Insights on the use of local sustainability indicators for national urban policy
Bibliographic record
Abstract
Abstract National governments play an essential role in supporting sustainability at the local level. However, they often struggle to design policies that are both coherent at scale and responsive to local diversity. Current approaches often shift between one-size-fits-all strategies, which overlook local variation, and fully customized interventions, which are resource-intensive and difficult to scale. This paper addresses this policy dilemma by proposing a three-step, data-driven approach that supports evidence-based differentiation of national urban policies, drawing on insights from archetype analysis in sustainability research. Step 1 involves developing sustainability profiles by combining environmental and socioeconomic indicators. Step 2 examines how commonly used policy criteria, such as provincial affiliation, urban typology, and population size, relate to these profiles. Step 3 identifies the issues that most strongly drive performance within each group, guiding the design of interventions. Applied to 171 cities across Canada’s ten provinces, the approach demonstrates how urban sustainability indicators can be used to determine when, how, and to what extent policies should be differentiated. While population size emerges as a consistent differentiator, regional and typological dynamics also influence outcomes, revealing distinctive strengths and weaknesses in both high- and low-performing cities. In contrast to static city classifications, this paper introduces a decision-support tool that adapts place-based policymaking to reflect local strengths, vulnerabilities, and policy goals.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".