Spatio-temporal Analysis of Car Distance and Greenhouse Gases and Effect of Built Environment: Latent Class Regression Analysis
Bibliographic record
Abstract
This paper has two main objectives: (i) to estimate greenhouse gas (GHG) emission inventories at the household level using link-level average speeds for three origin-destination surveys in Montreal, Canada for the years 1998, 2003 and 2008; (ii) to estimate the temporal and spatial variations of built environment characteristics and socio-demographics on car distance and GHG emissions at the household level. To estimate emissions, different sources of data are combined including road network, link-level average speeds during different hours, trip-level information and vehicle fleet characteristics. Urban form indicators over time such as population density, land use mix and transit accessibility are generated for each household in each of the three waves. A Latent Class (LC) regression modeling framework is then implemented to investigate the link between built environment, socio-demographics, and GHGs and car distance. The temporal and spatial changes on these two outcomes are determined. The data is divided into 3 classes where for each class a separate model is estimated. The authors recommend the use of LC regression models when the nature of the dataset has spatial and temporal variation. The authors findings on the effect of UF and TA on GHG and car distance travel are consistent with the literature. Also overall, the authors observe a declining trend in travel-related GHG emissions over time. By keeping everything set to the base case and observing a household across time, the authors observe that in general this household polluted 15% and 10% more GHG in years 1998 and 2003 than in 2008. This could be due to the better fuel economy of the auto-vehicles over time and increase in transit trips. Employment status also significantly affects household GHGs (with elasticities as high as 51% for each full-time worker added). As expected, and consistent with the literature, low and medium income households pollute less than high-income households (42% less GHG for low income class compared to high income).
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".