Background: Industry 4.0 (I4.0) technologies have transformed supply chain (SC) logistics and production. However, their environmental impact, particularly on CO₂ emissions and carbon footprints, remains underexplored. This study examines the impact of I4.0 tools on SCs sustainability, focusing on reducing carbon footprints. Methods: A bibliometric analysis was conducted in October 2023 to quantitatively evaluate the scientific literature, examining publication characteristics to assess current research and forecast future trends. The Scopus database was utilized with specific filters to identify studies on the impact of I4.0 technologies in SC domains on sustainability, focusing on CO₂ emissions and carbon footprint reduction. VOSviewer software version 1.6.15 was used to analyze selected papers, revealing key keyword clusters and relationships. Results: Five clusters were identified, offering insights for supply chain managers and highlighting links between I4.0 and CO₂ reduction in supply chains: “LCA towards zero carbon”, “Supply chain carbon footprint”, “Risk and decarbonization analysis”, “Industry 4.0 and stochastic models for sustainability”, and “Biodiversity and environmental impact”. Key findings emphasize the strong connection between LCA, carbon footprint analysis, emission control, and the role of I4.0 technologies like blockchain and IoT in reducing emissions. Conclusions: This study highlights the environmental benefits of I4.0 in SC management, supporting global decarbonization goals.

Pursuing sustain ability, the traditional “take- make-use-dispose” economy model is moving towards a circular approach by introducing the reverse flow for recovery of End- of-Life (EOL) or End-of-Use (EOU) products. This circularity of parts and information between industry and market has generated the Closed-Loop Supply Chain (CLSC). Circularity enhances reuse/recycle of parts and materials and products. However, it increases complexity in the supply as it inputs uncertainty sources regarding the returning and outgoing state of products, the required recovery processes, the expected consumer behavior and the unstable market trends. In this paper, an investigation of the uncertainties affecting CLSC and remanufacturing was selected. Authors aim at mapping, and reporting in connectivity, the sources of uncertainty affecting remanufacturing systems in order to identify the connections and causality relations between them. A holonic representation of the CLSC was elaborated for potential interdependencies assessment. Thus, a map of the uncertainties is then designed in the form of a labyrinth connecting the uncertainty drivers with the corresponding entities/holons: markEt, Consumer, Management, pRoduct and Process. The map is configured as a decision-support tool for application in remanufacturing context. The map allows to control the mutual effects between uncertainty sources in remanufacturing business environments.

The rise of sustainability in the luxury sector presents a paradox: for decades, luxury has been seen as antithetical to sustainability, yet younger socially conscious consumers increasingly demand eco-friendly and ethical practices from luxury brands. This study investigates the factors influencing consumer adoption of sustainable luxury products, with a focus on generational differences. We developed a framework identifying five categories of drivers of sustainable purchase intent and generated - using AI techniques - a simulated survey dataset of 1000 luxury consumers spanning four generations (Boomers, Gen X, Millennials, Gen Z). Using this dataset, we trained an ensemble of machine learning models - Random Forest and Neural Network - to test the possibility to predict purchase propensity and identify key drivers of sustainable luxury adoption. Feature importance analysis revealed that ethical sourcing was the top driver of purchase intent, followed by environmental impact and product quality. According to consumer segmentation, younger generations exhibited a higher propensity to adopt sustainable luxury, confirming a generational shift toward stronger sustainability values. Consumer adoption of sustainable luxury hinges on brands delivering superior quality and prestige alongside authentic sustainability commitments. The adaptive AI-driven profiling and forecasting can help tailor strategies to consumer segments, guiding the luxury sector toward more sustainable consumption. Across the identification of luxury value constructs and dimensions, we proposed strategies tailored on specific customer segments to predict luxury consumers behaviors and, eventually, to promote acceptance of sustainable practices.