Bibliographic record
Abstract
Both the European Union, which currently has an emissions trading scheme for greenhouse gases, and the USA, which is considering implementing such a program, are exploring the possibility of including foreign producers in that program by requiring importers of foreign goods to pay charges relating to the amount of CO emitted in the production of those goods. This article examines the legality of such measures under WTO law. While academic literature has in recent years considered carbon tariffs in the abstract, this article contextualizes that discussion by analyzing European and American proposals to enact carbon tariffs, focusing primarily on the 2008 Lieberman-Warner Bill. This article concludes that carbon tariffs are, subject to a number of constraints, generally permissible under WTO law. However, it also argues that while carbon tariffs may generally be legally permissible, additional domestic political constraints may significantly limit the set of legal carbon tariffs which are practically feasible in any given state. This article posits that any meaningful discourse on carbon tariffs must incorporate both political and legal constraints, and it seeks to contribute to this discourse by identifying the relevant constraints and exploring certain policy options which could satisfy those constraints.
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 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 | 0.036 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".