A Comprehensive Sustainability Assessment of Battery Electric Vehicles, Fuel Cell Electric Vehicles, and Internal Combustion Engine Vehicles through a Comparative Circular Economy Assessment Approach
Bibliographic record
Abstract
Transitioning to zero-emission vehicles (ZEVs) is thought to substantially curb emissions, promoting sustainable development. However, the extent of the problem extends beyond tailpipe emissions. To facilitate decision-making and planning of future infrastructural developments, the economic, social, and technological factors of ZEVs should also be addressed. Therefore, this work implements the circular economy paradigm to identify the most suitable vehicle type that can accelerate sustainable development by calculating circularity scores for Internal Combustion Engine Vehicles (ICEVs) and two ZEVs, the Battery Electric Vehicles (BEVs), and Fuel Cell Electric Vehicles (FCEVs). The circularity assessment presents a novel assessment procedure that interrelates the environmental, economic, social, and technological implications of each vehicle type on the three implementation levels of the circular economy (i.e., The macro, meso, and micro levels). The results of our analysis suggest that not all ZEVs are considered sustainable alternatives to ICEVs. BEVs scored the highest relative circularity score of 36.8% followed by ICEVs and FCEVs scoring 32.9% and 30.3% respectively. The results obtained in this study signify the importance of conducting circular economy performance assessments as planning tools as this assessment methodology interrelate environmental, social, economic, and technological factors which are integral for future infrastructural and urban planning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".