Life cycle impact evaluation of hydrogen-fueled railway locomotives in Canada
Bibliographic record
Abstract
Hydrogen (H 2 ), the most abundant element in the universe, offers a promising solution for decarbonizing the transportation sector. However, its sustainability assessment highly depends on the production pathway used. This study develops a comprehensive Multi-Criteria Decision Making (MCDM) framework by incorporating Life Cycle Assessment (LCA) analyses to evaluate the economic, environmental, and social implications of adopting H 2 in the railway sector. The MCDM framework considers five H 2 production pathways and four decision-making scenarios to compare hydrogen-powered and conventional diesel locomotives. Furthermore, an MCDM tool is developed to assist decision-makers in evaluating H 2 as an alternative fuel in the railway sector, considering region-specific conditions in Canada. When considering an environmental priority for the ranking scenarios, results show that hydrogen-powered locomotives using SMR with CCUS are the top alternative to diesel, especially in the provinces where energy is produced mainly from renewable sources. However, diesel remains the more sustainable option in regions reliant on fossil fuels. While hydrogen from electrolysis is the alternative with the highest emission reduction potential in provinces like British Columbia, Manitoba, Ontario, and Quebec, it involves higher economic costs and social impacts. Several technical and economic challenges must be overcome to ensure that adopting H 2 technology in the railway sector delivers both environmental benefits and a cost-effective solution for society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".