Optimal Intervention Policies in International Emissions Trading Considering Ancillary Benefits of Carbon Abatement
Bibliographic record
Abstract
This paper explores governments’ optimal intervention policies under imperfectly competitive international emissions trading (IET) considering ancillary benefits of carbon abatement (i.e., positive externalities). A sequential game is employed to conduct the analyses. It is found optimal for all countries to intervene in IET by imposing an import tariff (or export subsidy) equal to the marginal ancillary benefit of carbon abatement. Accordingly, the magnitude of ancillary benefits will affect the incentive for domestic abatement and the equilibrium of the IET market. Increasing ancillary benefits will enhance the intervention level and domestic abatement and leads to a fall in the equilibrium allowance price. However, its impact on the emissions for price-making country and that for price-taking countries are somewhat different. If the price-making country has larger ancillary benefit, she will be willing to abate more carbon emissions. By contrast, an increase in the ancillary benefits of a price-taking country will lead to an ambiguous impact on her abatement level.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".