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Sarah Spiteri
- 17 June 2026
- WORKING PAPER SERIES - No. 3248Details
- Abstract
- Europe is increasingly exposed to heat waves and droughts, but their short-term economic effects across sectors remain hard to predict. This study develops climate-augmented models to predict real growth in per capita value added across 1,117 EU regions (2002–2022), by combining economic indicators with high-frequency climate data. When using machine learning (ML, Random Forest and XGBoost), climate variables improve predictions in agriculture, while gains for other sectors are limited and do not outperform economic models. Heat wave indicators consistently enhance predictive performance, whereas drought effects vary by sector. Simulations of extreme combined heat and drought scenarios suggest that agricultural annual growth could fall by 1.9 to 7.6 percentage points in most regions, whereas industry, and manufacturing in particular, is less affected, although impacts are more pronounced in Eastern Europe and the Baltic states. Overall, ML models better reflect complex climate–economic interactions, supporting their use for early warning, policy planning, and targeted adaptation.
- JEL Code
- C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
Q54 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Environmental Economics→Climate, Natural Disasters, Global Warming
R15 : Urban, Rural, Regional, Real Estate, and Transportation Economics→General Regional Economics→Econometric and Input?Output Models, Other Models