Greenhouse gas emissions and urban form: Linking households’ socio-economic status with housing and transportation choices
Bibliographic record
Abstract
The main purpose of this research is to provide new insights for reducing greenhouse gas (GHG) emissions linked to transportation, furthering our knowledge on linkages between urban form and economic constraints, travel behaviour, and ability-to-pay of households based on residential choices and property ownership statuses. With Quebec City (Canada) as a case study, it combines an origin-destination (OD) survey, population census data and land use records for 2006 and rests on a series of structural equations models developed at the grid cell level (3,892 cells), which allows for testing for both direct and indirect effects of urban form, accessibility and socio-economic attributes on GHG emissions, households’ transportation and housing financial burdens and motorization rate. As expected, findings suggest that GHG emissions increase with higher incomes (and education), but mainly for homeowners. Tenants displaying a high expenditure-to-income ratio for housing tend to stay close to the city centre (and jobs), thereby minimizing their overall expenditures for transportation while lowering GHG emissions. Potential accessibility by car promotes urban sprawl, thereby contributing to increased GHG emissions. In contrast, increasing residential density and land use mix while providing a better walking access to jobs and local shops tends to favour active transportation, leading to a significant reduction in GHG emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".