Community Resilience to Climate Change: Theory, Research and Practice

122 (or used alongside) objective measures, for example, between rounds of an extended household survey; and that information about the relative influence of the objective measures on subjective resilience could enhance objective approaches, for example, by suggesting ‘weights’ that could be used in multidimensional indices. To address the research question, we present results from a nationally representative survey focused on the subjective resilience of households to flood risk in Tanzania. To our knowledge, this is the first time that such a tool has been applied nationally. We propose survey questions to explore key resilience-related capacities, namely the capacities to prepare for, cope with, and adapt to future flood risk. We then assess how these capacities vary across socioeconomic characteristics to understand how subjective resilience is manifest across different household profiles. The choice of methods and wording of questions used in this study leans on earlier theoretical work by Jones and Tanner (2017), who explore the merits and limitations of assessing subjective resilience at the household level. It also draws on related methodological insights from earlier work by Marshall and Marshall (2007) and Maxwell et al. (2015) that use similar survey-based approaches to examine subjective resilience: the former proposes a method of subjectively measuring ‘social resilience’ in coastal communities in Australia, while the latter sets out principles and guidelines for the subjective measurement of resilience. Based on these exercises, we provide insights into the factors that are associated with subjective assessments of household resilience in Tanzania and compare these with traditional assumptions, those based largely on objective markers. Last, we present future avenues for the methodological refinement and testing of subjective approaches to household resilience. BACKGROUND AND CONTEXT Resilience has its roots in several different disciplinary fields, ranging from mechanics to ecology and psychology (see Alexander 2013 for a comprehensive historical overview). However, the term’s more recent adoption across the sustainability sciences has bolstered its popularity amongst academic and policy communities alike. Indeed, resilience now forms a central pillar in many key international policy frameworks such as the United Nations’ Agenda 2030, Sendai Framework for Disaster Risk Reduction, and Paris Agreement (United Nations 2015a,b,c). Though the utility of a broad resilience framing has brought many benefits, it has also contributed to definitional and conceptual inconsistencies in its use (Olsson et al 2015). For example, early framings of resilience in describing social systems leant heavily on ecologic frameworks and revolved around the capacity to absorb change and disturbance in order to maintain core functions (Holling 1973, Walker et al. 1981, Odum 1985). Subsequent thinking within the field of social-ecological systems has challenged these frames when applying resilience to understand human responses to climate risk, encouraging greater recognition of the ability of social groups to adapt and change their core structure and functions (Berkes et al. 2002, Walker et al. 2004, 2006, Folke 2006, Bollettino et al. 2017). In some cases, it is argued that the complete transformation of a systemmay be a necessary component of a resilience process (Kates et al. 2012, Aldunce et al. 2015). Nevertheless, there is broad agreement across disciplines that resilience comprises a range of evolving capacities and processes rather than constituting a static state (Maguire and Cartwright 2008). For example, in the context of community resilience to disaster risk, Norris et al. (2008) propose that resilience can be broken down into three core capacities: robustness (the strength of a system’s resources); redundancy (the extent to which elements are substitutable in the event of disruption or degradation); and rapidity (how quickly the resource can be accessed and used). Béné et al. (2012) propose a framework consisting of the capacities to absorb, adapt, and transform. Many other such frameworks exist. However, agreement over the exact characterization of resilience is missing amongst the wider literature. These conceptual distinctions matter not only because the capacities needed to support them are different, but because they present a fundamentally different conceptualization of what a resilient system constitutes: “Such wide meanings may end up being contradictory as in the notion of ‘restoring equilibrium and getting away from it by moving to a new state’” (Alexander 2013, as cited in Olsson et al. 2015:22). As such, properties such as coping, adaptive and transformative capacities are often used in different ways and in different combinations when framing resilience (Bahadur and Pichon 2017). Indeed, they are frequently used interchangeably with resilience itself (Olsson et al. 2015). This lack of definitional and conceptual agreement presents challenges when seeking to track and measure resilience, but these difficulties notwithstanding, a wide range of measurement toolkits have emerged in recent years (Constas and Barrett 2013, FAO 2016, Frankenberger et al. 2014, Béné et al. 2016a). Most efforts to measure climate resilience use objective criteria, socioeconomic indicators and processes that are considered to support a household’s ability to deal with risk (Schipper and Langston 2015). “Objective,” in this context, tends to denote framings of resilience that are based on external judgement and verification (Maxwell et al. 2015). Such approaches tend to be guided by an overarching conceptual framework, usually designed by technical experts or those external to the individual or household themselves, though sometimes these draw on qualitative inputs from intended communities or the piloting of survey instruments (Jones and Tanner 2017). Yet there is no universal acceptance on how resilience can and should be measured and hence a plethora of different objective frameworks and indicator lists exist, some contrasting markedly (Constas et al. 2016). One widely used example is the United Nation’s Food and Agriculture Organization (FAO) Resilience Index Measurement and Analysis model (RIMA), which combines socioeconomic variables from five dimensions: access to basic services; assets; adaptive capacity; social safety nets; and sensitivity to shock. These are then further broken down into dozens of individual indicators (D’Errico and Giuseppe