The neglected electric vehicle adoption context: Expert perspectives concerning barriers to uptake in rural communities
Bibliographic record
Abstract
In an effort to decarbonize the transport sector, many countries are implementing policies to increase the uptake of Zero-Emission Vehicles (ZEVs). While ZEV adoption is on the rise, it is not occurring at the same rate geographically. With some exceptions, rural areas are adopting ZEVs at much slower rates compared to urban areas. It is likely that unique rural geographies require special policy considerations regarding ZEV uptake, yet few studies have focused on rural areas. This study addresses this gap through a qualitative investigation of barriers to ZEV adoption in rural areas. Twelve group interviews with experts in transport, energy, infrastructure, economics, and climate across Canada who serve on a Federal-Provincial-Territorial-Zero-Emission-Vehicle-Working-Group (FPT ZEV WG) were conducted. Group interviews were transcribed verbatim and analyzed using thematic analysis. Barriers to ZEV adoption that emerged from the analysis included logistical, perceptual, economic, and policy. Though provincial and territorial policies vary widely within Canada, a rural-urban ‘ one-size-fits-all ’ approach emerged. In other words, within provincial and territorial ZEV policy, there is a lack of distinguishment between rural and urban areas. Further, the heterogeneity of rural communities is rarely given explicit consideration in the policy landscape. Taken together, ZEV adoption policies may need to evolve to address rural blind spots that are apparent. • Using semi-structured interviews, rural barriers to ZEV uptake are identified. • Barriers are logistical, perceptual, economic, and policy related. • Barriers to uptake are more pronounced in rural areas. • Rural areas are heterogeneous and challenging to define. • Policy does not directly consider rural areas, nor their diversity.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".