Policy Responses to Climate Change in a Dynamic Stochastic Economy

Yongyang Cai, Ohio State University

Usage Details

Integrated Assessment Models (IAMs) of climate and economy aim to analyze the impact and efficacies of policy responses to climate change. However, the current IAMs often ignore many significant elements, such as uncertainties in climate and economic systems (e.g., climate tipping points), spatial heat and moisture transfer, competition between countries, international trade, competition between generations, robust decisions, renewable energy and learning, technology research and development, and so on. For example, DICE (Dynamic Integrated model of Climate and Economy) is the benchmark IAM model developed over the past 20 years by William Nordhaus. It is a simple perfect foresight forward looking model assuming that we know all of the future information and there is no heterogeneity, and it is used frequently in the literature, e.g., the United States Interagency Working Group on Social Cost of Carbon. Ignoring the listed significant elements is often excused on the grounds that computational limitations make it impossible to do better.

Our work from recent and previous Blue Waters projects clearly showed otherwise. The target problem for this project is a continuation of our previous successful Blue Waters projects, that is, to develop and solve new computational IAMs that merge the significant elements necessary. We will then use their solutions to do economic analysis about the optimal climate policy and how such a policy will impact the economic activities.