Work

Emulating an Integrated Assessment Model to Project Long-Term Emissions and Carbon Dioxide Removal Pathways to 2300

Year: 2025
Type: conference-paper
Abstract: Integrated Assessment Models (IAMs) are essential tools for exploring climate change mitigation pathways, but they typically generate emission scenarios only until 2100. To address the growing need for longer-term scenarios beyond 2100, we developed an emulator of the IAM REMIND-MAgPIE. This emulator, emIAM, captures the behavior of the IAM by using marginal abatement cost (MAC) curves for three major greenhouse gases (GHGs), two sectors, and five carbon dioxide removal (CDR) techniques. We coupled emIAM with the reduced-complexity climate model ACC2 to generate a suite of internally consistent scenarios extending to 2300, targeting a range of temperature stabilization levels with varying overshoot profiles. These scenarios go beyond previously employed idealized approaches by incorporating the emulated dynamic response of the IAM and by explicitly characterizing CDR deployment pathways. Our scenarios serve as input to Earth System Models (ESMs) investigating the long-term consequences of climate change mitigation strategies, particularly the implications of CDR deployment and associated Earth system dynamics over centennial timescales. Furthermore, the modular design of the IAM emulator facilitates coupling with other reduced-complexity climate models, broadening its applicability for exploring climate-economic interactions. Disclaimer This working paper, prepared for the 30th Annual Conference of the European Association of Environmental and Resource Economists (EAERE) (16-19 June, 2025, Bergen, Norway), presents preliminary results from an ongoing research focusing on the development and application of IAM emulators for generating long-term climate scenarios. The results and analyses presented here are subject to further refinement and should not be considered final. We welcome comments and suggestions that can inform the next stages of this research.
Language: English
Open Access status: green
OpenAlex ID: W7220686928
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