Climate change impacts on wind energy resources in North America based on the CMIP6 projections
Bibliographic record
Abstract
The mid- and long-term evolution of wind energy resources in North America is investigated by means of a multi-model ensemble selected from 18 global climate models. The most recent scenarios of greenhouse gases emissions and land use, the Shared Socioeconomic Pathways (SSPs), are considered - more specifically, the SSP5-8.5 (intensive emissions) and SSP2-4.5 (moderate emissions). In both scenarios, onshore wind power density in the US and Canada is predicted to drop. Under SSP5-8.5, the reduction is of the order of 15% overall, reaching as much as 40% in certain northern regions - Quebec and Nunavut in Canada and Alaska in the US. Conversely, significant increases in wind power density are predicted in Hudson Bay (up to 25%), Texas and northern Mexico (up to 15%), southern Mexico and Central America (up to 30%). As for the intra-annual variability, it is poised to rise drastically, with monthly average wind power densities increasing up to 120% in certain months and decreasing up to 60% in others. These changes in both the mean value and the intra-annual variability of wind power density are of consequence for the Levelised Cost of Energy from wind, the planning of future investments and, more generally, the contribution of wind to the energy mix.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".