Greenhouse gas emission intensity factors for marginal electricity generation in Canada
Bibliographic record
Abstract
In Canada, each province has its own electric utility system, and each system is responsible for meeting the demand of its customer base. Electricity demand in all provinces is highly variable throughout the day, as well as during the year. In order to achieve a good match between electricity demand and generation, a mix of base, intermediate and peaking load power plants is used, which uses different fuel sources. When a renewable energy technology or an energy efficiency measure that results in electricity savings is implemented on a regional, provincial and national scale, the electricity savings reflect in the peak (marginal) electricity generation. Thus, the greenhouse gas (GHG) emission reduction due to the reduction in electricity generation corresponds to the fuel used to generate the electricity at the margin. In Canada, the fuel used for marginal electricity generation varies from province to province and from hour to hour. To estimate the reduction in GHG emissions due to reducing electricity generation at the margin, it is necessary to have information on the fuel mix used to generate the marginal electricity for each province on a suitable time scale. With such information, it is possible to estimate a marginal GHG emission intensity factor for each province, which would provide the amount of GHG emissions produced as result of producing 1 kWh of electricity on the margin. However, such information is regarded confidential by most electric utilities and is not made public. In this paper, methodologies are presented to estimate the GHG intensity factors (GHGIFs) for marginal electricity generation for each province of Canada based on the information available in the public domain. The GHGIFs developed for each province are also presented, which are expected to be valid within the next 5-year horizon. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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 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".