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Water Resources Liability Measurement and the Driving Factors of Water Resources Liability Intensity

Author

Listed:
  • Dan-dan Zhang

    (Hohai University
    National University of Singapore)

  • Ju-qin Shen

    (Hohai University)

  • Fu-hua Sun

    (Hohai University)

Abstract

Water resources liability (WRL) is the responsibility and obligation assumed and repaid after human activities cause excessive consumption and damage to water resources. It is the creditor's right and debt relationship between the economy and natural environment based on water resources. With the intensification of water competition and the deterioration of water ecology, it is urgent to accurately calculate the real occupation of water resources by national and regional economic activities. From the perspective of sustainable utilization of water resources, this study constructs an accounting model of WRL and water resources liability intensity (WLI), analyzes the spatial and temporal characteristics of WLI, and discusses the driving factors of WLI. The results reveal that the total amount of WRL in China experienced a decreasing trend from 2012 to 2019. The increase in available water resources and the decrease in total water demand are the direct reasons for the decrease in WRL. China's average WLI experienced a downward trend, indicating a significant improvement in water use efficiency. The WLI was higher in the western region and lower in the eastern region. In terms of regional differences, the overall spatial differences of WLI were in the process of continuous adjustment. The analysis of the driving factors of WLI reveals that the urbanization rate and the degree of economic openness had a significant driving effect on reducing WLI. The WLI in China presented an inverted N-shaped Kuznets curve, and most of the regions were between the first and second inflection points.

Suggested Citation

  • Dan-dan Zhang & Ju-qin Shen & Fu-hua Sun, 2022. "Water Resources Liability Measurement and the Driving Factors of Water Resources Liability Intensity," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1553-1569, March.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:5:d:10.1007_s11269-022-03101-8
    DOI: 10.1007/s11269-022-03101-8
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    References listed on IDEAS

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    1. Yifan Gu & Yi Li & Hongtao Wang & Fengting Li, 2014. "Gray Water Footprint: Taking Quality, Quantity, and Time Effect into Consideration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3871-3874, September.
    2. Lange, Glenn-Marie & Mungatana, Eric & Hassan, Rashid, 2007. "Water accounting for the Orange River Basin: An economic perspective on managing a transboundary resource," Ecological Economics, Elsevier, vol. 61(4), pages 660-670, March.
    3. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    4. Chao Bao & Dongmei He, 2015. "The Causal Relationship between Urbanization, Economic Growth and Water Use Change in Provincial China," Sustainability, MDPI, vol. 7(12), pages 1-10, December.
    5. A. Hoekstra & A. Chapagain, 2007. "Water footprints of nations: Water use by people as a function of their consumption pattern," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(1), pages 35-48, January.
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