Krista Danielle S. Yu

A VULNERABILITY INDEX FOR POST-DISASTER KEY SECTOR PRIORITIZATION

Input–output-based techniques have proven to be effective in modeling how disasters lead to economic disruptions, while taking into account the structural connectivity of economic systems. In particular, through the inoperability input–output model (IIM), the degree of failure in an economic system can be quantified on a scale from 0 (normal state) to 1 (complete failure). This paper develops a vulnerability index that builds upon the foundations of the Leontief input–output model and the IIM, which is capable of identifying and prioritizing the key sectors in the aftermath of disasters. The key sector prioritization framework proposed in this paper is expected to contribute to the domain of disaster preparedness planning, such as enhancing the efficiency of resource allocation across various sectors. The proposed vulnerability index is formulated in terms of three underlying components: (1) economic impact, (2) propagation length, and (3) sector size. The vulnerability index captures the impact of investments to various sectors in times of disaster in order to yield the maximum benefits to the entire economy. This paper considers a baseline scenario that assumes that the decision-maker has an equal preference for all index components. Using Monte Carlo simulation and sensitivity analysis, we investigated the extent to which the key sector rankings could fluctuate with respect to variations in the decision-maker preferences. Key sectors tend to be sensitive to the weight assignments across the three vulnerability index components; nevertheless, some sectors are less sensitive to such weight variations and may persist on their level of priority, independent of the scenario. Using the Philippine input–output data, we found that the private services sector is consistently a high-priority sector, the trade sector is a mid-priority sector while the real estate and ownership of dwellings sector tend to be a low-priority sector.

Time-Varying Disaster Recovery Model For Interdependent Economic Systems Using Hybrid Input--Output And Event Tree Analysis

Disasters damage physical infrastructure systems, disrupt the movement of people and commodities, and cause significant economic losses. This paper develops an I–O model extension using an event tree analysis to assess the propagation of disaster effects across interdependent economic sectors using the inoperability and economic loss metrics. Inoperability, a dimensionless index that ranges between 0 and 1, indicates the extent to which a sectors production deviates below its normal state. On the other hand, economic loss is the monetary worth of the drop in output incurred in each sector of the economy due to the disaster. The new dynamic I–O extension is capable of adjusting the inoperability parameters within the disaster timeline to reflect events that can either degrade or enhance the predicted paths of sector recovery. It was implemented to the Nashville region – a metropolitan area in the USA known for its vibrant music and the tourism industry. The Nashville region is frequently hit by natural disasters such as tornadoes and floods, which makes it a suitable case study site for the model application. Results of the study can help identify critical economic sectors and ultimately provide insights for formulating preparedness decisions to expedite disaster recovery.

State of the art in risk analysis of workforce criticality influencing disaster preparedness for interdependent systems

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.

Analysis of drought risk management strategies using dynamic inoperability input-output modeling and event tree analysis

Abstract Climate change is expected to increase the frequency and intensity of droughts in many parts of the world. Since water is an essential resource for many economic activities, water scarcity can cause disruptions that manifest as losses in industrial outputs. These effects can propagate through economic systems as a result of the inherent interdependencies among economic sectors. Risk management strategies for droughts must therefore account for both direct and indirect effects of water supply disruptions. In this work, we propose a methodology for evaluating drought management strategies by combining economic input–output modeling with event tree analysis. We apply the methodology to a simulated drought scenario affecting the United States National Capital Region. Three risk management strategies, namely, reducing the initial level of water supply disruption, managing water consumption, and prioritizing water-use dependencies, are evaluated based on inoperability levels and cumulative economic losses. Results show that while managing water consumption yields the lowest cumulative economic losses in the region, reducing the initial level of water supply disruption and prioritizing water-use dependencies result in lower inoperability of critical sectors. These findings provide insights for decision makers in identifying critical sectors and formulating timely intervention strategies that minimize the overall effects of drought to economic systems. Further, the proposed modeling framework for drought risk assessment can be applied to other regions to evaluate the effects of drought severity and management strategies over the drought timeline.

A SHOCK ABSORPTION INDEX FOR INOPERABILITY INPUT–OUTPUT MODELS

Recent disasters have underscored the importance of enhancing resilience in economic systems. In this work, we propose a novel shock absorption index, which provides a measure of the ability of an economic system to tolerate disruptions. It is assumed that there are externally defined initial levels of system failure or disruption, as well as maximum allowable levels of inoperability for each sector. The shock absorption index is defined as the largest fraction of the anticipated initial disruption that can be absorbed by the predefined robustness limits. It provides an overall measure of the robustness of an economic system towards a disruptive event, which is driven by both the economic structure and the individual robustness of different sectors. The results of two case studies illustrate policy-making insights in identifying and prioritizing risk management strategies for critical systems.

Input–Output Modeling Approach to Sustainable Systems Engineering

Input–output (I–O) analysis is a quantitative methodology for modeling systems consisting of interdependent components. It provides an elegant mathematical framework for describing the interactions of the system components using a system of linear equations. Although originally developed for economic modeling purposes, the I–O framework has been extended to various systems that are characterized by high levels of internal connectivity. Its ability to capture the ripple effects that cascade through the system makes it a well-suited tool for analyzing interactions among man-made systems with the environment, via withdrawal of natural resources and generation of wastes and pollutants. It has thus been used to address contemporary sustainability issues such as that of climate change mitigation and adaptation through industrial systems optimization. This article provides a brief overview of I–O analysis as it applies to the sustainability analysis of such systems. An illustrative example is presented to demonstrate the computational principle of I–O models.

A weighted fuzzy linear programming model in economic input–output analysis: an application to risk management of energy system disruptions

Climate change exposes economic systems to numerous risks, including reduced agricultural production and electric power supply shortages. The interdependent nature of economic systems causes disruptions in any sector to cascade to other sectors via forward and backward linkages. This work develops an optimization model with which allocation of scarce goods or resources can be optimized; the model uses an overall index of satisfaction of fuzzy economic output goals under conditions of scarcity caused by climatic disruptions. The proposed model includes a vulnerability measure that integrates information elicited from expert judgment. A case study based on a scenario of drought-induced electricity shortage in the Philippine economy is examined. Results show that trade, transportation and service-oriented industries suffer losses in gross domestic product in the Philippine case.

Introduction to Input–Output Models

This chapter provides a general introduction to input–output analysis and input–output models. A brief description of the historical development of the framework, leading to its widespread use, is given. A qualitative discussion of the general framework is presented, followed by a discussion of the key assumptions that underlie input–output models as well as the resultant limitations. The chapter also provides an overview of the rest of the book.

Input–Output Models of Organizational Structures

The interactions among groups of employees or staff in organizational units involve transactions that are analogous to those reflected in large-scale input–output models. The main difference is that, in an organization, the interactions of personnel with internal or external customers are often measured in terms of time (e.g., person-hours), in contrast to the monetary and physical units used in traditional input–output models. This chapter discusses organizational input–output models based on this concept. A case study is solved under different scenarios to illustrate the framework, and the corresponding LINGO model formulations are given.

Input–Output Models of Industrial Plants

Physical input–output models can be used to aid in the synthesis (design) or operations of industrial plant. In this chapter, the use of such models is illustrated for the case of polygeneration plants. The first example illustrates a zero degree of freedom synthesis problem. A second example illustrates how an input–output model can be used to identify process bottlenecks in existing plants. A third example discusses a mixed-integer linear programming (MILP) formulation of an input–output model to optimize state operations when the system is subjected to a disruptive event. LINGO code is provided for all examples.