DSICE
/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:
- Tipping points
- Environmental tipping points and the cost-benefit assessment of climate policies.
- The social cost of stochastic and irreversible climate change.
Numerical Methods:
- Dynamic programming with Hermite interpolation.
- Advances in numerical dynamic programming and new applications.
- Continuous-Time Methods for Integrated Assessment Models.
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
People
Yongyang Cai | Kenneth Judd | Thomas Lontzek
Recent Publications
Thomas S. Lontzek, Yongyang Cai, Kenneth L Judd, and Timothy M. Lenton. Stochastic integrated assessment of climate tipping points indicates the need for strict climate policy. Nature Clim. Change, (5):441–444, 05 2015.
Yongyang Cai, Kenneth L Judd, and Thomas S Lontzek. The social cost of carbon with economic and climate risks. Technical report, arXiv preprint, 2015. arXiv:1504.06909.
Yongyang Cai, Kenneth L Judd, Timothy M Lenton, Thomas S Lontzek, and Daiju Narita. Environmental tipping points significantly affect the cost-benefit assessment of climate policies. Proceedings of the National Academy of Sciences, 112(15):4606–4611, 2015.
Cai, Yongyang, and Kenneth L. Judd. "Dynamic programming with Hermite approximation. Mathematical Methods of Operations Research," 81:245–267, 2015.
