An extended cross-efficiency evaluation method based on information entropy with an application to the urban logistics industry

JOURNAL OF MODELLING IN MANAGEMENT

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摘要
Purpose Green economy and economic development with high quality have set higher requirements for the development of the urban logistics industry. It can grasp the recent development level of the urban logistics industry by measuring its environmental efficiency to guide its future development direction. The purpose of this study is to improve the environmental efficiency and development level of the urban logistics industry by using a reasonable evaluation method. Design/methodology/approach This paper uses information entropy to directly aggregate index weights from different models to acquire comprehensive index weights (CIWs) for calculating peer-evaluation efficiency. Then, we weight self and peer-efficiencies to obtain final efficiency. The environmental efficiencies of the urban logistics industry in Anhui Province in 2019 are obtained according to the above method. Findings Several findings are summarized below. The logistics industry in Anhui is in urgent need of improving environmental efficiency. The environmental efficiency of the logistics industry in North Anhui is the highest one, showing that the logistics industry in North Anhui has achieved a relative balance between economic development and environmental protection. Their final cross-efficiency values based on the CIWs are smaller than those based on the comprehensive efficiency. And the environmental efficiency of almost all urban logistics industries is lower than its economic efficiency. The findings show that the proposed method is feasible and more reasonable. More economic implications and suggestions are proposed. Originality/value This paper proposes an extended cross-efficiency evaluation method based on information entropy to measure the environmental efficiency of the urban logistics industry, effectively avoiding the overestimation of efficiency results.
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关键词
Cross-efficiency, Undesirable outputs, Information entropy, Urban logistics industry, DEA, Efficiency analysis
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