Designing a response to global climate change requires analysis tools that can help decision makers understand the tradeoffs between the cost of a policy and its benefit in the form of avoided climate change. Both the costs and the benefits, however, are uncertain. To date, models used in studies of climate change have not represented all the critical uncertainties. One important omission is the representation of uncertainty in returns to research and development (R&D) directed to the improvement of energy technologies. Studies on R&D have shown that such returns are uncertain and highly skewed (i.e., have a small probability of a very large breakthrough). Ignoring this uncertainty and focusing on the average effect will produce biased estimates of the emissions reductions required to meet particular targets or the relative balance between near-term emissions constraints and investments in energy technology R&D.
This research will improve the analysis tools available to decision makers by developing a model framework that explicitly represents the uncertainty in the returns to R&D. The representation of technical change and its relation to R&D expenditure will be calibrated to historical experience with U.S. patents. The model will also improve the representation of the uncertainty in the climate response to greenhouse gas emissions by constraining that uncertainty to a range consistent with 20th century climate. These components will be integrated into a single framework that can be used to explore decision making about emissions reductions and R&D expenditures under uncertainty.
The results of this research will improve the analysis facilities that are applied to climate change assessment. The new tools are expected to give improved guidance in the timing of emissions reductions and investments in technology R&D by eliminating biases in current models.
