Quick Analysis Technique to Estimate GHG Emissions Based on Neighbourhood Built Form (Poster)
Bibliographic record
Abstract
There is growing awareness of climate change and its potential impacts. In response, many municipalities and provinces have set greenhouse gas (GHG) emission reduction targets. The transportation sector is one of the largest contributors to Canadian GHGs. As such, it will increasingly be called upon to help achieve GHG emission reductions. In the past few years, considerable research and policy in the transport sector has focused on reducing GHG emissions through improvements in technology, fuels and vehicles. However, there has been less attention given to travel activity. There are even fewer studies looking at how to reduce automobile usage and distances travelled, and especially less work investigating the relationship between built form (suburban vs. urban, type of infrastructure, etc.) and its impact on automobile usage. This study focuses on transportation related GHG emissions by examining the association between built form and travel activity. The objective is to present a quick-analysis tool to assess the GHG impacts based on changes in vehicle kilometers travelled (VKT): built form (density, mixed-uses, etc.), infrastructure (street connectivity, distance to high order transit, sidewalks, etc.) and context (location in region, demographics, TDM, etc.). This tool was developed to identify the most efficient improvement methods in a suburban context, without the need to develop an extensive travel activity model. This relationship is shown through the development of multiple scenarios to compare and contrast the GHG emissions of neighbourhoods with different characteristics (built form, infrastructure, regional context, etc.). Existing research on travel behaviour and the built form is used to analyse the relationship between the vehicle-kilometres driven by residents and the built form and regional context of a neighbourhood. This method is used to examine the effects of improving street grid connectivity, sidewalk coverage, cycling infrastructure and transit service areas on GHG emissions, for each neighbourhood scenario. In turn, this information provides a useful decision-support tool that enables to identify the most effective strategies for reducing GHG emissions in existing and new neighbourhoods, while taking into account the local and regional context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".