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Journal Article

Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)

Authors
/persons/resource/michaja.pehl

Pehl,  Michaja
Potsdam Institute for Climate Impact Research;

/persons/resource/Felix.Schreyer

Schreyer,  Felix
Potsdam Institute for Climate Impact Research;

/persons/resource/Gunnar.Luderer

Luderer,  Gunnar
Potsdam Institute for Climate Impact Research;

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Citation

Pehl, M., Schreyer, F., Luderer, G. (2024): Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0). - Geoscientific Model Development, 17, 5, 2015-2038.
https://doi.org/10.5194/gmd-17-2015-2024


Abstract
This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs).