Towards Carbon Neutrality: Machine Learning Analysis of Vehicle Emissions in Canada
Bibliographic record
Abstract
The transportation sector is a major contributor to carbon dioxide (CO2) emissions in Canada, making the accurate forecasting of CO2 emissions critical as part of the global push toward carbon neutrality. This study employs interpretable machine learning techniques to predict vehicle CO2 emissions in Canada from 1995 to 2022. Algorithms including K-Nearest Neighbors, Support Vector Regression, Gradient Boosting Machine, Decision Tree, Random Forest, and Lasso Regression were utilized. The Gradient Boosting Machine delivered the best performance, achieving the highest R-squared value (0.9973) and the lowest Root Mean Squared Error (3.3633). To enhance the model interpretability, the SHapley Additive exPlanations (SHAP) and Accumulated Local Effects methods were used to identify key contributing factors, including fuel consumption (city/highway), ethanol (E85), and diesel. These findings provide critical insights for policymakers, underscoring the need for promoting renewable energy, tightening fuel emission standards, and decoupling carbon emissions from economic growth to foster sustainable development. This study contributes to broader discussions on achieving carbon neutrality and the necessary transformations within the transportation sector.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".