Missing density: assessing support for compact cities among Canadian municipal officials and members of the public
Bibliographic record
Abstract
Developing compact urban form is a key strategy for the decarbonization of cities. Though many Canadian municipalities have declared climate emergencies, few have made progress on enabling compact land use. What, then, are the prospects for mitigating climate change via increased density? In this analysis, we examine two datasets: survey data of Canadian municipal elected officials (N = 1156), and a representative survey of the Canadian public (N = 3925). We find robust evidence in both samples that elected officials and members of the public who support local climate action are more likely to endorse low-carbon transport than to endorse compact cities. Among politicians, 35% strongly supported climate action, but only 17% strongly supported both climate action and increased density in established neighbourhoods. Since compact cities are critical to the success of low-carbon transport, voters may struggle to identify local candidates who endorse both climate action and the policies necessary to attain it. We further examine predictors of opposition to densification among members of the public, finding that, all else equal, home ownership is associated with a 33% decrease in the likelihood of a participant strongly supporting new housing in established neighbourhoods. While cities could use a variety of policy tools to increase density – including zoning changes, shorter public consultations, and reduced car parking – political will to implement these policies may be lacking. Given the obstacles to local action identified here, we suggest that provincial or national legislation may circumvent local opposition and accelerate the transition to a more compact urban form.Key policy insights Both city officials and members of the public who support climate action are more supportive of low-carbon transport than compact neighbourhoods. This represents a missed opportunity since dense housing complements low-carbon transport making it more cost-effective and efficient.Opposition to new housing in existing neighbourhoods is especially strong amongst homeowners (who possess disproportionate political power at the local level).Views on increased housing in existing neighbourhoods are not polarized along partisan lines, suggesting the possibility of bipartisan action at higher levels of government.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".