LEED’s Contribution to the United Nations’ Sustainable Development Goals
Bibliographic record
Abstract
Abstract Green and sustainable building standards strongly influence sustainable building activities. Therefore, it is essential to assess how current standards contribute to achieving the United Nations’ Sustainable Development Goals (SDGs). A comprehensive catalogue is developed, and the analysis of overlaps between the standard and the SDGs is automated through direct content analysis. LEED V4.1 BD+C for New Construction is selected as the leading green building standard globally. Similar to previous work, LEED generates matches with eight SDGs – SDG3 (health), SDG12 (sustainable consumption and production), SDG 11 (sustainable cities) being the highest-ranked, respectively This content analysis is complemented with a qualitative analysis founded on innovation and risk-management theory, to assess the transformative capacity of the standard. The findings indicate that only about 6% of LEED’s score calls for positive value creation and transformative change, with most of its credits directed towards the management and reduction of known building instigated environmental risks. The research concludes that while there are overlaps between the LEED credits and the SDGs' topics, further research is needed to validate its contribution to realizing the 2030 Agenda empirically.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".