Modelling the greenhouse gas emissions intensity of plug-in electric vehicles in Canada using short-term and long-term perspectives
Bibliographic record
Abstract
Plug-in electric vehicles (PEVs) have the potential to achieve deep greenhouse gas (GHG) emission reductions.However, the magnitude of these reductions depends largely on the source(s) of electricity, which can vary regionally and over time, making it unclear how policymakers should regulate PEVs over the short and long-term.To contribute to this discussion, I model the short and long-term operational (source-towheels) greenhouse gas emissions intensity of PEVs in the Canadian provinces of British Columbia, Alberta, and Ontario.I use empirical data on vehicle preferences, driving patterns, and potential recharge access from the Canadian Plug-in Electric Vehicle Survey (n=1754) to construct a temporally explicit model of PEV usage and emissions over the short-term.Fleet-wide emissions intensity of PEVs varies substantially between the three regions studied, with the greatest reduction potential in British Columbia (78-99%), followed by Ontario (58-92%) and Alberta (34-41%) relative to conventional (gasoline) vehicles.I then model the potential long-term dynamics of technology, behaviour, and emissions with the CIMS energy-economy model under three policy scenarios.Emissions intensity of electricity decreases by at least one-third by 2050 even under current policies, with the deepest reductions in Alberta (64%).Consequently, by 2050, fleet average PEV emissions are 23-40% (BC), 51-68% (Alberta), and 25-40% (Ontario) below 2015 levels.Despite the large range of emissions intensities between regions and over time, PEVs offer substantial GHG emissions benefits compared to conventional vehicles.Therefore, policy makers should look to design policies that concurrently promote vehicle electrification and decarbonisation of the electricity supply to help achieve long-term mitigation targets.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".