Quantifying potential contributions of green facades to environmental justice: a case study of a quarter in Berlin
Bibliographic record
Abstract
Abstract The potential of green facades (GFs) to enhance environmental justice (EJ) has not been quantified so far. EJ in Berlin, Germany is assessed by the core indicators (1) noise pollution, (2) air pollution, (3) bioclimatic stress, (4) provision of green space and (5) social status. Most of the inner city is rated “poorly” in one or multiple indicators. Based on literature and spatial data, status quo and target values are determined for indicators (1)-(4) for an exemplary, highly burdened quarter in Berlin. It is assessed if and how much GFs could potentially improve current EJ levels. The improvements due to GFs to reach target values are assessed in % for day/night and indoor/outdoor settings. It can be shown that installing GFs would improve statuses of the four indicators to different extents, with the biggest enhancement found regarding indicator (3) for indoors at daytime: 52%. Determining factors for the EJ improvement potential of GFs need to be further assessed. This feasible method for increasing the amount of urban green can be helpful for improving life in highly burdened quarters. Therefore, from the point of view of EJ, large-scale implementation of GFs in urban areas is recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".