Public Transport Decarbonization via Urban Bus Fleet Replacement in Portugal
Bibliographic record
Abstract
The transport sector accounts for around one-quarter of the GHG emissions in Europe, and, in Portugal, it represents almost one-third of the total emissions to the atmosphere. Unfortunately, these emissions have increased in recent years. Hence, cities and countries need plans to decarbonize their public transport fleets, and, more specifically, to replace fossil-fueled buses with electric buses that produce zero CO2 emissions. Thus, the main objective of this paper is to present a method to decarbonize the bus fleet in Portugal by a scheduled replacement of the current fleet, which is fueled by fossil fuels, with a completely electric fleet, in fourteen years. The study shows that it is possible to replace all Portuguese urban bus fleets with electric vehicles considering that all vehicles will be replaced when reaching the age of 14 years. Replacing the urban bus fleet with zero-emission buses would aid policymakers and bus companies to reduce the GHGs, and therefore contribute to fulfilling the Sustainable Development Goals of the United Nations 2030 Agenda for Sustainable Development, namely, Goal 13—take urgent action to combat climate change and its impacts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".