Forest carbon sequestration potential in China under the background of carbon emission peak and carbon neutralization

Zhang Ying; Li Xiaoge; Wen Yali

doi:10.12171/j.1000-1522.20210143

Journal of Beijing Forestry University > 2022 > 44(1): 38-47. > DOI: 10.12171/j.1000-1522.20210143

Zhang Ying, Li Xiaoge, Wen Yali. Forest carbon sequestration potential in China under the background of carbon emission peak and carbon neutralization[J]. Journal of Beijing Forestry University, 2022, 44(1): 38-47. DOI: 10.12171/j.1000-1522.20210143

Citation:

PDF (660 KB)

Forest carbon sequestration potential in China under the background of carbon emission peak and carbon neutralization

School of Economics & Management, Beijing Forestry University, Beijing 100083, China

More Information

Received Date: April 18, 2021
Revised Date: May 14, 2021
Available Online: June 15, 2021
Published Date: January 24, 2022

Graphical Abstract

Abstract

Abstract

Objective Carbon reserves and value of forest resources in China should be calculated to understand the status of forest resources and formulate a reasonable forestry development plan. Through the prediction of forest carbon stocks and carbon sequestration potential, it can improve the level of forest management and provide reference value for China to achieve the goal of carbon emission peak and carbon neutralization.
Method Based on the data of 9 forest inventories from 1973 to 2018 in China, the total carbon stocks of forest resources in China were calculated using forest volume method, and the carbon stocks and values of forest resources were calculated according to different forest types. This paper uses GM (1,1) grey model and power function model to predict the development potential of forest carbon sequestration in China, and analyzes the changing rate of carbon sequestration under different management levels by constructing the regression model of forest growth per unit area and carbon storage.
Result (1) Over the past 40 years, China’s average unit area volume of forest resources is 73.56 m³/ha, forest carbon stocks increased from 5.196 billion t in 1976 to 8.790 billion t in 2018, with an average annual increase of 0.085 57 billion t/year. The total carbon stocks of forest resources (including forest, woodland and understory vegetation) increased from 12.506 billion t to 21.439 billion t; among them, carbon stocks of plantation increased significantly, with an average annual increase of 5.05%. (2) The values of forest carbon stocks in China increased from 148.209 billion CNY in 1976 to 882.385 billion CNY in 2018, with an average annual increase of 17.480 billion CNY and a compound annual growth rate of 4.34%. Among them, the values of plantation carbon stocks increased by 8.24%. (3) The GM (1,1) grey model predicted that the forest carbon stocks will reach 10.013 billion t in 2030, the average annual increase of carbon sequestration will be 159 million t/year from 2018 to 2030, and the forest volume will reach 21.080 billion m³ in 2030; the forest carbon stocks in China will reach 18.032 billion t in 2060, and the average annual increase of carbon sequestration will be 236 million t/year from 2018 to 2060. The power function model predicted that China’s forest carbon stocks will reach 10.8 billion t in 2030, the average annual carbon sequestration will be 225 million t/year from 2018 to 2030, and the forest volume was expected to reach 22.738 billion m³ in 2030; China’s forest carbon stocks will reach 21.227 billion t in 2060, and the annual increase of carbon sequestration will be 312 million t/year from 2018 to 2060. (4) Based on the average benchmark of forest carbon stocks in recent 15 years, if the forest management level increase by 5%, the forest carbon stocks will increase by 4.30% − 6.86%; if increase by 10%, the forest carbon stocks will increase by 9.89% − 12.47%; if increase by 15%, the forest carbon stocks will increase by 15.48% − 18.09%; and if the forest management level increase by 20%, the forest carbon stocks will increase by 20.96% − 21.07%.
Conclusion Without considering the influence of external factors such as economy and policy, based on the changes of forest biomass and volume, forest carbon stocks and values in China are increased. According to this development trend, China can achieve the expected development goal of carbon emission peak and carbon neutralization for forestry in 2030 and 2060. If the current forest management level is further improved, the changing rate of forest carbon stocks will gradually increase, and the carbon sequestration potential will be huge.

FullText(HTML)

References (27)

References

[1]	Richards K R, Stokes C. A review of forest carbon sequestration cost studies: a dozen years of research[J]. Climatic Change, 2004, 63(1−2): 1−48.
[2]	姜霞, 黄祖辉. 经济新常态下中国林业碳汇潜力分析[J]. 中国农村经济, 2016(11):57−67. Jiang X, Huang Z H. Analysis of China’s forestry carbon sink potential under the economic new normal[J]. Chinese Rural Economy, 2016(11): 57−67.
[3]	国家林业和草原局. 中国森林资源清查报告(2014—2018)[M]. 北京: 中国林业出版社, 2019. National Forestry and Grassland Administration. Forest resources inventory report of China (2014−2018)[M]. Beijing: China Forestry Publishing House, 2019.
[4]	刘国华, 傅伯杰, 方精云. 中国森林碳动态及其对全球碳平衡的贡献[J]. 生态学报, 2000, 20(5):733−740. doi: 10.3321/j.issn:1000-0933.2000.05.004 Liu G H, Fu B J, Fang J Y. Carbon dynamics of Chinese forests and its contribution to global carbon balance[J]. Acta Ecologica Sinica, 2000, 20(5): 733−740. doi: 10.3321/j.issn:1000-0933.2000.05.004
[5]	张颖, 周雪, 覃庆锋, 等. 中国森林碳汇价值核算研究[J]. 北京林业大学学报, 2013, 35(6):124−131. Zhang Y, Zhou X, Qin Q F, et al. Value accounting of forest carbon sinks in China[J]. Journal of Beijing Forestry University, 2013, 35(6): 124−131.
[6]	马学威, 熊康宁, 张俞, 等. 森林生态系统碳储量研究进展与展望[J]. 西北林学院学报, 2019, 34(5):62−72. doi: 10.3969/j.issn.1001-7461.2019.05.10 Ma X W, Xiong K N, Zhang Y, et al. Research progresses and prospects of carbon storage in forest ecosystems[J]. Journal of Northwest Forestry University, 2019, 34(5): 62−72. doi: 10.3969/j.issn.1001-7461.2019.05.10
[7]	Paul S. Can intensive management increase carbon storage in forests[J]. Environment Management, 1991, 15(4): 475−481.
[8]	Golub A, Hertel T, Lee H L, et al. The opportunity cost of land use and the global potential for greenhouse gas mitigation in agriculture and forestry[J]. Resource and Energy Economics, 2009, 31(4): 299−319. doi: 10.1016/j.reseneeco.2009.04.007
[9]	Nepal P, Ince P J, Skog K E, et al. Projection of US forest sector carbon sequestration under US and global timber market and wood energy consumption scenarios, 2010−2060[J]. Biomass and Bioenergy, 2012, 45(45): 251−264.
[10]	续珊珊. 森林碳储量估算方法综述[J]. 林业调查规划, 2014, 39(6):28−33. doi: 10.3969/j.issn.1671-3168.2014.06.007 Xu S S. A review of forest carbon storage estimation methods[J]. Forest Survey and Planning, 2014, 39(6): 28−33. doi: 10.3969/j.issn.1671-3168.2014.06.007
[11]	郗婷婷, 李顺龙. 黑龙江省森林碳汇潜力分析[J]. 林业经济问题, 2006(6):519−522, 526. doi: 10.3969/j.issn.1005-9709.2006.06.008 Xi T T, Li S L. Analysis of forest carbon mitigation potential in Heilongjiang Province[J]. Issues in Forestry Economy, 2006(6): 519−522, 526. doi: 10.3969/j.issn.1005-9709.2006.06.008
[12]	方精云, 郭兆迪, 朴世龙, 等. 1981—2000年中国陆地植被碳汇的估算[J]. 中国科学(D辑: 地球科学), 2007, 37(6):804−812. Fang J Y, Guo Z D, Piao S L, et al. Estimation of terrestrial vegetation carbon sink in China from 1981 to 2000[J]. Science in China (Series D: Earth Sciences), 2007, 37(6): 804−812.
[13]	方精云, 陈安平, 赵淑清, 等. 中国森林生物量的估算: 对Fang等Science一文(Science, 2001, 291: 2320−2322)的若干说明[J]. 植物生态学报, 2002, 26(2):243−249. doi: 10.3321/j.issn:1005-264X.2002.02.018 Fang J Y, Chen A P, Zhao S Q, et al. Estimation of forest biomass in China: notes to Fang et al. Science (Science, 2001, 291: 2320−2322)[J]. Chinese Journal of Plant Ecology, 2002, 26(2): 243−249. doi: 10.3321/j.issn:1005-264X.2002.02.018
[14]	方精云, 陈安平. 中国森林植被碳库的动态变化及其意义[J]. 植物学报, 2001, 43(9):967−973. Fang J Y, Chen A P. Dynamic forest biomass carbon pools in China and their significance[J]. Acta Botanica Sinica, 2001, 43(9): 967−973.
[15]	张颖, 吴丽莉, 苏帆, 等. 我国森林碳汇核算的计量模型研究[J]. 北京林业大学学报, 2010, 32(2):194−200. Zhang Y, Wu L L, Su F, et al. An accounting model for forest carbon sinks in China[J]. Journal of Beijing Forestry University, 2010, 32(2): 194−200.
[16]	李奇, 朱建华, 冯源, 等. 中国森林乔木林碳储量及其固碳潜力预测[J]. 气候变化研究进展, 2018, 14(3):287−294. Li Q, Zhu J H, Feng Y, et al. Carbon storage and carbon sequestration potential of the forest in China[J]. Climate Change Research, 2018, 14(3): 287−294.
[17]	张煜星, 王雪军. 全国森林蓄积生物量模型建立和碳变化研究[J]. 中国科学: 生命科学, 2021, 51(2):199−214. Zhang Y X, Wang X J. Study on forest volume-to-biomass modeling and carbon change in forest dynamics in China[J]. Scientia Sinica Life Sciences, 2021, 51(2): 199−214.
[18]	李顺龙, 杜咏梅, 蒋敏元. 我国森林碳汇问题初探[J]. 林业财务与会计, 2004(7):5−6. Li S L, Du Y M, Jiang M Y. A preliminary study on forest carbon sink in China[J]. Forestry Finance and Accounting, 2004(7): 5−6.
[19]	新华网, 碳交易网. 习近平: 中国努力争取2060年前实现碳中和[EB/OL]. [2021−04−08]. http://www.tanpaifang.com/tanzhonghe/2020/0923/74144.html. Xinhua Net, Carbon Trading Net. Xi Jinping: China is striving to be carbon neutral by 2060 [EB/OL]. [2021−04−08]. http://www.tanpaifang.com/tanzhonghe/2020/0923/74144.html.
[20]	张颖, 潘静. 森林碳汇经济核算及资产负债表编制研究[J]. 统计研究, 2016, 33(11):71−76. Zhang Y, Pan J. The research on economic accounting of forest carbon sinks and its balance sheet establishment[J]. Statistical Research, 2016, 33(11): 71−76.
[21]	孙毅兵. 森林生态系统土壤碳影响因素研究[J]. 林业勘查设计, 2021, 50(3):73−75. Sun Y B. Research on influencing factors of soil carbon in forest ecosystem[J]. Forestry Work Research, 2021, 50(3): 73−75.
[22]	陈叙图, 李怒云, 高岚, 等. 美国林业碳汇市场现状及发展趋势[J]. 林业经济, 2009(7):76−80. Chen X T, Li N Y, Gao L, et al. The development background, development status and tide of the American forest carbon market[J]. Forestry Economics, 2009(7): 76−80.
[23]	中国外汇交易中心. 人民币汇率年平均汇率[EB/OL]. [2021−04−06]. http://www.chinamoney.com.cn/chinese/bkccpr/?tab=2. China Foreign Exchange Trade Center. The annual average exchange rate of RMB exchange rate [EB/OL]. [2021−04−06]. http://www.chinamoney.com.cn/chinese/bkccpr/?tab=2.
[24]	曾伟生, 孙乡楠, 王六如, 等. 东北林区10种主要森林类型的蓄积量、生物量和碳储量模型研建[J]. 北京林业大学学报, 2021, 43(3):1−8. doi: 10.12171/j.1000-1522.20200058 Zeng W S, Sun X N, Wang L R, et al. Developing stand volume, biomass and carbon stock models for ten major forest types in forest region of northeastern China[J]. Journal of Beijing Forestry University, 2021, 43(3): 1−8. doi: 10.12171/j.1000-1522.20200058
[25]	徐宁,丁松, 公彦德. 灰色GM(1,1)预测模型及拓展研究进展[J]. 数学的实践与认识, 2021, 51(13):52−59. Xu N, Ding S, Gong Y D. Advances in grey GM(1,1) forecasting model and its extension[J]. Mathematics in Practice and Theory, 2021, 51(13): 52−59.
[26]	陈景和, 王家福, 赵廷翠, 等. 我国与世界森林资源评估分析[J]. 山东林业科技, 2015, 45(3):94−96. doi: 10.3969/j.issn.1002-2724.2015.03.031 Chen J H, Wang J F, Zhao T C, et al. Analysis of forest resources assessment between China and the world[J]. Shandong Forestry Science and Technology, 2015, 45(3): 94−96. doi: 10.3969/j.issn.1002-2724.2015.03.031
[27]	陕西森工. 我国森林植被总碳储量已达92亿吨! 森林碳汇作用这么强大?[EB/OL].[2021−01−15]. https://m.sohu.com/a/444763670_187549/. Shaanxi Forest Industry. China’s total carbon storage of forest vegetation has reached 9.2 billion tons! Are forest sinks so powerful?[EB/OL]. [2021−01−15]. https://m.sohu.com/a/444763670_187549/.

[1]	Che Hailun, Li Hualin, Zhang Fan, Wu Fengyue, Xie Chenxin, Liu Ye. Using random forest model to analyze driving factors of extreme rainstorm peak discharge: taking the “23·7” rainstorm in Beijing as an example[J]. Journal of Beijing Forestry University, 2025, 47(1): 51-62. DOI: 10.12171/j.1000-1522.20240266
[2]	Gao Yushan, Peng Daoli, Zhang Nan, Yang Penghui, Yang Cancan, Chen Mingjie, Chen Jian. Remote sensing classification of stand type coupled with time series features[J]. Journal of Beijing Forestry University, 2024, 46(1): 68-81. DOI: 10.12171/j.1000-1522.20230093
[3]	Zhu Zhaoting, Sun Yujun, Liang Ruiting, Ma Jiaxin, Li Jiayi. Estimating DBH of Cunninghamia lanceolata based on crown and competition factors[J]. Journal of Beijing Forestry University, 2023, 45(9): 42-51. DOI: 10.12171/j.1000-1522.20230011
[4]	Wang Chunling, Fan Yilin, Pang Yong, Jia Wen. Extraction of deciduous coniferous forest based on Google earth engine (GEE) and Sentinel-2 image[J]. Journal of Beijing Forestry University, 2023, 45(8): 1-15. DOI: 10.12171/j.1000-1522.20220422
[5]	Zhang Mengku, Jiang Lichun. Prediction of bark thickness for Larix gmelinii based on machine learning[J]. Journal of Beijing Forestry University, 2022, 44(6): 54-62. DOI: 10.12171/j.1000-1522.20210097
[6]	Ou Qiangxin, Lei Xiangdong, Shen Chenchen, Song Guotao. Individual tree DBH growth prediction of larch-spruce-fir mixed forests based on random forest algorithm[J]. Journal of Beijing Forestry University, 2019, 41(9): 9-19. DOI: 10.13332/j.1000-1522.20180266
[7]	GAO Ruo-nan, SU Xi-you, XIE Yang-sheng, LEI Xiang-dong, LU Yuan-chang. Prediction of adaptability of Cunninghamia lanceolata based on random forest[J]. Journal of Beijing Forestry University, 2017, 39(12): 36-43. DOI: 10.13332/j.1000-1522.20170260
[8]	WEN Yi-bo, CHANG Ying, FAN Wen-yi. Algorithm for leaf area index inversion in the Great Xing'an Mountains using MISR data and spatial scaling for the validation[J]. Journal of Beijing Forestry University, 2016, 38(5): 1-10. DOI: 10.13332/j.1000-1522.20150204
[9]	HUANG Jian, Falk Huettmann, GUO Yu-min. Distribution modeling of breeding hooded crane in Heilongjiang basin[J]. Journal of Beijing Forestry University, 2015, 37(8): 40-47. DOI: 10.13332/j.1000-1522.20140178
[10]	WANG Xiao-guo, , HU Bo, AO Mei-rong, ANG Yan-qiang, HENG Xun-hua. Soil CO2 efflux and simulation of Forest-DNDC model in the mixed plantation of alder and cypress in hilly areas of the central Sichuan Basin[J]. Journal of Beijing Forestry University, 2008, 30(2): 27-32.

Cited By

Get Citation

PDF

XML

Article views (5043) PDF downloads (1243)

Turn off MathJax

Article Contents

Abstract

References

Forest carbon sequestration potential in China under the background of carbon emission peak and carbon neutralization

Abstract

References

Related Articles

Catalog

Forest carbon sequestration potential in China under the background of carbon emission peak and carbon neutralization

Abstract

References

Related Articles

Catalog

Export File

Citation

Format

Content