The objective of the study presented herein is to assess three commonly used CPT-based liquefaction evaluation procedures and three liquefaction severity index frameworks using data from the 2010–2011 Canterbury earthquake sequence. Specifically, post-event field observations, ground motion recordings, and results from a recently completed extensive geotechnical site investigation programme at selected strong motion stations (SMSs) in the city of Christchurch and surrounding towns are used herein. Unlike similar studies that used data from free-field sites, accelerogram characteristics at the SMS locations can be used to assess the performance of liquefaction evaluation procedures prior to their use in the computation of surficial manifestation severity indices. Results from this study indicate that for cases with evidence of liquefaction triggering in the accelerograms, the majority of liquefaction evaluation procedures yielded correct predictions, regardless of whether surficial manifestation of liquefaction was evident or not. For cases with no evidence of liquefaction in the accelerograms (and no observed surficial evidence of liquefaction triggering), the majority of liquefaction evaluation procedures predicted liquefaction was triggered. When all cases are used to assess the performance of liquefaction severity index frameworks, a poor correlation is shown between the observed severity of liquefaction surface manifestation and the calculated severity indices. However, only using those cases where the liquefaction evaluation procedures yielded correct predictions, there is an improvement in the correlation, with the Liquefaction Severity Number (LSN) being the best performing of the frameworks investigated herein. However scatter in the relationship between the observed and calculated surficial manifestation still remains for all liquefaction severity index frameworks.
The rapid classification of building damage states or placards after an earthquake is vital for enabling an efficient emergency response and informed decision-making for rehabilitation and recovery purposes. Traditional methods rely heavily on inspector-led on-site surveys, which are often time-consuming, resource-intensive, and susceptible to human error. This study introduces a machine learning-supported surrogate model designed to streamline the assessment of building damage, focusing on the automated assignment of damage placards within the context of New Zealand's post-earthquake evaluation frameworks. The study evaluates two key safety evaluation protocols—Rapid Building Assessment (RBA) and Detailed Damage Evaluation (DDE)—and integrates corresponding databases derived from the 2010–2011 Canterbury Earthquake Sequence (CES) in Christchurch. Six ML classifiers—Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and Gradient Bagging (GBag)—were rigorously tested across both databases. The results indicate that the RF-based surrogate model outperforms the other classifiers across both RBA and DDE protocols. Two distinct sets of critical predictors have been further identified for each protocol, allowing for the rapid retrieval of essential data for future on-site surveys, while retaining the RF model's predictive accuracy. The developed surrogate model provides a pragmatic tool for practising engineers to rapidly assign placards to damaged structures and for policymakers and building owners to make informed recovery decisions for earthquake-affected buildings.
The standard way in which disaster damages are measured involves examining separately the number of fatalities, of injuries, of people otherwise affected, and the financial damage that natural disasters cause. Here, we implement a novel way to aggregate these separate measures of disaster impact and apply it to two recent catastrophic events: the Christchurch (New Zealand) earthquakes and the Greater Bangkok (Thailand) floods of 2011. This new measure, which is similar to the World Health Organization’s calculation of Disability Adjusted Life Years (DALYs) lost from the burden of diseases and injuries, is described in detail in Noy (2014). It allows us to conclude that New Zealand lost 180 thousand lifeyears as a result of the 2011 events, and Thailand lost 2,644 thousand years. In per capita terms, the loss is similar, with both countries losing about 15 days per person due to the 2011 catastrophic events in these two countries. We also compare these events to other potentially similar events.
We examine the role of business interruption insurance in business recovery following the Christchurch earthquake in 2011 in the short- and medium-term. In the short-term analysis, we ask whether insurance increases the likelihood of business survival in the aftermath of a disaster. We find only weak evidence that those firms that had incurred damage, but were covered by business interruption insurance, had higher likelihood of survival post-quake compared with those firms that did not have insurance. This absence of evidence may reflect the high degree of uncertainty in the months following the 2011 earthquake and the multiplicity of severe aftershocks. For the medium-term, our results show a more explicit role for insurance in the aftermath of a disaster. Firms with business interruption insurance have a higher probability of increasing productivity and improved performance following a catastrophe. Furthermore, our results show that those organisations that receive prompt and full payments of their claims have a better recovery, in terms of profitability and a subjective ‘”better off” measure’ than those that had protracted or inadequate claim payments (less than 80% of the claim paid within 2.5 years). Interestingly, the latter group does worse than those organisations that had damage but no insurance coverage. This analysis strongly indicates the importance not only of good insurance coverage, but of an insurance system that also delivers prompt claim payments. As a first paper attempting to empirically identify a causal effect of insurance on business recovery, we also emphasize some caveats to our analysis.
Earthquakes are insured only with public sector involvement in high-income countries where the risk of earthquakes is perceived to be high. The proto-typical examples of this public sector involvement are the public earthquake insurance schemes in California, Japan, and New Zealand (NZ). Each of these insurance programs is structured differently, and the purpose of this paper is to examine these differences using a concrete case-study, the sequence of earthquakes that occurred in the Christchurch, New Zealand, in 2011. This event turned out to have been the most heavily insured earthquake event in history. We examine what would have been the outcome of the earthquakes had the system of insurance in NZ been different. In particular, we focus on the public earthquake insurance programs in California (the California Earthquake Authority - CEA), and in Japan (Japanese Earthquake Reinsurance - JER). Overall, the aggregate cost to the public insurer in NZ was $NZ 11.1 billion in its response to the earthquakes. If a similar-sized disaster event had occurred in Japan and California, homeowners would have received $NZ 2.5 billion and $NZ 1.4 billion from the JER and CEA, respectively. We further describe the spatial and distributive patterns of these different scenarios.
We measure the longer-term effect of a major earthquake on the local economy, using night-time light intensity measured from space, and investigate whether insurance claim payments for damaged residential property affected the local recovery process. We focus on the destructive Christchurch earthquake of 2011 as our case study. In this event more than 95% of residential housing units were covered by insurance, but insurance payments were staggered over 5 years, enabling us to identify their local impact. We find that night-time luminosity can capture the process of recovery and describe the recovery’s determinants. We also find that insurance payments contributed significantly to the process of economic recovery after the earthquake, but delayed payments were less affective and cash settlement of claims were more affective in contributing to local recovery than insurance-managed rebuilding.
We estimate the causal effects of a large unanticipated natural disaster on high schoolers’ university enrolment decisions and subsequent medium-term labour market outcomes. Using national administrative data after a destructive earthquake in New Zealand, we estimate that the disaster raises tertiary education enrolment of recent high school graduates by 6.1 percentage points. The effects are most pronounced for males, students who are academically weak relative to their peers, and students from schools directly damaged by the disaster. As relatively low ability males are overrepresented in sectors of the labour market helped by the earthquake, greater demand for university may stem from permanent changes in deeper behavioural parameters such as risk aversion or time preference, rather than as a coping response to poor economic opportunities.
