Carbon Mitigation Effects of Global Energy Interconnection : The Case Study of Connecting the Canada-US-Mexico Power Grids
Bibliographic record
Abstract
Widely known as the largest contributor to global carbon emissions, electricity sector has always been considered as a top priority for carbon emission reduction.Global energy interconnection (or, connecting power grids in different nations) could be one of the important measures to maximize the use of renewable/clean energy in different nations.However, its effects in carbon emission reduction have not well studied in a quantitative way.Taking North America as an example, this study uses a bottom-up energy system optimization model to study the carbon emission reduction effects of different interconnection scenarios.To capture the multiple possibilities of energy interconnection, we set up two scenarios for electricity demand: high and low; and two scenarios for power grid interconnection, weak connection and strong connection.The power electricity supply in different nations will change because of the different interconnection scenarios, which will make the CO 2 emission generated by electricity go up or down.When considering the power substitution and strong connection scenario, carbon dioxide emissions can be reduced by as much as 48% compared with the weak connection scenario.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".