长江经济带环境规制强度时空演变及障碍因子分析

Spatiotemporal Evolution and Obstacle Factor Analysis of Environmental Regulation Intensity in the Yangtze River Economic Belt

中文关键词:  环境规制强度  CRITIC-熵权组合权重法  障碍因子  空间相关性

英文关键词:Environmental Regulation Intensity  CRITIC-Entropy Weight Combination Method  Obstacle Factors  Spatial Correlation

中文摘要:

      科学监测环境规制强度并识别其障碍因子，是提升区域环境韧性、促进社会-经济-环境协同可持续发展的重要路径。本文基于政府主导、市场激励与公众参与三维框架，构建环境规制强度指标体系，采用CRITIC-熵权组合法对长江经济带2012-2022年各省市环境规制强度进行测度，系统解析其时空分异特征与障碍因子。研究发现：（1）研究期环境规制强度总体呈现出波动上升趋势，从0.359增加到0.468，年均增速2.19%，区域协同发展效应显著。（2）空间分异上，下游地区>中游地区>上游地区，均值分别为0.515、0.447及0.401。中游地区增速最快（3.64%），而上游地区在均值（0.401）与增速（2.87%）上均显滞后。（3）全局莫兰指数反映出空间集聚特征持续强化，空间联动性显著提升。局部空间格局表现为“高—高”型集聚或“低—低”型集聚，且“高—高”型集聚的省市逐渐增多。（4）政府主导、市场激励与公众参与的障碍度平均值分别为43.45%、34.67%、21.89%。最后本文针对以上结果提出了相关政策建议。

英文摘要:

      Scientific monitoring of environmental regulation intensity and identification of its obstacle factors are critical pathways to enhance regional environmental resilience and promote the coordinated sustainable development of society, economy, and environment. This study constructs an environmental regulation intensity index system based on a tripartite framework encompassing government-led initiatives, market incentives, and public participation. Employing the CRITIC-entropy weight combination method, we measure the environmental regulation intensity across provinces in the Yangtze River Economic Belt from 2012 to 2022, systematically analyzing its spatiotemporal differentiation characteristics and obstacle factors. The findings reveal that: (1) Environmental regulation intensity exhibited a fluctuating upward trend during the study period, increasing from 0.359 to 0.468 (annual growth rate: 2.19%), with significant regional synergistic effects. (2) Spatial heterogeneity followed a downstream > midstream > upstream gradient (decadal means: 0.515, 0.447, and 0.401, respectively), while the midstream region demonstrated the fastest growth (3.64%), contrasting with the upstream region's lag in both mean value (0.401) and growth rate (2.87%). (3) The global Moran’s index indicates intensifying spatial agglomeration and enhanced regional connectivity, with local spatial patterns dominated by "high-high" and "low-low" clusters, where "high-high" clusters expanded progressively. (4) The average constraint intensities of government-led measures, market incentives, and public participation were 43.45%, 34.67%, and 21.89%, respectively. Targeted policy recommendations are proposed to address these findings.