From Sparse to Representative: Machine Learning to Densify IAM Scenario Ensembles for Policy Insight

From Sparse to Representative: Machine Learning to Densify IAM Scenario Ensembles for Policy Insight (Proposals Track)

Georgia Ray (Imperial College London)

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Climate Science & Modeling Agriculture & Food Buildings Forests Heavy Industry and Manufacturing Power & Energy Public Policy Societal Adaptation & Resilience Supply Chains Transportation Generative Modeling Reinforcement Learning Uncertainty Quantification & Robustness

Abstract

This research addresses the challenge of extracting policy-relevant insights from Integrated Assessment Model (IAM) scenario ensembles, which are often sparse, non-representative, and inaccessible to non-experts. We propose a machine learning framework preserving high-dimensional dependencies between variables, enabling generation of plausible in-gap scenarios when one or more outputs are constrained. The intended output is a simplified exploration space for policymakers concerned with crucial climate policy exploration.