Applying grey systems to assess social impact on the extraction phase of the Palma mining project in Peru

Abstract

Nowadays, social impact assessment (SIA) is a very important factor in determining the viability and integration of a mining project. In this study, we analyzed the social conflict that the Palma project would have on the extraction phase using the grey clustering method, based on the grey systems theory. The case study was conducted in the town of Antioquia and its surroundings, where the mining project is located. Two stakeholder groups (rural and urban population) and six evaluation criteria (regarding the perception of social impact of the mining project) were identified. The results showed that in the future the project would have, for the rural group of study, a normal social impact, in the same way it would have a neutral impact over the urban stakeholder group. By applying the percentage system certain criteria it has been proved to be more negative or positive than others within each stakeholder group through a numerical quantifiable evaluation provided by such system. These results could help to the company in charge of the Palma project and local authorities to make the best decision about future conflicts in the extraction phase.

Analysis of Santa’s river water pollution caused by the environmental liability of Ticapampa using grey clustering method

“…3 were replaced into (1) -(3) in order to get the CTWF of the three grey classes. An example of the developed procedure for the third parameter Beryllium is shown in ( 6) - (8).…”

“…The Grey Clustering Method is based on the grey system theory which was developed by Deng [2], and CTWF (Center-point Triangular Whitenization weight Functions) and it will be used in order to classify our objectives in defined classes called grey classes [3]. Currently, there are several methods that perform the evaluation of natural resources such as the Adaptive Neuro-Diffuse Inference System (ANFIS) [4], [5],Delphi [6], Shannon Entropy [7], among others; however, the grey clustering method is the most accurate as it can consider uncertainty in its analysis [8], a helpful quality in the field of study of the present study. In addition, the focus of this method is located within the artificial intelligence that is related to the study of problems with little or limited sample information [9].…”