China’s water situation; the supply of water and the pattern of its usage

Udimal, Thomas Bilaliib; Zhuang, Jincai; Ayamba, Emmanuel Caesar; Owusu, Samuel Mensah

doi:10.1016/j.ijsbe.2017.10.001

Cited by 30 publications

(19 citation statements)

References 21 publications

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“…These results are consistent with the findings of Zhao et al [14]; in addition our results show that the top 10 blue and green virtual water exporting countries/regions to China did so mainly through the agricultural sector. From Figure 1, it can be seen that United States was the largest blue water exporter, contributing 3.3 km 3 blue water to China through its agricultural sector, with Brazil ranked second (1.0 km 3) . In terms of green water, Brazil contributed most to China's green VWI with 30.2 km 3 (95.0%) flowing into China's agricultural sector, United States ranked second with 24.7 km 3 , and Canada was third with of 5.4 km 3 .…”

Section: Discussionmentioning

confidence: 99%

Section: China's Blue and Green Water Footprint From The Production Pmentioning

confidence: 99%

“…As major importers of goods and services, developed countries were the main driving force behind China's production and resulting water use. The United States topped this list with a blue water import volume of 13.7 km 3 , followed by Japan (6.2 km 3 Figure 1 for details). The countries with the highest green VWI from China were consistent with those of blue VWI, albeit with a slightly different order.…”

Section: China's Blue and Green Water Footprint From The Production Pmentioning

confidence: 99%

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Blue and Green Water Footprint Assessment for China—A Multi-Region Input–Output Approach

et al. 2018

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Research on blue and green water footprints (WF) for China has typically been carried out based on bottom-up and top-down approach using a single-region input–output table. However, this research typically lacks detail on the sectoral interrelationships which exist between China and its trading partners in other countries/regions of the world. Here, a multi-region input–output approach using the WIOD database was applied to quantify the blue and green WF for China in 2009. The quantification was conducted from both production (WFP) and consumption (WFC) perspectives. The results show that the total WFP for China in 2009 was 1152.2 km3, second only to India. At 1070.9 km3, China had the largest WFC volume in the world. The internal WF was 953.5 km3, taking the substantial share for both the WFC and WFP. Overall, China’s trade resulted in a net export of 53.5 km3 virtual water. In contrast, the agricultural sector resulted in a net import of 70.6 km3 virtual water to China, with United States, Brazil, and Canada acting as major suppliers. This study suggests that quantifying the WF of China at global level through a MRIO framework is a necessary step towards achieving sustainability for China’s water management.

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Section: Discussionmentioning

confidence: 99%

Section: China's Blue and Green Water Footprint From The Production Pmentioning

confidence: 99%

Section: China's Blue and Green Water Footprint From The Production Pmentioning

confidence: 99%

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Blue and Green Water Footprint Assessment for China—A Multi-Region Input–Output Approach

et al. 2018

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“…In the case of groundwater resources, inappropriate management may result in the deterioration of water resources but also a decrease in the quality of groundwater [12]. Similar with the rest of the world, China faces increasing consumption of groundwater, making imperative the application of accurate methods for assessing groundwater potential [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models

Chen

Tsangaratos

et al. 2020

Applied Sciences

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This study presents a methodology for constructing groundwater spring potential maps by kernel logistic regression, (KLR), random forest (RF), and alternating decision tree (ADTree) models. The analysis was based on data concerning groundwater springs and fourteen explanatory factors (elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to streams, distance to roads, normalized difference vegetation index (NDVI), lithology, soil, and land use), which were divided into training and validation datasets. Ningtiaota region in the northern territory of Shaanxi Province, China, was considered as a test site. Frequency Ratio method was applied to provide to each factor’s class a coefficient weight, whereas the linear support vector machine method was used as a feature selection method to determine the optimal set of factors. The Receiver Operating Characteristic curve and the area under the curve (AUC) were used to evaluate the performance of each model using the training dataset, with the RF model providing the highest AUC value (0.909) followed by the KLR (0.877) and ADTree (0.812) models. The same performance pattern was estimated based on the validation dataset, with the RF model providing the highest AUC value (0.811) followed by the KLR (0.797) and ADTree (0.773) models. This study highlights that the artificial intelligence approach could be considered as a valid and accurate approach for groundwater spring potential zoning.

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“…A series of studies have been conducted to understand the quantity and quality of China's water resources (Jiang, 2009, Han et al 2016, Udimal et al 2017. However, most existing research has concentrated more on quantity and quality of direct water (production perspective) rather than embodied water (demand perspective), and the negligence would result in emerging water conflicts (De Angelis et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Quantity and quality of China's water from demand perspectives

Shan

Zhang

et al. 2019

Environ. Res. Lett.

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China is confronted with an unprecedented water crisis regarding its quantity and quality. In this study, we quantified the dynamics of China's embodied water use and chemical oxygen demand (COD) discharge from 2010 to 2015. The analysis was conducted with the latest available water use data across sectors in primary, secondary and tertiary industries and input-output models. The results showed that (1) China's water crisis was alleviated under urbanisation. Urban consumption occupied the largest percentages (over 30%) of embodied water use and COD discharge, but embodied water intensities in urban consumption were far lower than those in rural consumption.(2) The 'new normal' phase witnessed the optimisation of China's water use structures. Embodied water use in light-manufacturing and tertiary sectors increased while those in heavy-manufacturing sectors (except chemicals and transport equipment) dropped.(3) Transformation of China's international market brought positive effects on its domestic water use. China's water use (116-80 billion tonnes (Bts)) 9 and COD discharge (3.95-2.22 million tonnes (Mts)) embodied in export tremendously decreased while its total export values (11-25 trillion CNY) soared. Furthermore, embodied water use and COD discharge in relatively low-end sectors, such as textile, started to transfer from international to domestic markets when a part of China's production activities had been relocated to other developing countries.

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China’s water situation; the supply of water and the pattern of its usage

Cited by 30 publications

References 21 publications

Blue and Green Water Footprint Assessment for China—A Multi-Region Input–Output Approach

Blue and Green Water Footprint Assessment for China—A Multi-Region Input–Output Approach

Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models

Quantity and quality of China's water from demand perspectives

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