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• W4288987519 endingPage "109200" @default.
• W4288987519 startingPage "109200" @default.
• W4288987519 abstract "Identifying the spatial distribution characteristics and influencing factors (SDC-IF) of the social value of water resources (SVWR) within a basin is an effective means to realize the dynamic regulation of water resources. In this paper, the concept of the SVWR was based on energy flow in the water resources social subsystem. Spatial econometric methods were initially introduced into the study of the SVWR to establish an emergy-spatial autocorrelation-spatial regression (ESSR) model to explore the above problem. The study area, the Yellow River Basin (YRB), was used to verify model applicability, in which the analysis highlighted three key findings: 1) The Moran’s I of the SVWR exceeded 0, indicating the SVWR had spatial correlation in the YRB. 2) The SVWR in provinces (regions) and cities were 30.76–32.87 and 24.88–30.67 yuan/m3, respectively, and its spatial distribution in the YRB was non-uniform, highlighting inequity in current water allocation. 3) The greatest spatial influencing factors of the SVWR in provinces (regions) were agriculture and forestry water inputs, whereas it was domestic water consumption in cities. The factors with the least influence were the employees of agriculture-forestry-stockbreeding-fishery; these should be considered in future water resources management. The findings show that the dynamic equilibrium regulation of water resources in the basin can be realized by adjusting the SDC-IF of SVWR, contributing to the fair distribution of water resources. In addition, the model may provide technical references for similar studies in other watersheds, contributing to the sustainable development of global water resources." @default.
• W4288987519 created "2022-07-31" @default.
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• W4288987519 date "2022-09-01" @default.
• W4288987519 modified "2023-10-18" @default.
• W4288987519 title "Social values of water resources: Analyzing its spatial distribution characteristics and influencing factors using an ESSR model" @default.
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• W4288987519 doi "https://doi.org/10.1016/j.ecolind.2022.109200" @default.
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