Skip to main content

A holistic approach to assess the systemic resilience of critical infrastructures: Insights from the Caribbean island of Saint-Martin in the aftermath of Hurricane Irma

Saint Martin (France)
Publication date
View original


Recently, topics related to natural hazards and Critical infrastructure (CI) networks have deeply interested a large number of researchers (e.g. Beckers et al., 2013; Francis & Bekera, 2014; Ouyang, 2014; Queste, 2004; Zhong et al., 2014). However, CI is considered as an important emerging topic that is still under-researched (Fekete, 2020). Generally speaking, CI designate the physical and computer service networks/facilities/assets necessary for the proper functioning of a society and its economy (Gordon & Dion, 2008). Contemporary societies, called societies of risk (Clarke & Beck, 1994; Luhmann, 1996), considerably suffer from the dysfunctions of CI networks (Pederson et al., 2006). Dysfunctions can be exacerbated or even generated by CI interdependencies (Rey, 2015). The latter, meeting economic profitability requirements (Vuillet, 2016), increase the impact of hazards with their propagating and risk-amplifying aspects (Laugé et al., 2013). Interdependencies among CI, creating a “system-of-systems”/“network-of-networks” (Tolone et al., 2009; Eusgeld et al., 2011), are therefore assiduous drivers of systemic risks (regarded as internal/inherent threats) (figure 1). The failure of a system can have several downstream effects (cascading/domino effect) on one or more additional systems (Kotzanikolaou et al., 2013; Der Sarkissian et al., 2020). As Rinaldi, Peerenboom, and Kelly (2001) have widely developed, interdependencies can be physical, geographic, functional, spatial, cybernetic and logical, and consequently, failures can be cascading, escalating and common cause failures.

In order to reduce systemic risks, a systemic approach for achieving resilience of CI “networkof-networks” is needed. To situate the logic behind CI systemic resilience, a conceptual discussion of the term “resilience” is required. “Resilience” has been a vague buzzword in a variety of policy circles for over a decade now (Heinzlef et al., 2020; Keating & Hanger-Kopp, 2020; B. H. Walker, 2020). The absence of a conventional definition of "resilience" was recognized internationally at the "World Humanitarian Summit (WHS) 2016". The conceptual ambiguity of this term lies in the fact that it has come a long way (Manyena, 2006; Alexander, 2013) before being used by risk sciences with Torry in 1979 and officially adopted by “The United Nations International Strategy for Disaster Risk Reduction (UNISDR)” in The Hyogo Framework for Action 2005–2015. In addition, resilience is a multifaceted property and has multiple conceptual foundations that can’t be reduced to a single etymological lineage. The same term “resilience” is used to indicate diametrically opposed systemic capacities in different fields. However, this paper’s approach to CI systemic resilience integrates wider angles and enables a holistic conceptualization of resilience from an interdisciplinary perspective:

• CI systemic resilience draws primarily on specific elements of engineering resilience.
A rigid conceptualization of resilience is described herein (Sharifi & Yamagata, 2016) and optimization is a main goal. This equilibrium-centered resilience refers to a hardening of critical infrastructure (increasing robustness) that is meant to prevent disruptions from occurring, or when functionality is impacted, requires rapid recovery to a pre-disturbance state (Coaffee, 2008; Coaffee et al., 2009, 2018).

• CI systemic resilience also combines elements from ecological or ecosystem resilience (Guenderson & Holling, 2002). Ecological resilience, thoroughly influenced by Herbert Simon’s works (Grove, 2018), refers to the ability to undergo shocks, cope with and return not necessarily to the pre-disturbance equilibrium conditions (Gordon, 1978).
Resilience here refers to a systemic capacity to absorb shocks, adapt, change and transform to unpredictable disruptions– i.e., it allows disturbance to occur and uses adaptation to disturbance (Pelling, 2010) to improve system functioning (Grimm et al., 2000; Grimm et al., 2008). Adaptation is a matter of topological transformation (Cariolet et al., 2019b), a non-fundamental change in the form and function of a system that still preserves its identity and integrity (Holling, 1973; Holling, 2001; Walker & Salt, 2006;
Walker & Salt, 2012; Grove, 2018). Ecological resilience does not allow optimization, because a system cannot be understood with predictive certainty due to its complexities (Simon, 1955; Walker & Salt, 2012). In addition, a system has multipleequilibrium states that, instead of optimization, could only provide satisficing yet suboptimal outcomes (Holling, 1996).

• CI systemic resilience is influenced by economic resilience. The latter is a “collective” capability (Dollinger, 1990). CI systemic resilience follows the same principle of all CI operators collaborating, supported by governments, to reach resilience.

• Finally, CI systemic resilience also encompasses aspects from psychological resilience: thought in terms of internal capacities to develop along a normative trajectory despite external disturbances (Fleming & Ledogar, 2008).

Consequently, in comparison to urban resilience (Campanella, 2006; Comfort et al., 2010;
Lhomme, 2012; Diab, 2017) and its systemic property (Cariolet et al., 2019a), CI systemic resilience is defined as the capacity of multiple CIs to provide altogether trustworthy operations in hostile environments/degraded mode (systemic robustness) and to quickly regain (systemic recovery speed) an optimal level of functioning.

While systemic vulnerability of CI has been widely tackled (e.g. Hellström, 2007; Pitilakis et al., 2016; Grangeat, 2016; Tamima & Chouinard, 2017), the “systemic resilience” of CI has been rarely discussed in literature (e.g. Rey, 2015). In recent studies CI resilience has been generally assessed in silo and not in conjunction with how all CI interact together in the overall network-of-networks. This individual network level approach can make a CI more susceptible to cascading failures (Rogers et al., 2012; Mostafavi, 2017; Helfgott, 2018). Furthermore, neglecting the systemic level masks hidden vulnerabilities, underestimates systemic failures

(Grangeat, 2016; Rehak et al., 2019) and most importantly underrates their geographical and socio-economic extent. Latest literature reviews on CI resilience assessment pinpoint key areas where further research progress is needed. These needs, among others, are for further studies tackling: 1) urban resilience using a systemic approach (November, 2004; Quenault, 2014; Serre & Heinzlef, 2018), 2) “interrelations of interoperability and damage propagation” to improve overall system resilience (Cagno et al., 2011), 3) integrated frameworks that increase the resilience of systems as a whole (Hochrainer-Stigler et al., 2020), 4) more than one or two CIs at a time to capture interdependent behaviors and cover a systemic scale (Pant et al., 2018), 5) real-world observations and applications in contrast with probabilistic methods (Lundberg & Johansson, 2015), and 6) CI resilience in relation to the community of operators and users (Ouyang, 2014; Hosseini et al., 2016).

In response to the research gaps identified, this article addresses the urgency to extend the paradigm of network-based performance to the network-of-networks level. Accordingly, the systemic resilience of CI is assessed following a temporal dynamic analysis of post-Irma situation with a focus on Saint Martin’s CIs. The latter are considered as a network-of-networks which calls for a systemic resilience assessment. The graph theory modelling, taking all CI into account along with their interconnectedness, is used for this purpose. The graph theory has proved to be an accurate method for spatially assessing the resilience of interdependent urban networks (Lhomme et al., 2013;Aydin et al., 2018). More specifically, this article tackles the disruption of CI services and monitors their return to operation, considered as the most relevant approach from a societal perspective. The analysis of malfunctions caused by the hurricane and the causes behind it (whether of a technical, organizational and/or of human nature) will make it possible to reconstruct the failures of CIs. This approach is believed to deepen knowledge of vulnerabilities linked to interdependencies between infrastructures and thus facilitate the assessment of systemic resilience. Lessons learnt from Hurricane Irma can offer recommendations underlining the operational interest of studying the systemic resilience of CIs.

The remaining sections of this paper are organized as follows: Section 2 introduces the study area of Saint-Martin, section 3 presents the adopted methodology, section 4 illustrates and discusses the obtained results and section 5 concludes this paper by revealing the main contributions of this work and proposing some research perspectives.