中国农业碳排放密度的时空特征、动态演进与空间效应

doi:10.3864/j.issn.0578-1752.2025.18.010

Abstract

Abstract:

【Objective】This study aimed to compare the advantages of agricultural carbon emission density indicators, and to clarify the current situation, influencing factors, and spatial spillover effects of inter provincial agricultural carbon emission density in China, so as to provide a reference for exploring more potential space for agricultural carbon reduction. 【Method】On the basis of accurately measuring the carbon emission density of agriculture in China and its provinces, this study used methods such as kernel density estimation, spatial autocorrelation, and spatial Durbin model to investigate its spatiotemporal characteristics, influencing factors, and spatial spillover effects. 【Result】Although the agricultural carbon emission density has slightly decreased from 2005 to 2022, it was accompanied by significant interannual fluctuations, showing an overall change in three stages: "fluctuation decrease", "continuous increase", and "fluctuation decrease". Inter provincial development has shown a balanced trend, and although there was still an absolute gap, it was continuously narrowing; there was a strong and stable spatial dependence pattern in agricultural carbon emission density, with a local spatial heterogeneity pattern dominated by the "low-low" clustering type of interdependence between low-density provinces and multiple low-density provinces, and the "high-high" clustering type of interdependence between high-density provinces and multiple high-density provinces as the secondary dominant pattern; the carbon emission density of agriculture was comprehensively influenced by four levels: government responsibilities, economic development, social participation, and cultural education. There was an inverted U-shaped relationship between the level of agricultural economic development and agricultural industry agglomeration. Agricultural technological progress significantly promoted the reduction of agricultural carbon emission density, while the increases in agricultural public investment, financial support for agriculture, agricultural industrial structure, rural human capital, and industrialization level all led to an increase in agricultural carbon emission density; in terms of spatial spillover effects, the improvement of agricultural economic development level in surrounding areas would cause an initial increase and then a decrease in local agricultural carbon emission density. The increase in agricultural public investment in surrounding areas would promote a decrease in local agricultural carbon emission density, while the spatial spillover effects of fiscal support for agriculture and rural human capital would lead to an increase in local agricultural carbon emission density. 【Conclusion】There were still significant differences in agricultural carbon emission density among provinces in China; There is a strong spatial dependence between provinces across the country, and there were obvious spatial heterogeneity patterns in some regions; the carbon emission density of agriculture was influenced by four factors: government responsibilities, economic development, social participation, and cultural education. Continuously improving the level of agricultural economic development and increasing public investment in agriculture were beneficial for reducing the carbon emission density of agriculture in surrounding areas, while the spatial spillover effect of fiscal support for agriculture and rural human capital was the opposite.

Key words: carbon neutrality, agricultural carbon emissions, agricultural carbon emission density, spatiotemporal characteristics, spatial effect, China

YIN MinHao, CHEN ChiBo, LU YiHeng, TIAN Yun. China's Agricultural Carbon Emission Density: Spatiotemporal Characteristics, Dynamic Evolution, and Spatial Effect[J].Scientia Agricultura Sinica, 2025, 58(18): 3710-3727.

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Figures/Tables 10

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Table 4

Local spatial clustering of agricultural carbon emission density in China from 2005 to 2022"

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

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变量名称 Variable name	样本量 Sample number	平均值 Mean value	标准差 Standard deviation	最小值 Minimum value	最大值 Maximum value
农业碳排放密度（ACD） Agricultural carbon emission intensity (t·hm^-2)	540	2.940	2.439	0.263	13.147
农业发展水平（ADL） Level of agricultural development (10⁴yuan/person)	540	1.470	0.870	0.242	6.136
农业产业集聚（LQ） Agglomeration of agricultural industries	540	1.230	0.676	0.029	3.686
农业科技进步（TPC） Advances in agricultural science and technology (%)	540	1.733	1.012	0.549	6.770
农业产业结构（AIS） Agricultural industrial structure	540	0.822	0.104	0.538	0.964
农业公共投资（API） Public investment in agriculture（10⁴yuan）	540	562.953	727.160	1.100	4949.627
财政支农力度（FSA）Fiscal support for agriculture	540	0.105	0.036	0.012	0.190
城镇化率（UR）Urbanization rate	540	0.563	0.140	0.269	0.896
农村人力资本（RHC）Rural human capital	540	7.777	0.627	5.769	9.980
工业化水平（IL）Level of industrialization	540	0.493	0.079	0.212	0.637

省份（市、区） Province（City, District）	2005		2022		变动率 Change rate (%)
省份（市、区） Province（City, District）	密度Density (t·hm^-2)	排名Ranking	密度Density (t·hm^-2)	排名 Ranking	变动率 Change rate (%)
北京Beijing	3.50	10	0.78	27	-77.83
天津Tianjin	5.74	5	6.21	3	8.22
河北Hebei	3.55	9	2.13	15	-39.80
山西Shanxi	1.66	20	1.17	23	-29.77
内蒙古Inner Mongolia	0.37	29	0.52	29	38.11
辽宁Liaoning	2.33	18	2.16	14	-7.33
吉林Jilin	1.44	22	1.42	20	-1.20
黑龙江Heilongjiang	0.85	26	1.16	24	36.11
上海Shanghai	13.15	1	11.18	1	-14.94
江苏Jiangsu	8.62	2	10.62	2	23.19
浙江Zhejiang	3.21	12	2.49	12	-22.63
安徽Anhui	4.69	6	5.45	4	16.17
福建Fujian	2.36	17	1.70	18	-28.03
江西Jiangxi	3.41	11	3.79	9	11.15
山东Shandong	6.26	3	4.08	8	-34.86
河南Henan	5.79	4	4.46	5	-23.06
湖北Hubei	3.84	8	4.28	7	11.47
湖南Hunan	4.01	7	4.33	6	8.00
广东Guangdong	3.09	13	2.84	10	-8.11
广西Guangxi	2.58	16	1.91	16	-25.81
海南Hainan	3.09	14	2.53	11	-18.07
重庆Chongqing	2.90	15	2.31	13	-20.40
四川Sichuan	1.45	21	1.36	21	-6.30
贵州Guizhou	1.99	19	1.51	19	-24.20
云南Yunnan	1.08	24	1.17	22	8.91
陕西Shaanxi	0.89	25	0.89	26	0.75
甘肃Gansu	0.76	27	0.90	25	18.11
青海Qinghai	0.27	30	0.33	30	21.39
宁夏Ningxia	1.14	23	1.89	17	66.31
新疆Xinjiang	0.51	28	0.63	28	22.46

年份 Year	经济地理矩阵 Economic geography matrix	地理距离矩阵 Geographic distance matrix	邻接矩阵 Adjacency matrix
2005	0.272^***（4.554）	0.245^***（3.339）	0.508^***（4.872）
2006	0.286^***（4.591）	0.261^***（3.398）	0.541^***（4.985）
2007	0.294^***（4.777）	0.265^***（3.496）	0.537^***（5.022）
2008	0.298^***（4.858）	0.278^***（3.658）	0.533^***（4.858）
2009	0.293^***（4.895）	0.260^***（3.541）	0.507^***（4.904）
2010	0.288^***（4.819）	0.253^***（3.461）	0.499^***（4.833）
2011	0.285^***（4.763）	0.248^***（3.391）	0.495^***（4.785）
2012	0.280^***（4.660）	0.242^***（3.287）	0.491^***（4.718）
2013	0.281^***（4.699）	0.244^***（3.328）	0.486^***（4.696）
2014	0.283^***（4.684）	0.246^***（3.318）	0.488^***（4.663）
2015	0.283^***（4.644）	0.243^***（3.256）	0.488^***（4.617）
2016	0.282^***（4.592）	0.242^***（3.219）	0.487^***（4.569）
2017	0.284^***（4.638）	0.250^***（3.320）	0.480^***（4.525）
2018	0.292^***（4.743）	0.256^***（3.394）	0.475^***（4.481）
2019	0.294^***（4.784）	0.256^***（3.399）	0.467^***（4.421）
2020	0.286^***（4.659）	0.249^***（3.314）	0.454^***（4.297）
2021	0.281^***（4.562）	0.246^***（3.253）	0.449^***（4.230）
2022	0.273^***（4.454）	0.236^***（3.149）	0.437^***（4.133）

检验Test	统计量Statistic		检验Test		统计量Statistic
LM(error) test	230.830^***		LR(sdm sem) test		33.41^***
LM(lag) test	29.763^***		LR(sdm sar) test		33.40^***
Hausman test		Both（36.10^***）		联合显著性检验 Joint significance test		Ind (47.65^*) Time (1871.30^*)

变量 Variable	模型I Model I [SDM（W₁）]		模型II Model II [SDM（W₂）]		模型III Model III [SDM（W₃）]
变量 Variable	系数 Coefficient	Z统计值 Z statistical value	系数 Coefficient	Z统计值 Z statistical value	系数 Coefficient	Z统计值 Z statistical value
农业发展水平ADL	0.505^***	4.36	0.553^***	4.89	0.324^***	2.79
ADL²	-0.045^***	-2.89	-0.051^***	-3.29	-0.023	-1.55
农业产业集聚LQ	1.743^***	3.84	1.947^***	4.12	1.191^***	2.62
LQ²	-0.355^***	-3.32	-0.342^***	-3.23	-0.240^**	-2.18
农业科技进步TPC	-0.239^***	-5.15	-0.264^***	-5.63	-0.241^***	-4.98
农业产业结构AIS	3.584^***	7.14	2.960^***	5.83	2.946^***	6.14
农业公共投资API	0.075^***	2.78	0.085^***	3.33	0.064^**	2.42
财政支农力度FSA	2.254^**	2.55	2.494^***	2.94	1.817^**	2.04
城镇化率UR	1.186	1.27	0.668	0.73	3.257^***	3.86
农村人力资本RHC	0.174^**	2.44	0.182^***	2.66	0.135^*	1.92
工业化水平IL	2.376^***	3.77	2.660^***	4.56	2.917^***	4.82
W×ADL	0.927^**	2.49	1.660^***	5.69	0.715^***	3.08
W×ADL²	-0.150^***	-3.16	-0.231^***	-6.20	-0.099^***	-3.44
W×LQ	-1.750	-1.02	-0.764	-0.69	-3.993^***	-3.67
W×LQ²	0.314	0.47	0.137	0.47	1.489^***	4.26
W×TPC	-0.070	-0.56	-0.388^***	-3.00	-0.145	-1.35
W×AIS	1.627	1.20	-4.274^***	-3.47	-0.589	-0.53
W×API	-0.154^*	-1.86	-0.122^*	-1.84	-0.117^**	-2.14
W×FSA	7.270^*	1.89	1.791	0.73	3.581^*	1.96
W×UR	-1.422	-0.59	-2.496	-1.24	-5.177^***	-3.07
W×RHC	0.344^**	2.02	0.351^**	2.03	0.301^**	2.02
W×IL	-1.780	-0.94	2.090	1.44	0.975	0.74
个体固定效应 Individual fixed effects	控制Control		控制Control		控制Control
时间固定效应 Fixed time effect	控制Control		控制Control		控制Control
样本容量 Sample size	540		540		540

China's Agricultural Carbon Emission Density: Spatiotemporal Characteristics, Dynamic Evolution, and Spatial Effect

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变量 Variable	直接效应Direct effect		间接效应Indirect effects		总效应Total effect
变量 Variable	系数Coefficient	T值T value	系数Coefficient	T值T value	系数Coefficient	T值T value
农业发展水平ADL	0.506^***	4.29	0.917^**	2.37	1.423^***	3.56
ADL²	-0.054^***	-2.85	-0.149^***	-2.96	-0.194^***	-3.67
农业产业集聚LQ	1.797^***	4.14	-1.721	-1.00	0.077	0.04
LQ²	-0.367^***	-3.70	0.275	0.42	-0.092	-0.14
农业科技进步TPC	-0.236^***	-5.26	-0.063	-0.49	-0.299^**	-2.21
农业产业结构AIS	3.617^***	7.19	1.596	1.15	5.213^***	3.30
农业公共投资API	0.075^***	2.75	-0.156^*	-1.87	-0.080	-0.89
财政支农力度FSA	2.221^**	2.51	7.059^*	1.86	9.280^**	2.28
城镇化率UR	1.215	1.31	-1.561	-0.64	-0.346	-0.12
农村人力资本RHC	0.178^**	2.55	0.333^*	1.89	0.511^***	2.61
工业化水平IL	2.369^***	3.78	-1.810	-0.99	0.559	0.29

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