Canadian hydroelectricity imports to the U.S.; Modeling of hourly carbon emissions reduction in New England
Bibliographic record
Abstract
United States’ hydroelectricity imports from Canada have increased by > 1 TWh per year between 2007 and 2021. This occurs as policymakers in the U.S. try to ramp up the deployment of new carbon free electricity generation and transmission infrastructure. Furthermore, recent modeling in the northeast U.S. demonstrates that Canadian hydroelectricity will play a significant role in New England’s least-cost decarbonization scenario. Additionally, decarbonization targets are well- defined in all states within the New England region, making it a priority. Consequently, it is anticipated that more hydroelectricity will flow from Canada into New England, resulting in the expansion of transborder electricity interconnections. To characterize the costs and benefits of such projects as compared to alternatives, a high-resolution simulation (i.e., hourly) of the electric grid is needed. In this study, we utilize the U.S. Environmental Protection Agency's dataset on hourly electricity generation and carbon emissions. Using pre-established decarbonization scenarios, we can calculate the precise reduction in greenhouse gas and air pollutant emissions for each scenario. Our preliminary results demonstrate that the scenario projection for 2026–2027 by New England ISO, which involves a combination of Canadian hydroelectric imports (2100 MW summer, 826 MW winter), new wind (308 MW summer and 682 MW), and solar (92 MW summer, 28 MW winter) generation commitments, can effectively offset carbon emissions in New England. These results further support the current decarbonization policy, which relies on a diversified mix of carbon free electricity sources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".