Comparison of Assessment Systems for Green Building and Green Civil Infrastructure
Bibliographic record
Abstract
The assessment systems for green building have been developed and implemented for decades. Well-known systems include the U.S. system LEED, the U.K. system BREEAM, the Canadian system GB tools, and the Japanese system CASBEE. These systems will be discussed and compared together with Taiwan’s EEWH system. Each assessment system may contain a different set of evaluation items to evaluate the sustainability level of a building project. Contrarily, the assessment system for green civil infrastructure projects is rarely discussed and developed globally. In Taiwan, studies have been conducted to develop a new assessment system with some reasonable key indicators and evaluation items, serving as the tool to evaluate the sustainability level of a green civil infrastructure project. In this paper, the authors studied and summarized different key indicators and evaluation items, and made comparisons among some major assessment systems for both green building and green civil infrastructure projects. Based on the comparison of the various assessment systems, it is found that greenery, recycling of materials, water conservation, carbon emission reduction, and energy saving are considered in both green building and green civil infrastructure assessment systems. Nevertheless, external building structure, energy consumption, healthy air and temperature, illumination of the indoor environment, rainwater recycling, and underground reservoirs are considered only in green building assessments, but not in green civil infrastructure assessments. Moreover, durability, benefits, landscape, humanities, culture, and creativity, which are discussed adequately in green civil infrastructure assessments, are not highlighted in green building assessments. In addition, two construction projects in Taiwan, one green building project and one green civil infrastructure project, are presented to exemplify sustainability practices and assessments.
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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".