Inoperability Input-output Model Research Articles

The macroeconomic impact of a dengue outbreak: Case studies from Thailand and Brazil.

Dengue is spreading in (sub)tropical areas, and half of the global population is at risk. The macroeconomic impact of dengue extends beyond healthcare costs. This study evaluated the impact of dengue on gross domestic product (GDP) based on approaches tailored to two dengue-endemic countries, Thailand and Brazil, from the tourism and workforce perspectives, respectively. Because the tourism industry is a critical economic sector for Thailand, lost tourism revenues were estimated to analyze the impact of a dengue outbreak. An input-output model estimated that the direct effects (on international tourism) and indirect effects (on suppliers) of dengue on tourism reduced overall GDP by 1.43 billion US dollars (USD) (0.26%) in the outbreak year 2019. The induced effect (reduced employee income/spending) reduced Thailand's GDP by 375 million USD (0.07%). Overall, lost tourism revenues reduced Thailand's GDP by an estimated 1.81 billion USD (0.33%) in 2019 (3% of annual tourism revenue). An inoperability input-output model was used to analyze the effect of workforce absenteeism on GDP due to a dengue outbreak in Brazil. This model calculates the number of lost workdays associated with ambulatory and hospitalized dengue. Input was collected from state-level epidemiological and economic data for 2019. An estimated 22.4 million workdays were lost in the employed population; 39% associated with the informal sector. Lost workdays due to dengue reduced Brazil's GDP by 876 million USD (0.05%). The economic costs of dengue outbreaks far surpass the direct medical costs. Dengue reduces overall GDP and inflicts national economic losses. With a high proportion of the population lacking formal employment in both countries and low income being a barrier to seeking care, dengue also poses an equity challenge. A combination of public health measures, like vector control and vaccination, against dengue is recommended to mitigate the broader economic impact of dengue.

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

ABSTRACT 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.

Integrating household survey with inoperability input-output model of critical infrastructure systems: A case study of Hurricane Sandy.

Critical infrastructures are ubiquitous and their interdependencies have become more complex leading to their uncertain behaviors in the aftermath of disasters. The article develops an integrated economic input-output model that incorporates household-level survey data from Hurricane Sandy, which made its landfall in 2012. In this survey, 427 respondents who were living in the state of New Jersey during Hurricane Sandy were used in the study. The integration of their responses allowed us to show the probability and duration of various types of critical infrastructure failures due to a catastrophic hurricane event and estimate the economic losses across different sectors. The percentage of disruption and recovery period for various infrastructure systems were extracted from the survey, which were then utilized in the economic input-output model comprising of 71 economic sectors. Sectors were then ranked according to: (i) inoperability, the percentage in which a sector is disrupted relative to its ideal level, and (ii) economic loss, the monetary worth of business interruption caused by the disaster. With the combined infrastructure disruptions in the state of New Jersey, the model estimated an economic loss of $36 billion, which is consistent with published estimates. Results from this article can provide insights for future disaster preparedness and resilience planning.

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.

A decision-making model for efficient fair electricity rationing under major power outrage emergencies

Major power outages emergencies (MPOEs) are increasingly occurring with greater frequency and wreaking havoc, necessitating the effective decision-making for electricity rationing to mitigate economic losses incurred and maintain social stability. To address this issue, this paper proposes an efficient fair electricity rationing decision-making model under MPOEs, including two methods to quantify the efficiency and fairness of the electricity rationing. Firstly, the inoperability inputoutput model is employed to quantify the efficiency by assessing the business interruption costs caused by MPOEs. Secondly, the fairness is quantified by the fairness perception of the affected regions, which consider their bounded rational comparisons based on the Prospect theory. Then, the NSGA-II is utilized to solve the model. Finally, Sichuan MPOE in 2022 is designed as a numerical experiment to validate the proposed model, accompanied by corresponding discussions to demonstrate the impact of “supercities” on electricity rationing and the significance of the balance between efficiency and fairness.

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.

The impact of Japanese nuclear wastewater discharge into the sea on the global economy: Input-output model approach

As the division of work within the world economic system becomes increasingly complex, the impact of disturbing events on the economic system is expanding. Recently, Japan proposed to discharge nuclear wastewater into the Pacific Ocean, which will cause damage to marine fisheries, thereby seriously affecting fisheries and other industries in Japan and other countries and regions around the world. Considering different scenarios of final and intermediate demand shifting, this paper uses the Inoperability Input-Output Model (IIM) and Multi-Region Input-Output Model (MRIO) to simulate the economic consequences of nuclear wastewater discharge in Japan and calculate the economic changes of each industry and country (region). The results show that: In the short term, when only the final demand for Japanese fishery products decreases. (1) The ten countries (regions) with significant economic losses are Japan, the United States, Chinese Taipei, Canada, Chile, South Africa, Mexico, Peru, the United Kingdom, and Ireland. (2) The ten countries (regions) with a significant increase in total output due to demand shift are China (People's Republic of), the Rest of the World, India, Indonesia, Viet Nam, the Philippines, Brazil, Myanmar, the Russian Federation, and Malaysia. (3) A ranking of changes in the total output of different industries. In the long term, when both intermediate and final demand for Japanese fishery products decrease. (4) The change in value added in Japan. (5) The change in value added of 67 countries (regions) worldwide. The ten countries (regions) with the most significant increase in value-added are the Russian Federation, China (People's Republic of), the Rest of the World, the United States, Indonesia, Australia, Norway, Korea, Viet Nam, and Myanmar. The ten countries (regions) with the most significant decrease in value-added are Japan, Chinese Taipei, Chile, South Africa, Peru, Thailand, Mexico, Cambodia, Costa Rica, and Morocco. Changes in value added of 45 industrial sectors worldwide.

Influence of international trade disputes on the world industrial economic system based on inoperability input-output model

Since January 2018, the U.S. government has announced steep tariffs on a range of imports from China who then retaliated, thus triggering trade disputes. How much economic losses have the international trade disputes brought to the related parties? Which countries and industries are highly sensitive to the trade disputes? These are all important questions that have undoubtedly aroused mass concern of all circles. However, few scholars have carried out related empirical research on this issue, with little dynamic quantitative evaluation results on industry and country level obtained. Based on the static and dynamic inoperability input-output models and on the premise of several assumptions, the industrial economic system's indirect economic losses resulting from the trade disputes between China and the United States are evaluated according to the EU input-output table (WIOD2016) from January 1st to April 4th, 2018. The results show that the trade disputes severely affected the trading among China, the United States, and other countries (regions) in the world and brought about greater economic loss. Meanwhile, the countries and industries highly sensitive to the disputes are picked out. This paper provides an empirical reference for governments, industry management departments and related companies concerned with international trade disputes in assessing losses of similar events.

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.

A Model for Optimal Economic Lockdown to Contain Epidemic

This paper is concerned with the application of operations research in defining the optimal lockdown of economic activities to contain epidemic. The problem of optimal lockdown consists in deciding as best economic sectors can be lockdown with respect to fundamental sectors (essential goods and services) while disruptive impacts are minimized on the economy as a whole. Many countries around the world are currently implementing the lockdown of most economic activities to contain the spread of the Covid-19 pandemic. The lockdown brings health benefits for the society as it contains the spread of the virus, reducing the number of infections and allowing the health system to treat those infected better. This paper describes a Boolean linear programming model to deal with the problem of selecting several economic sectors to be shutdown. The objective function is linear and the constraints are linear inequalities related to the Leontief’s input-output table. The model permits to analyze the feasibility of national economic system in which some elements of the input-output table are set equal to zero. The mathematical approach to the shutdown problem permits to identify the greatest number of economic sectors that can be closed without destroying the fundamental sectors. Since solution of the shutdown problem is the greatest number of lockdown economic sectors, author believes the model allows to oppose effectively the spread of virus. Once spread of the virus decreases, another feature of the model is to support decision makers in assigning priorities to the economic sectors to be gradually unlocked. Standard input-output models are able to reveal how different sectors of an economy are interconnected and how changes in one sector affect all other sectors. Besides the use of the input-output data for descriptive analyses of economic interrelations, this table also provides the empirical fundament for a wide scale of impact analysis. input- output models greatly differ in size and possible applications. Simple static input-output models are used for comparative-static impact (scenario) analysis. With the help of input-output quantity models, statements can be made about direct and indirect effects based on exogenous changes in demand. More complex dynamic input-output models largely resolve the limitations and inherent assumptions of static input-output models. Time is considered explicitly; quantity and price reactions are modelled endogenously in a holistic approach and feedback effects are captured. Last models are not only suitable for scenario analysis and for forecasting as well. In “A Linear Programming Solution to Dynamic Leontief type Models” Harvey M. Wagner presents a general dynamic model of an economy and investigates a number of questions related to the feasibility of certain time profiles of demand and the rate of substitution between economic activities. The Inoperability Input-Output Model [1-4]. was developed to understand better the infrastructure interdependencies. Based on the Leontief Input- Output Model, the model is demand-driven, wherein perturbations to the final demand levels are considered the initiating event, and the impact to sectors’ production outputs are the direct and indirect effects resulting from sector interdependencies. Case study shows that the different perturbation classes (demand or supply, quantity or price) yield different rank orders of sectors impacted by the initiating event, thus providing various perspectives of impacts. The present study differs from most previous studies in two aspects. First, linear programming techniques have been widely used in Input-Output model: from the primary and dual formulation of the problem to the analysis of Lagrange multipliers in order to assess the mutual relative importance of sectors. Second, the impact of lockdown is measured in terms of annihilated interdependencies rather than in terms of canceled economic values such as revenues and monetary exchanges. For this reason, the proposed model takes into account the adjacency matrix got from input-output Table rather than the Table itself. This analysis is static in character (it is a static I-O model) because it does not take care explicitly of time, quantity and price reactions, consumption and production lags, growth of final demand (these ones are known as dynamic I-O models). The study simply considers the interruption of several activity as a consequence of a government measure adopted to reduce the risk of contagion. The proposed model answers to the basic question if the annihilation of N sectors is feasible, secondary the model identifies in the Lagrange multipliers the means to sequencing the sectors to be reopened.

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.

A Study on the Decay Model of Multi-Block Taxi Travel Demand under the Influence of Major Urban Public Health Events.

A sudden major public health event is likely to have a negative impact on public transport travel for residents, with public travel modes such as the metro and conventional buses experiencing varying degrees of decline in patronage. As a complement to public transport, taxi travel will suffer the same impact. Land use and population density among various functional blocks in a city are different, and therefore their changing rates in taxi travel demand are varied. This paper reveals the taxi travel demand correlations between urban blocks and then constructs a taxi travel demand decay model based on the Dynamic Input-Output Inoperability Model (DIIM) to simulate the decay degree of taxi travel demand in each block. When a major public health event occurs, the residential panic levels in different functional blocks may vary. It results in variable changing speeds of residential travel demand in each block. Based on this assumption, we use the intensity of travel demand as a correlation strength factor between blocks, and equate it with the technical coefficient in the DIIM model. We also define other variables to serve in model construction. These variables include the decay degree of travel demand intensity, residential travel willingness, coefficient of travel demand decay, derivative coefficient of travel demand interdependency, and demand perturbation coefficient. Lastly, we select a central area of Ningbo as the study area, and use taxi travel data in Ningbo during the COVID-19 pandemic of 2020 as input, simulate taxi travel demand dynamics, and analyze the accuracy and sensitivity of the model parameters. The relative errors between the five types of blocks and the actual decay of travel demand intensity are 8.3%, 3.8%, 8.7%, 5.5%, and 5.3%, respectively, which can basically match the actual situation, proving the validity of the model. The results of the study reveal the pattern of taxi travel demand decay among various blocks after major public health events. It provides methodological reference for decision makers to understand the development trend of multi-block taxi travel demand, so as to help form effective emergency plans for different blocks.

Developing operational resilience within CO2 utilisation networks: Towards ensuring business continuity through risk management

This study considers the business continuity management system (BCMS) through the development of operational risk management within networks. The objective is to assess the resilience level of a proposed CO2 network by evaluating its readiness and capacity to withstand disruptions that can trigger major production and economic losses. Through the incorporation of a resilience index within risk management, this study identifies the critical components, and evaluates network robustness in terms of its ability to withstand disruptions in terms of recovery time. Moreover, the network interdependency between the elements is evaluated by applying the inoperability input–output model (IIM), and dynamic IIM to calculate the recovery behavior of the inoperable components. The outcomes determine that the L13 and QAFCOH2 are the most important factors, while the resilience index is 0.42. Therefore, to enhance resilience index, a CO2 storage is to be located within node 6 serving more than 60% of the sinks.

Modeling economic losses and greenhouse gas emissions reduction during the COVID-19 pandemic: Past, present, and future scenarios for Italy

Unprecedented nationwide lockdowns were adopted because of the COVID-19 pandemic. Understanding the socioeconomic impact of the past and future restrictions while assessing the resilience of a local economy emerged as a worldwide necessity. To predict the economic and environmental effects of the lockdowns, we propose a methodology based on the well-established input–output inoperability model, using Italy as a case study. By reconstructing the 2020 restrictions, we analyzed the economic losses and greenhouse gas emissions reductions, identifying the most economically impacted sectors because of the restrictions and the sectoral interdependencies and those avoiding most air emissions. We constructed four partial-lockdown scenarios by minimizing the economic losses for increasing restrictions to highlight the model's utility as a tool for policymaking. By revealing the most interconnected and, thus, crucial sectors, the simulated scenarios showcase how the restrictions can be selected to avoid sudden and unpredicted economic damage.

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.