Diminishing Returns: Carbon Market Crisis and the Future of Market-Dependent Climate Change Finance
Bibliographic record
Abstract
The current and future costs of meeting climate change mitigation needs in the global South vastly exceed levels of available funding from public sources in the North. As a possible solution to this problem, policy-makers and various observers have pushed increasingly for the adoption of market-based carbon financing strategies, with the United Nations Clean Development Mechanism (CDM) representing the most consistent application of this approach to date. Nevertheless, market-based carbon finance remains highly volatile given its heavy dependence on conditions in the broader global carbon market which remains in the throes of a devastating crisis, earning carbon the distinction of 2011s worst performing global commodity. By demonstrating that it is through carbon's market price that finance-generating investment in the CDM is largely derived, and which also determines the ex post value of CDM projects, this paper argues for the decoupling of climate change finance from carbon's market value. The need to do so is particularly pressing since, it is argued, the current crisis in the global carbon market reflects an embedded crisis tendency in that market, born in part from the political machinations through which it was born and which leaves it prone to persisting crises of oversupply.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".