Ancillary Benefits of Carbon Mitigation: How Does it Affect Cost Effectiveness of the Annex-1 Emissions Trading?
Bibliographic record
Abstract
This paper explores how ancillary benefits of carbon mitigation may affect the cost effectiveness of Annex-1 emissions trading. We find that emissions trading could lead to cost-savings for both the Annex-1 countries as a whole and for the individual countries, as compared with the case of no-trading. The sum of the compliance costs is minimized under the cost-effective condition where marginal costs of domestic abatement are equalized across the Annex-1 countries. However, such a condition of cost effectiveness in emissions trading does not imply cost effectiveness in terms of the compliance of individual countries. The buyers of emission allowances, consisting of the European Union, the United States and Japan, could have even lower costs of compliance in the trading case where the ancillary benefits are taken into consideration. This result supports the intervention that takes account of the ancillary benefit in designing national carbon mitigation policies. To achieve the cost effectiveness in national carbon abatement, there should be regulatory interventions so that the price of carbon emission allowances can reflect the ancillary benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".