Capital structure optimization for build–operate–transfer (BOT) projects using a stochastic and multi-objective approach
Bibliographic record
Abstract
Private financing has long been recognized as playing an important role in providing public infrastructure facilities worldwide. Private investors–operators, however, are often exposed to the financial risk of low profitability due to the inaccurate forecast of facility demand, operating income, and maintenance costs. From the operator’s perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. To this end, operators are likely to reduce the equity amount to minimize the level of risk exposures, whereas creditors or lenders continue to raise it in an attempt to secure a decent level of financial responsibility from the operators. This paper presents an optimized capital structure model for both creditors and operators to reach an agreement for a balanced structure that synchronizes both profitability and repayment capacity. The model is developed with the use of Monte Carlo simulation and a multi-objective generic algorithm (GA) for drawing an optimal level of equity ratio. Results of a case study on a railway project show that the proposed model provides a proper range of capital structure for privately financed infrastructure projects while accounting for the project-specific risks under variable conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".