Avaliação da transição energética na Comunidade dos Países de Língua Portuguesa (CPLP): uma abordagem utilizando inferência fuzzy considerando aspectos sociais e eletroenergéticos

Abstract

This work presents an analysis regarding energy transition considering social and electroenergetic aspects in the Portuguese-speaking countries belonging to CPLP (Community of Portuguese Language Countries), created in 1996 and composed of nine member states: Angola, Brazil, Cape Verde, Guinea-Bissau, Equatorial Guinea, Portugal, Mozambique, São Tomé and Príncipe, and Timor-Leste. Through a meticulous data collection, each country is evaluated considering five axes of analysis including: (i) Human Development Index (HDI); (ii) access to electricity; (iii) energy generation; (iv) electricity production from renewable energy sources; and (v) installed generation capacity. Data on installed capacity from solar, wind, biomass, and hydraulic sources are used, obtained from information provided by the United States Central Intelligence Agency (CIA) and the International Renewable Energy Agency (IRENA). Additionally, HDI data were obtained from the United Nations Development Programme (UNDP) Human Development Reports. Through Pearson correlation, inferences are made about the relationship between the countries and the growth of renewable usage in each of them with their corresponding HDIs, highlighting a strong relationship between social development and energy transition in each country, despite the significant socio-economic differences presented throughout this dissertation. From the aforementioned data sampling, this work proposes a new index, created from a Fuzzy Inference System, which allows analyzing not only human development but also electroenergetic aspects in a manner coherent with the social and economic situation of each country, considering that energy transition will not occur equally in each country. In the proposed approach, each axis of analysis is treated as a variable to be fuzzified in order to assign linguistic variables that represent its classification subjectively. As an output of the inference system, the aim is to determine a numerical index that allows ranking the countries not only based on HDI, as traditionally done in the global context, but also based on other important axes of analysis. Results are presented based on a data sample collected over ten years for each of the countries and analyzed axes, demonstrating its applicability in the context of global energy transition.