ML-IAM: Emulating Integrated Assessment Models With Machine Learning (Papers Track)
Yen Shin (KAIST); Haewon McJeon (KAIST); Changyoon Lee (KAIST); Eunsu Kim (KAIST); Junho Myung (KAIST); Kiwoong Park (KAIST); Jung-Hun Woo (Seoul National University); Min-Young Choi (Seoul National University); Bomi Kim (Seoul National University); Hyun W. Ka (KAIST); Alice Oh (KAIST)
Abstract
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.