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

Peer-reviewed journal article in Energies 13(7) is available online (Publisher MDPI)

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 tCO2 (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.

The technology mix module combines both process-specific and process-unspecific GHG abatement measures. Process-unspecific GHG abatement measures are termed as cross-sectional measures. Cross-sectional measures refer to applications such as industrial lighting or gas consumption in the entire industry sector, whereas process-specific GHG abatement measures are always assigned to a specific process such as steel production. Figure 1 summarises the allocation of the technology mapping to the measure categories in the technology mix module.

Figure 1: Assignment of modelled technologies to the measure categories and effect level

Figure 2 shows the ramp-up of the individual measure categories (left). Furthermore, the figure compares the ramp-up of all process efficiency measures implemented in the model (71) (right). Figure 2 underlines that the energy savings achieved in TMI scenario, compared to the reference scenario, require the full implementation of almost all GHG abatement measures in the model. Only individual efficiency measures, CO2-capture and energy carrier change with technology change at process level still have GHG abatement potentials after 2050. However, it is also clear that the ramp-up of measures must begin as soon as possible in order to achieve the computed energy and emission savings by 2050. The left diagram shows the individual modelling of efficiency measures in SmInd. Figure 2 highlights that individual efficiency measures are above and below the scenario average (black dotted line) of the measure ramp-up with regard to their reinvestment cycle (replacement rate).

Figure 2: GHG abatement measure ramp-up of the different categories (left) and ramp-up of individual small-scale efficiency measures (right).

The project on which this publication is based was funded by the Federal Ministry of Education and Research (BMBF) under the funding code O3SFK300-2. The authors are fully responsible for the content of this publication.