Artificial intelligence for eco-design: a systematic review

Eco-design integrates environmental considerations into product design, recognizing 80% of sustainability impacts determined at the design phase. Artificial intelligence (AI) provides powerful tools for optimizing designs, assessing environmental impacts, and supporting circular economy, making eco-design proactive. Despite AI use in sustainable product development, no review has synthesized these efforts. Therefore, we conducted a systematic review using the PRISMA method, covering 38 studies from 2014 to 2024 applied AI in eco-design. This is the first review to consider all life-cycle stages with eco-design practices, integrating Ellen MacArthur circularity principles, United Nations sustainable development goals (SDGs), life cycle assessment (LCA), industrial applications, and AI methods. Our findings reveal: 1- an imbalanced focus across product life-cycle stages, with most studies addressing design and end-of-life, while production, use-life, and distribution remain underexplored. 2- Common eco-design practices include recycling, energy reduction, and disassembly, with less focus on non-hazardous materials, waste minimization, and remanufacturing. 3- While neural networks and hybrid AI methods are commonly applied for material compatibility and emissions prediction, more advanced AI-based approaches such as generative AI and LLMs have yet to be used in design, LCA, and circularity analysis. 4- No study applies all four Ellen MacArthur Technosphere circular economy strategies. 5- Researchers rarely couple LCA with cradle-to-cradle assessments or embed their results in real-time design simulations. 6- Case studies mostly focus on electronics and household appliances, with limited application in automotive, aviation, maritime, and healthcare. 7- SDG consideration mainly centers on SDGs 12 and 13, with more attention needed for other SDGs.