König, R., Pregger, T., Kronshage, S., Jochem, P., McCall, B., Holtz, G., Vishwanathan, S.S., Fragkos, P., Teng, F., Smith, S.J., Teske, S. (2025). How to consider feasibility aspects of transformation pathways for the industry sector - implications for energy systems modelling. Energy Strategy Reviews.
- Background knowledge integration from seven international energy systems models.
- Requirement analysis for feasibility evaluations for industry sector transition.
- Characterisation of feasibility indicator categories from various disciplines.
- Visual presentation of a process loop for improved feasibility assessments.
- Findings support translating scientific study results into industrial practices.
Abstract
The practical feasibility of GHG mitigation pathways is increasingly acknowledged as essential to climate scenario development. However, energy system models (ESMs) still lack a structured and comprehensive approach for integrating feasibility considerations. This study proposes a conceptual framework that addresses this gap by guiding the integration of feasibility aspects into model-based scenario studies, with a specific focus on the industrial sector.
At the heart of the proposed concept lies the Feasibility Loop—the core contribution of this work. It provides a structured, visual, and process-oriented approach to systematically link existing methods, indicators, and data sources across the entire modelling process. The loop identifies key steps in a model-supported feasibility assessment, clarifies how different types of methods contribute to these steps, and supports modellers in understanding where and how feasibility aspects can be meaningfully integrated.
The framework is built around three distinct types of feasibility constraints—hard, quantitative soft, and qualitative soft—which serve as a conceptual bridge between assessment content, modelling tools, and interdisciplinary knowledge. Supporting components include a 5W1H-based structuring of the research context, a typology of feasibility-relevant indicator categories, and guidance for modelling requirements such as granularity and adaptability.
Rather than prescribing a fixed workflow, the proposed concept serves as a flexible toolbox, enabling tailored application depending on available resources and research goals. Finally, it aims to improve the relevance, comparability, and transparency of scenario results and support more robust decision-making in the transformation of energy-intensive industry systems.