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Spatiotemporal Analysis, Predictive Modeling, and Driving Mechanism Investigation of Carbon Storage Dynamics in Changde City Under the Framework of LUCC

Author

Listed:
  • Ziyi Luo

    (College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China)

  • Caihong Chen

    (College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China)

  • Jiyun She

    (College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China)

  • Yamin Wang

    (College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China)

  • Wenfu Tong

    (Engineering Research Center for Forestry Equipment of Hunan Province, Central South University of Forestry and Technology, Changsha 410004, China)

  • Zexin Guo

    (College of Computer and Mathematics, Central South University of Forestry and Technology, Changsha 410004, China)

Abstract

In the context of the worldwide attention on climate change, examining how land use relates to the carbon sink functions of regions is essential. This research innovatively utilizes the 2000–2020 land use data of Changde City, integrating the PLUS and InVEST models to analyze spatiotemporal changes and predict scenarios. It also combines the parameter geodetector and multiscale geographically weighted regression model to dissect driving factor distributions and mechanisms, capture interactions and multiscale impacts, uncover underlying laws, pioneer new paths for similar studies, and support regional ecological sustainability. The results show that from 2000–2020, forest and arable land areas declined while construction land expanded, leading to a yij1,172,200-ton carbon storage reduction in Changde City. Carbon storage decreased under natural development and arable land protection scenarios but increased in the ecological scenario. The main drivers of carbon storage in Changde City are the DEM, slope, and annual average temperature, with their interactions enhancing spatial heterogeneity. Human activities, especially in mountains and urbanizing regions, negatively impact carbon storage. This study aids in optimizing land resource allocation, improving land use efficiency, and promoting coordinated and sustainable development in Changde City’s ecological, economic, and social systems.

Suggested Citation

  • Ziyi Luo & Caihong Chen & Jiyun She & Yamin Wang & Wenfu Tong & Zexin Guo, 2025. "Spatiotemporal Analysis, Predictive Modeling, and Driving Mechanism Investigation of Carbon Storage Dynamics in Changde City Under the Framework of LUCC," Sustainability, MDPI, vol. 17(3), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1273-:d:1583905
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    References listed on IDEAS

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    1. Ruei-Yuan Wang & Huina Cai & Lingkang Chen & Taohui Li, 2023. "Spatiotemporal Evolution and Multi-Scenario Prediction of Carbon Storage in the GBA Based on PLUS–InVEST Models," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    2. Nangware Kajia Msofe & Lianxi Sheng & James Lyimo, 2019. "Land Use Change Trends and Their Driving Forces in the Kilombero Valley Floodplain, Southeastern Tanzania," Sustainability, MDPI, vol. 11(2), pages 1-25, January.
    3. Mengyao Li & Hongxia Luo & Zili Qin & Yuanxin Tong, 2023. "Spatial-Temporal Simulation of Carbon Storage Based on Land Use in Yangtze River Delta under SSP-RCP Scenarios," Land, MDPI, vol. 12(2), pages 1-18, February.
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