Conference Agenda

People use weather forecasts to limit mortality from extreme temperatures, but relying on weather forecasts leaves them more exposed to errors in those forecasts. Exploiting daily variation in temperatures and temperature forecast errors between 2004 and 2022, we show that the U.S. population especially relies on forecasts on hot days. Extrapolating to the end of the 21st century, we estimate that climate change will cause an additional 3,600 heat-related deaths per year as the frequency of hot days increases. Disentangling the effects of predicted and surprising heat shows that a large fraction of these deaths can be prevented as they are due to inaccurate forecasts. We

predict that mortality on hot days can be reduced substantially to 900 deaths per year if forecast accuracy continues to improve at it’s historical rate. In ongoing work, we elicit experts’ opinions about the likely future improvement in forecast accuracy.

Using the meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and the workplace accident records from China Stock Market and Accounting Research (CSMAR) database, we investigate the impact of extreme temperatures on workplace accidents via fixed effect model, Poisson Pseudo Maximum Likelihood (PPML) estimation and Causal Forest-based DML. By referencing historical temperature distributions and constructing biometeorological indicators such as Heat Index (HI) and Wind Chill Index (WCI), we capture the extreme temperature events at both daily and hourly levels. Our findings indicate that both extreme heat and extreme cold increase the occurrence of workplace accidents, and the impacts of extreme temperature shocks on workers exhibit different patterns in the short term and long term. The potential mechanism through which extreme temperatures affect workplace accidents is by undermining workers’ positive mindset and reducing their work quality. Additionally, by employing a Causal Forest-based DML model, we investigate the non-linear heterogeneous variations in extreme temperature effects under different levels of air pollution, meteorological conditions, and urban development. Finally, we estimate the additional workplace injury and wage premium caused by extreme temperature events, and confirm the necessity of heat allowances. The findings of this study also provide some enlightenment into adapting to extreme temperatures from perspectives such as early warning systems, infrastructure development, and individual-level behavior.

This paper examines the impacts of climate extremes and variability on food security in Nigeria, focusing specifically on the aspect of households’ access to food. Employing high-resolution climate data and five waves of household-level surveys from 2010 to 2024, we analyse how temperature and precipitation extremes and climate anomalies affect food security indicators. In Nigeria, temperatures have exhibited a rapid increase over recent decades, accompanied by an overall negative trend and substantial variability in annual precipitation. Moderate and severe food insecurity have substantially increased during the study period, with moderate food insecurity being highly prevalent nationwide. By using three different measures of food security - the Household Dietary Diversity Score (HDDS), the Food Consumption Score (FCS), and the Food Insecurity Experience Scale (FIES) - different aspects of households’ access to sufficient and nutritious food are analysed. Employing linear regression models, we find that climate extremes significantly negatively affect food access in Nigeria, with pronounced effects on female-headed and rural households. This study contributes to an improved understanding of the interconnections between climate change and food insecurity, emphasising the importance of policies directed at enhancing the resilience of households and agricultural systems to the adverse impacts of climate change.

Migration is one of the most consequential disruptions to human society anticipated under climate change. Empirical evidence of climate-induced migration is, however, typically focused on individual regions, rendering global assessments of the scale of this issue challenging. We here provide such a quantification in a causal framework by leveraging rich data on sub-national migration with detailed analysis of climate exposure. Both, warming and exposure to extremes induce outward migration from poorer regions across the globe, with explicit cross-border flows from poor to rich regions identifiable. With climate change amplifying these effects, we project 210 million climate migrants from low-income regions by mid-century and about 370 million additional migrants in 2060 under a high-emission scenario.