Bibliographic record
Abstract
The many uncertainties in tidal energy conversion combine to form a significant barrier to raising private-sector capital. Mitigation and management of risk are essential if the industry is to attract equity investors. One way to manage the risk is through investment timing. The option to time an investment has value, which can be estimated. An analysis of an invest-vs-delay decision revealed a persistent, economicallyrational incentive to delay. Further inquiry identified a strategic rationale for delaying investment in tidal energy projects, given the uncertainty still present in the undertaking. As the largest sensitivity in the value of delay is the volatility of the investment’s expected cash flows, an investigation into the prevalent uncertainties was undertaken. This paper summarizes the real option valuation model. It then reports on results of a qualitative study of the predominant uncertainties facing developers and conditions that would help move the industry along. Predominant uncertainties reported revolve around technology reliability; site and resource knowledge; prospects for buildout; predictability of government policy and supports; prospects of off-take agreements; and supply chain capacity and costs. These are related back to the variables in the real option pricing model. The model is relevant for companies wishing to systematically evaluate timing options and communicate project value to the investment community. It can also be used by governments to evaluate the design of policies and financial supports in a way that is consistent with the priorities of financial markets.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".