Public support for energy portfolios in Canada: How information about cost and national energy portfolios affect perceptions of energy systems
Bibliographic record
Abstract
Public support for energy systems is a critical factor in the development and deployment of electricity-generating technologies. The publics’ support for energy developments may be driven by numerous factors, including the risks and benefits associated with the technology. It is well established that an important component in the deployment of energy systems is to assess the publics’ perceptions of the technology. There is also evidence that suggests providing information about the tradeoffs of different energy systems will encourage the public to make informed decisions regarding which energy technologies they support or oppose. To assess public perceptions of energy technologies, 1479 Canadians were surveyed about their preferences for nuclear, biomass, coal, wind, hydropower, solar, and natural gas. A portfolio approach was used to assess preferences for the seven technologies by asking respondents to create their ideal energy portfolio. In this manuscript, we examine (1) preferences for different energy sources, (2) whether preferences for these energy sources vary by province, and (3) whether providing information about the costs associated with the energy sources and the extent to which Canada relies on these different energy sources affects preferences for the technologies. Results indicate that participants were more likely to prefer energy portfolios that matched their current provincial energy portfolio. Results also show that participants were less supportive of expensive energy technologies and that providing information about the current state of electricity production may have a normalizing effect on energy perceptions. Implications for public policy and recommendations for communication about energy technologies are discussed.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".