Energy use, carbon dioxide emissions, GDP, industrialization, financial development, and population, a causal nexus in Sri Lanka: With a subsequent prediction of energy use using neural network

This study investigates the relationship between energy use, GDP, carbon dioxide emissions, population, financial development, and industrialization utilizing ARDL and artificial neural network for Turkey. The data covers the period from 1968 to 2013. The study performed a two stage analysis. At the first stage, we examined the long run relationship and causality between variables. The variables are found to be cointegrated. The Granger causality test results shows that there is a unidirectional causality running from energy use to both carbon dioxide emissions and industrialization. According to the artificial neural network results, the most important effect on energy use comes from GDP. The predicted energy use from 1968 to 2013 has maximum absolute error of % 11. 31 and minimum absolute error of %0.07. Neural network evidence shows that the R-square coefficient is 98% for the sample period.

Do green technology innovations, financial development, and renewable energy use help to curb carbon emissions?

The aim and novelty of this study consist of estimating the nexus between CO2 (carbon dioxide) emissions, energy use, economic growth, and financial development for ten Central and Eastern European countries (CEEC) over the 2000–2017 period, starting from Environmental Kuznets Curve (EKC) theory. The Fully Modified Ordinary Least Squares (FMOLS) method was used for testing the cointegration relationship. Granger causality estimation based on the Vector Error Correction Model (VECM) and Pairwise Granger causality test were applied to identify the causality relationships between the variables and to identify the direction of causality. The implementation of the tests led to significant conclusions. In the long run, the levels of CO2 emissions and energy use do not have any influence on economic growth. Furthermore, there is a bidirectional causality among economic growth in terms of GDP and financial development variables. Thus, increasing financial development will generate more CO2 emissions and more energy use, and increasing economic growth will lead to rising financial development. In the short run, increasing financial development will generate more CO2 emissions and will lead to increased energy use and economic growth. Also, a bidirectional causality is being revealed between financial development and CO2 emissions. This indicates that financial development may help to reduce CO2 emissions.

This study explored the long-run association among greenhouse gases (GHGs), financial development, forest area, improved sanitation, renewable energy, urbanization, and trade in 24 lower middle-income countries from Asia, Europe, Africa, and America (South and North) by using panel data from 1990 to 2015. Granger causality was tested by Toda and Yamamoto approach. The bi-directional causality was established among urbanization and GHGs (Asia), financial development and forest (Asia), energy use and renewable energy (Asia), renewable energy and forest (Asia), improved sanitation and forest (Asia, Africa, America), urbanization and forest (Asia), and improved sanitation and financial development (Europe). The GHG emission also shows one-way causality is running from financial development to GHG (America), energy to GHG (Asia), renewable energy to GHG (America), forest area to GHG (America), trade openness to GHG (Africa), urbanization to GHG (Europe), GHG to financial development (Europe), GHG to energy use (Europe, Africa, and America), and GHG to trade openness (Asia). On the basis of fully modified ordinary least square and generalized method of moment, the reciprocal relationship of GHGs was observed due to financial development in Asia and Africa; renewable energy in all panels; forest area in Asia, Europe, and America; improved sanitation in Asia, Africa, and America; trade openness in Africa; and urbanization in Europe and America. Policymakers should concentrate on these variables for the reduction in GHGs. The annual convergence towards long-run equilibrium was 50.5, 31.9, and 20.9% for America, Asia, and Africa, respectively.

ABSTRACTThe study examined the causal nexus between energy use, carbon dioxide emissions, and macroeconomic variables in Ghana with data spanning from 1960 to 2013 using the vector error correction model (VECM). It is evidential from the study that almost 12% of future fluctuations in energy use are due to shocks in financial development and 10% of future fluctuations in carbon dioxide emissions are due to shocks in energy use. There was evidence of a bidirectional causality between: energy use and financial development, energy use and industrialization, and energy use and population. Energy use in Ghana is predicted to decrease from 397 kg of oil equivalent per capita in 2013 to 374 kg of oil equivalent per capita in 2040. Energy policies that aim at increasing the share of renewable and clean energy technologies into Ghana’s energy portfolio will help mitigate climate change and its impacts.

Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition

Carbon emission, energy consumption, trade openness and financial development in Pakistan: A revisit

Artificial neural network with many types of algorithms is known as an efficient tool in forecasting as it is able to handle nonlinearity behaviour of data. This paper investigates the performances of Levenberg-Marquardt and gradient descent algorithms of back propagation neural networks carbon dioxide emissions forecast. The inputs for the model were selected and the ANNs were trained using the Malaysian data of energy use, gross domestic product per capita, population density, combustible renewable and waste and carbon dioxide intensity. The forecasting performances were measured using coefficient of determination, root means square error, mean absolute error, mean absolute percentage error, number of epoch and elapsed time. Comparison between these algorithms show that the Levenberg-Marquardt was outperformed the gradient descent in carbon dioxide emissions forecast.KeywordsRoot Mean Square ErrorArtificial Neural NetworkGradient DescentMean Absolute Percentage ErrorGradient Descent AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The impact of financial development, income, energy and trade on carbon emissions: Evidence from the Indian economy

The scholars of environmental economics have attempted the investigation of the impact of foreign direct investment-growth nexus, but they have missed the essential role played by technological innovation and financial development regarding the environmental costs. The notable economic growth and the consequent speedy process of urbanization in BRICS countries have brought about colossal escalation of energy needs leading to environmental degradation. The present study endeavors to explore the effect of foreign direct investment, technological innovation, and financial development on carbon emissions in BRICS member countries, with data from 1990 to 2017. The results verify a strong cross-sectional dependence within the panel countries. The Augmented Mean Group (AMG) estimator shows that foreign direct investment, technological innovation, and financial development in the BRICS countries possess a negative and statistically significant long-run association with CO2 emissions, while economic growth, trade openness, urbanization, and energy use are found to contribute statistically significant and positive with carbon emissions. The current study chose to employ the Dumitrescu and Hurlin panel causality test for examining the direction of causality. Findings reveal a bidirectional long-run causality running among financial development, economic growth, trade openness, urbanization, energy use, and CO2 emissions; on the contrary, unidirectional causality is found between foreign direct investment and carbon emissions. Consequently, for the BRICS member countries, the development of industries, financial institutions, and development of technological innovation are required to attract quality foreign direct investment. Moreover, urbanization contributes enormously to environmental degradation and necessitates urgent policy responses in these countries.

Forecasting energy usage in buildings is essential for implementing energy saving measures. Precisely forecasting building energy use is difficult due to uncertainty and noise disruption.To achieve enhanced accuracy in predicting energy use in buildings, a deep learning approach is proposed. This paper proposes a customized convolutional neural network with Q-Learning (CCNN-QL) based reinforcement learning algorithm for predicting energy consumption in building.The suggested CCNN-QL model offers an auto-learning feature that predicts building energy consumption through an automated method, continually improving its predictive accuracy.To assess its performance, various building types were selected to study the factors influencing excessive energy consumption, and data were collected from multiple Chinese cities. The suggested model’s performance has been assessed using evaluation metrics, resulting in a low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), indicating superior accuracy relative to comparable studies. Experimental results indicate that the suggested technique has superior predictive performance across several scenarios of building energy usage.

This report documents a demonstration of a software modeling tool from Romonet that was used to predict energy use and forecast energy use improvements in an operating data center. The demonstration was conducted in a conventional data center with a 15,500 square foot raised floor and an IT equipment load of 332 kilowatts. It was cooled using traditional computer room air handlers and a compressor-based chilled water system. The data center also utilized an uninterruptible power supply system for power conditioning and backup. Electrical energy monitoring was available at a number of locations within the data center. The software modeling tool predicted the energy use of the data center?s cooling and electrical power distribution systems, as well as electrical energy use and heat removal for the site. The actual energy used by the computer equipment was recorded from power distribution devices located at each computer equipment row. The model simulated the total energy use in the data center and supporting infrastructure and predicted energy use at energy-consuming points throughout the power distribution system. The initial predicted power levels were compared to actual meter readings and were found to be within approximately 10 percent at a particular measurement point, resulting in a site overall variance of 4.7 percent. Some variances were investigated, and more accurate information was entered into the model. In this case the overall variance was reduced to approximately 1.2 percent. The model was then used to predict energy use for various modification opportunities to the data center in successive iterations. These included increasing the IT equipment load, adding computer room air handler fan speed controls, and adding a water-side economizer. The demonstration showed that the software can be used to simulate data center energy use and create a model that is useful for investigating energy efficiency design changes.

The aim of this article is to investigate the relationship between air pollution, economic growth, energy use, trade openness, foreign direct investment, and financial development in N‐11 countries data period from 1980 to 2018. For this purpose, it is adopted the Panel Vector Autoregression (PVAR) model for the estimation of the long and short‐run effects. The results suggest that although energy consumption and financial development have a negative impact on CO2 emissions, foreign direct investment leads to an increase in pollution. In addition, there is bidirectional causality between financial development and CO2 emissions and energy use, carbon dioxide emissions and energy consumption, foreign direct investments and energy consumption, and financial development and energy consumption. In addition, there is unidirectional causality from carbon dioxide emissions to GDP, from energy consumption to GDP, from foreign direct investments to CO2 emissions and GDP, from financial development to GDP. Finally, impulse‐response functions indicate the validity of the EKC hypothesis in these countries.

CO2 emissions, energy consumption, economic growth, and financial development in GCC countries: Dynamic simultaneous equation models

This study was conducted in order to model energy consumption and greenhouse gas emissions for peanut production in Guilan province of Iran using artificial neural network (ANN). Also, the multi-objective genetic algorithm was used for optimization of energy inputs and GHG emissions in the region. Data were randomly collected from 120 farms in Astaneh Ashrafiyeh city with face to face questionnaire method. The results illustrated that the total energy consumption and the average yield were calculated as 19248.04 MJ ha-1 and 3488.39 kg ha -1 , respectively. Moreover, the results showed that the share of chemical fertilizers (mainly nitrogen) and diesel fuel energy to the total energy input were the highest. Also, the energy used efficiency ratio calculated as 4.53. The results of GHG emissions analysis showed the total GHG emissions were 571.18 kgCO2eq. ha -1 and the diesel fuel has the main reasonable of GHG emissions in peanut production. In this study, several direct and indirect factors have been identified to create a model based on ANN to predict energy use and GHG emissions in peanut production. The ANN model with 9-22-2 structure was capable of predicting the peanut yield and GHG emissions. Moreover, the results of the best topology showed that R 2 was 0.994 and 0.999, RMSE was 0.076 and 0.003 and MAPE was 0.174 and 0.009 for peanut yield and GHG emissions, respectively. The results of optimization indicated the total energy consumption and GHG emissions generation was calculated about 6888 MJ ha -1 and 159.08 kgCO2eq. ha -1 , respectively. The total GHG emissions reduction was found to be 412.09 kgCO2eq. ha -1 in optimal generation toward present farms.