Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Expert Systems with Applications

DOI: 10.1016/j.eswa.2025.127863

Authors: Rui Ding, Xiaofeng Ran, Shuyue Jiang, Bowen Zhang

Institutions: Guizhou University of Finance and Economics, Lancaster University

Language: English

Topic: Energy, Environment, Economic Growth

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $3,500