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Record W1855173003

BC's Carbon Tax Shift Is Working Well after Four Years (Attention Ottawa)

2013· article· en· W1855173003 on OpenAlexvenueaboutno aff
Stewart Elgie, Jessica McClay

Bibliographic record

VenueCanadian Public Policy · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical sciencePer capitaHumanitiesCarbon taxEconomyGeographyGreenhouse gasEconomicsArtDemographySociology
DOInot available

Abstract

fetched live from OpenAlex

La creation par la Colombie-Britannique en 2008 d’une taxe sur le carbone sans incidence sur les recettes a ete controversee. Dans cet article, nous comparons les changements qui sont survenus depuis dans la province en matiere de consommation de carburant, d’emissions de gaz a effet de serre et de produit interieur brut, et nous faisons la comparaison avec le reste du Canada. Nos resultats montrent que, depuis quatre ans que la taxe existe, la consommation de carburants a baisse de 19 % de plus que dans le reste du Canada. Ces resultats correspondent a ceux qu’ont obtenus les pays europeenes en creant eux aussi des taxes sur le carbone, et devraient donc enrichir le debat sur les politiques canadiennes en matiere de changements climatiques. Abstract: British Columbia’s introduction in 2008 of a revenue-neutral carbon tax shift was controversial. This analysis compares changes in fuel consumption, greenhouse gas emissions, and gross domestic product (GDP) between British Columbia and the rest of Canada. It finds that in the four years since the tax was introduced, British Columbia’s per capita consumption of fuels subject to the tax has declined by 19 percent compared to the rest of Canada. At the same time, its economy has kept pace with the rest of Canada. British Columbia’s experience mirrors the European experience with carbon tax shifting and should inform the federal debate on climate change policy.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.008

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 head0.053
GPT teacher head0.213
Teacher spread0.160 · 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 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

Citations25
Published2013
Admission routes2
Has abstractyes

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