Environmental and economic comparison of diesel and electric trucks in open-pit mining operations
Bibliographic record
Abstract
The global mining sector accounts for 4 %–7 % of greenhouse gas (GHG) emissions. Mining companies aim to drastically reduce Scope 1 and 2 emissions for environmentally sustainable operations by 2050. Transitioning from diesel to all-electric haulage fleets is a promising approach to eliminating Scope 1 emissions and aligning with net-zero objectives. Using battery electric vehicles in the industry has the potential to enhance operational efficiency by reducing operating costs, lowering energy consumption, and significantly decreasing GHG emissions. However, as electrification progresses, concerns arise over reliability, productivity, maintenance, capacity constraints, and the associated costs. This research addresses the prospective GHG impact of transitioning to electric trucks in open-pit haulage. A discrete event simulation model was developed as a generalized framework to compare diesel and electricity demands of haulage systems, considering factors like speed, load, maintenance, and resilience in adverse weather. Then, an economic assessment based on haulage energy demand identified operational cost-saving potential for electric trucks. Our modelling results show that GHG emissions could be significantly reduced by up to 92.6 % in a hypothetical case, and operating costs could be reduced by 40 %–62 %, underscoring the synergistic environmental and economic benefits of electrification in mining operations. • We built a reference model for fleet replacement when data availability is limited. • Our GHG analysis considered both Scope 1 and Scope 2 emissions for mining haulage. • Our model examined the impact of road profiles and weather conditions. • GHGs by replacing diesel trucks can be reduced by 50–92.6 % in Canada's context. • The related operating costs are predicted to be reduced by 40–62 %.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".