Managed retreat from high-risk flood areas: exploring public attitudes and expectations about property buyouts
Bibliographic record
Abstract
Increasing flood risk requires governments to develop innovative solutions for flood risk management. The effectiveness of these solutions depends, in part, on their social acceptability. This paper presents the findings of a national survey to explore the social acceptability of property buyouts as a form of managed retreat from flood risk in Canada. It discusses public attitudes and expectations towards property buyout programmes in high-risk flood zones, including their salience, essential design elements, and factors that would influence household acceptance of a property buyout offer. The results show there is an appetite for property buyout programmes to reduce flood risk in high-risk zones. Moreover, the social acceptability of such programmes is highest when participation is voluntary, flexible pricing options are combined with financial incentives, and programme design and implementation are transparent. Participants indicated costs for these programmes should be borne primarily by governments and shared between governments at different levels. The findings suggest that although property buyouts—and managed retreat more generally—are considered a socially acceptable approach to flood risk management, their efficacy will depend on programme design, stakeholder collaboration, and effective communication of risk to vulnerable populations. Policy recommendations are discussed in response to these findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".