The concept of resilience is increasingly applied to policy-making. However, despite its widespread use, resilience remains poorly defined, open to multiple interpretations, and challenging to translate into practical policy instruments. Three particularly problematic aspects of resilience concern its rigid conceptualisation of adaptation and learning, its de-politicised interpretation of participatory decision-making, and the ill-defined role and relevance of social vulnerability indicators. My research analyses these three aspects within the context of flood risk management in the UK, which is uniquely suited to studying the practicability of a cross-disciplinary concept like resilience, because it connects issues of natural resource management, social planning, and disaster management. First, I analyse two case studies of experimental pilot projects in natural flood management. Through studying project reports, and interviewing stakeholders involved in project implementation, I determine whether the theorised learning-by-doing method in resilience is reflected in experiences from real experimental projects. Secondly, I use one of these case studies to map out the political structure of local participatory bodies in flood management, and also conduct a small survey of local community groups. The purpose of this second study is to determine if collaborative methods can indeed lead to a knowledge-driven policy process as envisioned in resilience literature. Lastly, I use statistical analysis to compare a traditional flood management model and a socio-economic model. The aim of the statistical modelling is to determine whether socio-economic factors are indeed useful for informing flooding policy, and whether they offer new insights not already being used in modern flood management. I find that resilience gives insufficient consideration to the importance of political constraints and economic trade-offs in policy-making, and that evidence for the usefulness of socio-economic factors is inconclusive. Future work could focus on further refining the statistical modelling to pinpoint empirically verifiable indicators of resilience.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:744694 |
| Date | January 2018 |
| Creators | Gao, Shen |
| Contributors | Howarth, David |
| Publisher | University of Cambridge |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.repository.cam.ac.uk/handle/1810/274918 |
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