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农业科技投入对农业生态效率的空间效应分析

王辰璇 姚佐文

王辰璇, 姚佐文. 农业科技投入对农业生态效率的空间效应分析[J]. 中国生态农业学报(中英文), 2021, 29(11): 1−12 doi: 10.13930/j.cnki.cjea.210214
引用本文: 王辰璇, 姚佐文. 农业科技投入对农业生态效率的空间效应分析[J]. 中国生态农业学报(中英文), 2021, 29(11): 1−12 doi: 10.13930/j.cnki.cjea.210214
WANG C X, YAO Z W. An analysis of the spatial effect of agricultural science and technology investment on agricultural eco-efficiency[J]. Chinese Journal of Eco-Agriculture, 2021, 29(11): 1−12 doi: 10.13930/j.cnki.cjea.210214
Citation: WANG C X, YAO Z W. An analysis of the spatial effect of agricultural science and technology investment on agricultural eco-efficiency[J]. Chinese Journal of Eco-Agriculture, 2021, 29(11): 1−12 doi: 10.13930/j.cnki.cjea.210214

农业科技投入对农业生态效率的空间效应分析

doi: 10.13930/j.cnki.cjea.210214
基金项目: 国家社会科学基金重点项目(14AKS005)资助
详细信息
    作者简介:

    王辰璇, 主要研究方向为农业资源环境与生态。E-mail: wangcx133@163.com

    通讯作者:

    姚佐文, 主要研究方向为农业资源环境与生态。E-mail: yaozuowen@sina.com

  • 中图分类号: F323.3

An analysis of the spatial effect of agricultural science and technology investment on agricultural eco-efficiency

Funds: This study was supported by the Key Program of the National Social Science Foundation of China (14AKS005)
More Information
  • 摘要: 农业污染日益严重背景下, 探究农业科技投入对农业生态效率的作用机制, 对缓解农村生态压力、农村健康发展具有重要现实意义。鉴于此, 本文在采用超效率SBM (super-efficiency slack-based measure)模型测度2000—2018年我国东中西部省际农业生态效率基础上, 根据莫兰指数对农业生态效率及农业科技投入进行空间自相关检验, 采用空间计量模型剖析农业科技投入对农业生态效率影响的空间溢出效应与门槛特征。结果表明, 2010—2018年东中西部的农业生态效率呈现东西部高、中部低的态势; 2000—2018年东中西部的农业生态效率波动明显, 2000—2003年有小幅波动, 2004—2008年农业生态效率略有下降, 2008—2010年稍有上升, 2010年农业生态效率为0.731; 之后2011—2014年稍有下降, 2015—2017年全国农业生态效率分别下降到0.5894、0.5839、0.5159; 2018年农业生态效率提升到0.5453。农村科技投入对农业生态效率影响呈现为“倒U”型, 农业科技投入规模对农业生态效率有着显著的溢出效应。东中西部分组面板门槛回归显示: 东中西部的农业科技投入门槛效应差别较大, 东部表现为正向促进作用, 中部农业科技投入对农业生态效率的积极作用没有东部稳定, 西部农业科技投入对农业生态效率表现为负向抑制作用, 中西部地区农业发展中的科技投入要兼顾经济与生态效率。为此, 我国要大力推广绿色高效技术模式, 积极采取有机肥替代化肥行动, 加快实施科学施肥用药技术, 抓好示范带动减量增效, 提高农业生态效率。
  • 图  1  2000—2018年中国东中西部地区农业生态效率变化

    Figure  1.  Changes of agricultural eco-efficiency in the eastern, middle and western regions of China from 2000 to 2018

    表  1  中国农业生态效率指标体系

    Table  1.   Indexes system of agricultural eco-efficiency in China

    指标
    Index
    变量
    Variable
    变量说明
    Variable description
    单位
    Unit
    备注
    Notes
    要素投入
    Factor input
    机械投入
    Mechanical input
    农业机械总动力
    Agricultural machinery
    ×104 kW以农业机械作为农业现代化的代表
    With the agricultural machinery as the representative of agricultural modernization
    土地投入
    Land input
    农作物播种面积
    Area sown to crops
    km2反映农业生产过程中的耕作面积
    Reflecting the actual cultivated area in agricultural production
    劳动力投入
    Labor input
    农业从业人员数
    Number of agricultural employees
    ×104 persons农业从业人员数=第一产业从业人员×(农业总产值/农林牧渔业总产值)
    Number of agricultural employees = employees in the primary industry × (gross
    output value of agriculture / gross output value of agriculture, forestry, animal
    husbandry and fishery)
    灌溉投入
    Irrigation input
    有效灌溉面积
    Effective irrigated area
    km2以灌溉用水表征农业主要用水投入
    Using irrigation water to represent the main agricultural water input
    化肥投入
    Fertilizer input
    施用化肥折纯量
    Effective fertilizer
    ×104t化肥、农药、农膜、柴油等是农业生产中主要的污染源
    Fertilizer, pesticide, agricultural film, diesel oil are the main pollution sources in agricultural production.
    农药投入
    Pesticide input
    农药使用量
    Pesticide use
    ×104t
    农膜投入
    Film input
    农膜使用量
    Use of agricultural film
    ×104t
    能源投入
    Energy input
    农用柴油使用量
    Diesel consumption for agricultural use
    ×104t
    期望产出
    Expected output
    农业产出
    Agricultural output
    农业总产值
    Gross output value of agriculture
    ×108 ¥按指数(上年=100)折算为2000年不变价
    Coverting to invariabl price in 2000 according index (last year =100)
    非期望产出
    Undesired output
    农业碳排放
    Carbon emissions from agriculture
    农业碳排放
    Carbon emissions from agriculture
    ×104t参考李波等[23]的定义
    Refer to the definition of LI Bo, et al[23]
    下载: 导出CSV

    表  2  农村科技投入与农业生态效率影响的空间计量结果

    Table  2.   Spatial measurement results of the impact of rural scientific and technological input on agricultural eco-efficiency

    变量
    Variable
    OLS模型
    OLS model
    SLM模型
    SLM model
    SDM模型
    SDM model
    SEM模型
    SEM model
    随机效应
    Random effect
    固定效应
    Fixed effect
    随机效应
    Random effect
    固定效应
    Fixed effect
    随机效应
    Random effect
    固定效应
    Fixed effect
    lnKT−0.025
    (−0.43)
    −1.094
    (−2.17)**
    −2.320
    (−2.93)***
    −1.249
    (−1.73)*
    −3.136
    (−3.42)***
    −1.121
    (−2.18)**
    −2.366
    (−2.97)***
    lnMII−0.414
    (−3.39)***
    −0.131
    (−0.66)
    −0.196
    (−0.90)
    −0.135
    (−0.64)
    −0.223
    (−1.04)
    −0.137
    (−0.68)
    −0.209
    (−0.94)
    lnMCI0.355
    (4.69)***
    0.153
    (2.71)***
    0.128
    (3.39)***
    0.144
    (2.33)**
    0.0917
    (2.07)**
    0.153
    (2.72)***
    0.126
    (3.43)***
    lnCPS−0.842
    (−9.59)***
    −0.127
    (−0.51)
    0.015
    (0.06)
    −0.094
    (−0.39)
    0.066
    (0.29)
    −0.116
    (−0.44)
    0.043
    (0.16)
    lnTES0.737
    (4.64)***
    0.913
    (2.41)**
    1.777
    (2.84)***
    1.178
    (2.75)***
    2.152
    (3.26)***
    0.931
    (2.41)**
    1.802
    (2.88)***
    ln2TES0.035
    (2.17)**
    −0.011
    (−0.60)
    −0.043
    (−2.16)**
    0.014
    (0.41)
    −0.041
    (−1.26)
    −0.011
    (−0.61)
    −0.044
    (−2.18)**
    lnADR0.101
    (1.41)
    0.023
    (0.68)
    0.010
    (0.27)
    0.016
    (0.52)
    0.008
    (0.22)
    0.021
    (0.61)
    0.004
    (0.11)
    W×lnKT0.428
    (0.42)
    2.579
    (2.17)**
    W×lnMII0.378
    (0.63)
    0.756
    (1.21)
    W×lnMCI0.049
    (0.62)
    0.078
    (0.91)
    W×lnCPS−1.186
    (−1.26)
    −1.369
    (−1.59)
    W×lnTES−0.600
    (−0.74)
    −1.702
    (−1.58)
    W×ln2TES−0.028
    (−0.58)
    0.045
    (0.96)
    W×lnADR0.188
    (1.59)
    0.171
    (1.46)
      *、**和***分别表示在P<0.1、P<0.05和P<0.01的水平下显著。KT、MII、MCI、CPS、TES和ADR分别代表农业科技投入规模、农业机械强度、复种指数、种植结构、农业科技投入水平和农业受灾率。W为空间权重矩阵。OLS模型(普通最小二乘法)中, 括号内为t统计值; SLM(空间滞后模型)、SDM(空间杜宾模型)、SEM(空间误差模型)中, 括号内为z统计值。*, ** and *** indicate significant at the level of P<0.1, P<0.05, and P<0.01, respectively. KT, MII, MCI, CPS, TES and ADR respectively represent the agricultural technology investment scale, agricultural machinery intensity, multiple cropping index, planting structure, agricultural technology investment level and agricultural disaster rate. W is the spatial weight matrix. In the OLS (ordinary least squares), the t statistic is in parentheses; in the SLM (spatial lag model), SDM (spatial Dobbin model), and SEM (spatial error model), the z statistic is in the parentheses.
    下载: 导出CSV

    表  3  农村科技投入与农业生态效率影响的空间杜宾模型效应分解结果

    Table  3.   Decomposition results of spatial Dobbin model of the impact of rural science and technology investment on agricultural eco-efficiency

    变量
    Variable
    直接效应
    Direct effect
    间接效应
    Indirect effect
    总效应
    Total effect
    lnKT −3.145(−3.40)*** 2.493(2.22)** −0.652(−0.58)
    lnMII −0.241(−1.24) 0.757(1.18) 0.516(0.84)
    lnMCI 0.094(1.94)* 0.075(0.85) 0.169(1.87)*
    lnCPS 0.099(0.40) −1.226(−1.48) −1.127(−1.36)
    lnTES 2.086(2.87)*** −1.543(−1.53) 0.542(0.50)
    ln2TES −0.047(−1.46) 0.048(1.08) 0.002(0.06)
    lnADR 0.008(0.22) 0.189(1.75)* 0.197(1.82)*
      *、**和***分别表示在P<0.1、P<0.05和P<0.01的水平显著。KT、MII、MCI、CPS、TES和ADR分别代表农业科技投入规模、农业机械强度、复种指数、种植结构、农业科技投入水平和农业受灾率。括号内为z统计值。*, ** and *** indicate significant at the levels of P<0.1, P<0.05, and P<0.01, respectively. KT, MII, MCI, CPS, TES and ADR respectively represent the agricultural technology investment scale, agricultural machinery intensity, multiple cropping index, planting structure, agricultural technology investment level and agricultural disaster rate. The z statistic is in the parentheses.
    下载: 导出CSV

    表  4  农业科技投入影响农业生态效率的分组面板门槛回归

    Table  4.   Panel threshold regression of the impact of agricultural scientific and technological input on agricultural eco-efficiency

    变量
    Variable
    模型(9) (不含TES平方项)
    Model (9) (excluding TES square item)
    模型(10) (含TES平方项)
    Model (10) (including TES square item)
    东部
    East
    中部
    Middle
    西部
    West
    东部
    East
    中部
    Middle
    西部
    West
    lnKT−1.259
    (−4.02)***
    −5.379
    (−5.65)***
    0.797
    (5.09)***
    −0.849
    (−4.63)***
    1.928
    (5.76)***
    0.980
    (0.92)
    lnMII−0.795
    (−2.55)**
    0.559
    (1.64)
    0.513
    (1.57)
    −0.466
    (−2.85)***
    0.335
    (0.81)
    −0.694
    (−1.77)*
    lnMCI0.145
    (2.53)**
    0.023
    (0.04)
    0.279
    (3.69)***
    0.362
    (4.37)***
    0.120
    (0.19)
    0.283
    (2.96)***
    lnCPS−0.312
    (−1.49)
    0.494
    (1.83)*
    −1.493
    (−3.32)***
    −0.360
    (−2.05)**
    −0.039
    (−0.12)
    −2.168
    (−3.78)***
    lnTES (τitη1)1.275
    (4.48)***
    4.958
    (5.19)***
    −1.722
    (−6.32)***
    0.537
    (2.89)***
    −4.359
    (−3.06)***
    −3.503
    (−2.32)**
    lnTES (η1<τitη2)1.194
    (4.30)***
    4.839
    (5.09)***
    −1.878
    (−5.60)***
    0.609
    (3.35)***
    −4.298
    (−3.09)***
    −3.425
    (−2.24)**
    lnTES (τit>η2)−2.025
    (−5.52)***
    0.509
    (2.41)**
    −4.185
    (−3.05)***
    −3.644
    (−2.33)**
    ln2TES
    −0.065
    (−3.20)***
    −0.185
    (−1.63)
    −0.172
    (−1.75)*
    lnADR0.066
    (1.39)
    −0.010
    (−0.08)
    −0.009
    (−0.06)
    0.246
    (3.77)***
    0.018
    (0.12)
    −0.306
    (−1.78)*
      *、**和***分别表示在P<0.1、P<0.05和P<0.01的水平显著。KT、MII、MCI、CPS、TES和ADR分别代表农业科技投入规模、农业机械强度、复种指数、种植结构、农业科技投入水平和农业受灾率。τ为门槛变量, η1η2为估算的门槛值。括号内为t统计值。*, ** and *** indicate significant at the levels of P<0.1, P<0.05, and P<0.01, respectively. KT, MII, MCI, CPS, TES and ADR respectively represent the agricultural technology investment scale, agricultural machinery intensity, multiple cropping index, planting structure, agricultural technology investment level and agricultural disaster rate. τ is the threshold variable, η1 and η2 are the estimated threshold values. The t statistic is in the parentheses.
    下载: 导出CSV

    表  5  2000—2018年西部地区农业科技水平门槛区间

    Table  5.   Threshold interval of agricultural science and technology level in the western region of China from 2000 to 2018

    门槛区间
    Threshold interval
    2000—20052006—20102011—20152016—2018
    低 Low (TES≤0.3458%)
    中等 Medium (0.3458%<TES≤0.6288%)重庆 Chongqing
    陕西 Shaanxi
    青海 Qinghai
    四川 Sichuan
    云南 Yunnan
    贵州 Guizhou
    甘肃 Gansu,
    宁夏 Ningxia
    新疆 Xinjiang
    高 High (TES≥0.6288%)重庆 Chongqing
    陕西 Shaanxi
    青海 Qinghai
    四川 Sichuan
    云南 Yunnan
    贵州 Guizhou
    甘肃 Gansu
    宁夏 Ningxia
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-10
  • 录用日期:  2021-08-20
  • 网络出版日期:  2021-08-27

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