Behavioural nudging for greener travel: A discrete choice experiment in the Greater Toronto Area
Bibliographic record
Abstract
Environmental management through greenhouse gas (GHG) emission mitigation strategies is a collective responsibility shared by governments, communities, and individuals. Vehicular traffic is one of the leading causes of GHG emissions. Therefore, accelerating the shift from private vehicles to greener alternatives is crucial for achieving carbon neutrality . This study conducts controlled stated preference (SP) choice experiments on travellers' mode choices to nudge sustainable travel behaviours. The experiment aims to provide a quantitative understanding of the effectiveness of nudging for greener behaviour through environmental externality information (GHG emissions) and self-interest information (health implications). The SP experiment was conducted in the Greater Toronto Area, Canada, and had 606 valid samples. An error-component mixed logit model is empirically estimated to capture respondents' behavioural changes during various experiment stages. Based on parameter estimation results, the study also reports the Value of Greener (VOGer), defined as the willingness to pay for comparative GHG emission reductions achieved through greener travel modes. The study finds that the design of nudging interventions plays a crucial role in maximizing individuals’ VOGer. Travellers simultaneously aware of environmental and health-related implications are more likely to use greener travel modes (e.g., transit, biking, and walking).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".