A Real Options Approach for the Investment Decisions of a Farm‐Based Anaerobic Digester
Bibliographic record
Abstract
The profitability of anaerobic digesters (ADs) for Ontario dairy farmers are examined using real options under current and proposed government pricing policies and investment uncertainty. In the case of a renewable energy initiative such as an AD with large sunk costs and volatile returns, the value of deferring investment may be significant enough to offset the returns suggested by the net present value (NPV) approach. For a 150 cow herd, net revenues should be approximately $1.1 million before the AD is installed using the real options approach as compared to $0.5 million with the NPV approach. An AD is close to generating a positive NPV for a 600 cow herd if for either a 1% increase in the electrical price or decrease in the cost. However, farmers need not invest today and there is a value to delaying this decision from potential improvements in the technology that increase the efficiency and/or decrease operating costs of the AD. The real options analysis indicates that this option to delay investment has a value of approximately $300,000 for a typical Ontario dairy farm. Thus, either significant grant funding or higher feed‐in‐tariff rates are required to induce the increased adoption of AD technology in Ontario today even for the largest of dairy farms. Considering the probability of government support potentially ending, increases the value of investing today but a significant option value to defer still exists.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".