Towards Socially Sound Sustainable Building Projects with a Novel Life Cycle Assessment Method
Bibliographic record
Abstract
In architectural practice, social values are created through design choices made by architects. Integrating social intents, along with their corresponding design choices, into a design project has significant implications for the environmental impact of the building. This fac-tor is often inadequately addressed.This paper presents an initial study towards adopting a novel environmental life cycle assessment (eLCA) approach for assessing the environmental impact of social intents in building projects. The study focuses on exploring eLCA’s goal and scope setting through a methodological triangulation approach where the goal and scope are explored through various sources as follows: (a) a literature re-view of existing approaches within the building and construction industry, (b) narrative interviews and analysis with practicing architects, and finally (c) through a practical investigation carried out on social design intents from actual building projects. The preliminary findings indicate that incorporating social intentions into building projects yields significantly varying effects on the emissions of the building, contingent upon the specific design choices that facilitate these social intents. Furthermore, the findings underscore the necessity for additional research to reconcile the functional unit with the established units from other eLCA methodologies. Such an alignment is crucial for enabling a comprehensive evaluation of the impact magnitude of social intents within the entire building context.This work contributes to bridging the existing gap in the assessment of quantifiable environmental impacts versus qualitative intention-based social impacts, paving the way for more informed decision-making and socially sound, sustainable buildings.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".