The Scaling Potential of Experimental Knowledge in the Case of the Bauhaus.MobilityLab, Erfurt (Germany)

• Autor(a) principal: Kraaz, Luise
• Data de Publicação: 2022
• Outros Autores: Kopp, Maria, Wunsch, Maximilian, Plank-Wiedenbeck, Uwe
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://doi.org/10.17645/up.v7i3.5329
• topic: amplification processes; Bauhaus.MobilityLab; experimental knowledge; real-world experiments; real-world labs; scaling; urban transformation

Real-world labs hold the potential to catalyse rapid urban transformations through real-world experimentation. Characterised by a rather radical, responsive, and location-specific nature, real-world labs face constraints in the scaling of experimental knowledge. To make a significant contribution to urban transformation, the produced knowledge must go beyond the level of a building, street, or small district where real-world experiments are conducted. Thus, a conflict arises between experimental boundaries and the stimulation of broader implications. The challenges of scaling experimental knowledge have been recognised as a problem, but remain largely unexplained. Based on this, the article will discuss the applicability of the “typology of amplification processes” by Lam et al. (2020) to explore and evaluate the potential of scaling experimental knowledge from real-world labs. The application of the typology is exemplified in the case of the Bauhaus.MobilityLab. The Bauhaus.MobilityLab takes a unique approach by testing and developing cross-sectoral mobility, energy, and logistics solutions with a distinct focus on scaling knowledge and innovation. For this case study, different qualitative research techniques are combined according to “within-method triangulation” and synthesised in a strengths, weaknesses, opportunities, and threats (SWOT) analysis. The analysis of the Bauhaus.MobilityLab proves that the “typology of amplification processes” is useful as a systematic approach to identifying and evaluating the potential of scaling experimental knowledge.