Connected Autonomous Electric Vehicles as Enablers for Low-Carbon Future
Bibliographic record
Abstract
Transportation is the main cause of various harmful gases being released into the atmosphere. Due to dependency on fossil fuels, conventional internal-combustion engine vehicles cause major impacts on air pollution and climate change. Achieving greenhouse gas (GHG) reduction targets requires electrification of transportation at the larger scale. Zero-emission vehicles are developing rapidly with consequences for energy use and GHG emissions, and their penetration is rising throughout the world. Such vehicles are widely considered as a promising solution for GHG reduction and a key to low-carbon mobility future. Recent trend in transportation system is a rapid shift toward connected autonomous vehicles. Connected autonomous electric vehicle (CAEV) will play a vital role in emerging revolution in sustainable low-carbon mobility. They can result in major reductions in GHG emissions and be at the forefront of rapid transformation in transportation. CAEVs have great potential to operate with higher vehicle efficiency, if they are charged using renewable energy sources that will significantly reduce emissions and dependency on fossil fuels. This book chapter is intended not only to provide understanding of potential environmental implications of CAEV technologies by reviewing the existing studies and research works but also to discuss environmental impacts including GHG emissions and improvement of vehicle efficiency.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".