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Research on Carbon Emission Efficiency Space Relations and Network Structure of the Yellow River Basin City Cluster

Author

Listed:
  • Haihong Song

    (Urban and Rural Planning, School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

  • Liyuan Gu

    (Urban and Rural Planning, School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

  • Yifan Li

    (Urban and Rural Planning, School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

  • Xin Zhang

    (Urban and Rural Planning, School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

  • Yuan Song

    (Urban and Rural Planning, School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

Abstract

The Yellow River Basin serves as China’s primary ecological barrier and economic belt. The achievement of the Yellow River Basin’s “double carbon” objective is crucial to China’s green and low-carbon development. This study examines the spatial link and network structure of city cluster carbon emission efficiency in the Yellow River Basin, as well as the complexity of the network structure. It focuses not only on the density and centrality of the carbon emission efficiency network from the standpoint of city clusters, but also on the excellent cities and concentration of the city cluster ‘s internal carbon emission efficiency network. The results show that: (1) The carbon emission efficiency of the Yellow River Basin has been dramatically improved, and the gap between city clusters is narrowing. However, gradient differentiation characteristics between city clusters show the Matthew effect. (2) The distribution of carbon emission efficiency in the Yellow River Basin is unbalanced, roughly showing a decreasing trend from east to west. Lower-level efficiency cities have played a significant role in the evolution of carbon emissions efficiency space. (3) The strength of the carbon emission efficiency network structure in the Yellow River Basin gradually transitions from weakly correlated dominant to weakly and averagely correlated dominant. Among them, the Shandong Peninsula city cluster has the most significant number of connected nodes in the carbon emission efficiency network. In contrast, the emission efficiency network density of the seven city clusters shows different changing trends. Finally, this study suggests recommendations to improve carbon emission efficiency by adopting differentiated governance measures from the perspective of local adaptation and using positive spatial spillover effects.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12235-:d:926304
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    References listed on IDEAS

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    1. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    2. Qiongzhi Liu & Jun Hao, 2022. "Regional Differences and Influencing Factors of Carbon Emission Efficiency in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 14(8), pages 1-15, April.
    3. Zhang, Yue-Jun & Jiang, Lin & Shi, Wei, 2020. "Exploring the growth-adjusted energy-emission efficiency of transportation industry in China," Energy Economics, Elsevier, vol. 90(C).
    4. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    5. Feng Wang & Mengnan Gao & Juan Liu & Wenna Fan, 2018. "The Spatial Network Structure of China’s Regional Carbon Emissions and Its Network Effect," Energies, MDPI, vol. 11(10), pages 1-14, October.
    6. Wang, Qiang & Zhang, Chen & Li, Rongrong, 2022. "Towards carbon neutrality by improving carbon efficiency - A system-GMM dynamic panel analysis for 131 countries’ carbon efficiency," Energy, Elsevier, vol. 258(C).
    7. 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.
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    Cited by:

    1. Dong Feng & Jiayi Han & Han Jia & Xinyuan Chang & Jiaqi Guo & Pinghua Huang, 2023. "Regional Economic Growth and Environmental Protection in China: The Yellow River Basin Economic Zone as an Example," Sustainability, MDPI, vol. 15(14), pages 1-20, July.
    2. Lingzhi Ren & Ning Yi & Zhiying Li & Zhaoxian Su, 2023. "Research on the Impact of Energy Saving and Emission Reduction Policies on Carbon Emission Efficiency of the Yellow River Basin: A Perspective of Policy Collaboration Effect," Sustainability, MDPI, vol. 15(15), pages 1-17, August.
    3. Di Li & Peiyan Han & Kexin Li & Qianbin Di & Ye Duan, 2024. "A Study on the Impact of Urbanization Level on Carbon Emissions Based on the STIRPAT Model: A Case Study of Five Major Urban Agglomerations in Northern China," Sustainability, MDPI, vol. 16(14), pages 1-29, July.

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