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

摘要/Abstract

摘要：

【目的】比较农业碳排放密度指标优势，厘清中国省际农业碳排放密度现状、影响因素及空间溢出效应，为挖掘更多农业碳减排潜在空间提供参考。【方法】在精准测度中国及省域农业碳排放密度的基础上，利用核密度估计、空间自相关以及空间杜宾模型等方法对其时空特征、影响因素及空间溢出效应展开研究。【结果】2005—2022年间农业碳排放密度虽小幅下降但伴随着较大的年际波动，总体表现为“波动下降”“持续上升”“波动下降”3个阶段的变化，省际间已呈均衡化发展趋势，虽仍存在绝对差距，但该差距正不断缩小；农业碳排放密度存在极强且稳定的空间依赖性格局，局部形成以低密度省份与多个低密度省份相互依赖的“低-低”集聚类型为主导、高密度省份与多个高密度省份相互依赖的“高-高”集聚类型为次主导的空间异质性格局；农业碳排放密度受政府职责、经济发展、社会参与以及文化教育等四个层面的综合影响，农业经济发展水平与农业产业集聚对其存在“倒U”型影响关系，农业科技进步显著促进了农业碳排放密度降低，而农业公共投资力度、财政支农力度、农业产业结构、农村人力资本以及工业化水平的提升均导致农业碳排放密度增加；空间溢出效应方面，周边地区农业经济发展水平提升会引起本地农业碳排放密度先增后减，周边地区农业公共投资力度加大将促进本地农业碳排放密度降低，而财政支农力度与农村人力资本的空间溢出效应将导致本地农业碳排放密度增加。【结论】中国省际间农业碳排放密度仍存在较大差异，全国省域之间具有极强的空间依赖性，局部地区存在明显的空间异质性格局；农业碳排放密度受政府职责、经济发展、社会参与以及文化教育四个层面的因素共同影响，农业经济发展水平持续提高与加大农业公共投资力度有利于周边地区农业碳排放密度降低，财政支农力度与农村人力资本的空间溢出效应则与之相反。

关键词: 碳中和, 农业碳排放, 农业碳排放密度, 时空特征, 空间效应, 中国

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

尹忞昊, 陈池波, 卢奕亨, 田云. 中国农业碳排放密度的时空特征、动态演进与空间效应[J]. 中国农业科学, 2025, 58(18): 3710-3727.

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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链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2025.18.010

https://www.chinaagrisci.com/CN/Y2025/V58/I18/3710

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2005—2022年中国农业碳排放密度的局部空间聚类情况"

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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参考文献 32

[1]	WEST T O, POST W M. Soil organic carbon sequestration rates by tillage and crop rotation. Soil Science Society of America Journal, 2002, 66(6): 1930-1946.
[2]	JOHNSON J M, FRANZLUEBBERS A J, WEYERS S L, REICOSKY D C. Agricultural opportunities to mitigate greenhouse gas emissions. Environmental Pollution, 2007, 150(1): 107-124. pmid: 17706849
[3]	李波, 张俊飚, 李海鹏. 中国农业碳排放时空特征及影响因素分解. 中国人口·资源与环境, 2011, 21(8): 80-86.
	LI B, ZHANG J B, LI H P. Research on spatial-temporal characteristics and affecting factors decomposition of agricultural carbon emission in China. China Population, Resources and Environment, 2011, 21(8): 80-86. (in Chinese)
[4]	田云, 张俊飚, 尹朝静, 吴贤荣. 中国农业碳排放分布动态与趋势演进: 基于31个省(市、区)2002—2011年的面板数据分析. 中国人口·资源与环境, 2014, 24(7): 91-98.
	TIAN Y, ZHANG J B, YIN Z J, WU X R. Distributional dynamics and trend evolution of China’s agricultural carbon emissions: An analysis on panel data of 31 provinces from 2002 to 2011. China Population, Resources and Environment, 2014, 24(7): 91-98. (in Chinese)
[5]	田云, 尹忞昊. 中国农业碳排放再测算: 基本现状、动态演进及空间溢出效应. 中国农村经济, 2022(3): 104-127.
	TIAN Y, YIN M H. Remeasurement of agricultural carbon emissions in China: Basic status, dynamic evolution, and spatial spillover effects. Chinese Rural Economy, 2022(3):104-127. (in Chinese)
[6]	金书秦, 林煜, 牛坤玉. 以低碳带动农业绿色转型: 中国农业碳排放特征及其减排路径. 改革, 2021(5): 29-37.
	JIN S Q, LIN Y, NIU K Y. Driving green transformation of agriculture with low carbon: Characteristics of agricultural carbon emissions and its emission reduction path in China. Reform, 2021(5): 29-37. (in Chinese)
[7]	章胜勇, 尹朝静, 贺亚亚, 肖小勇. 中国农业碳排放的空间分异与动态演进: 基于空间和非参数估计方法的实证研究. 中国环境科学, 2020, 40(3): 1356-1363.
	ZHANG S Y, YIN C J, HE Y Y, XIAO X Y. Spatial differentiation and dynamic evolution of agricultural carbon emission in China: Empirical research based on spatial and non-parametric estimation methods. China Environmental Science, 2020, 40(3): 1356-1363. (in Chinese)
[8]	尹忞昊, 田云, 卢奕亨. 中国农业碳排放区域差异及其空间分异机理. 改革, 2023(10): 130-145.
	YIN M H, TIAN Y, LU Y H. Regional differences and spatial divergence mechanisms of agricultural carbon emissions in China. Reform, 2023(10): 130-145. (in Chinese)
[9]	何艳秋, 戴小文. 中国农业碳排放驱动因素的时空特征研究. 资源科学, 2016, 38(9): 1780-1790. doi: 10.18402/resci.2016.09.15
	HE Y Q, DAI X W. Phase characteristics and regional differences in agriculture carbon emissions in China. Resources Science, 2016, 38(9): 1780-1790. (in Chinese) doi: 10.18402/resci.2016.09.15
[10]	吴贤荣, 张俊飚. 中国省域农业碳排放: 增长主导效应与减排退耦效应. 农业技术经济, 2017(5): 27-36.
	WU X R, ZHANG J B. Agricultural carbon emissions in China Province: Growth leading effect and decoupling effect of emission reduction. Journal of Agrotechnical Economics, 2017(5): 27-36. (in Chinese)
[11]	魏玮, 文长存, 崔琦, 解伟. 农业技术进步对农业能源使用与碳排放的影响: 基于GTAP-E模型分析. 农业技术经济, 2018(2): 30-40.
	WEI W, WEN C C, CUI Q, XIE W. The impacts of technological advance on agricultural energy use and carbon emission-an analysis based on GTAP-E model. Journal of Agrotechnical Economics, 2018(2): 30-40. (in Chinese)
[12]	伍国勇, 刘金丹, 杨丽莎. 中国农业碳排放强度动态演进及碳补偿潜力. 中国人口·资源与环境, 2021, 31(10): 69-78.
	WU G Y, LIU J D, YANG L S. Dynamic evolution of China’s agricultural carbon emission intensity and carbon offset potential. China Population, Resources and Environment, 2021, 31(10): 69-78. (in Chinese)
[13]	徐清华, 张广胜. 农业机械化对农业碳排放强度影响的空间溢出效应: 基于282个城市面板数据的实证. 中国人口·资源与环境, 2022, 32(4): 23-33.
	XU Q H, ZHANG G S. Spatial spillover effect of agricultural mechanization on agricultural carbon emission intensity: An empirical analysis of panel data from 282 cities. China Population, Resources and Environment, 2022, 32(4): 23-33. (in Chinese)
[14]	张广胜, 王珊珊. 中国农业碳排放的结构、效率及其决定机制. 农业经济问题, 2014, 35(7): 18-26, 110.
	ZHANG G S, WANG S S. China’s agricultural carbon emission: Structure, efficiency and its determinants. Issues in Agricultural Economy, 2014, 35(7): 18-26, 110. (in Chinese)
[15]	郭四代, 钱昱冰, 赵锐. 西部地区农业碳排放效率及收敛性分析: 基于SBM-Undesirable模型. 农村经济, 2018(11): 80-87.
	GUO S D, QIAN Y B, ZHAO R. Analysis of agricultural carbon emission efficiency and convergence in western China: Based on SBM-undesirable model. Rural Economy, 2018(11): 80-87. (in Chinese)
[16]	谢会强, 吴晓迪. 城乡融合对中国农业碳排放效率的影响及其机制. 资源科学, 2023, 45(1): 48-61. doi: 10.18402/resci.2023.01.04
	XIE H Q, WU X D. Impact and its mechanism of urban-rural integration on the efficiency of agricultural carbon emissions in China. Resources Science, 2023, 45(1): 48-61. (in Chinese) doi: 10.18402/resci.2023.01.04
[17]	孟庆雷, 殷宇翔, 王煜昊. 我国农业碳排放的时空演化、脱钩效应及绩效评估. 中国农业科学, 2023, 56(20): 4049-4066. doi: 10.3864/j.issn.0578-1752.2023.20.010.
	MENG Q L, YIN Y X, WANG Y H. Spatial-temporal evolution, decoupling effect and performance evaluation of China’s agricultural carbon emissions. Scientia Agricultura Sinica, 2023, 56(20): 4049-4066. doi: 10.3864/j.issn.0578-1752.2023.20.010. (in Chinese)
[18]	谷家川, 查良松. 皖江城市带农田生态系统碳排放动态研究. 长江流域资源与环境, 2013, 22(1): 81-87.
	GU J C, ZHA L S. Dynamic research on carbon emissions of farmland ecological system of the Wan Jiang city belt. Resources and Environment in the Yangtze Basin, 2013, 22(1): 81-87. (in Chinese)
[19]	张红凤, 杨方腾, 李晓婷. 中国文化产业发展的空间布局、动态演进及形成机理. 改革, 2022(11): 106-118.
	ZHANG H F, YANG F T, LI X T. Spatial layout, dynamic evolution and formation mechanism of the development of Chinese cultural industry. Reform, 2022(11): 106-118. (in Chinese)
[20]	ANSELIN L. Local indicators of spatial association: LISA. Geographical Analysis, 1995, 27(2): 93-115.
[21]	袁华锡, 刘耀彬, 胡森林, 封亦代. 产业集聚加剧了环境污染吗? : 基于外商直接投资视角. 长江流域资源与环境, 2019, 28(4): 794-804.
	YUAN H X, LIU Y B, HU S L, FENG Y D. Does industrial agglomeration aggravate environmental pollution: From a perspective of foreign direct investment. Resources and Environment in the Yangtze Basin, 2019, 28(4): 794-804. (in Chinese)
[22]	徐维祥, 周建平, 刘程军. 数字经济发展对城市碳排放影响的空间效应. 地理研究, 2022, 41(1): 111-129. doi: 10.11821/dlyj020210459
	XU W X, ZHOU J P, LIU C J. The impact of digital economy on urban carbon emissions: Based on the analysis of spatial effects. Geographical Research, 2022, 41(1): 111-129. (in Chinese)
[23]	郑宏运, 李谷成. 城乡政策偏向对农业资源配置效率的影响研究. 农业技术经济, 2020(7): 79-92.
	ZHENG H Y, LI G C. The impact of urban-rural biased policy on agricultural resource allocation efficiency. Journal of Agrotechnical Economics, 2020(7): 79-92. (in Chinese)
[24]	田云, 尹忞昊. 产业集聚对中国农业净碳效应的影响研究. 华中农业大学学报(社会科学版), 2021(3): 107-117, 188.
	TIAN Y, YIN M H. Research on the impact of industrial agglomeration on China’s agricultural net carbon effect. Journal of Huazhong Agricultural University (Social Sciences Edition), 2021(3): 107-117, 188. (in Chinese)
[25]	盖美, 杨苘菲, 何亚宁. 东北粮食主产区农业绿色发展水平时空演化及其影响因素. 资源科学, 2022, 44(5): 927-942. doi: 10.18402/resci.2022.05.05
	GAI M, YANG Q F, HE Y N. Spatiotemporal changes and influencing factors of agricultural green development level in main grain- producing areas in Northeast China. Resources Science, 2022, 44(5): 927-942. (in Chinese)
[26]	陶群山, 胡浩. 环境规制和农业科技进步的关系分析: 基于波特假说的研究. 中国人口·资源与环境, 2011, 21(12): 52-57.
	TAO Q S, HU H. Analysis on the relationship of environmental regulation and agricultural technological progress: Based on the study of Porter’s hypothesis. China Population, Resources and Environment, 2011, 21(12): 52-57. (in Chinese)
[27]	陈雨生, 陈志敏, 江一帆. 农业科技进步和土地改良对我国耕地质量的影响. 农业经济问题, 2021, 42(9): 132-144.
	CHEN Y S, CHEN Z M, JIANG Y F. Effects of agricultural science and technology progress and land improvement on cultivated land quality in China. Issues in Agricultural Economy, 2021, 42(9): 132-144. (in Chinese)
[28]	逯进, 赵亚楠, 高艳云. 我国省域人口结构对环境污染影响的异质性研究: 基于有限混合模型. 统计研究, 2022, 39(11): 88-101.
	LU J, ZHAO Y N, GAO Y Y. Study on the heterogeneity of the effect of provincial population structure on environmental pollution in China: Based on the finite mixture model. Statistical Research, 2022, 39(11): 88-101. (in Chinese)
[29]	邹新阳, 温涛. 普惠金融、社会绩效与乡村振兴: 基于30省(区、市)的面板数据. 改革, 2021(4): 95-106.
	ZOU X Y, WEN T. Inclusive finance, social performance and rural revitalization: Based on panel data of 30 provinces. Reform, 2021(4): 95-106. (in Chinese)
[30]	邵帅, 范美婷, 杨莉莉. 经济结构调整、绿色技术进步与中国低碳转型发展——基于总体技术前沿和空间溢出效应视角的经验考察. 管理世界, 2022, 38(2): 46-69, 4-10.
	SHAO S, FAN M T, YANG L L. Economic restructuring, green technology progress and China's low-carbon transformation and development: An empirical investigation from the perspective of overall technology frontier and spatial spillover effect. Management World, 2022, 38(2): 46-69, 4-10. (in Chinese)
[31]	LEASAGE J P, PACE R K. Introduction to Spatial Econometrics. Boca Ration: CRC Press, 2009.
[32]	赵领娣, 贾斌, 胡明照. 基于空间计量的中国省域人力资本与碳排放密度实证研究. 人口与发展, 2014, 20(4): 2-10.
	ZHAO L D, JIA B, HU M Z. The impact of human capital on carbon emission density in Chinese provinces based on the spatial econometric analysis. Population and Development, 2014, 20(4): 2-10. (in Chinese)

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