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Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

Energy demand forecasting is crucial for effective resource management within the energy sector and is aligned with the objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis of different forecasting models to predict future energy demand trends in Brazil, improve forecasting methodologies, and achieve sustainable development goals. The evaluation encompasses the following models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Exogenous SARIMA (SARIMAX), Facebook Prophet (FB Prophet), Holt–Winters, Trigonometric Seasonality Box–Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS), and draws attention to their respective strengths and limitations. Its findings reveal unique capabilities among the models, with SARIMA excelling in tracing seasonal patterns, FB Prophet demonstrating its potential applicability across various sectors, Holt–Winters adept at managing seasonal fluctuations, and TBATS offering flexibility albeit requiring significant data inputs. Additionally, the investigation explores the effect of external factors on energy consumption, by establishing connections through the Granger causality test and conducting correlation analyses. The accuracy of these models is assessed with and without exogenous variables, categorized as economical, industrial, and climatic. Ultimately, this investigation seeks to add to the body of knowledge on energy demand prediction, as well as to allow informed decision-making in sustainable energy planning and policymaking and, thus, make rapid progress toward SDG7 and its associated targets. This paper concludes that, although FB Prophet achieves the best accuracy, SARIMA is the most fit model, considering the residual autocorrelation, and it predicts that Brazil will demand approximately 70,000 GWh in 2033. [ABSTRACT FROM AUTHOR]

Open water transposition channels in hot and arid regions, like those in the São Francisco River Integration Project (PISF) in Brazil, suffer significant water losses through evaporation. This paper proposes covering these channels with photovoltaic (PV) panels to reduce evaporation while simultaneously generating clean energy. The research aims to quantify water savings and energy generation potential across all channel lengths and assess whether the generated solar power can substitute grid electricity for powering the transposition pumps during peak hours, thereby enhancing energy efficiency. This study analyzed the state-of-the-art of PV generation and calculated their solar potential. Identified the specific characteristics of PISF channels and watercourses considering the regional geography, meteorology, irradiation, and social peculiarities. And, finally, assessed the feasibility of covering the watercourses with solar panels. The results reveal that covering all current PISF channels with PV panels could save up to 25,000 cubic meters of water per day, significantly contributing to water security and improving the quality of life for the local population. Additionally, the project could generate 1200 gigawatt-hours of electricity annually, meeting the energy demands of the transposition pumps during peak hours and promoting energy efficiency within the project. This research paves the way for utilizing PV technology to address water scarcity challenges and enhance the sustainability of water infrastructure projects in arid regions worldwide. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

Since the 1970s, Brazil has carried out the most successful world program of commercial biomass for use and production of energy by stimulating its sugarcane industry and promoting the large-scale production of ethanol nationwide in response to the first oil shock. Today, the technologies behind ethanol production are well established. Brazil is the world's largest sugarcane supplier, producing its ethanol at a competitive price. If other sugarcane producing countries decided to join Brazil's move toward the production of this biofuel, what impacts might there be for each country's economy and employment? This is what we investigate in this paper. Prime candidates for ethanol production include Australia, China, Colombia, India, Indonesia, Mexico, Pakistan, the Philippines, Thailand, and the United States of America. We evaluated the potential socioeconomic impacts of developing this promising industry by using an input–output approach. More specifically, we adapted the Brazilian method of producing ethanol to these countries' distinct economies. We augmented the input–output table of each country, inserting a new ethanol industry based on the Brazilian ethanol production model. We also augmented their new ethanol industry's sales following a hypothetical hydrous and anhydrous ethanol consumption scenario. Thereafter, we reconcile the national accounts, concluding our analysis by quantifying and comparing the different net effects of this new industry for each of the assessed countries for the year of 2009. We demonstrate which industries would be expected to be positively or negatively impacted by this substitution in each country; and find that not all of the countries we assessed would experience positive socioeconomic results from emulating Brazil's production of ethanol. [ABSTRACT FROM AUTHOR]

In the literature, it is well known that there is a bidirectional causality between economic growth and energy consumption. This is why it is crucial to forecast energy consumption. In this study, four deep learning models, i.e., Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), were used to forecast energy consumption in Brazil, Canada, and France. After a training test period, the performance evaluation criterion, i.e., R2, mean square error, root mean square error, mean absolute error, and mean absolute percentage error, was performed for the performance measure. It showed that GRU is the best model for Canada and France, while LSTM is the best model for Brazil. Therefore, the energy consumption prediction was made for the 12 months of the year 2017 using LSTM for Brazil and GRU for Canada and France. Based on the selected model, it was projected that the energy consumption in Brazil was 38 597.14–38 092.88, 63 900–4 800 000 GWh in Canada, and 50 999.72–32 747.01 GWh in France in 2017. The projected consumption in Canada was very high due to the country's higher industrialization. The results obtained in this study confirmed that the nature of energy production will impact the complexity of the deep learning model. [ABSTRACT FROM AUTHOR]

Two economic activities have been integrated in southern Brazil: olive cultivation and sheep farming. The favorable edaphoclimatic conditions in the southern region for cultivating olive trees and cultural preferences for the consumption of sheep meat make this integration a valuable strategy. Considering that every activity causes environmental impact and the exploitation of natural resources, new systems that promote sustainable development and the efficient use of these resources become necessary. This paper presents the first study of the carbon footprint of the life cycle of integrating of sheep and olive farming, with the objective to build an inventory based on primary data collected on olive and sheep farms in Brazil, determine the carbon footprint of the conventional and integrated system and estimate the potential for mitigating GHG emissions. The evaluation was performed using the life cycle assessment (LCA) methodology. The study focuses on the detailed analysis of the processes and activities that are responsible for GHG emissions, including enteric methane emissions, methane emissions from manure management, emissions from nitrogen fertilizer use and limestone application and energy consumption. For this, two major scenarios were considered, the first centered around on the traditional olive cultivation and sheep farming systems individually, and the second involved their synergistic integration. The findings indicate that the conventional system is associated with a global warming impact of 6.8 kg CO 2 eq per liter olive oil and kg live weight. In contrast, the integrated system demonstrates a significant mitigation potential of 27.7%, resulting in a reduced global warming impact of 4.92 kg CO 2 eq. This advantage is a consequence of the synergy between the systems, as they now occupy the same area, which allows the sharing of agricultural activities and agrochemical inputs, in addition to optimizing nutrient recycling and complementing each other. Additionally, the results of a sensitivity analysis showed that enteric methane from sheep and the application of limestone to the soil are the most influential parameters on the carbon footprint, indicating that they are key parameters for the development of GHG mitigation strategies, some of which were discussed in the article. • First life cycle assessment of integration between olive and sheep farming in Brazil. • The inventory was constructed with primary data collected in situ. • The study assesses the carbon footprint of the olive and sheep farming system. • The results show environmental advantages of the integration of olive and sheep farming systems. [ABSTRACT FROM AUTHOR]

Tourism serves as a crucial means of funding national development and ensuring the sustainability of local livelihoods in growing countries such as Brazil. Nevertheless, the accelerated growth of tourism in various nations might lead to significant environmental consequences due to heightened energy consumption. This surge in energy usage contributes to the exacerbation of global warming through the amplified release of carbon dioxide (CO2). In light of the increasingly evident impacts of climate change on the natural environment, a multitude of endeavors have been undertaken with the aim of attaining ecological sustainability. The objective of this study is to examine the dynamic effects of economic growth, energy consumption, and tourism on carbon emissions in Brazil by analyzing time series data spanning from 1990 to 2019. The stationarity of data was assessed through the application of unit root tests, while an autoregressive distributed lag (ARDL) approach was employed to analyze the relationship between the components, accounting for both the long-term and short-term dynamics. The empirical results indicate that a 1% rise in economic growth, energy consumption, and tourist arrivals leads to environmental deterioration by causing a respective increase in CO2 emissions of 1.37%, 1.06%, and 0.57% in the near term, and 0.72%, 0.62%, and 0.16% in the long term. This article presents policy ideas aimed at achieving carbon neutrality in Brazil by increasing the utilization of renewable energy sources, while simultaneously fostering sustainable tourism as a means to enhance the country's economy. Highlights •This study investigated the tourism-economy-energy-environment nexus in Brazil. •The econometric analysis revealed that tourism, energy use, and economic growth increase CO2 emissions. •This article offers policy suggestions to achieve sustainable tourism and carbon neutrality. [ABSTRACT FROM AUTHOR]