Sustainability Assessment of the Residential Land Use in Seven Boroughs of the Island of Montreal, Canada
Bibliographic record
Abstract
High resource utilization in the residential sector, and the associated environmental impacts, are central issues in the growth of urban regions. Land-use urban planning is a primary instrument for the proper development of cities; an important point is the consideration of the urban form’s influence on resource utilization intensity. Emergy synthesis, an energy-based methodological approach that allows the quantification and integration of both natural and human-generated flows interacting in urban environments, was used to assess sustainability of the residential land use of seven boroughs on the Island of Montreal. Natural resources, food, water, acquired goods and services, electricity and fuels were the main flows considered in the analysis. Results suggest that income, household size and distance to downtown are the variables affecting resource utilization intensity more noticeably and that allocation of green area coverage is an important parameter for controlling land use intensity. With the procedure used for calculating resource use intensity in the seven boroughs, it is possible to generate a tool to support urban planning decision-making for assessing sustainable development scenarios. Future research should consider urban green space potential for accommodating local waste treatment systems, acting as a greenhouse gas emissions sink and promoting human health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".