Enhancing carbon storage in terrestrial ecosystems has become a key strategy for mitigating climate change. The Tibetan Plateau holds a pivotal position in achieving carbon neutrality, with the structural pattern of its land use types directly impacting the region’s ecosystem carbon storage capacity. However, there is still a lack of understanding of the spatial distribution of carbon storage in their ecosystems. This study targeted the Tibetan Plateau, utilizing land use data from 2000 to 2020, and employed the Patch-generating Land Use Simulation (PLUS) model to project land use patterns for 2030. By integrating future climate change projections, this study forecasted land use under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was employed to quantify carbon storage from 2000 to 2030, while the GeoDetector model was used to explore the driving influences of factors such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Leaf Area Index (LAI), Net Primary Productivity (NPP), population density, and road network density on carbon storage. The results revealed that: (1) Grassland predominated the land use types on the Tibetan Plateau, with most types having a stability of over 70%, whereas significant changes were observed in the western Tibet Autonomous Region and southern Xinjiang Uygur Autonomous Region. (2) Carbon storage on the Tibetan Plateau generally followed a tendency towards an initial decrease followed by an increase, with an average annual reduction of 50,107,371.79 Mg. The SSP1-2.6 scenario demonstrated the most substantial increase in carbon storage, being 18 times the natural trend, while the SSP5-8.5 scenario indicated the largest decrease. (3) Over the two decades, NDVI emerged as the most influential driver of carbon storage on the Tibetan Plateau, which was maintained at around 0.4, with the interaction between NDVI and NDWI exerting the strongest driving force, which was maintained at around 0.45. The conversion to forestland and grassland was the primary factor accounting for the change in carbon storage. Based on these results, despite the absence of empirical carbon density data, the SSP1-2.6 scenario could be regarded as a reference pathway for carbon storage changes on the Tibetan Plateau. Solely focusing on enhancing carbon storage by converting low-carbon land uses to high-carbon land uses is misguided; sustainable development represents the optimal approach for augmenting carbon storage on the Tibetan Plateau.

Understanding the spatiotemporal dynamics and driving mechanisms of ecosystem services in response to land use change is critical for regional ecological security and sustainable development, especially under the combined pressure of intensive human activities and future climate change in tropical regions. Existing studies often lack an integrated framework for multi-scenario simulation, multi-dimensional ecosystem service quantification, and spatial driving factor identification. To support sustainable management, this study focused on Hainan Island and utilized land use data from 2000 to 2025. The Markov-Patch-generating Land Use Simulation (PLUS) model was employed to simulate land use patterns for 2050 under historical trend, SSP1-1.9, and SSP5-8.5 scenarios, incorporating future climate data. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to quantify habitat quality, carbon storage, water yield, and soil conservation. The Multi-weighted Entropy Ecosystem Service Index (MEESI) was established to evaluate ecosystem service performance. Furthermore, the GeoDetector model was applied to assess the explanatory power of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), and Bare Soil Index (BSI) on ecosystem service dynamics. The results indicated that: (1) during 2000–2025, land use change in Hainan Island was dominated by forest-to-cropland conversion and impervious surface expansion, while future suggestions included stronger ecological protection under SSP1-1.9 and greater ecological pressure under SSP5-8.5; (2) during 2000–2025, habitat quality and carbon storage generally declined, whereas water yield and soil conservation increased, and SSP1-1.9 maintained higher overall ecosystem service performance (habitat quality = 0.6207, carbon storage = 327.89 × 10⁶ t, and MEESI = 0.3509) than the historical trend and SSP5-8.5 scenarios in 2050; and (3) NDVI exhibited the strongest explanatory power for ecosystem service variation, whereas NDBI showed the weakest. These findings suggest that ecosystem management should consider the trade-offs and synergies among multiple ecosystem services rather than focusing on a single service. This study provides a systematic and spatially explicit framework for ecosystem service assessment under future scenarios. The study can also support scientific land use optimization, ecological conservation, and sustainable management decisions in tropical island regions.