Search

The energy needed for the dewatering and drying of wet paper web represents around half of the energy consumption of papermaking processes. The present work examined whether the dewatering and drying of paper could be enhanced during a previous pulp drying process by pretreating the fibers with polyamide-epichlorohydrin (PAE). According to the hypothesis, the cured PAE restrains swelling and water absorption of water-wetted fibers by forming a fiber-bound, self-crosslinked polymer-network on the fiber surfaces. The hypothesis was tested by adding PAE to never-dried kraft pulp slurry followed by pulp thickening, drying, and final curing of the PAE-resin. After this, the PAE-treated fibers were dispersed in water, and their water retention values (WRV) and Shopper-Riegler values (○SR) were measured. The PAE pretreatments notably decreased the fibers´ WRV and ○SR, indicating improved water removal of paper web in the paper machine forming and drying section. Compared to chemical crosslinking pretreatments, which also can be used to decrease fibers WRV and ○SR-value, a notable advantage of PAE-pretreatment is milder required curing conditions of the PAE, which makes implementation of the method easier in practice. Due to decreased fiber-to-fiber bonding capability, the PAE-treated specialty fibers could take advantage especially as a bulking aid of paperboard, tissue, and absorbent materials.

The energy needed for the dewatering and drying of wet paper web represents around half of the energy consumption of papermaking processes. The present work examined whether the dewatering and drying of paper could be enhanced during a previous pulp drying process by pretreating the fibers with polyamide-epichlorohydrin (PAE). According to the hypothesis, the cured PAE restrains swelling and water absorption of water-wetted fibers by forming a fiber-bound, self-crosslinked polymer-network on the fiber surfaces. The hypothesis was tested by adding PAE to never-dried kraft pulp slurry followed by pulp thickening, drying, and final curing of the PAE-resin. After this, the PAE-treated fibers were dispersed in water, and their water retention values (WRV) and Shopper-Riegler values (○SR) were measured. The PAE pretreatments notably decreased the fibers´ WRV and ○SR, indicating improved water removal of paper web in the paper machine forming and drying section. Compared to chemical crosslinking pretreatments, which also can be used to decrease fibers WRV and ○SR-value, a notable advantage of PAE-pretreatment is milder required curing conditions of the PAE, which makes implementation of the method easier in practice. Due to decreased fiber-to-fiber bonding capability, the PAE-treated specialty fibers could take advantage especially as a bulking aid of paperboard, tissue, and absorbent materials.

A key challenge in prevention of global warming is how to increase energy efficiency, to be able to deal with increased fossil CO2 emissions from rising energy usage. Increasing energy efficiency will decrease energy usage and is in a key role in emission mitigation. The focus is the pulp and paper industry, which is energy-intensive. Development of industrial energy efficiency has been studied before but the role of industrial transformation is still mostly unknown. The knowledge must be improved, to be able to predict future developments in the most effective way. In this research, impact of various production unit closures and start-ups on energy efficiency of the Finnish pulp and paper industry were studied utilizing statistical analysis. Results indicate that about 20% of the Finnish pulp and paper industry energy efficiency improvement between 2011 and 2017 is caused by the major structural changes. The rest, 80% of the progress, was mainly due to improved technology and more optimal operational modes. Additional findings suggest that modern mill start-ups have a significantly greater potential to reduce energy consumption than old mill closures.

Abstract In the realm of green communications, the focus is on achieving high spectrum efficiency and low energy consumption. This paper addresses the crucial goal of reducing energy usage in green cognitive radio networks (CRNs) during communication between secondary users (SUs) and primary users (PUs). This paper proposed an energy consumption optimization model (ECOM) for green CRN utilizing collaborative spectrum sensing thereby minimizing the environmental impact and prolonging the operational lifetime of devices. The collaborative spectrum sensing proved its role in optimizing the energy consumption in the green CRN. An energy-efficient scheduling algorithm is implemented in ECOM, in which the SUs can be scheduled to perform their sensing process in a time-division manner to reduce energy consumption. Applied collaborative spectrum sensing serves as a valuable resource for researchers, network operators, and policymakers seeking to balance the increasing demand for wireless communication services with the imperative of sustainability. The simulation results and mathematical proof emphasize that ECOM demonstrates reduced energy usage and increased average effective throughput when compared to other recent models.

Energy consumption prediction in buildings is a major issue in the field of energy efficiency and resource management. In recent decades, the use of Artificial Intelligence (AI) has become an increasingly common approach to improve the accuracy and reliability of these predictions. In this paper, the most used digital technologies such as: AI, Big Data, Internet of Things (IoT), Blockchain, Cloud computing and 5G, which can fundamentally transform the way energy consumption predictions are made in buildings, are presented; the different types of data used in energy consumption prediction are analysed, such as weather data, energy use data and building characteristics; the various AI methods and algorithms are presented, e.g. neural networks, decision trees, support vector and machine learning algorithms, used to improve prediction accuracy. This paper focuses on a conceptual energy prediction model developed for the project "Intelligent system for predicting energy consumption in buildings (PRECONERG)", a project in the first stage of development.

The rapid development in the field of electric vehicles requires a careful evaluation of the design process. The presence of a simulation model of the electric vehicle can effectively detect many faulty areas during the development process without risks. The MATLAB and Simulink environment is considered one of the most important tools used in the simulation process. In this paper, we will present the model of electric car used for transporting postal parcels (postal cars). The model includes simulating the operation of a permanent magnet synchronous electric motor. We will assume that the car is moving according to a driving cycle. The results will show the torque forces required to achieve the required speed. We will further calculate the traction and the resistance forces during the driving cycle and the engine efficiency in addition. Perhaps the most important problem facing electric car designers is calculating the amount of energy consumed from the battery or hydrogen fuel, and this is what was achieved as the result of the simulation process in this research. In the end, use one of the artificial intelligence tools (fuzzy controller) to improve battery life by providing the electric car driver with an alert system that will increase the ability to monitor the battery condition and thus increase battery life. The benefit of this paper emerges in realizing the importance of modeling and using simulation using artificial intelligence in developing the design of the electric car, specially the electric motor and battery size, and thus achieving one of the most important goals of the United Nations of preserving the environment and reducing carbon emissions.

Abstract 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 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.

The purpose of this paper is to assess the energy intensity of production in the Polish agri-food (agribusiness) sector, split into agriculture, food industry and supply subsectors, in the light of input-output tables. The study relies on input-output tables and physical energy flow accounts. Energy plays a key role in today’s society as it affects economic growth and the standards of living, while also being at the core of international conflicts, mainly caused by its limited resources. Compared to other sectors, agribusiness demonstrates particularly high levels of energy consumption. Agriculture, which is part of it, has a large share in the consumption of fuels derived from crude oil. This paper presents the consumption of energy in each of the four aggregates of agribusiness, as well as their GDP (in PPS) and energy consumption mix (split into 6groups of energy carriers). The authors found that between2014 and2019, energy consumption in the agribusiness industry increased by ca.9% and was accompanied by ca.8% growth in GDP. The sector’s energy intensity was half more than the average level for the economy as a whole, and remained at ca.5.6TJ per EUR1million of GDP in PPS. Only the food industry was at a level of energy efficiency similar to that of the economy as a whole, whereas the agricultural supply subsector was the only one to demonstrate lower levels. Both the economy as a whole and the agribusiness sector clearly tend to reduce the share of coal and related products in the energy mix.

To effectively address climate change, it is necessary to quantify the carbon emissions in high energy-consuming regions, analyze driving factors, and explore effective pathways for achieving green development. Therefore, this paper takes Liaoning Province as research area, using extended Kaya identity and LMDI method to analyze the driving factors of carbon emissions from energy consumption in five major industries and the residential consumption sector from 2011 to 2020 in Liaoning Province. Furthermore, this paper uses the Tapio model to explore the decoupling relationship between carbon emissions and economic development. The results show that: 1) From 2011 to 2020, total carbon emissions from energy consumption in five major industries showed a trend of initially declining and then rising, while carbon emissions from the residential consumption sector exhibited an upward trend. 2) For carbon emissions from the industrial sector, economic output and industrial structure are the primary factors that promote and inhibit carbon emissions respectively. The inhibitory effects of energy structure and energy intensity are not significant. Population scale has a certain promoting effect on carbon emissions. For residential energy consumption carbon emissions, Household consumption expenditure, residential energy structure, and residential population scale are driving factors that promote the growth of carbon emissions, while residential energy intensity restrains the growth of carbon emissions. 3) From 2011 to 2018, carbon emissions from the industrial sector have been decoupled from economic output, and the decoupling state is dominated by weak decoupling. However, carbon emissions are once again correlated with economic development in 2019–2020. Carbon emissions from residential energy consumption have not yet decoupled from consumption expenditure, and its decoupling state is unstable and has no obvious change rule.

This paper studies the impact of digital economy development on carbon emissions, which is a hot topic in the field of environmental economics. This topic is within the scope of the Frontiers in Environmental Science and is particularly in line with the theme of Environmental Economics and Management section in the journal. Firstly, this paper constructs the Digital Economy Development Index and its variable indicators to measure the development of the digital economy. Secondly, this paper uses urban panel data to reveal the impact of digital economy development on carbon emissions, and further adopts IV method to deal with endogeneity issues. Thirdly, this paper performs the heterogeneity analyses depending on the characteristics of the city. The results can provide a basis for formulating differentiated policies. Fourthly, this paper further explores the mechanism by which the digital economy affects carbon peaking. The results show that the digital economy may influence carbon peaking through upgrading of the industrial structure, technical innovation, and energy consumption.

This paper evaluates GHG emissions and energy usage in “short” and “long” cold chains for oranges, table grapes, and apples transported from South Africa to a retail store in Scotland. Novel formulae assess energy usage and emissions based on cold chain duration. “Short” chains show carbon footprints between 0.87 and 1.28 kg CO2e/kg of saleable fruit, contrasting starkly with extended cold chains. Extending storage durations increases emissions; a one-month extension results in 24–27 % emissions for oranges and grapes and 16 % for apples. Six months of CA storage of apples increases emissions by 96 % compared to “short” cold chains. Energy consumption follows a similar trend as emissions. This research informs policymakers and consumers, emphasising the need for sustainable and “short“ cold chains. This is also the first paper that comprehensively assesses both the energy requirements and emissions outputs in a fruit supply chain based on the combined transport and storage duration of the cold chain from tree to retail markets.

Based on panel data for 30 provinces in China from 2000 to 2020, this paper uses entropy method to construct the integrated development indicators of energy Internet, and explores the impact of the development of the energy Internet on reducing carbon emissions in China and its internal transmission mechanism. The research results show that the development of energy Internet can significantly reduce regional carbon dioxide emissions, and increasing the use of renewable energy is an important mechanism for the comprehensive development of energy Internet to curb carbon emissions. Heterogeneity analysis shows that the emission reduction effect brought by the development of energy Internet is more remarkable in the regions with lower carbon emissions and western China. Furthermore, this paper finds that in the setting of the development of energy Internet, the development of renewable energy can promote natural gas consumption and advance energy conversion efficiency. The government’s emphasis on regional technology development has an external strengthening effect on carbon emission reduction. The research in this paper expands and diversifies carbon reduction pathways and provides reference for more developing countries to utilize the development of energy internet to achieve carbon reduction.

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

Capacitive deionization is used to purify water by applying an electric charge across two porous electrodes. These electrodes are positioned in a spacer channel that transports the desalinated water. Capacitive deionization is a more energy-efficient approach to water purification than traditional methods. The process is believed to create clean water and store energy in a capacitive electrical double layer near the charged surface. Unlike previous review papers in this area, which mostly focus on energy recovery in terms of the technological advancement, this paper aims to conduct a bibliometric review and analysis of capacitive deionization cells, specifically focused on energy recovery mechanisms for treating contaminated water. The manuscript also analyzes capacitive deionization energy efficiency and energy-efficient capacitive deionization technology. Various relevant databases are utilized to select articles under specific conditions. Our findings suggest that the utilization capacitive deionization’s energy recovery mechanism from industrial wastewater has valuable opportunities for further research and growth. The selection and review of relevant articles and their analysis in this paper can aid in the systematic development of the capacitive deionization module. This would offer viable suggestions for future research towards more efficient energy recovery and consumption, which will subsequently contribute to net zero transitioning.

Abstract 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 Urbanization in Egypt detracts from green spaces, reduces the per capita green ratio, and increases adverse effects such as heat islands, air pollution, and energy consumption. In addition, it affects social human comfort issues. In this context, building rooftops is a potential solution that could reduce the impact of green space scarcity. Such a solution has multiple evidence-based environmental, economic, and social benefits. Consequently, numerous governmental and private initiatives have spread the rooftop greening concept in Egypt. These initiatives have adopted several planting systems, such as soil-based, deep-water culture, and nutrient film technique systems. This manuscript examines these prevalent systems through environmental, economic and social lenses. This paper pioneers a user-centric tool to facilitate the system selection that aligns with individual needs. An analysis was conducted to ascertain the value of various factors influencing system choice, encompassing a literature review, expert opinion solicitation, market survey, and energy simulation. The Analytical Hierarchy Processes methodology was proposed to appraise the factors, aiding in arriving at an informed decision. The paper presents a novel contribution by studying many factors spanning diverse scientific domains. Furthermore, creating an accessible decision-support tool encapsulates a substantial addition to the body of knowledge.

An automated storage and retrieval system (AS/RS) is a key component of enterprise logistics. Its performance metrics include, e.g., order fulfillment time and energy consumption. A crane-based automated storage and retrieval system (CB-AS/RS) is used as the study subject in this paper to build a location allocation model with the goal of minimizing order fulfillment time and minimizing energy consumption. The two-objective problem is transformed into a single-objective problem by the weight method. A genetic algorithm (GA) is used to optimize and simulate the model using spatial mapping coding. A permutation-combination heuristics (PCH) is proposed that follows the coding method and cross-operation of the GA and conducts both arrange-operation and change-operation. During the simulation, the influence of different storage utilization rates and different output and input instruction quantities in a batch of orders on the results is considered. Experimental results show that the results of the PCH algorithm are better than the GA and the optimization results are more stable. In this paper, we provide an optimization idea for the CB-AS/RS researchers and managers.

The pulp and paper industry, which is closely related to national economic and social development, is an important industry but also contributes high carbon emissions. Therefore, with the advent of the low-carbon economic era, ways to reduce the carbon emissions and to bring about a low-carbon industrial transition of the pulp and paper industry is becoming one of the important academic projects. A system for carbon footprint assessment, namely the Publicly Available Specification (PAS) 2050 methodology, is introduced in this paper. Based on the analysis and assessment of the carbon footprint (CO2 equivalent emissions) for the Coated Ivory Board production lines, it was used to provide a scientific basis and approach for reduction of carbon emissions and formulate the corresponding measures for carbon emissions reduction of China's pulp and paper industry. The business to business carbon footprint, for which steps of the life cycle are included in Coated Ivory Board production, was analyzed and calculated. The results showed that there were 888 kg of CO2 equivalent emissions per metric ton of Coated Ivory Board, in which the largest part, accounting for 57.5%, was associated with purchased electricity, followed by fuel oil at 40.2%, and others accounted for 2.3% of the CO2 equivalent emissions.

Mobile Edge Computing (MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy requirements. In this paper, we propose a flexible MEC-based requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy consumption. To address the dimensional differences between time and energy, we introduce two normalized parameters and then derive the computational overhead of processing tasks. Different from existing works, this paper considers practical variations in the user request patterns, and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay, task workload and power constraints. Since the resulting problem is non-convex, we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading solution. Numerical experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.

Energy consumption classification is one of the most widely-used approaches in the energy area that is applied in various applications such as household, commercial, urban, rural, industrial, etc. Energy consumption due to its substantial positive influence on the quality of made decisions, production and distribution management, and cost reduction, has considerable literature. In the classification literature, numerous statistical, intelligence, and hybrid methods have been developed and proposed in order to yield more accurate results. The common point of all these models is that whole of techniques follow a prevalent and repetitive procedure of learning process based on continuous distance-based cost function. While the objective function of classification is discrete. While the mismatch of the continuous cost function in classification problems with the discrete objective function causes them to be illogical or inefficient. Therefore, in this paper, a novel discrete learning process is offered to eliminate the inconsistency between the cost function and the classification objective function. The main difference between the proposed learning methodology rather than conventional versions is its cost function. In the proposed learning methodology, a mismatching function is considered as a cost function, which is dissimilar to previously developed ones, which are continuous functions based on distance, is a discrete function based on direction. In this way, in the proposed learning process, unknown parameters are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the unknown parameters are continuously adjusted and step-by-step near the target. In this paper, deep multilayer neural networks (DMNs) have been exemplarily applied to implement the proposed learning algorithm. Although more consistency of goal and cost functions will logically has not a negative effect on the classification rate of classifiers. However, in this paper, to show the superiority of the proposed discrete deep multilayer neural network (DDMN) over conventional continuous-based classifiers, four energy consumption benchmark data sets have been employed. These data sets include the Tamilnadu Electricity Board Hourly Readings, Tetouan City Energy Consumption, Household Electric Power consumption, and steel industry Energy Consumption. Empirical results indicate that the proposed DDMN model, as pre-expected, can yield better performance in all cases than the DMN model. The DDMN model can achieve a 93.27% classification rate on average and upgrade its classic version approximately by 4.77%.

As the EU strives to achieve its climate goals, it is becoming increasingly crucial to understand the complex relationships between economic activity, energy consumption, and carbon emissions. In this context, our paper aims to investigate the correlation between carbon emissions, energy consumption, and economic development. To fulfill our aim, we have used Eurostat and OECD data for the EU-27 member states for a period of 13 years (2010–2022), using a linear regression as the main analysis method. Our results have shown that there is a strong correlation between demand-based and production-based CO2 emissions as well as between production-based CO2 emissions and final energy consumption, while at the same time, our findings have shown that there is no direct correlation between energy consumption and economic development, aligning our study with the neutrality hypothesis of the energy growth nexus. This paper expands the ongoing discussion on sustainable development and climate change mitigation by conducting a thorough analysis of the EU-27 countries over a span of thirteen years. The results emphasize the need for integrated strategies that address both production and consumption emissions, emphasize the vital role of energy efficiency, and raise questions about the effectiveness of increasing energy consumption to enhance economic productivity or CO2 efficiency.

High-temperature molten salt capture and electrolysis technology of CO2 for the effective preparation of elemental carbon materials are considered to be an exceedinglyare gaining traction as promising carbon capture utilization and storage technology. The reason is their excellent selectivity and ease of operation, making them suitable for the effective preparation of elemental carbon materials from CO2. Nonetheless, the current research on the kinetic mechanism of CO2 capture in high-temperature molten salt systems is inadequate. Furthermore, the energy consumption and production costs associated with the process of preparing elemental carbon materials through CO2 molten salt electrolysis are relatively high. Therefore, achieving efficient CO2 capture in high-temperature molten salts and low-energy consumption electrolysis is very crucial. This paper delves into the thermodynamics of CO2 capture by various alkali metals/alkaline earth metal oxides. It also compares the theoretical decomposition voltage and energy consumption of various alkali metals/alkaline earth metal carbonates. The capture behavior of CO2 in typical binary chloride molten salts is also examined. The study further explores the thermodynamics of CO2 capture in molten salts to carbonates and calculates the theoretical voltage and power consumption during carbonate electrolysis in a high-temperature molten salt system. The kinetics of CO2 capture in typical CaCl2-based molten salts are examined by advanced technologies such as online gas mass spectrometry, carbon–sulfur analyzer, X-ray diffraction, and energy spectroscopy. The effects of temperature and voltage on the electrolysis process, electrolytic energy consumption, and product structure of elemental carbon materials prepared by CO2 electrolysis are investigated in typical ternary Na2CO3–K2CO3–Li2CO3 molten salts and ternary CaCl2–NaCl–CaO molten salts using scanning electron microscopy (SEM), Raman spectroscopy, and constant voltage electrolysis. The results indicate that CaO outperforms alkali metal oxide as a CO2 capture agent, and CaCO3 obtained by energy and conversion exhibits the lowest theoretical decomposition voltage and electrolytic energy consumption. In a typical CaCl2–3.0% CaO (mass fraction) molten salt, the CO2 capture capacity is 0.016 gCO2·g−1. Compared with the Na2CO3–K2CO3–Li2CO3 molten salt, the CaCl2–NaCl–CaO molten salt has a lower electrolysis voltage and energy consumption. Moreover, the current efficiency of the electrolytic elemental carbon material reaches up to 95.3%, with the minimum electrolytic energy consumption being only 14.1 kW·h·kg−1 under the optimal conditions of 750 ℃ and 1.5 V. Therefore, this study provides a foundation for exploring CO2 molten salt capture materials that combine excellent performance, low cost, and environmental friendliness. It also offers theoretical and data support for optimizing the process and reducing the cost of the actual electrolysis.

In recent years, the integration of digital technologies has grown rapidly in the field of thermal comfort and energy efficiency for buildings. The concept of a digital twin, incorporating multiple digital technologies, has gained increasing attention. The literature lacks a review of the digital twin concept in thermal comfort and energy consumption for existing buildings. This paper conducts a review of the current state-of-the-art in digital twin (DT) technology for thermal comfort/ energy consumption in buildings. The review employs a scientometric approach and examines various technologies used in creating DTs and a systematic analysis of the methods, technologies, algorithms, and approaches used in digital twin experiments. The results show a growing number of studies in this area, with a focus on thermal comfort monitoring, visualization, tracking, energy management, prediction, and optimization for existing buildings. Furthermore, the prediction of energy consumption using algorithms such as Artificial Neural Networks (ANN), Artificial Intelligence (AI), deep neural networks, and YOLOv4 have been used in buildings. However, the wider adoption of a DT that can facilitate occupants, and thermal sensations, enhance human-centered solutions, and improve energy prediction levels are necessary. There is a need for further international collaboration to expand the studies on digital twins for thermal comfort and energy efficiency. The review highlights the limitations and areas of improvement, such as the limited adoption of sensors for environmental measures, the need for more focus on the subjective perception of occupants, and the need for more comparative studies of algorithms for predicting energy consumption. Further studies can be conducted in areas such as understanding occupant psychological responses/behaviors to comfort in the digital world. This will enhance a more consolidated and robust validation for building performance.

Abstract Educational buildings have a large share and impact on urban development. While research shows a significant portion of non-industrial energy consumption in these buildings, obtaining optimal thermal comfort in educational buildings remains one of the main concerns in achieving the grounds to promote students’ best performance and efficiency. Extensive research has been done in this field, however, this research presents a new approach to the diverse use of nanotechnology techniques which improve its properties and components in the buildings, aiming to reduce energy consumption and increase thermal comfort. In this paper, thermal comfort and energy consumption are evaluated in a 12-class elementary school located in Shiraz City. Aeropan and nano-Phase change materials (nano-PCMs) is used in the window glass and walls of the studied case. This evaluation presents the simulation and experimental analysis of thermal comfort (PMV) and energy consumption of three classroom alignments in the school building including the Linear-shape (LS), the Integrated Linear-shape (ILS), and the U-shaped (US) alignment. The simulation was performed using EnergyPlus 9.6 software, while the experimental data was collected using TESTO 425 device. The result of this research shows that after applying nano-PCM and Aeropan techniques in window glass and walls, the US alignment has the highest reduction in energy consumption (monthly average of 11.80%) compared to LS and ILS alignments. This alignment includes an energy consumption reduction of 12.03% in the coldest, and, 11.66% in the hottest day of the year in addition to increasing the monthly average thermal comfort of school by the use of nanomaterials.

Mixed-mode ventilation system (MMVS) can significantly decrease the energy consumption of Egyptian office buildings while meeting indoor air quality requirements and thermal comfort of occupancy. This paper studies the effect of using mixed-mode ventilation system (MMVS) in achieving both energy efficiency and thermal comfort in office buildings. The research specifically focuses on investigating the energy-saving potential and thermal performance of existing office buildings in Egypt by merging natural ventilation (NV) with mechanical ventilation (MV) to reach an effective mixed-mode ventilation system (MMVS). Additionally, an energy performance simulation was used to study how to achieve thermal comfort and the building’s indoor temperature. Firstly, a selected office building in Cairo was used as a case study, and its thermal performance was studied by simulation. Secondly, site measurements were conducted to record the real thermal performance of the tested office building. These measurements were used for the validation phase. The validation demonstrated a satisfactory agreement between the building simulation and measurement data for air temperature (T) and relative humidity (RH). Thirdly, building energy simulations and analysis were used for testing the energy performance and thermal comfort of the tested scenarios that have different alternatives in the operation settings. The results revealed that it is possible to increase the thermal comfort hours by merging natural ventilation and applying the mixed-mode ventilation system only by changing the operation settings on the MMVS performance. The building’s annual energy consumption was reduced by approximately 27 % compared with the use of HVAC in the building. Natural Ventilation hours increased by approximately 27 % to reach 427 hours (h) and the HVAC hours decreased by 17 % to reach 898 hours (h).

Rail traction and resistance play an essential role in the efficient operation of rail systems. The nature of traction is based on the balance between static friction and generated force at the perimeter of the driving wheels. The main objective of this paper is the development of a methodology and a modeling procedure for the design of train speed trajectory diagrams for the suburban railway. The model is applied to the Athens–Chalkida railway line (in Greece). For this purpose, geometric data for the above-mentioned railway line is collected from the Hellenic Railways Organization (OSE) and then recorded and digitized. A code is developed in MATLAB to calculate the total resistance of the railway line at each kilometer position. The traction elements of the trains operating on the Athens–Chalkida–Athens line, as well as other representative trains, and the magnitudes of their mechanical-aerodynamic resistances are recorded. The MATLAB program generates and compiles the train speed trajectory diagrams and the traction-resistance matrices. Finally, a comparison is made between the time, energy, CO2 emissions, and fuel costs of the rail in relation to the competing mode of transportation, which, for the specific line studied, is the tourist bus.

The integration of electricity grids with technologies for small-scale distributed generation based on natural gas consumption is one of the most important innovative directions for improving the reliability and energy efficiency of power systems. Synchronous operation of large energy consumers and distributed generation systems is a new element of the electricity system. This element is an industrial microgrid, which has a unique ability to control the operating modes of the electrical energy system compared to conventional industrial electricity consumers. The aim of this study is to develop a theory and methodology for the use of microgrids in industrial areas under the market-based conditions of electricity and natural gas purchase. An analysis of trends in the use of small-scale distributed generation shows that the operation of distributed generation based on renewable energy sources is not economically feasible in all countries. In some countries, such as Russia, for example, the use of distributed energy sources powered by natural gas is the most important. The study of pricing principles for the purchase of energy in the wholesale and retail electricity markets and for the purchase of natural gas from regional suppliers and on commodity exchanges shows that managing the volatility of energy demand schedules can have a significant impact on the prices of electricity and natural gas used by industrial companies and large consumers of energy resources. As part of a unified power system, industrial enterprises and small distributed generation systems simultaneously consume electricity from the Unified Energy System, generate electricity through a distributed generation system into the internal network of the enterprise and external network the power system, and consume natural gas through the enterprise consumption system and the distributed generation system. Synchronised operation of an industrial enterprise and a small system of distributed generation system in the unified control system of a microgrid enables the enterprise to reduce the combined costs of purchasing energy resources and increase the efficiency and reliability of power supply to equipment. The article presents a system of factors influencing the demand of microgrids for electricity and natural gas consumption and develops a model for the integrated control of industrial microgrids under their integration with the technology for managing the demand of industrial enterprises for electricity and natural gas consumption. The control algorithm developed for a microgrid takes into account the cross effects of changes in its energy and gas demand, energy market price factors, internal constraints for industrial load management, external external of the Unified Energy System and the Unified Gas Supply System, and the ability to meet the energy demand of external consumers of the microgrid. The paper is of scientific and practical importance and can be used in the process of developing, implementing and managing microgrids at industrial enterprises in Russia and around the world.

Abstract In the cooperative vehicle‐infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing‐intensive delay‐sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles’ tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy‐packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions.

Sanitation is a critical issue in internally displaced people (IDP) camps, where the lack of proper infrastructure and services can pose major health and environmental dangers. This paper presents a comprehensive analysis of the sanitation situation in Kabarto 2 IDP Camp in Iraq using the Smart Sanitation Approach comprising of three tools: SFD (Shit Flow Diagram), CSDA (Citywide Sanitation Diagnostic Assessment), and ECAM (Energy and Carbon Assessment for Environmental Management). The results show that the camp's sanitation infrastructure is wholly unsafe, with faecal sludge (FS) not being properly contained, emptied, or treated. The operation of the desludging trucks and the incomplete treatment are the main causes of the sanitation system's greenhouse gas emissions. The paper offers recommendations for enhancing the design and construction of the containment systems and restoring or upgrading the treatment plant. In conclusion, the study suggests improving data collection and monitoring, clear roles and responsibilities for the developing a faecal sludge management policy. The paper also makes suggestions for potential future applications of these tools, including the use of these technologies in more IDP camps or humanitarian settings to produce more data and knowledge about the provision of sanitation services in these settings and promote service improvement in the region.

This paper systematically examines how workplace automation impacts energy poverty from a demand-side perspective, revealing a new challenge for Sustainable Development Goal 7 (SDG 7) in the context of technological revolution. Our research demonstrates that workplace automation significantly increases household energy poverty. This finding is robust when using the instrumental variable approach to tackle endogeneity, as well as employing different automation and energy poverty measures, placebo tests, and machine learning methods for robustness checks. Automation's impact mechanism is that it reduces people's income and work-related social capital, thus exposing households to higher risks of energy poverty. Moreover, its consequences are more prominent for rural households, less educated people, non-migrants, those without labor contracts, non trade-union members, and out-of-system workers. Thus enhancing human capital, promoting free movement of workers, and providing better labor protection contribute to weakening the adverse impact of the technological shock. Meanwhile, we find that improving the price reasonability, stability, security and accessibility of energy supply can also mitigate the negative effects of workplace automation on household energy consumption. In the dual context of the fourth technological revolution promoting industrial automation as well as the increasing urgency to achieve SDG 7, findings of this paper have important policy implications.

Starting from the premise that an increase in the use of energy determines a corresponding economic growth, this article aims to determine if at the community level of the European Union there is a causality between energy consumption and economic performance. Thus, using the multiple regression analysis, we sought to determine if, at the level of all 28 member states of the EU, the economic performance, measured through GDP, can be explained by the simultaneous variation in the consumption of the 5 types of primary energy: solid fuels, electricity, natural gas, oil and renewable energy. The results, of particular importance in the development of measures through the European energy policy, show a significant impact of the use of electricity, oil and renewable energy on the European GDP, while the influence of coal and natural gas consumption has a low intensity.

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

The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles.

Recently, the effectiveness of image-based sensors, which are based on image-processing techniques, has been analysed in terms of occupancy lighting control in comparison with a passive infrared (PIR) sensor, which monitors changes in infrared radiation from moving occupants. However, the image-based sensor has two shortcomings: occupants cannot be accurately detected in a dark environment, and the overall energy consumption from the light and sensor is excessive. Therefore, we evaluated a combined approach that employs both the PIR sensor and the image-based sensor simultaneously. Because the combined sensor uses the image-based sensor only in a sufficiently bright environment, the first shortcoming can be overcome. In addition, an experiment, was conducted at a single office, demonstrating that the combined sensor's overall energy consumption decreased by 3.59% or more compared with either of its constituent sensors individually. Thus, the combined sensor resolves the major shortcomings of the image-based sensor's performance. Further discussion and careful analysis are included in this paper.

Reducing carbon emissions is a basic policy long insisted on by the Chinese government, and the calculation of carbon footprint is of great application significance for reducing carbon emissions and realizing green consumption. Based on the multi-objective planning model this paper establishes a carbon footprint assessment model based on multi-objective planning from the economic, environmental, and social benefits of carbon footprint. It optimizes the particle swarm algorithm to solve the objective function of the carbon footprint multi-objective assessment model. The designed model is utilized in green consumption decision-making to forecast the carbon footprint and structure of green energy consumption in the future. The comparison shows that after the optimization of the carbon footprint assessment model based on multi-objective planning designed in this paper, the proportion of coal and oil consumption in the carbon footprint of energy consumption decreases by 1.2% and 0.5%, respectively, and the energy intensity can be optimized. The per capita carbon footprint of energy consumption of residents in eastern, central, and western China grows by 11.3%, 15.2%, and 2.7%, respectively, from 2024 to 2026. The carbon footprint of per capita energy consumption in the three regions is quite different. The results of the two projections show that the carbon footprint assessment model based on multi-objective planning designed in this paper is important for improving the energy consumption structure in different regions and realizing green consumption decisions.

This review paper presents a comprehensive analysis of Life Cycle Assessment (LCA) methodologies applied to construction materials. It begins with an introduction highlighting the significance of LCA in the construction industry, followed by an overview of LCA principles, phases and key parameters specific to construction materials. The methodological approaches utilised in LCA, including inventory analysis, impact assessment, normalisation, allocation methods and uncertainty analysis, are discussed in detail. The paper then provides a thorough review of LCA studies on various construction materials, such as cement, concrete, steel and wood, examining their life cycle stages and environmental considerations. The review also explores recent advances in LCA for construction materials, including circular economy principles, renewable alternatives, technological innovations and policy implications. The challenges and future directions in LCA implementation for construction materials are discussed, emphasising the need for data quality, standardisation, social aspects integration and industry-research collaboration. The provides valuable insights for researchers, policymakers and industry professionals to enhance sustainability in the construction sector through informed decision-making based on LCA.

This paper investigates the impact of climate change and technological innovation on economic growth, energy consumption, and carbon emissions by using annual observations of a wide range of variables of Asian and European countries from 1996 to 2021. The study uses a dynamic GMM model and reveals significant regional heterogeneity and time-varying effects of technological innovation on economic growth. Technological innovation plays a more substantial role in promoting sustainable economic growth in European countries compared to Asian countries. The study also uncovers a spatial linkage and dependence between climate change and technological innovation. The technological transfer from European countries has increased energy consumption and carbon emissions in Asian countries, leading to abnormal climate change. The paper underscores the importance of targeted technological innovation strategies in Asian countries to improve energy efficiency and mitigate climate change. The study also confirms the EKC hypothesis with an observed inverted U-shaped relationship between economic growth and carbon emissions.

Abstract 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.

CO2 capture from coal power plants is an important and necessary solution to realizing carbon neutrality in China, but CCS demonstration deployment in power sector is far behind expectations. Hence, the reduction potential of energy consumption and cost for CCS and its competitiveness to renewable powers are very important to make roadmaps and policies toward carbon neutrality. Unlike the popular recognition that capturing CO2 from flue gases is technically and commercially mature, this paper notes that it has been proved to be technically feasible but far beyond technology maturity and high energy penalty leads to its immaturity and therefore causes high cost. Additionally, the potential energy penalty reduction of capture is investigated thermodynamically, and future CO2 avoidance cost is predicted and compared to renewable power (solar PV and onshore wind power). Results show that energy penalty for CO2 capture can be reduced by 48%-57%. When installation capacity reaches a similar scale to that of solar PV in China (250 GW), CO2 capture cost in coal power plants can be reduced from the current 28-40 US$/ton to 10-20 US$/ton, and efficiency upgrade contributes to 67%-75% in cost reduction for high coal price conditions. In China, CO2 capture in coal power plants can be cost competitive with solar PV and onshore wind power. But it is worth noting that the importance and share of CCS role in CO2 emission reduction is decreasing since renewable power is already well deployed and there is still a lack of large-scale CO2 capture demonstrations in China. Innovative capture technologies with low energy penalties need to be developed to promote CCS. Results in this work can provide informative references for making roadmaps and policies regarding CO2 emission reductions that contribute towards carbon neutrality.

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

Improving the performance, efficiency and productivity of the machining, reducing energy consumption and establishing control over the machining process are the main goals of the production technologies development. This paper provides the basis for establishing of a process management and determining of optimal conditions for performing high-feed milling, as a special machining method. An experimental study of the analysis of the influence of process parameters, namely depth of cut and feed rate, on the output parameters of high-feed milling were carried out. As output indicators of the performance of the high-feed milling, namely the energy efficiency of the machine tool and the productivity of the machining process were analysed. Based on experimental data, analysis and modelling of the material removal rate per unit of time and electrical energy consumption were carried out. A workpiece made from aluminium was machined on a three-axis machining centre. By using the boundaries of the domain of process parameters and the optimization objective function, the optimization of the values of the process parameters was carried out. As an optimal solution, the following process parameters were obtained: cutting depth ap = 2 mm and feed rate vf = 1500 mm/min.

Abstract In filter bank multi‐carrier (FBMC) with offset quadrature amplitude modulation (OQAM) systems, the major limitation of FBMC‐OQAM signaling is the high peak‐to‐average power ratio (PAPR). High PAPR results in increased hardware complexity and is also prone to non‐linear effects of high‐power amplifiers (HPA). The existing HPA energy‐efficient optimization strategies are constrained severely by the scarcity and heterogeneity of signal distribution in the degree of freedom. This paper theorizes the optimal FBMC‐OQAM signal distribution, which has a global and steady feature and evaluates the maximal HPA efficiency‐suited multi‐carrier modulation (MCM) signal. This paper first proposes that the Binary Particle Swarm Optimization based Partial Transmit Sequence (BPSO‐PTS) with the Input‐Back Off Modulation Error Ratio (IBO‐MER) scheme traces the signal distribution for maximal HPA efficiency to save supply‐side energy. This paper further proposes Continuous‐Unconstrained Particle Swarm Optimization based PTS(CUPSO‐PTS) with IBO‐MER to obtain the theoretical boundaries and drastically accelerate convergence in the continuous‐unconstrained searching space. To decrease the computational complexity of MER calculation, this paper proposes the CUPSO with IBO Signal‐to‐Distortion Ratio (IBO‐SDR), enabling it to meet more practical applications. The simulation results show that the proposed scheme outperforms conventional IBO and HPA efficiency schemes.

With the increasing volume of shipping containers, container multimodal transport and port scheduling have attracted much attention. The allocation and dispatching of handling equipment to minimize completion time and energy consumption have always been a focus of research. This paper considers a scheduling problem at an auto-mated landmaritime multimodal container terminal with multi-size containers, in which operating facilities and equipment such as quay cranes, vehicles, yard cranes, and external container trucks are involved. Moreover, the diversity of container sizes and the location of handshake areas in yards are concerned. A mixed integer program-ming model is established to schedule all operating facilities and equipment. To solve the mathematical model is a NP-hard problem, which is difficult to be solved by conventional methods. Then we propose a heuristic algorithm which merges multiple targets into one and designs an improved genetic algorithm based on the heuristic combi-nation strategy in which 20-ft containers are paired-up to the same yard before allocation. After that, some exper-iments are designed to prove the effectiveness of the model and the algorithm. The effect of configurations on efficiency and energy consumption under different conditions is discussed, and the influences of different parame-ters and the proportion of 20-ft containers are also compared. Furthermore, the influence of locations of hand-shake area with different yard quantities are compared. To conclude, there is an optimal number of equipment to be allocated. If few equipment is used, the operation time will be prolonged; if too many, the energy consumption will be increased. When the yard operation is the bottleneck, the handover location should be in the centre, other-wise other locations might be feasible. When the proportion of 20-ft containers that can be combined is large, the method proposed in this paper has advantages over traditional methods. The proposed algorithm has made a breakthrough in improving efficiency and reducing energy consumption.

The characterization of the competitive interdependence among power consumers in power trading system contributes to pursuing efficient power supply and suppressing peak–valley load differences. Even though Stackelberg–Nash game approach has been applied to retail electricity trading with a single utility company and a group of end-users under competitive interdependence, the extension to multi-cluster retail electricity markets is not focused yet and the best strategies of end-users are still decoupled to each other. To bridge this gap this paper investigates the price-based demand response problem in multi-cluster retail electricity markets to coordinate the energy consumption behavior of the end-users and the retail price behavior of the utility companies. Adopting the structure of hierarchical game theory, the cluster retail price is controlled by the utility company to affect the energy consumption demand of end-users within the corresponding cluster and hence increase the income of both the utility companies and the end-users. In order to describe the coupled competition relationship among the bottom end-users in the system, the Cournot competition model of the differentiated oligopoly market is constructed by taking energy consumption as the individual strategy of end-users. The existence and uniqueness of Nash equilibrium and Stackelberg Nash equilibrium in such a hierarchical game system are proved, and a seeking algorithm is proposed to seek the Stackelberg Nash equilibrium, which is regarded as the extension from the existing algorithm for multi-cluster setup. This work gives an insight to theoretically check whether the utility companies need to redesign the contract with the power generator to ask for larger maximum supply.

Chip Multiprocessors (CMPs) leverage multiple processing units to improve computational speed and efficiency. Routing algorithms in NoC (Network-on-Chip) architectures ensure efficient data communication between these units, addressing challenges such as congestion, latency, and power consumption. This paper delves into an extensive analysis of the performance evaluation concerning the Dynamic Adaptive Deterministic (DyAD) routing for Network-on-Chip (NoC) and Wireless Network-on-Chip (WiNoC) configurations within the framework of a 64-core and 100-core multicore architecture. The evaluation involved a congestion threshold analysis of data transmission latency, the efficiency of network data throughput, and the amount of energy consumed. To substantiate our conclusions, we conducted simulations of the 64-core and 100-core mesh-based NoC and WiNoC architectures under the Video Image Processing System (VIPS) benchmark workload. These simulations were executed utilizing the Noxim simulator, a well-recognized tool acclaimed for its capacity to provide cycle-accurate simulations. Analyzing the simulation outcomes, it becomes evident that the DyAD routing approach for the 64-core and 100-core WiNoC architecture performs better in terms of network performance. It is characterized by enhanced capabilities at higher Packet Injection Rate (PIR) saturation loads, improved network throughput, and minimized latencies. This dominance is evident from its ability to handle heavier workloads and achieve lower delays in the investigated VIPS workload situations, compared to the 64-core and 100-core NoC multicore architecture.

Underwater Wireless Sensor Networks (UWSNs) are essential for a number of environmental and oceanographic monitoring applications. However, they face different and more complex challenges than terrestrial wireless sensor networks (TWSNs). The main challenges faced by UWSNs are limited include high propagation delays, poor bandwidth, low throughput, and high energy consumption. Replacing sensor batteries in such networks becomes extremely difficult as they are usually deployed in remote areas where limited human interaction is possible. The unbalanced and inefficient usage of energy by various network nodes poses another issue, as it may reduce the applicability and feasibility of the network. Therefore, proposing Energy-Efficient Routing Protocols (E-ER-Ps) is crucial to improve the performance and lifespan of these networks. Due to the challenges mentioned earlier, this research presents an extensive analysis of several different E-ER-Ps intended for UWSNs. We compare contemporary approaches that use machine learning (ML) with conventional protocols, as ML-based approaches have shown significant potential in resolving the intricate challenges faced by UWSNs. This paper aims to present a critical review of different E-ER-Ps from various prospects for UWSNs. To better comprehend the structure and uses of these protocols, we provide an innovative taxonomy for their classification. While ML-based protocols are evaluated for their flexibility, predictive power, and overall efficiency advancements, traditional protocols are evaluated based on their routing tactics and energy-efficiency improvements. A thorough comparative analysis highlights the advantages, disadvantages, and possible uses for different protocols. Furthermore, a critical analysis of ML’s function, incorporating intelligent and adaptive routing approaches, is presented, highlighting the technology’s potential to completely alter UWSN management. To formulate and implement E-ER-Ps for UWSNs, the article concludes by highlighting the present obstacles, including the need for real-time flexibility, resilience to environmental alters, and interaction with pre-existing network infrastructures. The development of ML-based approaches, hybrid approaches that combine conventional and ML-based methodologies, and the design of protocols that can adapt dynamically to the changing circumstances of underwater habitats are highlighted as future research objectives. This research provides the foundation for future advancements in this crucial field by presenting a comprehensive overview of the state-of-the-art UWSN E-ER-Ps.

Nowadays fleet electrification appears the most concrete opportunity to reduce the environmental burdens of the transportation sector. However, Battery Electric Vehicle penetration has to deal with crucial challenges, such as new BEVs high price, customers’ reluctant and scarcity of materials. In this regard, electric retrofit is emerging as a low-cost and short-term solution to deal with these issues. However, to date only few studies focus on regulatory framework of electric retrofit and fewer quantify the environmental benefits of a converted BEV used in a certain context. This paper analyses a retrofit industrial prototype made by a local start up with all features available for a future homologation and it proposes a comparative Life Cycle Assessment, stressing the significant environmental hotspots to suggests in which geographical context governments should promote retrofit conversion. Two scenarios composed by two time-related parts are evaluated: a vehicle replacement with a new Internal Combustion Engine Vehicle and a retrofit conversion process of a SmartForTwo W450. The production stage of conversion kit is assessed mainly through primary data, while operation consumption is calculated through an analytic model implemented in MATLAB- Simulink. A detailed sensitivity analysis is performed in which different options for electricity grid mix, energy consumption, battery pack maintenance and EoL pathways are combined, for a total of 274 scenario combinations. The results reveal that retrofit conversion allows achieving about 45 % Greenhouse Gases saving compared to new replacement, in particular when urban driving and renewable electricity grid mix are considered.

Digital technology plays a vital role in driving toward a zero-carbon future. This paper explores whether new infrastructure, serving as carriers for digital technology, contributes to carbon reduction and efficiency gains in cities. Drawing on panel data from 280 Chinese cities spanning 2011 to 2019, we establish a theoretical analytical framework to investigate how new infrastructure influences urban carbon performance through the perspectives of influence, threshold, and spatial spillover effects. The study reveals that new infrastructures development has a contributing effect on the total factor carbon productivity of cities, with consistent results across various testing methods. Additionally, the threshold effect test suggests that the dual threshold of regional energy consumption influences the enhancing effect of new infrastructure on total factor carbon productivity, exhibiting nonlinear characteristics. Furthermore, the spatial spillover effect test suggests that new infrastructure construction accelerates the local carbon emission performance, while having a positive spillover effect on neighboring cities. This study provides innovative ideas and experiences from China for the global realization of simultaneous promotion of digital economy development and energy conservation and emission reduction.