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Research on the spatiotemporal evolution characteristics and driving factors of the spatial connection network of carbon emissions in China: New evidence from 260 cities

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  • Wang, Longke
  • Zhang, Ming
  • Song, Yan

Abstract

China is currently facing enormous pressure to reduce emissions, and in order to realize synergistic emission reduction between regions, the spatial connection effect of carbon emissions must be considered comprehensively. Based on the data of 260 cities from 2001 to 2021, the social network analysis (SNA) method is utilized to explore the evolution characteristics and driving factors of the spatial connection network of carbon emissions (SCNCE) in China from the temporal and spatial dimensions. The research results show that: (1) The network has a complex spatial structure, with a progressively tighter overall structure, a weaker hierarchical structure, and a gradual stabilization of network connections. (2) The network is characterized by an obvious “core-edge” structure, with some cities at the core of the network, acting as important controllers or bridges in the network. (3) Cities in the network can be categorized into four regional blocks, with a distinct geographic distribution, and the size of inflow blocks are increasing. (4) The adjacent space, similar urbanization levels, and the widening gap in economic development, industrial structure, and technological innovation dynamics is the key to driving the formation of carbon emissions connections.

Suggested Citation

  • Wang, Longke & Zhang, Ming & Song, Yan, 2024. "Research on the spatiotemporal evolution characteristics and driving factors of the spatial connection network of carbon emissions in China: New evidence from 260 cities," Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:energy:v:291:y:2024:i:c:s0360544224002196
    DOI: 10.1016/j.energy.2024.130448
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    Cited by:

    1. Kai Wan & Xiaolin Yu & Kaiti Zou, 2024. "Assessing the Spatial Distribution of Carbon Emissions and Influencing Factors in the Yellow River Basin," Sustainability, MDPI, vol. 16(22), pages 1-20, November.
    2. Hailing Wu & Yuanjun Li & Kaihuai Liao & Qitao Wu & Kanhai Shen, 2024. "Structural Characteristics of Expressway Carbon Emission Correlation Network and Its Influencing Factors: A Case Study in Guangdong Province," Sustainability, MDPI, vol. 16(22), pages 1-20, November.

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