Flood Insurance, Building Codes, and Public Adaptation: Implications for Airport Investment and Financial Constraints
Bibliographic record
Abstract
This paper investigates the impact of flood management policies on airport investment and the resulting financial constraints. Specifically, it examines the effects of flood insurance, building codes, and public adaptation investment on the investment decisions of 100 United States airports located in flood-prone areas. The paper estimated the financial loss from extreme precipitations and flooding using novel data from the United States Federal Emergency Management Agency, and a differences-in-differences framework leveraging the introduction of the 2012 Biggert–Waters reform of the National Flood Insurance Program. The findings reveal that while flood insurance costs negatively influence overall airport investment, they do not significantly affect investment–cash sensitivity. On the other hand, the introduction of stricter building codes and public adaptation investment leads to increased cash usage for investment purposes, particularly among airports exposed to extreme precipitation and flood risks. Furthermore, the analysis suggests that the observed increase in financial constraints resulting from stricter building codes and public adaptation investment is likely driven by the asymmetry of information rather than the materiality of flood risk. In other words, public investment in flood risk reduction appears to signal to investors that the airport is exposed to flood risk, potentially leading to increased financial constraints. This finding highlights the importance of considering information asymmetry when assessing the impact of flood management policies on financial constraints. Understanding the underlying drivers of these effects is crucial for supporting resilient infrastructure development and informing effective decision-making in flood-prone areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".