Aiello, G.; Quaranta, S.; Inguanta, R.; Certa, A.; Mario, V. A Multi-Criteria Decision-Making Framework for Zero Emission Vehicle Fleet Renewal Considering Lifecycle and Scenario Uncertainty. Energies2024, 17, 1371.
Aiello, G.; Quaranta, S.; Inguanta, R.; Certa, A.; Mario, V. A Multi-Criteria Decision-Making Framework for Zero Emission Vehicle Fleet Renewal Considering Lifecycle and Scenario Uncertainty. Energies 2024, 17, 1371.
Aiello, G.; Quaranta, S.; Inguanta, R.; Certa, A.; Mario, V. A Multi-Criteria Decision-Making Framework for Zero Emission Vehicle Fleet Renewal Considering Lifecycle and Scenario Uncertainty. Energies2024, 17, 1371.
Aiello, G.; Quaranta, S.; Inguanta, R.; Certa, A.; Mario, V. A Multi-Criteria Decision-Making Framework for Zero Emission Vehicle Fleet Renewal Considering Lifecycle and Scenario Uncertainty. Energies 2024, 17, 1371.
Abstract
In the last decade, with the increased concerns about the global environmental situation, attempts have been made to promote the replacement of fossil fuels with sustainable sources. For transport, which accounts for around a quarter of total greenhouse gas emissions, meeting climate neutrality goals will require replacing existing fleets with electric or hydrogen propelled vehicles. This research addresses the problem of transport fleet renewal by proposing a multi-criteria decision-making approach, taking into account the multiple propulsion technologies currently available and the objectives of the EU Green Deal, as well as the uncertainty related to the technological scenario, in a generalized Life Cycle framework. The proposed approach, based on the TOPSIS model, demonstrates how the optimality of the choice is related to the different technological scenarios and to the risky attitude of the decision maker. The proposed methodology is finally validated against a practical case referred the strategic fleet renewal decision process, and the results obtained demonstrate how the decision maker’s perception about the technological evolution of the propulsion technologies influences the decision process, thus leading to different optimal choices.
Keywords
Hydrogen; Electric; Last mile; TCO; WTW; Topsis; Decarbonization; Green Mobility
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
