How Policy Can Build the Plug-in Electric Vehicle Market: Insights from Respondent-Based Preferences and Constraints (REPAC) Model
Bibliographic record
Abstract
Forecasts for alternative-fuel passenger vehicles sales have varied widely over the past three decades, often proving overly optimistic. In the recent case of plug-in electric vehicles (PEVs), published forecasts of new market share in North America have ranged from 1% to 28% in 2020, and from 1% to 70% by 2030. To improve their understanding of such forecasts, the authors develop a model with the goal of effectively representing key components of PEV demand, PEV supply and relevant policy in the REspondent-based Preference and Constraint (REPAC) model. Specifically, to represent consumer interest in PEVs the authors estimated a latent class discrete choice model based on data collected via a 2013 survey of 531 new vehicle-buying households in British Columbia, Canada. REPAC treats these choice model results as unconstrained (or latent) demand for PEVs. REPAC then adds “real-world” constraints based on other survey data collected in the same survey (PEV awareness and home charging access) as well as adding supply constraints to represent the limited variety and availability of PEV models. With such constraints, REPAC’s baseline (“no-policy”) forecast for annual PEV sales from 2020 through to 2030 is around 1% new market share. Forecasts for 2030 range from 1-10% with demand- focused policies in place (e.g. purchase subsidies), while strong supply-focused policy is also required to achieve 2030 market shares over 30% (i.e. a Zero-Emissions Vehicle mandate). REPAC’s forecasts are most sensitive to assumptions about PEV availability and variety, home charging access, and consumer familiarity with PEVs, but not gasoline or electricity costs.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".