Green walls and roofs: A mandatory or voluntary approach for Australia? Literature
Bibliographic record
Abstract
A review of literature on mandatory and voluntary approaches to the delivery of green roofs and walls (GRGW) globally. The key findings and patterns emerging around; Drivers for living architecture (LA) GRGW. As cities grow, increases in GHG emissions, air pollution, impervious surfaces urban temperatures, loss of tree canopy cover and land for food production. LA can mitigate the negative aspects. GRGW have social, economic, health and environmental benefits. Barriers are social, economic, technological and environmental. Costs are a significant barrier and lack of construction industry experience. Industry and BE professional capacity is in developing phase and not fully ready to implement on a larger scale. Training and skill development needed. There is significant potential to retrofit existing buildings, feasibility determined partly by structural capacity to sustain additional loads and; this needs to be more fully understood by stakeholders. Lack of policy and regulations to integrate LA practices in new build and retrofit. No consistent policy approach found in Australia. No states have GRGW policy (COS & COM councils have policies for LGAs. NSW, Vic, SA & WA have guidelines and policies referring to GRGW. Overall a lack of policy to promote LA. US Cost Benefit Analysis found a viable case for large-scale retrofit of GR. Increases in residential property value with green infrastructure between 6 to 15%, (AECOM, 2017). Wide-scale adoption of GR in Toronto could attenuate the UHI by 0.5 to 5o C - as heatwave is a resilience issue for Sydney, Melbourne and Adelaide, wide-scale adoption could be beneficial.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".