Carbon Sink of Forest Land and Other Biomass in the Beijing-Tianjin-Hebei Region in 2020

doi:10.13466/j.cnki.lyzygl.2022.05.006

Abstract

Abstract:

Based on the data of carbon measurement and monitoring system of China's forestry addressing climate change,carbon storage and carbon sink of forest land and other biomass in the Beijing-Tianjin-Hebei Region in 2020 andthe contribution of the key ecological projects and the key strategic implementation area to the carbon sinks in the Beijing-Tianjin-Hebei Region were estimated by using the carbon measurement model and parameters established by the specific survey of forest carbon pools,also combining the previous national forest inventory data.The results showed that:1)In 2020,the carbon sink of forest land in the Beijing-Tianjin-Hebei Region was 23.918 Tg CO₂/a,and the carbon sink of other biomass such as scattered trees and four-side trees was 0.467Tg CO₂/a.The arbor forestland accounted for the largest proportion of the total carbon sink of forest land and other biomass.Among all 3 provinces/cities in the Beijing-Tianjin-Hebei Region,the carbon sink of forest land and other biomass of Hebei Province took the 1st place,while Tianjin was the last.The biomass contributed the most(92.13%)to the carbon sinks of forest land and other biomass.‘Land that has always been forest land’ contributed the most to the carbon sinks of forest land and other biomass(87.67%),of which the carbon sink generated by the land that has always been arbor forest accounted for 79.78%.2)The contribution of three key ecological programs(namely,the Beijing-Tianjin Sandstorm-Control Program,the Three-North Shelterbelt Program and the Taihang Mountain Afforestation Program)and one key strategic implementation area(namely,the Beijing-Tianjin-Hebei Coordinated Development Zone)to the carbon sinks of forest land in the Beijing-Tianjin-Hebei Region was measured and analyzed.Among the three key ecological programs and one key strategic implementation area,the Beijing-Tianjin-Hebei Coordinated Development Zone made the largest contribution(65.13%),followed by the Beijing-Tianjin Sandstorm-Control Program and the Taihang Mountain Afforestation Program(37.55% and 34.16%,respectively),while the Three-North Shelterbelt Program had less impact.

Key words: Beijing-Tianjin-Hebei Region, carbon pool, carbon storage, carbon sink, carbon neutrality

CLC Number:

S718.56

HOU Ruiping, CHEN Jian, XIA Chaozong, SONG Jiageng, HUANG Xiang, ZHENG Qianhui, AN Tianyu, HAO Yuelan. Carbon Sink of Forest Land and Other Biomass in the Beijing-Tianjin-Hebei Region in 2020[J]. FOREST RESOURCES WANAGEMENT, 2022, 0(5): 42-52.

Figures/Tables 10

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References 42

[1]	张煜星, 王雪军, 蒲莹, 等. 1949—2018年中国森林资源碳储量变化研究[J]. 北京林业大学学报, 2021, 43(5):1-14.
[2]	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.
[3]	Koch G, Mooney H. Carbon dioxide and terrestrial ecosystems[J]. Journal of Applied Ecology, 1997, 34:543. doi: 10.2307/2404900
[4]	Grier C C, Vogt K A, Keyes M R, et al. Biomass distribution and above- and below-ground production in young and mature Abiesamabilis zone ecosystems of the Washington Cascades[J]. Canadian Journal of Forest Research, 1981, 11(1):155-167. doi: 10.1139/x81-021
[5]	Woodwell G M. The global carbon cycle[J]. Science, 1988, 241(4874):1736-1737. pmid: 17783113
[6]	Schimel D S. Terrestrial ecosystems and the carbon cycle[J]. Global Change Biology, 1995, 1(1):77-91. doi: 10.1111/j.1365-2486.1995.tb00008.x
[7]	Leavit S W. Biogeochemistry,an analysis of global change[J]. Eos,Transactions American Geophysical Union, 1998, 79(2):20.
[8]	Brown S L, Schroeder P, Kern J S. Spatial distribution of biomass in forests of the eastern USA[J]. Forest Ecology and Management, 1999, 123(1):81-90. doi: 10.1016/S0378-1127(99)00017-1
[9]	Hughes R F, Kauffman J B, Jaramillo V J. Biomass,carbon,and nutrient dynamics of secondary forests in a humid tropical region of méxico[J]. Ecology, 1999, 80(6):1892-1907.
[10]	王万同, 唐旭利, 黄玫. 中国森林生态系统碳储量——动态及机制[M]. 北京: 科学出版社, 2018.
[11]	王效科, 冯宗炜, 欧阳志云. 中国森林生态系统的植物碳储量和碳密度研究[J]. 应用生态学报, 2001, 12(1):13-16.
[12]	Dixon R K, Solomon A M, Brown S, et al. Carbon pools and flux of global forest ecosystems[J]. Science, 1994, 263(5144):185-190. pmid: 17839174
[13]	李国栋, 张俊华, 陈聪, 等. 气候变化背景下中国陆地生态系统碳储量及碳通量研究进展[J]. 生态环境学报, 2013, 22(5):873-878.
[14]	孙丽娜. 山西省森林生物量碳密度空间格局和影响因素研究[D]. 太原: 山西大学, 2020.
[15]	Eggleston S, Buendia L, Miwa K, et al. 2006 IPCC Guidelines for National Greenhouse Gas Inventories[M]. Japan: Institute for Global Environmental Strategies (IGES), 2006.
[16]	Liu Shuqin, Bian Zhen, An Tianyu, et al. Carbon pools of biomass and dead organic matter in typical forest ecosystems of Tibet:a new estimation based on the first forestry carbon sequestration monitoring undertaken in China[J]. Land Degradation & Development, 2021, 32(10):2877-2891. doi: 10.1002/ldr.3828
[17]	Köhl M, Lasco R, Cifuentes M, et al. Changes in forest production,biomass and carbon:results from the 2015 UN FAO global forest resource assessment[J]. Forest Ecology and Management, 2015, 352:21-34. doi: 10.1016/j.foreco.2015.05.036
[18]	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.
[19]	Xu Bing, Guo Zhaodi, Piao Shilong, et al. Biomass carbon stocks in China's forests between 2000 and 2050:a prediction based on forest biomass-age relationships[J]. Science China Life Sciences, 2010, 53(7):776-783. doi: 10.1007/s11427-010-4030-4 pmid: 20697867
[20]	Liu Yingchun, Yu Guirui, Wang Qiufeng, et al. Carbon carry capacity and carbon sequestration potential in China based on an integrated analysis of mature forest biomass[J]. Science China Life Sciences, 2014, 57(12):1218-1229. doi: 10.1007/s11427-014-4776-1 pmid: 25424432
[21]	Guo Zhaodi, Hu Huifeng, Li Pin, et al. Spatio-temporal changes in biomass carbon sinks in China's forests from 1977 to 2008[J]. Science China Life Sciences, 2013, 56(7):661-671. doi: 10.1007/s11427-013-4492-2 pmid: 23722235
[22]	刘领, 王艳芳, 悦飞雪, 等. 基于森林清查资料的河南省森林植被碳储量动态变化[J]. 生态学报, 2019, 39(3):864-873.
[23]	陈璐, 王顺忠, 桑卫国. 我国暖温带森林碳储量特点研究——以北京、河北为例[J]. 中央民族大学学报:自然科学版, 2021, 30(2):34-42.
[24]	毕君, 王超, 尤海舟. 基于温室气体清单的河北省森林碳汇量研究[J]. 生态科学, 2016, 35(4):113-118.
[25]	毕君, 王超, 李联地, 等. 基于IPCC 的河北省2005 年森林碳储量[J]. 东北林业大学学报, 2011, 39(12):36-38.
[26]	张滨, 张丽娜, 刘秀萍, 等. 河北省北部森林植被碳储量和固碳速率研究[J]. 中国生态农业学报, 2016, 24(3):392-402.
[27]	王光华, 刘琪璟. 基于TM 影像和森林资源二类调查数据的北京森林碳汇估算[J]. 应用基础与工程科学学报, 2013, 21(2):224-235.
[28]	樊登星, 余新晓, 岳永杰, 等. 北京市森林碳储量及其动态变化[J]. 北京林业大学学报, 2008, 30(2):117-120.
[29]	杨芝歌, 周彬, 余新晓, 等. 北京山区生物多样性分析与碳储量评估[J]. 水土保持通报, 2012, 32(3):42-46.
[30]	Fang Jingyun, Chen Anping, Peng Changhui., et al. Changes in forest biomass carbon storage in China between 1949 and 1998[J]. Science, 2001, 292(5525):2320-2322. pmid: 11423660
[31]	Poorter L, van der Sande M T, Thompson J, et al. Diversity enhances carbon storage in tropical forests[J]. Global Ecology and Biogeography, 2015, 24(11):1314-1328. doi: 10.1111/geb.12364
[32]	Durán S M, Sánchez-Azofeifa G A, Rios R S, et al. The relative importance of climate,stand variables and liana abundance for carbon storage in tropical forests[J]. Global Ecology and Biogeography, 2015, 24(8):939-949. doi: 10.1111/geb.12304
[33]	刘魏魏, 王效科, 逯非, 等. 全球森林生态系统碳储量、固碳能力估算及其区域特征[J]. 应用生态学报, 2015, 26(9):2881-2890.
[34]	李平, 肖玉, 杨洋, 等. 天津平原杨树人工林生态系统碳储量[J]. 生态学杂志, 2014, 33(3):567-574.
[35]	Huang Congde, Zhang Jian, Yang Wanqin, et al. Spatiotemporal variation of carbon storage in forest vegetation in Sichuan Province[J]. Chinese Journal of Applied Ecology, 2007, 18(12):2687-2692. pmid: 18333440
[36]	卫晓庆. 京津冀地区土地利用碳排放特征及影响因素分析[D]. 西安: 西安科技大学, 2019.
[37]	2021年京津冀各城市GDP排行榜京津冀地区哪个城市经济最高[EB/OL].(2022-05-26)[2022-10-11].https://www.maigoo.com/news/627244.html.
[38]	Penman J, Gytarsky M, Hiraishi T, et al. Good practice guidance for land use,land-use change and forestry[M]. Japan: Institute for Global Environmental Strategies(IGES), 2003.
[39]	李海奎, 雷渊才. 中国森林植被生物量和碳储量评估[M]. 北京: 中国林业出版社, 2010.
[40]	中国林业科学研究院木材工业研究所. 中国主要树种的木材物理力学性质[M]. 北京: 中国林业出版社, 1982.
[41]	2021年中国国土绿化状况公报[EB/OL].(2022-03-11)[2022-10-11].http://www.forestry.gov.cn/stzx/2/20220314/171445436366898.html.
[42]	李海奎. 碳中和愿景下森林碳汇评估方法和固碳潜力预估研究进展[J]. 中国地质调查, 2021, 8(4):79-86.

省 (市)	优势树种	BEF	D/ (t/m³)	R	CF/ (tC/td.m.)	省 (市)	优势树种	BEF	D/ (t/m³)	R	CF/ (tC/td.m.)
北京	油松	2.4850	0.4157	0.2259	0.5184	天津	其它硬阔类	1.2838	0.6062	0.2237	0.4795
	柏木	3.6810	0.4722	0.2278	0.5088		杨树	2.2995	0.3644	0.2088	0.4502
	栎类	2.1057	0.6119	0.2642	0.4798		柳树	2.5977	0.4409	0.2023	0.4803
	榆树	1.6011	0.4868	0.2170	0.4803		其它软阔类	2.1953	0.4222	0.2096	0.4519
	刺槐	1.4823	0.6720	0.2182	0.4750		其它松类	2.1030	0.4440	0.2288	0.5184
	其它硬阔类	1.7769	0.6062	0.2212	0.4813	河北	落叶松	1.9065	0.5053	0.3132	0.5137
	椴树	2.3974	0.4177	0.2597	0.4392		油松	1.8713	0.4157	0.2252	0.5184
	杨树	2.1759	0.3644	0.2303	0.4502		栎类	1.9828	0.6119	0.2879	0.4798
	其它软阔类	2.1213	0.4222	0.2285	0.4508		白桦	1.5523	0.4969	0.3201	0.5055
	阔叶混	1.8814	0.5222	0.2351	0.4657		枫桦	1.4108	0.5770	0.2509	0.4803
	其它松类	2.7802	0.4415	0.2499	0.5177		其它硬阔类	1.6280	0.6062	0.2253	0.4885
天津	油松	1.8988	0.4157	0.2255	0.5184		杨树	2.5588	0.3644	0.2110	0.4502
	柏木	2.3072	0.4722	0.2320	0.5088		其它软阔类	2.1200	0.4222	0.2121	0.4504
	栎类	2.0636	0.6119	0.2654	0.4798		阔叶混	1.8045	0.5222	0.2351	0.4728
	榆树	1.7864	0.4868	0.2143	0.4803		针阔混	2.0037	0.4754	0.2218	0.4866
	刺槐	1.4978	0.6720	0.2182	0.4750

气候带	树种	起源	模型方程	R²	气候带	树种	起源	模型方程	R²
温带大陆气候	云杉	天然林	B=3.89V^-0^.²⁴	0.81	温带季风气候	落叶松	人工林	B=1.02V⁰^.⁰⁶	0.71
	油松	人工林	B=0.67V^-0^.⁴²	0.86			天然林	B=113.45V⁰^.⁸⁰	0.81
		天然林	B=0.67V⁰^.¹⁵	0.69		其它松类	人工林	B=2.08V^-0^.¹⁰	0.76
	柏木	天然林	B=0.16V⁰^.⁵¹	0.70			天然林	B=108.05V^-1^.⁰¹	0.83
	落叶松	天然林	0.006			柏木	人工林	B=1.23V⁰^.⁰⁶	0.78
	其它针叶	人工林	B=6.68V^-0^.⁴³	0.78			天然林	B=44.08V^-0^.⁸⁷	0.72
		天然林	B=1.36V^-0^.⁰³	0.72		针叶混交	天然林	B=0.0098V	0.54
	栎类	天然林	B=3.52V^-1^.²³	0.82		栎类	天然林	B=2.47V^-0^.¹³	0.70
	桦森	天然林	B=9.51V^-1^.⁴⁷	0.81		刺槐	人工林	B=0.59V⁰^.²⁶	0.77
	杨树	人工林	B=0.01V¹^.⁵²	0.61		桦木	天然林	B=2.70V⁰^.⁰³	0.75
		天然林	0.071			杨树	人工林	0.009
	其它软阔类	天然林	B=2.64V^-0^.⁰⁹	0.87		椴树	天然林	B=2.10V⁰^.²⁷	0.88
	其它阔叶	人工林	B=0.95V⁰^.³³	0.67		软阔类	天然林	B=0.23V⁰^.⁵¹	0.70
		天然林	B=3.73V^-0^.¹⁸	0.82		硬阔类	天然林	B=7.07V^-0^.³⁴	0.79
	阔叶混	天然林	B=0.0180V	0.68		其它阔叶	人工林	B=0.69V⁰^.¹⁵	0.71
	针阔混交	人工林	B=0.0226V	0.71			天然林	0.037
		天然林	B=0.0298V	0.64		阔叶混交	天然林	B=0.16V^-0^.⁸²	0.74
温带季风气候	云冷杉	天然林	B=53.01V^-0^.⁶⁸	0.81		针阔混	人工林	B=5.76V^-0^.¹⁸	0.86
	油松	人工林	B=0.006V⁰^.⁴⁸	0.61			天然林	B=1.10V⁰^.¹¹	0.72
		天然林	B=0.71V⁰^.²⁹	0.72

气候带	森林类型	模型	R²
温带大陆气候	人工林/天然林	B=0.07176 V-4.46139P-9.31450	0.89
温带季风气候	人工林(B)	B=0.003777V	0.73
	天然林(A)	B=0.01015V	0.60

气候带	树种	起源	模型方程	R²	气候带	树种	起源	模型方程	R²
温带大陆气候	云杉	天然林	B=11.08V^-0^.³⁷	0.81	温带季风气候	其它松类	人工林	B=5.26V⁰^.⁰⁹	0.76
	油松	人工林	B=2.34V^-0^.⁰⁸	0.81			天然林	B=11.66V^-0^.¹²	0.82
		天然林	B=2.07V⁰^.¹⁶	0.78		柏木	人工林	0.1
	落叶松	天然林	B=96.54V^-0^.⁸⁰	0.87			天然林	B=0.41V⁰^.⁴⁴	0.71
	柏木	天然林	B=1.34V⁰^.⁰⁶	0.76		针叶混交	天然林	B=9.14V^-0^.¹⁶	0.83
	其它针叶	人工林	0.12			栎类	天然林	B=1.93V⁰^.²⁰	0.71
		天然林	B=2.20V^-0^.⁰⁴	0.74		刺槐	人工林	B=0.84V⁰^.³⁷	0.75
	栎类	天然林	0.13			桦木	天然林	B=10.64V^-0^.⁰⁷	0.82
	桦木	天然林	0.07			杨树	人工林	B=0.35V⁰^.³⁰	0.72
	杨树	人工林	0.06				天然林	0.09
		天然林	B=1.51V^-0^.⁰¹	0.84		椴树	天然林	B=1.75V⁰^.²⁰	0.66
	其它软阔	天然林	B=0.45V⁰^.⁴⁹	0.94		其它软阔	人工林	B=0.44V⁰^.³²	0.75
	阔叶混	天然林	B=19.71V^-0^.²⁴	0.92			天然林	B=19.72V^-0^.²⁹	0.88
	针阔混交	人工林	B=1.37V⁰^.⁴⁵	0.71		其它硬阔	天然林	B=2.97V⁰^.⁰⁵	0.79
		天然林	0.16			其它阔叶	人工林	B=0.78V⁰^.⁴⁴	0.76
温带季风气候	云冷杉	天然林	B=0.43V⁰^.³⁷	0.64			天然林	B=2.98V⁰^.¹²	0.64
	油松	人工林	B=4.82V⁰^.⁰³	0.72		阔叶混	天然林	B=11.26V^-0^.¹⁸	0.75
		天然林	0.11			针阔混交	人工林	B=1.99V⁰^.²⁶	0.79
	落叶松	人工林	B=1.95V⁰^.³²	0.72			天然林	B=1.31V⁰^.²⁸	0.87
		天然林	B=2.85V⁰^.²⁶	0.73

土纲	土壤类型	样点数/ 个	容重/(g/cm³)			有机质含量/(g/kg)			碳密度/(Mg C/hm²)
土纲	土壤类型	样点数/ 个	均值	标准差		均值	标准差		均值	标准差
淋溶土	棕壤	119	1.09	0.24		29.96	27.10		185.9	155.0
半淋溶土	褐土	70	1.20	0.28		20.14	13.95		130.8	77.2
钙层土	栗钙土	24	1.31	0.20		20.82	14.95		134.6	94.5
水成土	沼泽土	24	0.88	0.33		127.69	159.75		395.5	449.1
	水稻土	21	1.30	0.14		19.01	12.87		156.9	106.7
半水成土	水稻土	21	1.30	0.14		19.01	12.87		156.9	106.7
	潮土	24	1.32	0.26		9.04	5.87		99.8	55.6
	草甸土	20	1.11	0.25		27.38	21.41		224.6	181.5
	岩成土	风沙土	38	1.51	0.12		6.50	4.14		85.5	48.8
高山土	山地草甸土	3	1.02	0.28		41.80	23.92		267.5	135.3