The Influencing Factors of Water Uses in the Yellow River Basin: A Physical, Production-Based, and Consumption-Based Water Footprint Analysis by the Random Forest Model

Abstract

The strategy of “Basing city, land, population and production on water resources”, clarifying the water uses of each province and the influencing factors are crucial to the conservation and intensive use of water resources for the Yellow River basin. In this study, physical water use, the production-based water footprint, and the consumption-based water footprint of nine provinces in the Yellow River Basin from 2007 to 2017 are measured. Then, the key influencing factors of three kinds of water use are analyzed by the random forest model. The results show that (1) the three kinds of water use in the Yellow River basin all showed a trend of first increasing and then decreasing. Physical water use and the production-based water footprint present the geographical differentiation in the declining order from the upper reach to the lower reach, and then the middle reach, while the order for the consumption-based water footprint is the lower reach, the upper reach, and the middle reach. (2) Agriculture, forestry, animal husbandry, fisheries, electricity and hot water production, supply, and chemicals are the dominant sectors of physical water use. Agriculture, forestry, animal husbandry, fisheries, food processing, tobacco, and construction are the key sectors for production-based and consumption-based water footprints. (3) The results of the random forest model show the influencing factors and their interactions of three kinds of water use in the Yellow River basin present obvious sectoral differences. The key influencing factors exhibit a linear or nonlinear response to water use in the three perspectives. The influencing factors of water use are also differentiated among the three perspectives.

Revealing the changes in water footprint at the provincial level and their drivers in the Yellow River Basin, China

“…The regional distribution of the WF in the YRB shows that provinces with a high WF are concentrated in the middle and lower reaches. These results were consistent with those reported by Zhang et al (2023).…”

“…Therefore, exploring dynamic changes in WF and its driving mechanisms can help to fully understand the changing characteristics of WF and clarify the driving factors behind its impact on water resource utilization, so that corresponding measures can be taken to reduce WF, alleviate local water shortages, and achieve sustainable water resource utilization. However, existing research on water resources in the YRB mainly focuses on the measurement of WF and the study of WF drivers in specific industries, such as agriculture, coal, and chemicals (Liu et al 2022, Zhang et al 2023, while there is insufficient research on the mechanisms driving regional WF changes. In this study, an environmentally extended multi-regional input-output model (EE-MRIO) and structural decomposition analysis (SDA) were used to measure regional WF and investigate the driving factors of regional WF changes in the YRB from 2012 to 2017.…”

Structure and stability characteristics of zonal soil aggregates in the Three Rivers Source of the Qinghai‐Tibetan Plateau

“…It is a characteristic part of digital mapping to determine the model and select co‐variables based on the attributes of the research object (Lu et al., 2019; Zhang et al., 2022). In this study, when predicting soil aggregates with different stabilities in alpine meadows and grasslands, variables not relevant to the corresponding size aggregates were removed from the model (prediction of 0.5–0.25 mm mechanically stable particle size aggregates excluding altitude and mean annual temperature variables, 0.5–0.25 mm particle size water‐stable aggregates, and >2 mm mechanically stable particle size aggregates excluding altitude variables).…”

China’s Water Footprint on Urban and Rural Food Consumption: A Spatial–Temporal Evolution and Its Driving Factors Analysis from 2000 to 2020

“…This is because the amount of total water resources (wr) affects the water consumption in the food production and consumption process, thereby influencing the per capita food WF. Residents in water-rich areas tend to consume more food with higher virtual water content, and those in areas with abundant water resources may have a relatively weak awareness of water conservation [22]. When total water resources (wr) reach around 250 billion m 3 , residents' food consumption and food structure essentially stabilize, leading the per capita food WF to stabilize as well.…”

“…Additionally, the abundance or scarcity of water resources directly impacts agricultural production. There are arguments suggesting a correlation between the level of education of residents and their water conservation awareness [22]. In terms of research methods for the driving factors of the food water footprint, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model and the method of structural decomposition analysis (SDA), and Life Cycle Assessment (LCA) are widely used [23,24].…”