Renewable Energy, Infrastructure and GHG Implication of Electrified Transportation: Metro Vancouver Case Study
Bibliographic record
Abstract
This study is aimed to assess the fleet composition for the new portion of light and medium duty vehicles (LMDV) in Metro Vancouver forecasted for the year 2020. Accordingly, the analysis evaluates the sensitivity of the regional electricity demand on transportation electrification policies. Considering electricity and hydrogen as transportation infrastructures, sixteen scenarios of zero tailpipe emission Electric Vehicle (EV) penetration in the new fleet are investigated. The study assesses the efficiency of EV technologies, quantifies energy demand for the electric transportation, and summarizes the implications of using renewable electricity to power the transportation sector.\n The analysis shows that wind energy is the superior resource in terms of life cycle Greenhouse Gases (GHGs). The life cycle GHGs of electricity production via wind turbines ranges from 390-3000 tonnes yr- 1 and for photovoltaic cells from 1300-9900 tonnes yr-1 of CO2eq across the scenarios. Furthermore, it is observed that 92% to 96% of life cycle greenhouse emissions could be reduced by deploying zero emission vehicles, which utilize solar or wind energy as a renewable resource. In this category, battery electric vehicles enable larger energy efficiency. Moreover, the results show that in order to respond to FCEV demand by 2020, the number of on-site hydrogen refueling stations should vary between 3 and 62, across different scenarios. The electricity demand to power these stations ranges from 32 to 248 GWh yr-1 which translates to annual production of 5 to 37 wind turbines with 2.24 MW of rated capacity, or alternatively 0.2 to 1.6 km2 of photovoltaic cell surface.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".