文章基本信息

标题：Impactos ambientais associados à instalação e ao funcionamento de parques solares: estudo de nível de interesse por análise cognitiva de dados TREND DATA
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作者：Marcos Guedes do Nascimento ; Bruno César Bezerra Nóbrega de Souza ; Raimundo Aprígio de Menezes Júnior 等
期刊名称：Research, Society and Development
电子版ISSN：2525-3409
出版年度：2022
卷号：11
期号：13
页码：1-15
DOI：10.33448/rsd-v11i13.35265
语种：English
出版社：Grupo de Pesquisa Metodologias em Ensino e Aprendizagem em Ciências
摘要：The objective of this research is to assess people's level of interest in relation to the main environmental impacts associated with the installation and operation of solar parks, using an algorithm made for cognitive analysis of data extracted from TREND DATA. Initially, articles were consulted using the CAFe search tool of the CAPES journals research portal, using the keyword “solar plants AND environmental impact”. A total of 233 texts were selected, from which the main impacts were extracted. From them, five descriptors were defined, considering data recorded in the period from 2010 to 2020. To adjust the level of interest of users in relation to each descriptor, a score from 0 to 100 was established, which indicates the number of filtered content that deal with the descriptor, every hundred texts that deal with “solar energy” and “environmental impacts”. The results showed that the increase in animal death and temperature, in that order, were the most mentioned descriptors, with average scores of 65 and 38. In third and fourth places were the increase in pollution and deforestation, respectively, both presenting average scores 28. The descriptor with the lowest degree of interest was water waste, with an average score of 27. Through this research, it was observed that listing descriptors related to the main environmental impacts and establishing the level of interest of network users in relation to each descriptor , is vital for proper prioritization in the planning and execution of photovoltaic power plant projects.
关键词：Renewable energy;Cognitive analysis;Big data;Data science.