管理评论

管理评论 ›› 2020, Vol. 32 ›› Issue (6): 72-81.

• 经济与金融管理 • 上一篇    下一篇

产业生态化与空间集聚效应研究——来自中国31个省、市、自治区的面板数据

袁世一, 李永武, 陈维国, 谢启伟   

  1. 北京工业大学经济与管理学院, 北京 100124
  • 收稿日期:2019-10-21 发布日期:2020-07-10
  • 通讯作者: 李永武(通讯作者),北京工业大学经济与管理学院副教授,硕士生导师,博士
  • 作者简介:袁世一,北京工业大学经济与管理学院博士研究生;陈维国,北京工业大学经济与管理学院博士研究生;谢启伟,北京工业大学经济与管理学院教授,博士生导师,博士。
  • 基金资助:
    国家自然科学基金面上项目（61673381）；北京市自然科学基金项目（9192001；9202002）。

Study on Industrial Ecology and Spatial Agglomeration Effect——Panel Data from 31 Chinese Regions

Yuan Shiyi, Li Yongwu, Chen Weiguo, Xie Qiwei   

  1. School of Economics and Management, Beijing University of Technology, Beijing 100124
  • Received:2019-10-21 Published:2020-07-10

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摘要： 利用2011—2017年中国大陆31个省、市、自治区的相关数据分析了省际产业生态化水平并探究了产业生态化的影响因素。首先借助基于非期望产出的SBM-DEA模型对各省、市、自治区的产业生态化进行了测度，然后利用Moran's I指数检验了各省、市、自治区产业生态化水平的空间相关性，最后利用空间计量模型分析与探究产业生态化水平的时空分异特征及影响因素。研究结果表明：产业生态化总体平均水平呈下降趋势；产业生态化在空间上的集聚性逐渐增强；相邻地区人均GDP、相邻地区固定资产、相邻地区研究与实验发展（R&D）经费情况和相邻地区外商投资总量对基于非期望产出“三废”测度的产业生态化的影响不显著，相邻区域工业化程度、人均公共绿地面积对产业生态化影响系数较大且显著。

关键词: 产业生态化, SBM-DEA, 空间相关性, 空间计量分析

Abstract: Relevant data of 31 Regions in China from 2011 to 2017 are used to analyze the ecological level of inter-provincial industry and explore the influencing factors of the ecological level of industry. Firstly, the SBM-DEA model based on non-expected output is used to measure the industrial ecology of each province. Then Moran's I index is used to test the spatial correlation of the industrial ecology level of each province. Finally, the spatial econometric model is used to analyze and explore the spatial-temporal differentiation characteristics and influencing factors of the industrial ecology level. The results show that the overall average level of industrial ecology is decreasing, and the spatial agglomeration of ecological industry is gradually strengthened, and GDP per capita, fixed assets, science and technology expenditure and total foreign investment in neighboring regions have no significant influence on the eco-industry based on the "three wastes" measure of non-expected output, while the degree of industrialization of neighboring regions and the per capita public green space have a large and significant influence on the eco-industry coefficient.

Key words: ecological industry, SBM-DEA, spatial correlation, spatial econometric analysis