Local Government Disaster Risk Reduction Model
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The document presents a framework called the Context Capacity and Performance Model of Local Government Disaster Risk Reduction (DRR). It aims to understand the drivers and barriers to DRR action by local governments. The model hypothesizes relationships between the context local governments operate in, their capacities for DRR, and their performance and action. An expert survey was conducted to validate the model and found experts had varying opinions, with context and capacity seen as equally influential, and all capacities viewed as similarly important, though financial capacity was overestimated and policy context was underestimated. More research is needed, including broader expert and local government surveys, and case studies applying the model.
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Local Government Disaster Risk Reduction Model
- 1. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Untangling the Drivers of Disaster Resilience: Developing a Context, Capacity and Performance Model of Local Government Disaster Risk Reduction Benjamin Beccari, PhD Candidate, Scuola Universitaria Superiore IUSS Pavia
- 2. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Rationale Preparedville • Active disaster committee • High building code compliance • Strong investment in DRR Wontappenere • Committee doesn’t meet • 15 year old disaster plan • Unknown code compliance Why do some local governments have strong DRR action and others don’t?
- 3. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org The Context Capacity and Performance Model of Local Government DRR PerformanceCapacityContext
- 4. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org The Context Capacity and Performance Model of Local Government DRR What is it? • A framework for understanding the range of drivers and barriers to local government action on DRR • Hypotheses on the relationships between context, capacity and performance factors
- 5. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org The Context Capacity and Performance Model of Local Government DRR
- 6. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Validation Options • Meta-Analysis • Pros – Use Existing Data – Better review present studies – Identify better measures of context and capacity • Cons – Insufficient overlap in studied variables and areas • Local Government Survey • Pros – Data gathered on consistent basis – Test and generate new hypotheses – Include broad variety of contexts • Cons – Very low response rate – No organisation would agree to assist in distribution
- 7. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Expert Survey and stochastic-DEMATEL Analysis • Pros – More achievable than a local government survey – Understand directionality and explore causal relationships • Cons – Less reliable and robust than a survey based on local government data • Online survey distributed through social media – Questions about relationship between CCPDRR model elements – E.g. “How much does dispersion or concentration of political power directly influence coordination of other organisations to reduce disaster risk?” – 49 quality controlled responses received • Results analysed with stochastic-DEMATEL method
- 8. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Key Results • Experts have wide variance in opinions • Context as influential as Capacity • All capacities have similar levels of importance • Experts overstate importance of Financial Capacity, understate importance of Policy Context
- 9. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org More Research Needed • Broader and larger expert survey on influences of local government DRR • Large, cross national survey of local governments • Application of CCPDRR model to design of other large-N studies of local governments and the development of case studies • More and more available cross-national data on local governments that cover all context, capacity and performance elements
- 10. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Thank you ben@benbeccari.com www.benbeccari.com @casuscalamitas
Editor's Notes
- Thanks for sticking around until the end today. This evening I’d like to present the main focus of my PhD research which I’m due to defend later this year.
- We all know that local government are critical actors in DRR without which we can’t hope to reach the targets in the Sendai Framework. At this conference we’re hearing about many examples of local governments that are achieving great things in DRR – examples like preparedville….. But we don’t often hear about cities like Wontappenere, or won’t happen here. Despite being superficially similar to Preparedville…… But what’s the difference – Why do some local governments have strong DRR action and others don’t.
- To help answer this question I‘ve developed the Context, Capacity and Performance model of Local Government Disaster Risk Reduction At a base level it consists of Performance – Activities undertaken that reduce disaster risk Capacity – The general and disaster specific resources that enable it to perform and determine its level of performance Context – Those elements outside of a local government's organisation that influence its operation
- The CCPDRR model does two things Firstly it is..... Provides a taxonomy of these drivers and barriers. Secondly it is......
- Here is a scheme of the model and its components, with most of the relationships removed for clarity. I don’t have the time to go into all of these in detail, but the factors include the Disaster context - disaster risk and recent disaster experience Policy Context - levels of advocacy for DRR and central government DRR policies Leadership Capacity - leader risk perception and commitment to DRR Institutional Capacity - quality of governance, communication and risk perception Core services – basic infrastructure and social services that reduce underlying risk and vulnerabilities DRR Coordination - coordination of other organisations to reduce disaster risk DRR Services - activities to reduce disaster risk using the local governments own resources
- To expand on and validate this model I’ve explored a number of options to collect data: Meta-analysis using latent variable modelling of existing datasets on local government DRR performance in the USA. I contacted many organisations with access to large networks of local governments and none were willing to assist in distribution of the survey.
- So I turned to using an expert survey on the relationships between the factors in the Context, Capacity and Performance Model of Local Government DRR Now there are more experts than there are local governments and they’re more accessible. This, plus a shorter and easier survey increases the response rate and questions based on relationships themselves reduces the sample size requirement. Examining expert opinion on the relationships between factors means that….. Social media such as linkedin and facebook groups, twitter and the IAEM newsletter. There were 139 questions in total with each respondent being presented with about 55 randomly selected questions with the option to answer more. The stochastic DEMATEL method is a technique used to convert the opinions of large numbers of experts on the relationships between different factors into information on the importance and effect of those factors.
- Experts have a wide variety of opinions – there was significant variance in the responses given. The experts mostly considered the context factors to have similar influence on DRR performance as the capacity factors. This is counter to the CCPDRR model which hypothesises that the Capacity factors should have more influence. Compared to the empirical literature there is a disagreement about the role of Financial Capacity with experts saying it is the second most important factor, whereas the empirical literature says it is one of the least important. On the other hand experts say that Policy Context is the least important factor, whereas the empirical literature says it is one of the most important contextual factors.
- These disagreements indicate that much more research is needed. Broader survey to enable sub-setting of responses to compare the opinions of different groups to see how robust they are. Implementation of the local government survey that I originally proposed would provide a much better test of the CCPDRR model and its hypotheses. Greater application of the CCPDRR model to large sample size studies could enable future latent variable analyses and application to case studies could enable future Quantitative Case Analysis. Two systematic review techniques. The lack of good data on local governments – especially cross-national data – is a big problem in the broader public administration literature. We need more investment in efforts to collect, collate and disseminate this data to researchers and the public alike. And we need much more investment, especially from multinational collaborations and the international community in efforts to gather this type of data.