IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v510y2025ics0304380025002790.html

Scenario-driven modeling of mountain ecosystems: land use-carbon dynamics simulation based on the coupled SD-FLUS-InVEST framework

Author

Listed:
  • Yaowei, Qin
  • Yu, Yan
  • Jiaqi, Dong
  • Zhenyu, Zhao
  • Shuangjiang, Li
  • Jiansheng, Cao
  • Jieying, Xiao

Abstract

Understanding land use and carbon dynamics was crucial for optimizing resource management and promoting carbon neutrality. With a focus on the sustainable development of mountain systems, this study presented a coupling framework of SD-FLUS-InVEST and used the Yanshan-Taihang mountainous area as a case study to explore land use transitions and the mechanisms by which the carbon cycle responded. This model coupled the entire policy-space-ecology chain, improved computational efficiency compared to traditional models, achieved high-precision spatial allocation (Kappa > 0.83), and supported multi-scenario simulations. Empirical studies showed that the region's carbon imbalance had continued to worsen from 2000 to 2035. Spatial differentiation expanded; high-value carbon sink areas clustered in the mountains, and carbon hotspots expanded along the North China Plain. The potential for carbon neutrality declined. Thus, core forest land had to be strictly protected, and periurban carbon-sinking agriculture had to be developed to increase the region's potential for carbon neutrality. The modeling framework could be generalized for the sustainable management of fragile ecosystems worldwide.

Suggested Citation

  • Yaowei, Qin & Yu, Yan & Jiaqi, Dong & Zhenyu, Zhao & Shuangjiang, Li & Jiansheng, Cao & Jieying, Xiao, 2025. "Scenario-driven modeling of mountain ecosystems: land use-carbon dynamics simulation based on the coupled SD-FLUS-InVEST framework," Ecological Modelling, Elsevier, vol. 510(C).
  • Handle: RePEc:eee:ecomod:v:510:y:2025:i:c:s0304380025002790
    DOI: 10.1016/j.ecolmodel.2025.111293
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380025002790
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2025.111293?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:510:y:2025:i:c:s0304380025002790. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.