Affiliations: [a] School of Economics and Management, Dalian Jiaotong University, Dalian, Liaoning, China | [b] Research Institute of the Belt and Road, Dalian Jiaotong University, Dalian, Liaoning, China | [c] School of Traffic and Transportation Engineering, Dalian Jiaotong University, Dalian, Liaoning, China
Correspondence:
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Corresponding author: Haoyue Wang, School of Economics and Management, Dalian Jiaotong University, Dalian, Liaoning, China. E-mai: 1315481013@qq.com.
Abstract: China is a large road transport country as its logistics costs account for 18% of GDP. In order to further reduce logistics costs, it is necessary to optimize the energy-saving and efficient comprehensive transportation structure in China. In this paper, we analyze the characteristics of comprehensive transportation efficiency evaluation, then based on data envelopment analysis (DEA), we select a fixed return method to scale CCR model and a variable return BCC model to establish a comprehensive transportation efficiency evaluation model to evaluate comprehensive transportation efficiency. we select comprehensive transportation efficiency evaluation indicators as a combination of the input-output evaluation indicator system. Input indicators include energy consumption, number of employees, and mileage value, and output indicators are converted turnover; and finally We build a comprehensive transportation structure optimization model based on the linear programming method, the minimum comprehensive redundant input under the transportation efficiency evaluation as the objective function, and we want to take the unit GDP transportation energy consumption intensity drop by 15% in this way; Finally, we analyze the transportation structure of Liaoning Province in China, respectively calculate the input and output index datum and carry out DEA efficiency evaluation analysis. The results show that the energy intensity of road transportation is much higher than other two modes of transportation, and the efficiency of road transportation is relatively low.
