Practical Solutions to Decarbonize Mining Operations: A Case Study of a Copper Mine in British Columbia Using Renewable Diesel
Bibliographic record
Abstract
The mining sector is responsible for extracting essential minerals vital for the clean energy transition, and its operations must be conducted sustainably to lower GHG emissions while maintaining productivity. This study examines the feasibility of using renewable diesel to reduce carbon emissions from haul truck fleets at a copper mine in British Columbia. It highlights the role of renewable diesel in achieving regional carbon reduction targets for diesel-intensive mining. A mixed methodology combining case study and quantitative analysis of two scenarios, the base case (100% traditional diesel) and a blend (renewable and traditional diesel at varying ratios), was employed. Results show no performance loss (0.34 L/t ore versus 0.33 L/t ore) with blends, a 42% annual GHG reduction, and 59% lower emission intensity. Emission levels below benchmarks enable significant tax savings. The findings confirm renewable diesel as a cost-effective solution for decreasing haul truck emissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".