摘要
为进一步提高长江经济带省会城市的物流效率,提升区域物流发展质量,以物流业从业人数、物流业财政支出和等级公路里程作为投入指标,以城市GDP和商品进出口总额作为输出指标,运用DEA-Malmquist指数模型对2016—2020年长江经济带11个省会城市的物流效率进行测算并结合K-means聚类算法对其进行聚类分析,最后利用灰色关联度模型对其物流效率影响因素进行剖析。结果表明,11个省会城市的物流效率发展情况差异较大;技术效率是限制贵阳和长沙全要素生产率的主要因素;各省会城市物流效率的高低与其经济实力并非完全对等;城市GDP及物流业从业人数对物流业效率产生较大影响。基于上述结论提出优化物流业规模、加强技术投入等建议,以期促进长江经济带省会城市物流高质量发展。
To further improve the logistics efficiency of the capital cities of the Yangtze River Economic Belt and improve the quality of regional logistics development,the number of employees in the logistics industry,the fiscal expenditure of the logistics industry and the mileage of graded highways are taken as the input indicators.The city’s GDP and total import and export of commodities are used as the output indicators.The logistics efficiency of 11 provincial capitals in the Yangtze River Economic Belt from 2016 to 2020 is measured by the DEA-Malmquist index model,and the clustering analysis is combined with the K-means clustering algorithm.Finally,the grey correlation model is used to analyze the factors influencing its logistics efficiency.The results show that the development of logistics efficiency in 11 provincial capitals varies greatly.Technological efficiency is the main factor limiting the total factor productivity of Guiyang and Changsha.The logistics efficiency of provincial capitals is not completely equal to their economic strength.Urban GDP and the number of employees in the logistics industry have a great impact on the efficiency of the logistics industry.Based on the above conclusions,suggestions are put forward to optimize the scale of logistics industry and strengthen technical investment,to promote the high-quality development of logistics in provincial capitals of the Yangtze River Economic Belt.
作者
谷子硕
周书灵
GU Zishuo;ZHOU Shuling(School of Economics and Management,Anhui University of Science and Technology,Huainan 232001,China;School of Business,Suzhou University,Suzhou 234000,China)
出处
《广东石油化工学院学报》
2024年第1期107-112,共6页
Journal of Guangdong University of Petrochemical Technology
基金
安徽省高校人文社会科学研究重大项目(SK2021ZD0092)。