The social return on investment of an urban regeneration project using real-world data: the Connswater Community Greenway, Belfast, UK
Bibliographic record
Abstract
Previous research has illustrated the role of urban green and blue spaces in improving the economic, social, environmental, and health-related outcomes of urban populations. The Connswater Community Greenway is presented as a case study to assess the social value of an urban regeneration project. Using real-world data from two time points (2012 and 2017), our analysis focussed on eight key elements: property values; flood alleviation; tourism; biodiversity; climate change; health and wellbeing; crime; and employment and productivity. Using social return on investment analysis, we estimated the value of the Connswater Community Greenway over a 40-year horizon. The total value was estimated to be between £56.8m and £67m. After subtracting the costs (£42.2m), the net present value of the Connswater Community Greenway was £14.6m - £24.8m. The benefit-cost ratio was 1.34 – 1.59, meaning that for every £1 invested in the Connswater Community Greenway, the local economy gains between £1.34 and £1.59. Overall, the Connswater Community Greenway will provide a positive return on investment which will be realised after 30 years. Social return on investment analysis provides a framework for the incorporation of many multifunctional benefits of urban green and blue spaces into economic evaluation, providing a more complete analysis of value.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".