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Record W4407121570 · doi:10.1111/caje.12753

How protective are border carbon taxes for Canadian industry? The critical role of <scp>US</scp> emissions pricing

2025· article· en· W4407121570 on OpenAlexaffvenueabout
Christoph Böhringer, Thomas F. Rutherford, Eric J. Stewart

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsGreenhouse gasEmissions tradingBusinessEconomicsCarbon fibersNatural resource economicsIndustrial organizationComputer scienceEcology

Abstract

fetched live from OpenAlex

Abstract Border carbon taxes are considered as an important instrument to promote sustainable practices abroad and to level the playing field for domestic emission‐intensive and trade‐exposed (EITE) industries. We find that US emissions pricing plays a critical role in the effectiveness of border carbon taxes in protecting the international competitiveness of Canadian EITE producers. Border carbon taxes are more effective when the US follows the other OECD countries with stringent CO2 emissions pricing than when the US abstains from emissions pricing. In the latter case, border carbon taxes reduce the competitiveness of Canadian EITE export supply to the US (Canada's most important export destination), weakening the initial protective effect of border carbon taxes on the Canadian domestic market.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0050.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.137
GPT teacher head0.223
Teacher spread0.086 · 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 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
Published2025
Admission routes3
Has abstractyes

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