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The purpose of this article is to examine the correlations between greenhouse gas (GHG) by CO2eq emissions and the use of electricity on the Praia Vermelha campus of the Universidade Federal Fluminense (UFF) located in Brazil. The study favors understanding and management for the sustainability of the campus operations. The research used electricity consumption data from 2017 to 2019 and quantitative and qualitative data collected in 2020, through online research applied at Praia Vermelha campus. An exploratory and descriptive study, providing familiarity with the problem and deepening knowledge about the phenomenon, seeking a correlation between variables and proposing reflections. We observed a reduction in electricity consumption on the campus in the period surveyed from 2017 to 2019, however, there wasn't proportional and significant reduction CO2eq emissions in the period between 2018 until 2019. During this period occurred fluctuations in the dispatches of the different power generating plants, considering renewables and fossils sources. We also found that most of survey respondents agree with to implementation of incentive programs and the dissemination of information about GHG emissions on campus to promote the engagement of the academic environment in the efficient use of electricity. We found few peer-reviewed studies dealing with GHG emissions generated in South American universities. Through a bibliographic review, we investigated the commitment of the universities to sustainability and the management of efficient use of electric energy, as well as the methodology for calculating indirect GHG emissions, in CO2eq, related to purchase energy from the System National Interconnected System (SIN) of Brazil. [ABSTRACT FROM AUTHOR]

Rising energy costs and the current concern about sustainability issues directly impact the competitiveness and survival of organizations, promoting the search for continuous improvement in industrial production processes. The present study, by identifying the absence in the literature of models based on Value Stream Mapping and integrated with Industry 4.0 technologies that promote better energy efficiency practices in industrial processes, proposes a new Lean Management model called Real-time Energy Flow Mapping. This innovative model, provide capabilities to a visual, digital and sustainable management system of all energy behavior in machines and equipment using fully parameterizable metrics and rules. Also, estimulating stakeholders with the ability to predict and monitor energy costs, as well as identify possible problems due to changes in the behavior of energy consumption in machines, thus helping managers to make decisions faster and more dynamically. As a result, the literature search turned up 17 Value Stream Mapping based models with a focus on energy efficiency, along with 31 metrics grouped on energy, environmental and production processes. After discussions by the Focus Groups, 16 resulting metrics were submitted to expert panel, in which the least relevant were discarded and the 13 most relevant classified using Fuzzy Delphi. An analysis to verify the interrelationships between the metrics is performed using the Fuzzy DEMATEL, being finally applied in the proposed model. A case study of the model was carried out, submitting it operationally at the LabFaber 4.0, empirically validating the study. The Real Time Energy Flow Map fills the gap in the literature on Value Stream Mapping models to improve energy efficiency, monitoring energy consumption through dynamic mapping of the energy flow of production processes, identifying the area with increased potential for energy savings through changes in operating behavior that reduce overall energy consumption and environmental impacts. • Lean model integrated with energy, environmental and production process metrics. • Value Stream Mapping model for best practices in energy efficiency. • Real-time energy consumption modeling of industrial production processes. • Lean model in a real-world case study to analyse the benefits of energy efficiency. • Analysis of the most relevant energy, environmental and production process metrics. [ABSTRACT FROM AUTHOR]