An XGBoost–SHAP Explainable Machine Learning Approach Linking Urban Park Cooling Effects in a Megacity Core to Landscape Features Inside and Outside the Parks

Urban parks, as essential components of green infrastructure, play a critical role in mitigating the urban heat island effect and improving thermal comfort. This study evaluated the cooling performance of 170 parks in the urban center region of Beijing using seven cooling effect indicators. An integrated framework combining XGBoost, SHAP, and Ordinary Least Squares was proposed to examine the nonlinear relationships and threshold effects between cooling effect indicators and landscape features. Moreover, the Elbow method and K-means clustering were applied to classify parks based on their cooling performance. The results showed that both internal and external landscape features are closely associated with park cooling effects. Eight features were identified as key landscape features, possessing higher relative importance. Pronounced nonlinear relationships and threshold effects were identified. For example, the optimal park area is approximately 25 hm2, while more regular shapes enhance cooling performance, with an optimal ratio of perimeter and area of about 0.05 m−1. Furthermore, four distinct park types with significantly different cooling performance were identified, reflecting different capacities for regulating urban thermal environments. These findings improved the understanding of park cooling effects and provided guidance for optimizing park design in megacity cores.