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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]

VANETs (Vehicular Ad-hoc Networks) are a kind of Mobile Ad-hoc Networks (MANETs) with increased mobility as well as occasional geographic changes. Clustering is used in VANETs to divide networks into groups of adaptable vehicles to optimize routing, information gathering, as well as routing. As large data block transmission contains several stages and inside those, one of the important stages such as optimal model selection was these selections of quintessential features helps in a further stage called data transmission much more effective. This stage will also bring energy optimization and also improves the effectiveness of the optimal path for data transmission. This paper brings a comparative analysis of this optimal relay node selection. Here, we use Linear Scaling based Red Colobuses Monkey technique and are contrasted with other state-of-the-art models over various measures. Analysis proves that the proposed optimal relay node selection technique boosts the data transmission model. [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]

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 last few decades, the environmental degradation, global warming, and climate change remained the life-threatening issues across the globe. Specifically, the use of non-renewable fossil fuels is regarded as the most significant reason of such inclined issues. However, the policy-makers and scholars are now more concerned about the recovery of environmental quality, where renewable energy is considered as the primary solution to the existing issue. The developed economies and environment related international organizations rapidly enhances the use and promotion of renewable energy after the taking pledge in the Kyoto Protocol Agreement (1997). Nonetheless, the scholars are participating in analyzing the key drivers of renewable energy. Still, the proper drivers of renewable energy are not properly addressed in the existing literature. To fill this gap, current study analyzed the group of seven (G7) economies over the period 1990-2020. Using various panel data techniques such as slope heterogeneity, cross-section dependence, unit root, and cointegration test, the results indicates that the slopes are heterogeneous, and the cross-section dependence, as well as cointegration exists among the panel economies. Besides, the irregular distribution of data leads to the adoption of novel Method of Moments Quantile Regression accommodating four quantiles, i.e., ( Q 0.25 , Q 0.50 , Q 0.75 , and Q 0.90 ). The examined results asserted that economic growth and energy efficiency negatively and significantly affects renewable energy consumption ( REC). Whereas, developed environmental related technologies, environmental taxes, and composite risk index are positively affecting REC in the study panel. Besides, improved energy efficiency (energy efficiency squared) also contributed to the promotion of renewable energy consumption. The Granger causality test estimates reveals bidirectional and unidirectional causal association between the variables. Based on the empirical results, policies are provided that could help developed economies in the promotion of renewable energy consumption, improve economic growth, and enhances environmental sustainability. [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]

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]

Energy efficiency and renewable energy strategies have been insufficient in achieving rapid and profound reductions of energy-related greenhouse-gas (GHG) emissions. Consequently, energy sufficiency has gained attention as a complementary strategy over the past two decades. Yet, most research on energy sufficiency has been theoretical and its implementation in policy limited. This study draws on the growing sufficiency literature to examine the presence of sufficiency as a strategy for reducing energy-related GHG emissions in Sweden, a country often regarded as a "climate-progressive" country. By conducting a keyword and content analysis of energy policies and parliamentary debates during four governmental terms of office (2006–2022), this research explores the extent to which sufficiency is integrated into Swedish energy policy, as well as potential barriers to its adoption. The analyses revealed a scarcity of sufficiency elements. Although some policies could potentially result in energy savings, they are infrequent and overshadowed by the prevailing emphasis on efficiency and renewable energy. Furthermore, Sweden lacks a target for sufficiency or absolute energy reductions. The main impediments to sufficiency implementation include the disregard of scientific evidence in the policy-making process and the perceived contradiction between sufficiency and industrial competitiveness. This study thus concludes that sufficiency at best remains at the periphery of Swedish energy policy. Given the reinforced ambitions within the European Union, this raises questions regarding the validity of Sweden's reputation as a climate-progressive country. [ABSTRACT FROM AUTHOR]

To broaden the efficient operating zone and increase the energy efficiency of a multi-stage double-suction centrifugal pump, a multi-component and multi-condition optimization design method involving high-precision performance predictions, a flow loss visualization technique based on entropy production theory, and machine learning is proposed. First, the accuracy of the baseline pump numerical methodology is verified via a grid convergence analysis and experiments. Thereafter, nine design parameters of the impeller and double volute are selected as design variables. Subsequently, 150 designs are created according to the Latin hypercube sampling method (LHS) and numerically simulated using an automatic simulation program. A backpropagation neural network (BPNN) and a multi-objective genetic algorithm (MOGA) are adopted to maximize the efficiency at 0.6Qd, 1.0Qd, and 1.2Qd. Finally, the optimal results are verified via numerical calculations and analyzed. The results indicate that the efficiency of the optimized pump is increased by 2.05%, 3.56%, and 5.36% at 0.6Qd, 1.0Qd, and 1.2Qd, respectively. The comparative analysis of the energy characteristics reveals that the improved performance of the optimized pump can be attributed to the improved matching between the rotor and stator. This research further demonstrates the accuracy and reliability of the optimization method using an artificial neural network (ANN). [ABSTRACT FROM AUTHOR]

Since the last few decades, scholars and policy-makers have been struggling to find ways to achieve carbon neutrality target or a low carbon economy. To contribute to the existing literature regarding the said issue, this study aims to investigate whether energy efficiency could lead to achieving carbon neutrality target in the case of China. Also, this study analyses the association of economic growth to energy-related greenhouse gas emissions while using quarterly data over the period from 1990Q1 to 2014Q2. Empirical findings of the study suggest the mixed order of integration and Cointegration between economic growth, energy efficiency, and energy-related greenhouse gas emissions. This study employed a Quantile-on-Quantile regression approach to examine the long-run association at various quantiles. The estimated results asserted that energy efficiency holds a weaker relationship in the lower and medium quantiles, while relatively higher association to energy-related emission in the upper quantiles. On the other hand, economic growth and its squared are found significantly and highly associated with enhancing energy-related emissions in the country. Besides, the frequency domain causality indicates a causal association running from energy efficiency and economic growth to energy-related greenhouse gas emissions. This study recommends revised policies for energy efficiency and suggests that economic growth could be used as a remedial measure for environmental recovery by enhancing investment in the renewable energy sector, energy efficiency, and structural transformation of the industrial sector. [ABSTRACT FROM AUTHOR]

There are very few studies on the role of financial inclusion and energy efficiency in promoting a sustainable environment in the existing literature. These studies do not address or link financial inclusion to CO2 emissions in any way. Therefore, the purpose of this study is to look into the role of financial inclusion and energy efficiency on carbon emissions, as well as exports, imports, and gross domestic product (G.D.P.) in the BRICS economies from 1990 to 2020. The study additionally considers the panel data's integration, cointegration, cross-country interdependence, and heterogeneity features, resulting in reliable findings and well-founded policy recommendations. The panel Westerlund cointegration tests confirm the long-run relationships among CO2 emissions, financial inclusion, energy efficiency, exports, imports, and G.D.P. Furthermore, the long and short-run outcomes of CS-ARDL revealed that financial inclusion, imports size, and G.D.P. raise CO2 emissions, while energy efficiency and exports size reduce CO2 emissions. The study proposes increasing financial inclusion for controlling pollution and achieving sustainable environmental goals in light of these findings. Public-sector efforts are needed to integrate financial inclusion goals with continued improvements in energy efficiency and environmental policies. [ABSTRACT FROM AUTHOR]

Emerging seven (E7) are some of the rising economies in the world and are expected to be economically strengthened in the coming few decades due to rapid economic growth. Besides, financial inclusion and globalization are also rising in these economies, which compel them to adopt energy saving techniques to lower carbon (CO2) emissions in the region. This study aims to explore the influence of these variables on CO2 emissions in E7 economies over the period from 2004 to 2019. Various panel econometric approaches reveal that all the variables are stationary at first difference. Also, the long-run cointegration association exists between them. The non-normal distribution of data leads to the adoption of the panel quantile estimator for the long run estimations across three quantiles (i.e., q0.25, q0.50, and q0.75). The empirical findings illustrate that energy efficiency is negatively associated to CO2 emissions in all the quantiles. However, financial inclusion, economic growth, globalization, and composite risk index are the prominent factors of CO2 emissions. Such factors are the primary reasons for environmental degradation in the region. The estimated panel causality test results confirm the feedback effect for the variables except for globalization, which runs toward CO2 emissions. Based on findings, this study suggests policies regarding the encouragement of energy efficiency and alteration of economic growth from non-renewable energy sources to renewables. Devotion of financial inclusion towards green finance and green bonds promotion and reducing composite risk to promote environmental sustainability. [ABSTRACT FROM AUTHOR]

This article measures the impact of R&D investment and carbon dioxide (CO2) emission of the Shanghai and Shenzhen stock market listed companies on the green governance efficiency (G.G.E.). An econometric analysis based on the data from 2015 to 2019 is used to measure the impact. The study's findings reveal that research and development (R&D) investments significantly boost G.G.E. On the other hand, CO2 emission and energy intensity reduce G.G.E. The study results show that the listed companies' performance would have a significant role in achieving the Chinese government's carbon neutrality goal. The study provides policy recommendations to promote green governance performance in China and other developing countries. [ABSTRACT FROM AUTHOR]

Since the last four decades, energy demand has been reached to the utmost level, which also leads to emissions and causes environmental degradation, global warming and climate change all over the world. In this sense, policy makers have suggested various measures including renewable adoption and energy efficiency. Current study aims to investigate the influence of economic growth, energy consumption, renewable electricity output, and energy efficiency on the energy related emissions. A panel of 12 RCEP economies are examined covering the period 1990-2020. Since the data follows irregular path, therefore a novel method of moment panel quantile regression is employed along with the Granger causality test. The empirical results indicate that economic growth and energy consumption significantly enhances energy related emissions, where the magnitude and significance level is found strengthening from lower to upper quantiles (Q0.25, Q0.50, Q0.75 and Q0.90). Conversely, renewable electricity and energy efficiency are the significant tools for lowering energy related emissions in the region. Additionally, a unidirectional causality is found from energy consumption and renewable electricity output to energy related emissions. However, a feedback effect is validated between economic growth, energy efficiency, and energy related emissions. Based on the empirical findings, this study suggests enhancement of renewable electricity output and adoption of energy efficient technologies to reduce environmental degradation and emission level. [ABSTRACT FROM AUTHOR]

The role of sustainable financial inclusion is considered crucial for attaining energy efficiency. It is vital for acquiring low carbon energy sources for an economy. This study attempt to integrate financial inclusions as a major defining factor of efficient energy combined with the role of economic growth, environment-related technologies, and the human capital index for RCEP countries from 2004 to 2019. The Westerlund error correction cointegration test is adopted for co-integration purposes, while the augmented mean group (AMG) method is carried for short-run and long-run analysis, and the dynamic ordinary least square (DOLS) test is used to ensure the robustness of the model. The outcomes indicate that financial inclusion induces a positive impact on renewable energy efficiency and ensures low energy sources for RCEP economies. Further, the role of economic growth, environment-related innovation, and human capital is also important for achieving energy efficiency. Furthermore, in the short-run, the Dumitrescu and Hurlin (Economic Modelling, 29(4), 1450–1460, 2012) panel heterogeneous non-causality test underscores causal bi-directional relationship among these variables. Based on these findings, the policymakers ensure improvement in the financial system and promote financial inclusion to increase energy efficiency and improve their citizen's living standards. [ABSTRACT FROM AUTHOR]

Using efficient and cleaner energy is environmentally friendly and is vital for combating the negative effect of emissions on the environment. Focusing on both developed and developing nations, it is important to report their environmental conditions while targeting economic and energy-related factors. In this regard, the current study is an attempt to investigate the influence of energy efficiency (ENEF), financial inclusion (FIN), economic growth (GDP), environmental-related technological innovation (ERTI), and human capital index (HCI) on the carbon dioxide (CO2) emissions for the five selected nations of RCEP. The variables are found associated in terms of long-run cointegration relationships. The panel quantile regression estimator is utilized for empirical estimations, which provide highly significant estimates across the three selected quantiles (25th, 50th and 75th). The results report that FIN and GDP significantly aggravate environmental degradation by enhancing the CO2 emission level, among which the strongest CO2 emission growth is found in the second quantile. Besides, the ENEF, ERTI and HCI significantly reduce CO2 emission. Based on the empirical findings, this study provides practical implication focusing on the improvement of energy efficiency policies and revising financial inclusion policies in promotion of green investments. [ABSTRACT FROM AUTHOR]

Continuous rise in a global economy with a 3–4% annual growth rate poses a severe risk to environmental sustainability due to high energy demand. Since the Paris climate accord, countries worldwide have implemented numerous strategies to attain the target of carbon neutrality. With the rising environmental challenges, it is important to consider global financial inclusion (F.I.) policies. This study uses panel data for the B.R.I.C.S. countries to investigate the impact of F.I. and energy efficiency in limiting trade adjusted emissions (T.A.E.) taking technological innovation and trade as control variables. This study uses panel data consisting small sample size and large time period; therefore, keeping in mind the potential econometric problems, this study uses AMG method, which can efficiently deal with endogeneity problems and small sample bias. We find a positive impact of F.I. and energy efficiency on CO2 emissions. Moreover, we find that technological innovation, exports and output amplify CO2 emissions. [ABSTRACT FROM AUTHOR]

The popularity of green innovation has dramatically increased in the recent times because of the potential benefits attached with it. Therefore, in order to make the technology more affordable, green innovation is the key to enhancing the affordability factor. On the other hand, in order to safeguard the environment, the role of media is one of fundamental importance. In contrast, energy consumption is often regarded as a key indicator of economic prosperity, mostly at the cost of the environment. Hence, the present study attempts to explore the asymmetric effect of the film and drama industry, energy efficiency, and economic growth on green innovation, with the help of the latest quantile autoregressive distributed lag (QARDL) method for the period 2000Q1 to 2019Q4. The results have reported a positive and significant association of the Film and Drama Industry, Energy Efficiency, and economic growth on the quantiles of Green Innovation. Based on the findings, it is recommended that there is a dire need to develop content that promotes the green innovation, whereas, more investments are to be sought after, so as to enhance the level of energy efficiency. [ABSTRACT FROM AUTHOR]

This study examines causality relation between the quality of national institutional frameworks designed to ensure higher energy efficiency and the distance to the achievement of the EU Commission targets in terms of energy consumption, using a panel of 21 EU member states. Through panel unit root tests and an estimated VAR model, our results reveal that the quality of a nation's institutional and economic energy efficiency framework does not significantly influence the gap between annual energy consumption and related EU targets, as prescribed by the energy efficiency EU Directives. However, the results show a bi-directional causality relationship suggests that the nation's institutional framework on energy efficiency should be improved. According to the impulse response function, this relationship is more related to a short-run perspective. Hence, a strong correlation exists between the level of achieving the reduction targets on energy consumption and the level of gas emissions. Our findings further reveal that the level of reported GHCs significantly influences national regulations and strategies for gas emission reductions. This research highlights the importance of each EU member state creating an appropriate legislative and institutional framework that promotes energy efficiency. [ABSTRACT FROM AUTHOR]

This paper presents the improved version of our cool Cracker cluster (cCc), a heterogeneous distributed system for parallel and energy-efficient bcrypt password hash computation. The cluster consists of up to 8 computational units (nodes) with different performances measured in bcrypt hash computations per second [H/s]. In the cluster, nodes are low-power heterogeneous embedded systems with programmable logic containing specialized hash computation accelerators. In the experiments, we used a combination of Xilinx Zynq-series SoC boards and ZTEX 1.15y board which was initially used as a bitcoin miner. Zynq based nodes use the improved version of our custom bcrypt accelerator, which executes the most costly parts of the bcrypt hash computation in programmable logic. The cluster was formed around the famous open-source password cracking software package John the Ripper (abbr. JtR). On the communication layer, we used Message Passing Interface (MPI)library with a standard Ethernet network connecting the nodes. To mitigate the different performances among the cluster nodes and to balance the load, we developed and implemented password candidate distribution scheme based on the passwords' probability distribution, i.e. the order of appearance in the dictionary. We tested individual nodes and the cluster as a whole, trying different combinations of nodes and evaluating our distribution scheme for password candidates. We also compared our cluster with various GPU implementations in terms of performance, energy-efficiency, and price-efficiency. We show that our solution outperforms other platforms such as high-end GPUs, by a factor of at least 3 in terms of energy-efficiency and thus producing less overall cost of password attack than other platforms. In terms of the total operational costs, our cluster pays off after 4500 cracked passwords for a bcrypt hash with cost parameter 12, which makes it more appealing for real-world password-based system attacks. We also demonstrate the scalability of our cCc cluster. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms behind memory, learning, and behavior is crucial to human development and significant research has been done in this area. Classical and operant conditioning and other theories of learning have elucidated different mechanisms of learning and how it modulates behavior. Even with advances in this area, questions remain on how to unlearn faulty ideas or extinguish maladaptive behaviors. In this paper, a novel theory to improve our understanding of this area is proposed. The theory proposes that as a consequence of the brain's energy efficiency evolutionary adaptations, all learning following memory consolidation, reconsolidation, and repeated reinforcements or strengthening over time, results in a phenomenon called mental representation block. The implications of this block on learning and behavior are significant and broad and include cognitive biases, belief in a creator or God, close-mindedness, dogmatism, physician misdiagnosis, racism, homophobia, and transphobia, susceptibility to deception and indoctrination, hate and love, artificial intelligence and creativity. [ABSTRACT FROM AUTHOR]

This study applies the slack-based measure-data envelopment analysis (SBM-DEA) method to measure energy efficiency and tests the spatial spill-over effects of fiscal decentralisation, industrial structure and energy efficiency using the spatial Durbin model. The results show that China's energy efficiency presents a clear geographical step distribution, and the Eastern and Western regions have higher energy efficiency than the Central region. Fiscal decentralisation has a positive effect on the energy efficiency of the Eastern and Central regions by upgrading the industrial structure. In addition, fiscal decentralisation has significant, positive externalities on the surrounding areas, promoting environmental protection and energy conservation in all regions of China. The results are in line with the tenets of environmental federalism (Oates & Schwab): through the allocation and transfer of industrial factors, fiscal decentralisation affects energy governance. Therefore, local governments should formulate policies and targets according to their regions' different economic development levels, and the Central and Western areas, which have greater space for improvement and low energy efficiency, should receive attention to balance regional differences. [ABSTRACT FROM AUTHOR]