Risk management

4.3.1 What is risk management?

Risk management as a concept has traditionally paid attention to the likelihood of various outcomes and the associated magnitude of harms. This has pervaded risk assessment and its management. Such an approach has its origins in the work of the economist Knight (1921) who made a distinction between ‘risk’ and ‘uncertainty’, defining the former as being present where an action can result in different mutually exclusive outcomes where the probability is known, and the latter where the probabilities are unknown. This formed the basis of risk analysis as a technical assessment tool which was initially applied to potentially dangerous technologies and industrial processes, with strong roots in fact gathering for the modelling of risk events (Krimsky & Golding, 2006) in the fields of toxicology and engineering. Although not the first to do so, Vick (2002) made the distinction between ‘objective frequency’, based on computation, and ‘subjective beliefs’ based on expert judgement. Historically, risk analysis has concentrated on the former, while underestimating the existence and power of the latter. This has significant implications in risk assessments where the probability is low but the consequences of damage are large, leading to Vick’s observation that the adequacy of risk assessment often rests on which of the two distinctions is appropriate. This has implications for managing climate change as a problem, because ‘objective frequency’ which is relied upon in institutional settings that seek certainty, can only partially address uncertainty and dynamic changes over long timeframes.

Climate change risk management has been influenced by scholarship on the social dimensions of risk, starting with White (1945), and followed by Kates (1971). They sought to explain human adjustment to natural hazards and challenged notions of natural causes by placing hazards within human choices theories. Such studies led to the emergence of risk classifications based on the nature of the hazard, the medium of exposure and the nature of the consequences (Burton, Kates, & White, 1994). Cultural theory of risk (Rayner, 1992) placed the perception of risk within the attributes of the cultural group, ideology and organisational norms that determine lifestyle choices and behaviours. What emerged was a discourse about the inter-relationships between the social and physical systems and the role of organisations and actors within them, as discussed in Chapter 2. Risk analysis has demonstrated this distinction between the social and physical world, where

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the technical analysis purports to quantify the risk, while societal perceptions do not lend themselves to such treatment. These foundations formed the basis of how risk became framed in IPCC assessments.

The IPCC identified three important epistemological constructs (Jones et al., 2014)—idealised risk, meaning the conceptualising of the particular problem (for example, dangerous anthropogenic interference with the climate system); calculated risk, meaning the product of modelled historical and observed, and theoretical information; and perceived risk, meaning the subjective judgement that people make about idealised risk. These three types of risk combine at a societal level as an objective threat of harm and a product of social and cultural experience, thus reflecting socially constructed risk. Social and cultural values and beliefs have a strong influence where there are controversial risks (Leiserowitz, 2006). Climate change risk therefore sits within a wider spectrum of risk conceptualisation. This means that the management of risk will require both predictive types of risk analysis and more qualitative types that enable those affected by policy to be involved. In the climate change context, there has been a move from the technical, calculated, framing of risk often based on averages and historic records which are projected forward, to a more iterative form of risk management in which reassessment follows assessment and action over long timeframes (Jones et al., 2014). These latter approaches to risk management acknowledge the social determinants of risk that affect its perception and acceptability (Adger, 2006) and that technical assessments are insufficient by themselves for making ‘good decisions’ (Jones et al., 2014; Pidgeon & Fischhoff, 2011). However, iterative risk management has been slow to be embedded into practice, in part because the institutions within which risk management is embedded (for example, in Cabinet guidance and standards in the United Kingdom and elsewhere) still reflect the former conceptualisation of risk, which is difficult to change (Pidgeon & Butler, 2009).

Another related issue with risk management as a decision approach is how risk is communicated and therefore understood (discussed in Chapter 2), highlighting how statistical probability statements are often not well understood. Cognitive biases contribute to perceptions of what is ‘risk-based’, which differs amongst professional disciplines; engineers can call an approach ‘risk- based’ when it is reflecting only the physical risk, rather than the differential vulnerabilities within communities arising from societal, cultural or economic factors in combination with the physical risk. Like the precautionary principle, risk management as a decision approach can become limited if the way it is used differently, in different contexts, is not made transparent.

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4.3.2 Does risk management address uncertainty and dynamic change?

The technical framing of risk or ‘objective frequency’ has become a dominant risk paradigm. This is despite the well-developed methodologies available for iterative risk assessment that have been applied using robust decision-making approaches (Lempert et al., 2003). Where both physical and societal perceptions are changing over time, an iterative risk management process can overcome the limitations of technical quantitative framings of risk; for example, for the extremes of flood risk, and for sea-level rise where uncertainty is high in some respects. Systems for assessing such risks have been developed in a climate change context, where expert judgement of likelihood statements (probability) are assigned in terms of confidence and described qualitatively (Mastrandrea et al., 2010) (for example, as used in the 2007 and 2013 IPCC assessment reviews) thus addressing both ‘objective frequency’ and ‘subjective beliefs’.

Where there is deep uncertainty, however, risk management often does not take account of the implications of extreme events (for example, financial crises or unfamiliar risks not experienced before (Kousky, 2009; Taleb, 2010)). The different types of uncertainty that typify climate change as a policy problem (Chapter 2) contribute to the limitations of quantitative risk assessments as often applied in situations of deep uncertainty. Ambiguity and ignorance, as discussed by Wynne (1992), are often neglected areas of uncertainty, thus the limitations of a typically applied risk assessment will not be considered, leading to ‘one track to the future’ approaches that ‘close down’ potential alternatives (Wise et al., 2014) and create constraints on the development of innovations that respond to risk.

In his discussion of the heuristics and biases associated with risk assessment, Freudenburg (1992) suggested that many of the probabilistic techniques used can be prone to systematic errors which are overlooked when final estimates are presented. He attributes this to five problems: overconfidence in the ability to foresee all possible failure modes; insufficient sensitivity to small sample size problems; failure to see system interactions and interdependencies; calibration errors; and cognitive dissonance. These factors become especially problematic when addressing issues with low probability estimates (for example, extreme climate events). We cannot test such phenomena. They are non-falsifiable and are prone to unforeseen changes. Risk management applied with such biases are incomplete, especially when they are constrained by reliance on quantified and quantifiable inputs alone, outside the social determinants of risk. The most significant limitation of risk management for addressing uncertainty and dynamic climate change arises from assuming that risk management requires simply hazard identification, risk estimation

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and risk evaluation; the management of changing risk profiles is the challenge for managing climate risk.

On the positive side, risk management can address known uncertainties where parameters can be quantified and where uncertainty and change pervade an issue. More fundamentally, risk by definition is about uncertainty, meaning that there is no single estimate of the future. This also means that risk management approaches need to be designed differently for low-probability/high- impact outcomes, compared with high-probability/low-impact outcomes when deciding on response options; the appropriate level of expert and community deliberation will need to be different (Glavovic, 2014). Risk management can support new approaches (for example, sensitivity testing of policy choices, using models and climate change scenarios to characterise changing risk conditions (Lempert & Collins, 2007; Lempert et al., 2003)).

Risk management enables the consequences of the potential impact to be considered explicitly where those consequences are high and can be quantified. However, this depends on how it is applied. It can have limitations in situations of deep uncertainty where the future is unlikely to be like the past, the consequences are high and where dynamic climate changes are likely and the uncertainties cannot be quantified. Risk management can also be prone to an overly rationalist, top-down development of risk management decisions. This implies that concepts that can address change over time and can engender bottom-up, actor-centric development of responses may offer alternative entry points for considering uncertainty and dynamic climate change over long timeframes. Robust decision making (see Chapter 2) can be used in this way. In addition, risk governance scholarship (Renn, 2008) has developed to address the role deliberative processes can play in engendering greater consideration of values and preferences of actors and communities in decision making, when risk is characterised by high levels of ambiguity or deep uncertainty. Thus risk management decision making and adaptive management can be linked.