Electrification decisions of traditional automakers under the dual-credit policy regime. (September 2021)
- Record Type:
- Journal Article
- Title:
- Electrification decisions of traditional automakers under the dual-credit policy regime. (September 2021)
- Main Title:
- Electrification decisions of traditional automakers under the dual-credit policy regime
- Authors:
- He, Haonan
Li, Shiqiang
Wang, Shanyong
Chen, Zhuru
Zhang, Jinxi
Zhao, Jie
Ma, Fei - Abstract:
- Highlights: Dual-credit policy has made electrification of traditional automakers inevitable. This study considers stochastic credit prices and time-dependent investment costs. It presents an optimal decision model with three indicators for automakers. Electrification is not optimal for medium-sized automakers at current credit price. Tightening the rules is not necessarily beneficial for facilitating electrification. Abstract: The newly introduced dual-credit policy has made electrification inevitable for traditional automakers. This study considers stochastic credit prices and time-dependent electric vehicle (EV) investment costs to present a novel optimal decision model with three indicators: investment timing, research and development intensity, and product line allocation. This combinatorial optimization problem is solved by developing a genetic algorithm. The simulation result shows that the high profitability of EVs can accelerate electrification, while rapid credit price increases may instead d[1]elay it. Meanwhile, the Corporate Average Fuel Consumption (CFAC) credit rules outperform the New Energy Vehicle (NEV) credit rules in facilitating electrification and driving long-term cumulative EV productions. Interestingly, despite the pressure these rules bring, tightening them is not necessarily beneficial for boosting electrification. Overall, introducing credit ceiling and floor prices, or coordinating policy parameters by steadily tightening CAFC rules whileHighlights: Dual-credit policy has made electrification of traditional automakers inevitable. This study considers stochastic credit prices and time-dependent investment costs. It presents an optimal decision model with three indicators for automakers. Electrification is not optimal for medium-sized automakers at current credit price. Tightening the rules is not necessarily beneficial for facilitating electrification. Abstract: The newly introduced dual-credit policy has made electrification inevitable for traditional automakers. This study considers stochastic credit prices and time-dependent electric vehicle (EV) investment costs to present a novel optimal decision model with three indicators: investment timing, research and development intensity, and product line allocation. This combinatorial optimization problem is solved by developing a genetic algorithm. The simulation result shows that the high profitability of EVs can accelerate electrification, while rapid credit price increases may instead d[1]elay it. Meanwhile, the Corporate Average Fuel Consumption (CFAC) credit rules outperform the New Energy Vehicle (NEV) credit rules in facilitating electrification and driving long-term cumulative EV productions. Interestingly, despite the pressure these rules bring, tightening them is not necessarily beneficial for boosting electrification. Overall, introducing credit ceiling and floor prices, or coordinating policy parameters by steadily tightening CAFC rules while appropriately moderating NEV rules, would effectively accelerate electrification and promote EV productions. … (more)
- Is Part Of:
- Transportation research. Volume 98(2021)
- Journal:
- Transportation research
- Issue:
- Volume 98(2021)
- Issue Display:
- Volume 98, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 98
- Issue:
- 2021
- Issue Sort Value:
- 2021-0098-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-09
- Subjects:
- Dual-credit policy regime -- Traditional automaker -- Electric vehicles -- Electrification decision -- Genetic algorithm
Transportation -- Research -- Periodicals
Transportation -- Environmental aspects -- Periodicals
354.76 - Journal URLs:
- http://www.sciencedirect.com/science/journal/13619209 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.trd.2021.102956 ↗
- Languages:
- English
- ISSNs:
- 1361-9209
- Deposit Type:
- Legaldeposit
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