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Analysis of carbon emission performance and regional differences in China’s eight economic regions: Based on the super-efficiency SBM model and the Theil index

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  • Yuan Zhang
  • Zhen Yu
  • Juan Zhang

Abstract

China’s carbon emission performance has significant regional heterogeneity. Identified the sources of carbon emission performance differences and the influence of various driving factors in China’s eight economic regions accurately is the premise for realizing China’s carbon emission reduction goals. Based on the provincial panel data from 2005 to 2017, the super-efficiency SBM model and Malmquist model are constructed in this paper to measure regional carbon emission performance’s static and dynamic changes. After that, the Theil index is used to distinguish the impact of inter-regional and intra-regional differences on different regions’ carbon emissions performance. Finally, by introducing the Tobit model, the effect of various driving factors on carbon emission performance differences is analyzed quantitatively. The results show that: (1) There are significant differences in different regions’ carbon emission performance, but the overall carbon emission performance presents an upward fluctuation trend. Malmquist index decomposition results show substantial differences in technology progress index and technology efficiency index in different regions, leading to significant carbon emission performance differences. (2) Overall, inter-regional differences contribute the most to the overall carbon emission performance, up to more than 80%. Among them, the inter-regional and intra-regional differences in ERMRYR contributed significantly. (3) Through Tobit regression analysis, it is found that residents’ living standards, urbanization level, ecological development degree, and industrial structure positively affect carbon emission performance. On the contrary, energy intensity presents an apparent negative correlation on carbon emission performance. Therefore, to improve the carbon emission performance, we should put forward targeted suggestions according to the characteristics of different regional development stages, regional carbon emission differences, and influencing driving factors.

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  • Yuan Zhang & Zhen Yu & Juan Zhang, 2021. "Analysis of carbon emission performance and regional differences in China’s eight economic regions: Based on the super-efficiency SBM model and the Theil index," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    • Handle: RePEc:plo:pone00:0250994
    DOI: 10.1371/journal.pone.0250994
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    1. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
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    2. Guoyin Xu & Tong Zhao & Rong Wang, 2022. "Research on Carbon Emission Efficiency Measurement and Regional Difference Evaluation of China’s Regional Transportation Industry," Energies, MDPI, vol. 15(18), pages 1-19, September.
    3. Mushtaq Taleb & Ruzelan Khalid & Ali Emrouznejad & Razamin Ramli, 2023. "Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6627-6656, July.
    4. Liangjun Yi & Wei Zhang & Yuanxin Liu & Weilin Zhang, 2021. "An Analysis of the Impact of Market Segmentation on Energy Efficiency: A Spatial Econometric Model Applied in China," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    5. Yingqi Xu & Yu Cheng & Ruijing Zheng & Yaping Wang, 2022. "Spatiotemporal Evolution and Influencing Factors of Carbon Emission Efficiency in the Yellow River Basin of China: Comparative Analysis of Resource and Non-Resource-Based Cities," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    6. Dengjuan Liu & Wei Liu & Yuming He, 2024. "How Does the Intensive Use of Urban Construction Land Improve Carbon Emission Efficiency?—Evidence from the Panel Data of 30 Provinces in China," Land, MDPI, vol. 13(12), pages 1-22, December.
    7. Pan, Xiongfeng & Guo, Shucen, 2024. "Decomposition analysis of regional differences in China's carbon emissions based on socio-economic factors," Energy, Elsevier, vol. 303(C).
    8. Jianshi Wang & Chengxin Wang & Shangkun Yu & Mengcheng Li & Yu Cheng, 2022. "Coupling Coordination and Spatiotemporal Evolution between Carbon Emissions, Industrial Structure, and Regional Innovation of Counties in Shandong Province," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    9. Ta-Thi Huong & Liang Dong & Izhar Hussain Shah & Hung-Suck Park, 2021. "Exploring the Sustainability of Resource Flow and Productivity Transition in Vietnam from 1978 to 2017: MFA and DEA-Based Malmquist Productivity Index Approach," Sustainability, MDPI, vol. 13(21), pages 1-26, October.
    10. Tian Ma & Yisheng Liu & Meng Yang, 2022. "Spatial-Temporal Heterogeneity for Commercial Building Carbon Emissions in China: Based the Dagum Gini Coefficient," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    11. Niu, Xiaoqin & Yüksel, Serhat & Dinçer, Hasan, 2023. "Emission strategy selection for the circular economy-based production investments with the enhanced decision support system," Energy, Elsevier, vol. 274(C).
    12. Li, Panni & Lin, Zhongguo & Peng, Binbin & Du, Huibin, 2023. "Do CEOs’ social networks affect carbon emissions in China? The moderating role of CEO reputation," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 1122-1137.

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