Community Resilience to Climate Change: Theory, Research and Practice

237 Social systems are highly complex and are rarely modeled as such. Human actors respond to a range of stimuli in often irrational and highly context-specific ways, which makes it difficult to simulate human decisions at the societal scale in models (Goldspink 2000). For example, it is more difficult to model how an individual will respond to a flood than it is to model how a river system will respond to heavy rain. The result is that science-led, impacts-based approaches fail to represent the social nature of climate impacts. Risk assessment Climate science and impacts model results can be used in a variety of ways to support actual decisions. One of the most commonly advocated methods to support adaptation is to follow a risk management framework (Willows and Connell 2003, Jones and Preston 2011). Risk management is particularly appropriate because of the pervasive uncertainty involved with adaptation decisions. One way to undertake a climate risk assessment is to identify a long list of potential impacts and then to scientifically assess their likelihood and magnitude to identify a level of risk (Brown et al. 2011). This sort of risk assessment can be seen as a continuation of an impacts-based approach to adaptation decision making. It implies that there is a value-neutral or scientific measurement of risk. An alternative approach is objective-based risk identification (Institute of Risk Management 2002). This involves the assessment of a number of possible “risk drivers” on the cost or potential for achieving an explicit set of objectives. This sort of approach is more common in project or corporate risk assessment. It is equally applicable to policy-based organizations or governments, however. It requires decision makers to be explicit about their objectives, which often involves making normative choices of about what is most important, or what should be achieved. The civil servants and advisors who often undertake climate risk assessments aim to appear neutral in terms of future policy choices and are therefore often reluctant to specify strategic objectives. It is sometimes considered safer not to state normative preferences and base adaptation decision making on some value-free, scientific (impacts-based) approach. However, Bradbury (1989) argues that risk management is better when it is based on openly subjective preferences about what is important to society. If policy makers can be explicit about their objective to, for example, improve the quality of life for all citizens, or to reduce social inequalities, then objective-based risk assessments hold significant potential for capturing the social nature of climate impacts, risks, and vulnerability. Economic analyses Adaptation decisions can also be supported by economic analyses, including social or project cost benefit analysis (CBA) and, at the global scale, integrated assessment modeling (IAM). The objective of these tools is to identify efficient or optimal policy choices, not to consider equity as a priority criterion. Information on the costs of climate impacts and the benefits of adaptation are limited for most impacts and in most sectors (Watkiss 2011). However, investment decisions need to be informed by analysis of available options and in some instances, where investment costs and the value of avoided damages can be relatively well understood, e.g., for physical flood defences, CBA is an important and effective decision support tool. Social CBA seeks tomaximizewelfare froma utilitarian perspective, meaning that theremight bewinners and losers from an investment, but it will remain attractive as long as the winners are able to compensate the losers and still be better off. However, the distribution of the costs and (dis)benefits from social CBA for adaptation rarely receive much attention, and some argue that the treatment of time preference in CBA via discount rates also raises questions of intergenerational justice (e.g., Ackerman 2009). Similarly, the use of IAMs to inform decisions on adaptation policy design fails to shed light on social inequality or justice issues and may overestimate society’s ability to adapt because of the crude representation of adaptation decisions in such models (see Patt et al. 2009, Stanton et al. 2009). Economic analyses, although important in many respects, therefore fail to adequately account for the distribution of climate impacts across society. The use of impacts-based approaches can be generally characterized as “top-down” (Dessai and Hulme 2004). Top-down approaches, because they are based on climate scenarios, focus on exposure to harm and tend to see vulnerability as an “end-point” (Kelly and Adger 2000) or an “outcome” (O’Brien et al. 2007) and therefore static, as opposed to part of a social process. Top-down approaches, including relatively high-resolution maps or indicators, can also imply that vulnerability is heterogeneous across groups or places, which may be inaccurate and stigmatize certain people or places as being “high risk” (Benzie et al. 2011). An advantage of top-down assessments, however, is that they can generally be carried out for a large geographical area, for example across a country, or indeed globally. Vulnerability assessment Bottom-up approaches, also known as vulnerability-based assessments, on the other hand, tend to focus on the impacts of current climate variability and the underlying causes and processes that cause some people to be more vulnerable than others to those impacts (an example is Brown and Walker 2008). In this way, they place a greater emphasis on adaptive capacity rather than exposure in assessing vulnerability and try to avoid seeing vulnerability as an inevitable effect of certain socioeconomic characteristics (Spiers 2000). Bottom-up approaches are more likely to incorporate people’s own perception of vulnerability and attitudes toward risk, which