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

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  • Cheng, Hao
  • Wu, Boyu
  • Jiang, Xiaokun

Abstract

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.

Suggested Citation

  • Cheng, Hao & Wu, Boyu & Jiang, Xiaokun, 2024. "Study on the spatial network structure of energy carbon emission efficiency and its driving factors in Chinese cities," Applied Energy, Elsevier, vol. 371(C).
    • Handle: RePEc:eee:appene:v:371:y:2024:i:c:s0306261924010729
    DOI: 10.1016/j.apenergy.2024.123689
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    as
    1. Wolfgang Cramer & Joël Guiot & Marianela Fader & Joaquim Garrabou & Jean-Pierre Gattuso & Ana Iglesias & Manfred A. Lange & Piero Lionello & Maria Carmen Llasat & Shlomit Paz & Josep Peñuelas & Maria , 2018. "Climate change and interconnected risks to sustainable development in the Mediterranean," Nature Climate Change, Nature, vol. 8(11), pages 972-980, November.
    2. Peng, Benhong & Wang, Yuanyuan & Wei, Guo, 2020. "Energy eco-efficiency: Is there any spatial correlation between different regions?," Energy Policy, Elsevier, vol. 140(C).
    3. Mi, Zhifu & Zhang, Yunkun & Guan, Dabo & Shan, Yuli & Liu, Zhu & Cong, Ronggang & Yuan, Xiao-Chen & Wei, Yi-Ming, 2016. "Consumption-based emission accounting for Chinese cities," Applied Energy, Elsevier, vol. 184(C), pages 1073-1081.
    4. Jiang, Qichuan & Ma, Xuejiao, 2021. "Spillovers of environmental regulation on carbon emissions network," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    5. Najafi, Arsalan & Falaghi, Hamid & Contreras, Javier & Ramezani, Maryam, 2016. "Medium-term energy hub management subject to electricity price and wind uncertainty," Applied Energy, Elsevier, vol. 168(C), pages 418-433.
    6. Rendl, F. & Sotirov, R., 2007. "Bounds for the quadratic assignment problem using the bundle method," Other publications TiSEM b6d298bc-77c9-4a6d-a043-5, Tilburg University, School of Economics and Management.
    7. Heragu, Sunderesh S. & Kusiak, Andrew, 1991. "Efficient models for the facility layout problem," European Journal of Operational Research, Elsevier, vol. 53(1), pages 1-13, July.
    8. Wen, Lanjiao & Chatalova, Lioudmila & Gao, Xin & Zhang, Anlu, 2021. "Reduction of carbon emissions through resource-saving and environment-friendly regional economic integration: Evidence from Wuhan metropolitan area, China," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Bai, Rui & Lin, Boqiang, 2024. "An in-depth analysis of green innovation efficiency: New evidence based on club convergence and spatial correlation network," Energy Economics, Elsevier, vol. 132(C).
    10. Andrew P. Schurer & Michael E. Mann & Ed Hawkins & Simon F. B. Tett & Gabriele C. Hegerl, 2017. "Importance of the pre-industrial baseline for likelihood of exceeding Paris goals," Nature Climate Change, Nature, vol. 7(8), pages 563-567, August.
    11. Fan, Xiaojia & Wu, Sanmang & Li, Shantong, 2019. "Spatial-temporal analysis of carbon emissions embodied in interprovincial trade and optimization strategies: A case study of Hebei, China," Energy, Elsevier, vol. 185(C), pages 1235-1249.
    12. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    13. Cologni, Alessandro & Manera, Matteo, 2008. "Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries," Energy Economics, Elsevier, vol. 30(3), pages 856-888, May.
    14. Wang, Zhenshuang & Xie, Wanchen & Zhang, Chengyi, 2023. "Towards COP26 targets: Characteristics and influencing factors of spatial correlation network structure on U.S. carbon emission," Resources Policy, Elsevier, vol. 81(C).
    15. Morlet, Clémence & Keirstead, James, 2013. "A comparative analysis of urban energy governance in four European cities," Energy Policy, Elsevier, vol. 61(C), pages 852-863.
    16. Liu, S. & Xiao, Q., 2021. "An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model," Energy, Elsevier, vol. 224(C).
    17. Pang, Qinghua & Qiu, Man & Zhang, Lina & Chiu, Yung-ho, 2023. "Congestion effects of energy and capital in China's carbon emission reduction: Evidence from provincial levels," Energy, Elsevier, vol. 274(C).
    18. Lin, Boqiang & Ma, Ruiyang, 2022. "Green technology innovations, urban innovation environment and CO2 emission reduction in China: Fresh evidence from a partially linear functional-coefficient panel model," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    19. Rios, Vicente & Gianmoena, Lisa, 2018. "Convergence in CO2 emissions: A spatial economic analysis with cross-country interactions," Energy Economics, Elsevier, vol. 75(C), pages 222-238.
    20. Dong, Feng & Li, Xiaohui & Long, Ruyin & Liu, Xiaoyan, 2013. "Regional carbon emission performance in China according to a stochastic frontier model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 525-530.
    21. Yang, Jie & Huang, Yijing & Takeuchi, Kenji, 2022. "Does drought increase carbon emissions? Evidence from Southwestern China," Ecological Economics, Elsevier, vol. 201(C).
    22. Steven A. Melnyk & Barbara B. Flynn & Amrou Awaysheh, 2018. "The best of times and the worst of times: empirical operations and supply chain management research," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 164-192, January.
    23. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    24. Shohei Tokito & Tesshu Hanaka & Fumiya Nagashima, 2023. "Structural attribution of emissions along the global supply chain and implications for climate policy," Journal of Industrial Ecology, Yale University, vol. 27(6), pages 1488-1499, December.
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