Identification and attribution analysis of integrated ecological zones based on the XGBoost-SHAP model: A case study of Chengdu, China

• XGBoost-SHAP model assesses the impact of feature factors on ecological zoning. • Integrated ESV and LER for a comprehensive ecological zoning approach. • Gradual restoration of ecosystems in the study area observed. • Significant variation in the influence of driving factors across different ecological zones. Rapid urbanization has intensified pressure on regional ecosystems, constraining sustainable development. Constructing a scientific ecological zoning framework is essential for environmental protection and refined territorial spatial management. Taking Chengdu, China, as a case study, this study develops an ecological zoning framework based on the eXtreme Gradient Boosting-SHapley Additive exPlanations (XGBoost-SHAP) model. The framework integrates Ecosystem Service Value (ESV) and Landscape Ecological Risk (LER) as core indicators, applies Z-score standardization and quadrant classification to delineate four ecological zone types with distinct ecological functions, and further employs the XGBoost-SHAP model to identify key natural and anthropogenic drivers and explain their roles in spatial environmental evolution. The results show that: (1) Farmland and forest were the dominant land use types, accounting for over 87 % of the total area. From 2000 to 2020, farmland decreased by 11.64 %, while ecological land increased by 219.68 km 2 . (2) ESV followed a “rise–decline–rise” trend with a net decrease of 0.752 billion CNY and a spatial pattern of “high in the northwest, low in the center.” LER exhibited a “low in the northwest-central, high in the southeast” pattern, with low-risk areas gradually expanding. (3) The Ecological Control Zone (ECZ) and Ecological Improvement Zone (EIZ) dominated the study area, covering over 9,591 km 2 . At the same time, the Ecological Rehabilitation Zone (ERZ) and Ecological Conservation Zone (ECOZ) showed stable growth driven primarily by natural factors. (4) The XGBoost-SHAP model demonstrated high interpretability and attribution accuracy, effectively revealing the driving mechanisms behind zoning evolution. This ecological zoning framework is refined, interpretable and data-driven, providing scientific support for spatial planning and sustainable ecosystem management in rapidly urbanizing regions.