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Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance. [ABSTRACT FROM AUTHOR]

Energy efficiency is crucial in contemporary industry and controlling the resource power state by switching off/on commands is a promising measure. The control problem of deciding when to switch off/on the machines depending on the state of the system at a given time is not trivial due to the effect the control might have on the system production rate. Threshold-based policies using buffer occupancy information to control the machines can be effectively used to reduce energy consumption. Nevertheless, highly complex control policies are difficult to be applied and costly to be managed in practice. Buffer-based threshold policies to control multiple machines simultaneously in a serial production line for energy efficiency purposes are analysed in this work. The optimal control minimises the energy consumption while assuring a certain target production rate for the system. The effects of controlling different combinations of machines simultaneously with different number of thresholds have been investigated through numerical experiments with discrete event simulation. Insights regarding the trade-off between the complexity of the control and the performance gains are provided. The proposed policy works effectively and the effect of a proper selection of the controlled machines or thresholds is significant. [ABSTRACT FROM AUTHOR]

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]

The rental market, notably within the UK, is facing increased scrutiny with the tightening of regulation in relation to energy performance and the increases in energy costs and rental unaffordability. Whilst a sizeable volume of research has examined the pricing effects of EPCs and house prices, scrutiny of this relationship within the private rental sector remains more embryonic. This study, using 2,914 transactions for Northern Ireland, extends the traditional analysis beyond the conditional mean estimate by examining the quantiles of the relationship between EPCs and rental prices. The findings provide evidence of a rental premium of 0.2% for a one-point improvement in energy efficiency. In terms of EPC ratings, we find premiums for B- and C-rated dwellings of 8.2% and 2.4% and also discount effects for E, F and G-rated properties ranging between 3.9%-5.5%. The quantile findings exhibited a parabolic effect across the price distribution demonstrating the lowest and highest priced properties to comprise higher price premiums of 13% and 16% for B-rated dwellings, however more pronounced discount effects for F- and G-rated properties at the lowest and highest deciles. The findings provide evidence to help alleviate the split-incentive problem between landlords and tenants within the UK. [ABSTRACT FROM AUTHOR]

Energy efficiency is considered critical towards sustainability for all businesses, given that traditional energy intensification is not only an expenditure but also a proportional source of carbon emissions. However, it is unclear how investors consider a firm's energy efficiency while deciding on its cost of debt. This study aims to analyse the relationship between firms' energy efficiency and the cost of debt using panel data regression with a fixed effect on 17,175 firm-year observations of 2284 companies. The endogeneity effects of variables have been controlled using instrumental variables. The study found evidence that energy efficiency leads to a reduction in the cost of debt. This study also analyses the impact of energy efficiency on investments made in research and development. The findings suggest that efficient energy firms are technologically advanced through higher investments in research and development, effectively improving the firm's values and helping to reduce their cost of debt. The results support that energy-efficient firms are technologically advanced and better positioned regarding firm value. Thus, they are regarded as secure funding with reduced costs of debts. This research demonstrates that lenders must be responsive to modern-day cultures' social concerns through better social engagements in the context of global warming and climate change. [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]

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]

Residential energy policies for individual dwellings predominantly provide minimum design standards for thermal performance and energy efficiency on a square metre basis. This paper quantifies predicted dwelling energy use on a national scale to enable evaluation of the effectiveness of this policy model. Utilizing Australia as a case study, analysis of 580,956 thermal energy design certificates for new detached dwellings verifies that, although minimum energy efficiency design standards were achieved, predicted energy use for thermal comfort increased by 10.4% per dwelling and 5.6% nationally in 2022 compared to 2018. The study examines the impact of both the number and floor area of new dwellings on dwelling energy use and highlights the disparate jurisdictional outcomes resulting from inconsistent implementation of national policy across multi-state governance. To deliver reductions in household energy use it is imperative that residential energy policies target household energy use, rather than only energy efficiency, of new dwellings, of which floor area is a key determinant. Furthermore, to achieve global climate change objectives, there is a need for complementary policy mechanisms that consider the collective energy use of the residential dwelling sector. [ABSTRACT FROM AUTHOR]

Energy saving and the development of renewable energies already form part of the daily discourse in our society. Climate change and the environmental problems arising from it affect all of us on the planet. In view of the recent energy supply crisis in Europe and in line with the EU's Sustainable Development Goals, it is necessary to accelerate the ecological transition. This means, among many other things, encouraging energy efficiency and energy savings. This research is based on the EURONET 50/50 max project, which applied a methodology with no upfront capital outlay, but based on changing user habits to save energy in educational buildings. It was carried out in twelve educational centres of the Spínola Foundation throughout Spain. Significant reductions in energy and water consumption were achieved. The maximum reduction obtained in water consumption was 29%, 24% in electricity consumption and 33% in heating consumption. The average reduction in consumption obtained was 13% compared to 11% achieved in the 50/50 max project. It was concluded that, on the journey to optimising our energy consumption, working on patterns of usage is fundamental, raising awareness and involving users in order to give rise to permanent processes over time, gradually reversing climate change. [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]

Improving the overall energy efficiency of thermodynamic cycles relies heavily on the replacement of traditional pure fluids with zeotropic mixtures. The selection of the optimal components within the zeotropic mixture depends on the specific operating conditions of the thermodynamic cycle. Therefore, significant enhancements in performance can be achieved across varying operating conditions by effectively controlling the composition of zeotropic mixtures in the cycle. According to the gas-liquid equilibrium characteristics of the zeotropic mixtures, the adjustment of the medium components and the boundary conditions can be realized if the components can be adjusted through the gas-liquid separation. In this study, a novel device is proposed for automatically regulating the speed of gas-liquid separation in order to separate the components of the zeotropic mixtures. The CFD simulation was utilized to analyze the structural parameters and boundary conditions that impact the efficiency of gas-liquid separation. The results indicate that the adjustable mass flow range of the optimal structure is broadened by 5.5 times compared to conventional gas-liquid separation devices, ranging from 0 kg/s to 0.15 kg/s. Additionally, within this range, the gas-liquid separation efficiency exceeds 95%, representing a 10% improvement over traditional phase separators. [ABSTRACT FROM AUTHOR]

One of the top priorities of most countries around the world is sustainable development, to achieve which financial inclusion is identified as one of the key elements. Thus, unlike previous studies, the current study aims to channel the impact of financial inclusion on energy efficiency by constructing a parametric-based index that covers availability, accessibility, and usage of the former. The index provides an overall picture of financial services in all the three covered aspects. The analysis based on novel econometric time-series approaches, the method of moment quantile regression and the robust least square approach, shows that financial inclusion increases energy efficiency. The impact is overall positive for each quantile, i.e. 25th–90th. The impact of green innovation, human capital, and political risk is also supportive for energy efficiency. However, the impact of political risk improvement exhibits negative in the initial stages and turns positive later. In terms of policy implications, broadening financial inclusion base with the promotion of green innovation and human capital is important to achieve higher energy efficiency. In the meantime, improvement in the political risk profile should also be considered for a stable financial system to pave the way for increasing energy efficiency. [ABSTRACT FROM AUTHOR]

Reducing the power consumption of oil production wells is one of the important directions of oil industry development in the context of transition of many fields to the late stages of operation. At the same time, there is often a need to limit hydrocarbon production, which is often accomplished by shutting down wells completely. In this paper, the authors propose an approach to improve the energy efficiency of the electric centrifugal pump unit through the use of periodic operation mode, which allows to reduce the specific power consumption of the electric centrifugal pump unit. Also, this mode can be considered as a way to regulate the well flow rate. According to the results of calculations it was obtained that the reduction of specific power consumption of the unit when operating at nominal frequency can reach 5% relative to the continuous mode of operation. An assessment was made of the effect of reducing the frequency of the supply voltage in a periodic mode on the parameters of the operation of the installation and it was found that the well flow rate increased by 1.43% with a reduction in specific power consumption by 1.64%. [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]

In this study, we employ the Shephard energy distance function to evaluate the impacts of three broad aspects (i) government involvement, (ii) firm's managerial behaviour and (iii) technology adoption on energy (in)efficiency. Using a true fixed-effect model with a firm-level dataset of eight major industries in Vietnam from 2011 to 2016, the results suggest that these three aspects influence energy efficiency at varying degrees. The novel finding is that government involvement in business improves energy efficiency in less efficient industries. Furthermore, import activities negatively impact energy efficiency while the quality of labour increases efficiency. However, production expansion investment worsens the energy inefficiency situation. [ABSTRACT FROM AUTHOR]

Puppeteer crawler technology is used to obtain regional industrial policy documents, and we make classification and assignment from the perspective of the legal effect of industrial policy documents to quantify industrial policy variable. At the same time, Super-SBM model is used to measure energy efficiency in different areas, and spatial Durbin model is constructed according to the panel data of 30 provinces in China from 2006 to 2018. The main conclusions are as follows : (1) The spatial distribution of China's energy efficiency has a remarkable positive correlation from the global perspective. (2) The local industrial policy has a significant positive effect on its energy efficiency, while a negative spatial effect on energy efficiency in neighbouring regions. [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]

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]

The aim of this paper is to investigate agricultural sustainability as a collective issue involving multiple rather than individual farms. Through the utilization of energy consumption as a proxy, we propose a novel methodology that evaluates the impact of farm consolidations on agricultural sustainability while accounting for resource preferences. Our approach incorporates the ordered weighted averaging (OWA) operator within an inverse data envelopment analysis (IDEA) model to identify post-merger farms that meet preset efficiency targets. We employ a DEA cross-efficiency (DEA-CE) procedure to select merger plans that maximize agricultural sustainability for each preference scenario. By analysing a case study of 43 tomato greenhouse farms in Biskra, northern Algeria, our findings demonstrate that mergers can significantly enhance agricultural sustainability, surpassing the potential of individual farms by a factor of over 15. Additionally, the adoption of the most sustainable merger plan can lead to energy savings of more than 69%. Irrespective of the preference scenario, substantial energy savings in machinery, fertilizers, diesel, and electricity ranging from 22.92% to 73.73% were observed. These results emphasize the strategic role of merger processes in promoting agricultural sustainability and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

There is a lack of studies on joint optimisation of flexible job-shop scheduling problem (FJSP) considering energy consumption and production efficiency in the machining-assembly system. Thus, in this paper, we propose a methodology for multi-objective optimisation of energy-aware flexible job-shop scheduling during machining and assembly operations. First, a mixed integrated mathematical model is developed to improve production efficiency and minimise energy consumption. Then, a novel heuristic algorithm integrated particle swarm optimisation (PSO) and genetic algorithm (GA) is developed to address the established multi-objective problem. Moreover, numerical examples are carried out to verify the validity and performance of the solving methods in achieving energy awareness in the manufacturing system. Computational results are presented to demonstrate the advantage of solving the problem compared with the exact method and common heuristic algorithms, and the trade-off between production efficiency and energy efficiency is analysed to make the final decision for managers. [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]

Cognitive radio is a successful technique for utilizing the unused and under-used spectrum, and dynamic spectrum access is one of the major facilitators in making this happen. When a secondary user (an unlicensed user) interferes with the licensed user, the idea of using unused or under-utilized spectrum offers a challenge. Therefore, effective spectrum sensing is necessary to ensure the primary user's protection and the successful transmission of data by the secondary user. An Optimal Incentive algorithm is suggested to meet this need. It effectively uses the available idle channel based on the joint optimization of sensing time and transmission time without interfering with the primary user. The proposed work also contributes to a significant increase in energy efficiency with minimal interference. Simulation results show an increase in efficiency when compared with the algorithms, namely, exhaustive search and sub-optimal algorithms. [ABSTRACT FROM AUTHOR]

This study aims to analyse the connection between energy efficiency and renewable energy consumption in the emerging seven (E7) economies during the period 1990–2020. This study also examines the impact of economic growth, carbon emissions and technological innovation on renewable energy. This study employs various panel data approaches that validate the irregular distribution of data and the heterogeneous slopes coefficients. The cross-section dependence test confirms that cross-section dependence is present in the study variables. While these variables are cointegrated. Using non-parametric panel data approaches, the moments' quantile regression results unveil that economic growth is positively associated with renewable energy in all quantiles. Whereas energy efficiency and carbon emissions showed mixed results, negatively affect renewable energy consumption in the lower quantiles, insignificant in the medium quantiles and positive in the higher quantiles. On the other hand, technological innovation is found negatively related to renewable energy consumption. Bidirectional causal association is found between explanatory variables and renewable energy consumption. Based on the empirical findings, this study suggests policies to divert economic growth from fossil fuel energy consumption, enhancing investment in the renewable energy sector, promoting energy efficiency and investment in environmental-related technologies to promote renewable energy. [ABSTRACT FROM AUTHOR]

In the recent literature, energy efficiency got the attention of scholars due to its discouraging impact on CO2 emissions, which is considered the most prevalent greenhouse gas that human activities produce. Data reports that China is the leading CO2 emitting country across the globe, and still the environmental degradation is in progress. Thus, the current paper empirically investigates the impact of energy efficiency (ENEF), financial inclusion (FD), GDP, export diversification (EXD), and human capital index (HCI) on the environmental degradation of China over the period from 1988 to 2018. This study uses various time-series tests to empirically investigate the determinant of CO2 emissions, including normality tests, unit root tests, and combined cointegration tests. Besides, the long-run coefficients are analyzed via the fully modified ordinary least square (FMOLS), dynamic OLS (DOLS), and the Canonical Cointegrating Regression (CCR) estimators. The empirical findings reveal that all the variables are cointegrated in the long run. However, the coefficient estimate shows that ENEF and HCI significantly promote environmental sustainability. While GDP, FD, and EXD significantly promote environmental degradation by enhancing the CO2 level in the atmosphere. This study recommends practical policy implications based on the empirical findings: energy-efficient products and energy sources could be promoted. [ABSTRACT FROM AUTHOR]

This study examined renewable energy, energy efficiency which is very essential to boost up environmental quality. This research also takes other factors which has impact on greenhouse gas emissions in the BRICS nations between 2000 and 2019. On the other hand, this research differs from previous research in that it considers the impact of Energy Efficiency and Renewable Energy Electricity on greenhouse gas emissions in BRICS economies, despite the fact that Energy Efficiency and Renewable Energy Electricity have the greatest impact on greenhouse gas emissions in BRICS economies. The influence of GDP and political risk index on energy-related greenhouse gas emissions also comes under the scope of this study. Further, the order of unit root was determined using cross-sectional dependency and slope heterogeneity. The results were achieved utilising moment quantile regression (MMQ). The analysis shows that GDP causes an increase in Energy-Related Greenhouse Gases (ERGHG) at the 25th, 50th, 75th and 90th quantiles. At the 25th, 50th, 75th and 90th quantiles, however, Energy Efficiency and Renewable Energy Electricity reduce Energy-Related Greenhouse Gases. Furthermore, political risk index reduces Energy-Related Greenhouse Gas emissions. Moreover, policymakers should investigate the asymmetric behaviour of Energy Efficiency, Electricity from Renewable Energy and GDP when adopting BRICS economies energy, environment and growth policies based on empirical data. [ABSTRACT FROM AUTHOR]

Fuel consumption is an important factor affecting both costs and environmental impact of a logging operation. Traditionally, fuel consumption has been estimated through studies of individual machines, through follow-up studies based on company records or through questionnaires. Using automatically collected data from machine computers, in the form of standardized harvester production (hpr), forwarder production (fpr), and machine operational monitoring (mom) files, offers a new way to estimate fuel consumption. This study aims to estimate fuel consumption in cut-to-length logging operations based on automatically recorded data, and to evaluate whether this data is sufficient on its own or needs to be supplemented with additional data. Average fuel consumption per harvested m3 varied from 1.4 to 3.4 l, depending on machine size, tree size, and transport distance. The study shows that fuel consumption per machine type or logging team can largely be explained using the machine data. Classification of machines into size classes is a challenge, as machine configurations vary between individual machines, but such classification may not be needed. Forwarded volumes are a source of error, since this data is manually fed into the machine computer and, therefore, often deviates from the felled volume in a harvesting block. This will probably cause a slight overestimation of forwarder fuel consumption. [ABSTRACT FROM AUTHOR]

This study aims to investigate organisational factors of blockchain adoption for carbon trading and energy efficiency. This study develops a new theoretical model based on technology, organisation and environment (TOE), and Diffusion of Innovation (DOI) theories to fill the literature gaps. The underpinning theories are utilised to capture the motivation of manufacturing industry firms to adopt blockchain technology while supporting carbon trading activities. Using survey data, we examine the perception of early adopters of blockchain technology and their relationships to energy efficiency. The results show that attributes such as the contract system, firm size, and compatibility significantly affect energy efficiency. Paradoxically, top management support, competitive pressure, regulatory support, relative advantage, and complexity were not, as prior literature suggests, crucial drivers. Adopting blockchain technology can help a firm improve business transparency, manage energy consumption, and provide a strong business case for firms in this study. [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]

Although the nexus between urbanization and energy efficiency has been evidenced at the macro level, we have less knowledge about the effects of administrative level of cities on energy efficiency. Understanding this impact mechanism is necessary to achieve goals of energy conservation and sustainable development. We estimate the relationship between the administrative level of cities and energy efficiency in China by adopting the province-level fixed-effect models from a panel dataset. The empirical findings present that energy efficiency of capital cities is 0.46 (95% Confidence Interval: −0.713, −0.206) lower than that of general cities. Moreover, these effects of administrative level are more substantial in underdeveloped cities. These findings suggest that more political sources and autonomy in the higher-level cities might lead to low energy efficiency. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is an important pathway for the development of green and low-carbon economy. Based on the evidence from China's green finance reform pilot zone, we apply the data of Chinese prefecture-level cities from 2005 to 2020 to analyze the action mechanism among green finance, technological innovation and energy efficiency. We find that green finance effectively promotes energy efficiency. Moreover, the analysis of action mechanism reveals that while green finance can drive green technology innovation, only high-quality green technology innovation (green invention patents) can significantly increase energy efficiency. The heterogeneity analysis reveals that green finance has a more significant effect on increasing energy efficiency in regions with large-scale economic development and weak environmental regulation. Furthermore, digital technology can amplify the driving effect of green finance on energy efficiency, and the higher the level of digital technology development, the greater the driving effect of green finance. The findings provide empirical evidence and policy implications for building a market-based green financial system and promoting green and efficient development in regions. [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]

Energy efficiency is one of the most researchable topics of the energy constrained Wireless Sensor Networks (WSN). Due to the widespread usage of WSN in real-time applications, network lifetime is the serious concern and numerous research solutions are presented in the literature for handling this issue. Several techniques are employed to handle energy consumption and the most promising techniques are clustering and routing other than data compression. Hence, this paper presents a clustering and routing-based solution for energy efficient WSN with trust notion as its base. The nodes are clustered with the help of Dempster-Shafer Theory (DST), while the best route is selected by employing Lion Optimization Algorithm (LOA). The work efficiency of the proposed work is proven with reasonable outcomes with respect to different performance measures. This work proves an average packet delivery rate of 94.24%, average latency of 13.43 s and 191 alive nodes at the end of 500th second better network lifetime when compared to the existing works. [ABSTRACT FROM AUTHOR]

GHG emissions growth of Vietnam is highest all over the globe and the carbon intensity of this economy is considered second highest in Asian economies. As the energy intensity level is rising constantly, thereby, the predictions indicate that Vietnam will become highest GHG emission in the region. In order to address the issue, the current article aims to investigate the impact of green finance, technology advancement, energy efficiency, industrialization, and population growth on sustainable development in Vietnamese context. The time chosen for the study is 1991 to 2020. The study has used the Dynamic Auto-regressive Distributed Lags (DARDL) and the Bayesian Auto-regressive Distributed Lags (BARDL) model for data analysis. Findings exposed that green finance, technology advancement, REO, REC, industrialization, and population growth all are positively connected with sustainable development. The study guides the regulators in establishing regulations related to sustainable development through the adoption of green finance, energy, and technology. [ABSTRACT FROM AUTHOR]

It is reasonable to expect that the energy needs of the top emerging economies will increase over the coming decades as their economies expand rapidly. There may be environmental costs to using conventional energy from fossil fuels to meet increased energy demand. Therefore, this study aims to investigate the link between energy efficiency and energy transition along with additional control variables like research and development expenditures, trade, and gross domestic product for the top 10 emerging countries between 1990 and 2021. To study the case of these targeted economies, the authors use panel data and novel panel data econometrics techniques for a long-run relationship like the MMQR to observe changes over time between the variables, which is useful in the empirical evidence. The models' primary findings are as follows: The panel cointegration tests confirm log-run associations among the targeted variables. Energy efficiency has the largest influence on the energy transition. The control variables like RDR and TRA decrease renewable energy consumption in the targeted economies. The findings also support the notion that GDP drives renewable energy consumption in the leading emerging economies. Numerous important ecological sustainability-related plans are recommended to the concerned governments based on research findings. [ABSTRACT FROM AUTHOR]

The digital economy has become important in the world's major economies. Improving energy efficiency is the key to achieving stable and sustainable economic growth and carbon emissions reduction. However, the impact of the digital economy on energy efficiency remains unclear. Accordingly, this study examines the relationship between the digital economy and energy efficiency from the Environmental Kuznets Curve (EKC) perspective. In doing so, this study confirms that the digital economy follows the EKC in energy utilization efficiency, and there is a U-shaped relationship between the digital economy and energy efficiency, although this relationship differs from one region to another. This study also discusses the moderating effects of environmental regulation and innovation capability in this U-shaped relationship, confirming that they have a moderating effect on this relationship, reducing the inflection point of the U-shaped relationship, and reducing the negative influence of the digital economy on the energy efficiency and enhancing the positive impact. This study can serve as a reference for policymakers and professionals in emerging economies, helping them achieve a win-win situation for economic development and reducing carbon emissions. [ABSTRACT FROM AUTHOR]

The issue of climate change and its impact on every field of life has increased manifold during the 4.0 industrial revolution. We explore the driving factors of a sector-level carbon intensity which is essential to determine the targeted emissions reduction strategy in the developing economy. To execute this purpose, the study has been integrated by joining production and index decomposition with a spatial-temporal decomposition analysis to estimate the comparative performance of a sector. We cover nine significant factors for this purpose: the economic efficiency effect, the intensity effect, the gross domestic product gap effect, the structure effect, and the energy use efficiency effect. Moreover, this study utilized an updated set of data from three economic sectors, including the agriculture, services, and industrial sectors during 2006–2019 to estimate the energy-related carbon dioxide emission. According to our results based on the above classification, the performances of all these sectors are relatively either above, average, or below level. The economic and energy usage efficiency effects have a high association with one another, and both have above-average performance; however, the GDP gap effect has a lower performance. The service sector shows mixed results, whereas the performance of the agriculture sector remained unsatisfactory in this perspective. [ABSTRACT FROM AUTHOR]

Countries around the globe are rapidly targeting energy efficiency goal achievement due to the unproductive and inefficient use of traditional energy sources. Several factors are discovered that are critical for energy efficiency in the region. Still, there are many economic, financial, energy, and research and development factors that could influence energy efficiency and remained ignored in the scholarly research, which is important from economic growth as well as environmental sustainability perspective. This research contributes to the existing literature by providing novel factors affecting energy efficiency in the developed nations. Specifically, the current study investigates the influence of financial inclusion, energy R&D, political-economic-financial risk index, and the energy-related inflation on the energy efficiency of G7 economies covering the period from 2004 to 2020. This study employed the slope heterogeneity and cross-section dependence test, which led to using the second-generation unit root test. For empirical estimations, the current study utilizes the panel Quantile regression, and the outcomes reveal that all the considered variables positively influence the energy efficiency in the region. However, the influence of these variables increases except for the energy-related inflation when moving from lower quantile Q0.25 to medium Q0.50 to higher quantile Q0.75, respectively. The estimated results are found robust, confirmed by the FMOLS estimator. Based on the empirical findings, it is recommended that financial inclusion and energy-related research and development be enhanced to achieve the region's energy efficiency. [ABSTRACT FROM AUTHOR]

The manufacturing organizations are threatening the earth and its wildlife because of their growing concern about environmental pollution and industrial waste. Hence, in the present study, the three potential solutions, Green Intellectual Capital, Environmental Management Accounting and Energy Efficiency, are evaluated for excelling the organizational operations towards business sustainability and attaining the Competitive Advantage. With the assistance of 'Partial Least Square-Structural Equation Modelling' on the dataset of 364 respondents from the manufacturing organizations in China, the outcome reported the positive and significant impact of all of the studied potential solutions in excelling and enhancing business sustainability and competitive advantage. Based on the findings, it is proposed that manufacturing organizations need to apportion due attention to developing the green intellectual capital, improve the level of consumption of energy and need to disclose their environmental management through proper Environmental Management Accounting. [ABSTRACT FROM AUTHOR]

Environmental degradation is one of the main concerns for the countries across the globe, where energy efficiency (E.N.E.F.) is regarded as one of the substantial remedial measures. Still, the factors affecting E.N.E.F. is not extensively explored in the empirical literature. In this sense, the current study tends to analyse the influencing factors of E.N.E.F. in case of the G7 economies throughout 1990–2020. Since this study is dealing with the panel data, therefore, various panel data specifications are used, which validates the slope heterogeneity, panel cross-section dependence (C.D.), and the existence of cointegration between E.N.E.F., economic growth, renewable energy, energy related inflation, and political risk index (P.R.I.). Due to mixed integrating order, this study employed Cross-Sectional Autoregressive Distributed Lag (C.S.-A.R.D.L.) approach, which reveals that all the variables are significant and positive factors of E.N.E.F. in both short and long-run. Also, the results reveals the convergence of model towards the equilibrium with 83.7% speed of adjustment. To tackle the panel data issues such as slope heterogeneity and C.D., this study employed Dynamic Common Correlated Effects–Generalised Method of Moment (D.C.C.E.–G.M.M.), which also indicates the positive and significance influence of the selected variables on E.N.E.F. The estimated results are validated by Augmented Mean Group (A.M.G.) estimator. Moreover, bidirectional causal nexus is found between E.N.E.F. and regressors (economic growth, renewable energy, energy related inflation, and P.R.I.). This study also provides relevant policy measures at the end. [ABSTRACT FROM AUTHOR]

The world is facing enormous challenge of climate change and global warming due to increased emission level. In order to overcome such challenges, economies are adopting energy efficient techniques to control the carbon emissions and improves environmental sustainability. This study analyses the influencing factors of environmental quality from a global perspective throughout the last three decades. In this regard, advanced time series approaches are used to identify the association between factors such as economic growth, energy efficiency (E.N.E.F.), and carbon emissions – covering global data over the period 1990Q4–2020Q4. From the time series methods, this study observed the stationarity of all variables at first difference. The empirical outcomes also validates the long-run equilibrium relationship between the variables. Due to asymmetric distribution of the variables, this study uses the novel Quantile-on-Quantile (Q.Q.) regression approach, which reveals that increasing economic growth harms environmental quality by increasing the carbon emissions level. However, E.N.E.F. is a prominent factor of environmental sustainability, that reduces the level of carbon emissions in the atmosphere. Employing the pairwise Granger causality test, this study observed the unidirectional causality from economic growth to carbon emissions, while a two-way causal nexus is found between economic growth – E.N.E.F. and E.N.E.F. – carbon emissions. Based on the empirical results, this study suggests that economic growth should be regulated in a sense that it contribute towards the improvement of E.N.E.F., which ultimately leads to reduce the emissions level and promote environmental sustainability. [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]

There is a need to increase energy efficiency and use resources sustainably by means of energy-efficient building systems. In the study, vertical greening systems were integrated with a Double skin façade system and were applied as a Double skin green façade. Different improvement scenarios were produced for the cities of Karabük and Antalya, which have two different climates. Then, the scenarios were simulated with DesignBuilder program, and energy consumption values were determined accordingly. As a result, the energy efficiency of the DSF scenario applied to the south and west façades for Karabük and the scenarios with 30% greenery coverage on the façade were determined to be better. DSF applied on the south side of building was considered to be more appropriate for Antalya. [ABSTRACT FROM AUTHOR]

In this case study, the fuel consumption of a laden (i.e., the laden portion of the transport cycle) 92-tonne (92 t) high-capacity transport (HCT) timber truck combination was measured, modeled and compared to that of a normal 76 t timber truck combination in Finland. Fuel and CO2 emission savings, as well as potential resulting cost savings were also calculated for a future scenario where HCT (92 t) timber trucks are commonly utilized instead of normal (76 t) timber trucks for industrial roundwood hauling from wood terminals to mill yards. The data were collected during a long-term follow-up study in 2019 and 2020. The results revealed that the average payload weight was 27% higher for the HCT 92 t timber truck combination than the 76 t truck combination. Fuel consumption (using all two indicators, L 100 km−1 and mL tkm−1) for timber hauling with both laden vehicle combinations was higher in wintertime than summertime. The laden HCT 92 t timber truck combination had 8–11% lower fuel consumption (in mL tkm−1) than the 76 t truck combination over transport distances of 100–300 km. The calculations showed that there is a significant potential to achieve fuel, CO2 emission and cost savings using HCT vehicle combinations. On the basis of the study, it can be recommended to increase the number of energy-efficient HCT vehicle combinations in timber hauling. Consequently, lower fuel consumption and CO2 emissions and higher environmental performance of industrial roundwood transport can be achieved. [ABSTRACT FROM AUTHOR]

Ever since energy efficiency provisions were introduced into the National Construction Code in 2003, there have been increased requirements for energy-efficient measures with each iteration of the code, particularly around insulation. This is based on the often-repeated objective of reducing Australia's greenhouse gas emissions in the light of climate change. However, there is limited evidence whether the code achieved its stated objectives and whether these will continue to be suited to a changing climate. In fact, there is evidence that the intended and achieved goals are substantially different. This paper is a critical essay, set in the context of the Australian Building Codes Board's scoping study into "Energy efficient measures for 2022 and beyond", and makes the case that current insulation requirements are already excessive against considerations that should be made, as a higher priority, for fire safety, thermal bridging, condensation, heat stress resilience and thermal comfort. [ABSTRACT FROM AUTHOR]