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Record W2101682481 · doi:10.5558/tfc2014-128

Economic consequences of increased bioenergy demand

2014· article· en· W2101682481 on OpenAlexafffundvenue
Craig Johnston, G. Cornelis van Kooten

Bibliographic record

VenueThe Forestry Chronicle · 2014
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsRenewable energyWood fuelPelletsEnvironmental scienceBioenergyNatural resource economicsFossil fuelElectricityBiomass (ecology)BusinessEconomicsWaste managementEngineeringEcology

Abstract

fetched live from OpenAlex

Although wind, hydro and solar are the most discussed sources of renewable energy, countries will need to rely much more on biomass if they are to meet renewable energy targets. In this study, a global forest trade model is used to examine the global effects of expanded demand for wood pellets fired with coal in power plants. Positive mathematical programming is used to calibrate the model to 2011 bilateral trade flows. To assess the impact of increased demand for wood pellets on global forest products, we consider a scenario where demand for wood pellets doubles. Findings indicate that production of lumber and plywood is likely to increase in most of the 20 model regions, but outputs of fibreboard, particleboard and pulp will decline as these products must compete with wood pellets for residual fibre. Ultimately, policies promoting aggressive renewable energy targets cause wood pellet prices to more than double in our scenarios, which could increase the cost of generating electricity to such an extent that, in some regions, electricity producers will continue to use fossil fuels as their primary fuel, while some others might find it worthwhile to rely more on nuclear energy for base load power.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.160

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.199
Teacher spread0.190 · 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 designBench or experimental
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 routes3
Has abstractyes

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