Planning for Change Adaptability and Circularity of Communities and Homes
Bibliographic record
Abstract
This paper addresses the need to decarbonize built environments by altering community planning and home design. By employing principles of Circular Economy at the community and dwelling levels, the paper argues that having flexible design strategies can contribute to the reduction of the carbon footprint of urban areas by minimizing the need for demolition and, as a result, material waste. Currently, depending on location and cultural and economic conditions, large-scale developments might take many years to approve and construct. Moreover, when changes are to be introduced to an initially approved masterplan, the process of obtaining municipal planning approval can be lengthy. By using a case study-based methodology for community and home design, this research argues that, given the rapidly emerging new social challenges, neighbourhoods can be designed to adapt and accommodate changes they may encounter throughout their development and in subsequent years. The proposed approval procedure that this research introduces provides a step-by-step approach to planning that can be readily adjusted based on market demand and newly developing economic and social conditions. To demonstrate his research in partnership with a private developer and the municipality, the author utilized these techniques in the design of a community in La Prairie, a town near Montreal, in Quebec, Canada. The paper also introduces the concepts of adaptability and circularity at the dwelling unit level to minimize demolition and waste. The energy efficient design incorporates demountable partitions and specialized conduits for the installation and improvement of utility lines. By investigating the macro and micro levels, the author concludes that changes to the current system, while considering the needs of key stakeholders, stand to reduce demolition and waste.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".