Abstract
Environmental indicators are often aggregated into a single index in environmental studies. Commonly, an aggregated index is derived in a specific weighting scheme imposed from the outside. The paper presents a novel approach by letting each unit under study choose a set of weights. It applies the concept of self- and cross-appraisal in generating various aggregated indices from two linear programming optimization models. The proposed method is illustrated via a case study of the Mid-Atlantic region. Results show that the derived aggregated indices reveal environmental conditions of the study area in an objective and robust fashion. The proposed method is a valuable tool for integrated environmental assessment.
Published Version
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