Well-to-Propeller environmental assessment of natural gas as a marine transportation fuel in British Columbia, Canada
Bibliographic record
Abstract
This paper examines the environmental impact of Natural Gas (NG) as a transportation fuel, particularly for marine transportation use. The aim is to provide a systematic evaluation of Greenhouse Gas (GHG) emissions in the upstream fuel supply chain of NG fuel in British Columbia (BC), Canada. The recent introduction of Liquefied Natural Gas (LNG) fuel for ferry operations in western Canada presents a major step towards the large-scale adoption of NG as a cleaner and lower-cost fuel. This makes a systematic approach for accurate Lifecycle Assessment (LCA) of GHG emissions for the NG/LNG fuel more important and urgent. An analysis using operation and fuel consumption data from vessels with different engine technologies and types of fuel shows that the diesel cycle NG engine will produce 2% less CO2e emissions in comparison to the low sulphur petroleum diesel engine, while other NG engine technologies, such as the lean-burn Otto cycle engine or dual-fuel gas engine, will result in 4% higher CO2e emissions. This study clears doubts on well-to-pump (WTP) NG emissions, supports the wide adoption of NG fuel and promotes further pump-to-propeller (PTP) emission improvements in marine propulsion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".