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Carbon Border Adjustment Mechanism: A Cross-Country Comparative Analysis

2025· article· W7123504702 on OpenAlexaboutno aff
Ekta Kumawat, Shurveer Bhanawat, Udaipur MLSU

Bibliographic record

VenueInternational Journal of Advanced Research in Commerce Management & Social Science · 2025
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsCredibilityCarbon fibersGreenhouse gasClimate changeCarbon footprint

Abstract

fetched live from OpenAlex

The relationship between international trade and climate-based regulations has intensified in recent years as countries enact regulations on how much companies should charge for emitting carbon dioxide. One of the main problems associated with implementing unilateral climate-related policies is that some industries may move carbon-emission-intensive manufacturing processes to countries where environmental regulations are not as strict (carbon leakage). To address these issues, the EU created a Carbon Border Adjustment Mechanism (CBAM) that proposes to equalize carbon costs on imported and domestic products. Many of the other nations, such as the USA and Canada, propose or develop their CBAM-type systems, following the introduction of the CBAM by the European Union. This research compares the carbon border adjustment systems proposed by select nations and assesses several factors, including the way the policies have been designed and implemented, what sectors are affected, what components are expected to drive carbon emissions, and how these systems will benefit the environment. A mixed methods design has been adopted using a combination of secondary quantitative indicators for determining the effectiveness of the carbon border adjustments compared to the levels of carbon pricing, carbon emissions intensity, and carbon trade exposure. The findings of this study indicate that a CBAM will enhance the environmental credibility of domestic political action on climate change and help mitigate carbon leakage, but it will significantly vary in terms of its economic impact based on jurisdiction. Finally, the issues associated with setting up the measurement, reporting, and verification component of a CBAM raise significant difficulties with regard to how effective these policies will be in practice. This research will enhance and extend the knowledge regarding CBAM design from a cross-country perspective and also be used to inform global climate policies and global trade policies. The findings suggest that phased implementation of CBAM through international cooperation is a sustainable method of aligning climate ambition with trade objectives.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.144
GPT teacher head0.483
Teacher spread0.339 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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