Machine Learning-Based CO2 Emission Prediction to Support Sustainable Urban Development
Bibliographic record
Abstract
Accurate identification of CO2 emissions from vehicle has become important for sustainable urban planning strategies aimed at mitigating global warming. This study presents a framework for predicting CO2 emissions using data collected from Canada’s complete vehicle fleet. We have applied a voting-based ensemble approach that aggregates five feature selection algorithms such as SelectKBest, Lasso, Recursive Feature Elimination, Random Forest importance, and mutual information to identify the most influential predictors in the dataset. Subsequently, we have evaluated the predictive performance of various machine learning (ML) and deep learning (DL) models using the six highest-ranked features, including combined fuel consumption, engine size, and fuel type. Our analysis shows that the Random Forest Regressor substantially outperforms competing models, achieving a R² value of 0.9976 including the lowest root mean squared error (RMSE) 2.847 g/km. These results highlight the strength of the ensemble framework in generating precise CO2 emission estimates. For sustainable urban transportation planning, the suggested method offers a practical and data-driven framework for decision making. This application can help planners and policymakers to formulate comprehensive strategies to reduce carbon emissions and encourage low-carbon urban development.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 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".