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Environmental Performance of China’s Industrial System Considering Technological Heterogeneity and Interaction

Author

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  • Lei Li

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    School of Business, Applied Technology College of Soochow University, Suzhou 215325, China)

  • Ruizeng Zhao

    (School of Economics and Management, Southwest University of Science and Technology, Mianyang 621000, China
    School of Management, University of Science and Technology of China, Hefei 230026, China)

  • Feihua Huang

    (School of Business, Soochow University, Suzhou 215012, China)

Abstract

The industrial sector, the backbone of China’s economic development, is a key field that requires environmental management. The purpose of this study is to propose an improved data envelopment analysis (DEA) model to analyze the performance of provincial industrial systems (ISs) from 2011 to 2020 in China. To comprehensively characterize the operational framework of ISs, this study proposes an improved meta-frontier network DEA model. Unlike the existing models, the one proposed in this study not only considers the technical heterogeneity of ISs, but also reflects the interaction between IS subsystems. The empirical analysis yields valuable research findings. First, the overall environmental performance of Chinese ISs is generally low, with an average performance of 0.50, showing a U-shaped trend during the study period. Furthermore, significant regional differences are observed in the environmental performance of Chinese ISs. Second, the average performance of the production subsystem is 0.75, while the average performance of the pollution control subsystem (PTS) is 0.44. The low performance of the PTS pulls down the overall performance of Chinese ISs. Third, the technological level of Chinese ISs is low, with about 50% improvement potential. Finally, targeted suggestions to promote the green development of ISs are proposed on the basis of the empirical results.

Suggested Citation

  • Lei Li & Ruizeng Zhao & Feihua Huang, 2023. "Environmental Performance of China’s Industrial System Considering Technological Heterogeneity and Interaction," Sustainability, MDPI, vol. 15(4), pages 1-17, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3425-:d:1067300
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    1. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    2. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.
    3. Jie Wu & Qingyuan Zhu & Pengzhen Yin & Malin Song, 2017. "Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices," Operational Research, Springer, vol. 17(3), pages 715-735, October.
    4. Zhu, Qingyuan & Aparicio, Juan & Li, Feng & Wu, Jie & Kou, Gang, 2022. "Determining closest targets on the extended facet production possibility set in data envelopment analysis: Modeling and computational aspects," European Journal of Operational Research, Elsevier, vol. 296(3), pages 927-939.
    5. 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.
    6. Tao Ding & Huaqing Wu & Qianzhi Dai & Zhixiang Zhou & Changchun Tan, 2020. "Environmental Efficiency Analysis of Urban Agglomerations in China: A Non-Parametric Meta-Frontier Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(13), pages 2977-2992, October.
    7. Ning Ma & Yanrui Wu & Jianxin Wu, 2018. "Environmental efficiency and its distribution dynamics in Chinese cities," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 16(4), pages 417-445, October.
    8. Li, Ke & Lin, Boqiang, 2015. "Metafroniter energy efficiency with CO2 emissions and its convergence analysis for China," Energy Economics, Elsevier, vol. 48(C), pages 230-241.
    9. Xiaoling Wang & Feng He & Linfeng Zhang & Lili Chen, 2018. "Energy Efficiency of China’s Iron and Steel Industry from the Perspective of Technology Heterogeneity," Energies, MDPI, vol. 11(5), pages 1-11, May.
    10. Yin, Jianhua & Zheng, Mingzheng & Chen, Jian, 2015. "The effects of environmental regulation and technical progress on CO2 Kuznets curve: An evidence from China," Energy Policy, Elsevier, vol. 77(C), pages 97-108.
    11. Chen, Lei & Huang, Yan & Li, Mei-Juan & Wang, Ying-Ming, 2020. "Meta-frontier analysis using cross-efficiency method for performance evaluation," European Journal of Operational Research, Elsevier, vol. 280(1), pages 219-229.
    12. Qu, Jingjing & Wang, Baohui & Liu, Xiaohong, 2022. "A modified super-efficiency network data envelopment analysis: Assessing regional sustainability performance in China," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    13. Wu, Wei & Zhang, Tingting & Xie, Xiaomin & Huang, Zhen, 2021. "Regional low carbon development pathways for the Yangtze River Delta region in China," Energy Policy, Elsevier, vol. 151(C).
    14. Wen, Huwei & Lee, Chien-Chiang & Zhou, Fengxiu, 2021. "Green credit policy, credit allocation efficiency and upgrade of energy-intensive enterprises," Energy Economics, Elsevier, vol. 94(C).
    15. Huang, Jian-Bai & Luo, Yu-Mei & Feng, Chao, 2019. "An overview of carbon dioxide emissions from China's ferrous metal industry: 1991-2030," Resources Policy, Elsevier, vol. 62(C), pages 541-549.
    16. Yongjun Li & Xiao Shi & Ali Emrouznejad & Liang Liang, 2018. "Environmental performance evaluation of Chinese industrial systems: a network SBM approach," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(6), pages 825-839, June.
    17. Zhang, Lin & Zhao, Linlin & Zha, Yong, 2021. "Efficiency evaluation of Chinese regional industrial systems using a dynamic two-stage DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    18. Huang-Chu Huang & Cheng-Feng Hu, 2021. "Performance Measurement for the Recycling Production System Using Cooperative Game Network Data Envelopment Analysis," Sustainability, MDPI, vol. 13(19), pages 1-13, October.
    19. Wang, Qunwei & Hang, Ye & Su, Bin & Zhou, Peng, 2018. "Contributions to sector-level carbon intensity change: An integrated decomposition analysis," Energy Economics, Elsevier, vol. 70(C), pages 12-25.
    20. Jie Wu & Beibei Xiong & Qingxian An & Jiasen Sun & Huaqing Wu, 2017. "Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs," Annals of Operations Research, Springer, vol. 255(1), pages 257-276, August.
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