Driving the Future: An Analysis of Total Cost of Ownership for Electrified Vehicles in North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the number of electric vehicles (EVs) on North American roads continues to rise, driven by the shift toward sustainable transportation, understanding the economic implications of this transition is crucial. This review paper prioritizes an evaluation of the Total Cost of Ownership (TCO) for various types of EVs, providing insights into how different driving profiles align with the financial benefits of EV adoption. It demonstrates that at-home charging and government incentives are pivotal in reducing TCO. The analysis also offers a comprehensive overview of the factors driving EV growth, including declining operating and maintenance costs. Additionally, the paper explores adoption rates, charging infrastructure, and other non-monetary factors that influence consumer decisions in the shift to EVs. Conclusions emphasize that while EVs offer a financial advantage for many drivers, the success of broader adoption depends on decreasing the initial cost of EVs, developing charging infrastructure, and investing in charging networks.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it