Incorporating Climate Change into Risk Assessment Using Grey Mathematical Programming

Climate change presents problems for risk assessment procedures due to the difficulty of assigning a measure of probability to any future scenario. Grey systems theory provides an alternative means of quantifying uncertainty based on interval numbers. Within a mathematical programming model, grey systems theory provides a means for working with uncertainties that are not amenable to stochastic or fuzzy quantification. An example of forestry and agricultural expansion in the Mackenzie River Basin is used to illustrate grey mathematical programming in a hop, skip and jump formulation. In this example, climatic constraints are implicitly contained in other parameters which did not incorporate the different components of uncertainty associated with meteorological observations. These components can be combined into a numerical interval that can be used in determining a grey number. However, most of these uncertainties are negligible in climatic data sets due to the number of observations. Nevertheless, these uncertainties point to some of the problems in assessing the risks of climate change, and a grey mathematical programming algorithm is useful for assessing the sensitivity of a decision to climatically sensitive parameters.