Toward a Sustainable Digital Footprint in Industry 4.0: Predicting Green AI Adoption Among Gen Z Manufacturing Technicians

The digital carbon footprint denotes the environmental impact generated by digital technologies throughout their lifecycle. Industry 4.0 manufacturing environments rely extensively on data processing, information storage, and artificial intelligence, thereby increasing energy demand and associated carbon emissions. These conditions have intensified interest in Green AI, particularly in applications such as predictive maintenance and collaborative human–machine systems. This research investigates determinants of behavioural intention to adopt Green AI through an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model tailored to Industry 4.0 and sustainability contexts. The framework incorporates performance expectancy, Industry 4.0 eligibility, technology influence, digital manufacturing competence, sustainability conditions, Green AI recognition, and green manufacturing concern. Data were obtained from an anonymous survey of 1003 Generation Z students enrolled in technical disciplines and preparing for manufacturing-oriented careers. Relationships among constructs were analysed using partial least squares structural equation modelling (PLS-SEM). The model demonstrates strong explanatory and predictive capability. Adoption intention is primarily associated with performance expectancy, Industry 4.0 eligibility, and digital manufacturing competence, while sustainability-oriented perceptions play a contextual rather than direct behavioural role. The study offers a domain-specific empirical extension of UTAUT within pre-workforce technical education rather than proposing a new acceptance theory. The findings reflect intention formation prior to labour-market entry and require validation in operational manufacturing settings before broader generalisation.