Electric bus fleet transition: assessment approach considering economic and environmental impacts, and its application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In our society, global warming is considered one of the most serious problems. According to scientists, the world has been warmed by 3 degrees per year, which will be catastrophic to our world. To reduce CO2 emission, an electric bus is one way to solve the problem. In this article, we use four different models: Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average Model (ARIMA), Ecological Assessment Model, Bus Fleet Replacement Financial Model, and Integer Programming Model to determine the number of carbon emissions, the least money that government need to spend on transitions, and future blueprint; these help to predict the overall benefits for countries turn into absolutely electric bus society. Our research stands from the sustainable point of view; we view better environment as the goal. By applying these models to three different countries: London, and Toronto, and Philadelphia which is our main focus, we find out that the air quality will be increased by reducing different kinds of pollution. Moreover, by constructing a ten-year blueprint, we find out the best way to spend least money and make the environment gradually become better.
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.
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 it