Emotions and climate change: The role of emotion regulation in climate action
Bibliographic record
Abstract
Although there is strong evidence for the role of emotion in climate-mitigating behaviours (Brosch, 2021), little is known about the role of emotion regulation in climate action (Panno et al., 2015). Our studies (a correlational study and an experiment, conducted in 2022) investigated the effect of emotion regulation on emotional responses to climate change and the likelihood of taking climate-mitigating actions. In Study 1, 151 participants from the US and Canada read about the detrimental effects of climate change before recording their emotional responses, emotion regulation strategies used in response to climate change information, and climate actions (pro-environmental behaviours and civic engagement in environmental actions). Emotion regulation was a significant predictor of climate action even when controlling for demographic variables. In Study 2, 245 participants from the US watched a video on the negative consequences of climate change, rated their emotions, and were randomly assigned to distraction, worrying, or positive reappraisal in response to the video. Next, they were given the option to sign an environmental petition and donate money to an environmental organization, before rating their intentions to engage in climate actions. Distraction reduced negative emotion and positive reappraisal increased hope. The effects of emotion regulation on engagement in climate action were mixed. Exploratory mediation analyses indicated that worrying and reappraisal increased intentions to engage in climate action, via negative emotion. Further experimental research on the effects of emotion regulation on climate action will be important for informing communications about climate change, to protect mental health while motivating action.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".