Simon Scheidegger (Lausanne), 29.05.2024

This paper presents a comprehensive method for efficiently solving stochastic Integrated Assessment Models (IAMs) and performing parametric uncertainty quantification. Our approach consists of two main components: a deep learning-based algorithm designed to globally solve IAMs as a function of endogenous and exogenous state variables as well as uncertain parameters within a single model evaluation. Additionally, we develop a Gaussian process-based surrogate model to facilitate the efficient analysis of key metrics, such as the social cost of carbon, with respect to uncertain model parameters. To demonstrate the effectiveness of our method, we posit a high-dimensional stochastic IAM that aligns with cutting-edge climate science. Our computations reveal that most of the variability in the social cost of carbon stems from the parametric uncertainty in the equilibrium climate sensitivity and in the damage function.