No Emissions Trading Scheme is an Island: Building a Global Linking Agreement from the "Bottom Up"
Bibliographic record
Abstract
The global climate change regime is at a crossroads. There remains little prospect states will agree to urgently needed binding commitments through a "top down" climate agreement, but the ability of national and sub-national policies to produce sufficient emission reductions in its place faces significant challenges. This paper proposes a possible alternative model for building a centralised climate regime from the "bottom up" through linking national and sub-national emissions trading schemes in stages under a global linking agreement. This model is argued as preferable to waiting for a global carbon price to develop from decentralised linkages, or for a "top down" linking agreement to emerge. The paper then considers how the legal design of such an agreement could best facilitate linking of different schemes, concluding that parties should agree to "low end" mutual recognition of allowances under an umbrella treaty structure, with less essential elements left to non-binding arrangements.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".