In China, the transportation sector contributes about 18% of the total carbon emissions. This research contributes to measuring the energy and carbon emission efficiency (ECEE) of regional transportation systems (RTS) in China considering uncertain carbon emissions. A radial chance-constrained data envelopment analysis (DEA) model is developed to estimate the overall efficiency, and a nonradial chance-constrained DEA model is presented to evaluate the pure energy efficiency (PEE) and the pure carbon emission efficiency (PCEE). We prove that the proposed chance-constrained DEA models can effectively address the uncertain carbon emissions when measuring efficiency. We find that most of China’s RTS have low ECEE and the inefficiencies are mainly due to the lower gasoline utilization efficiency and the lower kerosene utilization efficiency. In addition, east China performs better than central China, and central China performs better than west China. In China, the unbalanced regional development of the ECEE in transportation corresponds with the unbalanced regional economic development. We provide some valuable suggestions based on the evaluation of the potential cuts in each kind of energy and the potential decreases in carbon emissions.

Pallet is a very important innovation in logistics industry. Pallets are so widely used that we can find them in nearly every logistical operation scenario. In order to manage pallets efficiently, researchers have developed several pallet management strategies (PMS). The most common and widely accepted PMS includes extensive management of pallets (EMP), transfer of pallet’s ownership (TPO), and pallet rent (PR). This paper addresses mainly on how to help pallet managers choose a certain kind of PMS from the perspective of supply chain cost. Firstly, cost models of three kinds of PMS are presented. Secondly, all parameters involved in the models are valued based on data that is collected from industry survey. The results show that the cost of PR is constantly lower than EMP, and also lower than TPO when the operation period is no more than 37 months. Finally, the effect of several important parameters on the cost is studied by sensitivity analysis. The selection strategies of PMS are proposed based on the results.

Pallets play an important role in logistics. It is well known that pallet rental is beneficial to both pallet users and society, and the pallet rental industry is very large nowadays. A non-oriented super-efficiency integer-valued DEA (data envelopment analysis) model for the performance evaluation of pallet rental companies is presented. The method differs from traditional models in that (1) it can be used to accurately estimate the performance of efficient pallet rental companies with integer-valued and real-valued variables; (2) it is an alternative approach to providing numerical super-efficiency scores for efficient pallet rental companies when input-oriented or output-oriented VRS (variable returns to scale) super-efficiency models are infeasible for them. A case study of nine pallet rental companies proves the advantages of the proposed model, and it shows that the efficiency gap among pallet rental companies is very large. Based on the results, some suggestions on the development of the pallet rental industry are proposed. The methodology and results would provide valuable contributions to the decision-making processes of both pallet rental companies and their customers.

Development of sharing economy has brought new opportunities to the development of green logistics. Pallet pooling is a typical type of sharing economy. It has been recently promoted in almost every country in the world. Researchers have reached a common agreement that establishing an effective pallet pool is of great importance to the pallets use efficiency. It is also believed that the implementation of pallet pool will substantially contribute to the economic and social development. Detailed analysis of pallet pooling such as benefit analysis, mode choose, pallet allocation model & algorithm, quality control, etc. are discussed in this paper. Based on literature review, several important problems are listed for future study. These problems are information management, supply chain management, rental pricing, pallets tracking and allocation, quality control, sustainable development (carbon emissions), and “internet plus pallet pooling”. Some suggestions on China’s pallet pooling development are proposed as well.

Pallets are the most common equipment for transporting and storing goods. More and more companies are willing to rent pallets. Pallet rental companies need to transport pallets from their pallet rental service stations to customers and take these pallets back when they are unloaded. Hence, managers should scientifically configure vehicles for their pallet rental service stations. The fleet size, which indicates the amount and types of vehicles, can significantly affect the efficiency and costs of empty pallet allocation. Therefore, an optimization model for fleet sizing and empty pallet allocation is proposed using the methods of mixed-integer programming and stochastic programming. The objectives of this model are to maximize the profits of pallet rental companies and minimize carbon dioxide (CO₂) emissions from vehicles. A particle swarm optimization algorithm with inertia weight (IPSO) is developed to solve the proposed model because IPSO can avoid becoming trapped in local optima and is able to find a globally optimal solution within a reasonable number of iterations. A numerical example proves the effectiveness of the proposed model and IPSO. The results of numerical tests show that the amount of CO₂ emissions from vehicles can affect the decision on fleet sizing and empty pallet allocation. However, if the price, rental fees, or idle costs of the vehicles with low CO₂ emissions are too high, managers would not choose them.

With the ever-increasing focus on sustainable development, recycling waste and renewable use of waste products has earned immense consideration from academics and policy makers. The serious pollution, complex types, and strong infectivity of medical waste have brought serious challenges to management. Although several researchers have addressed the issue by optimizing medical waste management networks and systems, there is still a significant gap in systematically evaluating the efficiency of medical waste recycling systems. Therefore, this paper proposes a two-stage data envelopment analysis (DEA) approach that combines the virtual frontier and the global bounded adjustment measure (BAM-VF-G), considering both undesirable inputs and outputs. In the first stage, the BAM-G model is used to evaluate the efficiency of medical waste recycling systems, and the BAM-VF-G model is used to further rank super-efficient medical waste recycling systems. In the second stage, two types of efficiency decomposition models are proposed. The first type of models decompose unified efficiency into production efficiency (PE) and environment efficiency (EE). Depending upon the system structure, the second type of models decompose unified efficiency into the efficiency of the medical waste collection and transport subsystem (MWCS) and the efficiency of the medical waste treatment subsystem (MWTS). The novel approach is used to measure the efficiency of the medical waste recycling systems in China’s new first-tier cities, and we find that (1) Foshan ranks the highest in efficiency, followed by Tianjin and Qingdao, with efficiency values of 0.386, 0.180, and 0.130, respectively; (2) the EE lacks resilience and fluctuated the most from 2017 to 2022; and (3) the efficiency of MWCSs has always been lower than that of MWTSs and is a critical factor inhibiting the overall efficiency of medical waste recycling systems.