Analysis of Energy Use and CO<sub>2</sub> Emissions in the U.S. Refining Sector, With Projections for 2025
Bibliographic record
Abstract
This analysis uses linear programming modeling of the U.S. refining sector to estimate total annual energy consumption and CO(2) emissions in 2025, for four projected U.S. crude oil slates. The baseline is similar to the current U.S. crude slate; the other three contain larger proportions of higher density, higher sulfur crudes than the current or any previous U.S. crude slates. The latter cases reflect aggressive assumptions regarding the volumes of Canadian crudes in the U.S. crude slate in 2025. The analysis projects U.S. refinery energy use 3.7%-6.3% (≈ 0.13-0.22 quads/year) higher and refinery CO(2) emissions 5.4%-9.3% (≈ 0.014-0.024 gigatons/year) higher in the study cases than in the baseline. Refining heavier crude slates would require significant investments in new refinery processing capability, especially coking and hydrotreating units. These findings differ substantially from a recent estimate asserting that processing heavy oil or bitumen blends could increase industry CO(2) emissions by 1.6-3.7 gigatons/year.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".