Innovative Technologies for Waste Reduction in the Extraction and Processing of Critical Raw Materials
Bibliographic record
Abstract
This study addresses the pressing challenge of substantial waste generation during the extractionand processing of critical raw materials (CRMs), which are essential for emerging technologies and thetransition to a sustainable economy. While previous research has documented individual technologies andpractices, it has often lacked an integrated, cross-regional perspective and a strategic framework forimplementation. To fill this gap, this study develops an integrated conceptual model of zero-waste miningsystems, informed by a systematic comparative analysis of innovative technologies and best practices fromEurope, Australia, and Canada. The research adopts a mixed-method approach combining a comprehensiveliterature review, in-depth case studies, and a strategic SWOT analysis to evaluate the feasibility, scalability, andtransferability of advanced waste-reduction technologies. Key findings demonstrate that precision miningtechniques, sensor-based selective extraction, advanced flotation and leaching methods, and tailings valorizationcan significantly reduce waste—by up to 60%—while enhancing resource recovery and generating additionaleconomic value. The proposed conceptual model synthesizes these practices within the circular economicframework, emphasizing the need for technological integration, supportive regulatory environments, and crossindustry collaboration. This study contributes to the literature by offering a systematic, cross-regional synthesisof innovative CRM waste-reduction strategies and proposing a transferable model that bridges the gap betweendescriptive case evidence and actionable strategies for sustainable mining. The findings provide both theoreticaland practical insights, supporting policymakers, practitioners, and researchers in advancing circular economyprinciples in the CRM sector
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".