Electric vehicle adoption in growing Canadian cities: Assessing barriers to electric vehicle adoption in the City of Kamloops
Bibliographic record
Abstract
Greenhouse gas emissions from transportation are a significant contributor to climate change. An effective method of reducing these transportation emissions is to electrify transportation. Local governments are creating electric vehicle policies to increase electric vehicle adoption in their communities. The City of Kamloops released their EV Strategy to encourage local electric vehicle uptake in 2020 but encountered barriers to local electric vehicle adoption such as a lack of charging infrastructure, a hesitancy to new technology, and the high prices of EVs. There is no significant research on EV adoption and barriers within a smaller city, regional hub, or Canadian context. To address this, I conducted a content analysis, literature review, and key informant interviews with six stakeholder groups in Kamloops to assess the perceived barriers to EV adoption. My research compares the perceived barriers within the context of Kamloops and provides policy solutions to the City of Kamloops to overcome these barriers and reduce transportation emissions. The City should continue to prioritize EV adoption through public education campaigns, encourage the building of charging stations, and focus on densification for an overall reduction of emissions and to provide convenient places for EV drivers to charge their vehicles.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".