Assessing the relationship between climate change anxiety, ecological coping, and pro-environmental behavior: Evidence from Gen Z Bangladeshis
Bibliographic record
Abstract
Climate change has been speculated to cause frequent, long-lasting, and adverse weather events and would affect people's lives-and-well-being. Bangladesh, a low-lying delta is vulnerable to climate change, experiencing natural disasters each year with physical, mental, and economical impact on the population. The primary objective of the present study was to investigate the relationship between climate change anxiety (CCA), climate change-related coping strategies, and pro-environmental behavior (PEB) among Gen-Z Bangladeshis. The secondary objective was to assess CCA level and its associated coping approach among different disaster types witnessed by the respondents, and in disaster-affected vs. non-affected group. A total 557 participants between 18- and 25 years old participated in a cross-sectional survey including 13-items CCA Scale (CCAS), 15-items Ecological Coping Scale (ECS), demographics, and PEB questions. Results demonstrated that functional-impairment subscale and cognitive-impairment subscale of CCAS, MFC (meaning-focused coping), denial, and problem-focused coping (PFC) subscale of ECS, and PEBs were reliably correlated to varying degrees. Moreover, the disaster-affected group had significantly higher cognitive-impairment, functional-impairment, denial, and PFC use than non-affected group. Also, flood-witnessed people demonstrated more cognitive-impaired, functional-impaired, and used more MFC and PFC than storm and drought witnessed people. These findings highlight the awareness level of climate change impact among Gen-Z Bangladeshis, assisting professionals to formulate a tailored intervention.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".