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Record W4223438260 · doi:10.32721/ctj.2022.70.1.ozai

Designing an Equitable Border Carbon Adjustment Mechanism

2022· article· en· W4223438260 on OpenAlexvenueaboutno aff
Ivan Ozai

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasCarbon leakageCommissionUnited Nations Framework Convention on Climate ChangeConventionNormativeBusinessDeveloping countryInternational tradeNatural resource economicsEconomicsEmissions tradingPolitical scienceEconomic growthKyoto ProtocolLawFinanceEcology

Abstract

fetched live from OpenAlex

Policy makers worldwide have increasingly considered the adoption of a carbon adjustment at the border to equalize carbon pricing on foreign goods with carbon policies imposed on domestic production. The implementation of a border carbon adjustment (BCA) in the European Union has been recently proposed by the European Commission, followed by similar plans in the United States and Canada, as an instrument designed to address concerns about competitiveness and emissions leakage resulting from the absence of a global price on carbon or an internationally coordinated carbon-pricing system. Despite its potential to address these issues, the implementation of a BCA raises concerns with respect to its impact on developing countries. A BCA will likely impose a disproportionate burden on developing countries with limited capacity to cut back emissions and thus violate the principle of common but differentiated responsibilities (CBDR) established in the United Nations Framework Convention on Climate Change. The main goal of this article is to examine CBDR's normative requirements and determine its legal implications for BCA design. The article further offers policy guidelines for implementing a CBDR-compliant BCA that addresses its ultimate purpose of reducing global greenhouse gas emissions while also supporting the development needs of less affluent countries.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.059
GPT teacher head0.221
Teacher spread0.162 · 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
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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Tax Journal/Revue fiscale canadienneSame topicClimate Change Policy and EconomicsFrench-language works237,207