Carbon emissions and subjective well-being in Blue Zone Ikaria and Athens, Greece
Bibliographic record
Abstract
Reducing carbon emissions has become largely synonymous with personal sacrifice that can decrease subjective well-being (i.e., happiness, life satisfaction). However, a growing body of research suggests that pro-environmental behavior is positively associated with subjective well-being. To further examine this relationship, this exploratory study examined individual carbon emissions and subjective well-being in Blue Zone Ikaria, Greece, using Athens as a comparison site. Structured interviews and questionnaires with 46 participants (22 in Ikaria, 24 in Athens) revealed that Icarian participants reported higher mental well-being and lower carbon emissions from air travel and clothing consumption than Athenian participants. Icarian participants were also more likely to grow their own food and identify as part of a tight-knit community. These findings suggest that community-focused lifestyles may promote mental well-being while reducing carbon emissions. Future research with larger, more representative samples and objective emissions data is needed to further explore this relationship in Ikaria and other non-WEIRD (Western, Educated, Industrialized, Rich, Democratic) societies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".