Life cycle assessment of electric scooters for mobility services: A green mobility solutions
Bibliographic record
Abstract
The gradual rise in global warming owing to fossil fuels usage is causing massive environmental problems and global temperature rise and countries are focusing on the negative emissions technologies (NETs) to restrict the average global temperature rise to 1.5°C by 2050. As Italy instigates its stable recovery from the coronavirus emergency, the Italian government is offering Italian people financial support of up to €500 to buy a personal bicycle or e-scooter, to reduce dependence on private cars and global warming issues and people inItaly have greeted this opportunity for new personal e-scooters. This paper aims to employ a life cycle assessment (LCA) onthe personally owned electric scooters (PEOS) that are circulating in Turin. The results are reported for five impact categories: global warming potential (GWP), acidification potential, eutrophication potential, ozone layer depletion, and human toxicity potential. We found that the environmental load associated with the usage and charging of e-scooters is less compared with the materials and manufacturing burdens of e-scooters. Regarding the GWP, the results of Analysis methodology of the use of the PEOS generate 21 g of CO2-eq. per passenger-kilometer dominated by around 50% from materials processing, 17.5% from Li-ion battery production, 1.6% from transportation, and 30.9% from usage and charging of e-scooter. Four scenarios are compared with the base case situation, which are the substitution of alternate materials, use of 50% recycled aluminum, transportation of e-scooters via plan, and charging with solar power. Results from these scenarios are proved to be highly sensitive to baseline scenarios. Results also revealed that PEOS has higher (21 g CO2 eq./km) environmental consequences on the global warming potential as compared to bicycles (8 g CO2 eq./km) and lower (21 g CO2 eq./km) environmental impacts as compared with electric bicycles (40 g CO2 eq./km) and battery electric vehicles (80 g CO2 eq./km).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".