Assessing a decision-support tool to estimate the cooling potential and economic savings from urban vegetation in Singapore
Bibliographic record
Abstract
• The InVEST Urban Cooling model accurately estimated urban temperatures in a tropical city. • Model parameters were calibrated using observed temperature data. • Urban cooling from vegetation has economic benefits through reduced energy use. • We estimated $47.14 million SGD annual savings in public residential buildings. • With minimal data requirements, the model can be applied to other tropical cities. To mitigate the growing threat of urban heat, cities are implementing greening strategies such as tree planting or the development of parks. Effectively integrating these solutions into planning requires quantitative information on the cooling effect of urban vegetation. Here we examined the performance of an open-source decision-support tool, the Integrated Valuation of Ecosystem Services and Tradeoffs Urban Cooling model, to estimate the cooling effect and economic benefits from urban vegetation in a tropical city context, using Singapore as an exemplar case study. Using observed temperature data, we calibrated the model to estimate the spatial distribution of annual average day- and night-time temperature at 10 m spatial resolution and validated the results using leave-one-out cross validation. The calibrated models performed well to estimate annual average daily mean and maximum (day), and minimum (night) temperatures (R 2 of 0.78, 0.65, and 0.52, respectively). We estimated that urban cooling in Singapore provides economic savings of $47.14 million SGD annually from reduced energy consumption in public residential buildings, based on the relationship between energy consumption and mean temperature. Our results give confidence in the model as a decision-support tool to estimate urban heat island effects and evaluate heat mitigation strategies in tropical cities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".