Subnational Climate Clubs: An Interactional Approach to Transnational Lawmaking
Bibliographic record
Abstract
Abstract Predominant climate club research emphasizes state-centric clubs that alter the incentive structure and bargaining context for climate cooperation. This focus on national governments, however, leaves climate clubs vulnerable to political turbulence afflicting individual club members. Subnational governments are an important yet often overlooked type of actor in the club literature. This article contributes to understanding the role and nature of subnational government-led clubs in transnational climate governance and lawmaking through qualitative case studies of the Western Climate Initiative and the C40 Cities Climate Leadership Group. I identify the distinguishing characteristics that these clubs manifest in their membership and functions, as formalized through legal arrangements. I demonstrate that these clubs have the potential to increase structural stability, withstand political changes, and enhance the legitimacy and efficacy of climate action. They do so by functioning not only as organizations that create incentives for committing to legal norms and mechanisms for deterring free riding but also as communities of practice that generate shared understandings, resources, and norms to sustain club cooperation in pursuing a shared commitment to climate action. As such, each club applies a mix of rationalist approaches to benefit generation and constructivist approaches to community building.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".