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中国农业碳排放效率测度、空间溢出与影响因素

吴昊玥 黄瀚蛟 何宇 陈文宽

吴昊玥, 黄瀚蛟, 何宇, 陈文宽. 中国农业碳排放效率测度、空间溢出与影响因素[J]. 中国生态农业学报(中英文), 2021, 29(10): 1−12 doi: 10.13930/j.cnki.cjea.210204
引用本文: 吴昊玥, 黄瀚蛟, 何宇, 陈文宽. 中国农业碳排放效率测度、空间溢出与影响因素[J]. 中国生态农业学报(中英文), 2021, 29(10): 1−12 doi: 10.13930/j.cnki.cjea.210204
WU H Y, HUANG H J, HE Y, CHEN W K. Measurement, spatial spillover and influencing factors of agricultural carbon emissions efficiency in China[J]. Chinese Journal of Eco-Agriculture, 2021, 29(10): 1−12 doi: 10.13930/j.cnki.cjea.210204
Citation: WU H Y, HUANG H J, HE Y, CHEN W K. Measurement, spatial spillover and influencing factors of agricultural carbon emissions efficiency in China[J]. Chinese Journal of Eco-Agriculture, 2021, 29(10): 1−12 doi: 10.13930/j.cnki.cjea.210204

中国农业碳排放效率测度、空间溢出与影响因素

doi: 10.13930/j.cnki.cjea.210204
基金项目: 国家自然科学基金项目(71704127)和四川省社会科学研究“十三五”规划项目(SC18TJ018)资助
详细信息
    作者简介:

    吴昊玥, 主要研究方向为农业碳排放。E-mail: wuhaoyue@sicau.stu.edu.cn

    通讯作者:

    陈文宽, 主要研究方向为资源配置与可持续利用。E-mail: 11454@sicau.edu.cn

  • 中图分类号: F323

Measurement, spatial spillover and influencing factors of agricultural carbon emissions efficiency in China

Funds: This study was supported by the National Natural Science Foundation of China (71704127) and Sichuan Provincial Social Science Research “13th Five-Year Plan” Project (SC18TJ018)
More Information
  • 摘要: 准确测度农业碳排放效率并分析关键影响因素, 可为加快实现农业减排增效提供理论参考。已有研究未将碳排放与其他要素的共同作用进行分离, 研究实质为碳排放约束下的农业生产效率, 而非农业碳排放效率。为完善既有测算思路, 本文在农业全要素框架下搭建碳排放效率的理论模型, 基于GB-US-SBM模型测算2000—2019年间中国30省(市、自治区)的农业碳排放松弛量, 根据碳排放松弛量与实际值计算农业碳排放效率。在此基础上, 从产业、要素、环境3个方面出发, 采用空间杜宾模型探讨农业碳排放效率的影响因素与溢出效应。结果表明: 研究期内中国农业碳排放效率均值为0.778, 具有较大减排潜力。省级层面上, 仅内蒙古和青海两地的农业碳排放效率达1.000, 其余地区均存在不同规模的减排空间; 根据总量与效率的双重特征, 将30省(市、自治区)分为高排高效区、低排高效区、高排低效区和低排低效区。中国农业碳排放效率全局Moran’s I显著大于0 (P<0.01), 说明效率整体存在空间自相关性。空间杜宾模型结果显示, 农业碳排放效率具有显著的正向溢出效应, 表明邻近地区间的效率呈良性互动的演进特征。就直接效应而言, 本省的农业产业结构、农业投资强度、财政支农力度和受灾程度对本省农业碳排放效率存在负向影响, 有效灌溉率和城镇化率则表现为正向作用。从溢出效应来看, 邻近地区的受灾程度将负向影响本省农业碳排放效率, 而城镇化率则呈积极影响。研究结果可为我国分区域、分类别推进低碳农业发展提供理论依据。
  • 图  1  基于非期望产出思想的农业碳排放效率理论模型

    X: 投入; P(X): 生产可能性的集合; Yg: 期望产出; Yb: 非期望产出; g=(−Yb/X, Yg/X): 预期产出方向; A(Yb0/X, Yg0/X): 某一时刻的产出决策点; B(Yb1/X, Yg1/X): A在生产前沿上的投影点。X is the input; P(X) is the production possibilities set; Yg is the desirable output; Yb is the undesirable output; g=(−Yb/X, Yg/X) denotes the expected output direction; A(Yb0/X, Yg0/X) is the decision point of output; B(Yb1/X, Yg1/X) is the projection point of point A on the production frontier.

    Figure  1.  Theoretical model of agricultural carbon emissions efficiency under the perspective of undesirable output

    图  2  基于农业碳排放总量与效率双重维度的省域类别划分

    1: 湖南; 2: 江西; 3: 湖北; 4: 安徽; 5: 福建; 6: 浙江; 7: 广东; 8: 江苏; 9: 宁夏; 10: 陕西; 11: 山西; 12: 云南; 13: 四川; 14: 天津; 15: 辽宁; 16: 重庆; 17: 上海; 18: 海南; 19: 贵州; 20: 甘肃; 21: 山东; 22: 河北; 23: 广西; 24: 黑龙江; 25: 河南; 26: 北京; 27: 吉林; 28: 新疆; 29: 内蒙古; 30: 青海。1: Hunan; 2: Jiangxi; 3: Hubei; 4: Anhui; 5: Fujian; 6: Zhejiang; 7: Guangdong; 8: Jiangsu; 9: Ningxia; 10: Shaanxi; 11: Shanxi; 12: Yunnan; 13: Sichuan; 14: Tianjin; 15: Liaoning; 16: Chongqing; 17: Shanghai; 18: Hainan; 19: Guizhou; 20: Gansu; 21: Shandong; 22: Hebei; 23: Guangxi; 24: Heilongjiang; 25: Henan; 26: Beijing; 27: Jilin; 28: Xinjiang; 29: Inner Mongolia; 30: Qinghai.

    Figure  2.  Classification of the 30 provinces (cities, autonomous regions) of China based on the dual dimension of quantity and efficiency of agricultural carbon emissions

    表  1  全要素框架下的农业生产投入产出指标体系

    Table  1.   Input-output indicators of agricultural production under the total-factor framework

    指标类型 Type of indicator具体指标 Specific indicators单位 Unit
    投入 Inputs 劳动力 Labor 农业从业人数 Quantity of agricultural employees persons
    土地 Land 耕地面积 Cropland area hm2
    机械 Machine 农业机械总动力
    Total power of agricultural machinery
    kW
    灌溉 Irrigation 农业灌溉用水总量 Total water used for irrigation m3
    肥料 Fertilizer 化肥施用量 Total fertilizer application t
    产出 Outputs 期望 Desirable 经济产出 Economic output 农业总产值 Total agricultural output CNY
    生态产出 Ecologic output 农业碳吸收量 Agricultural carbon sequestration t
    非期望 Undesirable 环境代价 Environmental cost 农业碳排放量 Agricultural carbon emissions t
    下载: 导出CSV

    表  2  农业碳排放效率影响因素变量说明及描述性统计分析

    Table  2.   Description and statistical analysis of factors influencing the efficiency of agricultural carbon emissions

    变量
    Variable
    符号
    Symbol
    计算方法
    Calculation
    均值
    Mean
    标准差
    Standard deviation
    最小值
    Minimum
    最大值
    Maximum
    产业
    Industry
    农业产业结构
    Agro-industrial structure
    ind 农业总产值/农林牧渔总产值
    Total agricultural output value / total agricultural,
    forestry, livestock husbandry and fishery output value
    0.524 0.086 0.339 0.740
    作物种植结构
    Crop structure
    cro 粮食种植面积/作物播种面积
    Grain sown area / crop sown area
    0.659 0.130 0.354 0.971
    产业集聚程度
    Degree of industrial agglomeration
    agg 参考文献[32]
    Reference [32]
    1.191 0.635 0.043 4.209
    要素
    Factor
    耕地规模化程度
    Scale of cropland
    lan 耕地面积/农业从业人数
    Cropland area/quantity of agricultural employees (hm2∙capita−1)
    0.499 0.386 0.104 2.812
    农业投资强度
    Agricultural investment intensity
    cap 农业固定资产投资/耕地面积
    Investment in agricultural fixed assets / cropland
    area (104 ¥∙hm−2)
    0.241 0.414 0.001 3.618
    农技人员密度
    Density of agricultural technicians
    tec 农业技术人员/耕地面积
    Agricultural technicians / cropland area
    (persons∙hm−2)
    0.008 0.004 0.002 0.025
    有效灌溉率
    Effective irrigation rate
    irr 有效灌溉面积/耕地面积
    Effective irrigated area / cropland area
    0.561 0.227 0.203 1.000
    环境
    Environment
    农业受灾程度
    Extent of agricultural disaster
    dis 受灾面积/农作物播种面积
    Agricultural disaster area / crop sown area
    0.238 0.162 0.002 0.936
    城镇化率
    Urbanization rate
    常住人口城镇化率
    Urbanization rate of resident population
    0.448 0.273 0.100 0.901
    财政支农力度
    Financial support for agriculture
    fis 农林水务支出/财政总支出
    Agricultural financial expenditure /
    total financial expenditure
    0.089 0.041 0.012 0.190
    下载: 导出CSV

    表  3  2000—2019年中国30省(市、自治区)主要年份农业碳排放效率

    Table  3.   Efficiencies of agricultural carbon emissions of 30 provinces (cities, autonomous regions) in China in major years of 2000 to 2019

    省(市) Province (city, autonomous region)20002005201020152019年均增长率 Annual growth rate (%)均值 Mean
    湖南 Hunan0.3230.3120.3150.3240.3460.370.326
    江西 Jiangxi0.4320.3460.3380.3601.0004.520.389
    湖北 Hubei0.6230.4730.5330.5600.519−0.950.539
    安徽 Anhui0.4960.5380.5670.5700.5070.110.540
    福建 Fujian0.4570.4880.5620.6240.8143.080.569
    浙江 Zhejiang0.4480.3820.4760.5841.0004.320.569
    广东 Guangdong0.4980.5380.5530.6321.0003.740.604
    江苏 Jiangsu0.4760.4900.5190.7921.0003.980.633
    宁夏 Ningxia0.7190.7150.7040.7120.7330.110.719
    陕西 Shaanxi0.7440.7710.7400.6260.8680.810.721
    山西 Shanxi0.7420.7550.7270.7470.7990.390.758
    云南 Yunnan0.7760.7370.7060.7290.8380.400.768
    四川 Sichuan1.0000.7510.7240.7420.811−1.100.773
    天津 Tianjin0.7070.7770.7010.7481.0001.840.788
    辽宁 Liaoning0.6440.7980.7110.8311.0002.340.805
    重庆 Chongqing1.0000.9750.8860.7511.0000.000.863
    上海 Shanghai0.6960.9231.0000.7880.8481.040.873
    海南 Hainan0.8340.7480.6850.8291.0000.960.877
    贵州 Guizhou1.0000.9470.7890.8280.959−0.220.881
    甘肃 Gansu0.9960.9690.8500.7971.0000.020.890
    山东 Shandong0.9580.8270.8520.9161.0000.230.891
    河北 Hebei0.9270.7950.8800.9331.0000.400.911
    广西 Guangxi0.6251.0000.8870.9071.0002.500.915
    黑龙江 Heilongjiang0.8280.8800.9201.0001.0001.000.924
    河南 Henan0.9370.9580.9240.9331.0000.340.942
    北京 Beijing0.8980.8750.9771.0001.0000.570.950
    吉林 Jilin0.8401.0000.9010.9701.0000.920.957
    新疆 Xinjiang1.0000.9360.9651.0001.0000.000.977
    内蒙古 Inner Mongolia1.0001.0001.0001.0001.0000.001.000
    青海 Qinghai1.0001.0001.0001.0001.0000.001.000
    全国 Nationalwide0.7540.7570.7460.7740.9010.940.778
    下载: 导出CSV

    表  4  2000—2019年中国农业碳排放效率Moran’s I测算结果

    Table  4.   Moran’s I of agricultural carbon emissions efficiency in China from 2000 to 2019

    年份 YearMoran’s IzP年份 YearMoran’s IzP
    20000.3594.0690.00020100.2252.7100.003
    20010.3373.8590.00020110.2723.2110.001
    20020.3934.4590.00020120.2452.9520.002
    20030.4064.5840.00020130.2593.0670.001
    20040.3153.6480.00020140.2002.4560.007
    20050.3043.5240.00020150.2903.4250.000
    20060.2983.4540.00020160.3223.7370.000
    20070.2252.6990.00320170.3033.5610.000
    20080.2132.5660.00520180.2573.1460.001
    20090.1892.3220.01020190.0410.8420.200
    下载: 导出CSV

    表  5  农业碳排放效率影响因素的空间计量模型估计结果

    Table  5.   Estimation of influencing factors of the agricultural carbon emissions efficiency based on spatial econometric model

    变量
    Variable
    SDM (W0)SDM (W1)SDM (W2)
    系数 Coefficientz-statistics系数 Coefficientz-statistics系数 Coefficientz-statistics
    Ind−0.986***−3.06−0.718**−2.14−1.385***−2.97
    cro−0.237−1.15−0.212−1.03−0.126−0.65
    agg−0.006−0.270.0160.780.0010.05
    ln(lan)0.1131.530.0191.000.0030.16
    ln(cap)−0.049***−2.92−0.015−0.47−0.084−0.65
    ln(tec)−0.037−1.01−0.040−1.02−0.025−0.64
    irr0.297**2.090.268**2.060.248*1.80
    dis−0.070**−2.14−0.082***−2.66−0.066**−2.13
    urb0.113*1.770.109*1.780.0671.09
    fis−0.753***−3.01−0.393−1.50−0.840***−3.52
    W×ind0.460*1.920.368*1.830.3411.33
    W×cro0.783*1.800.626*1.641.0551.42
    W×agg−0.022−0.39−0.048−1.07−0.122−0.80
    W×ln(lan)0.0400.93−0.038*−1.77−0.055**−2.28
    W×ln(cap)0.0100.580.1111.460.1131.52
    W×ln(tec)0.0870.940.0180.33−0.048−0.24
    W×irr−0.257−1.52−0.178−1.45−0.370−1.14
    W×dis−0.079−1.63−0.018−0.41−0.220**−2.38
    W×urb0.234*1.770.173*1.750.419**2.26
    W×fis0.5031.55−0.082−0.300.6171.52
    ρ0.185**2.100.254***4.440.320***3.22
    Hausman33.65***32.32***37.25***
    Wald-lag63.28***56.00***43.71***
    Wald-err58.88***48.91***40.04***
    LR-lag60.24***53.51***42.50***
    LR-err58.41***49.07***41.04***
    R20.25920.25300.2449
    Log-pseudolikelihood692.7242697.6138690.8701
    observations600600600
      *、**和***分别表示估计系数通过P<10%、P<5%和P<1%显著性水平下的z检验。各变量的意义见表2。*, ** and *** indicate that the estimated coefficients pass the z-test at P<10%, P<5%, and P<1% levels of significance, respectively. The meaning of each variable is shown in the table 2.
    下载: 导出CSV

    表  6  农业碳排放效率影响因素的总效应、直接效应及溢出效应

    Table  6.   Total, direct and spillover effects of influencing factors of agricultural carbon emissions efficiency

    变量
    Variable
    总效应 Total effect直接效应 Direct effect溢出效应 Spillover effect
    系数 Coefficientz-statistics系数 Coefficientz-statistics系数 Coefficientz-statistics
    ind−0.656**−2.25−1.093***−3.060.437*1.83
    cro0.7491.47−0.219−1.120.969*1.73
    agg−0.035−0.48−0.005−0.24−0.030−0.43
    ln(lan)0.188*1.680.116*1.680.0721.29
    ln(cap)−0.049**−2.54−0.058***−3.070.0080.52
    ln(tec)0.0650.49−0.032−0.830.0960.85
    irr0.0940.340.290**2.11−0.196−0.85
    dis−0.188***−2.95−0.075**−2.38−0.113**−1.96
    urb0.441***2.940.127**2.030.314**2.08
    fis−0.334−0.95−0.738***−2.990.4041.15
      *、**和***分别表示估计系数通过P<10%、P<5%和P<1%显著性水平下的z检验。各变量的意义见表2。*, ** and *** indicate that the estimated coefficients pass the z-test at P<10%, P<5%, and P<1% levels of significance, respectively. The meaning of each variable is shown in the table 2.
    下载: 导出CSV
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  • 收稿日期:  2021-04-05
  • 录用日期:  2021-06-03
  • 网络出版日期:  2021-07-26

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