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In light of a circular economy, to encourage core returns, the remanufacturer charges a deposit and refund it to the customer based on quality inspection of cores. Generally, two types of classification errors exist and interact with each other during the inspection process: either low-quality cores are sorted as remanufacturable, or high-quality cores are sorted as non-remanufacturable. The remanufacturer needs to choose refund policies and determine a reasonable deposit value, considering customers' potential responses. This paper firstly develops analytical solutions for these issues within a game theory framework. The effect of inspection information transparency is evaluated by comparing two settings: the information of inspection errors is available to customers or not. The study results show the advantage of inspection information transparency from the remanufacturer's perspective. The analysis indicates the importance of avoiding overestimating customers' payoff of products and the significance of inspection accuracy. The study also highlights that the salvage value of different cores significantly influences the remanufacturer's profits, and the improvement of inspection accuracy does not necessarily reduce the customer's return of low-quality cores. [ABSTRACT FROM AUTHOR]

Acquisition of cores is important for the success of remanufacturing business, but evaluating the value of cores is difficult due to many uncertain issues in the remanufacturing system. In this paper, we examine the value of cores using the real option valuation (ROV) approach, which well captures the production flexibility and represents different operations options in remanufacturing. First, in a system where the demand of remanufactured products is low, we focus on the uncertainty of price. We compare the cases where disposing cores occurs only at the expiration time and before that. The model also identifies under which circumstances disposing core early brings in more benefit. Furthermore, we study a remanufacturing system where demand and return are both uncertain. In this case, a spread option is used for evaluating the marginal value of obtaining additional cores. This approach also brings the advantage of linking the correlation between demand and return, which in fact is often exhibited in remanufacturing. The numerical experiments indicate that this correlation has an impact on collecting policies. This study illustrates the possibility of using ROV for core pricing modelling, and the study results also provide some managerial insights in managing returned cores. [ABSTRACT FROM AUTHOR]

With the growing demand for sustainable development, tobacco stalks, as a resource-rich and low-cost renewable resource, hold the potential for producing high-value chemicals and materials within a circular economy. Due to the complex and unique structure of tobacco stalk biomass, traditional methods are ineffective in its utilization, making the pretreatment of tobacco stalk lignocellulose a crucial step in obtaining high-value products. This paper reviews recent advancements in various pretreatment technologies for tobacco stalk lignocellulosic biomass, including hydrothermal, steam explosion, acid, alkaline, organic solvent, ionic liquid, and deep eutectic solvent pretreatment. It emphasizes the impact and efficiency of these pretreatment methods on the conversion of tobacco stalk biomass and discusses the advantages and disadvantages of each technique. Finally, the paper forecasts future research directions in the pretreatment of tobacco stalk lignocellulose, providing new insights and methods for enhancing its efficient utilization., Competing Interests: NH, XL, SW, JY, WW, BL, and TT were employed by China Tobacco Hubei Industrial Co., Ltd. NH, XL, SW, JY, WW, BL, and TT were employed by Hubei Xinye Reconstituted Tobacco Development Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hu, Liu, Wei, Yao, Wang, Liu, Tang, Jiang and Wang.)