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<p>Understanding the spatial network structure and driving forces of pollution reduction, carbon mitigation, green expansion, and growth (PR-CM-GE-R) is essential for regional ecological protection and sustainable development. Using panel data for the Middle Reaches of the Yangtze River Urban Agglomeration from 2010 to 2023, this study employs social network analysis and the XGBoost-SHAP approach to examine the spatial network characteristics and nonlinear driving mechanisms of PR-CM-GE-R synergistic development. Compared with previous studies, this study incorporates the four subsystems into a unified analytical framework, thereby extending the scope of synergy research. It further introduces machine learning methods to identify the nonlinear effects and dual-factor interaction effects of key driving factors, addressing the limitations of traditional linear models in capturing complex relationships. In addition, by integrating social network analysis, this study reveals the network structural characteristics of regional synergistic development and deepens the understanding of the spatial mechanisms underlying collaborative governance. The results show that: (1) the coupling coordination degree of PR-CM-GE-R exhibited an overall upward trend, although significant spatial disparities remained; (2) the PR-CM-GE-R synergistic effect formed a complex spatial association network centered on Wuhan and Changsha, with increasingly close intercity connections; (3) the block structure evolved from a three-block structure to a four-block structure, indicating a clearer regional spatial division of functions and a more complete network hierarchy; (4) industrial structure upgrading, technological innovation, and mobile penetration rate were the key drivers of synergistic effects, all of which exhibited nonlinear characteristics and threshold effects; and (5) strong dual-factor interaction effects were identified between industrial structure upgrading and technological innovation, between industrial structure upgrading and urbanization level, and between technological innovation and mobile penetration rate.</p>

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Evaluating the synergistic effects of pollution reduction, carbon mitigation, green expansion and growth: Evidence from social network analysis and XGBoost-SHAP

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Xiaoxiao Song,
  • Zaijie Zhang

摘要

Understanding the spatial network structure and driving forces of pollution reduction, carbon mitigation, green expansion, and growth (PR-CM-GE-R) is essential for regional ecological protection and sustainable development. Using panel data for the Middle Reaches of the Yangtze River Urban Agglomeration from 2010 to 2023, this study employs social network analysis and the XGBoost-SHAP approach to examine the spatial network characteristics and nonlinear driving mechanisms of PR-CM-GE-R synergistic development. Compared with previous studies, this study incorporates the four subsystems into a unified analytical framework, thereby extending the scope of synergy research. It further introduces machine learning methods to identify the nonlinear effects and dual-factor interaction effects of key driving factors, addressing the limitations of traditional linear models in capturing complex relationships. In addition, by integrating social network analysis, this study reveals the network structural characteristics of regional synergistic development and deepens the understanding of the spatial mechanisms underlying collaborative governance. The results show that: (1) the coupling coordination degree of PR-CM-GE-R exhibited an overall upward trend, although significant spatial disparities remained; (2) the PR-CM-GE-R synergistic effect formed a complex spatial association network centered on Wuhan and Changsha, with increasingly close intercity connections; (3) the block structure evolved from a three-block structure to a four-block structure, indicating a clearer regional spatial division of functions and a more complete network hierarchy; (4) industrial structure upgrading, technological innovation, and mobile penetration rate were the key drivers of synergistic effects, all of which exhibited nonlinear characteristics and threshold effects; and (5) strong dual-factor interaction effects were identified between industrial structure upgrading and technological innovation, between industrial structure upgrading and urbanization level, and between technological innovation and mobile penetration rate.