A Survey on Optimization, Pricing, and Market Design for Power & Energy Systems under Carbon Pricing Coupling
Bibliographic record
Abstract
After three years of operation, China's national carbon market (CEA) is evolving from a "single-sector + spot" model to a "multi-sector + combined futures and spot" model. Carbon prices are gradually being explicitly transmitted to electricity spot markets and benchmark electricity prices. Pilot programs in southern China and Shandong have incorporated a "carbon cost coefficient" into the clearing equation. Internationally, the EU ETS, the California-Quebec WCI, the Northeast RGGI, South Korea's K-ETS, and Japan's GX-ETS exhibit a stepped distribution of auction ratios, price ranges, and electricity price transmission paths: "deep auctions, high prices, strong transmission, and deep derivatives." This article summarizes the unified clearing, offset ratios, and local innovative practices of "carbon-electricity-green certificates," systematically analyzing these findings to form a comprehensive review of optimization, pricing, and market design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".