Deep ‘Climate’ Retrofit: Assessing Life-Cycle Thinking Of Emission Calculators In Construction
Bibliographic record
Abstract
Maintaining the existing built environment is crucial to achieving substantial, near-term carbon and emissions reductions in the construction industry. Retrofitting existing building stock to avoid embodied emissions from new construction and upgrading and electrifying existing buildings reduces building operational emissions. For the buildings constructed between now and 2050, more than half of their emissions will be from embodied carbon. Estimates show that reusing and retrofitting the most carbon-intensive parts of buildings – the structure and envelope – can save 50% to 75% of the embodied carbon emitted by constructing similar new buildings. Yet, a significant challenge to adopting low-carbon building practices in deep energy retrofit projects is the complexity of calculating the embodied and operational emissions of proposed designs according to the needs and priorities of various stakeholders. Recently, tools for calculating buildings’ embodied and operational emissions have been introduced and are being rapidly adopted by industry stakeholders to aid decision-making. Yet, these assessment tools often produce significantly different results, depending on the assumptions and calculations used to weigh various factors. The varied and sometimes contradictory results create uncertainty for designers and building stakeholders throughout the design process, and a better understanding of the impact these tools and their assumptions have on the design process is necessary.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".