Immigration status, flood risk perception, and policy support : lessons from Richmond, Canada
Bibliographic record
Abstract
Coastal flood protection measures are increasingly vital due to climate change and rising sea levels, and their long-term success depends on both technical efficacy and local community support. Coastal green infrastructure (CGI) supplements traditional hard structures (THS) for managing floods, offering both coastal erosion prevention and ecological benefits. Prior studies show that immigrant and non-immigrant groups differ in environmental risk perceptions and landscape preferences; however, little is known about their respective views on climate change-related coastal flooding. This study explores factors that influence public perceptions of coastal flood risk and support for CGI, focusing on how such perceptions and support vary across population subgroups based on immigration status. Three groups were compared: recent immigrants and temporary residents (RITR, defined as those having migrated to Canada within the last 5 years and those on study and work permits), long-term immigrants (LI, defined as those having migrated to Canada over 5 years ago), and those born in Canada (BIC). A questionnaire survey was conducted in Richmond, British Columbia – where immigrants constitute over 60% of the population – between July and September 2023. Results showed high general perception of coastal flooding risks among participants, with LI and BIC groups exhibiting higher risk perceptions than RITR group. These differences were partly due to immigration status moderating the relationships between risk perception and its determining factors: for LI and BIC groups, risk perceptions were primarily driven by hazard awareness; for the RITR group, factors such as awareness of existing flood protections, access to flood-related information, and homeownership also played significant roles. Most participants supported including CGI in flood risk management strategies. More BIC participants favored hybrid solutions, while more LI and RITR participants abstained from an opinion. Immigration status again moderated the relationships between flood risk management preferences and its determining factors: for all groups, knowledge about CGI’s benefits and higher CGI familiarity positively correlated with support for CGI; while for LI and RITR groups, support for CGI was also correlated with environmental values. These findings highlight the importance of targeted communication strategies to enhance flood risk awareness and support for CGI across diverse immigration groups.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".