July 23, 2017 | Autor: Verna Nel | Categoría: Resilience, Urban Planning, Complex Adaptive Systems
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Professor Verna Nel Urban and Regional Planning Faculty of Natural and Agricultural Sciences University of the Free State Tel: +27 (0)51 401 2499 Cell: +27 (0)83 657 2965 Fax: +27 (0)51 401 3049 E-mail: [email protected] Darren Nel Assistant Lecturer University of Pretoria Dept. of Town and Regional Planning Tel: 012 420 6958 Email: [email protected]

Word count: 5745 including references


Abstract Cities have demonstrated great resilience over time, surviving wars, social unrest, economic crises and natural disasters. Urban resilience - the ability to adapt to changes or prevent or recover from a setback - emerges in socio-ecological systems as they are complex adaptive systems and therefore share their attributes. The latter enable a system to function in a relatively stable state by absorbing and adapting to change. In this paper we discuss the characteristics of socioecological systems that give rise to resilience and using those traits and propose a framework for assessing the resilience of a socio-ecological urban system. We further indicate how the framework can be applied to the ecological, social, economic, infrastructure and governance components of the urban system.

Key words: Resilience; socio-ecological systems; complex adaptive systems; urban; city;



INTRODUCTION Although environmental concerns have long been a part of urban and regional planning Patrick Geddes, one of the founding fathers of the profession included the natural environment in his famous survey-before-plan formula (Taylor, 1998:62 ) while McHarg (1969) reminded planners of the importance of planning with nature in the late 1960s - the past decades have seen a growing awareness of the importance of sustainable development along with the issues related to climate change (Wilkinson, 2012: 157). Most definitions of sustainable development acknowledge that a systemic view of the social, ecological, environmental and institutional frameworks is essential. “Issues about sustainability are not merely complicated; they involve subsystems at a variety of scale levels, and there is no single privileged point of view for their measurement and analysis. Such problems can neither be captured nor solved by sciences that assume that the relevant systems are simple. If something really is complex, it cannot be described by means of a simple theory” (Hjorth and Bagheri, 2006: 76). Ahern (2011: 341) notes that during the latter part of the twentieth century a growing awareness of the complexity of the natural and built environments arose, a view contrary to previous more static conceptions. Urban areas are a socio-ecological system that impacts on, and is influenced by the natural environment (Du Plessis, 2008; Coelho and Ruth, 2006, Pickett et al, 2004). For Pelling (2003:11) urban areas should be seen “in a regional and global, as well as local, context and that the components of urban development are viewed as an interacting whole.” This view is echoed by Ahern (2011: 341), Ernstson et al, (2010:531) and Godfrey (2010: 220). It is these mutual interactions, characteristic of complex adaptive systems that often create the conditions for vulnerability, disaster and collapse, but also for change, growth and resilience (Ernstson et al, 2010: 531). This paper constitutes an initial foray into the concept of urban resilience through the lens of complex adaptive systems from an urban planning perspective. We describe a complex adaptive system and discuss definitions of resilience within the framework of such systems 3

before developing a framework for the application of a resilience assessment within an urban system. COMPLEX ADAPTIVE SYSTEMS Complex adaptive systems are largely defined through their properties. They are non-linear self-organising networks of diverse elements, agents or nodes whose interactions both define the system and enable new patterns to emerge that are more than merely the sum of the parts (Holland, 1992; Godfrey 2010: 221). These interactions represent the flows of energy and information, often the form of feedback loops, throughout the system which are the source of the system’s behaviour and constitute system memory (Innes and Booher, 2010; Miller, 2010; Wilkinson 2012). As with most systems, they cannot be analysed solely in terms of their individual components but must be viewed holistically (Hjorth and Bagheri, 2006). There are multiple dynamic interactions between elements. While such interactions may be short-range, the nature of the connections may result in long range influences (Celliers 1998). Such influences may not always be immediately evident as there is seldom a direct link between cause and effect (Helbing, 2009) or there may be time lags that mask the impetus for the changes in system behaviour (Meadows, 2008; Coelho and Ruth, 2006). As such systems are non-linear, small events may be amplified or major events may result in only small perturbations due to the reinforcing or dampening nature of the feedback loops. These restrict the level of predictability as occasionally a ‘gateway event’ may move a system from one state to another (Geyer and Rihani, 2010; Ernstson et al, 2010, Folke et al, 2010). They are also open, dissipative systems (Pickett et al, 2004:374) that are far from equilibrium, but maintain their integrity through the richness of their interactions. The structure of a system reflects the attributes of its components and the nature of the interactions. The organisation of the system may extend over multiple scales as the elements form groups or hierarchies where the ‘higher’ levels serve the ‘lower’ (Meadows, 2008). The system structure, including the diversity of its components and the complexity of the connections and interdependencies, also influences its stability (Du Plessis, 2008). A key feature of all complex adaptive systems is self-organisation that arises from competition and cooperation and increases the complexity of the system with time (Celliers, 1998). Increasing complexity is also associated with increased diversity that stimulates the 4

emergence of persisting patterns and novel behaviour (Holland, 1998). However, this complexity is often the function of the re-iteration of simple rules mediated through exchanges and flow that gives rise to large scale order. As the designation suggests, complex adaptive systems are constantly transforming and adapting in response to both external stimuli and the influence of other elements (Folke et al, 2010). “Systems are part of a broader environment so as the environment changes, systems change to ensure best fit. This in turn influences the wider environment, and creates a constant cycle of change as the system develops to adapt to the environment and the environment changes as a result of system alterations” (Health Foundation, 2010: 8). As these systems are continually evolving, they remain in sub-optimal, ‘good-enough’ states of ‘becoming’ (Holland, 1992). Although complex adaptive systems are in constant state of flux they are usually balanced between the extremes of order (stasis) and anarchy (chaos). This can be envisaged as attractors, the stable states of the system (Folke et al, 2010). A system that has very few attractors will not easily shift from that state as the energy demand is too high, while an unstable system has many attractors and may move chaotically and randomly between them. A system with several attractors can adapt fairly readily. Consequently, self-organising systems find a balance between too few and too many attractors, optimising the number of attractors in the system and avoiding instability (Celliers, 1998: 97). Change from one state occurs if the system reaches a critical state. Helbing (2009: 427) notes that as systems approach such critical points, small influences may lead to increasing fluctuations of a greater magnitude, an indicator that the system is unstable. Socio-economic or engineering systems may be driven to such critical points through eroding safety margins. When a threshold is reached, cascading effects may become evident which often underlie crises and disasters (Helbing, 2009; Pelling, 2003, Wilkinson, 2012). The structure of a complex adaptive system contributes to self-organisation and its continuous coherence. Elements may change, such as students in a university, but the overall structure remains. Few connections between elements can result in stultifying inactivity or stasis while multiple connections ensure stability and resilience (Nel, 2009; Geyer and Rihani, 2010, Bennett et al, 2005). DEFINITIONS OF RESILIENCE 5

Resilience is a property of complex adaptive systems, demonstrated in their ability to adapt and respond to changes in their environment, thus retaining their longevity or sustained existence. Fleischhauer (2008:277) describes it as a way of coping with uncertainty and dealing with disasters. While the term originally meant to bounce back and was initially used in this context to describe properties of materials, it has been expanded to encompass several other dimensions (Davoudi et al, 2012: 300). ‘Engineering resilience’ is a definition related to the initial definition of resilience that is concerned with stability or steady states, and the ability and speed with which a system can return to such a steady state (Gunderson and Holling, 2002: 27; Davoudi et al, 2012). Efficiency is a key parameter in determining the resilience of such systems (Fleischhauer 2008: 277). A far broader definition is that of ‘ecological resilience’, which is more concerned with the continued existence of the system (Gunderson and Holling, 2002:27; Davoudi et al, 2012, Folke, 2006:254-256; Folke et al, 2010). This definition of resilience acknowledges that a system may have several stable states or attractors to which it could move. The emphasis is on the “ability of the system to maintain functions and processes in the face of stresses and pressures by either resisting or adapting to change” or to “re-organise and build its capacity to adapt to change” (Marshall and Schuttenburg, 2006: 71). Pasteur expands this definition to include socio-economic systems. “Resilience refers to the ability of a system, a community or society to resist, absorb, cope with and recover from the effects of hazards and to adapt to longer terms changes in a timely and efficient manner without undermining food security or wellbeing” (emphasis in original) (Pasteur, 2011: 13). Consequently resilience must be viewed from a social, economic, ecological and political perspective. Other definitions include the ability to survive a crisis, respond creatively to stresses or absorb radical change (Innes and Booher, 2010) and the “capacity to adjust to threats and mitigate or avoid harm” (Pelling, 2003:5). Folke et al, (2010) distinguish between specified resilience, that is resilience relating to certain risks or shocks, and general resilience to unexpected jolts. Resilience is a means of managing uncertainty that combines strength and flexibility (Fleischhauer, 2008:277). Common to these concepts of resilience is the ability to anticipate, prepare and respond to threats, a trait of anthropogenic systems. It also implies


the ability to learn and pre-empt possible detrimental changes (Senge, 2006; Innes and Booher, 2010). Although resilience in an ecological system is generally desirable, not all socio-economic systems are just and advantageous to the community (Folke 2006:259, 260) and the resilience of such systems may be questionable. There are rewards and punishments as well as winners and losers in social systems. “Resilience for some people or places may lead to the loss of resilience for others. Therefore in a social context we cannot consider resilience without paying attention to issues of justice and fairness in terms of both the procedures for decision-making and the distribution of burdens and effects” (Davoudi et al, 2012: 306). THE STRUCTURE OF RESILIENCE Resilience is an emergent feature of complex adaptive systems that arises from the structure of the system. Barnett (2001 in Pelling, 2003:6) identifies several properties of resilient systems including feedback, stocks and buffers and overlapping functions. Systems maintain their function through feedback loops that enable learning and carry early warning signals (Folke 2006:262). This is critical to adaptability, which is defined as “the capacity of a SES to adjust its responses to changing external drivers and internal processes and thereby allow for development within the current stability domain” (Folke et al, 2010: n.p.) Systems that can transmit this information effectively are more resilient. Strong hierarchies tend to be rigid, which reduces their ability to cope with shocks or disruptions and makes them slow to respond and adapt. This is equally true of top heavy systems. Furthermore, hierarchal structures are vulnerable to information ‘short-circuits’ if critical links are missing or overloaded so that essential feedback loops cannot function (Helbing, 2009 : 430). Besides richness and effective feedback, the stocks or stores within a system are important. Excess capacity or buffers act as reserves that can be drawn on in times of need. Diverse sources of stocks or means of the delivery, means that if one source is not available a system can turn to another supply. Overlapping functions in a system (diversity and redundancy) allows vital systems to continue functioning if others are incapacitated, or while the system adapts. A rapid turnover of resources through a system means that more resources are


available when required. Holland (1995: 26) notes that systems that are able to recycle resources will be more resilient. Learning, foresight and planning are additional properties of adaptive human systems (Senge, 2006; Innes and Booher, 2010). For Ernstson et al, innovation, defined as “incremental or radical changes in ideas, practices and products; including novel ways of organising society, changing its rules and institutions” (2010:538), is at the crux of understanding the role of resilience in human systems. They also identify the ability to process information as a key component of socio-ecological systems. This aspect is reiterated by Folke et al, (2010) who see innovation and information processing along with adaptability and transformation (the ability of a system to change from one regime to another) as key elements of resilience. RESILIENCE IN AN URBAN SYSTEM In order to understand urban resilience, the properties of a resilient system are discussed in relation to an urban system. This section concludes with an explorative framework for assessing the resilience of the urban environment based on these components. Examples of the application of the framework are illustrated in Table 1. Resilience in an urban system emerges from the structure (Fleischhauer, 2008: 274) (including the nature of the networks and its feedback loops) and the adaptive capacity of the system including its ability to learn and predict as well as reserves within the system (Gunderson and Holling, 2002: 32; Wilkinson 2012: 153). The following section describes how the attributes of complex adaptive systems enable and support the resilience of the urban social economic system. Structure, linkages and networks Most definitions of systems emphasise the importance of connections that structure the system and determine its behaviour and responsiveness to change (Meadows, 2008; Holland 1995; Geyer and Rihani, 2010). “In systems terms, changing structure means changing of the information links in a system: the content and timeliness of the data that actors in the system have to work with, and the goals, incentives, costs, and feedbacks that motivate or constrain behaviour” (Hjorth and Bagheri, 2006: 80). It is through the systems network that all information and resources are transferred.


The implications for urban resilience are manifold. Among the most important of these being the structure, whether the system is strongly hieratical or has a hub-and-spoke configuration or it is flat with multiple connections; the strength of connections (e.g. how easily information moves through the system) and the arrangement or combinations of the structuring elements (Fleischhauer, 2008) and the flows of information and the effectiveness of the information flows. “Resilience grows as networks strengthen linkages in the system…[that] facilitate the self-organisation of nodes of interaction, dialogue and collaboration to address emerging problems or crises” (Innes and Booher, 2010: 209). Networks with unidirectional links, or very few links, are more vulnerable (that is less resilient); if a key link fails part of the network or system will be unable communicate with the remainder (Wilkinson, 2012:162). This has critical implication for urban infrastructure that is often built on a hierarchical basis as well as decision-making organisations. Large, top-heavy organisations are notorious for their inflexibility and sluggishness in adapting to change. This can be ascribed to their hierarchal structure and configuration of information flows. Resilient networks foster information flows; this enables the system to create new behaviours as different elements and agents will be communicating differently along the new network structure (Hjorth and Bagheri, 2006: 80). Connectivity for ecological systems is equally important to retain biodiversity and prevent fragmentation of the system (Ahern, 2011:342). Within a city the “multiplicity of connections enhances the resilience of a city and its possibilities of evolution, change and adaption. In fact, the more connections there are, the more likely they are to be redundant. Thus if the connections are cut… the city can continue to exist even as it changes” (Salat, 2011: 111). Multiple connections are therefore essential in building the resilience of a system as they allow for the free flow of information as well as building redundancies into the system. Redundancy Redundancy implies duplication or back-ups of that part of or function of the system. If one part collapses there is another that will take its place and perform the same function (Fleischhauer 2008: 277). It is the “extent to which different system elements can satisfy the same functional requirements; a diversity of pathways (or potential for creating a diversity of pathways) for achieving the same goal.” (United Nations and Asian Development Bank, 2012: 90). It can take different forms including connectivity to other systems to substitute for broken or missing links. 9

Page (2011: 228) describes two types of redundancies, namely pure redundancy (or modularity as described by Ahern (2011: 342)) and degeneracy. Pure redundancy refers to having numerous duplicates (carbon copies) of the same part; for example having several spare light bulbs so that a defunct bulb can be replaced with another that is exactly the same. In an urban system this could be spare municipal busses, or a back-up electricity supply system, for example local generators, to provide power in the event of a system-wide failure. It is commonly used in computer networks to safeguard data. Degeneracy on the other hand can be described as structures or elements that can perform the same function but are physically different (Page, 2011: 54); for example, pen and paper, a computer, a cell phone, tablet or a white board can all be used to record information or make notes of some kind. In a socio-economic system redundancy couldinclude different forms education such as state, private or religious schools, colleges or universities or private tutors or home schooling. For urban resilience, redundancy of key infrastructure – from road networks and bridges to emergency services - is critical (Pelling, 2003), while Newman et al, (2009) point out the need for distributed infrastructure and preparation for “when (not if) a system fails” (Ahern 2011: 342). For this reason centralised systems are more vulnerable to failure than local and dispersed systems. Diversity Diversity can be expressed as having a range of types of functions or parts. It is also one of the properties of a complex adaptive system. It can be expressed as variations between one type of element, or different types of elements (e.g. different types of species in an ecosystem or different modes of transport in a transport system, or a range of ecosystem services all performing a similar function of retarding run-off and lowering flood peaks) as well as differences in the arrangement of the different types (Page, 2011: 20; Gunderson and Holling 2002: 407; Ahern 2011: 342; Wilkinson, 2012:162). An increase in diversity of a function, i.e. transport also facilitates the creation of redundancy within that function (Page 2011: 229-230). “A diverse system with multiple pathways and redundancies is more stable and less vulnerable to external shock than a uniform system” (Meadows, 2008: 3). Diversity not only creates choice, but new niches. It creates the richness of an ecosystem, where multiple species can play the same or a similar role. Diversity of incomes creates a measure of resilience in a household. Socio-economic diversity in a community contributes to more economically and socially vibrant societies as well as innovation that fosters 10

resilience and recovery from shocks. The development of new products, services or production methods all contribute to diversity within the economy. Diversity of views is the foundation of a democracy. Miller (2010) notes the importance of the ‘competition of ideas’, and concept also espoused by Page (2011). While diversity creates stability, too much diversity within a system can lead to a breakdown in resilience (Page, 2011). One example is a municipal workshop where all the vehicles to be maintained are made by different manufacturers, requiring unique spare parts. Such a situation can easily arise through tendering processes that have a short term, immediate cost perspective, rather than a longer term, systemic view of the costs of acquiring and maintaining the asset. Diversity is often a response to change (Wilkinson 2011: 160; Ernstson et al, 2010:539). While repeated disasters (or disturbances to an ecological system) can cause the collapse of a system, change can give rise to innovation and hence diversity, strengthening the system. Buffers, stores and capital Related to diversity and redundancy are the stores or buffers held within the system. These can be resources such as seeds locked up within an eco-system or reserves such as savings or capital in an economy. Spare capacity in infrastructure, or emergency supplies that can used when required contribute to urban resilience (Pelling, 2003; United Nations and Asian Development Bank, 2012). Holland (1995: 29-30) points out the importance of recycling resources, “agents that participate in cyclic flows cause the system to retain resources. The resources so retained can be further exploited - they offer new niches to be exploited by new kinds of agents. Parts of cas [complex adaptive systems] that exploit these possibilities, particularly parts that further enhance recycling will thrive. Parts that fail to do so will lose their resources to those that do.” Social and human capital is critical to sustainable socio-ecological systems (Fleischhauer, 2008:277; Ahern, 2011:342). Campenella (2006: 145) and Ernstson et al, (2010: 539) describe the crucial role of communities and social capital in New Orleans in response to the disaster caused by Hurricane Katrina. In much of his writing on urban living Simone (2004, 2010) describes how many citizens rely on networks of contacts – social capital – to survive in precarious circumstances. Ability to adapt 11

The ability of the system to adapt to its changing environment is one of the defining features of a complex adaptive system (Wilkinson, 2012: 154). Adaption can present itself in the form of natural selection in an ecosystem or learning in a social system. Adaptation within and of a system arises due to adaptation of individual elements, which adapt to the changes around them. This leads to a system wide adaption (Page, 2011: 25). A resilient system must be in constant state of adaption; change is not just normal, it is essential. “If the entities adapt, then the system has a greater capacity to respond to changes in the environment” (Page, 2011: 25). A stagnant system will eventually collapse (Innes and Booher, 2010: 206; Hjorth and Bagheri, 2006: 84). Adaptive co-management merges approaches from ‘scientific’ natural resource management and user-participation and codecision-making (Wilkinson 2012: 154; Folke et al, 2010). For Ahern (2011:343) it is not just the need to accept change, but the ability to plan and manage in uncertainty, “safe-to fail” planning. In an urban planning context this implies system-wide planning (Wilkerson, 2012: 163) and local and incremental planning, spatial planning on various scales and sectoral planning (Fleischhauer, 2008: 293-296). Learning, problem solving, foresight and prediction,

Systems that learn not only draw from lessons learnt from the past, but use those lessons to solve problems, anticipate possible future difficulties and in the processes avoid, minimise or mitigate the effects of those difficulties (Holland 1995: 24; Hjorth and Bagheri, 2006: 84). According to Innes and Booher (2010: 206) “development and use of knowledge are primary tools for resilience”. It is this ability to learn, solve problems and anticipate that helps to improve the system’s ability to adapt and by improving its adaptive capacity it will improve its ability to manage changes, large and small, in its environment. Thus the system becomes less susceptible to fluctuations in its environment and ultimately more resilient (Innes and Booher, 2010: 205). Learning includes transferring of experience from one situation to another (Miller, 2010; Holland, 1995) while recombination stimulates innovation. Learning systems, although they can be found in nature (e.g. the immune system), are primarily a property of human systems (Page, 2011; Senge, 2006; Gunderson and Holling, 2002). “We need to encourage reflection, learning and personal choice. Thus, we need to 12

learn how to learn; learn how to manage social change and how to increase the pace of social learning and become a learning society” (Hjorth and Bagheri, 2006: 84). It entails building social capital along with other reserves. Resilience, through learning also requires “a full range of knowledges, including those that are seldom heard” (Innes and Booher, 2010: 171) and the involvement of citizens in collaborative governance (Pelling 2003; United Nations and Asian Development Bank, 2012; Geyer and Rihani, 2010; Ahern 2011:343). Not only can societies learn how to adapt to changes, they can also prevent unwanted change or disasters through foresight, or at least mitigate them. This often requires research and long term predictions of possible consequences to provide sufficient warning of hazards and risks. Collaborative governance Inflexible and unresponsive systems are far more vulnerable than adaptive, learning systems. In human societies, this implies robust two-way communication flows between policy-makers and citizens, strong social organisation with local involvement, particularly by marginalised members of society (who are often the most vulnerable) and the redistribution of power (Pelling, 2003: 64; Innes and Booher, 2010). Ernstson et al (2010: 541) point out that collaborative governance should include both the political economy and by implication the voices of the marginalised, as well as an ‘ecological economy’ that prioritises the sustainable use of resources. Table 1 illustrates some examples of application of the properties of resilience within the context of an urban system to demonstrate the application of the framework discussed above. For the purposes of this paper, these properties have been applied to the main domains or sub-systems that constitute an urban system. In this example, some of the attributes of resilience have been grouped together for convenience while the context is limited to five broad areas or domains; social, economic, natural (ecological), governance and infrastructure. Infrastructure is included due to the heavy reliance of urban residents on infrastructure such as water, roads, sanitation and telecommunications (Swilling, 2006, Ahern 2011: 343). In a thorough study both the resilience attributes and these broad domains can be divided into smaller units for analysis purposes allowing for a more detailed investigation into the resilience and risks associated with each sub-domain and attribute. 13

Table 1: Illustration of properties of resilience as applied to an urban system

Redundancy /duplication (back-ups)


Social capital / networks

Economic / financial

More than one source of aid (relief, counselling, support)

Several firms providing similar goods and services – alternatives available (opposite of JIT supplies that are vulnerable to interruptions)

Savings (resources to fall back on) Favours owed


Contacts and friends

Different skills and abilities, occupations and interests Cosmopolitan population – cultural diversity

Natural environment

Many individuals in a population or many populations

Stores and reserves in shops More than one economic base Many retail outlets providing different services at different levels of convenience Choice of products and services

Infrastructur e

Institutional/ governance

Alternatives routes e.g. roads or bus routes

Institutional knowledge and capacity of the state

Alternative facilities e.g. hospitals, fire stations, schools, many smaller sewerage plants

Skills available Vulnerability in hierarchy if key people/ resources are missing

Dual systems

Other agents/ species can fulfil a similar function Generic diversity

Alternative power sources (solar, wind, hydroelectric, nuclear) Alternatives modes of transport available

Wisdom of crowds; diversity of knowledge, experience and perspectives Democracy; Collaborative planning

Choice of housing types


Network structure

Ability to adapt Foresight, Prediction, Learning, and problem solving


Social capital / networks Research, learning, adequate capacity (time, education, health) Technologies to solve socioecological problems and avoid risks

Innovation leading to new products and services

Flat and flexible organisations

Collaborative governance

Decentralisati on

Contacts (small world) and social networks


Natural environment

Response to market and supply

Broad-based community participation

Favours owed


Economic / financial

Interdependencies (rather than dependencies)

Liquidity, savings and stock

Immune system

Infrastructur e

Institutional/ governance

Retrofitting (e.g. earthquakeproofing buildings)

Disaster risk prevention

Upgrading of infrastructure

Learning organisation

Improving efficiency (e.g. street lights)

Structure of the food web (Hierarchy and diversity)

Reserves and buffers, food stores

Road network: connections and strength of hierarchy Reticulation of other services: redundancy and back-up

Spare capacity of services

Size, structure, concentration of decisionmaking and authority, Level of delegation Political representivity of local communities’ needs and issues Financial reserves, skills and expertise

Access to Skills and skills and Political expertise expertise* influence *e.g. when private firms are able to provide specialist skills to assist a community, or through NGO networks Source: Authors

CONCLUSION Urban resilience emerges in socio-ecological systems because they are complex adaptive systems and therefore share their attributes. Resilience enables a system to function in a 15

relatively stable state by absorbing and adapting to change. However, if that resilience is eroded over time through many small events or there is one or more major impacts on the system, it may shift into another state that is less resourceful with a lower adaptive capacity. In socio-ecological systems such moves will have a detrimental effect on the resources available, the quality of life and eventually the ability of the system to provide eco-system services to the residents. Consequently, it is essential to assess the state of the system and determine areas of risk where the resilience may be low and the reasons therefor. These reasons may include loss of diversity, low redundancy with heavy reliance on only one or two sources, dwindling resources, a structure that restricts information flows and limited learning. Timely analysis may enable timely intervention to prevent collapse. This paper provides a framework for assessing the resilience of a socio-ecological urban system. It identifies the key attributes of a complex adaptive system and indicates how these can be applied to the ecological, social, economic, infrastructure and governance components of the urban system. The framework can form the basis for further research on urban resilience, particularly in terms of the social, economic and governance domains to complement the growing body of research on ecosystem resilience. The value of this framework is that it may be used to identify areas of resilience and vulnerability. This will enable appropriate research, learning, planning and action to be taken to avoid, mitigate and adapt to enable a resilient and sustainable future for our cities.


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