Essays on the economics of electric vehicle markets: Effect of subsidies on adoption, infrastructure development, and environmental outcomes
Bibliographic record
Abstract
This thesis explores the role of governmental subsidies in promoting electric vehicle adoption, and their potential for decarbonizing the Canadian transportation sector. The first chapter studies the impact of subsidies on electric vehicle adoption in Ontario and Quebec. I leverage the fact that Ontario significantly increased electric vehicle subsidies in 2016 to identify the causal effect of subsidies on electric vehicle sales using a difference-in-difference analysis. I find evidence that the increase in subsidies led to an increase in electric vehicle adoption, although the effect occurred with a lag. I find no evidence that the policy change generated a response from charging station operators directly, suggesting that network operators do not respond to rebate programs before the increase in demand is realized. The second chapter studies the ‘Roulez Vert’ program which was implemented in Quebec in 2012. I study in more depth how these subsidies impact the market for electric vehicles. A key feature of this work is that not only do I study how consumers react to the policy, but also how charging station operators alter the configuration of local charging station networks, and how car manufacturers update prices in reaction to the increased demand for electric vehicles. My analysis relies on a structural demand estimation. I find that the Roulez Vert program was responsible for 45% of electric vehicle sales and 26% of charging station installations between 2012 and 2018. I estimate the marginal cost of abating emissions using this program to be $340 per ton, well above conventional estimates for the social cost of carbon. This result raises questions about the cost-effectiveness of these rebate programs
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".