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

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

This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

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

This paper investigates the asymmetric demand for clean energy for a panel of OECD countries. We construct a price index for clean energy based on information on the energy composition of electricity generation, and focus on the possibility of non-symmetric clean and dirty clean consumption due to rising and falling energy prices, and to positive income growth and negative income growth. Using an Autoregressive Distributed Lag (ARDL) model that encapsulates both long-run equilibrium among energy demands, energy prices and income, and short-run dynamics. We find evidence that clean energy demand adjusts more aggressively to a price decline than a price rise. In addition, we find that clean energy consumption is more responsive to negative income changes than to positive income changes. A climate policy implication is that policy should account for reversal effect of clean energy consumption during periods of economic downturn. [ABSTRACT FROM AUTHOR]

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

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

We investigate the power system and climate policy implications of the anticipated expansion in electricity demand from datacentres. We perform a joint optimization of Generation and Transmission Expansion Planning for the island of Ireland considering uncertainty in future datacentre growth under various climate policies. Datacentre expansion imposes significant extra costs on the power system. A renewable energy target is more costly than a technology-neutral carbon reduction policy due to the restriction to invest solely in renewable energy, and the divergence in costs increases non-linearly in electricity demand. A carbon reduction policy is more robust to uncertainties in projected demand than a renewable policy. The increased demand from datacentres, which is relatively constant, is best matched by a power supply with constant output, generally fossil-fuelled power plants, potentially in combination with carbon capture and sequestration technology, than variable supply. Datacentre demand increases power system costs by 6% under a carbon reduction policy, and by 9% to 15% under renewable energy targets. However, the abatement cost of carbon under renewable targets decreases at higher levels of demand, while the opposite is true for the abatement cost under technology-neutral targets. High renewable targets crowd out other low-carbon options, namely CCS, leading to a higher cost solution for the same reduction in emissions, although this result is sensitive to increases in fossil fuel prices. The results suggest that climate-related energy policy should focus on technology-neutral carbon reduction policies. The climate impacts of datacentres on energy demand and thus supply requirements should also feature in climate and energy policy discussions. Climate policy that focuses on renewable energy only is more expensive than a technology-neutral climate policy The costs of a renewable-only focused climate policy increase non-linearly as demand increases [ABSTRACT FROM AUTHOR]

Investing in renewable energy is of utmost importance, especially in the context of addressing climate change. However, while numerous studies have explored the role of renewable energy in achieving carbon neutrality, there is a noticeable gap in the literature regarding how uncertainties affect the impact of renewable energy on electric power CO2. To bridge this gap, the present study employs recently developed nonparametric techniques, namely multivariate quantile-on-quantile regression (MQQR) and time-varying quantile causality (TVQC), to investigate the relationship between renewable energy and electric power CO2 in the presence of uncertainties. The study utilizes monthly data spanning from January 1988 to May 2023. The bivariate results reveal that renewable energy and low uncertainties contribute to improving ecological quality by reducing electric power CO2. Furthermore, the multivariate quantile-on-quantile regression results highlight the substantial influence of low uncertainties on the impact of renewable energy in lowering electric power CO2. Additionally, the TVQC analysis demonstrates that both renewable energy and uncertainties possess predictive power regarding electric power sector CO2. The study discoveries showcase that the influence of renewable energy on electric power CO2 is subject to external moderation. The suggested policy framework in this study is structured to assist the United States in accomplishing the goals outlined in SDG 7. [ABSTRACT FROM AUTHOR]

Global concern over socioeconomic activities' environmental impact is undeniable, given their implications for climate and human welfare. In our study, we analyze Pakistan's landscape, with emphasis on the effects of electricity generation and consumption (both symmetric and asymmetric), energy trade, and the role of information and communication technology (ICT). We harnessed data from 1993 to 2020, sourced from World Development Indicators, Climate Watch, and the USA EIA. Our analysis involved application of the Nonlinear Autoregressive Distributed Lag model and the Autoregressive Distributed Lag bounds testing methods. Our results reveal that electricity consumption, whether experiencing positive or negative shocks, exhibits a tendency to elevate emissions over the long term. Similarly, shocks in electricity generation from fossil fuels, whether positive or negative, have adverse environmental consequences both in the short and long run. A negative shock in electricity generation from renewable and alternative sources triggers environmental degradation. Remarkably, nuclear energy plays a pivotal role in bolstering environmental sustainability in Pakistan. Conversely, we identified factors contributing to ecological degradation, including imports of coal, crude oil, metallurgical coke, and ICT usage, in the long run. Equally, trade and natural resources rent emerge as mitigating factors over time. Ultimately, our study underscores the importance of diversifying the energy mix, with a particular emphasis on expanding the renewable sources for reducing emissions and fostering environmental sustainability in Pakistan. Additionally, our research suggests that leveraging natural resources rent and trade capabilities can pave the way for an eco-friendlier future in the country. This study assesses the environmental impact of electricity generation by source, electricity consumption, energy trade, and ICTs in Pakistan. The findings confirm that both positive and negative shocks in fossil fuel electricity generation and consumption detrimentally affect the environment. Coal, crude oil, metallurgical coke imports, and ICTs degrade the environment, while nuclear electricity generation, natural resources rent, and trade improve it. Integrating energy diversification with focus on promoting renewable energy sources has the potential to significantly reduce emissions and advance long-run environmental sustainability. Strategically utilizing natural resources rent and trade capabilities can aid in establishing an environmental sustainability in Pakistan. [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid system for an integrated solar and natural gas hybrid model with photovoltaic/thermal (PV/T) collectors and gas turbines (GT). The proposed optimization method combines both Dynamic Differential Annealed Optimizations (DDAO) and Radial Basis Function Neural Network (RBFNN), so it is called the DDAO-RBFNN approach. Natural gas, solar energy, coal, and geothermal energy are the sources of the system, while electricity, cooling, and heating are the energy needs of the system. A multi-objective stochastic model incorporates load and renewable energy uncertainty to obtain more efficient, economic, and environmental decisions. By utilizing an existing data set, the proposed approach is to forecast a new set. Some characteristics, such as historical weather data, building loads, and market information are required for modelling the energy hub and unpredictable factors. The proposed RBFNN method is used to model the energy hub through data collection, analysis, and initial sampling of uncertainty factors. To provide optimal outcomes with great computational time, the accuracy of DDAO approach is used. Smart operation managements consider both the energy supply and demand sides to achieve optimal state functioning of the hybrid CCHP scheme. Finally, the proposed system is performed in MATLAB/Simulink site and the performance of the proposed method is compared with existing methods. [ABSTRACT FROM AUTHOR]

There is a growing demand for electricity in homes in Indonesia. In this study, traditional Javanese houses were analyzed and redesigned to suit Indonesian modern lifestyles. The new modern Javanese houses are used as benchmarks for evaluating energy-efficient designs in Jakarta, Indonesia. It was compared to several rumah type 36's models in order to analyze it. The results show that a modern Javanese house can reduce its total electricity consumption by 31%. There is a high consumption of electricity in home appliances related to cooking and miscellaneous appliances. Heat can be faster transferred from these cooking appliances by designing an open layout and atrium. These design can lowered the nighttime heat temperature by about 5°C compared to the daytime temperature, and by about 1.6 °C compared to the temperature outdoors. Energy consumption in urban areas can be reduced by adapting a traditional house concept to modern homes. [ABSTRACT FROM AUTHOR]

In this research, proportional-integral (PI) and Fuzzy logic control (FLC) strategies are designed to control wastewater treatment processes at the whole plant level. Two control combination strategies are implemented to minimize the operational cost index (OCI) and effluent quality index (EQI). The benchmark simulation model (BSM2-P) is used as a working platform. In close resemblance to practical operations, the sensors are considered as nonideal with delay and also having measurement noise. Binary-level control topology is designed in which the lower-level PI controller is used to control the dissolved oxygen (DO) levels in the second aerobic reactor by manipulating the oxygen mass transfer coefficient (KLa) of the last three aerobic reactors and FLC is designed at the supervisory level based on Ammonia levels. Ammonia in the second aerobic reactor is taken as a feedback signal for FLC to compute the DO setpoints. In comparison with non-supervisory control structures, operational cost is improved in PI-Fuzzy by 6.5%. Compared to the open-loop control, the PI controller improves effluent quality by 2.1%. Nevertheless, the combination of PI and fuzzy control shows high greenhouse gas production rates. [ABSTRACT FROM AUTHOR]

A hybrid workstyle is becoming more common post-COVID-19, and longer occupancy hours at home are increasing household electricity consumption. Small homes are regarded as a potential for improving energy efficiency in the residential sector, and a home consists of mixed-function spaces with dynamic occupancy behaviors. These underpin the opportunity to optimize presence sensing lighting in small homes for energy efficiency and user-behavioral needs. A comprehensive overview of presence sensing approaches, comparing four types of non-wearable sensors connected to home lighting is made. A bibliometric mapping of the reviewed literature visually reinforces a significant research gap that presence sensing studies were not connected to home lighting but inclined toward the commercial and institutional context. Next, a non-exhaustive example of commercially available presence sensing products applicable to residential lighting for small homes is analyzed, and their general characteristics and technologies are synthesized. The literature and product overview identified five significant product knowledge gaps. Incorporating the gathered information leads to the proposal of a conceptual flexible radar-based sensor (prototype design), addressing a wish list with three important criteria to optimize future presence sensing lighting in a mixed-function small home. Future radar sensing studies are expected to develop an anticipatory lighting system that processes real-time multi-user vital signals for smarter localized and personalized lighting options for (small) living environments. [ABSTRACT FROM AUTHOR]

This study numerically estimates the effect of increased Chinese import penetration using U.S. manufacturing-level panel data from 1997 to 2005. It also decomposes this effect into factor substitution effects and output scale effects. Since import penetration rates may have endogeneity problems, we use China's share of world trade to quantify Chinese import penetration. The results show that increased imports from China increase fuel and electricity consumption: the marginal effect of Chinese import penetration is small but statistically significant, ranging from about 0.05% to 0.08%. Interestingly, the decomposition reveals opposite effects across energy types: the factor substitution effect for fuel is positive, while the factor substitution effect for electricity is negative, despite the impact of Chinese imports. The effects on fuel and electricity are both significantly positive, while the effect on electricity is negative. These results suggest the need for different policy responses across industries to the effects of trade, with implications for the environmental problems caused by energy use. [ABSTRACT FROM AUTHOR]

Self-harvesting and consumption of electrical energy from a small-scale photovoltaic (PV) system became quite a beneficial option for households who seek for an economical, independent and environment-friendly power alternative. However, in practice, prosumers without battery storage systems face some energy flow management issues, when only part of the generated energy is consumed instantly in place, while the rest is exported to the distribution grid or simply lost. The paper examines profiles of some preselected prosumers with small-scale PV installations with the purpose to evaluate losses caused by the inability to consume instantly all generated energy and thus resulting in redundant energy exchange between prosumer's microgrid and distribution grid. The analysis reveals certain anomalies, when local PV system produces enough power to cover all the electricity demand of the household, but energy is still imported from the DSO grid at the same time. These simultaneous flows of import and export active energy were calculated and showed quite significant numbers. The paper digs into this problem through a quantitative evaluation of energy flows within one preselected microgrid using one-second resolution experimental data. The results presented here help us distinguish the main factors leading to inefficient distribution of generated power in households. [ABSTRACT FROM AUTHOR]

Veneer drying usually consumes a significant amount of energy including heat and electricity. The soaring energy price as well as the substantial social-environmental concerns regarding energy use have urged veneer manufacturers to adapt and become more efficient in energy consumption. Different from the physics-based methods commonly seen in the literature, this research embraced a data-driven approach to analyze and predict unit gas and electricity consumption during industrial veneer drying. Both linear regression and random forest (RF) algorithms were deployed for prediction. Based on cross-validation evaluations, the RF model with all explanatory variables slightly outperformed two linear models regarding almost all accuracy metrics, although linear models had the advantage of providing an easy-to-interpret solution. [ABSTRACT FROM AUTHOR]

In developed countries, people spend nearly 90% of their time in buildings or during transportation. Recent research studies demonstrated that occupant behaviors have a significant impact on building performance in relation to the indoor environment and energy use. This paper presents the ASHRAE Global Occupant Behavior Database which aims to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems. This database includes 34 field-measured occupant behavior datasets for both commercial and residential buildings, contributed by researchers from 15 countries and 39 institutions covering 10 different climate zones. It includes occupancy patterns, occupant behaviors, indoor and outdoor environment measurements. The database is open source, a public website was developed for the users to interactively explore, query, and download datasets. This paper focuses on a detailed data analysis to investigate patterns of nine occupant behavior types, examining impacted factors such as building type, country, and climate zone. EnergyPlus simulations have been implemented based on the occupancy profiles derived from this database, and results showed overall building electricity consumption can be reduced up to around 27% in Summer and around 10% in Winter. [ABSTRACT FROM AUTHOR]

To investigate how to obtain the optimal balance between energy consumption and economic development, this paper proposes a multifactor optimization model (MFOM). Different from previous input–output optimization models, our proposed MFOM comprehensively considers the direct and indirect impacts of sectoral consumption activities on total energy consumption. The results show that MFOM achieves higher energy-saving goals by smaller adjustment to consumption activities than the previous input–output optimization models do. The results also show that the consumption of some products should be limited to achieve the energy-saving and economic growth goals, such as Non-ferrous Metal Ore Mining, Chemical Products and Ferrous Metal Ore Processing. Furthermore, the results indicate that the share of some sectors, especially high-level manufacturing sectors, significantly decreases in the consumption structure after optimization. To maintain the sustainable development of these sectors, the dependence of their production activities on energy-intensive products should be reduced. [ABSTRACT FROM AUTHOR]

With the rapid uptake of renewable energy generation and increasing price volatility, there are multiple opportunities emerging for businesses to earn additional revenue and reduce electricity bills by implementing demand response. However, commercial and industrial consumer implementation of demand response is not well understood and largely absent in energy management guidelines, which focus on reducing energy consumption and driving energy efficiency. Based on interviews with managers from 24 commercial and industrial businesses, we describe a practical implementation framework for demand response. The framework identifies unique implementation features for demand response - the activation steps. Energy management guidelines may have underemphasised approaches to demand response and inclusion of its unique features for businesses because benefits to be gained from demand response have focussed on benefits for the utilities in the electricity system, not benefits for individual businesses. The article concludes there is an opportunity for market operators to encourage organisations who produce energy management guidelines to include demand response, to promote awareness of the opportunities for businesses and provide practical guidance for implementation, therefore providing support for greater renewable energy penetration and reduced energy costs for businesses. [ABSTRACT FROM AUTHOR]

The Environmental Kuznets Curve (EKC) hypothesis has been used to explain the relationship between environmental degradation and economic condition. This study formulates an augmented EKC framework to incorporate the effect of agriculture, forestation and energy consumption on CO2 emission and estimates the model using the data from 24 OECD countries between 1990 and 2018. The study utilizes individual-country analysis and panel dynamic analysis for the empirical investigation. The results show a positive relationship between CO2 emission and fossil fuel use – a 1% increase in fossil fuel consumption will increase CO2 emission by 0.76%, and a negative relationship with renewable energy – a 1% increase in renewable energy consumption will reduce CO2 emission by 0.14%. The impact of forest cover and agricultural production on CO2 emission is mixed at the single-country level. The panel fixed-effect results reveal that a 1% increase in agricultural production and forest cover leads to a 0.04% and 0.63% increase in CO2 emission, respectively. There is also strong evidence supporting the presence of the EKC hypothesis. The global CO2 emission reduction strategies, such as carbon taxes and emission trading schemes, must acknowledge and incorporate the interplay among -key macro-level variables revealed in this study to enhance their effectiveness. [ABSTRACT FROM AUTHOR]

This study investigated the impact of various energy conservation measures (ECMs) on total annual electricity consumption on a single level, two-bedroom residential building in an arid climate and in a very remote area of Australia. Base case scenario of annual energy consumption profiles of the building and its existing systems were modelled and simulated using DesignBuilder software, and the results were validated by on-site measured electricity data. Two categories of ECMs (major intervention and minor intervention) were investigated and analysed. The findings show that the total annual energy consumption of the case study building can be reduced by up to 44% (2 kWh/m2·yr) when compared against the base model and measured data if selected ECMs are implemented. Significant savings from implementing selected ECMs can help alleviate the cost-of-living pressure and stress currently experienced by many households on low incomes especially for residents of very remote communities in Australia. [ABSTRACT FROM AUTHOR]

This study aims to analyze vital indicators for urban consumer's behavior regarding electricity saving appliances (ESA) to reduce energy consumption at home in Indonesia. The studied appliances are limited to two, refrigerator and air conditioner, as these appliances are the most dominant in consuming electricity energy. There are two equation models proposed for each appliance by using consumers' perceptions (general and specific perceptions) and external (social environment, weather, and mass media) group variables, usage and habitual behaviors. Using stepwise regression and statistical approaches for 232 surveyed household consumers in Makassar city, the adjusted R2 value for four composed models is between 52% and 59%. Study findings reveal that specific perception significantly influences the usage of both appliances including the habit of consumers for energy saving. Among the examined external factors, the variable of mass media has significance in almost all models. Implications for policymakers suggest applying a priority approach in promoting energy saving to consumers with low specific perception based on the aspect of installed electric power capacity at home, and giving more information via mass media about attributes of ESA including introducing intervention strategies is essential to support sustainable electricity consumption from the consumer's side. [ABSTRACT FROM AUTHOR]

Although nearly all households in Vietnam have connected to the national grid, electricity shortage in the residential sector is still an issue. One measure of residential electricity shortage is the micro-renewable energy system (micro-RES), but little is known about the drivers for household adoption of such system and whether the presence of a micro-RES can help to reduce household energy consumption. This paper examines the characteristics of households that have adopted a micro-RES and investigates whether the presence of micro-RES would result in a decrease in energy demand by using fixed-effects models. Analysis was carried out on the 2016 and 2018 Vietnam Household Living Standard Survey data sets. The results show that the micro-RES adopters are likely to have lower educational level, lower income or living conditions, and live in the mountainous regions. Micro-RES adopters are found to spend less on electricity and fuel than the non-adopters and this finding is consistent across households of different income groups. The results also suggest that the support schemes may be beneficial to households adopting micro-RES because they really need it to meet the increasing demand in electricity. [ABSTRACT FROM AUTHOR]

This study aims to scrutinize the determinants of household energy consumption needs in Kebbi State, Nigeria. The data for the study were sourced from household heads within the study area. The paper analyzes the determinants of household energy consumption using six energy consumption indicators (household expenditure on energy, electricity, LPG, kerosene, charcoal, and biomass). To analyze the data, the study used descriptive statistics and binary logistic regression (which has rarely been used in this kind of study) which broaden our understanding of social, economic, and environmental perspectives on energy usage. Our empirical strategy indicates that all the instruments used are appropriate based on Cronbach's alpha scale value of greater than 0.9. Education level was found to be a significant factor in energy expenditure by household, electricity, LPG, and kerosene usage, while negatively correlated with biomass usage. A binary logit regression model revealed that household head income, availability of different energy choices, reliability, and affordability are the major determinants of household energy consumption needs. Findings further show that low-income household heads which account for more than 60% of the respondents rely heavily on the traditional methods of biomass to meet their energy needs. The finding further revealed that 72.80% of the respondents confirmed that accessibility is one of the driving forces which determines their energy choice. Based on the findings, the study therefore, recommends the need to ensure the availability and affordability of safer forms of energy as well as invest more in making renewable energy available and affordable. [ABSTRACT FROM AUTHOR]

The current study looked at the influence of fossil-fuel energy (E.U.) consumption, renewable power generation and greenhouse gas emissions in Brazil, Russia, India, China, and South Africa (B.R.I.C.S.) between 1990 and 2020. The latest study also takes into account the influence of gross domestic product (G.D.P.) and technological innovation on carbon emissions. Using cross-sectional dependence and slope heterogeneity, the order of the unit root is also determined. The findings acquired by the application of moment quantile regression. The research finds that G.D.P. and the usage of E.U. increase carbon emissions at the 25th, 50th, 75th and 90th quantiles. On the other hand, renewable energy generation and technical innovation reduce carbon emissions at the 25th, 50th, 75th and 90th quantiles. Furthermore, while implementing B.R.I.C.S. economies' energy, environment, and growth policies based on empirical data, policymakers should analyse the asymmetry behaviour of G.D.P., E.U. consumption, renewable power output and technological innovation. [ABSTRACT FROM AUTHOR]

This paper examines the impact of tourist arrivals on environmental degradation, as well as highlights the implications of this interaction on environmental sustainability targets, while controlling for globalization, energy consumption and economic growth. The study adds to the existing literature by employing the quantile-on-quantile regression (QQR) to examine the nonlinear relationship between carbon emissions and the regressors over the period 1995-2018, using quarterly data. As a robustness check, the conventional quantile regression (QR) model is also applied to capture the nexus across quantiles (0.1-0.9). Furthermore, quantile causality is utilized to capture the causal interrelationships between CO2 and the regressors across quantiles (0.1-0.90). Overall, results show that globalization, tourist arrivals, economic growth and energy consumption increase environmental degradation at different quantiles. The quantile causality results also reveal evidence of causality in mean and variance from globalization, tourist arrivals, economic growth and energy consumption to CO2 emissions in majority of the quantiles. From a policy standpoint, it is recommended that policymakers in Thailand should come up with sound and effective environmental policies that would help the economy achieve sustainable environmental quality for both the immediate and future generations. [ABSTRACT FROM AUTHOR]

Trends indicate that the tourism and hospitality (TH) industry is significantly contributing to the socio-economic conditions of the economies worldwide. However, TH-led economic development is attained at the cost of environmental pollution. This research explores four TH subindustries' impacts on greenhouse gas (GHG) emissions and air pollutants in the US. We also considered energy consumption, economic growth, and globalization to normalize TH subindustries' environmental impacts. The ARDL bounds test approach is applied on a quarterly (2005-2019) time-series data to analyze TH environmental impacts. The findings uncovered that food and drink places (FSDP) contribute higher to GHG (CO2, CH4, N2O) emissions in the long-run than the rest of the subindustries. Compared to the other subsectors, the accommodation (AC) sector contributed higher to air pollutants (CO, NH3, NOx, SO2, VOC, and PM2.5). All the four subindustries positively contribute to energy consumption; however, FSDP, amusement, gambling, and recreation (AGR) subindustries consume higher energy levels. Economic growth has mixed impacts on GHG emissions and air pollutants. Interestingly, globalization shows negative impacts on GHG emissions and air pollutants. Granger causality results show that FSDP, AC, AGR, and performing arts and sports subindustries cause PM2.5. Key implications and policy initiatives are provided. [ABSTRACT FROM AUTHOR]

Digital technology developments shape the behaviour, performances, standards of society, organizations and individuals imposing new ways of payments and new forms of money. In this environment in 2008 was developed a new type of currency, namely Bitcoin. Cryptocurrency, as this new form of money has been generically called, puts pressure on the traditional concept of money. Today, the economic value of cryptocurrencies is attested by their circulation and acceptance by user communities for trade. However, establishing this value raises debates in the literature. The research from this paper investigates and analyses if there is a strong enough connectedness between Bitcoin price evolution and energy consumption tendency (for mining), to influence Bitcoin value. Public data from January 2014 to July 2021 is used. An Artificial Neural Network (ANN) was used to study and predict the tendency of Bitcoin price and energy consumption. A comparison between the forecasting trend and the real trend (the evolution of energy consumption and Bitcoin price) was made. The conducted research starts with a quantitative one and ends with a qualitative one (trends). The obtained results show that qualitatively, there is a good correlation between monthly average values of BTC prices and electricity consumption for mining. [ABSTRACT FROM AUTHOR]

This study quantifies the spatio-temporal distribution of electric buses' (EBs) electricity consumption characteristics and identifies the most electricity-saving operations from both instantaneous movements and driving conditions considering road types and bus routes. First, the real-world EB operating data collected from four EBs are divided into segments. Factors that influence the value of two energy consumption indexes (electricity consumption rate, ECR, and electricity consumption factor, ECF) are assessed. The study finds that an increasing average speed of a bus leads to a lower value of ECF, suggesting a less aggressive and a more efficient driving style. Further, ECFs are clustered into four states (very low/low/medium/high) to identify the energy-saving situation. The result shows that the most energy-saving speed ranges for expressways, arterial roads, and local roads during peak hours are 45–50 km/h, 35-40 km/h and 25-30 km/h, respectively. Findings from this study can be applied for eco-driving suggestions to drivers in order to improve the efficiency of EB operation system. [ABSTRACT FROM AUTHOR]

This paper analyses the nexus between renewable and non-renewable energy consumption, financial development, Information and Communication Technology (ICT) diffusion and economic growth, in MENA countries, over the period 1980–2018. We use the novel Cross-Section augmented Autoregressive Distributed Lag (CS-ARDL) estimation technique which accounts for cross-sectional dependence and cross-country heterogeneity issues. We find a positive impact of renewable and non-renewable energy on economic growth, but a negative effect of financial development on economic growth. We also find a positive and statistically significant influence of ICT on Gross Domestic Product (GDP). Renewable energy and ICT diffusion can be considered important determinants of improved economic activity, job creation and better environmental quality. Pairwise Dumitrescu-Hurlin panel causality tests were used to examine the causal relations among the variables. The findings of this study have considerable policy implications for the selected countries. [ABSTRACT FROM AUTHOR]

Though the relationship between temperature and energy consumption has been rigorous, not all energy sources used for space heating and cooling are investigated. Moreover, most studies consider the usage of energy by limited sectors or even do not separate consumption of energy by sectors, but treat the economy as a whole. By subdividing energy sources into six (coal, petroleum, city gas, electricity, heat, and new & renewable energy) and sectors into five (residential, commercial, industrial, public, and transport), we investigate the relationship between temperature and energy consumption by energy sources and sectors. We used two different regression models, which are time series model and decomposed multiplicative model, using temperature and energy consumption data of South Korea. The result from time series model shows that consumption of city gas (by public and industrial sectors), electricity (by residential, commercial, industrial, and transport sectors), new & renewable energy (by transport sector) is U-shaped in relation with temperature, while consumption of electricity and heat by public sector shows diminishing marginal consumption. On the contrary, consumption of coal (by residential sector), heat (by residential sector), and new & renewable energy (by res/com sector) is linearly related to the temperature. On the other hand, the result from multiplicative model shows that consumption of city gas (by public sector), electricity (by commercial sector), and heat (by commercial and public sector) is U-shaped, while consumption of city gas (by res/com sector), heat (by residential sector), and new & renewable energy (by res/com sector) shows diminishing marginal consumption. However, city gas consumption by industrial sector and new & renewable energy consumption by public sector turns out to be linearly related to the temperature. The relationship between temperature and energy consumption by sectors is also visualized. The implications, limitations, and suggestions for future research are also presented. [ABSTRACT FROM AUTHOR]

This paper presents a validation by simulation of feasibility analysis for the installation of the ground source heat pump systems that have been proposed the modeling study for use in Algeria. We design and calculate the energy needs of an example building with the COMSOL Multiphysics software and Geo TSOL Basic. The study addresses heating needs for the climate conditions of western Algeria, in one of North Africa's biggest cities. The houses energy needs are assumed to be covered by a ground source heat pump (GSHP) system coupled with solar panels. The GSHP consists of a vertical ground heat exchanger and water-to-water heat pumps and is analyzed using an in-house developed and validated code. Energy consumption was calculated and validated by a simulation of the building's properties. Results show that installation of the ground source heat pump system substantially reduces the building's energy consumption. Finally, an economic analysis of the proposed system for construction practices in Algeria was carried out. The main novelty of this article, being that the study was conducted to seek a solution to reduce the energy bill of a house located in a city of a country Natural Gas Producer. Algeria is one of the 10 largest producers of natural gas in the world. More than 80% of the energy bill of a house located in Algeria, is due to the consumption of gas (Heating, Hot Water and Cooking), the electricity is mainly used in summer for air conditioning. The use of renewable energy will cost more than the use of natural gas. It is for this main cause that renewable energies are struggling to develop in Algeria, the cost of gas is very low. The economic analysis shows that in countries like Algeria, where the cost of conventional energy is heavily subsidized, solar thermal is not competitive with natural gas. It is only by coupling the GSHP with solar collectors that solar can take off and attract households. As a result, the only renewable way to compete with natural gas is the combination of GSHP with solar collectors. The only solution adopted to reduce the energy bill in a house located in Algeria, is the solution presented in this article. This is the first technical–economic study of how a GSHP could contribute to the energy needs of buildings in the city of Tlemcen ALGERIA. [ABSTRACT FROM AUTHOR]

Even though the Paris Agreement and the Sustainable Development Goals say that low-carbon economic growth is essential, more research must be done to determine how the tourism sector affects carbon productivity. So, to see if increased energy usage, tourism, and economic growth jointly raise carbon productivity in Kuwait, this study uses a vector error correction strategy to look at the years 1995–2020. Predictions about how sustainable tourism will affect energy efficiency, and carbon productivity improvements are also an excellent way to learn more about this subject. As the amount of carbon dioxide in the air increases, tourism will go down by 0.13 percent. Inverse cointegration is the term for this phenomenon. However, the vector error correction model showed that carbon emissions go down as the economy grows and people pay more attention to how much energy they use. Nevertheless, Granger's theory of cause and effect says that carbon emissions, energy use, and economic growth can only lead to more tourism in one way. [ABSTRACT FROM AUTHOR]

The use of environmental taxes can encourage a shift toward eco-friendly choices. When used in conjunction with other policy tools available, environmental taxes can help bring about the adjustments needed in order to address our current environmental and climate challenges. Therefore the objective of this study was to examine the impact of environmental taxes on energy consumption and energy intensity using panel data covering the period 1995–2014 from 35 OECD countries. I employed environmental tax to total tax ratio, total energy consumption, and total energy intensity to estimate the relation between energy consumption and environmental taxes. Using the fully modified and dynamic OLS techniques and I showed that environmental taxes have a negative effect on energy consumption and energy intensity in the long run. Furthermore, using the Dumitrescu and Hurlin's panel granger causality test I found a bi-directional long-run causality between environmental taxes and energy consumption and intensity. With regards to the disaggregated effect of environmental taxes, this study found that energy taxes (including CO2 taxes) have a larger effect on energy consumption and energy intensity than pollution and transport taxes. To test for the robustness and sensitivity of my model, I resorted to the total environmental tax to GDP ratio and employed both GMM and quantile regressions. Thus, I concluded that environmental taxes have a significant impact on energy consumption and energy intensity among OECD member countries. [ABSTRACT FROM AUTHOR]

Bangladesh—recently graduated to developing nation category from a least developed country with an emerging economy also is one of the severely affected countries by climate change—is heading towards a coal-intensive electricity generation mix contrary to global decarbonisation efforts. It is facing formidable challenges in achieving universal access to affordable, reliable, and sustainable electricity, decarbonising the energy mix by 2030 to achieve the objective of Sustainable Development Goal (SDG) 7, despite a 285% increase of installed capacity between 2008–09 and 2020–21 and aiming at achieving 40 GW and 60 GW by 2030 and 2041 with planned expansions, respectively. This study reviewed Bangladesh's electricity sector developments—demand, generation, transmission, and distribution (T&D)—to identify progress in policies, drivers, and challenges behind the Greenhouse gas (GHG) emissions-intensive future direction. The rapid population and economic growth and shift towards industry-based economy drove the exponential growth in energy demand, eventually influencing the rapid generation capacity and T&D infrastructure development. However, Bangladesh has targeted transitioning from natural gas to coal dominating fuel mix due to the lower renewable potential, energy, and food security challenges, because of the anticipated substantial future electricity demand for becoming an Upper Middle and a High-income country by 2031 and 2041, respectively. We also recommended nuclear energy, (renewable) electricity import and floating solar plants to decarbonise the current trajectory. [ABSTRACT FROM AUTHOR]

This paper intends to know the impact of information and communication technology (ICT) on the economic progress and renewable energy consumptions in two Asian economic giants namely India and China. To achieve the objectives, generalised method of moments (GMM) and pooled ordinary least square (OLS), fixed effect and random effect models have been applied using yearly panel data of individuals using internet as ICT, growth rate of gross domestic product (GDP) as economic progress, electric power consumption as energy consumptions and foreign direct investment (FDI). This study observes significantly positive impact of ICT on renewable energy consumptions while negative and insignificant impact on the economic progress in India and China. It is observed that countries are similar in renewable energy expansion, time spent on internet, electricity generation and policy support for renewable energy generation while they significantly differ in the number of internet subscribers, internet-based entrepreneurships and sources of renewable energy. This study proposes to apply ICT in all possible sectors to boost-up economic growth as well as to enhance the demand for renewable energy consumptions. [ABSTRACT FROM AUTHOR]

Since the debate concerning economic growth, energy consumption, and environmental degradation is a burning issue in the last few decades. Yet the outcomes are controversial and requires more empirical work. This study used novel research methods to investigate the said issue in case of China since is the leading energy importing and pollution emitting economy. The time series data covers the period from 1969 to 2018 is analyzed via employing the novel Quantile-on-Quantile regression methods. The estimated results unveil that fossil fuel consumption significantly enhances carbon emissions in the country in the lower and middle quantiles. Besides, the economic growth showed an inverted U-shaped impact on the carbon emissions, where the lower-medium quantiles showed positive and the upper quantiles exhibit negative impact on carbon emissions. Besides, the positive association between economic growth and fossil fuel emissions are also evident in the analysis. The Granger causality indicates that there is a bidirectional causality between the study variables – validating that economic growth and fossil fuel consumption are the primary sources of increased emissions level. On the basis of empirical results, this study suggests the adoption of energy efficient/saving products and services, diverting economic growth to renewable and environmentally friendly energy resources, technological innovation and increased investment in the research and development to reduce dependency on fossil fuel energy consumption and attain low carbon economy. [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]

Unlike in the past, as the scale of buildings expands from a single building to a group of buildings, it is necessary to respond to the demand for energy consumption for the entire community. This study attempted to improve the electricity consumption prediction performance of single buildings by reflecting the inherent occupant behaviour patterns embedded in the shared electricity consumption data of single buildings. The proposed method utilized electricity consumption data for all other 7 target buildings as a part of LSTM model inputs in combination with meteorological data to predict the electricity consumption profile of a specific building. As a result, the proposed method reduced the error by utilizing the similar energy profiles of the surrounding buildings, *and the prediction performance of the total electricity consumption of the community was increased by 5.11% based on RMSE. The newly proposed method reflects the inherent occupant behaviour patterns and temporal electricity consumption patterns from energy profiles of nearby buildings. [ABSTRACT FROM AUTHOR]

A new bottom-up white-box building stock energy modelling approach is presented in this paper. The model is a physics simulation-based stock model that can be used to compare the base case building stock with technological variations for comparative assessments. The model accuracy is compared to known stock data for a variety of categories, including end-use, energy source and building type. The model predicts the energy use of a building stock in a Canadian region with good agreement across all categories, with the total energy consumption of the model within 1.5% of the real stock energy use. Our analysis shows that modelling a sufficiently large sample of stock is required to reduce the expected deviation for lesser-represented portions of the stock. A case study illustrates how the model can be applied to compare different heating system distributions, in order to assess their impact on greenhouse gas emissions and peak electricity demand. [ABSTRACT FROM AUTHOR]

This paper develops a dual sectors dynamic equilibrium model and introduces agricultural energy consumption and agricultural water consumption in a growth model that tested by using a panel data set from 2004 to 2016 in China. It presents a theoretical prediction on the interactions between agricultural energy consumption, agricultural water consumption, and food yield. Consistent with this prediction, agricultural electricity consumption and agricultural water consumption by themselves appear to have significant effects on food yield. The fixed-effects models verify that agricultural electricity consumption, agricultural water consumption, and food yield have long-run equilibrium relationships. The results of weather-based robustness checks reveal that food yield is positively correlated with agricultural electricity consumption. Also consistent with theory, agricultural water consumption is positively associated with food yield. These results are generally stable and hold with tests of panel data unit roots, with alternative estimation strategies, and with or without controlling for covariates. [ABSTRACT FROM AUTHOR]

By comparing ecological footprint and biocapacity, the load capacity factor follows a given ecological threshold, thus allowing for a thorough study of environmental deterioration. As a result, this research uses the load capacity factor (LCF) as a distinct proxy for ecological deterioration, resulting in a detailed environmental evaluation tool that considers both ecological footprint and biocapacity at the same time. Based on this knowledge, the current research investigates the relationship between load capacity factor (LCF) and fossil fuel (FF), renewable energy (REC), economic complexity (ECI) and foreign direct investment (FDI) in Spain utilizing a dataset covering the period between 1970Q1 and 2017Q4. We applied the novel wavelet coherence technique to assess the connection and/or causality interrelationship between LCF and the exogenous variables at various frequencies and different timeframes. The outcomes of the wavelet correlation disclosed that: (i) In the short and medium term, REC enhances the quality of the environment; (ii) In the short and medium term, fossil fuels worsen the quality of the environment; (iii) At all frequencies, FDI inflows enhance the quality of the environment; and (iv) In the short, medium and long term, economic complexity hinders the quality of the environment. In addition, the MWC and PWC outcomes are consistent with the wavelet coherence outcomes. Moreover, the wavelet-based Granger causality revealed that all the variables can predict each other at various frequencies. [ABSTRACT FROM AUTHOR]

Knowing of energy consumption behaviors and similarities between consumers becomes extremely important for electric power systems operators to apply green energy trends by ensuring the stability and flexibility of the grid. Especially in the modern power systems and the energy markets, consumer-based market structures must be implemented. Consumer types, energy needs, and demand behaviors of the distribution grid can affect cumulatively the demand behavior of the full electricity system. Thus, understanding the consumption behaviors and similarities among different distribution grid operator can provide a significant advantage in determining the types and applications of demand response activities in terms of the transmission grid. In this study, the consumption of distribution grids analyzes statistically according to the consumer types (like residential, commercial, industrial), and to identify the consumption similarities making clustering analysis. For this reason, first, the total consumption reflected on the transmission grid is examined periodically concerning the distribution grids. Second, we group distribution grids by consumption similarity using past real consumption data using a MATLAB application developed. A data-driven regional demand aggregation approach is shown together with the cluster analysis. Finally, the demand response potential got from distribution loads has been evaluated. [ABSTRACT FROM AUTHOR]

Metro transportation systems are significant energy consumers. Apart from the traction system, considerable amounts of electricity are consumed in metro stations. In the present study, energy audits of two Athens metro stations were conducted to ensure a detailed overview of energy consumption per stationary load. Data from on-site surveys as well as real-time measurements were elaborated. The energy profile over time was developed for the measured electrical loads. Results have shown that the total energy consumption of Sepolia and Peristeri stations was 117.09 and 99.17 kWh/m2/year respectively, as well as 0.12 and 0.52 kWh/passenger/year. Lighting and small power were significant consumers (23.3% of total energy consumed in Sepolia station, 51.5% in Peristeri station). The natural, piston effect induced ventilation system in Peristeri station showed significantly lower energy consumption (7.4%) than that of Sepolia station (25.9%) using forced ventilation for stations and tunnels. [ABSTRACT FROM AUTHOR]

Although DC power is claimed to be more energy-efficient than AC power for EHD drying, experimental studies on this subject are still insufficient. This research compares the efficiency of DC versus AC (60 Hz frequency) power for EHD drying by using 1 × 1 and 2 × 2 multi-pin discharge electrodes. Energy indicators were determined from the measurements of electric power consumption at the corresponding drying rate of the wet tissue paper. The experiments revealed the key benefits of DC drying, such as enhanced drying rate, higher energy efficiency, and lower specific energy consumption. For the comparable drying flux of 0.2 g/(m2 s), the specific energy consumption of DC drying was in the range from 350 J/g (2 × 2) to 750 J/g (1 × 1) as compared with 2000 J/g (2 × 2) to 5000 J/g (1 × 1) for AC. The specific energy consumption of a 2 × 2 discharge electrode was consistently smaller than the 1 × 1 one, indicating the role of emitters spacing in the process efficiency. The specific energy consumption of both electrodes increased with the drying rate. [ABSTRACT FROM AUTHOR]

Energy demand and consumption across the world are increasing, putting much pressure on current supply. The solar PV system has become one of the most promising renewable energy technologies. The potential of solar PV differs from one province and household to another depends on many attributes and variables. A household energy survey was conducted through questionnaires and interviews from September 2014 to March 2015 in order to determine perceptions on household energy consumption and solar PV's potential across household categories in South Africa. The data from the residential households were collected in nine provinces across South Africa using random sampling methods (stratified and systematic). The number of questionnaires (n = 13,007) were completed across South Africa nine provinces. This study found that 25% and 43% of the middle- and high-income residents respectively are interested in paying for electricity based on green sources. These two income groups consume 9.6 and 3 TWh/year, which means that there is a potential of 12.6 TWh/year that can be generated from solar PV technology in the residential sector. [ABSTRACT FROM AUTHOR]

Based on data from a household survey completed in 2016, this paper estimated a model for residential electricity consumption in Kuwait. The main objective of estimating the model was to examine the impact of curtailment and efficiency energy conservation measures on household electricity consumption. Given Kuwait's demographic composition and variations in electricity consumption across months, the model was estimated for two population groups (nationals and expatriates) and three periods (annual, summer months, and winter months). Prior to estimating the model, tests were conducted to determine if the observed differences in consumption across the population groups were sufficiently different to warrant separate treatment of groups. The survey data revealed that despite electricity consumption being heavily subsidized, households made efforts to conserve electricity by investing in efficient technologies as well as changing habits and practices. While the curtailment measures reported to be adopted by households were found to lower electricity consumption of the national households, the efficiency measures were found to impact electricity consumption of expatriate households. Awareness of conservation campaigns and conservation issues were found to lower electricity consumption, particularly in the case of expatriate households. [ABSTRACT FROM AUTHOR]

Coal dominates China's energy consumption and causes significant air pollution and carbon emissions. By examining recent policy imperatives of coal sector restructuring, this article unpacks the contradictory policy goals on cutting coal production capacity and mitigating climate change, as well as the challenges to reducing coal consumption. These challenges largely result from the regulatory framework governing China's electricity sector, which lacks effective mechanisms to reduce coal consumption. By doing so, this article aims to shed some light on the role state-owned enterprises play in the coal sector, or the role they could play, towards climate change mitigation. [ABSTRACT FROM AUTHOR]

Conventional drying technologies generally rely on drying processes driven by using hot air, where these processes have significantly high energy consumption and greenhouse gas emissions. With our knowledge based on the limited nature of fossil fuels and the impact of climate change, it is clear that every technology has to be cleaner, cheaper, and more efficient to limit our carbon footprint while maintaining sustainable development. In this regard, the present study comprehensively reviews the recent advances in drying technologies and energy consumption and the efficiency and economic performances of several alternative drying technologies. The reviewed drying technologies include adsorption mediated, agitated thin film, electrotechnologies, hybrid systems, impingement, heat pumps, microwave, ohmic-heating, refractance window, rotating drum, superheated steam, and vacuum. The selected performance criteria are final heat use, final electricity use, primary energy use, thermal and energy efficiencies, and operating cost. With these criteria taken into account, the results show that the refractance window has the highest sustainability ranking (8.28/10), and superheated steam has the lowest sustainability ranking (2.90/10). Besides, the sustainability of the different drying fuels and technologies is conducted, and the results show that renewables have the highest sustainability ranking. Besides, it is seen that some novel approaches in drying, such as solar-based hybrid drying systems and refractance window drying, are amongst the most promising sustainable technologies. They could be the key to clean, affordable, reliable, and efficient drying of different kinds of products in industrial processes. [ABSTRACT FROM AUTHOR]