A Multi-stages Decision Approach for Managerial Flexibility of Energy R&D Project under Fuzzy Environment
Bibliographic record
Abstract
In recent years, many countries and firms seek the new and renewable energy to cope with the impending global environmental crisis, such as depletion of fossil-based energy, climate change to control emissions of greenhouse gases. This paper aims to take the perspective of the firm, which undertakes the energy R\&D project to maximize profits implying minimization of total cost as well. Incorporating technical and market risks into energy R\&D project is crucial, in that the managers often face the rapidly changing environment full of uncertainties. The firms should incorporate managerial flexibility into energy R\&D project decision not only reducing uncertain risks, but also increasing potential market payoff. This research considers a multi-stages decision model in which real-option-based analysis is applied for energy R\&D project under fuzzy environment. Specifically, the market payoff is obtained when the new and renewable energy product is commercialized to market, while energy R\&D investment costs are exhausted gradually. Furthermore, the uncertain development performance and market information are described as fuzzy variables by credibility theory. Instead of the traditional real option pricing methods, the dynamic programming methodology that captures the uncertain product development performance and final market return is developed to more effectively characterize the managerial flexibility. This method can reflect the multi-stages nature of R\&D programme, while helping decision-makers take the optimal investment decision and capture future market opportunities of energy products.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".