High-Resolution Modeling and Projecting Local Dynamics of Differential Vulnerability to Urban Heat Stress

Abstract

Climate change-induced heat stress has significant effects on human health, and is influenced by a wide variety of factors. Most assessments of future heat-related risks however are based on coarse resolution projections of heat hazards and overlook the contribution of relevant factors other than climate change to the negative impacts on health. Research highlights sociodemographic disparities related to heat stress vulnerability, especially among older adults, women and individuals with low socioeconomic status, leading to higher morbidity and mortality rates. There is thus an urgent need for detailed, local information on demographic characteristics underlying vulnerability with refined spatial resolution. This study aims to address the research gaps by presenting a new population projection exercise at high-resolution based on the Bayesian modeling framework for the case study of Madrid, using demographic data under the scenarios compatible with the Shared Socioeconomic Pathways. We examine the spatial and temporal distribution of population subgroups at the intra-urban level within Madrid. Our findings reveal a concentration of vulnerable populations, as measured by their age, sex and educational attainment level in some of the city's most disadvantaged neighborhoods. These vulnerable clusters are projected to widen in the future unless a sustainable trajectory is realized, driving vulnerability dynamics toward a more uniform and resilient change. These results can guide local adaptation efforts and support climate justice initiatives to protect vulnerable communities in urban environments.