Empowering sustainable development through circular economy practices in rare-earth sector
Bibliographic record
Abstract
Rare-earth elements are essential mineral raw materials, but it is predicted that in a few years global mining output will not be able to meet the demand for them. The current study examines the connection between circular-economy practices in the rare-earth sector and sustainable development within the framework of the United Nations’ Sustainable Development Goals (the 17 SDGs). A structural review of the literature identified 69 relevant articles in peer-reviewed journals. There are five key findings. (1) The different circular strategies are not equally employed within the rare-earth sector; (2) the recovery strategy is the main one employed; (3) the chief challenges to the implementation of circular-economy practices are operational and financial; (4) there is a direct relationship between circular-economy practices and sustainable development, especially for SDGs 6, 7, 11, 12, and 13; and (5) robust policy will help the implementation of circular-economy practices and the achievement of sustainable development. This study is novel because it sets out the present situation of the rare-earth sector in relation to the implementation of circular-economy practices in the production and operation phases to accomplish sustainable development. It enhances theory and practice by deepening our understanding of circular-economy practices in the rare-earth sector, particularly with respect to their impact on sustainable development.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".