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Record W2899389743 · doi:10.36688/imej.1.35-40

Strategic timing of commercial-scale tidal energy investment

2018· article· en· W2899389743 on OpenAlexafffund
Shelley L. MacDougall

Bibliographic record

VenueInternational Marine Energy Journal · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsAcadia University
FundersHarrison McCain Foundation
KeywordsOption valueValuation (finance)Cash flowFinanceVolatility (finance)IncentivePredictabilityBusinessNet present valueInvestment decisionsEconomicsMicroeconomicsProduction (economics)

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.615
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.037
GPT teacher head0.236
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes2
Has abstractyes

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