Three Essays on the Effects of Government Taxation and Incentive Policies on Consumers' New Vehicle Purchase Decisions
Bibliographic record
Abstract
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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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".