Addressing the electric vehicle adoption gap for small fleets: A case study of local energy transitions in British Columbia
Notice bibliographique
Résumé
• 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».