Eco-emotions in children and adolescents: A rapid review of the qualitative literature
Bibliographic record
Abstract
The global environmental crisis, driven by climate change and environmental degradation (CCED), profoundly impacts the mental and emotional well-being of children and adolescents. This is of concern as children and adolescents are likely to have disproportionate and long-term CCED-related mental health impacts due to their developmental stage, limited influence over environmental decision-making, and long-term exposure to CCED impacts. Recent research has increasingly sought to understand the psychological impacts of emotions related to the awareness of CCED, referred to as eco-emotions. However, much of this research has focused predominantly on fear and anxiety, commonly termed climate- and eco-anxiety. To address the gap in understanding the broader spectrum of eco-emotions experienced by children and adolescent, a rapid review of qualitative literature was conducted. This rapid review searched six electronic databases and synthesized findings from 48 qualitative and mixed methods studies. Across many studies, emotions such as worry, fear, anxiety, and anger were reported by many participants. Emotions such as sadness, grief, powerlessness, and helplessness were also reported. While the eco-emotions experienced were predominantly found to be negative, eco-emotions such as hope, optimism, and happiness were also observed, particularly among those participating in CCED-focused programming (e.g., educational initiatives, community-based environmental projects, or programs explicitly addressing eco-emotions). Apathy toward CCED was noted in some cases and was often linked to perceptions about its relevance or perceived immunity to its impacts. Some studies reported on deleterious mental health and well-being impacts related to negative eco-emotions. Methods of coping with eco-emotions were also extracted and synthesized. Children and adolescents reported the use of problem-focused and emotion-focused coping strategies, such as engagement in pro-environmental behaviours and connecting with friends and family for support. In a small number of studies, meaning-focused strategies such as positive reframing of the global environmental crisis were also noted. Findings from this review highlight the need for eco-emotions research beyond climate- and eco-anxiety and underscore the importance of tailored CCED-programming to support children and adolescents experiencing negative eco-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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".