The Canterbury earthquakes caused huge amounts of damage to Christchurch and the surrounding area and presented a very challenging situation for both insurers and claimants. While tourism has suffered significant losses as a result, particularly due to the subsequent decrease in visitor numbers, the Canterbury region was very fortunate to have high levels of insurance coverage. This report, based on data gathered from tourism operators on the ground in Canterbury, looks at how this sector has been affected by the quakes, claims patterns, and the behaviour and perceptions of tourism operators about insurance.
On 22 February 2011, Canterbury and its largest city Christchurch experienced its second major earthquake within six months. The region is facing major economic and organisational challenges in the aftermath of these events. Approximately 25% of all buildings in the Christchurch CBD have been “red tagged” or deemed unsafe to enter. The New Zealand Treasury estimates that the combined cost of the February earthquake and the September earthquake is approximately NZ$15 billion[2]. This paper examines the national and regional economic climate prior to the event, discusses the immediate economic implications of this event, and the challenges and opportunities faced by organisations affected by this event. In order to facilitate recovery of the Christchurch area, organisations must adjust to a new norm; finding ways not only to continue functioning, but to grow in the months and years following these earthquakes. Some organisations relocated within days to areas that have been less affected by the earthquakes. Others are taking advantage of government subsidised aid packages to help retain their employees until they can make long-term decisions about the future of their organisation. This paper is framed as a “report from the field” in order to provide insight into the early recovery scenario as it applies to organisations affected by the February 2011 earthquake. It is intended both to inform and facilitate discussion about how organisations can and should pursue recovery in Canterbury, and how organisations can become more resilient in the face of the next crisis.
This report examines and compares case studies of labour market policy responses in APEC economies to natural disasters. It first reviews the policies and practice within APEC economies and internationally in managing the labour market effects of natural disasters. By using comparative case studies, the report then compares recent disaster events in the Asia-Pacific region, including: - the June 2013 Southern Alberta floods in Canada; - the 2010 and 2011 Queensland floods in Australia; - the 2010 and 2011 Canterbury earthquakes in New Zealand; - the 2011 Great East Japan Earthquake and Tsunami in Japan; and - the 2008 Wenchuan earthquake in China.
The 4 September, 22 February, and 13 June earthquakes experienced in Canterbury, New Zealand would have been significant events individually. Together they present a complex and unprecedented challenge for Canterbury and New Zealand. The repetitive and protracted nature of these events has caused widespread building and infrastructure damage, strained organisations’ financial and human resources and challenged insurer and investor confidence. The impact of the earthquakes was even more damaging coming in the wake of the worst worldwide recession since the great depression of the 1930s. However, where there is disruption there is also opportunity. Businesses and other organisations will drive the physical, economic and social recovery of Canterbury, which will be a dynamic and long-term undertaking. Ongoing monitoring of the impacts, challenges and developments during the recovery is critical to maintaining momentum and making effective mid-course adjustments. This report provides a synthesis of research carried out by the Resilient Organisations (ResOrgs) Research Programme1 at the University of Canterbury and Recover Canterbury in collaboration with Opus Central Laboratories (part of Opus International Consultants). The report includes discussions on the general state of the economy as well as data from three surveys (two conducted by ResOrgs and one by Recover Canterbury) on business impacts of the earthquakes, population movements and related economic recovery issues. This research and report offers two primary benefits:
The use of post-earthquake cordons as a tool to support emergency managers after an event has been documented around the world. However, there is limited research that attempts to understand the use, effectiveness, inherent complexities, impacts and subsequent consequences of cordoning once applied. This research aims to fill that gap by providing a detailed understanding of first, the cordons and associated processes, and their implications in a post-earthquake scenario. We use a qualitative method to understand cordons through case studies of two cities where it was used in different temporal and spatial scales: Christchurch (2011) and Wellington (Kaikōura earthquake 2016), New Zealand. Data was collected through 21 expert interviews obtained through purposive and snowball sampling of key informants who were directly or indirectly involved in a decision-making role and/or had influence in relation to the cordoning process. The participants were from varying backgrounds and roles i.e. emergency managers, council members, business representatives, insurance representatives, police and communication managers. The data was transcribed, coded in Nvivo and then grouped based on underlying themes and concepts and then analyzed inductively. It is found that cordons are used primarily as a tool to control access for the purpose of life safety and security. But cordons can also be adapted to support recovery. Broadly, it can be synthesized and viewed based on two key aspects, ‘decision-making’ and ‘operations and management’, which overlap and interact as part of a complex system. The underlying complexity arises in large part due to the multitude of sectors it transcends such as housing, socio-cultural requirements, economics, law, governance, insurance, evacuation, available resources etc. The complexity further increases as the duration of cordon is extended.
The research is funded by Callaghan Innovation (grant number MAIN1901/PROP-69059-FELLOW-MAIN) and the Ministry of Transport New Zealand in partnership with Mainfreight Limited. Need – The freight industry is facing challenges related to climate change, including natural hazards and carbon emissions. These challenges impact the efficiency of freight networks, increase costs, and negatively affect delivery times. To address these challenges, freight logistics modelling should consider multiple variables, such as natural hazards, sustainability, and emission reduction strategies. Freight operations are complex, involving various factors that contribute to randomness, such as the volume of freight being transported, the location of customers, and truck routes. Conventional methods have limitations in simulating a large number of variables. Hence, there is a need to develop a method that can incorporate multiple variables and support freight sustainable development. Method - A minimal viable model (MVM) method was proposed to elicit tacit information from industrial clients for building a minimally sufficient simulation model at the early modelling stages. The discrete-event simulation (DES) method was applied using Arena® software to create simulation models for the Auckland and Christchurch corridor, including regional pick-up and delivery (PUD) models, Christchurch city delivery models, and linehaul models. Stochastic variables in freight operations such as consignment attributes, customer locations, and truck routes were incorporated in the simulation. The geographic information system (GIS) software ArcGIS Pro® was used to identify and analyse industrial data. The results obtained from the GIS software were applied to create DES models. Life cycle assessment (LCA) models were developed for both diesel and battery electric (BE) trucks to compare their life cycle greenhouse gas (GHG) emissions and total cost of ownership (TCO) and support GHG emissions reduction. The line-haul model also included natural hazards in several scenarios, and the simulation was used to forecast the stock level of Auckland and Christchurch depots in response to each corresponding scenario. Results – DES is a powerful technique that can be employed to simulate and evaluate freight operations that exhibit high levels of variability, such as regional pickup and delivery (PUD) and linehaul. Through DES, it becomes possible to analyse multiple factors within freight operations, including transportation modes, routes, scheduling, and processing times, thereby offering valuable insights into the performance, efficiency, and reliability of the system. In addition, GIS is a useful tool for analysing and visualizing spatial data in freight operations. This is exemplified by their ability to simulate the travelling salesman problem (TSP) and conduct cluster analysis. Consequently, the integration of GIS into DES modelling is essential for improving the accuracy and reliability of freight operations analysis. The outcomes of the simulation were utilised to evaluate the ecological impact of freight transport by performing emission calculations and generating low-carbon scenarios to identify approaches for reducing the carbon footprint. LCA models were developed based on simulation results. Results showed that battery-electric trucks (BE) produced more greenhouse gas (GHG) emissions in the cradle phase due to battery manufacturing but substantially less GHG emissions in the use phase because of New Zealand's mostly renewable energy sources. While the transition to BE could significantly reduce emissions, the financial aspect is not compelling, as the total cost of ownership (TCO) for the BE truck was about the same for ten years, despite a higher capital investment for the BE. Moreover, external incentives are necessary to justify a shift to BE trucks. By using simulation methods, the effectiveness of response plans for natural hazards can be evaluated, and the system's vulnerabilities can be identified and mitigated to minimize the risk of disruption. Simulation models can also be utilized to simulate adaptation plans to enhance the system's resilience to natural disasters. Novel contributions – The study employed a combination of DES and GIS methods to incorporate a large number of stochastic variables and driver’s decisions into freight logistics modelling. Various realistic operational scenarios were simulated, including customer clustering and PUD truck allocation. This showed that complex pickup and delivery routes with high daily variability can be represented using a model of roads and intersections. Geographic regions of high customer density, along with high daily variability could be represented by a two-tier architecture. The method could also identify delivery runs for a whole city, which has potential usefulness in market expansion to new territories. In addition, a model was developed to address carbon emissions and total cost of ownership of battery electric trucks. This showed that the transition was not straightforward because the economics were not compelling, and that policy interventions – a variety were suggested - could be necessary to encourage the transition to decarbonised freight transport. A model was developed to represent the effect of natural disasters – such as earthquake and climate change – on road travel and detour times in the line haul freight context for New Zealand. From this it was possible to predict the effects on stock levels for a variety of disruption scenarios (ferry interruption, road detours). Results indicated that some centres rather than others may face higher pressure and longer-term disturbance after the disaster subsided. Remedies including coastal shipping were modelled and shown to have the potential to limit the adverse effects. A philosophical contribution was the development of a methodology to adapt the agile method into the modelling process. This has the potential to improve the clarification of client objectives and the validity of the resulting model.
Christchurch City Council (Council) is undertaking the Land Drainage Recovery Programme in order to assess the effects of the earthquakes on flood risk to Christchurch. In the course of these investigations it has become better understood that floodplain management should be considered in a multi natural hazards context. Council have therefore engaged the Jacobs, Beca, University of Canterbury, and HR Wallingford project team to investigate the multihazards in eastern areas of Christchurch and develop flood management options which also consider other natural hazards in that context (i.e. how other hazards contribute to flooding both through temporal and spatial coincidence). The study has three stages: Stage 1 Gap Analysis – assessment of information known, identification of gaps and studies required to fill the gaps. Stage 2 Hazard Studies – a gap filling stage with the studies identified in Stage 1. Stage 3 Collating, Optioneering and Reporting – development of options to manage flood risk. This present report is to document findings of Stage 1 and recommends the studies that should be completed for Stage 2. It has also been important to consider how Stage 3 would be delivered and the gaps are prioritised to provide for this. The level of information available and hazards to consider is extensive; requiring this report to be made up of five parts each identifying individual gaps. A process of identifying information for individual hazards in Christchurch has been undertaken and documented (Part 1) followed by assessing the spatial co-location (Part 2) and probabilistic presence of multi hazards using available information. Part 3 considers multi hazard presence both as a temporal coincidence (e.g. an earthquake and flood occurring at one time) and as a cascade sequence (e.g. earthquake followed by a flood at some point in the future). Council have already undertaken a number of options studies for managing flood risk and these are documented in Part 4. Finally Part 5 provides the Gap Analysis Summary and Recommendations to Council. The key findings of Stage 1 gap analysis are: - The spatial analysis showed eastern Christchurch has a large number of hazards present with only 20% of the study area not being affected by any of the hazards mapped. Over 20% of the study area is exposed to four or more hazards at the frequencies and data available. - The majority of the Residential Red Zone is strongly exposed to multiple hazards, with 86% of the area being exposed to 4 or more hazards, and 24% being exposed to 6 or more hazards. - A wide number of gaps are present; however, prioritisation needs to consider the level of benefit and risks associated with not undertaking the studies. In light of this 10 studies ranging in scale are recommended to be done for the project team to complete the present scope of Stage 3. - Stage 3 will need to consider a number of engineering options to address hazards and compare with policy options; however, Council have not established a consistent policy on managed retreat that can be applied for equal comparison; without which substantial assumptions are required. We recommend Council undertake a study to define a managed retreat framework as an option for the city. - In undertaking Stage 1 with floodplain management as the focal point in a multi hazards context we have identified that Stage 3 requires consideration of options in the context of economics, implementation and residual risk. Presently the scope of work will provide a level of definition for floodplain options; however, this will not be at equal levels of detail for other hazard management options. Therefore, we recommend Council considers undertaking other studies with those key hazards (e.g. Coastal Hazards) as a focal point and identifies the engineering options to address such hazards. Doing so will provide equal levels of information for Council to make an informed and defendable decision on which options are progressed following Stage 3.
