Multi-criteria analysis (MCA) is a structured procedure to compare and rank options while considering more than one criterion. This is achieved by normalizing all quantitative and/or qualitative information about an option, assigning weights to all criteria, and then combining the information into a single, integrated value. Multi-criteria analysis is a tool for dealing with the inevitable trade-offs within complex decision-making situations, which may also feature high uncertainty, different forms of information, and multiple stakeholder interests and perspectives. For this reason, MCA has gained momentum as a methodology for the evaluation of sustainability (Adams & Ghaly, 2007).
In the context of impact assessments, multi-criteria analysis can be used at the end of the research to combine the indicator values of multiple impact areas into integrated results and to rank the investigated management options.
Multi-criteria problems are common occurrences. For example, a farmer’s decision over which crops to plant may depend on factors such as expected revenues, risk of crop failure, time requirements, or personal preferences. Weighing them against each other in order to find the optimal solution is difficult because factors are assessed in different units that can not objectively be converted into each other (e.g., how much additional revenue would exactly balance a higher risk of crop failure or a reduction in spare time?). These decisions always involve an element of normative valuation. Usually, they are made intuitively based on previous experience. In impact assessments, however, a structured approach is required to avoid subjectivity, guarantee transparency, and to address problems that are too complex to solve intuitively. Multi-criteria analysis facilitates this by providing a method to combine scientific assessments with normative value choices. Indicator values from different impact areas are first normalized to a common, dimensionless scale and then weights are assigned, based on the perceived importance of the impact area for the respective assessment. This second step is purely normative and should ideally be based on stakeholder involvement or on societal norms codified in laws or regulations. Because normative decisions are always subjective and the weights assigned may not be reproducible with a different set of stakeholders or with the same set of stakeholders at another time, it is very important to document how decision makers or stakeholder groups were selected, or by what method weights were derived from laws or regulations. Finally, for each option analysed in the assessment, the normalized indicator values representing an impact area are multiplied by the weights assigned to that area and all products are summed up to generate a total, integrated score. If the impact assessment investigated different options, they may then be ranked based on the integrated score.
