基于超效率三阶段SBM模型的安徽省农业生产效率测度及其时空差异分析

Measurement of Agricultural Production Efficiency and Its Spatial-temporal Differences in Anhui Province Based on Three-stage SBM Model

  • 摘要: 基于安徽省16个地级市数据,选取2010—2020年作为研究时间段,综合考虑环境因素及随机干扰的影响,采用超效率三阶段SBM模型及Malmquist指数方法从静态和动态角度分析安徽省的农业生产效率与时空特征。研究结果表明,环境因素与随机干扰均显著影响农业生产效率,排除上述干扰之后:(1)2010—2020年安徽省农业生产平均效率整体稳定并呈增长态势,只有3年非DEA有效。测度出的16个地级市中,有7个地区平均效率处于DEA有效状态,有9个地区处于非DEA有效状态;(2)根据时空特征分析结果,2010—2020年平均效率有效的地市主要集中在安徽的西南区域,而东北区域平均效率相对较低;(3)Malmquist指数结果表明安徽省全要素年平均增长率为3.9%。各地级市中排名前3的地区全要素生产率的改善主要得益于技术效率贡献;排名3~9的地区全要素生产率改善主要得益于技术进步要素的贡献,而排名在第9名以后的地级市主要是由于规模效率影响技术效率值增加,从而影响全要素生成产率的改善。针对安徽省农业生产效率尚存在的不足,提出相关政策性的建议。

     

    Abstract: Based on the data of 16 prefectures in Anhui Province, this article selects the period of 2010—2020 as the study time period, takes into account the influence of environmental factors and random disturbances, and analyzes the agricultural production efficiency and temporal and spatial characteristics of Anhui Province from static and dynamic perspectives by using the super-efficient three-stage SBM model and Malmquist index method. The results show that both environmental factors and random disturbances significantly affect agricultural production efficiency. After excluding the above disturbances:(1)The average efficiency of agricultural production in Anhui Province is stable and growing overall from 2010 to 2020, with only three years of non-DEA validity. Among the 16 prefecture-level cities measured, 7 regions have average efficiency in DEA effective state and 9 regions are in non-DEA effective state.(2)According to the analysis results of spatial and temporal characteristics, the average efficiency and efficiency of cities from 2010 to 2020 are mainly concentrated in the southwest region of Anhui, while the average efficiency in the northeast region is relatively low.(3)Malmquist index results show that Anhui province has the highest annual average growth rate of total factors. Among prefecture-level cities, the top three regions’ total factor productivity improvement is mainly due to the promotion effect of technical efficiency, while the third to ninth regions’ total factor productivity improvement is due to the promotion effect of technological progress factors. The prefecture-level cities ranked after the 9th are mainly due to the scale efficiency which affects the increase of technical efficiency value and thus the improvement of total factor productivity. Finally, the article proposes policy recommendations to address the remaining shortcomings of agricultural production efficiency in Anhui Province.

     

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