环境规制视角下农业基础设施对粮食生态全要素生产率的影响

李自强; 叶伟娇; 梅冬; 郑茨文

doi:10.12357/cjea.20220214

环境规制视角下农业基础设施对粮食生态全要素生产率的影响

1.
华中农业大学经济管理学院　武汉　430070
2.
首都经济贸易大学工商管理学院　北京　100070
3.
中国农业科学院农业资源与农业区划研究所　北京　100081

基金项目: 教育部哲学社会科学重大攻关项目(20JZD015)、“十四五”国家重点研发计划项目(2021YFD1600505)资助

详细信息

作者简介:
李自强, 主要研究方向为资源与环境经济学。E-mail: liziqiang@webmail.hzau.edu.cn

通讯作者:
叶伟娇, 主要研究方向为粮食安全、资源环境经济与政策研究。E-mail: yeweijiao@163.com

中图分类号: F326.11
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出版历程
- 收稿日期: 2022-03-23
- 录用日期: 2022-06-21
- 网络出版日期: 2022-06-21
- 刊出日期: 2022-11-09

Impacts of agricultural infrastructure on ecology total factor productivity of grain from the perspective of environmental regulation

1.
College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China
2.
College of Business Administration, Capital University of Economics and Business, Beijing 100070, China
3.
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Funds: This study was supported by the Key Project of Philosophy and Social Sciences Research, Ministry of Education of China (20JZD015), and the National Key Research and Development Program (2021YFD1600505).

More Information

Corresponding author:
YE Weijiao: E-mail: yeweijiao@163.com

摘要

摘要: 保护生态环境前提下实现粮食增产是保障我国粮食安全可持续发展的重要前提。为探寻提升我国粮食生态全要素生产率的有效策略, 本研究在考虑粮食种植生态价值基础上, 运用Global-Malmquist-Luenberger (GML)指数测算出粮食生态全要素生产率, 以农业基础设施为切入点实证探究粮食生态全要素生产率的提升路径。结果表明: 1)农业基础设施各维度都能有效提高粮食生态全要素生产率, 但存在时间上的滞后性; 其中, 农业水利设施对其的影响呈“倒U型”关系。2)与全样本回归结果不同的是, 分南北样本回归结果显示南方地区农业水利设施及其滞后项对粮食生态全要素生产率不具有显著影响, 且农业电力设施及滞后项对其具有抑制作用; 分产区样本回归结果显示粮食主产区农业交通设施及其滞后项不能显著影响粮食生态全要素生产率, 同时, 非粮食主产区农业电力设施及滞后项对其也不具有显著影响。3)从调节作用检验结果看, 环境规制在农田水利设施对粮食生态全要素生产率的影响中具有正向调节作用。进一步研究发现, 分组调节回归结果中相对于粮食生态全要素生产率较高的区域, 在粮食生态全要素生产率较低区域内, 环境规制能够发挥更强的正向调节作用。据此, 建议政府应超前规划布局农业基础设施建设投资体系, 制定并宣传科学、合理、弹性的环境法规制度。
- 环境规制 /
- 农业基础设施 /
- 粮食生态全要素生产率 /
- 粮食种植生态价值
Abstract: Increasing grain production while protecting the environment is an important prerequisite for ensuring sustainable development and food security of China. To explore effective strategies to improve the ecology total factor productivity of grain (ETFP) in China, this study used the Global-Malmquist-Luenberger (GML) index to measure the ETFP based on the ecological value of grain production. Based on the theory of public goods, we empirically explored the path to improve ETFP with agricultural infrastructure as the point of penetration. We found: (1) The ecological value of grain production per hectare in 30 provinces (cities, autonomous regions) of China increased from 1993 to 2019. The average ETFP during 1993–2019 generally showed a fluctuating upward trend. Among them, the ETFP of the Middle and Lower Reaches of the Yangtze River and the Northeast China were higher than the national average in most years. (2) Agricultural water conservancy facilities, agricultural electric power facilities, and agricultural transportation facilities can effectively improve ETFP, but there was a lag in time. Among these factors, the impact of agricultural water conservancy facilities on ETFP showed an “inverted U” shape. This finding suggests that there is an optimum value for the provision of agricultural field water conservancy facilities in the process of ecological food production. (3) In contrast to the full-sample regression results, the regression results of samples of northern and southern regions showed that the agricultural water conservancy facilities and their lag terms had no significant impact on ETFP, and the agricultural electric power facilities and their lag terms had a reducing effect on ETFP in southern region. The results of the sample regression showed that agricultural electric facilities and their lag items in main grain-producing areas had no significant impact on ETFP, while agricultural electric power facilities and their lag items in non-main grain-producing areas had no significant impact on ETFP. (4) The results of the moderating effect test indicated that environmental regulation had a positive moderating effect on ETFP. Further study found that, in the grouping regulation regression results, environmental regulation could play a stronger positive regulatory role in the region with a lower ETFP than in the region with a higher ETFP. Therefore, on the basis of this research, we recommend that the government should plan and invest in agricultural infrastructure construction in advance and formulate and publicize scientific, reasonable, and flexible environmental laws and regulations. This study innovatively incorporates the ecological value of grain production into the measurement of ETFP. While broadening the research boundary of agricultural infrastructure construction planning, it provides a basis for improving the ETFP in China.
- Environmental regulation /
- Agricultural infrastructure /
- Ecology total factor productivity of grain /
- Ecological value of grain cultivation

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图 1 1993—2019年不同区域粮食种植生态价值变化特征

Figure 1. Characteristics of changes in ecological values of grain production in different regions from 1993 to 2019

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图 2 1993—2019年不同区域粮食生态全要素生产率(ETFP)累计增长情况

Figure 2. Cumulative growth of grain ecological total factor productivity (ETFP) in different regions from 1993 to 2019

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表 1 粮食生态全要素生产率测量指标

Table 1 Measurement indicators of ecological total factor productivity of grain

指标类别 Indicator category	指标 Indicator	计算方法 Calculation method	单位 Unit
期望产出 Expected output	粮食种植生态价值 Ecological value of food cultivation	通过生态系统服务价值评价法测算所得 Measured by ecosysytem services value method	无 No
期望产出 Expected output	粮食产量 Food production	统计数据 Statistics	×10⁴ t
非期望产出 Undesired output	面源污染 Non-point source pollution	农业面源污染×A Agricultural non-point source pollution×A	×10⁴ t
非期望产出 Undesired output	碳排放量 Carbon emissions	农业碳排放量×A Agricultural carbon emissions×A	×10⁴ t
投入要素 Input element	土地投入 Land input	粮食播种面积 Grain sown area	×10³ hm²
	劳动力投入 Labor input	第一产业从业人员×B Number of employees in primary industry×B	×10⁴ persons
	化肥投入 Fertilizer input	化肥施用量×A Amount of chemical fertilizer applied×A	×10⁴ t
	农药投入 Pesticide input	农药使用量×A Amount of pesticides used×A	×10⁴ t
	机械投入 Machinery input	农业机械总动力×A Total power of agricultural machinery×A	×10⁴ kW
	塑料薄膜投入 Plastic film input	农用塑料薄膜使用量×A Amount of agricultural plastic film used×A	×10⁴ t
	水资源投入 Water input	农业用水量×A Amount of water used in agriculture×A	×10⁸ m³
A=粮食播种面积/农作物播种总面积; B=A×(农业产值/农林牧渔总产值)。A=grain sown area/total crop sown area; B=A×(agricultural output value/total output value of agriculture, forestry, animal husbandry and fishery)

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表 2 粮食生态全要素生产率相关变量及计算方法

Table 2 Variables and calculation methods of ecological total factor productivity of grain

变量类别 Variable category	变量 Variable	符号 Symbol	计算方法 Calculation method	单位 Unit
被解释变量 Explained variable	粮食生态全要素生产率 Ecology total factor productivity of grain	ETFP	通过Global-Malmquist-Luenberger指数测算所得 Measured by the Global-Malmquist-Luenberger index	无 No
核心解释变量 Core explanatory variables	农业基础设施 Agricultural infrastructure	Infr
	农业水利设施 Agricultural water conservancy facilities	Irri	有效灌溉面积/农作物播种面积 Effective irrigated area/crop sown area	无 No
	农业电力设施 Agricultural electric power facilities	Elec	电力消费量×C Electricity consumption×C	×10⁷ kW∙h
	农业交通设施 Agricultural transportation facilities	Road	公路里程×耕地面积/省域国土面积 Road mileage×arable land area/provincial land area	×10⁴ km
调节变量 Moderator	环境规制 Environmental regulation	Envi	环境污染治理投资总额×C Total investment in environmental pollution control×C	×10⁸ ¥
控制变量 Control variable	户均耕地规模 Average size of arable land per household	Cult	耕地面积/乡村户数 Arable land area/number of rural households	hm²∙household
	除涝面积 Flood control area	Logg	统计数据 Statistics	×10⁶ hm²
	产业结构水平 Industrial structure level	Stru	(第二产业增加值+第三产业增加值)/GDP总值 (Value added of secondary sector+value added of tertiary sector)/gross GDP	无 No
	农村固定资产投资 Rural fixed asset investment	Inve	农村农户固定资产投资/乡村人口数 Rural farm household fixed asset investment/number of rural population	×10³ ¥∙person⁻¹
	受灾率 Disaster rate	Disa	农作物受灾面积/农作物总播种面积×100 Crop affected area/total crop sown area×100	%
	粮价波动 Food price fluctuation	Pric	粮食商品零售价格指数 Food commodity retail price index	无 No
	技术密度 Technology density	Tech	技术市场成交额×C/乡村从业人员数 Technology market turnover×C/number of rural employees	×10² ¥∙person⁻¹
C=A×(农业总产值/GDP总值), A的计算方法见表1注。C=A×(total agricultural output value/GDP value). Caculation method of A is illustrated in the note of table 1.

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表 3 农业基础设施与粮食生态全要素生产率回归结果

Table 3 Regression results between grain ecological total factor productivity and agricultural infrastructure

变量 Variable	因变量: 粮食生态全要素生产率 Dependent variable: grain ecological total factor productivity
变量 Variable	模型1 Model 1	模型2 Model 2	模型3 Model 3	模型4 Model 4	模型5 Model 5	模型6 Model 6	模型7 Model 7	模型8 Model 8	模型9 Model 9	模型10 Model 10
Irri		1.574^*** (4.20)	3.308^*** (3.18)					1.855^*** (4.95)	3.879^*** (3.75)
Sq_Irri			−1.514^* (−1.78)						−1.746^** (−2.08)
Elec				1.166^** (2.12)	3.255^** (2.10)			1.116^** (2.03)	2.946^* (1.93)
Sq_Elec					−4.869 (−1.44)				−3.839 (−1.15)
Road						0.059^*** (3.42)	0.112^** (2.42)	0.064^*** (3.71)	0.127^*** (2.74)
Sq_Road							−0.004 (−1.24)		−0.005 (−1.39)
Lag_Irri										1.952^*** (4.86)
Lag_Elec										0.850 (1.53)
Lag_Road										0.077^*** (4.43)
Cult	1.324^*** (8.37)	1.369^*** (8.80)	1.324^*** (8.49)	1.310^*** (8.31)	1.262^*** (7.91)	1.223^*** (7.85)	1.149^*** (7.03)	1.253^*** (8.22)	1.083^*** (6.79)	1.263^*** (8.26)
Logg	0.208 (1.57)	0.219 (1.59)	0.183 (1.34)	0.157 (1.17)	0.129 (0.96)	0.045 (0.33)	0.036 (0.27)	−0.016 (−0.11)	−0.093 (−0.68)	−0.050 (−0.36)
Stru	−0.716^*** (−3.58)	−0.660^*** (−3.34)	−0.664^*** (−3.36)	−0.800^*** (−3.93)	−0.859^*** (−4.14)	−0.755^*** (−3.79)	−0.770^*** (−3.86)	−0.775^*** (−3.89)	−0.860^*** (−4.24)	−0.657^*** (−3.37)
Inve	−0.340^*** (−5.39)	−0.377^*** (−5.99)	−0.383^*** (−6.09)	−0.352^*** (−5.57)	−0.355^*** (−5.62)	−0.349^*** (−5.55)	−0.342^*** (−5.42)	−0.404^*** (−6.46)	−0.407^*** (−6.50)	−0.405^*** (−6.43)
Disa	−0.251 (−1.44)	−0.264 (−1.54)	−0.277 (−1.62)	−0.245 (−1.41)	−0.243 (−1.40)	−0.214 (−1.23)	−0.204 (−1.18)	−0.219 (−1.29)	−0.217 (−1.28)	−0.167 (−0.98)
Pric	0.164 (0.31)	0.0692 (0.13)	0.002 (0.00)	0.159 (0.30)	0.175 (0.33)	0.169 (0.32)	0.187 (0.36)	0.053 (0.10)	0.006 (0.01)	0.052 (0.09)
Tech	0.030^* (1.74)	0.028 (1.63)	0.032^* (1.88)	0.035^** (2.00)	0.041^** (2.30)	0.035^** (2.03)	0.034^** (1.99)	0.037^** (2.16)	0.047^*** (2.67)	0.038^** (2.25)
Time	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Wald test	561.77^***	596.62^***	602.48^***	569.97^***	573.50^***	583.85^***	586.75^***	636.17^***	652.65^***	616.01^***
Cons	0.581 (0.75)	0.088 (0.12)	−0.150 (−0.20)	0.647 (0.86)	0.640 (0.86)	0.722 (0.97)	0.722 (0.97)	0.223 (0.30)	−0.058 (−0.08)	0.057 (0.06)
N	810	810	810	810	810	810	810	810	810	780
Sq_Irri、Sq_Elec、Sq_Road分别表示Irri、Elec、Road的平方项; Lag_Irri、Lag_Elec、Lag_Road分别表示Irri、Elec、Road的一阶滞后项; Time表示时间效应; Ind表示个体效应; 其他变量说明见表2。*、、分别代表在P<0.01、P<0.05、P<0.1水平显著; 括号内的数为t值。Sq_Irri, Sq_Elec, and Sq_Road denote the squared terms of Irri, Elec, and Road, respectively; Lag_Irri, Lag_Elec, and Lag_Road denote the first-order lagged terms of Irri, Elec, and Road, respectively; Time denotes time effect; Ind denotes individual effect. The explaination of other variables are shown in the table 2. , and * denote significance at P<0.01, P<0.05, and P<0.1 levels, respectively. The numbers in parentheses are t-values.

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表 4 南方和北方地区农业基础设施与粮食生态全要素生产率回归结果

Table 4 Regression results of grain ecological total factor productivity and agricultural infrastructure in north and south regions

变量 Variable	因变量: 粮食生态全要素生产率 Dependent variable: grain ecological total factor productivity
	北方地区 North region			南方地区 South region
	模型11 Model 11	模型12 Model 12	模型13 Model 13	模型14 Model 14	模型15 Model 15	模型16 Model 16
Irri		3.118^*** (6.77)			−0.016 (−0.02)
Elec		1.872^*** (3.24)			−3.150^** (−2.49)
Road		0.054^** (2.16)			0.071^*** (2.76)
Lag_Irri			3.481^*** (6.74)			−0.145 (−0.20)
Lag_Elec			1.394^** (2.36)			−2.491^* (−1.91)
Lag_Road			0.071^*** (2.79)			0.074^*** (2.88)
Cult	1.719^*** (10.10)	1.603^*** (10.00)	1.585^*** (9.66)	0.183 (0.51)	−0.050 (−0.13)	0.019 (0.05)
Logg	1.306^*** (3.56)	0.700^** (2.42)	0.612^** (2.26)	−0.333^** (−1.98)	−0.350^* (−1.91)	−0.365^** (−1.97)
Stru	−0.665^*** (−3.27)	−0.804^*** (−4.11)	−0.602^*** (−3.13)	−0.263 (−0.36)	−0.518 (−0.65)	−0.694 (−0.85)
Inve	−0.263^*** (−2.94)	−0.324^*** (−3.86)	−0.337^*** (−3.93)	−0.431^*** (−4.97)	−0.430^*** (−4.72)	−0.419^*** (−4.57)
Disa	−0.073 (−0.30)	−0.018 (−0.08)	0.029 (0.13)	−0.423 (−1.57)	−0.358 (−1.34)	−0.292 (−1.08)
Pric	−0.228 (−0.26)	−0.225 (−0.27)	0.259 (0.30)	0.888 (1.20)	0.90 (1.23)	0.183 (0.22)
Tech	0.032 (1.63)	0.025 (1.35)	0.0329^* (1.72)	0.050 (1.53)	0.023 (0.67)	0.013 (0.37)
Time	Yes	Yes	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes	Yes	Yes
Wald test	458.40^***	593.36^***	560.16^***	230.94^***	248.28^***	230.22^***
Cons	−0.645 (−0.53)	−1.290 (−1.14)	−2.245^* (−1.66)	0.293 (0.24)	0.616 (0.49)	1.676 (1.15)
N	405	405	390	405	405	390
Lag_Irri、Lag_Elec、Lag_Road分别表示Irri、Elec、Road的一阶滞后项; Time表示时间效应; Ind表示个体效应; 其他变量说明见表2。*、、分别代表在 P<1%、P<5%、P<10%水平显著; 括号内的数为t值。Lag_Irri, Lag_Elec, and Lag_Road denote the first-order lagged terms of Irri, Elec, and Road, respectively; Time denotes time effect; Ind denotes individual effect. The explaination of other variables are shown in the table 2. , , and * denote significance at P<1%, P<5%, and P<10% levels, respectively. The numbers in parentheses are t-values.

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表 5 粮食主产区和非粮食主产区农业基础设施与粮食生态全要素生产率回归结果

Table 5 Regression results of grain ecological total factor productivity and agricultural infrastructure in different grain production areas

变量 Variable	因变量: 粮食生态全要素生产率 Dependent variable: ecological total factor productivity of grain
	粮食主产区 Main grain-producing areas			非粮食主产区 Non-main grain-producing areas
	模型17 Model 17	模型18 Model 18	模型19 Model 19	模型20 Model 20	模型21 Model 21	模型22 Model 22
Irri		7.724^*** (9.47)			0.859^** (2.20)
Elec		4.531^*** (5.14)			−0.787 (−1.22)
Road		−0.009 (−0.44)			0.236^*** (4.90)
Lag_Irri			8.250^*** (9.68)			0.651 (1.55)
Lag_Elec			3.004^*** (3.29)			−0.445 (−0.69)
Lag_Road			0.012 (0.56)			0.251^*** (5.41)
Cult	2.310^*** (10.49)	1.636^*** (9.24)	1.743^*** (9.47)	−0.176 (−0.82)	−0.717^*** (−3.00)	−0.660^*** (−2.86)
Logg	0.139 (0.83)	−0.393^*** (−2.71)	−0.371^** (−2.50)	0.834 (1.05)	−0.028 (−0.03)	−0.474 (−0.54)
Stru	−0.770^*** (−4.01)	−1.083^*** (−5.95)	−0.591^*** (−3.48)	−0.621 (−0.86)	−1.032 (−1.45)	−1.082 (−1.51)
Inve	−0.113 (−0.94)	−0.185^* (−1.75)	−0.241^** (−2.22)	−0.431^*** (−6.10)	−0.395^*** (−5.60)	−0.378^*** (−5.38)
Disa	0.215 (0.81)	0.0499 (0.22)	0.180 (0.76)	−0.383^* (−1.72)	−0.302 (−1.40)	−0.183 (−0.85)
Pric	−0.759 (−0.98)	−1.493^** (−2.21)	−0.566 (−0.69)	0.922 (1.33)	0.935 (1.39)	0.620 (0.84)
Tech	0.113^*** (3.31)	0.115^*** (3.47)	0.0973^*** (2.83)	−0.011 (−0.55)	−0.012 (−0.61)	−0.014 (−0.71)
Time	Yes	Yes	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes	Yes	Yes
Wald test	605.03^***	949.67^***	885.81^***	179.98^***	220.91^***	212.21^***
Cons	0.859 (0.76)	0.414 (0.43)	−1.391 (−1.06)	0.424 (0.39)	0.746 (0.69)	1.161 (0.93)
N	351	351	338	459	459	442
Lag_Irri、Lag_Elec、Lag_Road分别表示Irri、Elec、Road的一阶滞后项; Time表示时间效应; Ind表示个体效应; 其他变量说明见表2。*、、分别代表在 P<0.01、P<0.05、P<0.1水平显著; 括号内的数为t值。Lag_Irri, Lag_Elec, and Lag_Road denote the first-order lagged terms of Irri, Elec, and Road, respectively; Time denotes time effect; Ind denotes individual effect. The explaination of other variables are shown in the table 2. , , and * denote significance at P<0.01, P<0.05, and P<0.1 levels, respectively. The numbers in parentheses are t-values.

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表 6 环境规制在农业基础设施影响粮食生态全要素生产率中的调节作用

Table 6 Test results of the moderating effect of environmental regulation on the impact of agricultural infrastructure on grain ecological total factor productivity

变量 Variable	因变量: 粮食生态全要素生产率 Dependent variable: grain ecological total factor productivity
变量 Variable	模型23 Model 23	模型24 Model 24	模型25 Model 25	模型26 Model 26	模型27 Model 27	模型28 Model 28
Irri	0.339^** (2.57)	0.294^** (2.17)
Irri×Envi		0.0193^* (1.78)
Elec			−0.621^*** (−2.64)	−0.674^* (−1.92)
Elec×Envi				0.003 (0.20)
Road					0.040^*** (4.68)	0.037^*** (2.75)
Road×Envi						0.0001 (0.29)
Envi	0.007^*** (3.91)	−0.001 (−0.20)	0.009^*** (4.16)	0.009^*** (2.80)	0.002 (1.21)	0.002 (0.82)
Cult	0.0848^* (1.88)	0.149^*** (3.27)	0.050 (1.09)	0.050 (1.09)	0.165^*** (3.19)	0.259^*** (4.16)
Logg	−0.065^*** (−2.92)	−0.083^*** (−3.78)	0.017 (0.53)	0.018 (0.55)	−0.098^*** (−3.01)	−0.073^** (−2.21)
Stru	0.066 (0.22)	0.495^* (1.87)	−0.153^** (−2.42)	−0.154^** (−2.43)	0.473^* (1.67)	0.745^** (1.97)
Inve	−0.012 (−0.43)	−0.024 (−0.76)	−0.044^* (−1.81)	−0.043^* (−1.80)	−0.015 (−0.40)	0.005 (0.10)
Disa	−0.129 (−1.31)	−0.243^*** (−2.71)	−0.067 (−1.18)	−0.067 (−1.17)	−0.284^*** (−2.96)	−0.371^*** (−3.22)
Pric	0.075 (0.82)	0.104 (1.43)	−0.265 (−1.44)	−0.266 (−1.45)	0.051 (0.70)	−0.030 (−0.35)
Tech	0.034^* (1.70)	0.041^*** (2.61)	0.018^** (2.57)	0.018^** (2.50)	0.045^* (1.93)	0.060^* (1.95)
Time	Yes	Yes	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes	Yes	Yes
Wald test	134.76^***	136.30^***	130.98^***	130.83^***	135.61^***	134.70^***
Cons	2.491 (0.07)	2.506 (0.05)	2.646 (0.05)	2.623 (0.07)	2.541 (0.08)	2.570 (0.05)
N	810	810	810	810	810	810
Irri×Envi、Elec×Envi和Road×Envi分别表示环境规制与农田水利设施、环境规制与农田电力设施和环境规制与农业交通设施的交互项; Time表示时间效应; Ind表示个体效应; 其他变量说明见表2。*、、分别代表在 P<0.01、 P<0.05、 P<0.1的水平上显著; 括号内的数为t值。Irri×Envi, Elec×Envi, and Road×Envi denote the interaction between environment regulaiton and Irri, Elec, and Road, respectively; Time denotes time effect; Ind denotes individual effect. The explaination of other variables are shown in the table 2. , , and * denote significance at P<0.01, P<0.05, and P<0.1 levels, respectively. The numbers in parentheses are t-values.

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表 7 环境规制在农业基础设施影响粮食生态全要素生产率中的调节作用的分组检验结果

Table 7 Group test results of the moderating effect of environmental regulation on the impact of agricultural infrastructure on grain ecological total factor productivity

变量 Variable	因变量: 粮食生态全要素生产率(ETFP) Dependent variable: grain ecological total factor productivity (ETFP)
	低ETFP组 Low ETFP group			高ETFP组 High ETFP group
	模型29 Model 29	模型30 Model 30	模型31 Model 31	模型32 Model 32	模型33 Model 33	模型34 Model 34
Irri		0.481^*** (3.87)	0.449^*** (3.50)		1.055^*** (3.22)	0.842^* (1.90)
Envi		0.006^*** (3.41)	−0.004 (−0.89)		0.012^* (1.86)	−0.003 (−0.12)
Irri×Envi			0.024^** (2.43)			0.044 (0.66)
Cult	0.086^* (1.92)	0.087^* (1.75)	0.105^** (2.04)	−0.028 (−0.48)	−0.053 (−1.17)	−0.050 (−1.11)
Logg	0.054^* (1.93)	0.042 (1.23)	0.026 (0.75)	−0.090^** (−2.46)	−0.157^*** (−4.55)	−0.157^*** (−4.62)
Stru	−0.212 (−1.17)	−0.341^* (−1.70)	−0.263 (−1.29)	−0.236^** (−2.24)	−0.418^*** (−3.15)	−0.432^*** (−3.15)
Inve	−0.026 (−1.17)	−0.047^** (−2.12)	−0.053^** (−2.38)	0.066 (0.48)	−0.003 (−0.02)	−0.008 (−0.06)
Disa	0.067 (1.13)	0.081 (1.40)	0.081 (1.42)	−0.476^*** (−3.45)	−0.483^*** (−3.75)	−0.487^*** (−3.79)
Pric	−0.228 (−1.15)	−0.246 (−1.28)	−0.250 (−1.33)	−0.738^** (−2.07)	−0.774^** (−2.16)	−0.780^** (−2.17)
Tech	0.018^*** (2.62)	0.015^** (2.15)	0.015^** (2.28)	0.040 (0.53)	0.043 (0.63)	0.043 (0.64)
Time	Yes	Yes	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes	Yes	Yes
Wald test	96.99^***	112.42^***	117.34^***	65.35^***	68.88^***	68.77^***
Cons	2.295 (0.05)	2.192 (0.05)	2.095 (0.07)	3.392 (0.06)	3.311 (0.04)	3.406 (0.04)
N	405	405	405	405	405	405
Irri×Envi表示环境规制与农田水利设施的交互项; Time表示时间效应; Ind表示个体效应; 其他变量说明见表2。*、、分别代表 P<0.01、P<0.05、P<0.1水平显著; 括号内的数为t值。Irri×Envi denotes the interaction between environment regulation and Irri; Time denotes time effect; Ind denotes individual effect. The explaination of other variables are shown in the table 2. , and * denote significance at P<0.01, P<0.05, and P<0.1 levels, respectively. The numbers in parentheses are t-values.

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