Modeling Uncertainty in Integrated Assessment Models

TL;DR

This paper reviews how Integrated Assessment Models (IAMs) incorporate and manage uncertainty, emphasizing recent methodological advances to improve climate policy robustness.

Contribution

It provides a comprehensive overview of uncertainty handling in IAMs, highlighting recent computational and methodological developments.

Findings

Identification of key uncertainty types in IAMs

Discussion of advanced modeling approaches for uncertainty

Implications for more robust climate policy design

Abstract

Integrated Assessment Models (IAMs) are pivotal tools that synthesize knowledge from climate science, economics, and policy to evaluate the interactions between human activities and the climate system. They serve as essential instruments for policymakers, providing insights into the potential outcomes of various climate policies and strategies. Given the complexity and inherent uncertainties in both the climate system and socio-economic processes, understanding and effectively managing uncertainty within IAMs is crucial for robust climate policy development. This review aims to provide a comprehensive overview of how IAMs handle uncertainty, highlighting recent methodological advancements and their implications for climate policy. I examine the types of uncertainties present in IAMs, discuss various modeling approaches to address these uncertainties, and explore recent developments in the field, including the incorporation of advanced computational methods.