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Driving Forces of Water Intensity in China’s Industrial Sector: A Global Meta-Frontier Production–Theoretical Decomposition Analysis

Author

Listed:
  • Tao Ding

    (School of Economics, Hefei University of Technology, Hefei, Anhui 230000, P. R. China)

  • Jiangyuan Li

    (School of Economics, Hefei University of Technology, Hefei, Anhui 230000, P. R. China)

  • Xing Shi

    (School of Economics, Hefei University of Technology, Hefei, Anhui 230000, P. R. China)

  • Huaqing Wu

    (School of Economics, Hefei University of Technology, Hefei, Anhui 230000, P. R. China)

Abstract

The shortage of water resources has prohibited the sustainable growth of China. Identifying the driving forces of water intensity is critical to initiating cost-effective policies and regulations to reduce water consumption across China. We develop a global meta-frontier production decomposition approach, which could simultaneously address the spatial and temporal heterogeneities, to decompose the water intensity of the industrial sector in China at various levels from 2011 to 2015. Results show that the industrial water intensity in all provinces except Shanxi has been declining over the sample period, with little potential for a further reduction. Second, at the national level, the potential water usage factor and the temporal catch-up effect of water usage technology are two significant contributors in reducing the industrial water intensity. Third, we find that some factors have mixed results at the regional and provincial levels, calling for customized policies in these aspects. Our approach provides a more precise decomposition and reveals more details in China’s variations of industrial water intensity, which has manifold implications for regional water management.

Suggested Citation

  • Tao Ding & Jiangyuan Li & Xing Shi & Huaqing Wu, 2021. "Driving Forces of Water Intensity in China’s Industrial Sector: A Global Meta-Frontier Production–Theoretical Decomposition Analysis," Water Economics and Policy (WEP), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 1-25, July.
  • Handle: RePEc:wsi:wepxxx:v:07:y:2021:i:03:n:s2382624x21500107
    DOI: 10.1142/S2382624X21500107
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    Cited by:

    1. Ding, Tao & Li, Jiangyuan & Shi, Xing & Li, Xuhui & Chen, Ya, 2023. "Is artificial intelligence associated with carbon emissions reduction? Case of China," Resources Policy, Elsevier, vol. 85(PB).

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