IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v368y2024ics0306261924007360.html
   My bibliography  Save this article

Identifying the optimal node group of carbon emission efficiency correlation network in China based on pinning control theory

Author

Listed:
  • Du, Ruijin
  • Zhang, Nidan
  • Zhang, Mengxi
  • Kong, Ziyang
  • Jia, Qiang
  • Dong, Gaogao
  • Tian, Lixin
  • Ahsan, Muhammad

Abstract

Improving carbon emission efficiency (CEE) levels is essential for achieving the 2030 carbon emission peak target. It is unrealistic to raise CEE levels in all regions in the short term, due to its high macroeconomic costs that have a significant impact on overall economic development. A more pragmatic strategy is controlling only some provinces to take the lead in raising CEE levels and indirectly drive other provinces based on network connectivity until the goal of region-wide CEE improvement, which will greatly save policy and economic costs. From the fresh perspective of complex network pinning control theory, this study contributes to propose the optimal node group screening theory to achieve the precise identification of provinces with radiation-led effect in China’s inter-provincial CEE correlation networks in 2012 and 2020. The following conclusions are obtained: (1) CEE levels are spatially heterogeneous, showing the distribution characteristics of higher levels in the east and south, and lower levels in the west and north of China. (2) The optimal pinned node group contains high-, medium- and low-efficiency provinces, and is geographically dispersed. (3) In the pinned node group, low-efficiency provinces radiate downstream to provinces with high betweenness centrality, which is an important “driver” of CEE improvement. (4) Compared with other node selection strategies, the pinned node group screening algorithm designed can achieve better network connectivity by controlling fewer provinces. This work provides a theoretical basis and methodological reference for policy makers to implement policies accurately and smooth the CEE impact pathway.

Suggested Citation

  • Du, Ruijin & Zhang, Nidan & Zhang, Mengxi & Kong, Ziyang & Jia, Qiang & Dong, Gaogao & Tian, Lixin & Ahsan, Muhammad, 2024. "Identifying the optimal node group of carbon emission efficiency correlation network in China based on pinning control theory," Applied Energy, Elsevier, vol. 368(C).
    • Handle: RePEc:eee:appene:v:368:y:2024:i:c:s0306261924007360
    DOI: 10.1016/j.apenergy.2024.123353
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924007360
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.123353?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chai, Jian & Tian, Lingyue & Jia, Ruining, 2023. "New energy demonstration city, spatial spillover and carbon emission efficiency: Evidence from China's quasi-natural experiment," Energy Policy, Elsevier, vol. 173(C).
    2. Chen, Qiong & Wu, Ting-Ting & Fang, Ming, 2013. "Detecting local community structures in complex networks based on local degree central nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 529-537.
    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. Du, Minzhe & Wu, Fenger & Ye, Danfeng & Zhao, Yating & Liao, Liping, 2023. "Exploring the effects of energy quota trading policy on carbon emission efficiency: Quasi-experimental evidence from China," Energy Economics, Elsevier, vol. 124(C).
    5. Du, Yanan & Zhou, Jianping & Bai, Jiancheng & Cao, Yujia, 2023. "Breaking the resource curse: The perspective of improving carbon emission efficiency based on digital infrastructure construction," Resources Policy, Elsevier, vol. 85(PB).
    6. 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.
    7. Xing, Peixue & Wang, Yanan & Ye, Tao & Sun, Ying & Li, Qiao & Li, Xiaoyan & Li, Meng & Chen, Wei, 2024. "Carbon emission efficiency of 284 cities in China based on machine learning approach: Driving factors and regional heterogeneity," Energy Economics, Elsevier, vol. 129(C).
    8. Song, Malin & Zheng, Huanyu & Shen, Zhiyang, 2023. "Whether the carbon emissions trading system improves energy efficiency – Empirical testing based on China's provincial panel data," Energy, Elsevier, vol. 275(C).
    9. 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.
    10. Douglas, Paul H, 1976. "The Cobb-Douglas Production Function Once Again: Its History, Its Testing, and Some New Empirical Values," Journal of Political Economy, University of Chicago Press, vol. 84(5), pages 903-915, October.
    11. Hu, Shuo & Yuan, Zheng & Wang, Ailun, 2024. "Improving carbon emission efficiency in Chinese manufacturing: A study considering technological heterogeneity and noise," Energy, Elsevier, vol. 291(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    2. Du, Xiaoyun & Meng, Conghui & Guo, Zhenhua & Yan, Hang, 2023. "An improved approach for measuring the efficiency of low carbon city practice in China," Energy, Elsevier, vol. 268(C).
    3. Zhao, Xing & Guo, Yifan & Feng, Tianchu, 2023. "Towards green recovery: Natural resources utilization efficiency under the impact of environmental information disclosure," Resources Policy, Elsevier, vol. 83(C).
    4. Min Ge & Kaili Yu & Ange Ding & Gaofeng Liu, 2022. "Input-Output Efficiency of Water-Energy-Food and Its Driving Forces: Spatial-Temporal Heterogeneity of Yangtze River Economic Belt, China," IJERPH, MDPI, vol. 19(3), pages 1-15, January.
    5. Lijie Wei & Zhibao Wang, 2022. "Differentiation Analysis on Carbon Emission Efficiency and Its Factors at Different Industrialization Stages: Evidence from Mainland China," IJERPH, MDPI, vol. 19(24), pages 1-14, December.
    6. Feng Dong & Chang Qin & Xiaoyun Zhang & Xu Zhao & Yuling Pan & Yujin Gao & Jiao Zhu & Yangfan Li, 2021. "Towards Carbon Neutrality: The Impact of Renewable Energy Development on Carbon Emission Efficiency," IJERPH, MDPI, vol. 18(24), pages 1-23, December.
    7. Hanhua Shao & Jixin Cheng & Yuansheng Wang & Xiaoming Li, 2022. "Can Digital Finance Promote Comprehensive Carbon Emission Performance? Evidence from Chinese Cities," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    8. Wang, Xinru & Long, Ruyin & Sun, Qingqing & Chen, Hong & Jiang, Shiyan & Wang, Yujie & Li, Qianwen & Yang, Shuhan, 2024. "Spatial spillover effects and driving mechanisms of carbon emission reduction in new energy demonstration cities," Applied Energy, Elsevier, vol. 357(C).
    9. Zhou, Na & Wu, Qiaosheng & Hu, Xiangping & Xu, Deyi & Wang, Xiaolin, 2020. "Evaluation of Chinese natural gas investment along the Belt and Road Initiative using super slacks-based measurement of efficiency method," Resources Policy, Elsevier, vol. 67(C).
    10. 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.
    11. Chenxu Liu & Ruien Tang & Yaqi Guo & Yuhan Sun & Xinyi Liu, 2022. "Research on the Structure of Carbon Emission Efficiency and Influencing Factors in the Yangtze River Delta Urban Agglomeration," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    12. Hu, Shuo & Wang, Ailun & Lin, Boqiang, 2024. "Marginal abatement cost of CO2: A convex quantile non-radial directional distance function regression method considering noise and inefficiency," Energy, Elsevier, vol. 297(C).
    13. Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & de Pinho Matos, Giordano Bruno Braz, 2015. "Statistical evaluation of Data Envelopment Analysis versus COLS Cobb–Douglas benchmarking models for the 2011 Brazilian tariff revision," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 47-60.
    14. Lingzhang Kong & Jinye Li, 2022. "Digital Economy Development and Green Economic Efficiency: Evidence from Province-Level Empirical Data in China," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
    15. Ashrafi, Ali & Seow, Hsin-Vonn & Lee, Lai Soon & Lee, Chew Ging, 2013. "The efficiency of the hotel industry in Singapore," Tourism Management, Elsevier, vol. 37(C), pages 31-34.
    16. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    17. Xu, Xianghui & Chen, Yingshan & Zhou, Yan & Liu, Wuyuan & Zhang, Xinrui & Li, Mo, 2023. "Sustainable management of agricultural water rights trading under uncertainty: An optimization-evaluation framework," Agricultural Water Management, Elsevier, vol. 280(C).
    18. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    19. Jael, Paul, 2019. "Does Marginal Productivity Mean Anything in Real Economic Life ?," MPRA Paper 97968, University Library of Munich, Germany, revised Jan 2020.
    20. Ke Liu & Yurong Qiao & Qian Zhou, 2021. "Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR," IJERPH, MDPI, vol. 18(8), pages 1-22, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:368:y:2024:i:c:s0306261924007360. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.