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The global transition towards environmentally friendly energy sources plays a major role in addressing both energy security and climate change. Brazil is at the forefront of this transition due to its rich natural resources and increasing investments in biofuels. Therefore, this investigation examines the consumption patterns and interactions between ethanol, primarily sourced from sugarcane, and gasoline within Brazil’s energy framework. Ethanol’s renewability, reduced environmental impact, and superior combustion characteristics position it as a feasible substitute for traditional fossil fuels. Nonetheless, obstacles like competition for land use and inadequate distribution infrastructure impede its widespread acceptance. This study explores the economic interaction between ethanol and gasoline, focusing on pricing dynamics and regional influences. Using consumer preferences and the accessibility of ethanol, this research identifies a range of price ratios within which consumer preferences shift from gasoline to ethanol in various Brazilian regions. The study also classifies Brazilian states into three distinct ranges based on the ethanol-to-gasoline price ratio in 2023 for a granular analysis of the economic dynamics influencing fuel choice. The research identifies states with competitive and dominant ethanol markets by examining the interplay between ethanol market share, fuel prices, and the adoption of flex-fuel vehicles (FFVs) in the country. Lastly, the findings support the importance of regional economic conditions and the influence of price ratios on consumer behavior, highlighting that ethanol’s market share does not always correlate with favorable pricing.

The use of biofuels represents a promising means of achieving a sustainable future and offers considerable economic and environmental benefits. Since they are derived from organic sources, such as vegetable oils and animal fats, biofuels can mitigate the effects of greenhouse gas emissions, improve air quality, support local agriculture, create employment opportunities, and enhance energy security by reducing dependence on fossil fuels. However, introducing these alternative fuels to the aviation sector remains a significant challenge. Thus, it is vital to investigate the potential of sustainable aviation fuel (SAF) and discover how to overcome the technological obstacles to its integration into mainstream aviation to attain broader decarbonization objectives. This article seeks to contribute to a discussion about SAF by examining how it has evolved and its connections to related patents. This article is a comprehensive study of biofuel innovation, highlighting the complex relationships between academia, industry, and other stakeholders. It is hoped that the findings from this study will provide a clearer understanding of the catalysts involved in SAF innovation and provide valuable insights for policymakers, academics, and professionals in the field who are committed to shaping the trajectory of sustainable energy technologies in the future.

The academic community has witnessed a notable increase in paper publications, whereby the rapid pace at which modern society seeks information underscores the critical need for literature mapping. This study introduces an innovative automatic model for categorizing articles by subject matter using Machine Learning (ML) algorithms for classification and category labeling, alongside a proposed ranking method called SSS (Scientific Significance Score) and using Z-score to select the finest papers. This paper’s use case concerns industry productivity. The key findings include the following: (1) The Decision Tree model demonstrated superior performance with an accuracy rate of 75% in classifying articles within the productivity and industry theme. (2) Through a ranking methodology based on citation count and publication date, it identified the finest papers. (3) Recent publications with higher citation counts achieved better scores. (4) The model’s sensitivity to outliers underscores the importance of addressing database imbalances, necessitating caution during training by excluding biased categories. These findings not only advance the utilization of ML models for paper classification but also lay a foundation for further research into productivity within the industry, exploring themes such as artificial intelligence, efficiency, industry 4.0, innovation, and sustainability.