Spatial Planning of Electric Vehicle Infrastructure for Belo Horizonte, Brazil
Bibliographic record
Abstract
In pursuit of a more sustainable transportation system, electric vehicles (EVs) have the potential to play a fundamental role due to their improved efficiency and lower emissions. The absence of an adequate electric vehicle supply equipment (EVSE) network has been one of the major obstacles for the mass adoption of EV, in large municipalities of developing countries. This is the case in Belo Horizonte (BH), Brazil, which also has a high motorization rate (7 light-duty vehicles per 10 inhabitants). The purpose of this study is to measure and identify the optimal locations for EVSE according to selected criteria to meet the needs of light-duty electric vehicles (LDEV) corresponding to a penetration of 1% by 2025 in the municipality of BH. The study highlights the most important attributes that need to be considered for the installation of an EVSE network in an urban space for a developing country. Multi-Criteria Decision Making (MCDM), the Weighted Linear Combination (WLC) method, and the Analytical Hierarchy Process (AHP) technique based on the inputs from a group of Brazilian electrical mobility specialists, coupled with a Geographic Information System (GIS) modeling tool, were used for this study. The results revealed that around 1,200 EVSE units are needed, with a large concentration of EVSE in a small region. We also illustrate where stakeholders should focus their attention for the successful promotion of EV. The development methodology has the potential to be applied in other future EVSE development projects.
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 | 0.000 | 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 | 0.000 | 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".