Inequities blocking the path to circular economies: A bio-inspired network-based approach for assessing the sustainability of the global trade of waste metals
Why this work is in the frame
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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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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 | 0.002 | 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 | 0.000 |
| 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