Predição e avaliação de significância de impacto ambiental: Análise da qualidade de estudos de impacto ambiental em Minas Gerais

Abstract

Since the 18th century, the increasing use of natural resources and the advancement of human activities have resulted in a situation of fragility in the planet's environmental quality. Faced with this challenge, Environmental Impact Assessment (EIA) is used in environmental planning and represents an object of discussions regarding the benefits and opportunities for streamlining its processes, since it often represents an obstacle to economic development. Due to the need to improve this important environmental management instrument, we intend to analyze the effectiveness of the practice of EIA in Minas Gerais (MG) through Environmental Impact Statements (EIS), based on the quality to report the prediction and assessment of impacts in the environmental licensing processes of that state. To this end, an applied and exploratory research was conducted, with a qualitative approach and with content analysis, documentary research and case study tools. The scope covered 57 processes submitted to the regional superintendencies of MG, from 2018 to 2021. After the elaboration of 18 good practice criteria, it was concluded that stages of prediction and significance of the environmental impacts of EIS submitted for approval by the environmental agency in MG are, to some extent, adherent to the principles of international good practices. However, significant deficiencies were identified to be addressed by the State Secretariat for Environment and Sustainable Development (SEMAD), with the scope of improving the EIA system. The criteria with more than 50% compliance are related to the content of the description and assessment of impacts, the environmental factors involved and some attributes (nature, order, duration, reversibility, area of coverage and magnitude). Meanwhile, the unsatisfactory factors referred to the attributes of cumulativeness and synergy, determination of significance, level of uncertainty and community involvement – in the overall performance, only 43% of EISs met more than 60% of the requirements. These results reinforce the need to optimize the quality of information in the prediction and significance assessment stages that have been submitted to environmental licensing and can compromise the judgment of the environmental viability of projects with significant environmental impact.