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Analysis of Forestry Carbon Sequestration based on Grey Prediction

Bobo Liu, Xiangyu Lan, Haochen Huang

Abstract


Forest is an important factor to improve climate. Trees absorb carbon dioxide in the air and seal it in the form of carbon.
Compared with the benefits of carbon sequestration without deforestation, the carbon sequestration method combining forest
products and regenerated forests can store more carbon dioxide over time, and has a sustainable prospect of carbon sequestration.
Therefore, the optimized forest management strategy should find a balance between the value of forest products generated
by deforestation and the value of carbon dioxide sequestration that allows the forest to continue to grow, so as to achieve the
best benefits. In this paper, the improved grey prediction model is used to predict the carbon sequestration stock, and the best
calculation method is determined according to the principle of minimum sum of squares of errors, so as to improve the accuracy
of prediction. In order to determine the best carbon sequestration strategy, three indicators, namely phytolith carbon, average forest
carbon sequestration and forest litter, are selected to calculate the average carbon sequestration rate of different types of forests
according to the three indicators, and formulate the corresponding optimal forest management plan according to the average
carbon sequestration rate.

Keywords


Carbon sequestration;Gray prediction;Forest management

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References


[1] Ying Yuqi, xiangtingting, liyongfu, wujiasen, jiangpeikun Estimation of carbon sequestration potential of important subtropical trees in China [j] Journal of natural resources, 2015,30 (01): 133-140.

[2] Renjiqin, zhenghuishe Comparative study on carbon storage status of urban trees in China and the United States [j] Forest engineering, 2016,32 (04): 28-30 DOI:10.16270/j.cnki. slgc. 2016.04.006.

[3] Yangjunzhe, wangzhenrong, Xufeng, wangyongsheng Research on remote directional continuous monitoring technology for carbon dioxide storage [j] Coal engineering, 2020,52 (09): 37-41.




DOI: https://doi.org/10.18282/l-e.v10i9.3209

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