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Effectiveness of battery electric vehicle promotion on particulate matter emissions reduction

Abstract Quantifying environmental benefits from battery electric vehicles (BEVs) requires an understanding of the long-term substitutional relationships among different vehicle technologies. This study analyzes how BEV promotion can contribute to reductions in particulate matter (PM) emissions in Korea and investigates effective long-term policy options. Using a choice model, this study simulates consumers’ vehicle replacement behavior under various policy scenarios from 2018 to 2025. The results show that BEV promotion alone has limited effectiveness at reducing PM emissions because BEV purchases were predominantly made by gasoline vehicle owners while diesel vehicles are the key source of emissions. This finding is significant because investment in BEV promotion in Korea specifies PM emissions reductions as their core objective and legal basis. Investigation of additional policy options suggests that the combination of direct regulations on diesel vehicles and a targeted rebate to promote substitution between diesel vehicles and BEVs can reduce PM emissions by ~13%.
- Seoul National University Korea (Republic of)
- Hong Kong University of Science and Technology (香港科技大學) China (People's Republic of)
- Hong Kong Polytechnic University China (People's Republic of)
- Hong Kong University of Science and Technology (香港科技大學) China (People's Republic of)
- Seoul National University Korea (Republic of)
330, Feebate policy, Electric vehicle, 629, Fine dust, Vehicle adoption, Particulate matter
330, Feebate policy, Electric vehicle, 629, Fine dust, Vehicle adoption, Particulate matter
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).24 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
