Electric vehicles — A ‘one-size-fits-all’ solution for emission reduction from transportation?
Bibliographic record
Abstract
Electric vehicles are broadly considered to have a great potential for reducing emissions from transportation and are sometimes presented as a `one-size-fits-all' solution. A simulation study was performed to forecast least emitting options for single vehicles as well as for total light duty vehicle fleets in the Canadian provinces of Québec, Ontario and Alberta for the year 2025. The study used the Plug-in Electric Vehicle - Charge Impact Model (PEV-CIM), a software tool developed by Natural Resources Canada for evaluating the impact of PEVs on the electricity grid, on fuel costs, and on emissions. Simulation results from PEV-CIM indicate that battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVS) offer a great potential to drastically reduce GHG emissions in the provinces of Québec and Ontario thanks to the low emission intensity of their electrical grids. However, the slow turnover of the light duty vehicle fleet limits the overall emission reduction of the provincial fleets for the year 2025 to only 5-12%. Power generation in the province of Alberta is dominated by the use of coal and natural gas. Its GHG emission intensity is higher than the threshold of 720 gCO2eq per kWh at which the emissions of BEVs and PHEVs are equal to those of hybrid electric vehicles (HEVs). For this province, HEVs will give the lowest emissions. Electric vehicles do reduce GHG emissions compared to gasoline vehicles. However, a `one-size-fits-all' does not exist as local conditions greatly influence which type of electric vehicle (HEV, PHEV, or BEV) is the best option. Besides, short term solutions may differ from those for the long term.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.285 | 0.119 |
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".