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Urbanization and large-scale meteorological forcing intensify impacts from extreme heat events globally, with most heat-related deaths occurring indoors. Understanding the relationship between heat and where people live is therefore an urgent topical area. Here, we introduce a Monte Carlo approach that bridges nationally representative building models with local property records for developing the heat impact - housing analysis. Using our novel approach, we improve mapping by reducing uncertainty from 40–60 % to below 3 %. This allows the development of more representative physiological heat risk metrics quantifying both cumulative and consecutive exposure to dangerous conditions. We demonstrate our methodology for Austin, Texas, and identify three distinct vulnerability profiles during heatwave-blackout scenarios: high-resilience buildings providing safe conditions, moderate-vulnerability buildings approaching dangerous thresholds, and critical-risk buildings rapidly crossing survivability thresholds. While 85 % of buildings currently face significant heat risk for elderly occupants, additional temperature increases will dramatically increase vulnerability for younger populations (from 15 % to 65 % of buildings). In a psychrometric analysis we map physiological survivability limits onto temperature–humidity parameters at the building level, while at the neighborhood-scale, we demonstrate how the temperature increase will eliminate safety buffers, transforming isolated risk hotspots into widespread danger zones. These findings provide evidence-based criteria for targeted retrofitting and emergency response planning. The relevance of our extreme event scenario modeling is evidenced by the recent 2024 Houston Derecho where power outages during extreme heat resulted in multiple fatalities. • Novel Monte Carlo approach bridges national building models with local property data, reducing mapping uncertainty from 40–60 % to < 3 %. • Physiological survivability metrics enable direct quantification of heat-related mortality risk from building simulation data. • Framework identifies transitional building stock vulnerable to climate-driven risk increases across the entire city. • Three distinct vulnerability profiles guide evidence-based retrofit prioritization and emergency response planning. • Methodology demonstrates scalable approach for high-resolution heat vulnerability assessment applicable beyond US contexts.
Published in: Building and Environment
Volume 289, pp. 114070-114070