Who and what are carbon markets for? Politics and the development of climate policy
Bibliographic record
Abstract
Why have carbon markets been rapidly adopted as policy solutions to climate change in the last decade? Perhaps surprisingly, this question has attracted virtually no attention in the large literature on such markets. The standard arguments given for why carbon markets are good ways to respond to climate change do not explain why such markets have flourished as governance mechanisms in relation to climate. Carbon markets have spread and become taken-for-granted because of the potential they give to certain powerful actors (financiers, specifically) to create new cycles of investment, profits and growth. As a consequence, they make possible a political coalition combining financiers with environmentalists. This coalition has considerable potential to legitimize substantial cuts in carbon emissions in the face of continued opposition from other interests. It is the combination of these two elements – the promotion of specific growth sectors and the construction of a political coalition – that constitutes the principal political virtue of carbon markets. In order to demonstrate this claim, the history of emissions trading is traced and the implication of this analysis is explored for the further building of climate governance centred on carbon markets.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".