The achievement of climate neutrality in the European Union by 2050 will not be possible solely through a reduction in fossil fuels and the development of energy generation from renewable sources. Large-scale implementation of various technologies is necessary, including bioenergy with carbon capture and storage (BECCS), carbon capture and storage (CCS), and carbon capture and utilisation (CCU), as well as industrial electrification, the use of hydrogen, the expansion of electromobility, low-emission agricultural practices, and afforestation. This research is devoted to an analysis of BECCS as a negative emissions technology (NET) and the assessment of its implementation impact upon the possibility of achieving climate neutrality in the EU. The modelling approach utilises tools developed within the LIFE Climate CAKE PL project and includes the MEESA energy model and the d-PLACE CGE economic model. This article identifies the scope of the required investment in generation capacity and the amount of electricity production from BECCS necessary to meet the greenhouse gas (GHG) emission reduction targets in the EU, examining the technology’s impact on the overall system costs and marginal abatement costs (MACs). The modelling results confirm the key role of BECCS technology in achieving EU climate goals by 2050.
Background: The transition to a carbon neutral society such as that envisaged in the European Union Green Deal requires careful and comprehensive planning. Integrated assessment models (IAMs) and energy system models (ESMs) are both commonly used for policy advice and in the process of policy design. In Europe, a vast landscape of these models has emerged and both kinds of models have been part of numerous model comparison and model linking exercises. However, IAMs and ESMs have rarely been compared or linked with one another. Methods: This study conducts an explorative comparison and identifies possible flows of information between 11 of the integrated assessment and energy system models in the European Climate and Energy Modelling Forum. The study identifies and compares regional aggregations and commonly reported variables We define harmonised regions and a subset of shared result variables that enable the comparison of results across the models. Results: The results highlight similarities and differences on final electricity demand, electricity supply and hydrogen across three levels of aggregation. However, the differences between the regional aggregation of the models limit detailed analysis. Conclusions: This first-of-its-kind comparison and analysis of modelling results across model type boundaries provides modellers and policymakers with a better understanding of how to interpret both IAM and ESM results. It also highlights the need for community standards for region definitions and information about reported variables to facilitate future comparisons of this kind.
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