Climate action at the neighbourhood scale: Comparing municipal future scenarios
Bibliographic record
Abstract
Municipalities face the direct impact of climate change events, but many are challenged to assess the potential outcomes of future climate action policies. It is essential for local municipalities to be able to evaluate cross-sector and cross-scale policy interventions, but many lack the expertise and resources for wholistic forecasting. This paper used a hybrid inter-scalar and interdisciplinary modeling approach to evaluate, compare, visualize, and reveal the performance of climate actions at building and neighbourhood scales of future ‘what-if’ scenarios for three neighbourhood urban form types (dispersed, corridor, nodal) in three cities in British Columbia, Canada (Vancouver, Victoria, Prince George). Results found that increases in population density combined with strict building standards and retrofitting older buildings decreased per person emissions per year by up to 84% by 2050. Within the neighbourhood-scale areas, building form and location had less impact. Population density and frequency of transit service were most important for mobility mode shifts. Concentrating density at transit nodes or along commercial corridors improved the percentages of residents within a five-minute walk of those services, but proximity to greenspaces showed mixed results. <em><strong>Policy relevance</strong></em> Municipal climate action policies cross sectors and scales. This analysis of future urban form and energy scenarios at neighbourhood scales reveals livability and greenhouse gas (GHG) outcomes. It provides evidence that compact neighbourhood form coupled with stringent building codes reduces GHG emissions. (1) Concentrating density at transit nodes or along commercial corridors improved the percentages of residents within a five-minute walk of those services, but proximity to greenspaces showed mixed results. (2) Within neighbourhood-scale areas urban form (building form and location) had less influence on shifting mobility modes than did overall population density and transit frequency. (3) British Columbia’s net-zero-ready building code, a performance-based code, resulted in notable energy-use reductions and related emissions reductions associated with the new building stock. (4) Retrofitting the existing building stock resulted in notable emissions reductions in the three neighbourhoods; however, for cities with growing high land value, the potential for sale and redevelopment is a disincentive to retrofitting.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".