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黄河流域绿色发展绩效评价、差异分解及驱动因素 被引量:13

Performance evaluation,difference decomposition and driving factors of green development in the Yellow River basin
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摘要 文章采用基于MinDS模型的Global-Luenberger指数测算了2005—2018年黄河流域城市绿色发展绩效,利用Dagum基尼系数考察其空间差异大小及具体来源,借助方差分解和分位数回归方法从内源和外源双重视角综合分析黄河流域城市绿色发展绩效差异的驱动因素。研究发现:①样本考察期内,黄河流域城市绿色发展绩效年均增长0.40%,上游和下游地区城市绿色发展绩效明显快于中游地区。②城市绿色发展绩效差异主要来源于区域间差异,尤其应注重黄河上游与下游地区发展的协同性;区域内绿色发展绩效非均衡现象也普遍存在。③从内源驱动因素看,期望产出和非期望产出生产率差异的作用强度高于投入要素生产率差异。其中,投入要素生产率增长差异问题在下游地区最为严重,期望产出和非期望产出生产率增长差异分别在中游和上游地区相对较高。④从外源驱动因素看,经济增长、教育程度和人口密度对黄河流域不同水平城市绿色发展绩效的影响均表现出强者愈强、弱者愈弱的“马太效应”,导致全域绿色发展绩效存在显著差异。其中,经济增长和产业结构是上游区域内差异形成的最大外源动力,经济增长和教育程度则导致中游地区极化现象的形成,经济增长和人口密度引致下游地区绿色发展绩效差异扩大。据此,文章从改善资源利用效率、构建联防联控机制、健全人才交通体系等方面提出相关政策建议,要求在打破城市间信息溢出壁垒的同时防范极化问题重演,为促进黄河流域绿色发展绩效协同提升提供有益借鉴。 This study used the Global-Luenberger index based on the MinDS model to calculate the urban green development perfor⁃mance of the cities in the Yellow River basin from 2005 to 2018.The Dagum Gini coefficient was used to investigate the size and specif⁃ic sources of its spatial difference.With the aid of variance decomposition and quantile regression methods,the driving factors of the ur⁃ban green development performance in the Yellow River basin were comprehensively analyzed from the internal and external sources.The study found that:①During the sample survey period,the urban green development performance in the Yellow River basin grew at an average annual rate of 0.40%,and the performance of urban green development in upstream and downstream regions was significant⁃ly faster than that in midstream regions.②The difference in urban green development performance was mainly derived from regional differences,and particular attention should be paid to the synergy of upstream and downstream regional development.The imbalance of green development performance in the region was also widespread.③From the perspective of internal power,the difference between the expected and undesired output productivity was higher than the input factor productivity difference.Among them,the difference in productivity growth of input factors was the most significant in downstream regions,and the difference in productivity growth of expect⁃ed output and that of undesired output were relatively significant in the middle reaches and upstream regions,respectively.④From the perspective of external power,the impact of economic growth,education level and population density on the green development perfor⁃mance at different levels in the Yellow River basin all showed the‘Matthew Effect’,resulting in significant differences in the green de⁃velopment performance across the cities.Among them,economic growth and industrial structure were the largest external sources of dif⁃ferences in the upstream region.Economic growth and education level led to the formation of polarization in the middle reaches.Eco⁃nomic growth and population density led to the expansion of regional differences in the green development performance of downstream cities.Based on this,this article puts forward relevant policy recommendations in terms of improving resource utilization efficiency,building joint prevention and control mechanisms,and improving the talent transportation system.It is required to prevent the recur⁃rence of polarization problems while breaking the barriers of information overflow between cities,in order to provide a useful reference for promoting the coordinated improvement of the green development performance of the Yellow River basin.
作者 陈明华 刘文斐 王山 岳海珺 CHEN Minghua;LIU Wenfei;WANG Shan;YUE Haijun(School of Economics,Shandong University of Finance and Economics,Jinan Shandong 250014,China;The Wang Yanan Institute for Studies in Economics,Xiamen University,Xiamen Fujian 361005,China;School of Economics,Shandong University,Jinan Shandong 250100,China)
出处 《中国人口·资源与环境》 CSSCI CSCD 北大核心 2022年第4期126-133,共8页 China Population,Resources and Environment
基金 山东省自然科学基金项目“强可持续视角下山东省生态福利绩效评价及提升路径研究”(批准号:ZR2021MG045)。
关键词 黄河流域 绿色发展绩效 驱动因素 方差分解 分位数回归 Yellow River basin green development performance driving factor variance decomposition quantile regression
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