Climate Governance: Comparing Centralized and Decentralized Approaches
Bibliographic record
Abstract
Climate governance is entering a period of turbulence, with policy reversals in some democracies and rapid expansions elsewhere. This paper compares how centralized, decentralized (federal), and polycentric/hybrid governance designs shape mitigation and adaptation outcomes. Using a qualitative comparative approach across China, the United States, Canada, Türkiye, Norway, and Saudi Arabia, assessing policy ambition, legal instruments, implementation capacity, subnational authority, stakeholder participation, finance mobilization, and equity considerations. A qualitative comparative approach is applied across six country cases - China, the United States, Canada, Türkiye, Norway, and Saudi Arabia - evaluating policy ambition, legal instruments, implementation capacity, subnational authority, stakeholder participation, finance mobilization, and equity considerations. Insights are then extended to the Central Asian context, where climate governance remains predominantly centralized, shaped by Soviet-era institutional legacies, uneven local capacity, and constrained civic participation. The analysis demonstrates that no model is universally superior; the most effective arrangements combine top-down coherence with bottom-up experimentation and social legitimacy. Norway’s polycentric governance model and Türkiye’s hybrid approach illustrate how localized climate planning can be integrated within broader national frameworks. For Central Asia, pragmatic hybrid pathways are recommended that align national targets and financing with empowered regional pilots, transparent monitoring, and inclusive engagement. These context-sensitive combinations offer the best prospects for durable emissions reductions, climate resilience, and just transition outcomes in the region.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".