Coping with climate emotions: A qualitative study using interviews and letters in remote, rural and small communities across Canada
Bibliographic record
Abstract
Abstract The consequences of climate change are becoming more severe and widespread, highlighting the growing need to understand and address the emotional dimensions of the climate crisis. Although research on climate emotions has grown substantially over the past decade, empirical work on how people are coping with climate emotions is very limited. Research oriented to lived experiences is needed. The objective of the study was to explore and describe how people living in remote, rural and small communities across Canada are coping with climate emotions. Data were collected through semi‐structured interviews ( N = 27) and asynchronous letter writing ( N = 23) and analysed using thematic network analysis. Five organizing themes emerged that illustrate the diverse and dynamic ways people cope with climate emotions and related consequences: (1) coping through distance and avoidance; (2) coping through feeling and talking about emotions; (3) coping through relationships and care; (4) coping through learning about and taking action; and (5) coping through resistance of despair and disempowerment. The findings underscore that coping is complex, dynamic and diverse. The findings also underscore the particularly important role of coping through caring relationships with people and nature. In the discussion section, key contributions relevant to the literature on coping with climate emotions broadly are outlined. Subsequently, the concept of connected coping is proposed and three insights relevant to supporting connected coping in remote, rural and small communities are discussed. First, the importance of coping through connection and care for the natural world is particularly important. Second, there is a growing need for community‐based and relationship‐focused processes that offer and create safe and accessible spaces for sharing experiences of climate emotions. These interventions must address the social silencing around climate change and related emotions that are particularly problematic in this context. Third, collective action in response to climate change and collective action aimed at protecting and caring for the natural world is an important pathway for supporting connected coping. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".