Application of sustainability indicators in decision-making processes for urban regeneration projects
Bibliographic record
Abstract
Birmingham Eastside, an area of 170 ha, is located to the eastern side of Birmingham's city centre in the UK. Over a 10-year period this once-deprived inner city area is being regenerated through public and private finance estimated at £6 billion. The regeneration scheme is bringing about changes to the local environment, economy and society. The key players (e.g. landowners, developers and planners) involved in the decision-making processes for Eastside have the power to see that these changes are brought about in a more sustainable manner. To achieve this it is necessary to assess in which direction the development should go, and to provide benchmarks for implementing and measuring sustainable changes along the way. The process can be facilitated by the use of sustainability indicators, of which there are many. This paper outlines a variety of sustainability indicators (e.g. Spear, Breeam, sustainability checklists and other benchmarks), including those used within the decision-making processes for Eastside. In particular, it details those indicators operating at city level, quarter level and then individual development site level. Several case study sites are included (Masshouse, City Park Gate, the learning and leisure quarter, the new technology institute and Warwick Bar). The paper discusses the role of indicators in achieving a more sustainable development (SD). The development timeline framework (DTF) is used to analyse how and when indicators have formed an integral part of the decision-making process for various sites in Eastside. The responsibility for implementing SD and the role of participation are discussed and generic lessons learned for the application of indicators, including aspects of timing, are set out.
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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 | 0.089 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".