A review of current state-of-the-art road vehicle life cycle assessment tools
Bibliographic record
Abstract
With the increasing availability of vehicle Life Cycle Assessment (LCA) tools, practitioners need a clear understanding of each tools' modeling methodology to select the one best fitting their needs to reach robust conclusions on whether and how a reduction of certain environmental burdens could be realized from advanced technologies, such as electric vehicles. We reviewed five free open-source LCA tools evaluating four wheeled vehicles, namely, GREET (2022 rev1), AFLEET (2023), carculator (v1.8.4), GHGenius (v5.02), and EV Footprint (2023). We conducted the review through 1) deciphering the LCA methodology of each tool, 2) benchmarking key inventory data to understand where and why tool-derived discrepancies could happen, and 3) developing a framework for assessing tools' adaptability and suitability to aid practitioners in choosing and customizing tools as needed. The results showed that each tool is developed with different scopes, adopting different model mechanism, sourcing different background data and characterizing different impact categories. At inventory stage, data on fuel feedstock, electricity mix, battery chemistry are sourced and aggregated differently, and different types of on-road emissions are reported. The life cycle impact assessment implementation does not significantly contribute to discrepancies where carbon footprint is the only commonly reported impact indicator across tools. Using the newly proposed framework, we found carculator to have the highest adaptability, followed closely by GREET. The framework proposes aligning user needs with each tool's goals and scope to determine final suitability scores. We finally identified key input parameters for each tool, with examples of parameter customization provided. • Five free open-source vehicle Life Cycle Assessment (LCA) tools are reviewed. • Each tool covers different model mechanism, technological and geographical scope. • Major result discrepancies are due to different background data sources adopted. • Use carculator for a full range of LCA impacts, other tools only cover carbon footprint. • A framework to help users selecting a tool is proposed based on adaptability score.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".