FELLOWS ADDRESS California Dreaming: The Economics of Renewable Energy
Bibliographic record
Abstract
California was the first jurisdiction to mandate a reduction in greenhouse gas (GHG) emissions by 80% below 1990 levels by 2050. This target was subsequently endorsed by the G8 in 2009 and the European Commission in 2014, and is the guiding principle of the 2015 Paris Agreement. To achieve these targets will require near elimination of fossil fuels and/or a technological breakthrough that might be considered a black swan event. Eschewing nuclear power, countries are relying on renewable energy sources to meet future energy needs. In this paper, I examine the prospects of reducing GHG emissions by 80% by first summarizing extant global energy sources and production, trends, and projections of energy demand, and the potential mix of future energy sources. I consider the role of conservation and then focus on the electricity sector to determine how wind and biomass could contribute to the 80% target. I conclude that these ambitious targets cannot be attained without nuclear power.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.004 |
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".