Understanding the Determinants of X-Minute City Policies and the differences and barriers to achieving X-minute city policies
Bibliographic record
Abstract
The concept of a x-minute (or 15-minute) city has recently gained prominence globally as an influential urban planning approach, endorsed by policy makers for its potential to enhance economic, environmental, and social outcomes, including quality of life and community cohesion. Despite its popularity, there is a dearth of research into the determinants and comparability of x-minute city policies, leading to a dispersion in thematic and geographic policy direction. To address this gap, this research was done with the aim of addressing two objectives. The first objective of this research was to examine the development of x-minute city policies across the United States, Canada, and Australia. Utilizing scholarly research, news articles, and a sequence of steps, 15 cities with recent x-minute city plans were identified and analyzed. The study found that while cities aim for complete local living through the incorporation of x-minute city concepts, there are variations in the modes of transportation, temporal cut-off values, and targeted destinations. The second objective aims to understand correlations and differences between these policies while understanding the impacts of the city’s structuring elements on the probability of achieving them. Using a wide array of spatial and transportation data for the City of Saskatoon, this research develops five different x-minute city policies based on four different city plans at the parcel level. Additionally, this research explores how physical elements in cities, such as highways, large parks, and rail lines, can impact the realization of x-minute city goals. Overall, the study shows considerable differences between policies in terms of the conclusions they convey. Different policies also led to diverse results regarding their relationship with people's socioeconomic issues. Additionally, the study shows that some physical elements such as highways, large parks, and rail lines have a consistent negative impact on the probability of realizing 15-minute city goals, regardless of the used policy. Other elements had a mixed effect according to the used policy. This research aids transit practitioners and planners in integrating x-minute city concepts by offering insights into policy determinants. It also helps cities in understanding the performance of different 15-minute city policies and the relative challenges in realizing them. With an increased attention to climate change issues, this research helps cities by providing important information that supports the implementation of such a concept, helping them with achieving their broader sustainability and equity 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".