基于农户视角农业绿色全要素生产率的测度与分析

程永生; 张德元; 汪侠

doi:10.12357/cjea.20220562

基于农户视角农业绿色全要素生产率的测度与分析

doi: 10.12357/cjea.20220562

程永生^{1, 2, ,},
张德元^2,,
汪侠¹

1.
阜阳师范大学商学院　阜阳　236032
2.
安徽大学创新发展战略研究院　合肥　230601

基金项目: 安徽省2022年度哲学社会科学规划项目(AHSKQ2022D039)资助

详细信息

通讯作者:
程永生, 主要研究方向为绿色高质量发展与产业经济。E-mail: chengyongsheng@aliyun.com

中图分类号: F32
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出版历程
- 收稿日期: 2022-07-21
- 录用日期: 2022-10-20
- 网络出版日期: 2022-11-25
- 刊出日期: 2023-05-10

Measurement and analysis of agricultural green total factor productivity based on farmers’ perspectives

1.
School of Business, Fuyang Normal University, Fuyang 236032, China
2.
Academy of Strategies for Innovation and Development, Anhui University, Hefei 230601, China

Funds: The study was supported by the Philosophy and Social Science Foundation of Anhui Province (AHSKQ2022D039).

More Information

Corresponding author: CHENG Yongsheng, E-mail: chengyongsheng@aliyun.com

摘要

摘要: 提升农业绿色全要素生产率, 加快农业绿色转型是全面建成社会主义现代化强国的必然选择。研究以中国家庭追踪调查(China Family Panel Studies, CFPS)的全国性大容量样本农户数据为蓝本, 在微观测度方法比较分析的基础上, 基于技术优化的Malmquist-Luenberger指数为基准, 测度分析了农户层农业绿色全要素生产率的状况, 并进一步选用核密度估计法和Dagum基尼系数法, 揭示了微观样本农业绿色全要素生产率的动态演变规律及其区域差异特征。主要研究发现如下: 1)技术优化的Malmquist-Luenberger指数测度显示, 2014年、2016年和2018年3期样本农户的农业绿色全要素生产率均值为1.0030, 总体发展态势良好; 农业绿色技术变化、绿色技术效率变化的共同作用是驱动农户层面农业绿色发展变化的主要引致因素, 且后者的影响程度远大于前者; 农户资源配置、管理模式及组织方式的改善优化, 在现阶段是农户发展绿色农业的提升关键, 其影响相对高于农户农业生产技术的革新。2)通过核密度估算发现, 2016年和2018年样本农户的绿色全要素生产率集中度较高, 农业绿色技术效率并未出现两级分化, 但农业绿色技术进步呈现上升趋势。3) Dagum基尼系数法结果表明, 农户层面农业绿色全要素生产率的区域差距不断缩小, 区域差距的降幅达22.32%, 超变密度是引致主因; 在区域内差距上, 东、西、中部地区内部, 农户的绿色农业差距依次递减; 在区域间差距上, 东西、东中、中西部间差距不断缩小、协同性不断增强, 但差距易受到环境因素影响。
- 农户视角 /
- 农业绿色全要素生产率 /
- 技术优化的Malmquist-Luenberger测度 /
- 差距识别
Abstract: Improving agricultural green total factor productivity (AGTFP) and hastening agricultural green transformation are unavoidable choices for comprehensively building a strong socialist, modernized country. Based on a comparative analysis of micro-measurement methods, this study analyzed the status of AGTFP at the farmer household level based on the technically optimized Malmquist-Luenberger index. The kernel density estimation method and the Dagum Gini coefficient method were further used to reveal the dynamic evolution of AGTFP and its regional differences in the micro-sample. The main findings are as follows: 1) From the measurement results, the mean value of AGTFP in the microfield in 2014, 2016 and 2018 was 1.0030, with a good overall development trend. The mean value of AGTFP of farmers in 2016 was 1.0099, and agricultural green development had a good growth trend. The mean values of technical efficiency change and technical progress change were 1.0165 and 0.9928, respectively, indicating that the improvement in farmers’ green agricultural technical efficiency was the main driving factor while the change in technical progress was relatively slow. In 2018, the mean value of AGTFP by farmers was 0.9960, which showed a decreasing trend. The corresponding mean values of technical efficiency change and technical progress change were 0.9765 and 1.0200, respectively, indicating that the technical efficiency improvement of green agriculture did not achieve a sustainable spillover effect and that the innovation function of technical progress change played a role in the improvement. 2) In terms of contributing factors, the use of subjective environmental assessment scores or objective provincial-level environmental pollution data as proxies for non-desired outputs among farmers with higher levels of AGTFP, agricultural green technological progress, and agricultural green technological efficiency was found to be more effective. For farmers with high levels of AGTFP, both green technological advances and green technological efficiency in agriculture were drivers of green growth, and the contribution of the latter was greater than that of the former. 3) From the perspective of a dynamic evolution pattern, in terms of AGTFP, the concentration in 2016 and 2018 was high, showing distinct clustering; however, the divergence phenomenon was not obvious, and the number of farmers with a high level of green development in 2018 was much higher than that in 2016; in terms of the agricultural technical efficiency of farmers, there was no bifurcation in 2016 and 2018. The number of low-level farmers in 2018 was higher than that in 2016, indicating that there was a regression phenomenon, and the difference between the agricultural technical efficiency of high- and low-level farmers was obvious. In terms of agricultural green technical progress of farmers, the overall trend was increasing, the number of low-level farmers in 2016 was lower, and the number of high-level farmers was relatively higher, while in 2018, the number of high- and low-level farmers remained the same, and a spatial clustering effect was evident. In 2018, the number of farmers with low levels of agricultural green technology progress decreased “precipitously.” On the premise that the number of farmers remained unchanged, this part of the low-level farmers moved to the middle- and high-level groups, forming the dynamic transfer effect of “internal push and external pull.” 4) From the perspective of regional disparity, the overall gap in AGTFP in the sample period was decreasing, with a decline of 22.32%. From the source decomposition, the hyper-variance density was the main cause of the overall regional disparity in AGTFP. From the contribution rate, the contribution rate of hyper-variance density was much higher than the contribution rate of intra- and inter-regional disparity, indicating that the cross-over problem between different regions was the main cause of the overall disparity in AGTFP at the farmer level. Further, from the intra-regional disparity, the disparity of AGTFP at the household level decreased within the eastern and western regions; from the inter-regional disparity, the disparity between the eastern and western, eastern and central, and central and western regions decreased continuously during the sample period, and the synergy was the highest, but this gap was susceptible to environmental factors.
- Farmers’ perspective /
- Agricultural green total factor productivity /
- Malmquist-Luenberger measure for technology optimization /
- Gap recognition

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图 1 农业绿色全要素生产率(a, b)、农业绿色技术效率变化(c, d)和农业绿色技术变化(e, f)的核密度分布

左图为使用虚拟户主的主观污染感知度作为非期望产出; 右图为使用农业化学需氧量、总氮、总磷等标排放量等客观农业面源污染作为非期望产出。The left panel uses the subjective pollution perception of the virtual household head as the non-desired output; the right panel uses objective agricultural non-point source pollution such as agricultural chemical oxygen demand, total nitrogen, total phosphorus equivalents emissions as the non-desired output.

Figure 1. Kernel density distribution of agricultural green total factor productivity (a, b), agricultural green technical efficiency change (c, d) and agricultural green technological change (e, f)

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表 1 改进后的农业绿色全要素生产率测算体系

Table 1. An improved system for measuring agricultural green total factor productivity

目标层 Target layer	一级指标 Primary indicator	二级指标 Secondary indicator	变量定义 Specific variable and description	指标单位 Indicator unit	符号 Symbol
农业绿色全要素生产率 Agricultural green total factor productivity	投入指标 Input indicators	资本 Capital	农业生产的流动性资本投入与固定性资本投入之和 Sum of liquid capital inputs and fixed capital inputs in agricultural production	¥	x₁
		劳动力 Labor	过去12个月参与的自家农业生产活动的家庭成员数 Number of household members involved in home-based agricultural production activities in the past 12 months	Persons	x₂
		土地 Land	承包地面积与租用地面积之和 Sum of contracted land area and leased land area	hm²	x₃
	期望产出指标 Desired output indicators	农产品总产出 Total agricultural output	过去12个月, 家庭所生产的农产品、养殖物及副产品销售收入以及自家消费总值之和 Sum of income from the sale of agricultural products, farm products and by-products produced by the household and the total value of own consumption in the past 12 months	¥	y₁
	非期望产出指标 Non-desired output indicators	农业面源污染 Agricultural non-point source pollution	农业化学需氧量(COD)等标排放量 Agricultural chemical oxygen demand (COD) equivalent emissions	t	y_u2
			农业总氮(TN)等标排放量 Agricultural total nitrogen (TN) equivalent emissions		y_u3
			农业总磷(TP)等标排放量 Agricultural total phosphorus (TP) equivalent emissions		y_u4
		主观污染感知度 Subjective pollution perception degree	采用农业活动管账人对环境污染问题严重度的感知, 0代表不严重, 10代表非常严重 Perception of the severity of environmental pollution problems by the custodians of agricultural activities: 0 = not serious, 10 = very serious.		y_u1

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表 2 农业绿色全要素生产率测算的投入和产出指标的描述性统计结果

Table 2. Results of descriptive statistics for input and output indicators for measuring agricultural green total factor productivity

指标 Index	样本量 Sample size	均值 Mean value	标准差 Standard deviation	最大值 Maximum value	最小值 Minimum value
资本 Capital	9735	11.0715	29.6284	0.0060	1000.0000
劳动力 Labor	9735	3.8680	1.8298	1.0000	21.0000
土地 Land	9735	0.8235	2.3192	0.0067	73.3333
农产品总产出 Total agricultural output	9735	16.4968	35.8345	0.0010	900.0000
主观污染感知度 Subjective pollution perception degree	9735	6.5300	2.5024	1.0000	10.0000
化学需氧量等标排放量 Chemical oxygen demand equivalent emissions	9735	2.0374	2.1763	0.0087	9.4930
总氮等标排放量 Total nitrogen equivalent emissions	9735	38.5440	27.1381	1.4973	131.6150
总磷等标排放量 Total phosphorus equivalent emissions	9735	13.5534	10.0784	0.9760	58.9300

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表 3 农业绿色全要素生产率测算的投入和产出指标的相关性检验

Table 3. Correlation test of input and output indicators for measuring agricultural green total factor productivity

指标 Index	资本 Capital	劳动力 Labor	土地 Land	农产品总产出 Total agricultural output	主观污染感知度 Subjective pollution perception degree	化学需氧量等标排放量 Chemical oxygen demand equivalent emissions equivalent emissions	总氮等标排放量 Total nitrogen equivalent emissions	TP等标排放量 Total phosphorus equivalent emissions
资本 Capital	1.0000	—	—	—	—	—	—	—
劳动力 Labor	0.0453^***	1.0000	—	—	—	—	—	—
土地 Land	0.0512^***	0.0191^*	1.0000	—	—	—	—	—
农产品总产出 Total agricultural output	0.7714^***	0.0507^***	0.0324^***	1.0000	—	—	—	—
主观污染感知度 Subjective pollution perception degree	0.0421^***	0.0400^***	0.0378^***	0.0233^**	1.0000	—	—	—
化学需氧量等标排放量 Chemical oxygen demand equivalent emissions	0.0473^***	0.0939^***	0.0568^***	0.0285^***	0.0435^***	1.0000	—	—
总氮等标排放量 Total nitrogen equivalent emissions	0.0377^***	0.0875^***	0.0703^***	0.0193^*	0.0161^***	0.3675^***	1.0000	—
总磷等标排放量 Total phosphorus equivalent emissions	0.0352^***	0.1113^***	0.0595^***	0.0415^***	0.0212^***	0.4296^***	0.8178^***	1.0000
*、、分别表示在1%、5%、10%水平显著。, and * denote significance at the 1%, 5%, and 10% levels.

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表 4 2016年和2018年基于技术优化Malmquist-Luenberger指数的农业绿色全要素生产率及其分解项

Table 4. Agricultural green total factor productivity and its decomposition terms based on technology-optimized Malmquist-Luenberger index for 2016 and 2018

年份 Year	排名 Rank	农户代码 Farmer code	绿色全要素生产率 ML(1)	绿色技术效率变化 MLTEC(1)	绿色技术进步变化 MLTC(1)	农户代码 Farmer code	绿色全要素生产率 ML(2)	绿色技术效率变化 MLTEC(2)	绿色技术进步变化 MLTC(2)
2016	前15名 Top 15	440560	3.7512	1.9630	1.9109	500233	3.7620	1.9889	1.8915
		350108	3.5632	1.9994	1.7821	510876	3.7029	1.9687	1.8809
		441716	3.4232	1.9810	1.7281	440560	3.6210	1.9862	1.8231
		510795	2.5510	1.5905	1.6039	441716	3.5514	1.9736	1.7994
		330177	2.4194	1.9837	1.2197	500236	3.2982	1.9926	1.6552
		360172	2.3063	1.7847	1.2923	510667	3.0436	1.9890	1.5302
		441941	2.0632	1.5001	1.3754	500238	2.7742	1.6172	1.7154
		510667	2.0528	1.9875	1.0329	510795	2.7659	1.5708	1.7608
		320134	1.8947	1.7166	1.1037	510790	2.6306	1.7399	1.5120
		130431	1.8898	1.5252	1.2390	500241	2.5685	1.6795	1.5294
		620847	1.8680	1.6140	1.1573	620847	2.5500	1.9676	1.2960
		140729	1.8670	1.5878	1.1758	441941	2.5474	1.6975	1.5007
		441738	1.8566	1.2592	1.4744	621077	2.4891	1.9672	1.2653
		330175	1.8561	1.6069	1.1551	500285	2.4136	1.7599	1.3714
		441073	1.8424	1.4593	1.2625	220212	2.2019	1.8826	1.1696
	后15名 Last 15	450209	0.6130	0.5539	1.1067	500149	0.5028	0.5055	0.9947
		510401	0.6072	0.5112	1.1877	621322	0.5008	0.6390	0.7837
		621126	0.6049	0.5947	1.0172	620970	0.4994	0.5065	0.9861
		500149	0.5968	0.5002	1.1931	210937	0.4868	0.5110	0.9527
		440156	0.5893	0.6336	0.9301	211800	0.4843	0.5414	0.8946
		440508	0.5823	0.6476	0.8992	120093	0.4748	0.6871	0.6910
		620011	0.5526	0.5456	1.0128	621197	0.4733	0.6333	0.7474
		610328	0.5458	0.5096	1.0709	621480	0.4485	0.5712	0.7853
		210937	0.5419	0.5216	1.0388	610328	0.4414	0.5224	0.8451
		683126	0.5378	0.5494	0.9788	621126	0.4385	0.5567	0.7876
		211800	0.5197	0.5230	0.9937	621289	0.4197	0.5663	0.7412
		621289	0.5197	0.5768	0.9010	140344	0.4081	0.5782	0.7058
		530423	0.4893	0.6008	0.8144	620011	0.4053	0.5063	0.8007
		140344	0.4551	0.5345	0.8514	350100	0.4025	0.7712	0.5219
		140647	0.4346	0.5121	0.8486	621476	0.3775	0.5094	0.7410
	平均 Average	—	1.0099	1.0165	0.9928	—	1.0175	1.0294	0.9828
2018	前15名 Top 15	441652	3.5980	1.9986	1.8002	510650	5.4701	1.9934	2.7441
		510650	3.5806	1.9972	1.7928	441652	3.5961	1.9981	1.7998
		140152	2.8038	1.9511	1.4370	440341	3.2432	1.8815	1.7237
		370448	2.4048	1.9551	1.2300	140152	2.7156	1.8185	1.4933
		530136	2.3989	1.9887	1.2062	530136	2.4714	1.9874	1.2435
		440341	2.1583	1.7552	1.2297	621236	2.4492	1.9663	1.2456
		210940	2.1467	1.7203	1.2479	370448	2.4075	1.9584	1.2293
		140361	1.8543	1.6359	1.1335	620223	2.4038	1.9329	1.2436
		550566	1.6099	1.5606	1.0316	621285	2.0741	1.7226	1.2041
		621177	1.6086	1.2917	1.2454	621177	1.9621	1.4907	1.3162
		441562	1.5932	1.3010	1.2246	210940	1.9401	1.3990	1.3868
		410858	1.5904	1.3746	1.1570	621476	1.8889	1.6638	1.1353
		130928	1.5341	1.3709	1.1191	621275	1.7729	1.5616	1.1353
		370326	1.5237	1.2862	1.1846	550566	1.7526	1.6110	1.0879
		620223	1.4896	1.5113	0.9856	620549	1.7439	1.4478	1.2046
	后15名 Last 15	530335	0.6427	0.5446	1.1801	140729	0.5781	0.5790	0.9984
		530252	0.6312	0.5572	1.1328	520342	0.5767	0.6585	0.8758
		620847	0.6312	0.6205	1.0172	210822	0.5730	0.5098	1.1240
		320134	0.6239	0.5894	1.0586	500238	0.5722	0.6614	0.8651
			450240	0.6221	0.5370	1.1584	500285	0.5625	0.5658	0.9942
			450199	0.6198	0.6053	1.0240	360172	0.5311	0.5245	1.0126
			620878	0.6143	0.6085	1.0097	621077	0.5205	0.5394	0.9650
			441941	0.6026	0.5920	1.0179	500233	0.4526	0.5897	0.7676
			620827	0.6016	0.5211	1.1544	500236	0.4447	0.5030	0.8841
			360172	0.5269	0.5280	0.9978	620847	0.4243	0.5095	0.8329
			510667	0.5207	0.5000	1.0412	510667	0.4193	0.5000	0.8386
			441716	0.4873	0.5064	0.9622	440560	0.3103	0.5019	0.6183
			330177	0.4764	0.5049	0.9436	441941	0.3021	0.5399	0.5596
			510795	0.3445	0.5271	0.6535	510795	0.2993	0.5327	0.5617
			440560	0.2393	0.5061	0.4728	441716	0.2663	0.5043	0.5281
		平均 Average	—	0.9960	0.9765	1.0200	—	0.9974	0.9713	1.0259
限于篇幅, 仅展示出农业绿色全要素生产率排名前15位、后15位农户和样本农户年度均值的结果。其中, ML(1)、MLTEC(1)、MLTC(1)分别表示使用虚拟户主主观污染感知度作为非期望产出的农业绿色全要素生产率、绿色技术效率变化、绿色技术进步变化; ML(2)、MLTEC(2)、MLTC(2)分别表示使用农业化学需氧量、总氮和总磷等标排放量等农业面源污染作为非期望产出的农业绿色全要素生产率、绿色技术效率变化、绿色技术进步变化。Due to the limitation of space, only the results of the top 15 farmers, the bottom 15 farmers and the annual average value of the sample farmers are shown. Among them, ML(1), MLTEC(1), and MLTC(1) denote agricultural green total factor productivity, green technical efficiency change, and green technological progress change using virtual household head subjective pollution perception as non-desired outputs; ML(2), MLTEC(2), and MLTC(2) denote agricultural green total factor productivity, green technical efficiency change, and green technical progress change in agriculture using agricultural non-point source pollution such as agricultural chemical oxygen demand, total nitrogen, and total phosphorus equivalents emissions as non-desired output.

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表 5 农业绿色全要素生产率的区域差距及其来源

Table 5. Regional gaps in agricultural green total factor productivity in and their sources

	年份 Year	总体差距 Overall gap	区域内差距 Intra-regional gap	区域间差距 Inter-regional gap	超变密度 Super variable density	贡献率 Contribution rate (%)
	年份 Year	总体差距 Overall gap	区域内差距 Intra-regional gap	区域间差距 Inter-regional gap	超变密度 Super variable density	区域内差距 Intra-regional gap	区域间差距 Inter-regional gap	超变密度 Super variable density
(1)	2016	0.0466	0.0159	0.0027	0.0280	34.12	5.79	60.09
	2018	0.0362	0.0124	0.0002	0.0236	34.25	0.55	65.19
(2)	2016	0.0780	0.0274	0.0041	0.0465	35.13	5.26	59.62
	2018	0.0496	0.0174	0.0014	0.0308	35.08	2.82	62.10
(1)、(2)分别表示使用虚拟户主主观污染感知度作为非期望产出、使用农业化学需氧量、总氮、总磷等标排放量等客观农业面源污染作为非期望产出。(1) and (2) denote the use of virtual household subjective pollution perceptions as non-desired output and objective agricultural non-point source pollution such as agricultural chemical oxygen demand, total nitrogen, total phosphorus equivalents emissions as non-desired output.

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表 6 东、中、西部农业绿色全要素生产率的区域内差距和区域间差距

Table 6. Intra-regional and inter-regional disparities in green total factor productivity in agriculture in East, Central and West

	年份 Year	区域内差距 Intra-regional gap			区域间差距 Inter-regional gap
	年份 Year	东 East	中 Central	西 West	东—中 East−central	东—西 East−west	中—西 Central−west
(1)	2016	0.0551	0.0357	0.0477	0.0457	0.0515	0.0419
	2018	0.0398	0.0295	0.0382	0.0348	0.0390	0.0340
(2)	2016	0.0520	0.0450	0.0499	0.0537	0.0852	0.0857
	2018	0.0403	0.0348	0.0358	0.0377	0.0544	0.0515
(1)、(2)分别表示使用虚拟户主主观污染感知度作为非期望产出、使用农业化学需氧量、总氮、总磷等标排放量等客观农业面源污染作为非期望产出。(1) and (2) denote the use of virtual household subjective pollution perceptions as non-desired output and objective agricultural surface source pollution such as agricultural chemical oxygen demand, total nitrogen, total phosphorus equivalents emissions as non-desired output.

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