Integrated Carbon Stock Simulation in Jiangsu Province Using InVEST and Random Forest Under Multi-Scenario Climate and Productivity Pathways

Ting Shi, Wei Yan and Weixiao Chen ()
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Ting Shi: College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
Wei Yan: Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Weixiao Chen: College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China

Sustainability, 2025, vol. 17, issue 17, 1-23

Abstract: Carbon stock plays a crucial role in regulating atmospheric carbon dioxide concentrations and represents a vital ecological function for mitigating climate change and supporting long-term environmental sustainability. Jiangsu Province, a typical region experiencing rapid urbanization and land-use transformation in eastern China, serves as a representative case for regional-scale carbon assessment. This study employs the InVEST model, integrated with multi-source remote sensing data, a random forest algorithm, and a control variable approach, to simulate the spatiotemporal dynamics of carbon stock in Jiangsu Province under a set of climate, productivity, and population scenarios. Three scenario groups were designed to isolate the individual effects of climate change, gross primary productivity, and population density from 2020 to 2060, enabling a clearer understanding of the dominant drivers. The results indicate that the coupled model estimates Jiangsu’s 2020 carbon stock at 1.52 × 10 9 t C, slightly below the 1.82 × 10 9 t C estimated by the standalone InVEST model, with the coupled results closer to previous estimates. Compared with InVEST alone, the integrated model significantly improves numerical accuracy and spatial resolution, allowing for finer-scale pattern recognition. By 2060, carbon stock is projected to decline by approximately 24.4% across all scenarios. Among the features, climate change exerts the most significant influence, with an elasticity coefficient range of −37.76–1.01, followed by productivity, while population density has minimal impact. These findings underscore the dominant role of climate drivers and highlight that model integration improves both predictive accuracy and spatial detail, offering a more robust basis for scenario-based assessment. The proposed approach provides valuable insights for supporting sustainable carbon management, real-time monitoring, and provincial-scale decarbonization planning.

Keywords: carbon stock modeling; InVEST–random forest integration; climate change; scenario-based analysis; Jiangsu Province (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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