Lessons Learned in Mandatory Carbon Market Development
Bibliographic record
Abstract
The Kyoto Protocol introduced the notion of a global emissions trading scheme (ETS) to aid in meeting global emissions reduction targets. Since then, the share of emissions covered by carbon pricing has tripled and now encompasses approximately 12% of global emissions. This paper discusses the challenges in design and implementation of past and current ETSs to provide recommendations for ETS development and linkage. It summarizes seven major factors that should be considered for successful ETS implementation: cap setting, permit allocation, trading guidelines that avoid carbon leakage, regulation of offsets, high compliance, transparent and continuous monitoring, and careful collaboration between systems. Successes and failures in practical implementation of each factor are explored through various ETS case studies. If applied carefully, these factors could ensure high and consistent carbon prices, and coupled with strict regulations, could achieve an ETS that meets intended environmental benefits, while offering potential for bottom-up international linkage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".