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A stochastic fuzzy multicriteria methodology for energy planning decision support: Case study of the electrification of the Greek road transport sector

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  • Koutsandreas, Diamantis
  • Keppo, Ilkka

Abstract

Providing robust planning insights requires transitioning from single-criterion, definitive frameworks to approaches that handle trade-offs and communicate result conditionality. This paper introduces an integrated methodology for energy planning, combining life-cycle impact assessment with decision support. Our framework incorporates often-overlooked aspects in energy planning (e.g., resource depletion and biodiversity) and allows us to reflect stakeholder risk profiles, preferences, and uncertainties about future energy and economic states. Scenarios are handled with the fuzzy group utility and maximum regret measures, while strategies are evaluated with the fuzzy VIKOR and TOPSIS methods. The framework advances existing multicriteria approaches by innovatively combining and contextually adapting existing methods, embedding them within a robust modelling framework. It provides comprehensive decision support by identifying optimal strategies across decision-maker profiles and explicitly communicating result contingency and diversity linked to uncertainties and policy preferences. The methodology is demonstrated for Greece's road transport sector, evaluating three fleet electrification strategies for 2040. Results revealed that aggressive electrification may induce significant negative repercussions on resources, human health, and ecosystems. Conversely, moderate electrification emerged as the most effective strategy in 79 % of cases across risk profiles and policy objective portfolios, suggesting the combination of technological shifts with resource-neutral lifestyle changes for road transport decarbonization.

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  • Koutsandreas, Diamantis & Keppo, Ilkka, 2026. "A stochastic fuzzy multicriteria methodology for energy planning decision support: Case study of the electrification of the Greek road transport sector," Technological Forecasting and Social Change, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:tefoso:v:222:y:2026:i:c:s0040162525004354
    DOI: 10.1016/j.techfore.2025.124404
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