The life cycle greenhouse gas emissions implications of power and hydrogen production for oil sands operations
Bibliographic record
Abstract
'Full text:' The Alberta Oil Sands represent a major economic opportunity for Canada, but the industry is also a significant source of greenhouse gas (GHG) emissions. One of the sources of these emissions is the use of natural gas for the production of electricity, steam and hydrogen. Due to concerns around resource availability and price volatility, there has been considerable discussion regarding the potential replacement of natural gas with an alternative fuel. While some of the options are non-fossil and could potentially reduce GHG emissions (e.g., nuclear, geothermal, biomass), others have the potential to increase emissions. A comparative life cycle assessment was completed to investigate the relative GHG emissions, energy consumption and financial implications of replacing natural gas with coal, coke, asphaltenes or bitumen for the supply of electricity, steam and hydrogen to oil sands operations. The potential use of carbon capture and storage (CCS) was also investigated as a means of reducing GHG emissions. Preliminary results indicate that, without CCS, the natural gas systems currently in use have lower life cycle GHG emissions than gasification systems using any of the alternative fuels analysed. However, when CCS is implemented in both the coke gasification and natural gas systems, the coke systems have lower GHG emissions and financial costs than the natural gas systems (assuming a 30-year project life and a natural gas price of 6.5 USD/gigajoule). The use of CCS does impose a financial penalty though, indicating that it is unlikely to be implemented without some financial incentive. While this study has limitations and uncertainties, the preliminary results indicate that although the GHG emissions of oil sands development pose a challenge to Canada, there are opportunities available for their abatement. (author)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 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".