中国省域降碳潜力空间关联网络及其影响因素研究

Study on the spatial correlation network and its influencing factors of carbon reduction potential in China's provinces

摘要:

测度省域降碳潜力空间关联结构特征及作用机制,以期缩减生产中因时空产生的能源消耗,增加能源利用效率的同时减少碳排放,实现低碳经济持续稳定发展。本文研究基于2011—2020年中国省际面板数据,利用Super-SBM模型,从公平和效率出发对我国30个省份降碳潜力进行测度并评价,借助修正的引力模型和社会网络分析方法探究其空间关联网络和影响因素。结果表明:(1)我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)东部发达省市多处于关联网络网络核心位置,在省际降碳潜力空间关联中发挥着支配作用；(3)北京、天津、江苏和上海属于“净受益”板块,广东、浙江、重庆和福建属于“经纪人”板块,吉林、内蒙古等22省份属于“净溢出”板块以及“双向溢出”板块；(4)空间邻接关系、经济水平差异、能源消耗差异、产业结构差异、城镇人口差异共同驱动着中国省域降碳潜力空间关联网络结构的形成与演化。

Abstract:

Measure the spatial correlation structure characteristics and action mechanism of the provincial carbon reduction potential, with a view to reducing the energy consumption caused by time and space in production, increasing energy utilization efficiency and reducing carbon emissions, and achieving the sustainable and stable development of low-carbon economy. Based on the inter-provincial panel data of China from 2011 to 2020, this paper uses the Super-SBM model to measure and evaluate the carbon reduction potential of 30 provinces in China from the perspective of fairness and efficiency, and explores its spatial correlation network and impact factors with the help of the modified gravity model and social network analysis method. The results show that:(1) the spatial correlation of carbon reduction potential in China"s provinces is increasingly close, the network density and the number of network connections are increasing, and the network efficiency and network hierarchy are decreasing; (2) The developed provinces and cities in the east are mostly at the core of the network and play a dominant role in the spatial correlation of inter-provincial carbon reduction potential; (3) Beijing, Tianjin, Jiangsu and Shanghai belong to the "net benefit" sector, Guangdong, Zhejiang, Chongqing and Fujian belong to the "broker" sector, and Jilin, Inner Mongolia and other 22 provinces belong to the "net spillover" sector and the "two-way spillover" sector; (4) Spatial adjacency, economic level difference, energy consumption difference, industrial structure difference and urban population difference jointly drive the formation and evolution of China"s provincial carbon reduction potential spatial association network structure.