Is There a Future for Nuclear Power? Wind and Emission Reduction Targets in Fossil-Fuel Alberta
Bibliographic record
Abstract
This paper explores the viability of relying on wind power to replace upwards of 60% of electricity generation in Alberta that would be lost if coal-fired generation is phased out. Using hourly wind data from 17 locations across Alberta, we are able to simulate the potential wind power output available to the Alberta grid when modern, 3.5 MW-capacity wind turbines are spread across the province. Using wind regimes for the years 2006 through 2015, we find that available wind power is less than 60% of installed capacity 98% of the time, and below 30% of capacity 74% of the time. There is only a small amount of correlation between wind speeds at different locations, but yet it remains necessary to rely on fossil fuel generation. Then, based on the results from a grid allocation model, we find that CO2 emissions can be reduced by about 30%, but only through a combination of investment in wind energy and reliance on purchases of hydropower from British Columbia. Only if nuclear energy is permitted into the generation mix would Alberta be able to meet its CO2-emissions reduction target in the electricity sector. With nuclear power, emissions can be reduced by upwards of 85%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".