Developing tools for municipalities to meet carbon targets
Bibliographic record
Abstract
Abstract Carbon emissions must be cut in half by 2030 to meet the Paris Agreement’s goals, and cities and municipalities are at the forefront of the fight against climate change. In 2017, the energy use of buildings directly accounted for 51% of emissions in the City of Victoria and offered the greatest opportunity for the municipal government to act. Unfortunately, at this point, many cities and municipalities lack the tools and locally relevant data to make effective policy decisions. This research aims to develop a practical framework for analyzing and comparing the carbon impact of policies enacted by municipal governments, and is specifically focused on the energy consumption, and operating and embodied carbon related to single-family dwellings (SFDs) in the City of Victoria, which contains a heterogeneous building stock with construction dates ranging between 1860 to present day. The underlying model has been developed based on statistical modeling and agent-based behavioral responses to different policy actions. The agent-based modelling approach models stock development in terms of new construction, retrofit, and replacement by simulating individual decisions at the building level. The results can be used to identify optimal efforts to minimizing barriers or bottlenecks in achieving low-carbon ambitions while understanding or addressing related aspects such as housing affordability. Municipalities can use the dashboard to identify and prioritize climate solutions that meet their stringent obligations.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".