Optimization of Carbon Sequestration Decision-Making Based on Greedy Algorithm and Forest Valuation Decision-Making Models

Forest ecosystems have an important carbon sequestration function, and it has been gradually recognized that forest products also have a considerable carbon storage role, which indicates that blindly pursuing the preservation of forests as they are will not enable forests to maximize their carbon sequestration function. In addition, forests have certain economic and social attributes. The maximization of forest value can only be achieved by rational use of forests, which requires reasonable management decisions for forests. Firstly, a carbon sink calculation model is established, which combines the characteristics of the forest itself, and the results show that the annual growth rate of carbon dioxide sequestration in Schwarzwald, which adopts the ecological forestry management method, is higher than that of the other two management methods. Subsequently, a forest evaluation model, FEM, was established, and 15 indicators were screened from ecological, economic and social aspects, and the weights of the indicators were determined according to EWM and AHP. Five forests around the world were evaluated, and the evaluation results were basically consistent with the surveys conducted by the Food and Agriculture Organization of the United Nations (FAO) in various countries, which verified the accuracy of the model. In order to get a reasonable management plan, a decision model of FDA (Forest Decision Algorithm) was established on the basis of FEM.FDA is based on the greedy algorithm, which selects the one that can make the next state of the forest reach the maximum value in the FEM score in the predefined decision set. The FDA also introduces a climate factor to measure the natural growth rate of the forest under different climatic conditions. The FDA model was applied to Amazon and Schwarzwald and finally, in the sensitivity analysis, it was found that the robustness of the model could be improved.