Disaggregated Empirical Analysis of Determinants of Urban Travel Greenhouse Gas Emissions
Bibliographic record
Abstract
A disaggregate approach is proposed for estimating travel-related greenhouse gas (GHG) emissions at the individual level by using an in-depth multiday activity-based survey in Quebec City, Canada. A random-effect model is then estimated to quantify the impact on emissions of individual and household socioeconomic characteristics as well as urban form and transit supply indicators. The model results are obtained in terms of total individual emissions and by trip-end activity purpose such as work, leisure, and shopping. According to the results, female respondents produced, on average, emissions that were 22% lower than those of men. Evidence of economies of scale was found within households in the production of travel GHG emissions. A couple would produce only 64% more emissions than a single person. It was found that both urban form and transit supply around the residence have a significant impact on GHG emissions, though this impact is relatively limited; this finding implies that drastic land use changes would be required to significantly cut travel emissions. For example, a 10% increase over the mean in residential or job density would lower emissions by less than 2%. This result is consistent with recent studies examining the relationship between travel and land use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".