Disaster resilience – a challenged science

ABSTRACT

The concept of resilience has become prominent and now dominates thinking about risk management, notably including environmental hazard management. This paper examines the diverse origins of resilience and its conceptual roots within the hazard and disaster management field and then questions whether or not resilience is simply a re-branding of the concept of mitigation which has previously been widely employed in the hazard and disaster management field. The discussion leads to the conclusion that resilience is not a simple re-branding but is a concept that goes well beyond mitigation to embrace adaptation, change and transformation. Whether disaster resilience is a mature science is discussed next, providing evidence that it is not yet mature because there is currently no settled definitional, conceptual or theoretical basis for the science which is widely recognised and adhered to. Finally, the significant challenges that disaster science has in becoming a more mature and readily applicable science are discussed before the six papers in this Special Issue are introduced.

KEYWORDS:

• Resilience
• mitigation
• definitions
• conceptualisations
• contexts
• challenges

Introduction

Resilience has gained prominence and now dominates thinking about the management of risks facing humankind. For risks associated with climate change, environmental disasters, critical infrastructure, security and terrorism, or other kinds of risks, resilience has become an attractive notion. The concept is now embedded within international discourse and plays a key role in international, national and local policy and development. It underpins the UN’s Sendai Framework for Disaster Risk Reduction (United Nations Office for Disaster Risk Reduction [UNDRR], 2015), it’s Paris Agreement on Climate Change (United Nations [UN], 2015a) and it’s Sustainable Development Goals (United Nations [UN], 2015b). Forty-five OECD countries have adopted national resilience strategies¹ and international development is experiencing a ‘resilience revolution’.² Over 100 world cities are in the 100 Resilient Cities network³; over 1000 participate in the ‘Making Cities Resilient Campaign’ (UNISDR, 2012) to make their cities resilient to disasters; and local/community projects abound. Resilience citations in the Web of Science publications rose from almost zero in 1997 to nearly 30,000 in 2015 (Lovell, Bahadur, Tanner, & Morsi, 2016). This paper examines the origins and prominent early conceptualisations of hazard and disaster resilience and then questions if it is re-branding of the earlier mitigation concept. The paper argues that disaster resilience science is not yet a mature science and faces considerable challenges. Lastly, there is a brief review of this Special Issue’s papers

Origins of resilience

Resilience thinking has diverse origins. Physical, engineering and ecological scientists first used the concept but soon social scientists became attracted to the idea. Disciplines including psychology, psychiatry, leadership studies and regional economic analysis employed the concept. Psychologists and psychiatrists applied it to individuals and to human communities and became interested in connected ideas of self-reliance and human support systems. Alexander (2013) attributes the first serious use of the concept to Rankine who used it to characterise the mechanical strength and deformation of steel beams. Most scholars attribute the first modern adoption of resilience to Holling (1973, 1986) who analysed the stability of ecological systems which demonstrated attributes of rebounding by absorbing stresses (Folke, 2006). However, it was disaster vulnerability rather than resilience which stole the limelight during the 1990s (Blaikie, Cannon, Davis, & Wisner, 1994). 1990 heralded the start of the International Decade for Natural Disaster Reduction (IDNDR) which focused on the role of vulnerability in disaster reduction. Remarkably, the Final Report of the IDNDR’s Scientific and Technical Committee includes a section on vulnerability and risk assessment but it makes no mention of resilience (UNIDNDR, 1999). Even so resilience emerged rapidly thereafter to dominate disaster risk management thinking (Aldunce, Beilin, Handmer, & Howden, 2014; Cutter et al., 2008; Walker, Crawford, Holling, Carpenter, & Kinzig, 2004; Zhou, Wang, Wan, & Huicong, 2010)

Resilience has some roots in earlier conceptualisations of hazard management. The notion of adjusting to hazards goes back to White’s conceptualisation of human adjustment to floods which became dominant in the 1970s (White, 1945). In an influential work, White argued that ‘floods are largely acts of man’ and adjustments were a range of structural and non-structural measures which could reduce flood losses. Subsequently hazard mitigation became virtually synonymous with this idea, although surprisingly its adoption is comparatively recent in the UK and European Union (Macdonald, Chester, Sangster, Todd, & Hooke, 2011).

Resilience – a re-branding of mitigation?

The Dutch face managing one of the most flood-prone regions of the planet but have no word for resilience in their language.⁴ Instead, they think of risk consequence mitigation or management and apply this to strengthen their country against the threat of potentially devastating sea and river flooding. Risk consequence mitigation is about reducing probability and/or consequence where consequence is exposure × vulnerability. Introducing resilience into this context begs the question – is resilience simply mitigation re-branded or it is something else? On the face of it, the two concepts are quite similar. When applied to disasters, mitigation usually means lessening of hazard or disaster severity and their consequences. Resilience is similar but usually implies the ability to recover quickly from, or adjust easily to, a hazard or disaster and their consequences. Mitigation is therefore about and ‘lessening’ or ‘reducing’ whereas resilience is about ‘recovering’ and ‘adjusting’. Within White’s concept of ‘adjustment’ the notion of recovering from floods was implicit (White, 1945). However, despite the apparent closeness of the concepts of disaster mitigation and resilience, resilience has potentially much greater reach and penetration into the opportunities that social institutions and groups inherently possess to effectively manage future scenarios of disaster risk. In this sense resilience is not at all a re-branding of mitigation but something that goes potentially well beyond it.

Mitigation is mostly about reducing loss so that the status quo can be safely maintained whereas resilience is more than about bouncing back to this status quo: the difference between static and dynamic perspectives (Cutter, 2016). Resilience as simply bouncing back was not accepted by Handmer and Dovers (1996) who viewed it as an adaptive process which emphasised the notion of development and improvement beyond simply returning to pre-disaster conditions (Aldunce et al., 2014). Walker et al. (2004) were also attracted to adaptation and learning, change and transformation in their conceptualisation of the resilience of socio-ecological systems. Martin (2012) argued that realignment and adaptation is an integral part of the concept of resilience. Recent conceptual developments of resilience view it as an attribute of social systems (Adger, 2000) or of place (Cutter et al., 2008). Some recent constructions strongly reflect Blaikie et al.’s (1994) understanding of the root causes of disaster vulnerability. These are limited access to power, institutional structures and resources within political and economic systems – causes largely ignored in the ‘human adjustment’ conceptualisation and subsequent work of Burton, Kates, and White (1968). So some recent conceptualisations of disaster resilience concentrate on human agency, power relations, equity, adaptive capacities and transformation (e.g. Keck & Sakdapolrak, 2013).

A mature science?

Given the long evolutionary history of resilience and the impressive rise in resilience citations, a number of questions arise. Are the increasing rate of publication and the obvious attractiveness of resilience within policy development and implementation signs of subject maturity? Is the disaster resilience approach to disaster risk management based on a mature science? These questions exercise the minds of some academics and to which policy-makers and practitioners wishing to employ disaster resilience strategies need clear answers. Taking just one example, the Environment Agency – England’s flood risk management agency – has published a national strategy aiming to move from the concept of protection to resilience (Environment Agency, 2019). This strategy mentions resilience over 100 times and is clearly dependent on a resilience approach to flood and coastal erosion risk management working properly. A flood resilience framework and measurement method, anchored in theory and practically applicable, will undoubtedly be required to ensure that the strategy is effective. The policy-makers and practitioners will hope to found their strategy on a science which is sufficiently grounded and stable, well-structured and well-tested to meet their aims. However, is such a science currently available?

Recent literature on disaster resilience contains a superabundance of definitions (e.g. Aldunce et al., 2014; Alexander, 2013; Cutter, Ash, & Emrich, 2014; Demiroz & Haase, 2018; Koliou et al., 2018; Sudemeier-Rieux, 2014; Zhou et al., 2010). Published disaster resilience measurement methods and application case studies are rapidly expanding. As Zhou et al. (2010) point out, differences in understandings often stem from varied epistemological orientations. There is no single precise and universally accepted definition of resilience or means for how it should be measured, although there is some common ground (Heng et al., 2018; Koliou et al., 2018). Some degree of general consensus is emerging that resilience is about attributes (e.g. economic, social, informational) or capacities across a number of dimensions (e.g. disaster cycle stages) leading to groups of components of resilience indicators (e.g. the use of ‘Cs’ or capitals, as in the ‘5Cs’ such as physical, natural, economic, social and infrastructural capacities and the ‘4R’s which are attributes, qualities or properties of resilience: rapidity, robustness, redundancy and resourcefulness). However, notwithstanding indications of convergence over some aspects of measurement, the current degree of ambiguity about the concept and its measurement is unhelpful to policy–makers and practitioners. Partly because of different epistemological perspectives and contexts in which resilience is conceptualised, it will require an almost herculean effort to somehow integrate all the different definitions and achieve any kind of consensus. Ideas on the sources of disaster resilience or variables influencing it also vary widely. For example, Burton (2015) employed 64 variables to measure community resilience whereas the Zurich Alliance community flood resilience measurement framework drew upon 88 sources of resilience (Keating et al., 2017).

These differences and inconsistencies lead to a sense of disaster resilience not yet being paradigmatic⁵ i.e. not possessing a settled definitional, conceptual and theoretical basis which is widely recognised and adhered to. Disaster resilience science suffers from competing views (although some might regard this as beneficial) and only a limited consensus about measurement. The science lacks a unifying theory suggesting that it has yet to reach maturity. Of course, it may be argued that the lack of consensus, and indeed disunity, is not necessarily negative and indicative of immaturity because the circumstances and contexts in which disaster resilience needs to be defined and measured vary widely. These circumstances include whether the objective is to measure resilience at the national, regional or local/community scales and resilience to which hazard among the range of hazards is being considered. Also measuring disaster resilience in an advanced society where there is a relatively flat social structure and little social inequity presents quite different issues to measuring it in an international development context. It may be argued that somewhat different and nuanced approaches to definition and measurement are required – an argument that has some strength. Even so the current lack of orthodoxy in the definition of resilience as well as inconsistency in its conceptualisation and measurement identified by Cutter (2016) is surely a sign of scientific immaturity. However, it is not all bad news because there are signs of scientific progress, including initiatives to identify operational measures of disaster resilience, increasing research to measure resilience, and efforts to validate and verify measurement tools.

Challenges

Disaster resilience science is a challenged science. Challenges include developing a more mature science; effective and readily applicable. Some challenges are identified above including a unifying theory of disaster resilience and greater definitional consensus. More research into changes in resilience over time are required. Frazier (2012) observed regarding resilience indicators that the importance of differential weighting is often unconsidered and Keating et al. (2017) report ‘there is currently no empirical evidence to support a larger weight for any source over others’. They point out that resilience measurement frameworks often assume that ex-ante presence or absence of an indicator of resilience (representing a source of resilience) will impact ex-post resilience positively. Keating et al. (2017) reported that they were unable to find a single disaster resilience measurement framework or method that had been verified by a longitudinal study of resilience changes, as a result of different capacities and actions. However, Heng et al. (2018) identified 18 studies in which empirical validation of resilience indices and models with external reference data had been undertaken using qualitative or quantitative methods. Even so, these studies represented only about 10% of their analysed articles on disaster resilience measurement. Developing verified or validated disaster resilience measurement tools for different circumstances and making them more routine remain important challenges in the quest to make disaster resilience science more mature and dependable for policy-makers and practitioners. A different kind of challenge in which some progress is being made, includes overcoming the limited progress to date in modelling systemic physical interdependencies of infrastructure systems (e.g. lifelines, critical infrastructure, transportation systems) and their cascading impacts on socio-economic systems which need to be understood to improve resilience and adaptation to disruption risk (Koliou et al., 2018; Kyriazis & Argyroudis, 2013).

Numerous further challenges exist – too many to be comprehensively covered here. Resilience has strong dynamic spatial and temporal elements presenting challenges which need to be taken into account in measurement. Measurement is complicated by the three basic dimensions requiring consideration: the temporal scale characterised by the disaster cycle; the spatial scale; and hazard/disaster receptors (e.g. physical, economic and social). In addition many types of hazard need to be considered. The European Commission’s ENSURE project, which sought to integrate disaster vulnerability and resilience assessment, developed a matrix-based assessment framework for a range of hazards, containing vulnerability and resilience variables, indicators and parameters for the three dimensions. Although vulnerability was usefully integrated with resilience assessments, the resulting framework was unconvincing in capturing the dynamics of vulnerability and resilience (Menoni, Modaressi, Schneiderbauer, Kienberger, & Zeil, 2013). Difficulties also arose in developing such a framework across the European Union nations because of the incompatibilities of resilience parameter data-sets between nations. Because of these kinds of complexities, resilience measurement frameworks are often confined to a single hazard or scale (e.g. a national scale or a community scale). The Zurich Alliance flood resilience measurement framework (Keating et al., 2017) addresses both ex-ante and ex-post resilience measurement. It focuses at the community scale but takes into account that community resilience is impacted upon by the household and regional, national and global scales. An all-singing, all-dancing disaster resilience framework and measurement system in which the appropriate resilience-building roles of local, regional and national policy-makers can be identified may not be required very often but would present a major challenge.

A final challenge to resilience science is the risk, in some cases, of its practice being partially or wholly discredited by being ‘all too easily … captured by neo-liberal apologists, to bolster arguments in favour of the need for “flexibility”, “self-help” and “competitive fitness”’ (Martin, 2012). Berkes and Ross (2013) critiqued resilience thinking for simply accepting socio-economic conditions and circumstances as a given, thereby avoiding any questioning of underlying structural causes of vulnerability of the type that focused the minds of Blaikie et al. (1994). This challenge is eloquently argued by Cutter (2016) in which she questions whether we should ‘be more forceful in our conceptualisations of resilience and point out the inequalities in patterns, processes and perspectives’.

A brief review of the papers in this special issue

The Special Issue begins with two papers from the U.S.A. where both Cutter and Derakhshan are struggling with national level quantification of resilience. Examining disaster resilience across the US at county scale, they provide a rare monitoring of changes over time. The study is based on the DROP (Disaster Resilience of Place) theoretical framework (Cutter et al., 2008) and employs the BRIC (Baseline Resilience Index for Communities) measurement system which uses the ‘capitals’ approach mentioned above and focuses on measuring pre-event inherent resilience only in communities. Indicators on variables known to be influencers of resilience and others including from national databases were employed. The latter provide a degree of data quality consistency overcoming the data inconsistency faced by the international ENSURE project mentioned above. Results reveal the spatial pattern of increases and decreases in resilience over time and the main drivers of change. The authors fundamentally question the purpose of measuring and seeking to manage resilience which may lead to inequalities in outcomes which privilege some and perpetuate disadvantage in others. Kunreuther and Atreya suggest that a community’s resilience to floods may be assessed using the US National Flood Insurance Program’s (NFIP) Community Rating System (CRS). The CRS is an incentive system encouraging flood risk communities to improve their flood resilience through creditable activities in return for discounted flood insurance premiums. They also employ the ‘capitals’ approach to describe a community’s strengths together with the ‘4Rs’ (properties of resilience), and find a positive correlation between the creditable activities and at least one of the 6Cs and 4Rs. However, additional information is required because some dimensions of resilience, for example social vulnerabilities, are not covered when using the CRS. Benefit–cost analysis of resilience enhancing measures would also be required to make the best use of communities’ limited financial resources – a point that has wider applicability to resilience-building projects.

Chen’s and colleagues analysis of earthquake stricken areas in the Longmenshan fault zone of China’s Sichuan Province uses an entirely different, macro-economic, quantitative modelling approach to measuring disaster resilience. Characteristics of a region’s industrial structure before and after an earthquake are used to measure of resilience or resilient recovery. Employment and industrial structures usually coincide but fail to do so when plant and equipment is destroyed. Chen and colleagues employ a measure of this degree of coincidence as a resilience indicator together with per capita GDP growth rate which disasters slow. The effects of earthquakes on resilience decline over time but seismic intensity and spatial variations in topographic complexity differentially constrain the spatial pattern of resilience improvement. Unless they can find ways of enhancing it, the resilience of those occupying high-risk zones may be progressively reduced by disasters in successive years, especially if they are poor. This is precisely the case in riverine northern Bangladesh where the coping mechanisms and resilience of rural households to flooding are investigated by Thompson and Sultana. Using household surveys they discovered ways in which vulnerable households employ ‘hydro-social landscapes’, such as evacuating to flood protection embankments, to boost their resilience. Vulnerable households’ sources of resilience include use of community organisations to enhance livelihoods, for example by providing access to relief, and male seasonal migration to urban areas where employment prospects exist. These examples illustrate a key point made in the earlier discussion above – that resilience is very context-specific. Even so, in this case, governmental policies familiar with the context fail to support the vulnerable households.

Odiase’s and co-authors paper is an example of ex-ante community resilience assessment employing the Community Resilience Index to investigate, measure and calculate a baseline resilience of the Nigerian community in urban Auckland, New Zealand to multi-hazards. They use a variant of the ‘capitals’ methodology by identifying six ‘domains’, each represented by resilience indicators the data for which are from primary and secondary sources. As explained above, weighting of indicators is a contentious issue in disaster resilience measurement but Odiase and co-authors opt for equal weighting for all indicators because differential weighting undermines interdependence among resilience domains.

Learning through experience and acquisition of disaster knowledge plays a vital role in adapting to hazards and disasters and, as we have seen above, adaptation is central to the concept of disaster resilience. Zhang and colleagues’ paper focuses on ‘social learning’ at the individual, community and organisation levels as a policy option for managing urban disaster risks. They employ thematic content analysis on carefully selected studies from around the world and investigate evidence of how, through many different types of social learning (e.g. networking, stewardship, use of memories) and a mix of cognitive change, self-organisation, civil responsibility and open communication and deliberation, social learning can promote urban disaster resilience. The results reveal how social learning is operationalised to promote disaster resilience.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1 https://www.oecd.org/cfe/regional-policy/national-policy-resilience-frameworks.pdf.

2 https://www.odi.org/projects/2864-resilience-scan.

3 https://www.100resilientcities.org/.

4 Personal communication with Jaap Flikweert, Royal HaskoningDHV (the Netherlands based international engineering company).

5 Here a paradigm means a settled and widely accepted, and indeed dominating, framework containing all of the commonly established and accepted perspectives on a subject, including theories, common methods, conventions and standards (Kuhn, 1970).