The development of rural e-commerce has rapidly driven the development of rural logistics in China. Improving the service quality of the last mile delivery is an important measure to promote the sustainable development of rural e-commerce logistics. However, such work is challenging because the current rural last mile delivery is inefficient and unsustainable and is influenced by a set of interacting factors. It is necessary to explore the relationships among the sustainability influencing factors of rural last mile delivery. A total of 15 sustainability influencing factors are selected. The improved fuzzy analytic hierarchy process (FAHP) is used to assign the weights of the factors and then the interpretative structural model (ISM) is used to determine the hierarchical structure of each factor. The driving force-dependency quadrant graph is constructed by cross-impact matrix multiplication (MICMAC). The research results show that four factors, including “convenience of returning goods”, “integrity of goods”, “advance reservation of goods pickup”, and “delivery costs”, are the most basic factors affecting the sustainability of rural last mile delivery and are the deepest and most indispensable factors. This research provides valuable information for decision makers to develop proactive strategies and reinforcement policies to improve the service quality of rural last mile delivery, which could promote the sustainable development of rural logistics.

Carbon emissions from the logistics industry have been rising year after year. Correct handling of the relationship between economic development and environmental protection is of great significance to the implementation of green logistics, which is an important component of China’s strategy for strong transportation. This paper focuses on the evaluation of the carbon emissions efficiency of logistics industry from a new strong transportation strategy perspective. A super-efficiency slack-based measurement (Super-SBM) model and Malmquist index are combined to evaluate the static and dynamic carbon emissions efficiency of the logistics industry. The results indicate that compared with the SBM model, the Super-SBM model can more effectively measure the carbon emissions efficiency of the logistics industry. Pilot regions for the strong transportation strategy were divided into two categories, namely regions with slow carbon emission growth rates but high efficiency, and regions with high carbon emission growth rates but low efficiency. Some policy recommendations from the strong transportation strategy perspective were proposed to improve the carbon emissions efficiency of the logistics industry, especially for the second category of pilot regions. This study is expected to provide a basis for decision-making for efficient emissions reduction measures and policies, and to encourage the pilot regions to take the lead in achieving the goal of China’s strategy for transportation.

The development of the online to offline business has accelerated the growth of the online food ordering market in China. The widespread use of disposable takeaway containers has resulted in a large amount of waste, which seriously affects the ecological environment. This paper studied the collection modes of reusable takeaway containers and the preferences of consumers and merchants. First, after two rounds of discussion and revision, four takeaway container collection modes were designed. Second, based on the survey results of consumers and merchants, a binary logistic regression model was applied to analyze the preferences of consumers and merchants. The results showed that the consumers’ delivery requirements and the current disposal of takeaway containers had a significant impact on consumers’ preferences. Consumers were more concerned about the hygienic status of the containers, food health and safety, while the merchants were more concerned about the increased costs. The promotion of collection modes requires the special consideration of the locations of dishwashing facilities and increased costs. Finally, according to the preferences and concerns of consumers and merchants, several suggestions on promoting the collection mode, such as the use of different promotion strategies for different people, the short distance of dishwashing facilities, reward systems, and food safeguard measures were proposed. This research provides guidance for decision making regarding the sustainable consumption and the promotion of reusable takeaway containers, which will contribute to resource conservation, ecological environmental improvement and sustainability.

The advent of e-commerce has led to a rapid acceleration of rural logistics development in China. To enhance green and sustainable development of rural logistics, it is necessary to improve the service quality of the rural last mile delivery and analyze service quality evaluation indexes. An integrated methodology combing fuzzy comprehensive evaluation (FCE) and the interpretative structural model (ISM) is presented in the current paper to reveal the relationship between the service quality evaluation indexes of the rural last mile delivery. A total of 18 logistics service quality evaluation indexes in five dimensions are selected. The FCE is used to measure the service quality of rural delivery in an empirical research area, and the weight of each evaluation index is assigned by regression analysis. The ISM is adopted to judge the hierarchical structure of indexes, and a five-layer hierarchy is obtained. The results show that it is necessary to first focus on improving the evaluation indexes of accuracy of goods arrival and timely customer service response. In the case of Shunfeng Express, the company needs to additionally improve the timeliness and rationality of damaged or lost processing goods. Some countermeasures and suggestions are put forward. The proposed integrated method helps to reveal the key service quality evaluation indexes and the areas needing improvement. The use of regression analysis within the FCE method allows the estimation of weights in a relatively objective way. This research provides theoretical support for improving the service quality and customers’ satisfaction of the rural last mile delivery, and enhancing the green and sustainable development of rural logistics.

The powertrain characteristics of battery electric logistics vehicles (BELVs) are extremely suited for the urban driving context and have a higher environmental protection potential for sustainable development, which can provide solutions for urban logistics decarbonization. The BELV is an important part of the Chinese national new-energy vehicle program, and the market penetration of BELVs has greatly improved. However, BELVs still have many technical shortcomings, which seriously affect their widespread acceptance. Therefore, it is necessary to reveal the technological characteristics and development direction of BELVs based on a large amount of marketing and technological data. This research reviewed the Chinese incentive policies and industrialization progress of BELVs. An evaluation system comprising 8 technological indicators for evaluating BELV technological progress was proposed. The distribution characteristics of the curb mass, vehicle volume, all-electric range, top speed, driving power, energy efficiency, traction battery and motor technology were analyzed for 2017 and 2019 markets’ models. The vehicle total cost, vehicle performance and reliability of BELVs are not advantageous yet. However, all the performance parameters are improving year by year, constantly meeting logistics demands. The subsidies are needed for longer for the market to mature. Finally, several suggestions for the technical development direction of BELVs were proposed. All these results can be used to improve the vehicle performance, reliability and market penetration of BELVs.

This study employed big data analytics to investigate the impacts of land use and network features on passenger flow distribution at urban rail stations. The aim was to provide decision support for differentiated operational management strategies for various types of rail stations, thereby achieving refined operation and the sustainable development of urban rail systems. First, this study compared clustering results using different similarity measurement functions within the K-means algorithm framework, selecting the optimal similarity measurement function to construct clustering models. Second, factors influencing passenger flow distribution were selected from land use and network features, forming a feature set that when combined with clustering model results, served as input for the XGBoost model to analyze the relationship between various features and the station passenger flow distribution. The case study showed that (1) the clustering results using a dynamic time-warping distance as the similarity measurement function was optimal; (2) the results of the XGBoost model highlighted commercial services and closeness centrality as the most important factors that affected rail station passenger flow distribution; (3) urban rail stations in Nanjing could be categorized into four types: “strong traffic attraction stations”, “balanced traffic attraction stations”, “suburban strong traffic occurrence stations”, and “distant suburban strong traffic occurrence stations”. Differentiated operational and management strategies were developed for these station types. This paper offers a novel approach for enhancing the operational management of urban rail transit, which not only boosts operational efficiency but also aligns with the goals of sustainable development by promoting resource-efficient transportation solutions.