To understand what is driving the observed uncertainty in sustainability indicators for the system, sensitivity analysis is often used to apportion the uncertainty in the model outputs to different sources of uncertainties in the model inputs (i.e., the "effects" of the inputs ). The results of sensitivity analyses are represented as input sensitivity indices for each output of interest. In other words, for each model output, all uncertain model inputs will have one or more sensitivity index values to represent their relative importance to that output's uncertainty (Figure ). Although sensitivity analysis can be either global or local, global methods are recommended in QSD due to their more robust and informative nature, as they evaluate the effects of input variables across the entire problem space while the local methods only focus on the vicinity of a base point. In QSD, global sensitivity analysis can be applied to facilitate research prioritization (e.g., to achieve sustainability targets), model improvement (e.g., to simplify models and reduce computational intensity), and data collection (e.g., to narrow probability distributions). ) are the least computational intensive and sufficient for monotonic models, where "monotonic" indicates that the model output increases or decreases monotonically with individual model inputs over their ranges of uncertainty (e.g., water treatment plant annual material cost always increases with the increasing unit price of an input chemical). These methods are often performed in tandem with uncertainty analysis (i.e., using the results generated from the uncertainty analysis) as they do not require specific sampling techniques, and have been widely used sustainability analyses to highlight the significance of technological advancement or data collection (e.g., ).