Evaluating financial risk management strategies under climate change for hydropower producers on the <scp>G</scp>reat <scp>L</scp>akes
Bibliographic record
Abstract
Abstract Hydropower on the Great Lakes makes up a substantial fraction of regional electricity generation capacity. Hydropower producers on the Niagara River (flowing between lakes Erie and Ontario) operate as run‐of‐river, and changing lake levels alter interlake flows reducing both generation and revenues. Index‐based insurance contracts, wherein contract payouts are linked to lake levels, offer a tool for mitigating this risk. As a potentially useful tool, pricing of financial insurance is typically based on historical behavior of the index. However, uncertainty with respect to the impacts of climate change on lake level behavior and how this might translate to increased (or decreased) risk for those selling or buying the insurance remains unexplored. Portfolios of binary index‐insurance contracts are developed for hydropower producers on the Niagara River, and their performance is evaluated under a range of climate scenarios. Climate Informed Decision Analysis is used to inform the sensitivity of these portfolios to potential shifts in long‐term, climatological variations in water level behavior. Under historical conditions, hydropower producers can use portfolios costing 0.5% of mean revenues to increase their minimum revenue threshold by approximately 18%. However, a one standard deviation decrease in the 50 year mean water level potentially doubles the frequency with which these portfolios would underperform from the perspective of a potential insurer. Trade‐offs between portfolio cost and the frequency of underperformance are investigated over a range of climate futures.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".