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Article

Risk Assessment of Water Resources and Energy Security Based on the Cloud Model: A Case Study of China in 2020

1
Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China
2
College of Sciences, North China University of Science and Technology, Tangshan 063210, China
3
Argonne National Laboratory, Environment Science Division, Lemont, IL 60439, USA
4
Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Yurui Fan and Xiaosheng Qin
Water 2021, 13(13), 1823; https://doi.org/10.3390/w13131823
Received: 4 June 2021 / Revised: 26 June 2021 / Accepted: 28 June 2021 / Published: 30 June 2021
(This article belongs to the Special Issue Water Environmental System Analysis)
With the rapid development of economy and society, China’s demand for water resources and energy is increasing, and the supply situation is becoming increasingly severe. The correlation and binding characteristics between the two have become increasingly prominent, which will become bottlenecks in sustainable economic and social development in the future. In this paper, the Liang–Kleeman method was used to screen the risk factors of water resources and energy security, and then four major risk factors were selected. Based on the cloud model, the water resource and energy security risk assessment models were constructed combined with the predicted values using GM (1,1) and Pearson III curve methods, and the water resource and energy security risks of 30 provinces (cities) in 2020 were quantitatively assessed. The risk assessment results showed that the risk level zoning of water resource shortage with different guarantee rates in most regions has undergone little change, but the spatial distribution was quite different, showing the characteristics of “low in the South and high in the North”. When the guarantee rate changed from P = 25% to P = 95%, the risk level of water shortage in Sichuan, Jiangxi, Hunan, Hainan, Jilin, Ningxia and Nei Monggol significantly increased, and the spatial distribution of energy security risk and water resource shortage risk was obviously inconsistent. View Full-Text
Keywords: water resources–energy security; cloud model; Liang–Kleeman information flow; risk assessment; GM (1,1); Pearson III curve water resources–energy security; cloud model; Liang–Kleeman information flow; risk assessment; GM (1,1); Pearson III curve
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MDPI and ACS Style

Yang, Y.; Wang, H.; Zhang, Y.; Wang, C. Risk Assessment of Water Resources and Energy Security Based on the Cloud Model: A Case Study of China in 2020. Water 2021, 13, 1823. https://doi.org/10.3390/w13131823

AMA Style

Yang Y, Wang H, Zhang Y, Wang C. Risk Assessment of Water Resources and Energy Security Based on the Cloud Model: A Case Study of China in 2020. Water. 2021; 13(13):1823. https://doi.org/10.3390/w13131823

Chicago/Turabian Style

Yang, Yafeng, Hongrui Wang, Yuanyuan Zhang, and Cheng Wang. 2021. "Risk Assessment of Water Resources and Energy Security Based on the Cloud Model: A Case Study of China in 2020" Water 13, no. 13: 1823. https://doi.org/10.3390/w13131823

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