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Simulation D’un Marché De Certificats Verts Pour La Promotion De L’énergie éolienne En Belgique

2002· article· fr· W1572349169 on OpenAlexvenueno aff
Pierre L. Kunsch, Rafael Álvarez-Nóvoa Barrio, Johan Springael

Bibliographic record

VenueINFOR Information Systems and Operational Research · 2002
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyEnvironmental economicsElectricityFossil fuelElectricity marketProduction (economics)EconomicsNatural resource economicsBusinessEnvironmental scienceEngineeringMicroeconomicsElectrical engineeringWaste management

Abstract

fetched live from OpenAlex

Renewable sources used for the generation of electricity have today quite high production costs in Europe. The liberalisation of the electricity market makes their advancement still more difficult, as it is expected to exert pressures towards lower prices. There are however strong arguments supporting renewable electricity. First, it is highly recommended to preserve the non-renewable fossil natural resources. Second, the use of renewable resources reduces the emissions of CO2 considered as being the most important greenhouse gas. In Belgium the most promising renewable production source for electricity is wind energy. Suitable policies shall amplify a virtuous cycle rendering this type of production competitive with fossil sources on a short-time basis. The present paper presents a simulation with system dynamics of a supporting policy based on the creation of a green-certificate market. The objective of the study is to demonstrate the feasibility of this approach, and to provide to regulators a decision tool for properly designing the main policy parameters as necessary conditions for its success.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.220
GPT teacher head0.343
Teacher spread0.123 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2002
Admission routes1
Has abstractyes

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