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Record W2969674710 · doi:10.3138/cpp.2019-036

Analysis of Carbon Tax Treatment in Canada’s Equalization Program

2019· article· en· W2969674710 on OpenAlexaffvenueabout
Tracy Snoddon, Trevor Tombe

Bibliographic record

VenueCanadian Public Policy · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of CalgaryWilfrid Laurier University
Fundersnot available
KeywordsGreenhouse gasRevenueEqualization (audio)Carbon taxPaymentGovernment (linguistics)Public economicsNatural resource economicsEconomicsCarbon fibersDistribution (mathematics)Tax revenueEnvironmental economicsComputer scienceFinanceTelecommunicationsEcology

Abstract

fetched live from OpenAlex

Carbon taxes are not only an efficient tool to mitigate greenhouse gas emissions, but they are also an increasingly important source of government revenue. The uneven distribution of emissions, however, creates significant differences across provinces in terms of their revenue potential. Equalization payments can mitigate these differences, but little is known about how this program interacts with carbon taxes. In this article, we quantitatively analyze this interaction and explore alternative considerations for equalization design—such as which revenues to include or tax bases to use—that may motivate changes to improve the functioning and effectiveness of both equalization and climate 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
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 head0.029
GPT teacher head0.225
Teacher spread0.196 · 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 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

Citations6
Published2019
Admission routes3
Has abstractyes

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