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This paper aims to develop an integrated Quality–Cost–Environmental (QCE) indicator for the selection of photovoltaic panels (PVps) offered to customers, considering the following criteria: (i) quality satisfaction; (ii) cost-effectiveness; and (iii) environmental impact throughout the life cycle. The proposed QCE indicator was developed within a framework that incorporated an FAHP (Fuzzy Analytic Hierarchy Process), cost-effectiveness analysis (CEA), and life cycle assessment (LCA). The model test confirmed its effectiveness in choosing a PVp which combines environmental friendliness throughout its entire life cycle with satisfactory quality and a reasonable purchase price for customers. The proposed model can be utilized by individuals, businesses, and public entities for the selection of high-quality, cost-efficient, and environmentally friendly PVps—thereby promoting sustainable development. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative model designed to predict the optimal product prototype within the framework of sustainable development. The model takes into account both quality and environmental impact throughout the entire life cycle of the product, with a specific focus on light electric vehicles. By incorporating customer expectations using the Weighted Sum Model, the model assesses vehicle quality based on the importance of various criteria. Employing life cycle evaluations guided by the Pareto-Lorenz rule, environmental impact for vehicle prototypes is modeled. The resulting Quality-Environmental Indicator of LCA (QLCA) facilitates a qualitative environmental analysis, aiding in the selection of prototypes that align with customer expectations. The model proves effective in guiding decisions related to product development, providing a comprehensive approach to evaluating product value. Notable contributions to the scientific community include the introduction of the QLCA model, which integrates quality and environmental assessments, and underscores the significance of open innovation, sustainable development, and sensitivity analysis for organizational adaptation. Future research directions involve adapting the model to larger customer samples, exploring production costs, and assessing its applicability across diverse industries and contexts to enhance its universality and practical implications. [ABSTRACT FROM AUTHOR]