Revealing Spatiotemporal Evolution and Driving Mechanisms of Grey Water Footprint in Land Consolidation Areas Using Explainable Machine Learning Models: Evidence from Yan’an Region, Shaanxi Province

The grey water footprint (GWF) is a critical indicator for assessing the impact of socio-economic activities on the water resources environment. To address the dual challenges of economic growth and water pollution associated with Land Consolidation Projects (LCPs) in the Loess Plateau, this study systematically analyzes the spatiotemporal distribution of GWF in the Yan’an region from 2000 to 2023 and employs the eXtreme Gradient Boosting (XGBoost) model to comprehensively explore its driving mechanisms. The SHapley Additive Explanations (SHAP) method was employed to quantify the dynamic contributions of the driving factors of GWF, while the threshold effects of these factors were assessed using partial dependence plot analysis. Additionally, spatial matching patterns between agricultural GWF (GWFagr) and economic factors were examined using the Gini coefficient and imbalance index. These findings indicate that the total GWF (TGWF) peaked at 1.347 billion m3 in 2004 and declined due to improvements in water management efficiency. Spatially, TGWF is higher in the central and eastern regions, where GWFagr is predominant. The permanent population and per capita GDP are the key driving factors, accounting for 21.1% and 15% of the total change in TGWF, respectively. In the spatial coupling relationship between agricultural GDP and GWFagr, the overall imbalance index has significantly decreased. The synergistic effect between the Grain for Green Project and LCPs is becoming increasingly evident. These insights provide scientific support and policy guidance for the ecological protection and high-quality development of the Yellow River Basin.