Simulation and Analysis of Land Use and Multi-scenario Carbon Sink in the Upper Yangtze River
The purpose of this study is to investigate the carbon sink effects and enhancement potential of land use pattern changes in the upper Yangtze River to shape the territorial development and conservation framework under the dual carbon vision.The research methods are as follows.A detailed carbon density database with 10 km×10 km grid units for the upper Yangtze River was constructed,coupling the InVEST and PLUS models to simulate carbon sink changes based on LUCC.The research results show that 1)from 1990 to 2020,the carbon sink in the upper Yangtze River showed a fluctuating upward trend,with significant differences in carbon sink processes at the county level,indicating the need for continuous reinforcement of ecological conservation efforts.2)Forests and grasslands are the largest carbon sinks,annually sequestering 8.42 Gt and 6.97 Gt of carbon respectively.Transfers in and out of these land types can enhance or reduce regional carbon sink.3)Low-carbon development strategies mitigate uncontrolled urban expansion and alleviate fragmentation of ecological land.Under a scenario prioritizing land use carbon sink capacity,regional carbon sink is projected to increase by 1.27 Gt,with substantial control over the construction occupying ecological land between 2020 and 2035,and a notable expansion and concentration of forest and grassland.In conclusions,the upper Yangtze River region needs to improve ecosystem dynamic monitoring technologies and land use management policies based on carbon sink functions,and to strengthen the ongoing assessments and reinforcement of ecological restoration based on carbon sink outcomes.This study provides monitoring,assessment methods,and dynamic control ideas for optimizing the territorial space of the upper Yangtze region in China with a focus on natural carbon sink enhancement.
spatial optimization of territorial spaceInVEST modelPLUS modellow-carbon governancenatural carbon sinkthe upper Yangtze River
NETL
NSTL
万方数据
