A Stated Preference Study to Explore Market-Based Instruments to Reduce Car Usage
Bibliographic record
Abstract
Abstract Car dependency is becoming an increasingly difficult problem for policymakers to contend with, and requires targeted policy solutions that balance the need for greater urban mobility with reduced congestion. We investigated public preferences for welfare measures designed to encourage car use reduction and promote more sustainable urban environments. Cross-sectional survey data were obtained from n = 773 car owners living in Belfast, United Kingdom. A discrete choice experiment was used to elicit the willingness-to-pay (WTP) for a congestion charge that would finance policies to reduce car usage. A contingent valuation question assessed the willingness-to-accept (WTA) a monetary incentive to reduce car usage. WTP values were computed using a mixed logit model, and an interval data model was used to assess the factors that were correlated with WTA. We also calculated the benefit to the economy of reduced car usage. WTP for different policy measures ranged from £2.12 to £11. The highest WTP value was observed for improvements to public transport frequency, coverage, and connectivity. The median WTA value to reduce car usage by one day per week was £3. As a result of reduced emissions and road casualties, it was estimated that this intervention would generate benefits worth £3.83 m, however this was greatly outweighed by the costs involved.
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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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".