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Application of explainable machine learning in the study of urban ecological economic resilience

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  • Ran, Xiaofeng
  • Zhang, Bowen
  • Ding, Rui
  • Jiang, Shuyue
  • Yuan, Jiarui
  • Zheng, Yaoyao
  • Yang, Changpeng

Abstract

Enhancing the resilience of urban ecological economic systems is crucial for promoting sustainable societal development. This article constructs an analytical framework for ecological economic resilience (EER), employing a variety of spatial analysis methods to explore the evolution and distribution of EER across 287 cities in China. It also integrates machine learning and SHapley Additive exPlanations (SHAP) value algorithms to analyze the importance and spatiotemporal variations of influencing factors. The study reveals that: China's EER exhibits a generally progressive growth trend, yet there are significant disparities in growth rates among cities. The spatial agglomeration characteristics of EER are strong, with local agglomeration phenomena being evident, where hotspots are concentrated in the eastern regions and cold spots in the western regions. Machine learning combined with the SHAP uncovers that the dominant factors influencing EER vary across different regional scopes. The primary driving factors for EER in the northeastern, eastern, central, and western regions are clean energy, green technological innovation, informatization level, and transportation development level, respectively.

Suggested Citation

  • Ran, Xiaofeng & Zhang, Bowen & Ding, Rui & Jiang, Shuyue & Yuan, Jiarui & Zheng, Yaoyao & Yang, Changpeng, 2026. "Application of explainable machine learning in the study of urban ecological economic resilience," Ecological Modelling, Elsevier, vol. 511(C).
    • Handle: RePEc:eee:ecomod:v:511:y:2026:i:c:s0304380025003540
    DOI: 10.1016/j.ecolmodel.2025.111368
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