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Research on Industrial Ecological Efficiency Evaluation and Improvement Countermeasures Based on Data-Driven Evaluations from 30 Provinces and Cities in China

Author

Listed:
  • Fan Liu

    (Business School, Suzhou University, Suzhou 234000, China
    School of Economics, Anhui University, Hefei 230601, China)

  • Shuling Zhou

    (Business School, Suzhou University, Suzhou 234000, China)

  • Yaliu Yang

    (Business School, Suzhou University, Suzhou 234000, China)

  • Conghu Liu

    (Business School, Suzhou University, Suzhou 234000, China
    School of Economics and Management, Tsinghua University, Beijing 100084, China)

Abstract

Improving industrial ecological efficiency is important in promoting the industry’s sustainable development. However, the economy, resources, the environment, and other factors should be considered. This paper proposes a data-driven evaluation and promotion method for improving industrial ecological efficiency. Based on industrial input and output data, the super-efficiency slack-based model containing an unexpected output was used to measure industrial ecological efficiency. The kernel density estimation method was employed to analyze the time-series characteristics of industrial ecological efficiency. Using data from 30 provinces and cities in China, this study demonstrated the implementation of a data-driven method. The results show that China’s overall industrial ecological efficiency is increasing, and industrial ecological efficiency in the western region is rapidly improving. Differences exist between provinces and cities; the characteristics of polarization are significant, and there are short boards in the eastern, central, and western regions. Based on this, suggestions are made to improve the industrial ecological efficiency of the central region, narrow the gaps between the regions, and promote each region to develop its strengths and mitigate its weaknesses. This provides a basis for formulating policies related to ecological environment protection and industrial pollution control.

Suggested Citation

  • Fan Liu & Shuling Zhou & Yaliu Yang & Conghu Liu, 2022. "Research on Industrial Ecological Efficiency Evaluation and Improvement Countermeasures Based on Data-Driven Evaluations from 30 Provinces and Cities in China," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8665-:d:863505
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    References listed on IDEAS

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    1. Evert Nieuwlaar & Geert Warringa & Corjan Brink & Walter Vermeulen, 2005. "Supply Curves for Eco‐efficient Environmental Improvements Using Different Weighting Methods," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 85-96, October.
    2. Liu, Conghu & Gao, Mengdi & Zhu, Guang & Zhang, Cuixia & Zhang, Pan & Chen, Jianqing & Cai, Wei, 2021. "Data driven eco-efficiency evaluation and optimization in industrial production," Energy, Elsevier, vol. 224(C).
    3. Mastini, Riccardo & Kallis, Giorgos & Hickel, Jason, 2021. "A Green New Deal without growth?," Ecological Economics, Elsevier, vol. 179(C).
    4. Yang, Guo-liang & Fukuyama, Hirofumi, 2018. "Measuring the Chinese regional production potential using a generalized capacity utilization indicator," Omega, Elsevier, vol. 76(C), pages 112-127.
    5. 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.
    6. Han, Yonghui & Zhang, Fan & Huang, Liangxiong & Peng, Keming & Wang, Xianbin, 2021. "Does industrial upgrading promote eco-efficiency? ─A panel space estimation based on Chinese evidence," Energy Policy, Elsevier, vol. 154(C).
    7. Yanhua Guo & Lianjun Tong & Lin Mei, 2021. "Evaluation and Influencing Factors of Industrial Pollution in Jilin Restricted Development Zone: A Spatial Econometric Analysis," Sustainability, MDPI, vol. 13(8), pages 1-18, April.
    8. Qiucheng Li & Jiang Hu & Bolin Yu, 2021. "Spatiotemporal Patterns and Influencing Mechanism of Urban Residential Energy Consumption in China," Energies, MDPI, vol. 14(13), pages 1-17, June.
    9. Xingcheng Ge & Jun Xu & Yong Xie & Xin Guo & Deyan Yang, 2021. "Evaluation and Dynamic Evolution of Eco-Efficiency of Resource-Based Cities—A Case Study of Typical Resource-Based Cities in China," Sustainability, MDPI, vol. 13(12), pages 1-22, June.
    10. Zhang, Ren-Long & Liu, Xiao-Hong, 2021. "Evaluating ecological efficiency of Chinese industrial enterprise," Renewable Energy, Elsevier, vol. 178(C), pages 679-691.
    11. Wang, Xipan & Song, Junnian & Duan, Haiyan & Wang, Xian'en, 2021. "Coupling between energy efficiency and industrial structure: An urban agglomeration case," Energy, Elsevier, vol. 234(C).
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    1. Guokui Wang & Xiaojia Guo & Guoqin Wu & Yijia Zhu, 2023. "Investigating the Effects of Environmental Regulation on Industrial Ecological Efficiency in China Using a Panel Smooth Transition Regression Model," Sustainability, MDPI, vol. 15(21), pages 1-18, October.
    2. Jiantao Peng & Yihua Liu & Chong Xu & Debao Chen, 2024. "Unveiling the Patterns and Drivers of Ecological Efficiency in Chinese Cities: A Comprehensive Study Using Super-Efficiency Slacks-Based Measure and Geographically Weighted Regression Approaches," Sustainability, MDPI, vol. 16(8), pages 1-23, April.

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