Assessment of life cycle environmental impacts of materials, driving pattern, and climatic conditions on battery electric and hydrogen fuel cell vehicles in a cold region
Bibliographic record
Abstract
Battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs) can play an important role in addressing climate change by diminishing greenhouse gas (GHG) emissions in the worldwide road transportation sector. There is limited research on the implications of the use of lightweight materials, driving pattern, and climatic impact on the life cycle GHG emissions in a cold region. To address this limitation, we developed a framework to assess eighteen BEV and four HFCV scenarios for a cold region that consider aforementioned parameters through a combination of driving patterns (in rural, city, and highway driving) and climatic conditions (i.e., summer, mild winter, and severe winter) for both conventional and carbon fiber-reinforced plastic (CRFP)-based BEVs. A case study was conducted for Canada, considering its cold regions, using available data for HFCVs. We assessed city driving in summer and highway driving in severe winter conditions for conventional and CFRP-based HFCVs. The results show that the lowest GHG emissions are in cities in summer, with life cycle GHG emissions values of 68.7 g CO 2 eq/km for CFRP-based BEVs. The highest life cycle GHG emissions are 364.4 g CO 2 eq/km with conventional HFCVs on the highway in severe winter conditions' scenario. The operation phase emerges as the primary contributor to life cycle GHG emissions, closely trailed by the production phase. The analysis shows that the most sensitive parameters for CFRP-based BEVs in the city in summer scenario are vehicle lifetime and for conventional HFCVs in the highway in severe winter scenario, fuel cell efficiency. The analysis also shows the range of life cycle GHG emissions for a cold region, with conventional HFCVs on highways in severe winter conditions exhibiting the highest emissions (331.0 g CO 2 eq/km) and CFRP-based HFCVs in the city in summer scenario the lowest (51.0 g CO 2 eq/km). • Life cycle assessment of conventional and CFRP-based BEVs and HFCVs in a cold climate. • The prime factors considered for LCA are materials, driving pattern, and climate. • Lowest GHG emissions of 68.7 g CO 2 eq/km in CFRP-based BEV: city summer. • Highest GHG emissions of 364 g CO 2 eq/km in conventional HFCV: highway severe winter. • GHG emissions are sensitive to BEV lifetime and HFCV fuel cell efficiency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".