Travel behaviour and greenhouse gas impacts of income-conditioned e-bike purchase incentives
Bibliographic record
Abstract
This study investigates the travel behaviour and greenhouse gas (GHG) impacts of an electric bicycle (e-bike) purchase incentive program in Saanich, British Columbia, Canada, which distributed purchase rebates in three tiers conditioned on household income. A panel of 402 study participants (including a control group) was surveyed in three waves. We find that 23 % to 76 % would not have purchased an e-bike without the rebate, increasing with rebate amount, and that the purchased e-bikes were used regularly. Larger, income-conditioned incentives were associated with higher pre-purchase automobile use and consequently greater post-purchase automobile travel reduction. The incentive recipients reduced their GHG from travel by an average of 16 kg CO 2 e per week one year after purchase, greater for the larger, income-conditioned incentives. The marginal and non-marginal GHG abatement costs were CA$722 and CA$190 per tonne CO 2 e, respectively, which is cost-competitive with other types of transportation subsidies, but not the international carbon market.
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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 | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".