A k-means-based-approach to analyze the emissions of GHG in the municipalities of MATOPIBA region, Brazil

Authors

  • Lucas Ferreira-Paiva Programa de Pós-Graduação em Ciência da Computação, Departamento de Informática, Universidade Federal de Viçosa, Viçosa, Belo Horizonte https://orcid.org/0000-0003-4924-4666
  • Attawan Guerino Locatel Suela Programa de Pós-Graduação em Economia Aplicada, Departamento de Economia Rural, Universidade Federal de Viçosa, Viçosa, Belo Horizonte https://orcid.org/0000-0003-3475-4495
  • Elizabeth R. Alfaro-Espinoza Programa de Pós-Graduação em Bioinformática, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais https://orcid.org/0000-0003-0840-1039
  • Nancy A. Cardona-Casas Pós-Graduação em Solos e Nutrição de Plantas, Departamento de Solos, Universidade Federal de Viçosa, Viçosa, Belo Horizonte https://orcid.org/0000-0002-9637-372X
  • Domingos S. M. Valente Departamento de Engenharia Agrícola, Universidade Federal de Viçosa: Vicosa, MG, BR https://orcid.org/0000-0001-7248-8613
  • Rodolpho V. A. Neves Departamento de Engenharia Elétrica, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil https://orcid.org/0000-0002-0101-483X

Keywords:

biodiversity, carbon offset pricing, climate change impact, k-means, sustainability risk assessment, spatial autocorrelation, alergies

Abstract

Brazil is the sixth largest emitter of GHG, with land use change and agriculture being the main source of these emissions. The recent expansion of agriculture in Brazil has been occurring mainly in the MATOPIBA region, a territorial division inserted on Cerrado. Clustering methods are useful for driving hyper-local GHG emission reduction strategies, but they have yet to be applied at the municipal level, nor from the emissions of various economic sectors. In order to contribute to the identification of the most critical areas in relation to GHG emissions for MATOPIBA, this study proposes an approach of municipal clustering according to the percentage contribution of Agriculture, Land Use Change, Energy and Waste sectors in total emissions. The clustering was performed with the k-means algorithm, using the elbow method and the silhouette score to define the number of clusters. In addition, statistical and geostatistical analyses were conducted to assess the consistency and spatial autocorrelation of the groups formed. The approach was able to generate six clusters with distinct characteristics, showing the heterogeneous profile of GHG emissions from MATOPIBA. At the same time, the clustering of similar municipalities can help in making decisions about the best pro-environmental measures to reduce/remove GHG to contain global warming.

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Author Biographies

Lucas Ferreira-Paiva, Programa de Pós-Graduação em Ciência da Computação, Departamento de Informática, Universidade Federal de Viçosa, Viçosa, Belo Horizonte

graduated in 2020 with a Bachelor's degree in Electrical Engineering from the Universidade Federal de Viçosa (UFV) in Viçosa, Brazil. He is now pursuing a MSc in Computer Science at UFV and a Specialization in Data Science for Aeronautical Industry at Universidade Federal de Pernambuco in Recife, Brazil. His research interests are Computational Intelligence and Signal Processing.

Attawan Guerino Locatel Suela, Programa de Pós-Graduação em Economia Aplicada, Departamento de Economia Rural, Universidade Federal de Viçosa, Viçosa, Belo Horizonte

received a Bachelor's degree in Agribusiness from the Universidade Federal de Viçosa (UFV) in 2017, and a Master's degree in Applied Economics from UFV in 2019. He is currently a PhD candidate in Applied Economics at UFV. His research interests include research in agribusiness, environment, economics and modeling methods for data analysis.

Elizabeth R. Alfaro-Espinoza, Programa de Pós-Graduação em Bioinformática, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais

graduated from the Universidad Nacional de Trujillo in Trujillo, Peru, with a degree in Microbiology and Parasitology. She is a PhD student in Bioinformatics at the Federal University of Minas Gerais in Belo Horizonte, Brazil. His research interests include structural bioinformatics and software development for managing biological data.

Nancy A. Cardona-Casas, Pós-Graduação em Solos e Nutrição de Plantas, Departamento de Solos, Universidade Federal de Viçosa, Viçosa, Belo Horizonte

earned a Bachelor's degree in Agronomic Engineering from the Universidad Nacional de Colombia in Medellín, Colombia, and a Master's degree in Soils and Plant Nutrition from the Universidade Federal de Viçosa in Brazil, where she is currently completing her PhD. She has an experience in agronomy, with a focus on entomology and agroecology, and has worked mostly in family farming, soil management, conservation, and fruit producing.

Domingos S. M. Valente, Departamento de Engenharia Agrícola, Universidade Federal de Viçosa: Vicosa, MG, BR

Domingos S. M. Valente received a Dsc. in Agricultural Engineering at Universidade Federal de Viçosa, Brazil, in 2010, and a Sabbatical (post-doc) at the University of Illinois (Urbana-Champaign). Currently, he is an Associate Professor at UFV, in the Department of Agricultural Engineering, working in the areas of Agricultural Mechanization, Digital Farming, Machine Learning, Remote Sensing, and GIS.

Rodolpho V. A. Neves, Departamento de Engenharia Elétrica, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil

received a Bachelor's degree in Electrical Engineering from the Universidade Federal de Viçosa (UFV), Viçosa, Brazil, in 2011, and the M.Sc. and D.Sc. in Electrical Engineering from the University of São Paulo, São Carlos, Brazil, in 2013 and 2018, respectively. From 2015 to 2016, he was a Visiting Researcher at Aalborg University, Denmark. He is currently an Adjunct Professor in the Department of Electrical Engineering at UFV. His research interests include intelligent control of dynamic systems and management of micro power grids.

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Published

2022-08-22

How to Cite

Ferreira-Paiva, L., Locatel Suela, A. G., Alfaro-Espinoza, E. R., Cardona-Casas, N. A., M. Valente, D. S., & V. A. Neves, R. (2022). A k-means-based-approach to analyze the emissions of GHG in the municipalities of MATOPIBA region, Brazil. IEEE Latin America Transactions, 20(11), 2339–2345. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6738

Issue

Vol. 20 No. 11 (2022): Special Issue on Artificial Intelligence for Sustainability

Section

Special Isssue on AI for Sustainability

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