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How industrial water resources green efficiency varies in China: a case study of the Yangtze River Economic Belt considering unexpected output

Author

Listed:
  • Dalai Ma

    (Chongqing University of Technology)

  • Fengtai Zhang

    (Chongqing University of Technology)

  • Yaping Xiao

    (Chongqing University of Technology)

  • Lei Gao

    (CSIRO, Waite Campus)

  • Hongbo Liao

    (Chongqing Institute of Quality and Standardization)

  • Na Zhao

    (Chongqing University of Technology)

  • Yuedong Xiao

    (Chongqing University of Technology)

  • Xingyu Yang

    (Chongqing University of Technology)

  • Wenli Wu

    (Chongqing University of Technology)

Abstract

Strengthening the protection and utilization of water resources is very important for the development of the Yangtze River Economic Belt (YREB). The YREB spans the three regions of the East, Middle and West of China, which is not only the inland river economic belt with the largest economic aggregate in China, but also the leading demonstration belt for the construction of ecological civilization. This paper uses the minimum distance to the strong frontier (mSBM) model to evaluate the industrial water resources green efficiency (IWRGE) in the YREB from 2000 to 2018, taking the industrial gray water footprint as bad output, which can more accurately measure the water efficiency of the current industrial sector. Then, compared with the traditional industrial water resources utilization efficiency (IWRUE), the temporal and spatial differences and influencing factors of the IWRGE are further analyzed. The results are as follows: First, the IWRUE in the YREB has been improved to a certain extent in the sample period, and there are obvious differences in provinces. The IWRUE of economically developed provinces is higher than economically underdeveloped provinces. Second, the IWRGE after adding industrial gray water footprint is lower than the IWRUE in the YREB, indicating that the IWRUE without considering the cost of water ecological damage is "false high". Third, the IWRGE in the upper, middle and lower reaches of the YREB shows a "U" type change trend of "decreasing first and then rising". Fourth, the IWRGE in the YREB shows an obvious spatial distribution pattern of "both ends high and intermediate low". The pressure to save industrial water and reduce pollution is especially intense in the middle reaches. Finally, economic growth, property right structure, and opening up can significantly promote the IWRGE, while water endowment, water structure, technological progress, and environmental regulation significantly inhibit the improvement of the IWRGE.

Suggested Citation

  • Dalai Ma & Fengtai Zhang & Yaping Xiao & Lei Gao & Hongbo Liao & Na Zhao & Yuedong Xiao & Xingyu Yang & Wenli Wu, 2024. "How industrial water resources green efficiency varies in China: a case study of the Yangtze River Economic Belt considering unexpected output," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(1), pages 187-213, January.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:1:d:10.1007_s10668-022-02704-w
    DOI: 10.1007/s10668-022-02704-w
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    1. Daron Acemoglu & Philippe Aghion & Leonardo Bursztyn & David Hemous, 2012. "The Environment and Directed Technical Change," American Economic Review, American Economic Association, vol. 102(1), pages 131-166, February.
    2. Siyu Gao & Haixiang Guo & Jing Yu, 2021. "Urban Water Inclusive Sustainability: Evidence from 38 Cities in the Yangtze River Economic Belt in China," Sustainability, MDPI, vol. 13(4), pages 1-32, February.
    3. Zhang, Rui & Sharma, Rajesh & Tan, Zhixiong & Kautish, Pradeep, 2022. "Do export diversification and stock market development drive carbon intensity? The role of renewable energy solutions in top carbon emitter countries," Renewable Energy, Elsevier, vol. 185(C), pages 1318-1328.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Wenhao Song & Hongyan Yu, 2018. "Green Innovation Strategy and Green Innovation: The Roles of Green Creativity and Green Organizational Identity," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 25(2), pages 135-150, March.
    6. Hu, Jin-Li & Wang, Shih-Chuan & Yeh, Fang-Yu, 2006. "Total-factor water efficiency of regions in China," Resources Policy, Elsevier, vol. 31(4), pages 217-230, December.
    7. Gray, Wayne B, 1987. "The Cost of Regulation: OSHA, EPA and the Productivity Slowdown," American Economic Review, American Economic Association, vol. 77(5), pages 998-1006, December.
    8. Xuhui Ding & Zhu Fu & Hongwen Jia, 2019. "Study on Urbanization Level, Urban Primacy and Industrial Water Utilization Efficiency in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    9. Jiao, Zhilun & Sharma, Rajesh & Kautish, Pradeep & Hussain, Hafezali Iqbal, 2021. "Unveiling the asymmetric impact of exports, oil prices, technological innovations, and income inequality on carbon emissions in India," Resources Policy, Elsevier, vol. 74(C).
    10. G. R. Jahanshahloo & J. Vakili & M. Zarepisheh, 2012. "A Linear Bilevel Programming Problem For Obtaining The Closest Targets And Minimum Distance Of A Unit From The Strong Efficient Frontier," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 29(02), pages 1-19.
    11. Yiru Guo & Yan Hu & Ke Shi & Yuriy Bilan, 2020. "Valuation of Water Resource Green Efficiency Based on SBM–TOBIT Panel Model: Case Study from Henan Province, China," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    12. Juan Aparicio & José Ruiz & Inmaculada Sirvent, 2007. "Closest targets and minimum distance to the Pareto-efficient frontier in DEA," Journal of Productivity Analysis, Springer, vol. 28(3), pages 209-218, December.
    13. Frank M. Gollop & Gregory P. Swinand, 1998. "From Total Factor to Total Resource Productivity: An Application to Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(3), pages 577-583.
    14. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    15. 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.
    16. Scheel, Holger, 2001. "Undesirable outputs in efficiency valuations," European Journal of Operational Research, Elsevier, vol. 132(2), pages 400-410, July.
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