农村人口结构对农业绿色全要素生产率的影响

doi:10.3864/j.issn.0578-1752.2024.23.012

Abstract

Abstract:

【Objective】 The development level of agricultural green total factor productivity in China was measured, and the mechanism and path of rural population structure on agricultural green total factor productivity were analyzed, so as to provide the theoretical basis and decision-making reference for promoting agricultural green development in China. 【Method】 Based on the input-oriented, constant return to scale and the super-efficient SBM model with unexpected output combined with Malmquist- Luenberger (ML) index, the agricultural green total factor productivity of 31 provinces and cities in China from 2000 to 2021 was measured by using the software of MaxDEA. 【Result】 (1) The level of green total factor productivity in Chinese agriculture from 2000 to 2021 exhibited significant temporal and spatial differences. The level of green total factor productivity in agriculture showed a trend of slow decline followed by steady increase and then rapid increase, with the lowest value being 0.5016 in 2005 and the highest value being 0.8872 in 2020, divided by 2005 and 2013; the most provinces were at a medium to high level, with 1 province at a low level, 15 provinces at a medium level, accounting for 48.39%, 12 provinces at a medium to high level, accounting for 38.71%, and 3 provinces at a high level. (2) The various indicators of rural population structure had a significant inhibitory effect on agricultural green total factor productivity. (3) The impact of various indicators, such as rural child dependency ratio, gender ratio, Engel coefficient of rural residents, and average years of education of rural population, on agricultural green total factor productivity mainly depended on the efficiency of agricultural green technology in the rural population structure. (4) The impact of rural population structure on agricultural green total factor productivity had obvious regional characteristics, and the influence coefficients of various indicators varied significantly in different regions. Among the negative impacts of various indicators on agricultural green total factor productivity, the influence of rural child dependency ratio was most significant in the central region, the influence of rural gender ratio was most significant in the eastern region, the influence of average education years of rural population was most significant in the western region, and the influence of Engel coefficient of rural residents was relatively significant nationwide. 【Conclusion】 There were significant differences in the green total factor productivity of agriculture among different provinces in China, and the agricultural population structure inhibited the green development of agriculture, with regional variations. Therefore, it was necessary to build a sustainable rural labor input system, explore and cultivate rural female human resources, build a long-term mechanism for increasing farmers' income, promote the orderly transfer of rural labor, fill the gaps in regional opening up, and improve the ecological oriented financial investment structure for supporting agriculture.

Key words: rural population structure, agricultural green total factor productivity, green development, SBM model

DENG YuanJian, LIU Peng. Study on the Influence of Rural Population Structure on Agricultural Green Total Factor Productivity[J].Scientia Agricultura Sinica, 2024, 57(23): 4725-4745.

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Figures/Tables 15

Table1

Index system for calculating agricultural green total factor productivity"

指标类型 Index type	指标名称 Index name	指标说明 Index description	单位 Unit	参考文献 Reference
投入指标 Input index	劳动力投入 Labor input	农业从业人员 Agricultural employees	万人 Ten thousand people	吴传清等^[19]，杜红梅等^[27]，张丝雨等^[28] WU C Q, et al^[19], DU H M, et al^[27], ZHANG S Y, et al^[28]
	土地投入 Land input	农作物播种面积 Planting area of crops	10³hm²
	灌溉投入 Irrigation input	有效灌溉面积 Effective irrigation area	10³hm²
	化肥投入 Chemical fertilizer input	农用化肥施用量 Application amount of agricultural chemical fertilizer	10⁴t
	农药投入 Pesticide input	农药使用量 Amounts of pesticides used	t
	农膜投入 Agricultural film input	农用塑料薄膜使用量 Amounts of agriculture film used	t
	柴油投入 Diesel input	农用柴油使用量 Agricultural diesel consumption	10⁴t
	机械投入 Mechanical input	农业机械总动力 Farm machinery production	10⁴kW·h
期望产出指标 Expected output index	经济价值 Economic value	农业总产值 Total value of farm output	10⁸yuan	孙能利等^[21]，刘二阳等^[22]， Robert Costanza^[23]，谢高地等^[24] SUN N L, et al^[21], LIU E Y, et al^[22], Robert Costanza^[23], XIE G D, et al^[24]
期望产出指标 Expected output index	生态价值 Ecological value	农业现实生态价值量 Agricultural real ecological value quantity	10⁸yuan
非期望产出指标 Unexpected output index	农业碳排放 Agricultural carbon emissions	农业生产活动产生的碳排放总量 Total carbon emissions from agricultural production activities	10⁴t	郭海红等^[29]，鄢曹政等^[30]，田云等^[25]，姜松等^[26] GUO H H, et al ^[29], YAN C Z, et al ^[30], TIAN Y, et al ^[25], JIANG S, et al^[26]
非期望产出指标 Unexpected output index	农业面源污染 Agricultural non-point source pollution	农业生产活动造成的面源污染 Non-point source pollution caused by agricultural production activities	10⁴t

Table1

Table 2

Measurement results of agricultural green total factor productivity in 31 provinces (municipalities and autonomous regions) in China from 2001 to 2021"

省份 Province	年份 Year
省份 Province	2001	2003	2005	2007	2009	2011	2013	2015	2017	2019	2021
北京Beijing	0.481 1	0.542 2	0.585 3	0.683 9	0.706 0	0.725 9	0.786 4	0.921 0	1.080 6	0.867 0	1.008 5
天津Tianjin	0.399 5	0.431 3	0.404 9	0.437 5	0.428 0	0.510 5	0.556 0	0.668 3	0.805 3	0.936 1	1.087 7
河北Hebei	0.357 1	0.350 1	0.350 1	0.388 8	0.418 1	0.456 7	0.488 1	0.533 9	0.634 1	0.701 4	0.709 0
山西Shanxi	0.374 9	0.409 9	0.377 6	0.440 4	0.459 8	0.462 4	0.528 9	0.604 6	0.740 3	0.742 0	0.547 8
内蒙古Neimenggu	0.634 0	0.619 3	0.582 3	0.585 6	0.599 7	0.628 1	0.634 0	0.725 4	0.788 9	1.111 0	1.011 7
辽宁Liaoning	0.425 0	0.455 8	0.479 9	0.484 1	0.491 4	0.496 0	0.562 2	0.709 9	1.000 7	1.032 4	0.850 8
吉林Jilin	0.451 7	0.543 2	0.554 6	0.565 9	0.709 3	0.691 9	0.766 9	0.832 9	0.885 3	1.043 0	1.018 3
黑龙江Heilongjiang	0.532 2	0.577 3	0.659 3	0.620 6	1.037 0	0.605 7	0.641 1	0.834 2	0.852 2	1.207 7	1.011 6
上海Shanghai	0.490 9	1.006 0	0.912 6	1.003 4	1.002 0	0.668 5	0.520 4	0.526 8	0.524 9	0.546 2	0.587 9
江苏Jiangsu	0.466 3	0.464 0	0.501 1	0.529 2	0.537 5	0.574 6	0.612 4	0.655 1	0.703 5	0.762 5	0.754 0
浙江Zhejiang	0.404 1	0.424 0	0.442 9	0.467 6	0.482 4	1.005 4	0.510 2	0.572 4	0.669 4	0.851 1	1.024 0
安徽Anhui	0.313 9	0.289 6	0.297 8	0.319 6	0.380 4	0.382 0	0.389 2	0.432 4	0.482 6	0.510 0	0.485 9
福建Fujian	0.475 1	0.476 5	0.461 7	0.435 8	0.466 8	0.490 4	0.518 1	0.571 5	0.666 0	0.750 4	1.051 7
江西Jiangxi	0.426 3	0.411 2	0.391 4	0.398 9	0.409 0	0.410 2	0.480 5	0.541 6	0.611 5	0.677 3	0.668 5
山东Shandong	0.284 2	0.277 1	0.301 9	0.318 9	0.349 2	0.370 2	0.406 8	0.466 1	0.536 6	0.594 4	0.620 9
河南Henan	0.364 8	0.342 2	0.396 8	0.447 5	0.466 4	0.476 8	0.509 6	0.584 3	0.678 6	0.758 7	0.747 5
湖北Hubei	0.369 7	0.341 0	0.385 1	0.422 1	0.441 1	0.472 0	0.495 2	0.573 5	0.648 1	0.713 7	0.639 5
湖南Hunan	0.401 4	0.383 0	0.387 5	0.403 5	0.412 0	0.439 2	0.455 0	0.511 5	0.559 1	0.587 5	0.614 9
广东Guangdong	0.529 8	0.594 9	0.634 9	0.653 9	0.603 3	0.628 2	0.684 8	0.734 8	0.827 6	0.934 0	1.159 4
广西Guangxi	0.441 1	0.448 6	0.462 3	0.478 3	0.487 3	0.513 1	0.549 5	0.601 4	0.698 0	0.734 2	0.746 1
海南Hainan	1.207 6	1.001 6	0.705 3	0.645 2	0.660 4	0.647 8	0.676 4	1.005 7	1.004 5	1.003 5	1.000 0
重庆Chongqing	0.477 0	0.476 8	0.483 0	0.478 1	0.520 4	0.550 4	0.595 0	0.656 1	0.746 6	0.776 9	0.816 2
四川Sichuan	0.426 5	0.438 6	0.440 2	0.455 3	0.513 7	0.502 7	0.538 2	0.579 1	0.665 0	0.707 5	0.755 7
贵州Guizhou	1.009 7	0.793 8	0.705 2	0.593 2	0.732 7	0.545 6	0.622 8	0.804 1	1.031 2	1.087 5	1.000 0
云南Yunnan	0.423 7	0.432 0	0.422 4	0.487 9	0.459 8	0.445 9	0.455 8	0.529 8	0.609 5	0.838 9	1.013 1
西藏Xizang	1.083 8	0.839 9	0.693 8	1.022 7	1.015 6	0.719 4	0.647 3	0.586 0	0.475 3	1.008 7	1.000 0
陕西Shaanxi	0.560 7	0.600 5	0.616 6	0.692 9	0.651 6	0.762 8	0.751 8	0.855 6	0.970 1	1.002 2	1.056 7
甘肃Gansu	0.398 4	0.412 0	0.458 3	0.425 0	0.493 4	0.447 8	0.501 6	0.569 3	0.822 8	1.020 4	1.017 2
青海Qinghai	1.020 4	0.512 6	0.550 9	0.662 1	0.591 2	0.522 0	0.667 0	0.682 9	0.738 7	0.910 5	1.025 9
宁夏Ningxia	0.466 5	0.469 3	0.473 4	0.468 4	0.439 9	0.469 8	0.603 2	0.719 9	1.017 2	0.959 6	0.580 4
新疆Xinjiang	0.382 2	0.413 0	0.429 8	0.459 0	0.474 0	0.507 1	0.513 8	0.485 1	0.550 2	0.739 5	0.592 9

Table 2

Fig. 1

Table 3

Table 4

Fig. 2

Table 5

Table 6

Variable explanation and explanation"

变量类型 Variable type	变量名称 Variable name	变量符号 Variable symbol	变量解释 Interpretation of variable
被解释变量 Dependent variable	农业绿色全要素生产率 Agricultural green total factor productivity	GTFP	SBM-ML模型测算效率值 SBM-ML model calculates efficiency values
	农业绿色技术进步 Progress in green agricultural technology	TC	SBM-ML模型测算TC值累乘 SBM-ML model calculates TC value accumulation
	农业绿色技术效率 Agricultural green technology efficiency	EC	SBM-ML模型测算EC值累乘 SBM-ML model calculates EC value accumulation
解释变量 Explanatory variable	年龄结构 Age structure	lnx1	农村少儿抚养比 Rural children’s dependency ratio
	年龄结构 Age structure	lnx2	农村老年抚养比 Rural elderly dependency ratio
	性别结构 Gender structure	lnx3	农村人口性别比 Gender ratio of rural population
	家庭结构 Family structure	lnx4	农村平均家庭户规模 Average household size in rural areas
	消费结构 Consumption structure	lnx5	农村居民家庭恩格尔系数 Engel’s coefficient of rural households
	文化结构 Culture structure	lnx6	农村人口平均受教育年限 The average education years of rural population
	就业结构 Employment structure	lnx7	第一产业从业人数占比 The proportion of employees in the primary industry
控制变量 Control variable	对外开放程度 Extent of openness to the outside world	lncon1	地区进出口总额占地区生产总值比重 The proportion of total regional imports and exports to regional GDP
	财政支农水平 Financial support for agriculture level	lncon2	财政涉农支出占总财政支出比重 The proportion of fiscal agricultural expenditure to total fiscal expenditure
	城乡收入差距 Income gap between urban and rural areas	lncon3	农村居民与城镇居民家庭人均纯收入比值 The ratio of per capita net income between rural and urban households
	农村经济发展水平 Rural economic development level	lncon4	人均农林牧渔总产值的对数 The logarithm of the per capita total output value of agriculture, forestry, animal husbandry, and fishery
	农业受灾率 Agricultural disaster rate	lncon5	农业受灾面积占农作物总播种面积比重 The proportion of agricultural disaster affected area to the total sowing area of crops
	农业种植结构 Agricultural planting structure	lncon6	粮食播种面积占农作物播种面积比重 The proportion of grain sowing area to crop sowing area

Table 6

Table 7

Table 8

Regression estimation results of the impact of rural population structure on agricultural green total factor productivity"

变量 Variable	模型I Model I	模型II Model II	模型III Model III	模型IV Model IV	模型V Model V	模型VI Model VI	模型VII Model VII
lnx1	-0.285*** (0.059 2)	-0.285*** (0.059 5)	-0.285*** (0.057 6)	-0.260*** (0.058 2)	-0.255*** (0.058 5)	-0.256*** (0.058 2)	-0.266*** (0.057 2)
lnx2	0.055 4 (0.050 6)	0.054 8 (0.050 9)	0.073 1 (0.049 4)	0.052 9 (0.049 9)	0.042 3 (0.051 2)	0.052 5 (0.051 1)	-0.007 9 (0.051 7)
lnx3	-1.180*** (0.236 0)	-1.179*** (0.236 0)	-1.149*** (0.229 0)	-1.221*** (0.230 0)	-1.141*** (0.246 0)	-1.108*** (0.245 0)	-1.247*** (0.242 0)
lnx4	0.022 0 (0.037 3)	0.022 2 (0.037 3)	0.003 7 (0.036 3)	0.011 5 (0.036 3)	0.009 7 (0.036 3)	0.005 3 (0.036 2)	0.003 6 (0.035 5)
lnx5	-0.596*** (0.092 0)	-0.596*** (0.092 2)	-0.709*** (0.091 0)	-0.738*** (0.091 4)	-0.702*** (0.099 5)	-0.703*** (0.099 0)	-0.759*** (0.097 9)
lnx6	-1.214*** (0.183 0)	-1.211*** (0.186 0)	-1.196*** (0.180 0)	-1.086*** (0.185 0)	-1.068*** (0.186 0)	-1.076*** (0.185 0)	-0.989*** (0.182 0)
lnx7	0.087 1 (0.102 0)	0.087 1 (0.102 0)	0.042 5 (0.098 7)	0.032 2 (0.098 3)	0.031 8 (0.098 4)	0.034 4 (0.097 9)	0.034 5 (0.096 1)
		0.002 1 (0.020 0)	-0.007 1 (0.019 5)	0.009 3 (0.020 5)	0.010 1 (0.020 5)	0.011 5 (0.020 4)	0.012 7 (0.020 0)
lncon1			-0.193*** (0.029 9)	-0.170*** (0.031 3)	-0.173*** (0.031 4)	-0.160*** (0.031 6)	-0.153*** (0.031 1)
lncon3				-0.250** (0.099 9)	-0.268*** (0.102 0)	-0.290*** (0.102 0)	-0.243** (0.100 0)
lncon4					0.317 0 (0.348 0)	0.264 0 (0.347 0)	0.320 0 (0.341 0)
lncon5						-0.0262*** (0.009 7)	-0.0260*** (0.009 5)
lncon6							0.406*** (0.083 0)
系数 Coefficient	0.771*** (0.283 0)	0.768*** (0.284 0)	0.031 9 (0.298 0)	-0.325 0 (0.330 0)	-1.044 0 (0.856 0)	-0.899 0 (0.853 0)	-1.115 0 (0.839 0)
adj. R²	0.654 0	0.653 0	0.674 0	0.677 0	0.677 0	0.680 0	0.692 0
控制时间 Control time	控制Control	控制Control	控制Control	控制Control	控制Control	控制Control	控制Control
控制省份 Control the provinces	控制Control	控制Control	控制Control	控制Control	控制Control	控制Control	控制Control
样本数 Number of samples	682	682	682	682	682	682	682

Table 8

Table 9

Table 10

Table 11

Table 12

Table 13

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区域 Region	省份 Province	ML指数效率值 ML exponential efficiency value	EC指数效率值 EC exponential efficiency value	TC指数效率值 TC exponential efficiency value
东部地区 Eastern region	北京 Beijing	1.712 5	1.423 2	1.305 7
	天津 Tianjin	1.512 7	0.984 6	1.500 7
	河北 Hebei	1.357 5	1.049 3	1.277 9
	辽宁 Liaoning	1.526 7	0.944 4	1.605 3
	上海 Shanghai	1.756 8	0.870 0	2.093 9
	江苏 Jiangsu	1.319 2	1.076 0	1.253 6
	浙江 Zhejiang	1.553 9	1.010 6	1.522 5
	福建 Fujian	1.247 3	1.154 7	1.068 5
	山东 Shandong	1.426 5	0.976 3	1.448 6
	广东 Guangdong	1.353 6	1.136 8	1.190 5
	海南 Hainan	0.853 7	0.729 7	1.201 8
	均值 Mean	1.420 0	1.032 3	1.406 3
中部地区 Midland region	山西 Shanxi	1.373 9	1.137 0	1.240 7
	吉林 Jilin	1.431 5	1.035 2	1.378 3
	黑龙江 Heilongjiang	1.505 3	1.378 9	1.140 8
	安徽 Anhui	1.228 1	0.947 1	1.308 1
	江西 Jiangxi	1.152 9	0.808 2	1.505 8
	河南 Henan	1.476 0	1.265 0	1.168 0
	湖北 Hubei	1.359 3	1.053 9	1.330 4
	湖南 Hunan	1.151 9	0.851 7	1.452 8
	均值 Mean	1.334 9	1.059 6	1.059 6
西部地区 Western region	内蒙古 Neimenggu	1.108 4	0.809 5	1.385 6
	广西 Guangxi	1.281 6	1.146 2	1.115 6
	重庆 Chongqing	1.220 5	1.213 1	1.016 2
	四川 Sichuan	1.239 2	0.881 9	1.476 1
	贵州 Guizhou	0.828 9	0.942 3	0.881 1
	云南 Yunnan	1.306 2	1.043 9	1.235 9
	西藏 Xizang	0.402 2	0.780 3	0.536 2
	陕西 Shaanxi	1.463 3	1.164 8	1.263 1
	甘肃 Gansu	1.525 7	1.275 9	1.178 7
	青海 Qinghai	1.021 6	0.903 2	1.126 4
	宁夏 Ningxia	1.392 6	0.994 9	1.441 1
	新疆 Xinjiang	1.313 8	0.875 8	1.606 0
	均值 Mean	1.175 3	1.002 7	1.188 5

变量类型 Variable type	变量符号 Variable symbol	样本量 Number of samples	平均值 Average value	标准差 Standard deviation	最小值 Minimum	最大值 Maximum
被解释变量 Dependent variable	GTFP	682	0.610 0	0.210 0	0.270 0	2.200 0
	TC	682	1.030 0	0.240 0	0.480 0	2.330 0
	EC	682	1.290 0	0.480 0	0.330 0	3.960 0
解释变量 Explanatory variables	lnx1	682	0.290 0	0.090 0	0.060 0	0.540 0
	lnx2	682	0.150 0	0.060 0	0.060 0	0.460 0
	lnx3	682	1.060 0	0.040 0	0.930 0	1.320 0
	lnx4	682	3.350 0	0.660 0	0.470 0	8.770 0
	lnx5	682	0.400 0	0.090 0	0.240 0	0.790 0
	lnx6	682	7.260 0	0.940 0	2.240 0	9.660 0
	lnx7	682	5.860 0	0.590 0	1.320 0	7.620 0
控制变量 Control variable	lncon1	682	0.300 0	0.360 0	0.010 0	1.710 0
	lncon2	682	0.090 0	0.050 0	0.010 0	0.200 0
	lncon3	682	0.370 0	0.070 0	0.180 0	0.540 0
	lncon4	682	8.730 0	0.590 0	7.230 0	10.250 0
	lncon5	682	0.220 0	0.160 0	0.000 0	0.940 0
	lncon6	682	0.650 0	0.130 0	0.330 0	0.970 0

变量 Variable	农业绿色技术效率（EC） Agricultural green technology efficiency	农业绿色技术进步（TC） Progress in green agricultural technology
lnx1	-0.285*** (0.062 1)	0.022 7 (0.058 3)
lnx2	0.107* (0.056 1)	-0.124** (0.052 7)
lnx3	-0.743*** (0.263 0)	-0.535** (0.247 0)
lnx4	0.017 3 (0.038 6)	-0.011 7 (0.036 2)
lnx5	-0.405*** (0.106 0)	-0.337*** (0.099 8)
lnx6	-0.588*** (0.198 0)	-0.419** (0.186 0)
lnx7	0.017 1 (0.104 0)	0.016 2 (0.098 0)
系数 Coefficient	-1.653* (0.911 0)	1.099 0 (0.855 0)
控制其他变量 Control for other variables	控制 Control	控制 Control
adj. R²	0.141 0	0.726 0
控制时间 Control time	控制 Control	控制 Control
控制省份 Control the province	控制 Control	控制 Control
样本数 Number of samples	682	682

变量 Variable	模型X Model X	模型XI Model XI	模型XII Model XII
被解释变量 Dependent variable	农业绿色全要素生产率 Agricultural green total factor productivity	农业绿色技术效率（EC） Agricultural green technology efficiency	农业绿色技术进步（TC） Progress in green agricultural technology
lnx1	-0.345*** (0.060 4)	-0.344*** (0.064 3)	-0.000 6 (0.061 0)
lnx2	0.020 2 (0.053 9)	0.174*** (0.057 5)	-0.154*** (0.054 5)
lnx3	-1.195*** (0.248 0)	-0.564** (0.264 0)	-0.632** (0.251 0)
lnx4	-0.006 2 (0.034 3)	0.011 2 (0.036 6)	-0.017 4 (0.034 7)
lnx5	-0.803*** (0.101 0)	-0.453*** (0.107 0)	-0.350*** (0.102 0)
lnx6	-0.994*** (0.185 0)	-0.421** (0.197 0)	-0.573*** (0.187 0)
lnx7	0.046 0 (0.093 0)	0.025 2 (0.099 1)	0.020 8 (0.094 0)
系数 Coefficient	-1.339 (0.858)	-1.790* (0.914)	1.183 (0.867)
控制其他变量 Control for other variables	控制 Control	控制 Control	控制 Control
adj. R²	0.646	0.133	0.622
控制时间 Control time	控制 Control	控制 Control	控制 Control
控制省份 Control the province	控制 Control	控制 Control	控制 Control
样本数 Number of samples	620	620	620

变量 Variable	全国 The whole country	东部地区 Eastern region	中部地区 Central region	西部地区 Western region
lnx1	-0.266*** (0.057 2)	0.053 0 (0.107 0)	-0.314*** (0.066 8)	-0.096 9 (0.120 0)
lnx2	-0.007 9 (0.051 7)	-0.108 0 (0.110 0)	-0.038 5 (0.089 5)	0.041 5 (0.083 7)
lnx3	-1.247*** (0.242 0)	-1.754*** (0.371 0)	-0.790** (0.368 0)	0.283 0 (0.461 0)
lnx4	0.003 6 (0.035 5)	-0.301 0 (0.234 0)	0.066 1 (0.060 7)	-0.142 0 (0.093 3)
lnx5	-0.759*** (0.097 9)	-1.217*** (0.235 0)	-0.901*** (0.110 0)	-0.850*** (0.161 0)
lnx6	-0.989*** (0.182 0)	-2.152 0 (1.660 0)	-0.482* (0.271 0)	-1.714*** (0.485 0)
lnx7	0.034 5 (0.096 1)	2.753 0 (1.817 0)	-0.050 6 (0.046 6)	1.117 0 (0.699 0)
系数 Coefficient	-1.115 0 (0.839 0)	-2.273 0 (1.473 0)	-0.424 0 (2.388 0)	-13.60*** (2.730 0)
控制其他变量 Control for other variables	控制 Control	控制 Control	控制 Control	控制 Control
adj. R²	0.692 0	0.697 0	0.923 0	0.730 0
控制时间Control time	控制 Control	控制 Control	控制 Control	控制 Control
控制省份Control the provinces	控制 Control	控制 Control	控制 Control	控制 Control
样本数Number of samples	682	242	176	264

Study on the Influence of Rural Population Structure on Agricultural Green Total Factor Productivity

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变量 Variable	全国 The whole country	东部地区 Eastern region	中部地区 Central region	西部地区 Western region
lnx1	-0.285*** (0.062 1)	-0.060 0 (0.112 0)	-0.516*** (0.132 0)	-0.195 0 (0.120 0)
lnx2	0.107* (0.056 1)	-0.080 9 (0.115 0)	0.268 0 (0.178 0)	0.072 1 (0.084 1)
lnx3	-0.743*** (0.263 0)	-1.170*** (0.386 0)	-1.119 0 (0.730 0)	-0.363 0 (0.463 0)
lnx4	0.017 3 (0.038 6)	-0.104 0 (0.244 0)	0.216* (0.120 0)	0.033 9 (0.093 7)
lnx5	-0.405*** (0.106 0)	-0.254 0 (0.244 0)	-1.336*** (0.218 0)	-0.390** (0.162 0)
lnx6	-0.588*** (0.198 0)	-1.745 0 (1.727 0)	-1.111** (0.537 0)	-0.060 2 (0.487 0)
lnx7	0.017 1 (0.104 0)	1.554 0 (1.891 0)	-0.024 9 (0.092 4)	-0.275 0 (0.702 0)
系数 Coefficient	-1.653* (0.911 0)	-3.046** (1.532 0)	-5.128 0 (4.738 0)	-12.72*** (2.744 0)
控制其他变量 Control for other variables	控制 Control	控制 Control	控制 Control	控制 Control
adj. R²	0.141 0	0.260 0	0.364 0	0.273 0
控制时间 Control time	控制 Control	控制 Control	控制 Control	控制 Control
控制省份 Control the provinces	控制 Control	控制 Control	控制 Control	控制 Control
样本数 Number of samples	682	242	176	264

变量 Variable	全国 The whole country	东部地区 Eastern region	中部地区 Central region	西部地区 Western region
lnx1	0.022 7 (0.058 3)	0.122 0 (0.104 0)	0.176 0 (0.134 0)	0.103 0 (0.127 0)
lnx2	-0.124** (0.052 7)	-0.043 6 (0.107 0)	-0.250 0 (0.179 0)	-0.022 3 (0.088 7)
lnx3	-0.535** (0.247 0)	-0.666* (0.360 0)	0.262 0 (0.737 0)	0.692 0 (0.488 0)
lnx4	-0.011 7 (0.036 2)	-0.190 0 (0.228 0)	-0.160 0 (0.122 0)	-0.177* (0.098 9)
lnx5	-0.337*** (0.099 8)	-0.963*** (0.228 0)	0.489** (0.220 0)	-0.444** (0.171 0)
lnx6	-0.419** (0.186 0)	-0.469 0 (1.611 0)	0.839 0 (0.542 0)	-1.685*** (0.514 0)
lnx7	0.016 2 (0.098 0)	1.186 0 (1.764 0)	-0.023 5 (0.093 4)	1.392* (0.741 0)
系数 Coefficient	1.099 0 (0.855 0)	1.764 0 (1.429 0)	4.153 0 (4.786 0)	0.336 0 (2.894 0)
控制其他变量 Control for other variables	控制 Control	控制 Control	控制 Control	控制 Control
adj. R²	0.726 0	0.691 0	0.800 0	0.758 0
控制时间 Control time	控制 Control	控制 Control	控制 Control	控制 Control
控制省份 Control the provinces	控制 Control	控制 Control	控制 Control	控制 Control
样本数 Number of samples	682	242	176	264

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