Optimal climate change mitigation under long-term growth uncertainty: Stochastic integrated assessment and analytic findings

- Integrated assessment under long-term growth uncertainty.
- Stochastic dynamic programming approach.
- Analytic explanation of impact channels for generic IAM.
- Carbon tax impact ranges from −30% to +45% in stochastic DICE.
- Analysis for both standard and Epstein-Zin-Weil preferences.

Economic growth over the coming centuries is one of the major determinants of today׳s optimal greenhouse gas mitigation policy. At the same time, long-run economic growth is highly uncertain. This paper is the first to evaluate optimal mitigation policy under long-term growth uncertainty in a stochastic integrated assessment model of climate change. The sign and magnitude of the impact depend on preference characteristics and on how damages scale with production. We explain the different mechanisms driving optimal mitigation under certain growth, under uncertain technological progress in the discounted expected utility model, and under uncertain technological progress in a more comprehensive asset pricing model based on Epstein-Zin-Weil preferences. In the latter framework, the dominating uncertainty impact has the opposite sign of a deterministic growth impact; the sign switch results from an endogenous pessimism weighting. All of our numeric scenarios use a DICE based assessment model and find a higher optimal carbon tax than the deterministic DICE base case calibration.