Identifying urban transformation dynamics: Functional use of scenario techniques to integrate knowledge from science and practice
Many urban regions are exposed to rapid growth, leading to vast changes in land use with diverse ecological, socio-economic, and aesthetical impacts. Regional scenarios are suitable for identifying possible urban development patterns. However, one challenge of scenario construction is integrating the knowledge of both science and practice for a better understanding of the complex interactions between impact factors in the urban fabric. The objective of this research is to enhance process design for a collaborative scenario analysis in the context of urban development. The scenarios are constructed for a case study of the Limmattal region, a suburban agglomeration close to Zurich, Switzerland, and we demonstrate a functional structure for science–practice collaboration within the process of scenario building. The types of communication between science and practice are systematically varied, which leads to four consistent scenarios for 2030. Our analyses of regional system dynamics reveal the most important feedback loop among five impact factors within the region, which allows for a better understanding of the systemic interactions in regional transformation. This process design shows the potential to support knowledge integration in research processes involving science and practice, and assists informed planning strategies for urban transformation.
|Keywords||Formative scenario analysis (FSA), Urban system transformation, Feedback loop, Science–practice collaboration, Knowledge integration, Regional dynamics|
|Persistent URL||dx.doi.org/10.1016/j.techfore.2013.08.030, hdl.handle.net/1765/125257|
|Journal||Technological Forecasting and Social Change|
von Wirth, T., Wissen Hayek, U., Kunze, A., Neuenschwander, N., Stauffacher, M., & Scholz, R. W. (2014). Identifying urban transformation dynamics: Functional use of scenario techniques to integrate knowledge from science and practice. Technological Forecasting and Social Change, 89, 115–130. doi:10.1016/j.techfore.2013.08.030