Assessment of a critical mineral recycling network: A case study on nickel recovery from production waste in Korean eco‐industrial parks
Bibliographic record
Abstract
Abstract The shift in industrial paradigms toward achieving global carbon neutrality and strengthening national material security may initially appear unrelated; however, both domains share a crucial intermediary: critical minerals. Despite global initiatives aimed at securing critical minerals through established supply chains, persistent challenges have arisen owing to resource depletion, geopolitical instability, and intricate international dynamics. Eco‐industrial parks (EIPs) are instrumental in mitigating these challenges by facilitating the recycling of resources embedded within waste and by‐products. This strategy is essential to minimize resource consumption and foster resilient domestic supply chains, particularly in resource‐scarce nations. This study evaluates the recovery potential of nickel, a critical material for green technologies, within a closed‐loop system utilizing an industrial symbiosis development framework with public and open‐source data of industry. This approach enhances supply‐ and demand‐matching schemes within industrial symbiosis networks, specifically focusing on nickel recovery technologies within the Korean EIP project. The findings revealed that these networks within industrial complexes encompassed 86% of the manufacturing industry, thus establishing a cohesive framework for the development of a nickel integration network. Notably, among the 190 companies across 74 industrial complexes, 135 of the 27 designated EIPs participated in the recycling network. This indicates that EIPs could serve as a viable alternative for resource recovery to secure critical minerals. The implementation of such networks in concentrated industrial complexes with diverse manufacturing sectors is expected to significantly enhance critical mineral self‐sufficiency in high‐demand countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".