A real options-based decision-making framework for hydraulic infrastructure investments
Bibliographic record
Abstract
Planning hydraulic infrastructure is challenging as it requires the careful consideration of many uncertain factors, such as the evolution of future demands and supplies. The deep uncertainty attached to climate change makes traditional planning approaches based on well-characterized statistical distributions ill-suited. This has led to the emergence of a new paradigm, "prepare and adapt," which focuses on developing robust and adaptive systems that can perform well under a wide range of futures. This research presents a framework for planning new water resources infrastructure (e.g., reservoirs, hydropower plants) based on real options, deep uncertainty, and temporal multicriteria analysis (TMCA). The real option component essentially handles the issues associated with the timing, sequencing, sizing, and operating of those infrastructures. The deep uncertainty that characterizes future hydroclimatic conditions is captured by a large ensemble of GCM-based hydrologic projections. Finally, TMCA allows us to compare and rank the options with respect to several criteria reflecting the different water uses (e.g., irrigated agriculture, hydropower generation, navigation, fisheries, flood recession agriculture, municipal and industrial water supply) in a dynamically changing environment induced by both climate change and the options. The framework is applied to the Senegal River Basin (SRB) in West Africa. The SRB is a complex system with several planned hydropower projects and irrigation schemes, making it ideal for testing the proposed framework. The results identify development pathways associated with different tradeoffs between risk and reward. The framework also helps decision-makers understand the distributional effects of these development pathways on society.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".