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Spatial correlation network structure characteristics of carbon emission efficiency and its influencing factors at city level in China

Author

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  • Zhongrui Sun

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xianhong Cheng

    (Changzhou University)

  • Yumei Zhuang

    (Shandong University of Finance and Economics)

  • Yong Sun

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

The improved gravity model was used to determine the spatial correlation of carbon emission efficiency (CEE) among cities, and then the spatial correlation network of CEE of 284 Chinese cities from 2006 to 2019 was constructed for the first time. Based on the social network analysis method, the structural characteristics and influencing factors of the network were analyzed. It was found that: (1) The spatial correlation of CEE at city level is obvious, the overall correlation intensity is increasing, and the aggregation of correlation is significant. (2) The network shows a core–edge structure, cities with high economic development, such as Shenzhen, Beijing, Nanjing, Guangzhou and Shanghai, occupying the center of the network, while cities with relatively weak development such as Heihe, Jiuquan, Baotou and Baoshan are at the edge. (3) The network can be divided into four plates, with sparse association within the plate and the strong spillover effect between the plates. (4) Spatial adjacency relationship, differences in economic development, industrial development, green technology innovation and energy consumption have significant positive effects on establishing a spatial network, while geographical distance has significant negative effects on it. The results can help governments to clearly realize the correlation of CEE between cities and identify the role that each city can play in improving the overall CEE, so as to provide guidance for governments to further formulate accurate carbon emission reduction policies.

Suggested Citation

  • Zhongrui Sun & Xianhong Cheng & Yumei Zhuang & Yong Sun, 2024. "Spatial correlation network structure characteristics of carbon emission efficiency and its influencing factors at city level in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(2), pages 5335-5366, February.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:2:d:10.1007_s10668-023-02936-4
    DOI: 10.1007/s10668-023-02936-4
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    1. Haihong Song & Liyuan Gu & Yifan Li & Xin Zhang & Yuan Song, 2022. "Research on Carbon Emission Efficiency Space Relations and Network Structure of the Yellow River Basin City Cluster," IJERPH, MDPI, vol. 19(19), pages 1-19, September.
    2. Sheng, Pengfei & Li, Jun & Zhai, Mengxin & Huang, Shoujun, 2020. "Coupling of economic growth and reduction in carbon emissions at the efficiency level: Evidence from China," Energy, Elsevier, vol. 213(C).
    3. Fang, Guochang & Gao, Zhengye & Tian, Lixin & Fu, Min, 2022. "What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data," Applied Energy, Elsevier, vol. 312(C).
    4. Wang, Shaojian & Liu, Xiaoping, 2017. "China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces," Applied Energy, Elsevier, vol. 200(C), pages 204-214.
    5. Juanjuan Tian & Xiaoqian Song & Jinsuo Zhang, 2022. "Spatial-Temporal Pattern and Driving Factors of Carbon Efficiency in China: Evidence from Panel Data of Urban Governance," Energies, MDPI, vol. 15(7), pages 1-24, March.
    6. Fei Ma & Yixuan Wang & Kum Fai Yuen & Wenlin Wang & Xiaodan Li & Yuan Liang, 2019. "The Evolution of the Spatial Association Effect of Carbon Emissions in Transportation: A Social Network Perspective," IJERPH, MDPI, vol. 16(12), pages 1-23, June.
    7. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Di Costa, Flavia, 2020. "The role of geographical proximity in knowledge diffusion, measured by citations to scientific literature," Journal of Informetrics, Elsevier, vol. 14(1).
    8. Malte Meinshausen & Nicolai Meinshausen & William Hare & Sarah C. B. Raper & Katja Frieler & Reto Knutti & David J. Frame & Myles R. Allen, 2009. "Greenhouse-gas emission targets for limiting global warming to 2 °C," Nature, Nature, vol. 458(7242), pages 1158-1162, April.
    9. Mahapatra, Bamadev & Irfan, Mohd, 2021. "Asymmetric impacts of energy efficiency on carbon emissions: A comparative analysis between developed and developing economies," Energy, Elsevier, vol. 227(C).
    10. Jingyi Wang & Kaisi Sun & Jiupai Ni & Deti Xie, 2021. "Evaluation and Factor Analysis of Industrial Carbon Emission Efficiency Based on “Green-Technology Efficiency”—The Case of Yangtze River Basin, China," Land, MDPI, vol. 10(12), pages 1-23, December.
    11. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    12. Cai, Bofeng & Guo, Huanxiu & Ma, Zipeng & Wang, Zhixuan & Dhakal, Shobhakar & Cao, Libin, 2019. "Benchmarking carbon emissions efficiency in Chinese cities: A comparative study based on high-resolution gridded data," Applied Energy, Elsevier, vol. 242(C), pages 994-1009.
    13. Chen, Zhe & Sarkar, Apurbo & Rahman, Airin & Li, Xiaojing & Xia, Xianli, 2022. "Exploring the drivers of green agricultural development (GAD) in China: A spatial association network structure approaches," Land Use Policy, Elsevier, vol. 112(C).
    14. Zuoren Sun & Yi Liu & Lifeng Wu, 2021. "Does Industrial Agglomeration Promote Carbon Efficiency? A Spatial Econometric Analysis and Fractional-Order Grey Forecasting," Journal of Mathematics, Hindawi, vol. 2021, pages 1-18, July.
    15. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    16. Liu, Zhen & Shi, Yuren & Yan, Jianming & Ou, Xunmin & Lieu, Jenny, 2012. "Research on the decomposition model for China’s National Renewable Energy total target," Energy Policy, Elsevier, vol. 51(C), pages 110-120.
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