Bibliographic record
Abstract
Absent meaningful multilateral action on greenhouse gas emissions, countries wishing to combat climate change must decide whether to take or continue unilateral action. A significant obstacle facing many governments is how to maintain the competitiveness of domestic industries and minimize the leakage of carbon emissions through international trade without violating trade rules. One way to assuage these competitiveness and leakage concerns is to implement destination-based carbon pricing—carbon policy levied at the point of consumption, rather than production. This Article addresses whether one such form of climate policy—a consumption tax levied on the carbon footprint of goods consumed domestically—would be consistent with World Trade Organization (WTO) rules. Because a carbon footprint tax (CFT) would be levied at the point of consumption, goods would be treated equally regardless of whether they are imported or produced domestically. This Article analyzes the legal precedents for a footprint tax and identifies grounds upon which a CFT might possibly be challenged. Our assessment is that the most convincing challenge would be through the likeness criterion. Although the CFT would be a single-rate tax, the effective tax per-unit will vary across goods according to their carbon footprint. As a result, goods that are identical but for differences in their embodied emissions will face different tax burdens; if goods with different embodied carbon are deemed like goods, any variation in the per-unit tax could be interpreted as violating National Treatment. Recent precedents for treating goods as distinct because of consumer tastes and production externalities suggest that highand low-carbon goods may not be deemed like in the event of a challenge. Nevertheless, we also outline an alternate policy—one which pairs a uniform tax on goods with a consumption subsidy for low-carbon goods—that may survive challenge * Carol McAusland is an Associate Professor in the Faculty of Land and Food Systems at the University of British Columbia, and the Canada Research Chair in Trade and Environment. © 2015, Carol McAusland. † Nouri Najjar is a Ph.D. student in the Vancouver School of Economics at the University of British Columbia. Portions of this manuscript were originally circulated as part of a composite document titled “Carbon Footprint Taxes.” We are grateful to the following individuals for valuable feedback on earlier drafts of this Article: Steve Charnovitz, Aaron Cosbey, David Duff, Jennifer Hillman, Gary Hufbauer, Itziar Lazkano, Gabrielle Marceau, Charles McLure, Stephanie Monjon, and Joost Pauwelyn. © 2015, Nouri Najjar.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".