Is road safety effective in promoting eco-driving? An experiment on how benefit framing affects eco-driving intentions among Montreal drivers
Bibliographic record
Abstract
In Quebec, Canada, the transportation sector is a major contributor to greenhouse gas emissions. Given the widespread use of personal cars, raising awareness about eco-driving—a set of fuel-saving behaviours—can help encourage the adoption of lower-carbon practices. Framing eco-driving in terms of its economic and environmental benefits is a promising communication technique to promote this practice, although evidence of its effectiveness remains mixed. Furthermore, while certain eco-driving behaviours contribute to road safety, the use of safety framing—particularly when presented as a community benefit—had yet to be empirically tested as an a priori strategy for promoting eco-driving. To fill this gap, we examined how three distinct goal frames—economic (egoistic-gain), environmental (biospheric-moral), and road safety (altruistic-moral)—influence intentions to adopt four eco-driving behaviours: avoiding hard braking and acceleration, removing bulky external objects, avoiding very high speeds, and choosing the shortest route. We conducted an online experimental survey with drivers ( N = 620) from Montreal, Canada. Participants were equally divided into four groups and asked to indicate their intention to adopt the targeted eco-driving behaviours. Before rating their intention, the three experimental groups were presented with eco-driving advice framed in terms of environmental, economic, or road safety benefits, while the control group received no advice. Compared to the control condition, the road safety frame was the only one to generate significantly stronger intentions to adopt eco-driving. This effect was consistent across all four behaviours tested. Our findings highlight the importance of focusing on community benefits when promoting pro-environmental behaviours.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".