How differentiating design strategies across building components lead to maximum reduction of adverse environmental impacts
Bibliographic record
Abstract
Abstract Purpose The building sector is responsible for substantial adverse environmental impact and vast material consumption. Eco-design of buildings is a potential mitigation strategy; however, quantitative evidence of the mitigation potential is lacking. Therefore, the purpose of this study is to quantify the environmental consequences of applying multiple combinations of various eco-design strategies to a building, thereby providing new insights into eco-designing buildings and potential focal points to mitigate the environmental impacts of the building sector. Method A multi-step approach was used to quantify the environmental consequences of conjointly applying various eco-design strategies to a building. In this approach, combinations of the eco-design strategies were applied to eight different components of a case building, such as interior walls and floor separations. Life cycle inventories were compiled for the original building design and for when the combinations of eco-design strategies were applied. The inventories were used as input for a consequential LCA, quantifying the potential environmental impacts across 16 impact categories. The impacts were then summed up to represent the total impact of a building, resulting in almost 3 million different design scenarios, and thereby environmental impact scenarios. The results were interpreted using statistical analysis such as linear regression. Results and discussion Results show that to minimize adverse environmental impacts construction materials should be used in the following prioritized order, biotic materials, inert natural materials, inorganic materials, and finally reclaimed materials. The fact that reclaimed materials are the least favorable for impact reductions goes against the findings of previous studies. However, this is because previous studies apply attributional modeling, while this study appliesy consequential modeling. The results indicate that the impact category climate change, can be a good proxy for the overall impact reduction across categories. However, the results also show that the distribution of the impacts for the design scenarios differs greatly across the assessed impact categories, which would not have been identified if only focusing on climate change. Conclusions Noticeable impact reductions are observed when the same combination of eco-design strategies is applied to all building components; however, the greatest possible reductions are noticed when applying specific combinations to specific building components. We therefore recommended that building designers differentiate the design and material used for different types of building components. Furthermore, LCA practitioners should be included in the design process, to ensure that the proposed solutions contribute to mitigating adverse environment impacts.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".