Temporal and Spatial Evolution of Grey Water Footprint in the Huai River Basin and Its Influencing Factors

Xi Wang, Yushuo Zhang, Qi Wang, Jing Xu, Fuju Xie and Weiying Xu ()
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Xi Wang: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Yushuo Zhang: The Key Laboratory of Mountain Environment Evolution and Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Qi Wang: International Business School, Shaanxi Normal University, Xi’an 710100, China
Jing Xu: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Fuju Xie: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
Weiying Xu: School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China

Sustainability, 2025, vol. 17, issue 15, 1-23

Abstract: To evaluate water pollution status and sustainable development potential in the Huai River Basin, this study focused on the spatiotemporal evolution and influencing factors of the grey water footprint (GWF) across 35 cities in the basin from 2005 to 2020. This study quantifies the GWF from agricultural, industrial, and domestic perspectives and analyzes its spatial disparities by incorporating spatial autocorrelation analysis. The Tapio decoupling model was applied to explore the relationship between pollution and economic growth, and geographic detectors along with the STIRPAT model were utilized to identify driving factors. The results revealed no significant global spatial clustering of GWF in the basin, but a pattern of “high in the east and west, low in the north and south” emerged, with high-value areas concentrated in southern Henan and northern Jiangsu. By 2020, 85.7% of cities achieved strong decoupling, indicating improved coordination between the environment and economy. Key driving factors included primary industry output, crop sown area, and grey water footprint intensity, with a notable interaction between agricultural output and grey water footprint intensity. The quantitative analysis based on the STIRPAT model demonstrated that seven factors, including grey water footprint intensity and total crop sown area, exhibited significant contributions to influencing variations. Ranked by importance, these factors were grey water footprint intensity > total crop sown area > urbanization rate > population size > secondary industry output > primary industry output > industrial wastewater discharge, collectively explaining 90.2% of the variability in GWF. The study provides a robust scientific basis for water pollution control and differentiated management in the river basin and holds significant importance for promoting sustainable development of the basin.

Keywords: grey water footprint (GWF); temporal and spatial evolution; spatial autocorrelation; decoupling model; geographic detectors; STIRPAT model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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