Environmental performance of eco‐design strategies applied to the building sector
Bibliographic record
Abstract
Abstract The application of eco‐design principles in the building sector is considered a promising way to mitigate its substantial environmental impacts. However, quantitative evidence for this mitigation potential is lacking. The objective of this study was to quantify the environmental performance of diverse eco‐design strategies when applied to the building sector. A macroscale model capable of simulating the future demand for housing and related material flows within the urban building stock was developed based on an existing building stock model. These material flows were used to build inventories for a consequential life cycle assessment and, in turn, to quantify the potential environmental consequences of introducing eco‐design strategies in the building sector, assessed across 16 impact categories. Model outputs have a high level of uncertainty but are still useful for decision‐making, given the model's simplicity and transparency. The main results show that impact reductions can be obtained from specific uses of wood and wooden products, for example, when used for the walls in high‐rise buildings, whereas using hempcrete for partition walls increases the impact. Although the use of adaptability or disassembly strategies can reduce impacts, this pay‐off can only be obtained after a long period of implementation. In summary, the present study provides new quantitative insights into the ability of eco‐design strategies to mitigate environmental impacts in the building sector.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 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".