Interpretable machine learning for analyzing the spatial-temporal evolution and influencing factors of green economic development

Understanding the dynamics and influencing factors of green economy development (GED) is essential for fostering sustainable growth and environmental sustainability . This paper investigates the spatial–temporal evolution, transition and influencing factors of GED in 287 cities of China. A multidimensional evaluation framework combining the entropy weight method and Exploratory Spatio-Temporal Data Analysis (ESTDA) quantifies GED trajectories while revealing two critical dynamics: 1) a core-to-periphery diffusion pattern with persistent regional disparities, and 2) unstable development pathways of cities despite stable high-performance clusters in coastal major urban agglomerations. To decode complex influencing factors, we introduce SHAP (SHapley Additive exPlanations)-interpreted Random Forest modeling, identifying consumption, financial development, scientific and technological innovation and commercial development as dominant drivers. The results directly support evidence-based governance by operationalizing SHAP-driven insights: urban decision-makers should embed spatial SHAP heterogeneity analysis into cross-regional and adaptive policy systems that may trigger GED upgrades.