Energy Transition Planning and the Role of Long-Term Modeling in the Northern Canada
Bibliographic record
Abstract
Long term energy transition planning presents a significant challenge to planners in Canada striving to meet national climate goals. This is exaggerated for northern territories, where barriers and considerations arise due to low capacity, harsh environmental conditions, and vast geographical separation from the southern Canadian energy infrastructure. This paper aims to emphasize the unique context of the Yukon Territory by completing three main objectives: (1) Review current long term modeling strategies; (2) Analyze the potential implementations of the newly developed MESSAGEix-Canada integrated assessment model (IAM); and (3) Recommend future work to increase the capacity, transparency, and confidence of energy transition modeling in the Yukon. This study was informed by stakeholder engagement with the Yukon Government, local utility providers, industry experts, and modeling consultants. Key outcomes of this work include a policy overview outlining the Territory's ambitious 2030 goals, a record of long-term energy resource modeling considerations, potential pathways to address stakeholder concerns, and outputs from the first iteration of the MESSAGEix-Canada model. From here, the author synthesized strategies for appropriate sourcing of input datasets and summarized sensitivities and scenarios of interest for the region. Further, modeling tools and features were compared - most notably, the integration of soft linked models to consider hourly electricity accounting for successful integration of intermittent renewables, climate impacts, land use implications, and technology emissions. Finally, this paper recommends future work to increase collaboration and capacity for Yukon's long term energy planning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".