Climate change anxiety in China, India, Japan, and the United States
Bibliographic record
Abstract
Climate change anxiety is becoming recognized as a way in which climate change affects mental health. It is not only observed in populations that suffer the most from the direct impacts of climate change but also can be trigged by the mere thought and perception about such impacts. Although climate change is a global problem that is a cause for concern around the world, research on climate anxiety has only recently utilized validated measures, and it has mostly been conducted in Western and developed societies. In response to this research gap, we conducted a cross-national study of climate change anxiety using the Climate Change Anxiety Scale, with participants (N = 4000) from four of the top emitters in the world (China, India, Japan, and the U.S.) which vary in their climate change vulnerabilities and resilience. We demonstrated that the widely adopted measure of climate change anxiety exhibited configural and metric invariance in the four countries. Climate change anxiety was apparently higher in the Chinese and Indian populations than in the Japanese and American populations. There were some demographic correlates of climate change anxiety, but the pattern was not always consistent across the countries. Climate change anxiety was positively associated with engagement in climate action in all four countries, but apparently more so for sustainable diet and climate activism than resource conservation and support for climate policy. The effect was driven more robustly by the cognitive-emotional impairment dimension than the functional impairment dimension of climate change anxiety. Taken together, these observations suggest that the Climate Change Anxiety Scale can be used to assess climate change anxiety across countries, and that there are both similarities and variations across different societal contexts with respect to the experience of climate change anxiety. Future research must take these complexities into consideration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".