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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".