How Many Electric Vehicles Are Needed to Reach CO2 Emissions Goals? A Case Study from Montreal, Canada
Bibliographic record
Abstract
In the province of Quebec, Canada where the electricity is nearly carbon-free, the road transport sector represents 35.6% of all emissions. As such, electric vehicles (EVs) have been proposed as a means to reduce such emissions. However, it is not clear how many conventional vehicles (CVs) would need to change to electric in order to meet the greenhouse gas (GHG) emissions reduction target of reducing 1990 CO2 emissions by 37.5% by 2030 in the province. In fact, various considerations exist such as which vehicles will change and how those vehicles are used. This articleaddresses this issue in the case of Montreal, Canada. First, to create a baseline, direct emissions by all personal vehicles in Montreal in 2018 are calculated using data from the 2018 origin-destination (OD) survey and provincial vehicle registration. Next, five scenarios are studied to calculate the variations in the number of EVs needed in the fleet in order to achieve provincial targets. The most optimistic scenario shows that roughly 49% of the fleet would need to change. The most pessimistic scenario estimates that almost 73% of the fleet would need to be converted to EVs. It can be concluded that the strategy used can have a great impact on how many vehicles need to be replaced in the fleet. However, all simulations show that the necessary replacements are far from negligible. It must surely be coupled with other actions such as reducing veh.km travelled (vkmt) or increasing public transport use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".