Factors Explaining Municipal Climate Adaptation: Insights from Two Assessments of over 100 German Cities in 2018 and 2022
Bibliographic record
Abstract
Climate adaptation is becoming increasingly important for municipalities. Yet, key questions remain about why they engage with this agenda, particularly at different stages of the adaptation cycle and over time. This study examines how 17 different factors, grouped into four principal components (city size & scale; land use & compactness; socio-economics; and regional climate & exposure to extreme weather), influence municipal adaptation activities. It examines how these variables played out in 104 German cities, using the results of two assessment frameworks: one analysed municipal adaptation activities across five dimensions in 2018, while the other mapped them against three dimensions in both 2018 and 2022. Regression analysis indicates that larger, more compact and more exposed cities are generally more active in adaptation, whereas socio-economic factors have a minimal impact. City size & scale shows significant effects consistently across all assessment dimensions. All four components, including socio-economics, influence adaptation plan-related dimensions, whereas implementation of adaptation measures is primarily shaped by land use & compactness. The influence of city size & scale and regional climate & exposure declined between 2018 and 2022, suggesting a policy diffusion process. These findings reveal different nuances in factors influencing municipal adaptation, highlight the importance of including implementation in assessments of adaptation, and echo calls for further research into causal mechanisms and longitudinal studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".