Dynamic and Stochastic Integration of Climate and Economy

Global warming has been recognized as a growing potential threat to economic well-being. This concern has lead to an increasing number of national and international discussions on how to respond to this threat. Determining which policies should be implemented will require merging quantitative assessments of the likely economic impacts of carbon emissions with models of how the economic and climate systems interact. All agree that uncertainty needs to be a central part of analysis. We address these challenges by developing a computational, dynamic, stochastic general equilibrium framework for studying global models of both the economy and the climate with risks and uncertainties called DSICE (Dynamic Stochastic Integration of Climate and the Economy). In general, the solution to a dynamic programming problem with continuous states can only be approximated. This project provides a set of tools for verifying the accuracy of the computations and improving the efficiency of numerical dynamic programming, and apply them to compute optimal climate policy in DSICE.

Recent Studies: 

Numerical Methods:

Ongoing work:

  • Uncertain economic growth with long-run risk: a comprehensive quantitative analysis of the optimal carbon tax policy in the face of economic risk with empirically plausible specifications for the willingness.
  • Multi-stage tipping processes with uncertain damage levels and durations and the interaction between economic uncertainties and the climate tipping risks.
  • Bayesian Learning in DSICE: use Bayesian learning to model how actors learn uncertain key parameters over time to reduce their uncertainty in DSICE, and to analyze the impact of learning to the optimal climate policy.
  • Nonlinear certainty equivalent methods for stochastic dynamic problems 


Yongyang Cai | Kenneth Judd | Thomas Lontzek

Recent Publications