Investigating Drivers Stimulating Demand for Green Renovation of Existing Buildings and Systems
Bibliographic record
Abstract
The main purpose of the research is to investigate drivers that motivate homeowners, investors, government institutions etc., to undertake green renovation. Sustainable upgrade actions have been slow although new smart technologies such as solar panels, e-glazing, insulation systems, cogeneration etc., are developed or upgraded every year. At such a slow pace, the existing building stock presents a challenge as drivers are not rigorously identified and applied. A survey questionnaire was designed to examine all the drivers that encourage energy renovation. Extensive review of the literature provided a theoretical framework that supported the study. The survey was administered to energy consultants, architects, quantity surveyors, facility managers and engineers with sufficient professional experience. The data was analysed using means, T-test analysis and Mann–Whitney U test. The results establish a relationship between drivers and upgrade of existing buildings and systems. The findings identified a strong level of agreement among the respondents on the drivers of green renovation. Incentive and support systems, penalties for noncompliance, high energy bills, energy conservation and policy and regulations, awareness etc., are some of the motivating factors that drive energy management retrofit.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".