Growing environmental concerns and regulatory pressures are prompting firms to re-examine packaging design to advance sustainability. Focusing on eco-designed retail packaging in the new-style milk tea industry, this study investigates how specific attributes of eco-designed retail packaging influence consumers’ purchase intention. Data were collected from 425 university students in Wuhan. We measured eco-designed retail packaging (ECRP) with a six-dimension scale (functional, aesthetic, eco-materials, eco-information, eco-production, and innovation) and tested the mediating role of green perceived value (GPV) using structural equation modeling (SEM). Results show differentiated effects of ECRP dimensions on GPV and purchase intention. Functional design and clear eco-information increase both GPV and purchase intention, whereas using eco-materials while directly raising purchase intention reduces GPV. Aesthetics and innovation mainly operate through direct enhancement of purchase intention rather than via GPV. GPV mediates part of the effects of functional attributes, eco-materials, and eco-information on purchase intention. The findings imply that optimizing functionality, information clarity, and material choices in eco-designed retail packaging can simultaneously elevate GPV and purchase intention. As green packaging becomes an industry imperative, this study provides theoretical and practical guidance for sustainable packaging innovation and green industry development.

Design for remanufacturing (DfRem) is a green design mode that ensures good remanufacturability at the end-of-life (EOL) of the product. However, the diversity of service environments and operating modes makes it difficult to generate accurate DfRem solutions for the smooth implementation of remanufacturing. Moreover, the historical remanufacturing process contains a great deal of information conducive to DfRem. It will greatly enhance the efficiency and accuracy of remanufacturing design by feeding effective remanufacturing information back into the product design process. Unfortunately, there is a lack of direct correlation between them, which prevents remanufacturing information from effectively guiding DfRem. To improve the accuracy of DfRem solutions and the utilization rate of remanufacturing information, an intelligent design method for product remanufacturing based on remanufacturing information reuse is proposed. Firstly, rough set theory (RST) is used to identify key remanufacturability demand, and the quality function development (QFD) is used to establish a relationship between remanufacturability demand and engineering characteristics, which can accurately obtain the design objectives. Then, the correlation between remanufacturability demand, remanufacturing information, and DfRem parameters is analyzed, and the ontology technology is applied to construct the DfRem knowledge by ingratiating remanufacturing information. In addition, case-based reasoning (CBR) is applied to search for design cases from DfRem knowledge that best match the design objectives, and gray relational analysis (GRA) is used to calculate the similarity between design knowledge. Finally, the feasibility of the method is verified by taking an ordinary lathe as an example. This method has been implemented as a DfRem interface application using Visual Studio 2022 and Microsoft SQL Server 2022, and the research results indicate that this design method can accurately generate a reasonable DfRem scheme.