Decoupling analysis of water use and economic development in arid region of China – Based on quantity and quality of water use

Yao, Zhilei; Liu, Wenxin; Cai, Yu; Khan, Sufyan Ullah; Zhao, Minjuan

doi:10.1016/j.scitotenv.2020.143275

Cited by 50 publications

(26 citation statements)

References 17 publications

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“…VWF included six sections ( Figure 2 ): agricultural (GWF a ), industrial (VWF i ), domestic (VWF d ), ecological (VWF e ), imported (from another region) (VWF im ), and exported (to another region) (VWF ex ) [ 35 , 48 ]. (1) Agricultural products were divided into two categories: crops and livestock products.…”

Section: Methodsmentioning

confidence: 99%

“…GWF included three sections ( Figure 2 ): agricultural (GWF a ), industrial (GWF i ), and domestic (GWF d ) [ 34 , 48 ]. (1) GWF a required the annual amount of nitrogen fertilizer (N) applied to crops in each region, which were acquired from the China Environmental Statistical Yearbook [ 2 ] and local statistical yearbooks [ 43 , 49 , 50 ].…”

Section: Methodsmentioning

confidence: 99%

“…VWF indicates the amount of actual water consumed in the products and services required to sustain the normal life of a regional group under certain physical conditions of living standards, which can be estimated as follows [ 35 , 48 ]:

where VWF is the virtual water footprint (m 3 ), which indicates the total quantity of water consumed in the study area in a year (m 3 ); VWF a is the agricultural virtual water footprint, which expresses the amount of water consumed in agricultural production in the study area in a year (m 3 ), and consists of two components—the amount of water used in the production of crop products, and the amount of water used in the production of livestock products—and is calculated by multiplying the virtual water content per unit of agricultural product [ 16 , 48 ]; VWF i is the industrial virtual water footprint (m 3 ), which represents the amount of water used for industrial production; VWF d is the domestic virtual water footprint (m 3 ), which expresses the amount of water used for domestic use; VWF e is the ecological virtual water footprint (m 3 ), which represents the amount of water used for ecological purposes; VWF im is the imported virtual water footprint (m 3 ), which indicates the virtual water imported from other regions (m 3 ); and VWF ex is the export virtual water footprint (m 3 ), which represents the virtual water exported from the study area (m 3 ). VWF im and VWF ex are calculated by multiplying the total amount of import and export trade in each region by the water consumption per CNY 10,000 of GDP.…”

Section: Methodsmentioning

confidence: 99%

“…According to previous studies [ 25 , 56 ], agricultural water pollution is mainly caused by nitrogen (N), including the usage of nitrogen fertilizers and pesticide spraying, etc. Accordingly, the estimation model is as follows:

where GWF a is the agricultural grey water footprint (m 3 ); α is the rate of nitrogen fertilizer entering the water (%), and the national average nitrogen fertilizer inflow rate of 7% is chosen for calculation, based on previous studies [ 34 , 48 ]; N is the annual amount of nitrogen applied to the crop (kg); C max is the standard concentration of pollutant water quality (kg/m 3 )—with reference to the Environmental Quality Standards for Surface Water of the People’s Republic of China [ 57 ], this paper adopts the 3rd level (of a total of 5 levels) as the minimum requirement for wastewater pollution control, with the maximum allowable nitrogen concentration of 1 mg/L—and C nat is the natural concentration of water (kg/m 3 ), which is the concentration of water pollution under natural conditions without any anthropogenic interference, and is usually assumed to be 0 [ 8 , 26 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

“…GWF i is the amount of fresh water required to carry the water pollutants caused by industrial production activities [ 34 ]. According to the China Environmental Statistical Yearbook [ 2 ], COD and AN are the main pollutants discharged from industrial wastewater; thus, this study used COD and AN as the key components to calculate GWF i [ 8 , 48 ]—the estimation model is as follows:

where GWF i is the industrial grey water footprint (m 3 ); according to Hoekstra’s grey water footprint theory [ 26 ], GWF i is determined by the most critical pollutants in industrial wastewater, as the maximum GWF i(j) based on COD or AN (j represents COD and AN); L i(j) denotes the annual discharge of category j pollutants in industrial production (kg); IWD is the total annual discharge of industrial wastewater in the study area (m 3 ); C max denotes the pollutant water quality standard concentration, which is 20 mg/L and 1 mg/L for COD and AN, respectively [ 26 ]; C nat denotes the concentration of water pollution under natural conditions without any anthropogenic influence—based on the Water Footprint Assessment Manual [ 26 ], the natural water concentrations of COD and AN are 0 mg/L and 0.015 mg/L, respectively.…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Exploring the Regional Coordination Relationship between Water Utilization and Urbanization Based on Decoupling Analysis: A Case Study of the Beijing–Tianjin–Hebei Region

Shen

Yao

2022

IJERPH

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Understanding the potential association between the urbanization process and regional water shortage/pollution is conducive to promoting the intensive utilization of local water resources. In this study, the water footprint model was used to estimate water utilization status in terms of both water quantity (virtual water footprint (VWF)) and water quality (grey water footprint (GWF)) in the Beijing–Tianjin–Hebei region (China) during 2004–2017. Their potential coordination relationship with the local urbanization process represented by the gross domestic product (GDP), population (POP), and built-up area (BA) was examined using the Tapio decoupling model. The results showed that from 2004 to 2017, (1) VWF in Beijing and Tianjin showed non-significant decreasing trends, with reductions of 1.08 × 109 and 1.56 × 109 m3, respectively, while that in Hebei showed a significant increasing trend, with an increase of 5.74 × 109 m3. This indicated a gradually increasing water demand in Hebei and decreasing demand in Beijing and Tianjin. In all three regions, the agricultural sector accounted for a relatively high proportion of VWF compared to other sectors. (2) GWF in Beijing, Tianjin, and Hebei all showed declining trends, with reductions of 2.19 × 1010, 2.32 × 1010, and 1.66 × 1011 m3, respectively, indicating considerable local water quality improvement. The domestic sector contributed as the main component of GWF in Beijing, while agriculture was the main contributor in Hebei. The major contributor in Tianjin transitioned from the domestic (before 2015) to the agricultural sector. (3) We found good coordination between VWF and GDP in all three regions, as their local economic development was no longer overly dependent on water consumption. However, the expansion of urban built-up area or population would bring about accelerated depletion of water resources. (4) GWF in the three provinces showed good coordination with GDP, POP, and BA in most years, implying that the development of urbanization no longer strongly caused the pollution of water resources. In sum, policymakers should focus on improving agricultural irrigation efficiency and residents’ awareness of water conservation, so as to gradually achieve sustainable water resource management in the BTH region.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%