Quantifying the Carbon Footprint of the Alouette Primary Aluminum Smelter
Bibliographic record
Abstract
Abstract The Alouette primary aluminum smelter is the largest in the Americas, with an annual production of ~ 630,000 t of aluminum. In this collaborative study, a detailed product carbon footprint analysis was undertaken by Rain Carbon using a large body of primary emissions data to provide a complete cradle-to-gate analysis of the smelter’s emissions. The total carbon footprint of the smelter in 2019 was 3914 kg CO2e/t of aluminum for scope 1, 2, and 3 emissions, and 1835 kg CO2e for scope 1 and 2 emissions. The modeling results were compared to those for global average and Canadian average smelters, using reference datasets developed by the International Aluminium Institute (IAI) and GaBi Professional Database. Alouette’s carbon footprint is ~ 76% lower than a world average smelter and ~ 25% lower than a Canadian average smelter. For the scope 3 emissions, the primary contributors to the lower carbon footprint are lower emissions from the alumina supply and the calcined petroleum coke supply. Today, Alouette produces among the lowest carbon aluminum in the world, and this is set to decrease further following a switch from fuel oil to natural gas in the anode baking furnaces, and a switch to LNG at the alumina supplier refinery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".