Impact of the car fleet evolution on electricity demand in Québec
Why this work is in the frame
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Bibliographic record
Abstract
Under pressure to reduce its greenhouse gas emissions (GHG), the global passenger car market is experiencing a shift from vehicles powered by internal combustion engines towards hybrid and fully electric models. In colder regions such as northern North America and northern Europe, the adoption of electric vehicles is expected to generate an uneven demand on the electric grid, with peak demand coinciding with building heating requirements in winter. To quantify the impact of this transformation, this study projects, using registered light-duty vehicles in Quebec, Canada, from 2011 to 2021, the evolving demand for electricity of the fleet under various electrification scenarios. Two historical tendencies enhance potential demand for electricity: (i) vehicles become increasingly heavy, gaining an average of 11 kg per vehicle between 2011 and 2021; (ii) the number of registered vehicles shows a yearly increase of 67,276 vehicles, about 1.3% of the fleet. Based on these trends, and accounting for per-vehicle energy use by weight, mileage, and temperature-related range losses, the projections show that stabilizing electric vehicle mass at 2021 levels would lead to a fleet that is 25.9% lighter and reduce electricity demand by 17.6% in 2040 with respect to the business as usual scenario. While a life-cycle assessment shows the benefits of electric vehicles over gasoline vehicles in terms of GHG reduction in any scenarios, our results underline the importance of understanding the potential strain on the electric grid that can be caused by unbridled transformation of the car fleet from internal combustion engine to battery-electric models. • Quebec’s car fleet averages a growth of 67,276 more vehicles each year. • In Quebec, an average of 492,216 new vehicles are added on the road annually. • The average mass of the light-duty vehicle fleet increases by 11 kg each year. • Capping average car weight at 2021 values could cut electricity demand by 17.6%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it