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Record W4328097171 · doi:10.54691/bcpbm.v40i.4387

The Carbon Tax Effect on British Columbia Economy and Carbon Emission

2023· article· en· W4328097171 on OpenAlex
Haoran Liu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBCP Business & Management · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCarbon taxGreenhouse gasCarbon creditRevenueCarbon fibersTax reformNatural resource economicsGovernment (linguistics)Value-added taxColumbia universityEconomicsPublic economicsFinance

Abstract

fetched live from OpenAlex

A carbon tax is the most common carbon emission control policy widely used. British Columbia is the first Canadian province to use a carbon tax and implementing a carbon tax will impact British Columbia's environment and economy. This paper analyzes the scale of the carbon tax, energy consumption, GHG emission, and government revenue in British Columbia to evaluate the effect of the carbon tax. This paper finds that a carbon tax has no impact on dramatically reducing GHG British Columbia emissions, but it can help the government get more revenue. Although the carbon tax has little effect on reducing carbon emissions in British Columbia, the carbon emissions in British Columbia have been significantly reduced compared to other provinces. Therefore, we believe that the British Columbia carbon tax has a significant impact on controlling CO2 emissions, and the value of carbon emissions can be further controlled by increasing the carbon tax.

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.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.027
GPT teacher head0.213
Teacher spread0.187 · 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