Drivers of flood and climate change risk perceptions and intention to adapt: an explorative survey in coastal and delta Vietnam
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
This article contributes to current research about determinants of climate change and flood risk perception, and intentions to take adaptive measures. We propose a research model that distinguishes between vulnerability and severity components of perceived risks, and adds perceived adaptive capacity as a third factor to predict the intention to take adaptive measures. We used this combined model as a conceptual lens for an explorative survey among 1086 residents of coastal and delta communities in Vietnam. Pairwise analyses revealed a significant association of flood and climate change risk perceptions with individual’s flood experience, climate change knowledge, frequency of community participation and socio-demographic factors. However, in multivariate analysis, the influence of most socio-demographic factors became weak or patchy. Flood experience was the most influential driver of flood-related risk perceptions but weak for climate change-related risk perceptions and behavioural intentions. Knowledge strongly increased the intention to adapt to flood and climate risks and the perceived vulnerability to and severity of climate change risks, but reduced the perceived capacity to adapt to climate risks. Frequency of community participation increased the perceived vulnerability and severity of climate change risks, the intention to adapt to both climate and flood risks and the perceived capacity to adapt to flood risks, but reduced the perceived capacity to adapt to climate risks. Our research confirms earlier findings that individuals’ knowledge, place-specific experience and social-cultural influences are key predictors of both flood and climate change risk perceptions and intentions to take adaptive measures. These factors should therefore receive ample attention in climate risk communication.
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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 | 0.004 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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