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Overwintering insects are facing energetic challenges because of food shortage, low temperature, and desiccation stress. Paper wasps of the genus Polistes overwinter as mated adults (gynes) in hibernacula protecting them from predation, snow, and rain but barely from low environmental temperature. In different climates, they face differing overwintering temperature regimes, and therefore they may differ in their energy use. We investigated how much of energy resources built up until autumn is used during diapause dormancy in natural hibernacula by measuring lipid, glycogen, and free carbohydrate content in autumn and early spring in Polistes dominula from temperate European (Austrian) and warm Mediterranean (Italian) climate and Polistes gallicus from Mediterranean climate. Winter energy consumption amounted to ~ 339 and ~ 310 J per wasp in the Austrian and Italian Polistes dominula populations. The smaller Italian Polistes gallicus consumed ~ 247 J. This amounts to 2.62, 2.35, and 1.79 J per day. Of this, the energy demand was mainly fuelled by lipids (84%, 93%, and 90%, respectively), but glycogen stores contributed also considerably (16%, 6%, and 9%). Free carbohydrates decreased only by 0.7%, 1%, and 0.8%. While fat stores seem still sufficient in spring, the wasps depleted most of their carbohydrates. The energy reserves of 396, 400, and 147 J per wasp remaining in spring in the three populations seem sufficient to fuel rest or simple brood care activities for a whole summer but restrict foraging flights to a few hours (~ 3.5–6 h). Results suggest that energy supply might become challenging in expected future climate scenarios. [ABSTRACT FROM AUTHOR]

Toilet paper has been reported as one of the major insoluble pollutant components in the influent of wastewater treatment plants. Toilet paper fibers contribute to a large production of sewage sludge, resulting in a high treatment cost and high energy consumption. To find energy-efficient, cost-effective, and environment-friendly technologies for fiber removal and resource recovery from wastewater, a life-cycle assessment (LCA) was performed to analyze the wastewater treatment processes, including a sieving process for removing and recovering suspended solids before the biodegradation units. Based on the LCA results, it was estimated that the sieve screening process saved 8.57% of energy consumption. The construction phase of sieving consumed 1.31% energy cost compared with the operation phase. Environmental impact analysis showed that sieving reduced the impacts of climate change, human toxicity, fossil depletion, and particulate matter formation, which reduced the total normalized environmental impacts by 9.46%. The life-cycle analysis of the removal of toilet paper fibers from wastewater revealed the need to use more efficient methods to enhance the recovery of cellulose fibers. [ABSTRACT FROM AUTHOR]

In order to study the cutting performance of the straight edge hob and reduce the cutting power consumption model of the straight edge hob, this paper takes the cutting power of the straight edge hob as the minimum goal, and establishes the mathematical model of the optimization design of the straight edge hob based on the composite optimization method. The mathematical model is solved by MATLAB software. At the same time,the mowing characteristics of a roller blade were studied by investigating the relationship between the hob and the coordination of variables such as rotational speed and roller diameter with the mowing parameters. The parameter analysis of straight edge hob before and after structural parameter optimization is generated, and a design method is proposed based on this. After defining the objective function and constraint conditions, the influence of structural parameters on the power consumption and efficiency of the hob was determined by optimising the complex method; this could significantly adjust the hob parameters to lower its power consumption. The energy consumption of the optimized design is reduced by 11.1 % compared with the original scheme, and the optimization effect is remarkable. The results show that the best working parameters of the hob are cutting speed of 1000 r/min, sliding cutting angle and grinding edge angle of 25~30°. Moreover, practical tests demonstrated the feasibility of using the proposed method to design the straight edge hob to improve mowing performance and hob stability. This study can provide parameter foundation and an optimization method for lowering chopping power consumption of the roller mower blade. [ABSTRACT FROM AUTHOR]

Existing literature ignores to consider multiple types of pollutants when analyzing energy efficiency and its convergence. Under this background, using SuperSBM-GML model, the improved entropy method, and convergence model, this paper calculates the green total-factor energy efficiency and its evolution trend of 35 China's industrial sub-sectors considering multiple pollutants, and analyzes its convergence. The results indicate that the average score of industrial green total-factor energy efficiency is low, and there are significant differences among sub-sectors: "Utilization of waste resources" is the highest, while "Manufacture of paper and paper products" is the lowest. The green total-factor energy efficiency shows an overall upward trend from 2006 to 2021, the main driving force comes from technological progress, but numerous sub-sectors have not sufficiently caught up with existing cutting-edge technologies. Specifically, the growth rate of green total-factor energy efficiency in high-energy-consumption sub-sectors is higher than low-to-medium-energy-consumption sub-sectors. There are both σ-convergence and β-convergence in low-to-medium-energy-consumption group, indicating that development of sub-sectors is stable and outstanding. Nevertheless, the high-energy-consumption group only exhibits conditional β-convergence, revealing an imbalance in energy efficiency development. Consequently, formulating the benchmark level of energy efficiency and developing energy efficiency "leader" system are suggested for low-to-medium and high-energy-consumption sub-sectors, respectively. [ABSTRACT FROM AUTHOR]

The papers analyzed here were presented in the reposts of leading scientists in the processes and apparatuses of chemical technology and published in the Proceedings of the International Scientific and Technical Symposium "Improving the Energy and Recourse Efficiency and Environmental Safety of Processes and Apparatuses in the Chemical Industry and Allied Branches," which was dedicated to the 110th anniversary of A.N. Planovskii and held at Kosygin Russian State University. The scientific areas considered included the development of processes and apparatuses in chemical technology as a science and an educational discipline; the main trends in the theory of engineering of energy- and resource-efficient chemical engineering systems under the conditions of the digital economy; the possibilities of improving the efficiency of mass-transfer processes through the use of contemporary methods for the mathematical modeling and kinetic calculation of apparatuses, intensification, and energy and resource saving; the development of systems of computational fluid dynamics as applied to the spatial calculations of processes and apparatuses; innovative methods for the production of fibrous and nano materials; the fundamentals of methods for the intensification of basic processes in different industrial technologies; the scientific foundations for the creation of energy- and resource-efficient chemical engineering and other processes; considerations of environmental safety for technological processes and equipment; and efficient systems for the improvement of technospheric safety. [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]

Background: To reduce the effects of climate change, the current fossil-based energy system must transition to a low-carbon system based largely on renewables. In both academic literature and non-academic discourse concerning the energy transition, resilience is frequently mentioned as an additional objective or requirement. Despite its frequent use, resilience is a very malleable term with different meanings in different contexts. Main text: This paper seeks to identify how resilience is understood in the field of the energy system and whether there are similar aspects in the different ways the term is understood. To this end, we review more than 130 papers for definitions of energy system resilience. In addition, we use different aspects to categorize and examine these. The results paint a diverse picture in terms of the definition and understanding of resilience in the energy system. However, a few definition archetypes can be identified. The first uses a straightforward approach, in which the energy system has one clearly defined equilibrium state. Here, resilience is defined in relation to the response of the energy system to a disturbance and its ability to quickly return to its equilibrium. The second type of resilience allows for different equilibriums, to which a resilient energy system can move after a disruption. Another type of resilience focuses more on the process and the actions of the system in response to disruption. Here, resilience is defined as the ability of the system to adapt and change. In the papers reviewed, we find that the operational definition of resilience often encompasses aspects of different archetypes. This diversity shows that resilience is a versatile concept with different elements. Conclusions: With this paper, we aim to provide insight into how the understanding of resilience for the energy system differs depending on which aspect of the energy system is studied, and which elements might be necessary for different understandings of resilience. We conclude by providing information and recommendations on the potential usage of the term energy system resilience based on our lessons learned. [ABSTRACT FROM AUTHOR]

Recently, the concept of cloud computing has been extended towards the network edge. Devices near the network edge, called fog nodes, offer computing capabilities with low latency to nearby end devices. In the resulting fog computing paradigm (also called edge computing), application components can be deployed to a distributed infrastructure, comprising both cloud data centers and fog nodes. The decision which infrastructure nodes should host which application components has a large impact on important system parameters like performance and energy consumption. Several algorithms have been proposed to find a good placement of applications on a fog infrastructure. In most cases, the proposed algorithms were evaluated experimentally by the respective authors. In the absence of a theoretical analysis, a thorough and systematic empirical evaluation is of key importance for being able to make sound conclusions about the suitability of the algorithms. The aim of this paper is to survey how application placement algorithms for fog computing are evaluated in the literature. In particular, we identify good and bad practices that should be utilized respectively avoided when evaluating such algorithms. [ABSTRACT FROM AUTHOR]

This article describes the utilization of lignin-based biopolymers and provides a brief explanation of lignin's chemical structure, extraction, and processing. Using lignin as a natural feedstock is an excellent idea because it is cheap, plentiful, and efficient as petroleum-derived goods. In plants, lignin, the world's second-most abundant natural polymer, boosts mechanical strength by covalently linking to cellulose and hemicellulose. Bio-renewable polymers have emerged as solid contenders as an alternative to traditional metallic and organic materials. It is increasingly beneficial for innovating innovative materials in the market and business because of their biocompatibility, biodegradability, and low production costs. Lignin extraction for biodegradable products is discussed in detail in this article. This review also discusses how lignin's antioxidant and antibacterial properties came into play in biological applications. In addition, the manuscript also discusses lignin's uses in pulp and paper, medicine, and other industries. As a bonus, lignin has the potential to be a rich source of high-performance polymers, energy-dense fuels, phenolic chemicals, carbon fibers, and value-added commodities that can be used in place of fossil-fuel-based products. This research project aims to review current achievements in lignin conversion and its usage as an energy source. This article covers Lignin conversion research in this article, including recent breakthroughs. [ABSTRACT FROM AUTHOR]

This research addresses the issue of the heritage preventive conservation in the perspective of energy sustainability, for contributing to the achievement of the Sustainable Development Goals (SDGs) and towards the EU Green Deal. The study analyses and compares four cases associated with different microclimate thresholds as suggested by the standard EN 16893:2018 (Cases 1–3) and as derived from the outputs of three degradation models for preserving paper, wood, and canvas paintings (Case 4). Weather-based indices (degree and gram days) were calculated to estimate trends in the potential energy demand of collection facilities in three European cities belonging to different Köppen-Geiger climate zones (Cfb, Csa, and Dfb), under recent past (1981–2010) and near/far future climate scenarios (2021–2050 and 2071–2100) from two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The findings suggest that adapting facilities' management strategies to focus on collections preservation can facilitate the achievement of 5 out of 17 SDGs, offering a viable alternative to costly energy retrofits and encouraging the development of shared solutions for similar facilities in the same climate zone. The results can contribute to inform the revision of EN 16893 and to face major challenges such as the preservation of paper collections in southern latitudes. [ABSTRACT FROM AUTHOR]

Water, ecology, energy and food are indispensable resources for human survival and social progress, as well as the core of the regional sustainable development. They are interdependent and closely related. The importance of ecology support and feedback capability has been overlooked in traditional studies of the water–energy–food nexus. In this paper, the concept of water–ecology–energy–food (WEEF) nexus was proposed. Then, a comprehensive evaluation indicator system of WEEF nexus was established from three criteria of stability, coordination and sustainability based on synergy theory. A comprehensive variable fuzzy evaluation model was constructed to evaluate the coordinated development level of WEEF nexus in Tianjin, China. And the main obstacle factors that constrained the coordinated development of WEEF nexus were analyzed through the obstacle degree model. The results showed that: from 2010 to 2021, the comprehensive coordinated development level of the WEEF nexus showed an upward trend, with an average growth rate of 4.0%. The stability, coordination and sustainability all significantly affected the comprehensive coordinated development of regional WEEF nexus. The coordination of water–ecology, energy–ecology and food–ecology subsystems was relatively low. Food self-sufficiency rate, average water consumption for energy production, annual rainfall and ecological environment index were the main obstacle factors of WEEF nexus. In summary, the nexus framework and evaluation method proposed in this paper can provide a new perspective on the basic theory and application of regional WEEF nexus. [ABSTRACT FROM AUTHOR]

This paper uses the exogenous shock of China's formal implementation of the environmental information disclosure system in 2008 to construct a quasi-natural experiment. Based on the panel data of prefecture-level cities from 2003 to 2019, the propensity score matching and difference in difference (PSM-DID) approach was used to systematically evaluate the impact of environmental information disclosure on energy efficiency. Overcoming the difficulties in measuring environmental information disclosure and the endogenous problem, this paper investigates the energy-saving effect of environmental information disclosure and illustrates its mechanism for the first time. The results show that environmental information disclosure significantly improved energy efficiency, and public participation played an important role in energy conservation, a conclusion that remained true after a series of robustness tests. The test of the impact mechanism shows that environmental information disclosure can achieve the goal of improving energy efficiency by promoting industrial structure upgrading and technological innovation. This paper enriches the discussion on the relationship between environmental information disclosure and energy efficiency, and provides useful policy inspiration for improving the level of energy efficiency and achieving sustainable economic development. [ABSTRACT FROM AUTHOR]

The provision of sustainable housing should factor energy efficiency, the use of sustainable building methods and materials and address the current associated barriers. This consideration triggered an ongoing study in UK which has so far conducted a bibliometric analysis of 275 papers using the VOSviewer software. More papers have been published in the last 5 years compared to the previous 15, and the UK is less engaged in successful collaborations in comparison to other economically developed nations. The lesser use of innovation in the UK has been identified. Also, the uses of sustainable methods and materials in residential housing are not fully exploited for achieving energy efficiency. A future research direction emerging is the potential to study the combined impact of using innovative materials and methods to achieve greater energy efficiency in UK housing provision. Through technological innovations and the need to move from fossil fuels (for example, through the use of heat pumps) a new approach can move the UK closer to achieving net-zero in housing provision and operations. [ABSTRACT FROM AUTHOR]

A wireless sensor network is a wireless communication network, and each sensor node has several sensors to collect environmental information. Wireless sensor network nodes have limited energy resources and need optimal routing protocols to reduce energy consumption. Failure to reduce energy consumption by sensor nodes reduces network life and efficiency. The main problem in routing is finding optimal paths for sending packets by reducing energy consumption in sensor nodes. This paper proposes an optimal routing method to reduce energy consumption in wireless sensor networks. In the first step, wireless sensor nodes are clustered with the Water strider algorithms (WSA), and cluster heads are selected for routing. In the second step, a mobile sink collects the packets from the cluster heads and sends them to the base station. The mobile sink uses the Ant colony optimization (ACO) algorithms to travel a shorter path between the cluster heads. The authors contribute to presenting a discrete version of the WSA algorithm for cluster head selection to reduce energy consumption. The authors contribute by providing a more comprehensive objective function for clustering network nodes considering error rate, energy consumption, PDR rate, and Euclidean distance. Cluster head traversal with a version of the ACO algorithm to reduce energy consumption and cluster head traversal coding like the TSP problem is the contribution of other authors. The paper aims to reduce energy consumption, reduce the error rate of sending packets and increase the lifetime of the wireless sensor network. Experiments are simulated on several simulated scenarios in Matlab. Criteria such as energy consumption, Packet delivery ratio (PDR), package loss rates, and the number of alive nodes to evaluate the proposed method are used. Experiments show that the proposed algorithm reduces the energy consumption and loss rates of packages of the wireless sensor network by optimally selecting cluster heads and increasing the PDR and number of alive nodes. Comparisons show In terms of energy consumption, Packet delivery ratio (PDR), Loss rates of packages, and the number of alive nodes, the proposed method is more efficient than Particle swarm optimization (PSO), Grey Wolf Optimizer (GWO), Information-centric wireless sensor networks, and Cluster based routing (CBR) routing methods. The PDR index in the proposed method is equal to 97.3% and is higher than PSO, GWO, and CS algorithms. The delay of the proposed method in routing is 25.97%, 5.78%, and 17.98% less than HHO, WOA, and GWO algorithms, respectively. [ABSTRACT FROM AUTHOR]

Accurate identification of critical malicious drones is crucial for optimizing directed energy attacks and maximizing their effectiveness. However, current studies on critical drone identification are still in the preliminary stage and almost rely on the traditional centrality methods that do not address the distributed features of drone swarms. This leads to inaccurate identification of critical drones, resulting in the low efficiency of directed energy attacks. Therefore, this paper proposes a new critical drone identification method based on the distributed features, communication intensity, and communication scale of drones. Specifically, this paper first constructs a dynamic communication prediction network (DCPN) of drone swarms based on the 3D position and interaction range, which predicts the dynamic communication between drones. Then, this paper proposes a new method called dynamic giant connected component (GCC)-based scale-intensity centrality (DGSIC) that combines the local, global, and community structure of DCPN to identify critical nodes with stronger communication capabilities. The dynamic strategy involves the iterative identification of one critical node at each step, considering the evolving network configuration and ensuring the identified node remains the most critical in the present network. Additionally, the prioritization strategy is employed to identify the nodes within the GCC, which can significantly impact the network connectivity and communication. DGSIC optimizes the attack sequence for directed energy attacks, facilitating the rapid dissolution of malicious drone swarms. Extensive experiments in four simulated networks and eight real-world networks demonstrate the superior robustness and cascading failure performance of DGSIC. [ABSTRACT FROM AUTHOR]

Under the unique environment of high humidity and high salinity of nuclear power plants, the environment should be improved to ensure staff comfort and equipment safe operation. Meanwhile, because of energy shortage, it is of great significance for building to save energy and reduce consumption. This paper first introduces the concept of Majorization to ensure user comfort with control smoothness, improving the existing two-timescale heating, ventilation, and air conditioning (HVAC) optimization model. Then, considering energy consumption, this paper formulates a multi-objective programming (MOP) problem with the objective to seek a balance between user comfort and energy consumption. The MOP model is solved by constructing the trade-off curve, where a subobjective with Majorization is also a MOP problem nested, which can be solved more simply using the characteristic of two-timescale HVAC optimization. The numerical results demonstrate the Pareto optimal solutions and the better energy-saving effect compared with the existing two-timescale model. [ABSTRACT FROM AUTHOR]

Emission data collection has always been a significant burden and challenge for Chinese counties to develop a CO2 emission inventory. This paper proposed a fuel consumption-based method to develop a local-specific CO2 emission rate database for Chinese counties using only open-source big data. Localized vehicle fuel consumption data is obtained through natural language processing (NLP) algorithm and large language model (LLM). The emission rates derived by our proposed method are consistent with field test results in literature. Besides, the CO2 emission estimation results using local-specific traffic activity data indicate that our method could effectively improve the accuracy of vehicle emission assessment. Compared with conventional method, the novel approach proposed in this paper can provide a pathway for convenient, universal, and cost-saving assessment for local scale CO2 emission rates. With this method, it is possible to formulate a local-specific CO2 emission database in various Chinese counties using only open-access big data. [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]

With the proposed generalized, hitless, and simple control strategy, the delay tunability and delay bandwidth product of the delay line increases with the increasing order of the Mach-Zehnder-based stage. To support end-to-end wireless-wired deterministic communication in these private, professional environments, it is necessary to introduce TSN features to the wireless network segments. The article demonstrates the multi-stage Mach-Zehnder-based integrated continuously tunable optical delay line with an extensive tunability range increases transmission bandwidth and the vast delay-bandwidth product. [Extracted from the article]

Smart home technology is expected to be widespread in the future and to accommodate a green transition to reduce and time-shift energy consumption. However, smart technologies also have social consequences, which are important to understand. At a basic level, we need to know more about learning to live with these technologies and how they influence our everyday practices and routines. Providing in-depth longitudinal insights into these processes, this paper presents an auto-ethnography of living with smart home technology: a 20-month diary kept by one of the authors. The paper uses theories of practice to investigate details of learning processes when interacting with three selected technologies: smart alarm and lighting management, smart control of heating, and a smart electric vehicle (EV). Theories of learning have a well-established tradition within theories of practice, and the concept of "knowing how to go on" and the concept of practical intelligibility are central in this work. This paper investigates the adoption of new smart technologies and how they interact with learning processes in different material and social contexts. Such an approach can lay the groundwork for further empirical research with a broader set of materials. It can also provide knowledge to assist in the design of better technologies and in developing policies and regulations to promote this. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of mechanism design optimization, particularly focusing on the energy efficiency and design space of reciprocating mechanisms. The research question centers on how to effectively utilize Computer-Aided Design (CAD) simulations alongside Bayesian optimization (BO) and a constrained design space to streamline the design optimization process, overcoming the limitations of traditional kinematic and dynamic analysis methods. The objective is to investigate and develop a novel optimization framework that integrates CAD-based simulations with a BO approach. To achieve this, the study employs a methodological approach. At first, the feasibility of a chosen mechanism design is evaluated through a sequence of CAD-motion simulations to quantify the (in)feasibility of this design. When this design appears to be feasible, a CAD-based design evaluation method is started, in which the objective value is extracted by a sequence of CAD-motion simulations. In this paper, we advocate the use of non-parametric Gaussian processes to build a surrogate model of the objective function and the feasible design space constrained by static and dynamic constraints. The main research results demonstrate that the proposed CAD-based Bayesian optimization framework can effectively identify optimal design parameters that minimize the root mean square (RMS) torque while adhering to specified static and dynamic constraints. This optimization approach significantly reduces the complexity associated with analytic methods, making it scalable to more complex mechanisms and implementable by machine builders. In conclusion, the study successfully develops a novel optimization framework that leverages CAD-based simulations and Bayesian optimization to streamline the design process of mechanisms. The results of an emergency ventilator case study with three design parameters show a reduction of the RMS torque with 71% after 255 CAD-based design evaluations. Moreover, the results demonstrate the effectiveness of incorporating constraints into the design optimization process and the usage of Bayesian optimization (BO), providing insights into the obtained optimum. This approach offers probabilistic insights into the expected improvement of the optimum, highlighting that the full optimization potential is utilized in a computationally efficient manner. [ABSTRACT FROM AUTHOR]

Consensus mechanism (CM) is the heart and soul of blockchain, for it allows nodes in a blockchain network to reach an agreement on the state of system execution. Among the CMs, the right for constructing the next new block is regarded as the most important one since it is required for the blockchain to grow so that transaction data can be recorded in it. Proof-of-work (PoW) is an efficient CM since it allows all the nodes to participate equally in the competition for the right. However, high energy consumption makes PoW less desirable for applications. Moreover, application-oriented blockchains should employ CMs that could reflect some main characteristics of the applications to facilitate the development of other mechanisms, such as the incentive mechanisms. Proof-of-contribution (PoC) is an effective, application-oriented CM since the right for constructing the next new block is determined by contributions made by the nodes and the node that has accumulated the highest contribution value (CV) gets the right. PoC is a general-purpose CM since the behavior of nodes can be characterized in the form of contributions. However, the deterministic nature of PoC as the result of ranking nodes based on CVs may lower efficiency since nodes could fail to function due to network delay, node failure or node's simply dropping out of the network. This paper proposes to design highly effective and efficient CMs by integrating PoC with PoW, which we refer to as PoCW. In PoCW, nodes compete for the right for constructing the next new block based on PoW after being assigned different difficulty values (DVs) based on the ranking of their CVs. Since assigning DVs strictly according to the ranking of CVs would make PoCW resemble PoC while assigning the same DV to all the nodes would make PoCW the same as PoW, PoCW could be designed as a class of CMs through applying different DV assignment strategies to meet the effectiveness and efficiency requirements of a variety of applications. We can further apply the same principle at finer levels of granularity by dynamically grouping nodes along the ranking of CVs and then applying different DV assignment strategies to different groups. The paper will first describe a generic PoCW without involving node grouping and then present the design of a general PoCW through applying an example node grouping method to demonstrate the feasibility of PoCW as a general-purpose CM for blockchain-based applications. Experiments were also conducted to demonstrate the effectiveness and the efficiency of PoCW as well as its advantages over comparable CMs. [ABSTRACT FROM AUTHOR]

The evolution of the Internet of Things paradigm in recent years demonstrate a significant impact on the transportation sector, leading to the emergence of a new research field, known as the Internet of Vehicles (IoV). In the IoV, vehicles can exchange information with each other and with the roadside units making use of Vehicular Ad Hoc Networks (VANETs). As this technology reaches near-to-market maturity levels, several issues arise related to the protection of users' privacy, while the interest of adversaries for such private user data in IoV environments gets stronger. This paper aims to present a review of the state-of-the-art techniques tackling the protection of location privacy in IoV environments, as well as experimental evaluation findings regarding the usage of various cryptographic algorithms for the protection of information exchange in these networks. In the conducted evaluations, the AES algorithm has been used as the main standard, which has been coupled with several other encryption/decryption algorithms, such as RSA, ECC and NTRU. The metrics used for the evaluation include measurements over the key generation process, the certificate generation, the encryption/decryption times, the signature generation/verification times, etc. Moreover, the size of messages in the negotiation, the pseudonym exchange and the new pseudonym enabling phases has been recorded, while the energy consumption in the exchange pseudonyms phase has also been measured. All previous experiments have been carried out mainly on NS-3 and SUMO open-source software aiming to have an estimation of how the aforementioned algorithms behave under constrained resources such as CPU usage and power. [ABSTRACT FROM AUTHOR]

Energy management controllers (EMCs) are pivotal for optimizing energy consumption and ensuring operational efficiency across diverse systems. This review paper delves into the various control strategies utilized by energy management controllers and explores their coordination mechanisms. Additionally, it examines the architectures of energy management controllers and their real-world implementations. The paper surveys a spectrum of EMCs, including conventional-based, rule-based, optimization-based, hybrid methods, and commercial software-based approaches, highlighting their respective advantages and drawbacks. It investigates how these controllers are coordinated within complex energy systems to achieve optimal performance and adaptability. Furthermore, the review outlines different architectures of energy management controllers, ranging from centralized to decentralized designs, discussing their suitability for various applications and their impact on system performance. Real-world applications of energy management controllers in sectors such as smart grids, buildings, industrial processes, and transportation systems are examined. Case studies and examples demonstrate the efficacy of different control strategies and architectures in addressing specific energy management challenges and achieving desired outcomes. Overall, this review provides valuable insights into the current landscape of energy management controller design and implementation, offering direction for future research and development in the pursuit of energy optimization and sustainability. [ABSTRACT FROM AUTHOR]

Datacenters are the primary driving force behind the energy shift to renewable sources. This shift limits the environmental impact of non-renewable sources and decarbonizes datacenters. However, renewable energy generation fluctuates with time and location, which introduces uncertainty in fulfilling user requests (URs). Thus, non-renewable energy generation continues to power the datacenters to make stability. Recent works direct to the use of renewable energy generation followed by non-renewable generation while assigning the URs to the resources of the datacenters. These works do not model the uncertainty of renewable and non-renewable energy resources and the level of uncertainty. This paper extends the three benchmark algorithms, namely future-aware best-fit (FABEF), highest available renewable energy first (HAREF) and round-robin (RR), by incorporating uncertainty and its level (UNL), and we call them UNL-FABEF, UNL-HAREF and UNL-RR, respectively. The goal of UNL-FABEF is to minimize the overall cost, whereas UNL-HAREF is to maximize the available renewable energy usage. On the contrary, UNL-RR assigns the datacenter to the URs in a roundabout fashion. This paper also introduces the UNL multi-objective scheduling algorithm (UNL-MOSA) to make a trade-off between UNL-FABEF and UNL-HAREF. UNL-MOSA creates a balance between the overall cost and the available renewable energy usage. All four algorithms consider three UNLs of URs, namely low, medium and high, for renewable energy resources. These algorithms are tested using fifty instances of ten datasets with 200 to 2000 URs and 20 to 200 datacenters and compared using five performance metrics: the overall cost, number of used renewable and non-renewable energy resource slots, and uncertainty cost and time. The performance of four algorithms in these performance metrics is extensively examined to know their applicability. [ABSTRACT FROM AUTHOR]

This paper aims to determine the environmental benefits associated with the implementation of a solar PV system to complement the conventional Electricité Du Liban/genset electrical system for the delivery of energy to an industrial facility. The selected case study is a juice manufacturing industrial facility located in Lebanon. Its energy demand is 6000 MWh/year. The components of the PV/hybrid energy system are the following: one 420 modules PV generator, seven three-phase grid-dependent inverters, three 1000 kW generators and one 500 kW generator. The ecoinvent database is used to model the life cycle inventory, and the IMPACT 2002+ methodology is used for the life cycle impact assessment. Results show at the assembly level that the manufacturing of the photovoltaic modules, mounting system components, inverters and cables results in harmful emissions that increase the system's environmental impacts. However, a comparative analysis between the entire life cycle of the PV/hybrid system and the conventional system indicates that the incorporation of the photovoltaic system results in a reduction in environmental impacts across all categories. The end-of-life scenario comprises landfilling/incineration and recycling of the components. Results show that the end-of-life stage poses a minor contribution to the overall life cycle impacts. [ABSTRACT FROM AUTHOR]

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed. [ABSTRACT FROM AUTHOR]

The key advantage of power utility-owned smart meters over rotating-disc meters is the ability of transmitting electrical energy consumption data to power utilities' remote data centers. Besides enabling the automated collection of consumers' electrical energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence make plentiful consumer-centric use cases possible. One of the plentiful consumer-centric use cases is detection and classification of household activities of daily livings (ADLs). A smart meter can acquire composite/circuit-level electrical energy consumption, but it cannot disaggregate its acquired composite/circuit-level electrical energy consumption into appliance-level electrical energy consumption. In this paper, a non-context-aware human life-pattern identification mechanism based on non-intrusive load monitoring (NILM) is presented for detection and classification of household ADLs. Here, NILM disaggregates composite/circuit-level electrical energy consumption into appliance-level electrical energy consumption with no intrusive deployment of networked plug-level power meters as the fundamental constituents of a traditional home energy management system, and it automatically characterizes physical characteristics of relevant electrical appliances for detection and classification of household ADLs without user intervention. [ABSTRACT FROM AUTHOR]

Tourism has become a major driver of China's economic growth and consumes much energy causing environmental pollution problems. This paper combines the LMDI (logarithmic mean Divisia index) method and K-means clustering to analyze the factors influencing tourism energy consumption in seven Chinese provinces and discusses strategies for energy consumption in tourism. Specifically, firstly, this paper decomposes the tourism energy consumption factors in each province into six factors and identifies the driving forces of different factors on energy consumption. Secondly, K-means clustering method is used to classify different provinces into three categories using the latest dynamic influencing factors as clustering factors and provincial targeted suggestions are made according to the characteristics of different categories. This paper combines the LMDI model with cluster analysis to find targeted energy optimization strategies for the energy consumption of the Chinese tourism industry. [ABSTRACT FROM AUTHOR]

Typical public institutions such as government offices, hospitals, and schools play important leading and exemplary roles in the green and low-carbon development of the whole society. This paper analyzes the business and energy consumption characteristics of public institutions, as well as the characteristics of personnel travel, and constructs a carbon accounting model for public institutions. It innovatively proposes a methodology for carbon accounting of personnel travel related to public institutions and validates and analyzes it using a comprehensive hospital as an example. The paper analyzes the carbon emission characteristics of various energy types, systems, and types of travelers in hospitals, provides schemes for the transformation of main energy systems, and corresponding emission reduction effects, thereby providing technical support for the full-chain carbon accounting of public institutions. Additionally, this paper explores the carbon reduction pathways for hospitals to support the peak carbon and carbon neutrality goals of public institutions, and promote the high-quality development of public institutions in China. [ABSTRACT FROM AUTHOR]

Energy is known as the lifeline of national development, and from the last decade or so, the security of human energy supply has been frequently disturbed by natural disasters due to global warming and frequent geological activities. In this paper, by combing the literature, we found that the past literature on the relationship between energy and natural disasters mainly focused on measuring energy consumption under natural disasters, but less on the integrated evaluation of human economic activities, energy security and natural disasters, and less on the evaluation of the energy governance efficiency of each provincial, municipal, and autonomous region government in preventing and managing exposure to natural disasters from the perspective of Chinese provinces. Therefore, in order to fill the gap in the literature in this field, this paper collects panel data of energy consumption efficiency stages and natural disaster treatment stages from 2013 to 2017 for 30 provinces in China (excluding Hong Kong, Macao and Taiwan as well as Tibet Autonomous Region) and uses the two-stage undesirable dynamic DDF model as a framework to study the relationship between economic, environmental pollution and natural disasters for the sample data and to analyze the phase-by-phase evaluation of energy and natural disaster efficiency and make corresponding policy recommendations. The following conclusions are drawn: (1) The overall efficiency of China's eastern coastal provinces is higher than that of the central and western provinces. (2) The first-stage efficiency of Chinese provinces is better than the second stage, and the difference in efficiency of the first stage of each province is smaller than that of the second stage. (3) In terms of the efficiency of disaster prevention and relief inputs, the efficiency values are generally lower in most regions of China. (4) Finally, the annual efficiency of natural disaster losses is not high in all regions, and the efficiency values are higher in the eastern coastal regions than in the central and western regions. Accordingly, this paper proposes that each province should formulate relevant disaster prevention and economic development strategies according to regional characteristics, while the central government should also propose locally appropriate coordinated governance policies to effectively control carbon dioxide emissions and air pollution, as well as increase disaster prevention publicity and incorporate disaster prevention education-related work into the performance assessment mechanism of local governments to promote the interactive development of the two. [ABSTRACT FROM AUTHOR]

Mobile object detection and tracking is an important application of wireless sensor network. However, longevity of such application is a challenge as sensor nodes in wireless sensor network operates on battery power. Therefore, there is the requirement for increasing the lifetime of this application by optimal usage of energy while tracking. Of course, while optimizing the energy usage, tracking accuracy should not be compromised. A number of research works are carried out in the past to enhance the wireless sensor network lifetime but no work resolved the problem completely. This paper studies the issue of data routing to sink in energy constrained wireless sensor network based application where tracking information collected from different sensor nodes are processed to determine the object's presence in the monitoring area. This paper proposes an energy efficient hybrid artificial bee colony-modified Dijkstra (HABC-MD) algorithm for cluster based network for optimum usage of energy during routing of packet. The objective of this algorithm is to save the energy of the network by optimizing the number of node-to-node transmission while tracking. It uses metaheuristic approach based artificial bee colony algorithm for selection of cluster head in each round and heuristic approach-based Dijkstra algorithm for selection of optimum route between source node and sink for packet transmission. Modified Dijkstra algorithm considers both distance between source and destination and energy of the source node for determination of optimum route to sink. The performance of HABC-MD is compared with existing hybrid LEACH-Dijkstra algorithm and is found that performance of our algorithm is better in comparison to later algorithm. [ABSTRACT FROM AUTHOR]

Heat pumps are a solution for decarbonising home heating in the UK. However, the readiness of UK homes for heat pumps is an area of concern regarding the policies aimed at increasing heat pump adoption. This work combines multiple perspectives in evaluating the technical readiness of homes with the market readiness of installers and homeowners to proceed with installing heat pumps. The effectiveness of past heating and energy efficiency policies in the UK are reviewed, along with building regulations, incentives to promote energy efficiency and the effectiveness of heat pump technology in heating homes. Current policies support the cost of a heat pump but home improvements to make homes 'heat pump-ready' can be necessary to achieve optimal heat pump system performance. This paper suggests the UK will face three major challenges. First, analysis highlights an 'eligibility-readiness gap' describing the difference between homes 'eligible' (50%) for the Government's Boiler Upgrade Scheme—a subsidy for heat pump installations—and the likely level of homes that are heat pump-ready (11%) for successful heat pump installations. Second, semi-structured interviews with heat pump installers identified gaps in capacity to deliver the necessary works to make homes heat pump-ready. As small or medium enterprises, the majority of installers do not currently see adding home improvement services to their existing business model as beneficial. All installers highlighted the need for Government to address the cost of electricity relative to gas. Third, a national survey of homeowners in England with gas boilers (n = 1,021) revealed low awareness of the necessary work to make homes heat pump-ready and low willingness to spend money on them unless supported by Government. This paper shows that the processes and costs involved in making homes heat pump-ready before successful design and installation are underappreciated by homeowners, inadequately served by industry, and insufficiently supported by Government. [ABSTRACT FROM AUTHOR]

Emulsion polymerization is an effective means of synthesizing high-molecular-weight polymers in order to reduce the consumption of fossil energy. This paper adopts isoamyl octanoate to replace white oil as the continuous phase of emulsion polymerization to synthesize the high molecular weight polyacrylamide emulsion RSE. S-80 and T-80 were chosen as the emulsifiers, and the test results showed that the HLB value is 5.72 when S-80/T-80 is 6.5. When S-80/T-80 is 6.5, the HLB value is 5.72. Water and isoamyl octanoate can produce ultra-low interfacial tension and stable arrangement in the interfacial membrane to form a stable emulsion, and the particle size is uniformly distributed in a normal manner. The test of the exothermic process of polymerization shows that the emulsion can be stabilized into nuclei, and polyacrylamide polymers with a molar mass of more than 18 million are obtained. The microstructure test observes that RSE molecules are in an aggregated state in the aqueous solution. A three-dimensional spatial network structure is formed, and the polymerization can be stabilized in a three-dimensional network structure after 120 °C, 170 s−1 shear, which still has good stability. After the variable shear performance test proved that the aqueous solution of RSE belongs to the typical non-Newtonian fluids, to shock test the viscoelasticity of different concentrations of RSE polymer, G′ > G″, the RSE solution to elasticity is dominant. In this paper, the capacity to form stable emulsions of isoamyl octanoate in the emulsion polymerization process is investigated to provide a basis for seeking the selection of a continuous phase with little environmental pollution and high functionality. [ABSTRACT FROM AUTHOR]

Scheduling tasks in the cloud system is the main issue that needs to be addressed in order to improve customer satisfaction and system performance. This paper proposes DCOHHOTS, a novel multi-objective task scheduling algorithm based on a modified Harris hawks optimizer. In overall, this paper has two main stages. As the first step, DCOHHO is introduced as a new version of Harris Hawks Optimizer. Using the Differential Evolution algorithm, an optimal configuration is selected from the chaotic map, the opposition-based learning, and the ratio of the population. In order to improve the performance of the Harris Hawks Optimizer, this optimal configuration is applied to initialize the hawk's position. In the second stage, DCOHHOTS, a DCOHHO-based Task Scheduling algorithm, is proposed. Multi-objective behavior in the proposed task scheduling algorithm optimizes resource utilization to decrease the makespan, energy consumption, and execution cost. Moreover, prioritizing tasks before submitting them to the scheduler is done using the hierarchical process in the DCOHHOTS algorithm. For the purpose of investigating the performance of the proposed DCOHHO algorithm, a number of experiments are conducted using 20 standard functions and twelve algorithms. The experimental results demonstrate that the DCOHHO algorithm is superior at determining the optimal test function solutions. Additionally, makespan, execution cost, resource utilization, and energy efficiency of DCOHHOTS task scheduling algorithms are analyzed. Compared to existing algorithms, the proposed algorithm saves up to 16% energy in heavy loads. Additionally, resource utilization has increased by 17%. Compared to the conventional algorithm, the proposed algorithm reduced makepan and execution cost by 26% and 8%, respectively. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the nonlinear relationship between ICT and CO2 emissions by controlling for economic growth, foreign direct investment, energy consumption, and trade openness. Using data from 16 Middle East and North African (MENA) countries over the period 1990–2019, we apply the Panel Smooth Transition Regression (PSTR) model, as introduced by (González A, Teräsvirta T, vanDijk D (2005) Panel smooth transition regression models. SEE/EFI Working Paper Series in Economics and Finance, No. 604), to study the potential regime-switching behavior of the relationship between the variables. The results reveal the existence of a strong regime-switching effect between ICT and CO2 emissions. It was found that after reaching a certain threshold, ICT use and penetration starts to significantly mitigate environmental degradation. Our results show that high levels of ICT not only improve environmental quality but can also be part of the solution to combat the environmental challenges that the MENA region has faced over the past decades. In addition, to account for the potential endogeneity bias, we also develop and estimate a PSTR model with instrumental variables (IV-PSTR) using the approach of (Fouquau et al., Econ Model 25:284–299, 2008). The results obtained confirm those initially found by the PSTR model. The study concludes with policy implications. [ABSTRACT FROM AUTHOR]

Spiking neural network (SNN) based on sparse trigger and event-driven information processing has the advantages of ultra-low power consumption and hardware friendliness. As a new generation of neural networks, SNN is widely concerned. At present, the most effective way to realize deep SNN is through artificial neural network (ANN) conversion. Compared with the original ANN, the converted SNN suffers from performance loss. This paper adjusts the spike firing rate of spiking neurons to minimize the performance loss of SNN in the conversion process. We map the ANN weights to the corresponding SNN after continuous normalization, which ensures that the spike firing rate of the neuron is in the normal range. We propose a certainty interval reset mechanism (CIRM), which effectively reduces the loss of membrane potential and avoids the problem of neuronal over-activation. In the experiment, we added a modulation factor to the CIRM to further adjust the spike firing rate of neurons. The accuracy of the converted SNN on CIFAR-10 is 1.026% higher than that of the original ANN. The algorithm not only achieves the lossless conversion of ANN, but also reduces the network energy consumption. Our algorithm also effectively improves the accuracy of SNN (VGG-15) on CIFAR-100 and decreases the network delay. The work of this paper is of great significance for developing high-precision depth SNN. [ABSTRACT FROM AUTHOR]

The task offloading of mobile edge computing (MEC) is to find proper edge or cloud resources for the execution tasks to efficiently utilize resources and meet different user's requirements. However, it is difficult for task offloading when the number of tasks and resources providers increases and to optimize multiple objectives while satisfying users' requirements. In this paper, a new multi-objective strategy based on the biogeography-based optimization (BBO) algorithm is proposed for MEC offloading to satisfied users' multiple requirements (the execution time, energy consumption and cost). In this strategy, a time-energy consumption model and a cost model are constructed for task offloading firstly. Based on these models, the BBO algorithm is introduced into task offloading for MEC to solve the problem of multi-objective optimization. Compared with the traditional strategies, the offloading strategy based on BBO decreases the average task completion time by an average of 25.03%, and compared with the technique for order preference by similarity to an ideal solution (TOPSIS) strategy, the BBO offloading strategy proposed in this paper reduces energy consumption 75% and cost by 36.9%. The proposed strategy can well solve the problem of multi-objective optimization in the task offloading for MEC. [ABSTRACT FROM AUTHOR]

Corporate green innovation is the crucial driving force to promote green development and realize the construction goal of "Beautiful China." Meanwhile, the development of Fintech creates a more favorable external environment for corporate green innovation. Using the panel data of China's Digital Financial Inclusion Index and Energy Poverty Index at provincial level from 2011 to 2020, this paper studies the influence of Fintech on corporate green innovation by the evidence from Chinese heavily polluting enterprises. Based on stepwise regression, this paper further examines the mediating role of energy poverty, including energy consumption level, energy consumption capacity, and energy consumption structure, in the relationship between Fintech and corporate green innovation. The results show that (1) Fintech contributes to improving the level of green innovation of heavily polluting enterprises; (2) energy poverty acts as a mediator in the process of Fintech's influence on corporate green innovation; (3) Fintech can promote green innovation of heavily polluting enterprises by improving the level of regional energy consumption level, but it fails to influence corporate green innovation through energy consumption capacity and energy consumption structure. These results provide governments and companies with implications on facilitating corporate green innovation so as to further promote green development. [ABSTRACT FROM AUTHOR]

Computation offloading and service migration are two major research hotspots in the mobile edge computing (MEC) environment. However, in the existing MEC architecture, the idle computing resources of offsite edge servers are not fully utilized, which leads to the problem of high overall system time and energy costs. In this paper, we propose a multi-edge collaborative computation offloading strategy for this problem. The strategy analyzes and calculates the energy consumption and latency cost of task execution for local terminals, edge servers and central cloud, constructs a computation offloading model with the weighted sum of latency and energy consumption as the optimization objective, and then solves the model using an improved genetic algorithm to obtain the best computation offloading decision. On the other hand, the mobility of users in the MEC environment leads to service migration, which leads to unbalanced load on the edge servers and network congestion, etc. This paper proposes an energy threshold-based task migration strategy. The strategy analyzes the time and energy consumption of service execution and data transmission, designs an edge server selection algorithm based on the energy consumption threshold, constructs a service migration model, and finally solves the optimal service migration strategy by improving the genetic algorithm. Experimental results show that the multi-edge collaborative computation offloading strategy proposed can significantly improve the performance of data transfer cost, energy consumption, and task completion time. The proposed migration strategy based on energy consumption threshold can significantly improve the performance of mobile server energy consumption, service completion time, and data transfer energy consumption. [ABSTRACT FROM AUTHOR]

In this paper, the optimum design considering main operating points (MOPs) of the electric vehicle (EV) traction motor is conducted to reduce the torque ripple and improve the fuel economy. MOPs of the traction motor can be determined by considering ratios of number of operating points or considering energy consumption ratios based on the EV simulation. Specifically, the optimum design is conducted with the aim of minimizing the torque ripples at MOPs determined by considering ratios of number of operating points (RNOMOPs) and maximizing the inverter-motor system efficiencies at MOPs determined by considering energy consumption ratios (ECRMOPs) to simultaneously reduce the torque ripple and improve the fuel economy. To verify the effectiveness of the optimum design considering MOPs proposed in this paper, torque ripples at RNOMOPs and the fuel economies according to the initial model and the optimum model are compared. As a result, torque ripples of the optimum model at RNOMOPs are remarkably decreased by 58.84 %p and 57.77 %p, respectively, and the fuel economy according to the optimum model is improved by 1.72 %. Conclusively, the effectiveness of the optimum design considering MOPs of the EV traction motor for the torque ripple reduction and the fuel economy improvement is verified. [ABSTRACT FROM AUTHOR]

It can be observed from the existing energy literature the previous papers investigating the influence of renewables consumption on GDP for the USA commonly ignore structural breaks in the US economy. Hence, the purpose of this paper is to examine the impact of renewable energy consumption on economic growth for the USA using quarterly data over the period 1977Q1-2019Q3 through unit root and cointegration methods based on different approaches in modelling structural breaks. Our empirical evidence confirms that all unit root tests give similar outputs and show all the variables become stationary at 1st differences. Besides, cointegration tests show highly different results in terms of the statistical significance of the coefficients. For instance, the cointegration test without structural breaks indicates that renewable energy consumption has no impact on economic growth. With sharp structural breaks in the cointegration approach, there is no cointegration between the variables. The empirical findings of the cointegration test with sharp and gradual breaks imply that renewable energy consumption has positive effects on economic growth. This paper discusses the implications of the empirical findings. [ABSTRACT FROM AUTHOR]

In this paper, an electric servo actuator implementation scheme with an energy recovery link is proposed to address the potential energy being wasted when a load falls. The new electric servo actuator recovers the potential energy dissipated during the load-falling progress by using an accumulator. The accumulator can also provide extra thrust when the load is lifted. Moreover, a dynamic model is established based on the theory of power bond graphs to analyze the dynamic and energy consumption characteristics of the new electric servo actuator. The simulation results show that the new electric servo actuator can significantly reduce the required motor power and effectively recover the potential energy of the load. This achievement is very significant for improving the energy efficiency of electric servo actuators and mitigating global warming. In addition, the modeling method used in this paper has important reference significance for the energy consumption analysis of other mechanical systems with complex energy domains. [ABSTRACT FROM AUTHOR]

Transportation is the key carbon emission source after energy supply and industrial production. Under the vision of carbon peak and carbon neutralization, the pressure of reducing carbon emissions in transportation will be greater in the future. This paper constructs a model that takes transportation carbon emission as the main target and freight transportation utility value as the auxiliary target. The constructed model satisfies the constraints of freight turnover in the whole society, freight economic and social benefits, and the ecological constraints of the freight system. With MATLAB, the freight turnover of roadways, railways, and waterways (excluding ocean transportation) in 2030 is solved by using the adaptive genetic algorithm. The results indicate that (I) compared with the current freight structure of China, the roadway freight sharing rate in 2030 will decrease by 8.07%, and the railway freight sharing rate and the waterway freight sharing rate (excluding ocean transportation) will increase by 0.93% and 7.13%, respectively. (II) After optimization, the energy consumption and carbon emission are reduced by 42,471,500 tons (10.3%) and 91,379,400 tons (10.2%) of standard coal, respectively. (III) The adaptive genetic algorithm outperforms the traditional genetic algorithm in terms of convergence speed and accuracy. (IV) As the weight coefficient of carbon emission increases, the utility value of freight transportation consistently decreases, and the sensitivity increases. Meanwhile, as the carbon emission weight coefficient increases, carbon emission keeps decreasing, and the sensitivity decreases. [ABSTRACT FROM AUTHOR]

There is a growing research literature focused on ‘invisible energy policy’ that explores the complex links between policies in non-energy sectors and energy demand. Invisible energy policies are those that do not include energy as a visible policy objective but still pose implications for energy demand that are largely unrecognised in non-energy organisational settings. Within this innovative area of analysis, to date, little attention has been paid to how ‘energy’ features in the discourse of non-energy contexts. This paper makes a distinctive contribution by examining how energy issues, such as energy demand, travel, and energy skills, do or do not feature in the policy and wider strategy of a non-energy policy setting. The research focuses on the content of policies and other documents in the non-energy policy context of UK secondary schooling, using a case study approach. It aimed to identify how energy surfaces and/or remains absent in different types of documentary evidence at the school. The textual analysis shows how openings for energy to surface as a concern at the school are often heavily bounded in specific policy areas, such as sustainability and education. It also foregrounds the lack of recognition in the sample for how school operations and strategy can produce demand for energy more fundamentally. It argues that these openings for energy to surface can provide points of negotiation to discuss the more fundamental energy impacts of policy. The paper concludes by reflecting on the analysis’ implications for research on ‘invisible energy policies’ and low-carbon transitions. [ABSTRACT FROM AUTHOR]

As a major part of global development governance, G20 countries account for 80% of global carbon emissions. In order to achieve the goal of "carbon neutrality" proposed by the United Nations, it is important to compare and analyze the drivers of carbon emissions in G20 countries and provide recommendations for their carbon emission reduction. Based on the data of 17 G20 countries in the EORA database, this paper compares the drivers of carbon emissions of each country from 1990-2021 using weighted average structural decomposition and K-mean model. This paper focuses on four drivers, including carbon emission intensity, final demand structure, export structure, and production structure. Carbon emission intensity and final demand structure are the main factors of carbon emission reduction, and the other two factors have little influence. Among the G20 countries, the UK is in the first category because it does the best job on the four factors of carbon emissions, yet Italy is in the last category because it does not take full advantage of the four factors. Therefore, improving supply energy efficiency and adjusting demand, export, and industrial structure have become important tools for countries to transform and achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

With the development of urbanization, the problem of the disintegrated between industry and city became more prominent, exploring the reasons. The efficiency of new-type industry has been the crucial factor in city-industry integration. This paper constructs the measurement index system of new-type urbanization via DEA-BCC methodology, starting from the quality of urbanization to analyze the efficiency of urbanization. This paper chooses the total energy consumption, general public budget expenditure, and the proportion of employment personnel in the tertiary industry in all urban units as input variables. The total retail sales of consumer goods, urbanization rate, average annual concentration of pm2.5 (popW), and built-up area as output variables. This paper uses DEA method to measure the comprehensive efficiency value, technical efficiency value, and scale efficiency value of new urbanization in Shanghai, and analyzes the influencing factors of urbanization efficiency. The results show the following: (1) The overall level of comprehensive efficiency value, technical efficiency value, and scale efficiency of Shanghai's new-type urbanization are relatively high, especially the technical efficiency basically stays at a high level. The overall trend of scale efficiency and comprehensive efficiency is consistent, and the comprehensive efficiency is greatly influenced by scale efficiency. (2) The technical efficiency of urbanization in Shanghai is close to the optimal, and there is little space for further increasing technological input to improve the comprehensive efficiency of new-type urbanization. The scale efficiency is slightly lower than the technical efficiency, and there is still some space for optimization. (3) In terms of urbanization input indicators, Shanghai's total energy consumption and general public budget input were too much in the early years, which led to the reduction of urbanization efficiency, and the situation has been improved in recent years. In terms of the output index of urbanization, increasing the total retail sales of social consumer goods and the output of built-up area can make the urbanization efficiency of Shanghai reach the optimal efficiency. [ABSTRACT FROM AUTHOR]

Fog computing has been introduced in recent years to extend cloud services to the network edge in order to minimize network delay and network congestion and overcome cloud computing limitations. However, several challenges are yet to be addressed in order to achieve the full benefits of the fog-IoT paradigm for low latency applications. The foremost crucial challenge is designing efficient resource management schemes capable of maximizing the throughput and minimizing the delay of IoT application. In this paper, we propose a QoS-aware greedy edge placement scheme that defines the way in which application modules are distributed across fog devices in order to minimize the end-to-end latency of real-time IoT applications. The proposed scheme is composed of two stages: a greedy-delay minimizing application module selection stage and a greedy-delay minimizing application module placement stage. The first stage aims to reduce the end-to-end latency by determining the order in which the application modules are placed across fog devices to maintain high QoS of real-time applications. The second stage uses the depth first search algorithm to select the fog node that meets application modules' processing, storage and bandwidth requirements. To evaluate the performance of the proposed scheme intelligent surveillance through distributed camera networks application was used. The application modules are mapped onto fog nodes connected in different configurations. The experimental results demonstrate that the proposed scheme provides high QoS by reducing the end-to-end latency, the network usage and the energy consumption. [ABSTRACT FROM AUTHOR]

In the case of China, there is a need of green energy system to boost growth and environment. However, the current growth of urbanization is putting high pressure on energy system through financial capital. Hence, it is a need to develop such path through renewable energy consumption, capital development, and urbanization to enhance the performance of development and environment. Therefore, this paper contributes to the literature by adding the asymmetries between renewable energy, urbanization, economic growth, and capital investment by covering the period from 1970 to 2021. For this purpose, we apply the nonlinear autoregressive distributed lag model to find the nonlinearities between the variables under study. The findings confirm the asymmetric short- and long-term relationships between the variables. In this sense, capitalization shows the short- and long-term asymmetric impacts on renewable energy consumption. In addition, urbanization and economic growth have long-term asymmetric and positive impacts on renewable energy consumption. At last, this paper provides applicable and practical policy implications for China. [ABSTRACT FROM AUTHOR]

Based on the background of carbon neutrality targets, panel data of 30 Chinese provinces during 2000–2019 were selected in this paper to explore the nonlinear correlation mechanism among transportation, tourism development, renewable and non-renewable energy consumption and economic growth. Firstly, by employing a fixed effects model as the base model, we confirmed the existence of correlations among these variables. And the nonlinear moderating effects were also verified by setting interaction terms in the model. Secondly, we further discussed the characteristics of the nonlinear relationship among the variables by employing a threshold effects model. Our results demonstrated that the effect of transportation on economic growth is positive and significant if and only if renewable energy consumption surpasses a certain threshold. Meanwhile, we also found that the effect of tourism development on economic growth is positive when non-renewable energy consumption is under a certain threshold. Finally, our study indicated that transportation maintains a positive and significant effect on economic growth within any threshold of tourism development, and the positive effect gradually increases as the threshold rises. The findings of this paper suggested that for China, the contribution of transportation and tourism to economic growth can be expanded by further expanding the substitution of renewable energy for non-renewable energy. [ABSTRACT FROM AUTHOR]