Output‐based rebating of carbon taxes in a neighbour's backyard: Competitiveness, leakage and welfare
Bibliographic record
Abstract
Abstract We investigate how, in an open economy, carbon taxes combined with output‐based rebating (OBR) perform in interaction with the carbon policies of a large neighbouring trading partner. Analytical results suggest that, whether the purpose of the OBR policy is to compensate firms for carbon tax burdens or to maximize welfare (accounting for global emission reductions), the OBR rate should be positive in policy‐relevant cases. Numerical simulations for Canada, with the US as the neighbouring trading partner, indicate that the impact of US policies on the OBR rate will depend crucially on the purpose of the Canadian OBR policies. If, for a given US carbon policy, Canada's aim is to restore the competitiveness of domestic emission‐intensive and trade‐exposed (EITE) firms to the same level as before the introduction of its own carbon taxation, we find that the necessary domestic OBR rates will be insensitive to the foreign carbon policies. However, if not only the Canadian carbon tax but also an equally high US tax is introduced, compensatory Canadian OBR rates will be up to 50% lower, depending on the sector and on US OBR policy. If the policy objective is to increase economy‐wide allocative efficiency (welfare) of Canadian policies by accounting for carbon leakage, the US policies will have only a minor downward pressure on desirable OBR rates in Canada. Practical choices of OBR rates hardly affect overall domestic economic performance; thus, output‐based rebating qualifies as an instrument for compensating EITE industries without a large sacrifice in terms of economy‐wide allocative efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".