Public attitudes towards electricity decarbonization and meeting 2035 goals
Bibliographic record
Abstract
Varying levels of public acceptance of different low-carbon energy technologies can pose a barrier to progress on decarbonizing the electricity grid in the U.S. This study developed a survey system that allowed respondents to design their ideal 100 % low-carbon mix for 2035 by allocating generation across seven low-carbon technologies. Using data from 532 respondents, the authors analyzed overall public preferences and how demographics affect technology preferences. Contrary to what is often assumed, the authors found that approximately 75 % of respondents' ideal portfolios are diverse (5+ technologies), and many included novel technologies, with approximately 90 % including CCS or offshore wind. Regression analysis found that demographic factors affect the amount of a technology that a respondent chose to deploy in their portfolio. Demonstrating the public's willingness to accept a diverse electricity portfolio that includes novel technologies opens the door for energy analysts and energy developers to investigate diverse and creative solutions to achieve electricity decarbonization. Additionally, demographic factors that affect technology preferences create a new layer of consideration for project siting to increase the likelihood of acceptance. Engaging with the public and considering public preferences will be integral to achieving the U.S.’s decarbonization goals. • Used a unique online tool in which public respondents constructed their ideal portfolios of low-carbon generation for 2035. • Portfolios revealed an acceptance of a diverse array of technologies, including CCS and offshore wind. • Demographic factors were found to impact the odds of including technologies in an individual portfolio.
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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 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 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".