Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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Climate disasters: wildfires and floods Location: Auditorium C: Thore Johnsen Session Chair: Stefano Ceolotto, Euro-Mediterranean Center on Climate Change | |
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Climate Change Risk Indicators for Central Banking: Explainable AI in Fire Risk Estimations 1: Central Bank of Hungary, MNB; 2: ECB, Germany; 3: 3The London School of Economics and Political Science As central banks increasingly rely on forward-looking models to estimate the financial risks associated with climate change, there is a pressing need for accurate assessments of physical risks such as wildfires. This paper introduces a refined methodology for evaluating wildfire risk, designed to inform the European System of Central Banks’ Expert Group in Climate Change and Statistics. It examines the relationship between the Fire Weather Index (FWI), land cover types, and fire risk across Europe, using data from 2001 to 2022 and a 2.5 x 2.5 km grid. We compare the performance of logistic regression with extreme gradient boosting models (xgboost), both unconstrained and constrained, to capture the complex, nonlinear dynamics influencing fire risk. The findings reveal that although the unconstrained version offers the highest predictive accuracy, the constrained version aligns more closely with the expected relationship between FWI and fire risk. Under the RCP 8.5 scenario and using the constrained xgboost model, the area at high risk is projected to increase from 569,000 square kilometers in 2022 to 635,000 square kilometers by 2050. This highlights the relevance of using advanced modeling techniques in improving the accuracy of financial risks assessments associated with climate change-driven wildfires. | |