粮食主产区农业社会化服务对农业碳排放强度的影响及空间溢出效应

张甜; 张艳荣

doi:10.12357/cjea.20240274

粮食主产区农业社会化服务对农业碳排放强度的影响及空间溢出效应

张甜,
张艳荣^,

甘肃农业大学财经学院　兰州　730070

基金项目: 甘肃省乡村振兴战略的制度供给研究(GSAU-XKJS-2018-245)资助

详细信息

作者简介:
张甜, 主要从事农业生态环境研究。E-mail: 2404123448@qq.com

通讯作者:
张艳荣, 主要从事农村经济、农业生态等方面应用研究。E-mail: 1296503929@qq.com

中图分类号: F294
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出版历程
- 收稿日期: 2024-05-17
- 修回日期: 2024-06-20
- 录用日期: 2024-06-24
- 网络出版日期: 2024-06-24

Impact of agricultural social service on the intensity of agricultural carbon emissions in major grain-producing areas and its spatial spillover effect

ZHANG Tian,
ZHANG Yanrong^,

College of Finance and Economics, Gansu Agricultural University, Lanzhou 730070, China

Funds: This study was supported by the Study on institutional supply of rural revitalization strategy in Gansu Province (GSAU-XKJS-2018-245).

More Information

Corresponding author:
ZHANG Yanrong: E-mail: 1296503929@qq.com

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摘要

摘要:
粮食主产区承担着粮食和生态的双安全重担, 探究其农业社会化服务对农业碳排放强度的影响机制, 对于推进粮食主产区农业低碳转型具有现实意义。本文通过选取2008—2022年粮食主产区13个省份(自治区)面板数据, 运用普通最小二乘法、中介效应模型和时间固定效应的空间杜宾模型, 进一步探讨农业社会化服务对农业碳排放强度的直接影响、间接影响和空间溢出效应。本文主要结论如下: 1)农业社会化服务对农业碳排放强度具有显著“减碳效应”, 从农业社会化服务的4个维度来看, 农业信息化服务、农村公共服务和农业金融保险服务均显著降低了农业碳排放强度, 而农资服务提高了农业碳排放强度。2)基于经济发展水平将粮食主产区划分为三大流域发现: 长江流域和黄河流域农业社会化服务一定程度上降低了农业碳排放强度, 而松花江流域农业社会化服务在一定程度上提高了农业碳排放强度。3)农业经营规模和农业财政扶持在农业社会化服务对农业碳排放强度的作用过程中存在中介效应, 即农业社会化服务能够通过促进农业经营规模扩大和降低农业财政扶持所带来的负面环境效应间接降低农业碳排放强度。4)粮食主产区农业社会化服务对农业碳排放强度具有较强的负向空间溢出效应, 本省份农业社会化服务每增加1%, 邻近省份农业碳排放强度降低7.035%。基于上述结论得出如下政策启示, 政府应提高粮食主产区各省份农业社会化服务发展水平, 合理规划农业社会化服务发展方向和范围, 并构建粮食主产区各省份农业社会化服务和农业碳减排交流渠道, 以此来降低粮食主产区农业碳排放强度, 助力粮食主产区早日实现农业绿色低碳化转型。
- 粮食主产区 /
- 农业社会化服务 /
- 农业碳排放强度 /
- 中介效应 /
- 空间溢出效应
Abstract:
Major grain-producing areas bear the double burden of food and ecology security. Therefore, it is practically significant to explore the influence mechanism of agricultural social service on the intensity of agricultural carbon emissions to promote the low-carbon transformation of agriculture in the major grain-producing areas. This study analyzed panel data from 13 provinces (autonomous regions) in major grain-producing areas from 2008 to 2022 using the Ordinary Least Squares Method, the Mediation Effect Model, and Spatial Durbin Model of the time fixed effect to determine the direct and indirect impacts of agricultural social service on agricultural carbon emission intensity and spatial spillover effects. The main conclusions of this study were as follows: 1) Agricultural social service had a significant “carbon reduction effect” on agricultural carbon emission intensity. From the perspective of the four dimensions of agricultural social service, agricultural information services, rural public services, and agricultural finance and insurance services reduced agricultural carbon emission intensity, while agricultural material services increased agricultural carbon emission intensity. 2) Based on their level of economic development, the main grain-producing areas could be divided into three major river basins. It was found that the agricultural social service in the Yangtze and Yellow River basins reduced agricultural carbon emission intensity to a certain extent, while the agricultural social service in the Songhua River Basin increased agricultural carbon emission intensity to a certain extent. 3) The agricultural operation scale and agricultural financial support were found to have mediating effects on agricultural carbon emission intensity; that is, agricultural social service can indirectly reduce agricultural carbon emission intensity by promoting the expansion of agricultural operation scale and reducing the negative environmental effects caused by agricultural financial support. 4) Agricultural social service in the main grain-producing areas had a significant negative spatial spillover effect on the intensity of agricultural carbon emissions, and every 1% increase in agricultural social service in this province reduced the intensity of agricultural carbon emissions in neighboring provinces by 7.035%. Based on these findings, the government should improve the development level of agricultural social service in the provinces of the main grain-producing areas, as well as reasonably plan the development direction and scope of agricultural social service. Further developments should seek to build communication channels between agricultural social service and agricultural carbon emission reduction in the provinces of the main grain-producing areas. This will help reduce the intensity of agricultural carbon emissions and assist in transforming the main grain-producing areas into green and low-carbon agriculture in the near future.
- Major grain-producing areas /
- Agricultural social service /
- Agricultural carbon emission intensity /
- Mediation effect /
- Spatial spillover effect

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图 1 农业社会化服务对农业碳排放强度的影响机理

Figure 1. The influence mechanism of agricultural social service on agricultural carbon emission intensity

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表 1 农业生产碳排放源碳排放系数

Table 1 Carbon emission factor of carbon emission sources in agricultural production

	碳排放源 Carbon emission sources	碳排放系数 Carbon emission factor	数据来源 Data sources
作物种植 Crop cultivation	水稻 Rice	0.240 kg(N₂O)·hm⁻²	[16]
	小麦 Wheat	冬小麦: 1.750 kg(N₂O)·hm⁻² Winter wheat: 1.750 kg(N₂O)·hm⁻²
	小麦 Wheat	春小麦: 0.400 kg(N₂O)·hm⁻² Spring wheat: 0.400 kg(N₂O)·hm⁻²
	玉米 Maize	2.530 kg(N₂O)·hm⁻²	[17]
	豆类 Beans	0.770 kg(N₂O)·hm⁻²	[18]
	薯类 Tubers	0.950 kg(N₂O)·hm⁻²	[16]
农用物资投入 Input of agricultural materials	化肥 Chemical fertilizer	0.896 kg(C)·kg⁻¹	美国橡树岭国家实验室 Oak Ridge National Laboratory, USA
	农药 Pesticide	4.934 kg(C)·kg⁻¹	美国橡树岭国家实验室 Oak Ridge National Laboratory, USA
	农用薄膜 Agricultural plastic film	5.180 kg(C)·kg⁻¹	南京农业大学农业资源与环境研究所 Research Institute of Agricultural Resources and Environment, Nanjing Agricultural University
	农用柴油 Agricultural diesel	0.593 kg(C)·kg⁻¹	联合国政府间气候变化专门委员会 United Nations Intergovernmental Panel on Climate Change
	农业翻耕 Agricultural ploughing	312.600 kg(C)·km⁻¹	中国农业大学生物学院 College of Biology, China Agricultural University
	农业灌溉 Agricultural irrigation	266.480 kg(C)·hm⁻¹	[19]
本文采用的各省早季稻、晚季稻和中季稻CH₄排放系数源于参考文献[16]研究, 详见电子版附件。此外, 由于粮食主产区果树种植面积较粮食作物播种面积较少, 农用物资投入中农膜和农药在果树上的消耗较小, 故忽略不计。The emission coefficient of CH₄ of early-season rice, late-season rice and mid-season rice in this study was derived from reference [16], and details can be seen in the electronic attachments. In addition, the planting area of fruit trees in the main grain-producing areas is much less than that of grain crops, and the consumption of agricultural plastic film and pesticide on the fruit trees is very less and ignored in this study.

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表 2 粮食主产区农业社会化服务发展水平评价指标体系

Table 2 Evaluation index system of the development level of agricultural social service in major grain-producing areas

一级指标 Level 1 indicator	二级指标 Level 2 indicator	指标解释 Index interpretation	指标属性 Index attribute	权重 Weight
农资服务 Agricultural material services (A1)	农业机械供应量 Supply of agricultural machinery	农业机械总动力 Total power of agricultural machinery (×10⁴ kW)	+	0.081
	水利设施水平 Water conservancy facilities level	水库数量 Number of reservoirs	+	0.143
	农业生产服务价格指数 Agricultural production and service price index	农业生产服务价格指数 Agricultural production and service price index	+	0.063
农业信息化服务 Agricultural information services (A2)	移动电话普及量 Mobile phone popularity	移动电话普及率 Mobile phone penetration rate [phone·(100 people)⁻¹]	+	0.036
	农村邮政投递路线长度 Length of rural postal delivery routes	农村投递路线长度 Rural delivery route length (km)	+	0.081
	农村互联网使用水平 Internet use level in rural areas	农村宽带接入用户 Rural broadband access users (×10⁴ household)	+	0.132
农村公共服务 Rural public services (A3)	有效灌溉面积 Effective irrigation area	有效灌溉面积 Effective irrigated area (×10³ hm²)	+	0.062
	农村用电量 Rural electricity consumption	农村用电量 Rural electricity consumption (×10⁸ kWh)	+	0.181
	财政支农支出 Fiscal expenditure to support agriculture	地方财政农林水事务支出 Local financial expenditure on agriculture, forestry and water resources affairs (×10⁸ ¥)	+	0.048
农业金融保险服务 Agricultural finance and insurance services (A4)	农业贷款额 Agricultural loan amount	涉农贷款余额 Balance of agriculture-related loans (×10⁸ ¥)	+	0.098
农业金融保险服务 Agricultural finance and insurance services (A4)	农业保险水平 Agricultural insurance level	农业保险保费收入 Agricultural insurance premium income (×10⁶ ¥)	+	0.073

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表 3 各变量的描述性统计

Table 3 Descriptive statistics for each variable

变量种类 Variable species	变量 Variable	变量说明 Variable declaration	平均值 Average	标准差 Standard deviation	最小值 Minimum	最大值 Maximum
被解释变量 Explained variable	农业碳排放强度 Agricultural carbon emission intensity (ACI)	—	0.430	0.246	0.139	1.530
解释变量 Explanatory variable	农业社会化服务 Agricultural social service (ASS)	—	0.274	0.131	0.046	0.615
中介变量 Mediation variable	农业经营规模 Scale of agricultural operation (AOS)	农作物播种面积/农林牧渔业从业人数 Crop sown area / number of employees in agriculture, forestry, animal husbandry and fishery (hm²·cap.⁻¹)	0.914	0.497	0.418	2.920
中介变量 Mediation variable	农业财政扶持 Agricultural financial support (AFS)	农林水事务支出/财政一般性支出 Expenditure on agricultural, forestry and water affairs / general fiscal expenditure	0.115	0.023	0.069	0.190
控制变量 Controlled variable	农业产业结构 Agricultural industrial structure (AIS)	第一产业增加值/地区GDP The added value of the primary industry / regional GDP	0.117	0.039	0.043	0.259
	城镇化水平 Urbanization level (URB)	城镇人口/总人口 Urban population / total population	0.560	0.086	0.360	0.744
	工业化水平 Industrial level (LOI)	地区规模以上工业企业数的对数 Take the logarithm of number of industrial enterprises above regional scale	9.464	0.779	7.938	11.090
	人力资本水平 Human capital level (LHC)	农村平均受教育年限 The average number of years of education was received in rural areas (a)	9.075	0.549	7.440	10.560
	农业生产环境 Agricultural production environment (APE)	农作物成灾面积/当年实际播种面积 Crop disaster area / actual sown area of the year	0.080	0.068	0.005	0.376
—代表该变量由作者计算得到。以上变量观测值均为195。— represent that the variable was calculated by the authors. The number of all the above variables are 195.

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表 4 2008年和2022年粮食主产区各地区农业社会化服务水平及农业碳排放强度水平

Table 4 The level of agricultural social service and the level of agricultural carbon emission intensity in different regions of the major grain-producing areas in 2008 and 2022

地区 Region	农业社会化服务发展水平 Development level of agricultural social service				农业碳排放强度 Agricultural carbon emission intensity
地区 Region	2008	类型 Type	2022	类型 Type	2008	类型 Type	2022	类型 Type
江西 Jiangxi	0.159	B₃	0.297	B₂	1.530	C₄	0.616	C₂
安徽 Anhui	0.154	B₃	0.376	B₁	1.084	C₃	0.473	C₁
湖北 Hubei	0.173	B₃	0.327	B₂	0.841	C₃	0.314	C₁
湖南 Hunan	0.287	B₂	0.477	B₁	0.663	C₂	0.223	C₁
江苏 Jiangsu	0.318	B₂	0.504	B₁	0.808	C₃	0.290	C₁
四川 Sichuan	0.192	B₃	0.478	B₁	0.503	C₂	0.139	C₁
吉林 Jilin	0.046	B₄	0.132	B₄	0.507	C₂	0.309	C₁
黑龙江 Heilongjiang	0.075	B₄	0.253	B₂	0.611	C₂	0.207	C₁
辽宁 Liaoning	0.113	B₄	0.135	B₄	0.415	C₁	0.164	C₁
河北 Hebei	0.245	B₃	0.419	B₁	0.459	C₁	0.156	C₁
山东 Shandong	0.357	B₁	0.561	B₁	0.368	C₁	0.143	C₁
河南 Henan	0.225	B₃	0.483	B₁	0.423	C₁	0.159	C₁
内蒙古 Inner Mongolia	0.070	B₄	0.219	B₃	0.488	C₁	0.243	C₁
B₁: 高农业社会化服务水平; B₂: 中高农业社会化服务水平; B₃: 中低农业社会化服务水平; B₄: 低农业社会化服务水平; C₁: 低碳排放强度水平; C₂: 中等碳排放强度水平; C₃: 较高碳排放强度水平; C₄: 高碳排放强度水平。B₁: high level of agricultural social service; B₂: medium-high level of agricultural social service; B₃: medium-low level of agricultural social service; B₄: low level of agricultural social service; C₁: low carbon emission intensity level; C₂: moderate carbon intensity level; C₃: medium-high carbon intensity level; C₄: high carbon intensity level.

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表 5 农业社会化服务对农业碳排放强度的普通最小二乘法回归结果

Table 5 The regression result of Ordinary Least Square Method on agricultural carbon emission intensity of agricultural social service

变量 Variable	ACI
变量 Variable	模型1 Model 1	模型2 Model 2	模型3 Model 3	模型4 Model 4	模型5 Model 5	模型6 Model 6
ASS	−0.650^*** (0.127)	−0.863^*** (0.144)	−0.849^*** (0.129)	−0.857^*** (0.177)	−0.831^*** (0.173)	−0.764^*** (0.178)
AIS		−1.420^** (0.486)	−2.165^*** (0.446)	−2.142^*** (0.564)	−2.232^*** (0.551)	−2.277^*** (0.550)
URB			−1.247^*** (0.176)	−1.241^*** (0.196)	−0.430 (0.314)	−0.461^* (0.314)
LOI				0.003 (0.037)	−0.020^* (0.036)	−0.018^* (0.036)
LHC					−0.158^** (0.049)	−0.152^** (0.049)
APE						0.350^* (0.236)
常数项 Constant term	0.608^*** (0.039)	0.833^*** (0.086)	1.615^*** (0.134)	1.587^*** (0.424)	2.789^*** (0.555)	2.685^*** (0.558)
N	195	195	195	195	195	195
R²	0.115	0.148	0.322	0.319	0.352	0.356
: P<0.1; : P<0.05; **: P<0.01。ACI: 农业碳排放强度; ASS: 农业社会化服务; AIS: 农业产业结构; URB: 城镇化水平; LOI: 工业化水平; LHC: 人力资本水平; APE: 农业生产环境。括号内为标准误。ACI: agricultural carbon emission intensity; ASS: agricultural social service; AIS: agricultural industrial structure ; URB: urbanization level; LOI: industrial level; LHC: human capital level; APE: agricultural production environment. Values in parentheses were standard error。

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表 6 农业社会化服务不同维度异质性结果

Table 6 Heterogeneity results of different dimensions of agricultural social service

变量 Variable	ACI
变量 Variable	模型1 Model 1	模型2 Model 2	模型3 Model 3	模型4 Model 4
A₁	0.347 (0.404)
A₂		−2.842^*** (0.408)
A₃			−1.123^* (0.455)
A₄				−4.047^*** (0.822)
控制变量 Controlled variable	Yes	Yes	Yes	Yes
常数项 Constant term	3.641^*** (0.538)	2.266^*** (0.518)	3.074^*** (0.573)	2.881^*** (0.528)
N	195	195	195	195
R²	0.295	0.437	0.315	0.373
: P<0.1; **: P<0.01。ACI: 农业碳排放强度; A₁: 农资服务; A₂: 农业信息化服务; A₃: 农村公共服务; A₄: 农业金融保险服务。括号内为标准误。ACI: agricultural carbon emission intensity; A₁: agricultural material services ; A₂: agricultural information service; A₃: rural public services; A₄: agricultural finance and insurance services. Values in parentheses were standard error.

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表 7 流域异质性结果

Table 7 Results of basin heterogeneity

变量 Variable	ACI
变量 Variable	长江流域(模型1) Yangtze River Basin (Model 1)	黄河流域(模型2) Yellow River Basin (Model 2)	松花江流域(模型3) Songhua River Basin (Model 3)
ASS	−1.679^*** (0.310)	−0.573^*** (0.101)	0.287 (0.365)
控制变量 Controlled variable	Yes	Yes	Yes
常数项 Constant term	5.519^*** (1.068)	1.109^*** (0.274)	2.682^*** (0.326)
N	90	60	45
R²	0.612	0.902	0.863
***: P<0.01。ACI: 农业碳排放强度; ASS: 农业社会化服务。括号内为标准误。长江流域主要包括江西、安徽、湖北、湖南、江苏和四川, 黄河流域主要包括河北、山东、河南和内蒙古, 松花江流域主要包括吉林、辽宁和黑龙江。ACI: agricultural carbon emission intensity; ASS: agricultural social service. Values in parentheses were standard error。The Yangtze River Basin mainly includes Jiangxi, Anhui, Hubei, Hunan, Jiangsu and Sichuan, the Yellow River Basin mainly includes Hebei, Shandong, Henan and Inner Mongolia, and the Songhua River Basin mainly includes Jilin, Liaoning and Heilongjiang.

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表 8 中介效应检验结果

Table 8 Results of the mediation effect tests

变量 Variable	效应名称 Effect name	模型1 Model 1 (AOS)	模型2 Model 2 (ACI)	模型3 Model 3	模型4 Model 4 (AFS)	模型5 Model 5 (ACI)
ASS		0.009 (0.359)	−0.753^*** (0.206)		−0.124^*** (0.037)	−0.644^** (0.210)
AOS			−0.017 (0.045)
AFS						−0.876^* (0.434)
Bootstrap	_bs_1			0.321^*** (0.078)
Bootstrap	_bs_2			1.085^*** (0.139)
控制变量 Controlled variable		Yes	Yes		Yes	Yes
常数项 Constant term		−0.457 (0.684)	3.434^*** (0.392)		0.055 (0.070)	3.378^*** (0.388)
N		195	195		195	195
R²		0.956	0.941		0.777	0.942
: P<0.1; : P<0.05; **: P<0.01。AOS: 农业经营规模; ACI: 农业碳排放强度; AFS: 农业财政扶持; ASS: 农业社会化服务;_bs_1: 间接效应结果; _bs_2: 直接效应结果。括号内为标准误。AOS: scale of agricultural operation; ACI: agricultural carbon emission intensity; AFS: agricultural financial support; ASS: agricultural social service; _bs_1: indirect effect results; _bs_2: direct effect results. Values in parentheses were standard error.

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表 9 2008—2022年粮食主产区农业社会化服务发展水平和农业碳排放强度的全局Moran’s I指数

Table 9 Global Moran’s I index of the development level of agricultural social service and agricultural carbon emission intensity in major grain-producing areas from 2008 to 2022

年份 Year	农业社会化服务 Agricultural social service	农业碳排放强度 Agricultural carbon emission intensity
2008	0.089^**	0.110^***
2009	0.078^**	0.091^***
2010	0.051^**	0.050^**
2011	0.079^**	0.047^**
2012	0.061^**	0.049^**
2013	0.066^**	0.064^***
2014	0.078^**	0.059^***
2015	0.077^**	0.056^***
2016	0.033^**	0.012^*
2017	0.070^**	0.014^*
2018	0.087^**	0.011^*
2019	0.098^**	0.011^*
2020	0.140^***	0.02^*
2021	0.154^***	0.021^*
2022	0.163^***	0.023^*
: P<0.1; : P<0.05; **: P<0.01。全局Moran’s I是在地理距离权重矩阵条件下利用Stata 17所得。Global Moran’s I is obtained using Stata 17 under the geographic distance weight matrix condition。

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表 10 拉格朗日乘数检验(LM)、似然比检验(LR)、沃尔德检验(Wald)及豪斯曼(Hausman)检验结果

Table 10 Results of the Lagrange Multiplier Checkout (LM), Likelihood Ratio Checkout (LR), Wald Checkout and Hausman Checkout

检验方法 Method of calibration	检验项目 Inspecting item	地理距离权重矩阵 Geographic distance weight matrix
检验方法 Method of calibration	检验项目 Inspecting item	统计值 Statistical value	P值 P value
LM检验 LM Checkout	LM(误差)检验 LM-error	8.83	0.000
	稳健LM(误差)检验 Robust-LM-error	5.56	0.000
	LM(滞后)检验 LM-lag	45.30	0.000
	稳健LM(滞后)检验 Robust-LM-lag	42.03	0.000
LR检验 LR Checkout	LR(空间误差)检验 LR-spatial-error	67.00	0.000
LR检验 LR Checkout	LR(空间滞后)检验 LR-spatial-lag	69.07	0.000
Wald检验 Wald Checkout	Wald(空间误差)检验 Wald-spatial-error	78.71	0.000
Wald检验 Wald Checkout	Wald(空间误差)检验 Wald-spatial-lag	82.83	0.000
豪斯曼检验 Hausman Checkout	空间固定的豪斯曼检验 Space-fixed Hausman test	20.48	0.002
	时间固定的豪斯曼检验 Time-fixed Hausman test	67.03	0.000
	时间空间固定的豪斯曼检验 Hausman test with fixed space and time	30.42	0.000
SDM/SAR/SEM		时间固定效应的SDM模型 The SDM model for time-fixed effects
结果由Stata 17软件计算所得。SDM: 空间杜宾模型; SAR: 空间滞后模型; SEM:空间误差模型。Results were calculated by the Stata 17 software。SDM: Spatial Durbin Model; SAR: Spatial Lag Model; SEM: Spatial Error Model.

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表 11 地理距离权重矩阵下农业社会化服务对农业碳排放强度影响的分解效应

Table 11 Decomposition effects of the impact of agricultural social service on agricultural carbon emission intensity under the geographic distance weight matrix

变量 Variable	ACI
变量 Variable	模型系数 Model coefficient	直接效应 Direct effect	间接效应 Indirect effect	总效应 Gross effect
ASS	−1.664^*** (0.297)	−1.857^*** (0.372)	−7.035^** (2.626)	−8.892^** (2.927)
AIS	−2.656^** (0.953)	−3.040^** (1.085)	−11.699 (6.125)	−14.739^* (7.061)
URB	−0.487^** (0.381)	−0.517^** (0.401)	−2.105^* (2.880)	−2.623^* (3.099)
LOI	0.013^* (0.055)	0.037^* (0.068)	0.954^* (0.470)	0.991^* (0.527)
LHC	−0.105^** (0.070)	−0.118^** (0.077)	−0.298^*** (0.549)	−0.416^** (0.604)
APE	0.063^* (0.251)	0.095 (0.248)	0.842^** (1.323)	0.937 (1.371)
常数项 Constant term	0.027^*** (3.80)
rho	0.240^*** (1.55)
N	195
R²	0.318
: P<0.1; : P<0.05; **: P<0.01。ACI: 农业碳排放强度; ASS: 农业社会化服务; AIS: 农业产业结构; URB: 城镇化水平; LOI: 工业化水平; LHC: 人力资本水平; APE: 农业生产环境。括号内为标准误。ACI: agricultural carbon emission intensity; ASS: agricultural social service; AIS: agricultural industrial structure; URB: urbanization level; LOI: industrial level; LHC: human capital level; APE: agricultural production environment. Values in parentheses were standard error.

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表 12 内生性检验结果

Table 12 Results of the endogeneity test

变量 Variable	模型1 Model 1 (ASS)	模型2 Model 2 (ACI)
L-ASS	1.012^*** (0.021)
ASS		−0.802^*** (0.139)
AIS	−0.028 (0.061)	−2.211^*** (0.505)
URB	−0.017 (0.039)	−0.411^** (0.352)
LOI	−0.000 (0.003)	−0.016^* (0.033)
LHC	−0.003 (0.004)	−0.142^** (0.045)
APE	0.016 (0.022)	0.069^** (0.237)
常数项 Constant term	0.053 (0.043)	2.568^*** (0.538)
R²	0.981	0.332
第一阶段F值 The first stage F value	1226.95
: P<0.1; : P<0.05; **: P<0.01。ASS: 农业社会化服务; ACI: 农业碳排放强度; L-ASS:滞后一期的农业社会化服务; AIS: 农业产业结构; URB: 城镇化水平; LOI: 工业化水平; LHC: 人力资本水平; APE: 农业生产环境。括号内为标准误。ASS: agricultural social service; ACI: agricultural carbon emission intensity; L-ASS: lagged behind a period of agricultural social service; AIS: agricultural industrial structure; URB: urbanization level; LOI: industrial level; LHC: human capital level; APE: agricultural production environment. Values in parentheses were standard error.

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表 13 稳健性检验结果

Table 13 Results of the robustness test

变量 Variable	更换权重矩阵 Replace the weight matrix	更换被解释变量 Replace the explained variable
直接效应 Direct effect	−0.117^*** (0.178)	−0.999^*** (0.135)
间接效应 Indirect effect	−1.691^*** (0.281)	−4.391^*** (1.238)
总效应 Gross effect	−1.808^*** (0.298)	−5.390^*** (1.359)
空间系数rho Spatial coefficient rho	0.807^*** (0.071)	0.363^*** (0.134)
控制变量 Controlled variable	Yes	Yes
N	195	195
R²	0.541	0.020
***: P<0.01。括号内为标准误。Values in parentheses were standard error.

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