Carbon Sequestration Model Based on Decision Tree Regression and Logic Tree Model

Abstract

We established a carbon sequestration model including a decision tree regression model and a logic tree model. First, we used data from various regions of the world, selected ten indicators using climatic factors, geographical factors, and forest attribute factors, established a forest net productivity pre- diction model using the decision tree regression method, and then proposed the "logic tree". Then we proposed the concept of "logical tree", in which the whole forest is considered as one big tree, and the use time of forest products is calculated as the whole cycle of forest management by weighting method, and finally, we concluded that we select suitable tree species according to climate type, control the indicators of climate, geography, and forest attributes, and then adjust the types of forest products and processing methods appropriately to achieve the net productivity of forest and carbon storage of forest products. To maximize the benefits of forest net productivity and forest product carbon sequestration.