Expert Assessments of Future Photovoltaic Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Subjective probabilistic judgments about future module prices of 26 current and emerging photovoltaic (PV) technologies were obtained from 18 PV technology experts. Fourteen experts provided detailed assessments, including likely future efficiencies and prices under four policy scenarios. While there is considerable dispersion among the judgments, the results suggest a high likelihood that some PV technology will achieve a price of $1.20/Wp by 2030. Only 7 of 18 experts assess a better-than-even chance that any PV technology will achieve $0.30/Wp by 2030; 10 of 18 experts give this assessment by 2050. Given these odds, and the wide dispersion in results, we conclude that PV may have difficulty becoming economically competitive with other options for large-scale, low-carbon bulk electricity in the next 40 years. If $0.30/Wp is not reached, then PV will likely continue to expand in markets other than bulk power. In assessing different policy mechanisms, a majority of experts judged that R&D would most increase efficiency, while deployment incentives would most decrease price. This implies a possible disconnect between research and policy goals. Governments should be cautious about large subsidies for deployment of present PV technology while continuing to invest in R&D to lower cost and reduce uncertainty.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it