Analysis of GHG Emissions for City Passenger Trains: Is Electricity an Obvious Option for Montreal Commuter Trains?
Bibliographic record
Abstract
Alternative technologies have emerged to reduce the greenhouse gas (GHG) emissions of traditional commuter rail systems powered by diesel. Even larger reductions can be obtained with energy production from renewable resources. This paper uses the commuter rail system in Montreal, Quebec, as a case study for implementing alternative technologies, namely, complete electrification of the network (only one of the existing five lines is electrified) and hydrogen fuel cell-powered trains. It is important to note that the main source of electricity generation in Quebec is hydropower which is offered at a relatively low cost. Several criteria were considered to determine the most suitable alternative including GHG emissions from operation and fuel production, operation and capital costs, and technological and commercial viability. Electrification of the commuter rail system would decrease annual emissions by 98% which is more than 27,000 tons. The GHG reductions for hydrogen trains are lower than electric trains but still substantial. The operation costs favor the electrification scenario; however, the high costs of electrical infrastructure make hydrogen trains more competitive since additional infrastructure is unnecessary. However, hydrogen trains remain a new and unproven technology; uncertainties associated with it should be settled before full implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".