Can conservation tillage machinery drive the spatial spillover of the net carbon sink of conservation tillage?Based on the perspective of cross-zone service of agricultural machinery
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摘要: 农机跨区服务推动了以农业机械为载体的保护性耕作技术及其净碳汇的空间外溢。本文以2000—2020年中国30个省份(不含港澳台及西藏)为研究样本, 运用探索性空间数据分析法揭示保护性耕作机械及其净碳汇在空间上的集聚特征, 并通过空间杜宾模型定量分析保护性耕作机械对其净碳汇的空间溢出效应。研究发现: 1)保护性耕作机械动力及其净碳汇在2000—2020年期间均呈增长态势, 年均增长率分别为12.52%和7.42%, 两者在空间上主要表现为“高-高集聚”和“低-低集聚”的区域集聚特征。2)保护性耕作机械动力通过跨区服务能够显著带动保护性耕作净碳汇实现空间外溢。具体表现为保护性耕作机械动力对周边省份保护性耕作净碳汇会产生正向空间溢出效应, 且主要归因于秸秆还田机械的跨区作业。3)保护性耕作机械动力对其净碳汇的空间溢出效应因时间、地形、粮食作物主产区而异。具体来说, 其空间溢出效应在2004—2009年间和2010—2013年间显著为正, 并呈增加趋势; 在平原地区, 其空间溢出效应为正, 在丘陵山区则为负; 保护性耕作机械的空间溢出效应在水稻主产区更明显, 免耕机械的空间溢出效应在小麦主产区相对突出, 而秸秆还田机械的空间溢出效应在三大粮食作物主产区基本无差异。为此, 本研究提出加大保护性耕作推广力度、搭建农机服务信息化平台和提高保护性耕作农机装备水平的对策建议。Abstract: Conservation tillage is an environment-friendly agricultural cultivation technique that distinguishes itself from traditional tillage, and its implementation relies on agricultural machinery. China’s unique situation as a large country with many small-scale farms has led to the development of a distinctive path for agricultural machinery in the form of cross-regional agricultural machinery services. Therefore, it is worth exploring whether conservation tillage machinery drives the spatial spillover of the net carbon sink of conservation tillage in the context of cross-regional agricultural machinery services. This study used panel data from 30 provinces in China (excluding Hong Kong, Macao, Taiwan, and Tibet) from 2000 to 2020. First, an exploratory spatial data analysis was used to reveal the spatial agglomeration characteristics of conservation tillage machinery and its net carbon sink. Second, the spatial spillover effect of conservation tillage machinery on net carbon sink was quantitatively analyzed using the spatial Durbin model. Furthermore, this study analyzed the heterogeneity of the spatial spillover effect of conservation tillage machinery on its net carbon sink from the dimensions of time, topography, and major grain-producing areas. The study found that: 1) from 2000 to 2020, mechanical power and the net carbon sink of conservation tillage increased from 22.55 million kW and 7.93 million t C in 2000 to 238.63 million kW and 33.17 million t C in 2020, with average annual growth rates of 12.52% and 7.42%, respectively. The growth trends were significant, and their development was closely synchronized. The spatial correlation results indicated that both of them mainly exhibited regional agglomeration characteristics with ‘high-high’ and ‘low-low’, showing a significant positive spatial correlation. 2) In the context of cross-regional agricultural machinery services, conservation tillage mechanical power significantly drove the spatial spillover effect of net carbon sink of conservation tillage. This manifested as a positive spatial spillover effect of the mechanical power of conservation tillage on the corresponding net carbon sink in neighboring provinces. Specifically, straw-returning mechanical power exhibited a positive spatial spillover effect, whereas no-tillage mechanical power, owing to its long-term implementation, mainly showed a negative spatial spillover effect, which can lead to crop yield reduction. 3) The spatial spillover effect of conservation tillage mechanical power on the corresponding net carbon sink exhibited heterogeneity across different time periods, topographies, and major grain-producing areas. In the temporal dimension, the spatial spillover effect was significantly positive and increased during the 2004–2009 and 2010–2013 periods. In the topographic dimension, the spatial spillover effect was positive in plain areas but negative in hilly and mountainous regions. Among the major grain-producing areas, the spatial spillover effect of conservation tillage mechanical power on the corresponding net carbon sink was more pronounced in rice-producing areas. The spatial spillover effect of no-tillage mechanical power was relatively prominent in the wheat-producing areas. The spatial spillover effect of the straw-returning mechanical power was essentially the same across the three major grain-producing areas. This study proposes measures to promote conservation tillage, establish an agricultural machinery service information platform, and enhance the level of conservation tillage of agricultural machinery and equipment. Additionally, the research findings hold significant reference value for how the government can use conservation tillage to contribute to the dual-carbon target.
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表 1 变量的描述性统计分析(n=630)
Table 1. Descriptive statistical analysis of variables (n=630)
变量名称
Variable代码
Code平均值
Mean标准差
S.D.最小值
Minimum vlaue最大值
Maximum value被解释变量
Explained variable保护性耕作净碳汇
Net carbon sink of conservation tillage (×104 t C)CTNCS 60.573 93.692 −39.538 453.640 核心解释变量
Core explanatory variable保护性耕作机械动力
Conservation tillage mechanical power (×104 kW)CTMP 341.434 651.837 0.000 4356.868 免耕机械动力
No-tillage mechanical power (×104 kW)NTMP 84.951 196.332 0.000 1177.958 秸秆还田机械动力
Straw returning mechanical power (×104 kW)SRMP 256.402 476.065 0.000 3178.910 控制变量
Control variable种植结构
Planting structure (%)PLANT 47.214 29.335 0.000 95.605 技术推广强度
Intensity of technology popularization (%)CTEI 27.615 29.103 0.000 155.523 受教育水平
Education level (a)EDU 7.440 0.903 4.405 12.213 经济发展水平
GDP per capita (×104 ¥·cap.−1)PGDP 2.715 1.973 0.266 11.417 表 2 保护性耕作机械动力和保护性耕作净碳汇的全局莫兰指数
Table 2. The global Moran's index for mechanical power and the corresponding net carbon sink of conservation tillage
年份
Year机械动力
Mechanical power净碳汇
Net carbon sink年份
Year机械动力
Mechanical power净碳汇
Net carbon sink莫兰指数
Moran’s IP值
P value莫兰指数
Moran’s IP值
P value莫兰指数
Moran’s IP值
P value莫兰指数
Moran’s IP值
P value2000 0.099 0.119 0.132 0.083 2011 0.342 0.001 0.187 0.034 2001 0.069 0.176 0.097 0.137 2012 0.328 0.001 0.181 0.038 2002 0.094 0.127 0.104 0.126 2013 0.338 0.001 0.178 0.040 2003 0.079 0.145 0.138 0.078 2014 0.343 0.001 0.179 0.041 2004 0.123 0.084 0.210 0.023 2015 0.359 0.000 0.216 0.021 2005 0.106 0.109 0.221 0.019 2016 0.373 0.000 0.240 0.013 2006 0.131 0.075 0.216 0.021 2017 0.382 0.000 0.232 0.015 2007 0.148 0.055 0.230 0.011 2018 0.382 0.000 0.194 0.032 2008 0.322 0.001 0.217 0.019 2019 0.393 0.000 0.165 0.053 2009 0.346 0.001 0.262 0.007 2020 0.395 0.000 0.164 0.053 2010 0.358 0.000 0.218 0.018 表 3 2000年、2010年和2020年保护性耕作机械动力及其净碳汇的LISA集聚关系
Table 3. The relationship of LISA agglomeration for mechanical power and the corresponding net carbon sink of conservation tillage in 2000, 2010 and 2020
集聚类型
Agglomeration type机械动力 Mechanical power 净碳汇 Net carbon sink 2000 2010 2020 2000 2010 2020 高-高 High-high 15 12, 15, 16 12, 15, 16 4, 5, 16 3, 4, 12, 15, 16 4, 12, 16, 17 低-低 Low-low 18, 19, 24 23 23 20, 21, 24 21 9 低-高 Low-high 2, 4, 5, 6 4 4 6, 8, 28 非研究区 Non study area 26, 32, 33, 34 不显著 Not significant 其他 Others 其他 Others 其他 Others 其他 Others 其他 Others 其他 Others 表中为10%显著性水平下的结果。高-低集聚类型无数据, 故未列出。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; 32: 台湾; 33: 香港; 34: 澳门。Results in this table was at a significance level of 10%. There is no data for high-low agglomeration type, and this is not listed in the table. 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; 32: Taiwan; 33: Hong Kong; 34: Macao. 表 4 保护性耕作机械动力对其净碳汇空间计量模型检验
Table 4. The test of the spatial econometric model for conservation tillage mechanical power on the corresponding net carbon sink
检验方法
Calibration method统计量值
t testP值
P value豪斯曼检验 Hausman test 27.016 0.005 沃尔德(滞后)检验 Wald-lag test 32.849 0.000 沃尔德(误差)检验 Wald-error test 35.268 0.000 似然比(滞后)检验 LR-lag test 36.786 0.000 似然比(误差)检验 LR-error test 38.209 0.000 表 5 保护性耕作机械动力对其净碳汇空间杜宾模型的估计结果
Table 5. The estimation results of the spatial Durbin model for conservation tillage mechanical power on the corresponding net carbon sink
解释变量
Explanatory variable模型1 Model 1 模型2 Model 2 估计系数
Estimated coefficientt值
t value估计系数
Estimated coefficientt值
t valueCTMP 0.012** 1.961 NTMP −0.038 −1.248 SRMP 0.005 0.468 PLANT 0.361 0.763 1.322*** 2.950 CTEI 1.901*** 10.896 2.117*** 13.030 EDU −0.093 −1.207 −0.106 −1.492 PGDP −0.141*** −4.200 −0.188*** −5.975 W×CTMP 0.029*** 2.624 W×NTMP −0.533*** −9.339 W×SRMP 0.215*** 10.167 W×PLANT −2.393*** −2.803 −0.127 −0.153 W×CTEI −0.280 −0.731 0.649* 1.786 W×EDU 0.160 1.002 0.192 1.302 W×PGDP −0.192*** −3.580 −0.311*** −6.144 空间自回归系数
Spatial autoregressive coefficient0.147*** 2.850 0.066 1.282 R² 0.838 0.862 观测值数量
Number of observed value630 630 各变量的解释见表1。W表示空间权重, W×变量表示变量的空间滞后项。***、**和*分别表示1%、5%和10%的显著性水平。The explanation of each variable is shown in Table 1. W represents the spatial weight, and W×variable represents the spatial lag term of the variable. ***, ** and * represent the significance levels of 1%, 5% and 10%, respectively. 表 6 保护性耕作机械动力对其净碳汇的空间杜宾模型回归结果及效应分解
Table 6. The spatial Durbin model regression results and effect decompotion of conservation tillage mechanical power on the corresponding net carbon sink
解释变量
Explanatory variable直接效应 Direct effect 间接效应 Indirect effect 总效应 Total effect 估计系数
Estimated coefficientt值
t value估计系数
Estimated coefficientt值
t value估计系数
Estimated coefficientt值
t valueCTMP 0.013** 2.137 0.035*** 2.917 0.049*** 4.138 NTMP −0.045 −1.497 −0.569*** −9.092 −0.614*** −8.495 SRMP 0.009 0.782 0.228*** 9.752 0.237*** 9.843 PLANT 1.306*** 2.917 −0.002 −0.002 1.304 1.367 CTEI 2.132*** 13.173 0.830** 2.201 2.962*** 6.939 EDU −0.104 −1.469 0.191 1.233 0.087 0.488 PGDP −0.190*** −5.865 −0.343*** −6.074 −0.533*** −9.184 各变量的解释见表1。***、**和*分别表示1%、5%和10%的显著性水平。The explanation of each variable is shown in Table 1. ***, ** and * represent the significance levels of 1%, 5% and 10%, respectively. 表 7 时间维度下保护性耕作机械动力对其净碳汇的空间杜宾模型回归结果及效应分解
Table 7. The spatial Durbin model regression results and effect decomposition of conservation tillage mechanical power on the corresponding net carbon sink in the temporal dimension
解释变量
Explanatory variable2000—2003 2004—2009 直接效应
Direct effect间接效应
Indirect effect总效应
Total effect直接效应
Direct effect间接效应
Indirect effect总效应
Total effectCTMP 0.068*** (4.253) 0.015 (0.634) 0.083*** (3.204) 0.058*** (5.336) 0.043**(2.065) 0.101*** (5.793) NTMP 0.057* (1.744) 0.157** (2.630) 0.213*** (2.714) 0.038 (1.536) 0.004 (0.062) 0.042 (0.574) SRMP 0.088*** (4.741) −0.045 (−1.512) 0.043 (1.411) 0.071*** (3.529) 0.050* (1.828) 0.121*** (4.147) 控制变量
Control variable已控制 Controlled 观测值数量
Number of
observed value120 270 解释变量
Explanatory variable2010—2013 2014—2020 直接效应
Direct effect间接效应
Indirect effect总效应
Total effect直接效应
Direct effect间接效应
Indirect effect总效应
Total effectCTMP −0.005 (−0.347) 0.055** (2.455) 0.049** (2.293) −0.007 (−0.614) 0.010 (0.530) 0.003 (0.198) NTMP 0.101** (2.230) −0.272** (−2.430) −0.171 (−1.303) −0.112** (−2.198) −0.124 (−1.257) −0.236* (−2.019) SRMP −0.061*** (−3.099) 0.181*** (4.255) 0.119*** (3.118) 0.036 (1.411) 0.056 (1.211) 0.092* (1.961) 控制变量
Control variable已控制 Controlled 观测值数量
Number of observed value120 210 各变量的解释见表1。***、**和*分别表示1%、5%和10%的显著性水平。括号内为t值。The explanation of each variable is shown in Table 1. ***, ** and * represent the significance levels of 1%, 5% and 10%, respectively. The value in brackets is the t value. 表 8 地形维度下保护性耕作机械动力对其净碳汇的空间杜宾模型回归结果及效应分解
Table 8. The spatial Durbin model regression results and effect decomposition of conservation tillage mechanical power on the corresponding net carbon sink in the topographic dimension
解释变量
Explanatory variable平原地区 Plain area 丘陵山区 Hilly area 直接效应
Direct effect间接效应
Indirect effect总效应
Total effect直接效应
Direct effect间接效应
Indirect effect总效应
Total effectCTMP −0.004 (−0.551) 0.109*** (9.628) 0.105*** (10.168) 0.147*** (6.292) −0.212*** (−4.823) −0.064 (−1.428) NTMP 0.045 (1.267) 0.086 (1.256) 0.131 (1.457) −0.693*** (−8.416) −0.323*** (−3.567) −1.016*** (−12.316) SRMP −0.024 (−1.687) 0.127*** (6.646) 0.103*** (4.428) 0.239*** (8.654) −0.057 (−1.174) 0.182*** (5.054) 控制变量
Control variable已控制 Controlled 观测值数量 Number of
observed value252 378 各变量的解释见表1。***、**和*分别表示1%、5%和10%的显著性水平。括号内为t值。据已有研究[16,30], 将北京、天津、河北、内蒙古、辽宁、吉林、黑龙江、上海、江苏、安徽、山东和河南12个省划分为平原地区, 本研究中的其余18个省划分为丘陵山区。The explanation of each variable is shown in Table 1. ***, ** and * represent the significance levels of 1%, 5% and 10%, respectively. The value in brackets is the t value. According to the existing literatures[16,30], Beijing, Tianjin, Hebei, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Anhui, Shandong and Henan are divided into plain area, and the remaining 18 provinces of the selected areas in this study are divided into hilly area. 表 9 粮食作物主产区维度下保护性耕作机械动力对其净碳汇的空间杜宾模型回归结果及效应分解
Table 9. The spatial Durbin model regression results and effect decomposition of conservation tillage mechanical power on the corresponding net carbon sink in the dimension of different major grain-producing areas
解释
变量
Explanatory variable小麦
Wheat玉米
Maize水稻
Rice直接效应
Direct effect间接效应
Indirect effect总效应
Total effect直接效应
Direct effect间接效应
Indirect effect总效应
Total effect直接效应
Direct effect间接效应
Indirect effect总效应
Total effectCTMP −0.028***
(−3.225)0.002
(0.169)−0.026**
(−1.880)−0.001
(−0.129)0.024*
(1.988)0.024*
(1.987)−0.025**
(−2.781)0.071***
(4.620)0.045**
(2.683)NTMP −0.153***
(−5.340)−0.325***
(−6.474)−0.478***
(−8.783)−0.007
(−0.249)−0.191***
(−2.975)−0.198***
(−2.879)−0.023
(−0.644)−0.048
(−0.762)−0.072
(−0.923)SRMP 0.001
(0.085)0.097***
(5.040)0.098***
(5.700)0.008
(−0.582)0.094***
(3.821)0.086***
(3.666)0.025
(−1.666)0.098***
(4.592)0.073**
(2.695)控制变量
Control variable已控制 Controlled 观测值数量
Number of observed value315 420 273 各变量的解释见表1。***、**和*分别表示1%、5%和10%的显著性水平。括号内为t值。借鉴已有文献[31,32], 小麦主产区包括河北、山西、内蒙古、黑龙江、江苏、安徽、山东、河南、湖北、四川、云南、陕西、甘肃、宁夏和新疆; 玉米主产区包括河北、山西、内蒙古、辽宁、吉林、黑龙江、江苏、安徽、山东、河南、湖北、广西、重庆、四川、贵州、云南、陕西、甘肃、宁夏和新疆; 水稻主产区包括辽宁、吉林、黑龙江、河北、山东、河南、江苏、浙江、安徽、湖北、内蒙古、宁夏和云南。The explanation of each variable is shown in Table 1. ***, ** and * represent the significance levels of 1%, 5% and 10%, respectively. The value in brackets is the t value. According to the existing literatures[31,32], the main wheat-producing areas include Hebei, Shanxi, Inner Mongolia, Heilongjiang, Jiangsu, Anhui, Shandong, Henan, Hubei, Sichuan, Yunnan, Shaanxi, Gansu, Ningxia and Xinjiang. The main maize-producing areas include Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Jiangsu, Anhui, Shandong, Henan, Hubei, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Ningxia and Xinjiang. The main rice-producing areas include Liaoning, Jilin, Heilongjiang, Hebei, Shandong, Henan, Jiangsu, Zhejiang, Anhui, Hubei, Inner Mongolia, Ningxia and Yunnan. 表 10 保护性耕作机械动力对其净碳汇的空间杜宾模型回归结果稳健性检验
Table 10. The robustness test of the spatial Durbin model regression results for conservation tillage mechanical power on the corresponding net carbon sink
解释变量
Explanatory variable模型1 Model 1 模型3 Model 3 模型4 Model 4 估计系数
Estimated coefficientt值
t value估计系数
Estimated coefficientt值
t value估计系数
Estimated coefficientt值
t valueCTMP 0.012** 1.961 0.015** 2.755 CTMN 1.724*** 4.661 PLANT 0.361 0.763 0.457 0.983 0.492 1.175 CTEI 1.901*** 10.896 1.701*** 9.675 1.693*** 10.977 EDU −0.093 −1.207 −0.070 −0.933 −0.062 −0.904 PGDP −0.141*** −4.200 −0.123*** −3.747 −0.115*** −3.866 W×CTMP 0.029** 2.624 1.460** 1.956 0.033*** 3.315 W×PLANT −2.393** −2.803 −1.949** −2.314 −2.258*** −2.996 W×CTEI −0.280 −0.371 −0.512 −1.336 −0.316 −0.934 W×EDU 0.160 1.002 0.137 0.874 0.161 1.145 W×PGDP −0.192*** −3.58 −0.174*** −3.302 −0.210*** −4.433 空间自回归系数
Spatial autoregressive coefficient0.147*** 2.850 0.101* 1.913 0.135*** 2.600 R² 0.838 0.844 0.862 观测值数量 Number of observed value 630 各变量的解释见表1。***、**和*分别表示1%、5%和10%的显著性水平。 CTMN表示保护性耕作农机具数量。The explanation of each variable is shown in Table 1. ***, ** and * represent the significance levels of 1%, 5% and 10%, respectively. CTMN represents the number of conservation tillage agricultural machinery. -
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