ML-IAM: Emulating Integrated Assessment Models With Machine Learning

Integrated Assessment Models (IAMs) are essential for projecting future greenhouse gas (GHG) emissions and energy outputs, but they are computationally expensive and limited by model-specific idiosyncrasies. We present ML-IAM, a machine learning model trained on the AR6 Scenarios Database to emulate IAMs. ML-IAM generates results for new scenarios in seconds, avoids convergence failures, and produces model-agnostic outputs by learning from diverse model families. Among the tested models, XGBoost achieves the best performance with an $R^2$ of 0.98 with the original IAM data. ML-IAM enables rapid exploration of climate scenarios, complementing traditional IAMs with efficient and scalable computation for climate policy analysis.