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作 者:丁颖辉[1] 杨亚茹 Ding Yinghui;Yang Yaru(School of Economics,Hebei University,Baoding 071000,China)
出 处:《科技管理研究》2024年第5期199-208,共10页Science and Technology Management Research
基 金:河北省社会科学基金项目“河北省大气污染治理的外部性空间溢出与动态补偿研究”(HB19TJ004)。
摘 要:为深入理解省域降碳潜力空间关联网络的结构特征及其影响机制,加速推动区域绿色转型和协同降碳,基于Super-SBM模型测度2011-2020年中国30个省份降碳潜力,利用修改引力模型和社会网络分析方法探究其空间关联网络结构特征及影响因素。结果表明:(1)中国省域降碳潜力的空间关联日趋紧密,网络密度和网络关联数呈增长态势,网络效率和网络等级度呈下降态势;(2)东部省份多处于网络核心位置,发挥着支配作用;西部省份多位于网络边缘位置,对网络关联结构影响有限;(3)网络结构形成了净受益、净溢出、双向溢出和“经纪人”四大功能板块;(4)经济水平、能源消耗、产业结构和城镇人口等差异以及空间邻接关系共同驱动省域降碳潜力空间关联网络结构演变。据此提出提升省域降碳潜力、优化省域降碳潜力关联的政策建议。In order to deeply understand the structural characteristics and influencing mechanism of the spatial correlation network of provincial carbon reduction potential,and accelerate the regional green transformation and collaborative carbon reduction,this paper measures the carbon reduction potential of 30 provinces from 2011 to 2020 based on the Super-SBM model,and uses the modified gravity model and social network analysis method to explore the structural characteristics and influencing factors of their spatial correlation network.The results show that:(1)the spatial correlation of carbon reduction potential in China's provinces is becoming more and more close,the network density and network correlation number are increasing,and the network efficiency and network rank are decreasing;(2)The eastern provinces and cities are mostly at the core of the network and play a dominant role;Most of the western provinces are located at the edge of the network and have limited influence on the network association structure.(3)The network structure has formed four functional sections:net benefit,net spillover,two-way spillover and"broker";(4)Differences in economic level,energy consumption,industrial structure and urban population,as well as spatial adjacency,jointly drive the evolution of spatial correlation network structure of provincial carbon reduction potential.Therefore,some policy suggestions were put forward to improve the carbon reduction potential of provinces and optimize the correlation of carbon reduction potential of provinces.
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