Do one’s moral foundations impact how they respond to information on climate change emissions? A vehicle choice experiment
Bibliographic record
Abstract
Transportation is a major source of climate change emissions. Providing people with better information on those emissions is one means of helping individuals make climate-friendly choices. However, not everyone is influenced by the same type of information. Previous research has demonstrated that Goal Framing Theory could help improve the influence of climate change emissions information and that different framings have different levels of influence depending on a number of socio-demographic and attitudinal characteristics. However, apart from climate change motivation, what other underlying psychological factors might help us understand why the framings vary in their influence between individuals? Moral Foundation Theory (MFT) identifies key values that influence people’s moral decisions, providing a useful framework for understanding diverse responses to information. The objective of this study is to understand whether MFT can help explain different responses by individuals and identify which framings are associated with stronger responses for different moral foundations. This study investigates the moderating effects of moral foundations on individuals’ responsiveness to different emission information framings. Utilizing data from discrete choice experiments involving 2015 Canadian drivers, we examine how different moral foundations impact the willingness-to-pay (WTP) for reducing emissions. The results reveal that the impact of emissions information framing varies significantly according to individuals’ moral foundations. Specifically, moral values associated with Authority, Fairness, and Purity play negative moderating roles on WTP for CO 2 emissions under different framings, whereas Ingroup and Harm foundations have positive moderating effects on WTP with the framings tested. Additionally, innovative communication tools like new emojis demonstrated strong positive effects on WTP, especially among those with strong Ingroup, Fairness, and Purity values. Conversely, individuals with a strong Authority value showed the lowest WTP when presented with pressure gauge visuals. Using appropriate framing based on Moral Foundation Theory can considerably change the willingness-to-pay for climate change emissions for different parts of the population, with a notable increase in WTP observed among individuals inclined to alter their behavior. Future framings should incorporate MFT in their design.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".