Economic Impacts of Curtailing Wind Turbine Operations for the Protection of Bat Populations in Ontario
Bibliographic record
Abstract
Wind energy is a growing industry in Canada to meet the demand for a renewable supply of energy. However, wind turbine operation represents a high mortality risk for bat populations, and regulators often require that steps are taken to mitigate this risk. The result is concern among operators about lost revenue potential. This study was, therefore, designed to estimate the theoretical financial impact of curtailing turbine operations to mitigate for bat mortality for all wind farms that were constructed and operating in Ontario, Canada, as of 1 January 2020 (n = 87 wind farms). Empirical data from the Canadian Wind Farm SCADA and meteorological systems are not publicly available; thus, we were compelled to use data from the Canadian Wind Turbine database, the Environment and Climate Change Canada Wind Atlas, and the Independent Electricity System Operator to calculate the total theoretical energy production for all wind turbines in the province using manufacturer power curves and a measure–correlate–predict linear regression method. We estimated the financial impacts for all wind farms on the assumption that operations were curtailed when the Wind Atlas modelled local wind speed was <5.5 m/s between 6 pm of one day and 6 am the following day, between 15 July and 30 September, using the lower and upper limits of power-purchase agreement rates for Ontario wind farms: 115 and 150 CAD/MWh. We used generalized linear modelling to test whether the variability in production loss was predicted based on factors related to turbine design and site wind speeds. We estimated that total annual wind energy production would be reduced from 12.09 to 12.04 TWh if all Ontario wind farms implemented operational curtailment, which is equivalent to a difference of 51.2 GWh, or 0.42%. Production loss was related to turbine cut-in speeds and average site wind speeds recorded between 15 July and 30 September. The estimated profit losses were 6.79 ± 0.9 million CAD compared to estimated earnings of 1.6 ± 0.21 billion CAD, which suggests that mitigating bat mortality may represent a small cost to the industry relative to the conservation benefits for bat populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".