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Backcasting is a well-established methodology particularly suitable for analyzing complex problems where the business-as-usual projection is no longer appropriate, which can be used to effectively address the challenges of climate change mitigation in transport. It is characterised by designing endpoints in the future and working backward from these visions to establish policy pathways that can achieve desired futures. The visioning phase, which involves the construction of a set of scenarios describing alternative futures, is the first phase in backcasting, and engaging different stakeholders during the visioning phase is considered an important aspect in transport studies. This paper aims to demonstrate the findings from a participatory visioning exercise carried out as part of a backcasting study on sustainable transport in the islands of Malta. It is based on a methodological approach that combines social practice theory with stakeholder workshops. The visioning exercise resulted in the development of four different scenarios: the business-as-usual scenario and three alternative scenarios for transport in 2050 in Malta; High-Tech Mobility (with a focus on clean technology); Local Mobility (with more local travel and reducing the need to travel over longer distances); and Green and Active Mobility (where active forms of travel are prioritised over motorised forms of transport). In the alternative scenarios, the elements of mobility practices and other social practices influencing mobility have been reconfigured to allow for low-carbon travel and significantly reducing GHG emissions. The results of this study demonstrate how insights from a theory of social practices approach can be utilized to provide narratives for future visions in transport backcasting studies, and how this approach could open new possibilities for the transition towards more sustainable mobility through the reconfiguration of mobility and other everyday social practices.

Future Low-Carbon Transport Scenarios: Practice Theory-Based Visioning for Backcasting Studies