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中国区域公路物流效率差异及其影响因素研究 被引量:15

Efficiency Measurement of China’s Road Transport Logistics and the Determinants Analysis
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摘要 公路物流在现代物流发展中的作用无可替代,提升公路物流效率是振兴中国物流业的关键。本文通过建立非径向超效率DEA模型和TOPSIS效率测度模型,对中国31个省(自治区、直辖市)公路物流效率进行组合测度,并建立Tobit面板回归模型对其影响因素进行实证研究。结果表明:2010—2015年,中国公路物流效率值呈现中、东、西部地区阶梯下降变化特征,且各区域内省份之间呈现显著差异性,通过系统聚类分析发现,安徽、河北、内蒙古等地区公路物流效率在各自区域内处于较高水平。地区信息化水平、公路物流资源利用率等因素对中国公路物流效率具有重要影响,尤其是提高载货汽车利用率对促进公路物流效率的提升具有重大现实意义。 The road transport logistics is playing irreplaceable role in the development of modern logistics, improving the road transport efficiency which is the key way to revitalize China's logistics industry. Through setting up the radial super efficiency DEA model and Technique for Order Preference by Sim- ilarity to Ideal Solution (TOPSIS) model, the paper measured the road transport logistics efficiency of China's 31 provinces (autonomous regions and municipalities), and analysised the determinants of China's road transport logistics by Tobit panel regression model. The results showed that the value of road transport logistics efficiency in the east was the largest, then was the central region, the western region was the least from 2010 to 2015, and existed significant differences between provinces inside each area. Through the hierarchical clustering analysis, the article found that, Anhui, Hebei and Inner Mon- golia's road transport logistics efficiency were three highest regions in their respective area. The level of regional economic development, the informationization level, degree of logistics marketization, road transport logistics resource utilization played important influence on China's road transport logistics efficiency. Especially, it has great important significance to promote the road transport logistics efficiency by improving the utility rate of trucks.
出处 《数理统计与管理》 CSSCI 北大核心 2018年第2期318-328,共11页 Journal of Applied Statistics and Management
基金 国家自然科学基金项目(71363046) 国家社会科学基金项目(16BYJ063)资助
关键词 公路物流效率 超效率DEA TOPSIS Tobit面板回归模型 road transport logistics efficiency, super efficiency DEA, TOPSIS, Tobit panel regressionmodel
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