Climate Emotions, Pro-environmental Behaviours, and Activism among Canadian Youth
Bibliographic record
Abstract
Psychological research has made significant contributions to the current understanding of the role of emotions in promoting or hindering a person’s ability to engage with pro-environmental behaviours and climate action. While international research on this topic continues expanding, there is little research documenting the emotional impacts of climate change on Canadian youth, and the role emotions play in their ability to stay actively engaged with this global challenge. This study examines several psychological constructs (e.g., climate anxiety, generalized anxiety and depression, negative and positive affect, and emotional responses to climate change) known to be associated with different levels of engagement with climate activism and pro-environmental behaviours in a sample of 912 first- and second-year Canadian university students. Using data gathered online, we conducted a series of statistical analyses that revealed that climate worry and concern were common among our participants. Results also showed that participants experienced many different emotions towards climate change. Factor analysis led to a categorization of emotional responses into four factors: externalizing negative emotions, internalizing negative emotions, positive emotions, and neutral emotions. Further statistical modeling showed that, while common, negative emotions did not inhibit climate activism or pro-environmental behaviours, which instead were predicted by positive emotions. We interpret the findings in the context of positive psychology frameworks such as Fredrickson’s Broaden and Built theory and draw insights that may guide further research investigation in the burgeoning field of the psychology of climate emotions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".