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Record W1520737698

Making Federalism Work for Climate Change: Canada's Division of Powers Over Carbon Taxes

2008· article· en· W1520737698 on OpenAlexaffabout
Nathalie J. Chalifour

Bibliographic record

VenueSSRN Electronic Journal · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOpposition (politics)FederalismCarbon taxPower (physics)Work (physics)Law and economicsPublic economicsEconomicsBusinessPolitical scienceLawClimate changePolitics
DOInot available

Abstract

fetched live from OpenAlex

There is increasing agreement in Canada that we need carbon pricing policies. Indeed, Quebec and British Columbia have forged ahead with carbon taxes, and the Liberal opposition has included a carbon tax in its most recent platform. One is tempted to simply point to the respective federal and provincial taxation powers for jurisdictional authority.However, and perhaps surprisingly, the taxation powers are not the optimal source of authority. This paper shows that both levels of government have authority to implement carbon taxes under various heads of power, depending on the measure’s design. Federally, carbon taxes could be justified under the national concern branch of the POGG power, and possibly under the criminal law, trade and commerce and taxation powers. While the property and civil rights power, taxation power and authority over natural resources might justify carbon taxes provincially, the licensing power offers the strongest source of authority. Analysis of Quebec and B.C.’s measures show that they were likely designed to fit within this licensing power.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.864

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0150.007
Scholarly communication0.0090.002
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.042
GPT teacher head0.237
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2008
Admission routes2
Has abstractyes

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