Canadian oil sands industry GHG emissions intensity and mitigation potential of some key emerging technologies towards fulfilling its 2050 net-zero commitment
Bibliographic record
Abstract
• Canadian oil sands industry current and future carbon footprints are assessed. • Extractive emerging technologies lower upstream emissions from 78 to 57 kgCO 2 eq/bbl. • New upgrading technologies lower upstream emissions by an additional 20 kgCO 2 eq/bbl. • The studied emerging technologies can reduce total emissions by 16 % till 2050. • ∼25–185 Mt CO 2 eq estimated gap to reach net zero in upstream GHG emissions by 2050. Oil sands industry have pledged to make its onsite operations carbon neutral by 2050. First, the status of oil sands’ well-to-wheel GHG emissions of transportation fuels was evaluated by covering ∼75 % of bitumen production in 2018/2019 using open-source bottom-up life-cycle assessment tools, public/commercial data, and by unprecedented engagement of 11 oil sands stakeholders, and provincial/national research agencies. Next, several emerging oil sands technologies and their GHG emissions mitigation potential are explored, with extractive emerging technologies to lower upstream GHG intensities from 78 to 57 kgCO 2 eq/bbl while new upgrading technologies lower by an additional 20 kgCO 2 eq/bbl. The innovative technologies and other advancements (e.g., electricity co-generation, CCS) contributions on annual GHG emissions until 2050 are quantified and broader implications are discussed. The estimated cumulative emissions reduction capacity across the industry until 2050 is ∼700 MMt CO 2 eq (16 % reduction) relative to a business-as-usual scenario which is far from the industry 2050 commitment.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".