Analysis of Forestry Carbon Sequestration based on Grey Prediction

  • Bobo Liu North China University of Science and Technology
  • Xiangyu Lan North China University of Science and Technology
  • Haochen Huang North China University of Science and Technology
Ariticle ID: 3209
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DOI: https://doi.org/10.18282/l-e.v10i9.3209
Keywords: Carbon sequestrationï¼›Gray predictionï¼›Forest management

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.

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.

Published
2022-07-24
Issue
Vol. 10 No. 9 (2021)
Section
Articles

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