Search

Efficient waste paper recycling has a significant role in the sustainable environment. Recyclable waste paper as a fundamental ingredient of municipal solid wastes (MSWs) is indeed an 'urban ore'. Waste papers are considered as the solid recovered fuel which is recovered from MSW. Recyclable waste papers are segregated into various grades to produce high-quality products. Moreover, sorted paper streams save energy, chemicals, and water, as well as reduce sludge and rejects. Information technology is widely integrated with the waste management industry into its operations such as recycling, reuse, segregating based on categories and so on. This review article focuses on the life cycle of waste paper and existing waste paper sorting techniques. In the paper industry, many types of sensors are used in different mechanical and optical waste paper sorting systems. Such sensors include lignin, gloss, stiffness, mid-infrared, infrared, and color sensors. In this review, also described the effectiveness of different waste paper sorting systems, and finally, recommended appropriate waste paper sorting techniques based on effectiveness and low-cost implementation. [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]

This paper presents the eco-efficiency assessment of the pulp and paper industry in Myanmar by using the key indicators such as raw material consumption, energy consumption, total waste output, water consumption, and CO emissions. The study was carried out by using quantitative methods for data analysis of the production, consumptions and emissions from fiscal year 2001–2005. The results revealed that the level of economic and environmental performance using the eco-efficiency ratio for each fiscal year has decreased since year 2002, and factory tried to increase the level of eco-efficiency again in year 2005. There was the positive aspect that factory could optimize the waste utilization by transferring lime mud to the cement factory in the last two fiscal years. This analysis showed the root causes that led to the losses of material, energy and water consumption and discussed how to conserve those utilities. [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]

Trying to understand how the interdependence relationship among the sectors of a given economy and its impacts on the environment is fundamental to the identification of sectors that are more polluting and those that are considered key in the sense of being less polluting. In order to describe this relationship, this work has as general objective to measure the level of CO2 emission from the level of production in function of the consumption of electric energy in the state of Mato Grosso do Sul. In order to meet the general objective, from the state energy balance and the input–output matrix of 2010, a hybrid input–output matrix was derived. Thus, it was possible to identify that the service sector, although is the engine of the state GDP, represents only 16% of the emissions, agriculture and cattle raising 6% and industry 78%. The main sectors in CO2 emission are rubber and plastic articles and the pulp and paper production sector. Considering the estimates of growth of the state, the concern with the environment is emphasized as the driving sectors of the expansion being precisely those that emit more CO2. [ABSTRACT FROM AUTHOR]

Inspired by the natural shape-morphing abilities of biological organisms, we introduce a strategy for creating energy-efficient dynamic 3D metasurfaces through spatiotemporal jamming of interleaved assemblies. Our approach, diverging from traditional shape-morphing techniques reliant on continuous energy inputs, utilizes strategically jammed, paper-based interleaved assemblies. By rapidly altering their stiffness at various spatial points and temporal phases during the relaxation of the soft substrate through jamming, we enable the formation of refreshable, intricate 3D shapes with a desirable load-bearing capability. This process, which does not require ongoing energy consumption, ensures energy-efficient and lasting shape displays. Our theoretical model, linking buckling deformation to residual pre-strain, underpins the inverse design process for an array of interleaved assemblies, facilitating the creation of diverse 3D configurations. This metasurface holds notable potential for tactile displays, particularly for the visually impaired, heralding possibilities in visual impaired education, haptic feedback, and virtual/augmented reality applications. This paper introduces a load-bearing 3D dynamic metasurface that alters the stiffness of interleaved assemblies at various spatial points and temporal phases through jamming. This approach does not require continuous energy input and was demonstrated as a tactile display for the visually impaired. [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]

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]

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]

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]

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]

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]

In the last decade, the use of additive manufacturing (AM) systems in manufacturing has increased drastically and studies show that additive manufacturing has potential of becoming the next sustainable manufacturing solution. There are multiple additive manufacturing processes currently in research and practice and have varying degrees of energy consumption. Energy consumption could be an important consideration in adopting a specific AM Method. However, the current literature review reveals that there is a lack of well-established methodology to map the energy consumption in an AM process. This paper aims to narrow this gap by developing a structured and robust method to map energy footprint in additive manufacturing processes using modified Sankey diagrams. The proposed approach allows engineers and researchers to visualize energy consumption in different steps of the additive manufacturing process and select the optimal energy efficient method. The paper demonstrates the proposed methodology through two case studies involving selective laser melting (SLM) process and fused filament fabrication (FFF) process. The novelty of using Sankey diagrams for mapping energy consumption in AM lies in their ability to provide a clear and intuitive representation of complex energy systems. The modular architecture of the Sankey diagrams aids in the effective mapping, visualization, and analysis of energy flow. It is shown that the proposed methodology can provide insight into potential changes for energy saving in additive manufacturing processes. [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]

Background: The Association of Southeast Asian Nations (ASEAN) relies mainly on fossil fuels in their energy supply, leading to higher CO2 emissions, pollution, and further environmental degradation. This paper uses the panel vector autoregressive and the Granger non-causality test in the heterogeneous panels, together with long-run estimation techniques, to examine the dynamic link among energy consumption, economic growth, and carbon emissions with the focus on renewable energy for the ASEAN countries in the past three decades. Results: The findings from this paper indicate that carbon emissions are associated with energy consumption. In contrast, renewable energy usage reduces CO2 emissions, improving environmental quality. Economic growth is associated with increased energy consumption and carbon emissions in the ASEAN countries. The findings also indicate that the effects of energy consumption on economic growth are more significant than those of renewable energy in ASEAN. When considered together, these findings form a vicious circle regarding the energy–growth–emission nexus for the ASEAN economies. In addition, a bidirectional Granger causality among energy consumption, economic growth, CO2 emissions and renewable energy usage is confirmed. Conclusions: Renewable energy has emerged as an important viable option for the ASEAN nations to achieve their dual objectives of enhanced economic growth, reduced CO2 emission, leading to improved environmental quality. [ABSTRACT FROM AUTHOR]

Grinding, a critical precision machining process for difficult-to-cut alloys, has undergone continual technological advancements to improve machining efficiency. However, the sustainability of this process is gaining heightened attention due to significant challenges associated with the substantial specific grinding energy and the extensive heat generated when working with difficult-to-cut alloys, renowned for their exceptional physical and mechanical properties. In response to these challenges, the widespread application of massive coolant in manufacturing industries to dissipate grinding heat has led to complex post-cleaning and disposal processes. This, in turn, has resulted in issues such as large energy consumption, a considerable carbon footprint, and concerns related to worker health and safety, which have become the main factors that restrict the development of grinding technology. This paper provides a holistic review of sustainability in grinding difficult-to-cut alloys, encompassing current trends and future directions. The examination extends to developing grinding technologies explicitly tailored for these alloys, comprehensively evaluating their sustainability performance. Additionally, the exploration delves into innovative sustainable technologies, such as heat pipe/oscillating heat pipe grinding wheels, minimum quantity lubrication, cryogenic cooling, and others. These groundbreaking technologies aim to reduce dependence on hazardous coolants, minimizing energy and resource consumption and carbon emissions associated with coolant-related or subsequent disposal processes. The essence of these technologies lies in their potential to revolutionize traditional grinding practices, presenting environmentally friendly alternatives. Finally, future development trends and research directions are put forward to pursue the current limitation of sustainable grinding for difficult-to-cut alloys. This paper can guide future research and development efforts toward more environmentally friendly grinding operations by understanding the current state of sustainable grinding and identifying emerging trends. [ABSTRACT FROM AUTHOR]

This paper investigates the persistence of shocks on non-renewable and renewable energy consumption for 15 leading countries by renewable energy consumption, over the period 1980–2018. For this aim, we apply Christopoulos and Leon-Ledesma (J Int Money Finance 29(6):1076–1093, 2010) Fourier ADF unit root test which structural breaks are included by using a trigonometric function to allow for smooth temporary mean changes rather than jump functions. In application, we do not need to specify the numbers, locations and the forms of the structural breaks a priori. The results give that shocks on non-renewable energy consumption per capita are persistent for 13 countries except Sweden and the United States; shocks on renewable energy consumption per capita are persistent for 12 countries except Canada, Sweden and the United Kingdom. Overall, we conclude that persistent policy implications on non-renewable energy consumption are more effective tool than transitory policy stances for 13 countries except Sweden and the United States whereas persistent policy implications on renewable energy consumption are more effective for 12 countries except Canada, Sweden and the United Kingdom. In other words, since the energy consumption returns to its trend path quickly, any policy will not be effective on non-renewable energy consumption for Sweden and the United States, and also on renewable energy consumption for Canada, Sweden and the United Kingdom. [ABSTRACT FROM AUTHOR]

Green consumption has become an important part of an environmentally friendly society. Policy guidance can effectively promote green consumption. Green consumption policies are extensive because of the diversity of green consumption objects. However, the various objects are not completely isolated, and policy must have a certain level of consistency. Therefore, from the perspective of green consumption objects, this paper first uses the text mining method to mine 308 green consumption policies in China, analyzing the objects covered in China's green consumption policies. Then, the evaluation system of green consumption policy consistency was built, seven key policies were selected, and the PMC-index method was used to measure policy consistency. The results show that (i) the main objectives of China's green consumption policy are the promotion of green products and the efficient use of energy. Green services and green recycling have been subject to fewer policy efforts. (ii) In terms of the links involved in green consumption, China pays more attention to the supply and the consumption chain and less attention to the recycling chain. (iii) The quantitative evaluation results of the seven typical policies show that the policy consistency is good but not perfect. The policy shortcomings are mainly reflected in the incentive methods. Therefore, it is recommended to further improve the green consumption objectives and enrich the means and methods of green consumption guidance. [ABSTRACT FROM AUTHOR]

The majority of Europe's building stock consists of facilities built before 2001, presenting a substantial opportunity for energy efficiency improvements during their operation and maintenance phase. Digitalizing these buildings with digital twin technology can significantly enhance their energy efficiency. Reviewing the applications and trends of digital twins in this context is beneficial to understand the current state of the art and the specific challenges encountered when applying this technology to older buildings. This study focuses on the application of digital twins in building operations and maintenance (O & M), emphasizing energy efficiency throughout the building lifetime. A systematic process to select 21 pertinent use-case studies was performed, complemented by an analysis of six enterprise-level digital twin solutions. This was followed by an overview of general characteristics, thematic classification, detailed individual study analyses, and a comparison of digital twin solutions with commercial tools. Five main applications of digital twins were identified and examined: component monitoring, anomaly detection, operational optimization, predictive maintenance and simulation of alternative scenarios. The paper highlights challenges like the reliance on Building Information Modeling (BIM) and the need for robust data acquisition systems. These limitations hinder the implementation of digital twins, in particular in existing buildings with no digital information available. It concludes with future research directions emphasizing the development of methods not solely reliant on BIM data, integration challenges, and potential enhancements through AI and machine learning applications. [ABSTRACT FROM AUTHOR]

In this paper, a schmitt trigger-based single-sided 7T stable SRAM is proposed for ultra-low energy and near-threshold operation, which supports a bit interleaving scheme. The proposed ST-7T SRAM design improves the WSNM (Write Static Noise Margin) and RSNM (Read Static Noise Margin) and consumes less energy. Moreover, obtain high read strength by utilizing a single-sided ST (schmitt trigger) inverter. Furthermore, write ability is also enhanced by applying an ST inverter write assist scheme (Negative VWWL technique), which can limit the tripping voltage of the ST inverter circuit. The PST-7T (Proposed ST-7T) circuit minimizes the read power by 49.96%, write power by 39.27%, and leakage power by 39.17% compared to conventional-6T SRAM. The RSNM and WSNM of the proposed SRAM circuit are enhanced by 66.28% and 18.97% compared to conventional-6T SRAM. The write energy and read energy utilization are also lowered by 14.87% and 14.19% compared to the SE7T SRAM cell. [ABSTRACT FROM AUTHOR]

This literature review presents a comprehensive review of life Cycle Assessmernt (LCA), as an emerging tool in the field of cultural heritage research and demonstrate how this tool could be useful to support the development of green heritage science into an environmentally responsible field of scientific endeavour. LCA is a standardised, structured, comprehensive, international environmental assessment tool and a rapidly evolving field of research that leverages and harmonises efforts across many sectors to inform environmentally-friendly solutions and choices. LCA has been growing in importance as an evidence-based tool in the field of heritage science, being used as a decision-support tool at micro level (typically for questions related to specific products/processes) and macro levels (e.g. strategies, scenarios, and policy options). This review explores applications of LCA (and the complementary Life Cycle Cost Assessment (LCC), and Social Life Cycle Assessment (S-LCA)) to a wide array of conservation and preservation actions. The paper also examines challenges associated with the application of these life cycle-based methods in heritage science, in order to put forward a set of recommendations to guide the domain of heritage science towards greener and more sustainable practices and impacts. Based on a review of the principles of green chemistry, green analytical chemistry, green engineering, and nature conservation, the paper also attempts to formulate nine principles of green heritage science, taking into account the complexity of research challenges and the environmental and socio-economic sustainability. [ABSTRACT FROM AUTHOR]

The clustering algorithm is an effective method for developing energy efficiency routing protocol for wireless sensor networks (WSNs). In clustered WSNs, cluster heads must handle high traffic, thus consuming more energy. Therefore, forming balanced clusters and selecting optimal cluster heads are significant challenges. The paper proposes a sector clustering algorithm based on K-means called KMSC. KMSC improves efficiency and balances the cluster size by employing symmetric dividing sectors in conjunction with K-means. For the selection of cluster heads (CHs), KMSC uses the residual energy and distance to calculate the weight of the node, then selects the node with the highest weight as CH. A hybrid single-hop and multi-hop communication is utilized to reduce long-distance transmissions. Furthermore, the impact of the number of sectors, the threshold for clustering, and the network size on the performance of KMSC has been explored. The simulation results show that KMSC outperforms EECPK-means, K-means, TSC, LSC, and SEECP in terms of FND, HND, and LND. [ABSTRACT FROM AUTHOR]

DC microgrids are integral to smart grids, enhancing grid reliability, power quality, and energy efficiency while enabling individual grid independence. They combine distributed and renewable energy sources, reducing overall energy consumption. High-gain DC–DC converters are crucial for elevating voltages from low-voltage DC sources like solar panels and wind turbines in DC microgrids. This paper introduces a non-isolated DC–DC converter designed to achieve ultra-high step-up (UHSU) voltage conversion utilizing a two-winding coupled inductor (CI). The propounded UHSU configuration achieves a substantial voltage increase by employing low duty cycles and a decreased turn ratio for the CI, resulting in a smaller core size. Moreover, this UHSU circuit incorporates soft-switching capabilities for both power switches and diodes, enhancing its efficiency. By keeping the voltage stress on the switches low, the design minimizes losses and improves overall efficiency. The operational modes are thoroughly analyzed, and comparisons with other topologies are presented to demonstrate the effectiveness of the proffered UHSU circuit. Finally, the performance of the UHSU circuit is validated through the construction and testing of a 150-W laboratory prototype operating at a switching frequency of 50 kHz, with Vin = 20 V and Vout = 300 V. [ABSTRACT FROM AUTHOR]

Energy efficiency and physical-layer security are crucial considerations in the advancement of mobile edge computing systems. This paper addresses the trade-off between secure-reliability and energy consumption in finite blocklength (FBL) communications. Specifically, we examine a three-node scenario involving a user, a legitimate edge computing server, and an eavesdropper, where the user offloads sensitive data to the edge server while facing potential eavesdropping threats. We propose an optimization framework aimed at minimizing energy consumption while ensuring secure-reliability by decomposing the problem into manageable subproblems. By demonstrating the convexity of the objective function concerning the variables, we establish the existence of an optimal parameter selection for the problem. This implies that practical optimization of parameters can significantly enhance system performance. Our numerical results demonstrate that the application of FBL regime and retransmission mechanism can effectively reduce the energy consumption of the system while ensuring secure-reliability. For the quantitative analyses, the retransmission mechanism is 33.1% better than no retransmission, and the FBL regime is 13.1% better than infinite blocklength (IBL) coding. [ABSTRACT FROM AUTHOR]

Controller placement issue (CPI) in software-defined networks (SDNs) describes the controllers' number, location, and assigning of forwarding devices to controllers. Recently, mathematical formulations and algorithms have been proposed to solve various problems in SDNs and software-defined wide area networks. The comprehensive literature review can be divided into four groups according to objectives: (i) minimizing latency between forwarding devices or switches and their corresponding controllers, and minimizing latency between controllers, (ii) improving network resilience and stability, (iii) minimizing energy consumption and installation costs and (iv) using multi-objective approaches. In addition to the objectives of each research, the importance of this paper is to examine the CPI in terms of reducing the network search space in order to optimally place the controller and how to assign switches to the controllers. In this paper, first the mathematical formulations in previous studies will be examined and then, for solving CPI, the existing algorithms will be discussed. Different classifications of CPIs and related formulas/algorithms, descriptions, advantages and disadvantages will be separately provided. A comprehensive comparison of proposed approaches with their advantages and disadvantages in the summarized tables will be provided. Also, a comparative discussion of different statistics of CPIs will be presented in terms of some technical features such as objective-oriented problems and parameters in four categories, estimated environments, and efficient estimating factors in CPIs. Finally, we explained the future studies' challenges, problems related to CPIs, ideas and following orientations in this field. [ABSTRACT FROM AUTHOR]

The relationship between energy consumption, environmental pollution, and economic growth has been the subject of intense research over the past few decades. However, empirical evidence remains scarce and with mixed results. This paper throws a new light on the nonlinear impact of environmental degradation on economic growth depending on the level of energy intensity. This study provides an original approach in the context of Malaysia by utilizing an innovative threshold regression approach suggested by Hansen (Econometrica 68(3):575–603, 2000), which allows the data to endogenously divided into two regimes with an exogenous threshold variable. We find strong evidence that environmental degradation has a nonlinear effect on economic growth in Malaysia. In particular, CO2 has a negative effect on growth, but only after the primary energy intensity reaches a threshold level of 91.33 tons of oil equivalent (TOE)/GDP and the final energy intensity reaches 51.56 TOE/GDP, respectively. Our results are robust in terms of ecological footprint. Several policy implications are highlighted which aim to encourage sustainable energy use for sustainable growth in the future. [ABSTRACT FROM AUTHOR]

A large volume of solar energy dissemination in a supply grid originates extreme variations in the load, resulting in a duck-form load arc that can cause stability issues. Also, the cost of energy consumption is found to vary between the off-peak and peak loads observed in the duck-shaped load curve. Accurate load forecast and demand response program is a key task for duck curve management in a distribution structure. Hence, this work proposes a Demand Response (DR) program using deep learning neural networks namely Long Short-Term Memory (LSTM). The proposed DR program is implemented in a modified 12 bus radial distribution network for duck curve management, where voltage stability is taken care of simultaneously minimizing the electricity cost in an energetic pricing environment. LSTM is used for forecasting the load and linear programming is used for load shedding. Therefore, this paper resolves the dual aims of flattening the duck-shaped load arc and minimizing electricity costs by combining them into a single objective function. [ABSTRACT FROM AUTHOR]

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. [ABSTRACT FROM AUTHOR]

Renewable energies, such as solar and wind, traditionally suffer from temporal incongruity. Society's energy demand peaks occur at different times of day than the electricity generation potential of a photovoltaic panel or, often, a wind turbine. Heat demand, in particular, is subject to a significant mismatch between the availability of heat (in the summer) and the need for heat (in the winter). Thus, a future energy system design should incorporate underground thermal energy storage (UTES) to avoid this temporal mismatch and emphasize thermal applications. Such a basis of design would introduce new methods of energy arbitrage, encourage the adoption of geothermal systems, and decrease the carbon intensity of society. UTES techniques are becoming increasingly sophisticated. These methods of storage can range from simple seasonal storage for residential structures in a grouted borehole array (BTES), to aquifer thermal energy storage (ATES), deep reservoir storage (RTES) in basins, among others. The method that each of these techniques shares is the use of the earth as a storage medium. UTES can also be characterized for electricity production, but this work largely explores applications in heating and cooling, further limited in scope to sensible heat storage (SHS). Heating and cooling processes—residential, commercial, and industrial—make up large fractions of energy demand in North America. This is also true of other locales. With the increasing concerns of climate change, exacerbated by anthropogenic greenhouse gas emissions, developers and municipal planners are strategizing to decarbonize building heating and cooling at district scales. This review covers the integration of UTES techniques with thermal energy network (TEN) technology across large districts. Though storage has long been in use for conventional district heating networks, designs are rapidly innovating, indicating broader applications of UTES integration with a TEN is advantageous from both an efficiency and economic perspective. This rapid innovation indicates the need for the integrated review offered in this paper. [ABSTRACT FROM AUTHOR]

With the development of distribution generation (DG) technology, large amount of renewable energy connected to the microgrid, which has a significant impact on the consumption of renewable energy. The nonlinear load connected to the microgrid leads to the reduction of power quality, and the line impedance between the distribution generation and the point of common coupling (PCC) is different, resulting in the harmonic current cannot be reasonably shared, and the transmission of redundant information brings enormous pressure to communication system. To solve these problems, this paper proposed a microgrid harmonic power-sharing method based on an event-triggered consensus algorithm. Firstly, the hierarchical coordination control architecture of the microgrid is established. Then, when designing distributed frequency and harmonic power controllers based on event-triggered consensus, the impact of communication network time delay on system performance is considered. The status information is exchanged between distributed controllers only when the event is triggered, and the actual value is replaced by the estimated value at other times, which can effectively reduce the transmission of redundant information. Finally, this paper uses the Lyapunov method to prove the stability of the proposed control method, analyzes the event-triggered interval, and avoids the Zeno phenomenon. The effectiveness of the proposed power-sharing method is verified by the hardware-in-the-loop experiment. [ABSTRACT FROM AUTHOR]

The digital economy has played a vital role in changing economic and social development patterns and has had a multifaceted impact on carbon neutrality. This paper strives to contribute to the existing research by analyzing the relationship between the digital economy and electricity consumption intensity and providing a comprehensive understanding of the mechanisms that influence the digital economy and energy consumption, particularly focusing on electricity consumption intensity through the construction of a theoretical model. To fulfill this objective, this study introduces the development level of the digital economy into the production function of representative power enterprise, constructs a partial theoretical equilibrium analysis framework, calculates provincial digital economy development indices for Chinese provinces from 2013–2021 using the CRITIC method, and examines the impact of the digital economy on power consumption intensity. The results show that the digital economy can reduce the intensity of power consumption. A unit standard deviation increase in the digital economy development level reduces power consumption intensity by 0.1008 standard deviations. The study also conducts a mechanism analysis with economic growth as the mediating variable, which shows that the digital economy promotes economic growth and reduces electricity consumption intensity. The moderating effect of the digital economy on power consumption is further examined using local government fiscal pressure as the moderating variable. The results show that the impact of the digital economy on reducing power consumption intensity decreases with the increase of government fiscal pressure. This study not only contributes to practical applications but also enhances our comprehension of the interplay between the digital economy and power consumption. On the one hand, the development of the digital economy can reduce power consumption, thus reducing carbon emissions and achieving low-carbon growth. On the other hand, central governments can promote the development of the digital economy by reducing the fiscal pressure on local governments through transfer payments. [ABSTRACT FROM AUTHOR]

The unflinching upsurge in global warming has put the world into a state of disarray, and policymakers are not sure of what the future holds for the ecosystem. To get the global environment back on track of sustainability, efforts are relentlessly emanating from both developed and developing economies to identify the hindering and enhancing factors. Given the preceding narratives, this paper investigates the tripartite impacts of renewable and nonrenewable energy, trade openness, and institutions on sustainable environment in 54 African countries from 1996 to 2019. The study employs the two-step system generalized methods of moment (SYS-GMM) in estimating the effects of the regressors on four indicators of sustainable environment. Six findings are prominent from the study. First, nonrenewable energy (NRE) and trade openness (TO) hinder sustainable environment, while renewable energy enhances it. Second, the unconditional impacts of institutions drive the path to a sustainable environment. Third, the interaction of institutions with NRE and TO hinders sustainable environment. However, after conducting institutional enhancement estimations, the interactive terms became supportive of sustainable environment. Fourth, the thresholds are policy-relevant across the specifications. Specifically, a 31.11% utilization of nonrenewable energy and 43.75% openness to trade contribute to a sustainable environment. Sixth, the impacts of economic growth and its square empirically support the inverted U-shaped EKC. This study suggests the adoption of carbon-taxing on nonrenewable energy products, price-subsidization for renewable energy goods and services, and institutional quality reforms as pathways towards achieving a sustainable environment in Africa. [ABSTRACT FROM AUTHOR]

Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock's direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions. [ABSTRACT FROM AUTHOR]

In recent years, the widespread adoption of wireless sensor networks (WSN) has resulted in the growing integration of the internet of things (IoT). However, WSN encounters limitations related to energy and sensor node lifespan, making the development of an efficient routing protocol a critical concern. Cluster technology offers a promising solution to this challenge. This study introduces a novel cluster routing protocol for WSN. The system selects cluster heads and relay nodes utilizing the multi-strategy fusion snake optimizer (MSSO) and employs the minimum spanning tree algorithm for inter-cluster routing planning, thereby extending the system's lifecycle and conserving network energy. In pursuit of an optimal clustering scheme, the paper also introduces tactics involving dynamic parameter updating, adaptive alpha mutation, and bi-directional search optimization within MSSO. These techniques significantly increase the algorithm convergence speed and expand the available search space. Furthermore, a novel efficient clustering routing model for WSN is presented. The model generates different objective functions for selecting cluster heads and relay nodes, considering factors such as location, energy, base station distance, intra-cluster compactness, inter-cluster separation, and other relevant criteria. When selecting cluster heads, the fuzzy c-means (FCM) algorithm is integrated into MSSO to improve the optimization performance of the algorithm. When planning inter-cluster routing, the next hop node is selected for the relay node based on distance, residual energy, and direction.The experimental results demonstrate that the proposed protocol reduces energy consumption by at least 26.64% compared to other cluster routing protocols including LEACH, ESO, EEWC, GWO, and EECHS-ISSADE. Additionally, it increases the network lifetime of WSN by at least 25.84%, extends the stable period by at least 52.43%, and boosts the network throughput by at least 40.99%. [ABSTRACT FROM AUTHOR]

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively. [ABSTRACT FROM AUTHOR]

In recent times, photovoltaic (PV) power generation has been growing due to increase in energy demand. In grid-connected mode, achieving maximum power (MP) from the PV array is difficult by using conventional techniques due to various reasons like low tracking efficiency, stability issues, etc. This motivates the design of an appropriate control strategy to obtain the maximum power point tracking (MPPT) to harvest MP from the PV array. This paper proposes a combined higher order sliding mode observer (HOSMO)–super-twisting control (STC) for a grid-connected scenario. A perturb and observe (P &O) technique is employed to generate reference voltage, and a HOSMO is proposed to drive the STC by estimating the inductor current of the PV boost converter. The proposed controller performance is evaluated based on response time across various scenarios, including generation changes, dynamic faults, islanding and resynchronization, and load variations in comparison to other existing controllers. These microgrid test cases have been thoroughly simulated, and their effectiveness has been validated in real-time using OPAL-RT (OP4510). [ABSTRACT FROM AUTHOR]

This paper introduces a structural design which has superior energy harvesting ability. The proposed structure is based on the ability of most periodic structures to prohibit elastic waves from propagating in a specific frequency range of the phonon band gap. By leading into acoustic black hole (ABH) units and utilizing the energy concentration characteristics of the ABH structure, the filtering effect can be made more significant. These two characteristics can produce significant strain and energy localization, which is conducive to energy harvesting. In this paper, we obtained ABH unit capable of producing broadband gaps by adjusting the geometric parameters of the ABH structure, and chose a cantilever as the research object for energy harvesting, by applying a piezoelectric patch on the unit located near the fixed end to gain a broader application in the actual situation. The results show that the beam combining the energy concentration characteristics of band gap and ABH structure has a significant improvement in energy harvesting efficiency. Optimization on the parameters of ABH unit shows that the band gap characteristic of the periodic ABH structure can further optimize the energy harvesting efficiency of practical energy harvesters, and opens up a new possibility of energy harvesting of ABH structure. [ABSTRACT FROM AUTHOR]

Scholars have given enormous attention to the roles of energy use and trade in economic growth strategies; however, recent international trade literature suggests that not merely the volume of trade but the diversification of goods may have a potential effect on economic growth. Thus, this paper uses the diversification of export goods and diversification of import goods as explanatory variables along with energy consumption and assessed the role of these important variables in economic development. The empirical results from Westerlund's (J Appl Econom 23:193–233, 2008) and Westerlund's (Oxf Bull Econ Stat 69:709–748, 2007) tests disclosed the existence of cointegration. The long-run estimations obtained from the CUP-FM technique revealed that both import and export product diversifications positively influence economic growth. This presents a vital opportunity for emerging nations to expand growth by diversifying export and import products. Likewise, energy use, labor, and capital contribute to increasing economic growth. The estimates from the causality test illustrated bidirectional causality between export diversification and economic growth and unidirectional causality from energy to export diversification. Finally, policy suggestions to expand economic progress are discussed. [ABSTRACT FROM AUTHOR]

Urban energy issues have attracted global attentions due to the key role in controlling climate change. As one of the world's largest metropolises, Beijing has been focusing on improving urban energy performances to achieve green transition and sustainable development during the past decades, for which purpose the measurement and decomposition of energy performance is of vital importance. In this paper, exergy is introduced as one of the indicators under the frame of index decomposition analysis. An hierarchical framework is constructed to reveal the contributions from different level of urban energy system to overall energy performance. Beijing's energy and exergy consumption and intensity are then analyzed and compared from the city and sector level during 2008–2017. Results suggest that Beijing's city and sector-level energy performance has been improving during the last 10 years and technical improvements contribute significantly from both energy and exergy perspectives. Meanwhile, changes from Beijing's industrial structure and energy supply structure display neutral contributions, which probably indicate the underestimation of structural contribution is caused under the IDA framework. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed a severe challenge to the global economic recovery and China's economic growth. Although China has achieved stage victory against the epidemic, the impact and influence of the epidemic on China's economy will linger. On the positive side, the epidemic has led to a dramatic reduction in air pollution levels and a sharp slowdown in greenhouse gas emissions such as CO2. Forecasting China's CO2 emissions under the impact of the COVID-19 epidemic is crucial for formulating policies that contribute to smooth economic development and rational energy consumption. To this end, this paper improves on the traditional grey model, GM(1, N), by developing a novel fractional multivariate nonlinear grey model, FMNGM(q, N). These improvements include two key points. First, different power exponents are assigned to each relevant factor variable to explain their nonlinear, uncertain, and complicated relationships with the system characteristic variables. Second, the integer accumulation is changed to fractional accumulation to preprocess the data, and the Caputo-type fractional derivative is used to characterize the endogenous relationships among the system characteristic data. In addition, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the above parameters with the goal of minimizing MAPE. The collected data on China's CO2 emission from 2001 to 2018 were divided into two parts according to different stages of economic development: 2001–2009 and 2010–2018. Both sets of data were modelled and analysed separately and compared with other models. Results showed that the proposed model had better prediction accuracy than other models. Finally, the model built using the 2010–2018 data was used to forecast China's CO2 emissions. Results show that China's CO2 emissions in 2020 under the impact of COVID-19 will decrease by approximately 3.15% from 2019 to 9893 million tons. The results bear important policy implications for planners in investment in clean energy and infrastructure to achieving the goal of a low-carbon transition. [ABSTRACT FROM AUTHOR]