How Lack of Knowledge and Tools Hinders the Eco-Design of Buildings—A Systematic Review
Bibliographic record
Abstract
The building sector is responsible for extensive resource consumption and waste generation, resulting in high pressure on the environment. A way to potentially mitigate this is by including environmental considerations during building design through the concept known as eco-design. Despite the multiple available approaches of eco-design, the latter is not easily achieved in the building sector. The objective of this paper is to identify and discuss what barriers are currently hindering the implementation of eco-design in the building sector and by which measures building designers can include environmental considerations in their design process. Through a systematic literature review, several barriers to implementation were identified, the main ones being lack of suitable legislation, lack of knowledge amongst building designers, and lack of suitable tools for designers to use. Furthermore, two specific tools were identified that allow the inclusion of environmental consideration in building design, along with nine design strategies providing qualitative guidance on how to potentially minimize energy and material consumption, as well as waste generation. This paper contributes a holistic overview of the major barriers to and existing tools and method for the eco-design of buildings, and provides guidance for both future research and practice.
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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 | 0.033 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".