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The residential sector is a critical contributor to global energy consumption. In Ghana, it accounts for 47% of the total electricity consumed. However, inadequate knowledge of categories of residential consumers, their profiles, and the implications on their consumption patterns could impede the development of targeted and sustainable interventions. This study examined the behavioral patterns among low-income urban households in Kumasi, using their socio-demographic and housing characteristics, and self-reported values. This is coherent to the current discourse on Ghana’s transition to zero carbon by 2070 given that Kumasi is the fastest-growing city in the country. Using quantitative methods within a transdisciplinary research design, a survey of 285 households was conducted. The findings yielded varied outcomes. Households reported good electricity use behavior (e.g. covering liquids in the refrigerator) and poor behaviors (e.g. not adjusting the power setting of refrigerators to weather conditions). Logistic regression operations showed associations between energy use behavior, the type of housing units and the type of electricity meters. A significant proportion of respondents exhibited pro-environmental behaviors across all nine value domains. The study concludes that low-income households engage in energy-saving practices. We suggest that policy mediation could enhance the effectiveness of behavioral interventions toward electricity use efficiency. [ABSTRACT FROM AUTHOR]

This study aims to provide energy-efficient strategies for collections care in the face of financial and environmental challenges. Focusing on paper-based enclosures in storage rooms, the research proposes strategies to enhance energy efficiency by relaxing tight environmental controls. The five-stage process began with a standard test of hygrothermal properties and buffering capacity of the enclosures. In the second stage, a computer model was developed using the first stage results. This model was used in a series of specially designed tests simulating the transient heat, air, and moisture transfer between the room and enclosure to determine an acceptable room environment in which the enclosure conditions could meet collections care standards. The modelling results were used, in the third stage, to develop an AI model that predicted the enclosure conditions using room temperature and relative humidity (RH) inputs. In the final stage, a model for energy consumption of air-conditioning system operations was developed to predict across various control accuracies. Results indicate that the tight maintenance of room RH levels can be relaxed from 47–53% to 33–65%, with a corresponding reduction in energy consumption of approximately 16–18%. This relaxation, while maintaining enclosure conditions in line with conservation standards, was demonstrated through a simulation case at the National Library of Scotland. This study underscores the broader applicability of the developed procedure, asserting its relevance for cultural institutions seeking energy-saving potential through customized enclosure models. [ABSTRACT FROM AUTHOR]

AbstractTo elucidate the effects of energy input modes on energy efficiency and berry quality in continuous microwave drying, four energy input modes with variational working ways of magnetrons were investigated by using numerical simulation and bench experiments in a continuous microwave dryer. Energy efficiency was divided into absorption and conversion efficiency to study the change rule of drying process, and the retention rate of anthocyanins of berry’s representative nutrition ingredient was studied considering both of the temperature and its uniformity. Results indicate that the energy input modes affect the absorption efficiency by changing the distribution uniformity of electric field of material layer, and the conversion efficiency by altering the matching degree between the energy allocation and drying characteristics of berry, which are both determined by the ways of working magnetrons of different energy input modes. The conversion efficiency has a greater influence on the final energy efficiency, so the energy input mode should be paid attention to the provision of microwave energy by selecting proper way of working magnetrons according to the drying characteristics. The effect of temperature uniformity on anthocyanin degradation is more obvious with average temperature increasing gradually, where better uniformity of electric field strength should be provided to guarantee the quality of dried berry by adjusting the energy input mode. The research results provide significant insights to optimize the energy input mode of taking into account both the energy efficiency and dried quality of material. [ABSTRACT FROM AUTHOR]

Through energy and exergy analysis, the present work revisits the combustion processes generated using different fuels (CH4, C3H8, H2, LPG, Natural gas and Biogas) under the same operating conditions. First, numerical computations, in which the turbulent dynamic k-ϵ model coupled with a probability density function (PDF) approach together with different sub-models that ensure the reliability of the results, are used. Then, the First and Second Laws of thermodynamics are applied to all fuel combustion cases considered previously to predict the energy loss, exergy destruction and their corresponding efficiencies. Next, variations in the air factor, the air inlet temperature and the fuel inlet temperatures are considered to analyse the response of combustion system performance as well as energy and exergy losses for the different fuels under investigation. The results show an amelioration of combustion parameters for the mixture of fuel and air using values of λ greater than 1. Also, the preheating of the inlet air temperature has a positive effect on energetic parameters. However, preheating the inlet fuel does not take a considerable role in the efficiency amelioration and loss reduction of the combustion system. It further turns out that biogas features higher energy efficiency compared to other fuels, at approximately 89.64%, while hydrogen fuel exhibits higher exergy efficiency at around 78.70% followed by biogas with 74.96%. [ABSTRACT FROM AUTHOR]

Germany has not yet made significant progress toward achieving SDGs 7 and 13. This challenge can be attributed to the underlying financialization problem in Germany, as well as implementation issues related to energy efficiency, particularly in coal and gas, and renewable energy consumption. Given these circumstances, Germany is facing challenges in reducing greenhouse gas emissions. Addressing this issue may necessitate a policy realignment, which is the primary focus of this study. In this context, our study employs various wavelet-based tools, including wavelet coherence, multivariate wavelet coherence, and wavelet-based causality, to delve into the co-movements, causality, and direction of causality between the load capacity factor (LF) and key factors such as energy efficiency (gas and coal), technological innovation, renewable electricity, and financial development, focusing on Germany during the period from 1990Q1 to 2020Q4. The results from wavelet coherence, corroborated by wavelet cohesion, indicate that in the short and medium term, improvements in coal efficiency, gas efficiency, financial development, and renewable energy are associated with an increase in LF, thereby contributing to ecological quality. Furthermore, the multivariate wavelet coherence results underscore the significant impact of considering or not considering the effect of a third variable in the interrelationship between LF and its determinants, with the effect being more pronounced when the third variable is taken into account. Although this policy framework primarily targets the objectives of SDG 13 and 7, its applicability extends to other EU nations. The primary significance of this study is its proposal of an SDG-focused policy framework. [ABSTRACT FROM AUTHOR]

This paper was conducted to model the drying kinetics of Capparis spinosa buds using dragonfly swarm optimized nonlinear regression and to compare the impact of drying treatments on their quality. Experiments included hot-air convective drying (HACD) from 40 to 120 °C, vacuum drying (VD) at 40, 60, and 80 °C, microwave drying (MD) ranging from 200 to 1000 W. Out of 30 models tested, the drying kinetics fitted best with Jena Das model in both HACD and VD and modified Midilli for MD. The comparison between the drying treatments used indicated that when taking into account the quality of the dried caper, VD at 80 °C was the most effective, resulting in a high-quality caper with a well-preserved color (ΔE = 7.47), high bioactives (TPC = 30.18mgGAE/gDW and TFC = 10.27 mg QE/gDW) and radical scavenging abilities (DPPH = 0.249 and ABTS = 4.448 mg/mL) at the expense of long duration (6h) and high specific energy consumption (50.667 kWh/kg). On the other hand, when considering the drying behavior MD ranked best with samples dried at 1000 W showing high diffusivities 2.04e−8 m2s−1 low energy consumption (MER 0.1216 kg/h), and short drying times (12 mn). This research is critical in selecting better drying conditions for increased usage in the culinary, cosmetic, and pharmaceutical sectors. [ABSTRACT FROM AUTHOR]

This study aimed to develop a novel, sustainable, highly efficient, and affordable salt processing technology tailored to local salt producers. Novel technology converts raw solar sea salt into the finest dried salt, which is widely accepted by various industries. Experiments were conducted at a drying temperature of 80°C, with an air velocity of 30 m/s, and a feed rate of 125 kg per hour to evaluate the proposed novel salt processing technology. Performance evaluation was based on product quality parameters (e.g. wet moisture content, sodium chloride (NaCl) percentage, and whiteness) and specific energy consumption. The economic assessment relied on financial analysis and payback period parameters. The results indicated a reduction in salt sample moisture content from 10.37% ± 0.4% to 0.47% ± 0.02%, with NaCl percentage and whiteness of the salt produced at 98.5% ± 0.3% and 81 ± 0.7, respectively. The specific energy consumption for the novel salt processing technology was 0.119 ± 0.07 kWh/kg of the final salt product. Initial investment requirements totalled IDR 275,435,000, with a payback period of 1.66 years. This proposed novel salt processing technology has the potential to generate high-quality dried salt with a production efficiency of 89.5%. Consequently, it could enhance local producer’s businesses, seize opportunities in the national salt market, support rural industrialisation, and be best configured for Indonesia’s local salt producers. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid fuzzy-Particle Swarm Optimization (PSO) approach for power allocation in bidirectional Light Fidelity (LiFi)-enabled Internet of Things (IoT) communication systems. The approach integrates fuzzy logic and PSO to achieve efficient and robust power allocation, considering energy efficiency and Quality of Service (QoS) requirements. The hybrid approach leverages flexibility of fuzzy logic to handle uncertainties and variations in system parameters, such as channel conditions and QoS requirements. Fuzzy sets and membership functions are defined to represent linguistic variables, and fuzzy rules are formulated based on expert knowledge and system-specific considerations. This allows the approach to adaptively adjust power allocation based on varying channel conditions and QoS requirements, enhancing the robustness of the power allocation strategy. The PSO algorithm is employed for iterative optimization to explore for ideal power allocation solution. The particles in swarm signify possible power allocation configuration, and its position in the search space corresponds to a specific power allocation. The particles update their positions based on local best-known positions and globally best-known positions, allowing the swarm to converge toward the optimal solution. Through extensive simulations and analysis, the proposed hybrid fuzzy-PSO approach is evaluated with regard to energy efficiency and QoS satisfaction. The results demonstrate its effectiveness in achieving energy-efficient power allocation while meeting diverse QoS requirements. The proposed approach outperforms existing methods such as channel- and Orthogonal Multiple Access (OMA)-based power allocation schemes. [ABSTRACT FROM AUTHOR]

The gap in demand and supply of energy across Sub-Saharan African (SSA) countries has increased energy insecurity in the region. Therefore, power outages have become pervasive, causing unemployment and a decline in production output. Among the several energy-saving factors identified in the literature across developing economies, technological spillover driven by trade openness appears to be a prominent factor in improving energy efficiency. Thus, this study evaluates the impact of machinery imported from OECD and non-OECD countries and its corresponding research and development (R&D) spillover on energy efficiency performance in 18 SSA countries from 1995 to 2017. Using a stochastic energy distance function, we discover that aggregated data from OECD and non-OECD countries have no significant effect on energy efficiency performance across SSA countries. However, results from disaggregated data for OECD and non-OECD machinery imports show that OECD machinery imports improve energy efficiency contrary to non-OECD imports. Thus, technology spillover from OECD countries is advantageous for SSA countries to reduce long-term energy-based emissions. Furthermore, our results show that human capital has no significant effect on SSA energy efficiency. Consequently, the results possess some policy implications; for instance, policymakers responsible for promoting science and technology could increase investment in human capital development by developing technology and engineering expertise and increasing GDP allocation to R&D activities. For energy efficiency scores, we observe substantial differences in efficiency across SSA countries – implying potential improvements in energy efficiency across SSA countries. [ABSTRACT FROM AUTHOR]

This study validates the mechanism of ultrasonic dehydration without transition of water into vapor. The process can be realized by cavitation dispersion of liquid initiated by collapse of cavitation bubbles of cylindrical shape formed in capillaries. The developed mathematical model considers the complete life cycle of cavitation bubbles, encompassing three stages: slow growth, rapid expansion with deformation, and collapse. The optimum range of sound pressure level from 150 dB (critical level) to 170 (limit level) at which cavitation dispersion of liquid from capillaries takes place has been determined. The optimum sample size corresponding to the ultrasonic wavelength in gas, at which the dehydrated productivity is maximized, has been determined. The mechanism of ultrasonic dispersion of liquid during drying was confirmed by experiments. Practical implementation of ultrasonic drying of food products (carrots) confirmed effectiveness of implementation of dehydration mechanism and provided a reduction of drying time by 1.9 times with a reduction of power consumption by 1.7 times. [ABSTRACT FROM AUTHOR]

Due to the temperature control function, the unit energy consumption of cold chain transportation is more than that of ordinary transportation. Although rail transportation is known as an energy-efficient means, little attention has been given to the energy efficiency of railway cold chain transportation (RCCT). To address this gap, in this research, detailed waybill data for China's railway cold-chain is applied. First, we investigate the RCCT market in China and provide an energy consumption calculation method for RCCT. Then, the provincial energy consumption and the efficiency of RCCT are compared based on a DEA-SBM model. Finally, the Tobit model is utilized to assess how various factors influence energy efficiency. The findings could provide support for energy-saving policy-making in the cold chain transportation sector at the national and regional level, which has been discussed in scenario analysis. [ABSTRACT FROM AUTHOR]

AbstractIn this paper I discuss a case of ‘green’ redevelopment of a social housing complex in former East Germany, and unpack the key elements that have led to its comprehensive regeneration without widespread negative social externalities. In Drewitz, a former GDR housing estate in the Berlin/Brandenburg metropolitan region, it has been possible to preserve existing social housing units, achieve high standards of energy efficiency, expand public green space and provide improved opportunities for education and health. I identify the key factors that have enabled this ‘renovation without renoviction’: 1) redevelopment has been undertaken by a municipal housing company under public control; 2) the scheme has benefited from EU, national and local funding; 3) phased retrofit has made the scheme financially viable, while reducing the incidence of forced relocations; 4) community participation has led to greater acceptance and buy-in of the plan. Based on in-depth interviews with longstanding residents and local stakeholders, I assess the community’s response to the regeneration plan. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) are the future communication infrastructure for all devices that operate without the elaborate design found in wired infrastructure networks. Constructing a routing protocol that helps MANETs to achieve (QoS). It is quite challenging to achieve energy-efficient routing, due to its limited resources and node mobility concerns, which would impact node resource stability and lead to congestion and lower user QoS. A novel Trustworthy Energy Efficient AODV protocol has been proposed for improving path selection and locating trusted stations to maximize QoS. This approach employs location data, transmission speed, and directions to estimate the energy usage and trustworthiness of distinct nodes. The proposed TEE-AODV protocol includes two new control information namely Trust Request packet and Trust Reply. (T-RREP) AODV's packet includes a neighbor list and channel trustworthiness. The number of working neighbors in every path is counted, and this information is used to efficiently choose paths. The TEE-AODV calculates a QoS supporting metric for each detected route to identify a traffic-free path at any moment and determines whether it is a trusted route. The NS-2 simulator has been used to assess the suggested approach in terms of specific characteristics, including energy consumption (EC), (PDR), end-to-end delay (), and number of dead nodes. According to the experimental results, the suggested framework enhances reliable route selection while lowering e nd-to-end latency and improving energy efficiency. The experimental findings indicate that the suggested TEE-AODV protocol achieves high energy efficiency rate in the selection of trustworthy route and minimizing the $ {E_D} $ ED. [ABSTRACT FROM AUTHOR]

Limiting CO2 emissions is crucial for sustainable development, environmental protection, and climate change mitigation, ensuring a more resilient and cleaner future. Prioritizing sustainable urbanization renewable energy, and energy efficiency is vital for achieving this prosperous and sustainable future. As a result, we adopt the Fourier testing approach for stationary, cointegration, estimator, and causality, to scrutinize the role of renewable energy, energy efficiency, and urbanization on CO2 emissions in Finland over the period between 1990 and 2020, controlling economic growth and trade openness. The empirical analysis of the Fourier ARDL estimator establishes that energy efficiency measures such as gas and oil efficiency exhibit an adverse effect on CO2 emissions, so energy efficiency mitigates CO2 emissions. Similarly, the study also found that urbanization and renewable energy reduce CO2 emissions. Conversely, economic growth and trade openness induce CO2 emissions. Additionally, the Fourier-Toda-Yamamoto method provides robust evidence regarding the long-term implications of the examined factors. Based on this result, the study emphasizes that implementing a regulatory framework to enhance energy efficiency, promote renewable energy adoption, and support sustainable urban development will be an essential strategy in the global effort to attain the net-zero emissions target, which offers valuable insights to other countries seeking to transition towards cleaner and more sustainable energy sources. [ABSTRACT FROM AUTHOR]

In one of the largest demand-side energy efficiency programs at the time, the Mexican government supported more than 11 million, mostly low-income households in replacing their old light bulbs and appliances with more efficient models. Previous evaluations of this program focused exclusively on appliances, which made up almost 90 per cent of the total program cost, and found modest benefits in terms of energy savings. This study compares the respective effects of replacing light bulbs and appliances simultaneously in a single econometric framework, using data from nationally representative household surveys and a difference-in-differences approach which exploits geographical variation in treatment intensities. Despite using different sources and types of data, our results for the appliances replacement intervention are largely in line with the estimates of previous studies. In addition, we find that the impact on energy consumption (proxied by the amount paid for electricity) of replacing light bulbs was of comparable magnitude as that of replacing appliances, although the average cost per participating household was much smaller. Overall, our results suggest that low-cost investments that help poor households reduce their energy consumption for lighting can have high returns on energy efficiency in the residential sector. [ABSTRACT FROM AUTHOR]

Utilization of organic wastewater for hydrogen production has dual advantages of clean energy generation and bioremediation, which is sustainable for a longer period. To maximize the energy recovery from starch rich wastewater, a two stage system comprising of thermophilic dark fermentation coupled with microbial fuel cell was employed. A single parameter optimization strategy was implemented for the operation of the batch system. The maximum cumulative hydrogen production obtained was 2.56 L L−1 with a 48% reduction in COD under the optimal conditions of 35 g L−1 initial substrate concentration (COD), temperature 60 °C, and pH 6.5. The H2 yield and H2 production rate were 6.8 mol H2/kg CODreduced and 731.3 mL L−1 h−1, respectively. The effect of the organic loading rate (OLR) on H2 production rate was studied in a continuous stirred tank reactor (CSTR). A maximum hydrogen production rate of 913 mL L−1 h−1 was observed at an OLR of 5.6 g L−1 h−1. Effluent recycle played an important role in the improvement of H2 production. A maximum H2 production rate of 1224 mL L−1 h−1 was observed at a recycle ratio of 0.6. Power density of 4.2 W m−3 was observed with MFC using the dark fermentative spent media neutralized with carbonate buffer at an optimal pH of 7. A total COD reduction of 86% was observed. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks have been used for sensing and gathering data about an environment from a remote location for many years in a variety of engineering applications. In WSN, nodes must overcome energy consumption to function efficiently. To resolve these issues and extend the usefulness of the network, clustering and routing algorithms are promising. One of the main issues with WSNs is that they lack restricted energy sources. To overcome this issue, a Hybrid Optimized Energy-Efficient Adaptive Clustered Routing approach (HOEEACR) has been proposed for WSNs. This paper presents the Genetic Bee Colony (GBC) Algorithm for Cluster Head Selection by considering distances to neighbors, residual energy, node degrees, and node centralities. Furthermore, an optimal routing path for the cluster heads is found by utilizing the Aquila with African Vulture Optimization (AAVO) algorithm. The AAVO optimizes network performance using residual energy, node degree, and distance. The proposed HOEEACR method has been extensively tested to ensure network lifetime and energy efficiency. According to experimental results, the proposed HOEEACR method consistently outperforms existing techniques on a variety of performance metrics. [ABSTRACT FROM AUTHOR]

GreenTotal Factor Productivity (GTFP) is a critical indicator for measuring the green development and transformation of the economy, with profound implications for achieving energy conservation and developing the economy. In the context of the digital economy, digital entrepreneurship, as one of the important channels for digital industrialization, will have an effect on the promotion of GTFP. This study combines panel data from 219 Chinese cities at the prefectural level and above from 2007 to 2020 to examine the effect of digital entrepreneurship activities on GTFP. The empirical results show that digital entrepreneurial activity significantly reduces both GTFP and energy efficiency. Meanwhile, the effect of digital entrepreneurship activity on GTFP through industrial structure upgrading is not significant. Furthermore, a substantial negative correlation is identified between GTFP and digital entrepreneurship activity in the digital manufacturing industry, and in the eastern and central region. [ABSTRACT FROM AUTHOR]

Efficient energy management in WSNs is pivotal for prolonged network lifetime and sustained performance. This research introduces a novel approach to energy optimization through the integration of an ARIMA-driven feature selection process and an Actor-Critic Reinforcement Learning model. The Intel Lab dataset, encompassing data from 54 nodes distributed over a month, serves as the basis for experimentation. In the proposed methodology, the experimental setup ensures the validity of the study, leveraging the realism of the Intel Lab dataset. The data collection process captures the intricacies of WSN dynamics, with temperature records collected from strategically positioned nodes. The ARIMA-driven feature selection method refines the dataset, capturing temporal dependencies critical for energy prediction. The Actor-Critic model, a hybrid of policy and value-based reinforcement learning, dynamically adapts energy allocation strategies based on learned policies and offers a dynamic solution to the challenges posed by WSNs' ever-changing environments. Comparative analysis with methods reveals lower energy consumption of 0.32 mJ, an extended network lifetime of 1501 rounds, and higher prediction accuracy of 98%. The study's implications extend beyond the specific algorithms, suggesting a shift toward adaptive learning models in WSNs. The findings open avenues for future research in the integration of machine learning models for sustainable and efficient WSN deployments, emphasizing the growing importance of dynamic adaptation in sensor networks. Moreover, limitations, such as simulation realism and computational complexity, are acknowledged, prompting avenues for future research. [ABSTRACT FROM AUTHOR]

In situations where a regular network may not be appropriate or is unable to function correctly, data collection and real-time transmission are commonly done using Wireless Sensor Networks (WSNs). In order to accomplish this type of operation, the network's durability and performance are extremely vital. One of the key elements that directly affect a network's lifespan and performance is the routing protocol. This study proposes a Geographic Forwarding Energy Efficient Routing Protocol (GF-EERP), an enhanced version of the Geographic Energy Aware Routing (GEAR) protocol that helps to extend the network's lifespan and performance. The introduction of node categorization, a unique technique for choosing the region head, use of multi-hop communication method and removal of dead nodes serve as the foundation of the GF-EERP. In this study, the proposed protocol's performance is also compared to other protocols already in use, like Geographic Adaptive Fidelity (GAF), Geographic Energy Aware Routing (GEAR), Greedy Perimeter Stateless Routing (GPSR) and Multihop-Geographic Energy Aware Routing (M-GEAR), using a variety of performance metrics including Network Delay, Data Delivery Ratio and Network Throughput. The simulation outcomes prove that the performance of GF-EERP, with its distinct node categorization and region head selection mechanism is superior to the existing protocols. [ABSTRACT FROM AUTHOR]

This research suggests a new flexible method for the optimal operation of multi-carrier energy systems based on optimization techniques for different weather conditions and seasons. The results apply to different modes (a hot summer day, a cold winter day and a normal day). A new cost function from the operator's point of view is suggested considering the constraints on consumable gas supply and electrical energy in the cold and hot seasons respectively. Furthermore, a novel algorithm is suggested that gives the economical values of energy carriers, final cost, and the percentage of the direct electrical energy supply and outside the hub. In this method, load data, related prices, constraints, and penalties are applied to the suggested model, and afterward hub optimization assists in finding the optimal operation and costs. MATLAB software is used to check the results and the simulation outputs confirming the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The greenhouse gas emissions and global energy consumption associated with construction materials can be greatly reduced by incorporating phase change materials into buildings. These materials can absorb and release energy during a phase transition, thus reducing the energy consumption while achieving human thermal comfort. Molecular dynamics simulations were used to study the miscibility of both linear and branched polyethylene with two types of paraffin (octadecane and hexatriacontane) at two different temperatures. The results showed that blending was better at high temperatures for both polymers, and branched polyethylene was more miscible with both paraffins than linear polyethylene was. Hexatriacontane exhibited better blending characteristics. From the molecular dynamics results, we concluded that linear polyethylene/paraffin blends were more suitable as phase change materials in buildings than branched polyethylene/paraffin blends, as they were miscible at high temperatures but separated into two phases at ambient temperature. The optimal blends were subsequently incorporated into a cement model to study their influence on the mechanical properties of this construction material. The introduction of the blends into the cement model deteriorated its mechanical properties. [ABSTRACT FROM AUTHOR]

Green manufacturing and remanufacturing revolve around recycling end-of-life (EOL) products. One crucial aspect of this process is the disassembly line balancing problem (DLBP), which involves assigning disassembly tasks to workstations in a logical order. However, current DLBP models often overlook operator-related factors and energy efficiency. This paper introduces a multi-objective DLBP model that considers both operator and energy factors. The model aims to minimise disassembly line idle rate, improve line smoothness, reduce energy consumption, and balance operator workload under multiple constraints. Since this problem is NP-hard, we also develop a hybrid intelligent algorithm that combines simulated annealing and the water cycle algorithm (SWCA) to solve DLBP effectively using real-world case disassembly. For multi-objective optimisation problems, choosing multiple non-inferior solutions can be challenging. In this work, we embrace an ‘optimisation before decision-making' approach, integrating multi-criteria decision-making techniques with neural networks optimised by a particle swarm optimisation (PSO) algorithm to identify the most satisfactory solution. [ABSTRACT FROM AUTHOR]

This study develops a stylized theoretical model to analyze how digital economy agglomeration affects energy efficiency. Then, we employ a two-way fixed effect model to identify the impact of digital economy agglomeration on energy efficiency. We find that a higher degree of digital economy agglomeration significantly increases a city’s energy efficiency. This result is robust when we address endogeneity concerns by exploiting a policy shock, geographic distance, and historical variation as instruments. We explore several explanations, including advancements in green technology, upgrading of industrial structures, and enhancements in public and governmental environmental concerns. Our findings provide evidence that substantiates each of these mechanisms. Our heterogeneous analysis indicates that the impact of digital economy agglomeration on energy efficiency is only significant in cities with higher attention to digitization, stricter environmental regulations, greater energy consumption, and better industrial development. This study provides the first city-level evidence of the relationship between digital economy agglomeration and energy efficiency, which has important implications for the energy transition in developing countries. [ABSTRACT FROM AUTHOR]

Hybridisation of networks is a trend that will continue to evolve for more efficient communication solutions. Hybrid power line communication (PLC) and wireless networks deployed for the Internet of Things (IoT) applications have improved the efficiency of low-power and lossy networks (LLNs). State-of-the-art solutions on hybrid networks are mainly application specific and does not provide a mathematical model to evaluate the network performance. Furthermore, the parallel PLC and wireless networks result in more power consumption due to the additional circuitry required to support and switch between two networks. There is a need for a generalized study on hybrid network architectures. A mathematical model must be developed to evaluate the performance of hybrid networks. Unlike parallel PLC and wireless networks, some intermediate solutions must be proposed that can offer the benefits of wired and wireless networks without increasing power consumption and circuit complexity. The paper presents a generalized study on hybrid network architectures. A hybrid network has been proposed that can offer the benefits of wired and wireless networks in the best possible way. A novel Markov-based stochastic model has been developed to evaluate the reliability of hybrid network designs. The analytical results obtained from Continuous Time Markov Chains (CTMC) have been validated by simulation using MATLAB. Spectral and energy efficiency have been evaluated for the hybrid designs along with a trade-off analysis. The proposed hybrid network (with 50% jump nodes being hybridized) is 33.53% more reliable for data transmission than parallel wired and wireless networks after 100 hours of operation. [ABSTRACT FROM AUTHOR]

Position-controlled systems driving repetitive tasks are of significant importance in industrial machinery. The electric actuators used in these systems are responsible for a large part of the global energy consumption, indicating that major savings can be made in this field. In this context, motion profile optimization is a very cost-effective solution as it allows for more energy-efficient machines without additional hardware investments or adaptions. In particular, mono-actuated mechanisms with position-dependent system properties have received considerable attention in literature. However, the current state-of-the-art methods often use unbounded design parameters to describe the motion profile. This both increases the computational complexity and hampers the search for a global optimum. In this paper, Chebyshev polynomials are used to describe the motion profile. Moreover, the exact bounds on the Chebyshev design parameters are derived. This both seriously reduces the computational complexity and limits the design space, allowing the application of a global optimizer such as the genetic algorithm. Experiments validate the added value of the chosen approach. In this study, it is found that the energy consumption can be reduced by 62.9% compared to a standard reference motion profile. [ABSTRACT FROM AUTHOR]

Conditions that maximize the performance of an accelerator-driven system related to particle beam and energy and accelerator type are analyzed. The toolkit Geant4 simulated the interaction of protons and ions with masses up to 20Ne and energies from 0.2 to 2 GeV/n. The beam intensity considered is 1.5 × 1016 p/s. The core of the reactor is modeled as an assembly of fuel rods surrounding a cylindrical beryllium converter, with a criticality coefficient of 0.985 and lead-bismuth eutectic coolant. Lower enrichment generates better utilization of fuel (20% to 25% from the initial actinide mass can fission in a cycle keeping neutron damage in clad below 200 displacements per atom). Data on particle fluence and energy released obtained from the simulation are used to calculate total electric power produced and isotope evolution. Power spent to accelerate the beam depends on accelerator type and is calculated by scaling from data on accelerator efficiency for a reference particle. Optimal proton energy is ~1.5 GeV when the beam is accelerated in a linac with energy gain G ~ 14 and is 0.75 to 1 GeV in the case of a cyclotron (G ~ 12). Ion beams starting with 4He realize higher G values than protons: 20 to 50 in a linac and 15 to 35 in a cyclotron. [ABSTRACT FROM AUTHOR]

This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm [ABSTRACT FROM AUTHOR]

This research study presents a novel approach to enhance the efficiency and performance of Battery Energy Storage Systems (BESSs) within microgrids, focusing particularly on the integration of wind energy. The inherent inconsistency and unpredictability of Renewable Energy Resources (RERs) necessitate the development of effective integration solutions to accommodate them. Our article provides a customized iteration of the metaheuristic algorithm referred to as the Contracted fitness-dependent optimizer. The proposed methodology involves the adaptive adjustment of migration rates based on habitat suitability indices, while also considering variations in perturbations. The incorporation of Lévy flight and an elimination phase significantly enhances the algorithm’s efficacy in problem-solving. In order to establish the superiority of our strategy over alternative optimization approaches, we conduct simulations across diverse conditions and subsequently compare the outcomes. It was calculated that the daily scheduling cost was 235.2$ and the daily operational cost was USD 268$. In Scenario 3, the total operating expenses and arranging costs for 1 day, respectively, were determined to be 165$ and 115$. Further, the highest depth of discharge level was around 77%. Moreover, a −9.18 kW and 23.02 kW maximum charging and discharging power adjustment was made. The findings of our study emphasize the economic and operational benefits associated with appropriately sized BESSs within microgrid contexts. These advantages have the potential to enhance battery longevity and promote the development of more sustainable energy systems. [ABSTRACT FROM AUTHOR]

AbstractThe issue of designing servo-actuated systems for enhancing their energy efficiency is dealt with in this paper. Starting from a detailed representation of the energy dissipation in a motor-reducer set, the overall energy cost associated with the execution of a motion task is expressed, in a closed form, as a function of the main design parameters. The analysis is based on the use of scaling rules, which allow to condense the main physical parameters of the motors and of the gearboxes by means of a reduced set of parameters. The resulting framework is implemented into an automatic procedure which selects all feasible designs and evaluates their associated energy costs. Two design test cases are taken into consideration, and the results are used to discuss the traditional sizing method based on the inertia matching rule, showing that it is often unsuited to capturing an energy-optimal design. [ABSTRACT FROM AUTHOR]

Although severe air pollution has been recognised to be associated with coal combustion, coal is still the main living energy for domestic purposes because of the living habits and low fuel price. Pollutant emission control incoal combustion is urgently needed in the residential sector. In this study, the combustion performanceand gaseous pollutant emissions of a natural draft stove with cross-draft combustor were determined using Shenmu raw coal under different sieved sizes (8–16 mm, 16–20 mm, 20–25 mm, and 25–50 mm). The results showed that the particle size of coal had different effects on firepower and energy efficiency. The coal fuel with a large particle size (25–50 mm) achieved the highest energy efficiency of 82.0%, while the firepower was limited to 13.4 kW. Reducing particle size was helpful to increase the firepower. Low emission factors of CO, NO and SO2 were found in this combustion system with large size coal fuel and the changes were nonlinear. Moreover, manual operation played an important role in pollutant emissions. The heating phase with steady combustion released fewer gaseous pollutants than the ignition and complex cooking/heating phase. These results can provide support for the research on the matching of stoves and coal fuels to deliver clean combustion performance. [ABSTRACT FROM AUTHOR]

Power generation comprises high environmental and ecological impacts. The global power industry is under pressure to develop more efficient ways to operate and reduce the impacts of inherent process variability. With the rapid development of technologies within the energy sector, large volumes of data are available due to in-time operational measurements. With increased computer processing capabilities, machine learning is applied to explore these in-time operational measurements for improved process understanding. This research paper investigates machine learning algorithms for energy efficiency improvement at coal-fired thermal power plants by conducting a systematic literature review. This research is essential since it provides guidelines for applying machine learning towards sustainable energy supply and improved decision-making. Subsequently, efficient processes result in the reduction of fuel usage, which results in lower emission levels for equivalent power generation capacity. Furthermore, this study contributes towards future research by providing valuable insights from academic and industry-related studies. [ABSTRACT FROM AUTHOR]

The overwhelming effects of climate change on the living environments has prompted several countries, including India into rolling out different carbon neutrality agenda. On this background, this study activated policy framework towards the attainment of India's 2070 net-zero emission target via energy efficiency. The roles of green-technology, affluence and population were also rectified. With quarterly series spanning 1997Q1–2021Q4 and estimates of the novel wavelet quantile correlation technique, the following insights sufficed. Energy-intensity generated significant carbon de-escalation effects mainly in the medium and long term. There were evidence of long-term asymmetric effects between them. Carbon intensity as well as green technology aggravated carbon emissions in both short and medium term, however, over the long term, they generated carbon neutrality effects. The empirical estimates also validated the environmental Kuznets curve (EKC) ideology given the long-term environmental quality enhancing attributes of GDP. India's population represents a major challenge for the net-zero emission target. But this can be curtailed through adequate orientations as prescribed in the LiFE progrmme. Among other considerations, India's net-zero target is realisable if the country extends its low-carbon energy profiles and deploy more green technologies. [ABSTRACT FROM AUTHOR]

Fruits, vegetable, and root (FVR) crops are vital to achieve food and nutrition security (FNS), especially in Sub-Saharan Africa (SSA). However, their perishable nature results in losses across the value chain. The review discusses the application of dehydration technologies: solar, infrared (IR), microwave (MW), and radiofrequency (RF) to produce shelf-stable dried agricultural produce. Drying technologies for example IR, MW, and RF use radiation for heat transfer and are more energy efficient compared to traditional hot air drying. Due to shorter processing times and lower thermal load, the nutritional quality and functional properties of dried materials from IR/MW/RF are often superior compared to hot air convection ovens or solar drying. Combination methods with hot air, vacuum and ultrasonication, and pre-treatments are of great interest for higher efficiency and quality. There are, however, limited studies available on the use of IR/MW/RF dehydration technologies for FVR crops in SSA, albeit these technologies have potential and further investigations are required for adoption. [ABSTRACT FROM AUTHOR]

Coordinating the movement of Connected Automated Vehicles (CAVs) can significantly improve traffic operations at signal-free intersections. However, we show there is a tradeoff between the operational capacity and the battery loss of CAVs at intersections. This research aims to enhance the battery life of CAVs with the minimum impact on operational capacity. To this end, we develop a stochastic model for the battery-capacity loss of CAV platoons at signal-free intersections. We account for the stochasticity in traffic operations at intersections by considering a probability distribution for the operational error in synchronizing the arrival and departure of consecutive platoons in crossing directions. We then balance the tradeoff between the battery-capacity loss rate and intersection capacity by optimizing the platoon size, traffic speed, and marginal gap length at a macroscopic scale. The numerical results of the research show that adjusting the macro-level control variables can improve CAVs' battery life by 27.6% at the cost of a 3.5% reduction from the maximum capacity. [ABSTRACT FROM AUTHOR]

Stakeholder pressure is among the pathways through which firms are being prodded to adopt environmental management practices. Owing to research paucity from the context of developing countries and overall inconclusiveness, this research investigates whether mimetic, normative, and coercive pressures (which encompass stakeholder pressure) sway firms into adopting resource management and energy efficiency. An analysis of data from 852 firms in Kenya using a simple probit model, suggests that all the three types of stakeholder pressure positively influenced corporate resource management and energy efficiency. This augments our postulations on both stakeholder and neo-institutional theories. Based on these findings, it is plausible to view stakeholders as critical 'tools' that can foster corporate sustainability initiatives in developing countries. [ABSTRACT FROM AUTHOR]

Spray drying of milk is a highly energy intensive process. In this study, thermodynamic and exergoeconomic analyses of two-stage spray drying plant have been executed. This plant was analyzed on the basis of various parameters such as energy efficiency, exergy efficiency, energy improvement potential, exergy improvement potential, etc. The energy and exergy efficiency of the plant were 72.90 and 35.15%, respectively. The maximum energy improvement potential was for drying whereas maximum exergy improvement potential was for homogenizer. It indicates huge scope in the technical improvement for drying chamber and homogenizer of the plant. The highest cost of processing was for cyclone separator (percentage relative cost difference: 44%) followed by drying chamber (percentage relative cost difference: 40.71%). Highest energy destroyed was calculated for drying chamber (195.83 kJ/kg) followed by VFBD (87.62 kJ/kg). Lowest energy efficiency was calculated for VFBD (34.64%) followed by drying chamber (50.04%). Highest energy improvement potential was estimated for drying chamber (97.86 kJ/kg). Highest relative energy destruction ratio was calculated for drying chamber (52.04%) followed by VFBD (23.30%). Highest energetic factor was calculated for drying chamber (28.22%) followed by homogenizer (16.71%). Based on these analyses, it was realized that performance of the spray drying plant may be substantially improved by some design improvement of drying chamber and homogenizer only. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of resource allocation in wireless networks, given the increasing usage of mobile devices and sensors. To achieve energy efficiency, we propose a novel method called Hybrid Random Forest Ensemble Support Vector Machine (RFESVM) with Chaotic Cloud Quantum Bat Algorithm (CCQBA). The proposed method identifies the best power allocation for each user in Device-to-Device (D2D) communication, which can significantly improve coverage and lower data rates and latency. To overcome the shortcomings of existing methods, we use the chaotic cloud quantum bat algorithm that combines randomness, ergodicity of chaotic mapping, and stability inclination to enhance the convergence speed. To address the issue of class imbalance in the dataset, we combine SVM with RF and establish an ensemble technique called RFESVM. Our proposed method achieves higher energy efficiency compared to other techniques, demonstrating the effectiveness of optimal power allocation in D2D communication. Overall, the proposed method can significantly improve the systems' energy efficiency while ensuring high network performance in wireless networks. [ABSTRACT FROM AUTHOR]

Exploring a scientific quantitative evaluation for energy efficiency and its improvement in jet milling process is of great significance for energy saving and emission reduction. In this study, three classical comminution breakage models, E-S breakage probability model, A-b breakage model and E-M breakage model, were modified by conducting comminution experiments with an improved single particle impact comminution equipment. Based on modified breakage models, new formulas, describing relationship between the comminution energy efficiency and breakage characteristic, were proposed and verified with the variation of system holdup, inlet pressure and feeding initial particle size on energy efficiency in a lab scale fluidized bed opposed jet mill. Results show that modified breakage models all well fitted the comminution results, and modified E-M breakage model was with the biggest accuracy. The energy efficiency calculated from the three new models is basically consistent with the change of comminution conditions, varying from 0.15% to 1.35%, which represents great improvement potentiality. The proposal of energy efficiency model would provide foundation for the subsequent quantitatively evaluation on energy utilization and efficiency improvement of steel slag ultra-fine grinding by fluidized bed opposed jet mill with waste heat steam. [ABSTRACT FROM AUTHOR]

An experimental study involving tapered wire coil inserts in a double-tube heat exchanger using water-based phase change material (PCM)-dispersed mono/hybrid nanofluids is conducted. The effects of the wire coils (WC) with nanofluids on the hydrothermal performance of this system are studied for different coil arrangements: converging wire coils (CWC), diverging wire coils (DWC), and converging-diverging wire coils (CDWC) and different Reynolds numbers. The experimental results indicate that among all the tested coil arrangements, the DWC presents a higher thermal performance factor (TPF) than the other arrangements. Using the DWC, the TPF of the Al2O3+PCM hybrid nanofluid is augmented by 3.98%, 9.61%, and 3.04% as compared to that for the plain wire coil (PWC), CWC, and CDWC, respectively. Besides, the DWC configuration exhibits the minimum entropy generation among all arrangements. For the Al2O3+PCM hybrid nanofluid using the DWC, the entropy generation is decreased by approximately 13.36%, 9.78%, and 5.78% as compared to that of the PWC, CWC, and CDWC at a volumetric flow rate of 15 lpm. [ABSTRACT FROM AUTHOR]

Multi-core architectures have grown to be a popular choice for deploying Mixed Criticality Systems (MCS). The focus of research in MCS has been to provide timing assurances for jobs with different criticality levels. Due to their significant processing demands and energy-aware/constrained nature, energy conservation in these systems is becoming mandatory. This article presents, mcDVFS, an energy management technique based on Dynamic-Voltage-and-Frequency-Scaling for multi-core MCS. mcDVFS achieves significant energy reduction while maintaining timing guarantees. It also prioritizes quality of service whenever feasible. Extensive experimental simulations show energy savings of ≈ 52% and 34% when compared to EDF-VD and EDF-VD with QoS. [ABSTRACT FROM AUTHOR]

Virtually dividing thermal zones into micro-zones and conditioning them using occupant-centric control (OCC) has emerged as a significant method in reducing cooling energy. Hence, the need arises to study time-sensitive thermal behaviour within micro-zones under OCC as thermal coupling from adjacent micro-zones significantly influences cooling patterns. This will explain possible variations in cooling time and energy consumption under different occupancies and aid in analysing the feasibility of micro-zoning as an efficient strategy. This study aims at exploring the key parameters that influence cooling patterns under micro-zonal OCC. Effects of four factors, namely indoor ambient temperature, supply air velocity, thermostat location and occupancy are explored using validated transient state CFD simulations for a hypothetical open-plan office. Results indicate that compared to zone conditioning, micro-zonal OCC can reduce about 20%–70% overall cooling energy while maintaining setpoint conditions and show the following three patterns: rate of cooling in occupied micro-zones decreases as they start to cool unoccupied zones through thermal coupling; supply air velocity and ambient temperature impact thermal coupling and the size of a chosen micro-zone influences energy load. This study is the first attempt at transient exploration of thermal developments within micro-zones under the influence of relevant parameters and is a necessary precursor to experimental studies on micro-zonal OCC. HIGHLIGHTS • Dividing thermal zones into micro-zones under the OCC saves 20%–70% energy. • First attempt at transient exploration of thermal developments within micro-zones. • The supplied air velocity, ambient temperature and micro-zone size influence HVAC energy. [ABSTRACT FROM AUTHOR]

Any successful effort to address climate change over the next 25 years will involve a "credible swap" that greatly reduces greenhouse gas emissions from the burning of fossil fuels, provides energy in entirely different ways, and also reduces demand for energy. On the demand side, the next quarter century offers abundant high-leverage opportunities to reduce future emissions via intelligent urban design, building construction (notably in the gigantic apartment complexes), and efficient vehicles and appliances. On the supply side, there are three variants of the swap. In one, the work horse is renewable energy (solar power, wind power, hydropower, and power from biological feedstocks); in a second it is nuclear power; and in a third it is a reshaped fossil fuel economy. The three are by no means mutually exclusive. Each brings disruption and risks, rivaling those of climate change if done inattentively. Well-executed solutions will require threading a needle. [ABSTRACT FROM AUTHOR]

This paper proposes an Indirect Field Oriented Controlled Dual Stator Multiphase Induction Motor (IFOC-DSMIM) as a traction unit in an Electric Vehicle (EV) for the enhancement of its fuel economy and average loading capacity. The performance of the drive is studied by loading it under standard drive cycles that include free running, acceleration, and braking conditions using the MATLAB/Simulink environment. Its performance analysis is carried out to obtain efficiency curves under the three modes of excitation using a steady-state equivalent circuit approach. Studies on load sharing between the two stators, speed tracking and torque-producing capability of the drive are executed on its dynamic model and the simulation results are depicted. The average energy efficiency of the vehicle with the proposed DSMIM drive system is at least 10% higher than a Single Stator Multiphase Induction Motor (SSMIM) drive under different load percentages. Furthermore, under 75% of rated load conditions the energy efficiency is 20% higher. Thus, the proposed DSMIM configuration promises to provide a sizeable energy saving in the EV system, reducing the energy cost and increasing the driving range. [ABSTRACT FROM AUTHOR]

For a long time, electricity is supplied by means of firing the fossil fuels in thermal power plants. Now, it is well-known that the activity has many environmental and health issues, which are sharply menace the future of earth and humanity. Photovoltaic (PV) system is one of the popular renewable types of energy generation, which is available in most parts of the earth. Unfortunately, temperature rise in a PV module, leads to a significant reduction in its electrical efficiency. In this paper, two methods of wind blowing and mounting fins are considered separately and simultaneously, to overcome the mentioned weak-point. The wind velocity is set to 2 km/h. So, four cases are assessed, experimentally. As results, it was revealed that wind blowing of 2 km/h is more effective than fins in cooling PV module, so that, it can create more than 15 °C lower temperature than mounting fins. The mentioned temperature reduction leads to enhance the electrical efficiency and output power by 2.4% and 21.7%, respectively. In order to show the accuracy of results, all experimental outcomes in electrical efficiency were compared with the analytical formula. Moreover, the environmental evaluation of commissioning a 10 kW PV power plant with the proposed cases are reported, too. [ABSTRACT FROM AUTHOR]

Net power plant efficiency is the ratio of exportable electrical power output to the product of fuel consumption rate and fuel heating value. Conventionally, power plants are designed to attain the maximum net power plant efficiency by using regenerative feed water heating, in which extracted steam from steam turbine is used to increase feed water temperature. An important factor that determines the net power plant efficiency is the temperature of flue gas exhausted from the power plant. Decreasing this temperature increases the net power plant efficiency. However, exhaust flue gas temperature should not be lower than the lower limit in order to avoid the corrosion resulting from condensation of sulfuric acid vapor in flue gas on heat exchanger tubes. The lower limit can be raised by reducing fuel moisture content and raising air temperature at boiler inlet. In this paper, it is proposed that low-pressure extracted steam should be used for fuel drying in steam dryer, and vapor released from steam dryer should be used for air heating in heat recovery air preheater. By doing so, the lower limit of exhaust flue gas temperature is decreased, and the net efficiency of the power plant is increased. A case study of a 600-MW lignite-fired power plant is used to demonstrate that the integration of steam dryer and heat recovery air preheater is economically justified. [ABSTRACT FROM AUTHOR]

Today, energy management systems are usually assessed as key to sustainable development goals. One of the best criteria for energy management systems in construction projects is Life Cycle Energy Analysis (LCEA). This research is a quantitative-qualitative study based on a Multi-Criteria Decision-Making (MCDM) model. This research involves four stages: library study, statistical survey, descriptive study, and case study. The review of previous studies showed that the LCEA and MCDM alone could not provide a comprehensive energy consumption management assessment model, due to inherent uncertainty and the existence of various analysis parameters; however, when are used in an integrated framework; the LCEA & MCDM hybrid approach serves as the best instruments to assess energy management systems. This research has proposed a newly developed hybrid multi-objective mathematical decision-making model to select appropriate and optimum options for all building shell components to optimize and manage the Cost and Energy of the Building Life Cycle. The life cycle energy analysis of an educational building in a case study showed that optimization using the proposed model saves about 16% of energy during the lifetime of the building. In addition, the investment payback period for this optimization is less than seven months in the first year of operation. [ABSTRACT FROM AUTHOR]

Green buildings (GB) show performance gaps despite a plethora of materials, technologies and stringent certification systems. India is predicted to shape the global energy demand shortly; hence, its recent GB mandate of Energy Conservation Building Code (ECBC) will contribute significantly to the global Agenda 2030. However, its success depends on a holistic relook beyond the myopic design approach focused on energy efficiency. Therefore, a multi-criteria decision support system (DSS) based on the Best-Worst method was developed. It integrates ECBC and BIM-based energy simulation while facilitating maintainability performance prediction in the early phase of design of the GB envelope. The top five options out of 72 common envelope combinations for wall, fenestration and roof materials were chosen for energy performance. The DSS is flexible to accumulate other decision-making criteria as per project-specific requirements. Its efficacy was analysed with a GB located in a warm-humid climatic zone of India. Here, the material availability criterion was also added. The framework provides probing insights and facilitates decision-makers to act appropriately in the early design phase to ensure efficient performance in the long run. [ABSTRACT FROM AUTHOR]

During its Presidency of the Group of Twenty (G20) in 2020, Saudi Arabia launched the concept of the circular carbon economy (CCE) as a framework for reducing emissions to a level consistent with the goals of the Paris Agreement. The concept, which was endorsed by both G20 leaders and energy ministers, comes at a time when Saudi Arabia appears to have stabilised its domestic emissions after decades of rapid growth. This article describes the CCE concept and positions it alongside other related sustainability concepts, and assesses how it may be meaningfully applied. To that end, the article provides an analysis of key greenhouse gas (GHG) and carbon dioxide (CO2) trends in Saudi Arabia and existing climate change-related policies and measures using the CCE as a framing tool. Prominent data sources indicate that Saudi Arabia's total CO2 emissions experienced their first-ever significant decline in 2018, of 3.93%. The analysis suggests that policy-induced variables, namely energy price reform and more robust energy efficiency measures, played a significant role in this. The article also explores the potential of key mitigation options if limiting global warming below 2°C is to be achieved. Finally, the article discusses the international significance of the CCE concept and its potential to foster stronger engagement on net-zero pathways, particularly from fossil-fuel rich countries and hard-to-abate sectors. The CCE approach aspires to promote holistic assessments of how various carbon management options fit together to achieve a carbon balance, or net-zero emissions. Saudi Arabia's adoption of the concept signals a stronger level of engagement on climate policy. CCE's game-changing potential derives from how it allows aligning fossil fuel producers' interests with net-zero emissions trajectories. A meaningful operationalisation of the CCE in Saudi Arabia will still require a stronger CO2 accounting framework, aligned with international measurement, reporting and verification (MRV) standards. Achieving an emissions pathway consistent with a 2°C global warming target in Saudi Arabia would require significantly more policy support for carbon capture and storage (CCS) and hydrogen in particular, along with a continued emphasis on renewable energy and energy efficiency. The CCE should also be seen as a tool to build bridges between fossil fuel importers and exporters for holistic discussions around energy transition.. [ABSTRACT FROM AUTHOR]

A thermodynamic analysis was performed on a newly developed concentrated solar power (CSP) technology known as the Cross-Linear (CL), which addresses the issue of cosine loss in conventional CSP technology. The analytical model has been developed and validated using experimental data collected at the experimental site situated in Bhopal, India. The analysis was performed under direct normal irradiance of 775 W/m2 with the key objective to determine the optimum inlet condition of the heat transfer fluid (HTF) considering three optimization parameters: HTF outlet temperature, energy, and exergy efficiency of the receiver. Also, the exergy of the solar field is investigated depending on the available solar irradiation throughout the day for three different configurations of the heliostat field. After a comprehensive analysis of the results, the optimal air inlet temperature range was observed to be between 373 and 423 K, and the optimal air mass flow rate was overserved to be 0.0925 kg/s (333 kg/h), which provides energy and exergy efficiency values within 50% to 60% and 28% to 34%, respectively. The maximum electrical exergy efficiency for 30 kW plant capacity is observed to be around 70% at solar noon with a cosine factor of around 0.9 for 4-hour duration. [ABSTRACT FROM AUTHOR]