What drives national adaptation? A global assessment
Bibliographic record
Abstract
That the climate is changing and societies will have to adapt is now unequivocal, with adaptation becoming a core focus of climate policy. Our understanding of the challenges, needs, and opportunities for climate change adaptation has advanced significantly in recent years yet remains limited. Research has identified and theorized key determinants of adaptive capacity and barriers to adaptation, and more recently begun to track adaptation in practice. Despite this, there is negligible research investigating whether and indeed if adaptive capacity is translating into actual adaptation action. Here we test whether theorized determinants of adaptive capacity are associated with adaptation policy outcomes at the national level for 117 nations. We show that institutional capacity, in particular measures of good governance, are the strongest predictors of national adaptation policy. Adaptation at the national level is limited in countries with poor governance, and in the absence of good governance other presumed determinants of adaptive capacity show limited effect on adaptation. Our results highlight the critical importance of institutional good governance as a prerequisite for national adaptation. Other elements of theorized adaptive capacity are unlikely to be sufficient, effective, or present at the national level where national institutions and governance are poor.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".