Modeling the Demand for Electric Vehicles in Canadian Corporate and Government Fleets
Notice bibliographique
Résumé
This dissertation investigates factors affecting the acquisition of Electric Vehicles (EVs) in the Canadian fleet market. Data from a random sample of over 1,000 fleet operating entities (FOEs) that owned and operated light fleets (i.e., cars, pickup trucks and utility vehicles) in Canadian cities were collected via an online survey titled Canadian Fleet Acquisition Survey (CFAS) in December 2016. The CFAS gathered information about the general characteristics of the surveyed FOEs, their existing fleet characteristics, future acquisition plans and EV fleet prospects. A stated preference (SP) section was introduced in the CFAS to identify the circumstances that will lead to higher adoption rates of EVs for fleet usage. The SP responses were based on six choice scenarios, each featuring four powertrains (Internal-Combustion Engine Vehicles, Hybrid Electric Vehicles, Plug-in Hybrid Electric Vehicles and Battery Electric Vehicles). The CFAS also included attitudinal statements to understand the issues that support or deter EV acquisition in fleets. Chapters 4, 5 and 6 of the dissertation are dedicated to employing various modeling approaches including Exploratory Factor Analysis (EFA), Analytical Hierarchy Process (AHP), and advanced discrete choice models such as Latent Class (LC) and Ordered Logit (OL) models to investigate the feasibility of EVs in fleets from various perspectives. This includes investigating EV adoption with respect to entity type (i.e., corporate vs. government), fleet type (car fleets vs. pickup truck fleets vs. utility vehicles fleets), industry type (transportation and warehousing vs. retail trade) as well as the temporal dimension for fleet electrification (i.e., short run vs. long run). The estimated EFA models identify latent constructs of behavior on various aspects and attitudes relating to EV adoption and provides evidence of attitudinal heterogeneity in the corporate and government FOEs. The AHP approach validates the logical consistency of the attitudinal responses obtained from the sampled FOEs. The four latent classes of FOEs identified in the estimated LC choice model provide novel results regarding the factors that affect acquisition of EVs in fleets. The willingness-to-pay estimates from the LC model reflect the taste variation among the four latent classes for improvements in certain attributes of EVs. The results from the OL modelling exercise successfully explain the behavior governing the acquisition timeframe for battery electric vehicles in the sampled FOEs and highlight the heterogeneity in the factors affecting the acquisition timeframe. Finally, evidence-based policy guidelines are proposed to help stakeholders make informed decisions regarding the acquisition of EVs in fleets. Key guidelines include investment in public charging infrastructure, incentivizing on-site charging infrastructure, engaging FOEs with climate action plan, and harvesting positive attitudes towards fleet electrification through various campaigns. The research work described in this dissertation is the first of its kind to collect and analyze revealed and state preference data on the acquisition of EVs in Canadian fleets including the timeframes under which these vehicles will likely be acquired. The work is seminal as it fills an important gap in the current knowledge about the motivations and preferences towards fleet electrification in Canada.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 source (Gemma direct ou Codex distillé), 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 ».