Renewable energy in the mining industry: Status, opportunities and challenges
Bibliographic record
Abstract
Currently, the mining sector is confronted with declining ore grades, volatile energy prices, and environmental pollution from massive carbon emissions. Due to the advantages of RE in terms of emission reduction and cost, some mining companies are actively exploring integrating RE in production to alleviate these challenges. At the same time, the integration of RE in mining sites also faces many obstacles. This paper highlights the importance of incorporating RE into mining projects through a comprehensive review of existing research and analyzes the opportunities and challenges from multiple perspectives. Finally, the conclusions summarize the gaps in existing work and provide appropriate recommendations. The paper aims to inform and provide implications for the transition to RE practices in the mining industry. • This paper highlights the importance of integrating renewable energy (RE) into mining projects by a comprehensive review. • The feasibility and application potential of integrating RE in the mining industry are analyzed from multiple perspectives. • The current techno-economic analysis and MCDM for the deployment of RE projects in the mining industry are reviewed. • The advantages and opportunities of RE in mining are summarized in four areas: economic, environmental, social and policy. • The barriers are analyzed from five perspectives: technology, expertise, financing, legislation and related interests.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".