A review of uncertainty in environmental risk: characterising potential natures, locations and levels

Abstract

Uncertainties, whether due to randomness or human or system errors, are inherent within any decision process. In order to improve the clarity and robustness of risk estimates and risk characterisations, environmental risk assessments (ERAs) should explicitly consider uncertainty. Typologies of uncertainty can help practitioners to understand and identify potential types of uncertainty within ERAs, but these tools have yet to be reviewed in earnest.Here we have systematically reviewed 30 distinct typologies and the uncertainties they communicate, and demonstrate that they: (i) use terminology that is often contradictory; (ii) differ in the frequencies and dimensions of uncertainties that they include; (iii) do not uniformly use systematic and robust methods to source information; and (iv) cannot be applied, on an individual basis, to the domain of ERA. On the basis of these observations we created a summary typologyconsisting of seven locations (areas of occurrence) of uncertainty across five distinct levels (magnitude of uncertainty)specifically for use with ERAs. This work highlights the potential for confusion given the many versions of uncertainty typologies which exist for closely related risk domains and, through the summary typology, provides environmental risk analysts with information to form a solid foundation for uncertainty analysis (based on improved understanding) to identify uncertainties within an ERA.

The Uncertainty Contagion: Revealing the Interrelated, Cascading Uncertainties of Managed Retreat

“…In the assessment reports we see the gradual emergence of the main concerns and the preferred response strategies, for example, the growing use of multiple models, or multiple strands of evidence beyond models, the linking of observations and models, the rise of expert judgement to help narrow uncertainty ranges, the construction of future scenarios, and the use of precisely defined language to convey the assessed likelihood of an outcome. These issues similarly underpin managed retreat deliberations in the face of climate change which, as a starting point, invariably involves a scientific calculation of the level of risk that prevails in a locality, and is typically ascertained by modelling inputs and technical risk assessments [27,28]. These reports also help to highlight different dimensions of scientific uncertainty, such as technical, methodological, epistemic and aleatory sources of uncertainty.…”

“…These reports also help to highlight different dimensions of scientific uncertainty, such as technical, methodological, epistemic and aleatory sources of uncertainty. For instance, epistemic uncertainty is the imperfection of knowledge (also termed completeness, subjective, or systematic uncertainty), and aleatory uncertainty represents the inherent randomness of human and natural systems that cannot be reduced (also referred to as variability, stochastic, random or ontic uncertainty) [27,[29][30][31]. Unlike aleatory uncertainty, epistemic uncertainty can be quantified and mitigated, but even then new information can reveal new uncertainties [27].…”

“…These nuanced perspectives blur the delineation between environmental and process uncertainty [43] for decision-making, or may also question the simplistic 'tame-wicked' binary [44] associated with difficult decisions. For instance, in a review of environmental risk assessments, Skinner and colleagues [27] (p. 213-214) identify a concern regarding how uncertainty becomes known and understood by revealing inconsistencies and contradictions in the use of terminology and dimensions of interest between similar technical assessments. They develop a taxonomy that categorizes uncertainty into two main types (epistemic and aleatory), seven location-based types (system processes, data, model, human, language, variability and decision) and a further five levels of uncertainty (determinacy, statistical, scenario, ignorance and indeterminacy).…”

“…Other typologies highlight the difference between fields of uncertainty. These include, for instance, geopolitical, financial, institutional, spatial, strategic, and climate change uncertainty [27,[45][46][47][48][49][50]. With particular relevance for managed retreat, Walker, et al [51] point out that decision-making associated with the distant future is beset by uncertainties that cannot be reduced by information gathering and statistical analysis.…”

Uncertainty communication in the environmental life cycle assessment of carbon nanotubes

“…something which cannot be reduced. Epistemic uncertainty comes from a lack of, or imperfection in, knowledge about a system of interest [22,23]. Generally in LCA, types of uncertainty and variability are distinguished by location.…”