Inequities blocking the path to circular economies: A bio-inspired network-based approach for assessing the sustainability of the global trade of waste metals
Bibliographic record
Abstract
• We assess the network properties of the global trade of waste metals. • We study the distribution of material and monetary flows across trading countries. • The FCI captures material and monetary trade circulation due to network structure. • Network methods can support the study of the equity dimension in trade ecosystems. • Network methods can inform circular economy policies about trading patterns. Considering the importance of waste metals for the transition to circular economies, this study follows a bio-inspired approach to evaluate their material and monetary global trade patterns for sustainability and equity. Between 2000 and 2022, the global trade grew by 5 % in trading countries, by 37 % in trade links, by 71 % in material flows, and by 569 % in economic flows. Driven by indirect effects, the average circulation of material and monetary flows ranged between 21.8–34.9 % depending on the demand or supply perspective but showed a declining trend. Due to homogenization, high network redundancy, and low network efficiency the trade remained robust yet outside the "window of vitality" characterizing natural ecosystems. A few, mostly high-income countries dominated the market, consolidating imports of high-value metal waste mostly from low- and middle-income exporters. Policies should address circularity and trade inequities, accounting for environmental and social ramifications throughout the lifecycle of products and materials.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".