Sustainable Development for Urban Sprawl and Its Containment: A Hybrid Framework Formulation
Bibliographic record
Abstract
Abstract With proper long-term planning involving sustainable urban development concepts such as new urbanism, smart growth and proximity cities, urban environments are better equipped to tackle increased populations. Sustainable urban growth methods can be achievable and effective and have been proven in cities such as Copenhagen, Gothenburg, Glasgow, Singapore, Winnipeg and Auckland, among others. These cities are world leaders committed to the United Nations sustainability agenda. This study led to the development of an ‘Urban Development Hybrid framework’ based on the three pillars of sustainability. The formulation of a hybrid framework aimed at promoting sustainable development in the context of urban sprawl and its management is essential for addressing the challenges posed by rapid urbanisation. Above all, this framework is heavily influenced by the four goals outlined by the UN’s agenda and Plan Melbourne 2050. The framework was tested and applied to three urban planning schemes/case studies: Greater Melbourne, Australia (Plan Melbourne 2050); Greater Toronto, Canada (Toronto’s Official Plan 2051); and Greater Boston, USA (Imagine Boston 2030). According to the results of this research, the most used sustainable urban measures shared between the case studies were Walkability/Rideability, Mixed land use, Community and Densification & Redevelopment. On the contrary, the Safety, Technology and Innovation measures were the least frequent. Nonetheless, it was also identified that sustainable urban development has no sole measure but a collection of components working together to improve cities. Such findings can further assist planners to better understand and plan for urban sprawl and its containment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".