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机构地区:[1]长安大学经济与管理学院,中国陕西西安710064 [2]江苏省交通运输与安全保障重点实验室,中国江苏淮安223003 [3]长安大学电子与控制工程学院,中国陕西西安710064
出 处:《经济地理》2015年第12期43-49,共7页Economic Geography
基 金:高等学校博士学科点专项科研基金(20130205110001);教育部人文社会科学基金项目(11YJA630155);陕西省科技工业攻关项目(2015GY033);中央高校基本业务费项目(2013G6231001;2014G3322008);江苏省交通运输与安全保障重点建设实验室开放基金资助(TTS2015-04)
摘 要:从投入产出角度,在采用主成分分析提取投入产出主成分的基础上,运用超效率三阶段DEA模型,修正了环境变量与随机因素的影响,对2013年中国各省域的综合运输效率进行测算,最后采用空间自相关检验和空间散点图分布分析了我国综合运输效率的空间集群特征。结果表明:1剔除外部环境变量和随机因素等影响后,我国总体综合运输效率有所下降(调整前0.87→调整后0.74),呈现东部高、中部次之、西部效率低的空间格局;2调整前后,我国均有10省域处于运输效率前沿面,调整前后效率最优的省域分别为广东(调整前4.34→调整后1)、天津(调整前0.72→调整后1.15),效率最低的省份为西藏(调整前0.06→调整后0.05);3我国各省域综合运输效率存在空间正相关性和空间集聚效应,主要表现为低低聚集,高效率地区聚集效应不显著。From the perspective of input and output, used principal component analysis to extract input and output index, on this basis we adopted the three-stage super efficiency DEA model which eliminated the effect of environmental and random factors to measure provincial integrated transport efficiency in china of 2013, finally used the spatial autocorrelation test and the scatterplot distribution analyzed the spatial cluster of the integrated transport efficiency. Results show that: (1) After eliminating the effect of external environment variables and random factors, China's overall integrated transport efficiency decreased(before adjustment 0.87→after adjustment 0.74), and presents a spatial pattern with high efficiency in eastern, mid in central regions and low in western areas; (2)No matter before or after the adjustment, our country both has 10 provinces which are in the transport efficiency frontier, before and after the adjustment, the optimal efficiency of the province respectively is Guangdong (before adjustment 4.34---.after adjustment 1) and Tianjin (before adjustment 0.72→after adjustment 1.15), the lowest efficiency of the province is Tibet(before adjustment 0.06--~after adjustment 0.05); (3) The provincial integrated transport efficiency in china has spatial correlation and spatial agglomeration effect, mainly is low-low aggregate, high efficiency areas gathered is not significant.
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