Abstract
Predicting future carbon stock dynamics under different development scenarios is crucial for balancing regional economic growth with ecological protection and achieving sustainable development. Taking Sichuan Province as a case study, this study established a framework combining PLUS-InVEST-OMGD (Optimal Multivariate-Stratification Geographical Detector) model to assess changes in carbon stock and their driving factors from 2000 to 2020, and to predict carbon stock for 2040 and 2060 under three scenarios: the Natural Trend Scenario (NTS), Economic Priority Scenario (EPS), and Ecological Conservation Scenario (ECS). The results show the following: (1) Between 2000 and 2020, the carbon stock in Sichuan Province decreased by 32.61 × 106 t, with the areas of decline primarily concentrated in the central and southern parts of the Sichuan Basin. (2) NDVI (q = 0.276) emerged as a critical factor influencing spatial variation in carbon stocks and showed the most significant interactive effect with elevation (q = 0.399). (3) Compared with 2020 levels, carbon stocks under the NTS and EPS were projected to continue declining between 2040 and 2060. In contrast, carbon stocks under the ECS were expected to exhibit an increasing trend, with an additional increase of 44.31 × 106 t by 2060. This study offers critical insights into optimizing China’s spatial land-use patterns to achieve its “dual carbon” strategy objectives.
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- Ecology
- Environmental sciences
- Environmental social sciences
Funding
This work was funded by Natural Science Foundation of Henan Province (252300421461), Science and Technology Innovation Leading Talent Support Program of Henan Province (254000510057).
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Zhong, L., Ma, Y., Meng, F. et al. Assessing land use and carbon stock dynamics coupled with the PLUS-InVEST-OMGD model: a multi-scenario simulation in Sichuan Province, China.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-63470-7
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DOI: https://doi.org/10.1038/s41598-026-63470-7
Keywords
- Land use change
- Carbon stock
- Multi-scenario prediction
- Optimal multivariate-stratification geographical detector model
Source: Ecology - nature.com
