We build a two-stage stochastic R&D portfolio model for climate policy analysis. This model can help policy makers allocate a limited R&D budget to minimize the total social cost. We develop several methods, including genetic programming and a greedy algorithm, to deal with the computational challenges of the model that arise due to the inclusion of uncertainties. From the R&D model, we have several key results. First, the optimal portfolios are robust against the climate risks. Second, policy makers should put most of their investment into Carbon Capture and Storage (CCS) projects when the R&D budget is relatively low. We further show Fast Reactor (FR) and 3rd generation PV are the two most unattractive technologies in the portfolio. Finally, more sophisticated expert elicitations on climate change energy technologies should be done in the future, because the potential benefit can be up to 11 billion dollars.

ABSTRACT The large-scale integration of PV solar energy onto the electricity grid remains a major challenge because of the intermittency issues which affect the grid reliability. In this thesis, we investigate the impact of grid integration issues upon the optimal energy R&D portfolio for climate change under damage uncertainty. We especially look at how the following two contrasting assumptions about solar intermittency issues will impact the composition of the optimal energy R&D technology portfolio for climate change. The first assumption, which we term “costly solar storage”, implies that grid integration will have costs; the second assumption, which we term “free solar storage”, implies that grid integration will have no costs. To achieve this task, we first present a two-stage stochastic programming model for energy R&D portfolio for climate change and the solution methods used to solve it. We will refer to this model as the budget constraint model (BCM model). Then, we will introduce a relaxation of the BCM model by including the R&D budget as a cost in the objective function. We will call this the overall optimal model (OOM model). In order to represent the impacts of technical change, we will use the Mini-Climate Assessment Model (MiniCAM) to generate marginal abatement cost curves (MAC), which represent the cost of reducing an additional ton of CO2. Two sets of MAC curves based on the two assumptions are generated and used in our models to estimate the impacts of grid issues on the optimal R&D portfolios for climate change. The results of our analysis using the BCM model show that the composition of the optimal portfolio remains almost the same under the two grid assumptions. However, the results of the OOM model show some significant differences between the two assumptions, with considerably more solar R&D investment when intermittency issues are neglected. Our estimates of the costs of the grid range between 2.5 billion and 21 billion dollars.