Bibliographic record
Abstract
An obstacle for the implementation of climate adaptation projects is access to cheap financing. In this report, we review various solutions, all of which have their advantages and disadvantages. The described financing models can be combined into hybrids that are adapted to the specific context. The current financial conditions make it possible to take out very inexpensive loans. A prerequisite for the loans for climate adaptation projects to be affordable is that the borrowers are assessed to have a high credit rating. In this connection, municipalities and the state will ensure high creditworthiness by guaranteeing the loans. To the extent that municipalities and the utility company take responsibility for climate adaptation projects, the current financing option through KommuneKredit is attractive. The report reviews two foreign financing examples. We show that financing models in Germany, Canada, and Denmark are very different. For example, coastal protection in Germany is locked into dike solutions as the federal and local state funds up to 90 % of sea wall construction. In Canada, municipalities - and similar administrative entities - can apply for co-financing in funds paid for by the federal and local governments. Both the German and the Canadian solutions can serve as an inspiration and as a warning in relation to the developmentof new financing models in Denmark.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".