How many GHGs does a light bulb emit? GHG emissions associated with electricity consumption
Bibliographic record
Abstract
Greenhouse gases (GHGs) associated with electricity consumption are regularly included in GHG reporting protocols. Canadian examples are used to illustrate where issues arise and where there is room for improvement, specifically the electricity emission factor (EEF). The EEF is affected by a number of factors including: Uncertainty of GHG emissions; GHG accounting; and Grid supply mix. Fluctuation in fuel quality can affect overall CO2 emissions and the EEF. An internal study conducted by Environment Canada found that emissions data reported via stack-specific continuous emissions monitoring systems differed by between 2 and 6% from those developed using generic emission factors and fuel consumption data. The GHG accounting for Canada's EEFs includes combustion emissions only, excluding process or fugitive emissions and emissions associated with the transportation and distribution of electricity. The variability in the grid supply mix also has an impact on GHG emissions due to contributions by different types of generators. The supply of electricity in Ontario during typical work hours is compared to a 24-hour average, showing that coal fired generation provides a greater percentage of electricity between 8 a.m. and 5 p.m. than over a 24-hour period. The dynamic nature of the electricity supply, uncertainty and variability in generating facility GHG emissions and improvements in GHG accounting are all areas where there is room for improvement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".