Macroscopic Model of Greenhouse Gas Emissions for Municipalities
Bibliographic record
Abstract
In the challenge to reduce greenhouse gas (GHG) emissions from the transportation sector, urban municipalities hold significant responsibilities. The Municipal Transportation and Greenhouse Gas (MUNTAG) model was developed to help municipalities estimate their current transportation emissions, set future targets, and run forecasting scenarios and responses to policies. A set of seven criteria was adopted for the development of the model including low input, ease of use, and feasibility (i.e., the model includes only variables that are controlled by municipalities). The model contains five strategies: land use intensification, public transport, active transport, financial policies, and vehicle technology. Each use is addressed separately and then integrated into one working model. Motorized passenger kilometers traveled (PKT) per capita is first estimated with the gross domestic product per capita and population density. With transit infrastructure indicators, PKT per capita is calculated for each transit mode. Bicycle infrastructure is included to calculate the bicycle mode share. Response to several financial policies (e.g., parking price, area pricing) can be modeled by using elasticity statistics gathered from the literature. Finally, changes in vehicle technology (e.g., hybrid electric vehicles) can be modeled by adjusting the various emission factors. One advantage of the model is that all parameters can be adapted fairly easily to account for municipal specificities. Overall, it is a macroscopic, aggregate, and static model suited for medium-sized and large municipalities that can be useful as a screening tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".