摘要
深入研究非期望产出的我国物流业效率的空间关联网络对于推进物流业的低碳转型,促进物流产业区域协同发展具有重要的现实意义。基于2011—2020年我国内陆30个省份的数据,利用超效率SBM模型测算考虑非期望产出的物流业效率,借助修正的引力模型和社会网络分析方法对效率空间关联网络特征及其影响因素进行了分析。结果表明:考虑非期望产出的物流业效率空间关联效应已经突破了地理邻近范围,其空间关联网络结构较为稳定但松散。研究期内,空间关联网络中各地区物流产业辐射关联能力及中介作用表现情况不同。地理位置、经济发展水平差异、科技水平差异和政府支持力度差异对考虑非期望产出的物流业效率空间关联网络结构的形成有显著影响。
In-depth studies on the spatial correlation network of logistics efficiency considering undesirable outputs in China have great practical significance for promoting the low-carbon transformation and the region-coordinated development of the logistics industry.Based on the data of 30 inland provinces in China from 2011 to 2020,a super-efficiency SBM model with undesirable outputs was constructed to calculate the efficiency of the logistics industry in this paper.Moreover,a modified gravity model and social network analysis methods are used to analyze the characteristics of the efficiency spatial correlation network and its influencing factors.The results show that the spatial correlation effect of logistics industry efficiency,considering undesirable outputs,has expanded beyond geographical proximity,with a relatively stable but loose spatial correlation network structure.During the research period,logistics industries in different regions in the spatial correlation network exhibit different radiation and correlation performance and played different intermediary roles.Factors such as geographical location,economic development disparities,technological differences,and varying levels of government support significantly influenced the formation of the spatial correlation network structure for logistics industry efficiency considering undesirable outputs.
作者
花均南
胡美佳
HUA Junnan;HU Meijia(School of Business,Guilin University of Electronic Technology,Guilin 541000,Guangxi,China)
出处
《铁道运输与经济》
北大核心
2024年第4期42-50,共9页
Railway Transport and Economy
基金
广西哲学社会科学规划研究课题(17FJY009)。
关键词
非期望产出
物流业效率
超效率SBM模型
空间网络结构
社会网络分析
Undesirable Output
Logistics Efficiency
Super-Efficiency SBM Model
Spatial Correlation Network
Social Network Analysis