Energy Optimization and Environmental Impact of an Office Building at Biskra City, Algeria: Life Cycle Assessment, Applied to the Building Envelope in Hot and Dry Climate
Bibliographic record
Abstract
This work assesses the environmental impact generated by an office building in arid region throughout its life cycle (cradle to grave), by means of a Life Cycle Assessment (LCA). This study focuses on a comparison of different external wall systems that are conventionally used in building. With recycled materials and thermal insulation system, it’s possible to reduce demand of energy consumption, evaluate their environmental indicators impacts, and also reduce them, throughout the building life cycle. In doing so, this work can contribute not only to control energy, long-term economic growth, but also to address pressing social issues, and mainly environmental impacts. We use an environmental analysis with a thermal dynamic simulation, to test the hypothesis on a data base of hot and dry climate of Biskra city. The last part consists of a technical approach, indicating the economy is the use of ecological and recycled materials. The results of this study show that the exterior insulation system, obtained the best environmental scores, being 30% less than the interior insulation system and 50% less than the distributed insulation system. Also, recycled materials save energy in their manufacture, and building energy consumption for its use and have a reduced building impact on the environment throughout its life cycle (cradle to grave). This work shows how LCA application is not only feasible, but recommended because it is a decision support tool in the search for sustainability and make use of recycled materials.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".