Homeowners’ perceptions of property-level flood risk adaptation (PLFRA) measures: the case of the summer 2007 flood event in England
Bibliographic record
Abstract
Flood events have far-reaching consequences, not only in economic or financial terms but also in social and health-related impacts.There is a growing body of research that suggests that property-level flood risk adaptation (PLFRA) measures have the potential to benefit homeowners by reducing the impact of flooding on households.Emphasis has, therefore, been placed on the implementation of PLFRA measures, and yet despite this, the take-up among the at-risk residents in England is low.One of the reasons identified in the literature is that homeowners do not clearly recognise the benefits of the measures.This research uses a survey of households affected by the summer 2007 flood event in England to investigate the perception of homeowners in connection with the benefits of PLFRA measures.The results highlight that there is a consensus among respondents that implementing adaptation measures has the potential to reduce health-related flood impacts such as worrying, stress and strain between families.However, there was a high level of uncertainty with regard to potential financial benefits from investing in adaptation measures, in the form of premium reduction by insurers.It was evident from the analysis that knowledge of the frequency of future flood events and expected flood damage rated highly among the factors perceived by homeowners to influence the uptake of PLFRA measures.Furthermore, the results show that there is a wide range of opinion among the respondents as to who is responsible for protecting homes against flood risk.For instance, the government flood protection scheme has the potential to provide a confusing message to floodplain residents as to whose responsibility it is to protect properties against flood risk.It is, therefore, recommended that at-risk population should be made aware of the limits of the responsibilities of other stakeholders in the domain of flood risk management at household levels.However, it is anticipated that the introduction of the new UK flood insurance scheme, Flood Re, may help to bring more clarity.There is a need to increase the motivation of homeowners to invest in PLFRA measures, which could be achieved through a range of actions, including the provision of subsidies and incentives, which would help in promoting more sustainable behaviour.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".