长江流域资源与环境 >> 2023, Vol. 32 >> Issue (3): 571-581.doi: 10.11870/cjlyzyyhj202303012

• 生态环境 • 上一篇    下一篇

我国碳排放空间关联的网络特征及其影响因素研究

方大春,王琳琳
  

  1. (安徽工业大学商学院,安徽 马鞍山 243032)
  • 出版日期:2023-03-20 发布日期:2023-04-19

Network Characteristics and Influencing Factors of Spatial  Correlation of Carbon Emissions in China

FANG Da-chun,WANG Lin-lin   

  1. (School of Business,Anhui University of Technology,Maanshan 243032,China)
  • Online:2023-03-20 Published:2023-04-19

摘要:  碳排放调控不仅需关注自身减排,而且要考察省际碳排放联动关系。从全国层面厘清碳排放空间关联的网络特征及影响因素是统筹碳排放治理、实现可持续发展的基本前提。基于2015~2019中国省际面板数据,采用社会网络分析法(SNA)对碳排放空间关联性进行实证分析,并通过QAP方法探究其影响因素。研究表明,我国碳排放呈典型空间网络特征,且省际碳排放空间关联日益密切,网络稳定性及复杂程度逐渐提升;上海、江苏、北京等地居于网络中心,并发挥“桥梁”作用;经济发达的东部沿海地区在网络中扮演“双向溢出”及“净受益”角色,能源蕴藏丰富的中、西部等内陆地区属于“经纪人”及“净溢出”板块;地理邻接、产业结构、收入、技术创新及人口密度的差距对碳排放空间关联均产生显著正向影响,能源消耗越相似,碳排放关联性越强。为此,在碳排放治理中需立足区域协同治理理念、建立“引导-跟从”碳减排机制、发挥“全国一盘棋”战略优势、制定差异化碳减排政策,推动形成全国层面均衡碳减排格局。


Abstract: Carbon emission regulation should not only focus on its own emission reduction, but also examine the inter-provincial carbon emission linkage. To clarify the network characteristics and influencing factors of spatial correlation of carbon emission at the national level is the basic premise of overall carbon emission control and sustainable development. Based on the panel data of China's provinces from 2015 to 2019, social network analysis (SNA) was used to conduct empirical analysis on the spatial correlation of carbon emissions, and the influencing factors were explored by QAP method. The results show that China's carbon emissions show typical spatial network characteristics, and the inter-provincial carbon emissions are increasingly correlated, and the stability and complexity of the network are gradually improved. Shanghai, Jiangsu, Beijing and other places live in the network center, and play the role of "bridge"; The economically developed eastern coastal region plays the role of "two-way spillover" and "net benefit" in the network, while the central and western inland regions with rich energy reserves belong to the "broker" and "net spillover" plate. Differences in geographical proximity, industrial structure, income, technological innovation and population density have a significant positive impact on the spatial correlation of carbon emissions. The more similar the energy consumption is, the stronger the correlation of carbon emissions is. Therefore, in carbon emission governance, it is necessary to base on the concept of regional collaborative governance, to establish a "leader-follow" carbon emission reduction mechanism, to give full play to the strategic advantage of "a national chess game", to formulate differentiated carbon emission reduction policies, and to promote the formation of a balanced carbon emission reduction pattern at the national level.

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