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Environmental and social life cycle analysis of hydrogen-powered railway locomotives in Canadian context

2024· article· en· W4401477033 on OpenAlex
Lizette Correa, Faran Razi, Kasun Hewage, Rehan Sadiq

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Hydrogen Energy · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersTransport Canada
KeywordsContext (archaeology)HydrogenEnvironmental scienceEngineeringChemistryGeology

Abstract

fetched live from OpenAlex

Hydrogen locomotives offer a promising cleaner alternative to conventional diesel locomotives. However, hydrogen production methods and energy sources may introduce additional emissions. This paper evaluates the environmental and potential social impacts of hydrogen locomotives in Canada from a life cycle perspective, encompassing the locomotive's retrofitting components and the fuel life cycle. Results show varying emissions across different hydrogen production pathways and regions. Electrolysis has the highest emission reduction potential in provinces with cleaner electricity sources, such as Manitoba, Quebec and British Columbia, resulting in up to 47% reduction in life cycle emissions. Conversely, in Alberta and Saskatchewan, emissions are approximately three times higher than diesel due to reliance on fossil fuel-derived electricity. The social assessment underscores the imperative of considering emissions, costs, and technical implications to address potential social impacts. This positions hydrogen locomotives with significant challenges that necessitate resolution before they can be considered a superior alternative to diesel.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.005
GPT teacher head0.218
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it