The carbon storage calaulation and carbon sequestration potential analysis of the main artificial arboreal forest in China

doi:10.12302/j.issn.1000-2006.202109014

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (5): 11-19.doi: 10.12302/j.issn.1000-2006.202109014

Special Issue: “双碳”视域下的生态系统固碳增汇（2）

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The carbon storage calaulation and carbon sequestration potential analysis of the main artificial arboreal forest in China

WANG Dawei(), SHEN Wenxing()

College of Economic and Management, Nanjing Forest University,Nanjing 210037,China

Received:2021-09-07 Revised:2022-03-22 Online:2022-09-30 Published:2022-10-19
Contact: SHEN Wenxing E-mail:wdwnanjing@163.com;swx@njfu.edu.cn

Abstract

Abstract:

【Objective】Carbon from the forest plays a dominant role in contributing the development to terrestrial ecosystem. Therefore, the purpose of this study is to predict the carbon sink potential of artificial trees by determining their carbon density and value-added carbon storage potential. The results of the study can further improve the structure of the artificial main forest age groups, improve the sustainable management levels of the forest, provide scientific basis for increasing the artificial tree forest unit area, and achieve the goal of increasing the amount of foreign exchange in China.【Method】This study applied the data of area and volume of the dominate tree species in China. The data are all from the 8th (2009-2013) and the 9th (2014-2018) national forest inventory data of China. Moreover, adopted the IPCC volume-biomass method to calculate the carbon storage and carbon density of the six main artificial arboreal forest. Meanwhile, the changing patterns and age group structure characteristics of the carbon storage and density of artificial arboreal forests in China were also analyzed between the two inventories. The aim of this step was to comprehensively analyze and evaluate China's artificial forest under age structures of carbon sequestration function; furthermore, the fitted unit area accumulation-forest-age logistic regression growth equation was applied and the IPCC volume-biomass methods were combined to calculate the accumulation of different ages of each dominant tree species after decades. The purpose was to estimate the data of carbon storage and carbon density of China's existing artificial arboreal forest in future stages.【Result】During the two inventory periods, the total carbon storage of the main artificial forest increased by 498.81 Tg, with an average annual increase of 99.76 Tg. The carbon storage from the highest to the lowest forest age groups in China was in the following order: over-matured forests (439.19 Tg) > mature forests (426.43 Tg) > near-mature forests (359.75 Tg) > middle-aged forests (292.34 Tg) > young forests (105.15 Tg). Carbon density from the lowest to the highest age groups in China were in the following order: over-matured forests (59.17 Mg/hm²) < young forests (169.12 Mg/hm²) < mature forests (178.13 Mg/hm²) < near-mature forests (190.38 Mg/hm²) < middle age forests (348.09 Mg/hm²). With regard to the future carbon sequestration capacity of artificial forests in China, the analysis results predict that the carbon storage and density of artificial arboreal forests based on current data will increase to 1 716.27 Tg and 36.51 Mg/hm², with an increase of 92.92% and 93.17%, respectively, compared with the values in 2015.【Conclusion】The carbon storage of the six main artificial forest increased significantly between the two inventories. Carbon storage shows a linear positive increase trend, while carbon density does not show a linear increase due to the effect of accumulation of the area and the volume. Moreover, by the year of 2035, the carbon storage of artificial forest will account for about 20% of the total carbon storage, of which the area of young-aged and middle-aged trees will account for 64.66% of the total area of trees in China, and it can be predicted that the carbon storage of main artificial arboreal forest will have great potential for increase.

Key words: artificial arboreal forest, carbon density, carbon storage, carbon sequestration potential, volume-biomass methods, Logistic growth curves

CLC Number:

S757

WANG Dawei, SHEN Wenxing. The carbon storage calaulation and carbon sequestration potential analysis of the main artificial arboreal forest in China[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(5): 11-19.

Figures/Tables 9

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龄组 age class	面积/×10⁵ hm² area			总蓄积量/×10⁵ m³ total volume
龄组 age class	第8次 the 8th	第9次 the 9th	变化 change	第8次 the 8th	第9次 the 9th	变化 change
幼龄林young	125.2	232.6	107.4	2 531.3	5 854.1	3 322.8
中龄林middle-aged	108.3	169.7	61.4	6 886.6	11 144.5	4 257.9
近熟林near mature	48.9	80.7	31.8	4 333.1	7 226 9	2 893.8
成熟林mature	37.2	65.9	28.7	3 513.6	7 200.1	3 686.5
过熟林over-mature	11.4	22.3	10.9	980.6	2 450.4	1 469.8
合计 total	331.0	571.2	240.2	18 245.2	33 876.0	15 630.8

人工林 plantation	面积/×10⁵ hm² area		总蓄积量/×10⁵ m³ total volume		单位面积蓄积量/ (m³·hm^-2) volume
人工林 plantation	第8次 the 8th	第9次 the 9th	第8次 the 8th	第9次 the 9th	第8次 the 8th	第9次 the 9th
杉木林 Cunninghamia lanceolata forest	89.5	99.0	6 254.0	7 555.4	69.88	76.31
杨树林 poplar forest	85.4	75.7	5 029.6	5 463.0	58.89	70.49
桉树林 eucalyptus forest	44.6	54.7	1 603.3	2 156.3	35.95	39.42
落叶松林 larch forest	31.4	31.6	1 841.4	2 374.4	58.64	75.14
马尾松林 Pinus massoniana forest	30.6	25.2	1 715.5	1 876.3	56.06	74.46
油松林 Pinus tabuliformis forest	12.3	17.8	269.9	516.0	21.94	28.99
合计total	293.8	300.4	16 713.7	19 941.4

人工林 plantation	参数 parameter	幼龄林 young	中龄林 middle- aged	近熟林 near mature	成熟林 mature	过熟林 over- mature
杉木林 Cunninghamia lanceolata forest	B_EF	1.634	2.035	1.323	1.273	1.209
	R_SR	0.246	0.292	0.223	0.210	0.191
	W_D	0.307	0.307	0.307	0.307	0.307
	C_F	0.520	0.520	0.520	0.520	0.520
杨树林 poplar forest	B_EF	1.446	1.496	1.369	1.390	1.460
	R_SR	0.227	0.259	0.227	0.171	0.209
	W_D	0.378	0.378	0.378	0.378	0.378
	C_F	0.496	0.496	0.496	0.496	0.496
桉树林 eucalyptus forest	B_EF	1.263	1.297	1.178	1.165	1.138
	R_SR	0.221	0.219	0.221	0.181	0.270
	W_D	0.578	0.578	0.578	0.578	0.578
	C_F	0.525	0.525	0.525	0.525	0.525
落叶松林 larch forest	B_EF	1.416	1.644	1.281	1.229	1.150
	R_SR	0.212	0.205	0.211	0.188	0.239
	W_D	0.490	0.490	0.490	0.490	0.490
	C_F	0.521	0.521	0.521	0.521	0.521
马尾松林 Pinus massoniana forest	B_EF	1.472	2.033	1.305	1.185	1.250
	R_SR	0.187	0.196	0.181	0.167	0.196
	W_D	0.380	0.380	0.380	0.380	0.380
	C_F	0.460	0.460	0.460	0.460	0.460
油松林 Pinus tabuliformis forest	B_EF	1.589	1.811	1.519	1.468	1.571
	R_SR	0.277	0.247	0.264	0.196	0.234
	W_D	0.360	0.360	0.360	0.360	0.360
	C_F	0.500	0.500	0.500	0.500	0.500

人工林 plantation	a	b	c	R²
杉木林 Cunninghamia lanceolata forest	124.719	11.400	0.195	0.968
杨树林 poplar forest	116.847	16.262	0.257	0.995
桉树林 eucalyptus forest	73.730	8.556	0.247	0.888
落叶松林 larch forest	133.950	18.696	0.136	0.998
马尾松林 Pinus massoniana forest	174.735	7.014	0.055	0.955
油松林 Pinus tabuliformis forest	93.818	26.393	0.099	0.998

人工林 plantation	时间 time	幼龄林 young		中龄林 middle-aged		近熟林 near mature		成熟林 mature		过熟林 over-mature		平均碳密度 average carbon density	碳储量合计 total carbon storage
人工林 plantation	时间 time	碳密度 carbon density	碳储量 carbon storage	碳密度 carbon density	碳储量 carbon storage	碳密度 carbon density	碳储量 carbon storage	碳密度 carbon density	碳储量 carbon storage	碳密度 carbon density	碳储量 carbon storage	平均碳密度 average carbon density	碳储量合计 total carbon storage
杉木 Cunninghamia lanceolata forest	2009—2013	9.21	30.62	33.59	96.52	40.70	56.06	50.34	59.12	59.98	11.86	38.76	193.82
	2014—2018	10.18	47.34	37.21	93.05	34.42	37.86	34.89	46.75	38.11	11.81	30.96	236.81
杨树 poplar forest	2009—2013	11.29	34.05	20.78	53.77	25.34	27.57	27.33	30.62	22.47	16.29	21.44	107.48
	2014—2018	11.69	24.32	26.92	52.76	28.55	37.69	28.95	38.79	28.94	25.47	25.01	179.03
桉树 eucalyptus forest	2009—2013	5.73	9.95	17.57	27.64	24.21	13.40	31.10	15.04	39.20	4.26	23.56	70.65
	2014—2018	6.59	17.33	26.23	44.85	27.18	14.14	32.28	10.98	45.44	11.81	27.54	99.11
落叶松 larch forest	2009—2013	11.30	18.32	32.10	28.31	43.01	18.46	46.95	9.71	26.54	0.17	31.98	74.97
	2014—2018	15.78	22.72	44.28	37.64	48.65	24.33	50.82	18.30	39.41	0.24	39.79	103.23
马尾松 Pinus massoniana forest	2009—2013	7.06	5.50	14.07	14.95	18.71	15.50	25.09	9.36	40.04	0.77	21.00	46.08
	2014—2018	8.58	4.89	29.53	23.33	24.39	17.07	28.39	11.92	35.28	1.06	25.23	58.27
油松 Pinus tabuliformis forest	2009—2013	-	-	-	-	-	-	-	-	-	-	-	-
	2014—2018	6.71	6.56	10.74	3.64	13.81	1.54	18.91	1.03	31.02	0.32	16.24	13.09

The carbon storage calaulation and carbon sequestration potential analysis of the main artificial arboreal forest in China

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项目 item	清查时间 inventor time	幼龄林 young	中龄林 middle-aged	近熟林 near mature	成熟林 mature	过熟林 over-mature	合计 total
碳储量/ Tg carbon storage	2009—2013	68.31	203.81	284.44	335.93	231.61	1 124.10
	2014—2018	105.15	292.34	359.75	426.43	439.19	1 622.86
	变化change	37.00	88.53	75.20	90.50	207.58	498.81
碳密度/(Mg·hm^-2) carbon density	2009—2013	110.35	259.90	143.58	132.84	35.33	136.40
	2014—2018	169.12	348.09	190.38	178.13	59.17	188.98
	变化change	58.77	88.19	46.80	45.29	23.84	52.58

年份 year	碳储量/Tg carbon storage	碳密度/(Mg·hm^-2) carbon density
2015	889.61	18.90
2020	1 189.56	25.31
2025	1 403.34	29.86
2030	1 586.49	33.75
2035	1 716.27	36.51

省(市、区) region	单位面积蓄积量/ (m³·hm^-2) volume	碳储量/ Tg carbon storage	碳储量占比/ % percentage	碳密度/ (Mg·hm^-2) carbon density	省(市、区) region	单位面积蓄积量/ (m³·hm^-2) volume	碳储量/ Tg carbon storage	碳储量占比/ % percentage	碳密度/ (Mg·hm^-2) carbon density
北京Beijing	33.22	6.27	0.10	14.62	河南Henan	55.98	72.10	1.18	23.61
天津Tianjin	49.74	1.42	0.02	18.88	湖南Hunan	45.26	121.15	1.97	16.56
河北Hebei	34.65	43.03	0.70	13.83	广东Guangdong	49.92	151.69	2.47	21.22
山西Shanxi	46.28	44.04	0.72	20.93	广西Guangxi	56.34	202.93	3.31	22.45
内蒙古Inner Mongolia	78.53	565.52	9.22	33.01	海南Hainan	91.69	40.06	0.65	41.25
辽宁Liaoning	64.28	114.38	1.87	29.38	重庆Chongqing	69.47	51.62	0.84	24.48
黑龙江Heilongjiang	84.37	736.13	12.00	37.76	四川Sichuan	141.92	628.93	10.25	53.13
吉林Jilin	122.45	431.44	7.03	57.26	贵州Guizhou	62.83	113.26	1.85	23.66
上海Shanghai	42.74	0.85	0.01	19.57	云南Yunnan	110.88	767.20	12.50	50.24
江苏Jiangsu	51.69	22.06	0.36	17.63	西藏Tibet	266.59	888.05	14.47	104.66
浙江Zhejiang	52.87	82.46	1.34	20.11	陕西Shaanxi	61.93	202.53	3.30	31.68
安徽Anhui	61.97	66.70	1.09	22.87	甘肃Gansu	86.79	88.58	1.44	35.84
福建Fujian	100.20	237.85	3.88	39.20	宁夏Ningxia	41.66	2.81	0.05	17.75
江西Jiangxi	51.70	163.38	2.66	20.68	新疆Xinjiang	187.81	120.58	1.97	67.29
山东Shandong	55.25	33.47	0.55	20.73	全国national	89.79	6 135.68	100.00	37.28