In this dissertation we analyze the effects of low carbon energy technology R&D portfolios on the cost of climate change mitigation. We use the results to create the analytical foundation for a decision support system aimed at effectively communicating the effects of uncertainty to decision makers. Specifically, we focus on three main areas. The first is generating a correlated probability distribution around detailed energy price forecasts. The second is showing how the availability of advanced energy technologies and combinations of them affect the marginal abatement cost curve. The third is creating the analytic foundation for a decision support system (DSS) by using an integrated assessment model to analyze the effects of combinations of low carbon energy technologies on CO2 concentration stabilization costs and then combining the results with probabilistic data from expert elicitations to analyze R&D portfolios. The third part also involves creating a multivariate regression model to represent the relationship between variables for additional analysis.