Green Mobility Application in Malaga: Analysis of the Transition from ICE to EV
Bibliographic record
Abstract
Through the decarbonization process, worldwide authorities want to reach the target settled for the 2050 of Net-Zero Emissions. Nowadays, fossil fuels still represent the engine which supply electricity generation and transportation, that represent the two most pollutant sectors in terms of CO2emissions. In order to face this problem, many countries start to increment their Renewable Energy Sources (RESs) share and to renew the outdated fleets of public transports. This type of decarbonization process represents a significant effort for the transport service operations. This happens since Electric Vehicles (EV) present a reduced autonomy with respect to Internal Combustion Engine (ICE) vehicles, leading to a change in the organization of the service and an improvement of the resting phase, due to longer charging time. Following this trend, in this work is presented a case study evaluation about the replacement of old EURO-5 diesel vehicle, with a new electric one, in order to sustain the decarbonization possibility in a Spanish city. After the simulation, consideration about the optimization of the electrified transport service is proposed. Finally, after the case study is highlighted the difference in terms of carbon footprint between the ICE vehicle and EV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".