Post-combustion CO2 capture retrofit from diesel-powered Arctic mines – Techno-economic and environmental assessment
Bibliographic record
Abstract
• Diesel reliance escalates Arctic mine site CO 2 capture cost. • Carbon capture can reduce Arctic mining's carbon footprint. • Carbon pricing alone isn't enough to incentivize energy-intensive carbon capture. • Reduced carbon tax exposure results in substantial annual costs increase. • Cost driven by solvent regeneration heat demand. This study evaluates the economic feasibility and environmental impacts of retrofitting a diesel-based powerhouse in the Canadian Arctic with a post-combustion carbon capture process at an active gold mining site isolated from cheaper or cleaner electrical grids. A techno-economic analysis was conducted to determine the total annualized cost ( TAC ) of implementing a monoethanolamine (MEA) chemical absorption process to mitigate carbon dioxide emissions. The calculated cost per tonne of CO 2 captured of $420 reflects the challenges of operating northern sites reliant on diesel fuel. Electricity generation costs, estimated at 0.44 $/kWh, are found to explain most of the variance in cost per tonne compared to other studies. A profitability model, comparing the additional annual expenditure to the current carbon tax exposure ( CTE ), suggests that carbon pricing alone is insufficient to incentivize investment in energy-intensive carbon capture technologies such as amine-based absorption processes. The sensitivity analysis, which evaluates profitability relative to variations in key variables, highlights the significant impact of the solvent regeneration heat demand. This major cost driver also contributes substantially to the carbon footprint of 0.55 tonnes emitted per tonne captured, as determined by a complementary life cycle assessment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".