Monetizing Environmental Impacts of Nuclear Power: Cost-Benefit Analysis Using Social Cost of Carbon

This paper conducts a comprehensive cost-benefit analysis of nuclear energy’s contribution to climate change mitigation, with a focus on its potential to reduce the social cost of carbon (SCC). For 31 countries, we employ a four-step methodology to estimate SCC, comparing it against the lifetime costs associated with nuclear power generation. The Upstate New York case study in 2019 reveals that its three nuclear power plants could save approximately $675 million of the social cost of carbon (SCC) annually. Our SCC analyses suggest that the currently operating nuclear power plants worldwide can save the SCC, corresponding to 2.6% of the United States’ annual GDP. According to our cost-benefit analysis, among several countries that operate nuclear reactors, the US and China exhibit high environmental benefits from nuclear power, whereas other countries earn net environmental losses from it. Our study’s robust data analysis methods ensure the reliability and validity of our conclusions, enhancing the relevance of our findings for informing policy decisions. Despite variations in net benefits influenced by societal SCCs and levelized costs of energy (LCOE), nuclear energy emerges as a crucial component in transitioning to a zero-carbon grid. Our study advocates for the use of SCC analysis to incentivize the adoption of low-carbon energy sources.

Authors

Institutions

Publication Details

Journal
SAGE Open
Published
2024-10-01
DOI
https://doi.org/10.1177/21582440241301468
Citations
1
Primary Topic
Climate Change Policy and Economics
Type
article
Field-Weighted Citation Impact
1.13

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Monetizing Environmental Impacts of Nuclear Power: Cost-Benefit Analysis Using Social Cost of Carbon

Sung‐Tae Lee, Sun‐Moon Jung
1 citations
SAGE Open
Climate Change Policy and Economics
1.13
article

Monetizing Environmental Impacts of Nuclear Power: Cost-Benefit Analysis Using Social Cost of Carbon

Sung‐Tae Lee, Sun‐Moon Jung
article en
1 citations

Abstract

This paper conducts a comprehensive cost-benefit analysis of nuclear energy’s contribution to climate change mitigation, with a focus on its potential to reduce the social cost of carbon (SCC). For 31 countries, we employ a four-step methodology to estimate SCC, comparing it against the lifetime costs associated with nuclear power generation. The Upstate New York case study in 2019 reveals that its three nuclear power plants could save approximately $675 million of the social cost of carbon (SCC) annually. Our SCC analyses suggest that the currently operating nuclear power plants worldwide can save the SCC, corresponding to 2.6% of the United States’ annual GDP. According to our cost-benefit analysis, among several countries that operate nuclear reactors, the US and China exhibit high environmental benefits from nuclear power, whereas other countries earn net environmental losses from it. Our study’s robust data analysis methods ensure the reliability and validity of our conclusions, enhancing the relevance of our findings for informing policy decisions. Despite variations in net benefits influenced by societal SCCs and levelized costs of energy (LCOE), nuclear energy emerges as a crucial component in transitioning to a zero-carbon grid. Our study advocates for the use of SCC analysis to incentivize the adoption of low-carbon energy sources.

SAGE OpenVol. 14(4)
Dongguk University (KR), Hongik University (KR)
Hongik University
Openalex Percentile: Top 11%
Climate Change Policy and Economics
1.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.