Abstract

This article compares and contrasts resilience frameworks to identify commonalities and gaps. It proposes use of a coupled human–natural systems framework (CHNS) to analyze community resilience to disasters. CHNS builds on the human ecosystem model, which analyzes how institutions and social order shape fluxes and flows of resources between and within social and environmental systems. It expands on the model by including anthropological concepts of culture, agency, power, and discourse. The framework covers environmental and social legacies, predisaster trends and conditions, resilience measures, and system changes provoked by a disaster. The article proposes 11 categories of variables that affect resilience and discusses research steps for putting the framework into action. The CHNS framework can be used to predict system changes and identify resilience measures that allow communities to articulate and achieve their resilience goals.

Communities across the United States are at risk from natural and anthropogenic hazards, experiencing earthquakes, tornadoes, severe storms, oil spills, and chemical accidents in recent years. Disasters affect communities by displacing human populations, devastating the local economy, destroying critical infrastructure, damaging natural resources, and disrupting environmental services. Federal disaster policies have begun to espouse resilience, or “the ability to anticipate, prepare for, and adapt to changing conditions and withstand, respond to, and recover rapidly from disruptions” (Executive Order 13653, 2013; Presidential Policy Directive-21, 2013). In parallel, several scientific frameworks and models of resilience have emerged. This article compares and contrasts six such frameworks, discusses how society–environment relations mediate disasters, and proposes a coupled human–natural systems (CHNS) framework to analyze community resilience.

The six frameworks differ with respect to key elements, scale of analysis, and desired endpoint. There are gaps in their coverage of variables, or factors thought to affect resilience, and attributes, or characteristics of resilient systems. Models coming out of the disaster literature illuminate socioeconomic factors that affect vulnerability and the role of social capital in supporting resilience (e.g., Cutter, Barnes, Berry, Burton, Evans, Tate, & Webb, 2008; Norris, Stevens, Pfefferbaum, Wyche, & Pfefferbaum, 2008). They do not, however, systematically address environmental and ecological variables. The social–ecological systems (SES) framework, which originates in ecology, provides critical insights into the resilience of ecological systems and natural-resource-dependent communities (e.g., Adger, Hughes, Folke, Carpenter, & Rockström, 2005; Gunderson, 2000). Only recently has it begun to be used to conduct comparable analysis of environmental and energy systems (e.g., van der Merwe, Biggs, & Preisser, 2018). Its treatment of critical social science concepts such as power and agency is inadequate and its applicability to social systems has been questioned (Davidson, 2010; Hatt, 2013).

The proposed CHNS framework situates resilience within human ecosystem structures and processes. It advances beyond current resilience frameworks in three ways. It considers cultural dimensions as explicit variables. It situates social capital and disaster planning within broader societal and governance arrangements of power, agency, and discourse. It highlights the importance of environmental and ecological systems. The article describes the 11 resilience variables of the proposed framework and discusses how these variables came into play during recent disasters. It then provides an orientation to research steps for using the framework to analyze community resilience and conduct policy-oriented research that informs resilience policy and practice.

Review of resilience frameworks

Resilience frameworks and models have emerged from a variety of disciplines. Table 1 provides a brief comparison of six resilience frameworks: Communities Advancing Resilience Toolkit (CART; Norris et al., 2008; Pfefferbaum, Pfefferbaum, Van Horn, Klomp, Norris, & Reissman, 2013), Community Capitals (Magis, 2010; Swanson, Hiley, Venema, & Grosshans, 2007), Disaster Resilience of Place (DROP; Cutter et al., 2008), Los Angeles County Community Disaster Resilience (LACCDR; Chandra, Williams, Plough, Stayton, Wells, Horta, & Tang, 2013; Wells, Tang, Lizaola, Jones, Brown, Stayton, Williams, Chandra, Eisenman, Fogleman, & Plough, 2013), Population & Demographics, Environmental/ Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle & Community Competence, Economic Development, Social–Cultural Capital (PEOPLES; Renschler, Frazier, Arendt, Cimellaro, Reinhorn, & Bruneau, 2010), and Social–Ecological Systems (SES; Adger et al., 2005; Carpenter, Walker, Anderies, & Abel, 2001; Walker, Holling, Carpenter, & Kinzig, 2004). These frameworks were selected because they represent distinct disciplinary approaches to resilience, are commonly cited, and have been applied in theory or practice.

TABLE 1

Resilience frameworks compared.

FrameworkDisciplineKey elementsScaleEndpointSimilarity to CHNSDifference from CHNS
CART Psychiatry Connection & caring, resources, transformative potential, disaster management Community Health & well-being Variables No environmental & ecological system 
Community capitals Sociology Natural, financial, political, social, human, built capitals Community Disastecovery Variables Capitals 
DROP Geography Antecedent conditions, hazard event, preparedness, mitigation, total hazard impact Community Recovery  Variables No environmental & ecological system 
LACCDR Public health Social–economic well-being, organizational involvement, risk communication, physical & psychosocial health, social connectedness  Community Recovery, health security Variables No environmental & ecological system 
PEOPLES Engineering Population, economic development, physical infrastructure, social–cultural capital, lifestyle, community competence, environment ecosystem, government services  Community Recovery Variables Attributes 
SES Ecology Panarchy, complex adaptive system System Transform, adapt, resilient Cross-scalar feedbacks No power or agency, uses panarchy 
FrameworkDisciplineKey elementsScaleEndpointSimilarity to CHNSDifference from CHNS
CART Psychiatry Connection & caring, resources, transformative potential, disaster management Community Health & well-being Variables No environmental & ecological system 
Community capitals Sociology Natural, financial, political, social, human, built capitals Community Disastecovery Variables Capitals 
DROP Geography Antecedent conditions, hazard event, preparedness, mitigation, total hazard impact Community Recovery  Variables No environmental & ecological system 
LACCDR Public health Social–economic well-being, organizational involvement, risk communication, physical & psychosocial health, social connectedness  Community Recovery, health security Variables No environmental & ecological system 
PEOPLES Engineering Population, economic development, physical infrastructure, social–cultural capital, lifestyle, community competence, environment ecosystem, government services  Community Recovery Variables Attributes 
SES Ecology Panarchy, complex adaptive system System Transform, adapt, resilient Cross-scalar feedbacks No power or agency, uses panarchy 

One commonality the frameworks share is in the variables that are posited to play a role in resilience. Adaptive capacity, community competence, and social networks are mentioned in several, for example (Chandra et al., 2013; Carpenter et al., 2001; Magis, 2010; Norris et al., 2008; Renschler et al., 2010). Another commonality is that the frameworks pay limited attention to the role that environmental and ecological systems and services play in disaster resilience (e.g., drinking water, wastewater, water and air quality, waste management, ecosystem health). Community Capitals does propose natural capital as a variable, and PEOPLES proposes environmental/ecosystem. Neither, however, details a broader theoretical basis for how to analyze the complex society–environment dynamics that affects resilience. SES is a notable exception in its consideration of ecological variables. Its focus, however, has largely been on ecosystems and natural-resource-dependent communities (Olsson, Jerneck, Thoren, Persson, & O’Byrne, 2015), not the environmental systems and services upon which rural and urban areas depend.

Another way to compare the frameworks is how they indicate resilience of what, to what, and to what end. In terms of resilience of what, the frameworks address resilience of communities and operate at the community scale, with the exception of SES, which is applied to social–ecological systems (Berkes & Ross, 2013). SES also differs in that it addresses cross-scalar interactions and feedbacks more rigorously (e.g., Adger et al., 2005; Holling, 2001). In terms of resilience to what, DROP, CART, and PEOPLES address resilience to disasters. While they do not specify a type of disaster, their application has largely been limited to natural hazards such as earthquakes and flooding, not to environmental emergencies, terrorism, or other human-caused disasters. For DROP and PEOPLES, resilience to what end is community recovery from disasters. CART and LACCDR specify community health and well-being and health security as endpoints. SES asserts that adaptation, transformation, and resilience are all possible endpoints for socio-ecological systems (Walker et al. 2004).

A resilient system is less likely to collapse in the wake of a disaster. Identifying system-level properties that distinguish resilient from nonresilient systems is important. The Community Capitals, DROP, LACCDR, and SES frameworks do not specify attributes of resilient systems. PEOPLES proposes robustness, redundancy, resourcefulness, and rapidity as attributes. Three of CART’s domains correspond with system attributes: connection and caring, transformative potential, and resources. This lack of attention to attributes makes it more difficult to use the frameworks to analyze system-level properties that confer resilience.

Disasters, society, and the environment

Society and the environment are mutually constitutive, with implications for disaster causes and consequences (Oliver-Smith, 1999). Both social and environmental factors affect resilience and must be examined in concert (Adger, 2000; Berkes, 2007; Pickett, Cadenasso, & Grove, 2004). Communities rely on environmental and ecological systems for their economies, well-being, and quality of life. Disasters pose environmental risks to human health, however. Floods can generate mold growth inside buildings and deposit household hazardous wastes in nearby wetlands. Tornado debris may include asbestos-laden building materials or soils containing heavy metals. Communities with damaged drinking water and wastewater treatment systems must scramble to provide these services. Disaster debris must be disposed of while regular waste collection resumes. A systems approach to community resilience entails consideration of the interconnections among social, environmental, and ecological systems.

Community resilience involves complex, dynamic processes. Sector interdependencies are common, as with the water–energy nexus: energy production requires water; water treatment requires energy. Electrical wires downed in a storm can have unintended consequences for water supply if the affected lines serve the local drinking water treatment plant. Cross-scalar feedbacks occur: wetlands loss in coastal Louisiana increases flooding risk; storm losses entrench political support to build upstream levees even higher; decreased sedimentation from upper parts of the watershed exacerbates deltaic land loss and increases vulnerability to sea level rise (Burby, 2006; Sidle, Benson, Carriger, & Kamai, 2013). These feedbacks may involve political, social, economic, and biophysical processes. Building resilient communities requires attention to these connections.

The social processes of community resilience are complex and operate on numerous scales. In resilience lexicon, the term “community” often refers to a group of people who live in geographic proximity and have existing relationships. However, community is a contested concept, and membership, social hierarchies, and power relationships are fluid before and after disasters (Barrios, 2014; Kendra, Clay, & Gill, 2018; Titz, Cannon, & Krüger, 2018). In resilience practice, federal, state, and local governments offer a multitude of policies and programs. Numerous stakeholders bring competing values and interests to the table. Community residents do not make disaster-related decisions (e.g., evacuation, reoccupation) as independent entities. Their decisions are influenced by membership in social groups (e.g., family, neighborhood, house of worship), resource availability, and cultural values and worldviews (Browne, 2013, Dietrich, 2016; Maldonado, 2016). The local economy is affected by broader political economic trends and supply chain constraints, as experienced by the Gulf Coast shrimping industry after Hurricane Katrina. Even shrimpers with functional boats had difficulty resuming normal levels of activity due to damaged docks and shrimp processing facilities. Those who could continue fishing earned lower prices per pound for their take, as the few processing facilities that remained were each receiving an overabundance of supply. The shrimping industry, already suffering from high fuel prices and foreign competition, went into even stronger decline (Ingles & McIlvaine-Newsad, 2007).

Power, agency, and discourse are part of the fabric of how disasters interweave with society and the environment. These social science concepts have complex theoretical underpinnings and definitions. In disaster contexts, they can be thought of as follows: agency is the individual or collective capacity to reduce vulnerability or enhance resilience, power is the ability to influence disaster decision-making, and discourse is the language used to describe disaster situations. A major disaster declaration thus is at once reflective of power (of state and federal governments under the Stafford Act) and discourse (establishing what a disaster is).

CHNS framework

Key dimensions

The foundation of the CHNS framework is the human ecosystem model, which analyzes flows (e.g., energy, nutrients, waste, water, capital, information) within human societies and between societies and the environment (Machlis, Force, & Burch, 1997). These flows are shaped by, and in turn shape, resource conditions and population characteristics. Social institutions, order, and cycles affect the use of biophysical, socioeconomic, and cultural resources (Machlis et al., 1997). There are complex feedbacks and interdependencies within and across scales (Liu, Dietz, Carpenter, Alberti, Folke, Moran, Pell, Deadman, Kratz, Lubchenco, Ostrom, Ouyang, Provencher, Redman, Schneider, & Taylor, 2007; Pickett, Cadenasso, Grove, Boone, Groffman, Irwin, Kaushal, Marshall, McGrath, Nilon, Pouyat, Szlavecz, Troy, & Warren, 2011). The model integrates social and biophysical variables in explaining system trends, conditions, and dynamics. As Pickett et al. (2004, 378) note, it “may look like a shopping list of system components. However . . . the most significant feature of the framework is the fact that it points to the interactions among particular natural, social, and cultural components.”

The human ecosystem model has most often been used in ecosystem management and urban ecology. Its application to community resilience builds on its use in developing scenarios of cascading consequences from the Deepwater Horizon oil spill and Hurricane Sandy (DOI Strategic Sciences Group, 2013; Machlis & Ludwig, 2014; Machlis & McNutt, 2010). The CHNS framework extends the human ecosystem model by incorporating anthropological concepts of power, agency, and discourse. It includes environmental and social legacies, pre-disaster trends and conditions, resilience measures, and system changes provoked by a disaster.

Figure 1 illustrates key elements of how the CHNS framework approaches resilience. Pre-existing conditions include 11 system variables (here, only four are shown for illustrative purposes) and the interrelationships among them. Pre-existing conditions within a community and cross-scalar influences shape postdisaster system changes and can be underlying causes of the disaster itself (Oliver-Smith, 1999). After a disaster, system variables and their interrelationships may change (e.g., system boundaries, property rights over resources, rules over how resources are governed, rituals that compose everyday life). Tracking CHNS flows (e.g., humanitarian aid, debris, food) can help anticipate changes and predict recovery trajectories. Drinking water may be flown in by helicopter instead of coming through kitchen pipes, for example. These changes carry implications for human health and well-being, the environment, and local economy. Postdisaster conditions are not static, and eventually set the stage for when the next disaster strikes.

FIGURE 1

CHNS model of social and environmental system interactions that affect resilience over time (G = governance, E = environment, S = social, I = infrastructure).

FIGURE 1

CHNS model of social and environmental system interactions that affect resilience over time (G = governance, E = environment, S = social, I = infrastructure).

Close modal

Resilience measures may be taken before or after a disaster, with varying effects upon pre- and postdisaster conditions. These measures include formal preparedness, response, and recovery efforts, as well as informal processes involving social capital, learning, memory, and mobility (Adger et al., 2005; Aldrich, 2012). Environmental and social legacies also come into play. Legacy effects are the “impacts of prior human-nature couplings on later conditions” (Liu et al., 2007, p. 1515). For example, Joplin, Missouri, which was leveled by a 2011 tornado, is also the location of the Oronogo–Duenweg Mining Belt Superfund site. Historic zinc mining and smelting contaminated the site with lead, zinc, and cadmium. Post-tornado rebuilding in Joplin required testing residential soils for this legacy pollution at a cost of $5.4 million.

Variables affecting community resilience

CART, community capitals, DROP, LACCDR, PEOPLES, and SES suggest a wealth of variables that affect resilience. The frameworks use different terminologies, such as domains (Pfefferbaum et al., 2013) or core components and levers (Chandra et al., 2013), instead of variables, but include similar elements (e.g., leadership and wellness). This article proposes 11 categories of variables (Table 2). CHNS categorization of variables reflects underlying human ecosystem structures and processes. It addresses the environment and ecosystem gap in disaster frameworks and the power, agency, and discourse gap in the SES framework. It explicitly considers cultural variables, situates social capital and disaster planning within broader societal and governance arrangements, and highlights the importance of both environmental and ecological systems.

Communications involve pre-existing flows of information (e.g., porch stoop gossip, church bulletins, social media) and disaster-specific flows (e.g., hazard warnings). Interoperability is essential to its effectiveness as a resilience measure (Cutter et al., 2008). The U.S. Coast Guard, for example, established a variety of communications channels in advance of Hurricane Katrina, increasing redundancy in case a channel failed (Baker & Refsgaard, 2007). Interoperability involves more than technical modifications. It also requires attention to discourse and the cultural contexts in which information is transmitted and received. Impeding information flows has resilience implications, as well. During the 2001 Amerithrax incident, public health agencies were unable to meet media demands for information, contributing to confusion about health risks and appropriate remedies (Gursky, Inglesby, & O’Toole, 2003).

TABLE 2

Variables affecting community resilience.

VariableMeaning
Communication System interoperability; risk communication; media; social media; language 
Demographic Human population—composition, characteristics 
Disaster governance & planning Disaster mitigation, preparedness, response, recovery, rebuilding; laws, rules, policies; political participation; power dynamics 
Economic Resource quantity, distribution, flows; funding, aid; labor; property rights 
Environmental & ecological Ecological systems & services; land use land cover change; environmental systems & services; water, soil, air quality; environmental legacy pollution; distribution of environmental harms 
Health & well-being Human and public health (mental, physical); health care resources 
Infrastructure & built environment Critical infrastructure & lifelines; housing; built environment 
Institutions Structure & function of public, private, civic organizations, business, government; leadership; organizational norms, hierarchies, culture 
Knowledge, values, worldviews, & beliefs Science & technology; social norms; risk perception; discourse; trust; hazard knowledge; cultural values; myth 
Sense of place & identity Sense of place; social, ecological, cultural identity 
Social networks & collective action Social capital; social order & hierarchy; social networks; collective action 
VariableMeaning
Communication System interoperability; risk communication; media; social media; language 
Demographic Human population—composition, characteristics 
Disaster governance & planning Disaster mitigation, preparedness, response, recovery, rebuilding; laws, rules, policies; political participation; power dynamics 
Economic Resource quantity, distribution, flows; funding, aid; labor; property rights 
Environmental & ecological Ecological systems & services; land use land cover change; environmental systems & services; water, soil, air quality; environmental legacy pollution; distribution of environmental harms 
Health & well-being Human and public health (mental, physical); health care resources 
Infrastructure & built environment Critical infrastructure & lifelines; housing; built environment 
Institutions Structure & function of public, private, civic organizations, business, government; leadership; organizational norms, hierarchies, culture 
Knowledge, values, worldviews, & beliefs Science & technology; social norms; risk perception; discourse; trust; hazard knowledge; cultural values; myth 
Sense of place & identity Sense of place; social, ecological, cultural identity 
Social networks & collective action Social capital; social order & hierarchy; social networks; collective action 

A community’s demographic composition affects disaster vulnerability (Cutter, Burton, & Emrich, 2010). Vulnerability is associated with race, class, and ethnicity, among other demographic characteristics. Community subpopulations can be differentially exposed and susceptible to disasters. Vulnerability to heat waves, for example, is associated with age, poverty, household composition, and pre-existing health conditions (Reid, O’Neill, Gronlund, Brines, Brown, Diez-Roux, & Schwartz, 2009). In New Orleans, victims who perished from Hurricane Katrina were largely African-American, elderly, and lower middle class (Petterson, Stanley, Glazier, & Philipp, 2006). Vulnerability is exacerbated by social legacies (e.g., historic underrepresentation, urban development patterns, insurance redlining). After disasters, demographic changes to a community may be temporary or longer-term.

Disaster governance and planning include preparedness, response, and recovery decisions. Disaster planning may involve installing early warning systems, conducting scenarios, and acquiring necessary supplies. It is situated within disaster governance, or the “interrelated sets of norms, organizational and institutional actors and practices that are designed to reduce the impacts and losses associated with disasters” (Tierney, 2012, p. 344). Disaster governance takes place on multiple scales. States often set building codes, while local governments regulate zoning. The federal government develops national frameworks for preparedness, response, and recovery. Politics, power, and justice thus are critical, yet understudied, factors that affect resilience (Barrios, 2016; Berkes & Ross, 2013; Davoudi, 2012). Identifying who is involved in or excluded from disaster decision-making is critical to analyzing resilience.

Economic variables influence resilience in a variety of ways. Hurricane Katrina struck at the end of the month, when people were stretching already short paychecks, so a lack of ready cash became an obstacle to evacuation (Elder, Xirasagar, Miller, Bowen, Glover, & Piper, 2007). Communities that depend on a single industry may be more vulnerable (Cutter et al., 2010). Labor issues arise during recovery, as with the aftermath of Hurricane Katrina, which saw both high levels of unemployment and labor shortages (Petterson et al., 2006). Economic considerations include who has access to natural resources and how access changes with a disaster.

Environmental and ecological systems affect resilience. Ecological degradation can increase hazard risk and vulnerability, for example (Adger et al., 2005; Sidle et al., 2013). The CHNS framework approaches these systems in concert because of their interconnections. The presence of invasive species limited debris disposal options after Hurricanes Sandy and Katrina. An ecological problem thus affected postdisaster environmental management. During the 2014 Elk River chemical spill in West Virginia, over 20 million plastic water bottles were distributed to provide residents with drinking water. Most area households did not have curbside recycling, turning a water quality problem into a waste management challenge. Disasters can damage drinking water and wastewater treatment systems, disrupt electrical service, spread contaminants about, and generate tons of waste and debris.

Disasters affect community health and well-being (Norris et al., 2008), and pre-existing health conditions increase vulnerability (Reid et al., 2009). Ensuring that health care systems continue to function is an essential part of resilience. Hurricane Katrina caused physical damage to New Orleans hospitals. After the storm, hospitals remained closed or had limited capacity, affecting health care for returning residents and incoming construction workers (Taylor, 2007).

Infrastructure and built environment performance can worsen or reduce disaster impacts and extend or minimize the time needed to return to normal service (Chang, 2009; U.S. Environmental Protection Agency, 2015). Resilience measures often involve the built environment (e.g., permeable parking lots, green roofs, greenways in riparian areas). Water and wastewater systems are designated as critical infrastructure under Presidential Policy Directive-21 (2013). Water infrastructure conditions can present resilience challenges (e.g., leaking pipes, combined sewer systems). Some community infrastructure may be locally valued, even if it is not designated as critical. The 2013 floods in Colorado destroyed recreational trails. While trail maintenance was a lower recovery priority than meeting basic water and electricity needs, it was still a central concern for area residents.

Numerous institutions play roles in resilience (e.g., businesses, emergency management departments, civic organizations). Institutional structure and organizational culture affect resilience. Resilient institutions are said to have strong and supportive leadership, flexibility, and adaptive capacity (Adger et al., 2005; Kendra & Wachtendorf, 2003). Even though New York City’s Emergency Operations Center was physically destroyed when the World Trade Center fell, it rapidly resumed operations (Kendra & Wachtendorf, 2003). It was able to do so in part because it had already established relationships with other organizations (Kendra & Wachtendorf, 2003). In contrast, attempts by the UK water sector to implement resilience measures during a 2006 drought were impeded by institutional arrangements (Chappells & Medd, 2012). Resilience measures draw upon existing organizational relationships or launch new ones, as with public–private partnerships for disaster response.

Knowledge, values, worldviews, and beliefs affect resilience. This category includes a constellation of cultural factors such as social memory, trust, risk perception, social norms, myths, discourse, and environmental knowledge (Adger et al., 2005; Flint & Luloff, 2005; Wells et al., 2013). Attention to disaster-related information flows before an incident can indicate the level of hazard knowledge. After an incident, access to information can be as important as material aid to survivors (Glass & Schoch-Spana, 2002). Trust in institutions affects how people receive and act upon disaster information (Glass & Schoch-Spana, 2002). Discourse is intimately connected with power, requiring attention to whose words are used and whose knowledge counts. While research has shown for decades that social norms are often upheld even amidst the chaos of a disaster (Johnson, 1987), the myth that “people panic” still pervades preparedness and response efforts (Glass & Schoch-Spana, 2002). Discourse analysis can help reveal how this myth is perpetuated and how it might be countered.

Sense of place and identity are cultural variables that affect human behavior and collective action. Place is found to be central to disaster recovery (Cox & Perry, 2011). A sense of place influenced one Australian town’s decision not to relocate after it was damaged by flooding (Boon, 2014). Social and cultural identity is also a factor. During a heat wave in the United Kingdom, precautionary measures recommended for elderly, vulnerable populations were largely ignored. The intended recipients, it turned out, considered themselves neither elderly nor vulnerable (Abrahamson, Wolf, Lorenzoni, Fenn, Kovats, Wilkinson, Adger, & Raine, 2009).

Social networks and collective action signal community agency. The strength and form of social networks reflect social capital, or the “resources embedded in one’s social networks” (Aldrich, 2012, p. 20). Social capital and networks are shown to affect resilience positively (Aldrich, 2012; Meyer, 2018). Social ties centered on the Catholic church contributed to the successful evacuation and recovery of a Vietnamese American community in New Orleans after Hurricane Katrina (Airriess, Li, Leong, Chen, & Keith, 2008). Insufficient social capital can worsen individual stress and collective trauma after a disaster (Ritchie, 2012). Social capital is embedded in state-civil society relations, social hierarchies, and power relationships (Adger, 2003; Defillipis, 2001; MacLean, Cuthill, & Ross, 2014). It can also undermine resilience via cronyism, groupthink, and social exclusion (Adger, 2003; Aldrich, 2012; Meyer, 2018; Vásquez-León, 2009). The CHNS framework’s attention to social institutions and order helps unpack how social networks mobilize social capital, affecting resource flows into and within a community.

The principal difference of CHNS from CART, Community Capitals, DROP, LACCDR, and PEOPLES is its situation of resilience within broader human–environment relations. It shares the most similarities with SES. One key difference is that SES uses panarchy as an underlying theory of system change, where adaptive cycles go through four phases: growth (r), conservation (K), creative destruction (Ω), and reorganization (α). The cycle results from interactions among slow and fast variables across scales (Walker et al., 2004). Panarchy, rooted in ecological theory, is being applied to human–environment systems (Berkes, 2007). Social scientists, however, have critiqued its applicability to social systems (Davidson, 2010; Olsson et al., 2015) and its inadequate treatment of power and agency (Berkes & Ross, 2013; Brown & Westaway, 2011; Cote & Nightingale, 2012; Davidson, 2010; Davoudi, 2012; Hatt, 2013). SES researchers have begun to address property rights, institutions, and polycentric governance (Anderies, Jansson, & Ostrom, 2004; Garmestani & Benson, 2013), but broader social dynamics largely remains a black box. The CHNS framework further unpacks this black box by attending to culture, power, agency, and discourse.

Putting the framework into action

The CHNS framework can be put into action for systems research and for policy-oriented research to identify effective resilience measures. Disaster science investigates community resilience through case studies, infrastructure modeling and simulations, indicators, and health monitoring, among other research methods (Chang & Shinozuka, 2004; Norris, 2006; Cutter, Ash, & Emrich, 2014; Davidson & Nozick, 2018; Donner & Diaz, 2018). SES and human ecosystem research integrate social and ecological data from fieldwork and secondary sources into Geographic Information Systems (GIS) or models (Liu et al., 2007; Pickett et al., 2011). CHNS research utilizes similar research strategies, but also requires methods to systematically analyze culture, power, agency, and discourse. Table 3 highlights a few such methods for five select CHNS variables (for further details on specific methods, see Faulkner, Brown, & Quinn, 2018; Mazereeuw & Yarina, 2017; McCulloh, Armstrong, & Johnson, 2013; Merriam & Tisdell, 2015; Russell & Harshbarger, 2003). Uncovering the social and cultural dimensions of disaster institutions, for example, could involve social network analysis to identify intra-organizational relationships, institutional ethnography to critically examine power and decision making, or narrative inquiry to analyze the stories organization tell about disasters (McCulloh et al., 2013; Merriam & Tisdell, 2015; Smith, 2006).

TABLE 3

Research methods to analyze culture, power, agency, and discourse for five CHNS variables.

VariableResearch Methods
Disaster planning & governance Critical research
Participatory stakeholder identification
Policy network analysis
Text & document analysis 
Institutions Institutional ethnography
Narrative inquiry
Social network analysis 
Knowledge, values, worldviews, & beliefs Interviews, focus groups
Mixed methods
Participant observation
Q methodology 
Social networks & collective action Games & scenarios
Social media data mining
Social network analysis
Surveys 
Sense of place & identity Disaster design research
Interviews, focus groups
Participant observation
Participatory mapping, photo ranking 
VariableResearch Methods
Disaster planning & governance Critical research
Participatory stakeholder identification
Policy network analysis
Text & document analysis 
Institutions Institutional ethnography
Narrative inquiry
Social network analysis 
Knowledge, values, worldviews, & beliefs Interviews, focus groups
Mixed methods
Participant observation
Q methodology 
Social networks & collective action Games & scenarios
Social media data mining
Social network analysis
Surveys 
Sense of place & identity Disaster design research
Interviews, focus groups
Participant observation
Participatory mapping, photo ranking 

CHNS resilience research necessitates data collection before and after a disaster (Figure 2). A combination of qualitative and quantitative research methods is used, with one key aspect being data collection at different scales. Research on supply chain performance during disasters takes place at a regional or larger scale, for example, whereas demographic data is analyzed down to the census block (Davidson & Nozick, 2018; Pickett et al., 2011; Smith & Lawrence, 2014). Data synthesis and analysis uses interdisciplinary approaches (e.g., models, GIS) that operate at more than one scale and/or can capture social dynamics (e.g., agent-based, multiscale, multilevel, participatory, systems dynamics, or cultural models; Donner and Diaz, 2018; Norris, 2006; Paolisso, 2015; Pickett et al., 2011).

The objective of CHNS research is not to investigate all system components equally or collect data ad infinitum. Doing so runs the risk of generating a nebulous jumble of data points and overly complicated and ultimately meaningless models. Instead, its systems approach helps identify the most relevant variables on which to focus. It is also distinguished by its data interpretation and emphasis. Examining data on food availability after a disaster, for example, involves a dual lens of analyzing how nutrients flow (e.g., humanitarian aid, supply chain disruptions), and how cultural foodways are or are not supported during response and recovery. Using these lenses fosters establishment of general principles of system behavior, making it easier to identify place-specific data patterns when analyzing disaster impacts.

FIGURE 2

Qualitative and quantitative methods of investigating CHNS resilience on different spatial and temporal scales.

FIGURE 2

Qualitative and quantitative methods of investigating CHNS resilience on different spatial and temporal scales.

Close modal

Resilience research faces several challenges. For example, resilience indicators that measure predisaster adaptive capacity and vulnerability depend on secondary data (e.g., U.S. Census, Bureau of Labor Statistics) that may not be collected frequently enough to show postdisaster differences. Other resilience indicators require data that are simply not available locally (U.S. Environmental Protection Agency, 2017). Data collection after disasters is complicated by issues of time, access, and ethics (Donner and Diaz, 2018; Packenham, Rosselli, Ramsey, Taylor, Fothergill, Slutsman, & Miller, 2017). Qualitative research is particularly time-consuming. An alternative course of action is to use the CHNS framework in policy-oriented research that examines system dynamics in a targeted fashion. Using it to help identify limiting factors and that stem from cross-scalar interactions or unintended consequences of resilience measures is one way it can inform resilience policy and practice. For example, elevating coastal houses to enhance their capacity to withstand storm surge may have unintended consequences for neighborhood social dynamics and local sense of place. Another way it can inform resilience measures is by uncovering social and environmental legacies that affect resilience. Abandoned hazardous waste sites, for example, are environmental legacies that can be sources of contaminant exposure during disasters. They can also be sources of distrust in institutions on the part of historically marginalized populations in environmental justice communities. Its attention to cultural factors means that the CHNS framework can be used to inform development of resilience measures that avoid cultural conflicts. For example, culture brokers can help breach cultural divides and build trust between community members and disaster institutions (Browne, 2013). The CHNS framework can also be used to identify relevant questions to ask during postdisaster hotwashes that produce after-action reports that facilitate resilient learning for the next disaster. It can also be used to help recognize which stakeholders should be at the table to participate in disaster planning scenarios and develop techniques for valuing their knowledge and input.

Conclusions

This article proposes the CHNS framework to analyze community resilience. Resilience frameworks from the hazards and disasters literature do not adequately incorporate environmental and ecological systems. SES addresses ecological resilience but does not employ key social science concepts. A comprehensive approach to resilience is necessary because interplays between society and the environment shape disaster causes and consequences. The CHNS framework expands the human ecosystem model to include anthropological concepts of culture, power, agency, and discourse. It proposes 11 categories of variables that affect community resilience: communication; demographic; disaster governance and planning; economic; environmental and ecological; health and well-being; infrastructure and built environment; institutions; knowledge, values, worldviews, and beliefs; sense of place and identity; and social networks and collective action. Its organization of variables reflects underlying human ecosystem structures and processes. The framework explicitly considers cultural dimensions of resilience, situates social capital and disaster planning within broader societal and governance arrangements, and highlights the importance of both environmental and ecological systems.

The CHNS framework provides a means of analyzing the complex social and biophysical dynamics that affects community resilience. Research steps to investigate specific variables and synthesize interdisciplinary data are discussed. One future research priority is to establish which key variables affect resilience of different types of communities to different types of disasters. It is likely that not all variables will be relevant to all cases, and some may be weighted more heavily than others. Indicators could also be developed to measure system conditions before and after disasters. Further research is also needed on system attributes that enhance community resilience. CHNS resilience research can be more analytical or more policy-oriented in nature. The framework can be used to help identify effective measures to help communities articulate and achieve their resilience goals.

References

Abrahamson, V., Wolf, J., Lorenzoni, I., Fenn, B., Kovats, S., Wilkinson, P., Adger, W. N., & Raine, R. (2009). Perceptions of heatwave risks to health: Interview-based study of older people in London and Norwich, UK. Journal of Public Health, 31(1), 119–26.
Adger, W. N. (2000). Social and ecological resilience: Are they related? Progress in Human Geography, 24(3), 347–64.
Adger, W. N. (2003). Social capital, collective action, and adaptation to climate change. Economic Geography, 79(4), 387–404.
Adger, W. N., Hughes, T. P., Folke, C., Carpenter, S. R., & Rockström, J. (2005). Social-ecological resilience to coastal disasters. Science, 309(5737), 1036–39.
Airriess, C. A., Li, W., Leong, K. J., Chen, A. C.-C., & Keith, V.M. (2008). Church-based social capital, networks and geographical scale: Katrina evacuation, relocation, and recovery in a New Orleans Vietnamese American community. Geoforum, 39(3), 1333–46.
Aldrich, D. P. (2012). Building Resilience: Social Capital in Post-disaster Recovery. Chicago: University of Chicago Press.
Anderies, J. M., Janssen, M., & Ostrom, E. (2004). A framework to analyze the robustness of social-ecological systems from an institutional perspective. Ecology and Society, 9(1), 18 [online]. http://www.ecologyandsociety.org/vol9/iss1/art18.
Baker, D., & Refsgaard, K. (2007). Institutional development and scale matching in disaster response management. Ecological Economics, 63(2–3), 331–43.
Barrios, R. (2014). “Here, I’m not at ease”: Anthropological perspectives on community resilience. Disasters, 38(2), 329–50.
Barrios, R. (2016). Resilience: A commentary from the vantage point of anthropology. Annals of Anthropological Practice, 40(1), 28–38.
Berkes, F. (2007). Understanding uncertainty and reducing vulnerability: Lessons from resilience thinking. Natural Hazards, 41(2), 283–95.
Berkes, F., & Ross, H. (2013). Community resilience: Toward an integrated approach. Society and Natural Resources, 26(1), 5–20.
Boon, H. J. (2014). Disaster resilience in a flood-impacted rural Australian town. Natural Hazards, 71(1), 683–701.
Brown, K., & Westaway, E. (2011). Agency, capacity, and resilience to environmental change: Lessons from human development, well-being, and disasters. Annual Review of Environment and Resources, 36(1), 321–42.
Browne, K. (2013). Standing in the need: Communication failures that increased suffering after Katrina. Anthropology Now, 5(1), 1.
Burby, R. J. (2006). Hurricane Katrina and the paradoxes of government disaster policy: Bringing about wise governmental decisions for hazardous areas. Annals of the American Academy of Political and Social Science, 604, 171–91.
Carpenter, S., Walker, B., Anderies, J. M., & Abel, N. (2001). From metaphor to measurement: Resilience of what to what? Ecosystems, 4(8), 765–81.
Chandra, A., Williams, M., Plough, A., Stayton, A., Wells, K. B., Horta, M., & Tang, J. (2013). Getting actionable about community resilience: The Los Angeles County Community Disaster Resilience Project. American Journal of Public Health, 103(7), 1181–89.
Chang, S. (2009). Infrastructure resilience to disasters. The Bridge, 39(4), 36–41.
Chang, S., & Shinozuka, M. (2004). Measuring improvements in the disaster resilience of communities. Earthquake Spectra, 20(3), 739–55.
Chappells, H., & Medd, W. (2012). Resilience in practice: The 2006 drought in southeast England. Society and Natural Resources, 25(3), 302–16.
Cote, M., & Nightingale, A. J. (2012). Resilience thinking meets social theory: Situating social change in socio-ecological systems (SES) research. Progress in Human Geography, 36(4), 475–89.
Cox, R.S., & Perry, K. M. E. (2011). Like a fish out of water: Reconsidering disaster recovery and the role of place and social capital in community disaster resilience. American Journal of Community Psychology, 48(3–4), 395–411.
Cutter, S. L., Ash, K., & Emrich, C. T. (2014). The geographies of community disaster resilience. Global Environmental Change, 29, 65–77.
Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J. (2008). A place-based model for understanding community resilience to natural disasters. Global Environmental Change, 18(4), 598–606.
Cutter, S. L., Burton, C., & Emrich, C. T. (2010). Disaster resilience indicators for benchmarking baseline conditions. Journal of Homeland Security and Emergency Management, 7(1), 1–22.
Davidson, D. J. (2010). The applicability of the concept of resilience to social systems: Some sources of optimism and nagging doubts. Society and Natural Resources, 23(12), 1135–49.
Davidson, R. A., & Nozick, L. K. (2018). Computer simulation and optimization. In H. Rodriguez, W. Donner, & J. Trainor (Eds.), Handbook of Disaster Research (pp. 331–56). New York: Springer.
Davoudi, S. (2012). Resilience: A bridging concept or a dead end? Planning Theory and Practice, 13(2), 299–333.
Defillipis, J. (2001). The myth of social capital in community development. Housing Policy Debate, 12(4), 781–806.
Dietrich, A. (2016). Sandy on Staten Island: Culture, barriers to recovery, and the question of resilience. Natural Hazards Observer, 15(5), 10–15.
DOI Strategic Sciences Group. (2013). Operational Group Sandy Technical Progress Report. Washington, DC: U.S. Department of the Interior.
Donner, W., & Diaz, W. (2018). Methodological issues in disaster research. In H. Rodriguez, W. Donner, & J. Trainor (Eds.), Handbook of Disaster Research (pp. 289–309). New York: Springer.
Elder, K., Xirasagar, S., Miller, N., Bowen, S. A., Glover, S., & Piper, C. (2007). African Americans’ decisions not to evacuate New Orleans before Hurricane Katrina: A qualitative study. American Journal of Public Health, 97(S1), S124–S129.
Executive Order 13653. (2013). Preparing the United States for the impacts of climate change. 3-CFR-13653. Washington, DC.
Faulkner, L., Brown, K., & Quinn, T. (2018). Analyzing community resilience as an emergent property of dynamic social-ecological systems. Ecology and Society, 23(1), 24 [online].
Flint, C., & Luloff, A. E. (2005). Natural resource-based communities, risk, and disaster: An intersection of theories. Society and Natural Resources, 18(5), 399–412.
Garmestani, A., & Benson, M. (2013). A framework for resilience-based governance of social-ecological systems. Ecology and Society, 18(1), 9 [online].
Glass, T. A., & Schoch-Spana, M. (2002). Bioterrorism and the people: Hhow to vaccinate a city against panic. Clinical Infectious Diseases, 34(2), 217–23.
Gunderson, L. H. (2000). Ecological resilience – In theory and application. Annual Review of Ecology and Systematics, 31, 425–39.
Gursky, E. A., Inglesby, T. V., & O’Toole, T. (2003). Anthrax 2001: Observations on the medical and public health response. Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science, 1(2), 97–110.
Hatt, K. (2013). Social attractors: A proposal to enhance “resilience thinking” about the social. Society and Natural Resources, 26(1), 30–43.
Holling, C. S. (2001). Understanding the complexity of economic, ecological, and social systems. Ecosystems, 4(5), 390–405.
Ingles, P., & McIlvaine-Newsad, H. (2007). Any port in the storm: The effects of Hurricane Katrina on two fishing communities in Louisiana. Annals of Anthropological Practice, 28(1), 69–86.
Johnson, N. R. (1987). Panic and the breakdown of social order: Popular myth, social theory, empirical evidence. Sociological Focus, 20(3), 171–83.
Kendra, J., Clay, L., & Gill, K. (2018). Resilience and disasters. In H. Rodriguez, W. Donner, & J. Trainor (Eds.), Handbook of Disaster Research (pp. 87–107). New York: Springer.
Kendra, J., & Wachtendorf, T. (2003). Elements of resilience after the World Trade Center disaster: Reconstituting New York City’s Emergency Operations Centre. Disasters, 27(1), 37–53.
Liu, J., Dietz, T., Carpenter, S. R., Alberti, M., Folke, C., Moran, E., Pell, A. N., Deadman, P., Kratz, T., Lubchenco, J., Ostrom, E., Ouyang, Z., Provencher, W., Redman, C. L., Schneider, S. H., & Taylor, W. W. (2007). Complexity of coupled human and natural systems. Science, 317(5844), 1513–16.
Machlis, G. E., Force, J. E., & Burch, W. R. (1997). The human ecosystem. Part I: The human ecosystem as an organizing concept in ecosystem management. Society and Natural Resources, 10(4), 347–67.
Machlis, G. E., & Ludwig, K. A. (2014). Science during crisis: The application of interdisciplinary and strategic science during major environmental crises. In M. J. Manfredo, J. J. Vaske, A. Rechkemmer, & E. Duke (Eds.), Understanding Society and Natural Resources (pp. 47–65). Dordrecht: Springer.
Machlis, G. E., & McNutt, M. K. (2010). Scenario–building for the Deepwater Horizon oil spill. Science, 329(5995), 1018–19.
MacLean, K., Cuthill, M., & Ross, H. (2014). Six attributes of social resilience. Journal of Environmental Planning and Management, 57(1), 144–56.
Magis, K. (2010). Community resilience: An indicator of social sustainability. Society and Natural Resources, 23(5), 401–16.
Maldonado, J. (2016). Considering culture in disaster practice. Annals of Anthropological Practice, 40(1), 52–60.
Mazereeuw, M., & Yarina, E. (2017). Emergency preparedness hub: Designing decentralized systems for disaster resilience. Journal of Architectural Education, 71(1), 65–72.
McCulloh, I., Armstrong, H., & Johnson, A. (2013). Social network analysis with applications. 1st ed. Wiley: San Francisco.
Merriam, S., & Tisdell, E. (2015). Qualitative research: A guide to design and implementation. San Francisco: Jossey-Bass.
Meyer, M. A. (2018). Social capital in disaster research. In H. Rodriguez, W. Donner, & J. Trainor (Eds.), Handbook of disaster research (pp. 263–86). New York: Springer.
Norris, F. (2006). Disaster research methods: Past progress and future directions. Journal of Traumatic Stress, 19(2), 173–84.
Norris, F., Stevens, S. P., Pfefferbaum, B., Wyche, K. F., & Pfefferbaum, R. L. (2008). Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. American Journal of Community Psychology, 41(1–2), 127–50.
Oliver-Smith, A. (1999). “What is a disaster?”: Anthropological perspectives on a persistent question. In A. Oliver-Smith & S. Hoffman (Eds.), The Angry Earth: Disaster in Anthropological Perspective (pp. 18–34). New York: Routledge.
Olsson, L., Jerneck, A., Thoren, H., Persson, J., & O’Byrne, D. (2015). Why resilience is unappealing to social science: Theoretical and empirical investigations of the scientific use of resilience. Science Advances, 1(4 e1400217), 11.
Packenham, J. P., Rosselli, R., Ramsey, S., Taylor, H., Fothergill, A., Slutsman, J., & Miller, A. (2017). Conducting science in disasters: Recommendations from the NIEHS Working Group for special IRB considerations in the review of disaster related research. Environmental Health Perspectives, 125(9), 094503.
Paolisso, M. (2015). Understanding culture and environment dynamics using cultural consensus analysis. In M. Ruth, Handbook of Research Methods and Applications in Environmental Studies (pp. 81–101). Northampton: Edward Elgar.
Petterson, J., Stanley, L., Glazier, E., & Philipp, J. (2006). A preliminary assessment of social and economic impacts associated with Hurricane Katrina. American Anthropologist, 108(4), 643–70.
Pfefferbaum, R. L., Pfefferbaum, B., Van Horn, R. L., Klomp, R. W., Norris, F. H., & Reissman, D. B. (2013). The Communities Advancing Resilience Toolkit (CART): An intervention to build community resilience to disasters. Journal of Public Health Management and Practice, 19(3), 250–58.
Pickett, S. T. A., Cadenasso, M. L., & Grove, J. M. (2004). Resilient cities: Meaning, models, and metaphor for integrating the ecological, socio-economic, and planning realms. Landscape and Urban Planning, 69(4), 369–84.
Pickett, S. T. A., Cadenasso, M. L. Grove, J. M., Boone, C., Groffman, P. M., Irwin, E., Kaushal, S., Marshall, V., McGrath, B. P., Nilon, C., Pouyat, R., Szlavecz, K., Troy, A., & Warren, P. (2011). Urban ecological systems: Scientific foundations and a decade of progress. Journal of Environmental Management, 92(3), 331–62.
Presidential Policy Directive-21. (2013). Critical Infrastructure Security and Resilience. The White House. Washington, DC.
Reid, C. E., O’Neill, M. S., Gronlund, C. J., Brines, S. J., Brown, D. G., Diez-Roux, A. V., & Schwartz, J. (2009). Mapping community determinants of heat vulnerability. Environmental Health Perspectives, 117(11), 1730–36.
Renschler, C. S., Frazier, A. E., Arendt, L. A., Cimellaro, G.-P., Reinhorn, A. M., & Bruneau, M. (2010). A framework for defining and measuring resilience at the community scale: The PEOPLES resilience framework. Gaithersburg, MD: NIST.
Ritchie, L. A. (2012). Individual stress, collective trauma, and social capital in the wake of the Exxon Valdez oil spill. Sociological Inquiry, 82(2), 187–211.
Russell, D., & Harshbarger, C. (2003). GroundWork for community-based conservation. Walnut Creek, CA: AltaMira Press.
Sidle, R. C., Benson, W. H., Carriger, J. F., & Kamai, T. (2013). Broader perspective on ecosystem sustainability: Consequences for decision making. Proceedings of the National Academies of Sciences, 110(23), 9201–8.
Smith, D. E. (Ed.). (2006). Institutional ethnography as practice. Lanham, MD: Rowman & Littlefield.
Smith, K., & Lawrence, G. (2014). Flooding and food security: A case study of community resilience in Rockhampton. Rural Society, 23(3), 216–28.
Swanson, D. A., Hiley, J. C., Venema, H. D., & Grosshans, R. (2007). Indicators of adaptive capacity to climate change for agriculture in the prairie region of Canada: An analysis based on Statistics Canada’s Census of Agriculture. Winnipeg: International Institute for Sustainable Development.
Taylor, I. (2007). Hurricane Katrina’s impact on Tulane’s teaching hospitals. Transactions of the American Clinical and Climatological Association, 118, 69–78.
Tierney, K. (2012). Disaster governance: Social, political, and economic dimensions. Annual Review of Environment and Resources, 37, 341–63.
Titz, A., Cannon, T., & Krüger, F. (2018). Uncovering “community”: Challenging an elusive concept in development and disaster related work. Societies, 8(3), 71 [online].
U.S. Environmental Protection Agency. (2015). Systems measures of water distribution system resilience. Washington, DC.
U.S. Environmental Protection Agency. (2017). Evaluating urban resilience to climate change: A multi-sector approach. Washington, DC.
Van der Merwe, S. E., Biggs, R., & Preiser, R. (2018). A framework for conceptualizing and assessing the resilience of essential services produced by socio-technical systems. Ecology and Society, 23(2), 12 [online].
Vásquez-León, M. (2009). Hispanic farmers and farmworkers: Social networks, institutional exclusion, and climate vulnerability in southeastern Arizona. American Anthropologist, 111(3), 289–301.
Walker, B., Holling, C. S., Carpenter, S. R., & Kinzig, A. (2004). Resilience, adaptability and transformability in social–ecological systems. Ecology & Society, 9(2), 5 [online].
Wells, K. B., Tang, J., Lizaola, E., Jones, F., Brown, A., Stayton, A., Williams, M., Chandra, A., Eisenman, D., Fogleman, S., & Plough, A. (2013). Applying community engagement to disaster planning: Developing the vision and design for the Los Angeles County Community Disaster Resilience Initiative. American Journal of Public Health, 103(7), 1172–80.