This contribution presents a novel dimensionality reduction method-a Representative Objectives Method (ROM)-applied to environmental footprints, by which the number of environmental footprints within the multiobjective optimization (MOO) is reduced to a minimum number of representative ones. The number of footprints is reduced according to similarities among those footprints showing similar behavior. The proposed method consists of three steps: (i) generation of solution points for analysing similarities among footprints, (ii) identification of similarities among footprints, and the selection of representative footprints (those footprints that show similar behavior are grouped into subsets, each subset's representative footprint is then selected), and (iii) the performing of MOO for maximizing profit with respect to the representative footprints. In this way, the dimensionality of the criteria within the MOO can be significantly reduced. Rather than obtaining the remaining footprints through correlations among the representative "independent" footprints, they are read directly from Pareto solutions. The presented dimensionality reduction method is applicable in cases when the model is known. The presented approach is illustrated using a demonstration case study of different biomass energy supply chains. The similarities among carbon (CF), energy (EF), water (WF), water pollution (WPF), and land footprints (LF) were investigated, from which only two representative footprints, CF and WF, were selected. This case study indicated that using this novel approach makes MOO more practical for real life problems.