Anticipate, Adjust, Adapt: Managing Sustainability Transitions through multiple Scenarios of Urban Mobility Futures.

Résumé En

Designing and managing system transitions towards sustainable urban futures are crucial to fulfilling the Paris Agreement and the United Nations (UN) Sustainable Development Goals (SDGs). However, the complexity and multidisciplinarity of urban systems pose challenges to the holistic conceptualisation, design, and management of such transitions. Utilising complex system theory as the theoretical foundation, we apply transition concepts from Complex Adaptive Systems (CAS), Multi-Level Perspective (MLP), Social Practice Theory (SPT), and Future Scenario Methods (FSM) to explore the frameworks’ potentials and shortcomings when applied to the urban mobility system transition (UMST) of Paris and its region Île-de-France (ÎdF) between 2001 and 2021.Our leading and supporting research questions are:RQ1: To what extent can current transition frameworks represent the UMST?RQ1a: What are the key transition drivers (TD) of Paris’ UMST between 2001 and 2021? RQ1b: What are the key transition frameworks that have been applied to UMST?RQ1c: How adequately can these frameworks represent the UMST of Paris?To answer these questions, we introduce the challenge and the underlying complex system theory, followed by the case study description. Afterwards, each framework is described, applied to the case, and discussed. This is followed by a literature-driven analysis and discussion of complements, commonalities, and mismatches between the frameworks. Finally, the initial outcomes are discussed, and a perspective for the next steps building on this preparatory work is drawn.Key outcomes of the paper are:• A descriptive case study of the UMST of Paris between 2001 and 2021;• An interdisciplinary overview of UMST frameworks applied to the case study;• A discussion of complementary, overlapping, and competing properties of the transition frameworksthat will feed the next steps of the research towards the development of a holistic, heuristic transition framework for UMST.