Modeling the impacts of electric bicycle purchase incentive program designs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Governments are interested in incentivizing e-bike adoption, due to potential benefits from displacing travel by private automobile. To inform the development of e-bike purchase incentive programs, the objective of this paper is to determine how key elements of program design (particularly rebate amounts and structure) are expected to affect new e-bike purchases. An aggregate demand model is developed and applied to rebate scenarios to examine incentive effectiveness. Results show that rebate programs are expected to be bound by available rebates, not e-bike demand, and additional bike shop revenues exceed rebate costs. At a fixed program budget, fewer, larger rebates yield fewer additional sales, but a larger share of rebates go to low-income and new (marginal) purchasers. Flat and proportional rebate structures yield similar sales, although flat rebates are more income-equitable. Flat rebates are recommended for new e-bike incentive programs, with robust program evaluations to inform future program designs.
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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 | 0.001 |
| 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 it