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

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

王辰璇^1,,
姚佐文^2, ,

1.
安徽农业大学经济管理学院　合肥　230061
2.
淮北师范大学经济与管理学院　淮北　235000

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
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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
- 刊出日期: 2021-11-10

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

WANG Chenxuan^1
,,
YAO Zuowen^{2
, ,}

1.
School of Economics and Management, Anhui Agricultural University, Hefei 230061, China
2.
School of Economics and Management, Huaibei Normal University, Huaibei 235000, China

Funds: This study was supported by the Key Program of the National Social Science Foundation of China (14AKS005)

More Information

Corresponding author: E-mail: yaozuowen@sina.com

摘要

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, 2000—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。
- 农业科技投入 /
- 农业生态效率 /
- 空间计量模型 /
- 门槛效应
Abstract: With increasingly severe agricultural pollution, it is important to explore the effects of agricultural science and technology investments on agricultural ecological efficiency to alleviate rural ecological pressure and promote the healthy development of rural areas. This study used a super-efficiency slack-based measure (SBM) model to measure the agricultural ecological efficiency of provinces in eastern, middle, and western China from 2000 to 2018. According to the Moran index, the spatial autocorrelation of agricultural ecological efficiency and agricultural science and technology input were analyzed. The spatial spillover effect and threshold characteristics of the impact of agricultural science and technology input on agricultural ecological efficiency were explored by using the spatial econometric model. Results showed that from 2000 to 2018, the agro-ecological efficiency was high in the eastern and western regions and low in the middle region. The agro-ecological efficiency in the eastern, middle, and western regions fluctuated significantly from 2000 to 2018, with slight fluctuations in 2000–2003. Agricultural ecological efficiency declined slightly from 2004 to 2008 and then rose slightly from 2008 to 2010. In 2010, the agricultural ecological efficiency was 0.731, after which it declined slightly from 2011 to 2014. From 2015 to 2017, the national agricultural ecological efficiency dropped to 0.5894, 0.5839, and 0.5159, respectively. In 2018, the annual agricultural ecological efficiency increased to 0.5453. The impact of rural science and technology investments on agricultural ecological efficiency presented as an inverted U-shape, and the scale of agricultural science and technology investments had a significant spillover effect on agricultural ecological efficiency. The panel threshold regression showed that the threshold effect of agricultural science and technology investments in the eastern, middle, and western regions in China differed, and that in the eastern region had a positive promoting effect. The positive effect of agricultural science and technology input on agricultural ecological efficiency in the middle region was not as stable as that in the eastern region. The input of agricultural science and technology in the western region harmed agricultural ecological efficiency. The scientific and technological input in the agricultural development of the middle and western regions of China should consider economic and ecological efficiency. Therefore, China should vigorously promote the green and efficient technology mode, actively replace chemical fertilizers with organic fertilizers, accelerate the implementation of scientific fertilizer application techniques, pay close attention on demonstration, fertilizer reduction and fertilization efficiency, and improve agricultural ecological efficiency.
- Agricultural science and technology investment /
- Agricultural ecological efficiency /
- Spatial econometric model /
- Threshold effect

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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

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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]

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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
变量 Variable	OLS模型 OLS 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)
lnMCI	0.355 (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)
lnTES	0.737 (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)^***
ln²TES	0.035 (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)^**
lnADR	0.101 (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×lnKT				0.428 (0.42)	2.579 (2.17)^**
W×lnMII				0.378 (0.63)	0.756 (1.21)
W×lnMCI				0.049 (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×lnADR				0.188 (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.

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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.

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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)
变量 Variable	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)^*
lnMCI	0.145 (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 (τ_it≤η₁)	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<τ_it ≤η₂)	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 (τ_it>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)^*
lnADR	0.066 (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, η₁和η₂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, η₁and η₂ are the estimated threshold values. The t statistic is in the parentheses.

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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—2005	2006—2010	2011—2015	2016—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

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