Drivers of flood and climate change risk perceptions and intention to adapt: an explorative survey in coastal and delta Vietnam
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".