Deep ‘Climate’ Retrofit: Assessing Life-Cycle Thinking Of Emission Calculators In Construction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it