Advanced Predictive Modeling of Pollutant Gas Emissions in the Automotive Industry based on Machine Learning
Bibliographic record
Abstract
Predicting CO2emissions in the automotive industry is vital for driving innovation in fuel efficiency, shaping policies, and fostering a greener, sustainable future. An advanced predictive modeling approach for estimating CO2emissions in the automotive industry using machine learning techniques is presented in this paper. Data from 46 distinct automotive brands was incorporated, comprehensively analyzing various vehicles. The predictive model employed six numeric features, encompassing engine size, cylinder count, and diverse fuel consumption metrics, along with five categorical features concerning brand, model, vehicle class, transmission, and fuel type. Considerable results were achieved, with a mean squared error (MSE) of 29.99, a root mean squared error (RMSE) of 5.48, and an $R^{2}$ of 0.991, showcasing the model’s forecasting accuracy for CO2emissions. Therefore, this work underscores the effectiveness of machine learning in CO2emissions prediction and emphasizes the importance of considering diverse features and multiple automotive brands for constructing comprehensive and robust models in the context of environmental impact assessment, thereby contributing to a more sustainable automotive industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".