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Record W3020706344 · doi:10.5744/ftr.2020.1005

Carbon Tax Shifts and the Revenue-Neutrality Dilemma

2020· article· en· W3020706344 on OpenAlexaff
Rory Gillis

Bibliographic record

VenueFlorida Tax Review · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversity of TorontoWestern University
Fundersnot available
KeywordsNeutralityCarbon neutralityRevenueDilemmaPublic economicsEconomicsTax reformLaw and economicsIndirect taxLiberian dollarPolitical scienceLawFinanceGreenhouse gas

Abstract

fetched live from OpenAlex

Over the past two decades, numerous experts and politicians have proposed “revenue-neutral carbon tax shifts,” under which a government implements a carbon tax and uses the resulting revenue to cut other taxes by an equal dollar amount. These proposals commonly include legal or political mechanisms to bind governments to revenue neutrality over time. This Article’s central claim is that revenue neutrality suffers from conceptual and epistemic confusion that should lead to reconsideration of the policy merits of carbon tax shifts. To illustrate the argument, the Article provides the first retrospective review in the legal literature of carbon tax shifts from four jurisdictions, two of which were implemented and then repealed, and two of which were rejected by voters at the ballot box. In each jurisdiction, the carbon tax shift was afflicted by confusion between two substantially different conceptions of revenue neutrality, which can be termed “backward-looking” and “sideways-looking” revenue neutrality. This confusion is difficult to resolve because the choice between the two conceptions presents governments with a dilemma: backward-looking revenue neutrality is normatively undesirable, while sideways-looking revenue neutrality is difficult to enforce through legal and political mechanisms. This Article argues that the dilemma should be taken into account when choosing between carbon tax shifts and alternative uses of carbon pricing revenues. The dilemma also has implications for revenue-neutral tax reform in other contexts: it is far harder to separate the question of “how to tax” from the question of “how much to tax” than is commonly understood.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.589
Threshold uncertainty score0.414

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 communication0.0000.000
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 head0.034
GPT teacher head0.232
Teacher spread0.197 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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