Cut down or not: a forest management decision system

Abstract

The increasingly serious greenhouse effect has brought a huge threat to the safety of human life and property. Increasing carbon sequestration in forests is an effective means of protecting the Earth’s ecosystem and reducing the number of greenhouse gases in the atmosphere. We have developed the Forest Management Decision System to propose an effective forest management plan by taking the Daxing’anling, a pristine forest located in China, as the object of our study. This paper establishes the Forest Management Decision System. The model considers the influence of four factors of carbon sequestration, biodiversity, secondary and tertiary industry income on decision-making results, and obtains the weights of each factor through the Analytic Hierarchy Process. Firstly, based on Logistic Model, this paper proposes an optimized carbon sequestration model, which is affected by forest area, tree species, tree age and wood products. Next, this paper optimizes the Carbon Sequestration Model, taking into account the negative effects of the development of the secondary and tertiary industries on carbon sequestration. Thirdly, by constructing a symbiotic dependency model, a quantitative function of biodiversity can be obtained. Finally, this paper fits the functions of the secondary and tertiary industry income respectively based on real data. This paper proposes four schemes for forest management and calculates the value of each impact factor according to the Forest Management Decision System. Based on the TOPSIS Model, a unified comprehensive evaluation index S is established, and the scheme with the highest comprehensive score is selected. It is calculated that with the development of this forest management model, although the carbon sequestration capacity of forests has dropped by 0.9% in the short term, the GDP growth rate has reached 19%, and the comprehensive benefit has increased by 6 times. After calculation, it is found that this scheme has certain limitations, and the effect of long-term use cannot be maintained optimally. To calculate the transition point of the forest management model, we used Multivariate Linear Regression to find that in 2102, we should adjust the forest management plan to reduce the deforestation rate to 0.004%, and increase the tree growth rate to 0.0048% through artificial planting. This paper simulates the forest management plan within 100 years and provides a relatively complete management model. Under this management model, the carbon sequestration after 100 years increases by 0.93% compared with the situation without any changes, and at the same time, the economy increases by 7.28%.