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Bangladesh is well on course to become one of the leading emerging market economies in the world. Hence, it can be expected that the economic growth of Bangladesh would substantially increase over the next decade. This, in turn, is likely to boost the energy consumption levels of the nation whereby meeting the surge in the energy demand would be a crucial agenda of the government. Therefore, it is important to understand the factors that influence the nation’s energy demand. Against this backdrop, this paper aims to evaluate the macroeconomic determinants of total, renewable, and non-renewable energy demands in Bangladesh between 1980 and 2014. Besides, the analysis is conducted for both primary energy and electricity consumption levels. The econometric methods used in this study controlled for the structural break issues in the data. The key findings, in a nutshell, show that economic growth and household consumption expenditure positively influence the overall primary energy and electricity demands in Bangladesh while income inequality exerts opposite effects. Besides, technological innovations are found to be reducing the total and non-renewable energy demand in Bangladesh while increasing the demand for renewable energy. On the other hand, positive oil price shocks are found to be ineffective in influencing the renewable energy demand but slightly reducing the non-renewable energy demand. Finally, the causality estimates portray the feedback hypothesis in almost all the cases to highlight the inter-relationships between economic growth and energy demand in Bangladesh. Hence, in line with these findings several critically important policy implications are suggested for managing the overall energy demand in Bangladesh.

One of the strategic objectives of China is to increase renewable energy consumption by reducing non-renewable energy consumption. This motivates us to carefully investigate the asymmetric effects of financial deepening on renewable and non-renewable energy consumption for China, using annual data from 1990 to 2019. The results show that in China, a positive shock in bank deposits and broad money has a significant increasing effect on renewable energy consumption, while a negative shock in bank deposits and broad money has also a significant increasing effect on renewable energy consumption in the long-run. Moreover, positive change in bank deposits and broad money has an inverse impact on non-renewable energy consumption, while negative change has stimulating non-renewable energy consumption in long run. Thus, government and policymaker's policies aimed at promoting financial deepening in China must be persistent and sustainable to foster renewable energy consumption.

Previous infant literature has assessed the symmetric impact of monetary policy uncertainty on a few macro variables. Our study has considered asymmetric monetary policy uncertainty impacts on energy consumption. Our key concern in this study is to regulate whether US monetary policy uncertainty has an asymmetric impact on energy consumption. We employ the symmetric and asymmetric autoregressive distributed lag (ARDL) estimation methods, and we found that monetary policy uncertainty has short- and long-run negative effects on renewable energy consumption in the linear model, while decreased monetary policy uncertainty has a significant negative influence on renewable energy consumption in the USA in the non-linear model. However, in the short and long run, the measure of monetary policy uncertainty has an insignificant impact on non-renewable energy consumption, while increased monetary policy uncertainty in the USA has negative effects and decreased monetary policy uncertainty has positive effects on non-renewable energy consumption in the short and long run in the non-linear model. The effects are asymmetric in direction and magnitude. The study results call for vital changes in renewable and non-renewable energy policies to accommodate monetary policy uncertainties.

In order to address the issues of significant energy and resource waste, low-energy management efficiency, and high building-maintenance costs in hot-summer and cold-winter regions of China, a research project was conducted on an office building located in Nantong. In this study, a data-driven golden jackal optimization (GJO)-based Long Short-Term Memory (LSTM) short-term energy-consumption prediction and optimization system is proposed. The system creates an equivalent model of the office building and employs the genetic algorithm tool Wallacei to automatically optimize and control the building’s air conditioning system, thereby achieving the objective of reducing energy consumption. To validate the authenticity of the optimization scheme, unoptimized building energy consumption was predicted using a data-driven short-term energy consumption-prediction model. The actual comparison data confirmed that the reduction in energy consumption resulted from implementing the air conditioning-optimization scheme rather than external factors. The optimized building can achieve an hourly energy saving rate of 6% to 9%, with an average daily energy-saving rate reaching 8%. The entire system, therefore, enables decision-makers to swiftly assess and validate the efficacy of energy consumption-optimization programs, thereby furnishing a scientific foundation for energy management and optimization in real-world buildings.

With regard to environmental degradation in Japan, the world's third-largest economy, limited studies have been performed to illustrate the ecological aspects of the country's core and recent economic policies such as globalization, technological innovation, and renewable energy usage policies. Given this motivation, this research reveals a new perspective on the connection between CO2 emissions and GDP growth, renewable energy, technological innovation and globalization in Japan by employing wavelet statistical tools. The paper employs series of wavelet tools for datasets covering the period from 1990Q1 to 2015Q4. The empirical outcomes demonstrate proof of the interaction between renewable energy use, economic growth, technological innovation, globalization and CO2 emissions in both time and frequency. The empirical results of the wavelet analyses reveal that globalization, GDP growth, and technological innovation increase CO2 emissions in Japan, while renewable energy usage mitigates CO2 in the short and medium terms. The results demonstrate the significance of implementing policies effectively coordinated by the policymakers to curb the significant environmental degradation in Japan. Moreover, Japan should actively support renewable energy development and create a more competitive climate for investment in the renewable energy market.

The present study explores the effect of renewable energy consumption and public-private partnership investment in energy on consumption-based carbon dioxide emissions for India from 1990Q1 and 2015Q4 whilst controlling technology innovation and economic growth. The study employs the Maki cointegration, Bayer-Hanck cointegration, fully modified ordinary least squares, dynamic ordinary least squares, and frequency-domain causality tests to explore these dynamics. The outcomes of the present study reveal that (i) there is a long-run cointegration equation between consumption-based carbon dioxide emissions and its possible determinants; (ii) whilst renewable energy consumption is beneficial for lowering consumption-based carbon dioxide emissions, public-private partnership investment in energy makes a positive contribution to consumption-based carbon dioxide emissions in the long-run; and (iii) public-private partnership investment in energy and renewable energy consumption also significantly causes consumption-based carbon dioxide emissions at different frequency levels in India. The present study recommends that policymakers in India should apply a series of policies to discourage the use of non-renewable energy and raise the share of renewable energy in order to reduce consumption-based carbon dioxide emissions in the country. The present study also recommends that public-private partnership investment in renewable energy should increase to achieve cleaner production processes.

In recent years, a growing number of scholars have employed various proxies of environmental degradation to understand the reasons behind rising environmental degradation. However, very few studies have considered consumption-based carbon emissions, even though a clear understanding of the impact of consumption patterns is essential for redirecting the pattern to more sustainable consumption. Thus, this study takes a step forward by using consumption-based carbon emissions (CCO 2 ) as a proxy of environmental degradation using the novel non-linear ARDL technique for Chilefrom 1990 to 2018. To the best understanding of the investigators, no prior studies have investigated the drivers of consumption-based carbon emissions utilizing non-linear ARDL. The study employed ADF and KSS (non-linear) tests to check the data series’ stationary level. Additionally, the symmetric and asymmetric ARDL approaches are utilized to explore cointegration and long-run linkages. According to the results, there is no symmetric cointegration among the variables; however, the empirical estimates reveal a long-run asymmetric connection between the indicators and CCO 2 emissions. The novel results from the asymmetric ARDL indicate that negative and positive changes in economic growth deteriorate the quality of the environment. Interestingly, a reduction in economic growth makes a more dominant contribution to environmental degradation. Moreover, positive changes in renewable energy usage improve the quality of Chile’s environment, inferring that the country can achieve a reduction in environmental degradation by boosting renewable energy consumption. Surprisingly, the study found that technological innovation is ineffective in reducing consumption-based carbon emissions, which implies that Chile’s technological innovation is not directed towards manufacturing green technology. Finally, the policy implications are discussed with respect to reducing consumption-based carbon emissions.

This study examines the causal relationship between economic growth and renewable energy consumption using data for 28 countries of European Union, taken from Eurostat database for years from 1995 to 2015. In addition, motivated by EU Directive 2009/28/EC, the tendency of the share of renewable energy consumption into the final energy consumption is analysed. Various panel data techniques implemented in EViews are used. The empirical results suggest a positive impact of renewable energy consumption on economic growth, and emphasize bidirectional or unidirectional Granger causalities between the two macroeconomic indicators, for each country in the panel. These results justify the political decisions of EU concerning the necessity of increasing the renewable energy consumption, and prove that this type of energy consumption has a strong positive impact on economic growth. Thus, the inclusion of such policies in future EU and national strategies is further motivated. Finally, by means of linear regression, an increasing trend was found for the ratio between renewable energy consumption and final energy consumption for all but one of the EU countries.

Energy consumption has become one of the primary costs in the stone processing industry. Stereoscopic statue production, characterized by extensive material removal and prolonged cycles, consumes the most energy among stone products. Due to their high degrees of freedom, operational agility, precision, and broad scope, industrial robots are widely applied in stereoscopic statue processing. However, robotic processing of stereoscopic statues represents a quintessential high-energy-consuming process, especially during the rough machining phase, where energy consumption is particularly significant. Therefore, this paper proposes a method for predicting energy consumption during the rough machining phase of robotic stereoscopic statue processing and implementing energy-saving optimization. Firstly, a prediction model for the robot’s body power is established by analyzing the energy consumption characteristics of the robot system. Subsequently, the spindle power of the robot is predicted based on the relationship between force and power variations during the grinding process. Finally, energy consumption optimization is achieved using the proposed feed-speed dynamic programming method based on genetic algorithms. Experimental results show that using the feed-speed dynamic programming method reduces energy consumption during rough machining by 16.9%, and processing time is shortened by 19.5%.

With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption. In this work, we aim to model and to compare three different machine learning algorithms in making a time series power forecast. The proposed models are the Long Short-Term Memory (LSTM), the Gated Recurrent Unit (GRU) and the Drop-GRU. We are going to use the power consumption data as our time series dataset and make predictions accordingly. The LSTM neural network has been favored in this work to predict the future load consumption and prevent consumption peaks. To provide a comprehensive evaluation of this method, we have performed several experiments using real data power consumption in some French cities. Experimental results on various time horizons show that the LSTM model produces a better result than the GRU and the Drop-GRU forecasting methods. There are fewer prediction errors and its precision is finer. Therefore, these predictions based on the LSTM method will allow us to make decisions in advance and trigger load shedding in cases where consumption exceeds the authorized threshold. This will have a significant impact on planning the power quality and the maintenance of power equipment.