Assessing homeowners’ awareness of green technologies in residential housing development: evidence from Ghana
Bibliographic record
Abstract
Purpose Using and promoting green technologies in residential buildings might be a far more practical strategy for developing a sustainable built environment. The primary goal of this study is to examine homeowners' knowledge and awareness of the different green technologies and concepts that can be adopted to improve the quality of their homes. Design/methodology/approach The study employed a sequential mix technique methodology in order to accomplish its goal. A total of 156 respondents were chosen for a survey within the research areas using a simple random sample approach, while interviewees were chosen using a purposive sampling approach methodology. Descriptive and inferential statistics as well as content analysis were used to analyze the quantitative and qualitative data, respectively. Findings The findings indicate that homeowners have moderate knowledge of green technologies. It was also evident that print and electronic media are excellent at capturing and reaching a diverse range of homeowners interested in learning about sustainable development issues. Furthermore, the top three green technologies that most homeowners are aware of are using local materials over imported materials, grey water reclaiming and reuse technology and solar water heating technology. Subsequently, the homeowners in the three communities have differing opinions about the majority (82%) of the green technologies examined. Originality/value The findings will serve as a useful guide to assist practitioners and policymakers in implementing appropriate methods to integrate green technologies into housing projects and subsequently encourage their adoption.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".