Integrating System Dynamics, Land Change Models, and Machine Learning to Simulate and Predict Ecosystem Carbon Sequestration Under RCP-SSP Scenarios: Fusing Land and Climate Changes

Understanding the impacts of land use and vegetation carbon sequestration under varying climate scenarios is essential for optimizing regional ecosystem services and shaping sustainable socioeconomic policies. This study presents a novel research framework that integrates a system dynamics (SD) model, a patch generation land use simulation (PLUS) model, and the random forest algorithm, coupled with SSP-RCP scenarios from Coupled Model Intercomparison Project Phase 6 (CMIP6), to simulate future vegetation net primary production (NPP). A case study in Hubei Province, central China, demonstrates the framework’s effectiveness in elucidating the interactions between land use change, climate change, topography, and vegetation conditions on carbon sequestration. The integration of SSP-RCP scenarios provides a clear understanding of how different climate conditions influence regional carbon sinks, offering valuable scientific insights for regional carbon neutrality and sustainable development policymaking. The simulation results for Hubei Province across the years 2030, 2040, 2050, and 2060, under three pathways—SSP1-1.9, SSP2-4.5, and SSP5-8.5—reveal that SSP1-1.9 leads to the highest carbon sequestration, while SSP5-8.5 results in the lowest. The annual total carbon sink ranges from 115.99 TgC to 117.59 TgC, with trends varying across scenarios, underscoring the significant impact of policy choices on local ecosystems. The findings suggest that under low-carbon emission scenarios, there is greater potential for NPP growth, making carbon neutrality goals more achievable.