The role of industrial actors in the circular economy for critical raw materials: a framework with case studies across a range of industries
Bibliographic record
Abstract
Abstract In this article, we explore concrete examples of circularity strategies for critical raw materials (CRMs) in commercial settings. We propose a company-level framework for systematically evaluating circularity strategies (e.g., material recycling, product reuse, and product or component lifetime extension) in specific applications of CRMs from the perspectives of specific industrial actors. This framework is applied in qualitative analyses—informed by relevant literature and expert consultation—of five case studies across a range of industries: (1) rhenium in high-pressure turbine components, (2) platinum group metals in industrial catalysts for chemical processing and oil refining, (3) rare earth permanent magnets in computer hard disk drives, (4) various CRMs in consumer electronics, and (5) helium in magnetic resonance imaging (MRI) machines. Drawing from these case studies, three broader observations can be made about company circularity strategies for CRMs. Firstly, there are multiple, partly competing motivations that influence the adoption of circularity strategies, including cost savings, supply security, and external stakeholder pressure. Secondly, business models and value-chain structure play a major role in the implementation of circularity strategies; business-to-business models appear to be more conducive to circularity than business-to-consumer models. Finally, it is important to distinguish between closed-loop circularity, in which material flows are contained within the “focal” actor’s system boundary, and open-loop circularity, in which material flows cross the system boundary, as the latter has limited potential for mitigating material criticality from the perspective of the focal actor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".