Modelling On-Road Transport Greenhouse Gas (GHG) Emissions Under Various Land Use Scenarios
Bibliographic record
Abstract
This research aims to investigate the impacts of various transportation and land use planning policies on changing travel behaviour, and subsequently GHG emissions reduction. A novel trip- based urban transport GHG emissions model is presented. The developed model integrates two main components: a discrete mode shift model and an emissions forecasting model. Given personal, modal, and land use information, the mode shift model calculates the proportion of trips made by different travel options (e.g. car, bus, walk, etc.). The total Vehicle Kilometres Travelled (VKT) by each mode is then calculated by multiplying the proportion of trips made by each mode by respective average VKT. Finally, total GHG emissions are calculated by multiplying the total VKT by each mode by respective average emissions factor. The City of Kelowna, BC, Canada is selected as a case study. The model validation shows a difference of only -0.3% between the forecasted GHG emissions and the 2010 historical data. The developed model is intended to assist municipalities evaluate alternative policy scenarios and eventually select the one(s) that help them meet their future GHG emission targets.
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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) | 0.001 | 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 | 0.000 |
| 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".