Wind Energy Development in the Great Lakes Region: Current Issues and Public Opinion
Bibliographic record
Abstract
In the last decade, both Canada and the United States have seen exponential growth in the percentage of electricity that comes from wind energy. The Great Lakes Region is no exception. The Region had just over 344 megawatts (MW) of wind energy in 2000; by late 2013 that number had risen forty-fivefold to 15,505MW.Wind turbines, though, are not spread evenly across the Region. Nearly a quarter (23%) of the wind energy around the Great Lakes comes from Illinois, while Ohio produces less than 3% of the Region’s wind energy. Ontario accounts for 15% of the Region’s total, and leads Canada in provincial wind energy production. Further, even within a state or province, windfarms tend to be concentrated in specific rural areas where there are ample wind resources and proximity to transmission lines, but low enough population densities to safely site turbines. As a result, residents across the Great Lakes Region may have widely varying familiarity and experiences with wind development. While as a group, residents of the Great Lakes Basin overwhelmingly support additional wind power development within the Region and see the beneficial rather than harmful impacts of wind energy, there remain a number of skeptics. Specifically, residents in Ontario tend to be less supportive of additional wind development and more believing of the potential negative consequences of wind energy than their American counterparts. There are a number of possible explanations for this difference that we hope to further explore. It could be due to more first-hand experience with wind turbines, it could be a result of increased exposure to controversy surrounding windfarms as a result of the rapid expansion of wind development in the province, or it could be backlash against the provincial government taking windfarm siting authority away from local municipalities. Alternatively, Ontarians’ skepticism of wind could be the result of more peripheral energy issues: increasing electricity prices in Ontario in recent years, or general dislike with provincial energy policy given its many shifts over the past decade. In order to set the stage for understanding the differing levels of support for wind energy throughout the Great Lakes Region, this report first describes the renewable energy policy environment in each of the states and provinces, and how that has translated into additional wind energy development in the last decade. It then looks at the results of the Great Lakes Region Public Opinion Survey, the specifics of which are discussed in greater detail in sections to follow.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".