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Record W6907790183 · doi:10.25316/ir-16318

A comparative life cycle assessment of hydrogen production in British Columbia

2021· other· en· W6907790183 on OpenAlexfundaboutno aff

Bibliographic record

VenueVIURRSpace (Vancouver Island University) · 2021
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicKarst Systems and Hydrogeology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaRoyal Roads University
KeywordsHydrogen productionLife-cycle assessmentCarbon fibersGreenhouse gasProduction (economics)Hydrogen

Abstract

fetched live from OpenAlex

Hydrogen can play a key role in decarbonizing energy systems and the transition to a low carbon economy. While scaling up hydrogen production plays a critical role in initiating a hydrogen economy, it is also important to quantify the environmental impacts to determine what pathways will adequately contribute to emission reduction goals. To assess these impacts, a comparative well-to-gate life cycle assessment (LCA) was performed to determine the carbon intensity of three hydrogen production pathways in British Columbia (B.C.) using the GHGenius 4.03a software model. The hydrogen production pathways reviewed include steam methane reformation (SMR), SMR with carbon capture and storage, and electrolysis using the B.C. grid electricity mix. The LCA results were 82.75, 26.07, and 24.42 g CO2e/MJ HHV respectively. Additional objectives of this study include identifying barriers and recommending policies to increase production of low carbon hydrogen in B.C., and defining a carbon intensity benchmark.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

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