In the face of the population's growing awareness about environmental degradation, air pollutant emissions from electricity production become a very relevant issue. Therefore, the present work aims to evaluate the greenhouse gases (GHG), NO x and SO 2 emissions in the Brazilian electricity production, using the expected capacity expansion from Ten-Year Energy Expansion Plan-2027, the current installed capacity of power generation and the electrical load factor. This study was based on data provided by official institutions that are responsible for the electricity sector as well as academic studies of the area. In order to obtain a better analysis of the most likely air pollutant emission values bounds, a Monte Carlo simulation was performed. In addition, the 2017 energy production emissions from Brazil, France, China, and the USA were evaluated and compared.The results indicate that non-renewable sources of energy have a negative environmental impact. In general, the emissions of CO 2 -eq and NO x per MWh are increasing according to Brazilian energy generation projections, but when compared with global indicator Brazil has an affordable electricity mix in terms of air pollutant emissions.According to IEA (International Energy Agency) [1], in 2018 the world's electricity generation sources were divided into 38% of coal, 23% of natural gas, 19% of hydroelectric and other renewable sources, 10% of nuclear power plants, solar and wind participate together with 7% and 3% of electricity were produced from oil. In 2017, Brazil was the eighth country in the world's electricity production, with almost 585 TWh [2]. In the same year, the production of Brazilian electricity from renewable sources accounted for almost 80% of the total [3].According to the report of the Brazilian Electricity Regulatory Agency (BERA) [4], relating to the power plants in operation, hydroelectric plants are still the main source of electricity in Brazil, with 218 projects, which represent 60.18% of installed capacity. On the other hand, the thermoelectric plants totalize 25.80% of the installed power that are distributed among 3001 projects. In addition to hydroelectric and thermoelectric power plants, small hydroelectric plants (SHP) are also part of the electricity mix, with 3.19% of installed capacity, the thermonuclear plants, representing 1.24%, and the hydroelectric generating stations, totaling 0.43%.It is notorious that Brazil stands out concerning the use of renewable sources of electricity generation when compared to the world scenario. This can be attributed mainly to propitious geographic and hydrological conditions for hydroelectric implementation. However, since this source is dependent on the rainfall cycle, it is necessary to analyze the diversification of energy production.Actually, the different compositions of the electricity mix do not only influence the availability and cost of energy, but also present a crucial role in environmental protection. Although electricity is a pre-requisite for an efficient and continuous supply of...

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This study aims to define a municipal Solid Waste Management (SWM) strategy using Multi-objective Optimization Problem (MOP) and ELECTRE method. Four waste treatment technologies were considered: recycling, composting, incineration and landfilling. Firstly, a Multi-objective Linear Programing (MOLP) model was developed. The solutions obtained from this model were ranked using the ELECTRE method. It was considered as case study a hypothetical city of 1,000,000 of inhabitants, presenting the average Brazilian waste per capita generation and composition. The results indicate 21 optimal solutions. The top ranked solution presents a combination of all the technologies considered. This solution presents the following waste allocation: 51.4% composted, 18.4% recycled, 16.7% landfilled and 13.5% incinerated. A sensitivity analysis was carried out by varying waste composition and criterion weights. The waste composition sensitivity was evaluated by substituting the average waste characteristics of Brazil to Europe, Japan and USA ones. The sensitivity analysis indicates that the ranking of solutions was very sensitive to waste composition changes and low sensitive to the variation of criterion preferences. Thus, although the MOP model presented is a simple approach for SWM when compared with other literature models, it shows to be efficient. The proposed model is able to decide between the trades-off related with the material allocation.

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