The Mechanism of Carbon Regulation of Emissions in Industrial Energy
Bibliographic record
Abstract
The introduction of cross-border taxes on hydrocarbon-intensive goods in the EU is expected in 2023, and the draft proposals are expected in the second quarter of 2021. The conditions for collecting such a tax from the EU will affect the interests of the EU itself for the next two decades remaining under the Green Deal until zero emissions. Apparently, the mechanism of tax collection will soon become clear, where and for what the collected funds will be directed. The issue of the impact of new taxes on Russian exports shifts the internal debate over the nature of such regulation to several urgent issues. This is the need for a more accurate, reliable and documented accounting of emissions in the country. And this is the choice of the option of adapting the country’s tax system to minimize the losses of companies, which began to be actively discussed, since the time factor begins to operate. An important aspect of the problem is the need for the simultaneous and rapid introduction of measures in the field of regulation, taxes, statistics in a complex, depending on the chosen response option. The new measures will not only affect the country’s climate programs, taxation and foreign trade, but will also have implications for economic strategy and even regional development.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".