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Rapid urbanization, openness and growth in human development index are some of the leading determinants of energy consumption in developing countries, particularly in BRICS economies (Brazil, Russia, India, China and South Africa). Thanks to their innate tendency to converge to the growth path of developed nations, BRICS countries are under increasing pressure to limit high energy consumption — triggered by outsourcing from developed nations. This paper attempts to weigh the relative importance of various determinants of energy consumption in BRICS countries between 1980 and 2016, studying in-depth the long-run co-movement pattern of energy consumption with demographic characteristics (depicting demand pressure) and macroeconomic aggregates (depicting cheap production cost). By leveraging on the trade-off between domestic and foreign demand and by employing the autoregressive distributed lag bounds testing approach, we establish differential effects of various predictors: whilst an increase in population growth rate, gross domestic product and capital account openness exert a positive and significant impact on energy consumption in Brazil, China and South Africa, foreign direct investment (FDI) and human development appear to enhance energy consumption in India, China and South Africa. The growth in external demand and the FDI inflows appear to have pushed urbanization, leading to greater energy consumption during the study period. Keeping in mind the sustainability goal, stronger green energy practices and sustainable urbanization patterns are needed to curb excessive energy sources.

In Wireless Sensor Network (WSN), node localization is a crucial need for precise data gathering and effective communication. However, high energy requirements, long inter-node distances and unpredictable limitations create problems for traditional localization techniques. This study proposes an innovative two-stage approach to improve localization accuracy and maximize route selection in WSNs. In the first stage, the Self-Adaptive Binary Waterwheel Plant Optimization (SA-BWP) algorithm is used to evaluate a node’s trustworthiness to achieve accurate localization. In the second stage, the Gazelle-Enhanced Binary Waterwheel Plant Optimization (G-BWP) method is employed to determine the most effective data transfer path between sensor nodes and the sink. To create effective routes, the G-BWP algorithm takes into account variables like energy consumption, shortest distance, delay and trust. The goal of the proposed approach is to optimize WSN performance through precise localization and effective routing. MATLAB is used for both implementation and evaluation of the model, which shows improved performance over current methods in terms of throughput, delivery ratio, network lifetime, energy efficiency, delay reduction and localization accuracy in terms of various number of nodes and rounds. The proposed model achieves highest delivery ratio of 0.97, less delay of 5.39, less energy of 23.3 across various nodes and rounds.

Aluminum alloys are widely employed in design and material selection in the industry due to their superior ergonomic properties and low cost. Therefore, aluminum has been strengthened with various elements and powder metal reinforcements in recent studies. In this study, the 6061 aluminum alloy, which is used widely in production, was reinforced with B₄C powder metal and copper elements in a hybrid form. The mechanical, metallurgical, and machinability properties of all reinforced 6061 aluminum materials were examined and characterized. In particular, this study examined the machinability of the materials produced differently from the literature by using equations of energy power conversion and the Taguchi Method, which is one of the experimental design methods, and compared the results of the machining process for different materials. Furthermore, the effects of feed rate, cutting speed, and amount of passes on machinability properties were investigated by conducting the ANOVA analysis on the experimental design parameters and levels. Consequently, while Cu and B₄C reinforcement improved the hardness and mechanical properties, positive results were also obtained on machinability.

Cloud computing has attracted significant attention because of the growing service demands of businesses that outsource computationally intensive tasks to the data center. Meanwhile, the infrastructure of a data center is comprised of hardware resources that consume a great deal of energy and release harmful levels of carbon dioxide. Cloud data centers demand massive amounts of electrical power as modern applications and organizations grow. To prevent resource waste and promote energy efficiency, virtual machines (VMs) must be dispersed over numerous physical machines (PMs) in a data center in the cloud. The actual allocation of VMs to PMs can involve more complex decision-making processes, such as considering the resource utilization, load balancing, performance requirements, and constraints of the system. Advanced techniques, like intelligent placement algorithms or dynamic resource allocation, may be employed to optimize resource utilization and achieve efficient VM distribution across multiple PMs. Cloud service suppliers aim to lower operational expenses by reducing energy consumption while offering clients competitive services. Minimizing large-scale data center power usage while maintaining the quality of service (QoS), especially for social media-based cloud computing systems, is crucial. Consolidating VMs has been highlighted as a promising method for improving resource efficiency and saving energy in data centers. This research provides deep learning augmented reinforcement learning (RL)-based energy efficient and QoS-aware virtual machine consolidation (VMC) approach to meet the difficulties. The proposed deep learning modified reinforcement learning-virtual machine consolidation (DLMRL-VMC) model can motivate both cloud providers and customers to distribute cloud infrastructure resources to achieve high CPU utilization and good energy efficiency as measured by power usage effectiveness (PUE) and data center infrastructure efficiency (DCiE). The suggested model, DLMRL-VMC, offers a VM placement approach based on resource usage and dynamic energy consumption to determine the best-matched host and VM selection strategy, Average Utilization Migration Time (AUMT). Based on AUMT, deep learning modified reinforcement learning (DLMRL) will choose a VM with a low average CPU utilization and a short migration time. The DLMRL-VMC Energy-efficient, Resource Allocation strategy is evaluated on the trace of the CloudSim VM to attain good PUE and CPU utilization.

Energy Consumption (EC) in the process of mechanical manufacturing directly leads to environmental pollution and resource waste. However, the EC characteristics of machine tool processing are complex, and most energy-saving optimization models require accurate material performance data and cutting force models. In response to the above issues, the study first analyzes the structural composition of the machining system, clarifies the main variable parameters for optimization, and then establishes a mathematical model with the determined optimization variables to describe the EC characteristics. Finally, the established optimization model is solved using the adaptive particle swarm algorithm to find the optimal combination of process parameters and achieve energy-saving optimization. The improved adaptive particle swarm intelligence algorithm tends to converge after more than 50 iterations. When taking low cost and low EC as the optimization goal, the cutting EC of the optimization solution is 3.49 × 10⁵ J, the processing time is 42.68 s, and the processing cost is 46.71 points, and the processing cost and EC are between the single optimization goal of low cost and low EC. It is indicated that the proposed method provides a reasonable energy-saving optimization strategy for machining process parameters, and provides support for the implementation of energy-saving optimization of machining center process parameters.

Vehicles equipped with advanced driving assistance systems (ADAS) can acquire information about preceding and following vehicles, which will have an undeniable impact on the phase transition of traffic flow. Therefore, we establish a new heterogeneous continuum model of traffic flow considering the jerk effect mixed with human driving (HD) and ADAS vehicles. The neutral stability condition associated with the permeability of ADAS vehicle is inferred via stability analysis. According to simulation, shock wave occurs, implying that our model can emerge correct wave propagation trend. Moreover, the simulation of density evolution and flow changes also suggests that an increase in ADAS vehicle penetration and jerk effect can enhance traffic system stability and reduce energy consumption.

This study reexamines the relationship between energy consumption per capita and real GDP per capita for Indonesia, Malaysia, the Philippines, Singapore and Thailand using both panel data causality which is taking into account cross-sectional dependence and heterogeneity among the countries and time series causality tests for the period 1971–2009. The findings indicate that taking into account cross-sectional dependence has a substantial effect on the achieved results. The conservation hypothesis is supported for Indonesia, Malaysia and the Philippines. Although a bidirectional relation is found in the case of Thailand, since there is no positive effect of energy consumption on GDP, the conservation hypothesis is supported. In the pattern of Singapore, the neutrality hypothesis is supported. In addition, the increase in investment and labor force lead to more energy consumption in Indonesia, Malaysia and Thailand.

This paper investigates the relationship between economic growth and energy consumption through non-linear causality tests. Eight developing countries from Europe and Central Asia spanning the period 1993 to 2008 are selected for the purpose of panel empirical analysis. Panel unit root and panel cointegration tests with and without considering cross section dependency (CD) problems are implemented. Next, linear panel cointegration tests are employed and, finally, a two-regime Dynamic Panel Smooth Transition Vector Error Correction (PSTRVEC) model is estimated for testing the presence of non-linear short- and long-run causality. To this end, a new estimator, called the Dynamic Non-linear Pooled Common Correlated Effect Estimator (DNPCCEE) is proposed. The empirical findings indicate that short and long-run causalities are regime-dependent.

Increased consumption of nonrenewable energy sources may lead to more air pollution, resulting in negative health impacts in a society. The main purpose of this study is to investigate the relationship between fossil fuel energy consumption and health issues using generalized method of moments estimation technique for data from 18 Asian countries (both low- and middle-income) over the period 1991–2018. The findings demonstrate that fossil fuel energy consumption increases the risk of lung and respiratory diseases. In addition, the results demonstrate the significant effect of CO₂ emissions and fossil fuel consumption on undernourishment and death rates. Furthermore, we find that increases in the gross domestic product per capita and healthcare expenditure may help reduce undernourishment and death ratio. The conclusion recommends that diversification of energy in low- and middle-income countries from too much reliance on fossil fuels to more renewable energy sources can improve energy insecurity, at the same time reduce greenhouse gas emissions and minimize the negative impacts on human health.

This paper examines the relationships among energy consumption, economic growth, inequality, and poverty in Iran. We estimate these relationships at both the aggregate and sectoral level using instrumental variables to address endogeneity and simultaneous equation models to enhance efficiency. Results show that decreasing inequality will be beneficial for economic growth, poverty alleviation and energy access. Inequality can negatively affect GDP directly, as well as indirectly through its negative effect on energy consumption. Similarly, inequality can increase poverty both directly as well as indirectly through its negative effect on energy consumption. We also find that increasing energy consumption has multiple benefits: it increases GDP, tends to decrease inequality and decreases poverty. Energy consumption decreases poverty both directly as well as indirectly via its effect on decreasing inequality. Our results therefore suggest that policies to improve energy access are important, and will have the benefits of increasing GDP, decreasing inequality and decreasing poverty.

This study examines the drivers of environmental degradation in ASEAN + China. It focusses on three unresolved questions: (i) Does the inclusion of China in ASEAN panel aggravate environmental degradation, given that China is a high carbon emissions country? (ii) Does financial development moderate the impact of energy consumption on environmental degradation in ASEAN? (iii) Does urbanization moderate the impact of energy consumption on environmental degradation in ASEAN? It employs empirical strategies that account for heterogeneity, endogeneity and cross-sectional dependence. The results show that economic growth, energy consumption and non-renewable energy aggravate environmental degradation, whereas renewable energy, foreign direct investment and trade openness mitigate it. The inclusion of China in ASEAN panel weakens the EKC hypothesis. Financial development favorably moderates the effect of energy consumption on environmental degradation in ASEAN, but adversely moderates the effect in ASEAN + China. Urbanization adversely moderates the impact of energy consumption on environmental degradation in both panels. Hence, efforts to address environmental degradation should consider these different drivers.

Empirical studies on the effects of carbon emissions on population health are still in their infancy and its true implications have not yet been fully understood. The purpose of this study is to conduct a comparative analysis on the relationship between carbon emissions, energy consumption, income and public healthcare expenditure in Organisation for Economic Co-operation and Development (OECD) and non-OECD countries. The empirical research employs the dynamic common correlated effects of mean group (DCCEMG) and two-stage least square estimators. The findings indicate that carbon emissions increase healthcare spending only in non-OECD countries. The relationship between energy consumption and health expenditure varies significantly between OECD and non-OECD countries. Income increases health expenditure; however, the correlation is more robust in the OECD than in non-OECD countries. As a result, the findings recommend that non-OECD governments implement strategic environmental management policies that prioritize clean and healthy air to reduce healthcare costs.

In this paper, a unified model is presented for two-lane lattice traffic flow, with comparing different effects in the various lattice hydrodynamic models. Results of linear and nonlinear analysis show that multiple density difference effect (MDDE) is the strongest to enlarge the stable region in two-lane systems. Followed by density difference effect (DDE), multiple flux difference effect (MFDE), and finally flux difference effect (FDE). But when density is around 0.25, MFDE is better to enlarge the stable region than DDE. The reason is that a small flow-rate value might correspond to either a light traffic or a heavy traffic. Also energy consumption and traffic emissions are analyzed and shown to be the same marshaling sequence as linear and nonlinear analysis results. Numerical simulations validate theoretical analysis. And this is consistent with the realistic.

To provide a model for real-time prediction of cutting forces, vibrations and optimal energy consumption (EC) model, a new hybrid algorithm based on Back-Propagation Neural Network and Multi-Objective Particle Swarm Optimization (BPNN-MOPSO) was developed to determine the optimal cutting parameters with minimal total energy consumption. High-speed milling experiments were conducted to confirm the proposed online monitoring and optimal model’s accuracy and availability. The proposed optimal method, based on the proposed improvement model, can reduce EC by 10.49% compared to the empirical option method. The feasibility and effectiveness of the proposed model have been verified through experimental processes.

An improved model of energy consumption including toll station is presented in this paper. Using the model, we study the influences of mixed ratio, the idling energy consumption of vehicle, vehicle peak velocity, dwell time and random deceleration probability on energy consumption of Electronic Toll Collection or Manual Toll Collection mixed traffic flow on single lane under periodic condition. Simulating results indicate that the above five parameters are all increasing functions of total energy consumption, in which the idling energy consumption represents the major amounts with the increase of mixed ratio and occupancy rate. Thus, the existence of toll station has significant effect on the energy consumption of mixed traffic flow.

Discrete manufacturing system generates a large amount of data and information because of the development of information technology. Hence, a management mechanism is urgently required. In order to incorporate knowledge generated from manufacturing data and production experience, a knowledge network model of the energy consumption in the discrete manufacturing system was put forward based on knowledge network theory and multi-granularity modular ontology technology. This model could provide a standard representation for concepts, terms and their relationships, which could be understood by both human and computer. Besides, the formal description of energy consumption knowledge elements (ECKEs) in the knowledge network was also given. Finally, an application example was used to verify the feasibility of the proposed method.

Based on the two velocity difference model (TVDM), an extended car-following model is developed to investigate the effect of driver’s memory and jerk on traffic flow in this paper. By using linear stability analysis, the stability conditions are derived. And through nonlinear analysis, the time-dependent Ginzburg–Landau (TDGL) equation and the modified Korteweg–de Vries (mKdV) equation are obtained, respectively. The mKdV equation is constructed to describe the traffic behavior near the critical point. The evolution of traffic congestion and the corresponding energy consumption are discussed. Numerical simulations show that the improved model is found not only to enhance the stability of traffic flow, but also to depress the energy consumption, which are consistent with the theoretical analysis.

In this paper, a multi-objective flexible job shop scheduling problem (MOFJSP) was studied systematically. A novel energy-saving scheduling model was established based on considering makespan and total energy consumption simultaneously. Different from previous studies, four types of energy consumption were considered in this model, including processing energy, idle energy, transport energy, and turn-on/off energy. In addition, a turn-off strategy is adopted for energy-saving. A modified shuffled frog-leaping algorithm (SFLA) was applied to solve this model. Moreover, operators of multi-point crossover and neighborhood search were both employed to obtain optimal solutions. Experiments were conducted to verify the performance of the SFLA compared with a non-dominated sorting genetic algorithm with blood variation (BVNSGA-II). The results show that this algorithm and strategy are very effective.

Considering the effect of headway changes with memory, an improved continuum model of traffic flow is proposed in this paper. By means of linear stability theory, the new model’s linear stability with the effect of headway changes with memory is obtained. Through nonlinear analysis, the KdV–Burgers equation is derived to describe the propagating behavior of traffic density wave near the neutral stability line. Numerical simulation is carried out to study the improved traffic flow model, which explores how the headway changes with memory affected each car’s velocity, density and energy consumption. Numerical results show that when considering the effects of headway changes with memory, the traffic jams can be suppressed efficiently. Furthermore, research results demonstrate that the effect of headway changes with memory can avoid the disadvantage of historical information, which will improve the stability of traffic flow and minimize car energy consumption.

Urban rail transit plays an important role in the urban public traffic because of its advantages of fast speed, large transport capacity, high safety, reliability and low pollution. This study proposes an improved cellular automaton (CA) model by considering the dynamic characteristic of the train acceleration to analyze the energy consumption and train running time. Constructing an effective model for calculating energy consumption to aid train operation improvement is the basis for studying and analyzing energy-saving measures for urban rail transit system operation.