Subprime catalyst: Financial regulatory reform and the strengthening of US carbon market governance
Bibliographic record
Abstract
Abstract The 2008 financial crisis has had an important, but neglected, impact on carbon market governance in the United States. It acted as a catalyst for the emergence of a domestic coalition that drew upon the crisis experience to demand stronger regulation over carbon markets. The influence of this coalition was seen first in the changing content of draft climate change bills between 2008 and 2010. But the coalition's more lasting legacy was its role in shaping the content of, and supporting, the passage of the Wall Street Reform and Consumer Protection Act (the Dodd–Frank bill) in July 2010. Although that bill was aimed primarily at bolstering financial stability, its derivatives provisions strengthened carbon market regulation in significant ways. This policy episode demonstrates new patterns of coalition building in carbon market politics as well as the growing links between climate governance and financial regulatory politics. At the same time, the significance of these developments should not be overstated because of various limitations in the content and implementation of the Dodd–Frank bill, as well as the waning support for carbon markets more generally within the US since the bill's passage.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".