Collaborative Research: DRU: An Improved Model of Endogenous Technical Change Considering Uncertain R&D Returns and Uncertain Climate Response

设计应对全球气候变化的措施需要分析工具来帮助决策者了解政策成本与其避免气候变化带来的收益之间的权衡。然而，成本和收益都是不确定的。迄今为止，气候变化研究中使用的模型并未代表所有关键的不确定性。一项重要的遗漏是针对能源技术改进的研发（R&D）回报的不确定性。对研发的研究表明，这种回报是不确定的并且高度倾斜（即取得很大突破的可能性很小）。忽略这种不确定性并关注平均效应将对满足特定目标所需的减排量或近期排放限制与能源技术研发投资之间的相对平衡产生有偏差的估计。这项研究将通过开发一个明确表示研发回报不确定性的模型框架来改进决策者可用的分析工具。技术变革的表述及其与研发支出的关系将根据美国专利的历史经验进行校准。该模型还将通过将不确定性限制在与 20 世纪气候一致的范围内，改善对温室气体排放的气候响应的不确定性的表征。这些组成部分将被整合到一个单一的框架中，该框架可用于探索不确定性下的减排和研发支出决策。这项研究的结果将改进应用于气候变化评估的分析设施。新工具预计将通过消除当前模型中的偏差，为减排时机和技术研发投资提供更好的指导。

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.