From diesel to electric: potential of drayage trucks transition in Southern California
Bibliographic record
Abstract
Battery electric drayage trucks (BEDTs) offer an opportunity to decarbonize the drayage fleets. This article analyzes the potential of BEDTs using data on 1,051 drayage trucks in Southern California. A methodology is developed to evaluate energy and charger requirements across singleton, small, and large fleets. This study assesses the fraction of trucks that can be electrified using battery sizes from 100 to 1000 kWh. Our analysis reveals decreasing uncertainties for fleet electrification with increasing battery size and with offsite charging involved. Combining an 800-kWh battery with both depot and offsite charging using 350 kW chargers, approximately 95% of diesel drayage trucks can be electrified. However, singleton fleets demonstrate the lowest performance and experience substantial improvements through offsite charging. Preferred locations for depot and off-site chargers are identified near the Ports of Long Beach and Los Angeles, and the City of Ontario. These results provide essential guidance for electrifying drayage trucks.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".