How to model a complex national energy system? Developing an integrated energy systems framework for long-term energy and emissions analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to manage an energy system responsibly and maintain its benefits indefinitely, science-based decision-making should be valued during energy policy making and energy management. This research presents a framework for developing a scientific tool with the long-range energy alternatives planning (LEAP) system for evaluating energy consumption and greenhouse gas (GHG) emission mitigation pathways for a national energy system. The framework developed is applied to create a bottom-up (technology-explicit), data-intensive (over 2 million data points), multi-regional (13 integrated regions) energy model of Canada, one of the world's most energy and emission intensive nations. Model accuracy was validated with historical data showing emissions varied 0-1.2% proving the framework can provide accurate assessments. The model was used to generate baseline Canadian energy-emissions outlooks to 2050 that do not currently exist in literature. The developed framework provides robust capabilities that are helpful for energy efficiency analysis, energy planning, and GHG mitigation assessment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it