Household transportation lifecycle greenhouse gas emission prediction
Bibliographic record
Abstract
• A model is developed to predict household transportation life-cycle GHG emissions. • The strongest determinants of household transportation emissions are identified. • Light Gradient Boosting Machine is the most accurate model. • Considering tail-pipe emissions leads to underestimating the GHG emissions by 20%. • Different scenarios are presented to meet Canada goals for transportation GHGs. This investigation develops a model to predict household transportation life-cycle greenhouse gas (GHG) emissions and identifies the strongest determinants of these emissions. The impact of many variables on household transportation GHG emissions is examined. Ten machine learning methods are used for modeling and prediction. Shapley additive explanation is then applied to detect the relative influence of variables on household GHG emissions. Partial dependency plots are also employed to capture the direction of influence of top variables on household GHG emissions. Further analyses suggest that considering tail-pipe emissions rather than life-cycle emissions leads to underestimating the GHG emissions by roughly 20%. Replacing all gasoline vehicles with electric vehicles would reduce GHG emissions in Montreal by 57%. Then, the modal shifts required to meet the Government of Canada’s goals for transportation GHGs are determined. Finally, a scenario analysis is applied, and a number of GHG emission scenarios are presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.005 | 0.001 |
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".