Fuel Selections for Electrified Vehicles: A Well-to-Wheel Analysis
Bibliographic record
Abstract
Electrified vehicles (xEV), especially the battery electric vehicle (BEV), are burgeoning and growing fast in China, aimed at building a sustainable, carbon-neutral future. This work presents an overview and quantitative comparison of the carbon-neutral vehicles fuel options based on the well-to-wheel (WTW) analysis. A more intuitionistic figure demonstrates the fuel options for greenhouse gas (GHG) emissions and describes the sustainability. Electricity and hydrogen shift the tailpipe emissions to the upstream process, forming larger WTW emissions from a fuel cycle view. The electricity WTW GHG emission reaches as much as twice that of gasoline. However, the high efficiency of the electric drive system improves the WTW emission performance from a vehicle view, making the lowest WTW emission of BEV. The fuel options’ technical and environmental perspectives are presented. Finally, long-term carbon-neutral vehicle development is discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".