Enhancing the contribution of urban living labs to sustainability transformations: towards a meta-lab approach

Abstract

The contribution of the first generation of urban living labs (ULLs) to system-wide sustainability transformations is thus far less than expected. A possible explanation for this can be found in the focus of most ULLs on local, highly contextualized knowledge, and a missing link to system-wide transformations through diffusion and upscaling beyond the geographic boundaries of the lab. Meta-learning, i.e., learning across multiple, distributed experiments, through networked ULLs seems to offer a way forward. However, the literature on city networks shows that meta-learning cannot be effectively facilitated in horizontal networks without a learning infrastructure. To address this shortcoming and inspire a second generation of ULLs, this Perspective paper outlines a meta-lab approach actively facilitating the contribution of local living labs to wider sustainability transformations. We see a meta-lab as a transurban multi-actor network to connect and, where possible, align the learning processes across thematically related ULLs in different urban contexts through a central learning agenda. The meta-lab approach respects and supports local learning agendas and their focus on local solutions for local problems, while acknowledging and utilizing the potential of local experiments to contribute to a central learning agenda. Our paper argues that a meta-lab approach can act as a catalyst of learning in two important ways: (1) by accelerating local experimentation and learning processes by feeding them with lessons from other locations; and (2) by facilitating a more focused – local and transurban – learning process through a shared learning agenda. The meta-lab approach thus stimulates urban sustainability transformations by supporting faster, more focused and wider learning about effective innovations. We conclude this paper by outlining how common pitfalls in transurban learning can be avoided by a careful design of the meta-lab, or by meeting certain conditions when implementing this design.