The North American Electric Grid as an Exchange Network: An Approach for Evaluating Energy Resource Composition and Greenhouse Gas Mitigation
Bibliographic record
Abstract
Using a complex network framework, the North American electric grid is modeled as a dynamic, equilibrium-based supply chain of more than 100 interconnected power control areas (PCAs) in the contiguous United States, Canada, and Northern Mexico. Monthly generation and yearly inter-PCA exchange data reported by PCAs are used to estimate a directed network topology. Variables including electricity, as well as primary fuels, technologies, and greenhouse gas emissions associated with power generation can be traced through the network, providing energy source composition statistics for power consumers at a given location. Results show opportunities for more precise measurement by consumers of emissions occurring on their behalf at power plants. Specifically, we show a larger range of possible factors (∼0 to 1.3 kgCO2/kWh) as compared to the range provided by the EPA's eGRID analysis (∼0.4 to 1 kgCO2/kWh). We also show that 66-73% of the variance in PCA-level estimated emissions savings is the result of PCA-to-PCA differences that are not captured by the larger eGRID subregions. The increased precision could bolster development of effective greenhouse gas reporting and mitigation policies. This study also highlights the need for improvements in the consistency and spatiotemporal resolution of PCA-level generation and exchange data reporting.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".