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Small-Scale Modelling of Individual Greenhouse Gas Abatement Measures in Industry

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  • Tobias Hübner

    (Forschungsgesellschaft für Energiewirtschaft (FfE), Munich 80995, Germany
    Chair of Energy Economy and Application Technology, Technical University Munich (TUM), Munich 80333, Germany)

Abstract

The dynamic bottom-up modelling of greenhouse gas (GHG) abatement measures in industry makes it possible to derive consistent transformation paths on the basis of heterogeneous, process-specific developments. The main focus is on the development of a transparent methodology for small-scale modelling and combination of individual GHG abatement measures. In this way, interactions between GHG abatement measures are taken into account when deriving industrial transformation paths. The presented three-part methodological approach comprises the preparation (1) and implementation (2) of GHG abatement measures as well as the resulting effects on the output parameters (3) in a technology mix module. In order to consider interactions in the measures implementation, year-specific overall measure matrices are created and prioritised based on the GHG abatement costs. Finally, the three-part methodology is tested in a consistent technology mix scenario. The results show that the methodology enables integrated industrial technology mix scenarios with a high level of climate ambition based on a plausible development of energy consumption and emissions. Compared to the reference scenario, the process-and energy-related emissions decrease by 90 million tCO 2 (77% of the 1990 level in 2050). The developed methodology and the related technology mix scenario within the framework of the bottom-up industry model SmInd can support strategic decision-making in politics and an efficient transition to a greenhouse gas neutral industry.

Suggested Citation

  • Tobias Hübner, 2020. "Small-Scale Modelling of Individual Greenhouse Gas Abatement Measures in Industry," Energies, MDPI, vol. 13(7), pages 1-43, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1619-:d:340233
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