A regional classification framework integrating AI and causal inference revealing the drivers of lake eutrophication...

Lake eutrophication is a globally pervasive environmental issue. While its driving mechanisms exhibit significant spatial heterogeneity, the relative contributions of climate change versus anthropogenic pressure remain underexplored at large spatial scales. Leveraging the Trophic State Index (TSI) of 2693 lakes from 2000 to 2020, this study innovatively integrates a geographically explainable artificial intelligence framework (XGBoost-GeoShapley/SHAP) with causal inference (CausalForestDML) to elucidate the sensitivity of TSI to environmental factors, building upon the identification of regional driving patterns. We categorized Chinese lakes into three distinct driving patterns: Natural-Sensitive, Anthropogenic-Sensitive, and Mixed-Sensitive. Based on this classification, SHAP analysis identified the nonlinear threshold characteristics of key factors and the interactive regulatory effects of natural backgrounds on anthropogenic pressures. Causal inference further revealed sensitivity variations that conventional correlation analysis failed to capture. Specifically, potential evaporation exerted a significant positive driving effect in humid regions but shifted to a prominent inhibitory role in the arid Northwest (Mixed-Sensitive). In Anthropogenic-Sensitive regions, the eutrophication potential of impervious surfaces was confirmed to significantly outweigh their mitigation capacity; furthermore, these ecosystems exhibited heightened sensitivity to increased temperature and solar radiation. Notably, the causal model captured early signals of anthropogenic pressure encroaching upon the natural-dominated southeastern edge of the Qinghai-Tibet Plateau. By integrating the identification of dominant patterns with the quantification of heterogeneous causal effects, this study systematically elucidates the driving mechanisms of lake eutrophication. These findings provide a scientific basis for differentiated zonal governance and underscore the need for future management to proactively account for the potential impacts of climate change on the stability of these driving mechanisms.