Improving the Understanding of the Green Assessment System / LEED in Developing Countries: The Case of China
Bibliographic record
Abstract
Several green assessment systems, such as LEED in North America, BREEAM in UK, CASBEE in Japan, GBTool in Canada have been discussed in this paper, especially focus on the similarities and differences. These systems could be used to evaluate the environmental impact of various construction projects, to set up the standard for related assessment as well. They have similar sustainable concerns: sustainable site selection, energy efficiency, water efficiency, sustainable material and resource, and indoor environment quality. However, each one put different ratio on these categories.A review in the state-of-the-act of the green assessment system has been done in this paper, and some important findings have been highlighted in tables. Consequently, some hotspot issues, such as why LEED is preferred in China, how to use it in a proper way, and what can we do with the still existed challenges are discussed.
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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 | 0.004 | 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".