A Theoretical Modeling Framework to Support Investment Decisions in Green and Grey Infrastructure under Risk and Uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
Green infrastructure for source water protection in the form of forest protection and afforestation is gaining interest worldwide. It is considered more sustainable in the long-term than traditional engineering-based approaches. This paper presents a theoretical model to support investment decisions in green and grey infrastructure to deliver safe drinking water. We first develop a static optimal control model accounting for the uncertainties surrounding green infrastructure. This model is then extended to factor in key characteristics surrounding investment decisions aimed at optimizing the stock of green and grey infrastructure. We first include dynamic forest growth, followed by the risk of wildfires and finally the potential offsetting effect of carbon sequestration on long-term climate change and the reduced risk of wildfires. We provide a numerical example to analyze the performance of the different model specifications, interpret their outcomes and draw conclusions to guide future investment decisions in green and grey infrastructure.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it