Flooding: Toward a Municipal Contribution to Economic Risk Sharing
Bibliographic record
Abstract
In Québec, flood damage costs have risen sharply over the past 40 years, partly due to population and property growth in flood-prone areas. This phenomenon is exacerbated by extreme weather events, such as torrential rains, some of which are on the rise in southern Québec in spring. Today, these costs are primarily covered by provincial and federal financial assistance programs and, to a lesser extent, by private insurance. These cost-sharing mechanisms give rise to moral hazard because they do not encourage municipalities or disaster victims to reduce risk. Municipalities need to be included in cost sharing because of their crucial role in land use planning and risk management. Similarly, disaster victims need to be included because they also have a role to play in reducing risk. This paper proposes and analyzes an economic contribution mechanism for municipalities that distributes the cost of damage to residential buildings more equitably. (Equity refers to a fair and just distribution of the financial burden based on the relative level of exposure to risk and the ability to reduce the risk for all parties involved.) The contribution is calculated for three medium-sized municipalities in Québec based on the sum of the average annual damage to each of the residential buildings located in their jurisdictions, and on property values. Three observations are drawn from this analysis: 1) a municipality's level of exposure is not correlated with its property value; 2) the low damage rate of a majority of buildings located in flood-prone areas justifies maintaining these buildings in these zones, provided that mitigation measures are implemented; and 3) relocating a minimum number of buildings would considerably reduce the municipality's economic contribution to damage costs. Implementing an economic contribution mechanism for municipalities and exposed citizens is intended to reduce the moral hazard and inequity generated by the current approach and encourage municipalities to implement mitigation and risk reduction measures. All stakeholders could equitably finance these measures.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".