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 greater Wellington region, New Zealand, is highly vulnerable to large earthquakes. While attention has been paid to the consequences of earthquake damage to road, electricity and water supply networks, the consequences of wastewater network damage for public health, environmental health and habitability of homes remain largely unknown for Wellington City. The Canterbury and Kaikōura earthquakes have highlighted the vulnerability of sewerage systems to disruption during a disaster. Management of human waste is one of the critical components of disaster planning to reduce faecal-oral transmission of disease and exposure to disease-bearing vectors. In Canterbury and Kaikōura, emergency sanitation involved a combination of Port-a-loos, chemical toilets and backyard long-drops. While many lessons may be learned from experiences in Canterbury earthquakes, it is important to note that isolation is likely to be a much greater factor for Wellington households, compared to Christchurch, due to the potential for widespread landslides in hill suburbs affecting road access. This in turn implies that human waste may have to be managed onsite, as options such as chemical toilets and Port-a-loos rely completely on road access for delivering chemicals and collecting waste. While some progress has been made on options such as emergency composting toilets, significant knowledge gaps remain on how to safely manage waste onsite. In order to bridge these gaps, laboratory tests will be conducted through the second half of 2019 to assess the pathogen die-off rates in the composting toilet system with variables being the type of carbon bulking material and the addition of a Bokashi composting activator.
Predicting building collapse due to seismic motion is critical in design and more so after a major event. Damaged structures can appear sound, but collapse under following major events. There can thus be significant risk in decision making after a major seismic event concerning the safe occupation of a building or surrounding areas, versus the unknown impact of unknown major aftershocks. Model-based pushover analyses are effective if the structural properties are well understood, which is not valid post-event when this risk information is most useful. This research combines Hysteresis Loop Analysis (HLA) structural health monitoring (SHM) and Incremental Dynamic Analysis (IDA) methods to determine collapse capacity and probability of collapse for a specific structure, at any time, a range of earthquake excitations to ensure robustness. The nonlinear dynamic analysis method presented enables constant updating of building performance predictions using post-event SHM results. The resulting combined methods provide near real-time updating of collapse fragility curves as events progress, quantifying the change of collapse probability or seismic induced losses for decision-making - a novel, higher resolution risk analysis than previously available. The methods are not computationally expensive and there is no requirement for a validated numerical model. Results show significant potential benefits and a clear evolution of risk. They also show clear need for extending SHM toward creating improved predictive models for analysis of subsequent events, where the Christchurch series of 2010-2011 had significant post-event aftershocks after each main event. Finally, the overall method is generalisable to any typical engineering demand parameter.
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Prediction of building collapse due to significant seismic motion is a principle objective of earthquake engineers, particularly after a major seismic event when the structure is damaged and decisions may need to be made rapidly concerning the safe occupation of a building or surrounding areas. Traditional model-based pushover analyses are effective, but only if the structural properties are well understood, which is not the case after an event when that information is most useful. This paper combines hysteresis loop analysis (HLA) structural health monitoring (SHM) and incremental dynamic analysis (IDA) methods to identify and then analyse collapse capacity and the probability of collapse for a specific structure, at any time, a range of earthquake excitations to ensure robustness. This nonlinear dynamic analysis enables constant updating of building performance predictions following a given and subsequent earthquake events, which can result in difficult to identify deterioration of structural components and their resulting capacity, all of which is far more difficult using static pushover analysis. The combined methods and analysis provide near real-time updating of the collapse fragility curves as events progress, thus quantifying the change of collapse probability or seismic induced losses very soon after an earthquake for decision-making. Thus, this combination of methods enables a novel, higher-resolution analysis of risk that was not previously available. The methods are not computationally expensive and there is no requirement for a validated numerical model, thus providing a relatively simpler means of assessing collapse probability immediately post-event when such speed can provide better information for critical decision-making. Finally, the results also show a clear need to extend the area of SHM toward creating improved predictive models for analysis of subsequent events, where the Christchurch series of 2010–2011 had significant post-event aftershocks.
