Defence of Thesis 1 October: Mohamed Hossameldin Badr – Faculty of Engineering

Doctoral Candidate Mohamed Hossameldin Badr at the Department of Energy and Process Engineering will hold a trial lecture and defend his doctoral thesis for the degree of Philosophiae Doctor (PhD). The degree is administered by the Faculty of Engineering at the Norwegian University of Science and Technology (NTNU). Thesis title Uncertainty in Environmentally-Extended Multi-Regional Input-Output Models for an electronic version of the thesis, pleas contact trond.kvilhaug@ntnu.no Trial lecture To what extent is IO an appropriate tool for analyzing the crossing of planetary boundaries? Assessment committee The Faculty has appointed the following Assessment Committee to assess the thesis 1 st opponent: Professor Bart Los, University of Groningen, the Netherlands 2 nd opponent: Associate Professor Anne Owen, University of Leeds Administrator: Professor Francesca Verones, NTNU The committee has concluded that the thesis is worthy of public defense for the PhD degree. Supervisors Main supervisor Dr. Konstantin Stadler, Department of Energy and Process Engineering Co-supervisor Associate Professor Juudith Ottelin, Department of Energy and Process Engineering Time and venue Trial lecture : 1 October 2026 at 10:15 in Disputasrommet, Hovedbygget, NTNU Gløshaugen Public defence : 1 October 2026 at 13:15 in Disputasrommet, Hovedbygget, NTNU Gløshaugen You can follow the trial lecture and the defence via Zoom, by clicking on this link Summary Environmentally extended multi-regional input–output (EE–MRIO) models have become essential tools for quantifying consumption-based environmental pressures and supporting climate and resource policy. Despite their widespread use, uncertainty in EE–MRIO indicators remains insufficiently understood, particularly regarding how different sources of uncertainty arise, interact, and propagate through complex economic systems. This limits the interpretation of footprint results and the robustness of policy recommendations. This thesis addresses this gap through a systematic investigation of uncertainty in EE–MRIO modelling. It begins with a comprehensive review of the existing literature to establish a conceptual understanding of uncertainty sources and identify key research gaps. Building on this foundation, the thesis develops analytical error-propagation methods and Monte Carlo simulation approaches to examine how uncertainty propagates through EE–MRIO models and how model structure influences the sensitivity of environmental footprint estimates. It then extends these insights to policy analysis by explicitly incorporating uncertainty into the assessment of mitigation interventions. Overall, the thesis demonstrates that uncertainty propagation in EE–MRIO models is shaped not only by data quality but also by model structure and the characteristics of individual environmental extensions. The findings show that uncertainty can both amplify and attenuate as it propagates through supply chains, depending on the distribution of environmental pressures and the structure of production networks. The thesis highlights the importance of integrating uncertainty into both methodological development and policy applications, providing a more robust foundation for environmental footprint analysis and uncertainty-aware decision-making.

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