Social impact assessment using the grey clustering method: A case study on a mining project

Self Cite

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.

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Delgado¹

2020

Self Cite

“…As water contamination, especially linked to mining activities, usually generates social conflicts, social impact assessments (SIA) are very important. Like developed by [4] and also by [5], the SIA can be calculated with the grey clustering method. The different affected and involved urban and rural population groups, as well as expert groups were used as stakeholders and the data was obtained from their subjective evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the quality of water is affected by several activities, including mining industry. The risk associated to these effects needs to be evaluated by effective techniques, such as the grey clustering method, which is used in this work as well asthe results of previous water quality assessments [3]- [5]. The grey clustering method, first developed in the grey systems theory by Deng [6], can be applied to many different working fields.…”

Section: Introductionmentioning

confidence: 99%

Analysis of water contamination in the mining zone of Cerro de Pasco, Peru using the grey clustering method

2020

Water quality assessment is a complex topic, since many parameters have to be considered in order to perform a correct evaluation. For this type of studies, the grey clustering method represents a useful tool to determine the quality of water, using artificial intelligence criteria. In this work, fourteen monitoring points on three water bodies in Cerro de Pasco were assessed using the parameters established by MINAM in the Decreto Supremo N°015-2015. The data used from this study was collected from the Water National Authority of Peru. The results showed that 57.1% of the points presented an acceptable water quality that just needs disinfection to be purified; while 21.4% presented an intermediate water quality, which could be purified by conventional treatment and other 21.4% presented a poor water quality that needs a special treatment to be purified for human consumption. These results could be used by local authorities to evaluate, inspect and remediate the damage caused by mining activity. The grey clustering method could be applied to different types of assessments such as water, air or social impact.

“…Therefore, in this article, a water quality assessment is sought, which is why the Grey Clustering Method will be applied. 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.…”

Section: Introductionmentioning

confidence: 99%

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

2020

In Peru, many environmental liabilities produce Acid Mine Drainage (AMD) which flow into rivers affecting their water quality, likewise, many of these liabilities transport large amounts of heavy metals, endangering every water-dependent living organism. In this context, for the evaluation of the parameters involved, we used the grey clustering method, which, based on the theory of grey systems, is presented as a remarkable alternative to the classical statistical methods. In this study we assessed the impact of the Ticapampa environmental liability on the water quality of Santa river using data collected in 2010 from four strategically located monitoring points along the river. The analyzed parameters were evaluated according to the parameters established by DS N° 004-2017-MINAM. Results showed that water quality from monitoring points located upstream (P1) and downstream (P2) with respect to the location of the environmental liability and according to Peruvian law, were of A1 (good) quality; which means that it can be purified using disinfectants. On the other hand, at two points located within the limit of the liability, the water quality was identified as A2 (moderate) and A3 (low) quality, which means it can be purified using conventional and special treatment respectively. Therefore, the grey clustering method has proven to be of great utility for water-quality-assessment related studies and can be applied in other studies which focus on this kind of analysis. In addition, the present results can be useful for Peruvian authorities in their decision making benefiting the communities nearby, animals and environment of study.