Americans Support for Renewable Energy is Disconnected from their Understanding of Powerline Infrastructure as a Mechanism to Mitigate Climate Change
Bibliographic record
Abstract
As nations are transitioning to renewable energy sources, they will need to expand and upgrade their energy infrastructure, including high-voltage power lines (HVPL). We have conducted the first nation-wide survey in the last thirty years to assess public attitudes toward HVPL in the USA. The study evaluates perceptions, knowledge, and attitudes toward building new transmission lines, as these relate to renewable energy, place attachment, and environmental impacts. Our results show that Americans do not recognize how new HVPL could help reduce greenhouse gas emissions; instead, respondents favor moving from centralized energy (large power stations and HVPL) to decentralized energy (local power supply and small scale solar panels and wind turbines. Our findings are consistent with studies from Europe in that citizens recognize negative human impacts on the natural world and support renewable energy, however, they have a limited understanding of the role of HVPL infrastructure in mitigating climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".