A not‐so‐green choice? The high carbon footprint of long‐distance passenger rail travel in Canada
Bibliographic record
Abstract
It is commonly assumed that taking the train serves as a more climate‐friendly means of travel than flying by commercial aircraft. Nevertheless, in Canada, long‐distance rail services are powered by aging and inefficient diesel locomotives. Moreover, long‐haul passenger trains are not typically loaded to capacity, and they must travel longer distances than equivalent air routes (which are able to benefit from more direct flight paths). This viewpoint considers whether traveling long‐distance by train generates a larger climatic footprint than flying by commercial aircraft, and offers a basic carbon footprint analysis and modal comparison of three long‐distance routes in Canada. It finds that taking the train does indeed generate a larger climatic impact than flying, in the cases of VIA Rail's trips between Toronto and Vancouver and between Montreal and Halifax, even when taking air travel's additional non‐CO 2 warming impact into account. While travelling by train within the modernized Quebec City‐Windsor Corridor generates a smaller climatic footprint than flying, VIA Rail's greenhouse gas emissions factor within the Corridor is still far higher than international rail comparisons. The viewpoint concludes by enumerating some policy changes which could reverse this dynamic and help Canadian long‐distance rail fulfill its reputation as a green choice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".