Life Cycle Greenhouse Gas Emissions from Uranium Mining and Milling in Canada
Bibliographic record
Abstract
Life cycle greenhouse gas (GHG) emissions from the production of nuclear power (in g CO2e/kWh) are uncertain due partly to a paucity of data on emissions from individual phases of the nuclear fuel cycle. Here, we present the first comprehensive life cycle assessment of GHG emissions produced from the mining and milling of uranium in Canada. The study includes data from 2006-2013 for two uranium mine-mill operations in northern Saskatchewan (SK) and data from 1995-2010 for a third SK mine-mill operation. The mine-mill operations were determined to have GHG emissions intensities of 81, 64, and 34 kg CO2e/kg U3O8 at average ore grades of 0.74%, 1.54%, and 4.53% U3O8, respectively. The production-weighted average GHG emission intensity is 42 kg CO2e/kg U3O8 at an average ore grade of 3.81% U3O8. The production-weighted average GHG emission intensity drops to 24 kg CO2e/kg U3O8 when the local hydroelectric GHG emission factor (7.2 g CO2e/kWh) is substituted for the SK grid-average electricity GHG emission factor (768 g CO2e/kWh). This results in Canadian uranium mining-milling contributing only 1.1 g CO2e/kWh to total life cycle GHG emissions from the nuclear fuel cycle (0.7 g CO2e/kWh using the local hydroelectric emission factor).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".