Assessing the readiness for 15-minute cities: a literature review on performance metrics and implementation challenges worldwide
Bibliographic record
Abstract
The 15-minute city (FMC) has recently emerged as a popular planning paradigm. While the concept builds upon well-stablished urban planning principles, such as density, mixed use, and proximity, its operationalisation in research and practice faces methodological and contextual challenges. This study conducts a systematic review of FMC performance metrics, analysing thirty-nine peer-reviewed articles analysing how assessment metrics have been defined and used to evaluate the alignment of a region with FMC principles across different geographical contexts. We categorise performance metrics into six broad groups: amenity-based, population-based, distance-based, gravity-based, behaviour-based, and weighted scores. The findings reveal significant methodological diversity, particularly in time thresholds, transport mode choices, and the selection of amenities. European and Asian studies tend to focus on the spatial distribution of amenities, while North American research emphasises behavioural analysis, highlighting the challenges posed by car dependency and urban sprawl. This review identifies key research gaps, including the limited attention given to digitalisation and equity concerns. Additionally, we highlight the need for standardised performance metrics to allow for comparability across studies. Given regional variations in urban form and behaviour, we argue that FMC policies should not adopt a one-size-fits-all approach but rather be tailored to local contexts. The findings from this research can be of interest to policymakers interested in understanding the regional challenges and methodological variations of FMC performance metrics.
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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 | 0.036 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".