Efficiency and its influencing factors of urban water sector in China and major OECD countries

Chen, Jian; Du, Minzhe; Huang, Chukun

doi:10.1016/j.jclepro.2022.133885

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…In view of the double counting caused by considering both the quantity of livestock produced and the quantity of livestock stored at the same time, the annual quantity of livestock and poultry is classified according to the breeding cycle of livestock and poultry [47,48]. For cattle and sheep with a feeding cycle of more than or equal to 365 days and pigs, poultry and rabbits with a feeding cycle of less than 365 days, the year-end stock quantity is taken; this method is more objective in measuring the annual livestock and poultry breeding scale [49]. Since TN and COD are relatively high in the manure and urine pollutants of livestock and poultry, and water bodies can dilute TN and COD at the same time, the largest grey water footprint produced by TN and COD pollutants is selected as the grey water footprint of livestock and poultry industry.…”

Section: Discussionmentioning

confidence: 99%

“…Natural factors (WRE) Water resource endowment: per capita water resources (m 3 /person) [48] Socio-economic factors (UL) Urbanization level: urban population/total population (%) [49] (IL) Industrialization level: industrial value added/regional GDP (%) [50] (AWI) Agricultural water use intensity: total agricultural water use/total water use (%) [48] Policy factors (FEP) Agricultural financial expenditure ratio: expenditure on agriculture, forestry and water affairs/local general public budget expenditure (%) [2] Scientific and technological factors (WSL) Level of water-saving agriculture: arable (effective) irrigated area/crop sown area (%) [25] Environmental factors (GCS) Food cultivation structure: area sown with food crops/area sown with crops (%) [38] External opening factors (APT) Agricultural trade: total import and export of agricultural products/regional GDP (%)…”

Section: Factor Classification Explanatory Variables and Units Refere...mentioning

confidence: 99%

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Evolutionary Trends, Regional Differences and Influencing Factors of the Green Efficiency of Agricultural Water Use in China Based on WF-GTWR Model

Jianzhong

2023

IJERPH

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Improving the green efficiency of agricultural water use is a key way to promote the sustainable utilization of agricultural water resources and sustainable development of economy and society. This work calculated and analyzed the evolution trend, regional differences and driving factors of the green efficiency of agricultural water use in China from the perspective of the water footprint. The results show that the green efficiency of agricultural water use in China shows a fluctuation trend of first declining and then rising from 1997 to 2020, after which the average efficiency dropped from 0.538 in 1997 to 0.406 in 2009, and then rose rapidly to 0.989 in 2020, with an average annual growth rate of about 3.6%. From a regional perspective, the green efficiency of agricultural water use in the eastern region was the highest (0.594), above the national average (0.538), followed by the western region (0.522), with the central region in last (0.491), with significant regional differences. The spatial differences in the green efficiency of available agricultural water in China shows a fluctuating downward trend. The Gini coefficient fluctuated from 0.271 in 1997 to 0.182 in 2020, with an average annual growth rate of about −1.4%. The main source of this regional difference was super-variable density, followed by the difference between the eastern and the central regions. The influence of urbanization level, water-saving level and agricultural trade on the green efficiency of agricultural water use was always positive and the influence of industrialization level was always negative; among them, the urbanization level, water-saving level and industrialization level had a greater impact on Northeast China, and agricultural trade had a greater impact on Southeast China. Therefore, this work puts forward relevant policy recommendations.

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Section: Discussionmentioning

confidence: 99%