Comparative Climate Change Governance: Lessons from European Transnational Municipal Network Management Efforts
Bibliographic record
Abstract
ABSTRACT Cities and municipalities are vital actors in addressing climate change. Because they are directly affected by the consequences of environmental transformations, cities are motivated to shape adaptation and mitigation. This paper looks at the possible mechanisms which cities can use to engage in climate change issues without decoupling themselves from the national or sub‐national level and while remaining consistent with other local initiatives. The paper analyses the European approach towards transnational municipal networks (TMNs) and community collective efforts and assesses its possible application in other jurisdictions. We argue that while TMNs are the institutional foundation for a concerted effort in climate change within and between countries; they are also subject to provisions from national and regional governments, which might hamper their benefits. Based on a typology of TMNs and an analysis of the national contexts, the paper finds that those networks that target a specific region and are supported by government have the most benefits for climate change. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".