Designing More Cost-effective Trading Markets for Renewable Energy
Bibliographic record
Abstract
In this paper we study the design of renewable energy portfolio standards (RPSs). We focus on solar energy and analyze two common RPS rules: cross-state trading restrictions and state-specific interim annual targets. Using historically observed RPSs and an empirically calibrated model of state-level solar supply curves, we find that allowing for cross-state trading reduces cost by one-fifth and significantly changes the geographic distribution of new solar installations. Removing interim annual targets over the 2015 to 2019 period reduces cost by one-third by back-loading installations to later years. These cost reductions become much larger when considering more ambitious RPS targets. Our results suggest that more flexible program design such as allowing for cross-state trading, back-loading interim targets, or banking and borrowing renewable energy credits can avoid escalating costs and preserve the political feasibility of renewable energy standards, although such cost savings must be balanced against the social damages from delayed climate action and other economic and political considerations. JEL Classification: H23, Q41, Q42, Q48
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".