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The pulp and paper industry (PPI) has several opportunities to contribute to meeting prevailing climate targets. It can cut its own CO2 emissions, which currently account for 2% of global industrial fossil CO2 emissions, and it has an opportunity to produce renewable energy, fuels, and materials for other sectors. The purpose of this study is to improve understanding of the decarbonization prospects of the PPI. The study provides insights on the magnitude of needed annual renewal rates for several possible net-zero target years of industrial fossil CO2 emissions in the PPI and discusses decarbonization opportunities, namely, energy and material efficiency improvement, fuel switching, electrification, renewable energy production, carbon capture, and new products. The effects of climate policies on the decarbonization opportunities are critically evaluated to provide an overview of the current and future business environment of the European PPI. The focus is on Europe, but other regions are analyzed briefly to widen the view. The analysis shows that there are no major technical barriers to the fossil-free operation of the PPI, but the sector renovates slowly, and many new opportunities are not implemented on a large scale due to immature technology, poor economic feasibility, or unclear political environment. [ABSTRACT FROM AUTHOR]

The article presents studies on the impact of the significant process parameters of a paper machine with a Yankee cylinder on its production capacity and heat energy consumption for drying the paper web. Parameters such as machine speed, web moisture content before and after pressing, parameters of steam supplied to the cylinder and parameters of hot air flowing from the nozzles of the hood were analyzed. The study's results were used to optimize production to improve the energy efficiency and performance of the machine. In order to use the possible methods of improving the production capacity and heat energy consumption, the parameters of the production process were measured and the basic indicators characterizing the operation of the machine were calculated in the Yankee cylinder–dryer hood system. The correct functioning of the machine components and the possibility of their modernization were also analyzed. Technological and construction changes introduced based on the research results made it possible to increase the production capacity by 10% and to reduce the consumption of heat energy per 1 ton of produced paper by 16.3%. The article presents a description of changes in the technology of paper production and modernization of the tissue machine made in the years 2013–2022. [ABSTRACT FROM AUTHOR]

State-of-the-art manufacturing of tissue paper by Through Air Drying provides excellent product performance, although at a high production cost and energy use. In this work, a laboratory scale vacuum suction box was used to mimic the initial dewatering and the Through Air Drying molding, together with a pilot-scale trial. The purpose was to investigate both how the laboratory scale corresponds to pilot scale testing and investigate how fabric design, basis weight, beating, and fibers affect dewatering and sheet caliper. This study reevaluates dewatering mechanisms during molding, challenging the previous hypothesis of pure air displacement dewatering. Results show a parallel mechanism of compression dewatering and air displacement. The influence of rush transfer is examined, impacting the sheets' visual appearance, thickness, and solids content. Correlations between molding box solids content and headbox freeness emphasize significance of fibers and beating levels. Pilot results confirm the link between former solids and molding box solids. Pilot trials validate the laboratory results, facilitating comprehensive simulation of full-scale manufacturing. This research reveals dewatering mechanisms, highlights operational parameters, and enables effective Through Air Drying process design and refinement. [ABSTRACT FROM AUTHOR]

Although there are opportunities to reduce electrical energy demand in unit processes of mechanical pulp-based paper and paperboard production, this may not be financially beneficial. This is generally because energy optimization opportunities connected to reduced refiner electricity demand in mechanical pulping systems also results in less steam available for the drying of the paper. As modern high consistency refiner systems produce approximately one ton of steam for each MWh of electricity when producing one ton of pulp, a reduction in electric energy demand leads to increased fuel demand in steam boilers to compensate for the steam shortage. In this study, we investigated what the financial and environmental situation would look like if we were to expand the system border from a paper mill to a larger system consisting of a mechanical pulp-based paper or paperboard mill, a district heating system with an incineration boiler and a chemical pulp mill. Mechanical pulp production has a wood to product yield of >90%, a high electric energy demand to separate woodchips to pulp and is a net producer of heat and steam while chemical pulp-based production has a wood to product yield of 50%, a low electric energy demand and is a net heat and electricity producer due to the combustion of dissolved wood polymers. The aim of this research is to create useful and robust models of how to use excess heat from certain industry sites to cover the steam shortage in other industry sites by means of utilizing and optimizing the district heating systems connecting these sites. For this purpose, we used a simulation tool which dynamically allows us to evaluate different scenarios. Our results shows that there is great potential to reduce both carbon dioxide emissions and production costs for industry sites and society by means of these tools. [ABSTRACT FROM AUTHOR]

Producing tissue paper is an energy-demanding process; a significant amount of energy is expended when removing water by vacuum, mechanical pressing, and thermal drying. Because the water is most energy-demanding to remove in drying, making the preceding step of vacuum dewatering more efficient would benefit the whole process. This article focuses on developing a laboratory-scale method for verifying the nature of diminishing returns of water removal and investigating efficiency strategies of the vacuum dewatering. The theoretical concept of successive vacuum suction boxes with progressing vacuum levels was tested at the laboratory scale in order to show quantifiable results of the previously solely theoretical concept. The results confirmed that vacuum dewatering can be improved by adding progressively higher vacuum levels and that such a practice can benefit both outgoing dryness levels and expended vacuum pump energy. To truly examine the power of progression of vacuum levels, rewetting can be included in the calculations, by using an approximate value collected from pilot or fullscale measurements. For any new fiber mix, pulp type, vacuum level setup, basis weight, etc. the vacuum levels, rewetting, and dwell times need to be tuned to that specific case. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) architectures generally focus on providing consistent performance and reliable communications. The convergence of IoT, edge, fog, and cloud aims to improve the quality of service of applications, which does not typically emphasize energy efficiency. Considering energy in IoT architectures would reduce the energy impact from billions of IoT devices. The research presented in this paper proposes an optimization framework that considers energy consumption of nodes when selecting a node for processing an IoT request in edge-fog-cloud layered architecture. The IoT use cases considered in this paper include smart grid, autonomous vehicles, and eHealth. The proposed framework is evaluated using CPLEX simulations. The results provide insights into mechanisms that can be used to select nodes energy-efficiently whilst meeting the application requirements and other network constraints in multi-layered IoT architectures. [ABSTRACT FROM AUTHOR]

Recently, as the use of wearable devices and the demand for eco-friendly energy have increased, many studies have been conducted on triboelectric nanogenerators (TENGs), which can economically harvest energy. Paper is considered a promising substrate and frame material that can be used to manufacture self-powered TENGs, owing to its flexibility, low cost, and accessibility. Herein, we present a sandwich-structured foldable paper-based TENG (FP-TENG) that comprises flexible materials and uses paper as a substrate. The FP-TENG can generate up to 572 mW/m2 of power via contact–separation of the triboelectric electrified body at the top and bottom. With more folds of the FP-TENG, the triboelectric cross-sectional area increases, and, thus, the electrical output increases. In addition, the proposed TENG exhibits excellent durability without signal degradation under 5000 cycles of repeated pushing motions. To demonstrate its practicality, the FP-TENG was manufactured in the form of a wristwatch Velcro and connected to an electronic watch panel to supply power. Various deformations are possible with origami, and they can drive wristwatches through external forces. Therefore, the FP-TENG is expected to be utilized as a sustainable and promising eco-friendly energy source for small electronic devices. [ABSTRACT FROM AUTHOR]

Excellent mechanical and electrical properties of graphene-based paper-like materials are essential for applications in flexible conductors, energy-storage devices, etc. The graphene oxide (GO) supernatant separated by centrifugation and ultrasonication has been used to prepare graphene oxide paper and reduced graphene oxide paper (rGOP) with high strength and conductivity. However, this method has cumbersome steps and low supernatant concentration, which greatly increases time and energy consumption, and is not suitable for rapid batch preparation of high-strength and high-thickness rGOP. Herein, a high-speed mechanical shearing method has been used to efficiently exfoliate graphite oxide suspension into high-concentration GO slurry with large lateral size, and rGOP has been further prepared by blade coating and HI acid chemical reduction. Compared with the product prepared by ultrasonic exfoliation method, the average area of GO sheets obtained via the mechanical shear exfoliation method can reach around 16.31 μm2. As a result, the mechanical properties and conductivity of the prepared rGOP have been increased by 30% and 23.5%, respectively, with the tensile strength and electrical conductivity of 478.2 MPa and 208.4 S/cm being obtained. The developed method is of great significance for the large-scale production and emerging applications of lightweight and high-performance rGOP. [ABSTRACT FROM AUTHOR]

The research assessed waste-based briquettes consumption compared to conventional fuels in the Andes. Laboratory tests were conducted together with on-field analysis in Colquencha (Bolivia). The laboratory study shows that the performances of briquettes are better in terms of PM2.5 (933.4 ± 50.8 mg kg−1) and CO emissions (22.89 ± 2.40 g kg−1) compared to animal dung (6265.7 ± 1273.5 mgPM2.5 kg−1 and 48.10 ± 12.50 gCO kg−1), although the boiling time increased due to the lower fuel consumption rate and firepower compared to shrubs. The social survey organized with 150 Bolivian citizens suggested that low-income households are not able to pay for an alternative fuel: about 40% would pay less than 4 USD per month, while methane use for cooking is positively correlated with the income level (r = 0.244, p < 0.05). On field analysis suggested that local cookstoves are not appropriate for briquettes combustion since indoor air pollution overcomes 30 ppm of CO and 10 mgPM2.5 m-3. On balance, local small manufactures can be the main target for selling waste-based briquettes to reduce shrubs and wood consumption. However, briquettes production costs seem not yet competitive to natural easy-to-obtain fuels (i.e., animal dung). The research encourages the use of cellulosic and biomass waste-based briquettes in the Andean area for cooking, heating, or manufacturing and strongly advises policy-makers to introduce economic incentives for the recovery of secondary raw materials. • Analysis of waste-based briquettes use in Andean areas were conducted. • Laboratory analysis, social surveys, and on field evaluations are the methods. • Briquettes can be employed for reducing about 30% of the conventional fuels. • The research encourages the employment of waste-based briquettes in the Andes. • Incentives to briquettes production and consumption should be introduced. [ABSTRACT FROM AUTHOR]

The current study analyzed the high heating values (HHVs) of various waste biomass materials intending to the effective management and more sustainable consumption of waste as clean energy source. Various biomass waste samples including date leaves, date branches, coconut leaves, grass, cooked macaroni, salad, fruit and vegetable peels, vegetable scraps, cooked food waste, paper waste, tea waste, and cardboard were characterized for proximate analysis. The results revealed that all the waste biomass were rich in organic matter (OM). The total OM for all waste biomass ranged from 79.39% to 98.17%. Likewise, the results showed that all the waste biomass resulted in lower ash content and high fixed carbon content associated with high fuel quality. Based on proximate analysis, various empirical equations (HHV=28.296-0.2887(A)-656.2/VM, HHV=18.297-0.4128(A)+35.8/FC and HHV=22.3418-0.1136(FC)-0.3983(A)) have been tested to predict HHVs. It was observed that the heterogeneous nature of various biomass waste considerably affects the HHVs and hence has different fuel characteristics. Similarly, the HHVs of waste biomass were also determined experimentally using the bomb calorimeter, and it was observed that among all the selected waste biomass, the highest HHVs (21.19 MJ kg−1) resulted in cooked food waste followed by cooked macaroni (20.25 MJ kg−1). The comparison revealed that experimental HHVs for the selected waste biomass were slightly deviated from the predicted HHVs. Based on HHVs, various thermochemical and biochemical technologies were critically overviewed to assess the suitability of waste biomass to energy products. It has been emphasized that valorizing waste-to-energy technologies provides the dual benefits of sustainable management and production of cleaner energy to reduce fossil fuels dependency. However, the key bottleneck in commercializing waste-to-energy systems requires proper waste collection, sorting, and continuous feedstock supply. Moreover, related stakeholders should be involved in designing and executing the decision-making process to facilitate the global recognition of waste biorefinery concept. [Display omitted] • The high heating values (HHVs) of biomass waste were assessed with the aim for the sustainable consumption of waste as a source of clean energy. • All the biomass waste were rich in organic matter and fixed carbon content that ranged from 79.39% to 98.17% and 50.64%–54.26% respectively. • The maximum HHVs (21.19 MJkg−1) were recorded for cooked food waste. • The heterogeneous nature of various biomass waste considerably affects the HHVs and hence serve as suitable feedstock for various energy conversion technologies. [ABSTRACT FROM AUTHOR]

New‐type professional farmers are new‐type rural talents with scientific and cultural literacy and professional knowledge of agriculture under the background of rural revitalization. The new type of professional farmers is of great significance for accelerating agricultural modernization and promoting the construction of a powerful modern socialist country. In the process of cultivating new types of professional farmers in rural areas, it is necessary to break through the previous informatization constraints of poor information flow, and give full play to the role of advanced information technology to promote the sustainable economic development. This paper proposes a new type of professional farmer cultivation platform based on edge computing architecture to improve the cultivation efficiency of new type of professional farmers, so as to better promote the development of rural informatization. Aiming at the challenges of energy consumption under the edge platform architecture, this paper proposes a dynamic scheduling method for semi‐online tasks for edge computing platforms. In order to evaluate the effectiveness of the algorithm, the method in this paper is compared with the classic scheduling algorithm, and simulated and verified on the CloudSim platform. Experimental results show that the proposed method outperforms other algorithms in the task completion time metric. With the expansion of task scale, more energy consumption can be saved by using the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a proper noun “Region” is used for nm‐scale n‐type dopant‐rich region in p‐type Si crystal. Using this Region, certain solar cells have been assumed. By resonance absorption between photon energy and potential barrier of the Region, the cell can absorb most photons for visible light frequency without passing loss or thermal loss. This light absorption mechanism is different from conventional band gap absorption. Despite this benefit, output voltage is anticipated to decline according to the principle of detail balance. To control the decline, two methods are proposed in this paper. Theoretical energy conversion efficiencies for several cases are calculated with an ideal condition. The calculation result is over 70% as a theoretical value. [ABSTRACT FROM AUTHOR]

The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system's expansion is hindered by renewable generation's climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries' energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers. [ABSTRACT FROM AUTHOR]

With the intensifying global energy crisis and environmental issues, the energy consumption and environmental impact of the construction sector have garnered significant attention. The development and application of green building materials have become crucial research areas. Traditional building materials cause substantial resource waste and environmental pollution during production, usage, and disposal. In contrast, green building materials based on thermodynamic principles, owing to their superior thermal performance and energy storage capabilities, have gradually gained attention. This paper aims to systematically optimize the thermal performance of green building materials through a research approach grounded in thermodynamic principles. The study consists of two main parts: first, constructing the control equations for the phase change thermal storage process of green building materials, and analyzing the thermodynamic characteristics during the phase change process in detail; second, conducting an in-depth analysis of the heat transfer process in phase change thermal storage green building materials to explore their thermal performance in practical applications. Despite extensive research on the thermal performance of green building materials, there are still shortcomings in the systematic analysis and optimization of material thermal performance, and existing research methods often fail to comprehensively and accurately reflect the actual thermal behavior of materials. This paper aims to provide a scientific basis for the optimized design of green building materials, further promoting their application and development in the construction field. [ABSTRACT FROM AUTHOR]

The software-defined network paradigm, ensembled with a wireless sensor network, has emerged as a promising phenomenon to enable multi-tasking, re-configuration, and scalability. Termed the software-defined wireless sensor network (SDWSN), it divides the network into two planes: data and control. The data plane consists of software-defined sensor nodes (SDSN) that sense monitoring activity and generate data. On the other hand, the control plane has controller/control Nodes (CN) which collect data from SDSN, perform data aggregation, and then transmit toward the sink node. These CNs consume more amount of energy as compared to SDSNs as they perform multiple tasks. Following this scenario, this paper proposes an energy-efficient multi-objective optimization approach to solve the CNs placement problem through a meta-heuristic algorithm by considering the nodes' location, energy, and load distribution. This paper presents a particle swarm optimization-based controller placement and re-placement (PSO-CPR) algorithm for SDWSN. The PSO-CPR elects SDSNs to become CN based on their distance, residual energy, and capacity in the network. Moreover, the placement of a CN rotates within the cluster to avoid its failure and balance energy consumption. The simulation results show improved CN placement with respect to the state-of-the-art algorithms in terms of average delay by 23.5–37.4%, energy consumption by 18.6–32.6%, and probabilistic load distribution by 17.7–54.1%. Moreover, the comparative study also indicates that PSO-CPR achieve promising result by reducing packet loss by 14.4–27.5% and network re-clustering period by 32.3–68.3% and enhance the network lifetime by 22.6–42.5%. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) path planning problem of an Unmanned Aerial Vehicle (UAV) considering the effect of environmental wind in a dense city is investigated in this paper. The mission of the UAV is to fly from its initial position to its destination while ensuring safe flight. The dense obstacle avoidance and the energy consumption in 3D space need to be considered during the mission, which are often ignored in common studies. To solve these problems, an improved Deep Reinforcement Learning (DRL) path planning algorithm based on Double Deep Q-Network (DDQN) is proposed in this paper. Among the algorithms, the random obstacle training method is first proposed to make the algorithm consider various flight scenarios more globally and comprehensively and improve the algorithm's robustness and adaptability. Then, the linear soft update strategy is employed to realize the smooth neural network parameter update, which enhances the stability and convergence of the training. In addition, the wind disturbances are integrated into the energy consumption model and reward function, which can effectively describe the wind disturbances during the UAV mission to achieve the minimum drag flight. To prevent the neural network from interfering with training failures, the meritocracy mechanism is proposed to enhance the algorithm's stability. The effectiveness and applicability of the proposed method are verified through simulation analysis and comparative studies. The UAV based on this algorithm has good autonomy and adaptability, which provides a new way to solve the UAV path planning problem in dense urban scenes. [ABSTRACT FROM AUTHOR]

The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

3D integration is one of the scalable multiprocessor design solutions. The main challenge in 3D design is temperature traps, especially in the upper layers and hot spots. The current studies present solutions for solving temperature and hotspot problems in topology, routing, and mapping levels. Accordingly, the characteristics of temperature-aware core mapping are beneficial for dealing with the challenges of 3D chips. In this paper, the core temperature is modeled based on processing, communication power, neighbor node temperature, distance from the heat sink, and hotspot node. To find the optimal solution, this paper proposes thermal-aware application mapping techniques using genetic algorithms and fuzzy logic to minimize peak temperatures in 3D network-on-chip (NoC) architectures. Fitness functions include the above parameters with the same weight (GSM) and the fitness function with fuzzy logic by considering the effect of distance from heat sink (GFM1) and heat sink distance and the number of neighbor's core (GFM2). Specifically, the fuzzy logic-based GFM1 algorithm demonstrates superior performance over the genetic algorithm-driven GSM approach in reducing power consumption and energy costs associated with inter-core communication traffic. Across the different traffic patterns tested, GFM1 consistently achieves lower energy expenditure, with reductions upward of 50% compared to GSM. This underlines the strengths of the fuzzy technique in enabling thermal-aware mapping to minimize chip temperatures in the presence of intensive workloads and communication. In essence, the key takeaway is that GFM1 outperforms GSM in the context of power and energy metrics linked to handling on-chip traffic between processing cores. [ABSTRACT FROM AUTHOR]

Research on resource management focuses on optimizing system performance and energy efficiency by distributing shared resources like processor cores, caches, and main memory among competing applications. This research spans a wide range of applications, including those from high-performance computing, machine learning, and mobile computing. Existing research frameworks often simplify research by concentrating on specific characteristics, such as the architecture of the computing nodes, resource monitoring, and representative workloads. For instance, this is typically the case with cloud systems, which introduce additional complexity regarding hardware and software requirements. To avoid this complexity during research, experimental frameworks are being developed. Nevertheless, proposed frameworks often fail regarding the types of nodes included, virtualization support, and management of critical shared resources. This paper presents Stratus, an experimental framework that overcomes these limitations. Stratus includes different types of nodes, a comprehensive virtualization stack, and the ability to partition the major shared resources of the system. Even though Stratus was originally conceived to perform cloud research, its modular design allows Stratus to be extended, broadening its research use on different computing domains and platforms, matching the complexity of modern cloud environments, as shown in the case studies presented in this paper. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids' efficient, stable, and economical operation in their initial stages. [ABSTRACT FROM AUTHOR]

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system. [ABSTRACT FROM AUTHOR]

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future. [ABSTRACT FROM AUTHOR]

Underwater long-endurance platforms are crucial for continuous oceanic observation, allowing for sustained data collection from a multitude of sensors deployed across diverse underwater environments. They extend mission durations, reduce maintenance needs, and significantly improve the efficiency and cost-effectiveness of oceanographic research endeavors. This paper investigates the closed-loop depth control of actuation systems employed in underwater vehicles, focusing on the energy consumption of two different mechanisms: variable buoyancy and propeller actuated devices. Using a prototype previously developed by the authors, this paper presents a detailed model of the vehicle using both actuation solutions. The proposed model, although being a linear-based one, accounts for several nonlinearities that are present such as saturations, sensor quantization, and the actuator brake model. Also, it allows a simple estimation of the energy consumption of both actuation solutions. Based on the developed models, this study then explores the intricate interplay between energy consumption and control accuracy. To this end, several PID-based controllers are developed and tested in simulation. These controllers are used to evaluate the dynamic response and power requirements of variable buoyancy systems and propeller actuated devices under various operational conditions. Our findings contribute to the optimization of closed-loop depth control strategies, offering insights into the trade-offs between energy efficiency and system effectiveness in diverse underwater applications. [ABSTRACT FROM AUTHOR]

Under the background of green and low‐carbon transformation of industrial high‐energy‐consuming parks, in order to realize the coordinated development planning of energy and transportation under different peaking capacity requirements, this paper proposes a collaborative optimization model of industrial high‐energy‐consuming parks under green and low‐carbon. Firstly, by studying the energy balance mechanism between industrial load and new energy, an energy topology prior model of energy interaction in the next generation of industrial new energy power grid is constructed. Then, based on the probability characteristics of the equilibrium state of different regions in the high‐energy‐consuming industrial zone, combined with the prior knowledge of industrial load and low‐carbon power system, a collaborative development planning model of high‐energy‐consuming industrial parks is constructed. Finally, the proposed model is simulated. From the simulation results the authors can see the dynamic division model and solution algorithm of energy balance region established in this paper can effectively improve the energy balance ability of power grid, reduce peak shaving demand and use more renewable energy. [ABSTRACT FROM AUTHOR]

The paper presents an original method for synthesizing high-density composite ceramics with a Mo 2 C/Mo 3 Co 3 C composition using the technology of high-speed Spark Plasma Sintering (SPS) of a mechanically activated powder mixture of Mo 2 C-10 wt%Co. The activated homogeneous starting mixture was obtained by wet milling/activation of the initial powders of Mo 2 C and Co in an anhydrous isopropanol medium. The ceramic samples were consolidated by SPS under vacuum (10−5 atm.) at 1000, 1100, 1150, and 1200 °C with a heating rate of 87.3 °C·min−1 under constant external uniaxial pressing pressure of 50 MPa. The sintering kinetics were studied, the twofold nature of sintering was established, and the temperature range of active densification was determined. The ceramic synthesis is accompanied by a reactive interaction of the components, resulting in the formation of the Mo 3 Co 3 C phase and a change in the crystal lattice of the non-stoichiometric Mo 2 C x compound. The ceramic obtained in the temperature interval of the isothermal sintering stage of 1150–1200 °C is homogeneous in the distribution of Mo and Co, consists of a Mo 2 C/Mo 3 Co 3 C phase mixture, has a density value of 98.13 ± 0.5 % theoretical density, and has an average Vickers hardness value of ∼1526 HV. Increasing the sintering temperature to 1200 °C results in the formation of almost monolithic structures in the samples, but also induces the formation of large defects due to the collective consolidation of pores within the bulk material and increases the susceptibility of the ceramic to cracking. The paper presents preliminary results from electrochemical testing. The proposed method for synthesizing Mo 2 C/Mo 3 Co 3 C ceramics has potential for use in optimizing the composition and production methods of electrode materials for the realization of active components in heterostructured electrodes used for hydrogen evolution. [ABSTRACT FROM AUTHOR]

To enhance the utilization of clean energy sources, such as wind power and photovoltaic within virtual power plants, and mitigate carbon emissions, this paper proposes a virtual power plant participation in the electricity carbon joint market optimization operation model based on ensemble empirical mode decomposition–improved bat algorithm (IBA)–Markov chain new energy output prediction. First, complementary set empirical mode decomposition is performed on historical data to construct a Markov chain based wind power and photovoltaic prediction model optimized by IBA. Second, this prediction model is used to predict the daily generation power of wind power and photovoltaic power. Finally, with the optimization goals of maximizing the benefits and minimizing the carbon costs of virtual power plants, a virtual power plant system participating in the electricity carbon joint market model based on wind power and photovoltaic output prediction results is constructed. At the same time, demand response factors are introduced and solved using the NSGA-II algorithm. Taking a certain park as an example for simulation analysis, the research results show that the combined effect of carbon market and demand response can achieve 99.82% of new energy consumption in virtual power plants without significantly reducing profits, basically achieving complete new energy consumption, demonstrating the effectiveness of the proposed model in this paper. [ABSTRACT FROM AUTHOR]

This paper aims to study the influence of novel macro encapsulated phase change materials (PCMs) on the mechanical and thermal properties of concretes. Thermal energy storage aggregates (TESAs) were prepared using vacuum impregnation. The PCMs were a lauric-myristic acid (LA-MA) eutectic mixture and coconut oil, pure or mixed, and are impregnated in diatomite as a supporting material. Thermal energy storage concrete (TESC) was made by replacing gravel in ordinary concrete with TESAs. Thermal and mechanical properties of prepared TESC were studied using differential scanning calorimetry, thermogravimetric analysis, Fourier transform infrared spectroscopy, compressive strength, and thermal performance analysis. The results showed that the melting temperature of coconut oil and the eutectic mixture of LA-MA were 25.2°C and 34.5°C, respectively. TESC with 80 wt.% of gravel replaced with TESA had a compressive strength of higher than 17 MPa, which indicates its usability as structural concrete. The results of thermal performance analysis also revealed that prepared TESC had the capability of storing thermal energy and can reduce energy consumption by damping temperature fluctuations caused by the outside environment. In this paper, a thermal energy storage concrete (TESC) was prepared by embedding phase change materials (PCMs) in diatomite used in TESC. Using this type of concrete in the building will reduce the energy consumption of the building and increase its thermal comfort. The produced concrete containing 80 wt.% of gravel replaced with diatomite saturated with PCM had a desirable 28-day cylindrical sample strength of 21 MPa. In the designed thermal test, the time required for this concrete to reach 40°C is almost twice that of concrete without PCM. The results show a reduction of the influence of outside temperature on the temperature inside the buildings made with this type of concrete. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology is constantly developing and changing. Due to the continuous development of technology and production of carbon emissions, various problems have occurred, forcing various regions to start promoting low‐carbon work. What kind of method can effectively and quickly improve the performance of carbon emissions has become one of the current research topics that has attracted much attention. In response to this problem, it is of great significance to study in the field of carbon emission performance improvement methods. With the in‐depth research on carbon emissions, the research on the intervention of water conservancy policies in carbon emissions has gradually been carried out, and its functional effects are of great significance to solve the problem of improving carbon emissions performance. This paper has aimed to study the impact and mechanism of water conservancy policy on carbon emission performance in the context of artificial intelligence. Through the analysis and research of the water conservancy policy under artificial intelligence, the double difference model (DID) is used to evaluate it, so that it can show the impact and mechanism on carbon emission performance, so as to solve the problem of improving carbon emission performance. This paper has analysed water policy, carbon emission performance and DID model under artificial intelligence. Its influence and mechanism have been experimentally analysed, and related theoretical formulas have been used to explain. The results have shown that the water conservancy policy has a significant and continuous driving effect on the reduction of carbon emission intensity, and its interaction coefficient under the control variable is −0.934, which is significantly negative. Further analysis of variables has shown that the energy intensity coefficient is 0.714, and the secondary industry coefficient is 0.924, which are both positive and significant. Water conservancy policies mainly reduce carbon intensity through the improvement of energy efficiency and industrial structure upgrading. It can be seen that water conservancy policies under artificial intelligence can meet the needs of improving carbon emission performance, and the level of economic growth and green development has been greatly improved. [ABSTRACT FROM AUTHOR]

Energy demand is rising over time in both developing and developed countries. Therefore, finding new sources of energy is a prime concern now. For this effort, this paper presents the pyrolysis of onion (Allium cepa) shells in a reactor with a fixed bed for generating alternative liquid fuel. This paper also compares alternative fuel characteristics, including higher heating value, viscosity, density, pour point, and flash point, with conventional petroleum fuels at optimal process conditions. The work adopted pyrolysis to produce liquid fuel at a temperature range of 400–550 °C and utilized LPG to provide a heat source. The liquid product (fuel oil) was collected, and non-condensable gas was flared. The liquid product was tested for various properties, and the results of the analyses show that alternative fuel has a higher heating value of 12.227 MJ/kg, density of 800 kg/m3, viscosity of 4.3 cP at 30 °C, pour point below −6.2 °C, and flash point around 137 °C, with a variation due to the volatile matters. To obtain favorable conditions for pyrolysis, some parameters, including bed temperature, sample quantity, average particle size, and operating time, were varied and analyzed. The physio-chemical properties made the alternative fuels isolated from conventional petroleum fuels due to the variation in distillation temperature. This work shows that the fuel oil generated from the pyrolysis of onion shells could be considered an alternative source of fuel. [ABSTRACT FROM AUTHOR]

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources. [ABSTRACT FROM AUTHOR]

This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines (SCOPUS, Web of Science, and Google Scholar) were used to locate relevant papers and documents. The results revealed the high significance of digital technologies in supporting the renewable energy sector, with high costs and security risks representing the key challenges. Most papers reviewed had a positive outlook, but recommended further research and development for effective energy transition and resilient infrastructure. The current drivers of the integration of digital technologies to support the diffusion of renewable energy sources appear to extend beyond energy demand and involve many aspects of sustainability and sustainable development. Compared with previous reviews, this work has unique scope and novelty since it considers the bigger picture of the coupling between digitalization and the renewable energy sector, with a greater focus on critical areas in these two interconnected bodies that need to be addressed. The relatively small sample of relevant papers (69 from 836 hits) located in the literature review confirms the need for more research covering the subject in greater depth. [ABSTRACT FROM AUTHOR]

Integrated energy microgrids and shared energy storage have significant benefits in improving the energy utilization of the system, which is gradually becoming the current research hotspot. And the uncertainty of new energy output also significantly affects the stable and economic operation of integrated energy microgrid. So how to establish a set of integrated energy microgrids optimization operation model considering photovoltaic (PV) output uncertainty and shared energy storage is an urgent problem to be solved nowadays. Firstly, this paper introduces the framework of an integrated energy system microgrid containing a shared energy storage operator (ESO), and analyzes the scheduling method of the upper tier operator within the system as well as the economic benefits at the lower tier user end. Secondly, to address the randomness of PV output, Monte Carlo method is used to generate the scenarios, and then the scenarios are cut down by using the fast antecedent elimination technique. Then, an optimal operation model is established for micro grid operator (MGO) and user aggregator (UA), respectively, and based on the master-slave game relationship, so that the MGO is the leader and the UA is the follower, a Stackelberg game model is proposed to consider the integrated demand response of electricity and heat between the MGO and UA in the context of the participation of ESO in the auxiliary service of the UA. Finally, the proposed model is brought into a typical residential building community for simulation verification, and the results show that the model proposed in this paper can effectively balance the interests of MGOs and UAs, and realize win-win benefits for UA and ESO. [ABSTRACT FROM AUTHOR]

The pulp and paper industry faces several challenges linked to climate and environmental impact, resource efficiency, rising energy prices, increased competition for biomass resources, and declining demand for traditional printed paper products. However, these challenges also offer strategic opportunities for the industry to develop into a competitive, resource-efficient, and low-carbon industry in line with a biobased economy. Against this background, this paper aims to analyse current energy strategies in the pulp and paper industry in Sweden. Specifically, the paper analyses how companies combine continuous process efficiency to reduce energy costs with activities that could be developed into new energy-related products to increase revenue. Most of the analysed companies work to various degrees with both these strategies, employing methods that include improving energy efficiency, energy security, and energy conversion, as well as developing a wide range of biobased energy products. However, our study indicates that there is an untapped potential associated with energy product development, and we conclude that energy efficiency measures can free up resources, enabling the development of new energy products. Finally, several potential managerial outcomes and implications are outlined. • The paper analyses the energy strategies in the pulp and paper industry in Sweden. • Energy strategies focus both to improve energy efficiency and develop new energy-products. • The companies' energy strategies combined exploitation and exploration. • A number of managerial implications are presented. [ABSTRACT FROM AUTHOR]

The EU has set the ambitious target of raising the share of EU energy consumption produced from renewable resources to 32% by 2030, with a target of climate neutrality by 2050. The aim of this paper is to assess the role of biomass usage in the context of these targets. The paper identifies the progress made between 2013 and 2022 by focusing on a selection of EU countries. The largest bioenergy increments of 130, 77, and 60 PJ were reported for Poland, Sweden, and the Netherlands. This study evaluates the crucial role of co-generation and heat in EU regions, with biomass usage between 55 and 80% of the combined heat and power (CHP) energy in Nordic countries. The future perspectives for bioenergy based on EU policies, biomass resources, and technical issues were addressed. The EU possesses around 9% of the global biomass supply, ensuring a certain level of biomass resource dependence. Thus, the biomass usage demand in energy production, non-energy sectors, and transport is expected to rise, leading to increments of 13–76% on biomass imports. It appears that bioenergy development is mostly limited by economic issues and uneven support for bioenergy in different EU countries as well as environmental issues. The study shows a promising and sustainable potential of bioenergy in the EU as a renewable energy source while minimizing negative impacts on the environment and the economy. By 2050, liquid biofuels are likely to be increasingly used in the transport sector. Non-energy sector usage of biomass is still in an early stage of development, except for the pulp and paper industry, and significant use of biomass in non-energy sectors seems unlikely in the near future. [ABSTRACT FROM AUTHOR]

Grid-connected photovoltaic (PV) power systems are one of the most promising technologies to address growing energy demand and ecological challenges. This paper proposes smart switching to mitigate inrush currents during the connection of single-phase transformers used in PV systems. An effective inrush current mitigation contributes to the reliability of PV systems. The inrush current severity is influenced by the pseudorandom residual flux at the transformer core and the energization point-on-wave. The most common approach to avoid inrush currents is controlled connection, which requires prior knowledge of the residual flux. However, the residual flux can differ in each case, and its measurement or estimation can be impractical. The proposed smart switching is based on a comprehensive analysis of the residual flux and the de-energization trajectories, and only requires two pieces of data (ϕRM and ϕ0, flux values of the static and dynamic loops when the respective currents are null), calculated from two simple no-load tests. It has a clear advantage over common approaches: no need to estimate or measure the residual flux before each connection, avoiding the need for expensive equipment or complex setups. Smart switching can be easily implemented in practical settings, as it considers different circuit breakers with distinctive aperture features, making it cost-effective for PV systems. [ABSTRACT FROM AUTHOR]

Deploying renewable energy to unit design energy-efficient technologies may fulfill the additional demand of various energy-intensive industries for their sustainability and resiliency. Multiple Stage Evaporator (MSE) is one of the most energy-intensive units used to extract the water content of the weak liquor in various industries, including the paper industry. This energy intensiveness may be reduced by integrating various Energy Reduction Schemes (ERSs). Hence, this work proposes a ERSs integrated MSE model to analyze its energy efficiency. These ERSs includes Thermo-Vapor Compressor, Steam-, Feed- Split, Feed Preheater, and Flash Tanks. Further, the performance of the proposed model is investigated under two important real-time plant complexities: Boiling Point Elevation and Fouling to achieve more realistic results. The performance analysis is initiated with the formulation of a nonlinear constrained optimization problem to increase the steam economy (SE). Also, the simulation is extended to validate at two different product concentrations (52% and 65%) by employing state-of-art optimization algorithms: CONOPT, and SCA in GAMS and MATLAB respectively. The simulated results shows an increment 1.64% and 1.37% of SE for both 52% and 65% concentration of weak liquor respectively in case of CONOPT than SCA. Also, the waste heat of the condensate, feed, and product may be further utilized for the heat recovery by incorporating the flash tanks which leads to a countable energy saving. Eventually, integrating the solar fields: PTC and LFR ensures a notable reduction in conventional heat utilization by 85.96% and 92.85%, respectively and hence, enhance the energy efficiency. • Expounded energy modeling of ERSs based MSE with BPE and Fouling. • Implementation of NLP and metaheuristic approaches for performance optimization. • Reutilization of waste heat to maximize the energy efficiency. • Integration of solar fields (PTC and LFR) to reduce conventional fuel utilization. • Prospecting the challenges and direction of energy management optimization. [ABSTRACT FROM AUTHOR]

Current management of solid waste from pulp and paper activities represents an environmental and economic burden worldwide due to pollution emissions. This study investigates the potential of hydrothermal carbonisation (HTC) treatment as a sustainable alternative for producing cleaner and energy-dense solid fuel from paper mill sludge. The effect of process parameters (temperature, reaction time and solid load) on hydrochar fuel formation from paper sludge was evaluated and, for the first time, the paper sludge-derived hydrochar was optimised to maximise the mass yield and calorific value using response surface methodology (RSM). The physicochemical characteristics, thermal fuel behaviour, energy recycling efficiency and electricity generation potential were assessed by proximate and ultimate analysis, thermogravimetry, bomb calorimeter, scanning electron microscopy and process energy assessment. Results showed that hydrochar fuel formation and properties were mainly influenced by the process temperature and residence time, and governed by dehydration and decarboxylation reactions which reduced the atomic H/C and O/C ratios by 35.5% and 64%, respectively. The produced hydrochars presented low sulphur, nitrogen and ash content with a maximum calorific value (HHV) of 22.9 MJ/kg, equivalent to the HHV of coal for commercial utility in South Africa. The HHV of the hydrochar corresponded to a 49.80% increase over the HHV of the initial feedstock. The optimum operating conditions were 231 ± 1 °C and 1.99 h for a hydrochar yield of 74.4% and calorific value of 18.5 MJ/kg. The energy assessment showed that up to 58.34% of the energy produced by hydrochar fuel combustion may be recycled as heat or power, while the remaining 41.66% of the combustion energy could be utilised to sustain the HTC treatment of paper sludge. The substantial water demand was concluded to be a drawback. Thus, water recirculation and the potential to catalyse the HTC reactions to increase overall process efficiency will constitute a future study to make the process more environmentally friendly for industrial-scale application. [Display omitted] • Dehydration and decarboxylation were predominant. • Optimum energy yield of 18.5 MJ/kg and 74.4% hydrochar was achieved under 231 ± 1 °C and 1.99 h. • Higher HTC reaction severity enhanced the physicochemical properties of the hydrochar. • Hydrochars produced have low nitrogen and sulphur (<1%) content. [ABSTRACT FROM AUTHOR]

In this paper, a combined air/water source heat pump water heater is presented. At ordinary time, the heater can be used for hot water heating in water-source mode to improve the energy efficiency. While in cold season, it gives priority to air-source mode for water heating to improve operational safety. Experiments were conducted to investigate the thermodynamic performance of the heater in each operation mode, and the experiment results were analyzed. The research indicated that its operation was reliable during the long-time running. This paper offers a promising energy-saving method in future. [ABSTRACT FROM AUTHOR]

This paper studies a local fractional vibration system with a damped non-linear term to reveal its frequency property and its energy consumption. A modification of He's frequency formulation is recommended for this purpose. Some examples are given to illustrate the solving process and the reliability of the method. Additionally, the effect of the initial conditions on the vibrating properties is elucidated. This paper offers a new window for fast and effective insight into local fractional vibration systems. [ABSTRACT FROM AUTHOR]

The micro-electro-mechanical system is widely used for energy harvesting and thermal wind sensor, its efficiency and reliability depend upon the pull-in instability. This paper studies a micro-electro-mechanical system using He-Liu [34] formulation for finding its frequency-amplitude relationship. The system periodic motion, pull-in instability and pseudo-periodic motion are discussed. This paper offers a new window for security monitoring of the system reliable operation. [ABSTRACT FROM AUTHOR]

Energy-intensive enterprises lack a scientific and effective energy efficiency assessment framework and methodology. This lack leads to an inaccurate understanding of energy usage and its benefits. As a result, there is energy wastage and loss. This wastage and loss negatively affect product costs. They also present a challenge to effective energy management. To address these issues, this paper introduces a novel, comprehensive energy efficiency evaluation system. This system integrates both qualitative and quantitative measures. It proposes an evaluation model based on the Particle Swarm Optimization (PSO) combined with the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), wherein PSO is employed to optimize the weights determined by AHP, ensuring that the significance attributed to various indicators is scientific, objective, and rational. The FCE method is utilized to convert diverse factors affecting corporate energy efficiency, across different types and scales, into standardized 0–1 values, enabling a comparative analysis of the impact of each process and indicator on energy efficiency. Furthermore, the paper introduces an energy efficiency prediction model employing a multivariate linear regression algorithm, which demonstrates a good fit, facilitating the transition from retrospective energy efficiency evaluation to proactive improvements. Utilizing data on actual consumption of water, electricity, and steam from an enterprise, along with expert assessments on the implementation levels of new processes, technologies, equipment, personnel scheduling proficiency, steam recovery rates, and adherence to policies and assessments, a simulation experiment of the proposed model was conducted using Python. The evaluation yielded an energy efficiency score of 0.68; this is consistent with the real-world scenario of the studied enterprise. The predicted mean square error of 9.035416039503998 ×   10 − 9 indicates a high model accuracy, validating the practical applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The research problem of this paper is related to numerous open questions in the field of energy, its understanding, its use, and the challenges of the energy future. After the introductory part, in which a brief historical overview of energy literacy is provided, the paper focuses on energy literacy, its emergence, and the different approaches to its definition and measurement. The paper analyzes the energy literacy of students at the Faculty of Economics in Rijeka based on their cognitive, affective, and behavioral skills. Their knowledge about energy, energy processes, and the energy efficiency of the objects they encounter on a daily basis is examined. Their personal attitudes and values are examined through various forms of personal initiatives and active participation in energy sustainability projects. Finally, the habits and behavioral patterns that the respondents have in their daily lives and their tendencies to save energy resources and find energy-efficient solutions are examined. Despite some positive findings, the current results are not satisfactory and point to the creation of adequate public policies with a particular focus on education and the role of the corrective mechanisms of the state, but also the need for joint negotiations between policy makers, regulators, scientists, representatives of civil society, and the business community. The particular contribution of this work is reflected in being the first research of its kind conducted among Croatian economics students, as well as among Croatian students in general; in the creation of a research instrument that is conceptually consistent with the findings from the existing literature, but with an original set of questions within each energy literacy skill adapted to the cultural and sociological background of the respondents; and in a kind of progress from previous research by taking into account the quantitative aspect (in addition to the qualitative) assessment of the respondents' energy literacy. [ABSTRACT FROM AUTHOR]

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023–2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China. [ABSTRACT FROM AUTHOR]

Nowadays, one of the main challenges facing green innovation management is how to enhance the performance of innovation processes by utilizing asymmetric input and output data. Therefore, this paper develops an improved SBM model analysis framework for evaluating the green innovation efficiency of asymmetric input and output data. The framework is applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of new energy companies under three input variables (R&D personnel input, R&D capital input, and comprehensive energy consumption input), two desirable output variables (green technology output and economic output), and one undesirable output variable (greenhouse gas emissions). Then, environmental factors and random factors are eliminated from the obtained input slack variables based on the SFA model, placing decision-making units in a homogeneous environment. The results demonstrate that TE, PTE, and SE are improved after eliminating environmental factors and random factors. Subsequently, based on the entropy method, this paper classifies companies' green innovation patterns into four categories and provides targeted solutions. The purpose of this paper is to provide an evaluation method for new energy companies to understand green innovation efficiency and assist decision makers in identifying the most optimal resource allocation approach. The proposed improved SBM model contributes to the literature and to industry practice by (1) providing a reliable evaluation of green innovation efficiency under asymmetric input and output data; (2) determining effective improvement actions based on a slack analysis of environmental variables and random variables that lead to improved process performance; and (3) making fuzzy innovation performance efficient to facilitate understanding and managing innovation resource allocation quality. [ABSTRACT FROM AUTHOR]