Qiguang Zhao

Extreme heat has emerged as a pressing sustainability challenge in rapidly urbanizing metropolitan areas, where built environments intensify thermal exposure and its unequal distribution across socially vulnerable communities. Although previous studies have documented disparities in urban heat exposure, fewer have developed decision-oriented frameworks that can simultaneously quantify heat inequity, identify its dominant drivers, and evaluate mitigation strategies under an explicit equity objective. To address this gap, this study develops an interpretable machine-learning framework to support sustainable and equitable urban heat mitigation in Houston. Using 727 census tracts, we model summer daytime land surface temperature (LST) in 2022 as a function of tract-level natural and built-environment characteristics with XGBoost, interpret model behavior using SHAP, quantify inequity through a Concentration Index relative to social vulnerability, and compare targeted counterfactual intervention scenarios under a dual cooling–equity objective. The results show that heat exposure is disproportionately concentrated in more vulnerable communities, with mean LST increasing from 38.60 °C in low-vulnerability tracts to 39.10 °C in high-vulnerability tracts, alongside a positive and statistically significant Concentration Index. The model demonstrates solid predictive performance (R² = 0.774, RMSE = 0.793 °C), and SHAP results identify coastal distance, NDVI, building height, road density, and building coverage as the principal drivers of tract-level thermal variation. Under equity-targeted intervention scenarios, increasing NDVI and mean building height emerge as the clearest win–win strategies, reducing both average predicted LST and the unequal concentration of heat burden. Overall, this study provides a planning-relevant framework for identifying mitigation priorities that advance urban cooling, equity, and more just forms of climate adaptation.