管理评论 ›› 2020, Vol. 32 ›› Issue (8): 52-62,105.

• 经济与金融管理 • 上一篇    下一篇

省际环境效率俱乐部收敛及动态演进分析

朴胜任1,2   

  1. 1. 渤海银行博士后科研工作站, 天津 300012;
    2. 南开大学博士后流动站, 天津 300071
  • 收稿日期:2017-07-29 出版日期:2020-08-28 发布日期:2020-09-05
  • 作者简介:朴胜任,渤海银行博士后科研工作站、南开大学博士后流动站联合培养博士后,博士。
  • 基金资助:
    中国博士后基金(2019M651020)。

Analysis of Convergence of Provincial Environmental Efficiency of China and Dynamic Processes

Piao Shengren1,2   

  1. 1. Postdoctoral Research Station of Bohai Bank, Tianjin 300012;
    2. Postdoctoral Mobile Stations of Nankai University, Tianjin 300071
  • Received:2017-07-29 Online:2020-08-28 Published:2020-09-05

摘要: 鉴于传统收敛模型的局限性,基于PS模型对中国30个省际2005—2014年环境效率的收敛特征进行分析。研究结果表明:(1)我国环境效率整体上不存在收敛,但是存在4个收敛类型和1个发散类型,其中类型A和类型B的相对路径趋于上扬,其所包含的省际的环境效率均高于其他类型;类型C、类型D和类型E的相对路径均在1以下,其所包含的省际的环境效率均低于全国总体平均水平,而且这部分省际数量占据了全国总体数量的2/3,是拉低全国环境效率的主要因素。(2)研究期间省际环境效率呈现“两极分化”或“多极分化”的趋势。(3)进一步研究表明,经济发展、产业结构是俱乐部形成的主要因素。

关键词: 环境效率, SBM-超效率DEA, PS收敛模型, 核密度估计, 影响因素

Abstract: In view of the limitations of the traditional convergence models, the convergence characteristics of environmental efficiency in 30 provinces in China from 2005 to 2014 are analyzed based on the PS model. The results show that there is no convergence in China's environmental efficiency, but there are four types of convergence and one divergence type. The relative path of type A and type B tends to rise, and its intervening environmental efficiency is higher than that of other types. The average path of type C, type D and type E is below 1, and the province's environmental efficiency is lower than the national average, and this part of the inter-provincial quantity occupies 2/3 of the national average. These are core classes with reducing the environmental efficiency. During the study, provincial environmental efficiency shows a “polarization” or “multipolar differentiation” trend by nuclear density model. Further, economic development level and industrial structure are the main influencing factors for club.

Key words: environmental efficiency, SBM-super model, PS convergence model, Kernel density estimation, influencing factors