Carbon‐motivated Border Tax Adjustments: Old Wine in Green Bottles?
Bibliographic record
Abstract
Abstract (1285) Ben Lockwood and John Whalley 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 harmonisation target in the early 1960s. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".