© 2018 The Authors. Published by Elsevier Ltd. Governance is understood to have considerable influence on the success of recoveries following a natural disaster. What constitutes good governance and successful recovery in these circumstances? This question is discussed in relation to two recent recovery processes. Sri Lanka has, for all intents and purposes, recovered from the tsunami that struck there and other parts of southern Asia in 2004. Christchurch, New Zealand was devastated by a sequence of earthquakes during 2010 and 2011 and recovery there is now well under way. The paper discusses the governance structures that have guided these two recoveries. While it is understood that the effects of disasters could potentially be life long and recovery from them complex, compatibility of the process and outcomes in relation to cultural norms and the critical issue of housing are the key issues discussed across the two cases.
We examine the role of business interruption (BI) insurance in business recovery following the Christchurch earthquake in 2011. First, we ask whether BI insurance increases the likelihood of business survival in the immediate (3-6 months) aftermath of a disaster. We find positive but statistically insignificant evidence that those firms that had incurred damage, but were covered by BI insurance, had higher likelihood of survival post-quake compared with those firms that did not have any insurance. For the medium-term (2-3 years) survival of firms, our results show a more explicit role for insurance. Firms with BI insurance experience increased productivity and improved performance following a catastrophe. Furthermore, we find that those organisations that receive prompt and full payments of their claims have a better recovery than those that had protracted or inadequate claims payments, but this difference between the two groups is not statistically significant. We find no statistically significant evidence that the latter group (inadequate payment) did any better than those organisations that had damage but no insurance coverage. In general, our analysis indicates the importance not only of adequate insurance coverage, but also of an insurance system that delivers prompt claim payments.
This is a post-peer-review, pre-copyedit version of an article published in 'The Geneva Papers on Risk and Insurance - Issues and Practice'. The final authenticated version is available online at: https://doi.org/10.1057/s41288-017-0067-y. The following terms of use apply: https://www.springer.com/gp/open-access/publication-policies/aam-terms-of-use.
SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database.
Overview of SeisFinder SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database. The basic SeisFinder architecture is shown in Figure 1.
Recently developed performance-based earthquake engineering framework, such as one provided by PEER (Deierlein et al. 2003), assist in the quantification in terms of performance such as casualty, monetary losses and downtime. This opens up the opportunity to identify cost-effective retrofit/rehabilitation strategies by comparing upfront costs associated with retrofit with the repair costs that can be expected over time. This loss assessment can be strengthened by learning from recent earthquakes, such as the 2010 Canterbury and 2016 Kaikoura earthquakes. In order to investigate which types of retrofit/rehabilitation strategies may be most cost-effective, a case study building was chosen for this research. The Pacific Tower, a 22-storey EBF apartment located within the Christchurch central business district (CBD), was damaged and repaired during the 2010 Canterbury earthquake series. As such, by taking hazard levels accordingly (i.e. to correspond to the Christchurch CBD), modelling and analysing the structure, and considering the vulnerability and repair costs of its different components, it is possible to predict the expected losses of the aforementioned building. Using this information, cost-effective retrofit/rehabilitation strategy can be determined. This research found that more often than not, it would be beneficial to improve the performance of valuable non-structural components, such as partitions. Although it is true that improving such elements will increase the initial costs, over time, the benefits gained from reduced losses should be expected to overcome the initial costs. Aftershocks do increase the predicted losses of a building even in lower intensities due to the fact that non-structural components can get damaged at such low intensities. By comparing losses computed with and without consideration of aftershocks for a range of historical earthquakes, it was found that the ratio between losses due to main shock with aftershocks to the losses due to the main shock only tended to increase with increasing main shock magnitude. This may be due to the fact that larger magnitude earthquakes tend to generate larger magnitude aftershocks and as those aftershocks happen within a region around the main shock, they are more likely to cause intense shaking and additional damage. In addition to this observation, it was observed that the most significant component of loss of the case study building was the non-structural partition walls.
