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This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company's coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas.

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This study reexamines the causal nexus among electricity consumption (EC) and economic growth (EG) for a panel of 31 countries in Latin America and the Caribbean between 1980 and 2021. We find that there are statistically significant feedback impacts among the research variables in the long run. A 1 percent augment in EC raises EG by 0.5 percent and a 1 percent augment in EG produces a 1.54 percent increase in EC which reflects the nature of the latter as a luxury good and implies a tradeoff between economy and environment, since although greater electrical infrastructure drives EG, the latter also increases the EC whose use in a non-responsible manner could lead to environmental degradation through higher CO2 emissions. Therefore, the main policy implication is that, it is necessary to promote EG based on infrastructure focused on sustainable development, ensuring the well-being of present and future generations.

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The prediction of domestic electricity consumption is relevant because it helps to plan energy production, among many other benefits. In this work a dataset was collected from one house in an urban city of north-east of Mexico. An ad-hoc acquisition system was implemented to collect the data using a smart meter and the open weather API. The data was collected every minute over a period of 14 months since November 5, 2022, to January 5, 2024. The dataset contains 605,260 samples of 19 variables related with energy consumption and weather data. This dataset is specifically tailored for predicting domestic energy consumption and understanding consumption behaviours, filling a void in the existing literature where such datasets for Mexico are scarce. Moreover, the multivariate nature of the dataset allows researchers to investigate and propose new techniques for forecasting or pattern classification using multivariate data collected in a real scenario.

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This study delves into the critical nexus between wastewater treatment, energy consumption, and greenhouse gas emissions. Wastewater treatment is a linchpin of sustainable development, yet its energy-intensive processes contribute significantly to greenhouse gas emissions. The research focuses on wastewater treatment plants (WWTPs) in Mexico City (CDMX) and the Metropolitan Area of Barcelona (AMB), exploring the disparities between a developed country and a developing country. The study examines how factors such as water treatment technologies and electricity sources influence carbon emissions. The AMB exhibits superior performance by treating all wastewater, cogenerating energy from the biomass contained in the wastewater and generating 10% fewer emissions, in stark contrast to CDMX, which does not capture the CH4 produced during water treatment, on top of only treating the water of 14% of the city's agglomeration. It underscores the critical implications of WWTP efficiency on climate change and progress toward UN Sustainable Development Goals. Given the limited attention to the Global South, this research serves as a vital contribution to the discourse on sustainability and development.

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This unprecedented analysis of the factors that determine the performance of Brazil electric energy contradictory policies identifies the irregular path followed by one of the largest national oil companies in the world. As high convenience comparative case study, the increase and decrease of production and investment within the context of the Brazilian electric matrix in the last 20 years can shed light on Petrobras' disparities in relation to itself and in relation to external variables over time such as policies and investment choices, international oil prices and exploration cost.

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Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, such as smart grids, by introducing a real-time energy monitoring system. In light of this, smart meters can offer greater opportunities for sustainable and efficient energy use and improve the utilization of energy sources, especially those that are nonrenewable. According to the 2020 International Energy Agency (IEA) report, nonrenewable energy sources represent 65% of the global supply chain. The smart meter developed in this work is based on the ESP32 microcontroller and easily accessible components since it includes a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of developing the smart meters was to enhance the software and simplify the hardware. Unlike traditional meters that calculate electrical parameters by means of complex circuits in hardware, this project performed the calculations directly on the microcontroller. This procedure reduced the complexity of the hardware by simplifying the meter design. Owing to the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters could be achieved without the need for additional circuits. This software-driven approach with simplified hardware led to benefits, such as reduced production costs, lower energy consumption, and a meter with improved accuracy, as well as updates on flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the collected data to be transmitted to remote monitoring systems for later analysis. The innovative feature of this smart meter lies in the fact that it has readily available components, along with the ESP32 chip, which results in a low-cost smart meter with performance that is comparable to other meters available on the market. Moreover, it is has the capacity to incorporate IoT and artificial intelligence applications. The developed smart meter is cost effective and energy efficient, and offers benefits with regard to flexibility, and thus represents an innovative, efficient, and versatile solution for smart grid applications.

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We conduct a sectoral analysis of electricity consumption during the Coronavirus disease 2019 (COVID-19) pandemic for the primary sectors that make up Colombia's unregulated and regulated markets. Applying a model of seemingly unrelated regression equations to examine data between February 2015 and May 2021, we evidence the recomposition of electricity consumption related to mandatory preventive isolation during the pandemic. Average consumption in the residential sector increased by 16.9% as working from home became prevalent. In contrast, unregulated market sectors subjected to quarantines presented a significant decrease in consumption, up to 32% in the financial sector. While industries that were not subjected to mandatory confinement, such as health, food (agriculture), and water supply, had no significant effect. Our results are relevant for informing demand forecasts and planning network expansions to guarantee the reliability of the supply as pandemic practices such as working from home become permanent.

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End-use demand data availability is a catalyst for improving energy efficiency measures and upgrading electricity demand studies. Nevertheless, residential end-use public datasets are limited, and end-use monitoring is costly. The lack of electricity end-use data is even more profound in Latin America, where there are no public end-use datasets as far as the authors are concerned. Hence, we present the Residential Electricity End-use Demand Dataset of Costa Rica (REEDD-CR), containing the results of monitoring 51 Costa Rican households. The data set includes the aggregated and branch circuit measurements for every home with a sample time of 1 min for at least an entire week. The measurements were distributed all around the country. In addition, based on these sub-measurements, REEDD-CR includes a dataset of 197 load signatures composed of seven consumption and demand features for eight high-consuming appliances: refrigerator, stove, dryer, lighting, water heating, air conditioning, microwave, and washing machine. The features included on each load signature are average power, peak power, average daily events, average daily energy, day-use factor, night-use factor, and time of use. The single-appliance measurements used to calculate these load signatures are also part of the dataset. The release of REEDD-CR can serve as a tool for appliance modeling, demand disaggregation testing, feedback for energy demand models, and the overall upgrade of electricity supply and demand simulation studies with realistic and disaggregated data.

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The objective of this study is to compare the environmental and social performance of two small-scale avocado biorefineries implanted in a rural zone in the North of Colombia. Two small-scale biorefineries were proposed. Small-B1 addressed to produce avocado oil and animal feed, and Small-B2 focused on the guacamole production. The environmental analysis was done by applying the life cycle assessment methodology. Then, agronomic information and process simulation were required to complete the analysis. Moreover, the water footprint of the avocado crops was estimated. Both biorefineries were compared with the direct avocados production and commercialization. The social assessment was achieved by the estimation of quantitative indicators related to wages, jobs, and working hours. The agricultural carbon and water footprints of the creole avocado crop were 0.59 kg CO2-eq/kg and 2.13 m3/kg. In the same way, Small-B1 and Small-B2 obtained a carbon and water footprints of 8.99 kg CO2-eq/kg and 6.63 m3/kg and 0.72 kg CO2-eq/kg and 1.38 m3/kg, respectively. The hotspots of the creole avocado crop are related to the use of fertilizers and fungicides. Then, new strategies should be implemented to reduce the farmer's dependency. The social analysis exhibit a high resilience of the Small-B1 biorefinery since a salary increase to worker about 50% can be proposed. In addition, the installation of this biorefinery can create more than ten jobs. A disjunction was found between the economic, environmental, and social analyses. Thus, the need to establish a multidimensional strategy to design sustainable biorefineries is presented.

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Electricity consumption in Brazil increases significantly, making feasibility studies to diversify the Brazilian electricity mix with other renewable sources become essential. However, the electricity market continues to be primarily driven by government or regulatory incentives and economic status. With the coronavirus pandemic since the beginning of 2020, the market has been undergoing significant changes that cause uncertainty in consumer investments in grid-connected photovoltaic systems. This article presents an economic feasibility analysis of photovoltaic system installation for the Brazilian residential sector, estimating a cash and term investment and comparing the viability of the investment before the global pandemic (December 2019) with the pandemic scenario (April 2021) for each of the 27 Brazilian capitals. The baseline scenario for economic feasibility analysis calculations considered a power system of 4 kW. The results show that the effects of the pandemic in the electricity market were positive in the feasibility of investing in grid-connected photovoltaic systems. Also, financing the system increases the chances of profitability. The current scenario is possibly the most attractive ever experienced by potential investors. However, this favorable scenario could decline in the coming years due to climatic and governmental factors.

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More stringent standards for engines and fuels are progressively implemented as alternatives to reduce on-road vehicle emissions. While electric vehicles appear as a perfect alternative since their engines do not emit pollutants, wear and dust resuspension (W&R) and indirect emissions associated with electricity production remain significant sources of pollution. This work compares well-to-wheel emissions (WTW) and abatement strategies for various types of passenger vehicles in Bogotá and Santiago for different pollutants (CO, PM2.5, SO2, and NOx) and greenhouse gases like CO2 equivalent (CO2-Eq). Results show that WTW baseline emissions are more extensive in Bogotá than in Santiago (i.e., 58 and 30% for PM2.5 and CO2-Eq), mainly due to the higher vehicle activity and older state of Bogotá's fleet. We also evaluated extreme scenarios to assess the potential of a given vehicle technology or energy source to reduce emissions. We assessed, in particular, the replacement of all current vehicles by (1) conventional technologies with stricter emission standards and (2) battery electric vehicles powered with different energy resources. Our results indicate that replacing the current fleet with modern combustion technologies has a lower reduction potential than battery electric vehicles, but these reductions largely depend on the energy mix. Substitution by electric vehicles powered with electricity from renewable energies is the most efficient scenario in both cities. Finally, results also stress the importance of the resuspension of deposited road dust and brake and tire wear emissions in both cities as a crucial source of PM2.5, which must be better controlled.

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During the COVID-19 pandemic, policymakers needed to assess the impact of large monetary and fiscal policy interventions in as close to real time as possible-yet existing survey-based indicators are usually released monthly or quarterly. The use of high-frequency data to track economic activity has become widespread. This paper constructs a near real-time economic activity indicator for the Brazilian economy during the COVID-19 pandemic. Brazil's integrated national electricity sector, which covers over 98% of the population, allows us to construct an economic activity indicator based solely on electricity consumption data that are available at near real time and accounts for activity in the large informal sector of the economy. We construct our indicator by isolating the variability in electricity consumption that is not related to economic activity, then measure how well monthly and quarterly versions of our indicator track against standard economic indicators. The results show strong correlation with standard indicators, notably during economic shocks.

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This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

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The enormous environmental problems that arise from organic waste have increased due to the significant population increase worldwide. Microbial fuel cells provide a novel solution for the use of waste as fuel for electricity generation. In this investigation, onion waste was used, and managed to generate maximum peaks of 4.459 ± 0.0608 mA and 0.991 ± 0.02 V of current and voltage, respectively. The conductivity values increased rapidly to 179,987 ± 2859 mS/cm, while the optimal pH in which the most significant current was generated was 6968 ± 0.286, and the ° Brix values decreased rapidly due to the degradation of organic matter. The microbial fuel cells showed a low internal resistance (154,389 ± 5228 Ω), with a power density of 595.69 ± 15.05 mW/cm2 at a current density of 6.02 A/cm2; these values are higher than those reported by other authors in the literature. The diffractogram spectra of the onion debris from FTIR show a decrease in the most intense peaks, compared to the initial ones with the final ones. It was possible to identify the species Pseudomona eruginosa, Acinetobacter bereziniae, Stenotrophomonas maltophilia, and Yarrowia lipolytica adhered to the anode electrode at the end of the monitoring using the molecular technique.

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To stop the spread of COVID-19, governments have implemented confinement measures unprecedented in modern society. One of the main consequences has been the paralysis of commercial and industrial sectors worldwide, primary electricity consumers. This paper examines the impact of these measures on the electricity sector through a literature review accompanied by fieldwork on the impact of COVID-19 in Ibero-America and its energy regulatory response. First, we will review the causes of the reduction in electricity demand due to the confinement measures and their technical and financial consequences in the electricity sector. Second, we will examine the impact of COVID-19 on the wind and solar PV energy sectors, mainly affected by the paralysis of production and export of materials and components. Third, we will revise the regulatory measures implemented by the countries to avoid the interruption of electricity supply to households. This paper will end by reviewing economic recovery plans and their relationship to the energy transition. Although there are no fundamental differences between developed and developing countries in their regulatory reaction to this crisis in the energy sector, there are significant differences in economic recovery planning. While developed countries aim for a green economic recovery and the creation of green jobs, developing countries are allocating least resources to social protection and general economic stimulus programs, postponing climate objectives. In Latin America, this adds to the high levels of debt faced by utilities and the possible resurgence of social crises that were stopped by the COVID-19 outbreak, making a green recovery even more difficult.

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The government of Argentina has recently declared its objective of turning the nation carbon-neutral by 2050. Thus, it is essential to identify the relevant factors which can facilitate the attainment of this environmental development target. Against this backdrop, this study aims to evaluate the impacts of renewable electricity output, trade globalization, economic growth, financial development, urbanization, and technological innovation on sectoral carbon dioxide emissions in Argentina during the 1971-2014 period. The findings, overall, suggest that enhancing renewable electricity output share in the total electricity output figure of the nation helps to curb carbon dioxide emissions generated from Argentina's energy, manufacturing and industry, residential and commercial buildings, and transportation sectors. Contrarily, greater trade globalization is evidenced to boost carbon dioxide emissions in almost all the aforementioned economic sectors. Besides, the findings also validate the existence of the carbon dioxide emission-induced environmental Kuznets curve hypothesis for all four sectors. In addition, financial development and urbanization are also evidenced to exert carbon dioxide emission-stimulating impacts, while technological innovation is witnessed to be necessary for curbing sector-based carbon dioxide emissions in Argentina. Accordingly, to decarbonize the economy, this study recommends the government of Argentina to adopt necessary policies for fostering renewable energy transition within the electricity sector, greening the trade globalization strategies, achieving environmentally sustainable economic growth, developing the financial sector by introducing green financial schemes, planning sustainable urbanization, and financing technological development-oriented projects.

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The energy sector of Argentina is predominantly reliant on fossil fuels. Consequently, such fossil fuel dependency within the nation's power sector, in particular, has aggravated the environmental quality in Argentina by amplifying the nation's energy production-based carbon emission levels. However, keeping into consideration the international commitments pledged by Argentina under the Paris Accord and the Sustainable Development Goals agenda, it is pertinent for this South American country to curb its energy production-based emission of greenhouse gases, especially carbon dioxide. Against this milieu, this study examines the impacts of renewable electricity generation, economic globalization, economic growth, and urbanization on carbon dioxide emissions generated from the production of electricity and heat in the context of Argentina. Using annual frequency data from 1971 to 2016, recent econometric methods are applied to control for multiple structural breaks in the data. The major findings from the ecnometric analyses affirmed long-run associations between renewable electricity generation, economic globalization, economic growth, urbanization, and energy production-based carbon dioxide emissions in Argentina. Besides, enhancing renewable electricity output shares is found to curb these emissions while economic globalization and urbanization are witnessed to boost them. Moreover, renewable electricity generation and economic globalization are found to jointly reduce the energy production-related carbon dioxide emissions in Argentina. The results also validate the authenticity of the Environmental Kuznets Curve (EKC) hypothesis. Finally, the causality analysis reveals evidence of unidirectional causalities running from renewable electricity generation, economic globalization, economic growth, and urbanization to energy production-related carbon dioxide emissions in Argentina. In line with these findings, this study recommends several viable policies which can be implemented to help Argentina control the growth of its energy production-based carbon dioxide emissions.

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This work has its origin in the growing demands of energy regulations to meet future local targets and to propose a global implementation framework. A literature review related to conventional electrical energy storage systems has been carried out, presenting different cases analyzed at building scale to deepen in nature-inspired processes that propose reductions in environmental impact and present improvements in these storage devices. The use of batteries, especially lithium-ion batteries, is the most prominent among the electrical storage applications; however, improvements have been proposed through hydrogen batteries or the implementation of more environmentally friendly materials to manufacture the electrodes. In this sense, oriented to creating systems designed to protect the environment, important advances have been made in the development of storage systems based on biomimetic strategies. The latter range from the generation of energy through the respiratory processes of microorganisms to the recreation of the generation, storage, and release of energy using the thermoelectric and thermoregulatory characteristics of some insects. These facts show that the trend in research towards improving existing systems continues but reinforces the idea that new solutions must be environmentally friendly, so there is still a long way to improving the processes established thus far.

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Electrical energy is generated in different ways, each located at some specific geographical area, and with different impact on the environment. Different sectors require heterogeneous rates of energy delivery, due to economic requirements. An important problem to solve is to determine how much energy must be sent from each supplier to satisfy each demand. Besides, the energy distribution process may have to satisfy ecological, technological, or economic cost constraints.

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Since developing countries experience economic and environmental sustainability challenges, it is desirable digging into the linkages between economic and environmental parameters. The purpose of this work is to evaluate the existence of the environmental Kuznets curve (EKC) theory (i.e., the inverse U-shape connection between real GDP per capita and per capita carbon dioxide emissions) in the sample of 11 developing countries. By using balanced annual panel data in the period between 1992 and 2014 and two alternative estimation techniques, we explored the potential inverted U-shaped linkage between carbon dioxide emissions and real GDP per capita in the sample of interest. For analysis purposes, Pedroni and Westerlund co-integration techniques are employed. Then, fully modified ordinary least squares, pooled mean group methods are applied for long-run parameter estimations. And, the Dumitrescu-Hurlin causality approach is employed for causal directions. Firstly, this work's findings provide the supportive evidence to the inverse U-shaped linkage in the long-run, indicating that an increase in real GDP per capita and electricity consumption tends to mitigate long-run carbon dioxide emissions in the developing countries, for the whole sample. Secondly, the country-specific findings suggested the presence of EKC theory for Brazil, China, India, Malaysia, the Russian Federation, Thailand, and Turkey. It implicated that these countries are on the path of attaining environmental sustainability in the long-run. However, Mexico, Philippines, Indonesia, and South Africa failed to lend credence to the EKC theory. It manifested that these countries need to design strategies directed to reduce carbon dioxide emissions from economic activity and electricity generation through efficiency improvement or promotion of renewables. Finally, bidirectional causal links are observed among all the variables of interest. The findings suggest that country-specific targeted action plans should be implemented to ensure the environmental sustainability in the developing world.

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