The Velocity of Density: Can We Build More Sustainable Cities Fast Enough?
Bibliographic record
Abstract
Urban planners now commonly advocate for increases in density of the built environment to reduce car dependence and enhance the sustainability of cities. The analysis in this paper asks about the speed at which density as a sustainability policy can be implemented. The Greater Toronto Hamilton Area (GTHA) is used as a case study to measure how quickly existing areas could be densified to meet minimum transit supportive density thresholds. Almost 70% of existing residents live in neighborhoods with densities below minimum transit supportive densities. The findings show that increases in minimum densities could be attained roughly within the target time horizon of existing growth plans, but that these increases hinge on assumptions of continuing high growth rates. The sustainability of cities relies on a high ‘velocity of density’, a term proposed in the paper to refer to the speed at which density can be implemented. Density is often slowed or halted by local residents, which could prove problematic if sustainability objectives require speedy implementation, for instance to address climate change. Analysis of the velocity of density suggests that planning for sustainability, and climate change, in cities would benefit from considering a broader set of solutions to car dependence in existing low-density areas than changes to the density of the built form alone.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".