Classification, Valuation and Real Options Analysis of Climate Change Projects in Africa: A case study of Ghana in West Africa
Bibliographic record
Abstract
Projects and investments such as those of R & D and climate change are subject to several uncertainties. These uncertainties, if not properly managed, could defeat the actual purpose and target of the project. In this work, we identify flexibility as a way in which uncertainties can be managed. As the main aim of investments is to make profit, it is significant that investors conduct an in depth study of the project under consideration. This will aid in cost-benefit analysis to ascertain whether it is financially worth it or not. Real options in finance, is the tool that has proved effective in that regard. However, not every project can be analysed using real options. This thesis introduces real options in climate change investment in Africa (Ghana), 2014-2020. To determine whether real options could be applied, we introduce and estimate certain measures: flexibility, optionability and realizability. These metrics help us to identify the project in which real options can be used. In this case, we characterize real options into mechanisms and types. The mechanisms are noted to be the enablers of real options while the types are the particular ones enabled. This work also introduces the Decision Structure Matrix (DSM) in climate change investment. The logical Coupled Dependency Matrix (C-DSM) is used to specify the logical relations that exist among dependencies in the project. The logical dependencies are then used for the estimation of the metrics . This study serves as the basis for the application of real options analysis in climate change investments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".