The influence of familial nudging on attitudes toward climate change and monetary contributions: An exploration of ease and difficulty with evidence from Japan, Canada, the USA, and Norway
Bibliographic record
Abstract
• Nudging grounded in Evolutionary Psychology was applied to promote carbon neutrality. • The effects were analyzed using >15,000 responses from four countries. • Moderating perceived risks of climate change was successful in most cases. • Only Canada, recently hit by wildfires, showed a positive effect in willingness to pay. Climate change countermeasures require monetary investment for implementation through regulations, often in the form of public taxes. Therefore, public opinion on climate change is an important factor in achieving carbon neutrality (CN). How public perception is formed, leading to motivation to fund measures for CN, needs to be identified. This study aimed to identify the causal structures of attitude change leading to financial investment through two surveys in multiple countries using nudging messages emphasizing familial support (familial nudging), which is effective in promoting pro-environmental attitudes. Study 1 (2023) examined how familial nudging moderates risk-averse attitudes toward climate change. Despite successful overall nudging effects, no increase in willingness to pay for CN was observed. Causality analysis showed that willingness to pay for CN was influenced by perceived risks to oneself and future generations. Based on these findings, Study 2 (2024) introduced modified familial nudging, emphasizing milder climate risks for the current generation, but more severe climate risks for future generations, such as wildfires. The new nudging message unexpectedly increased the perceived risks to the current generation, and no positive impact on willingness to pay was observed. The exception was Canada, as respondents showed non-negative responses and significant increases compared to the other messages. The Canadian wildfire in 2023 may have influenced the perceived necessity of CN countermeasures. These findings highlight the challenge of nudging perceived impacts on current and future generations, separately simultaneously. Meanwhile, recent natural disasters may effectively moderate the decreased financial motivation for CN, regardless of familial support.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".