Cross-cultural comparison of nudging effects for environmental protection: A case-study of risk-averse attitudes toward disposable plastics
Bibliographic record
Abstract
Disposable plastics are drawing considerable attention as a source of environmental risk despite their benefits in daily life. Banning the use of disposable plastics could increase other types of risks, which may damage the public good in the long run. Considering the trade-off of the risks and benefits, one way to improve social welfare is to conduct proper recycling and to continue using plastics but limit them to essential use, avoiding an unnecessary ban. A potential barrier to such a policy might be risk-averse attitudes toward actions that are perceived to threaten future generations, which is a well-known phenomenon. We previously designed a framework for information provision using messages that remind individuals about familial support, which had significant effects in multiple countries on increasing positive attitudes toward air pollution caused by industrialization. We hypothesized that this information provision could also be effective for disposable plastic use. Thus, we conducted a randomized controlled trial via online surveys in Japan, Canada, and the US to identify the effects of our designed messages about recycling on increasing positive attitudes toward disposable plastics. The intervention effects were measured by the difference-in-difference method and panel analysis based on linear regression models using the respondents' attributes and personality traits. The effects were consistently correlated with a sense of familial support, with the effect sizes varying according to country (US > Japan > Canada). Attributes that positively contributed to the message being more effective were higher agreeableness, lower Machiavellianism, lower psychopathy, and being a woman. Although personal fear about COVID-19 moderated the message effects, concern about the threats to relatives and family boosted the effects. Although the effect sizes were influenced by external factors, the results suggested that our proposed framework for information provision has the potential to be applied to a wider variety of risk-related topics.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".