Dynamic Inoperability Input-output Model Research Articles

Risk-based dynamic inoperability input-output and non-linear optimisation models to analyse resilience in the construction industry

Pakistan has faced significant economic losses due to catastrophes, specifically floods. This study aims to assess the ripple effects of the 2022 floods, focusing on the Construction Industry, which has been gaining importance in the national economy, and on the resilience of economic sectors absorbing the negative shocks from floods. The study uses the Asian Development Bank's (ADB) Input-Output database for Pakistan using the Dynamic Inoperability Input-Output Model (DIIM) with the Fuzzy Full Consistency Method (F-FUCOM). The analysis revealed that the Construction Industry’s weak resilience resulted in a prolonged recovery period and that the lack of flood risk mitigation infrastructure was identified as the most critical. The results suggest that policymakers prioritise such infrastructures, like dams, early warning systems, and transportation infrastructure, to enhance the resilience of the overall economy.

Modeling the power system resilience in China under different natural disasters

A good understanding of the power system resilience is necessary for optimizing the investment strategies and supporting the emergency rescue, but the existing quantitative estimation results based on real outage events are still lacked due to the data limitations. Therefore, this study first establishes a unified framework to measure the power system resilience under different natural disasters, by integrating the electricity performance curve with the dynamic inoperability input-output model. Then, a database of 285 Chinese historical big power outage events caused by natural disasters is established, and the city-level power system resilience values are estimated. Finally, a benefit analysis is conducted for improving the power system resilience. Our major findings are that: (1) Electricity system recoveries quickest from hail (23.05 h), while restores slowest from snowstorm (117.31 h). (2) China's city electricity system is the most resilient to the thunderstorm, while is the least resilient to the earthquake. (3) Enhancing the power system resilience will significantly reduce the requirements for rescue resources, and the saved emergency rescue cost ranges from 0.57 million yuan to 12.08 million yuan with 1% reduction of initial inoperability.

Studying the effects of Türkiye earthquake disaster and its impact on real estate industry: A risk analysis based on input-output & non-linear optimization models

The real estate industry of Türkiye is highly susceptible to seismic activity. The recent earthquakes have severely impacted the industry and the country's economy. This study examines Türkiye's real estate resilience during earthquakes using the Dynamic Inoperability Input-Output Model (DIIM) and a non-linear optimization technique. According to the findings of DIIM, specific industries, including those dealing with the supply of electricity and gas, chemicals, non-metallic minerals, and financial and insurance intermediaries, suffer significant financial losses. The levels of inoperability are highest in the mining and quarrying industries, followed by the chemical and non-metallic mineral product industries, and then by mining support activities. The top nine most inoperable industries account for approximately $9.5 billion in economic losses, with real estate alone accounting for $27.4 billion. The Fuzzy-FUCOM technique was used to identify significant risk factors related to technological, political, and social hazards, assisting policymakers in making more informed decisions. The novelty of this study is in its application of the DIIM model and a non-linear optimization technique to the challenge of seismic hazard mitigation in Türkiye's real estate market. In conclusion, this study's findings provide insight into Türkiye's affected sectors' risks, recovery time, and adaptability.

A resilience analysis of the contraction of the accommodation and food service sector on the Scottish food industry

The Scottish economy, such as the United Kingdom (UK) economy, has been exposed to several adverse shocks over the past 5 years. Examples of these are the effect of the United Kingdom exiting the European Union (Brexit), the effects of the COVID-19 pandemic, and more recently Russia–Ukraine war, which can result in adverse direct and indirect economic losses across various sectors of the economy. These shocks disrupted the food and drink supply chains. The purpose of this article is 3-fold: (1) to explore the degree of resilience of the Scottish food and drink sector, (2) to estimate the effects on interconnected sectors of the economy, and (3) to estimate the economic losses, which is the financial value associated with the reduction in output. This article focuses on the impact that the sudden contraction that the “accommodation and food service activities”, resulting from the pandemic, had on the food and drink sectors. For this analysis, the study relied on the dynamic inoperability input–output model (DIIM), which takes into account the relationships across the different sectors of the Scottish economy over time. The results indicate that the accommodation and food service sector was the most affected by the COVID-19 pandemic lockdown contracting by approximately 60%. The DIIM shows that the disruption to this sector had a cascading effect on the remaining 17 sectors of the economy. The processed and preserved fish, fruits, and vegetable sector is the least resilient, while preserved meat and meat product sector is the most resilient to the final demand disruption in the accommodation and food service sector. The least economically affected sector was the other food product sector, while the other service sector had the highest economic loss. Although the soft drink sector had a slow recovery rate, economic losses were lower compared to the agricultural, fishery, and forestry sectors. From the policy perspective, stakeholders in the accommodation and food service sector should re-examine the sector and develop capacity against future pandemics. In addition, it is important for economic sectors to collaborate either vertically or horizontally by sharing information and risk to reduce the burden of future disruptions. Finally, the most vulnerable sectors of the economy, i.e., other service sectors should form a major part of government policy decision-making when planning against future pandemics.

A Demand-Side Inoperability Input–Output Model for Strategic Risk Management: Insight from the COVID-19 Outbreak in Shanghai, China

This paper proposes the dynamic inoperability input–output model (DIIM) to analyze the economic impact of COVID-19 in Shanghai in the first quarter of 2022. Based on the input–output model, the DIIM model introduces the sector elasticity coefficient, assesses the economic loss of the system and the influence of disturbances on other sectors through sectoral dependence, and simulates the inoperability and economic loss changes through time series. A multi-evaluation examination of the results reveals that the degree of inoperability of sub-sectors is inconsistent with the ranking of economic losses and that it is hard to quantify the impact of each sector directly. Different from the traditional DIIM model that only considers the negative part of the disaster, the innovation of this paper is that the negative value of the inoperability degree is used to measure the indirect positive growth of sectors under the impact of the Shanghai pandemic shock. At the same time, policymakers need to consider multi-objective optimization when making risk management decisions. This study uses surrogate worth trade-off to construct a multi-objective risk management framework to expand the DIIM model to enable policymakers to quantify the trade-off between economic benefit and investment costs when making risk management decisions.

Assessing Shock Propagation and Cascading Uncertainties Using the Input–Output Framework: Analysis of an Oil Refinery Accident in Singapore

The impacts of shock events frequently cascade beyond the primarily affected sector(s), through the interdependent economic system, and result in higher-order indirect losses in other sectors. This study employed the inoperability input–output model (IIM) and the dynamic IIM (DIIM) to model recovery of sectors after a shock event and quantify associated total losses. Considering data limitations and uncertainties regarding sectoral recovery time, a key variable in DIIM, a probabilistic approach is used for modelling uncertainty in recovery times. The event analyzed is the 2011 oil refinery fire accident in Pulau Bukom (PB) island, Singapore, which caused the refinery to shut down for 11 days and be partially operational for several days thereafter. The impacts are assessed using the regrouped 15-sector Singapore IO data of year 2010, with manufacturing sector as the directly affected sector. The initial economic impact of the PB refinery fire is assessed in the top-down framework using the refinery’s contribution to the manufacturing sector and nation’s GDP. The higher-order losses are quantified considering different recovery paths for the directly affected sector and accounting for its inventory. Simulation experiments using synthetic IO tables are also carried out to understand relationship between recovery characteristics of directly and indirectly affected sectors. The results from IIM analysis show that the indirect losses are about 35–38% of direct losses. The DIIM analysis reveal that the utilities sectors (e.g., electricity, water supply and treatment) suffer the largest inoperability among indirectly affected sectors for a given direct damage to the manufacturing sector. The results also illustrate the dependence of overall losses on the recovery path of the directly affected sector, and associated uncertainties in sectoral recovery times.

Assessing the economic ripple effects of critical infrastructure failures using the dynamic inoperability input-output model: a case study of the Taal Volcano eruption

ABSTRACT Critical infrastructure systems are essential in sustaining people’s livelihoods and the operation of economic sectors. In this paper, we extend the dynamic inoperability IO model (DIIM), we evaluate the resilience of economic sectors given the initial functionality loss and recovery time of an infrastructure. The resulting model is applied in a case study of the 2020 eruption of Taal Volcano in the Philippines. The initial inoperability and recovery period parameters are used in the 14-sector DIIM. The dynamic recovery behaviors of the sectors are plotted over the disaster timeline based on two metrics: (1) economic loss, which is the monetary value of the damage; and (2) inoperability, which is the dimensionless loss relative to the total production output of each sector. The DIIM template and case study results from this paper can provide policy insights to enhance disaster resilience planning for future disasters.

Measurement of Power Grid Resilience Based on a Dynamic Inoperability Input–Output Model

The increase in global natural disasters, emergencies, and terrorist attacks brings great challenges to the flexible operation of power grids. The research on the flexibility of power grids becomes more and more important. At the same time, the rapid development of renewable energy and smart grids also provides new opportunities for research of flexible power grids. Based on the dynamic inoperability input–output model (DIIM), this study creatively uses the node transmission power to represent the relationship between nodes in the power grid and then studies the resilience measurement of the power grid. First, the significance of studying power grid resilience is discussed, and the absorption and recovery capacity of power grid resilience is described. Second, the power grid resilience measurement model based on the DIIM is established, and a theoretical analysis is carried out. Using the characteristics of inoperability, the power grid resilience is measured from the time aspect, and the characteristics of the model are analyzed at the same time. On the basis of theoretical analysis, this study also provides a control strategy to improve the grid resilience, which has good convenience and applicability. Finally, the theoretical analysis method is applied to a power grid example, and the correctness and effectiveness are verified. The measurement model can intuitively display the resilience characteristics of power grids and provide theoretical support for management and emergency response.

Industrial water network vulnerability analysis using dynamic inoperability input-output model

Industrial parks provide opportunities for Process Integration among different enterprises. Inter-Plant Water Network Integration is an effective strategy for water conservation. However, increased interplant linkages can make the entire system vulnerable to cascading failures in case of loss of water flow in some plants. The potential indirect impact of water shortages on such integrated systems may not be evident without the use of appropriate models. This work defines inoperability as the fractional loss of water flow relative to normal operations. A comparison between the applicability of demand-driven versus supply-driven Inoperability Input-output Model (IIM) is conducted. Then, a Vulnerability Assessment Framework which integrates vulnerability indicators into the Dynamic Input-Output Model (DIIM) is developed to analyse failure propagation in water networks in an industrial park. The DIIM is then applied to simulate the cascading effects of disturbances. From a time perspective, the vulnerabilities of the industrial parks With Integrated Optimal Water Network (WWN) and Without Integrated Optimal Water Network (WOWN) are assessed considering robustness, adaptability, and recoverability as the indicators. The results indicate that supply-driven IIM is more suitable for cascading failure analysis of water networks. The average inoperability at 16% from supply-driven IIM is higher than that from demand-driven IIM. In the freshwater disturbance scenario, the dependence of the plant on freshwater is proportional to the rate of inoperability change, the time to reach a new equilibrium. In this study, the robustness of WWN is about fivefold that of WOWN, but the recovery rate is only one-sixth of the latter. This work can help identify system vulnerabilities and provide a scientific insight for the development of park-wide water management strategies.

The Economic Impact of Extreme Cyber Risk Scenarios

Numerous industry studies discuss the economic effects of potentially extreme cyber incidents, with considerable variation in the applied methodology and estimated costs. We implement a dynamic inoperability input–output model that allows a consistent analysis and comparison of the economic impacts resulting from six widely discussed cyber risk scenarios. Our model accounts for the frequently omitted qualitative context of the scenarios to be considered as part of the economic projection. Overall, our loss estimations remain in an insurable range from US$0.7 to 35 billion. To our knowledge, this is the first effort to develop a standardized evaluation framework that allows for a consistent assessment of cyber risk scenarios, thereby enabling comparability.

Assessing the business interruption costs from power outages in China

With the increasing frequency of extreme weather events, cyber attacks and natural disasters, power system reliability is facing unprecedented challenges. To contribute to a more targeted electricity reliability policy in China, this study develops a Dynamic Inoperability Input-output Model to assess the business interruption costs (BICs) from a provincial extremely big electricity outage event. The time-varying inoperability is first simulated for different sectors over the recovery period with consideration of the sectoral interdependencies. Then, the BICs are estimated for different sectors and the most vulnerable sectors to power outages are identified. At last, the impacts of four influencing factors on the estimated BICs are explored in the sensitivity analysis section. Our major findings are that: (1) The total BIC of an outage event is about 1.44 billion yuan, and the first 24 h of the recovery period account for about 70% of the total BICs. (2) 2% of a sector's inoperability caused by power outages will, on average, be transmitted to other sectors due to their interdependencies. (3) The chemical sector has the biggest economic losses from power outages, while water supply sector has the largest peak inoperability from power outages.

Vulnerability Analysis for Park-Wide Water Management Using Dynamic Inoperability Input-Output Model

Park-wide water management is an important way to conserve water resources. However, the enhancement of inter-plant connection will weaken the system stability and strengthen the cascade effect of disaster losses. In this work, a dynamic inoperability input-output model is applied to park-wide water network to predict the impact of disturbances. The inoperability is expressed as the degree of water loss. The dependence of different water networks on freshwater is presented. This work analyzes the impact of freshwater supply disturbance on the water volume. By simulating the time-varying trajectory of inoperability, the indicators (i.e., resistance to disturbance, adaptability, and recoverability) are determined to evaluate the vulnerability of the water network in each scenario. The results show that under the same freshwater disturbance, water networks that are less dependent on freshwater are more robust but weaker in recoverability. In the case of freshwater disturbance, the possibility of disruptions in the participating plants of the without integrated optimization water network is about 1.5 times that of the integrated optimization water network.

Development of an Input-Output Model Considering Simultaneous Effect of Risks in Infrastructure under Dynamic Conditions

In this paper, a multi-objective model is proposed based on Haimes and Jiang's Input-Output model [1], which was developed to examine the impact of each infrastructure's risks on the performance other infrastructures in an interconnected system. Also, the simultaneous effect of risks on the model was investigated. Additionally, to reduce the harmful effects of risks, risk response strategies and associated costs were identified for each risk. The proposed model selects the best response strategies, which minimizes the inoperability of any infrastructure considering the limited budget. Thus, the comparison between the static input-output inoperability model (IIM) and dynamic inoperability input-output model (DIIM) was made for each infrastructure's risks and associated risk responses considering the simultaneous effect of risks. In the first case, 32 out of the 48 selected risk responses have been eliminated the effect of the relevant risk, whereas in the second case only 9 risk responses had this capability. Therefore, the simultaneous occurrence of risks leads to a decrease in the performance of the infrastructure as well as an increase in the inoperability of the infrastructure, and as a result, the efficiency of the responses decreases.

Economic impact of electricity supply interruptions in South Africa

In past studies on the costs of interruptions of electricity supply in South Africa, the concepts of sporadic and chronic interruptions were introduced. Cost was modelled using different parameters, and the measurement of customer interruption cost (CIC) was acquired through surveys. Rotational load shedding, as experienced during more than a decade in South Africa, shares many characteristics with chronic interruptions and large system collapse scenarios. Since CIC data are based on electricity customers' valuations of their impacts from electricity interruptions, and allow only for a bottom-up estimation of the economic cost of large electricity interruption events (without considering sectoral interdependencies), an alternative assessment of the cost using a suitable macroeconomic model that employs less subjective data allows for a validation of CIC-based results and a determination of a plausible range of estimates of the cost of large electricity interruptions. We test this proposition using a combination of a time-dependent probabilistic CIC model based on CIC survey data, and a dynamic inoperability input-output model (DIIM) that accounts for sectoral interdependencies, economic resilience and the temporal variation of electricity interruption impacts. The results lead to estimates of the costs of large interruptions of electricity supply in South Africa.

Dynamic inoperability input-output modeling for economic losses estimation in industries during flooding

Among all natural disasters, flood stands as a recurrently happening disaster. It holds the aptitude to disrupt the organizations and to cause absenteeism of the workforce in industries. As the workforce is directly involve in the functioning of industries, work force absenteeism can cause reduced production and inoperability which outcomes in financial losses of industrial sectors. This research objects to estimate inoperability of industries due to distraction of workers by incorporating Dynamic Inoperability Input-Output Model (DIIM). Economic losses are determined from inoperability. Industrial area which is selected for the research includes local industries in Peshawar, Khyber-Pakhtunkhwa, Pakistan. Various industries are chosen and are ordered according to inoperability and economic losses. Industries having highest financial damages are: (i) Agriculture; (ii) Sugar mills; and (iii) Marble industry. These three industries hold liable for 40.6% of the overall financial losses of fifteen industries. Industries suffering from highest inoperability include (i) Sugar mills; (ii) Agriculture and (iii) Marble industry. A risk analysis frame work has also been developed to help industrial sectors to recover after a disaster. Besides, data of three different floods has also been taken for the above mentioned critical sectors to plot probability distributions for predicting economic losses of most frequent floods. Furthermore, this research methodology has been applied to flooding but it can be applied to any other disaster, everywhere.

A Two-Stage Restoration Resource Allocation Model for Enhancing the Resilience of Interdependent Infrastructure Systems

Infrastructure systems play a critical role in delivering essential services that are important to the economy and welfare of society. To enhance the resilience of infrastructure systems after a large-scale disruptive event, determining where and when to invest restoration resources is a challenge for decision makers. Comprehensively considering the recovery time of infrastructure systems and the overall losses resulting from a disaster, this study proposes a two-stage restoration resource allocation model for enhancing the resilience of interdependent infrastructure systems. First, to evaluate the effect of resource allocation during the recovery process, dynamic resilience is selected as the criterion for the recovery of infrastructure systems. Second, taking into consideration the decision makers’ point of view, a two-stage resource allocation model is proposed. The objective of the first stage is to quickly recover the infrastructure systems’ dynamic resilience to meet the basic needs of the users. The second stage is aimed at minimizing the overall losses in the following recovery process. The effects of infrastructure interdependencies on resource allocation are incorporated in the model using the dynamic inoperability input–output model. Through a case study, the proposed approach is compared with other resource allocation strategies. The results show that: (1) the restoration resource allocation strategy obtained from the proposed approach balances the recovery time and the overall losses to infrastructure systems; and (2) the value of the usage cost of the unit restoration resource has a significant impact on the recovery time and the overall losses under different strategies. The proposed model is both effective and efficient in solving the post-disaster resource allocation problem and can provide decision makers with scientific decision support.

Analysis of Vulnerability and Recoverability of the Energy System in China Based On Inoperability Input-Output Model and a Dynamic Extension

In this paper, the Inoperability Input-Output Model (IIM) is used to simulate the degree of functional decline of the energy sectors under the perturbation of demand and supply. The Dynamic Inoperability Input-Output Model (DIIM) is used to simulate the time-varying operation state of the energy sectors. The paper then performs some case study that utilizes accounts from 2012 Input-Output Tables of China. The vulnerability and recoverability of energy system are analyzed by using relevant indicators in order to manage and develop the energy system.

Simulating the impact of inventory on supply chain resilience with an algorithmic process based on the supply-side dynamic inoperability input–output model

ABSTRACTThe interdependent nature of highly interconnected systems, such as supply chains, renders them incapable of quickly resuming operation following a disruptive event. In the context of supply chains, previous works have demonstrated the efficacy of inventory as a mitigation strategy to delay the impact of disruptive events. However, current methods tend to focus only on a demand-driven approach, which may be inappropriate for some systems where a value-added price perturbation represents an impact. Thus, this paper offers a methodology for understanding the role of inventory in the supply-side dynamic inoperability input–output model (DIIM). To demonstrate the applicability of the proposed model, an illustrative example is presented using data from the 2012 Philippines Input–Output (I-O) Table in five different scenarios. Given a predefined planning horizon, the results of this work can serve as guidelines for decision-makers for the provision of inventory that will keep the overall inoperability of an entire supply chain at a minimum.

Analysing economic impact on interdependent infrastructure after flood: Pakistan a case in point

ABSTRACTA disaster such as floods can have a drastic impact on interdependent infrastructure and economic sectors. The resilience or the ability of the critical sector to recover quickly from the disruption can also reduce the consequences of the disaster. In this paper, through resilience and recovery time Dynamic Inoperability Input-Output model (DIIM) is applied. Thus, Input-Output (I-O) table is constructed for Pakistan's economic system and a case study is performed on the flooding in Pakistan 2011–12. The purpose of this study is to provide a ballpark estimate of the system-wide impact and ripple effect on the sectors that lasted for several days after the disruption. Furthermore, to analyze the inoperability and economic loss in the sectors caused by the disaster in a developing country. The findings of the research show that most of the critical sectors are associated with agriculture and service sector in terms of inoperability and economic loss respectively. The outcome of the study will be essential for the policy makers, disaster management authorities and health departments to respond accordingly.

Comparing the Economic Impact of Natural Disasters Generated by Different Input-Output Models: An Application to the 2007 Chehalis River Flood (WA).

Due to the concentration of assets in disaster-prone zones, changes in risk landscape and in the intensity of natural events, property losses have increased considerably in recent decades. While measuring these stock damages is common practice in the literature, the assessment of economic ripple effects due to business interruption is still limited and available estimates tend to vary significantly across models. This article focuses on the most popular single-region input-output models for disaster impact evaluation. It starts with the traditional Leontief model and then compares its assumptions and results with more complex methodologies (rebalancing algorithms, the sequential interindustry model, the dynamic inoperability input-output model, and its inventory counterpart). While the estimated losses vary across models, all the figures are based on the same event, the 2007 Chehalis River flood that impacted three rural counties in Washington State. Given that the large majority of floods take place in rural areas, this article gives the practitioner a thorough review of how future events can be assessed and guidance on model selection.