A Comparative Whole-Building Life Cycle Assessment of the Four Framing Systems of the Bakers Place Building Using the Tally LCA Tool
Bibliographic record
Abstract
The urgent need for climate change mitigation has increased the focus on reducing embodied carbon and energy, particularly in the construction sector. Utilizing sustainably sourced mass timber products provides a low-carbon alternative to traditional concrete and steel structural systems in buildings. These carbon impacts can be quantified by evaluating the total environmental impact of a building, from material extraction and product manufacturing to construction, operation, and demolition. This study evaluated the environmental impacts of a 14-storey mass timber–steel hybrid building in Madison, USA, through a Whole-Building Life Cycle Assessment (WBLCA) using the Tally LCA tool integrated with Autodesk Revit. The hybrid design was compared to full mass timber, full steel, and post-tensioned concrete structures, which are common structural systems for high-rise buildings, enabling meaningful comparisons of their environmental performance. The results showed that the full mass timber design had the lowest global warming potential (GWP), reducing emissions by 16% compared to the concrete structure. The hybrid design achieved a 14% reduction, with both timber-based systems demonstrating about 30% lower non-renewable energy use. In addition, they provided significant biogenic carbon storage during the building’s lifespan. However, the mass timber and hybrid systems showed higher impacts in categories such as acidification, eutrophication, ozone depletion, and smog formation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".