MétaCan
Menu
Back to cohort
Record W4412041137 · doi:10.32390/ksmer.2025.62.3.274

Cost Analysis of the Blue Hydrogen Supply from Canada to Korea

2025· article· en· W4412041137 on OpenAlexaboutno aff
Amisha Flowrence Gomes, Namhwa Kim, Hyundon Shin

Bibliographic record

VenueJournal of the Korean Society of Mineral and Energy Resources Engineers · 2025
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
FundersKorea Institute of Energy Technology Evaluation and PlanningMinistry of Trade, Industry and Energy
KeywordsCost analysisHydrogenEnvironmental scienceBusinessNatural resource economicsWaste managementEconomicsChemistryEngineeringOperations research

Abstract

fetched live from OpenAlex

This study conducts a cost analysis of blue hydrogen supply from Canada to South Korea by modeling two distinct supply chain scenarios using Monte Carlo simulation.Scenario 1 involves importing liquefied natural gas (LNG) from Canada for domestic hydrogen production in South Korea, coupled with carbon capture and storage (CCS) integration.Scenario 2 involves producing blue hydrogen in Canada and importing it to Korea using various carrier options, such as liquefied hydrogen, ammonia, and liquid organic hydrogen carriers.Blue hydrogen, which is produced primarily through steam methane reforming, partial oxidation, and autothermal reforming, incorporates CCS to reduce carbon emissions.The economic analysis using Monte Carlo simulations in both scenarios shows that blue hydrogen stored in ammonia from Scenario 2 has the lowest supply cost at 7.33 $/kgH 2 , making it the most cost-effective option.Although Canada's current LNG supply to Korea is limited, future strategic energy planning supports the feasibility of this scenario.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.587

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Korean Society of Mineral and Energy Resources EngineersSame topicHybrid Renewable Energy SystemsFrench-language works237,207