IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p8042-d1147427.html
   My bibliography  Save this article

Analysis of Carbon Emission Efficiency in the Yellow River Basin in China: Spatiotemporal Differences and Influencing Factors

Author

Listed:
  • Jiao Wang

    (Centre for Innovation Management Research, Xinjiang University, Urumqi 830046, China
    School of Economics and Management, Xinjiang University, Urumqi 830046, China)

  • Zhenliang Liao

    (School of Economics and Management, Xinjiang University, Urumqi 830046, China
    College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China)

  • Hui Sun

    (Centre for Innovation Management Research, Xinjiang University, Urumqi 830046, China
    School of Economics and Management, Xinjiang University, Urumqi 830046, China)

Abstract

A good grasp of the carbon emission efficiency (CEE) of the provinces in the Yellow River basin (YRB) in China, and its influencing factors, can help promote the sustainable development of the region and smooth realization of the national carbon emission reduction target. Based on stochastic frontier analysis (SFA), this paper calculates the CEE of nine provinces in the YRB from 2005 to 2019, and then, analyzes its spatial and temporal characteristics. The spatial Durbin model (SDM) with two-way fixed effects is selected to investigate the influencing factors of the CEE in the YRB. The results suggest that: (1) the overall CEE of the YRB shows a slow upward trend, and although the gap in CEE between provinces is large, it is slowly narrowing; (2) there is a significant negative spatial autocorrelation in the CEE of the provinces in the YRB; and (3) technological innovation capability, energy consumption structure, population density, and urban greening level are the most significant factors affecting the CEE of the YRB. Both population density and urban greening level have a positive effect on the improvement of the CEE of the provinces themselves and of the whole YRB, and there is also a spatial spillover effect on the improvement of CEE due to population density. Technological innovation capability and energy consumption structure had a negative impact on the overall CEE of the province and the basin during the research period. This study may have some reference value for improving the CEE of the YRB.

Suggested Citation

  • Jiao Wang & Zhenliang Liao & Hui Sun, 2023. "Analysis of Carbon Emission Efficiency in the Yellow River Basin in China: Spatiotemporal Differences and Influencing Factors," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8042-:d:1147427
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/8042/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/10/8042/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiangfeng Hu & Haoming Shi & Qinghua Huang & Yalan Luo & Yamei Li, 2020. "The Impacts of Freight Trade on Carbon Emission Efficiency: Evidence from the Countries along the “Belt and Road”," Complexity, Hindawi, vol. 2020, pages 1-15, December.
    2. Wang, Wei-Zheng & Liu, Lan-Cui & Liao, Hua & Wei, Yi-Ming, 2021. "Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries," Energy Policy, Elsevier, vol. 151(C).
    3. Sheng, Pengfei & Li, Jun & Zhai, Mengxin & Huang, Shoujun, 2020. "Coupling of economic growth and reduction in carbon emissions at the efficiency level: Evidence from China," Energy, Elsevier, vol. 213(C).
    4. 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.
    5. 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.
    6. 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.
    7. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    8. 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.
    9. Kuosmanen, Timo, 2006. "Stochastic Nonparametric Envelopment of Data: Combining Virtues of SFA and DEA in a Unified Framework," Discussion Papers 11864, MTT Agrifood Research Finland.
    10. Wood, Richard, 2009. "Structural decomposition analysis of Australia's greenhouse gas emissions," Energy Policy, Elsevier, vol. 37(11), pages 4943-4948, November.
    11. Lei Jin & Keran Duan & Chunming Shi & Xianwei Ju, 2017. "The Impact of Technological Progress in the Energy Sector on Carbon Emissions: An Empirical Analysis from China," IJERPH, MDPI, vol. 14(12), pages 1-14, December.
    12. Fan, Ying & Liu, Lan-Cui & Wu, Gang & Tsai, Hsien-Tang & Wei, Yi-Ming, 2007. "Changes in carbon intensity in China: Empirical findings from 1980-2003," Ecological Economics, Elsevier, vol. 62(3-4), pages 683-691, May.
    13. Xiaoxiao Chu & Hong Geng & Wen Guo, 2019. "How Does Energy Misallocation Affect Carbon Emission Efficiency in China? An Empirical Study Based on the Spatial Econometric Model," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    14. Lin, Boqiang & Du, Kerui, 2015. "Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach," Energy Economics, Elsevier, vol. 49(C), pages 550-557.
    15. Shijian Wu & Kaili Zhang, 2021. "Influence of Urbanization and Foreign Direct Investment on Carbon Emission Efficiency: Evidence from Urban Clusters in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 13(5), pages 1-22, March.
    16. 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.
    17. Herrala, Risto & Goel, Rajeev K., 2012. "Global CO2 efficiency: Country-wise estimates using a stochastic cost frontier," Energy Policy, Elsevier, vol. 45(C), pages 762-770.
    18. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    19. Yanmei Li & Xin Sun & Xiushan Bai, 2022. "Differences of Carbon Emission Efficiency in the Belt and Road Initiative Countries," Energies, MDPI, vol. 15(4), pages 1-17, February.
    20. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    21. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    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. Le Yang & Zhongqi Liang & Wentao Yao & Hongmin Zhu & Liangen Zeng & Zihan Zhao, 2023. "What Are the Impacts of Urbanisation on Carbon Emissions Efficiency? Evidence from Western China," Land, MDPI, vol. 12(9), pages 1-18, August.
    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.

    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. Minyoung Yang & Jinsoo Kim, 2022. "A Critical Review of the Definition and Estimation of Carbon Efficiency," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    2. Tan, Xiujie & Choi, Yongrok & Wang, Banban & Huang, Xiaoqi, 2020. "Does China's carbon regulatory policy improve total factor carbon efficiency? A fixed-effect panel stochastic frontier analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    8. Wang, Qunwei & Su, Bin & Sun, Jiasen & Zhou, Peng & Zhou, Dequn, 2015. "Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities," Applied Energy, Elsevier, vol. 151(C), pages 85-92.
    9. Xian’En Wang & Shimeng Wang & Xipan Wang & Wenbo Li & Junnian Song & Haiyan Duan & Shuo Wang, 2019. "The Assessment of Carbon Performance under the Region-Sector Perspective based on the Nonparametric Estimation: A Case Study of the Northern Province in China," Sustainability, MDPI, vol. 11(21), pages 1-23, October.
    10. 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.
    11. 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.
    12. Dong, Feng & Li, Xiaohui & Long, Ruyin & Liu, Xiaoyan, 2013. "Regional carbon emission performance in China according to a stochastic frontier model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 525-530.
    13. Guo, Xiao-Dan & Zhu, Lei & Fan, Ying & Xie, Bai-Chen, 2011. "Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA," Energy Policy, Elsevier, vol. 39(5), pages 2352-2360, May.
    14. Feng Dong & Ruyin Long & Hong Chen & Xiaohui Li & Qingliang Yang, 2013. "Factors Affecting Regional Per-Capita Carbon Emissions in China Based on an LMDI Factor Decomposition Model," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-10, December.
    15. Lin, Boqiang & Sai, Rockson, 2021. "A multi factor Malmquist CO2emission performance indices: Evidence from Sub Saharan African public thermal power plants," Energy, Elsevier, vol. 223(C).
    16. Lin, Boqiang & Xu, Mengmeng, 2018. "Regional differences on CO2 emission efficiency in metallurgical industry of China," Energy Policy, Elsevier, vol. 120(C), pages 302-311.
    17. 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).
    18. Zhang, Ning & Wei, Xiao, 2015. "Dynamic total factor carbon emissions performance changes in the Chinese transportation industry," Applied Energy, Elsevier, vol. 146(C), pages 409-420.
    19. Honma, Satoshi & Hu, Jin-Li, 2014. "A panel data parametric frontier technique for measuring total-factor energy efficiency: An application to Japanese regions," Energy, Elsevier, vol. 78(C), pages 732-739.
    20. Zhang, Ning & Wang, Bing & Chen, Zhongfei, 2016. "Carbon emissions reductions and technology gaps in the world's factory, 1990–2012," Energy Policy, Elsevier, vol. 91(C), pages 28-37.

    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:15:y:2023:i:10:p:8042-:d:1147427. 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.