How can CASBEE contribute as a sustainability assessment tool to achieve the SDGs?
Bibliographic record
Abstract
Abstract The purpose of this research was to understand the relationship between assessment items of the Comprehensive Assessment System for Built Environment Efficiency (CASBEE) and the UN’s Sustainable Development Goals (SDGs). The SDGs, the core of the 2030 Agenda for Sustainable Development, were adopted in 2015. In response, social demand for sustainable buildings, urban districts, and cities has grown. New assessment tools have been developed to quantitatively evaluate the sustainability of individual buildings, building clusters, urban districts, and cities worldwide. CASBEE was developed in Japan, with assessment items suited to each scale, from individual buildings to entire cities. These assessment items are expected to contribute to SDG 11 “Sustainable Cities and Communities” as well as the other 16 goals. However, the degree of correspondence between assessment items and SDGs remains unclear. Therefore, the relationships between CASBEE assessment items and SDGs were investigated to confirm their effectiveness as a tool for sustainable design. In this analysis, the number of corresponding goals increased as the scale became broader. At the scales of individual buildings and building clusters, many assessment items contributed indirectly to SDG 12 “Responsible Consumption and Production” while many assessment items contributed directly to SDG 11 in urban districts and cities.
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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 | 0.030 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".