Artificial intelligence for eco-design: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it