Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Efficacy of China’s clean air actions to tackle PM2.5 pollution between 2013 and 2020

Nature Geoscience volume 17, pages 987–994 (2024)Cite this article

Subjects

Abstract

Beginning in 2013, China launched two phases (2013–2017 and 2018–2020) of clean air actions that have led to substantial reductions in PM2.5 concentrations. However, improvement in PM2.5 pollution was notably slowing down during Phase II. Here we quantify the efficacy and drivers of PM2.5 improvement and evaluate the associated cost during 2013–2020 using an integrated framework that combines an emission inventory model, a chemical transport model and detailed cost information. We found that national population-weighted mean PM2.5 concentrations decreased by 19.8 μg m−3 and 10.9 μg m−3 in the two phases, and the contribution of clean air policies in Phase II (2.3 μg m−3 yr−1) was considerably lower than that of Phase I (4.5 μg m−3 yr−1), after excluding the impacts from meteorological condition changes and COVID-19 lockdowns. Enhanced structure transitions and targeted volatile organic compounds and NH3 reduction measures have successfully reduced emissions in Phase II, but measures focusing on the end-of-pipe control were less effective after 2017. From 2013 to 2020, PM2.5 abatement became increasingly challenging, with the average cost of reducing one unit of PM2.5 concentration in Phase II twice that of Phase I. Our results suggest there is a need for strengthened, well-balanced, emission control strategies for multi-pollutants.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Anthropogenic emissions in China from 2013 to 2020 and emission decreasing rates during the two phases.
Fig. 2: Reduction of PM2.5 concentrations from 2017 to 2020 in China and the three key regions.
Fig. 3: Drivers of PM2.5 variations from 2013 to 2020 over China.
Fig. 4: Contributions from the eight control measures to anthropogenic emission abatements, PM2.5 reductions and avoided premature deaths in phases I and II.
Fig. 5: The marginal abatement cost curve during the two phases of clean air actions.

Similar content being viewed by others

Data availability

The emission data developed by this work are publicly available from http://meicmodel.org.cn. The source data for figures presented in the main text and extended data are available at the figshare repository (https://doi.org/10.6084/m9.figshare.26411008 (ref. 58)). Source data are provided with this paper.

Code availability

The code for the WRF model is available at https://github.com/NCAR/WRFV3/releases/tag/V3.9 and the code for the CMAQ model is available at https://github.com/USEPA/CMAQ/tree/5.2.

References

  1. Cohen, A. J. et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 389, 1907–1918 (2017).

    Google Scholar 

  2. Yin, P. et al. The effect of air pollution on deaths, disease burden and life expectancy across China and its provinces, 1990–2017: an analysis for the Global Burden of Disease Study 2017. Lancet Planet. Health 4, e386–e398 (2020).

    Google Scholar 

  3. State Council of the People’s Republic of China. Notice of the General Office of the State Council on Issuing the Air Pollution Prevention and Control Action Plan. https://www.gov.cn/jrzg/2013-09/12/content_2486918.htm (accessed 30 June 2021).

  4. State Council of the People’s Republic of China. Notice of the General Office of the State Council on Issuing the Three-Year Action Plan for Winning the Blue Sky Defense Battle. http://www.gov.cn/zhengce/content/2018-07/03/content_5303158.htm (accessed 30 June 2021).

  5. Zhong, Q. et al. PM2.5 reductions in Chinese cities from 2013 to 2019 remain significant despite the inflating effects of meteorological conditions. One Earth 4, 448–458 (2021).

    Google Scholar 

  6. Geng, G. et al. Tracking air pollution in China: near real-time PM2.5 retrievals from multisource data fusion. Environ. Sci. Technol. 55, 12106–12115 (2021).

    CAS  Google Scholar 

  7. Liu, S. et al. Tracking daily concentrations of PM2.5 chemical composition in China since 2000. Environ. Sci. Technol. 56, 16517–16527 (2022).

    CAS  Google Scholar 

  8. Xiao, Q. et al. Tracking PM2.5 and O3 pollution and the related health burden in China 2013–2020. Environ. Sci. Technol. 56, 6922–6932 (2022).

    CAS  Google Scholar 

  9. Zhang, Q. et al. Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc. Natl Acad. Sci. USA 116, 24463–24469 (2019).

    CAS  Google Scholar 

  10. Geng, G. et al. Impact of China’s air pollution prevention and control action plan on PM2.5 chemical composition over eastern China. Sci. China Earth Sci. 62, 1872–1884 (2019).

    CAS  Google Scholar 

  11. Zheng, B. et al. Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 18, 14095–14111 (2018).

    CAS  Google Scholar 

  12. Tian, H. et al. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science 368, 638–642 (2020).

    CAS  Google Scholar 

  13. Zheng, B. et al. Changes in China’s anthropogenic emissions and air quality during the COVID-19 pandemic in 2020. Earth Syst. Sci. Data 13, 2895–2907 (2021).

    Google Scholar 

  14. Zheng, B. et al. Satellite-based estimates of decline and rebound in China’s CO2 emissions during COVID-19 pandemic. Sci. Adv. 6, eabd4998 (2020).

    CAS  Google Scholar 

  15. Liu, F. et al. Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19. Sci. Adv. 6, eabc2992 (2020).

    CAS  Google Scholar 

  16. Hammer, M. S. et al. Effects of COVID-19 lockdowns on fine particulate matter concentrations. Sci. Adv. 7, eabg7670 (2021).

    CAS  Google Scholar 

  17. Zhong, J. et al. Feedback effects of boundary-layer meteorological factors on cumulative explosive growth of PM2.5 during winter heavy pollution episodes in Beijing from 2013 to 2016. Atmos. Chem. Phys. 18, 247–258 (2018).

    CAS  Google Scholar 

  18. Zhai, S. et al. Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology. Atmos. Chem. Phys. 19, 11031–11041 (2019).

    CAS  Google Scholar 

  19. Yang, Y., Liao, H. & Lou, S. Increase in winter haze over eastern China in recent decades: roles of variations in meteorological parameters and anthropogenic emissions. J. Geophys. Res. Atmos. 121, 13,050–013,065 (2016).

    CAS  Google Scholar 

  20. Xiao, Q. et al. Separating emission and meteorological contributions to long-term PM2.5 trends over eastern China during 2000–2018. Atmos. Chem. Phys. 21, 9475–9496 (2021).

    CAS  Google Scholar 

  21. Xue, T. et al. Rapid improvement of PM2.5 pollution and associated health benefits in China during 2013–2017. Sci. China Earth Sci. 62, 1847–1856 (2019).

    Google Scholar 

  22. 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).

    Google Scholar 

  23. Li, M. et al. Anthropogenic emission inventories in China: a review. Natl Sci. Rev. 4, 834–866 (2017).

    CAS  Google Scholar 

  24. Shi, Q. et al. Co-benefits of CO2 emission reduction from China’s clean air actions between 2013–2020. Nat. Commun. 13, 5061 (2022).

    CAS  Google Scholar 

  25. Skamarock, W. C. et al. A Description of the Advanced Research WRF. Version 3, 113 (National Center for Atmospheric Research, 2008).

  26. CMAQ (version 5.2) (United States Environmental Protection Agency, 2017); https://doi.org/10.5281/zenodo.1167892

  27. Murray, C. J. L. et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1223–1249 (2020).

    Google Scholar 

  28. Wang, X., Dickinson, R. E., Su, L., Zhou, C. & Wang, K. PM2.5 pollution in China and how it has been exacerbated by terrain and meteorological conditions. Bull. Am. Meteorol. Soc. 99, 105–119 (2018).

    Google Scholar 

  29. Zhang, Y., Yin, Z., Wang, H. & He, S. 2020/21 record-breaking cold waves in east of China enhanced by the ‘Warm Arctic-Cold Siberia’ pattern. Environ. Res. Lett. 16, 094040 (2021).

    Google Scholar 

  30. Zhou, Z.-Q., Xie, S.-P. & Zhang, R. Historic Yangtze flooding of 2020 tied to extreme Indian Ocean conditions. Proc. Natl Acad. Sci. USA 118, e2022255118 (2021).

    CAS  Google Scholar 

  31. Li, H., Sun, B., Wang, H. & Yuan, X. Joint effects of three oceans on the 2020 super mei-yu. Atmos. Ocean. Sci. Lett. 15, 100127 (2022).

    Google Scholar 

  32. Marlier, M. E., Xing, J., Zhu, Y. & Wang, S. Impacts of COVID-19 response actions on air quality in China. Environ. Res. Commun. 2, 075003 (2020).

    Google Scholar 

  33. Levelt, P. F. et al. Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI. Atmos. Chem. Phys. 22, 10319–10351 (2022).

    CAS  Google Scholar 

  34. Sekiya, T. et al. The worldwide COVID-19 lockdown impacts on global secondary inorganic aerosols and radiative budget. Sci. Adv. 9, eadh2688 (2023).

    CAS  Google Scholar 

  35. Liu, Z. et al. Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic. Nat. Commun. 11, 5172 (2020).

    CAS  Google Scholar 

  36. Wang, Y., Zhang, Q., He, K., Zhang, Q. & Chai, L. Sulfate-nitrate-ammonium aerosols over China: response to 2000-2015 emission changes of sulfur dioxide, nitrogen oxides and ammonia. Atmos. Chem. Phys. 13, 2635 (2013).

    Google Scholar 

  37. Li, H. et al. Rapid transition in winter aerosol composition in Beijing from 2014 to 2017: response to clean air actions. Atmos. Chem. Phys. 19, 11485–11499 (2019).

    CAS  Google Scholar 

  38. WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide: Executive Summary (WHO, 2021).

  39. Zhai, S. et al. Control of particulate nitrate air pollution in China. Nat. Geosci. 14, 389–395 (2021).

    CAS  Google Scholar 

  40. 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  Google Scholar 

  41. Liu, Y. & Wang, T. Worsening urban ozone pollution in China from 2013 to 2017 – Part 2: the effects of emission changes and implications for multi-pollutant control. Atmos. Chem. Phys. 20, 6323–6337 (2020).

    CAS  Google Scholar 

  42. Le, T. et al. Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science 369, 702–706 (2020).

    CAS  Google Scholar 

  43. Huang, X. et al. Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. Natl Sci. Rev. 8, nwaa137 (2020).

    Google Scholar 

  44. Liu, Y. et al. Drivers of increasing ozone during the two phases of clean air actions in China 2013–2020. Environ. Sci. Technol. 57, 8954–8964 (2023).

    CAS  Google Scholar 

  45. Chen, Q. et al. Widespread 2013–2020 decreases and reduction challenges of organic aerosol in China. Nat. Commun. 15, 4465 (2024).

    CAS  Google Scholar 

  46. Streets, D. G. et al. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. J. Geophys. Res. Atmos. 108, 8809 (2003).

    Google Scholar 

  47. Zhang, Q. et al. Asian emissions in 2006 for the NASA INTEX-B mission. Atmos. Chem. Phys. 9, 5131–5153 (2009).

    CAS  Google Scholar 

  48. Crippa, M. et al. Gridded emissions of air pollutants for the period 1970-2012 within EDGAR v4.3.2. Earth Syst. Sci. Data 10, 1987–2013 (2018).

    Google Scholar 

  49. Zhao, Y., Nielsen, C. P., Lei, Y., McElroy, M. B. & Hao, J. Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China. Atmos. Chem. Phys. 11, 2295–2308 (2011).

    CAS  Google Scholar 

  50. Zhong, Z. et al. Recent developments of anthropogenic air pollutant emission inventories in Guangdong province, China. Sci. Total Environ. 627, 1080–1092 (2018).

    CAS  Google Scholar 

  51. Bauwens, M., Verreyken, B., Stavrakou, T., Müller, J. & De Smedt, I. Spaceborne evidence for significant anthropogenic VOC trends in Asian cities over 2005–2019. Environ. Res. Lett. 17, 015008 (2022).

    CAS  Google Scholar 

  52. Luo, Z. et al. Estimating global ammonia (NH3) emissions based on IASI observations from 2008 to 2018. Atmos. Chem. Phys. 22, 10375–10388 (2022).

    CAS  Google Scholar 

  53. Chang, X. et al. Full-volatility emission framework corrects missing and underestimated secondary organic aerosol sources. One Earth 5, 403–412 (2022).

    Google Scholar 

  54. Lu, Q. et al. Simulation of organic aerosol formation during the CalNex study: updated mobile emissions and secondary organic aerosol parameterization for intermediate-volatility organic compounds. Atmos. Chem. Phys. 20, 4313–4332 (2020).

    CAS  Google Scholar 

  55. Geng, G. et al. Drivers of PM2.5 air pollution deaths in China 2002–2017. Nat. Geosci. 14, 645–650 (2021).

    CAS  Google Scholar 

  56. Hu, J., Chen, J., Ying, Q. & Zhang, H. One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system. Atmos. Chem. Phys. 16, 10333–10350 (2016).

    CAS  Google Scholar 

  57. Wang, L. et al. Application of Weather Research and Forecasting Model with Chemistry (WRF/Chem) over northern China: sensitivity study, comparative evaluation and policy implications. Atmos. Environ. 124, 337–350 (2016).

    CAS  Google Scholar 

  58. Geng, G. & Zhang, Q. Efficacy of China’s clean air actions to tackle PM2.5 pollution between 2013 and 2020. figshare https://doi.org/10.6084/m9.figshare.26411008 (2024).

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (42222507 to G.G. and 41921005 to Q.Z.) and the New Cornerstone Science Foundation through the XPLORER PRIZE to Q.Z.

Author information

Authors and Affiliations

  1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China

    Guannan Geng, Yang Liu, Liu Yan, Hanwen Hu & Kebin He

  2. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China

    Yuxi Liu, Shigan Liu, Jing Cheng, Nana Wu, Dan Tong & Qiang Zhang

  3. State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing, China

    Yuxi Liu

  4. Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Bo Zheng

  5. Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China

    Zhicong Yin

Authors
  1. Guannan Geng
    View author publications

    Search author on:PubMed Google Scholar

  2. Yuxi Liu
    View author publications

    Search author on:PubMed Google Scholar

  3. Yang Liu
    View author publications

    Search author on:PubMed Google Scholar

  4. Shigan Liu
    View author publications

    Search author on:PubMed Google Scholar

  5. Jing Cheng
    View author publications

    Search author on:PubMed Google Scholar

  6. Liu Yan
    View author publications

    Search author on:PubMed Google Scholar

  7. Nana Wu
    View author publications

    Search author on:PubMed Google Scholar

  8. Hanwen Hu
    View author publications

    Search author on:PubMed Google Scholar

  9. Dan Tong
    View author publications

    Search author on:PubMed Google Scholar

  10. Bo Zheng
    View author publications

    Search author on:PubMed Google Scholar

  11. Zhicong Yin
    View author publications

    Search author on:PubMed Google Scholar

  12. Kebin He
    View author publications

    Search author on:PubMed Google Scholar

  13. Qiang Zhang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

G.G. and Q.Z. conceived the study. Yang Liu, J.C., L.Y., N.W., H.H., B.Z., D.T., G.G., K.H. and Q.Z. estimated China’s emissions. Yang Liu, D.T. and G.G. estimated the drivers of emission changes. Yuxi Liu, S.L. and J.C. conducted CMAQ simulations. Z.Y. contributed to the analysis of meteorological impacts. G.G. and Q.Z. interpreted the results. G.G. and Q.Z. wrote the paper, with input from all co-authors.

Corresponding author

Correspondence to Qiang Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Monica Crippa, Zbigniew Klimont, Jean-Francois Lamarque and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson, in collaboration with the Nature Geoscience team.

Additional information

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

Extended data

Extended Data Fig. 1 Summary of major control measures implemented during Phase I (2013–2017) and II (2018–2020).

The footnotes represent the title of each standard.

Extended Data Fig. 2 Anthropogenic emissions by sector in China during 2013–2020.

This is a supplement of Fig. 1, which presents the emission trends for PM10, BC, OC, and CO.

Source data

Extended Data Fig. 3 Emission trends compared with satellite- and ground-based observations.

The 2013–2020 trends in SO2 (blue solid curve) and NOx (orange solid curve) emissions are compared with OMI SO2 (blue dashed curve) and NO2 (orange dashed curve) tropospheric columns for eastern China (29°N–41°N, 108°E–123°E), respectively. The 2013–2020 trends in ground-based observations of SO2 (blue dotted curve) and NO2 (orange dotted curve) are also presented. Data are normalized by their corresponding value in 2013.

Source data

Extended Data Fig. 4 Drivers of emission changes of major air pollutants in China from 2013–2020.

Drivers of the national emission changes in (a) SO2, (b) NOx, (c) PM2.5, (d) NMVOC, (e) NH3, (f) PM10, (g) BC, (h) OC, and (i) CO. For each pollutant, the changes in emissions are decomposed into the drivers of activity rates and pollution controls by sector during Phases I and II. Numbers less than 0.01 Tg are not presented. Pow, Ind, Sol, Res, Tra and Agr represent power, industry, solvent use, residential, transportation and agriculture sector, respectively.

Source data

Extended Data Fig. 5 Comparison of meteorologically driven PM2.5 concentrations between 2017 and 2020.

PM2.5 simulations using fixed emissions at 2020 and meteorological conditions for 2017 and 2020.

Source data

Extended Data Fig. 6 Drivers of PM2.5 variations from 2017 to 2020 over the three key regions.

Estimations of drivers in (a) BTHSA, (b) YRD and (c) FWP. Values in parentheses show the 95% CI of our estimates.

Source data

Extended Data Fig. 7 Meteorologically driven variations in PM2.5 concentrations.

Monthly percentage anomalies of simulated meteorologically driven PM2.5 variations (population-weighted) and occurrence frequency of air stagnation days (population-weighted) from their 2017–2020 means for individual months in (a) China, (b) BTHSA, (c) YRD and (d) FWP.

Source data

Extended Data Fig. 8 Relative changes of major industrial and social-economic activities over China in each month between 2019 and 2020.

The row denotes different activities and the column represent each month.

Source data

Extended Data Fig. 9 Reduced PM2.5 concentrations from strengthening industrial emission standards and promoting clean fuels in the residential sectors.

Spatial distributions of the reduced PM2.5 concentrations contributed by (a) strengthening industrial emission standards and (b) promoting clean fuels in the residential sectors. (c) Daily variations of national population-weighted mean PM2.5 reductions contributed by these two measures.

Source data

Extended Data Fig. 10 Comparison of the changes between PM2.5 chemical composition concentrations and their precursor emissions during 2013–2020.

Comparison between (a) SO2 emissions and sulfate concentrations, (b) NOx emissions and nitrate concentrations, (c) NH3 emissions and ammonium concentrations, and (d) BC emissions and BC concentrations. PM2.5 chemical composition concentrations are national population-weighted mean excluding the impacts from changes in meteorological conditions. All data are percent changes relative to the value in 2013. The shades of the symbols’ colors denote the year.

Source data

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–15 and Tables 1–8.

Source data

Source Data Fig. 1

Estimated anthropogenic emissions by sector in China from 2013–2020.

Source Data Fig. 2

PM2.5 concentrations 2017–2020 in China and the three key regions from CMAQ simulations, TAP dataset and ground observations.

Source Data Fig. 3

Estimated impacts from meteorological variations, anthropogenic emission control and COVID-19 lockdown in China for phases I and II.

Source Data Fig. 4

Estimated contributions of the eight control measures to emission reduction, PM2.5 abatement and avoided premature deaths.

Source Data Fig. 5

Estimated cost for the eight control measures.

Source Data Extended Data Fig. 2

Estimated anthropogenic emissions by sector in China from 2013–2020.

Source Data Extended Data Fig. 3

Satellite- and ground-based observations from 2013–2020.

Source Data Extended Data Fig. 4

Estimated drivers of emission changes in major air pollutants in China from 2013–2020.

Source Data Extended Data Fig. 5

Monthly PM2.5 simulations under E20M17 and BASE20 scenario.

Source Data Extended Data Fig. 6

Estimated impacts from meteorological variations, anthropogenic emission control and COVID-19 lockdown in BTHSA, YRD and FWP in Phase II.

Source Data Extended Data Fig. 7

Monthly percentage anomalies of simulated meteorologically driven PM2.5 variations and occurrence frequency of air stagnation days from their 2017–2020 means for individual months.

Source Data Extended Data Fig. 8

Data of major industrial and social-economic activities over China in each month between 2019 and 2020.

Source Data Extended Data Fig. 9

Simulated national population-weighted mean PM2.5 from strengthening industrial emission standards and promoting clean fuels in the residential sectors.

Source Data Extended Data Fig. 10

Annual data for PM2.5 chemical composition concentrations and their precursor emissions during 2013–2020.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Geng, G., Liu, Y., Liu, Y. et al. Efficacy of China’s clean air actions to tackle PM2.5 pollution between 2013 and 2020. Nat. Geosci. 17, 987–994 (2024). https://doi.org/10.1038/s41561-024-01540-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-024-01540-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

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