IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i16p12638-d1221588.html
   My bibliography  Save this article

Analysis of Ningxia Hui Autonomous District’s Gray Water Footprint from the Perspective of Water Sustainability

Author

Listed:
  • Chen Yue

    (Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences (CAGS), Shijiazhuang 050061, China
    Key Laboratory of Groundwater Contamination and Remediation of Hebei Province and China Geological Survey, Shijiazhuang 050061, China)

  • Yong Qian

    (Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences (CAGS), Shijiazhuang 050061, China
    Key Laboratory of Groundwater Contamination and Remediation of Hebei Province and China Geological Survey, Shijiazhuang 050061, China)

  • Feng Liu

    (Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences (CAGS), Shijiazhuang 050061, China
    Key Laboratory of Groundwater Contamination and Remediation of Hebei Province and China Geological Survey, Shijiazhuang 050061, China)

  • Xiangxiang Cui

    (Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences (CAGS), Shijiazhuang 050061, China
    Key Laboratory of Groundwater Contamination and Remediation of Hebei Province and China Geological Survey, Shijiazhuang 050061, China)

  • Suhua Meng

    (Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences (CAGS), Shijiazhuang 050061, China
    Key Laboratory of Groundwater Contamination and Remediation of Hebei Province and China Geological Survey, Shijiazhuang 050061, China)

Abstract

Gray water footprint (GWF) is an effective method to evaluate the degree of water pollution and water quality. It is the amount of freshwater needed to dilute water pollutants to meet ambient water quality standards. Accounting and analyzing the GWF will be significant for promoting an improved water environment and sustainable water ecology in Ningxia Autonomous District. We accounted for the GWF of all cities in Ningxia from 2012 to 2020 and evaluated its spatial-temporal variations by the GWF accounting method proposed by Hoekstra. Then, the Logarithmic Mean Divisia Index (LMDI) method was applied to investigate the contributions of four driving factors: the population scale effect, economic development effect, technological effect, and industrial structure effect. And then, the changes in the GWF in the Ningxia region were analyzed. The results showed that the GWF in the Ningxia region changed from 79.21 × 10 8 to 29.09 × 10 8 m 3 /yr during 2012–2020, making a significant decreasing trend. Among all cities, Wuzhong City contributes the most in terms of the GWF. More specifically, economic development and technology structure are the positive and negative drivers of the GWF, respectively. The water pollution levels in Ningxia (0.49–1.3) indicated that the waste assimilation capacity has fallen short of taking up the pollutant load, which had an unfavorable impact on the groundwater according to actual water quality data. NO 3 -N and NH 3 -N are detected in the groundwater throughout the Ningxia region, with the highest NH 3 -N content in the groundwater in Yinchuan, which almost exceeded the groundwater quality standard of category III. Above all, this study reflected the current water pollution situation better by combining the GWF with actual water quality data in Ningxia. The finding of this study is valuable for addressing water quality threats and developing sustainable development.

Suggested Citation

  • Chen Yue & Yong Qian & Feng Liu & Xiangxiang Cui & Suhua Meng, 2023. "Analysis of Ningxia Hui Autonomous District’s Gray Water Footprint from the Perspective of Water Sustainability," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12638-:d:1221588
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/16/12638/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/16/12638/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhihui Li & Haowei Wu & Xiangzheng Deng, 2022. "Spatial Pattern of Water Footprints for Crop Production in Northeast China," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    2. Wu, Zheng & Tian, Guiliang & Xia, Qing & Hu, Hao & Li, Jiawen, 2023. "Connotation, calculation and influencing factors of the water-use rights benchmark price: A case study of agricultural water use in the Ningxia Yellow River irrigation area," Agricultural Water Management, Elsevier, vol. 283(C).
    3. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    4. Pier Paolo Miglietta & Pierluigi Toma & Francesco Paolo Fanizzi & Antonella De Donno & Benedetta Coluccia & Danilo Migoni & Francesco Bagordo & Francesca Serio, 2017. "A Grey Water Footprint Assessment of Groundwater Chemical Pollution: Case Study in Salento (Southern Italy)," Sustainability, MDPI, vol. 9(5), pages 1-10, May.
    5. Diego Voccia & Giacomo Mortella & Federico Ferrari & Maria Chiara Fontanella & Marco Trevisan & Lucrezia Lamastra, 2022. "The Anthropic Pressure on the Grey Water Footprint: The Case of the Vulnerable Areas of the Emilia-Romagna Region in Italy," Sustainability, MDPI, vol. 14(24), pages 1-13, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Huadun & Du, Qianxi & Huo, Tengfei & Liu, Peiran & Cai, Weiguang & Liu, Bingsheng, 2023. "Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector," Energy, Elsevier, vol. 263(PE).
    2. Zhang, Shulin & Su, Xiaoling & Singh, Vijay P & Ayantobo, Olusola Olaitan & Xie, Juan, 2018. "Logarithmic Mean Divisia Index (LMDI) decomposition analysis of changes in agricultural water use: a case study of the middle reaches of the Heihe River basin, China," Agricultural Water Management, Elsevier, vol. 208(C), pages 422-430.
    3. Lu, I.J. & Lin, Sue J. & Lewis, Charles, 2007. "Decomposition and decoupling effects of carbon dioxide emission from highway transportation in Taiwan, Germany, Japan and South Korea," Energy Policy, Elsevier, vol. 35(6), pages 3226-3235, June.
    4. Ronald E. Miller & Umed Temurshoev, 2017. "Output Upstreamness and Input Downstreamness of Industries/Countries in World Production," International Regional Science Review, , vol. 40(5), pages 443-475, September.
    5. Trotta, Gianluca, 2020. "Assessing energy efficiency improvements and related energy security and climate benefits in Finland: An ex post multi-sectoral decomposition analysis," Energy Economics, Elsevier, vol. 86(C).
    6. GUPTA Monika, 2019. "Decomposing The Role Of Different Factors In Co2 Emissions Increase In South Asia," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 14(1), pages 72-86, April.
    7. de Freitas, Luciano Charlita & Kaneko, Shinji, 2011. "Decomposition of CO2 emissions change from energy consumption in Brazil: Challenges and policy implications," Energy Policy, Elsevier, vol. 39(3), pages 1495-1504, March.
    8. Linghua Qiu & Junhao He & Chao Yue & Philippe Ciais & Chunmiao Zheng, 2024. "Substantial terrestrial carbon emissions from global expansion of impervious surface area," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    9. Wang, Miao & Feng, Chao, 2017. "Analysis of energy-related CO2 emissions in China’s mining industry: Evidence and policy implications," Resources Policy, Elsevier, vol. 53(C), pages 77-87.
    10. Zhen, Wei & Qin, Quande & Wei, Yi-Ming, 2017. "Spatio-temporal patterns of energy consumption-related GHG emissions in China's crop production systems," Energy Policy, Elsevier, vol. 104(C), pages 274-284.
    11. Román-Collado, Rocío & Colinet, María José, 2018. "Are labour productivity and residential living standards drivers of the energy consumption changes?," Energy Economics, Elsevier, vol. 74(C), pages 746-756.
    12. Mohlin, Kristina & Camuzeaux, Jonathan R. & Muller, Adrian & Schneider, Marius & Wagner, Gernot, 2018. "Factoring in the forgotten role of renewables in CO2 emission trends using decomposition analysis," Energy Policy, Elsevier, vol. 116(C), pages 290-296.
    13. Hwang, In Chang, 2013. "Stochastic Kaya model and its applications," MPRA Paper 55099, University Library of Munich, Germany.
    14. Zheng, Jiali & Mi, Zhifu & Coffman, D'Maris & Milcheva, Stanimira & Shan, Yuli & Guan, Dabo & Wang, Shouyang, 2019. "Regional development and carbon emissions in China," Energy Economics, Elsevier, vol. 81(C), pages 25-36.
    15. Congxin Li & Xu Zhang, 2022. "The Influencing Mechanisms on Global Industrial Value Chains Embedded in Trade Implied Carbon Emissions from a Higher-Order Networks Perspective," Sustainability, MDPI, vol. 14(22), pages 1-38, November.
    16. Ouyang, Xiaoling & Lin, Boqiang, 2015. "An analysis of the driving forces of energy-related carbon dioxide emissions in China’s industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 838-849.
    17. Ang, B.W. & Goh, Tian, 2019. "Index decomposition analysis for comparing emission scenarios: Applications and challenges," Energy Economics, Elsevier, vol. 83(C), pages 74-87.
    18. Ang, B.W. & Liu, Na, 2007. "Energy decomposition analysis: IEA model versus other methods," Energy Policy, Elsevier, vol. 35(3), pages 1426-1432, March.
    19. Kamel Louhichi & Aymeric Ricome & Sergio Gomez y Paloma, 2022. "Impacts of agricultural taxation in Sub‐Saharan Africa: Insights from agricultural produce cess in Tanzania," Agricultural Economics, International Association of Agricultural Economists, vol. 53(5), pages 671-686, September.
    20. Xuankai Deng & Yanhua Yu & Yanfang Liu, 2015. "Effect of Construction Land Expansion on Energy-Related Carbon Emissions: Empirical Analysis of China and Its Provinces from 2001 to 2011," Energies, MDPI, vol. 8(6), pages 1-22, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12638-:d:1221588. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.