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Committed to achieving carbon neutrality by mid-century, South America faces unique challenges in carbon mitigation due to its distinct socio-economic, technological, and institutional backgrounds compared to developed and other developing economies. Previous research on South America's carbon emissions has limited country and sector coverage and typically adopts a top-down approach that focuses on the overall emissions of an economy, neglecting individual energy sources and emission sectors. Therefore, this paper compiles a comprehensive inventory of CO 2 emissions for South American countries based on a meticulous bottom-up approach and multi-source data integration, followed by a thorough analysis of their temporal evolution patterns and the driving forces behind them. Between 2010 and 2020, South America's overall emissions peaked in 2014 at 1092.3 Mt., led by emissions from oil consumption and from the transport and electricity sectors, and dropped to 865.6 Mt. in 2020, due to the economic fluctuations and COVID-19. Specifically, Brazil was the largest emitter, Chile experienced the most significant increase in emissions, and Paraguay recorded the highest emission growth rate. Generally, expanding population was constant driver of emission growth. High energy intensity led to significant emission increases in Brazil and Ecuador, while elevated carbon intensity was the main driver in Argentina. Energy structure changes played a positive role in most countries to different extents (e.g., much more significant in Brazil than in Chile) but aggrevated emissions in Paraguay and Guyana. Therefore, carbon mitigation strategies should be tailored to each country's unique national circumstances. • During 2010–2020, South America's CO 2 emissions from fossil fuels peaked in 2014. • Transport and electricity sectors comprised over 60% of total emissions. • Brazil was the top emitter, while Chile saw the largest increase in emissions. • Emission patterns and driving factors varied significantly across countries. • Carbon reduction strategies should be tailored to local circumstances. [ABSTRACT FROM AUTHOR]

• Lifecycle benefits/harms of solar/electric ships were demystified quantitatively. • Live-Life Cycle Assessment was introduced to overcome the lack of data available. • Environmental performance of solar ships was subject to geographical conditions. • Green electric grids were suggested to contribute to net zero-emission shipping. This paper was born to introduce a novel methodology termed Live-Life Cycle Assessment using a simulation-based data generation technique that can remedy the inherent shortcomings of conventional practices of lifecycle assessment. To demonstrate its excellence, the proposed method was applied to one of the most challenging topics in the marine industry. That was to tackle the fundamental doubt of whether solar-electric propulsion ships could truly be the future energy solution of maritime transports by fulfilling new environmental conventions and goals around the world. The case study began with an existing hybrid short route ferry running on diesel and plug-in battery power. Credible PV systems for the case ship was modelled to produce ship performance data under various operational/environmental circumstances of the coastal zones across 29 countries in the platform of MATLAB/Simulink. As a key functionality of Live-Life Cycle Assessment, the produced data was directly fed, as inputs, into life cycle assessment to avoid conventional practices that heavily rely on outdated data libraries. Results of the case study clearly revealed and quantified the correlations of the performance of PV-battery systems with climate parameters such as temperature and irradiance of subject areas as well as national power production methods. For example, in terms of Global Warming Potential, the case ship with the PV system was estimated to reduce 40,812 kg CO 2 eq. per year in Brazil (average temp.: 27.4 ℃, major energy source: hydro). Interestingly, the same vessel was found to achieve greater reductions in India (average temp.: 27.5 ℃, major energy source: coal) or Australia (average temp.: 20.1 ℃, major energy source: coal) where are overly laden with coal-based power plants. Therefore, their reduction levels were estimated at 152,887 kg CO 2 eq. per year and 141,517 kg CO 2 eq. per year respectively. This paper clearly shows the excellence of the proposed method to demystify/quantify the impacts of parametric variables on the performance of the PV-electric ship. Moreover, it highly suggests the Live-Life Cycle Assessment could be a solution as a new standard to respond to strong demand to obtain general observation of lifecycle benefits/harms of new technologies across all industries, not necessarily limited to the maritime sector. [ABSTRACT FROM AUTHOR]

This paper proposes an Internet-of-Things (IoT) based energy management system (EMS) for the optimal operation of unbalanced three-phase AC microgrids. The system utilizes a software architecture based on microservices, which includes a stochastic economic dispatch optimizer (EDO), a database, a web-based graphical user interface (GUI), and an application programming interface (API). The EDO uses a mixed-integer linear programming (MILP) model to ensure the day-ahead dispatch of the distributed energy resources (DERs) in the microgrid while adhering to grid constraints such as voltage, current, and power limits. Additionally, the optimization module takes into account security constraints for unplanned islanded operation, as well as stochastic scenarios of local demand and renewable generation. To assess the performance of the proposed IoT-based EMS, tests are conducted using a real-time simulator in a software-in-the-loop (SIL) experimental setup. Actual data from a microgrid located at the State University of Campinas (UNICAMP) in Brazil is utilized for the tests. The microgrid consisted of a photovoltaic (PV) system, a battery energy storage system (BESS), a thermal generation unit, and variable demands. Results indicated the effectiveness of the proposed IoT-based EMS in monitoring the operation of the microgrid and defining the optimal day-ahead dispatch of local DERs. • An IoT-based energy management system (EMS) for microgrids is presented. • A database, a web-based GUI, an API, and an optimization module comprise the EMS. • Optimal day-ahead dispatch is defined considering grid and security constraints. • Real-time simulations in a software-in-the-loop environment are implemented. • EMS is efficient in defining the dispatch and monitoring the operation of microgrids. [ABSTRACT FROM AUTHOR]

• An innovative framework for energy benchmarking of buildings is proposed. • Entropy and cluster analysis reduced uncertainty in setting parameters of archetypes. • The machine learning technique used as a benchmarking tool showed high performance. • The benchmarking applied in actual buildings showed a high inefficiency ratio. Energy benchmarking of buildings has an important role in improving energy performance by establishing a reference for the energy efficiency of the building stock. The simulation of archetypes followed by a generalisation model has been widely used to obtain benchmarks. However, even though archetypes summarise the main features of the building stock, the uncertainties must be accounted for in the modelling process. Moreover, testing the response of the benchmarking model using the actual building stock data supports the reliability of the method. This paper aims to propose an innovative framework to reduce the uncertainty of archetypes for benchmarking buildings. A standard framework for data compiling is proposed and an assessment of the uncertainty of variables using entropy and cluster analysis allowed to obtain representative archetypes. An Artificial Neural Network (ANN) was used as a predictive tool, and it was applied to benchmark a sample of actual buildings. Also, the simulation outcomes were used to determine energy end-uses according to the climatic zones. The framework proposition is presented alongside with a practical application. The result is an unprecedented benchmarking model: the archetype considers more variation in parameters of the building stock with higher uncertainties. Additionally, the modelling process showed to be robust for combining different datasets, and the ANN achieved high-performance metrics. Conclusion indicates the potential of using the framework for other typologies. Moreover, the framework demonstration is used for a school stock in Brazil, showing a trend to the inefficiency while a specific case study was explored, showing the potential of the method to find faults in the building energy use. [ABSTRACT FROM AUTHOR]