Energy Efficiency in Buildings: Performance Gaps and Sustainable Materials
Bibliographic record
Abstract
Real-world energy efficiency in the building sector is currently inadequate due to significant discrepancies between predicted and actual building energy performance. As operational energy is optimized through improved building envelopes, embodied energy typically increases, further exacerbating the problem. This gap underscores the critical need to re-evaluate current practices and materials used in energy-efficient building construction. It is well established that adopting a life cycle view of energy efficiency is essential to mitigate the building sector’s contribution to rising global energy consumption and CO2 emissions. Therefore, this study aims to examine existing research on sustainable building materials for life cycle energy efficiency. Specifically, it reviews recent research to identify key trends, challenges, and suggestions from tested novel materials. A combination of theoretical analysis and narrative synthesis is employed in a four-stage framework discussing the challenges, context, concepts, and the reviewed literature. Key trends include the growing adoption of sustainable materials, such as bio-fabricated and 3D printed materials, which offer improved insulation, thermal regulation, and energy management capabilities. Multifunctional materials with self-healing properties are also emerging as promising solutions for reducing energy loss and enhancing building durability. The focus on reusing materials from the agricultural, food production, and paper manufacturing industries in building construction highlights the opportunity to facilitate a circular economy. However, the challenges are substantial, with more research required to ascertain long-term performance, show opportunities to scale the implementation of these novel materials, and drive market acceptance.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".