IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i10p6114-d818040.html
   My bibliography  Save this article

Research on the Structure of Carbon Emission Efficiency and Influencing Factors in the Yangtze River Delta Urban Agglomeration

Author

Listed:
  • Chenxu Liu

    (School of Geography Sciences, Nanjing Normal University, Nanjing 210023, China)

  • Ruien Tang

    (School of Geography Sciences, Nanjing Normal University, Nanjing 210023, China)

  • Yaqi Guo

    (School of Geography Sciences, Nanjing Normal University, Nanjing 210023, China)

  • Yuhan Sun

    (School of Geography Sciences, Nanjing Normal University, Nanjing 210023, China)

  • Xinyi Liu

    (Honorary College, Nanjing Normal University, Nanjing 210023, China)

Abstract

Climate change caused by CO 2 emissions has become one of the most serious environmental problems facing the world today, and it has a strong relevance to sustainability. This paper measures the carbon emission efficiency of the Yangtze River Delta urban agglomeration from 2001 to 2019 using the U-S SBM model. The modified gravity model and social network analysis methods are used to explore its spatially correlated network structure, and QAP regression is used to explore the influencing factors. The results show the following: (1) The spatial correlation of the carbon emission efficiency in the Yangtze River Delta urban agglomeration increased during the study period, showing a complex network structure with multiple threads and directions, and a strong mobility of the network. (2) The spatial network of the carbon emission efficiency in the Yangtze River Delta urban agglomeration gradually formed a core−edge structure with southern Jiangsu as the core area, northern Zhejiang and central Jiangsu as the secondary core area, and central Anhui and southern Zhejiang as the edge area during the study period. (3) The spatial correlation network of carbon emission efficiency in the Yangtze River Delta urban agglomeration is divided into “net benefit”, “net spillover”, “two-way spillover”, and “broker”. (4) Differences in energy intensity, government environmental regulations, technology research and development, and economic export orientation are the main factors affecting the spatial correlation of carbon emission efficiency in the Yangtze River Delta urban agglomeration.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6114-:d:818040
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/10/6114/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/10/6114/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marbuah, George & Amuakwa-Mensah, Franklin, 2017. "Spatial analysis of emissions in Sweden," Energy Economics, Elsevier, vol. 68(C), pages 383-394.
    2. 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).
    3. Ang, B. W., 1999. "Is the energy intensity a less useful indicator than the carbon factor in the study of climate change?," Energy Policy, Elsevier, vol. 27(15), pages 943-946, December.
    4. Sun, J. W., 2005. "The decrease of CO2 emission intensity is decarbonization at national and global levels," Energy Policy, Elsevier, vol. 33(8), pages 975-978, May.
    5. Hampf, Benjamin & Rødseth, Kenneth Løvold, 2015. "Carbon dioxide emission standards for U.S. power plants: An efficiency analysis perspective," Energy Economics, Elsevier, vol. 50(C), pages 140-153.
    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. Mielnik, Otavio & Goldemberg, Jose, 1999. "Communication The evolution of the "carbonization index" in developing countries," Energy Policy, Elsevier, vol. 27(5), pages 307-308, May.
    8. R. Ramanathan, 2002. "Combining indicators of energy consumption and CO 2 emissions: a cross-country comparison," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 17(3), pages 214-227.
    9. Grunewald, Nicole & Jakob, Michael & Mouratiadou, Ioanna, 2014. "Decomposing inequality in CO2 emissions: The role of primary energy carriers and economic sectors," Ecological Economics, Elsevier, vol. 100(C), pages 183-194.
    10. Liu, Qianqian & Wang, Shaojian & Zhang, Wenzhong & Li, Jiaming & Kong, Yunlong, 2019. "Examining the effects of income inequality on CO2 emissions: Evidence from non-spatial and spatial perspectives," Applied Energy, Elsevier, vol. 236(C), pages 163-171.
    11. Hampf, Benjamin & Rødseth, Kenneth Løvold, 2015. "Carbon dioxode emission standards for U.S. power plants: An efficiency analysis perspective," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77009, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    12. 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).
    13. 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.
    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. Mingming Zhu & Jigan Wang & Jie Zhang & Zhencheng Xing, 2022. "Urban Low-Carbon Consumption Performance Assessment: A Case Study of Yangtze River Delta Cities, China," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    2. Qi Fu & Mengfan Gao & Yue Wang & Tinghui Wang & Xu Bi & Jinhua Chen, 2022. "Spatiotemporal Patterns and Drivers of the Carbon Budget in the Yangtze River Delta Region, China," Land, MDPI, vol. 11(8), pages 1-18, August.
    3. Xiaochun Zhao & Huixin Xu & Qun Sun, 2022. "Research on China’s Carbon Emission Efficiency and Its Regional Differences," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    4. 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.

    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. Qizhen Wang & Qian Zhang, 2022. "Foreign Direct Investment and Carbon Emission Efficiency: The Role of Direct and Indirect Channels," Sustainability, MDPI, vol. 14(20), pages 1-23, October.
    3. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2017. "Non-radial metafrontier approach to identify carbon emission performance and intensity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 664-672.
    4. 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.
    5. Li-Ming Xue & Zhi-Xue Zheng & Shuo Meng & Mingjun Li & Huaqing Li & Ji-Ming Chen, 2022. "Carbon emission efficiency and spatio-temporal dynamic evolution of the cities in Beijing-Tianjin-Hebei Region, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 7640-7664, June.
    6. Qingyou Yan & Fei Zhao & Xu Wang & Tomas Balezentis, 2021. "The Environmental Efficiency Analysis Based on the Three-Step Method for Two-Stage Data Envelopment Analysis," Energies, MDPI, vol. 14(21), pages 1-14, October.
    7. 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.
    8. Wang, Q.W. & Zhou, P. & Shen, N. & Wang, S.S., 2013. "Measuring carbon dioxide emission performance in Chinese provinces: A parametric approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 324-330.
    9. Xintao Li & Dong Feng & Jian Li & Zaisheng Zhang, 2019. "Research on the Spatial Network Characteristics and Synergetic Abatement Effect of the Carbon Emissions in Beijing–Tianjin–Hebei Urban Agglomeration," Sustainability, MDPI, vol. 11(5), pages 1-15, March.
    10. Zhou, Di & Huang, Qing & Chong, Zhaohui, 2022. "Analysis on the effect and mechanism of land misallocation on carbon emissions efficiency: Evidence from China," Land Use Policy, Elsevier, vol. 121(C).
    11. Xinna Zhao & Li Guo & Zhiyuan Gao & Yu Hao, 2024. "Estimation and Analysis of Carbon Emission Efficiency in Chinese Industry and Its Influencing Factors—Evidence from the Micro Level," Energies, MDPI, vol. 17(4), pages 1-15, February.
    12. Zhang, Wei & Liu, Xuemeng & Wang, Die & Zhou, Jianping, 2022. "Digital economy and carbon emission performance: Evidence at China's city level," Energy Policy, Elsevier, vol. 165(C).
    13. 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.
    14. Yao, Xin & Guo, Chengwen & Shao, Shuai & Jiang, Zhujun, 2016. "Total-factor CO2 emission performance of China’s provincial industrial sector: A meta-frontier non-radial Malmquist index approach," Applied Energy, Elsevier, vol. 184(C), pages 1142-1153.
    15. Ling, Yantao & Xia, Senmao & Cao, Mengqiu & He, Kerun & Lim, Ming K. & Sukumar, Arun & Yi, Huiyong & Qian, Xiaoduo, 2021. "Carbon emissions in China's thermal electricity and heating industry: an input-output structural decomposition analysis," LSE Research Online Documents on Economics 112930, London School of Economics and Political Science, LSE Library.
    16. Juan Aparicio & Magdalena Kapelko, 2019. "Enhancing the Measurement of Composite Indicators of Corporate Social Performance," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(2), pages 807-826, July.
    17. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "Slacks-based efficiency measures for modeling environmental performance," Ecological Economics, Elsevier, vol. 60(1), pages 111-118, November.
    18. Ying Sun & Fengqin Liu & Huaping Sun, 2022. "Does Standardization Improve Carbon Emission Efficiency as Soft Infrastructure? Evidence from China," Energies, MDPI, vol. 15(6), pages 1-17, March.
    19. Zhou, P. & Ang, B.W. & Han, J.Y., 2010. "Total factor carbon emission performance: A Malmquist index analysis," Energy Economics, Elsevier, vol. 32(1), pages 194-201, January.
    20. Ying Li & Yung-ho Chiu & Tai-Yu Lin, 2019. "Research on New and Traditional Energy Sources in OECD Countries," IJERPH, MDPI, vol. 16(7), pages 1-21, March.

    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:jsusta:v:14:y:2022:i:10:p:6114-:d:818040. 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.