The adoption of electric heavy trucks holds great potential for decarbonising freight transportation, but the market remains nascent. Electrification of the road freight transportation system is complex, involving many interrelated variables, including vehicles, charging infrastructure, and various stakeholders. Effective policy interventions are crucial for accelerating the transition, and developing dynamic models is helpful for understanding the dynamics involved. This study develops a system dynamics model to explore the long-term adoption of electric trucks and charging infrastructure development, considering technology maturity, awareness, and cost. Using real-world data from Sweden (2017–2060), the model analyses various policy levers. The results show that increasing subsidies for charging stations leads to a considerable rise in electric truck adoption, while investments in vehicle technology maturity are the most cost-efficient when financial resources are constrained. By modelling policy interventions endogenously, the study highlights the dynamic impact of policymaking on accelerating the transition to sustainable road freight transport.

The electrification of heavy road freight is a promising pathway to decarbonise the sector, yet it depends on the electricity supply system, which itself is undergoing a complex transition to low-carbon energy. Achieving net-zero emissions requires understanding the interdependencies between transport and electricity systems. To address this challenge, we developed a system dynamics (SD) model to explore how freight electrification interacts with electricity supply capacity and market-based pricing. Drawing on literature review and expert interviews, the model integrates three modules: demand (electric truck fleet and charging behaviour), supply (capacity expansion with construction delays), and price (merit-order dispatch). Preliminary results indicate that e-truck adoption has limited direct impact on electricity prices under baseline assumptions. However, cross-sectoral competition emerges as critical, where accelerated electrification in other sectors may drive electricity prices upward, reducing electric truck competitiveness. Charging coordination strategies also influence electricity price dynamics. The model provides a holistic perspective and highlights the need for coordinated planning between transport and electricity sectors. However, model calibration is ongoing, and results should be interpreted cautiously.

The transition to electric trucks is a multi-system transition (MST), shaped by dynamic interactions between the transport and electricity systems. While scientific research has begun to consider the complexities of such MST phenomena, there is limited understanding of their temporal impact on transitions. This research provides an overview of the critical couplings shaping the e-truck MST using the technology-actor-institution structure and employs qualitative system dynamics (SD) modelling to identify emerging multi-system dynamics. We apply the SD models to develop two case studies of e-truck adoption in Swedish forestry and port hinterland transport in the Netherlands. The results show similarities and differences between cases, emphasising the context-dependency of road freight electrification transitions. The forestry case demands geographical extension of electricity grids, whilst the port case requires grid congestion management, with profound consequences for policies in both systems. We conclude that SD modelling supports mapping the multi-system dynamics of the road freight electrification transition, providing insights into temporal effects and feedback.

This paper explores the system-level impacts of electrification on road freight transport efficiency, a complex concept involving various stakeholders. Electrification adds further complexity by introducing new stakeholders, dynamics, and efficiency variables. The study applies System Dynamics modelling to explore interactions between efficiencies and the impact of electrification. The model is grounded in literature, expert interviews, and workshops, using Swedish data to simulate 2010-2050 for heavy trucks. Results highlight trade-offs among efficiencies and a worse-before-better behaviour in cost, as electrification initially increases costs but results in lower long-term costs. The model allows testing of policy interventions endogenously to explore their dynamic impacts. Findings show two phases of electric truck adoption. Policy analysis suggests focusing on charging infrastructure in the first phase and cost-orientated policies in the second. By increasing system-level understanding, this paper offers valuable knowledge to decision-makers navigating the transition towards a more efficient and sustainable system.

Road freight transport is a major contributor to global emissions. Decarbonising the sector is challenging but essential to achieving sustainability goals. While electrification of heavy-duty trucks offers a promising decarbonisation pathway, this transition is more than just a technological shift; it is a "system transition" shaped by multiple interconnections among technological innovation, infrastructure expansion, market adoption, stakeholder engagement, and policy interventions. Understanding this system transition requires moving beyond isolated technical or financial analyses toward a holistic perspective that captures how decisions by stakeholders such as freight operators, charging providers, vehicle manufacturers, electricity suppliers, and policymakers interact through feedback mechanisms that unfold over time.

This thesis investigates the dynamic complexity shaping the transition to electrified road freight transport using System Dynamics (SD) methodology. Through six appended papers, the research addresses three fundamental questions: How does electrification impact the freight system at the system level? How can this dynamic complexity be modelled? How can such modelling support informed decision-making toward sustainable transport?

The research begins by structuring future pathways for automation, electrification, and digitalisation using morphological analysis, mapping 23 technology parameters across four scenarios. A conceptual multi‑layer model then distinguishes direct electrification effects (e.g., vehicle cost, charging need) from induced effects that ripple through supply chains, transport markets and infrastructure, illustrated with causal loop diagrams (CLDs). Three quantitative SD models capture critical transition dynamics: the co-evolution of electric truck adoption and charging infrastructure development, revealing "chicken-and-egg" dynamics and policy leverage points; the complex trade-offs between business efficiency and societal efficiency, exposing potential rebound effects; and the cross-sectoral interdependencies between freight electrification and electricity supply, revealing how capacity constraints and price dynamics impact electric truck adoption trajectories. Finally, a multi‑system transitions (MST) perspective is combined with qualitative SD in two cases (forestry, port hinterland) to map technology, actor and institutional couplings between freight and electricity systems.

The thesis contributes with (1) a structured, system‑level framing of freight electrification that makes feedbacks and induced effects explicit; (2) calibrated SD models that quantify adoption‑infrastructure co‑evolution, system‑wide efficiency dynamics and transport‑electricity interdependencies; (3) a methodological advancement in applying SD to freight electrification transitions through integration with multi‑layer and multi‑system transition frameworks; (4) guidance on policy timing, mix and stability, including phase‑specific recommendations; and (5) participatory decision‑support tools that help public and private actors test interventions under uncertainty. Together, these contributions equip stakeholders with the system-level understanding needed to make strategic decisions and steer the transition toward a sustainable road freight transport system.

Godstransporter på väg är en betydande källa till globala utsläpp, och att ställa om sektorn är svårt men avgörande för att nå hållbarhetsmålen. Elektrifiering av tunga lastbilar (e-lastbilar) erbjuder en lovande väg, men omställningen är mer än en teknisk förändring; det handlar om en systemomställning som formas av samverkande kopplingar mellan teknisk innovation, utbyggnad av infrastruktur, marknadsintroduktion, aktörsengagemang och styrmedel. För att förstå dynamiken i systemet krävs ett helhetsperspektiv som går bortom isolerade tekniska eller finansiella analyser och fångar hur beslut av aktörer – såsom transportörer, laddoperatörer, fordonstillverkare, elbolag och beslutsfattare – påverkar varandra genom återkopplingsmekanismer över tid.

Denna avhandling undersöker den dynamiska komplexiteten i övergången till elektrifierade godstransporter på väg med hjälp av systemdynamik (System Dynamics, SD). Genom sex artiklar behandlas tre grundfrågor: hur elektrifiering påverkar godstransportsystemet på systemnivå, hur denna dynamik kan modelleras, och hur sådan modellering kan stödja välgrundat beslutsfattande för en hållbar transportsektor.

Arbetet inleds med att strukturera framtida möjliga utvecklingsvägar för automation, elektrifiering och digitalisering med morfologisk analys, där 23 teknologiparametrar kartläggs över fyra scenarier. Därefter presenteras en konceptuell flernivåmodell som skiljer mellan direkta elektrifieringseffekter (t.ex. fordonskostnad, laddbehov) och inducerade effekter som fortplantar sig genom leveranskedjor, transportmarknader och infrastruktur, illustrerade med kausala slingdiagram (causal loop diagrams, CLD). Tre kvantitativa SD-modeller fångar centrala dynamiker för omställningen. Den första modellen analyserar samspelet mellan införande av e-lastbilar och utbyggnad av laddinfrastruktur, som blottlägger "hönan-och-ägget-dynamik" och påverkan av olika styrmedel. Den andra modellen undersöker komplexa avvägningar mellan företagsekonomisk och samhällsekonomisk effektivitet, inklusive möjliga reboundeffekter. Den tredje modellen kartlägger tvärsektoriella kopplingar mellan elektrifiering av godstransporter och elsystemets kapacitet, där flaskhalsar och prisdynamik formar införandebanor. Avslutningsvis kombineras ett multisystemperspektiv (multi-system transitions, MST) med kvalitativ SD i två fall (skogsbruket respektive hamnens inlandstransporter) för att kartlägga tekniska, aktörsmässiga och institutionella kopplingar mellan transport- och elsystem.

Avhandlingen bidrar med (1) ramverk på systemnivå som explicitgör återkopplingar och inducerade effekter; (2) SD-modeller som kvantifierar samutvecklingen mellan införande av e-lastbilar och infrastruktur, systemomfattande effektivitetsdynamik samt beroenden mellan utvecklingen av transport- och elsystemet; (3) ett metodologiskt bidrag genom tillämpning av SD på elektrifieringsomställningen av godstransporter och integration med flernivåmodeller och ramverk för omställning av multisystem; (4) vägledning avseende timing, sammansättning och stabilitet för styrmedel, inklusive rekommendationer för olika omställningsfaser; samt (5) ett beslutsstöd som hjälper offentliga och privata aktörer att testa åtgärder under osäkerhet. Tillsammans ger dessa bidrag aktörer den systemförståelse som krävs för strategiska beslut och för att styra omställningen mot ett hållbart godstransportsystem på väg.

This paper explores the system-level impacts of electrification on road freight transport efficiency, as a complex concept with involvement of various stakeholders. Introducing electrification adds further complexity, bringing in new stakeholders, dynamics, and efficiencies. The paper utilises system dynamics modelling to understand the dynamics between different efficiencies and the impact of electrification. The results of the simulation model highlight that there are trade-offs between different kinds of efficiencies, and it is essential to achieve a balance. Moreover, worse-before-better behaviour should be expected, as electrification initially increases costs but has lower costs in the long term. The results point to two phases of E-truck adoption: during the first phase, the policy focus should be on expanding charging infrastructure; during the second phase, cost-focused policies become critical to boosting electrification. By understanding the impact of different policies, this paper offers valuable knowledge to decision-makers navigating the transition towards a more sustainable system. 

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.

This study aims to systematically investigate and structure future technological developments within automation, electrification, and digitalization (AED) in the transportation sector. To address the significant complexity and uncertainty of these developments, a scenario analysis technique known as morphological analysis is used. A set of 23 AED-related technologies and various alternatives for how each technology could develop are compiled in the form of a morphological box. Then, four scenarios are mapped to illustrate future development pathways. This type of holistic analysis provides decision-makers with a comprehensive picture of the future transportation system, allowing them to make more informed decisions. The main contribution of the study is a better understanding of how to approach and structure such a complex research question.

Purpose

Electrification of road freight transport is a promising solution to achieve transportation climate goals. This study aims to provide a system-level understanding of the impacts of electrification on the road freight transport system and to develop a model that describes the system and enables the analysis of the interrelationships between system variables

Methodology

Using a system dynamics approach, a causal loop diagram (CLD) model is developed. The model structure is based on the three-layer model proposed by Wandel et al. (1992) and developed by Browne et al. (2022). A mixed methods approach is employed, including group model building, an impact analysis workshop, and literature analysis.

Findings

The study presents a conceptual multi-layer model that illustrates the complex causal relationships between variables in different layers of the freight transport system. Moreover, two example CLD models are provided: one exploring the factors that affect the attractiveness of electric trucks and another illustrating the trade-off between battery size and charging infrastructure.

Research limitations 

The study's main limitation is the challenge of recognizing all variables and their interconnections in such a complex system. Rather than providing an accurate picture of the system, the study aims to demonstrate the system's complexity and highlight some important dynamics.

Original/value

The study's system dynamics models aim to provide decision-makers with a comprehensive understanding of the system, enabling them to make better-informed decisions that consider the impact of their policies on the system as a whole. 

The aim of this prestudy is to investigate how developments within automation, electrification and digitalization (AED) may affect the demand for passenger and freight transport in Sweden in terms of transport activity (ton-kilometers TKM and passenger-kilometers PKM), traffic activity (vehicle kilometers traveled VKT), modal distribution and other characteristics of the transport system, in order to assess whether the current base forecasts for 2040 that are developed and used by Trafikverket are still robust when accounting for developments and impacts of AED. Both freight and passenger transports are considered, as well as several transport modes. These include road (passenger cars, light and heavy trucks), rail (long and short distance), marine (ships and ferries) and air (planes). In addition, support infrastructure such as charging stations and goods terminals are considered. Automation technologies include automated vehicles and goods handling. Electrification refers to the replacement of conventional fuels with electric energy, as well as charging infrastructure. Digitalization is the broadest of the technological fields, and includes both digital services and digital infrastructure. The latter is furthermore an enabler for first and foremost automation, but also for electrification to some extent.

The theoretical perspective of the study is that transport demand is derived from the need to transport goods and people. Several drivers of transport demand (such as mode characteristics and economic structure) are presented and included in a general framework for assessing transport demand. The framework further incorporates a variety of previously constructed models and consists of three layers (activities & material flows, transport services and infrastructure) which connect in two markets (the transport and traffic market). The effects on transport demand are assessed from a set of demand parameters, including TKM, PKM and VKT. Finally, six mechanisms through which AED could affect transport demand are presented and integrated into the general framework. 

Through literature reviews and workshops, a set of general trends within AED were identified. Since there is a considerable uncertainty regarding how these trends could develop until 2040, an explorative scenario-based approach was employed. In order to structure this approach, a morphological analysis was conducted where the identified trends were formulated as parameters and their stages of development as attributes. Combined, these parameters and attributes formed a morphological box which could be used to illustrate different scenarios. In this study, four scenarios were then mapped in the morphological box: a base scenario intended to mimic explicit and implicit assumptions in the base forecast and three alternative scenarios (Partnership Society, Social Engineering 2.0 and Swimming in Data) intended to contrast the base scenario by illustrating alternative societal and technological development paths. 

These scenarios and their respective morphological box mappings were then analyzed based on the general framework. The first step in this impact analysis consisted of investigating possible separate impacts of the parameters on each layer and market in the general framework. The mechanisms of which each parameter would affect the system were also identified. Examples of effects include changes in generalized costs and service levels. In the second step, the impacts from combined AED development were studied based on the scenario mappings in the morphological box. This highlights possible synergies between the technologies. Finally, the combined effects were compared with the base scenario in order to reach the study’s aim.  

The results of the analysis show that automation, electrification and digitalization technologies separately could lead to changes in transport efficiency as well costs. Furthermore, synergetic effects leading to even stronger impacts on factors such as these could arise when they are combined. Through the general framework and the demand impact mechanisms, it was shown that factors such as these could lead to changes in the transport demand, modal distribution and transport system characteristics. Since the scenario mapping shows that the base forecasts do not consider development in automation and digitalization to a significant extent, the base forecasts would probably not be robust if these technologies see a continued development and implementation in the transport system.