Green networks in carless cities: reusing infrastructure as public open space in sustainable urban systems
Bibliographic record
Abstract
As global urbanization increases, cities face the challenge of becoming sustainable. To reduce emissions and traffic congestion, cities must rethink their circulation systems and rely less on private cars. This change would improve one of the urban quality of life aspects by upgrading public spaces, more specifically urban green spaces, by linking them to an urban green network. As the space for private cars will gradually decrease, the existing vehicular system can be reused for pedestrian purposes.This paper addresses the transformation of existing road networks into a system of public green spaces, one that will connect the urban local parks to the green lungs of the city. Through a theoretical framework and several examples, the report examines four key aspects for completing this transformation: urban sustainability, open space as network, transportation and circulation, and reusing infrastructure. Two Montreal projects, used as case studies, illuminate ways of reusing existing infrastructure. Last, based on the theoretical framework and the case studies, recommendations for further development and implications are suggested.This report draws from urban design theories that changed cities through counter process design, in order to learn from those experiences and introduce a new contour of sustainability. The paper suggests another layer in the attempt to address the challenges to develop sustainable cities without compromising the ability of future generations to meet their own needs.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".