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An Input–Output Analysis of the Water–Energy–Food Nexus Based on the Intensity and Quantity Index System—A Case Study of 30 Provinces in China

Author

Listed:
  • Ke Zhang

    (School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 102616, China)

  • Zihao Shen

    (School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 102616, China)

  • Chengshuang Sun

    (School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 102616, China)

Abstract

In the study of the water–energy–food nexus (WEF nexus), the importance of the intensity and quantity index system has been widely recognized. In order to study the impact of WEF on the economy, this paper establishes an intensity index system and a quantity index system, taking account of the impact of environmental pollution. Using a DEA model and China’s provincial data from 2019, this paper calculated the efficiency of the WEF nexus with the developed intensity and quantity index systems. The results show that the efficiency is not high in areas with a high economic development level, and efficiency is not the lowest in areas with a relatively low economic development level. When considering environmental pollution, the efficiency of some provinces has increased significantly, indicating that the WEF nexus has not caused environmental damage and is conducive to sustainable economic development. In the two intensity index systems, the efficiency of the production system is significantly lower than that of the consumption system, indicating that there is a serious waste of cultivated land per capita. Compared with the intensity index system, the efficiency of the quantity index system is low, and the polarization is obvious. A high level of GDP does not mean a high level of economic development. There may be a low level of resource utilization technology or environmental pollution underlying it. It is unscientific to evaluate local economic development only by GDP. When evaluating the urban economy and national economy, we should conduct an overall study of WEF and reasonably allocate WEF resources, which will not only help to alleviate the current situation of resource shortage in various countries but also effectively promote the coordinated development of national and regional economies. At the same time, environmental protection should also be taken into account. Compared with the economic development model of developing the economy first and then solving environmental problems, developing and solving at the same time is more conducive to the sustainable development of the national economy.

Suggested Citation

  • Ke Zhang & Zihao Shen & Chengshuang Sun, 2022. "An Input–Output Analysis of the Water–Energy–Food Nexus Based on the Intensity and Quantity Index System—A Case Study of 30 Provinces in China," Energies, MDPI, vol. 15(10), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3591-:d:815286
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Aysegül Kibaroglu & Sezin Iba Gürsoy, 2015. "Water-energy-food nexus in a transboundary context: the Euphrates-Tigris river basin as a case study," Water International, Taylor & Francis Journals, vol. 40(5-6), pages 824-838, September.
    3. Caroline King & Hadi Jaafar, 2015. "Rapid assessment of the water-energy-food-climate nexus in six selected basins of North Africa and West Asia undergoing transitions and scarcity threats," International Journal of Water Resources Development, Taylor & Francis Journals, vol. 31(3), pages 343-359, September.
    4. Johannes Halbe & Claudia Pahl-Wostl & Manfred A. Lange & Christina Velonis, 2015. "Governance of transitions towards sustainable development - the water-energy-food nexus in Cyprus," Water International, Taylor & Francis Journals, vol. 40(5-6), pages 877-894, September.
    5. Golam Rasul & Bikash Sharma, 2016. "The nexus approach to water–energy–food security: an option for adaptation to climate change," Climate Policy, Taylor & Francis Journals, vol. 16(6), pages 682-702, August.
    6. Dai, Jiangyu & Wu, Shiqiang & Han, Guoyi & Weinberg, Josh & Xie, Xinghua & Wu, Xiufeng & Song, Xingqiang & Jia, Benyou & Xue, Wanyun & Yang, Qianqian, 2018. "Water-energy nexus: A review of methods and tools for macro-assessment," Applied Energy, Elsevier, vol. 210(C), pages 393-408.
    7. Animesh K. Gain & Carlo Giupponi & David Benson, 2015. "The water-energy-food (WEF) security nexus: the policy perspective of Bangladesh," Water International, Taylor & Francis Journals, vol. 40(5-6), pages 895-910, September.
    8. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
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