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Record W3124595078 · doi:10.3386/w14025

Carbon Motivated Border Tax Adjustments: Old Wine in Green Bottles?

2008· preprint· en· W3124595078 on OpenAlexaff
Ben Lockwood, John Whalley

Bibliographic record

VenueNational Bureau of Economic Research · 2008
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsWestern University
Fundersnot available
KeywordsWineBusinessCarbon taxEconomicsFood scienceChemistryGreenhouse gasBiologyEcology

Abstract

fetched live from OpenAlex

We discuss emerging proposals for border tax adjustments (BTAs) to accompany commitments to reduce carbon emissions in the EU, the US and other OECD economies. The rationale offered for such border adjustment is that various entities, such as the EU, if making commitments to reduce emissions which go beyond those undertaken in other regions of the world, impose added costs on domestic producers which create a competitive disadvantage for them. Some form of remedy is viewed as reasonable to maintain the competitiveness of domestic industries when responding to global environmental problems. In this paper, we argue that despite its current carbon manifestation, the issue of border tax adjustments and both their rationale and their effects on trade are not new and, despite the present debate (which seems to overlook older literature), have arisen before. Earlier debate on border tax adjustments occurred at the time of the adoption of the Value Added Tax (VAT) in the EU as a tax harmonization target in the early 1960's. But academic literature of the time showed that a change between origin and destination basis in the VAT would be neutral and hence the use of a destination based tax in the EU to accompany the VAT offered no trade advantage to Europe. Here we argue that essentially the same arguments also apply for carbon motivated BTAs, and in the current debate there seems to be a misconception between price level effects and relative price effects stemming from a BTA, which needs correcting. We also argue that the impact of border tax adjustments should be viewed as independent of the motivation of the adjustments.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.193
GPT teacher head0.420
Teacher spread0.228 · 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 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

Citations34
Published2008
Admission routes1
Has abstractyes

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