Global and Regional Gallium Recycling Potential and Opportunities: Based on Historical Material Flow Analysis
Bibliographic record
Abstract
Gallium plays a critical role in high-tech industries but faces supply risks. Improving the efficiency of recycling and utilization of secondary resources has become the most reasonable approach to addressing this. This study employs historical material flow analysis (2000–2019) to evaluate global and regional gallium recycling potential. The results indicate that gallium recycling remains underdeveloped, with three key opportunities identified: recycling gallium from primary production, new scraps, and old scraps. During 2000–2019, the cumulative amounts available from these sources were 239,760 tons, 3464 tons, and 955 tons, respectively, yet their recovery rates remain as low as 1.74%, 27.28%, and 0.84%, respectively. Regional analysis shows that the recycling potential and opportunities of gallium varies significantly across China, America, the EU, Japan, and the rest of the world. Current recycling technologies have shown potential for efficiently recovering gallium, but their economic viability relies on economies of scale and policy incentives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".