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A Study of Carbon Sequestration Options based on Numerical Prediction Models

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  • NETL
  • Spie-Int Soc Optical Engineering
Nowadays， the study of carbon sequestration in forests is a good way of weighing up the relationship between man and nature， so it is necessary to optimise carbon sequestration models and improve forest management plans。 We have therefore evaluated carbon sequestration indicators in forests through a variety of factors in order to develop a grey correlation analysis model to analyse the effects of population， temperature and gross forestry product on the amount of carbon sequestered。 We then used the variables identified in the analysis to build grey models for different regions and tree species in order to find the predicted amount of CO2。 In addition to this， we also used principal component analysis to find that in addition to carbon sequestration， average annual temperature， gross national product and annual precipitation are the factors that have a greater impact on forest efficiency。 This analysis allows a good treatment of forest carbon sequestration， and then a linear regression model is used to model the amount of money invested in forests in the gross national product。 The study can predict the value of carbon sequestration and also balance the relationship between nature and humans， and thus determine forest management plans measured in time， which is a hot topic of research in this field。

Grey predictive modellingprincipal component analysiscarbon sequestrationforest management

ZhenZhen Zhao、JinRong Liu、ShuaneKai Wu

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Faculty of Science, Jiamusi University, Jiamusi 154007, Heilongjiang, China

School of Mechanical Engineering, Jiamusi University, Jiamusi 154007, Heilongjiang, China

International Conference on Applied Mathematics, Modelling, and Intelligent Computing

Kunming(CN)

2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing

122590W.1-122590W.7

2022

10.1117/12.2639497