How recycling mitigates supply risks of critical raw materials: Extension of the geopolitical supply risk methodology applied to information and communication technologies in the European Union
Bibliographic record
Abstract
The Geopolitical Supply Risk method, originally developed by Gemechu et al. (2016) and subsequently extended by Helbig et al. (2016a) and Cimprich et al. (2017, 2018), is aimed at incorporating supply risk assessment of “critical raw materials” as a complement to environmental life cycle assessment (LCA) within life cycle sustainability assessment (LCSA). In this article, we further extend the method to consider the risk-mitigating potential of domestic recycling – thus advancing considerations of “circular economy” strategies for managing materials criticality. Our method captures two mechanisms through which domestic recycling can affect supply risk: a reduction in total imports (the “reduction effect”), and a potential redistribution of the import supply mix (the “redistribution effect”). We consider a range of outcomes from a best-case scenario (displacing imports from the riskiest trade partners) to a worst-case scenario (displacing imports from the least risky trade partners). Using our recently developed automated calculation tool, which significantly improves the practical applicability of the method by facilitating the otherwise burdensome computations required, we test and demonstrate our method on 13 raw materials used for information and communication technologies in the European Union. Thus, we test the notion that recycling mitigates supply risk. The reality is more complex. To maximize risk mitigation, recycling should ideally take place domestically, recycled material should be reinserted into the domestic economy, and the import supply mix should be considered, especially given that the redistribution effect sometimes exceeds the reduction effect.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".