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Abstract：Logistics plays a basic supporting role in the growth of national economy. However, tail gas, noise, and traffic congestion caused by logistics have a negative impact on the environment. An effective evaluation mechanism for sustainable development of urban logistics industry is necessary. Data envelopment analysis (DEA) is a common tool for efficiency evaluation. But, DEA has a limited effect on resource allocation in advance because it is ex-post evaluation. It requires input-output indications and the output is after-the-fact data. This defect is particularly prominent in the evaluation of ecological logistics because pollution indicators belong to ex-post output data that threaten the human environment. First prediction and then evaluation is a possible idea. In addition, DEA efficiency ranking does not have a good discrimination due to its coarse granularity. To solve the issues, combining DEA with the Bayes method, we propose an efficiency evaluation model without after-the-fact data, where an efficiency level is predicted and an evaluation value is calculated according to different investment combinations. Then, it is applied to logistics industries of Jiangsu province in China. The results show that our DEA-Bayes method has good discrimination and is easy to operate; a city with geographical advantage and environmental awareness generally gets a higher efficiency score. So the method can help decision makers to allocate resources rationally and further promote the coordinated development of logistics industry.
1. Introduction
With increasingly serious energy and environment issues in recent years, sustainable development becomes the common development goal of all countries in the world. As logistics industry plays an increasingly important role in economic development [1], it is necessary to establish an effective evaluation system for measuring sustainable development ability for urban logistics. Logistics is a complex system, and data envelopment analysis (DEA) is an effective evaluation method for a multi-input and multioutput complex system. But, DEA is an ex-post analysis; it is difficult really to give some advice in advance before decisions. In China, e-commerce has brought tremendous opportunities for logistics industry. But it is the common phenomenon of sacrificing the environment for rapid economic growth. Some works [2–8] focus on socioeconomic contributions and ignore environmental effects. In fact, they are both the key contents of sustainable development. Therefore, besides the socioeconomic contribution indications, we choose dioxide emissions produced by logistics as evaluation indications from the perspective of low carbon economy. Since the Bayes classifier has the advantages of stable classification and simple implementation, we combine DEA with the Bayes method to establish an evaluation model without after-the-fact data, where the Bayes method is used to predict the DEA classification. The model can provide some proposals on resource allocation for logistics industry in advance, particularly from the perspective of sustainable development. Meanwhile, it is easy to operate. The remainder of this paper is organized as follows. Section 2 reports an in-depth literature survey that focuses on efficiency evaluation of logistic industry. Section 3 illustrates the approach to calculate the efficiency value, and an efficiency ranking algorithm is presented. Section 4 shows the application process of the approach and discusses some results of case study for urban logistics industries of Jiangsu province in China. Finally, Section 5 presents some concluding remarks....... 
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