Assessing the effectiveness of municipal climate actions: A scoring system for residential energy retrofit initiatives
Bibliographic record
Abstract
Given the sector's significant contribution to global emissions, achieving ambitious climate targets requires substantial improvements in building energy performance. Retrofitting buildings to enhance energy efficiency, decarbonize energy systems, and integrate renewable energy is essential to meeting these goals. Through their Climate Action Plans (CAPs), municipalities are uniquely positioned to lead this transformation. This paper introduces a comprehensive scoring framework to evaluate the effectiveness of CAPs in advancing energy retrofit initiatives. Building on prior research categorizing policy actions into four levers—Mandates, Price Signals, Public Investments, and Voluntary Actions—we analyze 1283 building-related actions across the CAPs of 104 Canadian municipalities. By incorporating 12 critical insights for effective energy retrofitting and linking them to municipal climate action portfolios, the framework assesses both the breadth and depth of building-related strategies. The scoring system evaluates the integration of these insights through the lens of the policy levers, highlighting strengths and gaps in municipal approaches. This analysis provides actionable guidance for policymakers to identify best practices, refine strategies, and accelerate progress toward net-zero emissions. By emphasizing residential energy retrofitting in existing building stocks, the findings contribute to advancing sustainable urban development and addressing urgent climate challenges. • Proposes a weighted scoring system tailored to residential energy retrofitting in CAPs. • Uses 12 retrofit insights mapped to four policy levers with impact-based weighting. • Analyzes 1283 building-related actions from 104 Canadian municipalities. • Identifies major regional and city-size disparities in retrofit strategies and leverage. • Recommends stronger mandates and price signals to boost CAP retrofit effectiveness.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".