Quantification of Technological Uncertainty in Evaluating the Feasibility of Meeting Emission Targets

Abstract

Recent studies have considered uncertainty for GHG reductions by using the stochastic programming model for energy systems. The models require values of input parameters in the form of a scenario tree to handle uncertainty. However, only few studies have used quantitative methods to generate scenarios of the future values of technological input parameters. We show that the scenarios of the future values of the technological input parameter for energy system models can also be generated by a quantitative methodology. In addition, we introduce how to quantify the risk for the feasibility of meeting GHG emission targets by incorporating the technological uncertainty.