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Record W2791340403

Équivalence du système de plafonnement et d’échange de droits d’émission de GES au Québec (SPEDE) avec les exigences du fédéral en termes de tarification du carbone

2017· article· fr· W2791340403 on OpenAlexaboutno aff
Pierre‐Olivier Pineau, Simon Langlois-Bertrand

Bibliographic record

VenueCIRANO Project Reports · 2017
Typearticle
Languagefr
FieldEnvironmental Science
TopicEnvironmental Policies and Emissions
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesForestryPhilosophyGeography
DOInot available

Abstract

fetched live from OpenAlex

Le Quebec a ete la premiere province au Canada a mettre en œuvre, des 2013, un marche du carbone avec plafonds d’emissions decroissants dans le temps, le systeme de plafonnement et d’echange de droits d’emission (SPEDE). Cela s’est fait dans un contexte d’objectifs de reduction d’emissions de gaz a effet de serre (GES) plus ambitieux qu’au Canada, alors que les emissions quebecoises par habitant sont les plus faibles de toutes les provinces. Par ailleurs, parce que les emissions non energetiques representent une plus grande proportion des emissions au Quebec qu’au Canada, il y a moins d’opportunites de miser sur la substitution par des energies a faible teneur en carbone et sur l’efficacite energetique. Le gouvernement federal a annonce en 2016 un plan canadien de lutte contre les changements climatiques. Celui-ci propose une tarification du carbone de deux types : une taxe (redevance) sur le carbone des 2018, pour les produits petroliers et le gaz naturel utilises en transport et dans les bâtiments, combines a un « regime de tarification fonde sur le rendement » (RTFR) pour les grands emetteurs de plus de 50 000 tonnes de CO2 equivalent (tCO2e) par an, pas avant 2019. La question de l’equivalence des approches quebecoises et federales se pose donc, pour evaluer notamment leur efficacite a reduire les emissions de GES.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.019
GPT teacher head0.277
Teacher spread0.258 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCIRANO Project ReportsSame topicEnvironmental Policies and EmissionsFrench-language works237,207