中国农业净碳汇再测算：现状特征、时空格局及其影响因素

doi:10.3864/j.issn.0578-1752.2024.22.010

Abstract

Abstract:

【Objective】Based on the current “dual carbon” strategic goal, this study aimed to clarify the current characteristics, spatio-temporal pattern and influencing factors of agricultural net carbon sink, so as to provide the important support for accelerating agricultural sink increase and emission reduction.【Method】Based on the scientific reconstruction of the index system, the carbon sink/carbon emission factor method was used to measure and analyze the current situation of China’s agricultural net carbon sink. Then the spatial autocorrelation model was used to discuss the spatial dependence and spatial heterogeneity. Finally, the least-squares method was used to analyze the main factors affecting the change of its intensity. 【Result】From 2005 to 2022, the total amount of agricultural net carbon sink in China was in an obvious upward trend, although there were some interannual fluctuations, and its evolutionary characteristics could be roughly divided into four stages, namely, “continuous rise”, “fluctuating decline”, “rapid rise”, and “slow rise”; the intensity of agricultural net carbon sink was also in an obvious upward trend, with only a slight difference in the trajectory of the evolution, and the difference in its growth rate could be roughly categorized into four stages: “continuous rapid growth”, “slow growth”, “fluctuating ups and downs”, and “slow growth”. 2022, the amount of agricultural net carbon sink had a large interprovincial difference, with Inner Mongolia being the first and Shanghai being the last, and compared with the year of 2005, all the provinces had a significant increase. In 2022, the net carbon sink intensity of agriculture would be the highest in Henan and the lowest in Qinghai, with all provinces showing different degrees of increase compared with 2005. China’s provincial agricultural net carbon sink intensity as a whole showed obvious spatial dependence, but there was also a local spatial clustering phenomenon, more than 70% of the provinces showed obvious spatial clustering characteristics, and the number of provinces located in the high-high clustering and the low-low clustering was approaching. The structure of arable land use, urbanization level, rural residents' income level and the internal industrial structure of agriculture all had a significant impact on the intensity of agricultural net carbon sink; specifically, the higher the ratio of sown area of grain crops, or the higher the urbanization rate, or the higher the income level of rural residents, or the larger the ratio of plantation industry to animal husbandry, the higher the intensity of net carbon sink in agriculture.【Conclusion】The total amount and intensity of China’s agricultural net carbon sink were in a fluctuating upward trend and there were obvious inter-provincial differences. The intensity of China’s agricultural net carbon sink showed obvious spatial dependence and spatial heterogeneity. The intensity of the agricultural net carbon sink was affected by the structure of arable land use, the level of urbanization, the level of rural residents' income, and the structure of the internal industries of agriculture. The measures should be taken to promote the enhancement of sink and emission reductions and to promote the enhancement of agricultural net carbon sink in agriculture, such as establishing a sound policy support system for the development of low-carbon agriculture, strengthening inter-provincial exchanges and cooperation, and increasing financial support for agriculture.

Key words: agricultural carbon emissions, agricultural net carbon sink, spatio-temporal pattern, influencing factors, China

TIAN Yun, WANG XiaoRui, YIN MinHao, ZHANG HuiJie. Re-Evaluation of China’s Agricultural Net Carbon Sink: Current Situation, Spatial-Temporal Pattern and Influencing Factors[J].Scientia Agricultura Sinica, 2024, 57(22): 4507-4521.

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Figures/Tables 7

Table 1

Table 2

Table 3

Table 4

Agricultural net carbon sinks and intensities in 30 province(municipality, autonomous region) of China"

地区 Region	2005				2022				总量变动率 Change rate of total amount (%)	强度变动率 Change rate of intensity (%)
地区 Region	总量 Total amount (×10⁴t)	排名 Rank	强度 Intensity (t·hm^-2)	排名 Rank	总量 Total amount (×10⁴t)	排名 Rank	强度 Intensity (t·hm^-2)	排名 Rank	总量变动率 Change rate of total amount (%)	强度变动率 Change rate of intensity (%)
北京Beijing	1273.28	28	10.97	18	1672.78	28	13.78	10	31.38	25.62
天津Tianjin	501.77	29	8.96	23	948.04	29	17.87	5	88.94	99.44
河北Hebei	15634.87	16	12.32	7	24359.15	10	15.90	7	55.80	29.06
山西Shanxi	9128.48	23	9.19	21	14708.64	21	10.76	24	61.13	17.08
内蒙古Inner Mongolia	56046.56	1	5.86	25	63323.77	1	7.04	26	12.98	20.14
辽宁Liaoning	13245.52	17	12.25	8	16694.47	20	13.74	11	26.04	12.16
吉林Jilin	20598.73	7	12.89	4	25397.26	9	14.96	8	23.30	16.06
黑龙江Heilongjiang	40832.16	2	11.05	17	53720.03	2	13.43	14	31.56	21.54
上海Shanghai	55.80	30	1.61	30	206.55	30	7.33	25	270.16	355.28
江苏Jiangsu	7084.43	24	12.41	5	10806.88	23	20.70	3	52.54	66.80
浙江Zhejiang	9500.58	22	11.31	15	10310.17	24	12.61	21	8.52	11.49
安徽Anhui	11288.70	21	11.35	14	16808.82	19	16.74	6	48.90	47.49
福建Fujian	12617.12	20	12.15	10	13998.79	22	13.08	17	10.95	7.65
江西Jiangxi	16097.89	13	11.85	12	17871.33	18	13.00	18	11.02	9.70
山东Shandong	15642.44	15	15.49	2	25937.07	8	24.81	2	65.81	60.17
河南Henan	18682.25	10	16.30	1	31257.03	7	24.94	1	67.31	53.01
湖北Hubei	15706.83	14	11.76	13	19530.47	14	13.37	15	24.34	13.69
湖南Hunan	19942.05	8	12.13	11	23616.03	12	13.62	12	18.42	12.28
广东Guangdong	17771.11	11	12.36	6	20523.93	13	14.48	9	15.49	17.15
广西Guangxi	26544.02	6	15.38	3	38639.18	6	18.19	4	45.57	18.27
海南Hainan	3068.96	26	10.97	19	3680.37	26	12.78	19	19.92	16.50
重庆Chongqing	6670.03	25	11.14	16	9215.98	25	13.44	13	38.17	20.65
四川Sichuan	38943.20	3	9.01	22	49439.12	3	11.92	23	26.95	32.30
贵州Guizhou	12956.07	19	8.90	24	19000.46	17	12.33	22	46.65	38.54
云南Yunnan	36678.14	4	12.16	9	45520.06	5	13.31	16	24.11	9.46
陕西Shaanxi	19345.97	9	10.06	20	23812.55	11	12.68	20	23.09	26.04
甘肃Gansu	13137.75	18	5.72	26	19285.22	16	6.92	27	46.79	20.98
青海Qinghai	17028.59	12	3.90	29	19327.43	15	4.33	30	13.50	11.03
宁夏Ningxia	2277.41	27	5.44	27	2854.04	27	6.69	28	25.32	22.98
新疆Xinjiang	31274.80	5	5.02	28	47744.55	4	6.60	29	52.66	31.47

Table 4

Table 5

Table 6

Local spatial clustering of agricultural net carbon sinks intensity in 30 province (municipality, autonomous region) in main years"

年份 Year	高-高集聚区 High-high agglomeration	低-高集聚区 Low-high agglomeration	低-低集聚区 Low-low agglomeration	高-低集聚区 High-low agglomeration
2005	河北Hebei、辽宁Liaoning、吉林Jilin、黑龙江Heilongjiang、江苏Jiangsu、安徽Anhui、福建Fujian、江西Jiangxi、山东Shandong、河南Henan、湖北Hubei、湖南Hunan、广东Guangdong、广西Guangxi、海南Hainan、云南Yunnan、陕西Shaanxi	天津Tianjin、山西Shanxi、上海Shanghai、四川Sichuan、贵州Guizhou	内蒙古Inner Mongolia、甘肃Gansu、青海Qinghai、宁夏Ningxia、新疆Xinjiang	北京Beijing、浙江Zhejiang、重庆Chongqing
2010	河北Hebei、辽宁Liaoning、吉林Jilin、黑龙江Heilongjiang、江苏Jiangsu、福建Fujian、江西Jiangxi、山东Shandong、河南Henan、湖北Hubei、湖南Hunan、广东Guangdong、广西Guangxi、海南Hainan、重庆Chongqing	天津Tianjin、山西Shanxi、内蒙古Inner Mongolia、上海Shanghai、贵州Guizhou	浙江Zhejiang、安徽Anhui、四川Sichuan、陕西Shaanxi、甘肃Gansu、青海Qinghai、宁夏Ningxia、新疆Xinjiang	北京Beijing、云南Yunnan
2015	河北Hebei、辽宁Liaoning、吉林Jilin、黑龙江Heilongjiang、江苏Jiangsu、安徽Anhui、福建Fujian、江西Jiangxi、山东Shandong、河南Henan、湖北Hubei、湖南Hunan、广东Guangdong	天津Tianjin、山西Shanxi、内蒙古Inner Mongolia、上海Shanghai、海南Hainan、贵州Guizhou、陕西Shaanxi	北京Beijing、浙江Zhejiang、重庆Chongqing、四川Sichuan、甘肃Gansu、青海Qinghai、宁夏Ningxia、新疆Xinjiang	广西Guangxi、云南Yunnan
2022	北京Beijing、天津Tianjin、河北Hebei、辽宁Liaoning、吉林Jilin、黑龙江Heilongjiang、江苏Jiangsu、安徽Anhui、山东Shandong、河南Henan、湖北Hubei、湖南Hunan、广东Guangdong	山西Shanxi、内蒙古Inner Mongolia、上海Shanghai、福建Fujian、江西Jiangxi、海南Hainan	浙江Zhejiang、四川Sichuan、贵州Guizhou、云南Yunnan、陕西Shaanxi、甘肃Gansu、青海Qinghai、宁夏Ningxia、新疆Xinjiang	广西Guangxi、重庆Chongqing

Table 6

Table 7

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品种 Variety	经济系数Economic coefficient	含水量 Water content (%)	碳吸收率Carbon absorption rate	系数值Coefficient value	品种 Variety	经济系数Economic coefficient	含水量 Water content (%)	碳吸收率Carbon absorption rate	系数值Coefficient value
水稻Rice	0.45	12	0.414	0.8096	棉花Cotton	0.10	8	0.450	4.1400
小麦Wheat	0.40	12	0.485	1.0670	甘蔗Sugar cane	0.50	50	0.450	0.4500
玉米Corn	0.40	13	0.471	1.0244	甜菜Beet	0.70	75	0.407	0.1454
豆类Beans	0.34	13	0.450	1.1515	烟草Tobacco	0.55	85	0.450	0.1227
薯类Tubers	0.70	70	0.423	0.1813	蔬菜Vegetable	0.60	90	0.450	0.0750
花生Peanut	0.43	10	0.450	0.9419	其他作物Other crops	0.40	12	0.450	0.9900
油菜籽Rapeseed	0.25	10	0.450	1.6200

变量名称 Variable name		均值 Mean value	标准差 Standard deviation	最小值 Minimum value	最大值 Maximum value	样本数 Sample number
因变量 Dependent variable	农业净碳汇强度 Agricultural net carbon sink intensity (t·hm^-2)	12.195	4.109	1.610	25.030	540
自变量 Independent variable	耕地利用结构Cultivated land utilization structure	3.102	4.124	0.551	33.192	540
	城镇化水平Urbanization level	0.559	0.139	0.269	0.896	540
	农村居民收入水平 Rural resident income level (×10⁴ yuan/person)	0.832	0.470	0.188	2.683	540
	农村人力资本水平Rural human capital level (a)	7.601	0.685	5.139	9.915	540
	农村就业结构Rural employment structure	0.595	0.204	0.125	1.000	540
	农业内部产业结构Agricultural internal industrial structure	0.815	0.110	0.518	0.970	540

年份 Year	农业碳汇 Agricultural carbon sink	农业碳排放 Agricultural carbon emission	农业净碳汇 Agricultural net carbon sink		农业净碳汇强度 Agricultural net carbon sink intensity
年份 Year	数量 Amount (×10⁴t)	数量 Amount (×10⁴t)	数量 Amount (×10⁴ t)	增速 Growth rate (%)	数量 Amount (t·hm^-2)	增速 Growth rate (%)
2005	610565.56	100990.06	509575.50	—	9.03	—
2006	620667.85	102582.91	518084.94	1.67	9.18	1.66
2007	625016.22	97278.69	527737.53	1.86	9.53	3.81
2008	638158.47	94451.16	543707.31	3.03	9.82	3.04
2009	647829.64	96533.77	551295.87	1.40	10.45	6.42
2010	652340.66	98228.36	554112.30	0.51	10.45	0.00
2011	663031.86	99953.29	563078.57	1.62	10.58	1.24
2012	672508.50	102062.63	570445.87	1.31	10.67	0.85
2013	677468.81	101635.32	575833.49	0.94	10.73	0.56
2014	678804.76	103062.46	575742.30	-0.02	10.74	0.09
2015	680743.54	103855.15	576888.39	0.20	10.79	0.47
2016	678101.53	103651.19	574450.34	-0.42	10.75	-0.37
2017	698309.08	102941.51	595367.57	3.64	11.04	2.70
2018	720704.20	97733.99	622970.21	4.64	11.31	2.45
2019	758381.76	94067.21	664314.55	6.64	11.10	-1.86
2020	760839.20	93876.76	666962.44	0.40	11.16	0.54
2021	765649.65	96250.93	669398.72	0.37	11.20	0.36
2022	767151.95	96941.81	670210.14	0.12	11.22	0.18
平均增速 Average growth rate	1.35%	-0.24%	1.62%	—	1.29%	—

年份 Year	Moran’s I	Z值 Z statistics	P值 P value	年份 Year	Moran’s I	Z值 Z statistics	P值 P value
2005	0.156	2.074	0.019	2014	0.209	2.645	0.004
2006	0.194	2.468	0.007	2015	0.202	2.573	0.005
2007	0.187	2.398	0.008	2016	0.186	2.396	0.008
2008	0.205	2.599	0.005	2017	0.187	2.432	0.008
2009	0.205	2.613	0.004	2018	0.193	2.507	0.006
2010	0.203	2.591	0.005	2019	0.205	2.622	0.004
2011	0.217	2.731	0.003	2020	0.198	2.548	0.005
2012	0.214	2.705	0.003	2021	0.210	2.677	0.004
2013	0.210	2.659	0.004	2022	0.211	2.695	0.004

变量 Variable	模型I Model I	模型Ⅱ Model Ⅱ	模型III Model III	模型IV Model IV	模型Ⅴ Model Ⅴ	模型Ⅵ Model Ⅵ
clus	0.178^*** （5.45）	0.119^*** （4.57）	0.089^*** （3.51）	0.089^*** （3.52）	0.096^*** （3.73）	0.085^*** （3.27）
ul		10.845^*** （17.44）	4.562^*** （4.18）	4.454^*** （3.93）	4.512^*** （3.98）	3.985^*** （3.45）
rril			1.428^*** （6.88）	1.393^*** （6.04）	1.381^*** （5.99）	1.661^*** （6.31）
rhcl				0.063 （0.35）	0.033 （0.18）	0.060 （0.33）
res					-0.857 （-1.48）	-0.801 （-1.39）
ais						3.750^** （2.19）
常数项 Constant	11.644^*** （102.53）	5.760^*** （16.49）	8.180^*** （16.85）	7.787^*** （6.39）	8.487^*** （6.50）	5.287^*** （2.70）
R²	0.055	0.409	0.459	0.459	0.462	0.467

Re-Evaluation of China’s Agricultural Net Carbon Sink: Current Situation, Spatial-Temporal Pattern and Influencing Factors

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