灰水足迹视角下我国省域农业生态效率及其影响因素

doi:10.3864/j.issn.0578-1752.2022.24.008

摘要/Abstract

摘要：

【目的】从灰水足迹视角评价我国省域农业生态效率，揭示农业生态效率的空间分布特征，分析影响农业生态效率的主要因素，据此提出提升我国省域农业生态效率的政策建议。【方法】利用我国2000—2019年的省级面板数据，考虑非期望产出的超效率SBM模型对我国省域农业生态效率进行综合评价，采用空间杜宾模型对农业生态效率的空间差异与影响因素进行分析。【结果】（1）总体而言，农业灰水足迹呈下降趋势，但个别省（市、区）呈上升趋势。从灰水足迹由低到高的排名可以看出，处于前列（即灰水足迹较小）的省（市、区）经济发展水平较高或农业产值占比较低；处于后列（即灰水足迹较高）的省（市、区）经济发展水平较低或农业产值占比较高。（2）观测期内，农业生态效率总体平稳，个别年份波动较大，各省（市、区）的均值差距明显且分布极不平衡。（3）经济发展水平、种植业结构、技术进步、财政支农、农业受灾率等因素对中国农业生态效率的影响程度各异。随着经济发展水平与人们生活质量的双双提升，无论是农业经营者还是消费者对农业生态环境保护和农产品质量的重视程度日益提升，在一定程度上改善了区域农业生态效率水平，但是地区经济社会发展产生的污染也可能对农业生态效率产生负面影响；财政支农的大部分资金使用在对农药、化肥和农机等生产资料的补贴上，虽然改善了农业生产条件，提高了农业经济生产力和效率，但对农业生态效率的提升效果不显著；技术的发展在农业生产过程中很重要，使用得当会提高农业生态效率；农业受灾率的估计结果未通过显著性检验，可能是因为农业受灾面积的扩大导致农业生态效率的下降，但每年的受灾情况并不具有规律性；种植业结构的系数为负，其对农业生产效率产生了负面影响，可能是因为粮食作物种植面积占作物总播种面积比例较高，且消耗的氮肥数量较多。【结论】由于我国各个省（市、区）的农业灰水足迹演变趋势和差异明显，农业生态效率整体水平不高，且各个因素对农业生态效率的影响程度不一。因此，需要健全农业灰水足迹治理机制；优化农业产业结构，建立基于灰水足迹的农业水资源保护补偿机制；完善财政支农方式和政策，引导经营主体积极提升农业生态效率。

关键词: 农业生态效率, 农业灰水足迹, 超效率SBM模型

Abstract:

【Objective】This paper evaluated Chinese provincial agricultural ecological efficiency from the perspective of gray water footprint, revealed the spatial distribution characteristics of agricultural ecological efficiency, analyzed the main factors affecting agricultural ecological efficiency, and put forward policy suggestions to improve Chinese provincial agricultural ecological efficiency. 【Method】Based on the provincial panel data of China from 2000 to 2019, this paper comprehensively evaluated the agricultural ecological efficiency of Chinese provinces with the super efficiency SBM model considering the unexpected output, and used the spatial Dobbin model to analyze the spatial differences and influencing factors of agricultural ecological efficiency. 【Result】(1) In general, the agricultural grey water footprint showed a downward trend, but in some provinces (cities and districts), it showed an upward trend. From the ranking of grey water footprint from low to high, it could be seen that the provinces (cities and districts) in the forefront (i.e. with less grey water footprint) had a high level of economic development or a relatively low proportion of agricultural output value; the provinces (cities and districts) in the rear row (i.e. with more grey water footprint) had low economic development level or high agricultural output value. (2) During the observation period, the agricultural ecological efficiency fluctuated greatly in some years in the stable trend, and the average difference among provinces (cities and districts) was obvious and the distribution was extremely unbalanced. (3) Economic development level, fiscal expenditure for supporting agriculture, technological progress, agricultural disaster rate, planting structure and other factors had different impacts on Chinese agricultural ecological efficiency. With the improvement of both economic development level and people's living quality, both agricultural operators and consumers paid more attention to the protection of agricultural ecological environment and the quality of agricultural products, which have improved the level of regional agricultural ecological efficiency to a certain extent. But the pollution caused by regional economic and social development might also have a negative impact on agricultural ecological efficiency. Most of the financial support for agriculture was used to subsidize production links, such as pesticides, chemical fertilizers, and agricultural machinery. Although the agricultural production conditions have been improved and the agricultural economic productivity and efficiency have been improved, the improvement of agricultural ecological efficiency was not significant. The development of technology was very important in the agricultural production process, and the proper use of it would improve the agricultural ecological efficiency. The estimated results of agricultural disaster rate failed to pass the significance test, which might be because the expansion of agricultural disaster area would lead to the decline of agricultural ecological efficiency, but the annual disaster situation was not regular. The coefficient of planting structure was negative, which had a negative impact on agricultural production efficiency. This might be due to the high proportion of grain crop planting area in the total planting area of crops, and the high consumption of nitrogen fertilizer. 【Conclusion】As the evolution trend and difference of agricultural gray water footprint in various provinces (cities and districts) in China were obvious, the overall level of agricultural ecological efficiency was not high, and various factors have different impacts on agricultural ecological efficiency, it was necessary to improve the governance mechanism of agricultural gray water footprint; optimize the agricultural industrial structure and establish a compensation mechanism for agricultural water resources protection based on gray water footprint; improve the ways and policies of financial support for agriculture, and guide business entities to actively improve agricultural ecological efficiency.

Key words: agricultural ecological efficiency, agricultural grey water footprint, super-SBM model

邓远建,超博. 灰水足迹视角下我国省域农业生态效率及其影响因素[J]. 中国农业科学, 2022, 55(24): 4879-4894.

DENG YuanJian,CHAO Bo. Provincial Agricultural Ecological Efficiency and Its Influencing Factors in China from the Perspective of Grey Water Footprint[J]. Scientia Agricultura Sinica, 2022, 55(24): 4879-4894.

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链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2022.24.008

https://www.chinaagrisci.com/CN/Y2022/V55/I24/4879

图/表 12

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表3

2000—2019年我国30个省（市、区）农业生态效率测算结果"

省域 Province	年份 Year
省域 Province	2000	2002	2004	2006	2008	2010	2012	2014	2016	2018	2019
北京 Beijing	1.069	1.171	1.271	1.208	1.135	1.124	1.088	1.130	1.165	1.664	1.696
天津Tianjin	1.392	1.315	1.160	1.198	1.208	1.149	1.147	1.136	1.090	1.014	1.023
河北 Hebei	1.286	1.206	1.241	1.240	1.255	1.149	1.108	1.094	1.004	0.176	1.027
山西 Shanxi	0.723	1.003	1.024	1.056	1.000	1.013	0.845	1.059	0.578	0.167	0.469
内蒙古 Neimenggu	0.488	0.504	0.532	0.549	1.046	1.035	1.026	1.046	1.027	0.216	1.031
辽宁 Liaoning	0.426	0.451	0.468	0.532	0.514	0.536	0.533	0.573	0.588	0.162	0.574
吉林 Jilin	0.385	0.384	0.403	0.454	0.484	0.560	0.595	0.597	1.050	0.231	1.162
黑龙江 Heilongjiang	0.447	0.438	0.450	0.521	0.542	1.002	1.042	1.004	1.111	0.176	1.126
上海 Shanghai	1.062	1.037	0.844	1.102	1.136	1.257	1.320	1.155	1.037	1.025	1.026
江苏 Jiangsu	0.416	0.406	0.400	0.410	0.429	0.452	0.463	0.507	0.709	0.144	0.694
浙江 Zhejiang	1.023	1.007	0.681	1.001	0.677	0.681	0.668	0.665	0.746	4.036	0.650
安徽 Anhui	0.527	0.559	0.584	0.622	0.605	0.700	0.717	0.677	1.080	0.173	1.024
福建 Fujian	0.376	0.393	0.416	0.400	0.422	0.432	0.459	0.463	0.519	0.179	0.481
江西 Jiangxi	0.279	0.336	0.433	0.594	1.002	1.093	1.106	0.445	0.567	0.157	0.620
山东 Shandong	1.008	1.027	1.019	1.022	1.003	1.016	1.020	1.026	1.110	0.170	1.112
河南 Henan	0.631	0.648	0.685	0.689	0.712	0.705	0.728	0.750	1.024	0.147	0.831
湖北 Hubei	0.315	0.337	0.347	0.422	0.450	0.509	0.532	0.562	0.662	0.144	0.667
湖南 Hunan	0.449	0.475	0.506	0.557	0.598	0.641	0.674	0.705	1.081	0.185	1.031
广东 Guangdong	0.463	0.402	0.398	0.376	0.374	0.405	0.421	0.440	0.489	0.120	0.474
广西 Guangxi	0.422	0.440	0.449	0.413	0.450	0.502	0.540	0.568	1.018	0.154	1.014
海南 Hainan	1.089	1.005	1.012	1.008	1.015	1.017	1.030	0.759	1.039	0.456	1.066
重庆 Chongqing	0.304	0.349	0.388	0.403	0.404	0.445	0.444	0.475	0.596	0.214	0.622
四川 Sichuan	0.296	0.312	0.331	0.370	0.348	0.426	0.462	0.447	0.586	0.117	0.595
贵州 Guizhou	0.292	0.332	0.385	0.542	0.520	0.613	0.537	0.620	0.601	0.170	1.007
云南 Yunnan	0.357	0.371	0.387	0.393	0.417	0.502	0.491	0.492	0.674	0.123	0.471
陕西 Shaanxi	0.342	0.374	0.405	0.424	0.445	0.486	0.524	0.543	0.583	0.160	0.577
甘肃 Gansu	0.484	0.510	0.539	0.533	0.555	0.616	0.660	0.663	0.646	0.192	0.673
青海 Qinghai	1.868	1.773	1.588	1.702	1.503	1.397	1.598	1.054	1.024	1.035	1.061
宁夏 Ningxia	0.682	1.002	1.005	1.012	0.805	1.007	1.033	1.026	0.776	1.007	1.014
新疆 Xinjiang	0.357	0.371	0.383	0.403	0.415	0.508	0.528	0.369	0.503	0.144	0.485

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指标类型 Index type	指标类别 Index category	指标名称 Index name	指标说明 Index description
投入指标 Input index	资源类指标 Resource index	劳动力投入 Labor input	农林牧渔业从业人员（万人） Agricultural, forestry, animal husbandry and fishery employees (ten thousand people)
		土地投入 Land input	农作物播种面积 Planting area of crops (×10³hm²)
		化肥投入 Chemical fertilizer input	农用化肥施用折纯量 Net amount of agricultural chemical fertilizer application (×10⁴t)
		农业机械投入 Agricultural machinery input	农业机械总动力 Total power of agricultural machinery (×10⁴kW·h)
		灌溉投入 Irrigation input	有效灌溉面积 Effective irrigation area (hm²)
		农膜投入 Agricultural film input	农膜使用量 Amounts of agricultural film used (×10⁴ t)
		农药投入 Pesticide input	农药使用量 Amounts of pesticides used (×10⁴ t)
期望产出指标 Expected output index	经济类指标 Economic index	农业生产总值 Gross agricultural production	农林牧渔业生产总值 Gross output value of agriculture, forestry, animal husbandry and fishery (×10⁸ yuan)
非期望产出指标 Unexpected output index	环境类指标 Environmental index	农业灰水足迹 Agricultural grey water footprint	稀释农业生产活动排放的一定量的水污染物所需要的自然水体体积 Volume of natural water body required to dilute a certain amount of water pollutants discharged from agricultural production activities (m³)

省域 Province	均值与排名 Average value and ranking
	2000—2004年 From 2000 to 2004		2005—2009年 From 2005 to 2009		2010—2014年 From 2010 to 2014		2015—2019年 From 2015 to 2019
	排名 Ranking	均值 Average value (m³)	排名 Ranking	均值 Average value (m³)	排名 Ranking	均值 Average value (m³)	排名 Ranking	均值 Average value (m³)
青海 Qinghai	1	3.2	1	3.3	1	3.9	1	3.3
北京 Beijing	2	8.5	2	7.2	3	5.9	2	3.4
天津 Tianjin	3	10.1	4	12.2	4	10.9	4	6.6
海南 Hainan	4	10.9	5	13.1	5	14.5	5	14.9
上海 Shanghai	5	11.7	3	7.7	2	5.4	3	3.9
宁夏 Ningxia	6	14.1	6	16.6	6	17.9	6	16.8
甘肃 Gansu	7	33.8	7	37.7	8	39.8	8	34.4
山西 Shanxi	8	40.7	8	40.1	7	37.2	7	26.9
贵州 Guizhou	9	43.4	10	46.3	13	51.8	11	43.3
重庆 Chongqing	10	45.5	12	48.8	12	49.8	13	46.4
新疆 Xinjiang	11	45.9	18	69.2	20	96.4	24	110.4
江西 Jiangxi	12	46.9	9	44.6	9	42.8	9	35.9
内蒙古 Neimenggu	13	49.8	19	73.1	18	89.6	19	90.7
福建 Fujian	14	52.3	11	48.3	10	47.3	12	43.4
黑龙江 Heilongjiang	15	53.7	17	68.7	17	86.4	17	82.3
浙江 Zhejiang	16	56.5	13	53.7	11	49.4	10	40.7
广西 Guangxi	17	59.5	16	67.8	16	73.3	16	74.4
辽宁 Liaoning	18	64.2	15	65.7	14	68.3	14	55.8
吉林 Jilin	19	66.2	14	64.3	15	71.3	15	62.5
云南 Yunnan	20	73.6	21	90.3	23	109.8	23	108.1
陕西 Shaanxi	21	74.8	20	82.7	19	95.6	18	87.9
广东 Guangdong	22	96.2	22	97.2	21	102.1	21	95.8
湖南 Hunan	23	100.3	23	108.1	22	108.6	20	94.1
安徽 Anhui	24	117.7	24	111.9	24	112.3	22	97.3
四川 Sichuan	25	120.5	25	128.3	25	126.6	25	113.7
湖北 Hubei	26	135.6	26	148.5	27	151.2	26	119.9
河北 Hebei	27	149.3	27	154.1	26	150.6	27	126.6
江苏 Jiangsu	28	185.5	28	176.9	29	166.9	29	149.1
山东 Shandong	29	190.5	29	181.5	28	156.3	28	133.7
河南 Henan	30	216.9	30	239.4	30	242.9	30	209.9

年份Year	莫兰值Morans’I	P值P value	Z值Z value	年份Year	莫兰值Morans’I	P值P value	Z值Z value
2000	0.135	0.009	2.387	2010	0.204	0.001	3.210
2001	0.133	0.006	2.496	2011	0.227	0.000	3.519
2002	0.151	0.005	2.577	2012	0.211	0.000	3.365
2003	0.197	0.001	3.176	2013	0.154	0.006	2.536
2004	0.191	0.001	3.110	2014	0.179	0.002	2.868
2005	0.175	0.002	2.879	2015	0.191	0.001	3.028
2006	0.154	0.005	2.610	2016	0.075	0.071	1.465
2007	0.169	0.003	2.792	2017	0.034	0.164	0.980
2008	0.185	0.001	2.981	2018	0.012	0.329	0.433
2009	0.206	0.001	3.282	2019	0.132	0.010	2.310

因变量 Dependent variable	符号 Symbol	变量解释 Interpretation of variables
人均收入 Per capita income	RCI	农村居民人均可支配收入 Per capita disposable income of rural residents
财政支农支出 Fiscal expenditure on supporting agriculture	AFI	地方财政农林水事务支出/地方财政一般预算支出 Amount of agricultural, forestry and water affairs expenditure in local fiscal expenditure/ General budget expenditure of local finance (%)
技术水平 Technical level	TI	R&D投入/地方财政一般预算支出 R&D input/ General budget expenditure of local finance (%)
经济发展水平 Economic development level	LED	地区人均GDP Regional per capita GDP
农业受灾率 Agricultural disaster rate	RDR	农作物受灾面积/农作物总播种面积 Area affected by crops/ Total sown area of crops (%)
种植业结构 Planting structure	CS	粮食作物种植面积/农作物总播种面积 Grain crop planting area/ Total sown area of crops (%)

变量名称 Variable name	均值 Average value	标准差 Standard deviation	最小值 Minimum	最大值 Maximum	样本数 Number of samples
ECOE	0.69	0.38	0.11	4.04	600
RCI	7.8e	7.4e	1.41	1.3e	600
AFI	274.95	279.87	2.14	1.3e	600
TI	6.5e	7.4e	80.91	4.4e	600
LED	0.92	0.46	0.05	2.71	600
RDR	0.23	0.16	0.00	0.94	600
CS	0.53	0.09	0.34	0.75	600