Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

Nilsa Duarte da Silva Lima, Irenilza de Alencar Nääs, João Gilberto Mendes dos Reis and Raquel Baracat Tosi Rodrigues da Silva
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Nilsa Duarte da Silva Lima: Postgraduate Program in Production Engineering, Universidade Paulista - UNIP, Dr. Bacelar Street 1212, 04026002 São Paulo, Brazil
Irenilza de Alencar Nääs: Postgraduate Program in Production Engineering, Universidade Paulista - UNIP, Dr. Bacelar Street 1212, 04026002 São Paulo, Brazil
João Gilberto Mendes dos Reis: Postgraduate Program in Production Engineering, Universidade Paulista - UNIP, Dr. Bacelar Street 1212, 04026002 São Paulo, Brazil
Raquel Baracat Tosi Rodrigues da Silva: Postgraduate Program in Production Engineering, Universidade Paulista - UNIP, Dr. Bacelar Street 1212, 04026002 São Paulo, Brazil

Energies, 2020, vol. 13, issue 8, 1-16

Abstract: The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.

Keywords: biofuel policy; efficiency rating; ethanol; data mining (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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