Who wants zero-emissions vehicles and why? Assessing the Mainstream market potential in Canada using stated response methods
Bibliographic record
Abstract
Extensive deployment of zero-emissions vehicles (ZEVs) is likely essential for Canada to achieve its greenhouse gas reduction targets, including plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs). To effectively promote ZEVs, it is critical to understand the factors that influence consumer interest in ZEVs. In this study, I surveyed 2,123 Canadians that intend to buy new vehicles to develop insights into “latent demand” among consumers, (that is, what demand would be if the ZEVs were fully available in the market), including ZEV-related preferences and possible underlying motivations for interest. Specifically, I analyze results from two stated response methods: design exercises and a stated choice experiment. First, the design exercises reveal that 21% of respondents are interested in ZEVs (a proxy for latent demand), where interest is primarily in PHEVs, followed by BEVs and HFCVs. ZEV-interested respondents tend to be younger and have higher education and income levels, and are also unique in measures of lifestyle engagement, values, and environmental concern. The design exercises also revealed that HFCV-interested respondents are distinct from PHEV- and BEV-interested respondents in their values and possible underlying motivations. Using data from the stated choice experiment, I estimated a latent class discrete choice model, and identified five unique respondent segments. Thirty-six percent of respondents fall probabilistically into segments which have strong preferences for ZEVs, 20% of respondents are undecided about ZEVs but remain open to them, and 44% of respondents prefer conventionally fueled vehicles. The latent class model indicates that respondents who prefer ZEVs are younger and have higher education levels, and have greater environmental concern, more environmental-oriented lifestyles, and stronger pro-social values. Results from this study indicate that financial subsidies and home recharging could be effective in increasing latent demand. Policy makers would be wise to consider the range of preferences and possible motives for ZEV interest when designing ZEV-supportive policy.
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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 | 0.000 | 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 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".