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Record W4409787573 · doi:10.1016/j.trd.2025.104754

Can hydrogen fuel cell bus facilitate carbon neutrality in the transportation sector?

2025· article· en· W4409787573 on OpenAlexaff
Ziyi Liu, Chidong Zhou, Lei Liu, Xi Li, Bingya Xue, Kai Huang, Yajuan Yu

Bibliographic record

VenueTransportation Research Part D Transport and Environment · 2025
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsDalhousie University
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsFuel cellsCarbon neutralityHydrogenHydrogen vehicleNeutralityCarbon fibersBusinessHydrogen fuelAutomotive engineeringEngineeringChemistryEnvironmental scienceMaterials scienceChemical engineeringElectrical engineeringRenewable energyPolitical science

Abstract

fetched live from OpenAlex

Hydrogen fuel cell vehicles (HFCV) emerge as the promising alternative to internal combustion engine vehicles (ICEV). This study focuses on hydrogen fuel cell buses (HFCB) and assesses carbon footprint (CF) across the life cycle. During the production phase, the average carbon emissions of HFCB are 84055.91 kgCO 2 eq, significantly higher than the 43881.92 kgCO 2 eq of ICEV. During the usage phase, HFCB’s emissions are significant, but the cleanliness of hydrogen production methods can significantly reduce the emissions. In the recycling phase, the average carbon reduction achieved by HFCB is 12897.11 kgCO 2 eq, surpassing the 10746.24 kgCO 2 eq reduction of ICEV. However, it is not sufficient to offset the carbon emissions generated throughout the life cycle of HFCB. The overall lifecycle CF of HFCB exceeds that of ICEV. Finally, with changes in power infrastructure and advancements in hydrogen production , HFCB will contribute positively to carbon neutrality , accelerating the achievement of the 2060 carbon neutrality target.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.264
Teacher spread0.222 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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