A STUDY ON THE ECONOMIC ANALYSIS FRAMEWORK OF THE ARCTIC ENERGY PLANT CONSTRUCTION PROJECT
Bibliographic record
Abstract
The construction projects in the Arctic region are considered highly uncertain due to the extremely cold construction environment. In this study, we presented an economic analysis framework applying rainbow options that can consider multiple uncertainties. The economic feasibility of oil & gas plant construction projects is mainly affected by fluctuation in oil price and construction cost. Thus, we considered oil price and construction cost fluctuation as main uncertainties. The proposed framework consists of five steps and aimed to be validated through a case study conducted in the Canadian context: (1) estimate construction cost uncertainty (estimate the volatility of construction cost with Monte-Carlo Simulation); (2) estimate oil price uncertainty (estimate the volatility of oil price with geometric Brownian motion); (3) Economic data can be categorized into project data and market data, and detailed data is defined and quantified; (4) the economic feasibility of the project is assessed using the quadrinomial lattice method, which is one of the techniques used to calculate real options; and (5) optimize project decision making. The research findings hold significant meaning as we propose a framework for evaluating the economic feasibility of an energy plant construction project using rainbow options. Furthermore, it is anticipated that the utilization of the proposed framework can enhance the profitability and make a significant contribution to investors planning to embark on energy plant construction projects.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".