Author
Listed:
- Wanxing Sheng
(China Electric Power Research Institute, Beijing 100089, China)
- Keyan Liu
(China Electric Power Research Institute, Beijing 100089, China)
- Dongli Jia
(China Electric Power Research Institute, Beijing 100089, China)
- Jun Zhou
(China Electric Power Research Institute, Beijing 100089, China)
- Zezhou Wang
(State Grid Zhejiang Electric Power Corporation Jiaxing Power Supply Company, Jiaxing 314000, China)
- Chenbo Wang
(State Grid Zhejiang Electric Power Corporation Jiaxing Power Supply Company, Jiaxing 314000, China)
- Xiang Li
(State Grid Zhejiang Electric Power Corporation Jiaxing Power Supply Company, Jiaxing 314000, China)
- Yuting Feng
(MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Data-Driven Management Decision Making Lab, Shanghai Jiao Tong University, Shanghai 200240, China)
Abstract
Fleet electrification is increasingly recognized as a cornerstone of urban decarbonization in high-density megacities. This study introduces a multi-scenario simulation framework integrating high-resolution mobile signaling data with traffic modeling to quantify the systemic environmental and energy impacts of road-based battery electric vehicle (BEV) integration in Shanghai. By evaluating both a fixed-fleet baseline and dynamic-fleet growth scenarios focused on the urban road network, we find that aggressive fleet electrification leads to a profound reduction in aggregate carbon emissions and criteria pollutants, effectively decoupling transit-related environmental burdens from urban growth. However, results also highlight a significant energy trade-off: while fossil fuel displacement accelerates, grid-based electricity demand increases under fleet growth conditions. Within this context, the expanded vehicle population exacerbates urban congestion, which disproportionately inflates the fuel consumption of remaining internal combustion vehicles. Their operational efficiency is severely compromised by frequent stop-and-go cycles, leading to an intensification of idling losses. Ultimately, this research highlights the capability of the proposed simulation framework to provide granular insights into urban emission dynamics, offering a quantitative foundation for policymakers to harmonize electrification targets with proactive traffic management and grid infrastructure strengthening to evaluate the systemic trade-offs toward achieving long-term urban sustainability.
Suggested Citation
Wanxing Sheng & Keyan Liu & Dongli Jia & Jun Zhou & Zezhou Wang & Chenbo Wang & Xiang Li & Yuting Feng, 2026.
"A Multi Scenario Simulation Study on the Systemic Benefits of Fleet Electrification for Urban Sustainability in Shanghai,"
Sustainability, MDPI, vol. 18(8), pages 1-17, April.
Handle:
RePEc:gam:jsusta:v:18:y:2026:i:8:p:4077-:d:1924016
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