Research on the effects of rural land consolidation on agricultural carbon emissions: a quasi-natural experiment based on the high-standard farmland construction policy
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摘要: 在“碳达峰、碳中和”目标下, 高标准农田建设被视为推动农业绿色低碳高质量发展的重要举措。本研究旨在深入探究高标准农田建设政策对农业碳排放的影响效应与作用机制, 为优化政策制定和农业碳减排提供经验依据, 促进低碳农业发展。本文基于2007—2017年中国30个省份的面板数据, 借助高标准农田建设政策和连续DID (Differences-in-differences)模型, 评估高标准农田建设政策对农业碳排放的影响。结果表明: 研究期间, 全国农业碳排放量呈先升后降的倒U型变化趋势, 2015年达到峰值。基准回归结果发现, 高标准农田建设政策显著抑制了农业碳排放。平均来看, 当其他条件不变时, 实施高标准农田建设政策可以显著减少10.1%的农业碳排放量。在替换解释变量和被解释变量以及剔除其他政策影响后, 高标准农田建设政策对农业碳排放的抑制作用依旧显著。动态估计结果显示, 高标准农田建设政策的减碳效应具有滞后性, 减碳效应于2013年显现出来并逐渐增强。机制分析发现, 高标准农田建设政策主要通过降低农业化学品投入强度和提高社会化服务来抑制农业碳排放。异质性分析发现, 高标准农田建设政策的减碳效应主要发生在土地流转程度高的省份和非粮食主产区, 而在土地流转程度低的省份和粮食主产区并未发挥相应的减碳效应。因此, 各级政府应差异化、精准化实施高标准农田建设政策, 关注农业化学化和社会化服务在减碳效应中的作用。Abstract: Under the carbon emission pattern of “carbon peak and carbon neutral”, agricultural carbon emissions, as one of the main sources of greenhouse gases, have become a key area for emission reduction. High-standard farmland construction is an important measure for promoting green, low-carbon, and high-quality agricultural development. An in-depth investigation of the effects and mechanisms of high-standard farmland construction policies on agricultural carbon emissions can provide an empirical basis for optimizing policy formulation and reducing agricultural carbon emissions. This is of great significance in promoting the development of low-carbon agriculture. Based on the theories of scale economy and division of labor, this study constructed a theoretical model of “high-standard farmland construction-agrochemical input intensity/socialized service-agricultural carbon emission”. Based on panel data from 30 provinces in China from 2007 to 2017, this study analyzed the effect and mechanism of the high-standard farmland construction policy on agricultural carbon emissions using a continuous differences-in-differences approach (DID) and mediation effect model. By measuring the agricultural carbon emissions of each province, this study found that national agricultural carbon emissions showed an inverted U-shaped trend, rising at the beginning, then declining, and peaking in 2015. Regions such as Henan, Shandong, Hebei, Jiangsu, and Anhui are at the forefront of agricultural carbon emissions nationwide, whereas regions such as Beijing, Shanghai, Tianjin, Hebei, and Shandong have higher rates of agricultural carbon emission reduction. The dynamic estimation results showed that the carbon reduction of the high-standard farmland construction policy had a lag effect, and the carbon reduction effect appeared in 2013 and continued to increase gradually. The results of the benchmark regression showed that a high-standard farmland construction policy significantly suppressed agricultural carbon emissions. On average, when all other conditions remained unchanged, implementing a high-standard farmland construction policy reduced agricultural carbon emissions significantly, i.e., by 10.1%. Robustness tests were conducted using the approach of substituting variables and considering the interference of other relevant policies. The results confirmed the positive effect of the high-standard farmland construction policy on reducing agricultural carbon emissions. The results of the mechanism analysis showed that agricultural chemical input intensity and agricultural socialized services played mediating roles in reducing agricultural carbon emissions through the construction of high-standard farmland. The construction of high-standard farmlands suppressed agricultural carbon emissions, mainly by reducing agricultural chemical input intensity and improving agricultural socialized services. Heterogeneity analysis revealed that the carbon reduction effect of the high-standard farmland construction policy mainly occured in provinces with a high degree of land transfer and in non-food-producing areas. In contrast, it did not have a corresponding carbon reduction effect in provinces with a low degree of land transfer and in food-producing areas. Therefore, the government should strengthen the construction of high-standard farmlands and differentiate the implementation of high-standard farmland construction policies according to local conditions and classifications to give full play to the emission reduction effect. In addition, the government should pay great attention to the role of agricultural chemicalization and socialized agricultural services in carbon reduction effects.
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表 1 高标准农田建设政策对农业碳排放影响的相关变量说明与描述性统计
Table 1. Definition and statistical description of relevant variables of high-standard farmland construction policy that have impacts on agricultural carbon emissions
类型
Type名称
Name符号
Symbol测量方法
Measurement method均值
Mean标准差
Standard
deviation最小值
Minimum
value最大值
Maximum
value被解释变量
Explained variable农业碳排放量
Agricultural carbon emission (×104 t)ACE 农业活动碳排放
Carbon emissions from agricultural activities286.463 200.177 12.159 874.300 核心解释变量
Core explaining variable土地整治面积占比
Percentage of rural land consolidation area to total area (%)HFC 中低产田改造与高标准农田面积/耕地
总面积
Ratio of area of improved land with low and medium yield and high-standard farmland to total arable land area39.239 22.711 8.012 130.039 控制变量
Control variable受灾程度
Degree of disaster (%)Dis 受灾面积/农作物播种面积
Ratio of disaster area to crop sown area20.790 14.567 0.945 69.545 环境污染治理投资Environmental pollution control investment (%) Env 环境污染治理投资/国内总产值
Ratio of environmental pollution control investment to Gross Domestic Product1.396 0.697 0.300 4.240 农业产业结构
Agricultural industry structure (%)Ind 农业总产值/农林牧渔业总产值
Ratio of total agricultural output value to total output value of agriculture, forestry, animal husbandry and fishery52.459 8.550 33.775 78.330 经济发展水平
Level of economic development (¥·cap.−1)Eco 人均国内总产值
Gross Domestic Product per capita42 556.380 23 928.040 6915.000 12 8994.100 财政支农力度
Financial support for agriculture (%)Fin 财政支农支出/总财政支出
Ratio of financial support for agriculture expenditure to total financial expenditure10.846 3.049 2.869 18.966 种植结构
Planting structure (%)Pla 粮食播种面积/农作物播种面积
Ratio of grain sown area to crop sown area65.417 12.965 32.815 95.847 城镇化率
Urbanization rate (%)Urb 城镇人口所占比例
Percentage of urban population54.437 13.568 28.240 89.600 土地经营规模
Land operation scale
(hm2∙cap.−1)Sca 农作物播种面积/农业劳动力
Ratio of crop sown area to agricultural labor1.266 0.545 0.588 3.761 中介变量
Intermediate variable农业化学品投入强度 Agricultural chemical products input intensity
(t∙hm−2)ACII 化肥、农药、农膜使用总量/
农作物播种面积
Ratio of total use amount of fertilizer, pesticide and agricultural film to crop sown area0.397 0.133 0.153 0.875 农业社会化服务
Agricultural socialization services (×107 ¥∙hm−2)ASS 农林牧渔服务业产值/
农作物播种面积
Raito of output value of agriculture, forestry, animal husbandry and fishery services to crop sown area0.224 0.146 0.030 0.903 表 2 2007—2017年各地区农业碳排放情况
Table 2. Agricultural carbon emissions in each region from 2007 to 2017
×104 t 地区 Region 2007 2015 2017 地区 Region 2007 2015 2017 北京 Beijing 25.086 18.415 15.310 河南 Henan 707.960 874.300 856.261 天津 Tianjin 41.084 36.836 32.160 湖北 Hubei 401.744 444.147 422.021 河北 Hebei 682.234 600.696 539.937 湖南 Hunan 314.679 360.992 357.680 山西 Shanxi 141.124 168.726 162.226 广东 Guangdong 305.186 362.672 362.644 内蒙古 Inner Mongolia 195.216 328.671 331.298 广西 Guangxi 279.245 338.073 336.485 辽宁 Liaoning 239.287 287.380 269.705 海南 Hainan 71.696 95.989 87.329 吉林 Jilin 216.945 314.227 312.849 重庆 Chongqing 112.751 135.393 133.294 黑龙江 Heilongjiang 303.266 416.230 414.070 四川 Sichuan 327.277 358.917 349.920 上海 Shanghai 36.865 28.903 26.977 贵州 Guizhou 102.826 136.151 130.082 江苏 Jiangsu 455.379 460.457 445.116 云南 Yunnan 228.728 353.006 356.067 浙江 Zhejiang 259.705 265.898 255.737 陕西 Shaanxi 197.149 295.596 297.065 安徽 Anhui 410.426 467.532 443.776 甘肃 Gansu 150.295 251.655 222.010 福建 Fujian 216.671 225.017 214.949 青海 Qinghai 12.159 18.352 17.539 江西 Jiangxi 201.855 226.769 216.292 宁夏 Ningxia 46.838 60.115 60.293 山东 Shandong 836.884 759.961 722.101 新疆 Xinjiang 239.069 439.550 435.613 表 3 高标准农田建设政策对农业碳排放的基准回归结果
Table 3. Baseline regression results of high-standard farmland construction policy on agricultural carbon emissions
变量
Variable模型1
Model 1模型2
Model 2HFC×T2011 −0.154*** (0.055) −0.101** (0.038) Dis 0.000 (0.000) Env 0.026 (0.027) Ind 0.003 (0.003) Eco 0.169 (0.116) Fin 0.019*** (0.006) Pla −0.003 (0.004) Urb 0.011 (0.009) Sca 0.069 (0.054) 常数项
Constant term5.187*** (0.024) 2.752** (1.205) 年份固定效应
Year fixed effect已控制 Control
已控制 Control已控制 Control
已控制 Control省份固定效应
Provincial fixed effect观测值数
Number of observations330 330 R2 0.4375 0.5664 HFC和T2011分别表示土地整治面积占比和高标准农田建设政策实施时点虚拟变量。其他变量解释说明见表1。*、**和***分别表示在10%、5%和1%的统计水平显著相关; 括号内为稳健标准误。HFC and T2011 are percentage of area of land consolidation to total arable land area and dummy variable of time related to policy implementation, respectively. The explanations of the other variables are shown in Table 1. *, ** and *** indicate significant correlations at 10%, 5%, and 1% levels, respectively. The robust standard errors are in parentheses. 表 4 高标准农田建设政策对农业碳排放影响的稳健性检验结果
Table 4. Robustness test results of the impact of high-standard farmland construction policy on agricultural carbon emissions
变量
Variable模型3 (替换核心解释变量)
Model 3 (substitution of core explaining variable)模型4 (替换被解释变量)
Model 4 (substitution of explained variable)剔除其他政策干扰
Remove other policy distractions模型5 (化肥农药零增长政策)
Model 5 (fertilizer and pesticide zero growth policy)模型6 (土地流转政策)
Model 6 (land transfer policy)HFC×T2011 −0.086** (0.035) −0.061* (0.031) −0.096*** (0.035) CADI×T2011 −0.053** (0.023) Tra −0.004** (0.002) 常数项 Constant term 2.894** (1.275) 9.425*** (1.430) 0.265 (1.330) 2.802** (1.072) 控制变量 Control variable
已控制 Control
已控制 Control
已控制 Control
已控制 Control
已控制 Control
已控制 Control
已控制 Control
已控制 Control年份固定效应
Year fixed effects省份固定效应
Provincial fixed effects观测值数
Number of observations330 330 240 330 R2 0.5524 0.9181 0.6698 0.5967 HFC、T2011、 CADI和Tra分别表示土地整治面积占比、高标准农田建设政策实施时点虚拟变量、单位农作物播种面积农业综合开发投入和土地流转水平。*、**和***分别表示在10%、5%和1%的统计水平上显著相关; 括号内为稳健标准误。HFC, T2011, CADI and Tra are percentage of area of land consolidation to total arable land area, dummy variable of time related to policy implementation, comprehensive investment for agriculture per unit crop sown area, and land transfer level, respectively. *, ** and *** indicate significant correlations at 10%, 5%, and 1% levels, respectively. The robust standard errors are in parentheses. 表 5 高标准农田建设政策对农业碳排放的动态影响估计结果
Table 5. Dynamic estimation results of the impact of high-standard farmland construction policy on agricultural carbon emissions
交互项
Interaction term估计系数
Estimated coefficient标准误
Standard
errorP值
P valueHFC×2007 0.060 0.043 0.168 HFC×2008 0.040 0.040 0.322 HFC×2009 0.038 0.025 0.145 HFC×2010 0.029 0.018 0.129 HFC×2012 −0.022 0.015 0.159 HFC×2013 −0.042 0.018 0.025 HFC×2014 −0.078 0.028 0.009 HFC×2015 −0.088 0.039 0.031 HFC×2016 −0.095 0.036 0.014 HFC×2017 −0.123 0.048 0.015 HFC表示土地整治面积占比。2007、2008、2009、2010、2012、2013、2014、2015、2016和2017表示年份虚拟变量。HFC refers to the percentage of land consolidation area to total arable land area. 2007, 2008, 2009, 2010, 2012, 2013, 2014, 2015, 2016 and 2017 refer to the year dummy variables. 表 6 高标准农田建设政策对农业碳排放影响的机制检验结果
Table 6. Mechanism test results of the impact of high-standard farmland construction policy on agricultural carbon emissions
变量
Variable模型7
Model 7 (ACII)模型8
Model 8 (ASS)模型9
Model 9 (ACE)模型10
Model 10 (ACE)HFC×T2011 −0.025* (0.013) 0.045* (0.025) −0.085** (0.038) −0.086** (0.037) ACII 0.645** (0.282) ASS −0.341* (0.194) 常数项 Constant term 1.054** (0.462) 1.144** (0.552) 2.071* (1.111) 3.142*** (1.122) 控制变量 Control variable
已控制 Controlled
已控制 Control
已控制 Controlled
已控制 Control
已控制 Controlled
已控制 Control
已控制 Controlled
已控制 Control年份固定效应 Year fixed effects 省份固定效应 Provincial fixed effects 观测值数 Number of observations 330 330 330 330 R2 0.5031 0.7943 0.5979 0.5851 HFC和T2011分别表示土地整治面积占比和高标准农田建设政策实施时点虚拟变量。ACII、ASS和ACE解释见表1。*、**和***分别表示在10%、5%和1%的统计水平显著相关; 括号内为稳健标准误。HFC and T2011 are percentage of area of land consolidation to total arable land and dummy variable of time related to policy implementation, respectively. Details of ACII, ASS and ACE can be seen in Table 1. *, ** and *** indicate significant correlation at 10%, 5%, and 1% levels, respectively. Robust standard errors are in parentheses. 表 7 高标准农田建设政策对农业碳排放影响的异质性回归结果
Table 7. Heterogeneity regression results of the impact of high-standard farmland construction policy on agricultural carbon emissions
变量
Variable模型11 (土地流转程度高)
Model 11 (high level
of land transfer)模型12 (土地流转程度低)
Model 12 (low level
of land transfer)模型13 (粮食主产区)
Model 13 (Major grain
producing areas)模型14 (非粮食主产区)
Model 14 (non-grain
producing areas)HFC×T2011 −0.120*** (0.036) −0.062 (0.067) −0.032 (0.023) −0.111* (0.054) 常数项 Constant term 3.085***(1.006) 4.167** (1.925) 5.630*** (1.246) 2.095 (1.585) 控制变量 Control variable 已控制 Controlled 已控制 Controlled 已控制 Controlled 已控制 Controlled 年份固定效应 Year fixed effects 已控制 Controlled 已控制 Controlled 已控制 Controlled 已控制 Controlled 省份固定效应 Provincial fixed effects 观测值数 Number of observations 143 187 143 187 R2 0.7282 0.6643 0.6427 0.7098 HFC和 T2011分别表示土地整治面积占比和高标准农田建设政策实施时点虚拟变量。*、**和***分别表示在10%、5%和1%的统计水平显著相关; 括号内为稳健标准误。HFC and T2011 are percentage of area of land consolidation to total arable land and dummy variable of time related to policy implementation, respectively. *, ** and *** indicate significant correlation at 10%, 5%, and 1% levels, respectively. Robust standard errors are in parentheses. -
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附表1 区域土地流转水平和农业功能区分区.docx
附表2 2007—2017年各地区农业碳排放情况.docx
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