Evaluation of price fluctuation risks in tradable green certificate market based on complex social network approach
Bibliographic record
Abstract
The intention of tradable green certificate (TGC) system is to leverage the market's price discovery function of renewable energy's environmental value. However, the government is concerned about excessive price fluctuation risks, which could reduce renewable energy investment willingness. Since the complex social network among TGC market entities affects information dissemination, it has significant impacts on price fluctuation. Thus, this paper simulates the evolution of complex social network based on entities' game strategies to examine the impacts of complex social network structure on price fluctuation risks. The study finds that (1) the game learning mechanism promotes the continuous evolution of complex social network toward an equilibrium state, increasing the network's clustering by four times, and ultimately forming several clusters around influential manufacturers (central nodes). (2) In the short term, the cluster structure of social network suppresses the widespread dissemination of information, causing price to continue their historical trend, which reveals momentum effect. As entities' interaction level increases to 130% of the initial state, TGC price returns to fundamental value. (3) In the long term, the degree of central nodes is 5 to 10 times that of other nodes; hence, other entities tend to excessively imitate the decisions of central nodes, leading to an overreaction in the market, causing high-frequency price fluctuations and resulting in reversal effect.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".