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Dynamic Evaluation of Water Resources Management Performance in the Yangtze River Economic Belt

Author

Listed:
  • Fuhua Sun

    (College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China)

  • Caiqin Miao

    (Business School, Hohai University, Nanjing 210098, China)

  • Shuqin Li

    (Business School, Hohai University, Nanjing 210098, China)

  • Juqin Shen

    (College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China)

  • Xin Huang

    (College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China)

  • Shengnan Zhang

    (College of Finance and Economics, Wan Jiang University of Technology, Ma’anshan 243031, China)

Abstract

The evaluation of water resources management performance (WRMP) can provide guidance for water resources management. This paper constructs a scientific WRMP evaluation index system based on “water resources–water environment–water ecology”. Secondly, the game variable weight matter–element extension model is appropriately introduced to dynamically evaluate the WRMP level of the provinces (cities) in the YREB from 2012 to 2021, and Arcgis is used to analyze the spatial and temporal variations in the performance level of each sub-system. Lastly, a geographical detector model is used to explore the main factors influencing the WRMP in the Yangtze River Economic Balt (YREB). The main findings are as follows: (1) The overall provincial WRMP level in the YREB has been improving from 2012 to 2021, and the performance of water resource utilization (WRU) and water environment treatment (WET) are high in the east and low in the west, while the performance of water ecological protection (WEP) shows a trend of continuous improvement. (2) Compared with the model without variable weight modification, the game variable weight matter–element extension model can reflect the influence of the measured value of the index on the evaluation result as much as possible. (3) The top eight factors that have a greater impact on the WRMP level are the industrial water conservation rate, water resource development and utilization rate, water resource sustainability index, sewage diameter ratio, urban water penetration rate, industrial wastewater treatment completion rate, ecological construction and protection of the year to complete the investment in GDP, and the water ecological carrying capacity growth rate. The interaction types of each influence factor are nonlinear enhancement and two-factor enhancement.

Suggested Citation

  • Fuhua Sun & Caiqin Miao & Shuqin Li & Juqin Shen & Xin Huang & Shengnan Zhang, 2024. "Dynamic Evaluation of Water Resources Management Performance in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:649-:d:1317421
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    References listed on IDEAS

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    1. Han Han & Huimin Li & Kaize Zhang, 2019. "Urban Water Ecosystem Health Evaluation Based on the Improved Fuzzy Matter-Element Extension Assessment Model: Case Study from Zhengzhou City, China," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-14, January.
    2. Dongying Sun & Jiarong Gu & Junyu Chen & Xilin Xia & Zhisong Chen, 2022. "Spatiotemporal differentiation and influencing factors of urban water supply system resilience in the Yangtze River Delta urban agglomeration," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(1), pages 101-126, October.
    3. Dagmawi Mulugeta Degefu & Weijun He & Liang Yuan & Jian Hua Zhao, 2016. "Water Allocation in Transboundary River Basins under Water Scarcity: a Cooperative Bargaining Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4451-4466, September.
    4. Zhang, Jie & Zhang, Ke & Zhao, Feng, 2020. "Research on the regional spatial effects of green development and environmental governance in China based on a spatial autocorrelation model," Structural Change and Economic Dynamics, Elsevier, vol. 55(C), pages 1-11.
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