Output-based rebating of carbon taxes in the neighbors backyard
Bibliographic record
Abstract
We investigate how carbon taxes combined with output-based rebating (OBR) in an open economy perform in interaction with the carbon policies of a large neighboring 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 second-best OBR rate should be positive in most cases. Further, it should fall with the introduction of carbon taxation in the neighboring country, particularly if the neighbor refrains from OBR. Numerical simulations for Canada with the US as the neighboring trading partner, indicates that the impact of US policies on the second-best OBR rate will depend crucially on the purpose of the domestic OBR policies. If the aim is to restore the competitiveness of domestic emission-intensive, trade exposed (EITE) firms at the same level as before the introduction of its own carbon taxation for a given US carbon policy, we find that the domestic optimal OBR rates are relatively insensitive to the foreign carbon policies. If the aim is to compensate the firms for actions taken by the US following a Canadian carbon tax, the necessary domestic OBR rates will be lower if also the US regulates its emissions, particularly if the US refrains from OBR. If the goal is rather to increase the efficiency of Canadian policies in an economy-wide sense by accounting for carbon leakage, the US policies have but a minor reducing impact on domestic optimal OBR rates.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".