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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".