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Empirical evidence of air pollution reduction from electric vehicle usage across Chinese cities

Nature Cities volume 3, pages 251–260 (2026)Cite this article

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Abstract

Large-scale adoption of electric vehicles (EVs) is expected to influence urban air quality, yet the effects of actual EV adoption and usage within cities remain poorly quantified. Leveraging detailed EV charging records from 2016 to 2023 across 292 Chinese cities, we empirically quantify these impacts. We find that a 10% increase in EV charging is linked to reductions in surface NO2 and PM2.5 concentrations by 0.08% and 0.04%, respectively, with no significant effect for ambient O3 concentrations. Notably, these benefits vary by ambient temperatures and regions: effects are more pronounced at extremely low temperatures (below −7 °C), despite reduced battery performance, and southern cities experience greater improvements than northern cities, probably due to higher utilization rates. Our findings highlight the heterogeneous nature of EV-related air quality benefits and provide actionable insights for policy-makers and operators to optimize EV resource allocation in cities and accelerate progress toward urban environmental targets.

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Fig. 1: Geographic distributions of EV charging and air quality across 334 cities in China, from 2016 to 2023.
Fig. 2: Temperature dependency of the estimated effects.
Fig. 3: Air quality benefits of EV charging usage.
Fig. 4: Projected NO2 and PM2.5 concentrations over 8 years (2016–2023) with and without EV deployment.

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Data availability

A collaboration with a leading Chinese EVCS operator yielded a proprietary dataset of detailed consumer charging records, provided under a non-disclosure agreement. To safeguard sensitive information while preserving the reproducibility of key findings, the data are desensitized and released in accordance with our partner’s privacy requirements. The air quality data are sourced from CNEMC at http://www.cnemc.cn/en/ and other data used in the analysis are drawn from publicly accessible sources, as detailed in the section ‘Datasets’ of Methods. The final compiled datasets used in the analysis are available via Zenodo at https://doi.org/10.5281/zenodo.17906782 (ref. 47).

Code availability

All data processing and analysis were performed using Stata 18.0 and R 4.2.0. The custom scripts used to clean, aggregate and analyze the data are available via Zenodo at https://doi.org/10.5281/zenodo.17906782 (ref. 47).

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Acknowledgements

X.L. and Y.M. were grateful for the support by the Engineering Technology Research Center for Ordinary Universities in Guangdong Province (grant no. 2024GCZX005) and Shenzhen Medical Research Fund (grant no. C2501033). We especially thank Z. Lu from Argonne National Laboratory, University of Chicago, for his insightful comments and valuable feedback.

Author information

Author notes

  1. These authors contributed equally: Yuanhong Ma, Minghao Qiu.

Authors and Affiliations

  1. Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen, China

    Yuanhong Ma & Xitong Li

  2. Guangdong Engineering Center for Social Computing and Mental Health, Shenzhen, China

    Yuanhong Ma & Xitong Li

  3. School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA

    Minghao Qiu

  4. Program in Public Health, Stony Brook University, Stony Brook, NY, USA

    Minghao Qiu

  5. Department of Earth System Science, Stanford University, Stanford, CA, USA

    Yuan Wang

  6. Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China

    Jiaofeng Pan & Jianfeng Guo

  7. School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China

    Jiaofeng Pan & Jianfeng Guo

  8. Center of Engineering Management, Polytechnic Institute of Zhejiang University, Zhejiang University, Hangzhou, China

    Fu Gu

  9. Department of Industrial and System Engineering, Zhejiang University, Hangzhou, China

    Fu Gu

  10. National Institute of Innovation Management, Zhejiang University, Hangzhou, China

    Fu Gu

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Contributions

Y.M., X.L., M.Q. and Y.W. developed the research concept and designed the methodology. X.L. acquired the funding, administered and supervised the project. J.G. and F.G. collected and preprocessed the data from the EVCS operator. M.Q., Y.M., X.L. and Y.W. carried out the investigation. Y.M., M.Q., Y.W. and X.L. performed the visualization. Y.M. and M.Q. drafted the paper. X.L., Y.W., J.P., J.G. and F.G. reviewed and edited the paper.

Corresponding authors

Correspondence to Jianfeng Guo, Fu Gu or Xitong Li.

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Nature Cities thanks Zhonghua Gou, Gang He, Guojun He and Daniel Mehlig for their contribution to the peer review of this work.

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Ma, Y., Qiu, M., Wang, Y. et al. Empirical evidence of air pollution reduction from electric vehicle usage across Chinese cities. Nat Cities 3, 251–260 (2026). https://doi.org/10.1038/s44284-026-00395-2

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