Buy them out before they are built: evaluating the proactive acquisition of vacant land in flood-prone areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rising flood damages have prompted local communities to implement buyout and property acquisition programmes to eliminate repetitive losses for at-risk properties. However, buyouts are often costly to implement and are reactionary solutions to flooding. This study quantifies the benefits of acquiring vacant private properties in flood-prone areas rather than acquiring such properties after they are built up. Using a geodesign framework that integrates concepts and analytical approaches derived from geographical, spatial and statistical-based disciplines, we analyse vacant properties with high development potential that intersect current and future floodplain areas in Houston (TX, USA). We use geospatial proximity analysis to select candidate properties, land-use prediction modelling to estimate future development and sea-level rise and benefit-cost analysis to assess the economic viability of buyouts. The results indicate that cumulative avoided flood losses exceed the cost of vacant land acquisition by a factor of nearly two to one, and up to a factor of ten to one in selected areas. This study emphasizes the benefits of proactive property buyouts that focus on acquiring parcels before they are built up, while also avoiding the social and institutional problems associated with traditional buyout programmes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it