The Economics of Electric Vehicles with Application to Electricity Grids
Bibliographic record
Abstract
Governments around the world promote the purchase of electric vehicles (EVs) as part of their climate change strategy, with many committing to EV-only sales of new passenger vehicles by 2035 and complete use of EVs by 2045 (California) or 2050 (e.g., Canada, EU). Rebates (purchase subsidies) are offered to consumers to promote uptake of EVs, and growth in their uptake has been quite strong, although EVs remain a small proportion of registered vehicles. In this study, we first analyze the economics of EV subsidies and then use Canadian electrical generation capacity, EV efficiency data, and distances driven, along with Monte Carlo simulation, to project the increased demands that greater numbers of EVs will place on an electrical grid. We find that the current grid’s capacity will not be adequate to power the anticipated growth in EVs, and major new power plants or hydroelectric dams will need to be constructed. The analysis suggests that Canada might need to build 17 new hydroelectric facilities or 14 additional gas plants, as there is likely to be much resistance to new hydroelectric projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".