Complex artificial intelligence models for energy sustainability in educational buildings.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Optimizing the Performance of the Sparse Matrix-Vector Multiplication Kernel in FPGA Guided by the Roofline Model.

The widespread adoption of massively parallel processors over the past decade has fundamentally transformed the landscape of high-performance computing hardware. This revolution has recently driven the advancement of FPGAs, which are emerging as an attractive alternative to power-hungry many-core devices in a world increasingly concerned with energy consumption. Consequently, numerous recent studies have focused on implementing efficient dense and sparse numerical linear algebra (NLA) kernels on FPGAs. To maximize the efficiency of these kernels, a key aspect is the exploration of analytical tools to comprehend the performance of the developments and guide the optimization process. In this regard, the roofline model (RLM) is a well-known graphical tool that facilitates the analysis of computational performance and identifies the primary bottlenecks of a specific software when executed on a particular hardware platform. Our previous efforts advanced in developing efficient implementations of the sparse matrix-vector multiplication (SpMV) for FPGAs, considering both speed and energy consumption. In this work, we propose an extension of the RLM that enables optimizing runtime and energy consumption for NLA kernels based on sparse blocked storage formats on FPGAs. To test the power of this tool, we use it to extend our previous SpMV kernels by leveraging a block-sparse storage format that enables more efficient data access.

Comparison of Collaborative and Cooperative Schemes in Sensor Networks for Non-Invasive Monitoring of People at Home.

This paper looks at wireless sensor networks (WSNs) in healthcare, where they can monitor patients remotely. WSNs are considered one of the most promising technologies due to their flexibility and autonomy in communication. However, routing protocols in WSNs must be energy-efficient, with a minimal quality of service, so as not to compromise patient care. The main objective of this work is to compare two work schemes in the routing protocol algorithm in WSNs (cooperative and collaborative) in a home environment for monitoring the conditions of the elderly. The study aims to optimize the performance of the algorithm and the ease of use for people while analyzing the impact of the sensor network on the analysis of vital signs daily using medical equipment. We found relationships between vital sign metrics that have a more significant impact in the presence of a monitoring system. Finally, we conduct a performance analysis of both schemes proposed for the home tracking application and study their usability from the user's point of view.

Fabrication of Activated Carbon Decorated with ZnO Nanorod-Based Electrodes for Desalination of Brackish Water Using Capacitive Deionization Technology.

Capacitive deionization (CDI) is a promising and cost-effective technology that is currently being widely explored for removing dissolved ions from saline water. This research developed materials based on activated carbon (AC) materials modified with zinc oxide (ZnO) nanorods and used them as high-performance CDI electrodes for water desalination. The as-prepared electrodes were characterized by cyclic voltammetry, and their physical properties were studied through SEM and XRD. ZnO-coated AC electrodes revealed a better specific absorption capacity (SAC) and an average salt adsorption rate (ASAR) compared to pristine AC, specifically with values of 123.66 mg/g and 5.06 mg/g/min, respectively. The desalination process was conducted using a 0.4 M sodium chloride (NaCl) solution with flow rates from 45 mL/min to 105 mL/min under an applied potential of 1.2 V. Furthermore, the energy efficiency of the desalination process, the specific energy consumption (SEC), and the maximum and minimum of the effluent solution concentration were quantified using thermodynamic energy efficiency (TEE). Finally, this work suggested that AC/ZnO material has the potential to be utilized as a CDI electrode for the desalination of saline water.

Role of country risks and renewable energy consumption on environmental quality: Evidence from MINT countries.

This study focuses on uncovering the effect of country risks and renewable energy consumption on environmental quality. In this context, the study examines Mexico, Indonesia, Nigeria, and Turkey (MINT) nations; takes economic growth, trade openness, and urbanization into account; includes data from 1990 to 2018; applies cross-sectional autoregressive distributed lag (CS-ARDL) as the main model while common correlated effects mean group (CCEMG) and augmented mean group (AMG) for robustness checks. The empirical results show that (i) economic growth, political risk, urbanization, and trade openness contribute to an increase in ecological footprint; (ii) economic and financial risks as well as renewable energy use have a positive influence on environmental quality; (iii) a unidirectional causality exists from economic risk, financial risk, political risk, economic growth, urbanization, and trade openness to the ecological footprint: (iv) the validity of the EKC hypothesis for the MINT economies is verified; (v) the robustness of CS-ARDL results are validated by CCEMG and AMG approaches. Based on these results, policymakers should promote a sustainable environment to lessen the ecological footprint. Additionally, governments should firmly support investments in green technology as well as economic and financial stability to boost energy efficiency and promote the adoption and usage of energy-saving products.

The energy-growth nexus in 3 Latin American countries on the basis of the EKC framework: in the case of Argentina, Brazil, and Chile.

In this paper, the effects of economic growth and four different types of energy consumption (oil, natural gas, hydroelectricity, and renewable energy) on environmental quality in terms of carbon dioxide (CO2) emissions were examined within the framework of the Environmental Kuznets Curve (EKC) for three Latin American countries, namely, Argentina, Brazil, and Chile, from 1975 to 2018. The autoregressive distributed lag (ARDL) in the form of Error Correction Mechanism (ECM) was used to verify the validity of the EKC hypothesis and the impacts of the variables in the short and the long run alike. Furthermore, the Toda-Yamamoto Granger causality test was carried out to identify the direction of causality between the variables. From ARDL-ECM estimation, the EKC was confirmed (inverted U-shaped curve between income growth and CO2 emissions) only in Argentina in the long run but not in Brazil and Chile. Based on the findings, renewable energy can have a great potential in reducing CO2 emissions in the future, but this advantage has not been fully exploited yet since a significant negative impact on CO2 emissions was only found in Chile. Also, the use of other less carbon-intensive energy sources such as natural gas and hydropower if they could be combined with renewable energy would be of great benefit and contribute to enhancing environmental quality and energy security in the short and the medium term and to successful low-carbon energy transition in the long run in Argentina, Brazil, and Chile.

Effects of exercise on human energy metabolism in high temperature and high humidity environment / Efeitos do exercício sobre o metabolismo energético humano em ambientes de alta temperatura e umidade / Efectos del ejercicio sobre el metabolismo energético humano en ambientes de alta temperatura y humedad

ABSTRACT Introduction Many exercise enthusiasts have started participating in sports in the high-temperature environment in recent years due to the increasing popularity of these sports habits. However, their scientific studies still have a gap in their safety and effectiveness. Objective Measure the energy supply characteristics of fat and sugar oxidation during exercise in different high-temperature and humidity environments. Methods 20 healthy adult subjects were exposed to fixed-intensity exercise for 20 minutes at 30-33 oC, 20% relative humidity (RH), and 50% RH, respectively. Results Under the silent exposure condition, compared with RH 20% and RH 50% under high temperature, sugar oxidation was significantly increased (P<0.01), while fat oxidation was significantly reduced (P<0.01), and total energy consumption was significantly increased (P<0.01). Under the condition of 65% VO2 max exercise, compared with RH 20% and RH 50% at high temperatures, the amount of sugar oxidation was significantly reduced (P<0.05), and the total energy consumption was significantly reduced (P<0.05). Conclusion Under 65% exercise under VO2 max in the high temperature and humidity-controlled environment, the high temperature and medium humidity (RH 50%) environment consumes more energy, and there is a greater amount of sugar oxidation. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMO Introdução Muitos entusiastas do exercício físico começaram a participar de esportes no ambiente de altas temperaturas nos últimos anos devido a crescente popularidade desses hábitos esportivos, embora seus estudos científicos ainda apresentem uma lacuna sobre sua segurança e efetividade. Objetivo Comparar as características do fornecimento de energia de oxidação de gordura e açúcar durante o exercício em ambientes de alta temperatura e umidade diferentes. Métodos Um total de 20 sujeitos adultos saudáveis foram expostos a exercícios de intensidade fixa durante 20 minutos a 30-33 oC, 20% de umidade relativa (RH) e 50% de RH, respectivamente. Resultados Sob a condição de exposição silenciosa, comparado com RH 20% e RH 50% sob alta temperatura, a oxidação do açúcar foi significativamente aumentada (P<0,01), enquanto a oxidação da gordura foi significativamente reduzida (P<0,01), e o consumo total de energia foi significativamente incrementado (P<0,01). Sob a condição de 65% de exercício de VO2max, comparado com RH 20% e RH 50% a altas temperaturas, a quantidade de oxidação do açúcar foi significativamente reduzida (P<0,05), e o consumo total de energia foi significativamente reduzido (P<0,05). Conclusão Sob a condição de 65% de exercício sob VO2max, no ambiente de alta temperatura e umidade controlados, o ambiente de alta temperatura e umidade média (RH 50%) consome mais energia, havendo uma maior quantidade de oxidação de açúcar. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción En los últimos años, muchos aficionados al ejercicio han comenzado a practicar deportes en el entorno de altas temperaturas debido a la creciente popularidad de estos hábitos deportivos, aunque sus estudios científicos aún presentan lagunas sobre su seguridad y eficacia. Objetivo Comparar las características de suministro energético de la oxidación de grasas y azúcares durante el ejercicio en diferentes entornos de alta temperatura y humedad. Métodos Un total de 20 sujetos adultos sanos fueron expuestos a ejercicio de intensidad fija durante 20 minutos a 30-33 oC, 20% de humedad relativa (HR) y 50% de HR, respectivamente. Resultados Bajo la condición de exposición silenciosa, en comparación con RH 20% y RH 50% bajo alta temperatura, la oxidación de azúcar se incrementó significativamente (P<0.01), mientras que la oxidación de grasa se redujo significativamente (P<0.01), y el consumo total de energía se incrementó significativamente (P<0.01). Bajo la condición de ejercicio VO2max 65%, en comparación con RH 20% y RH 50% a alta temperatura, la cantidad de oxidación de azúcar se redujo significativamente (P<0,05), y el consumo total de energía se redujo significativamente (P<0,05). Conclusión Bajo la condición de 65% de ejercicio bajo VO2max en el ambiente controlado de alta temperatura y humedad, el ambiente de alta temperatura y humedad media (RH50%) consume más energía y hay una mayor cantidad de oxidación de azúcar. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Power supply characteristics of basketball players at different training intensities / Características do consumo de energia dos jogadores de basquete com diferentes intensidades de treinamento / Características del consumo de energía de los jugadores de baloncesto con diferentes intensidades de entrenamiento

ABSTRACT Introduction: In competitive basketball sports, athletes must repeatedly perform movements of maximum intensity quickly, followed by rest. A training mode called high-intensity interval training (HIIT) has the same characteristics. Objective: Explore basketball players' energy supply characteristics and training changes under different exercise intensities. Methods: The effects of different recovery methods in the intermittent period on exercise capacity and aerobic metabolic energy supply of young male basketball players during high-intensity intermittent interval training (HIIT) were presented. Results: Increased aerobic energy production during HIIT was closely related to the acceleration of kinetics. However, although the time to exhaustion, a parameter characterizing exercise capacity, increased by 3.5% and 4.6%, respectively, in the HIITa group compared to HIITs and HIITp, there was no significant difference. After analyzing each group for the 30s, a gradual increase in strength was noticed. Conclusion: The use of HIIT as training is an important way to improve the physical performance of athletes. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

RESUMO Introdução: Na competição esportiva do basquete, os atletas precisam realizar repetidamente movimentos de intensidade máxima rapidamente, seguidos de repouso. Há um modo de treinamento chamado de treinamento de intervalo de alta intensidade (HIIT) que possui as mesmas características. Objetivo: Explorar as características de consumo de energia e as mudanças de treinamento dos jogadores de basquetebol sob diferentes intensidades de exercício. Métodos: Foram apresentados os efeitos de diferentes métodos de recuperação em período intermitente sobre a capacidade de exercício e fornecimento de energia metabólica aeróbica de jovens jogadores masculinos de basquetebol durante o treinamento intermitente de alta intensidade (HIIT). Resultados: O aumento da produção de energia aeróbica durante o HIIT foi estreitamente relacionado com a aceleração da cinética. Entretanto, embora o tempo de exaustão, parâmetro que caracteriza a capacidade de exercício, tenha aumentado em 3,5% e 4,6% respectivamente no grupo de HIITa em comparação com HIITs e HIITp, não houve diferença significativa. Depois de analisar cada grupo durante 30s, percebeu-se um aumento gradual da força. Conclusão: O uso do HIIT como treinamento demonstrou-se um meio importante para melhorar o desempenho físico dos atletas. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción: En los deportes de baloncesto de competición, los atletas necesitan realizar repetidamente movimientos de máxima intensidad de forma rápida, seguidos de descanso. Existe una modalidad de entrenamiento llamada entrenamiento por intervalos de alta intensidad (HIIT) que tiene las mismas características. Objetivo: Explorar las características del suministro de energía y los cambios en el entrenamiento de los jugadores de baloncesto bajo diferentes intensidades de ejercicio. Métodos: Se presentaron los efectos de diferentes métodos de recuperación en período intermitente sobre la capacidad de ejercicio y el suministro de energía metabólica aeróbica de jóvenes jugadores de baloncesto durante el entrenamiento de intervalos intermitentes de alta intensidad (HIIT). Resultados: El aumento de la producción de energía aeróbica durante el HIIT estaba estrechamente relacionado con la aceleración de la cinética. Sin embargo, aunque el tiempo hasta el agotamiento, un parámetro que caracteriza la capacidad de ejercicio, aumentó un 3,5% y un 4,6% respectivamente en el grupo HIITa en comparación con los HIIT y HIITp, no hubo diferencias significativas. Tras analizar cada grupo durante 30 segundos, se percibió un aumento gradual de la fuerza. Conclusión: El uso del HIIT como entrenamiento ha demostrado ser una forma importante de mejorar el rendimiento físico de los atletas. Nivel de evidencia II;Estudios terapéuticos - investigación de los resultados del tratamiento.

Investigation of tactile gait parameters based on deep learning of energy consumption estimation algorithm in sport / Investigação sobre parâmetros tácteis de marcha baseados no aprendizado profundo do algoritmo de estimativa de consumo energético no esporte / Investigación sobre los parámetros táctiles de marcha basados en el aprendizaje profundo del algoritmo de estimación del consumo de energía en el deporte

ABSTRACT Introduction In medicine, Deep Learning is a type of machine learning that aims to train computers to perform human tasks by simulating the human brain. Gait recognition and gait motion simulation is one of the most interesting research areas in the field of biometrics and can benefit from this technological feature. Objective To use Deep Learning to format and validate according to the dynamic characteristics of gait. Methods Gait was used for identity recognition, and gait recognition based on kinematics and dynamic gait parameters was performed through pattern recognition, including the position and the intensity value of maximum pressure points, pressure center point, and pressure ratio. Results The investigation shows that the energy consumption of gait as modeled analyzed, and the model of gait energy consumption can be obtained, which is comprehensively affected by motion parameters and individual feature parameters. Conclusion Real-time energy measurement is obtained when most people walk. The research shows that the gait frequency and body parameters obtained from the tactile parameters of gait biomechanics can more accurately estimate the energy metabolism of exercise and obtain the metabolic formula of exercise. There is a good application prospect for assessing energy metabolism through the tactile parameters of gait. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMO Introdução Na medicina, o aprendizado profundo é um tipo de aprendizado de máquina que visa treinar computadores para a realização de tarefas humanas simulando o cérebro humano. O reconhecimento da marcha e a simulação do movimento de marcha são um dos pontos de maior interesse da investigação no campo da biometria e pode ser beneficiado com esse recurso tecnológico. Objetivo Utilizar o aprendizado profundo para formatar e validar, de acordo com as características dinâmicas da marcha. Métodos A marcha foi utilizada para o reconhecimento da identidade, e o reconhecimento da marcha baseado na cinemática e parâmetros dinâmicos de marcha foi realizado através do reconhecimento de padrões, incluindo a posição e o valor de intensidade dos pontos de pressão máxima, ponto central de pressão e relação de pressão. Resultados A investigação mostra que o consumo de energia da marcha como modelado analisado, e o modelo de consumo de energia da marcha pode ser obtido, o qual é afetado de forma abrangente pelos parâmetros de movimento e pelos parâmetros de características individuais. Conclusão A medição de energia em tempo real é obtida quando a maioria das pessoas caminha. A investigação mostra que a frequência da marcha e os parâmetros corporais obtidos a partir dos parâmetros tácteis da biomecânica da marcha podem estimar com maior precisão o metabolismo energético do exercício e obter a fórmula metabólica do exercício. Há uma boa perspectiva de aplicação para avaliar o metabolismo energético através dos parâmetros tácteis da marcha. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción En medicina, el aprendizaje profundo es un tipo de aprendizaje que pretende entrenar a los ordenadores para que realicen tareas humanas simulando el cerebro humano. El reconocimiento de la marcha y la simulación de su movimiento es uno de los puntos más interesantes de la investigación en el campo de la biometría y puede beneficiarse de este recurso tecnológico. Objetivo Utilizar el aprendizaje profundo para formatear y validar según las características dinámicas de la marcha. Métodos Se utilizó la marcha para el reconocimiento de la identidad, y el reconocimiento de la marcha basado en la cinemática y los parámetros dinámicos de la marcha se realizó mediante el reconocimiento de patrones, incluyendo la posición y el valor de la intensidad de los puntos de presión máxima, el punto de presión central y la relación de presión. Resultados La investigación muestra que el consumo de energía de la marcha, tal y como se analizó, y el modelo de consumo de energía de la marcha se puede obtener, que es ampliamente afectado por los parámetros de movimiento y los parámetros de las características individuales. Conclusión La medición de la energía en tiempo real se obtiene cuando la mayoría de la gente camina. La investigación muestra que la frecuencia de la marcha y los parámetros corporales obtenidos a partir de los parámetros táctiles de la biomecánica de la marcha pueden estimar con mayor precisión el metabolismo energético del ejercicio y obtener la fórmula metabólica del mismo. Existe una buena perspectiva de aplicación para evaluar el metabolismo energético a través de los parámetros táctiles de la marcha. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Long-run economic and social determinants of the ecological footprint of latin america: a panel causality approach.

As a region rich in natural resources and biodiversity, Latin America is particularly vulnerable to environmental crises. The ecological footprint (EF) of Latin America and the variables that affect it in the long run are examined through a panel data approach and causality, using a sample of 12 countries in the region over the period 1990 to 2014. The study uses human development instead of human capital, because the former considers health and standard of living in addition to schooling. Applying a recent methodology that contains tests and estimators for various specification problems including cross-sectional dependence, the study finds that economic growth and imports damage environmental quality while human development, renewable energy consumption and exports tend to mitigate the ecological footprint. Human development and imports have a unidirectional effect on the EF while there is feedback between economic growth, renewable energy consumption and the EF. Urbanization and financial development do not play a significant role, and the Environmental Kuznets Curve theory is not validated for the region. Policies are proposed for EF management to ensure sustainable development in Latin America.

Desalination Using the Capacitive Deionization Technology with Graphite/AC Electrodes: Effect of the Flow Rate and Electrode Thickness.

Capacitive deionization (CDI) is an emerging water desalination technology whose principle lies in ion electrosorption at the surface of a pair of electrically charged electrodes. The aim of this study was to obtain the best performance of a CDI cell made of activated carbon as the active material for water desalination. In this work, electrodes of different active layer thicknesses were fabricated from a slurry of activated carbon deposited on graphite sheets. The as-prepared electrodes were characterized by cyclic voltammetry, and their physical properties were also studied using SEM and DRX. A CDI cell was fabricated with nine pairs of electrodes with the highest specific capacitance. The effect of the flow rate on the electrochemical performance of the CDI cell operating in charge-discharge electrochemical cycling was analyzed. We obtained a specific absorption capacity (SAC) of 10.2 mg/g and a specific energetic consumption (SEC) of 217.8 Wh/m3 at a flow rate of 55 mL/min. These results were contrasted with those available in the literature; in addition, other parameters such as Neff and SAR, which are necessary for the characterization and optimal operating conditions of the CDI cell, were analyzed. The findings from this study lay the groundwork for future research and increase the existing knowledge on CDI based on activated carbon electrodes.

Experimental and Numerical Study of a Turbulent Air-Drying Process for an Ellipsoidal Fruit with Volume Changes.

It is common in the numerical simulations for drying of food to suppose that the food does not experience a change of volume. The few numerical studies that include volume changes assume that the shrinkage occurs symmetrically in all directions. Therefore, this effect has not been fully studied, and it is known that not considering it can be detrimental for the accuracy of these simulations. The present study aims to develop a three-dimensional model for the simulation of fruits that includes the volume changes but also takes into consideration the asymmetry of the shrinkage. Physalis peruviana is taken as the subject of study to conduct experiments and imaging analyses that provided data about the drying kinetics and asymmetric shrinkage mode. The effective diffusion coefficient is found to be between 10-12 m2 s-1 and 1.75 × 10-9 m2 s-1. The shrinkage occurs essentially in only one direction, with an average velocity of 8.3 × 10-5 m/min. A numerical modelling scheme is developed that allows including the shrinkage effect in computer simulations. The performance of the model is evaluated by comparison with experimental data, showing that the proposed model decreases more than 4 times the relative error with respect to simulations that do not include volume changes. The proposed model proves to be a useful method that can contribute to more accurate modeling of drying processes.

Development of a low-cost IoT system based on LoRaWAN for monitoring variables related to electrical energy consumption in low voltage networks.

Energy efficiency is an issue that is currently gaining relevance, high electricity demands worldwide generate a negative impact on the planet caused by the natural depletion of resources associated with production processes. In this regard, the technologies associated with the Internet of Things (IoT) are considered as a tool to optimize processes and resources through the monitoring of variables. In this context, this work proposes a low-cost electronic system with IoT architecture used in the monitoring of electrical variables, this becomes a support tool in the estimation of energy consumption in internal distribution electrical circuits of homes or small industries. This device generates information to recognize consumption patterns and load balances per electrical phase, contains two hardware modules and a software user interface. The first is an electronic node that includes a high-performance polyphase meter based on the Atmel M90E32AS chip, which is controlled by an ESP32 chip, for wireless communication is used a Radio Frequency (RF) module in the 915 MHz band and LoRa protocol based on the Semtech SX1278 transceiver, this node is able to measure and transmit variables such as current, voltage, active energy, reactive energy, power factor and other electrical variables in circuits of up to three phases. For the study, a calibration process was carried out in an accredited laboratory (Metrex S.A. in Colombia), then tests were performed by monitoring a three-phase 110V electrical circuit in a small factory, with the information generated it was possible to identify consumption patterns over a period of seven consecutive days, important data such as times when energy is wasted due to improper use of loads connected to the network, electric stoves, computer equipment turned on during non-working hours are examples of the results obtained.

A static and dynamic copula-based ARIMA-fGARCH approach to determinants of carbon dioxide emissions in Argentina.

This paper attempts to model both static and dynamic dependence structures and measure impacts of energy consumptions (both renewable (EC) and non-renewable (REN) energies), economic globalization (GLO), and economic growth (GDP) on carbon dioxide (CO2) emissions in Argentina over the period 1970-2020. For analyses purpose, the current research deploys the novel static and dynamic copula-based ARIMA-fGARCH with different submodels. The static bivariate copula results show that the growth rates of the pairs EC-CO2 and GDP-CO2 are asymmetrically positive co-movements and have high left tail (extreme) dependencies, implying that the increase in non-renewable energy and economic growth can critically contribute to the environmental degradation, and the decrease in the consumption of non-renewable energy at a high level will consequently reduce the CO2 emissions at the same level. Based on several copula-based dependence measures, we document that between the two factors, the non-renewable energy has a stronger impact than the economic growth regarding the CO2 emissions. On the other hand, the growth rates of both economic globalization and renewable energy symmetrically negatively co-move with the growth rates of the CO2 emissions, but they have no extreme dependencies, indicating that these factors contribute to Argentina's environmental quality, in which the factor of renewable energy has a greater impact. Furthermore, the dynamic copula outcomes show that the (tail) dependencies of CO2 emissions on the non-renewable energy and economic growth are time-varying, while the pairs REN-CO2 and GLO-CO2 possess only dynamic dependencies, but no dynamic tail dependencies. Moreover, through the dynamic copula-based dependence, the environmental Kuznets curve (EKC) hypothesis can be estimated and illustrated explicitly. In addition, we leverage multivariate vine copulas for modelling dependence structures of the five variables simultaneously, which can reveal rich information regarding conditional associations among the relevant variables. Some policy implications are also provided to mitigate CO2 emissions.

Degradation of carbendazim in aqueous solution by different settings of photochemical and electrochemical oxidation processes.

The present study analyzed the performance of photochemical and electrochemical techniques in the degradation and mineralization of the pesticide carbendazim (CBZ). Direct photolysis (DP), heterogeneous photocatalysis (HP), photoelectrocatalysis (PEC), and electrochemical oxidation (EO) were tested, and the influence of UV radiation, current density (j), and supporting electrolyte concentration were evaluated. The results suggest that CBZ is only degraded by DP when UV-C254nm is used. For HP, the CBZ degradation was observed both when UV-A365nm or UV-C254nm were used, which is related to the reactive oxygen species (ROS) formed by the photocatalytic activity (photon-ROS). Neither DP nor HP were able to mineralize CBZ, demonstrating its resistance to photomediated processes. For EO, regardless of the j, there were higher CBZ degradation and mineralization than those observed when using DP and HP. The increase in the supporting electrolyte concentration (Na2SO4) did not affect the levels of degradation and mineralization of CBZ. Concerning the PEC, a CBZ mineralization of 52.2% was accomplished. These findings demonstrate that the EO is the main pathway for CBZ mineralization, suggesting an additional effect of the electro-ROS on the photon-ROS and UV-C254nm. The values of mineralization, kinetics, and half-life show that PEC UV-C254nm with a j of 15 mA cm-2 was the best setting for the degradation and mineralization of CBZ. However, when the values of specific energy consumption were considered for industrial applications, the use of EO with a j of 3 mA cm-2 and 4 g L-1 of Na2SO2 becomes more attractive. The assessment of by-products formed after this best cost-efficient treatment setting revealed the presence of aromatic and aliphatic compounds from CBZ degradation. Acute phytotoxicity results showed that the presence of sodium sulfate can be a representative factor regarding the toxicity of samples treated in electrochemical systems.

Air transport and rail transport impact on environment: evidence from India, China, Brazil, Mexico, Indonesia, Turkey and Russia.

Over the years, the world has been plagued by issues brought about by environmental degradation, climate change, and environmental health issues. Core to the environmental risk and security issues is the greenhouse gas emission which reflects carbon dioxide emissions effect on global climate. In order to better understand this stuff, the study explored the combined effect of increasing railway transport, air transport and urbanisation on the environment in emerging economies from 1995 to 2016. The study employed the Pesaran CD, average mean group (AMG), correlated effect mean group (CCEMG) and cointegration test approach. The study revealed that rail transport and urban population show good statistical strength to improve the environment. Findings from the study indicate that the proliferation of the emerging economies such as India, China, Brazil, Mexico, Indonesia, Turkey and Russia has greatly contributed to the growth of environmental sustainability. There is a lot of policy blueprint mentioned in this study, in which when adhere to could provide pertinent decision advocate in building quality environmental economies.

A new perspective into the impact of renewable and nonrenewable energy consumption on environmental degradation in Argentina: a time-frequency analysis.

In most nations across the world, the fundamental goal of economic policy is to achieve sustainable economic growth. Economic development, on the other hand, may have an influence on climate change and global warming, which are major worldwide concerns and problems. Thus, this research offers a new perceptive on the influence of renewable and nonrenewable energy consumption on CO2 emissions in Argentina utilizing data from the period between 1965 and 2019. The current research applied the wavelet tools to assess these interconnections. The outcomes of these analyses reveal that the association between the series evolves over both frequency and time. The current analysis uncovers notable wavelet coherence and significant lead and lag connections in the frequency domain, while in the time domain, contradictory correlations are indicated among the variables of interest. From an economic perspective, the outcomes of the wavelet analysis affirm that in the medium and long term, renewable energy consumption contributes to environmental sustainability. Furthermore, in the medium term, trade openness mitigates CO2, although in the long term, no significant connection was found. Moreover, both nonrenewable energy and economic growth contribute to environmental degradation in the short and long term. Finally, the frequency domain causality outcomes reveal that in the long term, economic growth, trade openness, and nonrenewable energy can predict CO2 emissions. The present analysis offers an innovative insight into the interconnection and comovement between CO2 and trade openness, renewable energy utilization, and GDP in the Argentinean economy. The findings from this research should be of interest to economists, researchers, and policymakers.

Environmental benefit of clean energy consumption: can BRICS economies achieve environmental sustainability through human capital?

This paper scrutinizes the asymmetric impact of education and education expenditure on clean energy consumption and CO2 emissions in the BRICS economies using annual data for the period 1991-2019. The analysis employs a nonlinear autoregressive distributed lag (ARDL) framework. Findings unfold that a positive change in education contributes to increasing clean energy consumption in Brazil, Russia, India, and China. This finding implies that a negative change in education contributes to reducing clean energy consumption in Brazil, Russia, and India in the long run. Nonetheless, a positive change in education expenditure increased the clean energy consumption in Brazil, Russia, and India, while it has decreased in South Africa. On the dark side, a negative change in education expenditure degrades clean energy consumption in India, China, and South Africa in the long run. The asymmetric empirical results of CO2 emissions are mixed, economy-specific, and vary across group countries in the long run. We find that the education and education expenditure has long-run asymmetric effects in BRICS industries. Thus empirical findings give us robust policy implications for BRICS economies.

Ecological footprint, public-private partnership investment in energy, and financial development in Brazil: a gradual shift causality approach.

The present study assesses the effect of public-private partnerships in energy and financial development on Brazil's ecological footprint and also takes into account the role of renewable energy and economic growth using data spanning from 1983 to 2017. The study utilized several techniques including autoregressive distributive lag (ARDL) and dynamic ordinary least square (DOLS) to examine the relationship between ecological footprint and the determinants, while the gradual shift causality test was utilized to capture the causal linkage between the series in the presence of a single structural break. The outcomes of the Maki co-integration test revealed evidence of a long-run association among the variables of interest. Furthermore, the results of the ARDL and DOLS tests revealed that economic growth and public and private investment in energy increase environmental degradation, while it is mitigated by both renewable energy and financial development. Moreover, the gradual shift causality test revealed a bidirectional causal linkage between ecological footprint and economic growth. The present study recommends the establishment of a forum that will foster public and private partnerships to enhance communication, which will promote collaboration on new initiatives involving green technological innovations.

Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble.

The employment of smart meters for energy consumption monitoring is essential for planning and management of power generation systems. In this context, forecasting energy consumption is a valuable asset for decision making, since it can improve the predictability of forthcoming demand to energy providers. In this work, we propose a data-driven ensemble that combines five single well-known models in the forecasting literature: a statistical linear autoregressive model and four artificial neural networks: (radial basis function, multilayer perceptron, extreme learning machines, and echo state networks). The proposed ensemble employs extreme learning machines as the combination model due to its simplicity, learning speed, and greater ability of generalization in comparison to other artificial neural networks. The experiments were conducted on real consumption data collected from a smart meter in a one-step-ahead forecasting scenario. The results using five different performance metrics demonstrate that our solution outperforms other statistical, machine learning, and ensembles models proposed in the literature.