Land Use and Ecosystem Services
Open access
Peer reviewed
Abstract
Abstract
Rapid urbanization reshapes Production–Living–Ecological Space (PLES), creating challenges for metropolitan spatial planning, ecological protection, and adaptive land governance. However, the temporal heterogeneity and nonlinear mechanisms associated with PLES change remain insufficiently explored. Taking the Changsha–Zhuzhou–Xiangtan Metropolitan Area (CZXMA) as an empirical study, this research develops an integrated framework to identify stage-based land transitions, dynamic predictor importance, and nonlinear response patterns of PLES from 2010 to 2025. The study aims to clarify how PLES transition intensity changes across development stages and how key predictive factors vary over time. The results show the following: (1) PLES evolution is characterized by persistent expansion of living space and contraction of ecological space, with living space predominantly encroaching upon production space, while overall change intensity peaked during 2010–2015. (2) The dominant driving forces shifted from administrative planning and proximity to government in the early stage to demographic and market-oriented factors during later metropolitan integration. (3) SHapley Additive exPlanations (SHAP) analysis reveals nonlinear responses, with population growth showing an inverted U-shaped association with living-space expansion, suggesting possible land-use saturation. Compared with conventional static monitoring or single-method driver detection, this framework improves the diagnosis of dynamic land-system change by linking transition intensity with interpretable, period-specific predictive associations. The main policy insight is that metropolitan land governance should move from static zoning toward adaptive planning that monitors expansion intensity, demographic pressure, and ecological constraints. This study supports more resilient and efficient land-use strategies in rapidly urbanizing metropolitan regions.
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@article{Liao2026Uncovering,
title = {Uncovering Dynamic and Nonlinear Driving Mechanisms of Production–Living–Ecological Space Change in Metropolitan Areas Using Interpretable Machine Learning},
author = {Jia Liao and Bin Quan and Kui Liu and Zhiwei Deng},
journal = {Sustainability},
year = {2026},
doi = {10.3390/su18125894},
url = {https://doi.org/10.3390/su18125894}
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