Conference Agenda

The extent to which changes in labour force outcomes can be attributed to historic climate change is currently unknown. Here, we combine robust estimates of the impact of climatic stressors on labour supply, labour productivity, and a combined metric of the two - effective labour, with novel historic climate forcing data to quantify the effect of historic climate change on labour. We do this at the global and regional level, taking explicit account of heterogeneity of working conditions. Globally, effective labour in outdoor working conditions was 1.8 percentage points lower in 1901-2019 than it would have been without climate change. The attributable declines have increased over time, rising to 3.6 percentage points between 2001 and 2019. The highest declines have been in the relatively lower-income regions of Western Africa, South-Eastern Asia, and Middle Africa, where climate change has increased workforce inequalities. We also estimate the economic effects through impacts on GDP and find up to a 12% GDP loss for some regions. Our findings can help improve the design of better labour protections, improve worker health, enhance productivity and economic growth, and inform better climate adaptation and resilience.

Climate change is producing significant transformations in the labour market, in

intensifying inequalities due to its heterogeneous effects. This paper proposes an empirical analysis for Italy on the causal relationship between high temperatures and

work-related injuries, at the provincial and daily levels, for the period 2014-2022, by

exploiting within-country local variation. Our analysis is the first to compare the

estimated effects of heat on injuries at the workplace using, besides air temperature,

two other meteorological indicators which are comprehensive human heat stress indexes

not applied yet in economics: the wet-bulb globe temperature (WBGT) and the uni-

versal thermal climate index (UTCI). Our findings confirm that higher temperatures

significantly increase the risk of work-related injuries, with coefficients rising across

temperature thresholds and varying by indicator. Heterogeneity analyses reveal that

vulnerability differs by gender, age, nationality, economic sector, and geographical area.

The study highlights the importance of employing advanced climatic metrics and stratified analyses to better understand the impact of heat stress on occupational health,

especially in regions like Italy that are highly susceptible to global warming.

The existing literature on the macroeconomic impacts of climate change has

established links between climatic shifts and economic growth. However, impact

projections vary across studies and are difficult to validate in hindsight,

as the counterfactual growth path is unknown. This paper addresses this issue

by leveraging the IMF’s semi-annual World Economic Outlook forecasts from

1990-2022 as a baseline counterfactual. First, we validate that these forecasts

do not capture climate impacts by showing that the identifying variation for

regression models in the literature stems from growth variation not predicted by

IMF. Then, we produce GDP nowcasts for different damage functions by adding

climate impacts to IMF forecasts for recent years and validate nowcasts against

actual growth. We find that most damage functions would have improved IMF

growth forecasts, albeit not more than roughly 20%. Importantly, the most severe

damage functions nowcast substantial recessions in many high-income countries

between 2014–2019 that cannot be reconciled with actual observations. Our

findings highlight the potential of nowcasting to validate and constrain damage

functions, which typically focus on distant futures.

Integrated Assessment Models (IAMs) are foundational tools for assessing climate change impacts and informing policy decisions. However, traditionally, IAMs rely on aggregate variables and decisions of actors representing global regions. As a results, IAMs are criticized for oversimplifying representation of heterogeneity and human behavior, in particular on climate adaptation. This paper introduces the first bidirectional coupling between a Cost-Benefit IAM (RICE50+) and an agent-based model (CRAB) that incorporates multiple adaptation strategies against different climate hazards. Our framework enables climate-economy interactions where global temperature changes affect local flood probabilities and labor productivity, while regional adaptation investments feed back into macro-level damage functions and resources left for mitigation strategies. Using detailed spatial and behavioral data from the Netherlands and USA, we demonstrate significant regional variation in adaptation patterns and efficiency. Cost-benefit analysis reveals consistently positive but regionally variable returns of private adaptations (benefit-cost ratios of 5.0 in USA and 2.0 in Netherlands). Moreover, we find that adaptation complements rather than substitutes mitigation efforts. This coupled modeling approach provides a robust platform for evaluating climate strategies that bridge local adaptation decisions with global climate dynamics.