Energy retrofit evaluation in residential construction : a life cycle thinking approach
Bibliographic record
Abstract
Buildings play an essential role in energy use and climate change mitigation, consuming more than 40% of the global energy and releasing one-third of total greenhouse gas (GHG) emissions. The deteriorating energy and emission performances of existing buildings have been identified as a critical concern in this sector. Older buildings mostly use non-renewable energy resources and create much more GHG emissions compared to new construction. Therefore, promoting the energy efficiency of old buildings is extremely important in reducing GHG emissions through retrofitting. However, retrofit decision-making has not been explored sufficiently in literature. Evaluation of retrofit strategies should not be limited to technical feasibility and environmental impacts and should also include the economic aspects. This research aimed to develop a life cycle thinking-based energy retrofits evaluation and decision support framework to identify optimal retrofit packages for Canadian residences. This research proposed a multi-stage approach. In the first stage, life cycle assessment models were developed to evaluate the overall environmental and economic impacts of energy retrofits. Next, a community-level energy retrofits decision support framework was developed by coupling building energy simulation software packages with the Pareto optimization approach developed in the Python coding environment. The proposed framework can optimize retrofit solutions by accounting for the environmental and economic impacts. Finally, the policy recommendations for the penetration of retrofit schemes were proposed. The proposed framework will assist policymakers, planners, and homeowners in determining building archetypes that need to be prioritized for retrofitting, identifying optimal retrofit packages, and designing retrofit strategies for the penetration of retrofit schemes.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".