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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".