Construction professionals’ environmental perceptions of lumber, concrete and steel in Japan and China
Bibliographic record
Abstract
As green building programs (GBPs) are introduced in Asian countries, the question of how GBPs evaluate the “greenness” of construction practice becomes one of the most critical uncertainties for the building material industry. To better understand construction professionals’ perception of greenness and the greenness of different building materials in Japan and China, surveys were conducted in both countries in 2009 and 2010. The surveys were designed to evaluate professionals’ perceptions of the relative importance of the major environmental attributes of resource efficiency. Chinese and Japanese professionals ranked the environmental attributes similarly even though their construction practices are very different. They perceive that saving energy and saving water are substantially more important environmental attributes than using renewable materials, having a low carbon footprint and using recycled materials. Second, the survey was designed to gain insights into the respondents’ perceptions of the relative environmental performance of the three major structural building materials (wood, concrete and steel). In both countries, construction professionals perceive lumber as having the best environmental performance relative to concrete and steel. However, Chinese and Japanese professionals have different perceptions of lumber in terms of its environmental friendliness. In Japan, energy efficiency of houses, the level of pollution generated during the manufacturing process, and CO 2 emissions contribute to the evaluation of the environmental friendliness of lumber. In China, the sustainability of the resource is the main factor perceived as promoting the environmental friendliness of lumber.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".