Economic effects of regulating the seismic strengthening of older buildings

The marked increase in the awareness of earthquake risk following the Canterbury earthquakes in New Zealand offered a unique opportunity to investigate the economic effect of disaster-mitigation regulations on the commercial building stock. A difference-in-differences (DD) framework was used to determine whether earthquake risk has been capitalized into the property prices of buildings constructed prior to 1976, as a response to the national policy requiring assessment and strengthening (or demolition) of the existing earthquake-prone building stock. A negative externality is found in the policy announcement on affected (pre-1970s) office and retail buildings which caused office buildings to suffer a 12.5% stigma discount. However, retail properties were less impacted suffering a 2.3% stigma loss. The value of the commercial building stock has been affected by the policy. These findings provide policy-makers with timely evidence as to the economic effects of New Zealand’s earthquake-prone buildings policy. Facing losses in property value and financial responsibility for retrofitting their assets, building owners will be looking for a workable set of regulatory and non-regulatory incentives to encourage disaster risk management and protect the built environment.     

Local demand shocks and firms' survival: An application to the Italian economy during the Great Recession

The Italian economy is characterised by a large number of micro-firms and small firms and by a long-lasting gap between North and South Italy. Therefore, global shocks – such as the Great Recession – have had a heterogeneous impact at the local level: the collapse in private demand was unequally distributed across different products, services and regions. The aim of this paper, therefore, is to evaluate the impact of the Great Recession on firm's survival for the universe of Italian firms in the light of two relevant indicators: local exposure to crisis and local demand shocks. The results suggest a strong negative impact of local demand shocks on the survival of firms, whereas the degree of exposure to the crisis generally has a weak effect.     

The three Rs of river ecosystem resilience: Resources,recruitment, and refugia

Resilience in river ecosystems requires that organisms must persist in the face of highly dynamic hydrological and geomorphological variations. Disturbance events such as floods and droughts are postulated to shape life history traits that support resilience, but river management and conservation would benefit from greater understanding of the emergent effects in communities of river organisms. We unify current knowledge of taxonomic‐, phylogenetic‐, and trait‐based aspects of river communities that might aid the identification and quantification of resilience mechanisms. Temporal variations in river productivity, physical connectivity, and environmental heterogeneity resulting from floods and droughts are highlighted as key characteristics that promote resilience in these dynamic ecosystems. Three community‐wide mechanisms that underlie resilience are (a) partitioning (competition/facilitation) of dynamically varying resources, (b) dispersal, recolonization, and recruitment promoted by connectivity, and (c) functional redundancy in communities promoted by resource heterogeneity and refugia. Along with taxonomic and phylogenetic identity, biological traits related to feeding specialization, dispersal ability, and habitat specialization mediate organism responses to disturbance. Measures of these factors might also enable assessment of the relative contributions of different mechanisms to community resilience. Interactions between abiotic drivers and biotic aspects of resource use, dispersal, and persistence have clear implications for river conservation and management. To support these management needs, we propose a set of taxonomic, phylogenetic, and life‐history trait metrics that might be used to measure resilience mechanisms. By identifying such indicators, our proposed framework can enable targeted management strategies to adapt river ecosystems to global change.     

Between-herd variation in resilience and relations to herd performance

Resilient cows are minimally affected in their functioning by infections and other disturbances, and recover quickly. Herd management is expected to have an effect on disturbances and the resilience of cows, and this effect was investigated in this study. Two resilience indicators were first recorded on individual cows. The effect of herd-year on these resilience indicators was then estimated and corrected for genetic and year-season effects. The 2 resilience indicators were the variance and the lag-1 autocorrelation of daily milk yield deviations from an expected lactation curve. Low variance and autocorrelation indicate that a cow does not fluctuate much around her expected milk yield and is, thus, subject to few disturbances, or little affected by disturbances (resilient). The herd-year estimates of the resilience indicators were estimated for 9,917 herd-year classes based on records of 227,655 primiparous cows from 2,644 herds. The herd-year estimates of the resilience indicators were then related to herd performance variables. Large differences in the herd-year estimates of the 2 resilience indicators (variance and autocorrelation) were observed between herd-years, indicating an effect of management on these traits. Furthermore, herd-year classes with a high variance tended to have a high proportion of cows with a rumen acidosis indication (r = 0.31), high SCS (r = 0.19), low fat content (r = ?0.18), long calving interval (r = 0.14), low survival to second lactation (r = ?0.13), large herd size (r = 0.12), low lactose content (r = ?0.12), and high production (r = 0.10). These correlations support that herds with high variance are not resilient. The correlation between the variance and the proportion of cows with a rumen acidosis indication suggests that feed management may have an important effect on the variance. Herd-year classes with a high autocorrelation tended to have a high proportion of cows with a ketosis indication (r = 0.14) and a high production (r = 0.13), but a low somatic cell score (r = ?0.17) and a low proportion of cows with a rumen acidosis indication (r = ?0.12). These correlations suggest that high autocorrelation at herd level indicates either good or poor resilience, and is thus a poor resilience indicator. However, the combination of a high variance and a high autocorrelation is expected to indicate many fluctuations with slow recovery. In conclusion, herd management, in particular feed management, seems to affect herd resilience.     

Assessing supply chain robustness to links failure

Supply chain networks need to respond efficiently to operation disruptions, as one of their aims is to guarantee the on time delivery of products. Hence, robustness has become one of the important issues to consider when designing supply networks. There are alternative ways to measure what robustness means in this context. In this paper, we propose a new metric based on the effect on service level of the collapse of active transportation links. Numerical experiments are carried out to understand how different design factors affect robustness. Robustness under a targeted attack is compared with robustness to random failures. Results show that flow complexity (i.e. the number of potential transportation links between supply network nodes) is the most influential factor affecting supply network and its robustness, as well as the service level that can be maintained after disruptions. Thus, diversification both in supply sources and transportation routes seems to be key to robustness.     

Evaluating the role of resilience in reducing economic losses from disasters: A multi-regional analysis of a seaport disruption

Models to estimate economic impacts of disasters have recently been augmented to include resilience. However, most research has incorporated only a limited set of resilience tactics and has not estimated their individual loss reduction effect. We present a comprehensive framework for estimating the relative effects of a broad set of post-disaster resilience tactics. Our methodological innovation is illustrated by adapting the TERM multi-regional CGE model for a seaport disruption, distinguishing inherent resilience working through the price system from adaptive resilience and other inherent tactics to cope with input shortages. We also overcome a path-dependency problem in the modelling process.     

Everyday work investigations for safety

A key theme in safety is the investigation and improvement of work processes and conditions. This is built on a tradition of studying accidents, but there is growing interest in the investigation of everyday work. Researchers have started using terms such as success, normal and everyday work investigations, but behind these can be different activities. This paper makes explicit some considerations behind investigations of everyday work, how these considerations shape an investigation, and how they reflect different schools of thought. These considerations can be used as an aid for designing everyday work investigations for safety and serve as a first step towards critical reflection on investigating everyday work.     

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services. The ISNs are open systems with structural dynamics since the firms may exhibit multiple behaviours by changing the buyer-supplier roles in interconnected or even competing SCs. From the positions of resilience, the ISNs as a whole provide services to society (e.g. food service, mobility service or communication service) which are required to ensure a long-term survival. The analysis of survivability at the level of ISN requires a consideration at a large scale as resilience of individual SCs. The recent example of coronavirus COVID-19 outbreak clearly shows the necessity of this new perspective. Our study introduces a new angle in SC resilience research when a resistance to extraordinary disruptions needs to be considered at the scale of viability. We elaborate on the integrity of the ISN and viability. The contribution of our position study lies in a conceptualisation of a novel decision-making environment of ISN viability. We illustrate the viability formation through a dynamic game-theoretic modelling of a biological system that resembles the ISN. We discuss some future research areas.     

Restoration and functionality assessment of a community subjected to tornado hazard

The loss of infrastructure functionality for a community from a tornado can significantly affect economic vitality and result in substantial social disruption. The duration of the loss of functionality is a decisive factor in the magnitude of the economic disruption caused by a tornado. Therefore, quantitative models for the post-tornado restoration processes are needed in order to provide risk-informed decision support. In this study, the water network, electric power network, school buildings, residential buildings and businesses were modelled with their relative spatial distribution along with select dependencies and cross-dependencies in order to investigate the restoration of a community in the aftermath of a disaster. A tornado path was simulated using statistics from a historical tornado database. Based on the damage level of the components and their connectivity throughout the community, the functionality of the supplier nodes was assessed through a Monte Carlo simulation. Finally, the repair time of the damaged components was modelled to capture how the demands may be satisfied over time until eventually full restoration is achieved. Then, two metrics were defined to highlight business and social disruption following a tornado. The approach presented herein can be implemented in future studies to quantify indirect economic loss and social disruption resulting from a tornado.     

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population''s activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.