The Investigation of the Barriers in Developing Green Building in Malaysia
Bibliographic record
Abstract
Green building is the foundation of the sustainable construction development. Construction industry with the high contributes with gross domestic product, has undeniable impacts on the economy. Although Green buildings provide a wide range of benefits for the society, green building development suffers from different kinds of market barriers in developing countries including Malaysia. In order to meet green building development in Malaysia, this study aims to investigate the level of developing green building in the current situation, to find important key players and to identify, and to eliminate the important obstacles to green building development. In this research, the respondents were randomly selected from the professionals of Malaysian construction industry across the country and the method applied for collecting data is questionnaire survey. All the questionnaires were sent out to the respondents manually or through e-mail. A total of 673 sets of questionnaire were sent out and 167 (24.81%) questionnaires were received. The quantitative method was used for analysing data through SPSS version 19. Based on the results, the level of developing green building in Malaysia is not satisfied and government has a key role in the development of green buildings in Malaysia. The main barriers can be listed as: lack of credit resources to cover up front cost, risk of investment, lack of demand as well as higher final price.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".