Machine Learning Insights into Supply–Demand Mismatch, Interactions and Driving Mechanisms of Ecosystem Services Across Scales: A Case Study of Xingtai, China

To reveal the cross-scale trade-offs and synergies of ecosystem services (ESs) in resource-based cities, this study took Xingtai City, Hebei Province, as a case. Six ESs—water yield (WY), soil retention (SDR), habitat quality (HQ), urban cooling (UC), net primary productivity (NPP), and PM2.5 removal—were quantified at the 1 km grid, township, and county scales. Using Spearman correlation, geographically weighted regression (GWR), and the XGBoost-SHAP framework, we analyzed the spatiotemporal evolution of the ecosystem service supply–demand ratio (ESDR) from 2000 to 2020 and identified the dominant driving mechanisms. The results indicate the following: (1) The mean ESDR in Xingtai decreased sharply from 0.14 in 2000 to 0.008 in 2020, a decline of 94.3%, showing a pronounced “high in the western mountains–low in the eastern plains” gradient pattern and an increasingly severe supply–demand imbalance. (2) Synergistic relationships dominated among the six ESs, accounting for over 80%. Strong synergies were observed between supply-related services such as WY–SDR and HQ–NPP, with correlation coefficients ranging from 0.65 to 0.88, whereas weak trade-offs (<20%) occurred between UC and PM2.5 removal in urbanized areas, which diminished with coarser spatial scales. (3) Population density (Pop), elevation (DEM), cropland proportion (Crop), and vegetation index (NDVI) were identified as the key driving factors, with a combined contribution of 71.4%. NDVI exhibited the strongest positive effect on ecosystem service supply (mean SHAP value = 0.24), while Pop and built-up land proportion showed significant negative effects once exceeding the thresholds of 400 persons/km2 and 35%, respectively, indicating nonlinear and threshold-dependent responses. This study quantitatively reveals the spatiotemporal synergy patterns and complex driving mechanisms of ecosystem services in resource-based cities, providing scientific evidence for differentiated ecological restoration and multi-scale governance, and offering essential insights for enhancing regional sustainability.