Carbon intensity of global crude oil trading and market policy implications
Bibliographic record
Abstract
Abstract The energy mix transition has accelerated the need for more accurate emissions reporting throughout the petroleum supply chain. Despite increasing environmental regulations and pressure for emissions disclosure, the low resolution of existing carbon footprint assessment does not account for the complexity of crude oil trading. The lack of source crude traceability has led to poor visibility into the “well-to-refinery-entrance” carbon intensities at the level of granular pathways between producers and destination markets. Using high-fidelity datasets, optimization algorithms to facilitate supply chain traceability and bottom-up, physics-based emission estimators, we show that the variability in global “well-to-refinery-entrance” carbon intensities at the level of crude trade pathways is significant: 4.2–214.1 kg-CO 2 -equivalent/barrel with a volume-weighted average of 50.5 kg-CO 2 -equivalent/barrel. Coupled with oil supply forecasts under 1.5 °C scenarios up to 2050, this variability translates to additional CO 2 -equivalent savings of 1.5–6.1 Gigatons that could be realized solely by prioritizing low-carbon supply chain pathways without other capital-intensive mitigation measures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".