Comparative analysis of the existing and proposed ETS
Bibliographic record
Abstract
Emissions trading schemes (ETS) have been operational to control greenhouse gas emissions in European Union since 2005. Under the EU ETS, the governments of the Member States agree on national emission caps, allocate allowances to industrial operators, track and validate the actual emissions and retire allowances at the end of each year. ETS have been proposed to be introduced in New Zealand, Australia, Japan, US, Canada, Korea, India and two Chinese provinces in the near future. The main idea of the ETS is to create the market for pollution which will provide economic agents with incentives to reduce their emissions ( Stavins, et al., 2003). The design of ETS plays an important role in reducing greenhouse gas emissions and promoting environmental and economic sustainability. There are several designs of ETS including cap-and-trade, baseline-and-credit and hybrid, however, cap-and-trade scheme is the most popular among the proposed ETS. The purpose of this paper is to perform a comprehensive review of the existing and the proposed ETS focusing on design issues. Findings of this research will be useful for countries with existing and proposed ETS and for countries intending to adopt ETS in the future.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".