Explainable Machine Learning Reveals Seasonal Dynamics of Heat Inequality and Cooling Efficiency Bias Across 15 Chinese Cities

Urban heat inequality represents a critical barrier to inclusive climate-resilient governance. While existing research has extensively mapped surface temperature patterns, the dynamic evolution of human thermal stress and the divergent regulatory efficiencies of cooling features across socio-economic contexts remain poorly understood. This study integrates multi-source datasets from 15 typical Chinese cities, employing a machine learning framework and GeoShapley interpretation to resolve the drivers of heat inequality across spatio-temporal and mechanistic dimensions. The findings demonstrate that high-density urbanization in China leads to a spatial synchronization of wealth and heat exposure, contrasting with the “Luxury Effect” observed in low-density Western contexts and indicating that high-income urban cores bear significantly higher absolute thermal stress. This inequality exhibits pronounced seasonal dynamics, where extreme summer conditions non-linearly amplify exposure gaps between socio-economic groups. Crucially, the results identify a systemic failure of cooling mechanisms in low-income communities, where the empirical thermal response of physical features deviates from expected patterns, failing to mitigate or even exacerbating perceived heat stress. These results emphasize that urban mitigation should move beyond quantitative resource expansion toward efficiency restoration, utilizing targeted spatial optimization to achieve precision climate justice.