Assessing and Managing the Direct and Indirect Emissions from Electric and Fossil-Powered Vehicles
Bibliographic record
Abstract
Efforts to improve air quality and concerns about global warming make transportation mediums that do not produce emissions more attractive to end users. Meanwhile, some of these transportation mediums are powered by an electricity grid that generates a great deal of emissions. This study compared the greenhouse gas GHG emissions for both electric and fossil-powered vehicles using estimates of tailpipe emissions of fossil-powered vehicles and the indirect emissions from the electricity grid. Furthermore, a system dynamic model was developed for a more holistic review of the GHG emissions for both electric and fossil-powered vehicles. The result indicated that in terms of associated emissions from the grid, electric-powered vehicles are not always better than fossil-powered vehicles when the electricity is not from a renewable source. The GHG emissions for electric-powered vehicles are dependent on both the electricity usage rate of the vehicle and the GHG emissions that are associated with the production of that amount of electricity. Further opportunities exist in renewable and clean energy technologies for various operations. Based on reports from previous works, this report also presented potential strategies to achieve a significant reduction in GHG emissions for both the electricity grid and fossil fuel refining processes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".