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Record W1996948850 · doi:10.5558/tfc2014-120

Evaluation of environmental impacts of harvest residue-based bioenergy using radiative forcing analysis

2014· article· en· W1996948850 on OpenAlexvenueno aff
Francesca Pierobon, Indroneil Ganguly, Tommaso Anfodillo, Ivan Eastin

Bibliographic record

VenueThe Forestry Chronicle · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureNorthwest Advanced Renewables AllianceU.S. Department of Agriculture
KeywordsBioenergyGreenhouse gasEnvironmental scienceBiomass (ecology)Carbon neutralityLife-cycle assessmentCarbon sequestrationRadiative forcingFossil fuelBio-energy with carbon capture and storageCarbon dioxideBiofuelClimate change mitigationClimate changeWaste managementEcologyEngineeringProduction (economics)

Abstract

fetched live from OpenAlex

The “carbon neutrality” assumption plays an important role in the evaluation of the global warming potential (GWP) of bioenergy relative to fossil fuels. In the case of woody bioenergy, this assumption implies that the carbon dioxide emitted during the combustion of the biomass is equal to the carbon dioxide sequestered from the atmosphere within that biomass. However, the collection and conversion of woody biomass requires energy inputs in various forms that produce emissions to the air or water. To be able to estimate the overall environmental burdens associated with converting woody biomass to bioenergy, and the net reduction in greenhouse gas (GHG) emissions to the atmosphere by avoiding the use of fossil fuel, a life cycle assessment (LCA) is the internationally recognized method of choice. However, the carbon neutrality of woody biomass and the environmental impacts associated with wood-based bioenergy are hotly debated in national and international arenas. This study presents a comprehensive evaluation of the environmental impacts of woody biomass-based bioenergy and proposes a GWP impact assessment methodology using radiative forcing for incorporating the dynamics of carbon sequestration, decomposition of residues and biomass processing in the life cycle assessment of bioenergy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
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
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.270
Teacher spread0.252 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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