Three Essays on the Effects of Government Taxation and Incentive Policies on Consumers' New Vehicle Purchase Decisions
Notice bibliographique
Résumé
Chapter 1. This chapter aims to find the effects of financial point of sales incentives on the sales of electric vehicles across the Canadian provinces from September 2012 to December 2016. The findings of my study indicate that purchase incentives cause the sales of new electric vehicles to increase by 8 percent on average due to a $1000 increase in incentives. I find that 47% of electric vehicle sales across the rebating provinces (Ontario, Quebec, and British Columbia) are attributed to the purchase incentives. Results of my counter-factual simulations imply that the cost of eliminating one tonne of carbon emissions across the provinces that offer incentives over the years of my study is, on average, $216/tonne CO2. Chapter 2. In light of the rapid increase in Canadian gasoline prices from 2000 to 2010, this chapter focuses on the relationship between gasoline price and demand for vehicle fuel efficiency across the Canadian forward sortation areas (FSA) over this period. I find that consumers respond to variations in gasoline price when deciding the fuel efficiency of their new vehicle; increases in gasoline price result in shifts in demand for more fuel-efficient vehicles and therefore improve the average fuel efficiency of the new vehicle fleet. I find that the elasticity of fuel economy with respect to gasoline price for new vehicles sold across the Canadian forward sortation areas (FSA) from 2000 to 2010 is -0.06 to -0.16. Results of further analyses imply that consumer are more responsive to rising and constant gasoline prices than falling prices and that urban residents are slightly more responsive to variations in gasoline price compared to residents of suburb regions. Chapter 3. This chapter investigates the effect of the carbon tax policy implemented by the Canadian Province of British Columbia on households’ new vehicle purchase decisions. I dis-aggregate the effects of gasoline price into two effects: the carbon tax and carbon tax-exclusive gasoline price. These effects are both measured along the extensive margin of replacing a fuel inefficient vehicle with a fuel-efficient vehicle. The results indicate that there is a significant negative relationship between both effects and fuel efficiency substitutions. However, vehicle fuel economy is more sensitive to changes in the carbon tax than to equivalent changes in the carbon tax-exclusive gasoline price. I find that the elasticity of fleet fuel economy with respect to the carbon tax ranges from -0.22 to -0.26 whereas this elasticity changes between -0.1 and -0.15 with respect to gasoline price (net of the carbon tax). I obtain consistent results when estimating the effect of both factors on fleet fuel economy conditional on fleet composition, indicating that almost all vehicle segments respond more strongly to changes in the carbon tax component of gasoline price than other components. Results also imply that, among all segments, the fuel consumption of compact sport utility vehicles (SUVs), minivans, and luxury high-end cars respond the most to the carbon tax.
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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 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,001 |
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 ».