COPTEM: A Model to Investigate the Factors Driving Crude Oil Pipeline Transportation Emissions
Bibliographic record
Abstract
Previous transportation fuel life cycle assessment studies have not fully accounted for the full variability in the crude oil transport stage, for example, transporting a light crude through a high-diameter pipeline, vs transporting a heavy crude through a small-diameter pipeline. We develop a first-principles, fluid mechanics-based crude oil pipeline transportation emissions model (COPTEM) that calculates the greenhouse gas (GHG) emissions associated with pipeline transport as a function of crude oil parameters, pipeline dimensions, and external factors. Additionally, we estimate the emissions associated with the full life cycle of pipeline construction, maintenance, and disposal. This model is applied to an inventory of 62 major Canadian and U.S. pipelines (capacity greater than 100 000 barrels/day) to estimate the variability of GHG emissions associated with pipeline transportation. We demonstrate that pipeline GHG emissions intensities range from 0.23 to 20.3 g CO 2 e/(bbl·km), exhibiting considerably greater variability than data reported in other studies. A sensitivity analysis demonstrates that the linear velocity of crude transport and pipeline diameter are the most impactful parameters driving this variability. To illustrate one example of how COPTEM can be used, we develop an energy efficiency gap analysis to investigate the possibilities for more efficient pipeline transport of crude oil.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".