Identification and attribution analysis of integrated ecological zones based on the XGBoost-SHAP model: A case study of Chengdu, China
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 dev...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25007174 |
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| Summary: | 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 km2. (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 km2. 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. |
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| ISSN: | 1470-160X |