Public Preferences in a Shifting Energy Future: Comparing Public Views of Eight Energy Sources in North America’s Pacific Northwest
Bibliographic record
Abstract
The U.S. and Canada continue to face major changes in energy production. Mounting awareness of the climate crisis has placed increasing importance on developing renewable energy sources, however, advances in fossil fuel extraction technology have opened vast domestic reserves of oil and natural gas. Public preferences for energy policy play a role in determining energy futures, but researchers rarely simultaneously compare public views across multiple renewable and non-renewable energies or across country boundaries. Here, we used a 2019 online survey sample (n = 1500) to compare predictors of support for eight fuel sources for electricity generation in British Columbia, Canada, and Washington and Oregon, USA. Results indicate the highest support overall for renewables (wind, solar, wave/tidal energy, geothermal) and the lowest for fossil fuels (coal and natural gas), nuclear, and hydropower. Mixed-effects regression modeling indicates that views on climate and the balance between environment and economy were consistent predictors of support across most energy types, while political ideology was less consistent. Perceived local importance of both extractive and renewable energy industries were significant predictors of support for some, but not all, energy sources, as were education and gender. Overall, our research suggests that while divisions persist in public energy preferences for both renewable and non-renewable sources, there is the broadest support for renewable energy technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".