Comparison of using two LCA software programs to assess the environmental impacts of two institutional buildings
Bibliographic record
Abstract
The current trends in climate change have captured the attention of stakeholders across multiple industries, including the building sector. With the introduction of innovative building materials such as mass timber products (MTPs), it has become essential to evaluate their environmental performance. In response, a variety of life cycle assessment (LCA) software programs are available to meet this need. However, it is crucial to understand how different LCA software and databases might influence the results. This study was aimed at exploring the impact of two widely used LCA software programs, SimaPro and Athena Impact Estimator, on LCA results. Two buildings were employed to conduct this study, a traditional institutional building and a mass timber building currently under construction. By comparing the numerical outputs from both software programs, it was discovered that while both could reach similar conclusions regarding the environmental impacts of a building, their use is limited to comparative purposes only. The software programs produced distinct numerical values in their outputs and attributed the sources of impacts differently, indicating they cannot be used interchangeably. However, either SimaPro or Athena Impact Estimator was suitable for estimating the global warming potential of a building during stages A1 to A3.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".