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Record W1536128209 · doi:10.4337/9781849806138.00014

Optimal Climate Change Tax Policy for Small Open Economies

2011· book-chapter· en· W1536128209 on OpenAlexaffabout
Arthur J. Cockfield

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2011
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsQueen's University
Fundersnot available
KeywordsCarbon taxTransparency (behavior)EconomicsClimate changeGlobal warmingInternational economicsOpen economyConsistency (knowledge bases)Small open economyTax policyTax reformPublic economicsMacroeconomicsPolitical science

Abstract

fetched live from OpenAlex

What are the best climate change tax policies for governments with relatively small open economies such as the Canadian one? This chapter assesses recent Canadian government climate change tax policy initiatives, discusses the merits of carbon taxes versus cap and trade solutions then considers the constraints imposed on optimal climate change (or global warming) tax policy by increasing regional and global economic interdependence. The perhaps obvious conclusion is that governments with small open economies should seek collective action solutions to confront climate change challenges: the suggested approach is to develop consensus surrounding the imposition of a global carbon tax with, at least initially, a low rate. In particular, carbon taxes have the virtue of transparency and consistency that responds to concerns set out in the optimal tax and compliance theory literature. An international agreement that focuses on the price of carbon can, at the beginning stages, incorporate regional cap and trade (or other) programs with the aim of ultimately evolving into a global carbon tax.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.162
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.232
GPT teacher head0.274
Teacher spread0.041 · 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 designTheoretical or conceptual
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
Published2011
Admission routes2
Has abstractyes

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Same venueEdward Elgar Publishing eBooksSame topicClimate Change Policy and EconomicsFrench-language works237,207