IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i19p12235-d926304.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/19/12235/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/19/12235/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. 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.
    5. 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).
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Hongtao Jiang & Jian Yin & Yuanhong Qiu & Bin Zhang & Yi Ding & Ruici Xia, 2022. "Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces," Land, MDPI, vol. 11(8), pages 1-22, July.
    3. Huaming Chen & Jia Liu & Ying Li & Yung-Ho Chiu & Tai-Yu Lin, 2019. "A Two-stage Dynamic Undesirable Data Envelopment Analysis Model Focused on Media Reports and the Impact on Energy and Health Efficiency," IJERPH, MDPI, vol. 16(9), pages 1-23, April.
    4. Davtalab-Olyaie, Mostafa & Begen, Mehmet A. & Yang, Zijiang & Asgharian, Masoud, 2024. "Incentivization in centrally managed systems: Inconsistencies resolution," Omega, Elsevier, vol. 129(C).
    5. Honma, Satoshi, 2012. "Environmental and economic efficiencies in the Asia-Pacific region," MPRA Paper 43361, University Library of Munich, Germany.
    6. Le Sun & Congmou Zhu & Shaofeng Yuan & Lixia Yang & Shan He & Wuyan Li, 2022. "Exploring the Impact of Digital Inclusive Finance on Agricultural Carbon Emission Performance in China," IJERPH, MDPI, vol. 19(17), pages 1-18, September.
    7. 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).
    8. Zhipeng Yu & Yi Liu & Taihua Yan & Ming Zhang, 2024. "Carbon emission efficiency in the age of digital economy: New insights on green technology progress and industrial structure distortion," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 4039-4057, July.
    9. Suzuki, Soushi & Nijkamp, Peter, 2016. "An evaluation of energy-environment-economic efficiency for EU, APEC and ASEAN countries: Design of a Target-Oriented DFM model with fixed factors in Data Envelopment Analysis," Energy Policy, Elsevier, vol. 88(C), pages 100-112.
    10. Haixiang Xu & Rui Zhang, 2024. "Dynamic Analysis of Urban Land Use Efficiency in the Western Taiwan Strait Economic Zone," Land, MDPI, vol. 13(8), pages 1-26, August.
    11. Zhijiang Li & Decai Tang & Mang Han & Brandon J. Bethel, 2018. "Comprehensive Evaluation of Regional Sustainable Development Based on Data Envelopment Analysis," Sustainability, MDPI, vol. 10(11), pages 1-18, October.
    12. Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
    13. Fan Wang & Lili Feng & Jin Li & Lin Wang, 2020. "Environmental Regulation, Tenure Length of Officials, and Green Innovation of Enterprises," IJERPH, MDPI, vol. 17(7), pages 1-16, March.
    14. Rongrong Xu & Yongxiang Wu & Gaoxu Wang & Xuan Zhang & Wei Wu & Zan Xu, 2019. "Evaluation of industrial water use efficiency considering pollutant discharge in China," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-22, August.
    15. Chia-Nan Wang & Jen-Der Day & Nguyen Thi Kim Lien & Luu Quoc Chien, 2018. "Integrating the Additive Seasonal Model and Super-SBM Model to Compute the Efficiency of Port Logistics Companies in Vietnam," Sustainability, MDPI, vol. 10(8), pages 1-17, August.
    16. Wang, Ke-Liang & Sun, Ting-Ting & Xu, Ru-Yu & Miao, Zhuang & Cheng, Yun-He, 2022. "How does internet development promote urban green innovation efficiency? Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    17. Zhang, Wei & Liu, Xuemeng & Zhao, Shikuan & Tang, Tian, 2024. "Does green finance agglomeration improve carbon emission performance in China? A perspective of spatial spillover," Applied Energy, Elsevier, vol. 358(C).
    18. Song, Malin & Zheng, Wanping & Wang, Zeya, 2016. "Environmental efficiency and energy consumption of highway transportation systems in China," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 441-449.
    19. Josef Jablonsky, 2022. "Individual and team efficiency: a case of the National Hockey League," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(2), pages 479-494, June.
    20. Vicente J. Bolós & Rafael Benítez & Vicente Coll-Serrano, 2023. "Continuous models combining slacks-based measures of efficiency and super-efficiency," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 363-391, June.

    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:gam:jijerp:v:19:y:2022:i:19:p:12235-:d:926304. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.