Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Air quality and health benefits from fleet electrification in China


China has emerged as a leading electric vehicle (EV) market, accounting for approximately half of the global EV sales volume. We employed an atmospheric chemistry model to evaluate the air quality impacts from multiple scenarios by considering various EV penetration levels in China and assessed the avoided premature mortality attributed to fine particulate matter and ozone pollution. We find higher fleet electrification ratios can synergistically deliver greater air quality, climate and health benefits. For example, electrifying 27% of private vehicles and a larger proportion of certain commercial fleets can readily reduce the annual concentrations of fine particulate matter, nitrogen dioxide and summer concentrations of ozone by 2030. This scenario can reduce the number of annual premature deaths nationwide by 17,456 (95% confidence interval: 10,656–22,160), with the Beijing–Tianjin–Hebei, Yangtze River Delta and Pearl River Delta regions accounting for ~37% of the total number. The high concentration of health benefits in populous megacities implies that their municipal governments should promote more supportive local incentives. This study further reveals that fleet electrification in China could have more health benefits than net climate benefits in the next decade, which should be realized by policymakers to develop cost-effective strategies for EV development.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Changes in annual average PM2.5 concentration in Scenario EV compared with Scenario w/o EV.
Fig. 2: Changes in annual average NO2 concentration in Scenario EV compared with Scenario w/o EV.
Fig. 3: Changes in the monthly average 8 h maximum O3 concentration in Scenario EV compared with Scenario w/o EV.
Fig. 4: Avoided premature deaths and economic benefits in Scenario EV compared with Scenario w/o EV.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

Code availability

The code that supports the findings of this study is available from the corresponding author upon request.


  1. 1.

    Williams, J. H. et al. The technology path to deep greenhouse gas emissions cuts by 2050: the pivotal role of electricity. Science 335, 53–59 (2012).

    CAS  Article  Google Scholar 

  2. 2.

    Melton, N., Axsen, J. & Sperling, D. Moving beyond alternative fuel hype to decarbonize transportation. Nat. Energy 1, 16013 (2016).

    Article  Google Scholar 

  3. 3.

    Global EV Outlook 2019: Scaling-Up the Transition to Electric Mobility (IEA, 2019).

  4. 4.

    Wu, Y. et al. On-road vehicle emissions and their control in China: a review and outlook. Sci. Total Environ. 574, 332–349 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Mock, P. & Yang, Z. Driving Electrification: A Global Comparison of Fiscal Incentive Policy for Electric Vehicles (ICCT, 2014).

  6. 6.

    China Association of Automobile Manufacturers: the annual sales of new energy vehicles exceeded 1.25 million in 2018. D1EV (2019).

  7. 7.

    Ke, W. et al. Assessing the future vehicle fleet electrification: the impacts on regional and urban air quality. Environ. Sci. Technol. 51, 1007–1016 (2016).

    Article  Google Scholar 

  8. 8.

    Peng, W., Yang, J., Lu, X. & Mauzerall, D. L. Potential co-benefits of electrification for air quality, health, and CO2 mitigation in 2030 China. Appl. Energy 218, 511–519 (2018).

    CAS  Article  Google Scholar 

  9. 9.

    Soret, A., Guevara, M. & Baldasano, J. M. The potential impacts of electric vehicles on air quality in the urban areas of Barcelona and Madrid (Spain). Atmos. Environ. 99, 51–63 (2014).

    CAS  Article  Google Scholar 

  10. 10.

    Petroff, A. These countries want to ditch gas and diesel cars. CNN Business (2017).

  11. 11.

    Jonson, J. E. et al. Impact of excess NOx emissions from diesel cars on air quality, public health and eutrophication in Europe. Environ. Res. Lett. 12, 094017 (2017).

    Article  Google Scholar 

  12. 12.

    Anenberg, S. C. et al. Impacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets. Nature 545, 467–471 (2017).

    CAS  Article  Google Scholar 

  13. 13.

    Bernard, Y., German, J., Kentroti, A. & Muncrief, R. Catching Defeat Devices: How Systematic Vehicle Testing Can Determine the Presence of Suspicious Emissions Control Strategies (ICCT, 2019).

  14. 14.

    The European Parliament & the Council of the European Union Regulation (EU) 2019/631: Setting CO2 emission performance standards for new passenger cars and for new light commercial vehicles, and repealing Regulations (EC) No 443/2009 and (EU) No 510/2011. EUR-Lex (2019).

  15. 15.

    Huo, H., Zhang, Q., Liu, F. & He, K. Climate and environmental effects of electric vehicles versus compressed natural gas vehicles in China: a life-cycle analysis at provincial level. Environ. Sci. Technol. 47, 1711–1718 (2013).

    CAS  Article  Google Scholar 

  16. 16.

    Ke, W., Zhang, S., He, X., Wu, Y. & Hao, J. Well-to-wheels energy consumption and emissions of electric vehicles: mid-term implications from real-world features and air pollution control progress. Appl. Energy 188, 367–377 (2017).

    CAS  Article  Google Scholar 

  17. 17.

    Thompson, T. M., King, C. W., Allen, D. T. & Webber, M. E. Air quality impacts of plug-in hybrid electric vehicles in Texas: evaluating three battery charging scenarios. Environ. Res. Lett. 6, 24004–24015 (2011).

    Article  Google Scholar 

  18. 18.

    Schnell, J. L. et al. Air quality impacts from the electrification of light-duty passenger vehicles in the United States. Atmos. Environ. 208, 95–102 (2019).

    CAS  Article  Google Scholar 

  19. 19.

    Tessum, C. W., Hill, J. D. & Marshall, J. D. Life cycle air quality impacts of conventional and alternative light-duty transportation in the United States. Proc. Natl Acad. Sci. USA 111, 18490–18495 (2014).

    CAS  Article  Google Scholar 

  20. 20.

    2018–2020 Three-Year Action Plan for Winning the Blue Sky War Guofa No. 22 (State Council, 2018).

  21. 21.

    Li, K. et al. Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China. Proc. Natl Acad. Sci. USA 116, 422–427 (2019).

    CAS  Article  Google Scholar 

  22. 22.

    China 2030: Building a Modern, Harmonious, and Creative Society (World Bank & Development Research Center of the State Council, 2013).

  23. 23.

    Chen, X. et al. Impacts of fleet types and charging modes for electric vehicles on emissions under different penetrations of wind power. Nat. Energy 3, 413–421 (2018).

    CAS  Article  Google Scholar 

  24. 24.

    Tong, D. et al. Targeted emission reductions from global super-polluting power plant units. Nat. Sustain. 1, 59–68 (2018).

    Article  Google Scholar 

  25. 25.

    Peng, W. et al. Managing China’s coal power plants to address multiple environmental objectives. Nat. Sustain 1, 693–701 (2018).

    Google Scholar 

  26. 26.

    Kempton, W. & Tomić, J. Vehicle-to-grid power implementation: from stabilizing the grid to supporting large-scale renewable energy. J. Power Sources 144, 280–294 (2005).

    CAS  Article  Google Scholar 

  27. 27.

    Hall, D., Cui, H. & Lutsey, N. Electric Vehicle Capitals of the World: What Markets Are Leading the Transition to Electric? (ICCT, 2017).

  28. 28.

    He, X. et al. Individual trip chain distributions for passenger cars: implications for market acceptance of battery electric vehicles and energy consumption by plug-in hybrid electric vehicles. Appl. Energy 180, 650–660 (2016).

    Article  Google Scholar 

  29. 29.

    Zhang, S. et al. Fine-grained vehicle emission management using intelligent transportation system data. Environ. Pollut. 241, 1027–1037 (2018).

    CAS  Article  Google Scholar 

  30. 30.

    China Announced 2019 Subsidies for New Energy Vehicles (ICCT, 2019).

  31. 31.

    China Manufacturing 2025 Key Areas of Technology Roadmap (in Chinese) (Advisory Committee of National Manufacturing Power-Building Strategy, 2015).

  32. 32.

    Wang, S. et al. Target and measures to prevent and control ambient fine particle pollution in China (in Chinese). Chin. J. Environ. Manag. 7, 37–43 (2015).

    CAS  Google Scholar 

  33. 33.

    Wang, S. X. et al. Emission trends and mitigation options for air pollutants in East Asia. Atmos. Chem. Phys. 14, 6571–6603 (2014).

    Article  Google Scholar 

  34. 34.

    Zhao, B. et al. Change in household fuels dominates the decrease in PM2.5 exposure and premature mortality in China in 2005–2015. Proc. Natl Acad. Sci. USA 115, 12401–12406 (2018).

    CAS  Article  Google Scholar 

  35. 35.

    Yang, D. et al. High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets. Atmos. Chem. Phys. 19, 8831–8843 (2019).

    CAS  Article  Google Scholar 

  36. 36.

    Shen, W., Han, W. & Wallington, T. J. Current and future greenhouse gas emissions associated with electricity generation in China: implications for electric vehicles. Environ. Sci. Technol. 48, 7069–7075 (2014).

    CAS  Article  Google Scholar 

  37. 37.

    Wang, S. et al. Verification of anthropogenic emissions of China by satellite and ground observations. Atmos. Environ. 45, 6347–6358 (2011).

    CAS  Article  Google Scholar 

  38. 38.

    Zhao, B. et al. Quantifying the effect of organic aerosol aging and intermediate-volatility emissions on regional-scale aerosol pollution in China. Sci. Rep. 6, 28815 (2016).

    CAS  Article  Google Scholar 

  39. 39.

    Zhao, B. et al. Impact of national NOx and SO2 control policies on particulate matter pollution in China. Atmos. Environ. 77, 453–463 (2013).

    CAS  Article  Google Scholar 

  40. 40.

    Shen, H. et al. Urbanization-induced population migration has reduced ambient PM2.5 concentrations in China. Sci. Adv. 3, e1700300 (2017).

    Article  Google Scholar 

  41. 41.

    Rockhill, B., Newman, B. & Weinberg, C. Use and misuse of population attributable fractions. Am. J. Public Health 88, 15–19 (1998).

    CAS  Article  Google Scholar 

  42. 42.

    Flegal, K. M., Panagiotou, O. A. & Graubard, B. I. Estimating population attributable fractions to quantify the health burden of obesity. Ann. Epidemiol. 25, 201–207 (2015).

    Article  Google Scholar 

  43. 43.

    Zheng, X. et al. Association between air pollutants and asthma emergency room visits and hospital admissions in time series studies: a systematic review and meta-analysis. PLoS ONE 10, e0138146 (2015).

    Article  Google Scholar 

  44. 44.

    Ding, D., Xing, J., Wang, S., Liu, K. & Hao, J. Estimated contributions of emissions controls, meteorological factors, population growth, and changes in baseline mortality to reductions in ambient PM2.5 and PM2.5-related mortality in China, 2013–2017. Environ. Health Perspect. 127, 067009 (2019).

    Article  Google Scholar 

  45. 45.

    Yin, P. et al. Long-term fine particulate matter exposure and nonaccidental and cause-specific mortality in a large national cohort of Chinese men. Environ. Health Perspect. 125, 117002 (2017).

    Article  Google Scholar 

  46. 46.

    Burnett, R. T. et al. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc. Natl Acad. Sci. USA 115, 9592–9597 (2018).

    CAS  Article  Google Scholar 

  47. 47.

    Burnett, R. T. et al. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environ. Health Perspect. 122, 397–403 (2014).

    Article  Google Scholar 

  48. 48.

    Xue, T. et al. Change in the number of PM2.5-attributed deaths in China from 2000 to 2010: comparison between estimations from census-based epidemiology and pre-established exposure–response functions. Environ. Int. 129, 430–437 (2019).

    Article  Google Scholar 

  49. 49.

    Stanaway, J. D. et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1923–1994 (2018).

    Article  Google Scholar 

  50. 50.

    Hammitt, J. K. & Graham, J. D. Willingness to pay for health protection: inadequate sensitivity to probability? J. Risk Uncertain. 18, 33–62 (1999).

    Article  Google Scholar 

  51. 51.

    Kan, H. et al. Differentiating the effects of fine and coarse particles on daily mortality in Shanghai, China. Environ. Int. 33, 376–384 (2007).

    Article  Google Scholar 

  52. 52.

    Deng, X. Economic costs of motor vehicle emissions in China: a case study. Transp. Res. D. 11, 216–226 (2006).

    Article  Google Scholar 

  53. 53.

    Wang, H. & Mullahy, J. Willingness to pay for reducing fatal risk by improving air quality: a contingent valuation study in Chongqing, China. Sci. Total Environ. 367, 50–57 (2006).

    CAS  Article  Google Scholar 

  54. 54.

    Li, M. et al. Air quality co-benefits of carbon pricing in China. Nat. Clim. Change 8, 398–403 (2018).

    Article  Google Scholar 

  55. 55.

    Guidelines for Preparing Economic Analyses (EPA, 2014).

  56. 56.

    Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis ­ Under Executive Order 12866 (Interagency Working Group on Social Cost of Greenhouse Gases, US Government, 2016).

  57. 57.

    Revesz, R. et al. Best cost estimate of greenhouse gases. Science 357, 655 (2017).

    CAS  Article  Google Scholar 

  58. 58.

    IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer, L. A.) (IPCC, 2014).

  59. 59.

    Long, Y. Redefining Chinese city system with emerging new data. Appl. Geogr. 75, 36–48 (2016).

    Article  Google Scholar 

Download references


We are grateful to the National Key Research and Development Program of China (grant no. 2017YFC0212100) and the National Natural Science Foundation of China (grant no. 91544222 and 21625701) for supporting this research. S.Z. was supported by the David R. Atkinson Center for a Sustainable Future while at Cornell University. X.H. is supported by the University of Michigan–Ford Alliance Project. K.M.Z. acknowledges his support from the National Science Foundation through grant no. 1605407.

Author information




X.L. and S.Z. contributed equally to this study. X.L., Y.W., S.Z. and J.H. conceived the research idea; X.L., S.W., J.X. and S.Z. prepared the emission inventory data; X.L. and J.X. conducted air quality modelling and health impact assessments; X.H. provided the individual travel pattern dataset and UF analytic method; X.L., Y.W. and S.Z. analysed the data; J.H., J.X. and S.W. provided valuable discussions; X.L., Y.W., K.M.Z. and S.Z. wrote the paper with contributions from all authors.

Corresponding author

Correspondence to Ye Wu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary methods, Notes 1–3, tables, Figs. 1–15 and refs. 1–44.

Reporting Summary

Supplementary Datasets

Includes data organized in three tables.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liang, X., Zhang, S., Wu, Y. et al. Air quality and health benefits from fleet electrification in China. Nat Sustain 2, 962–971 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing