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Carbon emission reduction benefits of ride-hailing vehicle electrification considering energy structure

Author

Listed:
  • Zhang, Zhe
  • Yu, Qing
  • Gao, Kun
  • He, Hong-Di
  • Liu, Yang
  • Huang, Haichao

Abstract

Ride-hailing services provided by companies like Uber, Lyft, and Didi have rapidly grown, leading to increased traffic congestion and greenhouse gas emissions. The transition of ride-hailing fleets to Electric vehicles (EVs) presents a considerable opportunity to reduce emissions in the transportation sector. Despite this potential, the carbon emission benefits of electrifying ride-hailing vehicles remain inadequately quantified. This study introduces a framework designed to assess carbon emission reductions resulting from EVs, specifically accounting for emissions transferred from electricity during the operational phase of ride-hailing vehicles. The study employs field data from Chengdu and Xi’an, China for case studies using the proposed framework. Our findings indicate that emission reductions are markedly influenced by the grid electricity emission factors specific to each city. The daily reduction in emissions due to electrification of ride-hailing vehicles is equivalent to eliminating approximately 133,307 and 63,162 trips of gasoline vehicle in the ride-hailing services of Chengdu and Xi’an, respectively. More importantly, this study identifies equilibrium points that establish the necessary grid electricity emission factors for achieving emission reductions across all ride-hailing trips when transitioning from gasoline to EVs through sensitivity analysis. For Chengdu and Xi’an, the thresholds of grid electricity emission factors are 156.25 g/kWh and 131.09 g/kWh, respectively. This study offers an applicable analytical framework to evaluate emission reductions of ride-hailing electrification across various urban contexts, thereby aiding in the determination of conditions conducive to effective integration of EVs into ride-hailing services.

Suggested Citation

  • Zhang, Zhe & Yu, Qing & Gao, Kun & He, Hong-Di & Liu, Yang & Huang, Haichao, 2025. "Carbon emission reduction benefits of ride-hailing vehicle electrification considering energy structure," Applied Energy, Elsevier, vol. 377(PA).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pa:s0306261924019317
    DOI: 10.1016/j.apenergy.2024.124548
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    1. Yang, Guixiang & Zhang, Hao & Qiu, Lin, 2026. "Graph-based multi-agent reinforcement learning with an enriched environment for joint ride-sharing and charging optimization," Applied Energy, Elsevier, vol. 405(C).
    2. Guan, Qingshu & Cao, Hui & Tan, Junkai & Jia, Lixin & Yan, Dapeng & Chen, Badong, 2025. "Token-specific deep reinforcement learning for energy-efficient capacitated electric vehicle routing problems," Applied Energy, Elsevier, vol. 396(C).

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