Financial Feasibility and Social Acceptance for Reducing Nuclear Power Plants: A Contingent Valuation Study
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sustainability-10-03833.pdf
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Author(s)
Woo, JongRoul
Lim, Sesil
Lee, Yong-Gil
Huh, Sung-Yoon
Date Issued
October 2018
Journal
Sustainability
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Woo, JongRoul et al. "Financial Feasibility and Social Acceptance for Reducing Nuclear Power Plants: A Contingent Valuation Study." Sustainability 10, 11 (October 2018): 3833 © 2018 The Authors
Version
Final published version
Abstract
Social acceptance of nuclear power has become a decisive factor in framing a sustainable energy policy. This study examines social acceptance for cancelling the construction of planned nuclear power plants (NPPs) and replacing them with other energy sources. The contingent valuation method (CVM) and cost–benefit analysis (CBA) are used to access the social acceptance and financial feasibility of such projects. Empirical analysis is based on the case of South Korea, where a similar policy is in progress under the new government. The CVM results show that a Korean household was willing to pay an additional KRW 1922.45/month (USD 1.80/month) for replacing seven 1-Gigawatt NPPs with other energy sources, which is about 3.5% of a household’s current electricity bill. The CBA suggests that the annual costs of replacing this amount of nuclear power capacity with renewables or liquefied natural gas is KRW 1291.40 billion (USD 1.21 billion) or KRW 1180.38 billion (USD 1.11 billion) larger than its benefits, which amounts to about 3% of total annual electricity generation costs in South Korea. As the additional costs of nuclear power replacement cannot be fully covered by the mean willingness-to-pay of the current acceptance level, moderate levels of social resistance are expected if all the additional costs are passed on to the end-users. Keywords: social acceptance; nuclear power; renewable energy; liquefied natural gas; contingent valuation method; willingness to pay
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/su10113833
