Perceptions, Preferences, and Behavior Regarding Energy and Environmental Costs: The Case of Montreal Transport Users
Bibliographic record
Abstract
Providing travel-related fuel and environmental information to transport users is becoming increasingly relevant. However, the impact of providing such information on users’ travel behavior is yet to be determined. This research examined the perceptions and preferences related to the fuel consumption costs, greenhouse gas (GHG) social costs, and health-related air pollution costs, and the influence such information could have on travel behavior. Examining the case of Montreal transport users, the authors conducted a survey in which the respondents were asked general and stated preference questions. The respondents were found to be unaware of the energy and environmental footprints of their travel. Approximately 85% of the respondents were not able to estimate GHG social costs and health-related air pollution costs across different modes. The respondents generally overestimated these costs and they interestingly reported higher environmental costs for public transport (metro) compared to cars. They also preferred to receive such information in monetary units, and they were more comfortable in receiving the information through mobile applications over other tools/means. The research also found that fuel and environmental information influence respondents’ travel decisions especially their route choices. Finally, the respondents would be willing to pay an average of 7 Canadian dollars/month in exchange for obtaining the information.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".