Classifying Circular Mining Initiatives: A Global Review of Business Models and Geographic Trends
Bibliographic record
Abstract
The mining industry generates vast amounts of waste each year, often stored in tailings ponds, waste piles, and other containment structures. Yet, this waste has the potential to become a valuable resource. A circular economic approach—built on reducing, reusing, recycling, and recovering—could greatly reduce this volume of waste while supplying environmentally friendly materials to other industries. This paper examines mining waste recycling methods and case studies, drawing conclusions from the evaluation and comparison of different recycling business models. Data collection focused on major global mining companies, from which 40 circular recycling projects were identified and catalogued for analysis. These projects were grouped into five distinct recycling methods, allowing for meaningful comparison. Analysis across geographical locations revealed that companies are more likely to invest in circular projects within jurisdictions that provide supportive incentives or require public reporting. In addition, it was found that the market capitalization is not correlated to the number of recycling initiatives run by a company. The business model framework presented in this report establishes a foundation for future industry design and implementation, offering guidance to mining companies in selecting recycling approaches best suited to the specific conditions of their operations.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".