Long-term supply mix planning of power systems accounting for greenhouse gas emissions
Bibliographic record
Abstract
Recent scientific findings have generated considerable concern about the adverse effect of greenhouse gas (GHG) emissions on the world's climate in general and global warming in particular. In Canada and many other countries, this concern has led to the adoption of legal and political steps with the aim of curbing GHG emissions. The first part of this thesis describes the steps taken by Canada in this regard. Such steps provide a strong incentive to Canadian power systems to incorporate reduction of GHG emissions in their planning process. Thus, in the second part of the thesis, a long-term supply mix planning model is developed. Since significant decrease in GHG emissions is unlikely to occur without removal of highly polluting power plants, this model allows for decommissioning these power plants. Finally, the supply mix planning model is applied to evaluate the strategy of joint planning (as opposed to separate planning) of the power systems of Quebec and Ontario. Results obtained from the model leads to the conclusion that joint planning is preferable from the point of view of overall social and financial cost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".