Work

Market diffusion of Power-to-X fuels in Germany: A cognitive approach with robust reasoning for policy support

Year: 2024

Type: article

Abstract: The European Union has set the target to reduce greenhouse gas (GHG) emissions by 55% by 2030 (compared to 1990) and to achieve climate neutrality by 2050. While GHG emissions have fallen in most areas, efforts to reduce them in transport have been largely unsuccessful. Power-to-X (PtX) fuels are renewable fuels with significant potential to reduce emissions in parts of the transport sector that are not well suited for electrification. However, the market diffusion of PtX fuels is a complex process influenced by multi-dimensional factors. Overcoming these complexities requires a comprehensive approach in order to improve the governance dynamics of the regulatory framework. To systematically analyze the diffusion of PtX fuels, a cognitive approach based on Fuzzy Cognitive Mapping (FCM) is developed and enhanced using the extended Z-numbers theory. The extended Z-numbers theory provides a realistic and robust reasoning framework for decision analysis in complex systems by involving decision makers to determine the interrelationships in the FCM and experts to evaluate the decision makers’ opinions. A case study is conducted to investigate the market diffusion of PtX fuels in the German transport sector from a system perspective. Extensive sensitivity analyses are performed to understand the system’s behavior under different scenarios. The results indicate a high importance of cross-sectoral factors influencing the complex diffusion of PtX fuels. • Identification of cross-sectoral factors affecting the market diffusion of PtX fuels. • A novel approach to investigate the market diffusion from a system perspective. • Improved Fuzzy Cognitive Mapping with extended Z-numbers. • High influence of technical and economic factors on the market diffusion of PtX fuels. • Assessing impacts of policy scenarios on the diffusion process and the system.

Source: Applied Energy

DOI: 10.1016/j.apenergy.2024.124616

Authors: Ali Ebadi Torkayesh, Sandra Venghaus

Institutions: RWTH Aachen University, Forschungszentrum Jülich

Language: English

Topic: Cognitive Science and Mapping

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Climate action, Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,300