Accurate prediction and assessment of carbon storage are crucial in the context of global climate change. However, existing research has largely focused on large-scale regions, while studies on small-scale ecologically fragile alpine regions remain insufficient. This study focuses on Zoige County, integrating the PLUS model, InVEST model, and Random Forest model to form a composite analysis workflow. Through this workflow, we simulated the distribution of land use types in 2030 and quantified carbon storage from 1990 to 2030, subsequently analyzing their spatial distribution and driving factors. The key findings include: (1) Under the natural development scenario (NDS) and the ecological protection scenario (EPS) for 2030, the primary land use transition involved the conversion of grassland to forest and wetland. Conversely, wetland was converted into cropland under the economic development scenario (EDS). (2) Under the NDS, EDS, and EPS, carbon storage would be 8.396 × 107 t, 8.252 × 107 t, and 8.432 × 107 t, respectively. The EPS yielded the largest increase in carbon storage. (3) In all three scenarios, carbon storage showed a clustered distribution. Compared with 2020, the carbon storage hot spot areas under both NDS and EPS showed an expansion trend, whereas the cold spot areas also expanded in three scenarios. (4) The key drivers of carbon storage include slope, elevation, soil type, and mean annual temperature. This study concludes that the EPS represents the most favorable development pathway for carbon storage accumulation. This finding can provide a basis for future carbon storage dynamics and land use planning for Zoige County.
Lan et al. (Fri,) studied this question.