A Circularity Mapping Framework for Urban Policymaking
Bibliographic record
Abstract
In the context of urban policies, the circular economy can represent a virtuous model of sustainable and efficient management of resources and services for citizens, generating value for the community. Local policymakers play a central role in accelerating the circular economy transition, given that they organize and manage services that can significantly contribute to urban resilience. For policies to be properly designed, tools aimed at supporting territorial planning are needed to direct local policy towards choices that favor the circular economy and the resilience of cities. Among these urban planning tools, it is particularly important to have dashboards of comparative data on the degree of implementation of the circular economy. This paper provides a circularity mapping framework to map the degree of circularity and identify cities' strengths and weaknesses to design policies accordingly using a data-driven approach. Using a circular economy model based on 5 circular economy pillars, we identified 28 variables and assigned them to each of the pillars according to the variable's scope: sustainable inputs, social sharing, Product as a service, environmental policies, and resource efficiency. Both partial scores based on the five circular economy pillars, and a circularity index are provided for benchmarking and positioning analysis. Since urban life's environmental, economic, and social aspects are intertwined, only an integrated strategy can result in successful urban sustainable development. The paper supports policymakers in creating the conditions for efficient production and consumption markets and resource management systems while designing incentives and communications to citizens to support bottom-up initiatives and encourage virtuous behavior.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".