Emotion regulation, climate distress, and climate action in climate activist and student samples
Bibliographic record
Abstract
Emotions predict the likelihood that people will engage in behaviors to mitigate climate change. However, there has been little attention to the role of emotion regulation—how we increase, decrease, or maintain our emotional experiences—in climate action. Emotion regulation is critical for mental health and well-being, but emotion regulation strategies that benefit individuals’ psychological health may not necessarily benefit society through motivating climate action. In the current, exploratory study, we set out to test the associations between emotion regulation, climate distress, and climate action. We recruited participants from a small urban university and a local climate activism group. Participants completed measures of climate distress (climate change anxiety and worry), use of cognitive emotion regulation strategies in response to climate change information, and climate action (pro-environmental behaviors and civic engagement). Climate activist participants scored higher on climate distress, varied in their use of several emotion regulation strategies, and reported more engagement in climate action relative to the student sample. Specific emotion regulation strategies (rumination, catastrophizing, other-blame) were associated with climate distress. Specific strategies (rumination, positive reappraisal, catastrophizing, other-blame, and lower non-judgment and positive refocusing) predicted climate action. In addition, climate distress was associated with climate action. The results support the notion that negative affect may be a strong motivating factor for climate action and point to the need to consider emotion regulation as a potential means for transforming climate distress and for intervening to change climate-related behaviors.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".