The RIN Market as a New Stage in the Development of Environmentally Friendly Fuels
Bibliographic record
Abstract
Since the signing of the US Energy Policy Act of 2005 by US President G. W. Bush a new market has been organized for trading a specific financial asset called Renewable Identification Numbers (RIN). RIN is a security, whose price is a tax stimulating fuel blenders and consumers in the USA to use more ecological fuels. The aim of this working paper is to introduce Russian readers to the basics of the RIN market, as information about the market is still scarce in Russian. In particular, we discuss such significant factors influencing the RIN market as mandate and blend wall. We also note the close link of the number of RINs in circulation with ethanol production. In the nearest future the problem of ethanol overproduction is likely to arise in the USA which might be resolved by its export to the EC and Canada. The possible dynamics of the RIN market in that respect are certainly of interest. Finally, although the market is still young, some large-scale cases of fraud have already been revealed. Those cases have attracted attention of regulators and may soon provide a reason for revision of RIN market rules. (In Russian).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".