IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i4p546-d791679.html
   My bibliography  Save this article

Analysis of Regional Differences and Factors Influencing the Intensity of Agricultural Water in China

Author

Listed:
  • Jiaxing Pang

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
    Institute of County Economic Development, Lanzhou University, Lanzhou 730000, China
    Research and Assessment Center for Ecological Civilization Construction, Lanzhou University, Lanzhou 730000, China)

  • Xue Li

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China)

  • Xiang Li

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China)

  • Ting Yang

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China)

  • Ya Li

    (College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China)

  • Xingpeng Chen

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
    Institute of County Economic Development, Lanzhou University, Lanzhou 730000, China
    Research and Assessment Center for Ecological Civilization Construction, Lanzhou University, Lanzhou 730000, China)

Abstract

The output intensity of water resources has become a subject of increasing concern. Based on spatial autocorrelation, the Gini coefficient, the Theil index, and geographically and temporally weighted models, this work studied the spatial correlation and regional differences of the output intensity of agricultural water and the main factors influencing the output intensity of agricultural water from a spatial–temporal perspective in China from 2003 to 2019. The results show that the output intensity of agricultural water showed an upward trend and that the output in the central region was higher than the output in the eastern region, and the eastern region had higher output than the western region. By analyzing the spatial autocorrelation, it was found that the output intensity of agricultural water presented a significant spatial dispersion trend and showed the spatial difference. The overall difference in the output intensity of agricultural water in China showed an increasing trend, but the widening difference showed an alleviating trend; the main reason for this increase in the overall differences is that the intra-group differences in the three regions were increasing, with the largest intra-group differences being observed in the western region followed by the eastern region and the central region. Population scale, water use scale, water use structure, effective irrigation scale, urbanization, and industrial structure create significant spatial differences in the output intensity of agricultural water. However, the level of economic development positively impacts the agricultural water output intensity of all provinces. Therefore, water resource management departments should formulate water resource management policies based on regional water conditions and the differences between influencing factors.

Suggested Citation

  • Jiaxing Pang & Xue Li & Xiang Li & Ting Yang & Ya Li & Xingpeng Chen, 2022. "Analysis of Regional Differences and Factors Influencing the Intensity of Agricultural Water in China," Agriculture, MDPI, vol. 12(4), pages 1-20, April.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:4:p:546-:d:791679
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/4/546/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/4/546/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Atkinson, Anthony B., 1970. "On the measurement of inequality," Journal of Economic Theory, Elsevier, vol. 2(3), pages 244-263, September.
    2. Paolo D’Odorico & Davide Danilo Chiarelli & Lorenzo Rosa & Alfredo Bini & David Zilberman & Maria Cristina Rulli, 2020. "The global value of water in agriculture," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(36), pages 21985-21993, September.
    3. Chen, Jiandong & Xu, Chong & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2019. "Driving factors of CO2 emissions and inequality characteristics in China: A combined decomposition approach," Energy Economics, Elsevier, vol. 78(C), pages 589-597.
    4. Weinan Lu & Apurbo Sarkar & Mengyang Hou & Wenxin Liu & Xinyi Guo & Kai Zhao & Minjuan Zhao, 2022. "The Impacts of Urbanization to Improve Agriculture Water Use Efficiency—An Empirical Analysis Based on Spatial Perspective of Panel Data of 30 Provinces of China," Land, MDPI, vol. 11(1), pages 1-20, January.
    5. Krishna Malakar & Trupti Mishra & Anand Patwardhan, 2018. "Inequality in water supply in India: an assessment using the Gini and Theil indices," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(2), pages 841-864, April.
    6. Hao Jin & Shuai Huang, 2021. "Are China’s Water Resources for Agriculture Sustainable? Evidence from Hubei Province," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
    7. Liu, Jiaguo & Li, Sujuan & Ji, Qiang, 2021. "Regional differences and driving factors analysis of carbon emission intensity from transport sector in China," Energy, Elsevier, vol. 224(C).
    8. Entezari, A. & Wang, R.Z. & Zhao, S. & Mahdinia, E. & Wang, J.Y. & Tu, Y.D. & Huang, D.F., 2019. "Sustainable agriculture for water-stressed regions by air-water-energy management," Energy, Elsevier, vol. 181(C), pages 1121-1128.
    9. Daniel P. McMillen, 2004. "Geographically Weighted Regression: The Analysis of Spatially Varying Relationships," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 554-556.
    10. Jiang, Lu & Yu, Lu & Xue, Bing & Chen, Xingpeng & Mi, Zhifu, 2020. "Who is energy poor? Evidence from the least developed regions in China," Energy Policy, Elsevier, vol. 137(C).
    11. Kang, Shaozhong & Hao, Xinmei & Du, Taisheng & Tong, Ling & Su, Xiaoling & Lu, Hongna & Li, Xiaolin & Huo, Zailin & Li, Sien & Ding, Risheng, 2017. "Improving agricultural water productivity to ensure food security in China under changing environment: From research to practice," Agricultural Water Management, Elsevier, vol. 179(C), pages 5-17.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chin-Hung Kuan & Yungho Leu & Wen-Shin Lin & Chien-Pang Lee, 2022. "The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model," Agriculture, MDPI, vol. 12(8), pages 1-15, July.

    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. Guangming Yang & Guofang Gong & Qingqing Gui, 2022. "Exploring the Spatial Network Structure of Agricultural Water Use Efficiency in China: A Social Network Perspective," Sustainability, MDPI, vol. 14(5), pages 1-22, February.
    2. Li, Rongrong & Han, Xinyu & Wang, Qiang, 2023. "Do technical differences lead to a widening gap in China's regional carbon emissions efficiency? Evidence from a combination of LMDI and PDA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    3. Meng Yang & Yisheng Liu & Jinzhao Tian & Feiyu Cheng & Pengbo Song, 2022. "Dynamic Evolution and Regional Disparity in Carbon Emission Intensity in China," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    4. Xia, Yin-Shuang & Sun, Lu-Xuan & Feng, Chao, 2022. "What causes spatial inequalities of low-carbon development in China's transport sector? A newly proposed meta-frontier DEA-based decomposition approach," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    5. Yu, Ying & Dai, Yuqi & Xu, Linyu & Zheng, Hanzhong & Wu, Wenhao & Chen, Lei, 2023. "A multi-level characteristic analysis of urban agglomeration energy-related carbon emission: A case study of the Pearl River Delta," Energy, Elsevier, vol. 263(PB).
    6. Hao Jin & Shuai Huang, 2021. "Are China’s Water Resources for Agriculture Sustainable? Evidence from Hubei Province," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
    7. 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).
    8. Ashantha Ranasinghe & Xuejuan Su, 2023. "When social assistance meets market power: A mixed duopoly view of health insurance in the United States," Economic Inquiry, Western Economic Association International, vol. 61(4), pages 851-869, October.
    9. Cao, Zhaodan & Zhu, Tingju & Cai, Ximing, 2023. "Hydro-agro-economic optimization for irrigated farming in an arid region: The Hetao Irrigation District, Inner Mongolia," Agricultural Water Management, Elsevier, vol. 277(C).
    10. Gajdos, Thibault & Maurin, Eric, 2004. "Unequal uncertainties and uncertain inequalities: an axiomatic approach," Journal of Economic Theory, Elsevier, vol. 116(1), pages 93-118, May.
    11. Eckstein, Zvi & Zilcha, Itzhak, 1994. "The effects of compulsory schooling on growth, income distribution and welfare," Journal of Public Economics, Elsevier, vol. 54(3), pages 339-359, July.
    12. Ren, Dongyang & Xu, Xu & Engel, Bernard & Huang, Quanzhong & Xiong, Yunwu & Huo, Zailin & Huang, Guanhua, 2021. "A comprehensive analysis of water productivity in natural vegetation and various crops coexistent agro-ecosystems," Agricultural Water Management, Elsevier, vol. 243(C).
    13. Wu, Zhangsheng & Li, Yue & Wang, Rong & Xu, Xu & Ren, Dongyang & Huang, Quanzhong & Xiong, Yunwu & Huang, Guanhua, 2023. "Evaluation of irrigation water saving and salinity control practices of maize and sunflower in the upper Yellow River basin with an agro-hydrological model based method," Agricultural Water Management, Elsevier, vol. 278(C).
    14. Alain Chateauneuf & Patrick Moyes, 2005. "Lorenz non-consistent welfare and inequality measurement," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 2(2), pages 61-87, January.
    15. Rodriguez-Alvarez, Ana & Llorca, Manuel & Jamasb, Tooraj, 2021. "Alleviating energy poverty in Europe: Front-runners and laggards," Energy Economics, Elsevier, vol. 103(C).
    16. Roobavannan, M. & Kandasamy, J. & Pande, S. & Vigneswaran, S. & Sivapalan, M., 2020. "Sustainability of agricultural basin development under uncertain future climate and economic conditions: A socio-hydrological analysis," Ecological Economics, Elsevier, vol. 174(C).
    17. Oscar Volij, 2018. "Segregation: theoretical approaches," Chapters, in: Conchita D’Ambrosio (ed.), Handbook of Research on Economic and Social Well-Being, chapter 21, pages 480-503, Edward Elgar Publishing.
    18. Alessandro Spiganti, 2022. "Wealth Inequality and the Exploration of Novel Alternatives," Working Papers 2022:02, Department of Economics, University of Venice "Ca' Foscari".
    19. Günther Rehme, 2007. "Education, Economic Growth and Measured Income Inequality," Economica, London School of Economics and Political Science, vol. 74(295), pages 493-514, August.
    20. 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.

    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:jagris:v:12:y:2022:i:4:p:546-:d:791679. 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.