Adapting for Uncertainty: A Scenario Analysis of U.S. Technology Energy Futures
Bibliographic record
Abstract
Policymakers and managers in the U.S. energy sector will face complex multidimensional challenges as they confront potential supply shortfalls, infrastructure constraints, and environmental limitations in the years ahead. Using a technique known as scenario analysis, this paper investigates key energy issues and decisions that could improve or reduce the ability of the United States to deal with the uncertainties that may challenge the U.S. economy during the next fifty years. Four scenarios have been developed representing a diverse range of future worlds to explore the driving forces and critical uncertainties that may shape U.S. energy markets and the economy for the next fifty years. Each scenario has been quantified using a computable general equilibrium model, the All Modular Industry Growth Assessment model, also known as the AMIGA modeling system. The preliminary results from the scenario analysis suggest that the range of feasible U.S. energy futures is broad, but that energy use is expected to grow under all scenarios.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".