The economic costs of natural disasters, terrorist attacks, and other calamities: An analysis of economic models that quantify the losses caused by disruptions

Over the past decade, numerous studies have estimated the economic impacts of a variety of disruptions. Most of these studies are based on macroeconomic models that quantify the direct and indirect economic losses from a disruption. Direct economic losses occur due to damaged facilities or when consumers change their purchasing behavior because of the disruption. Indirect economic losses occur when directly impacted businesses consequently reduce their orders to their suppliers. Indirect economic losses are often larger than direct economic losses. This paper compiles the results from these economic models in order to compare the costs of different disruptions and help decision makers prioritize among disruptions. We compare the direct and indirect economic losses from a variety of disruptions, including earthquakes, hurricanes, terrorist attacks, pandemic diseases, and port closures. Some studies model hypothetical scenarios, but other studies quantify the economic losses from historical events such as the September 11 attacks and the 2011 Japanese tsunami. This paper provides a useful benchmark to understand the consequences from disruptions and highlight areas that public officials could address in planning for future disruptions

Estimating production losses from disruptions based on stock market returns: Applications to 9/11 attacks, the Deepwater Horizon oil spill, and Hurricane Sandy

The threats to human life and infrastructure are ever growing due to global terrorism, conflicts and climate change as well as the omnipresent threat of natural disruptions like earthquakes, volcanos, tsunamis etc. Disruptions or disasters lead to sudden changes in demand, production and supply. In case of such scenarios it is essential to optimize the utilization of available resources and avoid further wastage. In this study a model is presented to measure the changes in production due to changes in supply and demand of goods and services, and measure possible losses to industries during such disruptions. It is anticipated that there is a strong economic correlation of growth among the industries and there is a ripple effect causing losses to interdependent industries and economies in such scenarios. It is believed that, variability in the economy is preceded by stock market price fluctuations. The trend of any economy is reflected in the stock markets that it encompasses and these markets provide instantaneous feedback to changes in a state of normalcy. These stock markets have been used to study the variability in economic output of industries, and measure the dynamic changes in production or output of industries. The results of the study justify the existence of such a correlation between the gross output of industries and the stock indices that are related to these industries. Study of past disruptions is performed through a deterministic model and a stochastic model and the results obtained resonate with the existing estimates published by studies measuring the economic impacts of these disruptions. Such a study would enable governments, corporations and individual businesses to make informed decisions regarding the allocation of resources and contingency plans in case of such a disruption. The risk of monetary and market losses can be substantially reduced thus enabling faster recovery and higher resilience

Infrastructure Network Resilience and Economic Impacts: Applications in Multi-Modal Freight Transportation

The US has defined a number of critical infrastructures, the disruption of which “would have a debilitating impact on security, national economic security, national public health or safety, or any combination of those matters”. Among these critical infrastructures are transportation networks, which enable the flow of people and commodities, and recent reports suggest that many highways, bridges, and other transit assets in the US fall short of a state of good repair, potentially threatening the efficiency of the network. In 2013, 55 million tons of goods valued at more than $49.3 billion traversed the US freight transportation system each day, and freight tonnage and monetary value rose by 6.3 and 8.0 percent, respectively, over 2007 levels. Over the next 30 years, transportation’s contribution to the US gross domestic product is expected to grow to approximately$1.6 trillion. Given the potential for disruption by malevolent attacks, natural disasters, human-made accidents, or common failures, recent US planning documents focus on the criticality of transportation network preparedness. Emphasis has been placed on “securing and managing flows of people and goods” along transportation networks. The consequences of disruptions to critical infrastructures highlight the need to better understand resilience, or the ability to withstand the effects of and recover timely from a disruption. Particularly for critical infrastructures, the Infrastructure Security Partnership (2011) noted that a resilient infrastructure sector would “prepare for, prevent, protect against, respond or mitigate any anticipated or unexpected significant threat or event” and “rapidly recover and reconstitute critical assets, operations, and services with minimum damage and disruption.” As with any other critical infrastructure, resilience planning is important for multi-modal transportation networks due to their role in the economic vitality of states, regions, and the broader country. The functionality of this network is threatened by disruptive events that can disable the capacity of the network to enable flows of commodities in portions of nodes and links. This research creates a new paradigm with which to improve decision making for freight transportation network sustainment through an integrated duple of resilience and interdependent economic impact. Integrating a multi-commodity network flow formulation with an economic interdependency model, driven by publicly available data from Bureau of Economic Analysis and U.S. Department of Transportation, I have proposed a framework to quantify the multi-regional, multi-industry impacts of a disruption in the transportation network which has led to (i) defining a new measure of network component importance, (ii) planning for adaptive capacity through contingent rerouting, (iii) investing for absorptive capacity, and (iv) guiding network recovery and resilience. This work has been applied a multimodal freight transportation network in Oklahoma that connects the state to several regional trading states, enabling the flow of six important commodities that have interdependent effects on the Oklahoma economy (classified into 62 industry sectors)

Modelling the Economic Impacts of Compound Hazards through the Production Supply Chain in the Post-pandemic World.

Climate change and fast urbanization are increasing the likelihood of compound hazards - events where multiple drivers and/or hazards interact with multiplicatively destructive environmental and socio-economic consequences. This includes increases in the frequency of not only concurrent natural extremes (heatwaves and droughts, storm surges and extreme rainfalls, etc.), but also collisions between natural and manmade disasters (air pollution, infectious disease transmission, trade wars, etc.) particularly in the post-pandemic world. Entanglement of different hazardous factors increases the complexity of impact accounting and risk management and requires an integrated solution to tackle the vulnerabilities of human societies towards compound risks. However, most of the research in disaster analysis investigates one hazard at a time. Only a few emerging perspectives have noticed or warned the potential of compound hazards, but they are still far from capacity building for the compound resilience to future crises. This PhD thesis presents a full set of methodology to systematically assess the economic impacts from single to compound hazards. The concept of ‘disaster footprint’ is used here to capture the direct and indirect impacts rippling through the economic supply chain during a single or compound disaster event. A four-stage research framework is proposed. It starts from the direct disaster footprint assessment, which links physical characteristics of hazards with property damage or health impairment by simulating hazard-specific exposure-damage functions. The direct footprint is then fed into an input-output-based (IO-based) hybrid economic model to calculate the indirect disaster footprint that propagates through intersectoral and interregional connections to wider economic systems. The improved IO-based disaster footprint model is built here for single hazard analysis, with innovations regarding inventory adjustment and cross-regional substitutability. Third, within the same disaster footprint framework, the economic interplays between diverse types of hazards are synthesized into the impact assessment, and thereby a Compound Hazard Economic Footprint Assessment (CHEFA) model is developed for compound events. Finally, favourable response and recovery plans, which are aimed to mitigate the total disaster footprint, are suggested by comparing the modelling results under wide ranging scenarios and identifying crucial influencing factors through sensitivity analysis. A major contribution of this thesis is that it takes the first step in the field of disaster analysis to integrate multiple hazardous factors within a macro-economic impact assessment framework that accounts for both direct and indirect disaster footprint into sectoral and regional details. The proposed modelling framework is first applied to three types of hazards (i.e., heat stress, air pollution and climate extremes) on the provincial and national scales in China to demonstrate its flexibility for a wide range of disaster risks. The total economic costs of heat stress, air pollution and climate extreme events in China have increased from US$207.9 billion (1.79$317.1 billion (2.16% of GDP) in 2020. Despite the decreasing economic costs of air pollution and climate extreme events, the economic costs from heat-related health impacts have continued the concerning growing trend. Among the three types of hazards, the economic costs of heat stress were the biggest and accounted for over 70% of the total costs. Heat stress affects the economy mainly by reducing labour productivity. For each unit of direct costs, heat stress was also inclined to cause more indirect supply chain costs than air pollution and climate extremes. Most of the heat-induced direct costs occurred outdoors in the agriculture and construction sectors, while most of the heat-induced indirect costs happened indoors in the manufacture and service sectors. At the regional level, hotspot provinces with prominent economic risks from these hazards have been identified for China. Southern provinces were more economically vulnerable to heat stress than northern provinces, while northern provinces tended to suffer larger economic costs from air pollution than southern provinces. By contrast, the economic impacts of climate extreme events were more spatially distributed in China than the other two types of hazards. Location-specific economic impacts of climate change require location-specific responses, including enhancing inter-departmental cooperation, strengthening climate emergency preparedness, supporting scientific research, raising public awareness, and promoting climate change mitigation and adaptation. Economic implications of climate change are also evaluated with a focus on future flood risks in six developing countries (i.e., Brazil, China, India, Egypt, Ethiopia and Ghana) around the end of 21st century (2086-2115). A physical model cascade of climate-hydrological-flood models is linked with the disaster footprint economic model through a set of country and sector specific depth-damage functions. The total (direct and indirect) economic losses of fluvial flooding are projected for each country, with or without socio-economic development, under a range of warming levels from <1.5°C to 4°C. As a share of national GDP, Egypt suffers the largest flood-induced losses under both climate change (CC) and climate change plus socio-economic development (CC+SE) experiments, reaching 2.3% and 3.0% of GDP under 4°C warming. Climate change acts as a driving factor that increases the flood losses in all countries, but the effect of socio-economic development differs among the countries and warming levels. For Ethiopia and China, future flood losses as a proportion of GDP under different warmings decline from the baseline levels when socio-economic development is modelled, suggesting a more resilient economic growth that helps reduce future flood risks. However, for Brazil, Ghana, and India, while losses as a proportion of GDP initially decline at lower warming levels, increases are seen from 2.5°C or 3°C warming onwards, suggesting a tipping point where increasing flood risk outweighs any relative benefits of socio-economic development. These results highlight the importance of including socio-economic development when estimating future flood losses, essential to provide a more comprehensive picture of potential losses that will be important for decision makers. With the development of the CHEFA model, the economic interaction between concurrent hazardous factors comes into analysis. A hypothetical perfect storm consisting of floods, pandemic control, and trade restrictions (as a proxy for deglobalization) is assumed to test the applicability and robustness of the model. The model also considers simultaneously cross-regional substitution and production specialization, which can influence the resilience of the economy to multiple shocks. Scenarios are first designed to investigate economic impacts when a flood and a pandemic lockdown collide and how these are affected by the timing, duration, intensity/strictness of each event. The results reveal that a global pandemic control aggravates the flood impacts by hampering the post-flood capital reconstruction, but a flood exacerbates the pandemic impacts only when the flood damage is large enough to exceed the stimulus effect of the flood-related reconstruction. Generally, an immediate, stricter but shorter pandemic control policy would help to reduce the economic costs inflicted by a perfect storm. The study then examines how export restrictions and retaliatory countermeasures during the pandemic and floods influence the economic consequences and recovery, especially when there is specialization of production of key sectors. It finds that the trade restriction of a region to ‘protect’ its product that can be substituted by the same product made elsewhere, while hampering the global recovery, may alleviate the region’s own loss during the compound disasters if the increasing domestic demand exceeds the negative impacts of falling exports. By comparison, the trade restriction on a non-substitutable product has greater negative impacts on the global recovery, which ultimately propagates backward to the region through the supply chain and exacerbates its own loss. The results also indicate that the potential retaliation from another region and sector would further deteriorate the global recovery and make everyone lose, with the region which initiates the trade war losing even more when the retaliatory restriction is also imposed on a non-substitutable product. Therefore, regional or global cooperation is needed to address the spillover effects of such compound events, especially in the context of the risks from deglobalization. The CHEFA model has been then successfully applied to a real compound event of the 2021 extreme floods and a COVID-19 wave in Zhengzhou, the capital city of Henan province in China. The event was rare in history and has caused enormous economic consequences (direct damage worthy of 66,603 million yuan and indirect losses worthy of 44,340 million yuan) to the city, reaching a total of 10.28% of its GDP during the previous year. The negative impacts also spilled over to the whole nation through the production supply chain, making the total economic losses amount to 131,714 million yuan (0.13% of China’s GDP in the previous year). The local lockdown to control the spread of COVID-19 has increased the indirect losses by 77% and the indirect/direct loss ratio from 0.55 to 0.98. While a majority (29%) of direct losses happened in Zhengzhou’s real estate industry, the indirect losses were more distributed in Zhengzhou’s non-metallic mineral products (13%), food and tobacco (10%), and transportation services (10%). Zhengzhou’s non-metallic mineral sector is also a critical sector with strong propagation effects. The reduction in its production has triggered a supply chain loss of 10,537 million yuan in terms of trades with other sectors and regions, which nearly doubled its value-added loss. In regions outside Zhengzhou, the agriculture, mining, petroleum and coking, chemical products, accommodation and restaurants, and financial services were the sectors significantly affected by this compound event. Among them, the agriculture in Henan (outside Zhengzhou) suffered the greatest indirect (or value-added) loss at 2,760 million yuan. The study also finds that the post-disaster economic resilience is most sensitive to factors such as road recovery rate, reconstruction efficiency and consumption subsidies, and the COVID-19 control tends to reduce the marginal economic benefits of flood emergency efforts. As low-likelihood compound extreme events become more frequent with global warming, concerted actions are in urgent need to address the intricate dilemma between disaster relief, disease control and economic growth at both individual and institutional levels. Overall, this PhD study develops an integrated assessment framework for the direct and indirect economic impacts from single to compound hazardous events. Within this framework, consistent and comparable loss metrics are elicited for different types of hazards, either single or compound ones, advancing the understanding of their economic risk transmission channels through the production supply chain. Knowing the economic complexity intrinsic to the disaster mixes will foster a sustainable risk management strategy that balances different emergency needs at the minimal economic costs, and guide investment to risk preparedness against the growing threats under climate change. In addition, collaborative efforts are required from the local to global levels to enhance the economic resilience towards future crises in complex situations. This is crucial to achieve the mitigation and adaptation targets in the Paris Agreement and Sendai Framework for Disaster Risk Reduction