Climate effects of aerosols reduce economic inequality

Abstract

The climate effects of anthropogenic aerosols have masked some of the warming induced by GHGs1 along with some impacts of that warming2. These temperature effects may be beneficial but are almost certainly overwhelmed by aerosols’ negative health impacts3. Recent analyses of economic impacts have concluded that warming harms economies in warm climates, but provides economic benefits in cold climates4. Here we investigate whether aerosol-induced cooling would have a positive effect on less wealthy economies in hotter regions and a negative effect on wealthier economies in colder regions. Climate simulations over the historical period both with and without anthropogenic aerosol emissions, using a fully coupled ocean and atmosphere climate model, indicate that in year 2010 anthropogenic aerosol emissions were cooling the Earth by 0.72 ± 0.02 °C relative to a scenario without such emissions. Due to opposing economic impacts in different regions, the net economic impact of aerosol-induced cooling is likely to be small at the global scale. However, these results suggest that the cooling effects of anthropogenic aerosols benefit developing tropical economies while harming developed high-latitude economies, and thus the temperature effects of past aerosol emissions have probably diminished global economic inequality.

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Fig. 1: Trend of global mean surface temperature and anthropogenic aerosol emissions.
Fig. 2: Spatial distribution of climatic and economic impacts introduced by anthropogenic aerosol emissions.
Fig. 3: Country-level temperature changes and the associated annual GDP increment induced by anthropogenic aerosols.
Fig. 4: Country-level percentage changes in GDP associated with anthropogenic aerosol-induced cooling.

Data availability

All data used in this study are available at https://github.com/YixuanZheng/Aerosol_Inequality_2019.

Code availability

All scripts used to support the findings of this study are available at https://github.com/YixuanZheng/Aerosol_Inequality_2019.

References

  1. 1.

    Charlson, R. J. et al. Climate forcing by anthropogenic aerosols. Science 255, 423–430 (1992).

  2. 2.

    Samset, B. H. et al. Climate impacts from a removal of anthropogenic aerosol emissions. Geophys. Res. Lett. 45, 1020–1029 (2018).

  3. 3.

    Lelieveld, J. et al. Effects of fossil fuel and total anthropogenic emission removal on public health and climate. Proc. Natl Acad. Sci. USA 116, 7192–7197 (2019).

  4. 4.

    Burke, M., Davis, W. M. & Diffenbaugh, N. S. Large potential reduction in economic damages under UN mitigation targets. Nature 557, 549–553 (2018).

  5. 5.

    Zhang, Q., He, K. & Huo, H. Cleaning China’s air. Nature 484, 161–162 (2012).

  6. 6.

    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).

  7. 7.

    Ebenstein, A., Fan, M., Greenstone, M., He, G. & Zhou, M. New evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River Policy. Proc. Natl Acad. Sci. USA 114, 10384–10389 (2017).

  8. 8.

    Albrecht, B. A. Aerosols, cloud microphysics, and fractional cloudiness. Science 245, 1227–1230 (1989).

  9. 9.

    Wu, P., Christidis, N. & Stott, P. Anthropogenic impact on Earth’s hydrological cycle. Nat. Clim. Change 3, 807–810 (2013).

  10. 10.

    Myhre, G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 659–740 (IPCC, Cambridge Univ. Press, 2013).

  11. 11.

    Diaz, D. & Moore, F. Quantifying the economic risks of climate change. Nat. Clim. Change 7, 774–782 (2017).

  12. 12.

    Pretis, F., Schwarz, M., Tang, K., Haustein, K. & Allen, M. R. Uncertain impacts on economic growth when stabilizing global temperatures at 1.5 °C or 2 °C warming. Phil. Trans. R. Soc. A 376, 20160460 (2018).

  13. 13.

    Ricke, K., Drouet, L., Caldeira, K. & Tavoni, M. Country-level social cost of carbon. Nat. Clim. Change 8, 895–900 (2018).

  14. 14.

    Moore, F. C. & Diaz, D. B. Temperature impacts on economic growth warrant stringent mitigation policy. Nat. Clim. Change 5, 127–131 (2015).

  15. 15.

    Newell, R., Prest, B. & Sexton, S. The GDP–Temperature Relationship: Implications for Climate Change Damages (RFF, 2018); https://go.nature.com/2RROmZ8

  16. 16.

    Larsen, J. N. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Barros, V. R. et al.) 1567–1612 (IPCC, Cambridge Univ. Press, 2014).

  17. 17.

    Hurrell, J. W. et al. The Community Earth System Model: a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339–1360 (2013).

  18. 18.

    Lamarque, J. F. et al. Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application. Atmos. Chem. Phys. 10, 7017–7039 (2010).

  19. 19.

    Riahi, K. et al. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change 109, 33–57 (2011).

  20. 20.

    Kiehl, J. T. & Briegleb, B. P. The relative roles of sulfate aerosols and greenhouse gases in climate forcing. Science 260, 311–314 (1993).

  21. 21.

    Ricke, K. L. & Caldeira, K. Maximum warming occurs about one decade after a carbon dioxide emission. Environ. Res. Lett. 9, 124002 (2014).

  22. 22.

    Baker, L. H. et al. Climate responses to anthropogenic emissions of short-lived climate pollutants. Atmos. Chem. Phys. 15, 8201–8216 (2015).

  23. 23.

    Xie, S.-P., Lu, B. & Xiang, B. Similar spatial patterns of climate responses to aerosol and greenhouse gas changes. Nat. Geosci. 6, 828–832 (2013).

  24. 24.

    Cohen, J. et al. Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci. 7, 627–637 (2014).

  25. 25.

    Hartmann, D. L. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 159–254 (IPCC, Cambridge Univ. Press, 2013).

  26. 26.

    Menary, M. B. et al. Mechanisms of aerosol-forced AMOC variability in a state of the art climate model. J. Geophys. Res. Oceans 118, 2087–2096 (2013).

  27. 27.

    Persad, G. G. & Caldeira, K. Divergent global-scale temperature effects from identical aerosols emitted in different regions. Nat. Commun. 9, 3289 (2018).

  28. 28.

    Mastrandrea, M. D. et al. The IPCC AR5 guidance note on consistent treatment of uncertainties: a common approach across the working groups. Climatic Change 108, 675–691 (2011).

  29. 29.

    Sala-i-Martin, X. The world distribution of income: falling poverty and… convergence, period. Q. J. Econ. 121, 351–397 (2006).

  30. 30.

    Diffenbaugh, N. S. & Burke, M. Global warming has increased global economic inequality. Proc. Natl Acad. Sci. USA 116, 9808–9813 (2019).

  31. 31.

    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).

  32. 32.

    Santer, B. D. et al. Statistical significance of trends and trend differences in layer-average atmospheric temperature time series. J. Geophys. Res. Atmos. 105, 7337–7356 (2000).

  33. 33.

    Liu, X. et al. Toward a minimal representation of aerosols in climate models: description and evaluation in the Community Atmosphere Model CAM5. Geosci. Model Dev 5, 709–739 (2012).

  34. 34.

    Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

  35. 35.

    World Bank Open Data (World Bank, accessed 22 September 2018); https://data.worldbank.org/

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Acknowledgements

We thank F. Tong and L. Duan from the Department of Global Ecology, Carnegie Institution for Science, for their helpful comments on this work.

Author information

Y.Z., S.J.D. and K.C. conceived the study. G.G.P. helped with climate modelling. Y.Z. ran model simulations, conducted analysis and wrote the manuscript with contributions from all co-authors.

Correspondence to Ken Caldeira.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Luke Harrington, James Rising 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 Trend of global mean surface temperature in different forcing scenarios.

(a) Global mean surface temperature anomalies for scenarios with anthropogenic aerosol and without anthropogenic sulfate aerosol emissions and their difference. (b) Global mean surface temperature anomalies for scenarios without anthropogenic aerosol and anthropogenic sulfate emissions and their difference. Shaded lines show the 20-year moving averages (that is, centered on 1 January of the reported year) of temperature anomalies and the standard errors. Filled dots represent the simulated annual mean surface temperature anomalies. Temperature anomalies are calculated relative to the simulated preindustrial climatology (see Methods). Standard errors of the 20-year moving average of temperature anomalies are calculated with effective sample sizes adjusted for autocorrelation32.

Extended Data Fig. 2 Population distribution of climatic and economic impacts of anthropogenic aerosol emissions.

Identical to Fig. 2a–c but shows the population distribution: (a) Distribution of population exposed to aerosol optical depth; (b) distribution of population exposed to aerosol-induced temperature changes; (c) distribution of population exposed to different delays in GHGs-induced warming due to anthropogenic aerosols (that is, year in which 2010 temperatures would have been experienced without anthropogenic aerosol emissions). (d) Distribution of population exposed to percentage GDP changes associated with aerosol-induced cooling. Note that the scale of y-axis varies by subplots.

Extended Data Fig. 3 Zonal mean climatological surface air temperature from With-Aerosol scenario (a) and changes induced by anthropogenic aerosols (b).

Changes in zonal mean climatological surface air temperature induced by all aerosols is depicted by purple line and by sulfate only is depicted by the black line in (b).

Extended Data Fig. 4 Spatial distribution of climatic and economic impacts introduced by anthropogenic sulfate aerosol emissions.

Same as Fig. 2 but shows the results for the impacts of anthropogenic sulfate aerosols. (a) Changes in aerosol optical depth at 550 nm (unitless) in response to anthropogenic sulfate aerosols averaged over 2000–19. (b) Changes in surface air temperature in response to anthropogenic sulfate aerosols averaged over 2000-19. (c) The year that year 2010 temperature (averaged over 2000-19) would have occurred without anthropogenic sulfate aerosols. Results are calculated by comparing year 2010 temperature from the control scenario (that is, With-Aerosol scenario) with historical temperature (20-year running average) derived from the scenario with anthropogenic sulfate emissions set to 1850 levels (that is, No-Sulfate scenario); difference between 2010 and the central year of the matched time period is illustrated. (d) Country-level estimates of changes in annual GDP in response to anthropogenic sulfate-induced cooling averaged over 2000-2019 based on Burke et al.4 response functions (see Methods). Grid markings in (b) and (c) indicate regions that sulfate-induced temperature changes are not statistically significant at the 95% confidence level via one-sample t-test with an effective sample size adjusted for autocorrelation32. Countries and regions with missing value are shaded in grey in (d).

Extended Data Fig. 5 The probability that cooling associated with anthropogenic aerosols has resulted in economic benefits at the country-level.

(a) The probability associated with cooling induced by all considered aerosol (that is, OC, BC, and sulfate); (b) the probability associated with sulfate-induced cooling. The probability is calculated as the percentage of the ~8,000 realizations (see Methods) that show an increase in country-level GDP relative to a counterfactual world without anthropogenic aerosols. Here, low probability of benefits means a high probability of damages.

Extended Data Fig. 6 Spatial distribution of economic impacts introduced by anthropogenic aerosol-induced cooling.

Median estimates (of ~8,000 realizations, see Methods) of changes in annual GDP in response to anthropogenic aerosol-induced cooling averaged over 2000-19 based on Burke et al.4 response functions. The ocean and large lakes are masked in white. Antarctica is also masked in white as it does not have any associated GDP.

Extended Data Fig. 7 Country-level percentage changes in GDP associated with anthropogenic aerosol-induced cooling.

Countries are sorted by percentage change in GDP attributable to aerosol-induced cooling from the lowest on the left to the highest on the right. The x axis shows the share of global GDP for each country. The area of each bar represents the country-specific fraction of aerosol-induced cooling GDP changes out of global GDP in 2010. Bars are shaded by the country-level GDP per capita in 2010 (2010 US $). Countries that have large GDP increases from aerosol-induced cooling tend to have low per capita GDP, whereas countries that have large GDP decreases tend to have high per capita GDP. Error bars show the 90% CI (see Methods), which are plotted for countries with global GDP share larger than 0.5%.

Extended Data Fig. 8 GDP increment introduced by aerosol-induced cooling among the G20 nations.

(a) Median estimates (of ~8,000 realizations, see Methods) on changes in GDP in 2010 introduced by anthropogenic aerosols; (b) median estimates (of ~8,000 realizations, see Methods) on percentage changes in GDP in 2010 introduced by anthropogenic aerosols. Positive values indicate net benefits in GDP induced by the cooling effects of anthropogenic aerosols. Bars are shaded by the year 2010 population-weighted temperature of each country (derived from the ERA-Interim Reanalysis dataset, see Methods).

Extended Data Fig. 9 Impact of aerosol-induced cooling on country-level economic inequality.

Changes in global economic inequality were calculated as the percentage changes in the ratio of per capita GDP between the top and bottom population-weighted deciles (that is, 90:10 ratio) and quintiles (that is, 80:20 ratio) relative to the counterfactual world without anthropogenic aerosols. For example, the 50% line in the left box means that half of the ~8,000 combinations of response function parameters and model simulation ensemble members have >1% reduction in the ratio of country-level per-capita-GDP of the (population-weighted) 10% richest to 10% poorest countries – indicating that aerosol-induced cooling tend to reduce global inequality.

Extended Data Fig. 10 Impact of sulfate-induced cooling on country-level economic inequality.

Changes in global economic inequality were calculated as the percentage changes in the ratio of per capita GDP between the top and bottom population-weighted deciles (that is, 90:10 ratio) and quintiles (that is, 80:20 ratio) relative to the counterfactual world without anthropogenic sulfate aerosols. For example, the 50% line in the left box means that half of the ~8,000 combinations of response function parameters and model simulation ensemble members have >0.5% reduction in the ratio of country-level per-capita-GDP of the (population-weighted) 10% richest to 10% poorest countries – indicating that sulfate-induced cooling tend to reduce global inequality.

Supplementary information

Supplementary Information

Supplementary Discussion 1 and 2, Figs. 1–7 and Table 1.

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Zheng, Y., Davis, S.J., Persad, G.G. et al. Climate effects of aerosols reduce economic inequality. Nat. Clim. Chang. 10, 220–224 (2020). https://doi.org/10.1038/s41558-020-0699-y

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