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Record W4389154280 · doi:10.5539/ijef.v15n12p160

Real Options Analysis for Investment Decisions in Geothermal Energy

2023· article· en· W4389154280 on OpenAlexvenueno aff
Michel Becker, Marcus Vinícius Andrade de Lima, Juliana Baldessar Weber

Bibliographic record

VenueInternational Journal of Economics and Finance · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Geothermal gradientInvestment (military)Environmental economicsRenewable energyGeothermal energyInvestment analysisGreenhouse gasValue (mathematics)Risk analysis (engineering)Option valueBusinessEconomicsComputer scienceFinanceEngineeringMicroeconomicsFinancial riskManagement

Abstract

fetched live from OpenAlex

Geothermal renewable energy can contribute to the reduction of greenhouse gas emissions. However, difficulties have been encountered during its development. It is characterized by high-risk investment and irreversibility, whereas traditional investment analysis techniques have limited applications. As a tool for evaluating energy investments, the real options theory allows flexibility to be incorporated into project design in the face of an uncertain environment and has demonstrated the ability to add economic value to investments. Based on simulations of a geothermal project implemented in Brazil in a real-options framework specifically adapted for the analysis of geothermal investments, this study demonstrates that the tool is efficient in adding economic value to the analysis. In addition, it provides a comprehensive view of the project, identifying the managerial flexibilities and uncertainties that influence profits the most. This model is expected to encourage researchers and investors to evaluate geothermal energy projects in Brazil.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.256
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2023
Admission routes1
Has abstractyes

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