Global Prospects, Advance Technologies and Policies of Energy-Saving and Sustainable Building Systems: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automation, modernization, economic development and global progress depends on efficient extraction and utilization of energy. Power generation by burning fossil fuels makes various adverse impacts on the environment. Additionally, the worldwide fossil fuel reserve is limited and depleting very fast. Hence, efficient energy usage and savings are crucial to address the environmental issues to ensure sustainable development. Buildings, both commercial and residential, represent a major energy consumption sector. Approximately 40% of the total energy is reportedly consumed in the building sector. Worldwide building energy consumption, performance measuring systems and best practices, energy-saving techniques and policies are reviewed and summarized in this article. Underfloor air distribution, double-glazed windows, use of highly efficient electric motors and variable speed drives may play a great role in reducing building energy consumption. In the UK, the application of double-glazed windows in commercial buildings can save 39–53% energy. The proper maintenance of a building’s central heating system can save up to 11% energy. The automatic HVAC control system can reduce up to 20% of the building’s total heating load. Proper utilization of a VSD system in motor and building performance optimization by an ANOVA tool also proved instrumental in saving energy. Apart from this, the building codes of different countries also help to improve building performance by reducing energy consumption. This study will help building researchers and policymakers to make a framework for sustainable, green building.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it