基于技术效率及影子价格的农业灌溉弹性需水研究——以黑龙江省为例

doi:10.13249/j.cnki.sgs.2018.07.018

摘要/Abstract

摘要：

基于黑龙江省13个地市2000~2015年的农业投入和产出数据,采用随机非参数包络分析法（StoNED）,估算黑龙江省农业灌溉用水的技术效率和影子价格,提出了基于技术效率和影子价格的农业灌溉用水弹性需求分析模型,研究表明：在相同的产出条件下,2015年农业灌溉用水技术效率无效比2000年降低了23.68 %、农业灌溉用水技术效率有效提高了25.02%;哈尔滨市的农业灌溉用水技术效率最高,达到了0.978 8;齐齐哈尔市的农业灌溉用水技术效率最低,为0.685 4;黑龙江省农业灌溉用水影子价格平均值在2.04~3.86元/m³之间波动,各地市农业灌溉用水影子价格平均值波动性较大,其极差达到了11.92 元/m³;当其他投入保持不变的条件下,黑龙江省农业灌溉用水价格、农业总产出和农业用水技术效率每增加1%,农业灌溉用水量将分别减少4.64%、增加1.10%和减少0.20%。

关键词: StoNED模型, 农业灌溉用水, 影子价格, 技术效率, 黑龙江省

Abstract:

Based on the input-output datas of agriculture in 13 cities of Heilongjiang Province from 2000 to 2015, the input-output index system was built. The input indexes included cultivated area of the crops, total power of agricultural machinery, fertilizer amount, the number of agricultural employment and water consumption for agricultural irrigation and the output indexes included agricultural output value. Then the method of stochastic nonparametric envelopment of data was introduced to build the technical efficiency model of agricultural irrigation water. Based on the measurement results, the shadow price of agricultural irrigation water was calculated with the shadow price theory. Last the empirical model of flexible demand analysis on agricultural irrigation water was established using the Shephard theory. On the basis of the above analysis, the model bulit in this paper can estimate the noise in the data of the agricultural irrigation water sample correctly and formulate the economic relations between the agricultural irrigation water and the agricultural production adopting the crop production function reasonably. Compared with 2000, in the condition of the same output, the agricultural water use inefficienint in 2015 decreased by 23.68% and the agricultural water use efficienint increased by 25.02%. The most agricultural water use efficienint of all cities in Heilongjiang Province is Harbin with 0.978 8, the least is Qiqihar with 0.685 4. The average agricultural water shadow prices of Heilongjiang Province are between 2.04-3.86 yuan/m³. But the average agricultural water shadow prices of different cities differ largely, and the range is 11.92 yuan/m³. On the condition of other inputs no change, the agricultural water consumption will decrease by 4.64%, increase by 1.10% and decrease by 0.20% respectively with each increase of 1% of agricultural water price, total agricultural output and agricultural water efficiency. Due to the shortcomings of relying on the functional form of SFA (Stochastic frontier analysis) model and ignoring the data stochastic noise of DEA (Data envelopment analysis) model, the StoNED model can better solve these problems and satisfy the the concepts and principles of SFA and DEA. The empirical model for the analysis of agricultural irrigation water elasticity demand can effectively reveal the impact mechanism of the agricultural irrigation water price and the technical efficiency to the agricultural irrigation water demand in Heilongjiang Province. The research results have important theoretical and practical significance for drawing up the agricultural water use price scientific and rational, promoting the reform of agricultural water use price and saving agricultural water resources in Heilongjiang Province.

Key words: StoNED model, agricultural irrigation water, shadow price, technology efficiency, Heilongjiang Province

中图分类号:

P964

张向达, 朱帅. 基于技术效率及影子价格的农业灌溉弹性需水研究——以黑龙江省为例[J]. 地理科学, 2018, 38(7): 1165-1173.

Xiangda Zhang, Shuai Zhu. The Flexible Demand Analysis of Agricultural Irrigation Water Use Based on Tech-nical Efficiency and Shadow Price: Taking Heilongjiang Province for an Example[J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(7): 1165-1173.

图/表 6

图1

表1

表2

黑龙江省2000~2015年农业灌溉用水效率测度结果"

年份	平均无效率值	$σ ? ε 2$	$σ ? δ 2$	$σ ? ε 2$ + $σ ? δ 2$	平均效率值	信噪比 $σ ? ε$ / $σ ? δ$
2000	0.3277	0.0561	0.0262	0.0823	0.6723	1.4624
2001	0.2706	0.0156	0.0233	0.0388	0.7294	0.8180
2002	0.2666	0.0725	0.0128	0.0853	0.7334	2.3814
2003	0.2537	0.0583	0.0184	0.0766	0.7463	1.7822
2004	0.2492	0.0210	0.0190	0.0400	0.7508	1.0530
2005	0.2233	0.0581	0.0165	0.0746	0.7767	1.8741
2006	0.2177	0.0538	0.0772	0.1310	0.7823	0.8343
2007	0.2099	0.0711	0.0726	0.1438	0.7901	0.9894
2008	0.1927	0.0950	0.0746	0.1696	0.8073	1.1283
2009	0.1831	0.0862	0.0721	0.1584	0.8169	1.0935
2010	0.1459	0.0847	0.0567	0.1414	0.8541	1.2218
2011	0.1078	0.0927	0.0589	0.1516	0.8922	1.2549
2012	0.1068	0.0909	0.0432	0.1341	0.8932	1.4497
2013	0.1066	0.0803	0.0557	0.1361	0.8934	1.2006
2014	0.1052	0.0120	0.0172	0.0292	0.8948	0.8338
2015	0.1034	0.0128	0.0152	0.0280	0.8966	0.9162

表2

图2

图3

图4

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指标	平均值	最大值	最小值	方差	中位数	标准差
农业产值指数(10⁸元)	106.49	647.10	5.50	12919.61	70.19	113.66
农业灌溉用水量(10⁸m³)	323.52	377.02	265.14	350.15	321.65	18.71
农业从业人数(10⁴人)	55.60	183.73	1.50	2926.27	31.90	54.09
农机总动力(10⁴kW)	201.63	1024.50	20.80	40590.75	134.70	201.47
化肥施用量(10⁴t)	24.12	118.55	0.06	829.57	11.26	28.80
农作物播种面积(10⁴hm²)	76.90	229.43	8.77	4435.37	48.95	66.60