Life-Cycle Value Assessment (LCVA) of Fuel Supply Options for Fuel Cell Vehicles
Bibliographic record
Abstract
The fuel cell vehicle (FCV) has the potential to revolutionize the world's transportation systems. As choices are made on sources of fuel for FCVs it is important to consider the life-cycle implications of each option or system. This paper summarizes the methodology and results of a joint initiative to evaluate the life-cycle performance of 72 vehicle and fuel scenarios in 3 Canadian cities, comparing Proton Exchange Membrane (PEM) fuel cell vehicles and fuelling infrastructure with conventional and alternative fuel vehicles. The analysis is based on actual performance data of commercial and near-commercial technologies. The specific fuels investigated were gasoline, diesel, natural gas, methanol, hydrogen and electricity. The Pembina Institute's Life-Cycle Value Assessment (LCVA) methodology was used to compare the environmental, economic and social performance of each system. The stressor categories quantified include emissions of greenhouse gases and criteria air contaminants, resource consumption, fuel cost, and vehicle cost. A wide number of other stressor categories were identified as important to vehicle / fuel supply performance and should be considered when comparing systems. This paper details only the air emissions results from the full LCVA report1. The conclusions reached within the LCVA highlight that there are considerable differences in the life-cycle performance of vehicle systems and that fuel supply choices play a critical role.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".