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Artificial Intelligence–Driven Life Cycle Sustainability Assessment of UV and UV-LED Printing Ink Technologies

Year: 2026
Type: article
Abstract: The move towards sustainable manufacturing in the printing industry has really ignited interest in energy-curable ink technologies, particularly ultraviolet (UV) and ultraviolet light-emitting diode (UV-LED) systems. While UV-LED curing is often praised as a greener alternative to traditional mercury-based UV curing, comprehensive and data-driven assessments of its sustainability are still quite limited. This study presents an artificial intelligence (AI)–driven life cycle assessment (LCA) framework aimed at systematically evaluating and improving the environmental performance of both UV and UV-LED printing ink technologies. The framework utilizes machine-learning techniques for various tasks, including life cycle inventory (LCI) data collection, filling in gaps, modelling surrogate processes, predicting emissions, and optimizing decisions across multiple criteria. A comparative sustainability assessment is included, highlighting aspects like energy use, global warming potential (GWP), volatile organic compound (VOC) emissions, ozone depletion potential, and end-of-life risks. Insights from peer-reviewed studies and industry reports indicate that UV-LED curing can reduce energy consumption by around 60–80% compared to traditional UV systems, leading to significant cuts in greenhouse gas emissions. The integration of AI allows for rapid scenario analysis, improved uncertainty management, and informed decision-making, even when data is incomplete or proprietary. The study also tackles limitations, data quality concerns, and future research paths, underscoring the promise of AI-assisted LCA as a strategic tool for advancing sustainability in the printing and packaging industries.
DOI: 10.5281/zenodo.21049009
Language: English
Open Access status: green
OpenAlex ID: W7166555148
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