Forecasting of Transportation-related CO2 Emissions in Canada with Different Machine Learning Algorithms
Bibliographic record
Abstract
The amount of carbon dioxide in the atmosphere has risen over recent years, with a growth of over 40%. This study examines transportation-related carbon dioxide (CO 2 ) emissions in Canada, which contribute significantly to the country's overall emissions. The study investigates the rise of carbon dioxide (CO 2 ) due to various reasons such as economic development, transportation, as well as population growth, but the study focuses on transportationrelated CO 2 emission in Canada. Various machine learning algorithms, such as Deep Neural Networks, Support Vector Machines, and Random Forests, are utilized to forecast CO 2 emissions. The results show promising outcomes, with R2 values ranging from 0.9532 to 0.9996, RMSE values ranging from 1.0974 to 13.6561, MAPE scores from 0.0088 to 0.0010, MBE scores ranging from -0.0594 to 1.0366, rRMSE score ranging from 0.4259 to 5.3002, and MABE score ranging from 0.2643 to 5.6582 for all six (6) algorithms. To meet greenhouse gas reduction targets, this paper recommends further efforts to reduce CO 2 emissions from transportation sources and suggests the adoption of Vehicle Alternative Fuel Types and lowcarbon fuels.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".