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 multidimen...
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| Published in | Expert systems with applications Vol. 283; p. 127863 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
15.07.2025
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| Subjects | |
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| 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. |
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| AbstractList | 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. |
| ArticleNumber | 127863 |
| Author | Ding, Rui Jiang, Shuyue Ran, Xiaofeng Zhang, Bowen |
| Author_xml | – sequence: 1 givenname: Rui orcidid: 0000-0002-3542-3965 surname: Ding fullname: Ding, Rui email: 201801162@mail.gufe.edu.cn organization: College of Applied Economics, Guizhou University of Finance and Economics, Guiyang 550025, China – sequence: 2 givenname: Xiaofeng surname: Ran fullname: Ran, Xiaofeng email: 20231172111005@mail.gufe.edu.cn organization: College of Applied Economics, Guizhou University of Finance and Economics, Guiyang 550025, China – sequence: 3 givenname: Shuyue surname: Jiang fullname: Jiang, Shuyue email: 1637042383@qq.com organization: Lancaster University Management School, Bailrigg Lancaster LA1 4YW, United Kingdom – sequence: 4 givenname: Bowen orcidid: 0009-0001-2368-4930 surname: Zhang fullname: Zhang, Bowen email: 20241272111070@mail.gufe.edu.cn organization: College of Applied Economics, Guizhou University of Finance and Economics, Guiyang 550025, China |
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| Keywords | Green economy development SHAP Spatio-temporal Transition Influencing Factors Interpretable Machine Learning |
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