Analysis of GHG Emissions for city passenger trains: is electricity an obvious option for Montreal commuter trains?
Bibliographic record
Abstract
Alternative technologies such as hydrogen fuel cells and electric-powered trains have emerged to reduce the 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. Results also indicate that increasing ridership to full occupancy of the current commuter rail network would not lower emissions per passenger-kilometer to the emissions level of electric-powered trains. The operation costs favor the electrification scenario; however, the high costs of electrical infrastructure make hydrogen trains more competitive since additional infrastructure are unnecessary. The cost of electrification is estimated as $1,240.1 million which is within the $1,2-1,5 billion recently proposed by the Quebec government, compared with $414.8 million for hydrogen trains. Hydrogen trains remain a new and unproven technology; uncertainties associated with it should be settled before full implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".