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.

Health co-benefits of climate change mitigation depend on strategic power plant retirements and pollution controls

Abstract

Reducing CO2 emissions from fossil fuel- and biomass-fired power plants often also reduces air pollution, benefitting both climate and public health. Here, we examine the relationship of climate and health benefits by modelling individual electricity-generating units worldwide across a range of climate–energy policy scenarios. We estimate that ~92% of deaths related to power plant emissions during 2010–2018 occurred in low-income or emerging economies such as China, India and countries in Southeast Asia, and show that such deaths are quite sensitive to future climate–energy trajectories. Yet, minimizing future deaths will also require strategic retirements of super-polluting power plants and deployment of pollution control technologies. These findings underscore the importance of considering public health in designing and implementing climate–energy policies: improved air quality and avoided air pollution deaths are not an automatic and fixed co-benefit of climate mitigation.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Shares of PM2.5-related deaths from global power plants as of 2010.
Fig. 2: Mean annual change in CO2 emissions and PM2.5-related deaths.
Fig. 3: PM2.5 exposures and PM2.5-related deaths that are linked to emissions from global power plants.
Fig. 4: Cumulative avoided PM2.5-related deaths and CO2 emissions 2010–2050.

Data availability

The database GPED that supports the base-year findings of this study is available at http://www.meicmodel.org/dataset-gped.html. The base mortality incidences data during 2010–2018 are available at http://ghdx.healthdata.org/gbd-results-tool. The future base mortality incidences database is available at http://www.ifs.du.edu/ifs/frm_MainMenu.aspx. The future demographic structure database is available at https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=30. Emission data for other sectors are available at https://edgar.jrc.ec.europa.eu/emissions_data_and_maps. Emissions data of the power plants in scenarios produced that support the findings of this study are available at https://doi.org/10.5281/zenodo.5637476 (ref. 74).

Code availability

The code of the GEOS-Chem model to simulate the global PM2.5 concentrations is available at https://geos-chem.seas.harvard.edu/.

References

  1. 1.

    Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D. & Pozzer, A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367–371 (2015).

    CAS  Google Scholar 

  2. 2.

    Jackson, R. B. et al. Global energy growth is outpacing decarbonization. Environ. Res. Lett. 13, 120401 (2018).

    CAS  Google Scholar 

  3. 3.

    Tong, D. et al. Committed emissions from existing energy infrastructure jeopardize 1.5 °C climate target. Nature 572, 373–377 (2019).

    CAS  Google Scholar 

  4. 4.

    Tong, D. et al. Current emissions and future mitigation pathways of coal-fired power plants in China from 2010 to 2030. Environ. Sci. Technol. 52, 12905–12914 (2018).

    CAS  Google Scholar 

  5. 5.

    Wu, R. et al. Air quality and health benefits of China’s emission control policies on coal-fired power plants during 2005–2020. Environ. Res. Lett. 14, 094016 (2019).

    Google Scholar 

  6. 6.

    Ou, Y., West, J. J., Smith, S. J., Nolte, C. G. & Loughlin, D. H. Air pollution control strategies directly limiting national health damages in the US. Nat. Commun. 11, 957 (2020).

    CAS  Google Scholar 

  7. 7.

    West, J. J. et al. Co-benefits of mitigating global greenhouse gas emissions for future air quality and human health. Nat. Clim. Change 3, 885–889 (2013).

    CAS  Google Scholar 

  8. 8.

    Driscoll, C. T. et al. US power plant carbon standards and clean air and health co-benefits. Nat. Clim. Change 5, 535–540 (2015).

    CAS  Google Scholar 

  9. 9.

    Buonocore, J. J. et al. Health and climate benefits of different energy-efficiency and renewable energy choices. Nat. Clim. Change 6, 100–105 (2016).

    Google Scholar 

  10. 10.

    Shindell, D. T., Lee, Y. & Faluvegi, G. Climate and health impacts of US emissions reductions consistent with 2 °C. Nat. Clim. Change 6, 503–507 (2016).

    Google Scholar 

  11. 11.

    Millstein, D., Wiser, R., Bolinger, M. & Barbose, G. The climate and air-quality benefits of wind and solar power in the United States. Nat. Energy 2, 17134 (2017).

  12. 12.

    Silva, R. A. et al. Future global mortality from changes in air pollution attributable to climate change. Nat. Clim. Change 7, 647–651 (2017).

    Google Scholar 

  13. 13.

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

    Google Scholar 

  14. 14.

    Shindell, D., Faluvegi, G., Seltzer, K. & Shindell, C. Quantified, localized health benefits of accelerated carbon dioxide emissions reductions. Nat. Clim. Change 8, 291–295 (2018).

    CAS  Google Scholar 

  15. 15.

    Luderer, G. et al. Environmental co-benefits and adverse side-effects of alternative power sector decarbonization strategies. Nat. Commun. 10, 5229 (2019)..

  16. 16.

    Shindell, D. & Smith, C. J. Climate and air-quality benefits of a realistic phase-out of fossil fuels. Nature 573, 408–411 (2019).

    CAS  Google Scholar 

  17. 17.

    Scovronick, N. et al. The impact of human health co-benefits on evaluations of global climate policy. Nat. Commun. 10, 2095 (2019).

    Google Scholar 

  18. 18.

    Rogelj, J. et al. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).

    CAS  Google Scholar 

  19. 19.

    Rogelj, J. et al. in Special Report on Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) Ch. 2 (IPCC, WMO, 2018).

  20. 20.

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

    Google Scholar 

  21. 21.

    Luckow, P., Wise, M. A., Dooley, J. J. & Kim, S. H. Large-scale utilization of biomass energy and carbon dioxide capture and storage in the transport and electricity sectors under stringent CO2 concentration limit scenarios. Int. J. Greenh. Gas Control 4, 865–877 (2010).

    CAS  Google Scholar 

  22. 22.

    O'Neill, B. C. et al. A new scenario framework for climate change research: the concept of Shared Socioeconomic Pathways. Clim. Change 122, 387–400 (2014).

    Google Scholar 

  23. 23.

    Rao, A. B. et al. Evaluation of potential cost reductions from improved amine-based CO2 capture systems. Energy Policy 34, 3765–3772 (2006).

    Google Scholar 

  24. 24.

    van Horssen, A. et al. The Impacts of CO2 Capture Technologies in Power Generation and Industry on Greenhouse Gases Emissions and Air Pollutants in the Netherlands (TNO and Univ. of Utrecht, 2009); https://www.rivm.nl/bibliotheek/digitaaldepot/BOLK_II_CCS_Final-Version%20UPDATE%2028-07-2010.pdf

  25. 25.

    Air Pollution Impacts from Carbon Capture and Storage (CCS) EEA Technical Report No. 14/2011 (European Environment Agency, 2011); https://www.eea.europa.eu/publications/carbon-capture-and-storage

  26. 26.

    Koornneef, J. et al. Carbon Dioxide Capture and Air Quality: Chemistry, Emission Control, Radioactive Pollution and Indoor Air Quality (InTech, 2011); https://www.intechopen.com/chapters/16320

  27. 27.

    Bey, I. et al. Global modeling of tropospheric chemistry with assimilated meteorology: model description and evaluation. J. Geophys. Res. Atmos. 106, 23073–23095 (2001).

    CAS  Google Scholar 

  28. 28.

    Burnett, R. 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  Google Scholar 

  29. 29.

    Rauner, S. et al. Coal-exit health and environmental damage reductions outweigh economic impacts. Nat. Clim. Change 10, 308–312 (2020).

    Google Scholar 

  30. 30.

    Sampedro, J. et al. Quantifying the reductions in mortality from air-pollution by cancelling new coal power plants. Energy Clim. Change 2, 100023 (2021).

    Google Scholar 

  31. 31.

    Fofrich, R.A. et al. Early retirement of power plants in climate mitigation scenarios. Environ. Res. Lett. 15, 094064 (2020).

  32. 32.

    Sergi, B. J. et al. Optimizing emissions reductions from the U.S. power sector for climate and health benefits. Environ. Sci. Technol. 54, 7513–7523 (2020).

  33. 33.

    Hong, C. et al. Impacts of climate change on future air quality and human health in China. Proc. Natl Acad. Sci. USA 116, 17193–17200 (2019).

    CAS  Google Scholar 

  34. 34.

    van Vuuren, D. P. et al. The Representative Concentration Pathways: an overview. Clim. Change 109, 5–31 (2011).

    Google Scholar 

  35. 35.

    O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Google Scholar 

  36. 36.

    Davis, S. J. & Socolow, R. H. Commitment accounting of CO2 emissions. Environ. Res. Lett. 9, 084018 (2014).

    Google Scholar 

  37. 37.

    Cui, R. Y. et al. Quantifying operational lifetimes for coal power plants under the Paris goals. Nat. Commun. 10, 4759 (2019).

    Google Scholar 

  38. 38.

    Garbarino, E. et al. Best Available Techniques (BAT) Reference Document for the Management of Waste from Extractive Industries in accordance with Directive 2006/21/EC (Publications Office of the European Union, 2018); https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/best-available-techniques-bat-reference-document-management-waste-extractive-industries

  39. 39.

    Guideline on Best Available Technologies of Pollution Prevention and Control for Thermal Power Plant (Ministry of Ecology and Environment of the People’s Republic of China, 2016); http://www.mee.gov.cn/gkml/hbb/bgth/201610/t20161009_365147.htm

  40. 40.

    Koornneef, J. et al. The impact of CO2 capture in the power and heat sector on the emission of SO2, NOx, particulate matter, volatile organic compounds and NH3 in the European Union. Atmos. Environ. 44, 1369–1385 (2010).

    CAS  Google Scholar 

  41. 41.

    Brauer, M. et al. Ambient air pollution exposure estimation for the global burden of disease 2013. Environ. Sci. Technol. 50, 79–88 (2016).

    CAS  Google Scholar 

  42. 42.

    Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).

    Google Scholar 

  43. 43.

    Park, R. J., Jacob, D. J., Field, B. D., Yantosca, R. M. & Chin, M. Natural and transboundary pollution influences on sulfate–nitrate–ammonium aerosols in the United States: implications for policy. J. Geophys. Res. 109, D15204 (2004).

    Google Scholar 

  44. 44.

    Park, R. J., Jacob, D. J., Kumar, N. & Yantosca, R. M. Regional visibility statistics in the United States: natural and transboundary pollution influences, and implications for the Regional Haze Rule. Atmos. Environ. 40, 5405–5423 (2006).

    CAS  Google Scholar 

  45. 45.

    Park, R. J., Jacob, D. J., Chin, M. & Martin, R. V. Sources of carbonaceous aerosols over the United States and implications for natural visibility. J. Geophys. Res. 108, 4355 (2003).

    Google Scholar 

  46. 46.

    Liao, H., Henze, D. K., Seinfeld, J. H., Wu, S. & Mickley, L. J. Biogenic secondary organic aerosol over the United States: comparison of climatological simulations with observations. J. Geophys. Res. 112, D06201 (2007).

    Google Scholar 

  47. 47.

    Fairlie, D. T., Jacob, D. J. & Park, R. J. The impact of transpacific transport of mineral dust in the United States. Atmos. Environ. 41, 1251–1266 (2007).

    CAS  Google Scholar 

  48. 48.

    Zender, C. S., Bian, H. & Newman, D. Mineral dust entrainment and deposition (DEAD) model: description and 1990s dust climatology. J. Geophys. Res. 108, 4416 (2003).

    Google Scholar 

  49. 49.

    Alexander, B. et al. Sulfate formation in sea-salt aerosols: constraints from oxygen isotopes. J. Geophys. Res. 110, D10307 (2005).

    Google Scholar 

  50. 50.

    Jaeglé, L., Quinn, P. K., Bates, T. S., Alexander, B. & Lin, J. T. Global distribution of sea salt aerosols: new constraints from in situ and remote sensing observations. Atmos. Chem. Phys. 11, 3137–3157 (2011).

    Google Scholar 

  51. 51.

    Seinfeld, J. H. & Pankow, J. F. Organic atmospheric particulate material. Annu. Rev. Phys. Chem. 54, 121–140 (2003).

    CAS  Google Scholar 

  52. 52.

    Pye, H. O. T. et al. Effect of changes in climate and emissions on future sulfate–nitrate–ammonium aerosol levels in the United States. J. Geophys. Res. 114, D01205 (2009).

    Google Scholar 

  53. 53.

    Heald, C. L. et al. A large organic aerosol source in the free troposphere missing from current models. Geophys. Res. Lett. 32, L18809 (2005).

    Google Scholar 

  54. 54.

    van Donkelaar, A. et al. Analysis of aircraft and satellite measurements from the Intercontinental Chemical Transport Experiment (INTEX-B) to quantify long-range transport of East Asian sulfur to Canada. Atmos. Chem. Phys. 8, 2999–3014 (2008).

    Google Scholar 

  55. 55.

    Janssens-Maenhout, G. et al. HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution. Atmos. Chem. Phys. 15, 11411–11432 (2015).

    CAS  Google Scholar 

  56. 56.

    Bolshcer, M. et al. RETRO Deliverable D1-6 (RETRO Documentation, 2007).

  57. 57.

    Guenther, A. B. et al. The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions. Geosci. Model Dev. 5, 1471–1492 (2012).

    Google Scholar 

  58. 58.

    van der Werf, G. R. et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10, 11707–11735 (2010).

    Google Scholar 

  59. 59.

    Wang, Y., Jacob, D. J. & Logan, J. A. Global simulation of tropospheric O3-NOx-hydrocarbon chemistry: 1. Model formulation. J. Geophys. Res. 103, 10713–10725 (1998).

    CAS  Google Scholar 

  60. 60.

    Yienger, J. J. & Levy, H. Empirical model of global soil-biogenic NOx emissions. J. Geophys. Res. 100, 11447–11464 (1995).

    CAS  Google Scholar 

  61. 61.

    Murray, L. T., Jacob, D. J., Logan, J. A., Hudman, R. C. & Koshak, W. J. Optimized regional and interannual variability of lightning in a global chemical transport model constrained by LIS/OTD satellite data. J. Geophys. Res. 117, 20307 (2012).

    Google Scholar 

  62. 62.

    Ott, L. E. et al. Production of lightning NOx and its vertical distribution calculated from three-dimensional cloud-scale chemical transport model simulations. J. Geophys. Res. 115, D04301 (2010).

    Google Scholar 

  63. 63.

    Price, C. & Rind, D. Modeling global lightning distributions in a general circulation model. Mon. Weather Rev. 122, 1930–1939 (1994).

    Google Scholar 

  64. 64.

    Johnston, F. H. et al. Estimated global mortality attributable to smoke from landscape fires. Environ. Health Perspect. 120, 695–701 (2012).

    Google Scholar 

  65. 65.

    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).

    Google Scholar 

  66. 66.

    Jiang, X. et al. Revealing the hidden health costs embodied in Chinese exports. Environ. Sci. Technol. 49, 4381–4388 (2015).

    CAS  Google Scholar 

  67. 67.

    Lelieveld, J. et al. Loss of life expectancy from air pollution compared to other risk factors: a worldwide perspective. Cardiovasc. Res. 116, 1910–1917 (2020).

    CAS  Google Scholar 

  68. 68.

    Dicker, D. et al. Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1684–1735 (2018).

    Google Scholar 

  69. 69.

    Global Health Data Exchange (Institute for Health Metrics and Evaluation, accessed 17 March 2021); http://ghdx.healthdata.org/gbd-results-tool

  70. 70.

    Population Estimates and Projections (World Bank Group, 2011); https://databank.worldbank.org/source/population-estimates-and-projections

  71. 71.

    CIESIN Gridded Population of the World, Version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals, Revision 11 (NASA SEDAC, 2018).

  72. 72.

    Kc, S. & Lutz, W. The human core of the Shared Socioeconomic Pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017).

    Google Scholar 

  73. 73.

    Hughes, B. B. et al. Projections of global health outcomes from 2005 to 2060 using the International Futures integrated forecasting model. Bull. World Health Org. 89, 478–486 (2011).

    Google Scholar 

  74. 74.

    Tong, D. et al. Dantong2021/Dantong2021-Globalpower_in_scenarios: global power emissions. Zenono https://doi.org/10.5281/zenodo.5637476 (2021).

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant nos. 41921005 and 41625020) and the Energy Foundation (G-2009-32416). D.T. was supported by a gift to Carnegie Institution for Science from Gates Ventures LLC. C.H. and S.J.D. were supported by the US National Science Foundation (Innovations at the Nexus of Food, Energy and Water Systems grant no. EAR 1639318).

Author information

Affiliations

Authors

Contributions

Q.Z., D.T. and S.J.D. designed the study. D.T. performed the emission and health analyses with support from J.C., X.Q. and C.H. on analytical approaches. G.G. conducted GEOS-Chem simulations. D.T., S.J.D. and Q.Z. interpreted the data. D.T., S.J.D., G.G. and Q.Z. wrote the paper with input from all co-authors.

Corresponding authors

Correspondence to Qiang Zhang or Steven J. Davis.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Jan Steckel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 The framework of unit-level power emission projection model.

The figure shows the framework of unit-level power emission projection model developed for this study.

Extended Data Fig. 2 Regional average lifetimes for each retirement strategy.

The figure shows the regional average lifetimes of power plants for each retirement strategy.

Source data

Extended Data Fig. 3 Identified capacity and death contributions of 2010-coal super-polluting units.

The figure shows capacity and death contributions of 2010-coal super-polluting units in 2010 and 2018 across nine regions.

Source data

Extended Data Fig. 4 Mean annual change in CO2 emissions and PM2.5-related years of life lost.

The figure shows the relationship between annual average CO2 reduction rate and PM2.5-related years of life lost under the scenario assemble in (a) 2030 and (b) 2050, spanning four levels of climate ambition (RCP6.0, RCP4.5, RCP2.6, and RCP1.9) and three different retirement strategies (historical, performance-based, and early retirement) and two stringencies of pollution controls (that is strong and weak). The black circles show the mean annual change in CO2 emissions during 2010–2015 (and 2010-level PM2.5-related years of life lost), and 2010–2018 (and 2018-level PM2.5-related years of life lost), respectively.

Source data

Extended Data Fig. 5 Future emission reductions during 2010–2050 under various combined mitigation options.

The period during 2010–2018 show the real emission differences, equalling 0. The RCP6.0 with performance-based retirement and weak pollution control scenario was set as the base scenario for comparison, Figs. a1-a4 show the emission reductions among different ambitious climate–energy scenarios (that is RCP4.5, RCP2.6, and RCP1.9); Figs. b1-b4 show the emission changes among different retirement strategies (that is historical and early retirements) covering RCP6.0 and RCP1.9; Figs. c1-c4 show the emission reductions from weak to strong pollution controls covering RCP6.0 and RCP1.9.

Source data

Supplementary information

Supplementary Information

Supplementary Notes 1–7 and Figs. 1–9.

Supplementary Tables

Supplementary Tables 1–18.

Source data

Source Data Fig. 1

Source Data for Fig. 1.

Source Data Fig. 2

Source Data for Fig. 2.

Source Data Fig. 3

Source Data for Fig. 3.

Source Data Fig. 4

Source Data for Fig. 4.

Source Data Extended Data Fig. 2

Source Data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Source Data for Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Source Data for Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Source Data for Extended Data Fig. 5.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tong, D., Geng, G., Zhang, Q. et al. Health co-benefits of climate change mitigation depend on strategic power plant retirements and pollution controls. Nat. Clim. Chang. 11, 1077–1083 (2021). https://doi.org/10.1038/s41558-021-01216-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-021-01216-1

Further reading

Search

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