Approaches for aggregating preferences in participatory forest planning: An experiment with university students

Traditionally, the main focus of forest management planning has been the production of timber. However, forests are today regarded as a source for a wide range of commodities and services, such as biodiversity and recreation to name a few. This results in planning situations that often involve several stakeholders or social groups where a multiplicity of criteria of very different natures must be considered. An approach that has been proposed for situations like these is the combination of multiple criteria decision analysis (MCDA) and participatory planning. This type of merger has been applied in an increasing number of cases related to forestry during recent years. A crucial part of a participatory MCDA process is the aggregation of individual stakeholder preferences into a collective preference. Equitability and transparency are desirable properties of the aggregation mechanism, which will increase the participants’ trust in the process. Furthermore, the way stakeholders interact within the process will be of import for the outcome of the process. Successful communication and conflict management can increase the mutual understanding of values and objectives among stakeholders and form a basis for sound relations and future collaboration. This study aims to evaluate the outcome of different approaches for aggregation of stakeholders’ preferences in a participatory MCDA process. The outcome of the process can be evaluated by the actual result of the aggregation, the effort and time spent on the process by the stakeholders and the analyst, and by the potential benefits for the stakeholders. The study is based on data from a role playing case where university students have been acting as stakeholders in a participatory forest planning situation. A prepared objective hierarchy and five alternatives were presented to the students, who were asked to give their preferences on the criteria and the alternatives using “pairwise” comparisons. The students were asked to make the “pairwise” comparisons individually. After having given their individual preferences, the group together made “pairwise” comparisons to determine the relative importance of each stakeholder. The individual preferences were then aggregated into a collective preference by different approaches: Weighted arithmetic mean, geometric mean, and goal programming. The results show a variation in the performance of the different approaches. Thus, the aggregation procedure must be chosen with consideration to the particularities of the planning situation in question.