INOVAÇÃO SOCIAL E SUSTENTABILIDADE: CONSUMO DE ENERGIA ELÉTRICA EM COMUNIDADES CARENTES NO BRASIL

This study aims to understand the universalization of electricity consumption by poor communities in Brazil and raise the legal aspects and the universalization of electricity sector. The methodological procedures were based on qualitative strategy and documents analisys of two cases. Was used indicators of disadvantaged communities and sustainable energy development. The results show a tendency to make the power sector more socially inclusive, particularly for services universalization.Este trabalho objetiva compreender a universalização do consumo de energia elétrica por comunidades carentes no Brasil, levantar os aspectos legais e a universalização do setor de energia elétrica. Os procedimentos metodológicos basearam-se em estratégia qualitativa de base documental de dois casos estudados. Foram utilizados indicadores de comunidades desfavorecidas e de desenvolvimento energético sustentável. Tem-se uma tendência de tornar o setor elétrico socialmente mais inclusivo, principalmente pela universalização dos serviços

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Novas formas de gestão na saúde: descentralização e intersetorialidade

Este artigo discute a descentralização e a intersetorialidade como pressupostos de gestão municipal para melhorar a qualidade de vida dos munícipes. A descentralização transfere o poder de decisão para os níveis periféricos da cidade, mas não garante com as políticas setoriais a resolução dos problemas sociais. A intersetorialidade constitui uma possibilidade de encaminhar a resolução dos problemas da população, situada em determinado território, de maneira integrada. A partir desses pressupostos a Prefeitura de Fortaleza foi remodelada, configurando, a partir de 1997, uma nova estrutura de gestão municipal para melhorar a qualidade de vida da população fortalezense.<br>The present article discusses decentralization and intersectoriality as assumptions of municipal management for the improvement of quality of life of citizens. Decentralization transfers decision power to the peripheral levels of the city but does not guarantee the solution of social problemas through sectorial policies. Intersectorality constitutes a possibility of solving the problems of the population of a given area in an integrated manner. Fortaleza's City Hall was remodeled presenting as of 1997, a new structure of municipal management for the betterment of quality of life of the city's citizens