Decoding green space supply–demand mismatch through urban morphology: Toward equitable urban planning with explainable machine learning

Urban green spaces (UGS), as essential components of urban ecological infrastructure, play a vital role in improving residents’ quality of life and fostering spatial equity. However, rapid urbanization and intensive land use have intensified UGS supply–demand mismatches and associated “green inequities”, while their spatial drivers remain insufficiently understood. Taking the central urban area of Nanjing as a case study, this study developed a comprehensive framework to quantify UGS supply, demand, and spatial mismatches in 2022, thereby revealing inequities in green resource allocation. An explainable machine learning model (XGBoost–SHAP) was further applied to identify the urban morphological drivers of these mismatches. The results showed that all traffic analysis zones (TAZs) experienced varying degrees of spatial mismatch: 48 TAZs in urban core suffered from severe green space deficits, whereas 203 TAZs in peripheral areas exhibited evident surpluses. We found a clear gap between provision and residents’ actual benefits, with “quantitative abundance” not translating into “qualitative accessibility”. Regarding driving mechanisms, urban compactness and floor area ratio exerted significant negative impacts on supply–demand balance, while road density and land-use mix further amplified spatial inequities. In contrast, public facility coverage and the proportion of residential land played positive roles in mitigating mismatches. Based on these findings, we propose governance strategies that move beyond an “area-oriented” approach, emphasizing urban morphology optimization to align green space services with residents’ needs. Overall, this study offers practical insights for equity-oriented green space planning and spatial governance in rapidly urbanizing regions, advancing social equity and environmental justice.