Climate solutions to meet the suburban surge: leveraging COVID-19 recovery to enhance suburban climate governance
Bibliographic record
Abstract
Cities dominate the local climate governance arena, yet both the U.S. and Canada are suburban nations. The vast reaches of suburbia, which far exceed urban cores in land area, population, and emissions impacts, lag on climate action. At the same time, they are comparatively underserved by, and underrepresented within, the policy community. COVID-19 pandemic driven flight from cities to pastoral safety was hyped in the media, but the reality is a more mundane suburban acceleration. As always, suburbs provide more space, 2020s top commodity, at a lower cost. This stands at odds with the prescriptions of urban climate experts, where mixed-use, walkable density is the path to meeting Paris Agreement targets. Without a more expansive approach to subnational action, the popular movement around cities for climate will fail to deliver on its potential. We suggest going beyond narratives that position suburbs as helpful accessories, arguing instead that suburbs themselves can be a locus of climate action. In the context of a green recovery, additional steps include expanding suburban capacity through existing or new networks and supporting organizations engaged in suburban planning to adopt promising practices for equitable, integrated adaptation and mitigation. This approach could be an ideal use of recovery funds, with the potential to parlay immediate action into long-term gains. Governments have always subsidized the suburbs. COVID-19 recovery offers a narrow window to redirect and enhance those subsidies to bring the management of climate impacts within reach.Key policy insightsSuburbs, due to their share of population, land area and emissions, must become centres of climate action, not accessories to urban plans.Climate action should be designed to meet the specific circumstances of suburban communities. Examples include focusing on retrofitting existing buildings, integrating mixed-use zoning, and prioritizing demand-side management.Suburban communities can immediately begin adopting the most promising practices for equitable, integrated adaptation and mitigation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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; both teacher heads agree on what is shown here.
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".