The new paradigm of future cities? Facilitating dialogues between the 15-minute city and the 15-minute life circle in China based on a bibliometric analysis
Bibliographic record
Abstract
Cities worldwide are facing increasing disturbances due to the long-lasting COVID-19 pandemic, the unpredictable climate change, and the intensifying geopolitical clashes. The uncertainties associated with the future drive a backlash against contemporary planning practices that center on growth-oriented agenda and placemaking to survive neoliberal inter-city competitions. A noticeable paradigm shift towards micro-scale and human-centric planning practices has gained traction. The 15-minute city model (FMC), which was proposed by Carlos Moreno in 2016 and went viral after the pandemic, has become a prominent concept in steering future city development. Interestingly, China has also embarked on a transition from physical-oriented to human-centric planning, which is featured by the concept of the 15-minute life circle (FLC) and its planning guidelines first promulgated in Shanghai in 2016. While both concepts have thrived since then, few scholarly attempts have been made to advance the mutual learning process. This research aims to facilitate a dialogue between the two research communities, starting with a systematic literature review of the FMC. Using a bibliometric analysis, it maps the development and evolution of research on the FMC and identifies four key themes: sustainability, resilience, utopian city models, and technology. Based on the findings, the research discusses the complexity and current trends in the FMC literature. It compares the human-centric and chrono-urbanism planning principles in FMC and FLC cases, highlighting the differences in origins, priorities, and planning principles. The study concludes with three directions for mutual learning: integrating resilient thinking, synthesizing theoretical development and practical accumulation, and adopting a new urban regeneration perspective.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.040 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".