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date: 07 December 2022

# Socioeconomic Impacts of Infrastructure Disruptions

• Stephanie E. ChangStephanie E. ChangSchool of Community and Regional Planning, University of British Columbia

### Summary

Infrastructure systems—sometimes referred to as critical infrastructure or lifelines—provide services such as energy, water, sanitation, transportation, and communications that are essential for social and economic activities. Moreover, these systems typically serve large populations and comprise geographically extensive networks. They are also highly interdependent, so outages in one system such as electric power or telecommunications often affect other systems. As a consequence, when infrastructure systems are damaged in disasters, the ensuing losses are often substantial and disproportionately large. Collapse of a single major bridge, for example, can disrupt traffic flows over a broad region and impede emergency response, evacuation, commuting, freight movement, and economic recovery. Power outages in storms and other hazard events can affect millions of people, shut down businesses, and even cause fatalities. Infrastructure outages typically last from hours to weeks but can extend for months or even years. Minimizing disruptions to infrastructure services is thus key to enhancing communities’ disaster resilience.

Research on infrastructure systems in natural hazards has been growing, especially as major disasters provide new data, insights, and urgency to the problem. Engineering advances have been made in understanding how hazard stresses may damage the physical components of infrastructure systems such as pipes and bridges, as well as how these elements can be designed to better withstand hazards. Modeling studies have assessed how physical damage disrupts the provision of services—for example, by indicating which neighborhoods in an urban area may be without potable water—and how disruption can be reduced through engineering and planning. The topic of infrastructure interdependencies has commanded substantial research interest.

Alongside these developments, social science and interdisciplinary research has also been growing on the important topic of how infrastructure disruption in disasters has affected populations and economies. Insights into these impacts derive from a variety of information sources, including surveys, field observations, analysis of secondary data, and computational models. Such research has established the criticality of electric power and water services, for example, and the heightened vulnerability of certain population groups to infrastructure disruption. Omitting the socioeconomic impacts of infrastructure disruptions can lead to underinvestment in disaster mitigation. While the importance of understanding and reducing infrastructure disruption impacts is well-established, many important research gaps remain.

### Subjects

• Economic Analysis of Natural Hazards
• Infrastructure

### Infrastructure Systems

#### Definitions and Importance

Infrastructure systems provide services such as energy, water, sanitation, transportation, and communications. The terms “infrastructure systems,” “critical infrastructure,” and “lifelines” all refer to assets, networks, and systems in the built environment that provide essential services for social and economic activities. While the terms are often used interchangeably, some definitions distinguish between them. “Infrastructure systems” is the broadest in scope; “critical infrastructure” can be seen as a subset, while “lifelines” indicate those critical infrastructure systems that are characterized by spatially extensive network structures. In the hazards and disasters field, the term “infrastructure systems” is most commonly used. “Lifelines” is typically utilized in engineering contexts (e.g., lifeline earthquake engineering), and “critical infrastructure” is often employed in contexts related to terrorism and security.

The scope of the U.S. government’s National Infrastructure Protection Plan (NIPP) (Department of Homeland Security [DHS], 2013) reflects a framing of infrastructure around issues of risk management, resilience, and security. It defines infrastructure as “the framework of interdependent networks and systems . . . that provide a reliable flow of products and services essential to the defense and economic security of the United States, the smooth functioning of government at all levels, and society as a whole . . .” (p. 31). Critical infrastructure is considered to include “systems and assets, whether physical or virtual, so vital . . . that the incapacity or destruction of such systems and assets would have a debilitating impact on security, national economic security, national public health or safety, or any combination of those matters” (p. 29). Specifically, the NIPP addresses 16 critical infrastructure sectors: chemical; commercial facilities; communications; critical manufacturing; dams; emergency services; information technology; nuclear reactors, materials, and waste; food and agriculture; defense industrial base; energy; healthcare and public health; financial services; water and wastewater systems; government facilities; and transportation systems.

Other governments, organizations, and scholars define the scope of infrastructure systems somewhat differently. For example, the American Society of Civil Engineers (ASCE) regularly issues assessments (labeled “report cards”) on the condition of the nation’s infrastructure. These assessments pertain to an overlapping but somewhat different set of infrastructures than in the NIPP, emphasizing public investments in construction and engineering: water and environment (dams, drinking water, hazardous waste, levees, solid waste, wastewater); transportation (aviation, bridges, inland waterways, ports, rail, roads, transit); public facilities (public parks and recreation, schools); and energy. The Canadian federal government’s National Strategy for Critical Infrastructure (Public Safety Canada [PSC], 2009) addresses 10 critical infrastructure sectors: energy and utilities; finance; food; transportation; government; information and communication technology; health; water; safety; and manufacturing.

While specific definitions differ, infrastructure most commonly refers to energy (especially electric power), water, wastewater, transportation, and telecommunications systems. The U.S. National Research Council (NRC, 2009) defined critical infrastructure systems with this scope, because these systems are “the lifelines without which other types of infrastructure (e.g., banking and finance, government facilities, schools) cannot operate as intended” (p. 1). The current article similarly focuses on these five systems and the socioeconomic impacts of their disruption in disaster events.

Infrastructure systems are important in disasters because they affect all sectors of society. Infrastructure services are so ingrained in modern life that they tend to be both ubiquitous and taken for granted. For example, the operation of a restaurant requires potable water for drinking, washing, and cooking; electric power for lighting, heating, and refrigeration; transportation for bringing in employees, customers, and supplies; sewerage for sanitation; and communications for ordering supplies and making financial payments.

In addition to providing essential services, infrastructure systems typically comprise geographically extensive, interdependent networks that serve large populations. Many cities, for example, derive their potable water from a single network that distributes water from the source (e.g., a reservoir) to households and businesses throughout the city. Outages in one system such as electric power or telecommunications often affect other systems.

As a consequence, when infrastructure systems are damaged in disasters, the ensuing losses are often substantial and disproportionately large. Collapse of a single major bridge, for example, can disrupt traffic flows over a broad region and impede emergency response, evacuation, commuting, freight movement, and economic recovery. Power outages in storms and other hazard events can affect millions of people, shut down businesses, and even cause fatalities.

Infrastructure systems tend to remain in service for long periods of time, and older infrastructure is often especially vulnerable to disaster damage due to outdated engineering design and physical deterioration. In the United States, large segments of the nation’s infrastructure systems are 50 to 100 years old. Numerous calls have been made for greater investments in infrastructure maintenance and renewal (e.g., ASCE, 2013), as well as for new visions and coordinated policies that address how this renewal can be leveraged to simultaneously address other needs such as disaster resilience (NRC, 2009).

There is some evidence for an increasing trend in infrastructure disruptions. For example, in the United States, the number of weather-related blackouts has increased from 5 to 20 per year in the 1990s to 50~100 in recent years (Castillo, 2014; see also Executive Office of the President, 2013). While trends are difficult to establish comprehensively and explain conclusively, factors such as aging systems, increasing interdependence, climate change, and growing societal dependence on infrastructure (e.g., on communications systems) suggest that the overall risk of socioeconomic impacts from infrastructure disruptions will continue to rise.

#### Major Infrastructure Systems

Electric power infrastructure consists of three types of linked systems: generation, transmission, and distribution. In the generation system, electricity is produced from fuel sources (such as coal, natural gas, hydro, solar, or wind) at central power stations or distributed power generation facilities. Hydroelectric dams, for instance, are part of power generation systems. From generation facilities, electricity is conveyed over long distances on high-voltage transmission networks that consist of high-voltage substations, towers, and wires. Near urban areas, electricity is “stepped down” to lower voltages at distribution substations and transferred through electrical lines to the consumer.

Electric power infrastructure is vulnerable to damage in many types of natural hazards. Hurricanes and storms, including ice storms, cause over half of large power outage events in the United States (Hines et al., 2009). Cold weather events, snowstorms, lightning, tornadoes, and earthquakes, among other hazards, can also cause blackouts. The largest power outages are typically caused by hurricanes and tropical storms, which affect on average over 900,000 customers (Hines et al., 2009). In storms, the vast majority of damage is usually caused by wind damage to distribution systems, for example, from wind-blown tree damage to overhead power lines. Earthquakes, by contrast, more typically cause power outages by damaging substations.

It is important to note that electricity is not only vital for people and businesses, but also an essential input to other infrastructure systems. For example, transportation systems rely on power for many functions, including lighting, traffic signals, electric rail, and gas station pumps. Water and wastewater systems require power for operating pumps and treatment plants. Power outages, therefore, affect people both directly and through the indirect impacts on other dependent infrastructure systems.

Gas and liquid fuels are also moved over long distances from source to consumer via complex and spatially extensive networks. Between production areas (e.g., oilfields), processing facilities (e.g., refineries), storage facilities, and distribution terminals, fuel is conveyed through networks of gathering, transmission, and distribution pipelines. Transmission pipelines are like trunk lines, being larger in capacity than distribution pipes and thus potentially affecting much broader areas if disrupted. In addition to the pipes themselves, these systems involve many types of equipment, such as pumps, storage tanks, pressure regulators, and monitoring equipment. In many places, liquid fuels are also transported by ships, rail, and trucks (e.g., by liquefied natural gas [LNG] ships).

Fuel pipelines are vulnerable to damage from earthquakes, floods, and hurricanes, among other hazards. Earthquakes, for example, can cause liquefaction and landslides that damage buried pipelines. Gas and liquid fuel infrastructure systems can also be disrupted by loss of electric power that is needed to operate pump and compressor stations.

Potable water delivery systems also involve subsystems that pertain to water supply, treatment, transmission, and distribution. In some regions, the primary water source may be a reservoir; in others, water may be obtained from rivers, underground aquifers, or even the ocean (requiring desalination plants). Most water infrastructure systems filter, treat and/or disinfect water before conveying it to the consumer. While water is generally moved by gravity, many systems also require pumps and other equipment. Storage facilities such as tanks and reservoirs are also important.

Wastewater systems serve to remove water from places where it is used, such as homes and factories. They consist of subsystems for collection (e.g., drainage pipes), conveyance, treatment, and disposal. In some places, the infrastructure for moving raw sewage is combined with that for draining storm water, while other cities have separate systems. Treating wastewater is typically conducted at treatment plants that are located near water bodies (e.g., rivers, ocean) into which treated water is discharged. In some systems, however, wastewater is discharged with partial or no treatment. In a few systems, treated wastewater is recycled for use as an alternate water source.

The pipes, pumping stations, tanks, dams, and other elements of water and wastewater systems are vulnerable to damage in hazard events. For example, earthquakes have caused extensive damage to buried pipeline systems that have caused disrupted potable water supplies for weeks. Many societal functions depend upon water to be sufficiently available in terms of both quantity and quality. Hospitals, for example, are critically dependent upon a supply of clean water. In addition, some functions such as fire-fighting require piped water (e.g., at fire hydrants) to be sufficiently pressurized. When pipes, equipment, and other water infrastructure components are damaged in disasters, the piped water is often reduced in volume and contaminated. Emergency response measures may include issuing boil-water advisories or providing potable water through other means such as tanker trucks. Damage to wastewater systems can lead to sewage flooding or discharge of untreated sewage.

Broadly speaking, energy and water systems deliver critical resources from some source to the locations where people and businesses will utilize them. These infrastructure systems are thus structured in a somewhat hierarchical manner as described previously (e.g., generation/transmission/distribution). In contrast, the function of transportation and telecommunications systems is to enable connections across space. The networks are thus structured differently, and infrastructure service is closely related to the concepts of access and levels of service.

Modern telecommunications systems are multi-layered (Applied Technology Council [ATC], 2016). Conventional infrastructure carrying telephone service via copper wires, trunk lines, and telephone switches are still in use. In addition, more recently developed infrastructure such as cable TV, broadband Internet, and cellular networks now provide services that people, businesses, and governments have come to rely on. In some places, these services—which are often provided by the private sector—may have the same physical infrastructure. For example, in the United States, data networks providing Internet connectivity all rely on a few backbone networks. Communications infrastructure systems also differ from traditional utilities (e.g., electric power, water, natural gas) in that consumers have more choice regarding service providers; thus, the service areas of communications service providers are typically overlapping.

Communications infrastructure can be disrupted by many types of hazards. The major physical elements of these systems include: transoceanic fiber cables, “last mile” wired connections, “middle mile” connections, core or backbone network, data centers and central offices, and towers (e.g., cell towers). These assets may be damaged by natural hazards such as landslides, earthquakes, flooding, wildfires, etc., but they are also vulnerable to terrorism, in particular, cyber attacks. At the local and regional scale, the concentration of assets in telecom hotels and telecommunications centers presents a key source of vulnerability (Armbruster, Endicott-Popovsky, & Whittington, 2012).

Transportation systems convey people and goods both locally and across long distances. The major modes of transportation, which are interconnected in many ways, consist of road, rail, air, and marine transportation. Road infrastructure systems are composed of surface roads (from local streets to interstate highways), as well as bridges, tunnels, traffic signals, etc. Rail infrastructure includes roadbeds and tracks, bridges, tunnels, stations, and control facilities and equipment. Some urban areas have subway or light rail systems for transporting passengers. Heavy rail is primarily used for moving goods over long distances. Air transportation infrastructure includes airport structures such as runways, terminal buildings, control towers, and the like. Shipping transportation infrastructure pertains to movement of people and goods by water, whether across oceans, along coasts, or on rivers and other inland waterways. The physical infrastructure of these systems includes different types of port facilities, such as wharves, piers, cranes, and control centers. Vehicles (e.g., cars, trucks, ships, airplanes) are generally not considered part of the infrastructure systems, although they are necessary for the systems to be used. It is also important to note that the various transport modes are functionally connected (also referred to as interdependent or multimodal). For example, moving a manufactured product from the factory to the retail store may entail transportation by road, rail, and sea.

Transportation systems are highly vulnerable in natural hazard events. In road systems, bridges are often the weak links. Earthquakes can damage virtually all components of road, rail, marine, and air transport systems. Shaking can damage transportation structures such as bridges, and landslides can block key access routes. Ground failure (e.g., liquefaction) is a particular concern for ports, which are often built on waterfront areas with poorly consolidated soils (e.g., fill). Tsunamis and storm surge are particularly destructive to coastal infrastructure such as ports and coastal roads and bridges.

Network redundancy is an important characteristic of transportation networks. For analytical purposes, transportation networks are often described in terms of nodes and links, where nodes are the points of origin and destination for trips (e.g., cities in an interstate highway network) and links are the connected paths between them (e.g., highways). More redundant networks will have a greater availability of alternate routes that can be used to circumvent damaged nodes and links. Rail systems and inland waterways are generally much less redundant than road systems.

Although infrastructure outage durations vary across disasters and are not always consistently defined or measured, some broad observations are possible (see also ATC, 2016). Power outages often affect large numbers of people, but are generally relatively brief in duration, lasting from hours to days. The case of the catastrophic 1995 Kobe earthquake is illustrative: electric power and telecommunications required a few days to be essentially fully restored, while water and natural gas took 2–3 months, commuter rail about 7 months, and highways approximately 21 months (Chang & Nojima, 2001).

### Understanding Socioeconomic Impacts of Infrastructure Disruption

#### Development of the Research Area

Research on infrastructure systems in natural hazards has been growing, especially as major disasters provide new data, insights, and urgency to the problem. Engineering advances have been made in understanding how hazard stresses may damage the physical components of infrastructure systems such as pipes and bridges, as well as how these elements can be designed to better withstand hazards. Modeling studies have assessed how physical damage disrupts the provision of services, for example, by indicating which neighborhoods in an urban area may be without potable water. (Research has been published in a variety of forums such as journals dedicated to infrastructure systems and those focusing on natural hazards; in the earthquake area, conference proceedings from the Technical Council on Lifeline Earthquake Engineering provide useful compendia.) The topic of infrastructure interdependencies has commanded substantial research interest: since the publication of influential work in the early 2000s, in particular Rinaldi, Peerenboom, & Kelly,2001, the number of publications has grown exponentially (ATC, 2016).

While infrastructure systems are seen as a distinct research area in engineering, this is generally not the case in social science research on hazards and disasters. Rather, infrastructure tends to be discussed in the context of other issues, such as social vulnerability, emergency management, and disaster recovery. This may be largely because in the context of pre-disaster risk and post-disaster impacts, infrastructure is only one of many risk and loss factors; for example, in earthquakes, building damage typically causes much more loss of human lives than does infrastructure disruption. In addition, infrastructure disruptions tend to be short-term and resolved once damage to pipes, bridges, and the like are repaired; thus, infrastructure outages tend to be seen as more of an engineering problem than a social or planning problem.

Perhaps as a consequence, the literature that has emerged on the socioeconomic impacts of infrastructure disruptions has tended to be multidisciplinary or interdisciplinary, drawing on engineering advances as well as concepts and methods in the social sciences. (Key findings from this literature are discussed in the section on Impacts and Issues.) One major type of research activity has been large, coordinated programs and projects, where engineering research on infrastructure systems (e.g., electric power or maritime ports) has been conducted with participation by social scientists investigating aspects of socioeconomic impacts. In the United States, for example, earthquake engineering research centers generated substantial research in this vein (NRC, 2006; Shinozuka, Rose, & Eguchi, 1998), as have multidisciplinary research teams working on issues of sustainable and resilient infrastructures.

Disaster events have also provided major impetus for new research and knowledge advances. Virtually all natural disasters (and many human-induced ones) cause infrastructure disruption, demonstrating both the physical vulnerability of these systems and their importance for human life, economic activity, and societal functioning. For example, the 1989 Loma Prieta earthquake caused bridge collapses on the Cypress Viaduct freeway and the San Francisco–Oakland Bay Bridge. The Great 2008 Chinese Ice Storm caused electrical grid failure that triggered loss of water supply and heating, closure of hospitals and factories, flooding of mines, and disruption of railways; some 5.8 million people were stranded in railway stations alone (Zhou et al., 2011). With not only rail but also road and air transport immobilized by the ice storm, supply chains were disrupted, leading to shutdowns of powerplants, shortages of food, and escalation of food prices. Communication systems disruption hampered government response. Hurricane Sandy in 2012 caused electric power outages to over 8.6 million customers in a broad region of the eastern United States; power outage was a key factor in fuel shortages following the storm (U.S. Department of Energy [USDOE], 2013). In the 2011 Great East Japan triple disaster (i.e., earthquake, tsunami, and nuclear power accident), over 8.9 million households in 15 prefectures lost electric power, 2.2 million lost water, and 459,000 lost city gas supply (Nojima, 2012). Damage was also extensive to coastal infrastructure such as bridges, ports, and harbors. Terrorism events such as the 9/11 World Trade Center disaster have also yielded new insights into infrastructure vulnerabilities, including interdependencies (O’Rourke, 2007). Post-disaster investigations have provided a critical source of information on not only engineering aspects, but also socioeconomic impacts.

The importance of understanding and considering the impacts of infrastructure disruptions is becoming increasingly recognized in public policy. In particular, with the growing interest in fostering the disaster resilience of communities, attention is being paid to the role of infrastructure systems in community resilience. In the United States, infrastructure has been identified as a strategic priority for research and implementation in numerous reports and strategies, including at the national level the National Earthquake Hazard Reduction Program’s NEHRP Strategic Plan, Fiscal Years 2009–2013 (NEHRP, 2008), the National Research Council’s National Earthquake Resilience: Research, Implementation, and Outreach (NRC, 2011), its Disaster Resilience: A National Imperative (NRC, 2012), and the National Institute of Standards and Technology’s Community Resilience Planning Guide for Buildings and Infrastructure (NIST, 2016a).

#### Types of Impacts

In disaster events, infrastructure disruptions frequently cause or exacerbate many types of socioeconomic impacts, including health, social, economic, and environmental consequences. In terms of human health, infrastructure damage and disruption can cause human casualties. For example, people can be killed or injured by infrastructure failures such as bridge collapses and gas pipeline breaks that cause fires. More indirectly, power outages cause loss of heating, lighting, and elevators, which have caused deaths and injuries from hypothermia, carbon monoxide poisoning (as people try to heat their homes with alternative heat sources), accidents (e.g., falls in the dark), and heart attacks (e.g., from exertion in climbing stairs) (Yates, 2014). People can also become ill: power outages frequently cause food spoilage due to loss of refrigeration; damage to pipes can lead to contamination of tap water; and damage to wastewater systems can entail discharge of raw sewage.

In terms of social impacts, loss of infrastructure services—especially electric power, potable water, and sewerage—is a frequent cause of people being displaced from their homes in disasters. Even when residential buildings have survived the storm, earthquake, or other hazard event in intact condition, they may become uninhabitable without utility services. For example, one survey after the 1994 Northridge (Los Angeles) earthquake found that 14% of people in Red Cross shelters sought shelter there because their homes were uninhabitable, even though they had already been inspected and found to be structurally safe (Earthquake Engineering Research Institute [EERI], 1995).

Infrastructure loss can also impede the delivery of emergency and social services in a disaster, just when they are critically needed. Fire-fighting requires sufficient volumes of pressurized water, without which urban fires can spread rapidly and cause human and property losses, as seen in the 1995 Kobe (Japan) earthquake. Disruption to road networks by bridge damage, debris blockage, landslides, or other causes can also impede emergency response; for example, the ability of fire-fighters, search-and-rescue crews, ambulances, and utility repair workers to reach places where they are needed. Hospitals are very dependent upon electric power for potable water and communications, without which they may need to curtail healthcare services, evacuate patients, or even temporarily shut down (Kirsch et al., 2010). For example, in addition to lighting, heating, refrigeration of medical supplies, and operation of medical equipment, hospitals are also heavily reliant on electric power and communications for access to computerized medical records. Communications infrastructure systems are also required for warning and community alert systems, 911 and related emergency calls, dispatch of emergency responders, and general citizen information needs in a disaster. Social media, which require communications infrastructure, are being increasingly used in disaster response and recovery, both formally and informally (Yin, Lampert, Cameron, Robinson, & Power, 2012).

Businesses can be disrupted by infrastructure loss in many ways, leading to economic impacts such as lost production and sales, reduced income for employees, reduced tax payments to governments, and temporary or even permanent closure. Depending upon the type of business, loss of electric power, potable water, wastewater, or communications infrastructure may cause impacts ranging from minor inconveniences to immediate shutdown of operations. Transportation disruption can affect businesses by impeding employees’ ability to come to work, customer access, delivery of supplies, and transport of products. There may also be economic costs of response, repair, and cleanup, for example, the cost of disposing of and replacing spoiled foods in power outages.

Business interruption losses can be experienced directly as a result of infrastructure loss, or indirectly as a result of linkages with other businesses that have suffered loss. For example, even if a factory has no on-site damage or infrastructure disruption in a disaster, it may be idled if it cannot receive supplies from or sell its products to other businesses that have been affected by the disaster. Indeed, with modern supply chains, the effects of disasters can be felt across the globe: in the 2011 Great East Japan triple disaster, disruption to parts producers in northeastern Japan caused automobile manufacturers as far away as the United States and Europe to curtail production.

Infrastructure disruption can cause environmental impacts, especially through waste generation and contaminant releases. Damaged infrastructure such as rubble from earthquake-damaged bridges contributes to the problem of disaster debris. Food spoilage and food waste is a common consequence of power outages. Damage to wastewater pipelines and treatment facilities, as well as power outages, often leads to spillage of raw sewage into the environment. In coastal storms, damage to fuel storage tanks, harbor facilities, and related waterfront industry can release hazardous materials and other contaminants.

Many factors influence the severity of infrastructure disruption impacts. The location and spatial extent of outages directly influence the number of people affected. Duration of outage is also very important; a few hours without electric power may merely entail inconveniences, but outages that last weeks can cause more severe impacts, such as people being displaced from their homes or businesses closures. The time of year in which the disaster occurs also matters. In many regions of the world, for example, loss of electric power or natural gas for heating can be life-threatening in winter. In addition, as will be discussed further in the section on Impacts and Issues, many socioeconomic factors also affect impacts.

It is important to note that impacts are related to the disruption of infrastructure services, rather than necessarily to infrastructure damage itself. If an electric power line is damaged but the utility company can reroute electricity flows around it, there may be no loss of service to the population and no impacts, other than repair costs to the utility. If a bridge is damaged but alternate routes are intact, transportation services may be only minimally disrupted; however, if the damaged bridge provided the only path across a river or the only access to a remote town, transportation disruption would be very high and the consequences severe. If electric power is lost at an airport control tower but backup power is available from a generator, there may be no disruption to air transportation services. Similarly, potable water can be brought in by tanker trucks to alleviate shortages caused by damage to the piped water network. At the other extreme, infrastructure services can be disrupted in ways that involve no physical damage at all, such as in cases of road gridlock during mass evacuations before a hurricane, labor strikes, or terrorism threats that entail precautionary shutdowns.

Rapid restoration of infrastructure services after disasters is important not only for minimizing impacts to affected customers but also to enable reconstruction and recovery. Infrastructure systems are interdependent in the reconstruction phase of a disaster; for example, utility crews making repairs need to be able to access damaged areas using the road network. Cleaning up damage, removing debris, and other post-disaster restoration and recovery activities are also facilitated by functional road networks and availability of electric power, water, wastewater, and communications. Service restoration must, however, be coordinated. For example, because electric sparks can cause fire ignitions in the presence of natural gas leaks following earthquakes, it may be necessary to delay electric power resumption while broken gas mains are being inspected and repaired.

In general, infrastructure service disruptions in disasters are usually short-term in duration, typically lasting from hours to weeks. While their socioeconomic impacts may be acute, they are usually short-term and do not entail fundamental socioeconomic changes, such as population relocation or changes in regional economic structure. In some cases, however, infrastructure outages can extend for months, years, or even indefinitely. For example, in a catastrophic earthquake or tsunami, extensive damage to ports, buried pipeline networks, and major bridges can take months to repair. Some facilities may never be repaired if authorities decide to take the opportunity to enact long-term changes, as in the case of the demolished Embarcadero Freeway in San Francisco after the 1989 earthquake or the residential “red zones” in Christchurch, New Zealand, after the 2012 earthquake.

#### Methodological Approaches

Research on the socioeconomic impacts of infrastructure disruption has been conducted using various methodological approaches. Broadly, these include empirical approaches and modeling. Often, empirical studies seek to develop understanding of what happened in a disaster, or what could happen. Many focus on disaggregate units of analysis such as individual people, families, businesses, or other organizations. Modeling studies, in contrast, often seek to quantitatively estimate impacts (e.g., in dollar terms) and typically address phenomena involving systems interaction at the urban or regional scale of analysis.

Empirical studies have commonly included surveys of people and businesses affected by actual disasters, as well as surveys outside the context of any specific hazard event that investigate issues of preparedness, potential impacts in hypothetical disasters, and the like. Field observations, particularly in the immediate aftermath of disasters, provide another valuable source of grounded, albeit anecdotal, information. Empirical studies can also involve gathering information from infrastructure managers and operators who have detailed familiarity with particular systems, whether in terms of normal operations, potential disruption and decision-making in disasters, or actual disaster experiences. Expert interviews, focus groups, and sometimes workshops are appropriate in such cases because there are usually only a few people in any organization with such expertise and because the information required is detailed and nuanced. Secondary data from censuses or other statistical sources are rarely used in research on socioeconomic impacts of infrastructure disruption except to contextualize surveys and other primary data collection. Government and even media reports, however, often provide useful information, subject to the usual cautions regarding such sources. Post-disaster reports by utilities, transportation authorities, and other infrastructure providers are a valuable reference for some types of information such outage extent, duration, and restoration; however, they rarely venture to address socioeconomic impacts in any depth.

Modeling approaches have been used extensively to investigate the socioeconomic impacts of infrastructure disruptions. Drawing on methodological approaches in urban and regional economics, numerous studies have modeled the indirect economic impacts of disasters, including in particular of utility outages and transportation disruption. In some cases, modeling has included network analysis of how flows (e.g., of water or people) would be affected by network damage; in others, models abstract away from the spatial and topological aspects of networks to focus on economic or other types of linkages. Impact models and simulation models are advantageous in many ways; for example, they consider interactions (e.g., between economic sectors, or between people and businesses), network flows (e.g., traffic volumes), and aggregate impacts (e.g., value of overall economic disruption). They can also be used to explore “what if” types of questions, such as the extent to which impacts could be reduced if certain mitigation actions were undertaken. In many ways, therefore, empirical and modeling approaches contribute to the literature in complementary ways.

### Impacts and Issues

Research on the socioeconomic impacts of infrastructure disruption in disasters has investigated a range of issues and challenges. Three key areas are highlighted: conceptualizing and measuring infrastructure disruption in ways that support understanding socioeconomic impacts; modeling the urban and regional economic impacts of infrastructure loss; and understanding the impacts of infrastructure disruption on households and businesses. An additional area, developing tools and approaches to support risk reduction decision-making, is discussed in the section on Reducing Impacts through Mitigation and Planning.

#### Measuring Infrastructure Disruption

The measurement of infrastructure disruption has implications for how socioeconomic impacts can be analyzed, compared, and ultimately, reduced. In engineering studies of infrastructure risk, reliability, and resilience, infrastructure disruption is typically considered from a technical perspective. Disruption is generally considered in such terms as number of bridges that are closed, pipelines that are damaged, and utility customers who have no service. For example, a standard measure of outage in the electric power industry is the System Average Interruption Duration Index (SAIDI), which is calculated as the total number of customer-minutes of lost power divided by the total number of customers. In such formulations, there is no distinction between customer types; yet, as will be discussed in the section on Impacts to Households and Businesses, social science research has established that different population and business groups have different infrastructure vulnerabilities and sensitivities. One common approach in studies that have sought to link infrastructure disruptions to socioeconomic impacts is to use geographic information systems (GIS) to spatially overlay areas of lifeline service loss to data on socioeconomic characteristics of the populations and businesses in those areas (e.g., Chang, Svekla, & Shinozuka, 2002; Federal Emergency Management Agency [FEMA], 2003, chapter 14). That is, infrastructure disruption is assessed spatially, not just in terms of overall system functionality.

Measurement of disruption is relatively straightforward for utility infrastructures such as electric power and water that deliver services to consumers, but more complex for systems that serve to enable connections across space, namely, for communications and transportation systems. For example, the number of people without road transportation service is not a very meaningful concept. Faturechi and Miller-Hooks (2015) reviewed research on measuring transportation performance in disasters and identified three broad measurement approaches: topological, functional, and economic. Topological approaches focus on the relative locations of nodes and links and their interconnections; they measure performance in terms such as connectivity, rather than the operations of the system. Functional approaches most commonly assess system performance in terms of travel time (or distance), from which impacts such as the monetized value of travel time delays can be calculated. Other functional measures address throughput or flows, capacity, and accessibility. The latter concept pertains to the ability of people to access destinations (e.g., work, schools, shopping) using the transportation network and how this ability is degraded when transportation is disrupted in a disaster. Mattson and Jenelius (2015) further discuss the strengths and limitations of the topological and the systems-based research approaches.

#### Urban and Regional Impacts

At the urban and regional scales, studies focusing on specific infrastructure systems following disasters or in anticipation of future disasters have developed key insights regarding actual and potential socioeconomic impacts. In one particular dynamic area, researchers have drawn on modeling techniques in urban and regional economics—especially input-output (I-O) models, which account for inter-industry linkages, and related methods such as computable general equilibrium (CGE) modeling—to evaluate disaster impacts (Okuyama, 2007). Some of these studies have modified the basic methods to enable application to infrastructure system disruptions, typically seeking to estimate the magnitude of economic impacts. For example, Gordon, Richardson, and Davis (1998) estimated that transportation damage to highway bridges in the 1994 Northridge earthquake accounted for $1.5 billion, or a quarter of total, business interruption losses. For the same disaster, Rose and Lim (2002) estimated business disruption losses from the extensive but brief (< 36 hours) electric power outage to be over$88 million. Models have also been developed to estimate the indirect economic impacts of hypothetical disaster scenarios, often paying particular attention to how results would change depending upon assumptions made, behavioral and decision-making responses, and other variables: for example, Rose and Guha (2004) for electric power, Rose and Liao (2005) for water disruption, Cho et al. (2001) and Tatano and Tsuchiya (2008) for road transportation, Ham, Kim, and Boyce (2005) for multi-regional highway and railroad commodity flows, and Rose and Wei (2013) for seaports. Inoperability input-output models (IIMs) and variants, developed specifically to address the issue of infrastructure interdependencies and economic impact, have been applied to contexts such as electric power outages (Haimes et al., 2005; MacKenzie & Barker, 2013). In Brozovic, Sunding, and Zilberman’s (2007) model of economic losses to businesses and households from water loss in earthquakes, particular attention is paid to spatio-temporal outage patterns and impact thresholds.

Other research on urban and regional impacts has considered the simultaneous disruption of multiple infrastructure systems, thereby more closely examining or mirroring the complexity of actual disasters. Some studies have sought to clarify the role of infrastructure and infrastructure interdependencies in overall urban and regional socioeconomic impact. In terms of relative importance or priority, one California survey of public perceptions related to seismic risk found that out of 20 types of structures and systems in the built environment, residents assigned greatest importance to hospitals, public safety buildings, and infrastructure for natural gas, electric power, and water (ATC, 2016). Webb, Tierney, and Dahlhamer (2000) found that in the 1993 Midwest floods, businesses in one city suffered more economic loss from infrastructure outages (i.e., of water, electric power, and wastewater) than from actual physical flooding to their facilities. In a study of the 1989 Loma Prieta earthquake and 1992’s Hurricane Andrew, Wasileski, Rodriguez, and Diaz (2010) found loss of electric power and communications services to be most disruptive to businesses; however, the only infrastructure disruptions to significantly influence businesses’ decisions to temporarily close or relocate were transportation and wastewater. Similarly, Webb et al. (2000) found that utility disruptions were frequently cited as a source of business disruption, but longer-term recovery problems are affected by a range of operational problems, some of which (e.g., supply chain, employee access) may be related to longer-duration infrastructure disruptions such as to the road network. Overall, the literature suggests that the effects of utility disruptions tend to be severe but short-term, whereas longer duration infrastructure outages have effects on recovery.

A few modeling studies have estimated socioeconomic impacts by considering the influence of utility infrastructure disruptions in the context of other damage, in particular to buildings and building contents, for example, impacts on hospital functionality (Yavari, Chang, & Elwood, 2010), shelter populations (Federal Emergency Management Agency [FEMA], 2003; Chang, Pasion, Yavari, & Elwood, 2009), and direct business interruption (Chang & Lotze, 2014). Generally speaking, the marginal effect of infrastructure disruption diminishes when building damage is severe.

A range of approaches has been used to model infrastructure interdependencies and the socioeconomic impacts of their failures (see Hasan & Foliente, 2015). McDaniels, Chang, Peterson, Mikawoz, and D.Reed (2007) analyzed socioeconomic impacts associated with infrastructure failure interdependencies from power outages in several disasters. They found health impacts to be predominant in the hurricane and ice storm events but economic impacts to be prevalent in a major blackout caused by technical issues and human error. Failures of building support functions (e.g., heating, elevators) were most societally significant across all events, followed by impacts related to water, food, hospitals and healthcare, and transportation.

Research has found that although all infrastructure systems are important, some are especially vital from the perspective of socioeconomic impacts and tolerance for disruption. Electric power has consistently been found to be paramount, while evidence also indicates the relatively high importance of water, communications, and transportation (Chang, McDaniels, Fox, Dhariwal, & Longstaff, 2014). In both the United States and Japan, businesses have been found to be most resilient to natural gas outage, less resilient to water disruption, and least resilient to loss of electric power (Kajitani & Tatano, 2009).

The potential socioeconomic impacts of infrastructure disruption are also reflected in the literature on hazard vulnerability indicators. Because the purpose of indicators is generally to provide community overviews (e.g., of vulnerability), to compare the range of factors in a community influencing risk (including but not limited to infrastructure), enable vulnerability comparisons between communities, or potentially track progress in risk reduction or recovery, infrastructure is necessarily represented in this context in a more indicative than detailed manner. Infrastructure measures have long been included in indicators of social vulnerability, variously constructed to reflect attributes such as infrastructure density, redundancy, evacuation difficulty, travel time or distance in disasters, and populations’ dependence on the systems (see Holand, 2014). Here, an interesting debate concerns whether higher infrastructure exposure signifies higher vulnerability (i.e., more at risk) or lower vulnerability (i.e., more resources for response). In the growing literature on disaster recovery, infrastructure measures are the most commonly referenced indicators of recovery, potentially because infrastructure restoration is a precursor to social and economic recovery (Jordan & Javernick-Will, 2013). Various summary representations of infrastructure are also commonly used in indicators and frameworks of disaster resilience (Cutter, 2015). For example, continuity of critical services, as well as reliable communications and mobility, are highlighted as resilience drivers in the Rockefeller Foundation’s Resilient City Framework, which underpins its 100 Resilient Cities initiative (Rockefeller Foundation, 2015).

#### Impacts to Households and Businesses

As with other aspects of disasters, research has found that vulnerability to infrastructure disruption differs between population groups (see also ATC, 2016). For disasters in general, numerous studies have clarified the heightened vulnerability of population groups such as the elderly, children, linguistic minorities, and low-income households. Infrastructure disruption potentially contributes to population groups’ differential social vulnerability to hazards in several ways: among other mechanisms, population groups may face differential likelihood of experiencing infrastructure disruption in a disaster (i.e., different exposure); they may have differential capacity to withstand such disruption; they may have differential access to emergency assistance to alleviate infrastructure loss; and they may have differential resources to find infrastructure service alternatives. For example, medical patients requiring regular dialysis treatments rely on functional transportation systems, clean water, and electric power for regular treatments. Faber (2015) analyzed the impacts of subway transportation disruption in New York City after Hurricane Sandy and found that neighborhoods that were most severely affected by transit disruption differed demographically from those affected by coastal flooding, with the greatest access loss occurring in poor, predominantly Asian and Latino areas. Serulle and Cirillo (2014), in a study of evacuation in scenario floods, found that low-income populations have differential transportation accessibility to shelters and safe zones. Stough, Sharp,Resch, Decker, and Wilker (2015) note that persons with disabilities are especially dependent upon transportation that can meet their needs; lack of suitable transport is a key factor in their reluctance to evacuate before hurricanes and presents a barrier to post-disaster recovery.

Infrastructure disruptions also cause differential impacts across business types and economic sectors. As in the case of people and households, business vulnerability to infrastructure is closely linked to business vulnerability to hazards in general (see Webb, Tierney, & Dahlhamer, 2000). To capture the observation that the same infrastructure disruption may be experienced differently by different types of businesses—for example, that natural gas outage may immediately shut down a manufacturing plant but cause only inconveniences for a retail store—the concept of “resilience factors” has been proposed. An Applied Technology Council study (ATC, 1991) used expert elicitation methods to quantify this concept, originally termed “importance factors” in that study. Later studies, notably Kajitani and Tatano (2009) in a study of Japanese firms, gathered survey data to develop more empirically based and refined measures. They found, for example, that electric power loss would cause more severe impacts to manufacturing than to non-manufacturing firms. From a different perspective, Altay and Ramirez (2010) found statistical evidence for differentials in infrastructure impacts across echelons of the supply chain, specifically, between firms engaged in raw material supply, manufacturing, wholesale, and retail trade.

### Reducing Impacts through Mitigation and Planning

The socioeconomic impacts of infrastructure disruption can also be reduced through investments in mitigation and planning prior to disasters. Researchers have developed analytical approaches and decision-support systems to inform the strategic prioritization of transportation links, especially bridges, for pre-disaster strengthening. While many approaches focus on engineering or network criteria, some consider socioeconomic conditions and objectives such as economic importance (e.g., Sohn, Kim, Hewings, Lee, & Jang, 2003), travel delays (e.g., Croope & McNeil, 2011), emergency evacuation capacity (Chang, Peng, Ouyang, Elnashai, & Spencer, 2012), and criticality for social and economic activities (Oh, Deshmukh, & Hastak, 2013). Wein, Ratliff, Baez, and Sleeter (2014) analyzed the spatial distribution of socially vulnerable populations from the perspective of evacuation planning, considering such factors as demand for public transportation and immobility.

In comparison with transportation, there are relatively few studies on risk reduction considering socioeconomic impacts for other infrastructure systems. Analyzing options for earthquake mitigation in an urban water system, Chang (2003a) found that expected societal losses from an earthquake would outweigh utility agency losses by 100 times, and mitigation options that are not cost-effective for the utility can be very cost-effective from a societal standpoint. The implication is that ignoring societal benefits would lead to underinvestment in disaster risk reduction.

In comparison with research on reducing socioeconomic impacts through pre-disaster mitigation and preparedness, the area of loss reduction through post-disaster decision-making has received relatively little attention, both for transportation (Mattson & Jenelius, 2015) and for other infrastructure systems. This may be because infrastructure repair and restoration are viewed largely as engineering and emergency response problems where decision-making is guided by practitioner experience, knowledge, and conventions. Another reason may be the relatively short time frames (i.e., hours to days) for many infrastructure services to be restored following disasters; however, as noted previously, some infrastructure disruptions can extend for weeks or longer, especially in the case of catastrophic earthquakes. Xu and colleagues (2007) developed an approach to optimize post-earthquake restoration of electric power based on system performance metrics of customers without power. Chang (2003b) evaluated alternative post-earthquake repair and restoration strategies for passenger rail and road and highway transportation systems from the standpoint of accessibility and spatial equity.

Increasingly, researchers have been emphasizing the importance of stakeholder involvement in decision-making for infrastructure risk reduction. Gregory, Harstone,Rix, and Bostrom (2012) explored seismic risk decision-making at seaports and argue for greater attention to the quality of stakeholder participation in mitigation decision-making. Little, Loggins, and Wallace (2015) stress the value of stakeholder input in the development of a decision-support tool for restoration of interdependent infrastructures, including the importance for stakeholder adoption of the tool. To address coordination needs and information gaps across interdependent infrastructures, McDaniels and colleagues (2008, 2015) developed stakeholder-based approaches for understanding decision-making contexts, developing risk reduction strategies, and prioritizing them. Hasan and Foliente (2015) emphasize that the selection of modeling method for infrastructure interdependencies should be based on appropriateness for specific decision-making contexts and stakeholder needs.

In broader terms, infrastructure performance in disasters is being increasingly viewed not only as an issue to be described and understood, but also as a decision-making objective or a goal in community resilience. As noted earlier, various measures related to infrastructure systems are commonly found in community resilience metrics and frameworks. In some cases, post-disaster infrastructure performance is treated explicitly as a planning goal; for example, the San Francisco–based civic organization SPUR has developed infrastructure (and other) performance goals for potential earthquakes. By comparing these goals with how the infrastructure systems are expected to actually perform in disasters, the approach identifies priorities for regional infrastructure risk reduction (Poland, 2009). The Coastal Resilience Index (Emmer et al., 2008) incorporates community self-assessments regarding expected infrastructure performance and related risk reduction and preparedness actions. Similarly, the U.S. federal government has developed a Community Resilience Planning Guide for Buildings and Infrastructure (National Institute for Standards and Technology [NIST], 2016a) that emphasizes and supports developing performance goals for infrastructure systems (Box A).

#### Box A. NIST Community Resilience Planning Guide.

 In 2016, the National Institute for Standards and Technology, a unit of the U.S. federal government’s Department of Commerce, released a Community Resilience Planning Guide for Buildings and Infrastructure Systems. Intended as a living document, the guide provides a practical approach for leaders across the country to improve the disaster resilience of their cities, towns, and other communities. The approach consists of six steps: 1. Form a collaborative planning team 2. Understand the situation 3. Determine goals and objectives 4. Plan development 5. Plan preparation, review, and approval 6. Plan implementation and maintenance Setting performance goals and priorities for infrastructure systems after disasters—specifically in terms of recovery times for each system—is central to the process. Comparing performance goals and current performance expectations reveals gaps (schematically illustrated in Figure 1) that the community must address concretely in order to increase its disaster resilience.

Societal expectations, public knowledge, preparedness, and outage tolerance regarding infrastructure disruptions are also important—albeit understudied—topics. Data on these issues are sparse; moreover, there is considerable heterogeneity among the general public, and understanding such differentials is key to reducing impacts to the most vulnerable members of society (ATC, 2016). There is evidence to suggest that public acceptance of infrastructure disruptions depends not only on the actual experience of outage (e.g., duration), but also on the associated risk communication. Besides addressing public expectations, risk communication should aim to manage expectations (ATC, 2016). For example, Miles, Jagielo, and Gallagher (2015) found that in 2012’s Hurricane Isaac, although electric power restoration was not unusually slow, the utility was subject to substantial criticism because of poor communications with the public and government agencies. Public willingness to tolerate infrastructure disruption is also influenced by factors such as public trust and confidence in the particular infrastructure service provider (ATC, 2016). Public expectations may be changing, and some indication of this can be found in related laws and regulations, for example in judgments on utility liability following disasters. Nonetheless, the engineering codes, standards, guidelines, and performance requirements that currently govern infrastructure design and engineering practice in the United States rarely consider the socioeconomic impacts of infrastructure service disruption (ATC, 2016).

### Remaining Issues

In sum, infrastructure systems are vital to the disaster resilience of people and communities. Numerous disasters have demonstrated the physical vulnerability of electric power, gas and liquid fuels, water, wastewater, and communications systems—and the societal consequences of their disruption, ranging from human casualties and displaced populations to business disruption and supply chain losses. The vulnerabilities associated with infrastructure interdependencies are being increasingly understood, even as they may be growing. Reducing the likelihood of damage through pre-disaster mitigation, reducing the duration of outage through rapid restoration, and enhancing the capacity of people and businesses to withstand outage disruption through preparedness and emergency response planning will all enhance disaster resilience. While research on infrastructure systems is still predominantly engineering-based, increasingly, social science and interdisciplinary studies have developed new knowledge about the socioeconomic impacts of infrastructure disruption and approaches to addressing them.

Many research needs and gaps remain. The classic challenges of research on infrastructure systems persist—for example, impact attribution, generalizability, system complexity, infrastructure interdependencies, and security concerns (e.g., related to disclosing information on infrastructure location and vulnerability)—but researchers have been developing approaches to address them.

Broader insights are needed into infrastructure vulnerability and resilience. For example, research is needed to investigate how different types of places (e.g., large metropolitan areas vs. rural towns) vary in their vulnerability to infrastructure disruption, their capacity to withstand such loss, and appropriate strategies for enhancing their resilience. Similarly, the infrastructure vulnerability of different socioeconomic groups remains only superficially understood. More research is needed on societal understanding of infrastructure vulnerability, expectations regarding outages and tolerance thresholds (e.g., tolerable durations of outages), and effective risk communication approaches. Some types of impacts (e.g., environmental consequences) tend to be less visible and require greater attention. Similarly, infrastructure disruption in some types of hazards (e.g., floods) is relatively under-studied. Insights are needed into people’s and businesses’ capacities to adapt to infrastructure outages and shortages. Relatively little is understood about the social impacts of infrastructure disruption, in comparison with economic impacts. Models are needed that can identify infrastructure mitigation and restoration priorities from the standpoint of minimizing socioeconomic impacts, particularly to the most vulnerable groups in society. Collaborative research is needed to ensure that knowledge gained from research informs practical decision-making.

Infrastructure disruption is also important in disasters occurring in the Global South (i.e., developing countries), but under-researched. While many of the issues there may be similar to those discussed in this article, there are also key contextual differences that must be recognized. For example, access to infrastructure services such as water and transportation may be very uneven and inadequate prior to a disaster, especially for the urban poor. Municipal piped water and wastewater systems may serve only some areas of a city. Disaster reconstruction and recovery may present key opportunities for infrastructural change and improvement.

Finally, research is needed on how infrastructure vulnerability, societal impacts, and resilience-building opportunities may be changing over time. The effects of socioeconomic and technological changes on impacts of infrastructure disruption need to be better understood, for example, changes such as aging populations, increasing reliance on communications systems (e.g., cloud-based information technology), growth of global supply chains, greater use of renewable energy sources, and climate change effects on infrastructure services supply and demand (ATC, 2016). Anticipating the influence of such issues will be important for reducing the socioeconomic impacts of infrastructure disruptions in future disasters.

#### References

• Altay, N., & Ramirez, A. (2010). Impact of disasters on firms in different sectors: Implications for supply chains. Journal of Supply Chain Management, 46(4), 59–80.
• American Society of Civil Engineers. (2013). 2013 Report Card for America’s Infrastructure.
• Applied Technology Council. (2016). Critical assessment of lifeline system performance: Understanding societal needs in disaster recovery. Report no. NIST CGR 16-917-39. Gaithersburg, MD: National Institute of Standards and Technology.
• Applied Technology Council (ATC). 1991. “Seismic vulnerability and impact of disruption of lifelines in the conterminous United States.” report ATC-25, Redwood City, Calif.
• Armbruster, G., Endicott-Popovsky, B., & Whittington, J. (2012). Are we prepared for the economic risk resulting from telecom hotel disruptions? International Journal of Critical Infrastructure Protection, 5, 55–65.
• Brozovic, N., Sunding, D. L., & Zilberman, D. (2007). Estimating business and residential water supply interruption losses from catastrophic events. Water Resources Research, 43.
• Castillo, A. (2014). Risk analysis and management in power outage and restoration: A literature survey. Electric Power Systems Research, 107, 9–15.
• Chang, L., Peng, F., Ouyang, Y., Elnashai, A. S., & Spencer, B. F. (2012). Bridge seismic retrofit program planning to maximize postearthquake transportation network capacity. Journal of Infrastructure Systems, 18(2), 75–88.
• Chang, S. E. (2003a). Evaluating disaster mitigations: Methodology for urban infrastructure systems. Natural Hazards Review, 4(4), 186–196.
• Chang, S. E. (2003b). Transportation planning for disasters: An accessibility approach. Environment and Planning A, 35, 1051–1072.
• Chang, S. E., & Lotze, A. (2014). Infrastructure contribution to business disruption in earthquakes: Model and application to North Vancouver, Canada. In Proceedings of the 10th National Conference on Earthquake Engineering. Anchorage, AK: Earthquake Engineering Research Institute.
• Chang, S. E., McDaniels, T., Fox, J., Dhariwal, R., & Longstaff, H. (2014). Toward disaster-resilient cities: Characterizing resilience of infrastructure systems with expert judgments. Risk Analysis, 34(3), 416–434.
• Chang, S. E., & Nojima, N. (2001). Measuring post-disaster transportation system performance: The 1995 Kobe earthquake in comparative perspective. Transportation Research Part A, 35, 475–494.
• Chang, S. E., Pasion, C., Yavari, S., & Elwood, K. (2009). Social impacts of lifeline losses: Modeling displaced populations and health care functionality. In A. Tang & S. Werner (Eds.), Proceedings of the 2009 ASCE Technical Council on Lifeline Earthquake Engineering (TCLEE) Conference (pp.563–572). Oakland, CA (June 28–July 1, 2009).
• Chang, S. E., Svekla, W. D., & Shinozuka, M. (2002). Linking infrastructure and urban economy: Simulation of water disruption impacts in earthquakes. Environment and Planning B, 29(2), 281–301.
• Cho, S., Gordon, P., Moore II, J. E., Richardson, H. W., Shinozuka, M., & Chang, S. (2001). Integrating transportation network and regional economic models to estimate the costs of a large urban earthquake. Journal of Regional Science, 41(1), 39–65.
• Croope, S. V., & McNeil, S. (2011). Improving resilience of critical infrastructure systems postdisaster: Recovery and mitigation. Transportation Research Record, 2234, 3–13.
• Cutter, S. L. (2015). The landscape of disaster resilience indicators in the USA. Natural Hazards, 80(2), 741–758.
• Department of Homeland Security. (2013). NIPP 2013: Partnering for critical infrastructure security and resilience. Washington, DC: Department of Homeland Security.
• Earthquake Engineering Research Institute. (1995). Northridge Earthquake of January 17, 1994: Reconnaissance report, vol. 1. Oakland, CA: EERI. See especially, pp. 400–401.
• Emmer, R., Swann, L., Schneider, M., Sempier, S., Sempier, T., & Sanchez, T. (2008). Coastal Resilience Index: A community self-assessment: A guide to examining how prepared your community is for a disaster. NOAA Publ. No. MAS GP-08-014.
• Executive Office of the President. (2013). Economic benefits of increasing electric grid resilience to weather outages. Washington, DC: President’s Council of Economic Advisers.
• Faber, J. W. (2015). Superstorm Sandy and the demographics of flood risk in New York City. Human Ecology, 43, 363–378.
• Faturechi, R., & Miller-Hooks, E. (2015). Measuring the performance of transportation infrastructure systems in disasters: A comprehensive review. Journal of Infrastructure Systems, 21(1), 04014025.
• Federal Emergency Management Agency. (2003). HAZUS-MH MR1 technical manual. Washington, DC: FEMA.
• Fujimi, T., & Chang, S. E. (2014). Adaptation to electricity crisis: Businesses in the 2011 Great East Japan triple disaster. Energy Policy, 68, 447–457.
• Gordon, P., Richardson, H. W., & Davis, B. (1998). Transport-related impacts of the Northridge Earthquake. Journal of Transportation and Statistics, 1(2), 21–36.
• Gregory, R., Harstone, M., Rix, G. J., & Bostrom, A. (2012). Seismic risk mitigation decisions at ports: Multiple challenges, multiple perspectives. Natural Hazards Review, 13(1), 88–95.
• Haimes, Y. Y., Horowitz, B. M., Lambert, J. H., Santos, J. R., Lian, C., & Crowther, K. G. (2005). Inoperability input-output model for interdependent infrastructure sectors. II: Case studies. Journal of Infrastructure Systems, 11(2), 80–92.
• Ham, H., Kim, T. J., & Boyce, D. (2005). Assessment of economic impacts from unexpected events with an interregional commodity flow and multimodal transportation network model. Transportation Research Part A, 39(10), 849–860.
• Hasan, S., & Foliente, G. (2015). Modeling infrastructure system interdependencies and socioeconomic impacts of failure in extreme events: Emerging R&D challenges. Natural Hazards, 78, 2143–2168.
• Hines, P., Apt, J., & Talukdar, S. (2009). Large blackouts in North America: Historical trends and policy implications. Energy Policy, 37, 5249–5259.
• Holand, I. S. (2014). Lifeline issues in social vulnerability indexing: A review of indicators and discussion of indicator application. Natural Hazards Review, 16(3), 04014026.
• Jordan, E., & Javernick-Will, A. (2013). Indicators of community recovery: Content analysis and Delphi approach. Natural Hazards Review, 14(1), 21–28.
• Kajitani, Y., & Tatano, H. (2009). Estimation of lifeline resilience factors based on surveys of Japanese industries. Earthquake Spectra, 25(4), 755–776.
• Kirsch, T. D., Mitrani-Reiser, J., Bissell, R, Sauer, L. M., Mahoney, M. Holmes, W. T., Santa Cruz, N., & de la Maza, F. (2010). Impact on hospital functions following the 2010 Chilean earthquake. Disaster Medicine and Public Health Preparedness, 4(2), 122–128.
• Little, R. G., Loggins, R. A., & Wallace, W. A. (2015). Building the right tool for the job: Value of stakeholder involvement when developing decision-support technologies for emergency management. Natural Hazards Review, 16(4), 05015001.
• MacKenzie, C. A., & Barker, K. (2013). Empirical data and regression analysis for estimation for infrastructure resilience with application to electric power outages. Journal of Infrastructure Systems, 19(1), 25–35.
• Mattson, L.-G., & Jenelius, E. (2015). Vulnerability and resilience of transport systems—a discussion of recent research. Transportation Research Part A, 81, 16–34.
• McDaniels, T., Chang, S., Cole, D., Mikawoz, J., & Longstaff, H. (2008). Fostering resilience to extreme events within infrastructure systems: Characterizing decision contexts for mitigation and adaptation. Global Environmental Change, 18(2), 310–318.
• McDaniels, T., Chang, S. E., Hawkins, D., Chew, G., & Longstaff, H. (2015). Towards disaster-resilient cities: An approach for setting priorities in infrastructure mitigation efforts. Environment, Systems and Decisions, 35(2), 252–263.
• McDaniels, T., Chang, S., Peterson, K., Mikawoz, J., & Reed, D. (2007). Empirical framework for characterizing infrastructure failure interdependencies. Journal of Infrastructure Systems, 13(3), 175–184.
• Miles, S. B., Jagielo, N., & Gallagher, H. (2015). Hurricane Isaac power outage impacts and restoration. Journal of Infrastructure Systems, 22(1), 05015005.
• National Earthquake Hazards Reduction Plan. (2008.) Strategic plan for the National Earthquake Hazards Reduction Program, fiscal years 2009–2013. Washington, DC: NEHRP.
• National Institute for Standards and Technology. (2016a.) Community resilience planning guide for buildings and infrastructure, Vols. I and II. Gaithersburg, MD: NIST.
• National Institute for Standards and Technology. (2016b). Toward a more resilient community: An overview of the Community Resilience Planning Guide for Buildings and Infrastructure. Gaithersburg, MD: NIST.
• National Research Council. (2006). Facing hazards and disasters: Understanding human dimensions. Washington, DC: National Academies Press.
• National Research Council. (2009). Sustainable critical infrastructure systems: A framework for meeting 21st century imperatives. Washington, DC: National Academies Press.
• National Research Council. (2011). National earthquake resilience—research, implementation, and outreach. Washington, DC: National Academies Press.
• National Research Council. (2012). Disaster resilience: A national imperative. Washington, DC: National Academies Press.
• Nojima, N. (2012). Restoration processes of utility lifelines in the Great East Japan Earthquake Disaster, 2011 Proc. 15th World Conference on Earthquake Engineering. Lisbon, Portugal. Paper no. 2088.
• Oh, E. H., Deshmukh, A., & Hastak, M. (2013). Criticality assessment of lifeline infrastructure for enhancing disaster response. Natural Hazards Review, 14(2), 98–107.
• Okuyama, Y. (2007). Economic modeling for disaster impact analysis: Past, present, and future. Economic Systems Research, 19(2), 115–124.
• O’Rourke, T. (2007). Critical infrastructure, interdependencies, and resilience. The Bridge, 37(1), 22–29.
• Poland, C. (2009). The resilient city: Defining what San Francisco needs from its seismic mitigation policies. San Francisco: San Francisco Planning + Urban Research Association.
• Rinaldi, S., Peerenboom, J., & Kelly, T. (2001). Identifying, understanding, and analyzing critical infrastructures interdependencies. IEEE Control Systems Magazine, 21, 11–25.
• Rockefeller Foundation. (2015). City resilience framework.
• Rose, A. (2004). Defining and measuring economic resilience to disasters. Disaster Prevention and Management, 13(4), 307–314.
• Rose, A., & Guha, G. (2004). Computable general equilibrium modeling of electric utility lifeline losses from earthquakes. In Y. Okuyama & S. Chang (Eds.), Modeling the spatial economic impacts of natural hazards (pp. 118–141). Heidelberg, Germany: Springer.
• Rose, A., & Liao, S. (2005). Modeling regional economic resilience to disasters: A computable general equilibrium analysis of water service disruptions. Journal of Regional Science, 45(1), 75–112.
• Rose, A., & Lim, D. (2002). Business interruption losses from natural hazards: conceptual and methodological issues in the case of the Northridge earthquake. Environmental Hazards, 4, 1–14.
• Rose, A., & Wei, D. (2013). Estimating the economic consequences of a port shutdown: The special role of resilience. Economic Systems Research, 25(2), 212–232.
• Schneider, P., & Schauer, B. (2006). HAZUS—Its development and its future. Natural Hazards Review, 7(2), 40–44.
• Serulle, N. U., & Cirillo, C. (2014). Accessibility of low-income populations to safe zones during localized evacuations. Transportation Research Record 2459, 72–80.
• Shinozuka, M., Rose, A., & Eguchi, R. T. (Eds.). (1998). Engineering and socioeconomic impacts of earthquakes. Buffalo, NY: Multidisciplinary Center for Earthquake Engineering Research.
• Sohn, J., Kim, T. J., Hewings, G. J. D., Lee, J. S., & Jang, S.-G. (2003). Retrofit priority of transport networks links under an earthquake. Journal of Urban Planning and Development, 129(4), 195–210.
• Stough, L. M., Sharp, A. N., Resch, J. A., Decker, C., & Wilker, N. (2015). Barriers to the long-term recovery of individuals with disabilities following a disaster. Disasters, 40(3), 387–410.
• Tatano, H., & Tsuchiya, S. (2008). A framework for economic loss estimation due to seismic transportation network disruption: A spatial computable general equilibrium approach. Natural Hazards, 44(2), 253–265.
• United States Department of Energy, Office of Electricity Delivery and Energy Reliability. (2013). Comparing the impacts of Northeast hurricanes on energy infrastructure. Washington, DC: U.S. Department of Energy.
• Wasileski, G., Rodriguez, H., & Diaz, W. (2010). Business closure and relocation: A comparative analysis of the Loma Prieta earthquake and Hurricane Andrew. Disasters, 35(1), 102–129.
• Webb, G. R., Tierney, K. J., & Dahlhamer, J. M. (2000). Businesses and disasters: Empirical patterns and unanswered questions. Natural Hazards Review, 1(2), 83–90.
• Wein, A., Ratliff, J., Baez, A., & Sleeter, R. (2014). Regional analysis of social characteristics for evacuation resource planning: ARkStorm scenario. Natural Hazards Review, A4014002.
• Xu, N., Guikema, S. D., Davidson, R. A., Nozick, L. K., Cagnan, Z., & Vaziri, K. (2007). Optimizing scheduling of post-earthquake electric power restoration tasks. Earthquake Engineering and Structural Dynamics, 36, 265–284.
• Yates, A. (2014). A framework for studying mortality arising from critical infrastructure loss. International Journal of Critical Infrastructure Protection, 7, 100–111.
• Yavari, S., Chang, S. E., & Elwood, K. (2010). Modeling post-earthquake functionality of regional health care facilities. Earthquake Spectra, 26(3), 869–892.
• Yin, J., Lampert, A., Cameron, M., Robinson, B., & Power, R. (2012). Using social media to enhance emergency situation awareness. IEEE Intelligent Systems, 27(6), 52–59.
• Zhou, B., Gu, L., Ding, Y., Shao, L., Wu, Z., Yang, X., Li, C., . . . Kong, W. (2011). The Great 2008 Chinese Ice Storm: Its socioeconomic-ecological impact and sustainability lessons learned. Bulletin of the American Meteorological Society, 92(1), 47–60.