Explainable AI Toward Data-Driven Policymaking for Urban Heat Island Climate Adaptation

The Urban Heat Island (UHI) phenomenon constitutes one of the most significant climate-related challenges for contemporary cities, intensifying thermal stress, energy demand, and social vulnerability. This study proposes a methodological framework that integrates multi-source data with explainable machine learning techniques in order to both analyse and support the refinement of climate adaptation policies. The approach combines satellite-derived land surface temperature from Sentinel-3, meteorological and air quality indicators, and biophysical and anthropogenic variables. After a preprocessing stage, clustering and classification models (Logistic Regression, Support Vector Classifier) were trained for the city of Madrid, with inference applied to Athens as a reference case. The evaluation of model performance was complemented by explainability techniques (Feature Importance and SHAP), which highlighted temporality, soil moisture, and urban morphology as the most decisive factors for UHI intensity, while atmospheric pollutants were found to play a secondary role. These insights were systematically compared with existing international, European, and national policy frameworks, including the Sustainable Development Goals, the European Green Deal, and Spain’s National Energy and Climate Plan. The findings demonstrate how interpretable, data-driven analysis can bridge the gap between predictive modelling and governance, providing a transparent basis for targeted and evidence-based urban climate adaptation strategies.