Effects of agricultural technical efficiency on agricultural carbon emission: Based on spatial spillover effect and threshold effect analysis
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摘要: 农业技术是促进农业产业发展的根本力量, 探究其对农业碳排放的影响机制, 对实现我国“双碳”目标具有重要意义。本文基于2001—2020年我国31个省、直辖市和自治区(港澳台地区以外)的面板数据, 使用随机前沿模型对农业技术效率进行测算, 并对各地区农业碳排放总量与强度进行核算, 构建空间杜宾模型和以农业技术效率为门槛变量的门槛模型, 探究农业技术效率和农业碳排放的空间效应与非线性关系。结果表明: 全国农业碳排放总量与强度近年来呈下降趋势。中部地区农业碳排放总量高于东西部地区, 东部地区农业技术效率高于中西部地区, 而农业碳排放强度则低于中西部地区。农业碳排放强度与农业技术效率具有空间自相关性, 并表现为集聚特征, 集聚类型以高高聚聚和低低聚集为主。农业碳排放强度具有正向空间溢出效应, 而农业技术效率对农业碳排放强度则表现为负向空间溢出, 此外城镇化、人力资本水平和人均耕地面积对农业碳排放强度具有负向影响, 农业经济发展水平和农业受灾程度为正向影响。农业技术效率与农业碳排放强度存在双门槛效应, 当农业技术效率达到“拐点”后, 其对农业碳排放强度的影响转变为负向, 当进一步提升农业技术效率水平后, 其影响力会因边际效应递减而减弱。本研究为探索实现“双碳”目标的路径提供理论基础与政策依据。Abstract: Global warming, caused by the greenhouse effect, has triggered numerous unprecedented extreme weather events globally. Agricultural technology is the fundamental force that promotes the development of the agricultural industry. Studying the impact mechanism of agricultural technology on agricultural carbon emissions will help transform traditional agriculture into ecological, green, and low-carbon modern agriculture, and it is of great significance to the realization of carbon neutrality and carbon peaks. This study used panel data from 31 provinces and cities in China from 2001 to 2020. First, the stochastic frontier model was used to extend existing research from a broad and narrow sense of agricultural technical progress to agricultural technical efficiency. The total agricultural carbon emissions and intensity of agricultural carbon emissions were then calculated and compared. Finally, we constructed the spatial Dubin model and the threshold model with agricultural technical efficiency as the threshold variable, which revealed the spatial effect and non-linear relationship between agricultural technical efficiency and agricultural carbon emissions. The results showed that the total and intensity of agricultural carbon emissions had decreased in recent years. Central China had more agricultural carbon emissions than eastern and western China, and eastern China had a higher technical efficiency of agriculture and a lower carbon emission intensity of agriculture than central and western China. Agricultural carbon emission intensity and technical efficiency had spatial autocorrelation and agglomeration characteristics, and high-high clustering and low-low clustering are the main factors among the provinces. Agricultural carbon emission intensity had a positive spatial spillover effect on itself, but agricultural technical efficiency had a negative spatial spillover effect, which was conducive to the overall reduction of agricultural carbon emissions. Additionally, urbanization, human capital level, and per capita cultivated land area also had negative effects on agricultural carbon emission intensity, but the level of agricultural economic development and the degree of agricultural disaster had positive effects. There was a double threshold effect between agricultural technical efficiency and agricultural carbon emission intensity, which meant that when agricultural technical efficiency reached the “inflection point”, its impact on agricultural carbon emission intensity became negative, and after the level of agricultural technical efficiency was further improved, its influence weakened due to the diminishing marginal effect. Most existing research began with a broad or narrow definition of technological progress, but this study used technical efficiency as the research object after the decomposing technological progress in a broad sense, which further validated the indisputable and decisive role of technological progress in agricultural energy conservation and emission reduction. This study provides a theoretical and policy basis for exploring the path to achieving the “double carbon” goal.
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图 1 2001—2020年中国不同地区农业碳排放总量(a)和农业碳排放强度(b)
Figure 1. Total amount (a) and intensity (b) of agricultural carbon emissions in different regions of China from 2001 to 2020
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图 2 2001—2020年中国不同地区农业技术效率
Figure 2. Agricultural technical efficiencies in different regions of China from 2001 to 2020
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图 3 2020年农业碳排放强度(a)与农业技术效率(b) Moran’s I散点图
1: 北京; 2: 天津; 3: 河北; 4: 山西; 5: 内蒙古; 6: 辽宁; 7: 吉林; 8: 黑龙江; 9: 上海; 10: 江苏; 11: 浙江; 12: 安徽; 13: 福建; 14: 江西; 15: 山东; 16: 河南; 17: 湖北; 18: 湖南; 19: 广东; 20: 广西; 21: 海南; 22: 重庆; 23: 四川; 24: 贵州; 25: 云南; 26: 西藏; 27: 陕西; 28: 甘肃; 29: 青海; 30: 宁夏; 31: 新疆。
Figure 3. Moran’s I scatter diagrams of agricultural carbon emission intensity and agricultural technical efficiency in 2020
1: Beijing; 2: Tianjin; 3: Hebei; 4: Shanxi; 5: Inner Mongolia; 6: Liaoning; 7: Jilin; 8: Heilongjiang; 9: Shanghai; 10: Jiangsu; 11: Zhejiang; 12: Anhui; 13: Fujian; 14: Jiangxi; 15: Shandong; 16: Henan; 17: Hubei; 18: Hunan; 19: Guangdong; 20: Guangxi; 21: Hainan; 22: Chongqing; 23: Sichuan; 24: Guizhou; 25: Yunnan; 26: Tibet; 27: Shaanxi; 28: Gansu; 29: Qinghai; 30: Ningxia; 31: Xinjiang.
表 1 各类农业碳排放源的碳排放系数及其来源
Table 1 Carbon emission coefficient of each carbon source of agriculture and its reference source
碳源
Carbon source计算方法
Computational method碳排放系数
Carbon emission coefficient数据参考来源
Reference source化肥 Chemical fertilizer 实际化肥投入量 Actual fertilizer input 0.859 kg∙kg−1 West, et al[28], ORNL[29] 农药 Pesticide 实际农药投入量 Actual pesticide inputs 4.934 kg∙kg−1 ORNL[29] 柴油 Diesel 农业机械消耗柴油量 Diesel fuel consumption by agricultural machinery 0.593 kg∙kg−1 IREEA 农膜 Agricultural film 农用塑料薄膜使用量 Amount of plastic film used for agriculture 5.18 kg∙kg−1 IPCC[30] 农地翻耕 Agricultural land tilling 农作物实际播种总面积 Actual total area sown of crops 312.6 kg∙hm−2 CAB[31] 农业灌溉 Agricultural irrigation 农作物实际灌溉面积 Actual irrigated area of crops 20.476 kg∙hm−2 RDRCH[5] ORNL: 美国橡树岭国家实验室; IREEA: 南京农业大学农业资源与生态环境研究所; IPCC: 政府间气候变化专门委员会; CAB: 中国农业大学农学与生物技术学院; RDRCH: 湖北农村发展研究中心。ORNL: Oak Ridge National Laboratory, USA; IREEA: Institute of Resource, Ecosystem and Environment of Agriculture, Nanjing Agricultural University; IPCC: Intergovernmental Panel on Climate Change; CAB: College of Agronomy and Biotechnology, China Agricultural University; RDRCH: Rural Development Research Center of Hubei. 
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表 2 农业碳排放研究相关变量说明及描述性统计
Table 2 Description and descriptive statistics of related variables of agricultural carbon emission research
变量类型
Type of
variable指标名称
Name of
index变量符号
Variable
symbol测算方法
Calculating
method单位
Unit均值
Mean
value标准差
Standard
deviation被解释变量
Explained variable农业碳排放强度
Carbon emission intensity
of agricultureCarbon I 农业碳排放/不变价格的种植业总产值
Agricultural carbon emissions /
gross plantation value at constant pricet·(104 ¥)−1 0.51 0.28 解释变量
Explaining variable农业技术效率
Agricultural technical
efficiencyATE 基于超越对数生产模型计算
Based on translog production model— 0.19 0.10 控制变量
Control variable城镇化率
Urbanization rateUrban 城镇常住人口/常住总人口
Permanent urban population /
total permanent population% 0.50 0.17 农业经济发展水平
Level of agricultural
economic developmentEy 农业总产值/农业从业人员
Total agricultural output value /
agricultural employees×104 ¥·capita−1 1.79 1.30 人力资本水平
Level of human capitalEdu 农村地区初中及其以上学历人数/
农村地区6岁以上人数
Number of people with junior high school
education or above in rural areas / number
of people over 6 years old in rural areas% 0.05 0.01 农业受灾程度
Degree of agricultural
damageDisaster 受灾面积/农作物播种面积
Affected area / crop sown area% 0.22 0.16 人均耕地面积
Arable land per capitaArea 农作物播种面积/第一产业从业人数
Crop sown area / number of
workers in primary industryhm2·capita−1 0.63 0.34 样本时间
Sample timeYear 年份(2001—2020年)
Year (2001—2020)— — — 研究对象
Object of studyProvince 省份(31个省、直辖市、自治区)
Provinces (31 provinces, cities, autonomous regions)
— — —
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表 3 2001—2020年农业碳排放强度和农业技术效率的全局Moran’s I指数及检验
Table 3 Moran’s I indexes and test of agricultural carbon emission intensity and agricultural technical efficiency from 2001 to 2020
年份
Year农业碳排放强度
Agricultural carbon
emission intensity年份
Year农业技术效率
Agricultural technical
efficiencyMoran’s I Z P Moran’s I Z P 2001 0.278 2.636 0.004 2001 0.338 3.562 0.000 2002 0.224 2.196 0.014 2002 0.345 3.592 0.000 2003 0.237 2.287 0.011 2003 0.351 3.619 0.000 2004 0.220 2.151 0.016 2004 0.356 3.643 0.000 2005 0.185 1.840 0.033 2005 0.362 3.665 0.000 2006 0.185 1.866 0.031 2006 0.366 3.684 0.000 2007 0.221 2.131 0.017 2007 0.370 3.701 0.000 2008 0.118 1.267 0.103 2008 0.374 3.717 0.000 2009 0.198 1.971 0.024 2009 0.378 3.73 0.000 2010 0.208 2.056 0.020 2010 0.381 3.742 0.000 2011 0.195 1.931 0.027 2011 0.384 3.752 0.000 2012 0.211 2.059 0.020 2012 0.386 3.761 0.000 2013 0.203 1.999 0.023 2013 0.389 3.769 0.000 2014 0.177 1.826 0.034 2014 0.391 3.776 0.000 2015 0.229 2.258 0.012 2015 0.393 3.782 0.000 2016 0.249 2.485 0.006 2016 0.394 3.787 0.000 2017 0.277 2.644 0.004 2017 0.396 3.792 0.000 2018 0.331 3.189 0.001 2018 0.397 3.795 0.000 2019 0.320 3.103 0.001 2019 0.399 3.798 0.000 2020 0.368 3.519 0.000 2020 0.400 3.801 0.000
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表 4 农业技术效率对农业碳排放强度空间面板回归模型的LM检验、LR检验、Wald检验和Hausman检验结果
Table 4 Results of LM test, LR test, Wald test and Hausman test of the spatial panel regression model of agricultural technical efficiency to agricultural carbon emission intensity
检验类别
Test category检验项目
Inspection itemW1 W2 W3 LM检验 LM test LM(误差)检验 LM (error) test 431.232*** 426.437*** 64.628*** 稳健LM(误差)检验 Robust LM (error) test 300.825*** 370.055*** 50.390*** LM(滞后)检验 LM (lag) test 134.108*** 77.351*** 15.260*** 稳健LM(滞后)检验 Robust LM (lag) test 3.701* 20.969*** 1.023 LR检验 LR test SDM与SLM的卡方检验 SDM&SLM chi2 88.50*** 68.00*** 46.97*** SDM与SEM的卡方检验 SDM&SEM chi2 64.73*** 44.35*** 49.50*** Wald检验 Wald test SDM与SLM的卡方检验 SDM&SLM chi2 27.73*** 36.70*** 31.27*** SDM与SEM的卡方检验 SDM&SEM chi2 24.76*** 21.59*** 18.58*** Hausman检验 Hausman test 显著性卡方检验 Prob>=chi2 54.41*** 73.37*** 124.75*** 拟合度 R2 时间效应 Time 0.0750 0.4271 0.0271 个体效应 Ind 0.8809 0.9098 0.8895 混合效应 Both 0.7442 0.7408 0.7247 模型选择 Model select 个体固定效应
的SDM模型
SDM model of individual
fixed effects个体固定效应
的SDM模型
SDM model of individual
fixed effects个体固定效应
的SDM模型
SDM model of individual
fixed effects***和*分别表示在1%和10%水平显著; W1、W2和W3分别代表邻接空间矩阵、地理距离矩阵和经济距离矩阵; LM检验、LR检验和Wald检验分别代表拉格朗日乘子检验、似然比检验和沃尔德检验; SLM、SEM和SDM分别代表空间滞后模型、空间误差模型和空间杜宾模型; Chi2为卡方检验。*** and * indicate significance at 1% and 10% levels, respectively. W1, W2 and W3 represent adjacency spatial matrix, geographical distance matrix and economic distance matrix, respectively. LM test, LR test and Wald test represent Lagrange multiplier test, likelihood ratio test and Wald test, respectively. SLM, SEM and SDM represent spatial lag model, spatial error model and spatial Durbin model, respectively. Chi2 is the chi-square test.
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表 5 农业技术效率对农业碳排放强度的邻接空间矩阵空间杜宾模型回归结果及效应分解
Table 5 Regression results and effect decomposition of the adjacency space matrix spatial Durbin model of agricultural technical efficiency to agricultural carbon emission intensity
变量
Variable模型估计系数
Main空间矩阵估计系数
Wx空间自相关估计系数
Spatial差异系数
Variance直接效应
Direct effect间接效应
Indirect effect总效应
Total effectATE 6.203***(0.452) −7.081***(0.473) 5.529***(0.431) −7.747***(0521) −2.217***(0.393) Urban −0.361*** (0.107) 0.095(0.128) −0.390***(0.095) −0.282(0.188) −0.672***(0.176) Ey −0.036*** (0.008) 0.090***(0.012) −0.021**(0.008) 0.160***(0.025) 0.139***(0.029) Edu 0.473(1.138) −4.457***(1.529) −0.416(1.077) −9.736***(2.763) −10.151***(3.004) Disaster 0.079***(0.030) 0.049(0.050) 0.099***(0.030) 0.222**(1.103) 0.321***(0.113) Area 0.007***(0.002) −0.016***(0.004) 0.004*(0.002) −0.029***(0.008) −0.024***(0.009) rho 0.602***(0.034) sigma2_e 0.005***(0.0003) ***、**和*分别表示在1%、5%和10%水平显著; Main意为本地区解释变量对本地区被解释变量的影响程度系数β, Wx意为相邻地区解释变量对本地区被解释变量的影响程度系数θ, Spatial意为相邻地区的被解释变量对本地区被解释变量的影响系数ρ, Variance意为差异系数; 直接效应、间接效应与总效应分别表示利用偏微分法的无偏估计结果, 即本地区解释变量对本地区被解释变量的影响程度, 相邻地区解释变量对本地区被解释变量的影响程度和直接效应与间接效应的总和; ATE、Urban、Ey、Edu、Disaster、Area、rho和sigma2_e分别代表农业技术效率、城镇化率、农业经济发展水平、人力资本水平、农业受灾程度、人均耕地面积、空间自相关系数ρ和个体效应的特异误差; 括号内为标准差。***, ** and * indicate significance at 1%, 5% and 10% levels, respectively. Main means the coefficient β of the influence degree of the explanatory variables in the local region on the explained variables in the local region; Wx means the coefficient θ of the influence degree of the explanatory variables in neighboring regions on the explained variables in the local region; Spatial means the influence coefficient ρ of the explained variables in neighboring regions on the explained variables in the local region; and Variance means the difference coefficient. Direct effect, indirect effect and total effect respectively represent the unbiased estimation results using partial differential method, that is, the degree of influence of local explanatory variables on local explained variables, the degree of influence of neighboring explanatory variables on local explained variables and the sum of direct and indirect effects. ATE, Urban, Ey, Edu, Disaster, Area, rho and sigma2_e represent agricultural technical efficiency, urbanization rate, agricultural economic development level, human capital level, agricultural disaster degree, per capital cultivated land area, spatial autocorrelation coefficient ρ and individual effect specific error, respectively. Standard deviations are in parentheses.
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表 6 农业技术效率对农业碳排放强度的地理距离矩阵空间杜宾模型回归结果及效应分解
Table 6 Geographical distance matrix spatial Durbin model regression results and effect decomposition of agricultural technical efficiency to agricultural carbon emission intensity
变量
Variable模型估计系数
Main空间矩阵估计系数
Wx空间自相关估计系数
Spatial差异系数
Variance直接效应
Direct effect间接效应
Indirect effect总效应
Total effectATE 7.110***(0.390) −7.914***(0.418) 6.672***(0.386) −9.018***(0.530) −2.346***(0.468) Urban −0.363***(0.096) 0.185(0.120) −0.374***(0.088) −0.143(0.199) −0.517***(0.185) Ey −0.040***(0.007) 0.075***(0.015) −0.032***(0.008) 0.139***(0.042) 0.107**(0.045) Edu 0.076(1.040) −3.762**(1.486) −0.468(0.995) −10.322***(3.365) −10.790***(3.579) Disaster 0.072***(0.027) 0.001(0.060) 0.079***(0.026) 0.133(0.154) 0.213(0.159) Area 0.009***(0.002) −0.005(0.056) 0.010***(0.023) 0.002(0.015) 0.011(0.017) rho 0.654***(0.037) sigma2_e 0.004***(0.0002) 同表5注释。Note the same as in Table 5.
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表 7 农业技术效率对农业碳排放强度的经济距离矩阵空间杜宾模型回归结果及效应分解
Table 7 Economic distance matrix spatial Durbin model regression results and effect decomposition of agricultural technical efficiency on agricultural carbon emission intensity
变量
Variable模型估计系数
Main空间矩阵估计系数
Wx空间自相关估计系数
Spatial差异系数
Variance直接效应
Direct effect间接效应
Indirect effect总效应
Total effectATE 6.393***(0.379) −7.741***(0.389) 5.837***(0.370) −8.755***(0.446) −2.918***(0.384) Urban −0.444***(0.093) 0.252**(0.126) −0.445***(0.085) 0.025(0.180) −0.420**(0.166) Ey −0.033***(0.008) 0.126***(0.016) −0.018**(0.008) 0.222***(0.031) 0.204***(0.033) Edu 3.181***(1.098) −10.768***(1.732) 1.943*(1.002) −18.431***(2.876) −16.488***(2.945) Disaster 0.102***(0.027) 0.028(0.054) 0.114***(0.028) 0.169(0.112) 0.282**(0.128) Area 0.010***(0.002) −0.018***(0.004) 0.008***(0.002) −0.027***(0.009) −0.019*(0.010) rho 0.537***(0.035) sigma2_e 0.005***(0.0003) 同表5注释。Note the same in Table 5.
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表 8 农业碳排放研究中变量的Pearson相关系数及VIF检验结果
Table 8 Pearson correlation coefficient and VIF test results of variables in the study of agricultural carbon emissions
变量
VariableUrban Ey Edu Disaster Area Urban 1.000 Ey 0.551*** 1.000 Edu 0.590*** 0.537*** 1.000 Disaster −0.363*** −0.446*** −0.239*** 1.000 Area 0.285*** 0.399*** 0.296** −0.030 1.000 VIF 2.66 2.35 2.05 1.35 1.26 ***、**分别表示在1%、5%水平显著; Urban、Ey、Edu、Disaster、Area和VIF分别表示城镇化率、农业经济发展水平、人力资本水平、农业受灾程度、人均耕地面积和方差膨胀因子。***, ** indicate significance at 1%, 5% levels, respectively. Urban, Ey, Edu, Disaster, Area and VIF represent the urbanization rate, agricultural economic development level, human capital level, agricultural disaster degree, per capital cultivated land area and variance inflation factor.
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表 9 农业碳排放研究中变量的单位根检验结果
Table 9 Results of unit root tests for variables in agricultural carbon emissions research
变量 Variable HT检验 HT test IPS检验 IPS test LLC检验 LLC test 平稳性 Stationarity Statistic P Statistic P Statistic P Carbon I 0.5601 0.0045 −4.9399 0.0000 −6.6489 0.000 平稳 Steady ATE 0.9396* 0.0000 −29.9033 0.0000 −3.1050 0.0010 平稳 Steady Urban 0.4386 0.0000 −6.3996 0.0000 −20.9000 0.000 平稳 Steady Ey 0.3116* 0.0000 −7.5412** 0.0000 −6.4405* 0.0000 平稳 Steady Edu 0.1314 0.0000 −7.5447 0.0000 −5.7000 0.0000 平稳 Steady Disaster −0.1156 0.0000 −12.6652 0.0000 −18.5207 0.0000 平稳 Steady Area −0.0036* 0.0000 −5.8085* 0.0000 −13.7496* 0.0000 平稳 Steady *表示进行了一阶差分, **表示进行了二阶差分; HT检验、IPS检验、LLC检验分别表示Harris-Tzavalis检验、Im, Pesaran and Shin检验和Levin-Lin-Chu 检验; Carbon I、ATE、Urban、Ey、Edu、Disaster和Area分别代表农业碳排放强度、农业技术效率、城镇化率、农业经济发展水平、人力资本水平、农业受灾程度和人均耕地面积。* means the first difference, ** means the second difference. HT test, IPS test and LLC test represent Harris-Tzavalis test, Im, Pesaran and Shin test, and Levin-Lin-Chu test. Carbon I, ATE, Urban, Ey, Edu, Disaster and Area represent agricultural carbon emission intensity, agricultural technical efficiency, urbanization rate, agricultural economic development level, human capital level, agricultural disaster degree, and per capital cultivated land area, respectively.
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表 10 农业碳排放研究中变量的协整检验结果
Table 10 Cointegration test results of the variables in the study of agricultural carbon emissions
检验类别 Kind of inspection Statistic P 佩德罗尼检验 Pedroni test MPP test 8.0859 0.0000 PP test −4.5221 0.0000 ADF test −3.1695 0.0008 卡奥检验 Kao test MDF test −2.9334 0.0017 DF test −4.4456 0.0000 ADF test −3.8192 0.0001 维斯特隆德检验 Westerlund test VR 3.3257 0.0004 MPP test、PP test、ADF test、MDF test、DF test和VR分别表示修正的菲利普斯-佩荣检验、菲利普斯-佩荣检验、增广迪基-富勒检验、修正的迪基-富勒检验、迪基-富勒检验和方差比。MPP test, PP test, ADF test, MDF test, DF test and VR represent Modified Phillips-Perron test, Phillips-Perron test, Augmented Dickey-Fuller test, Modified Dickey-Fuller test, Dickey-Fuller test and Variance ratio, respectively.
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表 11 农业技术效率对农业碳排放强度的门槛效应检验结果
Table 11 Test results of threshold effect of agricultural technical efficiency on agricultural carbon emission intensity
门槛检验 Threshold test F 临界值 Critical value 10% 5% 1% 单一门槛 Single threshold 197.94*** 46.5580 51.5043 65.6828 双重门槛 Double threshold 118.84*** 31.6226 35.4662 45.8873 三重门槛 Triple threshold 76.39 81.7964 94.8090 111.1802 ***表示在1%水平显著。*** indicates significance at 1% level.
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表 12 农业技术效率对农业碳排放强度的门槛模型回归估计结果
Table 12 Threshold model regression estimation results of agricultural technical efficiency on agricultural carbon emission intensity
变量
Variable系数估计值
Coefficient
estimation标准误
Standard
errorStatistic Urban −0.480*** 0.074 −6.46 Ey −0.011 0.009 −1.15 Edu 0.369 1.008 0.37 Disaster 0.1078*** 0.033 3.30 Area −0.003 0.003 −1.28 ATE (ATE≤0.0746) 0.496 0.309 1.61 ATE (0.0746<ATE≤0.2590) −2.122*** 1.150 −14.16 ATE (ATE>0.2590) −1.538*** 0.130 −11.84 Constant 1.082*** 0.056 19.30 Observations 620 F-value 542.31 R-squared 0.8819 ***表示在1%水平显著; Urban、Ey、Edu、Disaster、Area和ATE分别代表城镇化率、农业经济发展水平、人力资本水平、农业受灾程度、人均耕地面积和农业技术效率。*** indicates significance at 1% level. Urban, Ey, Edu, Disaster, Area and ATE represent urbanization rate, agricultural economic development level, human capital level, agricultural disaster degree, per capita cultivated land area and agricultural technical efficiency, respectively.
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表 13 农业技术效率门槛值及省份分布(2015年)
Table 13 Agricultural technical efficiency threshold and provincial distribution (2015)
门槛值及区间
Threshold value and interval省份
Province低 Low (ATE≤0.0746) 中等 Medium
(0.0746<ATE≤0.2590)新疆、西藏、湖南、吉林、四川、河南、甘肃、重庆、陕西、黑龙江、湖北、广西、江西、
云南、山西、贵州、青海、宁夏、安徽、内蒙古
Xinjiang, Tibet, Hunan, Jilin, Sichuan, Henan, Gansu, Chongqing, Shaanxi, Heilongjiang, Hubei, Guangxi, Jiangxi,
Yunnan, Shanxi, Guizhou, Qinghai, Ningxia, Anhui, Inner Mongolia高 High
(ATE>0.2590)北京、上海 、浙江、天津 、福建、广东 、辽宁、海南、山东、河北 、江苏
Beijing, Shanghai, Zhejiang, Tianjin, Fujian, Guangdong, Liaoning, Hainan, Shandong, Hebei, Jiangsu
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[1] 王一鸣. 百年大变局、高质量发展与构建新发展格局[J]. 管理世界, 2020, 36(12): 1−13 WANG Y M. Changes unseen in a century, high-quality development, and the construction of a new development pattern[J]. Management World, 2020, 36(12): 1−13
[2] 黄杰, 孙自敏. 中国种植业碳生产率的区域差异及分布动态演进[J]. 农业技术经济, 2022(7): 109−127 HUANG J, SUN Z M. Regional differences and distribution dynamics of carbon productivity in planting industry in China[J]. Journal of Agrotechnical Economics, 2022(7): 109−127
[3] 李劼, 徐晋涛. 我国农业低碳技术的减排潜力分析[J]. 农业经济问题, 2022, 43(3): 117−135 LI J, XU J T. Analyses of carbon reduction potential of low carbon technologies in China[J]. Issues in Agricultural Economy, 2022, 43(3): 117−135
[4] YANG L S, LI Z. Technology advance and the carbon dioxide emission in China - Empirical research based on the rebound effect[J]. Energy Policy, 2017, 101(2): 150−161
[5] 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[J]. 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[J]. China Population, Resources and Environment, 2011, 21(8): 80−86
[6] 刘杨, 刘鸿斌. 山东省农业碳排放特征、影响因素及达峰分析[J]. 中国生态农业学报(中英文), 2022, 30(4): 558−569 LIU Y, LIU H B. Characteristics, influencing factors and peak analysis of agricultural carbon emissions in Shandong Province[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 558−569
[7] ZHOU P, ANG B W, HAN J Y. Total factor carbon emission performance: a Malmquist index analysis[J]. Energy Economics, 2010, 32(1): 194−201
[8] 张永强, 田媛, 王珧, 等. 农村人力资本、农业技术进步与农业碳排放[J]. 科技管理研究, 2019, 39(14): 266−274 ZHANG Y Q, TIAN Y, WANG Y, et al. Rural human capital, agricultural technology progress and agricultural carbon emissions[J]. Science and Technology Management Research, 2019, 39(14): 266−274
[9] 庞丽. 我国农业碳排放的区域差异与影响因素分析[J]. 干旱区资源与环境, 2014, 28(12): 1−7 PANG L. Empirical study of regional carbon emissions of agriculture in China[J]. Journal of Arid Land Resources and Environment, 2014, 28(12): 1−7
[10] 邓祥征, 钟海玥, 白雪梅, 等. 中国西部城镇化可持续发展路径的探讨[J]. 中国人口·资源与环境, 2013, 23(10): 24−30 DENG X Z, ZHONG H Y, BAI X M, et al. Path of sustainable urbanization in Western China[J]. China Population, Resources and Environment, 2013, 23(10): 24−30
[11] 杨钧. 农业技术进步对农业碳排放的影响−中国省级数据的检验[J]. 软科学, 2013, 27(10): 116−120 YANG J. The effects of technological advances on agricultural carbon emission — Evidence from Chinese provincial data[J]. Soft Science, 2013, 27(10): 116−120
[12] VALIN H, HAVLÍK P, MOSNIER A, et al. Agricultural productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security?[J]. Environmental Research Letters, 2013, 8(3): 035019 doi: 10.1088/1748-9326/8/3/035019
[13] 周勇, 林源源. 技术进步对能源消费回报效应的估算[J]. 经济学家, 2007(2): 45−52 doi: 10.3969/j.issn.1003-5656.2007.02.007 ZHOU Y, LIN Y Y. The estimation of technological progress on the energy consumption returns effects[J]. Economist, 2007(2): 45−52 doi: 10.3969/j.issn.1003-5656.2007.02.007
[14] 李成龙, 周宏. 农业技术进步与碳排放强度关系−不同影响路径下的实证分析[J]. 中国农业大学学报, 2020, 25(11): 162−171 doi: 10.11841/j.issn.1007-4333.2020.11.17 LI C L, ZHOU H. Relationship between agricultural technology progress and carbon emission intensity: an empirical analysis under different influence paths[J]. Journal of China Agricultural University, 2020, 25(11): 162−171 doi: 10.11841/j.issn.1007-4333.2020.11.17
[15] 丁超. 三角贸易及其对中国劳动生产率的影响研究[D]. 厦门: 厦门大学, 2014 DING C. Study on present situation of triangle trade and its influence on China’s labor productivity[D]. Xiamen: Xiamen University, 2014
[16] 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[J]. 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[J]. China Population, Resources and Environment, 2022, 32(4): 23−33
[17] 陈军娟, 燕振刚, 李薇, 等. 基于系统动力学的民勤绿洲农业系统碳排放仿真模拟研究[J]. 西南农业学报, 2022, 35(6): 1432−1440 CHEN J J, YAN Z G, LI W, et al. Simulation study on carbon emission of Minqin oasis agricultural system based on system dynamics[J]. Southwest China Journal of Agricultural Sciences, 2022, 35(6): 1432−1440
[18] 何炫蕾, 陈兴鹏, 庞家幸. 基于LMDI的兰州市农业碳排放现状及影响因素分析[J]. 中国农业大学学报, 2018, 23(7): 150−158 HE X L, CHEN X P, PANG J X. Current situation and influencing factors of agricultural carbon emissions in Lanzhou based on LMDI[J]. Journal of China Agricultural University, 2018, 23(7): 150−158
[19] 贺青, 张虎, 张俊飚. 农业产业聚集对农业碳排放的非线性影响[J]. 统计与决策, 2021, 37(9): 75−78 HE Q, ZHANG H, ZHANG J B. Nonlinear effects of agricultural industry aggregation on agricultural carbon emissions[J]. Statistics & Decision, 2021, 37(9): 75−78
[20] 张抗私, 史策. 认知能力、技术进步与就业极化[J]. 现代财经(天津财经大学学报), 2022, 42(5): 95−113 ZHANG K S, SHI C. Cognitive ability, technological progress and employment polarization[J]. Modern Finance and Economics - Journal of Tianjin University of Finance and Economics, 2022, 42(5): 95−113
[21] 杨增旭, 韩洪云. 化肥施用技术效率及影响因素−基于小麦和玉米的实证分析[J]. 中国农业大学学报, 2011, 16(1): 140−147 YANG Z X, HAN H Y. Technical efficiency of fertilizer and its influencing factors: based on wheat and corn empirical study[J]. Journal of China Agricultural University, 2011, 16(1): 140−147
[22] 汪小勤, 姜涛. 基于农业公共投资视角的中国农业技术效率分析[J]. 中国农村经济, 2009(5): 79−86 WANG X Q, JIANG T. Analysis of China’s agricultural technical efficiency from the perspective of agricultural public investment[J]. Chinese Rural Economy, 2009(5): 79−86
[23] 高鸣, 宋洪远. 粮食生产技术效率的空间收敛及功能区差异−兼论技术扩散的空间涟漪效应[J]. 管理世界, 2014(7): 83−92 GAO M, SONG H Y. Spatial convergence and functional zone differences of technical efficiency of grain production: a discussion on the spatial ripple effect of technology diffusion[J]. Management World, 2014(7): 83−92
[24] 张思麒, 刘导波. 技术进步视角下中国产业结构高级化格局及影响因素[J]. 经济地理, 2022, 42(5): 104−113 ZHANG S Q, LIU D B. Spatial pattern and influencing factors of industrial structure advancement in China based on technological progress[J]. Economic Geography, 2022, 42(5): 104−113
[25] 杨义武, 林万龙, 张莉琴. 农业技术进步、技术效率与粮食生产−来自中国省级面板数据的经验分析[J]. 农业技术经济, 2017(5): 46−56 YANG Y W, LIN W L, ZHANG L Q. Agricultural technical progress, technical efficiency and grain production: an empirical analysis from provincial panel data in China[J]. Journal of Agrotechnical Economics, 2017(5): 46−56
[26] WANG Q Y. Fixed-effect panel threshold model using stata[J]. The Stata Journal, 2015, 15(1): 121−134 doi: 10.1177/1536867X1501500108
[27] HANSEN B E. Threshold effects in non-dynamic panels: Estimation, testing, and inference[J]. Journal of Econometrics, 1999, 93(2): 345−368 doi: 10.1016/S0304-4076(99)00025-1
[28] WEST T O, MARLAND G. A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture: comparing tillage practices in the United States[J]. Agriculture Ecosystems & Environment, 2002, 91(1/3): 217−232
[29] 智静, 高吉喜. 中国城乡居民食品消费碳排放对比分析[J]. 地理科学进展, 2009, 28(3): 429−434 ZHI J, GAO J X. Comparative analysis of carbon emissions from food consumption of urban and rural residents in China[J]. Progress in Geography, 2009, 28(3): 429−434
[30] IPCC. Climate Change 2007: The Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[M]. New York: Cambridge University Press, 2007
[31] 伍芬琳, 李琳, 张海林, 等. 保护性耕作对农田生态系统净碳释放量的影响[J]. 生态学杂志, 2007(12): 2035−2039 WU F L, LI L, ZHANG H L, et al. Effects of conservation tillage on net carbon release from farmland ecosystem[J]. Chinese Journal of Ecology, 2007(12): 2035−2039
[32] 傅晓霞, 吴利学. 前沿分析方法在中国经济增长核算中的适用性[J]. 世界经济, 2007, 30(7): 56−66 FU X X, WU L X. Applicability of frontier analysis method in China’s economic growth accounting[J]. The Journal of World Economy, 2007, 30(7): 56−66
[33] 白俊红, 江可申, 李婧. 应用随机前沿模型评测中国区域研发创新效率[J]. 管理世界, 2009(10): 51−61 BAI J H, JIANG K S, LI J. Stochastic frontier model is applied to evaluate the innovation efficiency of regional R&D in China[J]. Management World, 2009(10): 51−61
[34] LESAGE J, PACE R K. Introduction to Spatial Econometrics[M]. Boca Raton: CRC Press, 2009: 155–165
[35] 赵昕东, 刘成坤. 人口老龄化对制造业结构升级的作用机制研究−基于中介效应模型的检验[J]. 中国软科学, 2019(3): 153−163 ZHAO X D, LIU C K. Research on the mechanism of population aging to the upgrading of manufacturing structure — Based on the test of mediation effect model[J]. China Soft Science, 2019(3): 153−163
[36] 郭庆旺, 贾俊雪. 中国全要素生产率的估算: 1979—2004[J]. 经济研究, 2005, 40(6): 51−60 GUO Q W, JIA J X. Estimating total factor productivity in China: 1979–2004[J]. Economic Research Journal, 2005, 40(6): 51−60
