100% Renewable Energy Scenarios for North America—Spatial Distribution and Network Constraints
Bibliographic record
Abstract
The urgency to combat climate change and the widely distributed, increasingly competitive renewable resources in North America are strong arguments to explore scenarios for a renewable energy supply in the region. While the current power system of North America is heavily dependent on fossil fuels, namely natural gas, coal and oil, and some nuclear power plants, some current policies at the state level, and future federal policies are likely to push the share of different renewable sources available in Mexico, the U.S., and Canada. This paper explores three scenarios for a renewable energy supply, using a bottom-up energy system model with a high level of spatial and time granularity. The scenarios span the extremes with respect to connecting infrastructure: while one scenario only looks at state-level supply and demand, without interconnections, the other extreme scenario allows cross-continental network investments. The model results indicate that the North American continent (a) has sufficient renewable potential to satisfy its energy demand with renewables, independent of the underlying grid assumption, (b) solar generation dominates the generation mix as the least-cost option under given renewable resource availability and (c) simultaneous planning of generation and transmission capacity expansion does not result in high grid investments, but the necessary flexibility to integrate intermittent renewable generation is rather provided by the existing grid in combination with short-term and seasonal storages.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".