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<p>The Yangtze River Economic Belt has emerged as a significant contributor to carbon emissions, with this expansion closely linked to the region's green development policies. Using panel data spanning 2006–2021, this study adopts a production-living-ecological framework. First, the best–worst method, CRITIC and TOPSIS are applied to comprehensively evaluate the green development level (GDL), while the super-efficiency slacks-based measure model is utilized to assess carbon emission efficiency (CEE). Next, the coupling coordination degree (CCD) model and exploratory spatial data analysis is utilized to characterize the spatial distribution of CCD. Finally, the geodetector model is applied to identify the driving factors underlying CCD's spatial differentiation. The results reveal that GDL shows a steady upward trend, whereas CEE exhibits a "V-shaped" development pattern. CCD demonstrates a intermediate coordination with pronounced spatial disparities, consistent with the CEE trend. Economic development, government regulation, infrastructure construction, education level, openness level, and industrial structure are identified as the primary factors influencing CCD. These findings provide empirical evidence to promote regional synergy in line with Sustainable Development Goal 12.</p>

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Coordination between green development level and carbon emission efficiency: A production-living-ecology framework

  • 期刊论文

  • 发表日期：

    2026年2月20日

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  • Zhigang Li,
  • Shuhui Yang,
  • Xuefeng Huang

摘要

The Yangtze River Economic Belt has emerged as a significant contributor to carbon emissions, with this expansion closely linked to the region's green development policies. Using panel data spanning 2006–2021, this study adopts a production-living-ecological framework. First, the best–worst method, CRITIC and TOPSIS are applied to comprehensively evaluate the green development level (GDL), while the super-efficiency slacks-based measure model is utilized to assess carbon emission efficiency (CEE). Next, the coupling coordination degree (CCD) model and exploratory spatial data analysis is utilized to characterize the spatial distribution of CCD. Finally, the geodetector model is applied to identify the driving factors underlying CCD's spatial differentiation. The results reveal that GDL shows a steady upward trend, whereas CEE exhibits a "V-shaped" development pattern. CCD demonstrates a intermediate coordination with pronounced spatial disparities, consistent with the CEE trend. Economic development, government regulation, infrastructure construction, education level, openness level, and industrial structure are identified as the primary factors influencing CCD. These findings provide empirical evidence to promote regional synergy in line with Sustainable Development Goal 12.