Expert Assessments of Future Photovoltaic Technologies
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.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".