Comprehensive Assessment of the Impact of Green Roofs and Walls on Building Energy Performance: A Scientific Review
Bibliographic record
Abstract
Sustainability and energy efficiency are now two pivotal goals that society aims towards. Green roofs and facades have gained significant attention in this direction for innovative, sustainable solutions for enhancing building energy performance. With a focus on sustainable urban development and energy-efficient building practices, this study delves into the intricate relationship between these green infrastructure elements and the overall energy dynamics of constructed environments. Furthermore, a range of case studies from diverse geographical locations are presented to provide valuable insights into their practical implications as emerging technologies that contribute to improved insulation, reduced heat transfer, regulating indoor temperatures, and mitigation of urban heat island effects, thus reducing the need for artificial heating and cooling and optimizing overall energy consumption. This comprehensive review serves as a dataset for understanding and highlighting all the research findings of the numerical and experimental investigations invested in the field of greenery systems to encourage their integration, which is crucial for combating climate change and pollution. Previous research is often focused on isolated, short-term, or single-climate analyses of consumption; therefore, by providing an inclusive description of their practical benefits in both temperate and extreme climates, the gap in previous articles is tackled.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".