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Author ORCID Identifier



Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Industrial Engineering & Operations Research

Year Degree Awarded


Month Degree Awarded


First Advisor

Erin D. Baker

Subject Categories

Climate | Energy Policy | Environmental Policy | Policy Design, Analysis, and Evaluation


There is widespread consensus that low carbon energy technologies will play a key role in the future global energy system. Many of the low-carbon technologies under consideration are not yet commercially available, and their ultimate value depends on a host of deeply uncertain socioeconomic, environmental, and technological considerations. While it is clear that significant investment in the energy system is needed, the optimal allocation of these investments is unclear. This dissertation develops a methodology for (1) analyzing the impact of low carbon energy technologies on the cost of meeting emission reduction targets (policy cost) and (2) using this information to develop optimal R&D investment portfolios. We then apply this methodology to analyze the value of low carbon energy R&D across two key dimensions of uncertainty and two theoretical models. In the first part we apply a set of expert-elicitation derived future technology scenarios to the Global Change Assessment Model and conduct a large ensemble of model runs. We then use the results of these runs to develop our methodology for analyzing the impact of technological change in low carbon energy technologies on policy cost. The second part builds on the methodology of part one by adding probabilistic information to the analysis. This allows us to not only measure the impact of technological change on policy costs, but also to derive optimal R&D investment portfolios. We conduct a sensitivity analysis of our results across assumptions about the structure of the demand side of the energy system. In the third part we consider the influence of model choice on our results. We apply harmonized input assumptions to two different integrated assessment models and examine how the model outputs differ. We find that although the impacts of low carbon energy technologies vary widely across different scenarios of socioeconomic and technological development, as well as across the models used for the analysis, the optimal R&D investment portfolios are surprisingly robust. We also find that return to R&D investment is sharply decreasing.