Abstract
Environmental issues are an important issue facing the world in the 21st century. While China’s economy is developing rapidly, the problem of environmental pollution is becoming more and more serious, especially the problem of carbon emissions. Faced with the severe natural ecological environment, China has proposed a dual-carbon goal, that is, China will achieve carbon peaks by 2030 and carbon neutrality by 2060. In order to improve the ecological environment and complete the dual carbon goals on time, in addition to adjusting the industrial structure and improving the technical level to reduce carbon emissions, forestry carbon sink transactions should also be actively used. Forestry carbon sequestration is one of the few carbon sequestration measures that can be implemented at this stage, but the sustainable development of forestry carbon sequestration requires support from land resources, and reasonable land use planning is the premise to ensure forestry carbon sequestration. This research will use the FLUS model based on the artificial neural network algorithm (ANN) and cellular automata algorithm (CA) to analyze the future spatial changes of land use under forestry carbon sink trading and formulate reasonable land planning for sustainable forestry carbon sink trading. FLUS model is a land use simulation algorithm, which is specially used to study the development prediction of land use under different scenarios. The study found that if the forestry carbon sink transaction was implemented, the forest land area in Shenyang could be increased by 303 km2 and 454,500 tons of CO2 could be absorbed annually. The forest land would take the lead in choosing the northern and eastern hilly areas for expansion.