Understanding the Spatiotemporal Patterns and Drivers of Carbon Stock in Central-Southern China’s Hilly Regions Through Land Use Change and Scenario Simulation

Yali Zhang, Jia Tang, Xijun Hu (), Cunyou Chen, Ziwei Luo, Qian Li and Qizhen Li
Additional contact information
Yali Zhang: College of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China
Jia Tang: College of Landscape Architecture and Art, Jiangxi Agricultural University, Nanchang 330045, China
Xijun Hu: College of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China
Cunyou Chen: College of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China
Ziwei Luo: College of Landscape Architecture and Arts, Northwest Agriculture & Forestry University, Xianyang 712100, China
Qian Li: College of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China
Qizhen Li: College of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China

Sustainability, 2025, vol. 17, issue 12, 1-38

Abstract: Land use and land cover (LULC) changes play a crucial role in regional carbon dynamics and climate regulation. This study assesses the impact of LULC changes on carbon stocks in Hunan Province, China, from 2000 to 2035 using a MOP-PLUS–InVEST–OPGD integrated modeling framework. Results show that carbon stock declined by 45.96 million tons from 2000 to 2020 due to rapid urban expansion and conversion of forest and grassland to construction land. Scenario simulations reveal that by 2035, carbon stock will increase by 4.82% under the ecological protection scenario (EP) but decrease by 3.26% under the natural trend scenario (NT). Economic development scenario (ED) and sustainable development scenario (SD) produce intermediate outcomes. Spatially, high-carbon regions are concentrated in high-altitude forested areas, while urbanized lowlands exhibit the lowest carbon density. The optimal parameters-based geographical detector (OPGD) model identifies land use intensity, elevation, and net primary productivity as the dominant drivers of carbon stock variation, with significant interactions between natural and socioeconomic factors. These findings underscore the need for integrated land-use planning and ecological conservation policies that align with carbon neutrality goals. This study provides a replicable spatial framework and policy-oriented insights for managing carbon stocks in rapidly developing regions.

Keywords: land use transformation; carbon stock dynamics; InVEST model; future land use prediction; driving factors analysis; hilly region of China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/12/5578/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/12/5578/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:12:p:5578-:d:1681136

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().