Comprehensive decision-making considerations in the transition to electrification transportation system in a developing country
Bibliographic record
Abstract
Introducing suitable alternatives to the existing fossil fuels can provide insightful aspects of the expected benefits. In the energy sector, the transportation system is one of the key contributors to fossil fuel consumption and greenhouse gas productions. To elaborate on the performance of each introduced alternative, two perspectives are considered; short-term and long-term evaluations. In the short outlook, the PROMETHEE method is used for evaluation. Six scenarios are introduced based on the technical, economic, social, and policy criteria and each scenario benefits from different weights. In this scheme, the impacts of evolutions in the battery applications in the vehicles are investigated. Based on the short-term study, CNG and gasoline are considered the best options for the fuels of vehicles in Iran by taking into consideration the current situation. By viewing the technical and economic criteria, it was concluded that the Li-Ion battery provides better performance in comparison with gasoline in the long run. By 2040, the number of EVs will reach 10% of the overall vehicle production. It is obtained that the benefits of the electrical vehicles’ presence as the alternative to the internal combustion vehicles can provide growing interest in this outlook from 2.02 × 10 6 US$ in 2025 to 17.55 × 10 8 US$ in 2040.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".