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Policy Forum: Border Carbon Adjustments—Four Practical Challenges

2022· article· en· W4223458321 on OpenAlexvenueaboutno aff
Ken Boessenkool, Mike Moffatt, Aaron Cosbey, Michael A. Bernstein

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueValue (mathematics)AccommodationProcess (computing)Carbon fibersCarbon priceEconomicsBusinessGreenhouse gasPolitical sciencePublic economicsFinanceComputer science

Abstract

fetched live from OpenAlex

For Canadian policy makers, the relatively straightforward economics of a border carbon adjustment (BCA) is complicated in practice by four challenges. First, Canadian carbon-pricing regimes have been developed from the bottom up, being primarily designed at the provincial rather than the federal level. Second, a Canadian BCA must comply with international trade law. Third, while carbon taxes are applied on the carbon emitted at each stage of every industrial process, applying a BCA on each process and at each level of a value chain is extremely difficult. Fourth, there is the question of what to do with any revenues that a BCA generates. The key conclusion that emerges from the discussion in this article is that the federal nature of Canada will be an early and significant challenge for policy makers. Given that Canadian provinces have led the development of carbon-pricing schemes, it seems likely that most will continue to want to do so. Any BCA regime (which will necessarily be implemented at the federal level) will have to find ways to accommodate or account for the differences in provincial carbon-pricing regimes. That accommodation may not be impossible, but it will not be easy.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.833
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.291
Teacher spread0.255 · 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
GenreCommentary

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 routes2
Has abstractyes

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