Impact of Electric Vehicle Adoption on Urban Air Quality: A Simulation Study
Bibliographic record
Abstract
The growing popularity of electric vehicles (EVs) is anticipated to play a pivotal role in mitigating urban air pollution, a critical concern for public health and environmental sustainability. This research paper presents a comprehensive simulation study analyzing the potential impacts of EV adoption on urban air quality, employing advanced computational models to simulate scenarios of varying EV penetration rates within urban transport systems. The study integrates a multidimensional approach, considering factors such as vehicle emission reductions, changes in electricity generation mix, and traffic flow dynamics. Results indicate a significant potential for improvement in urban air quality, with notable reductions in pollutants such as nitrogen oxides (NOx), particulate matter (PM), and carbon monoxide (CO), contingent upon the extent of EV adoption and the decarbonization of the power sector. Furthermore, the paper elucidates the non-linear relationship between EV adoption levels and air quality benefits, highlighting the critical thresholds necessary to achieve meaningful improvements. The findings underscore the importance of supportive policy frameworks and infrastructure development to maximize the environmental benefits of EVs. This study contributes to the ongoing discourse on sustainable urban mobility, offering insights into the role of EVs in fostering cleaner urban environments.
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 | 0.001 |
| 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".