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Multi-Dimensional Assessment, Regional Differences, and Influencing Factors of Agricultural Water Pollution from the Perspective of Grey Water Footprint in Zhejiang Province, China

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  • Hua Zhu

    (School of Geomatics, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
    Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China)

  • Qing Zhang

    (School of Geomatics, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Hailin You

    (Institute of Watershed Ecology, Jiangxi Academy of Sciences, Nanchang 330096, China
    Poyang Lake Wetland Observation and Research Station, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Lushan 332800, China)

  • Ying Liu

    (Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China)

Abstract

The implementation of differentiated governance for agricultural water pollution (AWP) plays a significant role in alleviating the pressure on agricultural water resources. However, research that comprehensively assesses AWP and its influencing factors from a multidimensional perspective remains relatively limited. This study utilized the grey water footprint (GWF) model to quantify the agricultural grey water footprint (AGWF), agricultural grey water footprint efficiency (AGWFE), agricultural grey water footprint intensity (AGWFI), and agricultural water pollution level (AWPL) in Zhejiang from 2010 to 2020. Subsequently, we applied the standard deviational ellipse (SDE), the kernel density estimation (KDE), and the Dagum Gini coefficient to delve into the dynamic evolution and regional disparities of these indicators. Ultimately, we leveraged both the random forest model and the panel regression model to identify and examine the key factors shaping AGWF-related indicators. The results show that: (1) From 2010 to 2020, in Zhejiang, both AGWF and AGWFI exhibit a trend of first increasing and then decreasing, peaking in 2012. In contrast, AGWFE has consistently increased over the years, reaching an increase of 54.56 CNY/m 3 by 2020. Meanwhile, despite fluctuations, AWPL in Zhejiang shows an overall gradual decline. (2) The centroids of relevant indicators for AWP in Zhejiang are primarily located in Jinhua (for AGWF and AGWFI), Shaoxing (for AWPL), and in the area where AGWFE converge. (3) Compared to 2010, the regional disparities in AGWF and AWPL have shrunk significantly in 2020, whereas the regional differences in AGWFE and AGWFI have increased to some extent. In most years, the regional disparities in AGWF, AGWFI, and AWPL are more pronounced in Northeastern Zhejiang compared to the southwestern part. (4) The influencing factors of AGWF, AGWFE, and AGWFI exhibit significant regional heterogeneity. In Northeastern Zhejiang, the primary factors influencing them are technological innovation, resource endowment, and crop-cultivation methods. Conversely, in the southwestern region, the primary factors exerting the same influence are the application intensities of fertilizers, pesticides, and agricultural film application. The primary drivers of AWPL in Zhejiang are grain yield, water resource availability, and crop-planting structure. Notably, these factors do not exhibit regional heterogeneity. The paper proposes AWP control policies from both a comprehensive and multi-dimensional perspective.

Suggested Citation

  • Hua Zhu & Qing Zhang & Hailin You & Ying Liu, 2024. "Multi-Dimensional Assessment, Regional Differences, and Influencing Factors of Agricultural Water Pollution from the Perspective of Grey Water Footprint in Zhejiang Province, China," Agriculture, MDPI, vol. 14(11), pages 1-25, November.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:2031-:d:1518894
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    References listed on IDEAS

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    1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    2. Wei Xie & Anfeng Zhu & Tariq Ali & Zhengtao Zhang & Xiaoguang Chen & Feng Wu & Jikun Huang & Kyle Frankel Davis, 2023. "Crop switching can enhance environmental sustainability and farmer incomes in China," Nature, Nature, vol. 616(7956), pages 300-305, April.
    3. Chen, Zhao & Kahn, Matthew E. & Liu, Yu & Wang, Zhi, 2018. "The consequences of spatially differentiated water pollution regulation in China," Journal of Environmental Economics and Management, Elsevier, vol. 88(C), pages 468-485.
    4. Rui Zhang & Yanfeng Wang & Jie Lyu & Zhanxiang Sun, 2023. "Uncovering the Hidden Risks: A Bibliometric Investigation of Farmers’ Vulnerability to Climate Change," Agriculture, MDPI, vol. 13(9), pages 1-17, September.
    5. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    6. Qadir, M. & Boers, Th. M. & Schubert, S. & Ghafoor, A. & Murtaza, G., 2003. "Agricultural water management in water-starved countries: challenges and opportunities," Agricultural Water Management, Elsevier, vol. 62(3), pages 165-185, October.
    7. Rongwei Li & Shucheng Tan & Mingfei Zhang & Shaohan Zhang & Haishan Wang & Lei Zhu, 2024. "Geological Disaster Susceptibility Evaluation Using a Random Forest Empowerment Information Quantity Model," Sustainability, MDPI, vol. 16(2), pages 1-18, January.
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