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Electrification is a promising solution for decarbonising the road freight transport system, but it is challenging to understand its impact on the system. The purpose of this research is to provide a system-level understanding of how electrification impacts the road freight transport system. The goal is to develop a model that illustrates the system and its dynamics, emphasising the importance of understanding these dynamics in order to comprehend the effects of electrification.

The main methodological contribution of the study is the combination of the multi-layer model with system dynamics methodology. A mixed methods approach is used, including group model building, impact analysis, and literature analysis.

The study presents a conceptual multi-layer dynamic model, illustrating the complex causal relationships between variables in the different layers and how electrification impacts the system. It distinguishes between direct and induced impacts, along with potential policy interventions. Moreover, two causal loop diagrams (CLDs) provide practical insights: one explores factors influencing electric truck attractiveness, and the other illustrates the trade-off between battery size and fast charging infrastructure for electric trucks.

The study provides stakeholders, particularly policymakers, with a system-level understanding of the different impacts of electrification and their ripple effects. This understanding is crucial for making strategic decisions and steering the transition towards a sustainable road freight transport system.

The aim of this chapter is to analyse how the governing capacity of current policy instruments might be affected in futures of smart mobility. In order to explore this issue, the authors make use of the so-called NATO (nodality, authority, treasure, organisation) framework for analysing two contrasting scenarios. The analyses show that the overall governing capacity of many of the policy instruments is strengthened or maintained in both of the scenarios. However, the governing capacity of some policy instruments is reduced, and some seem to need calibration, not least because authorities’ access to and control over data are under question. Future governing capacity hinges on access to data, although all resources are, in one way or another, affected.

Automated vehicles are likely to have significant impacts on the transport system such as increased road capacity, more productive/enjoyable time spent travelling in a car, and increased vehicle kilometres travelled. However, there is a great risk that automated driving may negatively impact the environment if adequate policies are not put in place. This chapter examines the effects of driverless vehicles and the types of policies required to attain sustainable implementation of the technology. To understand the effects on a systemic level, and to understand the needs and impacts of policies, the dynamics must be understood. Therefore, a causal loop diagram (CLD) is developed and analysed. One important insight is that the effects of driverless vehicles are mainly on the vehicular level (e.g., the reduced number of accidents per vehicle). These effects can be cancelled out on a systemic level (e.g., due to increased vehicle-kilometre travelled (VKT) that increases total number of accidents). The marginal costs of road transport are central to both freight and passenger transport. Automation will reduce marginal costs and shift the equilibrium in the transport system towards a state with higher VKT. This will lead to greater energy consumption and higher emissions. To attain sustainability goals, there might be a need to balance this reduction of marginal costs by using policy instruments. In the work, CLDs is experienced to be a useful tool to support the collaboration between experts from different fields in the dialogue about policies.