Effects of asymmetric policies to achieve emissions reduction on energy trade: A North American perspective
Bibliographic record
Abstract
The implementation of asymmetric emission reduction policies can not only increase the cost of reducing emissions but also reduce the effectiveness of climate policies themselves, leading to policy inefficiencies such as carbon leakage. This paper investigates the impact of asymmetric emission reduction policies on the cost-effectiveness and efficiency of climate strategies in North America. Using a model inter-comparison approach, which combines two bottom-up global models and one top-down global model, this study assesses the effects of such policies on fuel substitution, global fossil fuel trade, and emissions in North America and globally. It is the first work where a multi-model approach is used for exploring how different energy systems react to asymmetric carbon policies. This provides critical insights into regional policy design within a global emissions framework. Quantitatively, the study reveals that asymmetric carbon pricing can lead to more than 60% global emissions reduction in certain models, but can also drive trade distortions , where U.S. exemptions result in emissions rising by more than 10% compared to reference scenarios. Qualitatively, significant fuel substitution patterns across Canada, Mexico , and the U.S. demonstrate increased coal consumption when carbon prices are unevenly applied. While no global emission increase was observed, asymmetric policies result in inefficiencies between local policy costs and emissions reduction outcomes, such as rising fossil fuel trade in non-abating regions. The findings suggest that harmonising carbon policies across regions would reduce inefficiencies and minimise carbon leakage.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".