Quantifying climatic drivers and identifying spatial risks of vegetation carbon sink instability: a case study of central–western Inner Mongolia

Vegetation carbon sinks are critical for the global carbon budget, yet their stability in vulnerable ecosystems remains poorly understood. This gap is pronounced in central–western Inner Mongolia due to the nonlinear responses of vegetation carbon sinks to climatic factors and spatial heterogeneity. This study integrates time-series analysis, XGBoost–SHAP, and an optimized MaxEnt model, utilizing MODIS/Terra MOD17A3HGF net primary productivity (NPP) data, interpolated climate data from the China Meteorological Data Service Centre, and future climate data from the BCC-CSM2-MR model, to identify climatic regulators of vegetation carbon sinks and project their spatial risk patterns. Key findings include: (1) During 2014–2023, regional net ecosystem productivity (NEP) showed a slight upward trend with noticeable interannual fluctuations; comparing 2014–2023, approximately 25,500 km 2 of vegetated area switched between carbon sink and source states. (2) Temperature-related interannual variability accounted for approximately 62% of NEP fluctuations, with the variability of maximum temperature of the warmest month (bio5 IVA ) contributing the most (19.4%). Precipitation-related variability contributed the remaining 38%, and interactions between temperature and precipitation jointly shaped ecosystem instability. (3) High-risk and extreme-risk areas clustered along desert margins, with risk levels declining outward. Risk patterns under SSP126 (+1.76 °C by 2040 relative to 2023) remained stable, whereas SSP245 (+2.28 °C) and SSP370 (+2.53 °C) induced widespread risk-level transitions, highlighting the acute vulnerability of ecological transition zones to climate forcing. Although recent studies highlight the important roles of vapor pressure deficit and soil moisture in regulating dryland carbon sinks, these factors were not directly quantified in this study. Future efforts will integrate them to refine the predictions. This framework will provide a mechanistic basis for early warning and targeted management of vegetation carbon sinks in vulnerable ecosystems.