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Record W4320493474 · doi:10.5539/jsd.v16n2p50

Life Cycle Assessment of Power Generation from Imported Woody Biomass Fuels

2023· article· en· W4320493474 on OpenAlexvenueno aff
Noriko Nishihara, Lisa Ito, Letícia Sarmento dos Muchangos, Akihiro Tokai

Bibliographic record

VenueJournal of Sustainable Development · 2023
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
FundersMinistry of Economy, Trade and Industry
KeywordsRenewable energyEnvironmental scienceBiomass (ecology)Life-cycle assessmentBioenergyElectricity generationRenewable fuelsEnvironmental impact assessmentFuel oilLiquefied natural gasWaste managementBiofuelEnvironmental protectionNatural gasPower (physics)EngineeringEcologyProduction (economics)

Abstract

fetched live from OpenAlex

With the promotion of woody biomass power generation in Japan, wood fuel imports have increased yearly. However, the environmental impact of the transportation process is expected to increase compared to procuring the fuel domestically. Therefore, this study aimed to evaluate the environmental impact of biomass power generation using imported woody fuel. Human health, ecosystem, and global warming were evaluated using the life cycle assessment (LCA) method under four scenarios: three scenarios for importing woody fuel and one scenario for procuring woody fuel domestically. The results reveal that replacing heavy oil with liquefied natural gas (LNG) or ammonia as ship fuel at the time of importation could reduce the environmental impact to the same level or up to 86% compared to the case wherein wood fuel is procured domestically. Thus, using next-generation vessels to import wood fuel and generate biomass power effectively reduces environmental impact. Furthermore, the use of wood fuel—a renewable energy source whose generation can be adjusted—should be promoted toward achieving carbon neutrality.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.594
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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.

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
Published2023
Admission routes1
Has abstractyes

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