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Design government incentive schemes for promoting electric taxis in China

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  • Yang, Jie
  • Dong, Jing
  • Hu, Liang

Abstract

This paper presents an optimization framework to determine the government incentive schemes to promote battery electric vehicle (BEV) taxis. The impacts of drivers’ operating behaviors, charger network coverage, BEV range, vehicle costs, and energy prices are taken into account. A two-stage optimization model is proposed, which describes the interplay between the government subsidy scheme and taxi drivers’ acceptance of BEVs. To quantify drivers’ acceptance, a data-driven microsimulation model is used to simulate driving and charging activities based on GPS trajectory data collected from conventional gasoline taxis in Changsha, China. The optimal government subsidy scheme is solved using the genetic algorithm. The key findings include: (1) detour for charging is inevitable for BEV taxis and would cause significant disruption in operational activities, especially for small-range BEVs (e.g. 150km). (2) Subsidizing on vehicle purchase is necessary, and the subsidy intensity is expected to maintain at the current level to achieve an electrification goal of more than 50%. The government should provide financial support for public charging exclusive of vehicle purchase. (3) Different taxi drivers might prefer different BEV ranges, thereby they should be allowed to select from diversified BEV models, instead of deploying a single vehicle model for the entire taxi fleet.

Suggested Citation

  • Yang, Jie & Dong, Jing & Hu, Liang, 2018. "Design government incentive schemes for promoting electric taxis in China," Energy Policy, Elsevier, vol. 115(C), pages 1-11.
    • Handle: RePEc:eee:enepol:v:115:y:2018:i:c:p:1-11
    DOI: 10.1016/j.enpol.2017.12.030
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    References listed on IDEAS

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    Cited by:

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    5. Zhishuang Zhu & Hua Liao, 2019. "Do subsidies improve the financial performance of renewable energy companies? Evidence from China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 241-256, January.
    6. Zhou, Min & Long, Piao & Kong, Nan & Zhao, Lindu & Jia, Fu & Campy, Kathryn S., 2021. "Characterizing the motivational mechanism behind taxi driver’s adoption of electric vehicles for living: Insights from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 134-152.
    7. Scorrano, Mariangela & Danielis, Romeo & Giansoldati, Marco, 2020. "Mandating the use of the electric taxis: The case of Florence," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 402-414.
    8. Yecid Alfonso Mu oz Maldonado & C sar Acevedo & Edward Jerez & Carlos Sarmiento & Miguel De La Rosa & Adalberto Ospino, 2021. "Transition of Electric Mobility in Colombia: Technical and Economic Evaluation of Scenarios for the Integration of E-taxis in Bucaramanga," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 461-469.
    9. Goldschmidt, Rüdiger & Richter, Andreas & Pfeil, Raphael, 2019. "Active stakeholder involvement and organisational tasks as factors for an effective communication and governance strategy in the promotion of e-taxis. Results from a field research lab," Energy Policy, Elsevier, vol. 135(C).
    10. Yang, W.H. & Wong, R.C.P. & Szeto, W.Y., 2018. "Modeling the acceptance of taxi owners and drivers to operate premium electric taxis: Policy insights into improving taxi service quality and reducing air pollution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 581-593.
    11. Luo, Qi & Yin, Yunlei & Chen, Pengyu & Zhan, Zhenfei & Saigal, Romesh, 2022. "Dynamic subsidies for synergistic development of charging infrastructure and electric vehicle adoption," Transport Policy, Elsevier, vol. 129(C), pages 117-136.

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