Uncertainty in Derivation of Transportation Sector Inputs and Parameters for a Canadian Energy System Optimization Model

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.