Mitigation and adaptation: Assessing the multi-value benefits of transmission expansion
Bibliographic record
Abstract
Previous research has shown that expanding transmission capacity facilitates the achievement of net-zero targets by improving variable renewable energy utilization. However, transmission expansion plans have traditionally only been assessed on the metrics of operational cost savings and curtailment reduction. In this study, a multi-value benefit planning framework has been applied to assess the value of transmission expansion more holistically, expanding past simply considering operational cost savings to include five other metrics: emission reduction, renewable expansion capital cost savings, risk mitigation under uncertain future conditions, resource adequacy analysis, and resilience benefits. This multi-value planning framework is used to assess transmission corridors that show significant opportunity for expansion under the Canadian Energy Regulations: British Columbia and Alberta, and Saskatchewan and Manitoba. Results indicate that there are significant benefits of expanding transmission in terms of improving the resilience and resource adequacy of the electricity grid, which have previously been unquantified with traditional transmission expansion assessments. These findings highlight that the value of transmission is not sufficiently captured through export revenues and that transmission is as much an adaptation initiative as a mitigation initiative.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".