Study on the spatial network structure of energy carbon emission efficiency and its driving factors in Chinese cities

被引:6
作者
Cheng, Hao [1 ,2 ]
Wu, Boyu [1 ]
Jiang, Xiaokun [3 ]
机构
[1] Nanning Normal Univ, Sch Econ & Management, Nanning 530299, Guangxi, Peoples R China
[2] Chinese Acad Social Sci, Res Inst Ecocivilizat, Beijing 100710, Peoples R China
[3] Guangxi Minzu Univ, Sch Econ, Nanning 530006, Guangxi, Peoples R China
关键词
Chinese cities; Energy carbon emission efficiency; Spatial correlation network structure; Driving factors; CO2; EMISSIONS; ECONOMIC-GROWTH; SBM MODEL; STRATEGIES; URBANIZATION; GOVERNANCE; IMPACT; TRADE; GAME; FLUX;
D O I
10.1016/j.apenergy.2024.123689
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
How to adapt to climate change while achieving sustainable economic and social growth has become a major topic of concern worldwide. With the constraints of the dual-carbon strategy, the integration of regional environmental governance and energy carbon reduction governance is a prevalent focus. This study focuses on analyzing 282 prefecture-level cities in China. The SNA approach and modified gravitation model are utilized to estimate the energy carbon emission efficiency of Chinese cities from 2006 to 2021. The spatial correlation network and the QAP model are ultimately utilized to investigate the factors. The study presented the following findings: (1) There are notable temporal and regional discrepancies in the energy carbon emission efficacy of Chinese cities. Generally, defined by high values in the east and low values in the west. (2) The efficiency of energy carbon emissions in networks connecting urban areas in China is multidimensional, complex, and organic and has improved stability. (3) The developed regions in the east exert a dominant influence on the geographical network, while the central and western parts of the country, which are distant, are considered peripheral. (4) There are few connections within each segment of the geographic correlation network for energy carbon emission efficiency in Chinese cities; however, there are substantial correlations between segments, indicating the presence of a substantial spillover effect. (5) The formation of energy-carbon emission efficiency correlation networks in Chinese cities is significantly influenced by disparities in economic development and government intervention. Conversely, the level of science and education exerts a significantly negative impact on this phenomenon. It is advisable to encourage the development of a spatial correlation network that connects urban energy carbon emission efficiency. This can be achieved through the implementation of specific measures, the establishment of a regional coordination mechanism, leveraging the strengths of energy-efficient regions in the east, maximizing the potential of each sector, and considering the factors that influence the outcomes. Compressing the driving factors and attributes of the spatial correlation network of energy carbon emission efficiency holds substantial practical importance for facilitating the ongoing expansion of the regional low-carbon energy network space and establishing a regional low-carbon synergistic energy governance system.
引用
收藏
页数:17
相关论文
共 50 条
[21]   Spatial-temporal evolution and influencing factors of net carbon sink efficiency in Chinese cities under the background of carbon neutrality [J].
Zhang, Aoxiang ;
Deng, Rongrong .
JOURNAL OF CLEANER PRODUCTION, 2022, 365
[22]   SPATIAL ECONOMETRIC ANALYSIS OF LIVING-ENERGY CARBON EMISSIONS IN CHINA AND ITS DRIVING FACTORS [J].
Tao, Dong ;
Li, Shuang ;
Tang, Yanyan ;
Xia, Qing .
FRESENIUS ENVIRONMENTAL BULLETIN, 2018, 27 (07) :4952-4964
[23]   Urban spatial structure and total-factor energy efficiency in Chinese provinces [J].
Yu, Binbin .
ECOLOGICAL INDICATORS, 2021, 126
[24]   Temporal-Spatial Evolution and Driving Factors of Global Carbon Emission Efficiency [J].
Cao, Ping ;
Li, Xiaoxiao ;
Cheng, Yu ;
Shen, Han .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (22)
[25]   Spatial correlation network structure of China's building carbon emissions and its driving factors: A social network analysis method [J].
Huo, Tengfei ;
Cao, Ruijiao ;
Xia, Nini ;
Hu, Xuan ;
Cai, Weiguang ;
Liu, Bingsheng .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2022, 320
[26]   Towards COP26 targets: Characteristics and influencing factors of spatial correlation network structure on US carbon emission [J].
Wang, Zhenshuang ;
Xie, Wanchen ;
Zhang, Chengyi .
RESOURCES POLICY, 2023, 81
[27]   A study on the spatial correlation network structure and its influencing factors of coupling coordination between FDI flow network and carbon transfer network in the belt and road initiative countries [J].
Huang, Yong ;
You, Di ;
Yu, Haozhen ;
Yang, Chengye ;
Mao, Jiawen .
FRONTIERS IN ENVIRONMENTAL SCIENCE, 2024, 12
[28]   How do CO2 emissions and efficiencies vary in Chinese cities? Spatial variation and driving factors in 2007 [J].
Tian, Yunyu ;
Zhou, Weiqi .
SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 675 :439-452
[29]   Effects of land-use change on carbon emission and its driving factors in Shaanxi Province from 2000 to 2020 [J].
Zhao, Chenxu ;
Liu, Yuling ;
Yan, Zixuan .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (26) :68313-68326
[30]   Spatial structure and carbon emission of urban agglomerations: Spatiotemporal characteristics and driving forces [J].
Wang, Yanan ;
Niu, Yujia ;
Li, Meng ;
Yu, Qianyu ;
Chen, Wei .
SUSTAINABLE CITIES AND SOCIETY, 2022, 78