Effect of electricity generation mix on battery electric vehicle adoption and its environmental impact
By Hyunhong Choi, Jungwoo Shin, Jongroul Woo. Energy Policy, 2018
150 citations recorded by the source corpus.
Battery electric vehicles (BEVs) are gaining much attention as the next technology paradigm in the transport sector because it can mitigate the environmental problems, such as greenhouse gas (GHG) emissions, of conventional internal combustion engine vehicles. Many countries are attempting to promote the consumer adoption of BEVs in this sense by providing subsidies and expanding their related infrastructure. The expected environmental effect of BEVs is an important factor in increasing the consumer adoption of BEVs, and the environmental impact of BEVs is directly related to the electricity generation mix. In this study, we analyze how the consumer adoption behavior of BEVs and their environmental impact can be changed by improving the environmental performance of BEVs based on the electricity generation mix. To this end, we estimate consumer preferences in vehicles by using a discrete choice experiment survey and a mixed logit model. Then, on the basis of the estimation results, we simulate changes in consumers’ vehicle adoption behavior according to various electricity generation mixes and measure the environmental impact of these changes. Analysis results show changing the electricity generation mix to renewable-oriented mix can promote BEV's market share up to 10% and reduce GHG emissions up to 5% by 2026.
Subfields: Automotive Engineering, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/55e623fdf539196994d47762218e3f49b05f0562
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In Automotive Engineering
In Electrical and Electronic Engineering
In Renewable Energy, Sustainability and the Environment
Citations in the corpus
- Willingness to pay and attitudinal preferences of Indian consumers for electric vehicles (2021, background): “Mixed-MNL (Choi et al., 2018; Cirillo et al., 2017) and latent class MNL (LC-MNL) (Ardeshiri and Rashidi, 2020; Axsen et al.”