Land Use Planning to Mitigate Climate Change in the Greater Golden Horseshoe: An Analysis of Potential Scenarios
Bibliographic record
Abstract
This paper assesses the potential effects of housing development on regional greenhouse gas emissions in Ontario’s Greater Golden Horseshoe. Using models of different development scenarios based on household vehicle kilometres travelled and energy use, we evaluate the impacts of different forms of new housing production on greenhouse gas reduction targets and suggest housing and land use best practices and policy approaches. We model core scenarios of development from 2023 to 2030 that reflect current debates on housing development and land use planning in the region that include Build as Usual (on-going intensification); All-Sprawl (under recent policy changes); and four alternatives: Business as Usual, Moderate, Limited, and No Sprawl. Our findings suggest that aggressive intensification would reduce greenhouse gas emissions by as much as 26 percent, with particularly significant and compounding effects to be expected over the long term. We conclude that progressive land use planning and other mechanisms by the provincial, regional, and municipal orders of government that reduce the emissions generated by buildings, preserve open space that provides critical carbon sequestration, and reduce vehicle miles travelled, should be aggressively strengthened to build on progress made under the Province’s Growth Plan for the Greater Golden Horseshoe.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".