Exploring stability and change in transport systems: combining Delphi and system dynamics approaches

Abstract

Transport is a vast and complex socio-technical system, and despite a clear need to reduce dependence on fossil fuels due to undesirable environmental impacts, it is largely locked into business-as-usual. Systems approaches are a useful way to help make sense of multiple competing influences which may be simultaneously driving change and supporting the status quo. This paper applies qualitative system dynamics modelling to help interpret the results of a Delphi study into global transport transitions, involving 22 international experts in various aspects of transport. The main contribution of the paper is its exploration of the use of system dynamics (SD) modelling to interpret the Delphi findings. SD modelling was used to reveal and elucidate the causal arguments put forward by the expert panel about the factors driving business-as-usual, the factors creating barriers to more sustainable transport systems, and the drivers of change. The SD model is used to explore and expose the key causal patterns at play, and how these interact to both support and hinder change. The resulting model shows the complex, interdependent dynamics involved in supporting the status quo. Even at the relatively high level of analysis reported here, the model is useful in revealing interdependencies between parts of the system, where change in one part may well have knock-on effects elsewhere in the system. In particular the model reveals the strong reinforcing loops that act to minimise the impact of change drivers and thus retain the dominance of automobility. The result is a system that is highly dependent on the continued existence of key reinforcements such as policies that subsidise fossil fuels. From a methodological perspective, the outcomes of the Delphi study provided a rich source of qualitative material which was highly suitable for developing a system dynamics model.

Fairness in Transport Policy: A New Approach to Applying Distributive Justice Theories

“…The viewing of transport policy as a social Conversion Factor has reconceptualised the way in which the Capabilities Approach is applied to transport policy. This aligns with and facilitates a systems view of transport [98][99][100] by highlighting the multiple ways in which transport policies can influence wellbeing across society, and the multiple ways in which transport is influenced by other policies, such as those influencing urban form. This makes it easier for policy makers to take into account the dynamic non-linearities and feedback loops that are a part of all complex social-ecological systems [50].…”

Coming and Going in Loops: Participatory Modelling of a System with All its Complexity

“…Participatory activities need lots of practice, patience and psychological empathy with a simultaneous consideration of procedural rigor and strict adherence to the protocols. Therefore, to combat this challenging character of group-based modelling activities, such tools as Delphi techniques can help generate qualitative data in a more efficient way (Rees et al 2017).…”

“…During participatory modelling, members of a modelling team explore circular causal relationships between system components via the application of specific concepts (e.g., a concept of feedback) and tools, like drawing causal loop diagrams (CLDs) to map feedback dynamics (Rees et al 2017;Sterman 2000). The essence of gathering stakeholder groups is to exchange mental models between their members in order to unravel and communicate important problem-affecting feedback loops, which include key elements and causal links between these elements, thus hypothesizing about system feedback behavior (Hovmand 2014).…”

Stakeholder consensus on the use of compressed natural gas as automotive fuel in Nigeria

“…Besides, the Delphi method has been applied successfully as a research method in a wide range of disciplines to examine major issues, seek group consensus in solving problems and to forecast the future. It has also been applied successfully in diverse transportation research contexts -as, for example, in Rees et al (2017), Wang et al (2014) Liimatainen et al (2014), Cafiso et al (2013) and Schuckmann et al (2012), among others.…”