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Reduced urban air pollution and mortality from the transition to new energy vehicles in China

Nature Health (2026) Cite this article

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Abstract

Vehicle emissions are an important source of urban air pollution. As the world’s largest market for new energy vehicles (NEVs), China has rapidly expanded NEV adoption to support green development. However, the environmental and health benefits of this transition remain unclear. Here, using high-resolution satellite-retrieved data and interpretable machine learning techniques, this study quantified the impact of NEVs on atmospheric pollution, specifically particulate matter particles with an aerodynamic diameter of 2.5 μm or less (PM2.5), nitrogen dioxide, carbon monoxide and particles with an aerodynamic diameter of 10 μm or less, and evaluates the corresponding health benefits. By 2023, NEVs led to reductions of 23.80% in particles with a diameter of 2.5 μm or less (8.97 µg m−3) and 30.67% in carbon monoxide (0.26 mg m−3), resulting in the prevention of approximately 262,000 non-accidental deaths and 75,000 all-cause deaths, respectively. Benefits were concentrated in economically developed cities, and reductions in coarse particles and nitrogen dioxide (1.81 µg m−3) were low. These findings highlight pollutant-specific disparities and socio-economic inequalities in NEV-related benefits, suggesting a need to accelerate heavy-duty diesel vehicle electrification and enhance NEV deployment in less-developed regions.

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Fig. 1: Interannual trends of pollutant concentrations from 2013 to 2023.
The alternative text for this image may have been generated using AI.
Fig. 2: Model accuracy statistics of the 100 RF-based regression models.
The alternative text for this image may have been generated using AI.
Fig. 3: SHAP analysis results.
The alternative text for this image may have been generated using AI.
Fig. 4: NEV-contributed roadside pollutant concentration changes.
The alternative text for this image may have been generated using AI.
Fig. 5: Spatial distribution of avoided deaths related to the NEV-contributed decrease in PM2.5, NO2, CO and PM2.5–10 in 150 Chinese cities.
The alternative text for this image may have been generated using AI.

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Data availability

Landsat 8 data were accessed using the Google Earth Engine (https://code.earthengine.google.com/). Provincial-level vehicle ownership data were obtained from the China Statistical Yearbook (https://data.stats.gov.cn/dg/website/page.html#/pc/national/publication), while city-level vehicle ownership data were provided by the Ministry of Public Security of China and are available from the corresponding authors upon reasonable request, subject to data use restrictions. Meteorological data were obtained from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/datasets/). In situ ground-level air pollution measurements can be downloaded from https://quotsoft.net/air/. Population data were derived from the 2020 China Census by the National Bureau of Statistics of China. Mortality data were sourced from PubMed (https://pubmed.ncbi.nlm.nih.gov/38762325/)34. Road network data were extracted from OpenStreetMap (https://download.geofabrik.de/asia/china.html#).

Code availability

The code to perform the analyses is publicly available via Zenodo at https://doi.org/10.5281/zenodo.19065317 (ref. 51) and can also be requested from the corresponding authors.

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Acknowledgements

This study was supported by grants awarded to M.G. from the National Natural Science Foundation of China (no. 42322902) and the Research Grants Council of the Hong Kong Special Administrative Region, China (nos. C2002-22Y, HKBU12201023 and HKBU12202824). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Geography, Hong Kong Baptist University, Hong Kong Special Administrative Region, China

    Qianqian Yang & Meng Gao

  2. Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, China

    Quan Yuan

  3. School of Geodesy and Geomatics, Wuhan University, Wuhan, China

    Qiangqiang Yuan

  4. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

    Huanfeng Shen

  5. School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, China

    Yuan Wang

  6. School of Urban Design, Wuhan University, Wuhan, China

    Hongzan Jiao

  7. The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

    Liangpei Zhang

  8. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Meng Gao

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Contributions

Q. Yang, Qiangqiang Yuan and M.G. designed the research. Q. Yang conducted the research. Q. Yang and Quan Yuan completed the analysis. Q. Yang wrote the paper with help from M.G., Qiangqiang Yuan, Quan Yuan, Y.W. and L.Z. All authors contributed to the review and revision of the paper.

Corresponding authors

Correspondence to Qiangqiang Yuan, Liangpei Zhang or Meng Gao.

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Nature Health thanks Thilo Erbertseder and M Omar Nawaz for their contribution to the peer review of this work. Primary Handling Editor: Ben Johnson, in collaboration with the Nature Health team.

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Extended data

Extended Data Fig. 1 Annual variation in FV and NEV ownership.

(a) Annual variation in FV ownership. (b) Annual variation in NEV ownership. Box plots show the distribution of vehicle ownership across 150 cities. The center line indicates the median; the black point indicates the mean value; box limits represent the interquartile range (25th–75th percentiles); whiskers extend to 1.5 × the interquartile range. Dashed lines show the fitted trends (linear in A, exponential in B). (c) Spatial distribution of the NEV ownership ranking score, representing the relative rank of NEV ownership among cities. Higher NEV ownership corresponds to redder colors, while lower ownership corresponds to greener colors. The administrative boundary basemap is derived from Amap. (d) Frequency distribution of NEV ownership across the 150 cities in 2023.

Extended Data Fig. 2 Comparison of the vehicle ownership data used in this study with data from China Statistical Yearbook at provincial level.

RE means relative error, defined as the ratio of the RMSE to the mean value.

Extended Data Fig. 3 Ten-fold cross-validation result of the retrieval model.

A1-E1 represent 10-fold cross-validation accuracy of phyMTDNN model 1 for pollutants PM2.5, NO2, CO, O3, and PM10. A2-E2 represent 10-fold cross-validation accuracy of phyMTDNN model 2 for pollutants PM2.5, NO2, CO, O3, and PM10.

Extended Data Fig. 4 Ten-fold cross-validation result of the RF-based fusion model.

A for PM2.5, B for NO2, C for CO, and D for PM10. For the results of each city in each year, we will establish a fusion model. Box plots here show the accuracy of the ensemble models for different cities (150 in total) and different years. The center line indicates the median; black star indicates the mean value; the box limits represent the interquartile range (25th–75th percentiles); whiskers extend to 1.5× the interquartile range; grey points indicate outliers.

Extended Data Fig. 5 Independent validation with the meteorological–PM2.5 dataset.

Scatter plot comparing retrieved and observed PM2.5 concentrations from the meteorological–PM2.5 dataset, with statistical metrics indicating model performance. The solid red line represents the linear fit, and the dashed black line indicates the 1:1 line. N denotes the sample size.

Extended Data Fig. 6 Scatterplot comparisons between our retrievals and the CHAP near-surface pollutant concentrations.

Validation results for (A) PM2.5, (B) PM10, (C) NO2, and (D) CO during 2019–2021. Our retrievals were resampled to 1 km resolution for comparison with the CHAP dataset.

Extended Data Fig. 7 Examples of retrieved air pollution maps.

Retrieved mean concentrations from 2013–2023 in Shanghai (A1–F1) and Wuhan (A2–F2) at 60-m resolution for PM2.5 (A1, A2), NO2 (B1, B2), CO (C1, C2) and PM10 (D1, D2). Panels E1 and E2 show the 2020 road networks of Shanghai and Wuhan, and F1–F2 present magnified views of the black rectangles in B1–B2. Zooming in reveals road network structures, particularly in NO2 maps, reflecting vehicle emissions. The administrative boundary basemap is derived from Amap.

Extended Data Table 1 Kruskal–Wallis test results
Full size table
Extended Data Table 2 Top 30 cities with the fastest growth rates of express parcel volume (unit: 10⁴ parcels, for the trend column, the unit is 10⁴ parcels per year)
Full size table
Extended Data Table 3 RF-based regression model accuracy for different NEV ownerships
Full size table

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Yang, Q., Yuan, Q., Yuan, Q. et al. Reduced urban air pollution and mortality from the transition to new energy vehicles in China. Nat. Health (2026). https://doi.org/10.1038/s44360-026-00120-2

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