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Precautionary Principles are often said to be appropriate for decision-making in contexts of uncertainty such as climate policy. Contexts of uncertainty are contrasted to contexts of risk depending on whether we have probabilities or not. Against this view, I argue that the risk-uncertainty distinction is practically irrelevant. I start by noting that the history of the distinction between risk and uncertainty is more varied than is sometimes assumed. In order to examine the distinction, I unpack the idea of having probabilities, in particular by distinguishing three interpretations of probability: objective, epistemic, and subjective probability. I then claim that if we are concerned with whether we have probabilities at all-regardless of how low their epistemic credentials are-then we almost always have probabilities for policy-making. The reason is that subjective and epistemic probability are the relevant interpretations of probability and we almost always have subjective and epistemic probabilities. In contrast, if we are only concerned with probabilities that have sufficiently high epistemic credentials, then we obviously do not always have probabilities. Climate policy, for example, would then be a case of decision-making under uncertainty. But, so I argue, we should not dismiss probabilities with low epistemic credentials. Rather, when they are the best available probabilities our decision principles should make use of them. And, since they are almost always available, the risk-uncertainty distinction remains irrelevant. [ABSTRACT FROM AUTHOR]

Flooding hazard evaluation is the basis of flooding risk assessment which has significances to natural environment, human life and social economy. This study develops a spatial framework integrating naïve Bayes (NB) and geographic information system (GIS) to assess flooding hazard at regional scale. The methodology was demonstrated in the Bowen Basin in Australia as a case study. The inputs into the framework are five indices: elevation, slope, soil water retention, drainage proximity and density. They were derived from spatial data processed in ArcGIS. NB as a simplified and efficient type of Bayesian methods was used, with the assistance of remotely sensed flood inundation extent in the sampling process, to infer flooding probability on a cell-by-cell basis over the study area. A likelihood-based flooding hazard map was output from the GIS-based framework. The results reveal elevation and slope have more significant impacts on evaluation than other input indices. Area of high likelihood of flooding hazard is mainly located in the west and the southwest where there is a high water channel density, and along the water channels in the east of the study area. High likelihood of flooding hazard covers 45 % of the total area, medium likelihood accounts for about 12 %, low and very low likelihood represents 19 and 24 %, respectively. The results provide baseline information to identify and assess flooding hazard when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in the study offer an integrated approach in evaluation of flooding hazard with spatial distributions and indicative uncertainties. It can also be applied to other hazard assessments. [ABSTRACT FROM AUTHOR]