Navigating the urban-rural divide in EV adoption: equity challenges and policy implications of mandatory zero-emission vehicle transitions in British Columbia
Bibliographic record
Abstract
The transition to electric vehicles (EVs) is crucial for reducing greenhouse gas emissions globally. As Canada commits to decarbonizing its transportation sector, aiming for 100% zero-emission vehicle (ZEV) sales in new light-duty vehicles by 2035 (Transport Canada, 2024), British Columbia's ZEV Act establishes even more ambitious targets of 90% by 2030 (Ministry of Energy, Mines and Low Carbon Innovation, 2024). While critical for climate change mitigation, this study investigates how the distribution and accessibility of electric vehicles are uneven; they are not uniformly available across different communities. By examining the transportation equity implications of EV adoption disparities between rural and urban areas in British Columbia (Metro Vancouver and the Kootenay region as comparative cases), this research analyzes multiple dimensions of geographical disparities in transportation electrification. Through descriptive analysis of publicly available data from government databases, non-profit & industry reports, we compare the factors that shape EV adoption in rural and urban areas and examine how universal ZEV mandates affect residents differently based on their geographic location. The findings reveal a nearly ninefold difference in adoption rates between Metro Vancouver and the Kootenay region (3.30% versus 0.38%). The regions also differ with regard to key factors that influence EV adoption, including limited charging infrastructure, higher financial burdens for rural households, severely limited public transit options, and reduced exposure & awareness to EV technology, impacting rural people's adoption decisions. These results demonstrate that British Columbia's ZEV mandate, without targeted interventions to address geographical inequities, risks creating a ‘green divide’ that reinforces rather than alleviates existing inequities and these disparities reflect inequitable policy choices rather than inevitable outcomes. This study contributes to transportation equity and sustainable energy policy discourse by providing empirical evidence of how sustainable mobility transitions affect different communities. It demonstrates that achieving equitable outcomes in transportation electrification requires moving beyond one-size-fits-all approaches to develop rural-specific interventions, including geographically differentiated infrastructure investment, rural-calibrated economic incentives, and targeted information campaigns. The implications extend beyond British Columbia to similar regions globally, underscoring the importance of considering geographical diversity in crafting effective and equitable transportation electrification policies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".