Abstract:In this paper, a methodology was developed to analyze the results of energy efficiency programs used in the electricity distribution sector in Brazil. Analyzing the gains obtained through the investments made, and classifying which actions resulted in the best performance, contributed to decision making on the best allocation of investments to obtain the greatest energy savings. The Brazilian Energy Efficiency program was analyzed with a developed non-parametric model, using the data envelopment analysis method, and the categories of projects with better performance were determined. A database of the results from 1704 projects, from 2008 to 2016 in the Energy Efficiency program in Brazil´s electricity distribution sector, was used. The results obtained show that the best performance was achieved by projects in the industrial and cogeneration categories; however, in Brazil these constitute only 4.24% of the projects presented and 5.28% of the total investments in the last eight years, indicating a need to review the regulatory strategies for energy efficiency in this country.
The world’s population has reached 8 billion and is projected to reach 9.7 billion by 2050, increasing the demand for food production. Artificial intelligence (AI) technologies that optimize resources and increase productivity are vital in an environment that has tensions in the supply chain and increasingly frequent weather events. This study performed a systemic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology on artificial intelligence technologies applied to agriculture. It retrieved 906 relevant studies from five electronic databases and selected 176 studies for bibliometric analysis. The quality appraisal step selected 17 studies for the analysis of the benefits, challenges, and trends of AI technologies used in agriculture. This work showed an evolution in the area with increased publications over the last five years, with more than 20 different AI techniques applied in the 176 studies analyzed, with machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision being the most used technologies. Considering a worldwide scope, the countries highlighted were India, China, and the USA. Agricultural sectors included crop management and prediction and disease and pest management. Finally, it presented challenges and trends that are promising when considering the future directions in AI for agriculture.
Este estudo realiza o mapeamento dos investimentos no setor de hidrogênio, assim como identifica as principais rotas de produção de hidrogênio no Brasil com foco no hidrogênio sustentável, obtido a partir de fontes renováveis de energia ou utilizando o processo de captura, utilização e armazenamento de carbono (CCUS) para neutralizar as emissões. Os resultados identificados apontam que o Brasil já está recebendo grande volume de investimento em projetos para produção de hidrogênio, com destaque para os clusters industriais costeiros, que podem apoiar a adaptação da utilização da CCUS às usinas de hidrogênio existentes e possuem localização estratégica para exportação. Além disso, foram identificadas as principais políticas públicas realizadas para o setor de hidrogênio no Brasil, como o Projeto de Lei no 725/2022, que inclui o hidrogênio como fonte energética na matriz brasileira e estabelece metas para a inserção nos gasodutos nacionais, e o Decreto no 11.075, de 19 de maio de 2022, que criou o mercado regulado de carbono no Brasil.
The growing global demand for soybean production combined with its increased market value could result in a new supercycle for this commodity. For Brazilian agribusiness, there has been an opportunity to increase exports, particularly in soybean production, in recent years, and therefore, soybean production has been expanding more and more across the states of the Brazilian Amazon. Soybean is the most important grain crop among temporary crops in the Brazilian Amazon; in 2019, it reached a value of USD 21.78 billion, using a planted area of 124,947 km2 (about 55% of the planted area). At the same time, overall deforestation increased significantly in recent years: 10,897 km2 in 2019 and 9811 km² in 2020. To study these changes, economic, social, and environmental sustainability indicators were identified and analyzed using a regression model, and changes in the main economic and socio-environmental indicators were observed that identified a strong positive correlation between agricultural GHG emissions and soybean-planted area. The impact on the local population was also analyzed between the years 2000 and 2019, and there was a mismatch between the population growth rate and the growth rate of the harvested area, which resulted in the displacement of the populations to the cities, and identified a strong positive correlation between the unemployment rate for young people and the soybean-planted area. In this context, this paper presents an analysis of the correlation between soy expansion and the main economic indicators and socioenvironmental impacts in the Brazilian Amazon.
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