Addressing the electric vehicle adoption gap for small fleets: A case study of local energy transitions in British Columbia
Bibliographic record
Abstract
• There is currently a gap in adoption of electric vehicles (EV) in small fleets. • This study surveys small fleet operators to understand barriers to adoption of EVs. • Barriers are related to cost, incompatibility (real or perceived) and availability of EVs. • Policymakers CAN use targeted programming, such as a bulk-buy, to increase adoption in small fleets. In the transition to replacing internal combustion engine vehicles with electric vehicles (EV), there remains a gap in adoption by small fleets. Researchers and practitioners have posited that this gap may exist for a range of reasons, including: that the fleet electrification is not economically rational, that the needs of fleet operators are too diverse for current market offerings, or that targeted government interventions for this segment are lacking. We conducted a survey (n = 68) of small fleet operators in British Columbia, Canada and categorized the responses into barriers related to cost, incompatibility (real or perceived) and availability. Current EVs are incompatible with the operational needs of some respondents but our results show that, in many cases, the incompatibility is perceived and EVs could meet the stated requirements of such small fleets. We also observed that common customizations to (or “upfitting” of) fleet vehicles can be readily applied to EVs, but specialized use cases must be produced by the manufacturer—which may be a supply-related barrier. We also used a total cost of ownership (TCO) to demonstrate that while economic rationality is generally stronger for lighter duty class vehicles, small fleets that drive longer distances have a greater advantage in electrification. Our findings suggest that government intervention targeted at small fleets, such as bulk purchasing programs, could increase the adoption of EVs in this segment when coupled with purchase incentives. This gap could potentially be filled by local agencies, which can play a critical role in brokering trust between parties involved by being the middle actor at the boundary of government, suppliers, and customers. Lastly, we observe that small fleet operators display some understanding of the TCO of EVs. Incorporating an educational component into a bulk purchase program, as observed in other successful procurement arrangements that we review, could enhance the confidence of fleet operators and ultimately, lead to further adoption.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".