The politics of decarbonization and the catalytic impact of subnational climate experiments
Bibliographic record
Abstract
The Paris Agreement of 2015 marks a formal shift in global climate change governance from an international legal regime that distributes state commitments to solve a collective action problem to a catalytic mechanism to promote and facilitate transformative pathways to decarbonization. It does so through a system of nationally determined contributions, monitoring and ratcheting up of commitments, and recognition that the practice of climate governance already involved an array of actors and institutions at multiple scales. In this article, we develop a framework that focuses on the politics of decarbonization to explore policy pathways and mechanisms that can disrupt carbon lock-in through these diverse, decentralized responses. It identifies political mechanisms-normalization, capacity building, and coalition building-that contribute to the scaling and entrenchment of discrete decarbonization initiatives within or across jurisdictions, markets, and practices. The role for subnational (municipal, state/provincial) climate governance experiments in this new context is especially profound. Drawing on such cases, we illustrate the framework, demonstrate its utility, and show how its political analysis can provide insight into the relationship between climate governance experiments and the formal global response as well as the broader challenge of decarbonization.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".