Ecosystem services supply-demand mismatches for urban heat mitigation

Urban areas, characterized by dense construction, often exhibit elevated land surface temperatures, leading to the formation of urban heat islands (UHIs). These UHIs pose significant environmental hazards, contributing to issues such as heat-related mortality, degraded air quality, and elevated heat stress on biodiversity and ecosystems. Moreover, the impact of UHIs is not uniformly distributed due to the heterogeneous nature of urban landscapes and socio-spatial inequities influencing factors like impervious surfaces and vegetation cover. Urban green infrastructure is increasingly valued as a nature-based solution to mitigate UHIs, offering essential ecosystem services (ES) like urban heat mitigation. To analyze the relationship between users' access and dependence on these benefits, we propose a modeling approach that integrates remote sensing, field, and socio-demographic data, along with Artificial Intelligence for Environment and Sustainability (ARIES) and GIS tools. This approach incorporates: i) indicators of UHI exposure and urban heat vulnerability indices; ii) spatial quantification of the supply and demand of urban green infrastructure related to ES for UHI mitigation; iii) spatially explicit (mis)matches of ES supply and demand balance and iv) coupled modelling. We applied it in the green city of Vitoria-Gasteiz, in the Basque Country as a case study. Our findings evidence the unequal distribution of UHI burdens, with individuals vulnerable to heat experiencing disproportionate impacts, including higher exposure and limited access to temperature-regulating ES. This mismatch between the supply and demand of ES particularly affects disadvantaged communities. Conversely, areas associated with higher income levels indicate reduced vulnerability to heat. Incorporating environmental justice principles into UHI mitigation strategies is essential to ensure equitable outcomes for all residents. By considering the socio-spatial inequalities associated with supply-demand mismatches in ES and their impact on vulnerability to heat, our approach enables evidence-based decision-making and spatial prioritization to address the specific needs of vulnerable populations.