Econometric Modelling Approach to Explore the EV Adoption and Charging Opportunities at Workplace
Bibliographic record
Abstract
The transportation sector is a significant contributor to CO 2 emissions, and it is imperative to electrify the existing fleet to mitigate this issue. Researchers worldwide are focused on identifying influential factors for electric vehicle (EV) adoption and the optimal location for charging stations. This study contributes to the current literature by utilizing the Halifax Sustainable Transport Survey to answer these research questions. The study employs Binary Logistic Regression (BLR) and Ordered Logistic Regression (OLR) models to identify the determinants of EV adoption and the importance of installing Electric Vehicle Charging Stations (EVCS) at workplaces. The study has yielded critical outcomes, such as individuals between the ages of 25 to 44 being more inclined to adopt EVs, particularly for shorter travel distances due to insufficient charging infrastructure for longer distances. The study also found that respondents who work daily or 3-4 times per week exhibit a greater interest in installing EV charging stations at their workplace. The implications of this research will aid policymakers in developing a sustainable transportation infrastructure to reduce vehicular emissions and provide a better living environment for the residents of Halifax.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".