Air quality and diet preference: do consumers purchase more healthy food when breathing polluted air?
Bibliographic record
Abstract
Purpose This research examines how air quality, as a direct environmental factor, impacts individual food choices. Design/methodology/approach Study 1 uses a panel of more than ten thousand people with one-year shopping records to examine the relationship between air quality and healthy food purchase. Study 2 employs a scenario-based experiment to investigate the underlying behavioral mechanisms regarding food consumption as individual response to mitigate perceived stress caused by air pollution. Findings Using data from a major Chinese supermarket chain in a large city, combined with daily local air quality measurements over a one-year period, Study 1 revealed a nonlinear relationship between air quality and healthy food purchases. Specifically, as air quality deteriorates to moderate levels, healthy food purchases decrease, reaching the lowest point under medium pollution levels. However, when air pollution becomes severe, healthy food purchases increase again, resulting in a “U-shaped” pattern. In a subsequent scenario-based experiment (Study 2), poor air quality was found to increase individuals’ perceived stress, which subsequently influenced food choices. This effect was moderated by the emotion regulation strategies individuals adopted, providing a behavioral explanation for the observed U-shaped relationship. Originality/value This research uncovers a nonlinear relationship between air quality and individual healthy food choices. Moreover, it highlights the role of emotion regulation strategy in shaping the effect of air quality on individual behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".