Barriers to Managing Smart Water Infrastructures – A Socio-Technical Network Approach

Digital transformation holds the potential to increase efficiency and improve performance of infrastructure systems. For instance, using real-time data from sensors employed within urban wastewater systems could foster a more evidence-based management, reduce investment costs and in the long-term lead to a better surface water quality. However, organizational barriers currently hinder the exploitation of the full value of such data. In practice, data often remains with the actor who obtained it (e.g. operator/utility), but is rarely exchanged with further potentially benefitting actors (e.g. planners, authorities). This paper addresses the socio-technical (mis)fit by analysing multi-level networks that comprise technical infrastructure units of the urban wastewater system as well as social actors who are involved in managing these systems. We study the overlap between infrastructure and governance systems in terms of data and information exchange. The goal of this paper is a better understanding of the substructures that support data and information exchange. By taking up a network perspective, we aim at identifying socio-technical configurations, in which access to data and information corresponds well with governance responsibilities. Our analysis provides a novel approach to study multi-level networks by investigating bidirectional relations between technical and social systems. We borrow concepts from socio-ecological networks (SEN) and substitute ecological units by technical units. We obtain network data in six case studies in Switzerland that vary in terms of size and innovativeness. Each case is limited by the spatial extent of the technical infrastructure, i.e. the catchment area of a wastewater treatment plant (WWTP), which represents an endpoint of all wastewater flows through the local or regional sewer system. Within these spatial boundaries, we include actors who are responsible for managing the technical system. Besides urban water management, our approach is applicable to other infrastructure sectors (e.g. energy, transportation, drinking water), especially with regard to the growing importance of using real-time data for managing such infrastructure systems.