Network governance and low-carbon transitions in european cities

The thesis investigates the role of governance networks in advancing sustainable energy transitions in the cities of Europe. By doing so, it aims to provide insights about the practical applicability of the Transition Management framework in different urban settings. Exploring this issue is timely as well as important due to parallel processes of the rising profile of cities in transition governance; and the perceived need in city authorities to develop new governance mechanisms to support low-carbon transitions on the urban scale. The main contribution to knowledge is the empirical evidence provided for the context-dependency of the connections between technological change required for urban low-carbon energy transitions and organisational change in local governance arrangements. The findings’ consequence for theory is that the implicit assumptions built into Transition Management about the functioning of collaborative governance networks limit its applicability in different cities. The evidence collected through the study also highlights problems with scaling down the Multi-Level Perspective to the urban scale. The findings are derived from a comparative study of three cities from across Europe with diverse characteristics in terms of historical sustainability agenda development, locally relevant rationales for transitions, and patterns of organisational fragmentation and power-distribution in local governance arrangements

The local governance of digital technology – Implications for the city-scale digital twin

The project set out to examine how governance structures, processes and socio-political systems affect the adoption of new (digital) technologies – e.g. City Digital Twins – that provide evidence for policy making and implementation in urban planning and the management of urban infrastructures. Situating City Digital Twins as next-generation urban models, we analysed the existing practice of using computerised models to support decision-making in the multi-actor governance context of the Cambridge city region in the United Kingdom. The study traced modelling practices and evidence informed decision-making processes across a variety of sectors: transport, energy, land-use planning and telecommunications. Outcomes include the mapping of governance stakeholders in the wider Cambridge area, and the analysis of network relationships, to develop recommendations for the design and implementation of a Cambridge City Digital Twin. The role of citizens in the production of evidence was also examined with a participatory research approach to analysing citizen engagement initiatives and the impact of digital tools on democracy, participation and transparency in the local context. The results of the Cambridge case study are contrasted with international practice and global experiences pertinent to City Digital Twins in British, European and international cities. This comparative perspective provides initial insights to understand generalisation possibilities from the Cambridge case study

Exploring Resilient Observability in Traffic-Monitoring Sensor Networks: A Study of Spatial–Temporal Vehicle Patterns

Vehicle mobility generates dynamic and complex patterns that are associated with our day-to-day activities in cities. To reveal the spatial–temporal complexity of such patterns, digital techniques, such as traffic-monitoring sensors, provide promising data-driven tools for city managers and urban planners. Although a large number of studies have been dedicated to investigating the sensing power of the traffic-monitoring sensors, there is still a lack of exploration of the resilient performance of sensor networks when multiple sensor failures occur. In this paper, we reveal the dynamic patterns of vehicle mobility in Cambridge, UK, and subsequently, explore the resilience of the sensor networks. The observability is adopted as the overall performance indicator to depict the maximum number of vehicles captured by the deployed sensors in the study area. By aggregating the sensor networks according to weekday and weekend and simulating random sensor failures with different recovery strategies, we found that (1) the day-to-day vehicle mobility pattern in this case study is highly dynamic and decomposed journey durations follow a power-law distribution on the tail section; (2) such temporal variation significantly affects the observability of the sensor network, causing its overall resilience to vary with different recovery strategies. The simulation results further suggest that a corresponding prioritization for recovering the sensors from massive failures is required, rather than a static sequence determined by the first-fail–first-repair principle. For stakeholders and decision-makers, this study provides insightful implications for understanding city-scale vehicle mobility and the resilience of traffic-monitoring sensor networks