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The potential influence of cost-related factors on the adoption of electric vehicle: an integrated micro-simulation approach

The potential influence of cost-related factors on the adoption of electric vehicle: an integrated micro-simulation approach

Zhuge, Chengxiang, Wei, Binru, Shao, Chunfu, Dong, Chunjiao, Meng, Meng ORCID logoORCID: https://orcid.org/0000-0001-7240-6454 and Zhang, Jie (2019) The potential influence of cost-related factors on the adoption of electric vehicle: an integrated micro-simulation approach. Journal of Cleaner Production, 250:119479. ISSN 0959-6526 (Print), 1879-1786 (Online) (doi:10.1016/j.jclepro.2019.119479)

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Abstract

Cost-related factors (e.g., subsides) play a vital role in the diffusion of Electric Vehicle (EV). However, it remains unclear how these factors would influence the diffusion and further the associated urban elements (e.g., infrastructures) at the micro scale. In response, this paper tried to quantify the influence of two types of cost-related factors on the adoption of Electric Vehicle (EV), namely upfront cost and usage-related cost, using purchase subsides and fuel prices as examples, respectively. An agent-based integrated micro-simulation model (SelfSim-EV) was used here to simulate how the EV market in Beijing might evolve from 2016 to 2020, within several “what-if” scenarios considering different Plug-in Hybrid Electric Vehicle (PHEV) subsides, petrol prices and electricity prices. The results suggested that 1) doubling the PHEV subsidy would make PHEV price competitive and thus increase the PHEV sale from around zero to 2500 in 2019. The PHEV sale price increases by around 3500 RMB (from around 261,000 to 264,500 RMB) due to the increase in the PHEV penetrate rate. This further gives rise to the changes in those urban elements connected with the EV market, including the urban environment, electricity and infrastructure systems, especially at the disaggregate level; 2) both electricity and petrol prices have little influence on the adoption of EVs at the macro level (i.e. the city level), but they do influence the spatial distributions of both CV and EV owners (based on the analyses of their residential locations) and further geographical distributions of vehicular emissions, EV-related facilities (e.g., charging posts) and electricity demand of EV at multiple resolutions, ranging from the facility level to district level.

Item Type: Article
Additional Information: ** Article version: AM ** Embargo end date: 31-12-9999 ** From Elsevier via Jisc Publications Router ** History: accepted 26-11-2019; issue date 27-11-2019. ** Licence for AM version of this article: This article is under embargo with an end date yet to be finalised.
Uncontrolled Keywords: Electric vehicle (EV); SelfSim-EV; Hybrid Electric Vehicle (PHEV); micro scale; EV market
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HD Industries. Land use. Labor
H Social Sciences > HE Transportation and Communications
Faculty / School / Research Centre / Research Group: Faculty of Business
SWORD Depositor: Users 6393 not found.
Last Modified: 12 Jun 2024 14:38
URI: http://gala.gre.ac.uk/id/eprint/26271

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