“Team Play” between Renewable Energy Sources and Vehicle Fleet to Decrease Air Pollution
Bibliographic record
Abstract
The reduction of air pollutants for the purpose of maintaining or improving air quality across the globe is a fundamental concern to which all modern governments are allocating varying amounts of attention and resources. The successful amelioration of air pollution requires strategic investments in the commercialization and adoption of “clean energy technologies” by both private and public entities, the conversion of contemporary houses to “smart houses”, the diffusion of Renewable Energy Sources (RES) including photovoltaic systems (PV), wind farms, and different forms of bioenergy, and the integration of electric-powered vehicles. In concert with these ideas, this paper aims to discuss the possibility of undertaking a feasibility study in two countries Canada and Italy concerning the integration of electric vehicles (EVs) and electric motorcycles (EMs). The proposed feasibility study would seek to assess the prospect of replacing the current vehicle fleets in these two countries with EVs in a manner that utilizes renewable energy sources and, thus, does not generate new toxic emissions. In conclusion, this study demonstrated that a pronounced introduction and distribution of RES, EVs, and EMs can operate as a great opportunity for both the environment and the capacities and needs of energy production. Today, the EV is not widespread. With this contribution, it is shown how EVs can be well integrated with renewable energy. Therefore, it is the duty of governments to implement policy strategies, in order to spread them across more territory.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".