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<p>As global resource depletion and environmental constraints intensify, the sustainable transformation of resource-based cities has become a critical imperative for balancing industrial growth with ecological integrity. While China’s National Green Mine (NGM) policy serves as a strategic cornerstone for promoting this transition, its actual effectiveness and the underlying differentiated mechanisms across diverse urban contexts remain empirically under-explored. Addressing this gap, this study utilizes a hybrid framework of Double Machine Learning (DML) and spatial econometrics to model the differentiated impacts of the National Green Mine (NGM) policy on industrial sustainability across 282 resource-based cities (2010–2022). The results reveal a nuanced and complex relationship between policy implementation and urban developmental maturity. While the NGM policy contributes to sustainability efficiency through fiscal coordination and industrial upgrading in growing and mature cities, these benefits are unevenly distributed, as declining cities experience structural rigidities. Furthermore, we identify significant mineral-dependent heterogeneity, with divergent outcomes between energy-based and non-metallic extractive industries. Mechanistically, the policy manages the trade-offs between environmental protection and industrial gain by optimizing fiscal incentives and reducing carbon intensities. This study concludes with recommendations for policy and practice, emphasizing stage-specific adaptation for the sustainable transition of global resource-dependent systems.</p>

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Examining the differentiated effects of national green mine policy on industrial sustainability: a modeling approach for resource-based cities in China

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Xiaoxuan Wu,
  • Tianming Gao,
  • Tao Dai

摘要

As global resource depletion and environmental constraints intensify, the sustainable transformation of resource-based cities has become a critical imperative for balancing industrial growth with ecological integrity. While China’s National Green Mine (NGM) policy serves as a strategic cornerstone for promoting this transition, its actual effectiveness and the underlying differentiated mechanisms across diverse urban contexts remain empirically under-explored. Addressing this gap, this study utilizes a hybrid framework of Double Machine Learning (DML) and spatial econometrics to model the differentiated impacts of the National Green Mine (NGM) policy on industrial sustainability across 282 resource-based cities (2010–2022). The results reveal a nuanced and complex relationship between policy implementation and urban developmental maturity. While the NGM policy contributes to sustainability efficiency through fiscal coordination and industrial upgrading in growing and mature cities, these benefits are unevenly distributed, as declining cities experience structural rigidities. Furthermore, we identify significant mineral-dependent heterogeneity, with divergent outcomes between energy-based and non-metallic extractive industries. Mechanistically, the policy manages the trade-offs between environmental protection and industrial gain by optimizing fiscal incentives and reducing carbon intensities. This study concludes with recommendations for policy and practice, emphasizing stage-specific adaptation for the sustainable transition of global resource-dependent systems.