Experienced climate change impacts help explain subjective well‐being—Evidence from 14 nature‐dependent communities
Bibliographic record
Abstract
Abstract Climate change profoundly affects well‐being in complex and interconnected ways. However, the relationship between climate change and well‐being has been explored in only a handful of settings, most of which are industrialized. Here, we investigate the association between perceived climate change impacts, their severity and subjective well‐being (measured as life satisfaction) using cross‐culturally comparable first‐hand reports from 2488 participants across 14 nature‐dependent communities. We find a negative association between site‐aggregated life satisfaction and different metrics of climate change: perceptions of local impacts, reported severity and instrumental measurements. Within sites, individual‐level associations between perceived severity of climate change impacts and life satisfaction are weak or absent. Further analysis suggests that site‐level characteristics play a crucial role in shaping these patterns. This could indicate that it is the overall vulnerability and exposure of a community to climate change impacts, rather than individual experiences that matters most. Our findings offer a nuanced understanding of how climate change impacts relate to well‐being, emphasizing the multidimensional character of climate change impacts and underscoring the importance of local context in shaping these relationships. 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".