Prevalence and predictors of wind energy opposition in North America
Bibliographic record
Abstract
Addressing climate change requires societies to transition away from fossil fuels toward low-carbon energy, including renewables. Unfortunately, large wind projects have proven politically controversial, with groups opposing them across advanced economies. To date, there are few large-scale, systematic studies to identify the prevalence and predictors of opposition to wind energy projects. Here, we analyzed a dataset of wind energy projects across the United States and Canada between 2000 and 2016. We found that during this period, in the United States, 17% of wind projects faced significant opposition, and in Canada, 18% faced opposition, with rates in both countries growing over time. Opposition was concentrated regionally in the Northeastern United States and in Ontario, Canada. In both countries, larger projects with more turbines were more likely to be opposed. In the United States, opposition was more likely and more intense in areas with a higher proportion of White people, and a smaller proportion of Hispanic people. In Canada, opposition was more likely and more intense in wealthier communities. The most common tactics used to oppose wind energy were court cases, legislation, and physical protests. The number of people engaging in opposition to wind projects is likely small: Across articles that cited the number of individuals engaging in protests, the median number was 23 in the United States and 34 in Canada. When wealthier, Whiter communities oppose wind projects, this slows down the transition away from fossil fuel projects in poorer communities and communities of color, an environmental injustice we call "energy privilege."
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".