Future insurance losses for pluvial flooding in Canada and the United States
Bibliographic record
Abstract
There is mounting pressure on the financial services industry to factor in climate extremes and climate change. As a result, new reporting and regulatory requirements are gradually being enforced on (re)insurers globally. One key requirement is physical risk assessment, that is, quantifying the financial impacts of climate change on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modelling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects. \n \nIn this paper, we introduce a data science approach to physical risk assessment of pluvial flooding for insurance portfolios over Canada and the United States. The underlying flood model is focused on quantifying the financial impacts of short-term (12-48 hours) precipitation dynamics over the present (2010-2030) and future climate (2040-2060) using a methodological approach that leverages statistical/machine learning and regional climate models. The flood model is designed for applications that do not require street-level precision as is often the case for scenario and trend analyses. It is performed at the full scale of Canada and the U.S. at 10 to 25 km resolution. \n \nOur models show that climate change and urbanization will typically increase losses over Canada and the U.S., while impacts are strongly heterogeneous from one state or province to another, or even within a territory. Portfolio applications highlight the importance for a (re)insurer to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses. While the overall methodology can be applied to physical risk assessment of various risks, we also provide detailed maps and tables of the impacts of climate change on pluvial flooding for use by researchers and practitioners.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".