Environmental Impact Assessment Of Anticipated 2037 Electrical Vehicle Adoption In Alberta
Bibliographic record
Abstract
This study assesses the environmental impacts of the projected adoption of electric vehicles in Alberta in 2037, evaluates receptiveness of the current regulatory environment and identifies policies to encourage adoption. Four scenarios are modeled including two electric vehicle uptake projections and two anticipated Albertan energy mixes. Findings suggest greenhouse gas reductions between 57.8% and 63% per kilometer could be achieved, with Alberta-wide reductions being between 1,910,000 and 3,120,000 tonnes GHG-100 annually. Substantial reductions of criteria pollutants can also be achieved but sulphur dioxide emissions will increase due to grid dependency upon natural gas. Policy research suggests the regulatory environment is somewhat receptive to electric vehicle adoption, but numerous potential incentives could further encourage uptake. Environmental benefits from adopting electric vehicles are currently minimal but these will become increasingly pronounced as the grid develops towards 2037 projections. Policies to encourage electric vehicle adoption should be scaled accordingly to maximize environmental benefits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".