to be investigated in the study is Cheongsapo OWP farm, it is necessary to compare the results with each other after applying the framework of the paper to other candidate sites for OWP farm in South Korea. As the government plans to drastically increase OWP farm in the near future, this work will be especially valuable in revising or consolidating the plan. It is also interesting to compare our results with the environmental costs of other ocean energy developments such as tidal power generation and tidal stream power generation. This is because the government is investing a lot of money in the development of other marine energy. A comparison of the results of our work with those of future works that will be undertaken in other countries will yield new implications.
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Declarations of interest
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None. [29]
Acknowledgement
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This research was a part of the project titled ‘Marine ecosystembased analysis and decision-making support system development for marine spatial planning’, funded by the Korean Ministry of Oceans and Fisheries (Grant number 20170325).
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