Analysis of Infrastructure Requirements for Sustainable Transportation Technologies
Bibliographic record
Abstract
At present, transportation energy comes primarily from fossil fuels. In order to mitigate the effects of greenhouse gas emissions, it is necessary to transition to low-carbon transportation technologies. These technologies can include battery electric vehicles, fuel cell vehicles and biofuel vehicles. This transition includes not only the development and production of suitable vehicles, but also the development of appropriate infrastructure. For example, in the case of battery electric vehicles, this infrastructure would include additional grid capacity for battery charging. For fuel cell vehicles, infrastructure could include facilities for the production of suitable electrofuels, which, again, would require additional grid capacity. In the present paper, we look at some specific examples of infrastructure requirements for battery electric vehicles and vehicles using hydrogen and other electrofuels in either internal combustion engines or fuel cells. Analysis includes the necessary additional grid capacity, energy storage requirements and land area associated with renewable energy generation by solar photovoltaics and wind. The present analysis shows that the best-case scenario corresponds to the use of battery electric vehicles powered by electricity from solar photovoltaics. This situation corresponds to a 47% increase in grid electricity generation and the utilization of 1.7% of current crop land.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".