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保护性耕作机械能否带动保护性耕作净碳汇的空间外溢?

李园园 薛彩霞 柴朝卿 李卫 姚顺波

李园园, 薛彩霞, 柴朝卿, 李卫, 姚顺波. 保护性耕作机械能否带动保护性耕作净碳汇的空间外溢?−基于农机跨区服务视角[J]. 中国生态农业学报 (中英文), 2023, 31(0): 1−14 doi: 10.12357/cjea.20230375
引用本文: 李园园, 薛彩霞, 柴朝卿, 李卫, 姚顺波. 保护性耕作机械能否带动保护性耕作净碳汇的空间外溢?−基于农机跨区服务视角[J]. 中国生态农业学报 (中英文), 2023, 31(0): 1−14 doi: 10.12357/cjea.20230375
LI Y Y, XUE C X, CHAI C Q, LI W, YAO S B. 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[J]. Chinese Journal of Eco-Agriculture, 2023, 31(0): 1−14 doi: 10.12357/cjea.20230375
Citation: LI Y Y, XUE C X, CHAI C Q, LI W, YAO S B. 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[J]. Chinese Journal of Eco-Agriculture, 2023, 31(0): 1−14 doi: 10.12357/cjea.20230375

保护性耕作机械能否带动保护性耕作净碳汇的空间外溢?基于农机跨区服务视角

doi: 10.12357/cjea.20230375
基金项目: 教育部人文社科项目(22YJA630042)、陕西省软科学项目(2023-CX-RKX-043)和西北农林科技大学经济管理学院研究生科技创新项目(JGYJSCXXM202206)资助
详细信息
    作者简介:

    李园园, 主要从事资源经济与环境管理研究工作。E-mail: lyy020924@nwafu.edu.cn

    通讯作者:

    薛彩霞, 主要从事农林经济管理、资源经济与环境管理研究工作。E-mail: xiaoxueacc@126.com

  • 中图分类号: F323.3

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

Funds: This study was supported by Ministry of Education, Humanities and Social Science Project (22YJA630042), Shaanxi Soft Science Project (2023-CX-RKX-043) and Postgraduate Science and Technology Innovation Project in College of Economics and Management, Northwest A& F University (JGYJSCXXM202206)
More Information
  • 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。
  • 图  1  保护性耕作机械对保护性耕作净碳汇的影响机制

    Figure  1.  Influencing mechanism of conservation tillage machinery on related net carbon sink

    图  2  2000—2020年中国保护性耕作机械动力及其净碳汇的时序变化

    Figure  2.  Time series changes of mechanical power and the corresponding net carbon sink of conservation tillage in China from 2000 to 2020

    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)
    CTNCS60.57393.692−39.538453.640
    核心解释变量
    Core explanatory variable
    保护性耕作机械动力
    Conservation tillage mechanical power (×104 kW)
    CTMP341.434651.8370.0004356.868
    免耕机械动力
    No-tillage mechanical power (×104 kW)
    NTMP84.951196.3320.0001177.958
    秸秆还田机械动力
    Straw returning mechanical power (×104 kW)
    SRMP256.402476.0650.0003178.910
    控制变量
    Control variable
    种植结构
    Planting structure (%)
    PLANT47.21429.3350.00095.605
    技术推广强度
    Intensity of technology popularization (%)
    CTEI27.61529.1030.000155.523
    受教育水平
    Education level (a)
    EDU7.4400.9034.40512.213
    经济发展水平
    GDP per capita (×104 ¥·cap.−1)
    PGDP2.7151.9730.26611.417
    下载: 导出CSV

    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 I
    P
    P value
    莫兰指数
    Moran’s I
    P
    P value
    莫兰指数
    Moran’s I
    P
    P value
    莫兰指数
    Moran’s I
    P
    P value
    20000.0990.1190.1320.08320110.3420.0010.1870.034
    20010.0690.1760.0970.13720120.3280.0010.1810.038
    20020.0940.1270.1040.12620130.3380.0010.1780.040
    20030.0790.1450.1380.07820140.3430.0010.1790.041
    20040.1230.0840.2100.02320150.3590.0000.2160.021
    20050.1060.1090.2210.01920160.3730.0000.2400.013
    20060.1310.0750.2160.02120170.3820.0000.2320.015
    20070.1480.0550.2300.01120180.3820.0000.1940.032
    20080.3220.0010.2170.01920190.3930.0000.1650.053
    20090.3460.0010.2620.00720200.3950.0000.1640.053
    20100.3580.0000.2180.018
    下载: 导出CSV

    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
    200020102020200020102020
    高-高 High-high1512, 15, 1612, 15, 164, 5, 163, 4, 12, 15, 164, 12, 16, 17
    低-低 Low-low18, 19, 24232320, 21, 24219
    低-高 Low-high2, 4, 5, 6446, 8, 28
    非研究区 Non study area26, 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.
    下载: 导出CSV

    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 test
    P
    P value
    豪斯曼检验 Hausman test27.0160.005
    沃尔德(滞后)检验 Wald-lag test32.8490.000
    沃尔德(误差)检验 Wald-error test35.2680.000
    似然比(滞后)检验 LR-lag test36.7860.000
    似然比(误差)检验 LR-error test38.2090.000
    下载: 导出CSV

    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 coefficient
    t
    t value
    估计系数
    Estimated coefficient
    t
    t value
    CTMP0.012**1.961
    NTMP−0.038−1.248
    SRMP0.0050.468
    PLANT0.3610.7631.322***2.950
    CTEI1.901***10.8962.117***13.030
    EDU−0.093−1.207−0.106−1.492
    PGDP−0.141***−4.200−0.188***−5.975
    W×CTMP0.029***2.624
    W×NTMP−0.533***−9.339
    W×SRMP0.215***10.167
    W×PLANT−2.393***−2.803−0.127−0.153
    W×CTEI−0.280−0.7310.649*1.786
    W×EDU0.1601.0020.1921.302
    W×PGDP−0.192***−3.580−0.311***−6.144
    空间自回归系数
    Spatial autoregressive coefficient
    0.147***2.8500.0661.282
    0.8380.862
    观测值数量
    Number of observed value
    630630
      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。******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.
    下载: 导出CSV

    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 coefficient
    t
    t value
    估计系数
    Estimated coefficient
    t
    t value
    估计系数
    Estimated coefficient
    t
    t value
    CTMP0.013**2.1370.035***2.9170.049***4.138
    NTMP−0.045−1.497−0.569***−9.092−0.614***−8.495
    SRMP0.0090.7820.228***9.7520.237***9.843
    PLANT1.306***2.917−0.002−0.0021.3041.367
    CTEI2.132***13.1730.830**2.2012.962***6.939
    EDU−0.104−1.4690.1911.2330.0870.488
    PGDP−0.190***−5.865−0.343***−6.074−0.533***−9.184
      各变量的解释见表1******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.
    下载: 导出CSV

    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 variable
    2000—20032004—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 effect
    CTMP 0.068*** (4.253)0.015 (0.634)0.083*** (3.204)0.058*** (5.336)0.043**(2.065)0.101*** (5.793)
    NTMP0.057* (1.744)0.157** (2.630)0.213*** (2.714)0.038 (1.536)0.004 (0.062)0.042 (0.574)
    SRMP0.088*** (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 value
    120270
    解释变量
    Explanatory variable
    2010—20132014—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 effect
    CTMP−0.005 (−0.347)0.055** (2.455)0.049** (2.293)−0.007 (−0.614)0.010 (0.530)0.003 (0.198)
    NTMP0.101** (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 value
    120210
      各变量的解释见表1******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.
    下载: 导出CSV

    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 effect
    CTMP−0.004 (−0.551)0.109*** (9.628)0.105*** (10.168)0.147*** (6.292)−0.212*** (−4.823)−0.064 (−1.428)
    NTMP0.045 (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 value
    252378
      各变量的解释见表1******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.
    下载: 导出CSV

    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 effect
    CTMP−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)
    SRMP0.001
    (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 value
    315420273
      各变量的解释见表1******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.
    下载: 导出CSV

    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 coefficient
    t
    t value
    估计系数
    Estimated coefficient
    t
    t value
    估计系数
    Estimated coefficient
    t
    t value
    CTMP0.012**1.9610.015**2.755
    CTMN1.724***4.661
    PLANT0.3610.7630.4570.9830.4921.175
    CTEI1.901***10.8961.701***9.6751.693***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×CTMP0.029**2.6241.460**1.9560.033***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×EDU0.1601.0020.1370.8740.1611.145
    W×PGDP−0.192***−3.58−0.174***−3.302−0.210***−4.433
    空间自回归系数
    Spatial autoregressive coefficient
    0.147***2.8500.101*1.9130.135***2.600
    0.8380.8440.862
    观测值数量 Number of observed value630
      各变量的解释见表1******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.
    下载: 导出CSV
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  • 收稿日期:  2023-07-05
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