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Improving energy efficiency is one of the most effective ways to address environmental constraints and achieve high-quality economic development. The existing literature suggests that technological progress has an important impact on energy efficiency, but ignores the role of different paths of technological progress. Herein, the impact of different technological progress paths on energy efficiency was analyzed using the dynamic panel data model and the threshold model based on the energy efficiency measurement using the Meta-frontier and Nonradial Directional Distance Function model. The findings of this study were as follows: (1) During the study sample period, a fluctuating upward trend was observed in China's energy efficiency, and there were significant differences in energy efficiency in different regions of China. (2) The energy efficiency in China could be significantly improved by domestic innovation and regional technology diffusion. Even though the imported technologies do not play a significant role in promoting energy efficiency and have no positive effect on the improvement of energy efficiency from the perspective of domestic technology absorption or foreign technology absorption. (3) The eastern and central regions of China improve their energy efficiency through domestic innovation. However, none of the technological progress paths in the western and low-energy-efficiency regions have played a positive role in promoting energy efficiency. (4) The effect of promoting domestic innovation and regional technology diffusion on energy efficiency is gradually increasing with human capital improvement, and the restriction of foreign technology import on energy efficiency has obviously decreased. In a nutshell, different paths of technological progress have differential effects on energy efficiency improvements. Based on the above results, this paper makes some targeted policy recommendations on the choice of technological progress paths to improve energy efficiency in different regions of China. [ABSTRACT FROM AUTHOR]

Energy supply and environmental capacity are important material foundations for social and economic development and constraints for scientific and sustainable development, and with achieving the strategic goals of carbon peak and carbon neutral at an early date, too. Therefore, the international community generally regards energy-economic- environment (3E) as a necessary research direction. This paper studies the relationship among effective energy utilization, economic growth protection and ecological environment protection by establishing the contribution rate model of energy consumption factors to economic GDP growth. At the same time, this paper puts forward the government needs to use carbon trading market, carbon tax, such as low carbon management mechanism based on the Pareto Optimality of carbon trading market, prompts the excess money of the country or the region back to actively invest in low-carbon production, lets more energy companies to clean energy and low carbon production transformation, and helps to achieve the goal of regional energy industry carbon neutral. [ABSTRACT FROM AUTHOR]

This conceptual paper proposes that all methodologies create a footprint like the carbon footprint. Design and implementation of new methodologies require limited resources and funding, and these resources are not equitably distributed on a global scale. Thus, we argue for more ecological uses of methodologies, especially in the context of data collection and interdependent relations of knowledge/information creation. Like the excessive use of energy sources, potentially unnecessary productions of new data, information, and evidence should not be regarded as unproblematic, let alone virtuous. Rather, qualitative researchers, funding agencies, and other bodies that evaluate research, should question whether new data, information, evidence are needed and at what cost. We also propose more data recycling, data sharing, open access data, and other ecological ways of supporting shared knowledge and monitoring excessive data production. [ABSTRACT FROM AUTHOR]

Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems to optimize energy consumption. However, these systems frequently encounter issues with anomalous data such as missing data, redundant data, and outliers data which can undermine their effectiveness. In this paper, an artificial neural network (ANN)-based approach for data imputation is specifically designed to deal with the anomalies in smart home energy consumption datasets. Our research harnesses the power of ANNs to model intricate patterns within energy consumption data, enabling the accurate imputation of missing values while detecting and rectifying anomalous data. This approach not only enhances the completeness of the data but also augments its overall quality, ensuring more reliable results. To evaluate the effectiveness of our ANN-based imputation method, comprehensive experiments were conducted using real-world smart home energy consumption datasets. Our findings demonstrate that this approach outperforms traditional imputation techniques like mean imputation and median imputation in terms of accuracy. Furthermore, it showcases adaptability to diverse smart home scenarios and datasets, making it a versatile solution for improving data quality. In conclusion, this study introduces an advanced data imputation technique based on ANNs, tailor-made for addressing anomalies in smart home energy consumption data. Beyond merely filling data gaps, this approach elevates the dataset's reliability and completeness, thereby facilitating a more precise analysis of energy consumption and supporting informed decision-making in the context of smart homes and sustainable energy management. Ultimately, the proposed method has the potential to contribute considerably to the ongoing evolution of smart home technologies and energy conservation efforts. [ABSTRACT FROM AUTHOR]

Due to climate change conditions, natural ventilation potential may reduce over the years and increase dependence on HVAC systems. Moreover, occupants' behaviour regarding natural ventilation is a significant parameter affecting the thermal-energy performance of residential buildings as people tend to occupy their homes differently depending on their life, work and cultural routines. Therefore, in this study, the thermal-energy performance of a Global South (GS) housing case study located in Brazil was assessed in a future weather context. This paper included two major steps: (1) Optimization procedure to create optimized models based on different occupancy patterns; and (2) Parametric analysis to explore the building's thermal-energy performance for a given constructive design option, occupant behaviour and weather data. The optimization procedure included a multi-objective optimization based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to minimize discomfort hours and cooling energy demand, while parametric analysis explored the occupants' behaviour varieties derived from alternative occupancy patterns, ventilation availabilities and HVAC operation modes. The obtained future context simulation results indicated an increase in discomfort hours and cooling energy demand, while the most appropriate architecture design might vary depending on the occupancy behaviour. [ABSTRACT FROM AUTHOR]

Promoting high-quality economic development, boosting high-level ecological environment protection, and integrating deeply digital economy and energy efficiency has become a general trend. This paper, based on 284 prefecture-level Chinese cities from 2008 to 2018, uses the entropy weight method and super-efficient SBM-DEA model to measure the digital economy and energy efficiency (decompose into energy pure technical efficiency and energy scale efficiency), respectively. After the spatial and temporal evolution characteristics, a fixed-effect model is constructed to study the impact of the digital economy on energy efficiency. The research results show that: (1) the development of the digital economy effectively promotes energy efficiency, mainly reflected in the improvement of pure technical efficiency of energy; (2) the development of both the digital economy and the energy efficiency in China has complex spatial and temporal evolutionary characteristics, and there is heterogeneity in the impact of the digital economy on energy efficiency at the temporal and spatial levels; and (3) digital economy effectively improves energy efficiency by reducing unnecessary energy consumption through the R&D innovation effect. The development of the digital economy has far-reaching significance on the optimization and upgrading of the economic system, accelerating the improvement of energy efficiency and achieving green transformation. [ABSTRACT FROM AUTHOR]

The globe is now in ecological turmoil as a result of the unrelenting increase in global warming. As a result, governments worldwide are committing to decarbonizing the environment, with the United Arab Emirates (UAE) and the Kingdom of Saudi Arabia (KSA) playing an important role in this effort. Hence, this paper evaluates the nonlinear (asymmetric) impact of natural gas consumption on renewable energy consumption and economic growth in the KSA and the UAE utilizing data stretching from 1990 to 2020. The study also considers other drivers of renewable energy consumption and economic growth, such as trade openness and CO2 emissions. The study utilizes nonlinear autoregressive distributed lag (ARDL) to evaluate these associations. The outcomes of bounds nonlinear ARDL (NARDL), affirm the long-run association between the variables in both countries. The nonlinear results show that positive and negative shocks in natural gas consumption have a negative impact on renewable energy in both UAE and KSA. In contrast, positive and negative shocks in natural gas consumption impact economic growth positively. The study proposed vital policy recommendations based on these results. [ABSTRACT FROM AUTHOR]

Investigating the current and future dynamics of energy consumption in modern economies such as the UK is crucial. This paper predicts the UK's energy consumption using data spanning January 1995 to March 2022 by comparing and evaluating the forecast performance of machine learning, dynamic regression, time series and combination modelling techniques. The analysis reveals that the seasonal ARIMA and TBATS hybrid models yield the lowest forecast errors in predicting the UK's electricity and gas consumption. Although the combination forecasts performed poorly relative to other models, machine learning techniques such as neural network and support vector regression produced better results compared to the dynamic regression models, whereas the seasonal hybrid model performed better than the machine learning and time series models. The results indicate that the UK's electricity consumption would either stabilise or decline over the forecast horizon, suggesting that it will take some years for electricity consumption to attain pre-2019 levels. For gas consumption, the results indicate that consumption would either maintain current levels or increase over the forecast period. We also show that combination forecasts do not often generate the best predictions, and therefore, choice of methodology matters in energy consumption forecasting. Overall, changing seasonal patterns, energy efficiency improvements, shift to renewable sources and uncertainties due to the COVID-19 pandemic, Brexit, and the Russia–Ukraine crisis appear to be significant drivers of energy consumption in the UK in recent times. These findings are expected to help in designing more effective energy policies as well as guide investor decisions in the energy sector. [ABSTRACT FROM AUTHOR]

This paper is concerned with the impact on the global greenhouse effect issues and focus on comprehensive multi-indicator though analyze the relationship between CO2 emissions and real GDP, energy intensity, fraction of the renewable energy supply out of the total energy supply, and energy consumption from different sectors, including the agricultural, service, industrial and transport sectors. The panel data encompass all 37 Organization for Economic Co-operation and Development (OECD) member countries covering 20 years from 2000 to 2019 and divide into two subsamples, the first and second ten years, for comparison. The empirical results of all parameters track the sound progress of green growth trend in OECD countries in the second ten years (2010–2019). According to the OECD, green growth strategy had been acknowledged in OECD Ministerial Council Meeting in June 2009. Thus, the study divided into the first and second ten years with 2010 as the boundary to compare and the empirical result also indicates the green growth strategy in OECD countries have gradually matured in the last ten years. The implementation of the policy framework and measures includes pricing, removing perverse subsidies, ensuring the regulatory standards of energy efficiency, innovation, and infrastructure investments might also provide a reference and important information for energy policy makers. [ABSTRACT FROM AUTHOR]

In line with the importance of SDG-7, a number of studies have endeavored to divulge the changes in renewable energy consumption (REC); however, the literature fails to either understand the importance of technology i.e., information communication technologies (ICT) and macroeconomic uncertainty in this context or employ robust econometric techniques. This research paper extends the prior literature by focusing on technology and macroeconomic uncertainty as novel determinants in addition to natural resources, human development, globalization, and economic growth as control variables of renewable energy for the top 10 renewable energy-consuming countries by applying several second and third generation econometric tests on annual data from 1990 to 2017. The empirical estimations determine ICT as a crucial factor of renewable energy, suggesting that it significantly triggers REC in the top economies. Conversely, the detrimental effects of uncertainty tend to shrink REC. Furthermore, natural resources, human development, globalization, and economic growth significantly boost REC as consistent with the existing literature. Based on these findings, this study suggests several SGD-oriented policies. [ABSTRACT FROM AUTHOR]

The increasing demand, rapid consumption, price increase, limited reserves, and environmental concern due to pollution produced by conventional fossil fuel (diesel & gasoline) are a few reasons why biofuels need to be explored. The present paper employs a systematic methodology to examine the performance of a 20% volumetric blend of Sal biodiesel (S20) blended with diesel using α-aluminium oxide (α-Al2O3) nanoparticles (NP) as additives and is compared with a diesel under like circumstances. The central composite design, Box-Behnken design (BBD) based response surface methodology, and desirability tests are used in the organized experiments on a diesel engine configuration to facilitate calibration. The created multivariate regression model yields all of the best engine inputs. Interaction effects are used to determine the most influential element by observing the interaction of two distinct input factors on a single response. According to the desirability tests, the highest estimated desirability was 0.579; the optimal input parameters found are 21°bTDC injection timing (IT), 238 bar injection pressure (IOP), 17 compression ratio (CR), and 74 ppm concentration of α-Al2O3NP, estimated the optimized response of brake thermal efficiency (BHTE) 31.18%, brake specific fuel consumption (BSFC) 0.2975 kg/kWh, carbon monoxide (CO) 0.0887%, hydrocarbon (HC) 31 ppm, oxide of nitrogen (NOx) 677 ppm, and smoke level 54.92%. These predicted values were validated with experimental results, and errors were within the range. The nanoparticle combination sample offers improved brake thermal efficiency (BTHE) and lower BSFC rate than the S20 while testing for the optimal parametric condition. Graphical abstract [ABSTRACT FROM AUTHOR]

This paper presents a virtual sensing approach for monitoring the elevator installations. The aim of the proposed virtual sensing approach is to monitor the condition of elevators without installing additional sensors. The adopted virtual sensing approach comprises a coupled-electromechanical elevator model presented in state space domain and a generalized output equation which includes not only the available measurements but also a set of virtual sensors. This virtual sensing approach employs an observer, and this observer estimates other physical quantities that cannot be directly measured with the already installed sensors. The estimated physical quantities are then employed for the calculation of four easy-to-interpret Key Performance Indicators (KPIs). These four indicators will enable to report the condition and triggers maintenance actions of elevator installations. Two of the indicators are related to the elevator ride quality and the energy efficiency which are currently employed by elevator manufacturers for evaluating their elevators. The rest two indicators are related to guide system diagnosis and electrical traction machine diagnosis. Finally, the proposed procedure is experimentally evaluated, by comparing the KPIs calculated based on the estimated virtual sensors and actual measurements, with the values obtained from the additional set of sensors installed in an elevator test tower. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBAN) is an emerging technology, a subset of Wireless Sensor Network. WBAN is a collection of pieces of tiny wireless body sensors with small computational capability and communicates short distance using ZigBee or Bluetooth. The main application of WBAN is in healthcare industry like remote patient monitoring. The small pieces of sensor monitor health factors like body temperature, pulse rate, ECG, heart rate etc., and communicate it to the base station or central coordinator for aggregation or for data computation. The final data is communicated to remote monitoring devices through internet or cloud service providers. The main challenge of this technology is dead nodes due to high energy consumption with all the wireless node working on battery. Minimization of the energy consumption extends life of the network. Security is another major challenge. There are possibilities of internal attacks being executed by malicious nodes, creating problems for the network. This paper proposes a model which provides solution for extending the life span of the network by minimizing energy consumption and also proposes model to provide solution for internal soft attacks created within the network through calculation or trust, computation among nodes to identify malicious nodes with the help of social-and QoS-based trust computation for secure clustering and communication. The proposed model is compared with LEACH and LEACH-MM protocol and performance is measured with various parameters. [ABSTRACT FROM AUTHOR]

This paper is the first to investigate the relationship between the energy efficiency of dwellings, measured by the energy performance certificate (EPC), and utility cost inclusion in rental prices. First, we investigate potential drivers behind the decision to include utility costs in rents. We find that labeled dwellings are more likely to include utility costs and that this likelihood is higher among energy-effi- cient dwellings than among inefficient dwellings. Next, we surprisingly find that utility costs seem to be under-capitalized in energy-inefficient dwellings. These results are confirmed with the counterfactual decomposition approach. Overall, the findings indicate that the EPC labeling policy may be important for both landlord and tenant decision-making and may enhance market efficiency. [ABSTRACT FROM AUTHOR]

In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption. [ABSTRACT FROM AUTHOR]

For the purpose of effectively providing protection and utilization of historical earthen sites, the fully-enclosed exhibition halls with electricity driven air-conditioning have been successfully adopted to provide a suitable preservation air environment. However, its electricity consumption and carbon emissions are considerable due to the continuous operation of environmental control, the energy consumption of air cooling in exhibition halls can reach more than 10 times that of office buildings. To further reduce energy consumption, this current study drew on the concept of NBS (Nature Based Solution) and proposed a low-carbon environmental control system for an enclosed exhibition hall for storage of earthen relics by applying natural energy (solar photovoltaic and earth-air-cooling) and space optimization. Firstly, a numerical model was established to simulate the preservation environment in exhibition halls and the electricity consumption of each energy subsystem. Then the model was applied to optimize the design parameters (e.g. earth-air tunnel and airflow pattern) for the environmental control system. Numerical results showed that the electricity consumption was reduced by 58% by applying earth-air-cooling and solar photovoltaic. After optimization of the exhibition hall height, electricity consumption was approximately reduced by 65%. This research is of great value to mitigate energy consumption problems encountered in earthen relics museums and other similar environments. [ABSTRACT FROM AUTHOR]

This study was carried out in a building located in Antalya in order to determine the optimum combinations of different levels of improvement and PV panel use in existing buildings in terms of energy saving and LCC. Within the framework of TS 825 Thermal Insulation Rules in Buildings, which is the national standard in Turkey, three different levels of insulation in walls, roofs, floors and windows and three different numbers of PV panels for energy production were determined. 18 different alternatives obtained using the orthogonal array for five factors with three levels analyzed have been made. In terms of energy savings, the scenario with the highest levels of insulation for walls, roofs, floors and windows and the use of 12 PV panels is the optimum combination that produces all of its energy needs and switches to surplus energy with +KW after consumption. In terms of LCC, the alternative with 12 PV panels, where wall, floor and windows are the highest and the roof is insulated at level 2, is the best combination. PV panel integration was found to be the most effective parameter in terms of energy savings and LCC. [ABSTRACT FROM AUTHOR]

The issue of marketing efficiency, or how to better transform inputs into outputs within a marketing system, has always occupied a special place in the macromarketing literature. The concept of marketing efficiency has not been studied in closed and isolated systems that exhibit distinctive behavioral patterns compared to conventional open systems and can explain important and understudied marketing phenomena, including autarky. This study challenges some of the conventional premises of marketing systems by defining autarky as a closed marketing system that 1) operates with the specific purpose of propaganda in a relatively predictable and efficient manner; 2) has minimal, though not entirely absent, interaction with the international environment; and 3) involves participants (actors) who do not engage in voluntary exchanges with one another but rather collectively, as well as individually, adhere consistently to the self-reliance ideology. We demonstrate how a classical thermodynamic model (i.e., the Carnot cycle for maximum energy efficiency) can be metaphorically associated with the behavior of the North Korean autarkic system to better understand the long-term efficiency of its propaganda machine. To the best of our knowledge, this is the first attempt to develop a holistic metaphorical framework for exploring the efficiency of an autarkic system and to establish common criteria for measuring relationships between autarkic properties and autarkic processes. [ABSTRACT FROM AUTHOR]

This study focused on enhancing the balance between sufficient daylighting and heat mitigation in lecture halls, set within the challenging hot-arid climate of Saudi Arabia, through the optimization of window designs and the incorporation of light-shelves. To achieve this, a combination of in-detail Parametric Sensitivity Analyses (PSA) was performed in the first stage to find the best configuration of windows, while a multi-objective optimization (MOO) strategy was applied in the second stage considering multiple objectives in each stage. Before running both analyses, the simulation model was validated by field measurement. The optimization results show that the optimal window configurations obtained through PSA and MOO offer significant potential for improving daylighting performance, energy efficiency and comfort. In terms of the overall best solutions from PSA and MOO, the PSA demonstrated substantial improvements, with Useful Daylight Illuminance (UDI) rising by 46.31% to 55.36%, glare decreasing by 52.17% to 82.61% and Energy Use Intensity (EUI) improving by 0.89% to 2.63%. Additionally, MOO solutions yielded even more significant enhancements, increasing UDI by 43.58% to 58.37%, reducing glare by 95% to 100% and enhancing EUI by 1.81% to 3.88%. This has resulted in a more evenly distributed and efficient daylighting throughout the space. [ABSTRACT FROM AUTHOR]

The present study investigates the effect of public–private partnership investment in energy and renewable energy consumption on carbon dioxide (CO2) emissions, taking into account the critical role of economic growth and trade openness in Pakistan from 1992 to 2019. The linear and nonlinear autoregressive distributed lag models are employed to check the co-integration link between dependent and independent variables, further estimate short-run and long-run associations, and examine the symmetric and asymmetric effects of public–private partnership investment in energy on CO2 emissions in Pakistan. The empirical findings show that public–private partnership investment in energy reduces environmental quality by increasing CO2 emissions. Similarly, economic development and trade openness harms the atmosphere by raising CO2 emissions. On the other hand, renewable energy consumption significantly negatively affects CO2 emissions. In addition, the findings also authenticate the asymmetric link between public–private partnership investment in energy and the environment, as CO2 emissions are caused mainly by positive shocks in public–private partnership investment in energy in the short and long run. This study proposes financing renewable energy projects through public–private partnership is needed for an environmentally friendly future. [ABSTRACT FROM AUTHOR]

In view of the United Nations' Sustainable Development Goals on clean and responsible energy consumption, climate change mitigation, and sustainable economic growth (UN-SDGs-7, 11–13), this study examines institutional quality (IQ)–carbon emissions nexus in the framework of the Environmental Kuznets Curve (EKC) hypothesis. Six dimensions of IQ from the World Governance Indicators (WGIs) were used while focusing on Sub-Saharan African (SSA) countries between 1996 and 2019. After controlling for growth, energy use, and industrialization levels, the empirical results validated the EKC hypothesis for the SSA as a unit increase in economic growth initially worsens the environment while further economic expansion eventually improves the environment. However, mixed results were obtained on the effects of IQ indicators. CO2 emissions are only substantially reduced by corruption control, regulatory quality, and the rule of law among other IQ measures. Furthermore, the causality analysis showed a unidirectional causality between growth and environmentally detrimental energy consumption levels coupled with a two-way emission-population growth causality flow as well as a two-way emissions—IQ causality channel. While economic growth, energy use, and industrialization levels undermine environmental sustainability in the SSA region via increased carbon emissions, the overall findings signal the moderating roles of IQ. Hence, the strengthening of institutions is recommended for environmental sustainability enhancement in the SSA region. [ABSTRACT FROM AUTHOR]

The current study evaluates the effect of financial development (FD), economic growth (GDP), trade globalization (TGLO), renewable energy consumption (REC), and human capital (HC) on ecological footprint (ECF) in Italy using data between 1985 and 2018. We further assess the combined impact of trade globalisation and human capital on ecological footprint as well as the combined effect of trade globalisation and human capital on ecological footprint. In order to explore these associations, the present research utilizes autoregressive distributed lag (ARDL) to catch both long and short-run associations between ecological footprint and its drivers. Furthermore, we use dynamic OLS (DOLS) and fully modified OLS (FMOLS) long-run estimators as robustness checks. Lastly, causality at different frequencies is captured using the frequency domain causality test. The outcomes of the ARDL estimator disclose that (a) a surge in ecological footprint is attributed to an increase in economic growth; (b) a decrease in ecological footprint is attributed to an increase in renewable energy, trade globalisation, and human capital; (c) the combined impact of human capital and trade globalisation lessen ecological footprint, and (d) combined effect of trade globalisation and human capital mitigate ecological footprint in Italy. Furthermore, the outcomes of the causality test revealed that all the exogenous variables can significantly predict ecological footprint in the long run with the exemption of financial development. Based on the study's findings, an SDG-focused policy framework is advocated. [ABSTRACT FROM AUTHOR]

With the Architecture, Engineering, and Construction industry representing a significant share of global energy consumption and greenhouse gas emissions, developing sustainable design and reducing buildings' environmental impacts has become a priority over the past decades. Adopting building information modeling tools and implementing them into life cycle analysis techniques at the early stages of design has been an effective method for buildings sustainability evaluation. With our environment constantly changing, it is reasonable that the construction industry should also aim to adapt to these changes and use them. However, the role of local climate features and their effects on a building's energy output is often neglected. By using building information modeling–life cycle analysis integration techniques, this research aims to consider the role of climatic attributes along with some passive design strategies in the life cycle assessment of the building and see how it affects that building's energy performance. By only applying passive design strategies (not including heating, ventilation, and air conditioning system type), the models' energy demand was reduced up to 30% of the original value. It is also witnessed that by using the proper equipment and construction materials that match the respective climate, up to 28% of the building's energy consumption during the operational phase, can be saved. Although insulation of the walls does reduce energy consumption values, it also contributes to more greenhouse gas emissions. The increase in greenhouse gas emissions was estimated to be around 3%, but since the insulation boards make up only 2% of the building's area, it is a considerable amount. [ABSTRACT FROM AUTHOR]

This study examines the relationship between financial inclusion, renewable energy, and CO2 emissions using data from 11 Middle East and North Africa (MENA) countries from 2004 to 2019. Evidence from fixed effects-ordinary least squares (FE-OLS), dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and canonical correlation regression (CRR) showed that financial inclusion contributes significantly to decarbonization. Country-specific analysis indicated that financial inclusion is associated with mitigating CO2 emissions in Egypt, Israel, Qatar, and Tunisia while significantly spurring CO2 emissions in Algeria, Lebanon, and Saudi Arabia. In addition, renewable energy contributes significantly to decarbonization in MENA, especially in Algeria, Lebanon, Tunisia, and Turkey. We recommend that policies promoting financial inclusion and renewable energy usage would contribute to the attainment of the carbon-neutrality goal by MENA countries. [ABSTRACT FROM AUTHOR]

Iconic destinations are benchmarks for tourism development, but their effects on the environment are debatable. To highlight the tourism attractiveness factors of sustainability in the world's most visited destinations, we applied clustering and multiple regression to specific indicators for the period 2000–2020. We observed a relatively high heterogeneity in terms of sustainability, with destinations split into four clusters: C1 (France, Spain, Italy, Germany and UK), C2 (Mexico and Thailand), C3 (USA, China) and C4 (Turkey). Unsustainable factors in all destinations include the industry and construction sector, social insecurity, inflation and transport services development. Renewable energy consumption is sustainable, while economic growth, education and tourism indicators have antagonistic effects. The effects of tourism attractiveness factors on the environment have decision-making implications at all levels. The novelty of this analysis lies in the tourism attractiveness factors examined, and the results can help shape tourism development policies in balance with structural policies such as energy and environment. [ABSTRACT FROM AUTHOR]

This study examines the determinants of modern cooking fuel choices in Korea. The ordered probit model is estimated using an extensive online survey of Korean household energy consumers. Our empirical results showed that age, gender, and education are significant determinants of modern cooking fuel choices among the socioeconomic demographic variables: females, older people, and highly educated people are more likely to prefer electricity. Also, electricity is more likely to be preferred over natural gas by people who have a higher preference for district heating. Two psychological factors significantly influence cooking fuel choices. People whose behaviors are environment-friendly and people who are more health-conscious are more likely to prefer electricity over natural gas or propane. There are three important insights into the cooking fuel transition toward electricity. First, as interest in health and climate change has significantly increased in recent years in Korea, it may stimulate the transition from natural gas or propane to electricity. Second, electrification of cooking methods will be beneficial for the environment if electricity generation becomes less carbon-intensive. Since electricity in Korea is mainly generated by fossil fuels, it is crucial to implement more aggressive policies toward renewable sources in the energy mix for electricity generation. Third, the public should better understand why this problem cannot be overlooked because the energy mix is important in mitigating climate change. The better the people understand the exact relationship between energy consumption and pollutant emissions, the more effective and environmentally sound will the energy mix policy become. [ABSTRACT FROM AUTHOR]

As the manufacturing industry is the foundation of the national economy, many countries have made concerted efforts to promote the transition upgrading of manufacturing enterprises from political, industrial, and academic circles. This study employed content analysis to explore the transition upgrading factors for manufacturing enterprises from an energy source perspective. With the help of FsQCA (Fuzzy-sets qualitative comparative analysis), which can mine multivariate combination relationships using a small number of samples, this study quantitatively calculated the transition paths and energy source combinations of manufacturing enterprises. Three transition upgrading paths were proposed, including an innovation-oriented path, a business-oriented path, and an efficiency-oriented path. The results indicate that manufacturing enterprises can realize transition upgrading based on three key methods, including innovation resources and innovation activities, strategic layout, and production operation + network relationship (peripheral). Such a finding validated the rationality of the theoretical path design and improved the research depth and precision. Multiple case analyses of Huawei, Haier, and Lenovo reveal that manufacturing enterprises used three ways to promote transition upgrading: the joint action of innovation activities and innovation resources; the uni-directional causal effect of strategic layout adjustment to increase market value; and the coordination and matching role of network relations dominated by production and operation. We conclude that this study provides an effective decision-making basis and management reference for manufacturing enterprises, so that enterprises can choose an appropriate energy accumulation method and scientifically realize the transition upgrading. [ABSTRACT FROM AUTHOR]

The existing constitutive models of shape memory alloy (SMA) cannot accurately describe the mechanical behavior in martensitic strengthening segment since their loading and unloading paths in strengthening segment completely overlap. This study proposes a multi-segment linear mathematical model for SMA, which realizes the separation of loading path and unloading path in strengthening segment. A four-story steel frame structure and corresponding braced structures with various SMA constitutive models are designed, and seismic control effects of SMA braces with these models are analyzed and compared through time history analysis. The results show that the sub-cycle unloading mode of SMA constitutive model affects the seismic mitigation capacity of SMA brace in a certain extent. The position of the inflection point of unloading path has very little influence on the structural control ability of SMA braces. The SMA brace using the SMA constitutive model with a single linear sub-cyclic unloading path has the minimal seismic response and the highest seismic reduction ratio. For most evaluation indexes, the seismic mitigation ability calculated by the proposed path separation constitutive model is in the middle of the results calculated by the existing models. SMA models without considering the mechanical behavior of strengthening segment may lead to an erroneous estimation of the energy consumption of SMA brace and the structural damage. The difference of simulation results among various SMA constitutive models is almost independent with ground motion. [ABSTRACT FROM AUTHOR]

This research aims to predict the cost and energy consumption associated with pocket and groove machining using the hybrid particle swarm optimization-artificial neurons network (PSO-ANN) algorithm and the response surface method (RSM). A parametric study was conducted to determine the best predictions by adjusting the swarm population size (pop) and the number of neurons (n) in the hidden layer. The results showed that machining strategies and sequences can have a significant impact on energy consumption, reaching a difference of 99.25% between the minimum and maximum values. The cost (C tot) and energy consumption (E tot) values with the PSO-ANN algorithm increased significantly by 99.99% and 92.41%, respectively, compared to the RSM model. The minimum mean square error values for E tot and C tot with the PSO-ANN models are 3.0499 × 10−5 and 4.6296 × 10−10, respectively. This study highlights the potential of the hybrid PSO-ANN algorithm for multi-criteria prediction and highlights the potential for improved machining of 2017A alloy. [ABSTRACT FROM AUTHOR]

The renewable electricity quota standard (RES) policy plays an important role in achieving sustainable and green development targets. Thus, it is essential to measure the sustainability of the electric power sectors in the context of RES. In terms of the energy and environmental efficiency (EEE) measurements, few studies divided energy inputs into renewable and non-renewable energy or explored the restricted problem of minimizing resource summation. To fill these gaps, we propose an improved fix-sum energy input data envelopment analysis (FSIDEA) model based on the adjustment strategy of weighted sum minimization of renewable energy inputs. The assurance region (AR) restriction and renewable energy input sum constraint (IC) are, respectively, imposed on the FSIDEA model to achieve the common equilibrium effective frontier (EEF). Finally, this study assesses the EEE of the electric power sectors in China's 30 main regions based on the common EEF. The result reveals that Beijing and Shanghai can be selected as benchmarks after efficiency comparison. In addition, our proposed model can compressively rank all decision-making units (DMUs) compared with traditional efficiency models. [ABSTRACT FROM AUTHOR]

As a result of high energy consumption and pollution from energy-intensive industries, the Chinese government has implemented a series of policies. A comprehensive assessment of these policies is necessary to guide the green transformation and upgrading of the industry, however, little research has been conducted on this issue. This article takes China's economic policy uncertainty (EPU) Index as a comprehensive and continuous policy shock and investigates the impact of EPU on the financial performance of energy-intensive firms. Based on 289 listed energy-intensive companies from 2003 to 2018, we find that EPU negatively affects the financial performance of firms, and the negative effect is robust after replacing the dependent variable and endogenous discussion. Further, the negative impacts are more pronounced for midwestern, non-SOE firms, as well as ferrous metal and chemical product manufacturers. Moreover, our results indicate that the negative impact of EPU on performance is mitigated for firms with high cash holdings and low debt ratios. [ABSTRACT FROM AUTHOR]

Several nations, including Mexico, are encountering problems with accomplishing the targets of sustainable development goals (SDGs). This study addresses how to develop SDG agenda for Mexico, which may also be applied to other emerging markets with comparable profiles. Therefore, this study utilizes a newly developed load capacity factor (LCF, a detailed environmental evaluation tool) that combines biocapacity (supply side) and ecological foot (demand side) to examine the effect of renewable energy consumption and trade openness on LCF while controlling for nonrenewable energy consumption and economic growth in the case of Mexico during 1970–2017. Using the dual adjustment approach, we find evidence of long-run cointegration nexus among the series. In addition, trade openness affects LCF positively, whereas renewable energy consumption, economic growth, and nonrenewable energy consumption impact LCF negatively. Furthermore, results of the frequency domain causality show that all the variables can predict LCF in the long-term. Based on these results, Mexico should focus on promoting public understanding of green energy and environmental preservation measures and participate in the production of nonenergy-consuming and ecologically friendly products while compelling polluting enterprises to migrate to countries with less stringent environmental restrictions. [ABSTRACT FROM AUTHOR]

Developing an inclusive policy agenda for ensuring sustainable development is a challenge for both developed and developing countries. The developing countries face challenges in designing policy that achieves sustainable development goals based on environmental awareness, and that is one of the main contributions of this research. By taking into account the SDG 13 (climate action) and 7 (clean and affordable energy) this study examines the influence of education, natural resource availability, financial development, urbanization, and economic growth for ASEAN nations from 1991 to 2018. For an empirical estimate, second-generation approaches are used. The findings demonstrate the energizing influence of renewable energy usage in altering the environmental quality. According to the empirical findings, renewable energy and education lower CO2 emissions by 0.46% and 0.22%, respectively. Financial development, urbanization, and natural resource depletion all have a 0.14%, 0.03%, and 0.08% impact on the environment, respectively. The heterogeneous causality analysis reveals the feedback effect, i.e., bidirectional causal links between education, carbon emissions, and the use of renewable energy. This empirical data implies that nations should enhance investment in renewable energy and education sectors, as well as prepare for renewable energy research and development, to ensure environmental sustainability. This research has policy implications for ASEAN countries in terms of renewable energy and education investments. Through this agenda, the objectives of SDG 13 and SDG 7 will be achieved, while SDG 4 will be targeted. [ABSTRACT FROM AUTHOR]

This research utilizes a bootstrap rolling-window (BRW) causality test to explore the causal interrelationship between nuclear energy consumption (NUC) and carbon dioxide emissions (CO2) in 6 developed countries from 1980 to 2020. When there are structural shifts in the full-sample time series, empirical research exploring causality between two-time series generates erroneous conclusions. On the other hand, the BRW method allows researchers to find potential time-varying causality between time series using sub-sample data. The outcomes of the BRW causality test disclosed the following results: (i) a unidirectional negative causality from NUC to CO2 without feedback was found for Japan; (ii) a negative causality at sup-sample periods from NUC to CO2 surfaced at the sub-sample period while a positive causality surfaced from NUC to CO2 in sub-sample period for the United States of America (USA) and France; (iii) a negative feedback causality between NUC and CO2 was found For Canada; (iv) a positive unidirectional causality surfaced from NUC to CO2 was found for Germany, which implies that consumption of NUC worsens the environment in the sub-sampled period. The results may have policy consequences for the selected developed countries regarding NUC and CO2 nexus. [ABSTRACT FROM AUTHOR]

Conventional panel models that overlook nonlinearity, nonstationarity, heterogeneity and cross-sectional dependency when analysing the energy-growth nexus might produce misleading conclusions. In addressing these issues, this study extends the examination of the nexus by applying nonstationary panel models with common correlated effects (CCE). They involve two estimators, namely CCE mean group (CCEMG) and augmented mean group (AMG) estimators. The main objective is to examine the effects of total and renewable energy consumption on the gross domestic product (GDP) by comparing the top oil-importing and top oil-exporting countries. The datasets are in annual frequency, ranging from 1992 to 2015. The results are compared to ordinary least squares (OLS)-type models that serve as benchmarks. This study innovates the models by incorporating the asymmetric effects of energy consumption into the estimations. The findings reveal a positive correlation between total energy consumption and GDP for both panel groups, and capital input is one of the determinants in all cases. The results disclose evidence of the asymmetric effects of total and renewable energy consumption. The positive impact of total/renewable energy consumption decreases for the top oil-exporting group is more apparent than its increases, signifying the importance of energy conservation. For top oil-importing nations, increased renewable energy consumption impedes the GDP, but the increase in total energy consumption accelerates it. Energy efficiency policies should be implemented in oil-importing countries since the positive impact of total energy consumption is smaller than in the other group. [ABSTRACT FROM AUTHOR]

In contemporary times, developed economies are adopting environmentally friendly initiatives such as green finance and energy efficiency. However, the factors affecting energy efficiency are widely explored, and green finance remains overlooked in the empirical literature. The present research examines the drivers of sustainable green finance in OECD economies during 2004–2021. The study uses several panel diagnostic tests and validates the mixed integration order of the variables. Still, the long-run equilibrium relationship is valid between financial inclusion, energy efficiency, human capital, foreign trade, composite risk, and green finance. Using panel autoregressive distributed lag model, the study found that financial inclusion, energy efficiency, foreign trade, and composite risk significantly improve green finance in the long run. However, only energy efficiency is effective in the short run. On the other hand, human capital exhibits a significant adverse influence on green finance. The long-run results are robust due to the significant estimates offered by panel dynamic ordinary least square. Besides, there exist one-way and two-way causal associations between variables. The study recommends improving the financial sector performance to enhance financial inclusivity, increase investment in the energy efficiency sector, and encourage trade in environmentally friendly initiatives. [ABSTRACT FROM AUTHOR]

The study inspects the seven sectors of the Association of Southeast Asian Nations (ASEAN) economies to explore the impact of consumption of clean energy, green bonds, and green economic advancement on environmental, social, and governance (ESG) practices to attain sustainable development goals (SDGs). The ESG aspects are individually explored from 2011 to 2020. It is observed that the key determinants of ESG practices are green economic advancement, clean energy, and green financing. Moreover, these determinants positively and significantly track the ASEAN economies toward attaining SDGs. The determinants play a vital role in sustainable advancement even when explored individually using different regression models. The linkages between the explanatory and outcome variables remain consistent after controlling other variables like climate regulatory quality, foreign direct investment, and gross domestic product. The study provides solid implications for stakeholders, environmental activists, and governments of ASEAN economies. The findings also infer sustainable business practices via creating an association between ESG practices, green economy, clean energy, and green financing. [ABSTRACT FROM AUTHOR]

Graphical abstract: EMS overview Burning fossil fuels results in more emissions than generating electricity from renewable sources. The transition to renewable energy from fossil fuels, which currently produce the majority of emissions, is essential to preventing the climatic disaster. Hybrid energy generation systems are still in their infancy. It is envisaged that future technology developments would lead to greater application and more economical goods. There will be more standardised designs, which will make it easier to select a system that is suitable for a certain application. The components will communicate more with one another. As a result, control, monitoring, and diagnosis will be made simpler. The hybrid energy system (HES), also known as hybrid power, is expected to be the long-term power solution for microgrid (MG) systems. This study compares and contrasts several theories and conventional approaches to controlling HRES's control and energy consumption. A successful energy management strategy has been created using a variety of methods and procedures. The effectiveness of an EMS is determined by its control architecture and the solution approach used; common topologies include hierarchical, decentralised and centralised EMS. Supply side management and demand side management, two EMS components, will be discussed later. The three EMS control architectures are examined in this section. In order to determine the most practical and dependable solution with the lowest Net present cost (NPC), COE and realistic environmental consequences, various hybridisation cases of a PV panel, wind turbine, battery storage and diesel generator are designed, analysed and compared using DSM. The results of taking into account DSM indicated a reduction in CO2 emissions of 25%, NPC emissions of 14.8%, COE emissions of 14% and an increase in RF emissions of 8.5%. Two fundamental metrics – the DSM Quality Index for technical benefits and the DSM Appreciation Index for economic advantages – are used to assess the technical and economic benefits of DSM. [ABSTRACT FROM AUTHOR]

The ambitious targets of peaking CO2 emissions before 2030 and reaching carbon neutrality before 2060 (Goal 3060) have emerged as the driving force in the development of China's low-carbon energy policy. Adopting a systematic review approach, this article provides a timely analysis of key Chinese renewable energy and energy efficiency policies under Goal 3060 across five sectors: electricity, industry, transportation, buildings, and local governments. In addition, the review identifies challenges to be addressed and offers policy recommendations. There are five main conclusions: (1) expedite the development of market-based and network-based governance systems; (2) pursue the harmonization of policies by avoiding contradiction and fragmentation; (3) develop new policies, especially in the building and transportation sectors; (4) embrace surveillance technologies to improve implementation and compliance; and (5) ensure just transition by implementing policy to reduce the negative socioeconomic impacts on fossil fuels-based communities and workers. This review serves as a useful guide for scholars interested in China's clean and low-carbon energy transition. [ABSTRACT FROM AUTHOR]

Rapid growth in the global population and associated elevated reliance on modern technology has resulted in increased demand for energy consumption. This has resulted in an increased focus on the development and generation of advanced sustainable energy systems. The swift implementation of sustainable renewable energy resource utilization and improvement in their efficiency by the modification of current technologies are the possible solutions that gave rise to the emergence of geothermal technology as a potential alternative. Geothermal technology is a non-carbon renewable energy resource that could be utilized efficiently to fulfil the energy demands while mitigating the climate change threat. According to the surveyed literature, the global geothermal energy power plant installation capacity has reached 14.3 GWe to successfully implement this sustainable alternative. In order to have a successful and uninterrupted way forward, it is essential to evaluate the constraints both in terms of technicality and economic feasibility to establish an approved framework. Moreover, the governance and monitoring regarding the social and environmental impact alongside the legal challenges should also be addressed. The significant barriers include increased capital cost, site selection, superiority of resources at diverse levels of rock bottoms, and obstruction from nearby residents that need to be addressed appropriately. As a result, policymakers will continue to seek measure that have least negative impact on environment. [ABSTRACT FROM AUTHOR]

Environmental degradation has been recognized as a global issue due to high energy consumption and economic growth. This situation needs researchers to focus on, thereby, the current article examined the impact of renewable energy production (REP), energy import, renewable energy consumption (REC), gross domestic product (GDP), inflation, and foreign direct investment (FDI) on the carbon dioxide (CO2) emission in China. The study considered secondary data and extracted it from the World Bank database covering the period 1981 to 2018. The current article has examined the stationarity of the constructs using Augmented Dickey-Fuller tests and investigated the association among constructs using the quantile autoregressive distributed lag (QARDL) model. The data revealed that REP, energy import, and REC, had a significant and negative linkage with CO2 emission in China. In contrast, GDP, inflation, and FDI are linked with CO2 emission in a positive manner. The article also guided the policymakers regarding the policy development related to reducing carbon emissions using renewable energy production and consumption. [ABSTRACT FROM AUTHOR]

In the context of ecological environment construction, how to create a new development pattern in which environment and economy are coordinated has become an important goal of the Chinese government. From the perspective of the symbiotic development of resource-based industries and non-resource industries, this study discusses the path of green credit optimization of energy consumption structure by using the space Durbin model, based on the panel data from 30 Chinese provinces from 2006 to 2019. The study results are as follows: Both green credit and energy consumption structure have a significant positive spatial correlation. Furthermore, the energy consumption structure of local and adjacent areas is significantly optimized by green credit. Additionally, for non-resource-based regions, although green credit has no spatial spillover, it can significantly improve the local energy consumption structure, while green credit in resource-based regions significantly inhibits the optimization of the energy consumption structure in local and adjacent regions. Finally, from the perspective of the symbiotic development of resource-based industries and non-resource industries, although green credit can significantly improve the local energy consumption structure, it will inhibit the optimization of the energy consumption structure in adjacent areas. Therefore, government departments not only need to implement green credit policies according to local conditions but also actively guide exchanges and cooperation between resource-based industries and non-resource industries. This move promotes the symbiotic development of resource-based industries and non-resource industries and simultaneously improves the utilization efficiency of green credit, and optimizes the energy consumption structure. [ABSTRACT FROM AUTHOR]

In recent years, the global business landscpae has witnessed a paradigm shift toward sustainable and responsible corporate practices. This transition has been particulalrly pronounced in the energy sector where companies are increasingly recongzing the importance of Non-Financial Reporting Initiatives (NFRI) in shaping their operational strategies. The current study endeavors to delive into the intricate relationship between NFRI and the Weighted Average Cost of Capital (WACC) for energy companies in Pakistan. The current study relied on Partial Least Square Structural Equation Modeling (PLS-SEM) technique to extract useful information about the study topic. The data collected for the period 1999 to 2020 from World Development Indicators. The findings of the study revealed that energy consuption factors are responsiblee for around 31.32% variations in WACC, while economic factors contribute 43.98% variations. The financial factors showed to have 35.72% variations in the WACC. The findings of current study revealed to implement policies fostering sustainable energy practices, robust economic growth, and prudent financial management to positively influence the Weighted Average Cost of Capital (WACC) for enhanced business competitiveness and investment attractiveness. [ABSTRACT FROM AUTHOR]

This study explores the optimization of kinetic facades to promote environmental sustainability in building designs, addressing the critical issues of high energy consumption and CO2 emissions prevalent in the construction sector. The focus is on achieving an intricate balance between maximizing natural daylight and minimizing solar radiation using innovative kinetic facade designs. Parametric modeling tools are utilized in the design process to experiment with various facade configurations. The effectiveness of these designs is then validated using both digital and physical prototypes, with their adaptability to diverse climatic conditions evaluated through dynamic simulations. A key component of the study is the application of the Wallacei plugin for Grasshopper, which assists in multi-objective optimization to determine the most effective facade aperture ratios. The results demonstrates a substantial reduction in solar radiation levels, with a 70% decrease on the first floor and a 76% decrease on the seventh floor, achieved by optimizing aperture ratios. The study concludes that optimizing kinetic facades significantly improves building performance compared to traditional glass facades, offering an effective balance between daylight enhancement and solar radiation reduction, influenced by seasonal changes. It also emphasizes the importance of factors such as building height and the surrounding environment in facade design. Overall, the findings highlight kinetic facades as a viable solution for improving building efficiency and occupant comfort, suggesting a promising avenue for advancements in architectural design and construction. [ABSTRACT FROM AUTHOR]