Application of re/insurance models to estimate increases in flood risk due to climate change
Bibliographic record
Abstract
Floods are the most common and most expensive natural hazard, and they are expected to become more frequent as the climate changes. This article presents research that used re/insurance catastrophe models to estimate the influence of climate change on flood-related losses. The geographic focus of the study was the Canadian Maritimes—specifically Halifax, Nova Scotia—and it sought to determine how municipal risks due to rainfall-driven riverine floods could change as a result of climate change. Findings show that annual flood losses could increase by up to 300% under a business-as-usual climate scenario by the end of the century (i.e., no mitigation or adaptation), even without accounting for changes to the built environment that could increase exposure (e.g., no population or economic growth). Increasing flood risk demands an open discussion about how much risk is acceptable to the community and what controls on further growth of exposure are necessary. Moreover, projected increases in flood losses put into question long-term insurability in the Halifax area, and highlight a broader problem facing manyother areas in Canada as well.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".