Environmental life cycle assessment of conventional and electric vehicles: lessons learned from selected countries
Bibliographic record
Abstract
Electric vehicle (EV) is an alternative expected to be tail-pipe emission-free and improve public health. Switching conventional or internal combustion engine vehicles (ICEVs) to EVs becomes a potential strategy for realizing urban sustainability. The study aims to review the environmental impact between ICEV and EV in Lithuania, China, Canada, Poland, Czech Republic, Italy, United States, and Australia. Then, the review result is compared to the Indonesia context as lessons learned. A comparative study with a qualitative descriptive method was carried out. The main activities are a literature review. The works of literature were collected, classified, and reviewed to find out significant findings on the environmental impact of ICEV and EV. Assessing the vehicle in all life-cycle (LC) phases is an essential issue. The entire LC of products may significantly impact the environment due to the utilization of raw materials through a process that causes adverse environmental impacts. Therefore, Life-cycle assessment (LCA) is proposed to estimate the environmental effects related to all the LC stages of EVs. Thus, LCA could be a critical tool. Numerous cases in several countries show that EVs were not always more environmentally friendly than ICEVs. The review indicates that EVs and electricity-generating mix scenarios play a significant role in performing LCA due to the performance of an EV is extremely dependent on the energy consumed through its operation phase. Additionally, the results show how significant renewable energy sources (RES) are in the electricity-generating mix that provides different environmental impacts. In the Indonesia context, the environmental impact of EV is predicted to be higher than ICEV due to the electricity generating mix is still lower than 20% in 2023. Optimizing the electricity generating mix scenario by increasing the RES, implementing clean technology power plants, and applying vehicle recycling are excellent strategies to promote sustainable development in the EV industry. However, economic and social aspects shall be considered to get comprehensive results in further research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".