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Academic Journal of Environment & Earth Science, 2022, 4(3); doi: 10.25236/AJEE.2022.040306.

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


Jiaying Yao

Corresponding Author:
Jiaying Yao

School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu, China


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.


Decision Tree Regression; Logic Tree; Carbon Sequestration

Cite This Paper

Jiaying Yao. Carbon Sequestration Model Based on Decision Tree Regression and Logic Tree Model. Academic Journal of Environment & Earth Science (2022) Vol. 4 Issue 3: 27-31. https://doi.org/10.25236/AJEE.2022.040306.


[1] Robert Jandl, Marcus Lindner, Lars Vesterdal, Bram Bauwens, Rainer Baritz, Frank Hagedorn, Dale W.Johnson, Kari Minkkinen, and Kenneth A.Byrne. How strongly can forest management influence soil carbon sequestration? Geoderma, 137: 253–268, 2007.

[2] Valentin Bellassenz and Sebastiaan Luyssaert. Carbon sequestration: Managing forests in uncertain times. Nature, 506: 153–155, 2014.

[3] A.Noormets, D.Epron, J.C.Domec, S.G.McNulty, T.Fox, G.Sun, and J.S.King. Effects of forest management on productivity and carbon sequestration: A review and hypothesis. Forest Ecology and Management, 355: 124–140, 2015.

[4] Engin Pekel. Estimation of soil moisture using decision tree regression. Theoretical and Applied Climatology volume, 139: 1111–1119, 2020.

[5] Yong Soo Kim. Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size. Expert Systems with Applications, 34: 1227-1234, 2008.

[6] David W. Pearce. The economic value of forest ecosystems. Ecosystem Health, 7: 284–296, 2013.

[7] Ing-Marie Gren and Mattias Carlsson. The economic value of carbon sequestration in forests under multiple sources of uncertainty. Journal of Forest Economics, 19: 174–189, 2013.

[8] C.Simon and M.Etienne. A companion modeling approach applied to forest management planning. Environmental Modelling Software, 25(11): 1371–1384, 2010.

[9] J.D. Fontes, L.and Bontemps, H. Bugmann, M. van Oijen, C. Gracia,K. Kramer, M. Lindner, T. Rötzer, and J.P. Skovsgaard. Models for supporting forest management in a changing environment. University & Research, 19:8– 29, 2010.