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Record W1802730896 · doi:10.22004/ag.econ.196915

Bioenergy trade, a theoretical analysis

2013· article· en· W1802730896 on OpenAlexaboutno aff
Jean‐Marc Bourgeon, Hélène Ollivier

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioeconomy and Sustainability Development
Canadian institutionsnot available
Fundersnot available
KeywordsBioenergyGreenhouse gasRenewable energyFossil fuelBiofuelNatural resource economicsEuropean unionAgricultural economicsRenewable fuelsInternational tradeEconomicsBusinessEngineeringWaste managementEcology

Abstract

fetched live from OpenAlex

Though the potential of bioenergy in the mitigation of greenhouse gases (GHG) coming from fossil energies is strongly debated, several developed countries such as the United States, the European Union and Japan have for several years already outlined ambitious objectives of incorporating bioenergy into their energy package in order to reduce their GHG emissions, notably in the field of transportation. Bioenergies are presented as an alternative to fossil fuels that is both renewable and relatively clean. The policies implemented give rise to little in the way of imports and yet, for biofuels to reach a 10% share of fuel consumption in transports, the United States, Canada and the EU would need to use 30%, 36% and 72% of their farm lands respectively (Von Lampe, 2006). A very simplified theoretical model of the world economy shows that opening up Bioenergy to trade would result in an increase in GHG emissions if Southern countries have a comparative advantage in the industrial sector, or conversely, a reduction thanks to bioenergy imports if Northern countries have the best performing industrial sector.

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 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 score0.747
Threshold uncertainty score0.980

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)0.0200.000

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 head0.183
Teacher spread0.172 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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