Many original equipment manufacturers (OEMs) face the choice of whether to license an independent remanufacturer (IR) to remanufacture their used products. In this paper, we develop closed-loop supply chain models with licensed and unlicensed remanufacturing operations to analyze the competition and cooperation between an OEM and an IR. The OEM sells new products and collects used products through trade-ins, while the IR intercepts the OEM's cores to produce remanufactured products and sell them in the same market. We derive optimal decisions for each of the two types of firms in licensed and unlicensed remanufacturing scenarios and identify conditions under which the OEM and the IR would be most likely to cooperate with each other in implementing remanufacturing. The results show although it is beneficial for an OEM to license an IR to remanufacture its cores, it is not always necessary for an IR to accept OEM's authorization. Moreover, we contrast the result for licensed remanufacturing scenario in the decentralized system with that in the centrally coordinated system to quantify potential inefficiency resulting from decentralization of decision making.

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In order to address the lack of collaborative decision and failure to notice the emergency and fairness of relief after disasters have occurred, a collaborative decision-making system for emergency relief materials dispatching is established. According to the forecast of the demand for postdisaster relief materials, the entropy weight-TOPSIS method is applied to measure the urgency of the disaster area; then, a “Hub-and-Spoke” dispatching network is constructed. In this paper, a multiobjective collaborative relief material dispatching model is built, which has great performance in terms of minimal distribution cost and maximal fairness, and the objective of fairness requires minimizing the penalty cost caused by unsatisfied demands. Based on the urgency of demand points, the simulated annealing algorithm is designed to solve the Pareto disaggregation of multiobjective optimization model. The performance of the model is verified with the case of Wenchuan Earthquake. The results indicate that if the fair distribution of supplies is emphasized, it will increase the number of rescue vehicles and the number of distribution batches. On the other hand, a variety of relief material dispatching plans can be provided based on calculation of the Pareto front for policy-makers.

In order to clarify the influencing factors of fresh produce e-commerce consumer satisfaction in the context of COVID-19, a hybrid approach based on LDA-SEM-XGBoost was proposed by studying online reviews. Firstly, topic elements were extracted through the LDA topic model, PLS-SEM was established to explore the paths between variables, and XGBoost models were applied to rank the importance of each topic variable based on satisfaction. The results showed that epidemic factors had a significant impact on logistics factors, product factors, and platform factors, with the epidemic factors having the greatest impact on logistics factors. Logistics factors, product factors, platform factors, and epidemic factors had a significant impact on consumer satisfaction, with logistics factors having the greatest impact on satisfaction. The topic variables affecting fresh produce e-commerce consumer satisfaction were, in order: logistics time, shipping speed, product quality, delivery speed, after-sales strategy, logistics packaging, product price, the impact of COVID-19, marketing strategy, and product brand. Based on these findings, recommendations are made for the sustainable production and marketing of fresh produce.

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In order to achieve net zero emissions, the global transportation sector needs to reduce emissions by 90% from 2020 to 2050, and road freight has a significant potential to reduce emissions. In this context, emission reduction paths should be explored for road freight over the fuel life cycle. Based on panel data from 2015 to 2020 in China, China's version of the GREET model was established to evaluate the impact of the crude oil mix, electricity mix, and vehicle technology on China's reduction in road freight emissions. The results show that the import share of China's crude oil has increased from 2015 to 2020, resulting in an increase in the greenhouse gas (GHG) emission intensity of ICETs in the well-to-tank (WTT) stage by 7.3% in 2020 compared with 2015. Second, the share of China's coal-fired electricity in the electricity mix decreased from 2015 to 2020, reducing the GHG emission intensity of battery electric trucks (BETs), which is approximately 6.5% lower in 2020 than in 2015. Third, different vehicle classes and types of BETs and fuel cell electric trucks (FCETs) have different emission reduction effects, and their potential for energy-saving and emission reduction at various stages of the fuel lifecycle are different. In addition, in a comparative study of the vehicle technology, the results show that: (1) for medium-duty truck (MDT) and heavy-duty truck (HDT), FCETs have lower GHG emission intensity than BETs, and replacing diesel-ICETs can significantly reduce GHG emissions from road freight; (2) for light-duty truck (LDT), BETs and FCETs have the highest GHG emission reduction potential; thus, improving technologies such as electricity generation, hydrogen fuel production, hydrogen fuel storage, and transportation will help to improve the emission reduction capabilities of BETs and FCETs. Therefore, policymakers should develop emission standards for road freight based on vehicle class, type, and technology.

The low efficiency of the closed-loop supply chain in waste tire recycling has hindered the green development of China’s automobile industry. Additionally, the government subsidy decision has a huge influence on green development. This study focuses on a closed-loop supply chain system that consists of five members, namely, manufacturer, retailer, Internet recycling platform, and government. It aims to investigate the effect of the government’s subsidy mechanism on the decision-making process of recycling units, as well as to reveal the optimal strategies under different conditions. Under the coexistence of the retailer and the Internet recycling platform recycling programs implemented simultaneously by themselves, a two-stage Stackelberg game model is developed to explore the optimal government subsidy decision and the optimal pricing decision of manufacturer, retailer, and network platform in the closed-loop supply chain. At the same time, this paper investigates the effects of government subsidies on social welfare and the profits of supply chain members under different scenarios and then verifies the optimal government subsidy decision with MATLAB software through numerical examples and sensitivity analysis. The results show that the government subsidy coefficient is positively correlated with social welfare under four subsidy scenarios. To maximize the economic profit and social welfare of the members of the closed-loop supply chain, the government should appropriately select different subsidy objects within the range of different subsidy coefficients. When the subsidy coefficient γ ∈ [0, 15] and the government chooses consumer as the subsidy object, the social welfare will be maximized when γ > 15 and the government chooses Internet recycling platform as the subsidy object. It is recommended that the government directly subsidizes the Internet recycling platform. However, in order to maintain the manufacturer’s core position in the closed-loop supply chain, the subsidy coefficient for the Internet recycling platform should not exceed the critical value of 18. These results provide managerial insights for the government, manufacturer, and the third party to make decisions in the field of waste tire recycling. This paper presents the different subsidy conditions under which the government should appropriately select different subsidy objects. It also provides a theoretical and practical basis for improving the recycling efficiency of waste tires.

In order to achieve net zero emissions, the global transportation sector needs to reduce emissions by 90% from 2020 to 2050, and road freight has a significant potential to reduce emissions. In this context, emission reduction paths should be explored for road freight over the fuel life cycle. Based on panel data from 2015 to 2020 in China, China's version of the GREET model was established to evaluate the impact of the crude oil mix, electricity mix, and vehicle technology on China's reduction in road freight emissions. The results show that the import share of China's crude oil has increased from 2015 to 2020, resulting in an increase in the greenhouse gas (GHG) emission intensity of ICETs in the well-to-tank (WTT) stage by 7.3% in 2020 compared with 2015. Second, the share of China's coal-fired electricity in the electricity mix decreased from 2015 to 2020, reducing the GHG emission intensity of battery electric trucks (BETs), which is approximately 6.5% lower in 2020 than in 2015. Third, different vehicle classes and types of BETs and fuel cell electric trucks (FCETs) have different emission reduction effects, and their potential for energy-saving and emission reduction at various stages of the fuel lifecycle are different. In addition, in a comparative study of the vehicle technology, the results show that: (1) for medium-duty truck (MDT) and heavy-duty truck (HDT), FCETs have lower GHG emission intensity than BETs, and replacing diesel-ICETs can significantly reduce GHG emissions from road freight; (2) for light-duty truck (LDT), BETs and FCETs have the highest GHG emission reduction potential; thus, improving technologies such as electricity generation, hydrogen fuel production, hydrogen fuel storage, and transportation will help to improve the emission reduction capabilities of BETs and FCETs. Therefore, policymakers should develop emission standards for road freight based on vehicle class, type, and technology.