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Record W4308067518 · doi:10.32721/ctj.2022.70.3.wood

Is Revenue Neutrality in Carbon Taxation Possible in Practice? Lessons from the Canadian Experience

2022· article· en· W4308067518 on OpenAlexvenueaboutno aff
Joel Wood

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon neutralityTax reformRevenuePublic economicsIndirect taxAd valorem taxValue-added taxEconomicsCarbon taxDirect taxTax creditDouble taxationBusinessFinanceGreenhouse gas

Abstract

fetched live from OpenAlex

While the potential economic efficiency, equity, and political acceptability benefits of a revenue-neutral carbon tax have been well studied, a deeper question remains about the feasibility of revenue neutrality in practice. This article provides perspective on this issue by assessing different definitions of revenue neutrality and presenting an in-depth discussion of the motivations for the adoption of revenue-neutral carbon taxes. Two examples of carbon taxes and revenue recycling implemented in Canada are examined: British Columbia's revenue-neutral carbon tax (a carbon tax with offsetting tax cuts) and the Canadian federal government's fuel charge and climate action incentive tax credit (a carbon tax and dividend). The BC case serves to highlight the inherent difficulties of assessing revenue neutrality owing to uncertainty about what would have occurred in the absence of the tax. As time passes following initial implementation of the tax, it becomes increasingly difficult to determine whether, and to what extent, government revenue, income tax rates, the overall tax structure, and the tax base might have differed had the tax not been adopted. The federal example suggests that a carbon tax and dividend policy would be better able to ensure revenue neutrality.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
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
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.238
Teacher spread0.189 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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