Abstract for: Incorporating heterogeneity of stakeholder perspectives in System Dynamics with Q-methodology: the case of zero-emission truck adoption in port region

In road freight transport decarbonisation, CLDs are a useful tool for analysing the causal relations among different decarbonisation pathways, such as reducing the carbon content of assets, utilising assets, reducing freight demand, or modal shift (Ghisolfi et al., 2022; Raoofi et al., 2024). Standard approaches like group model building do not guarantee sufficient representation of stakeholders nor the accurate representation of heterogeneity of opinions. Q-methodology offers a possible solution but has hardly been applied to support SD modelling. Using Q-methodology, we analyse stakeholders' dominant opinions on the drivers of challenges and opportunities for accelerating e-truck uptake in port hinterland transport. This will feed into our CLDs by validating existing relations and helping to elaborate them, so that heterogeneity of stakeholder interests and opinions is accounted for. The extended CLD and dominant opinions can then serve as a basis for policy interventions and scenarios for system evolution. Interim results include an initial CLD as a basis for the concourse of the Q-methodology, the Q-set of statements and the analysis of 14 interviews. After completing the interviews, the interviewees' opinions will be incorporated into the initial CLD to validate and enhance the causality, and to further inform the creation of scenarios for the mathematical model. We contribute, first, to the stream of work on e-truck adoption for port hinterland transport by integrating the dominant perspectives of a fragmented stakeholder field, with different views on the major barriers and opportunities that can drive e-truck adoption. Secondly, we contribute to the literature on participatory methods of CLD creation by testing Q-methodology as an alternative approach to incorporating stakeholders' subjective perspectives through group model building and cognitive mapping.