Decomposing the Driving Factors of Water Use in China

Li, Wei; Xi-feng, Wang; Liu, Jiahong; Jia, Yangwen; Qiu, Yaqin

doi:10.3390/su11082300

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…We calculated the standard structure of the multiregional input–output table for the Yellow River based on the multi-sectoral input–output table for China. Due to data availability limitations, we used a non-competitive model 39 , which assumed that products imported, transferred from outside the Yellow River, and obtained from the Yellow River differed. Therefore, these products do not have a competitive relationship as substitutes.…”

Section: Methodsmentioning

confidence: 99%

Optimal water resource allocation considering virtual water trade in the Yellow River Basin

Wang,

2024

Sci Rep

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Water can be redistributed physically and virtually. We explored water allocation optimization to mitigate water stresses by constructing a physical–virtual dual water system and optimizing the 1987 Yellow River water allocation scheme. We calculated the virtual water volume, identified the virtual in-basin, out-of-basin, and export water volumes, and compared the total regional water demand (i.e., combined physical and virtual water volumes) with regional water planetary boundaries to optimize basin water allocation schemes. Virtual water accounted for > 90% of the total regional demands, whereas physical flows did not significantly impact them. Moreover, allocation quotas for Qinghai and Inner Mongolia should be reduced by 0.113 and 1.005 billion m3, respectively, for sustainability. Furthermore, improving the efficiency of water-intensive sectors and limiting virtual water outflows from heavy industry to out-of-basin sectors are vital to water intensification. Increased attention should be directed toward physical–virtual water demands than the current focus on supply-oriented water allocation.

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Section: Methodsmentioning

confidence: 99%

Optimal water resource allocation considering virtual water trade in the Yellow River Basin

Wang,

2024

Sci Rep

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“…Compared with the existing literature [29][30][31][32], in this research, we studied the driving effects of the total water use evolution in China from the perspective of multi-year long time-series in the whole country for the first time. This could comprehensively explain the influence mechanism of the total water use evolution in China.…”

Section: Analysis Of the Driving Effects Of The Total Water Use Evolu...mentioning

confidence: 99%

“…The agricultural, industrial, and other sector water use intensity separately decreased from The innovation and application of watersaving technology is conducive to improving the water use efficiency in different sectors; the emerging irrigation technology is conducive to improving the effective coefficient of irrigative water utilization; the development of industrial clean technology is conducive to improving the reuse rate of industrial water; and the in-depth awareness of citizens with respect to water-saving is conducive to improving the domestic water use efficiency, thus reducing the total water use. Compared with the existing literature [29][30][31][32], in this research, we studied the driving effects of the total water use evolution in China from the perspective of multi-year long time-series in the whole country for the first time. This could comprehensively explain the influence mechanism of the total water use evolution in China.…”

Section: Wuiementioning

confidence: 99%

“…Compared with the existing literature [29][30][31][32], in this research, we also considered the driving effects of the total water use evolution when the population or economic proportion changed in the six major districts in China for the first time. This could comprehensively reflect the actual water demand in these districts.…”

Section: Analysis Of the Dppe And Depe Of The Total Water Use Evoluti...mentioning

confidence: 99%

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The Driving Effects of the Total Water Use Evolution in China from 1965 to 2019

Wang,

Qin,

Han

2023

Water

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To understand the influence mechanism of the total water use evolution in a certain region more deeply, it is necessary to accurately identify the driving effects of the total water use evolution, and quantitatively analyze the influence of the driving effects on the total water use evolution. In this research, we studied the driving effects of the total water use evolution in China from the perspective of multi-year long time-series in the whole country for the first time. Through the logarithmic mean Divisia index (LMDI) decomposition method, we constructed an LMDI decomposition model for the regional total water use evolution, and decomposed the total water use evolution in China and its five stages from 1965 to 2019 into the water use intensity effect (WUIE), sector proportion effect (SPE), per capita total economy effect (PCTEE), and total population effect (TPE). We also considered the driving effects of the total water use evolution when the population or economic proportion changed in the six major districts in China for the first time. Based on the LMDI decomposition method, we separately added the district population proportion variable and the district economic proportion variable to contrast a logarithmic mean Disivia index-population (LMDI-P) decomposition model and a logarithmic mean Divisia index-economic (LMDI-E) decomposition model for the regional total water use evolution. Compared with the LMDI decomposition model, the district population proportion effect (DPPE) and the district economic proportion effect (DEPE) were separately added. We calculated the value and proportion of the driving effects of the total water use evolution in China and analyzed their influence mechanisms. Our findings provide better decision-making reference for water resource planning and management in China. The results show the following: (1) According to the overall situation from 1965 to 2019, the prohibitive role played by the PCTEE (total 22,263.79 × 108 m3) and the TPE (total 2945.38 × 108 m3) with respect to the total water use increasing in China offset the inhibitive role played by the WUIE (total −16,094.31 × 108 m3) and the SPE (total −5930.02 × 108 m3) with respect to the total water use increasing in China; (2) According to the overall situation from 1965 to 2019, both the DPPE and DEPE had heterogeneity in the total water use evolution in the six major districts in China. The DPPE played a prohibitive role in the three population inflow districts (Southeast China, Central South China, and Northwest China) with respect to the total water use increasing (total 291.09 × 108 m3), and an inhibitive role in the other three population outflow districts (North China, Central South China, and Southwest China) with respect to the total water use increasing (total −207.78 × 108 m3). The DEPE played a prohibitive role in the three economically developed districts (North China, Southeast China, and Central South China) with respect to the total water use increasing (total 428.26 × 108 m3), and an inhibitive role in the other three economically underdeveloped districts (Northeast China, Southwest China, and Northwest China) with respect to the total water use increasing (total −477.74 × 108 m3).

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“…In comparison, quantitative methods can provide a more nuanced understanding of the impacts of different driving forces of water use 13 . SDA is particularly suitable for socio-economic problems 14 and it makes use of input-output tables as data source and requires intense computations [15][16][17] . IDA is also a widely used analytical tool for socio-economic problems.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Water Use Driving Effects and Dynamic Water Demand Research Based on the Kaya-LMDI-STIRPAT Model

Wang,

Liu

et al. 2024

Preprint

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A better understanding of the driving effects of socio-economic water use is essential to obtain accurate water demand prediction and to implement water resources management. In this study, six driving effects, including scale effect, structure effect, economic level effect, policy rationality effect, water price policy effect, and scientific and technological level effect, are considered. A Kaya-LMDI model is constructed to identify the driving effects of domestic, industrial and agricultural water use, and then a STIRPAT model is established for water demand production. Annual precipitation is introduced into the model for predicting agricultural water demand. The model is applied to Hebei province, China. The main conclusions are summarized as follows: ① The economic level effect plays a role in promoting the socio-economic water use in all prefecture-level cities of Hebei province; The water price policy effect plays a role in inhibiting the domestic and agricultural water use, while the scientific and technological level effect plays a role in inhibiting the industrial water use. ② The water use is mainly inhibited by the effects of policy rationality and water price policy before 2015 but mainly by the effects of water price policy and scientific and technological level after 2015; ③ There is a clear spatial difference in the driving effects of the socio-economic water use among the prefecture-level cities, and the economic level effect plays a major role in promoting the socio-economic water use in all prefecture-level cities; the water price policy effect plays an inhibitory role in nine cities; while the policy rationality effect plays an inhibitory role in two cities; ④ The water demand prediction results suggest that the water demand of Hebei province in 2030 is 22.01 billion m3 in normal years (P = 50%) and 24.33 billion m3 in medium-dry years(P = 75%), which are consistent with the red line set by the government. This study may contribute to optimizing the economic structure and provides guidance for water use management.

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Decomposing the Driving Factors of Water Use in China

Cited by 4 publications

References 17 publications

Optimal water resource allocation considering virtual water trade in the Yellow River Basin

Optimal water resource allocation considering virtual water trade in the Yellow River Basin

The Driving Effects of the Total Water Use Evolution in China from 1965 to 2019

Multiscale Water Use Driving Effects and Dynamic Water Demand Research Based on the Kaya-LMDI-STIRPAT Model

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