This thesis examines the closing of Aranui High School in 2016, a low socio-economic secondary school in eastern Christchurch, New Zealand, and reflects on its history through the major themes of innovation and the impact of central government intervention. The history is explored through the leadership of the school principals, and the necessity for constant adaptation by staff to new ways of teaching and learning, driven by the need to accommodate a more varied student population – academically, behaviourally and culturally – than most other schools in wider Christchurch. Several extreme changes, following a neoliberal approach to education policies at a national government level, impacted severely on the school’s ability to thrive and even survive over the 57 years of its existence, with the final impact of the 2010 and 2011 Canterbury earthquakes leading indirectly to Aranui High’s closure. The earthquakes provided the National government with the impetus to advocate for change to education in Christchurch; changes which impacted negatively on many schools in Christchurch, including Aranui High School. The announcement of the closure of Aranui High shocked many staff and students, who were devastated that the school would no longer exist. Aranui High School, Aranui Primary School, Wainoni Primary School and Avondale Primary School were all closed to make way for Haeata Community Campus, a year 1 to 13 school, which was built on the Aranui High site. Aranui High School served the communities of eastern Christchurch for 57 years from 1960 and deserves acknowledgment and remembrance, and my hope is that this thesis will provide a fair representation of the school’s story, including its successes and challenges, while also explaining the reasons behind the eventual closure. This thesis contributes to New Zealand public history and uses mixed research methods to examine Aranui High School’s role as a secondary school in eastern Christchurch. I argue that the closure of Aranui High School in 2016 was an unjustified act by the Ministry of Education.
Meeting the Sustainable Development Goals by 2030 involves transformational change in the business of business, and social enterprises can lead the way in such change. We studied Cultivate, one such social enterprise in Christchurch, New Zealand, a city still recovering from the 2010/11 Canterbury earthquakes. Cultivate works with vulnerable youth to transform donated compost into garden vegetables for local restaurants and businesses. Cultivate’s objectives align with SDG concerns with poverty and hunger (1 & 2), social protection (3 & 4), and sustainable human settlements (6 & 11). Like many grant-supported organisations, Cultivate is required to track and measure its progress. Given the organisation’s holistic objectives, however, adequately accounting for its impact reporting is not straightforward. Our action research project engaged Cultivate staff and youth-workers to generate meaningful ways of measuring impact. Elaborating the Community Economy Return on Investment tool (CEROI), we explore how participatory audit processes can capture impacts on individuals, organisations, and the wider community in ways that extend capacities to act collectively. We conclude that Cultivate and social enterprises like it offer insights regarding how to align values and practices, commercial activity and wellbeing in ways that accrue to individuals, organisations and the broader civic-community.
The supply of water following disasters has always been of significant concern to communities. Failure of water systems not only causes difficulties for residents and critical users but may also affect other hard and soft infrastructure and services. The dependency of communities and other infrastructure on the availability of safe and reliable water places even more emphasis on the resilience of water supply systems. This thesis makes two major contributions. First, it proposes a framework for measuring the multifaceted resilience of water systems, focusing on the significance of the characteristics of different communities for the resilience of water supply systems. The proposed framework, known as the CARE framework, consists of eight principal activities: (1) developing a conceptual framework; (2) selecting appropriate indicators; (3) refining the indicators based on data availability; (4) correlation analysis; (5) scaling the indicators; (6) weighting the variables; (7) measuring the indicators; and (8) aggregating the indicators. This framework allows researchers to develop appropriate indicators in each dimension of resilience (i.e., technical, organisational, social, and economic), and enables decision makers to more easily participate in the process and follow the procedure for composite indicator development. Second, it identifies the significant technical, social, organisational and economic factors, and the relevant indicators for measuring these factors. The factors and indicators were gathered through a comprehensive literature review. They were then verified and ranked through a series of interviews with water supply and resilience specialists, social scientists and economists. Vulnerability, redundancy and criticality were identified as the most significant technical factors affecting water supply system robustness, and consequently resilience. These factors were tested for a scenario earthquake of Mw 7.6 in Pukerua Bay in New Zealand. Four social factors and seven indicators were identified in this study. The social factors are individual demands and capacities, individual involvement in the community, violence level in the community, and trust. The indicators are the Giving Index, homicide rate, assault rate, inverse trust in army, inverse trust in police, mean years of school, and perception of crime. These indicators were tested in Chile and New Zealand, which experienced earthquakes in 2010 and 2011 respectively. The social factors were also tested in Vanuatu following TC Pam, which hit the country in March 2015. Interestingly, the organisational dimension contributed the largest number of factors and indicators for measuring water supply resilience to disasters. The study identified six organisational factors and 17 indicators that can affect water supply resilience to disasters. The factors are: disaster precaution; predisaster planning; data availability, data accessibility and information sharing; staff, parts, and equipment availability; pre-disaster maintenance; and governance. The identified factors and their indicators were tested for the case of Christchurch, New Zealand, to understand how organisational capacity affected water supply resilience following the earthquake in February 2011. Governance and availability of critical staff following the earthquake were the strongest organisational factors for the Christchurch City Council, while the lack of early warning systems and emergency response planning were identified as areas that needed to be addressed. Economic capacity and quick access to finance were found to be the main economic factors influencing the resilience of water systems. Quick access to finance is most important in the early stages following a disaster for response and restoration, but its importance declines over time. In contrast, the economic capacity of the disaster struck area and the water sector play a vital role in the subsequent reconstruction phase rather than in the response and restoration period. Indicators for these factors were tested for the case of the February 2011 earthquake in Christchurch, New Zealand. Finally, a new approach to measuring water supply resilience is proposed. This approach measures the resilience of the water supply system based on actual water demand following an earthquake. The demand-based method calculates resilience based on the difference between water demand and system capacity by measuring actual water shortage (i.e., the difference between water availability and demand) following an earthquake.
The increase of the world's population located near areas prone to natural disasters has given rise to new ‘mega risks’; the rebuild after disasters will test the governments’ capabilities to provide appropriate responses to protect the people and businesses. During the aftermath of the Christchurch earthquakes (2010-2012) that destroyed much of the inner city, the government of New Zealand set up a new partnership between the public and private sector to rebuild the city’s infrastructure. The new alliance, called SCIRT, used traditional risk management methods in the many construction projects. And, in hindsight, this was seen as one of the causes for some of the unanticipated problems. This study investigated the risk management practices in the post-disaster recovery to produce a specific risk management model that can be used effectively during future post-disaster situations. The aim was to develop a risk management guideline for more integrated risk management and fill the gap that arises when the traditional risk management framework is used in post-disaster situations. The study used the SCIRT alliance as a case study. The findings of the study are based on time and financial data from 100 rebuild projects, and from surveying and interviewing risk management professionals connected to the infrastructure recovery programme. The study focussed on post-disaster risk management in construction as a whole. It took into consideration the changes that happened to the people, the work and the environment due to the disaster. System thinking, and system dynamics techniques have been used due to the complexity of the recovery and to minimise the effect of unforeseen consequences. Based on an extensive literature review, the following methods were used to produce the model. The analytical hierarchical process and the relative importance index have been used to identify the critical risks inside the recovery project. System theory methods and quantitative graph theory have been used to investigate the dynamics of risks between the different management levels. Qualitative comparative analysis has been used to explore the critical success factors. And finally, causal loop diagrams combined with the grounded theory approach has been used to develop the model itself. The study identified that inexperienced staff, low management competency, poor communication, scope uncertainty, and non-alignment of the timing of strategic decisions with schedule demands, were the key risk factors in recovery projects. Among the critical risk groups, it was found that at a strategic management level, financial risks attracted the highest level of interest, as the client needs to secure funding. At both alliance-management and alliance-execution levels, the safety and environmental risks were given top priority due to a combination of high levels of emotional, reputational and media stresses. Risks arising from a lack of resources combined with the high volume of work and the concern that the cost could go out of control, alongside the aforementioned funding issues encouraged the client to create the recovery alliance model with large reputable construction organisations to lock in the recovery cost, at a time when the scope was still uncertain. This study found that building trust between all parties, clearer communication and a constant interactive flow of information, established a more working environment. Competent and clear allocation of risk management responsibilities, cultural shift, risk prioritisation, and staff training were crucial factors. Finally, the post-disaster risk management (PDRM) model can be described as an integrated risk management model that considers how the changes which happened to the environment, the people and their work, caused them to think differently to ease the complexity of the recovery projects. The model should be used as a guideline for recovery systems, especially after an earthquake, looking in detail at all the attributes and the concepts, which influence the risk management for more effective PDRM. The PDRM model is represented in Causal Loops Diagrams (CLD) in Figure 8.31 and based on 10 principles (Figure 8.32) and 26 concepts (Table 8.1) with its attributes.
