Measuring infrastructure investment option value
Bibliographic record
Abstract
Purpose – The purpose of this paper is to propose a risk-based framework to estimate the option value of infrastructure investment, accounting for the stochastic behavior of both financial and physical (engineering) variables. Design/methodology/approach – This study uses a real-options approach and computes the optimal investment dates and option values using Least Squares Monte Carlo, both the original Longstaff – Schwartz algorithm and the constrained Least Squares approach of Le tourneau – Stentoft. Findings – Real-option value for infrastructure investment is substantial. It is beneficial to model jointly financial and engineering risks to better understand the timing and real-option value of infrastructure investment. The analysis further shows which variables are option value drivers. Research limitations/implications – Future work could integrate financing constraints into the model, consider path dependency in the physical state variables or integrate sovereign risk, expropriation risk, operational risk or other project risks. Practical implications – Financial practitioners and investment managers interested in infrastructure risk finance or project finance will benefit from a novel framework to analyze infrastructure investments in which engineering and financial risks interact in a tractable way. Social implications – Public decision-makers will benefit from a better understanding of what determines the value of infrastructure investments, how real-option value affects optimal investment timing and how both are determined by financial and engineering risks. Originality/value – The analysis considers financial and engineering risks in a single framework to better understand option value in infrastructure investment. The framework and findings are useful both to risk finance and project finance practitioners and investors as well as engineers and public sector decision-makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".