Mitigating urban pollution: A comparative life cycle assessment of hydrogen, electric, and diesel buses for urban transportation
Bibliographic record
Abstract
Urban transportation systems, particularly public buses, contribute significantly to global pollution, creating an urgent need for sustainable solutions. Alternative fuel buses and other disruptive technological advancements in this field are essential to resolve these problems. The absence of studies on the life cycle assessment (LCA) of hydrogen-fueled buses, along with comparative analyses of alternative-fueled buses, makes this research particularly timely. This study develops a comprehensive LCA framework to measure the economic and environmental impact of using different technologies (i.e., hydrogen-fueled, electric, and diesel buses). Different fuel production methods were examined, considering operational factors such as energy consumption across various routes. This study contributes to enhancing the LCA methodology for public bus operations by using machine learning algorithms to cluster routes and identify optimal demonstration routes for analysis. The results highlight the impact of fuel production methods for hydrogen-fueled buses in the significant pollutant reductions (e.g., CO 2 and NO x ), despite their high life cycle costs. The proposed framework is validated with real data from Halifax, Canada, and expanded to assess public bus networks in cities with varying routes, topology, and population levels. The paper’s analyses consider future technological advances to lower costs, aligning them with electric buses over time. This study helps policymakers choose the best public bus alternatives to improve the economic, environmental, and social sustainability of urban transportation. • Explores environmental impact and costs of three urban bus transport alternatives. • Uses real data and machine learning to propose urban transportation policies. • Advocates the superiority of hydrogen-fueled buses in reducing urban pollutions. • Projects hydrogen buses to match electric ones in life cycle costs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".