Medição de sustentabilidade econômica, ambiental e social no setor de mineração utilizando Data Envelopment Analysis e o indicador financeiro EBITDA

Abstract

By reaching new levels of maturity of productive processes, there is a need of balance that contemplates the search for excellence and parallel objectives would offer the ideal of sustainability for organizations and society. Sustainability has rooted cumulative principles to those focused only on environmental issues, such as: economic and social development. By framing the mineral extraction sector in this scenario, it is expected a certain counterpart to its activities, which, by their nature, are configured with a recognized degree of environmental degradation and large social impacts. In this context, this work aimed to evaluate the performance of the sustainable initiatives of mining companies through the analysis of the sustainability indicators of their annual reports. The proposed model, based on Data Envelopment Analysis (DEA), suggested an establishment of an efficiency benchmark considering a set of variables that permeate the environmental and social dimensions of sustainability. A total of 18 units, from 5 different companies in distinct periods, composed a data panel that fed the model and as a result, 2 of them were considered efficient. This result was possible due to the calculation of the cross-efficiencies, that increased the discriminatory capacity of the model. An additional analysis of the EBITDA margin deepened the comprehension of the economical dimension results and was created to compare it with the efficiencies scores, having the purpose to affirm that companies with greater operational results are more likely to act based on sustainable targets. This analysis, by the use of Pearson's correlation coefficient was null. Guidance on the use of GRI standard indicators opens the possibility to apply this model in other industries, areas or economic activities. A limitation of the study refers to the inability to consider peculiarities of each type of mineral extraction and, consequently, to deepen the analysis with the inclusion of production and process technical data, specific variables of the mining industry.