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Excise Taxation To Preserve Health and To Protect the Environment: A Review

2022· review· en· W4321513726 on OpenAlexvenueno aff
Sijbren Cnossen

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2022
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsExciseExternalityContext (archaeology)RevenueConsumption (sociology)BusinessPublic economicsHealth careEconomicsFinanceEconomic growthMicroeconomics

Abstract

fetched live from OpenAlex

Excise duties continue to be coveted sources of revenue, particularly in developing countries, because they are easy to collect and cause few economic distortions. In developed economies, on the other hand, excise duties are increasingly considered useful instruments for enhancing social and market outcomes by internalizing the financial, physical, and psychological costs that abusive consumers and producers impose on other people (that is, external costs) and, unintentionally, on themselves (that is, internal costs). By internalizing these costs in the price of goods and thereby confronting consumers with the true cost of consumption, excise duties on tobacco, alcohol, sugar, and gambling can induce these consumers to preserve the quality of their health. Similarly, excise duties on carbon, cement, transport, and plastics can correct for the human-induced degradation of the environment. In this paper, the author identifies the revenue-raising and externality- and internality-correcting features of excise taxation and reviews, in this context, some of the main developments and issues encountered in subjecting particular excisable goods and services to excise taxation.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.610
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
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)0.0020.001

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 head0.087
GPT teacher head0.243
Teacher spread0.156 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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