Impact of solar geoengineering on temperature-attributable mortality

Abstract

Decisions about solar geoengineering (SG) entail risk-risk tradeoffs between the direct risks of SG and SG's ability to reduce climate risks. Quantitative comparisons between these risks are needed to inform public policy. We evaluate idealized SG's effectiveness in reducing deaths from warming using two climate models and an econometric analysis of temperature-attributable mortality. We find SG's impact on temperature-attributable mortality is uneven with decreases for hotter, poorer regions and increases in cooler, richer regions. Relative to no SG, global mortality is reduced by over 400,000 deaths annually [90% CI: (-1.2 million,2.7 million)] for cooling of 1 °C from 2.5 °C above preindustrial in 2080. We find no evidence that mortality reduction achieved by SG is smaller than the reduction from equivalent cooling by emissions reductions. Combining our estimates with existing estimates of sulphate aerosol injection direct mortality risk from air quality and UV-attributable cancer enables the first quantitative risk-risk comparison of SG. We estimate with 61% probability that the mortality benefits of cooling outweigh these direct SG risks. We find the benefits outweigh these risks by 13 times for our central estimates, or 4 deaths per 100,000 per 1 °C per year [90% CI: (-11,23)]. This is not a comprehensive evaluation of the risk-risk tradeoffs around SG, yet by comparing some of the most consequential impacts on human welfare it is a useful first step. While these findings are robust to a variety of alternative assumptions, considerable uncertainties remain and require further investigation.

Keywords: human mortality; risk–risk analysis; solar geoengineering.

Fig. 1.

Regional mortality rate impact with income growth and climate adaptation. Panels (A) and (C) show the impact of SG on temperature-attributable mortality. Blue indicates regions SG reduces mortality risk. Red indicates regions SG increases mortality risk. Panels (B) and (D) show the difference in mortality risk impact between SG and emissions reductions. Blue indicates regions where mortality risk is lower in a world cooled with SG. Red indicates regions where mortality risk is higher in a world cooled with SG. Panels (A) and (B) report impacts as deaths per 100,000 per year per 1 °C. Panels (C) and (D) report impacts as deaths per area per year, converting mortality rates to number of deaths using population in 2080. Maps present estimates for the FLOR climate model simulations. Zonal averages for both the FLOR and GLENS climate model simulations are shown to the right of each map. FLOR estimates assume income growth to 2080 and climate adaptation to each respective climate model experiment. Estimates for the GLENS climate model are averaged over the 2050–2059 decade with income growth and climate adaptation.

Fig. 2.

Temperature response to SG relative to emissions cuts. Across columns from left to right we consider the response of dry-bulb temperature, wet-bulb temperature, wet-bulb globe temperature, and population-weighted zonal averages. Across rows from top to bottom we consider the response of annual mean temperature, heatwave intensity, and coldwave intensity. The intensity of cold (heat) extremes are measured as the 10th (90th) percentile of the rolling 5-d maximum (minimum) daily temperatures annually. Results for other percentiles can be found in SI Appendix, Figs. S3 and S4. Responses are measured using the ratio (r) that compares the effect of SG normalized per degree of global mean dry-bulb temperature change relative to the effect of emissions reductions normalized per degree of global mean dry-bulb temperature change. Displayed values are the median over 100 climate simulation years. Blue grid-cells indicate SG reduces temperatures more than emissions reductions and red grid-cells indicate SG reduces temperatures less than emissions reductions. Crosshatches indicate statistical significance at 90% confidence level using a Wilcoxon signed rank test corrected following the false discovery rate procedure. Right-hand subpanels show the population-weighted zonal average of the Left-hand subpanels.

Fig. 3.

Regional mortality rate impact by heat/cold with income growth and climate adaptation. Impact of SG on (A) heat-attributable mortality and (C) cold-attributable mortality. Blue indicates regions SG reduces mortality risk. Red indicates regions SG increases mortality risk. Difference in (B) heat-attributable and (D) cold-attributable mortality impact between SG and emissions reductions. Blue indicates regions where mortality risk is lower in a world cooled with SG. Red indicates regions where mortality risk is higher in a world cooled with SG. Impacts are reported as deaths per 100,000 per year per 1 °C. Maps present estimates for the FLOR climate model simulations. Zonal averages for both the FLOR and GLENS climate model simulations are shown to the right of each map. FLOR estimates assume income growth to 2080 and climate adaptation to each respective climate model experiment. Estimates for the GLENS climate model are averaged over the 2050–2059 decade with income growth and climate adaptation.

Fig. 4.

Uncertainty in global mortality rate impact. (A) Distribution of estimates of global mortality rate impact of SG normalized per degree of global mean cooling across climate model simulations and adaptation assumptions. (B) Distribution of estimates of difference in global mortality rate impact for SG relative to emissions reductions normalized per degree of global mean cooling across climate model simulations and adaptation assumptions. FLOR climate model estimates use SSP3 2080 population and SSP3 income for 2015 with no income growth and 2080 with income growth. Estimates for the GLENS climate model are averaged over the 2050 to 2059 decade and use SSP3 population and income. For each distribution of estimates, the middle dotted line denotes the median estimate and the upper and lower dotted lines denote the 90% CI. Numbers above and below distributions denote the percentage of estimates above and below 0, respectively.