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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ferreira, Igor José Malfetoni; Campanharo, Wesley Augusto; Fonseca, Marisa Gesteira; Escada, Maria Isabel Sobral; +7 Authors

    This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5). The dataset is composed of four files in GeoTIFF format: calibrated-AGB-distribution.tif: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha estimated-uncertanty-for-calibrated-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage. projected-AGB-distribution-under-rcp45.tif: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha estimated-uncertanty-for-projected-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage. Spatial resolution: 0.0083 degree (ca. 1 km) Coordinate reference system: Geographic Coordinate System - Datum WGS84

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • Authors: Vasconcelos, Miguel; Vasconcelos, Miguel; Cordeiro, Daniel; Da Costa, Georges; +3 Authors

    L'empreinte carbone des technologies numériques est une préoccupation depuis plusieurs années. Cela concerne principalement la consommation électrique des datacenters; beaucoup de fournisseurs dans le domaine du cloud s'engagent à n'utiliser que des sources d'énergie renouvelables. Cependant, cette approche néglige la phase de fabrication des composants des infrastructures numériques. Nous considérons dans ce travail de recherche la question du dimensionnement des énergies renouvelables pour une infrastructure de type cloud géographiquement distribuée autour de la planète, considérant l'impact carbone à la fois de l'électricité issue du réseau électrique local en fonction de la location de sa production, et de la fabrication des panneaux photovoltaïques et des batteries pour la part renouvelable de l'alimentation des ressources. Nous avons modélisé ce problème de minimisation de l'impact carbone d'une telle infrastructure cloud sous la forme d'un programme linéaire. La solution est le dimensionnement optimal d'une fédération de cloud sur une année complète en fonction des localisations des datacenters, des traces réelles des travaux à exécuter et valeurs d'irradiation solaire heure par heure. Nos résultats montrent une réduction de l'impact carbone de 30% comparés à la même architecture cloud totalement alimentée par des énergies renouvelables et 85% comparés à un modèle qui n'utiliserait qu'une alimentation via le réseau local d'électricité. The carbon footprint of IT technologies has been a significant concern in recent years. This concern mainly focuses on the electricity consumption of data centers; many cloud suppliers commit to using 100% of renewable energy sources. However, this approach neglects the impact of device manufacturing. We consider in this work the question of dimensioning the renewable energy sources of a geographically distributed cloud with considering the carbon impact of both the grid electricity consumption in the considered locations and the manufacturing of solar panels and batteries. We design a linear program to optimize cloud dimensioning over one year, considering worldwide locations for data centers, real-life workload traces, and solar irradiation values. Our results show a carbon footprint reduction of about 30% compared to a cloud fully supplied by solar energy and of 85% compared to the 100% grid electricity model. Données computationnelles ou de simulation: En tenant compte des données en entrée (description de la fédération de centres de données, fichiers de configuration appropriés, conditions météorologiques, etc.), le logiciel est capable de proposer un dimensionnement optimal pour la fédération des datacenters à faible émission de carbone distribuée à l'échelle mondiale : surface des panneaux photovoltaïques et capacité des batteries pour chaque datacenter de la fédération. Des scripts sont disponibles pour mettre en forme les solutions proposées. Simulation or computational data: Considering given inputs (datacenter federation, appropriate configuration files, weather conditions, etc.), the software is able to propose an optimal sizing for the globally distributed low carbon cloud federation: surface area of solar panels, battery capacity for each data center location. . Scripts are available to shape the optimal configuration. Audience: Research, Policy maker UpdatePeriodicity: as needed

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dias de Lima, Tayenne; F. Franco, John; Lezama, Fernando; Soares, Joao; +1 Authors

    Dataset of the paper: Joint optimal Allocation of Electric Vehicle Charging Stations and Renewable Energy Sources including CO2 emissions, Energy Informatics, 2021 (Presented in EIA 2021) Table 1 shows the operational scenarios, while the data for the substations is shown in Table 2. The demand data for each node is shown in Table 3. The parameters related to RES are shown in Table 4 and Table 5. The PV units have a nominal power capacity of 100 kW and are composed by 40 modules with 2.5 kW each. A maximum of 60 generators of this type can be installed in each node. The CO2 factor emission is defined as 𝜁𝑝𝑣=0.0584 ton/MWh. The candidate nodes for the installation of wind turbines, photovoltaic modules, and EV charging stations, are respectively: Ω𝑤𝑡 = {3, 4, 5, 9, 11, 14, 16, 19}, Ω𝑝𝑣 = {3, 4, 6, 8, 10, 13, 14, 15, 19}, and Ω𝑅 = {3, 6, 8,14, 15}. The power factors for PV and WT units are defined as 0.98 and 0.90, respectively. Table 6 presents the data for the two EV chargers alternatives. Finally, Fig.1 shows the initial system topology. R&D center: http://www.gecad.isep.ipp.pt/ and https://www.feis.unesp.br/#!/lapsee Project website: http://www.gecad.isep.ipp.pt/CENERGETIC/ This work has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE2020), under Project POCI-01-0145-FEDER- 028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017(CENERGETIC), CEECIND/02814/2017,and UIDB/000760/2020. The brazillan team (CENERGETIC partners) was supported by the Brazilian institutions Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, CNPq (process 313047/2017-0) and São Paulo Research Foundation (FAPESP), grants 2015/21972-6, 2017/02831-8, 2018/23617-7, and 20018/08008-4 (CENERGETIC research project).

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    ZENODO
    Dataset . 2021
    License: CC BY NC ND
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY NC ND
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY NC ND
    Data sources: Datacite
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      ZENODO
      Dataset . 2021
      License: CC BY NC ND
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY NC ND
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY NC ND
      Data sources: Datacite
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The World Bank Open ...arrow_drop_down
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    The World Bank Open Data
    Dataset . 2018
    License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The World Bank Open ...arrow_drop_down
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      The World Bank Open Data
      Dataset . 2018
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Junior, Celso H. L. Silva; Heinrich, Viola H. A.; Freire, Ana T. G.; Broggio, Igor S.; +8 Authors

    We discontinued this version of the dataset.

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    Smithsonian figshare
    Dataset . 2020
    License: CC BY
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    ZENODO
    Dataset . 2020
    Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      Smithsonian figshare
      Dataset . 2020
      License: CC BY
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      ZENODO
      Dataset . 2020
      Data sources: Datacite
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    Authors: Tangarife Escobar, Andres; Guggenberger, Georg; Feng, Xiaojuan; Muñoz, Estefania; +4 Authors

    Files for the manuscript “Radiocarbon Isotopic Disequilibrium Shows Little Incorporation of New Carbon in Soils and Fast Cycling of a Boreal Forest Ecosystem” 1. “Raw_Data” folder contains the files in .xlsx: - Lab_Atmospheric_Samples: D14C results from ambient air at the sampled heights. - Lab_Soil_Respiration: D14C results with date and integration time for the FFSR sampling campaign. - Lab_Solid_Samples: D14C and TOC results for soil, vegetation, roots, fungi and incubation samples.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
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      ZENODO
      Dataset . 2024
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      ZENODO
      Dataset . 2024
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  • Authors: Pinto, Janete Maria Barreto;

    This work addresses biodiesel by transesterification from the use of waste frying oil as a possible technological alternative for both reducing greenhouse gas emissions and by presenting themselves as an environmental call to designate a rational use of oil when no longer played in the environment to become renewable energy. It has proposed location of a residual oil and fat treatment plant to produce biodiesel, using models of Location and Routing for the improvement of routes. To achieve the goal, questionnaires were administered in establishments that use oil or vegetable fat in their productive activities in order to quantify the residue, to analyze actions and environmental perception of people who work directly with the residue on the destination you are being given to oil and fat used. It has indicated using of two single setup location, the method of Center of Gravity and the model of Ardalan, a geographical point that minimizes the costs of transporting waste to the treatment plant. Actions have been proposed for the improvement of collection routes this residue using the Routing Method of Scanning, as an illustration. The results demonstrated the lack of knowledge of the people who deal directly with large amounts of waste, on the environmental impacts caused by their incorrect disposal. The models used were uniform since point out to neighborhoods in similar regions. The neighborhoods of Lagoa Nova / Morro Branco (Ardalan) and Nova Descoberta (Center of Gravity) as ideal for the installation of treatment plant. However, it is suggested to be tested other models that take into account new variables than those used (supply of waste and the distance between points). The routing through the method of scanning has shown that it is possible, in a simple way to optimize routes in order to reduce distances and therefore the logistics costs in the collection of such waste. Introducing a route as a test to gather the twenty largest oil suppliers used in sample frying, using as a main factor time 8 hour of ...

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: João Paulo Soto Veiga; Teresa Losada Valle; José Carlos Feltran; Waldir Antonio Bizzo;

    Abstract This study sought to quantify and characterize cassava waste as fuel. The wastes from three cultivars were collected to study and were divided into three distinct parts of the cassava plant: seed stem, thick stalks, and thin stalks. Physical and chemical analyzes were carried out to determine the elemental composition of the waste: volatile matter; fixed carbon; ash; moisture; lignin; cellulose; hemicellulose; ash composition and higher heating value were determined. We conducted a thermogravimetric analysis in oxidizing and inert atmospheres to study the behavior of the waste as fuel. The root productivity obtained ranged from 7.7 to 13.0 t ha−1 yr−1 on a dry basis (db), and the ratio between waste and roots varied from 0.36 to 0.91. The physical and chemical properties of cassava waste are analogous to those of woody biomass regarding the elemental composition, the higher heating value, and thermogravimetric analysis. Ash content varied from 2.5% to 3.5%, reaching around 6.0% in samples unwashed. Approximately 60% of the ashes are alkali oxides, especially P2O5, K2O, and CaO, which have low melting points. The alkali index calculated suggests that there is a strong tendency that the combustion process leads to ash fouling and the formation of ash deposits.

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    Renewable Energy
    Article . 2016 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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      Renewable Energy
      Article . 2016 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: R. Apolonio; Ozlean de Lima Dantas;

    Os sistemas fotovoltaicos conectados à rede vêm cada vez mais ganhando espaço no Brasil. Isto é fruto de uma política energética regulatória iniciada em 2012, expansão mundial dessa tecnologia e o alto potencial de irradiação solar disponível no país. Este trabalho tem por objetivo comparar grandezas elétricas medidas no ponto comum de conexão (PCC) de uma unidade consumidora (UC) com perfil de carga comercial e sua microusina fotovoltaica (FV) de 45kWp conectada à rede elétrica em relação ao atendimento dos parâmetros da qualidade da energia elétrica (QEE) em regime permanente estabelecidos no Módulo 8 do Procedimento de Distribuição (PRODIST) da Agência Nacional de Energia Elétrica (ANEEL). Os parâmetros analisados no PCC da instalação foram: perfil de tensão, fator de potência, distorção harmônica total de tensão, fator de desequilíbrio e frequência. Também é apresentado o consumo e geração da unidade consumidora com a usina fotovoltaica para um dia útil e um dia do final de semana. Os resultados obtidos mostram que a UC com a usina FV conectada à rede elétrica em operação não impacta na QEE da rede de distribuição e os parâmetros analisados estão dentro dos limites estabelecidos pelo Módulo 8 do PRODIST da ANEEL, com exceção da tensão que requer ajustes no comutador de carga do transformador da distribuidora que alimenta a instalação.

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    E&S Engineering and Science
    Article . 2018 . Peer-reviewed
    Data sources: Crossref
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    E&S Engineering and Science
    Article . 2018
    Data sources: DOAJ
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      E&S Engineering and Science
      Article . 2018 . Peer-reviewed
      Data sources: Crossref
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      Article . 2018
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  • Authors: Nascimento Neto, Francisco Vieira do;

    This work has as object of study some of the expressions of the water privatization process in contemporary Brazil. The objective was to analyze different expressions of the water privatization process in Brazil. To achieve this general objective, we seek to: Discuss the fundamentals of the environmental issue and its relationship with the finitude of natural resources in contemporary capitalism; To situate water as one of the main resources inherent to the production and valorization processes in the capitalist mode of production; To bibliographically map some expressions of water privatization in the Brazilian State. Methodologically, the research is characterized as exploratory, with data of a bibliographic and qualitative nature. In the case of data collection, we use printed and electronic references of different natures, such as: monographs, theses and dissertations, scientific journals, annals of scientific meetings, among others. Regarding the data source, we define two types of water privatization: 1) Electricity generation (dams / hydroelectric plants); 2) Water distribution by companies / environmental sanitation. A research script was used as a data collection instrument, which guided the selection of the sample of texts based on three aspects of categorization prioritized in the script, namely: characteristics, favorable and contrary arguments; developments and social consequences identified. We analyzed the data in the light of the historical-dialectical materialist method, understanding that it establishes an analysis of reality from the category of totality, representing it in a historical-critical way. The results obtained through this research demonstrate that water privatization is an essential mechanism for the accumulation and valorization of capital to the detriment of the social use of this natural resource to satisfy the needs of living beings. Thus, it highlights the relevance of this study for conducting a contemporary and necessary debate, with theoretical and practical content ; Este ...

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ferreira, Igor José Malfetoni; Campanharo, Wesley Augusto; Fonseca, Marisa Gesteira; Escada, Maria Isabel Sobral; +7 Authors

    This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5). The dataset is composed of four files in GeoTIFF format: calibrated-AGB-distribution.tif: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha estimated-uncertanty-for-calibrated-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage. projected-AGB-distribution-under-rcp45.tif: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha estimated-uncertanty-for-projected-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage. Spatial resolution: 0.0083 degree (ca. 1 km) Coordinate reference system: Geographic Coordinate System - Datum WGS84

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
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  • Authors: Vasconcelos, Miguel; Vasconcelos, Miguel; Cordeiro, Daniel; Da Costa, Georges; +3 Authors

    L'empreinte carbone des technologies numériques est une préoccupation depuis plusieurs années. Cela concerne principalement la consommation électrique des datacenters; beaucoup de fournisseurs dans le domaine du cloud s'engagent à n'utiliser que des sources d'énergie renouvelables. Cependant, cette approche néglige la phase de fabrication des composants des infrastructures numériques. Nous considérons dans ce travail de recherche la question du dimensionnement des énergies renouvelables pour une infrastructure de type cloud géographiquement distribuée autour de la planète, considérant l'impact carbone à la fois de l'électricité issue du réseau électrique local en fonction de la location de sa production, et de la fabrication des panneaux photovoltaïques et des batteries pour la part renouvelable de l'alimentation des ressources. Nous avons modélisé ce problème de minimisation de l'impact carbone d'une telle infrastructure cloud sous la forme d'un programme linéaire. La solution est le dimensionnement optimal d'une fédération de cloud sur une année complète en fonction des localisations des datacenters, des traces réelles des travaux à exécuter et valeurs d'irradiation solaire heure par heure. Nos résultats montrent une réduction de l'impact carbone de 30% comparés à la même architecture cloud totalement alimentée par des énergies renouvelables et 85% comparés à un modèle qui n'utiliserait qu'une alimentation via le réseau local d'électricité. The carbon footprint of IT technologies has been a significant concern in recent years. This concern mainly focuses on the electricity consumption of data centers; many cloud suppliers commit to using 100% of renewable energy sources. However, this approach neglects the impact of device manufacturing. We consider in this work the question of dimensioning the renewable energy sources of a geographically distributed cloud with considering the carbon impact of both the grid electricity consumption in the considered locations and the manufacturing of solar panels and batteries. We design a linear program to optimize cloud dimensioning over one year, considering worldwide locations for data centers, real-life workload traces, and solar irradiation values. Our results show a carbon footprint reduction of about 30% compared to a cloud fully supplied by solar energy and of 85% compared to the 100% grid electricity model. Données computationnelles ou de simulation: En tenant compte des données en entrée (description de la fédération de centres de données, fichiers de configuration appropriés, conditions météorologiques, etc.), le logiciel est capable de proposer un dimensionnement optimal pour la fédération des datacenters à faible émission de carbone distribuée à l'échelle mondiale : surface des panneaux photovoltaïques et capacité des batteries pour chaque datacenter de la fédération. Des scripts sont disponibles pour mettre en forme les solutions proposées. Simulation or computational data: Considering given inputs (datacenter federation, appropriate configuration files, weather conditions, etc.), the software is able to propose an optimal sizing for the globally distributed low carbon cloud federation: surface area of solar panels, battery capacity for each data center location. . Scripts are available to shape the optimal configuration. Audience: Research, Policy maker UpdatePeriodicity: as needed

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    Authors: Dias de Lima, Tayenne; F. Franco, John; Lezama, Fernando; Soares, Joao; +1 Authors

    Dataset of the paper: Joint optimal Allocation of Electric Vehicle Charging Stations and Renewable Energy Sources including CO2 emissions, Energy Informatics, 2021 (Presented in EIA 2021) Table 1 shows the operational scenarios, while the data for the substations is shown in Table 2. The demand data for each node is shown in Table 3. The parameters related to RES are shown in Table 4 and Table 5. The PV units have a nominal power capacity of 100 kW and are composed by 40 modules with 2.5 kW each. A maximum of 60 generators of this type can be installed in each node. The CO2 factor emission is defined as 𝜁𝑝𝑣=0.0584 ton/MWh. The candidate nodes for the installation of wind turbines, photovoltaic modules, and EV charging stations, are respectively: Ω𝑤𝑡 = {3, 4, 5, 9, 11, 14, 16, 19}, Ω𝑝𝑣 = {3, 4, 6, 8, 10, 13, 14, 15, 19}, and Ω𝑅 = {3, 6, 8,14, 15}. The power factors for PV and WT units are defined as 0.98 and 0.90, respectively. Table 6 presents the data for the two EV chargers alternatives. Finally, Fig.1 shows the initial system topology. R&D center: http://www.gecad.isep.ipp.pt/ and https://www.feis.unesp.br/#!/lapsee Project website: http://www.gecad.isep.ipp.pt/CENERGETIC/ This work has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE2020), under Project POCI-01-0145-FEDER- 028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017(CENERGETIC), CEECIND/02814/2017,and UIDB/000760/2020. The brazillan team (CENERGETIC partners) was supported by the Brazilian institutions Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, CNPq (process 313047/2017-0) and São Paulo Research Foundation (FAPESP), grants 2015/21972-6, 2017/02831-8, 2018/23617-7, and 20018/08008-4 (CENERGETIC research project).

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    ZENODO
    Dataset . 2021
    License: CC BY NC ND
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY NC ND
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY NC ND
    Data sources: Datacite
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      ZENODO
      Dataset . 2021
      License: CC BY NC ND
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY NC ND
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY NC ND
      Data sources: Datacite
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The World Bank Open ...arrow_drop_down
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    The World Bank Open Data
    Dataset . 2018
    License: CC BY
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      The World Bank Open Data
      Dataset . 2018
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Junior, Celso H. L. Silva; Heinrich, Viola H. A.; Freire, Ana T. G.; Broggio, Igor S.; +8 Authors

    We discontinued this version of the dataset.

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Smithsonian figshare
    Dataset . 2020
    License: CC BY
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    ZENODO
    Dataset . 2020
    Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Smithsonian figshare
      Dataset . 2020
      License: CC BY
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      ZENODO
      Dataset . 2020
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tangarife Escobar, Andres; Guggenberger, Georg; Feng, Xiaojuan; Muñoz, Estefania; +4 Authors

    Files for the manuscript “Radiocarbon Isotopic Disequilibrium Shows Little Incorporation of New Carbon in Soils and Fast Cycling of a Boreal Forest Ecosystem” 1. “Raw_Data” folder contains the files in .xlsx: - Lab_Atmospheric_Samples: D14C results from ambient air at the sampled heights. - Lab_Soil_Respiration: D14C results with date and integration time for the FFSR sampling campaign. - Lab_Solid_Samples: D14C and TOC results for soil, vegetation, roots, fungi and incubation samples.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2024
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  • Authors: Pinto, Janete Maria Barreto;

    This work addresses biodiesel by transesterification from the use of waste frying oil as a possible technological alternative for both reducing greenhouse gas emissions and by presenting themselves as an environmental call to designate a rational use of oil when no longer played in the environment to become renewable energy. It has proposed location of a residual oil and fat treatment plant to produce biodiesel, using models of Location and Routing for the improvement of routes. To achieve the goal, questionnaires were administered in establishments that use oil or vegetable fat in their productive activities in order to quantify the residue, to analyze actions and environmental perception of people who work directly with the residue on the destination you are being given to oil and fat used. It has indicated using of two single setup location, the method of Center of Gravity and the model of Ardalan, a geographical point that minimizes the costs of transporting waste to the treatment plant. Actions have been proposed for the improvement of collection routes this residue using the Routing Method of Scanning, as an illustration. The results demonstrated the lack of knowledge of the people who deal directly with large amounts of waste, on the environmental impacts caused by their incorrect disposal. The models used were uniform since point out to neighborhoods in similar regions. The neighborhoods of Lagoa Nova / Morro Branco (Ardalan) and Nova Descoberta (Center of Gravity) as ideal for the installation of treatment plant. However, it is suggested to be tested other models that take into account new variables than those used (supply of waste and the distance between points). The routing through the method of scanning has shown that it is possible, in a simple way to optimize routes in order to reduce distances and therefore the logistics costs in the collection of such waste. Introducing a route as a test to gather the twenty largest oil suppliers used in sample frying, using as a main factor time 8 hour of ...

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: João Paulo Soto Veiga; Teresa Losada Valle; José Carlos Feltran; Waldir Antonio Bizzo;

    Abstract This study sought to quantify and characterize cassava waste as fuel. The wastes from three cultivars were collected to study and were divided into three distinct parts of the cassava plant: seed stem, thick stalks, and thin stalks. Physical and chemical analyzes were carried out to determine the elemental composition of the waste: volatile matter; fixed carbon; ash; moisture; lignin; cellulose; hemicellulose; ash composition and higher heating value were determined. We conducted a thermogravimetric analysis in oxidizing and inert atmospheres to study the behavior of the waste as fuel. The root productivity obtained ranged from 7.7 to 13.0 t ha−1 yr−1 on a dry basis (db), and the ratio between waste and roots varied from 0.36 to 0.91. The physical and chemical properties of cassava waste are analogous to those of woody biomass regarding the elemental composition, the higher heating value, and thermogravimetric analysis. Ash content varied from 2.5% to 3.5%, reaching around 6.0% in samples unwashed. Approximately 60% of the ashes are alkali oxides, especially P2O5, K2O, and CaO, which have low melting points. The alkali index calculated suggests that there is a strong tendency that the combustion process leads to ash fouling and the formation of ash deposits.

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    Renewable Energy
    Article . 2016 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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      Renewable Energy
      Article . 2016 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: R. Apolonio; Ozlean de Lima Dantas;

    Os sistemas fotovoltaicos conectados à rede vêm cada vez mais ganhando espaço no Brasil. Isto é fruto de uma política energética regulatória iniciada em 2012, expansão mundial dessa tecnologia e o alto potencial de irradiação solar disponível no país. Este trabalho tem por objetivo comparar grandezas elétricas medidas no ponto comum de conexão (PCC) de uma unidade consumidora (UC) com perfil de carga comercial e sua microusina fotovoltaica (FV) de 45kWp conectada à rede elétrica em relação ao atendimento dos parâmetros da qualidade da energia elétrica (QEE) em regime permanente estabelecidos no Módulo 8 do Procedimento de Distribuição (PRODIST) da Agência Nacional de Energia Elétrica (ANEEL). Os parâmetros analisados no PCC da instalação foram: perfil de tensão, fator de potência, distorção harmônica total de tensão, fator de desequilíbrio e frequência. Também é apresentado o consumo e geração da unidade consumidora com a usina fotovoltaica para um dia útil e um dia do final de semana. Os resultados obtidos mostram que a UC com a usina FV conectada à rede elétrica em operação não impacta na QEE da rede de distribuição e os parâmetros analisados estão dentro dos limites estabelecidos pelo Módulo 8 do PRODIST da ANEEL, com exceção da tensão que requer ajustes no comutador de carga do transformador da distribuidora que alimenta a instalação.

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    E&S Engineering and Science
    Article . 2018 . Peer-reviewed
    Data sources: Crossref
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    E&S Engineering and Science
    Article . 2018
    Data sources: DOAJ
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      E&S Engineering and Science
      Article . 2018 . Peer-reviewed
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      E&S Engineering and Science
      Article . 2018
      Data sources: DOAJ
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  • Authors: Nascimento Neto, Francisco Vieira do;

    This work has as object of study some of the expressions of the water privatization process in contemporary Brazil. The objective was to analyze different expressions of the water privatization process in Brazil. To achieve this general objective, we seek to: Discuss the fundamentals of the environmental issue and its relationship with the finitude of natural resources in contemporary capitalism; To situate water as one of the main resources inherent to the production and valorization processes in the capitalist mode of production; To bibliographically map some expressions of water privatization in the Brazilian State. Methodologically, the research is characterized as exploratory, with data of a bibliographic and qualitative nature. In the case of data collection, we use printed and electronic references of different natures, such as: monographs, theses and dissertations, scientific journals, annals of scientific meetings, among others. Regarding the data source, we define two types of water privatization: 1) Electricity generation (dams / hydroelectric plants); 2) Water distribution by companies / environmental sanitation. A research script was used as a data collection instrument, which guided the selection of the sample of texts based on three aspects of categorization prioritized in the script, namely: characteristics, favorable and contrary arguments; developments and social consequences identified. We analyzed the data in the light of the historical-dialectical materialist method, understanding that it establishes an analysis of reality from the category of totality, representing it in a historical-critical way. The results obtained through this research demonstrate that water privatization is an essential mechanism for the accumulation and valorization of capital to the detriment of the social use of this natural resource to satisfy the needs of living beings. Thus, it highlights the relevance of this study for conducting a contemporary and necessary debate, with theoretical and practical content ; Este ...

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