No abstract

Increased extreme heat is among the clearest impacts of global warming, but the economic effects of heat waves are poorly understood. Using subnational economic data, extreme heat metrics measuring the temperature of the hottest several days in each year, and an ensemble of climate models, we quantify the effect of extreme heat intensity on economic growth globally. We find that human-caused increases in heat waves have depressed economic output most in the poor tropical regions least culpable for warming. Cumulative 1992–2013 losses from anthropogenic extreme heat likely fall between $5 trillion and $29.3 trillion globally. Losses amount to 6.7% of Gross Domestic Product per capita per year for regions in the bottom income decile, but only 1.5% for regions in the top income decile. Our results have the potential to inform adaptation investments and demonstrate how global inequality is both a cause and consequence of the unequal burden of climate change.

Quantifying which nations are culpable for the economic impacts of anthropogenic warming is central to informing climate litigation and restitution claims for climate damages. However, for countries seeking legal redress, the magnitude of economic losses from warming attributable to individual emitters is not known, undermining their standing for climate liability claims. Uncertainties compound at each step from emissions to global greenhouse gas (GHG) concentrations, GHG concentrations to global temperature changes, global temperature changes to country-level temperature changes, and country-level temperature changes to economic losses, providing emitters with plausible deniability for damage claims. Here we lift that veil of deniability, combining historical data with climate models of varying complexity in an integrated framework to quantify each nation’s culpability for historical temperature-driven income changes in every other country. We find that the top five emitters (the United States, China, Russia, Brazil, and India) have collectively caused US$6 trillion in income losses from warming since 1990, comparable to 14% of annual global gross domestic product; many other countries are responsible for billions in losses. Yet the distribution of warming impacts from emitters is highly unequal: high-income, high-emitting countries have benefited themselves while harming low-income, low-emitting countries, emphasizing the inequities embedded in the causes and consequences of historical warming. By linking individual emitters to country-level income losses from warming, our results provide critical insight into climate liability and national accountability for climate policy.

High‐impact poor air quality events, such as Beijing's so‐called “Airpocalypse” in January 2013, demonstrate that short‐lived poor air quality events can have significant effects on health and economic vitality. Poor air quality events result from the combination of the emission of pollutants and meteorological conditions favorable to their accumulation, which include limited scavenging, dispersion, and ventilation. The unprecedented nature of events such as the 2013 Airpocalypse, in conjunction with our nonstationary climate, motivate an assessment of whether climate change has altered the meteorological conditions conducive to poor winter air quality in Beijing. Using three indices designed to quantify the meteorological conditions that support poor air quality and drawing on the attribution methods of Diffenbaugh et al. (2017, https://doi.org/10.1073/pnas.1618082114), we assess (i) the contribution of observed trends to the magnitude of events, (ii) the contribution of observed trends to the probability of events, (iii) the return interval of events in the observational record, preindustrial model‐simulated climate and historical model‐simulated climate, (iv) the probability of the observed trend in the preindustrial and historical model‐simulated climates, and (v) the relative influences of anthropogenic forcing and natural variability on the observed trend. We find that anthropogenic influence has had a small effect on the probability of the January 2013 event in all three indices but has increased the probability of a long‐term positive trend in two out of three indices. This work provides a framework for both further understanding the role of climate change in air quality and expanding the scope of event attribution.