Prospecting and modeling of primary energy production indicators in Brazil supported by graph theory

Authors

DOI:

https://doi.org/10.33448/rsd-v10i10.19199

Keywords:

Primary energy; Graph theory; Ucinet software; SNA.

Abstract

This research presents a study on the scenario of primary energy production in Brazil over the period from 1970 to 2018, as well as the main sources that contributed to the national energy matrix. To map trends in primary energy production, Social Network Analysis was applied. Also are presented the mathematical models that represent the variation in the centrality and density of primary energy production. Based on the results and the literature on the economy of Brazil in the period between the years 1970 to 2018, it discuss the movements carried out by public policymakers that culminated in a reduction of investments in the sector, even that demand would always be growing. However, it would continue to be linked to the results of small increases in GDP and HDI. Another result was the evolution and of oil as a non-renewable primary source offer for the entire period of the research. Was perceived the alternation of offers from non-renewable sources that, starting with the predominance of firewood, passing on to the generation of hydraulic energy, the most important for two decades, and the substitution by-products derived from sugarcane, which extends until the year 2018. It was also observed that in the period from 2010 to 2018, the share of supply from renewable primary sources, in percentage terms, it is no longer so distant from the share of offers from non-renewable primary sources, almost even dividing availability for the composition of the Brazilian matrix.

Author Biography

Mario Mollo Neto, São Paulo State University

Prof. Dr. Mario Mollo Neto, CNPq Scholar - DT-II Process: 313339 / 2019-8 - Productivity in Technological Development and Innovative Extension, Free Lecturer in Digital Circuits at Universidade Estadual Paulista "Júlio de Mesquita Filho" UNESP; (2019). He has a Post Doctorate in Biosystems Engineering in the area of Rural Constructions and Ambience, from the State University of Campinas (2009), a Doctorate in Agricultural Engineering (CAPES Concept 5) in the area of Rural Constructions and Ambience from the State University of Campinas (2007) , Master's in Production Engineering (CAPES Concept 5) from Universidade Paulista UNIP (2004), and a degree in Industrial Engineering from the São Judas Tadeu University (USJT) (1987). He is currently an Associate Professor in the Biosystems Engineering Course at the Faculty of Science and Engineering (FCE) at Universidade Estadual Paulista - UNESP in TUPÃ.

Department of Biosystems Engineering.

References

Andrada, A. (2017). Uma breve história da economia brasileira (1948-2018). https://www.huffpostbrasil.com/alexandre-andrada/uma-breve-historia-da-economia-brasileira-1948-2018_b_8720394.html

ANEEL. (2008). Atlas de Energia Elétrica do Brasil. http://www2.aneel.gov.br/arquivos/PDF/atlas_par2_cap5.pdf

ANEEL. (2019). BIG - Banco de Informações de Geração. http://www2.aneel.gov.br/aplicacoes/capacidadebrasil/capacidadebrasil.cfm

Bassanezi, R. (2002). Ensino - aprendizagem com Modelagem matemática. 3ed. Ed. Contexto.

Bellingieri, J. C. (2005). A economia no período militar (1964-1984): crescimento com endividamento. Revista Online Fabibe. 1(1), 1-13.

BEN. (2019) Balanço Energético Nacional: Relatório Síntese BEN 2019 Ano Base 2018. Rio de Janeiro: EPE. http://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-377/topico-470/Relat%C3%B3rio%20S%C3%ADntese%20BEN%202019%20Ano%20Base%202018.pdf

Biembengut, M. S. & Hein, N. (2003). Modelagem Matemática no ensino. 3. Ed. Contexto.

Borgatti, S. P., Everett, M. G. & Freenan, L. C. (2002). Ucinet 6 for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies.

British Petroleum, B. P. (2018). British Petroleum Statistical Review of World Energy. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2018-full-report.pdf

Carley, K. M., Desner, J., Reminga, J. & Maksim, T. (2007). Toward an Interoperable Dynamic Network Analysis Toolkit. Decision Support Systems, 43(4), 1324–47.

CONAB, (2019). Conab Produção de etanol no Brasil mantém recorde com 33,14 bilhões de litros. Companhia Nacional de Abastecimento. https://www.conab.gov.br/ultimas-noticias/2859-producao-de-etanol-no-brasil-mantem-recorde-e-alcanca-33-58-bilhoes-de-litros

Conway, F. (1962). A First Course in Mathematical Statistics. By C. E. Weatherburn. Pp. Xii + 277. 1961. 18s. 6d. (Cambridge University Press). The Mathematical Gazette, 46, ( 356), 158–158.

Costa, F. de A. (2016). Ensino matemática por meio da modelagem matemática. Ensino da Matemática em Debate. 3, (1), 58-69.

Creswell, J. W. (2007). Projeto de Pesquisa: Métodos qualitativo, quantitativo e misto. 2. ed. Artmed.

Daniel, D. (2016). Modelagem por Polinômios no Ensino Médio. 2016. Unicamp, 127 f. Dissertação (mestrado) Instituto de Matemática, Estatística e Computação Científica. Universidade Estadual de Campinas. Campinas.

Dehmer, M., Emmert-Streib, F. & Shi, Y.(2017). Quantitative Graph Theory: A New Branch of Graph Theory and Network Science. Information Sciences, 418–419, (1), 575–580.

Demirel, Y. (2012). Energy and Energy Types. In: Demirel, Y. (Ed.). Energy. Ed. Springer London.

Emirbayer, M. & Goodwin, J. (1994). Network Analysis, Culture, and the Problem of Agency. American Journal of Sociology. 99, (6), 1411–1454.

EPE. (2018). World Energy Outlook 2018. Empresa de Pesquisa Energética – EPE. http://www.epe.gov.br/sites-pt/sala-de-imprensa/noticias/Documents/12%20April%20_%20EPE%20WEO%20launch_Clean%20(002).pdf

Firjan. (2019). Anuário da Indústria de Petróleo no Rio de Janeiro Panorama 2019. Rio de Janeiro: Firjan – Federação das Indústrias do Estado do Rio de Janeiro. https://www.firjan.com.br/publicacoes/publicacoes-de-economia/anuario-petroleo-e-gas.htm

Hanneman, R. & Riddle, M. (2005). Social Network Data. In: Introduction_to_Social_Network_Methods. University of California, Riverside: Department of Sociology.

Izquierdo, L. R. & Hanneman, R. A. (2006). Introduction to the Formal Analysis of Social Networks Using Mathematica. Published in digital form, Burgos, Spain. http://luis.izqui.org/papers/Izquierdo_Hanneman_2006-version2.pdf.

Koche, J. C. (2011). Fundamentos de metodologia científica. Ed. Vozes. http://www.brunovivas.com/wp-content/uploads/sites/10/2018/07/K%C3%B6che-Jos%C3%A9-Carlos0D0AFundamentos-de-metodologia-cient%C3%ADfica-_-teoria-da0D0Aci%C3%AAncia-e-inicia%C3%A7%C3%A3o-%C3%A0-pesquisa.pdf

Levine, S. S. & Kurzban, R. (2006). Explaining Clustering in Social Networks: Towards an Evolutionary Theory of Cascading Benefits. Managerial and Decision Economics, 27, (2–3), 173–187.

Ludke, M. & Andre, M. E . D. A. (2013). Pesquisas em educação: uma abordagem qualitativa. Ed. E.P.U.

Macarini, J. P. (2005). A política econômica do governo Médici: 1970-1973. Nova Economia, 15, (3), 53–92.

Mahalingam, B. & Orman, W. H. (2018). GDP, and Energy Consumption: A Panel Analysis of the US. Applied Energy, 213, (1),208–218.

Makagon, M. M., McCowan, B. & Mench, J. A. (2012). How Can Social Network Analysis Contribute to Social Behavior Research in Applied Ethology? Applied Animal Behaviour Science, 138, (3–4), 152–161.

Marangoni, G. (2012). IPEA – Instituto de Pesquisa Aplicada. Anos 1980, década perdida ou ganha? Revista de informações e debates. 72, (1), 1-2. http://www.ipea.gov.br/desafios/index.php?option=com_content&id=2759:catid=28&Itemid=23

Masquietto, C. D., Sacomano Neto, M. & Giuliani, A. C.(2011). Centrality and Density in Interfirm Networks: A Study of an Ethanol Local Productive Arrangement. Review of Administration and Innovation - RAI, 8, (1), 122-147.

Mollo Neto, M. (2015). Análise de Redes. In: REIS, J. G. M. (Ed.). Qualidade em Redes de Suprimentos: a qualidade aplicada ao supply chain management. Ed. Atlas.

Mollo Neto, M., Carreira Junior, E. F., Gonçalves Junior, O. E., Romano, S. M. V. & Morales, V. (2014). Análise de redes para prospecção de indicadores da produção de biodiesel no brasil. Energia na agricultura, 29, (4), 306–316.

Mollo Neto, M., Nääs, I.A., Vendrametto, O. & Okano, M.T. (2010). Quantitative analysis supported in sna of the production milk chain in Brazil, CIGR XVIIth World Congress – Québec City, Canada , 1, (1), 1-9.

Nascimento, C. S. D. (2013). PANDORA - Uma Ferramenta para Visualização Incremental e Análise de Redes Sociais Acadêmicas. Universidade Federal do Rio Grande do Sul, Dissertação (mestrado) Programa de Pós-Graduação em Computação - Porto Alegre. https://lume.ufrgs.br/bitstream/handle/10183/67851/000874023.pdf?sequence=1&isAllowed=y

Nogueira, L. P. P., Lucena, A. F. P., Rathmann, R., Rochedo, P. R. R., Szklo, A. & Schaeffer, R. (2014). Will Thermal Power Plants with CCS Play a Role in Brazil’s Future Electric Power Generation? International Journal of Greenhouse Gas Control, 24, (1), 115–123

Ouedraogo, N. S. (2013). Energy Consumption, and Economic Growth: Evidence from the Economic Community of West African States (ECOWAS). Energy Economics, 36, (1), 637–647.

Peng, H., Bao, M., LI, J., Bhuiyan, M. Z. A., Liu, Y., He, Y. & Yang, E. (2018). Incremental Term Representation Learning for Social Network Analysis. Future Generation Computer Systems, 86, (1), 1503–1512.

Pereira, A. S., Shitsuka, D. M., Parreira, F. J. & Shitsuka, R. (2018). Metodologia da pesquisa cientifica. [free e-book]. Santa Maria: UAB/NTE/UFSM. https://repositorio.ufsm.br/bitstream/handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1

Ren 21. (2019). Renewables 2019 global status report. France: Frankfurt School UNEP Collaborating Centre for Climate & Sustainable Energy Finance, Bloomberg NEF and UN Environment. Ed. REN21 Secretariat. https://www.ren21.net/wp-content/uploads/2019/05/gsr_2019_full_report_en.pdf

Rossoni, L., Silva, A. J. H. & Ferreira Júnior, I. (2008). Aspectos estruturais da cooperação entre pesquisadores no campo de administração pública e gestão social: análise das redes entre instituições no Brasil. Revista de Administração Pública, 42, (6), 1041–1067.

Scott, J. (1996). Software Review : A Toolkit for Social Network Analysis. Acta Sociologica, 39, (2), 211–216.

Silveira, D. (2019). Crise econômica freia consumo de energia primária no Brasil, aponta Firjan. https://g1.globo.com/economia/noticia/2019/08/07/crise-economica-freia-consumo-de-energia-primaria-no-brasil-aponta-firjan.ghtml

Sorrell, S. (2015). Reducing Energy Demand: A Review of Issues, Challenges, and Approaches. Renewable and Sustainable Energy Reviews, 47, (1), 74–82.

Souza, F. B. de. (2018). Blog 2 Engenheiros: Como obter a equação de regressão de um conjunto de dados no Excel? https://2engenheiros.com/2018/12/18/equacao-regressao-excel

Stokman, F. N. (2001). Networks: Social. In: SEMELSER N.J., B. P. B. (Ed.). International Encyclopedia of the Social & Behavioral Sciences. Ed. Elsevier.

Tainter, J. A. (2011). Energy, Complexity, and Sustainability: A Historical Perspective. Environmental Innovation and Societal Transitions, 1, (1), 89–95.

Tolmasquim, M. T., Guerreiro, A. & Gorini, R. (2007). Matriz energética brasileira: uma prospectiva. Novos Estudos - CEBRAP, 79, (1), 47–69.

Tomaél, M. I. & Marteleto, R. M. (2006). Redes sociais: posições dos atores no fluxo da informação. Encontros Bibli: revista eletrônica de biblioteconomia e ciência da informação, 11, (1), 75–91.

Yin, R. K. (2015). O estudo de caso. Ed. Bookman.

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Published

16/08/2021

How to Cite

MOLLO NETO, M. .; CASAGRANDE, L. M. .; CREMASCO, C. P. .; GABRIEL FILHO, L. R. A. . Prospecting and modeling of primary energy production indicators in Brazil supported by graph theory. Research, Society and Development, [S. l.], v. 10, n. 10, p. e485101019199, 2021. DOI: 10.33448/rsd-v10i10.19199. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/19199. Acesso em: 27 oct. 2021.

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Section

Engineerings