A study of carbon sequestration options based on numerical prediction models

JinRong Liu; ZhenZhen Zhao; JinRong Liu; ShuangKai Wu

doi:10.1117/12.2639497

17 May 2022 A study of carbon sequestration options based on numerical prediction models

JinRong Liu, ZhenZhen Zhao, JinRong Liu, ShuangKai Wu

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122590W (2022) https://doi.org/10.1117/12.2639497
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

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.

Citation Download Citation

JinRong Liu, ZhenZhen Zhao, JinRong Liu, and ShuangKai Wu "A study of carbon sequestration options based on numerical prediction models", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122590W (17 May 2022); https://doi.org/10.1117/12.2639497

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KEYWORDS

Carbon sequestration

Carbon

Carbon dioxide

Atmospheric modeling

Data modeling

Pollution control

Systems modeling

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