Uncertainty in Derivation of Transportation Sector Inputs and Parameters for a Canadian Energy System Optimization Model
Bibliographic record
Abstract
Energy system models (ESMs) provide an evidence base for climate policy analysis. However, model prognoses vary dramatically across different ESMs, undermining credibility and hindering knowledge transfer. This is compounded by limited access to disaggregated energy data in Canada, leading to reliance on foreign sources and heuristic assumptions, while underrepresenting key sectors, including transportation and chemical fuels supply. Rather than comparing the systematic differences between ESMs, this thesis steps back to demonstrate the influence that input data derivation and parameterization have on model results, with emphasis on road transportation in Ontario. Using Tools for Energy Model Optimization and Analysis (Temoa), this thesis evaluates system responses to different modeling choices and parameterization methods. It addresses uncertainties related to: (i) using ad hoc constraints to capture market and political dependencies, (ii) technological change in efficiency and projections, (iii) electric vehicle charging demand representation, and (iv) global sensitivities of decision variables to transportation parameters.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".