A linguistic fusion approach for heterogeneous Environmental Impact Significance Assessment
Abstract Environmental Impact Significance Assessment (EISA) is usually modeled as a multi criteria decision making process for determining the importance of project's impacts over involved environment, considering subjective judgments provided in a qualitative and/or quantitative way. Classical EISA methods are not efficient in handling heterogeneous contexts since experts are forced to use numerical scales even for assessing subjective environmental indicators and they also obtain numerical outputs of low interpretability. In this paper is proposed a new approach for heterogeneous EISA based on the linguistic 2 tuple fusion model for dealing with heterogeneous information. It provides a flexible evaluation framework in which experts can supply their preferences using different information domains conform to the nature and uncertainty of criteria as well as their level of knowledge and experience. Moreover the approach applies a multi step aggregation process to obtain interpretable significance values without loss of information.