Research on the impact of certification emission reduction price on energy price based on big data
Bibliographic record
Abstract
Global warming has seriously affected human production and life. At present, countries around the world are committed to finding ways to save energy and reduce emissions. The Kyoto Protocol introduced a market mechanism to trade the power of carbon dioxide and other greenhouse gas emissions as a commodity. In the process of actual economic operation, due to advanced emission reduction technology, extensive use of new energy, implementation of environmental protection policies and other factors, certification emission reduction (CER) in some countries are less than the emission limit. At present, the scarcity of CER is mainly determined by the government, and the government can intervene in the dynamics of the carbon emission trading market through various means, such as formulating different carbon quota allocation methods, or holding periodic auction of emission rights, etc. This paper studies the phenomenon that CER is a commodity traded in the market, and the tight relationship between supply and demand of CER determines the carbon price. This paper analyzes the application of big data in the price control of CER, and the influence mechanism of CER on energy prices. Finally, the development plan and prospect of the exploration market are put forward.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".