Natural Gas, Wind and Nuclear Options for Generating Electricity in a Carbon Constrained World
Bibliographic record
Abstract
A linear programming model is used to examine the impact of carbon taxes on the optimal generation mix in the Alberta electrical system. The model permits decommissioning of generating assets with high carbon dioxide emissions and investment in new gas-fired, wind and, in some scenarios, nuclear capacity. Although there is an intertie from Alberta to the U.S., the focus is on the connection to British Columbia as wind energy can potentially be stored in reservoirs behind hydroelectric dams. However, storage can also be used to smooth out the net load facing nuclear facilities. A carbon tax facilitates early removal of coal-fired capacity, which is replaced by low-emissions gas plants. It is only when the carbon tax exceeds $125/tCO2 that wind enters the system, although wind is displaced by nuclear power if that option is permitted. Although upfront costs of adding nuclear capacity are prohibitive, nuclear outcompetes wind because wind farms have low capacity factors and, importantly, because a great deal of gas-plant capacity is required to support wind, something avoided when nuclear energy is added. Finally, an intertie with British Columbia is beneficial because of the support it provides for wind and nuclear energy, but the role of natural gas is more important in facilitating a transition to lower system-wide carbon dioxide emissions.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".