Implementing adaptation in developed countries: an analysis of progress and trends
Bibliographic record
Abstract
Little attention has been paid thus far to the experiences of developed countries in adapting to climate change. This article addresses this research gap by providing an assessment of broad trends in progress on planning and implementing adaptation in developed countries. Primary inputs are the National Communications (NCs) by these countries to the United Nations Framework Convention on Climate Change (UNFCCC), although the article also discusses illustrative examples of recent adaptation activities that have not been covered in the NCs. NCs reflect ‘whole government’ perspectives and follow a standardized reporting format, which facilitates cross-national comparisons. The analysis shows that impacts and adaptation receive limited attention within NCs. The discussion on impacts and adaptation has typically been dominated by climate scenarios and impacts analysis, while the discussion on adaptation is often limited to the identification of generic options. There are signs of recent progress, however, in the Third and especially the Fourth NCs, in which a growing number of developed countries report on establishing frameworks for adaptation and on efforts to implement adaptation measures that take future climate into account. Although an encouraging sign, it is still too early to assess the eventual impact of such measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".