Improving accuracy, complexity and policy relevance: a literature survey on recent advancements of climate mitigation modeling

Process-based Integrated Assessment Models (IAMs) play a crucial role in climate agenda-setting and progress monitoring. They advise climate negotiations, inform nationally determined contributions (NDCs), and help create scenarios for central banks. Recent developments have enhanced IAMs' policy scope and accuracy, including the incorporation of industrial policies, improved sectoral details, and modeling of consumer behavior. Despite these advancements, challenges remain, particularly in improving spatio-temporal and sectoral resolution, adapting to fast-changing sector-specific policies, and addressing complex dynamics beyond the traditional techno-economic cost-minimization framework. This literature review explores Directed Technical Change (DTC) growth models, Agent-Based Modeling (ABM), and game theory to complement mainstream IAM approaches, especially in integrating political economy considerations. DTC emphasizes the role of public research and development (R&D) investment in supporting early-stage mitigation technologies. ABM highlights the decision-making processes and behaviors of heterogeneous agents, while game theory examines market dynamics, such as newcomer vs. incumbent competition, strategic pricing, and resource extraction. While these models cannot replace IAMs, they can broaden the scenario design space and improve the complexity and policy relevance of IAM-based mitigation modeling.