Search results

This study aims to investigate the impact of total primary energy consumption and CO₂ emissions on the economic development in 16 emerging countries.

The panel model was used taking the period 1980-2008.

The results showed that a long-run relationship is present between total primary energy consumption, CO₂ emission, and economic development in the countries under investigation. It was also found that both total primary energy consumption have a positive causal relationship with the economic development and other economic aspects playing an important role in achieving high economic performance with the consequence of higher pollution.

The main recommendation of this study is to increase their investment and government spending on green energy projects to increase the share of green energy out of their total energy consumption. This can be considered a good solution for their energy woes.

Different from the previous studies, it was also found that total primary energy consumption have a positive causal relationship with the economic development and other economic aspects playing an important role in achieving high economic performance with the consequence of higher pollution. In addition, there are a number of countries that had not investigated before.

The purpose of this paper is to attempt to analyze the situation and trend of China's energy consumption structure and predict it.

Starting from the situation of China's energy consumption structure, a quadratic programming model is created to analyze the trend of it. A homogeneous Markov chain is chosen to predict China's energy consumption structure with the data collected from China's Statistical Yearbook. Finally, the implication of the prediction is explained.

The results are convincing: the substitution of different energies are found, China will not enter the oil era, natural gas and non‐fossil energy will rapidly develop.

The results of this article can provide an important basis for the government to make a non‐fossil energy development plan and energy policies.

The paper succeeds in revealing and predicting China's energy consumption structure by quadratic programming and homogeneous Markov chain.

The paper aims to study the relationship between economic growth, nuclear energy consumption and carbon dioxide (CO₂) emissions for a panel of 25 countries over a period of 1993-2010. Through this study, the author has provided an insight into one of the available sources of energy, i.e. nuclear energy and its impact on economic growth and CO₂ emissions.

Separate panels are created for developing and developed economies. Short- and long-run causalities between the variables are established using error correction mechanism.

For the developed countries, short-run causality running from CO₂ emissions to economic growth was estimated, whereas strong form of causality indicated the dependence of CO₂ emissions on economic growth and nuclear energy consumption was seen to impact CO₂ emissions. For the developing countries, both the short-run and strong-form causality estimates indicate that economic growth causes CO₂ emissions.

On policy front, developing countries can safely adopt CO₂ cut-back policies as they are not found to impact economic growth. For the developed countries, such policies may impede growth in the short run, but in the long run these policies do not affect the economic growth.

Keeping in mind the significance of nuclear energy consumption in economic growth and less/no GHG emissions generated by nuclear energy, this study validates its significance. This study, to the best of the author's knowledge, considers the largest panel (i.e. 25 countries) to date and the only study that focuses on studying three different panels (complete dataset, developed countries, developing countries) in one study and applies the vector error correction mechanism to study the causal relationship between nuclear energy consumption, CO₂ emissions and economic growth.

This study investigates the impact of smart city construction (SCC) on urban energy consumption.

The focus is on a panel of 285 prefecture level cities in China from 2010 to 2021. The empirical evidence is based on the difference-in-difference (DID) method. We uses per capita coal consumption as a proxy variable to measure urban energy consumption energy consumption. We set the SCC as a policy dummy variable, with pilot cities set to 1 and non-pilot cities set to 0. We also selected a series of control variables that affect urban energy consumption, such as urbanization rate, labor force, road density, number of college students per 10000 people, regional economic development level and so on.

(1) SCC significantly reduces urban energy consumption, and the conclusion still holds after conducting robustness testing; (2) SCC reduces urban energy consumption is mainly effective in those cities with larger scale, stronger human capital, larger financial services and better information infrastructure construction; (3) The technological innovation and industrial structure upgrading are the main mechanisms for the SSC policy to reduce urban energy consumption.

The results in this study provide evidence for achieving an environmentally friendly society.

This research investigates the complex relationship between economic policy uncertainty (EPU), energy consumption and institutional factors in the Gulf region. The purpose of this study is to examine how institutional factors moderate the impact of EPU on energy consumption in Gulf countries.

This paper uses the dynamic panel autoregressive distributed lag (PARDL) method, over a period stretching from 1996 to 2021 in the Gulf countries.

The results show that, only in the long term, EPU has a positive and significant impact on energy consumption, suggesting that increased EPU leads to increased energy use. Furthermore, this study found that, only in the long term, government effectiveness and regulatory quality have positive and significant effect on energy consumption. Accordingly, the two institutional factors play a moderating role in the EPU−energy consumption nexus.

This study highlights the importance of considering the time dimension when formulating energy and economic policies in Gulf countries. Policymakers should take into consideration the nature of these relationships to make informed decisions that promote energy efficiency and economic stability in the region.

To the best of the authors’ knowledge, this is the first study examining the relationship between EPU and energy consumption in the Gulf countries while incorporating the role of institutional factors as potential mediators.

This proposal aims to forecast energy consumption in residential buildings based on the effect of opening and closing windows by the deep architecture approach. In this task, the developed model has three stages: (1) collection of data, (2) feature extraction and (3) prediction. Initially, the data for the closing and opening frequency of the window are taken from the manually collected datasets. After that, the weighted feature extraction is performed in the collected data. The attained weighted feature is fed to predict energy consumption. The prediction uses the efficient hybrid multi-scale convolution networks (EHMSCN), where two deep structured architectures like a deep temporal context network and one-dimensional deep convolutional neural network. Here, the parameter optimization takes place with the hybrid algorithm named jumping rate-based grasshopper lemur optimization (JR-GLO). The core aim of this energy consumption model is to predict the consumption of energy accurately based on the effect of opening and closing windows. Therefore, the offered energy consumption prediction approach is analyzed over various measures and attains an accurate performance rate than the conventional techniques.

An EHMSCN-aided energy consumption prediction model is developed to forecast the amount of energy usage during the opening and closing of windows accurately. The emission of CO₂ in indoor spaces is highly reduced.

The MASE measure of the proposed model was 52.55, 43.83, 42.01 and 36.81% higher than ANN, CNN, DTCN and 1DCNN.

The findings of the suggested model in residences were attained high-quality measures with high accuracy, precision and variance.

The Gulf Cooperation Council (GCC) of countries has some of the highest electricity consumptions and carbon dioxide emissions per capita in the world. This poses a direct challenge to the GCC government’s ability to meet their CO₂ reduction targets. In this review paper the current household electricity consumption situation in the GCC is reviewed.

Three scenarios for reducing energy consumption and CO₂ emissions are proposed and evaluated using strengths, weaknesses, opportunities and threats (SWOT) as well as the political, economic, social, technical, legal and environmental (PESTLE) frameworks.

The first scenario found that using solar Photovoltaic (PV) or hybrid solar PV and wind system to power household lighting could save significant amounts of energy, based on lighting making up between 8% to 30% of electricity consumption in GCC households. The second scenario considers replacement of conventional appliances with energy-efficient ones that use around 20% less energy. The third scenario looks at influencing consumer behavior towards sustainable energy consumption.

Pilot trials of these scenarios are recommended for a number of households. Then the results and feedback could be used to launch the schemes GCC-wide.

The proposed scenarios are designed to encourage responsible electricity consumption and production within households (SDG12).

All three proposals are found viable for policymakers to implement. However, to ensure successful implementation GCC Governments are recommended to review all the opportunities and challenges associated with these schemes as laid out in this paper.

This study aims to revisit the empirical debate about the asymmetric relationship between oil prices, energy consumption, CO₂ emissions and economic growth in a panel of 184 countries from 1981 to 2020.

A relatively new research method, the PVAR system GMM, is applied.

The outcome of the PVAR system GMM model at the group level in the study suggests that oil prices exert a positive but statistically insignificant effect on economic growth. Energy consumption is inversely related to economic growth but statistically significant, and the correlation between CO₂ emissions and economic growth is negative but statistically insignificant. The Granger causality test indicates that oil prices, CO₂ emissions, oil rents, energy consumption and savings jointly Granger-cause economic growth. A unidirectional causality runs from energy consumption, savings and economic growth to oil prices. At countries’ income grouping levels, oil prices, oil rent, CO₂ emissions, energy consumption and savings jointly Granger-cause economic growth for the high-income and upper-middle-income countries groups only, while those variables did not jointly Granger-cause economic growth for the low-income and lower-middle-income countries groups. The modulus emanating from the eigenvalue stability condition with the roots of the companion matrix indicates that the model is stable. The results support the asymmetric impacts of oil prices on economic growth and aid policy formulation, particularly the cross-country disparities regarding the nexus between oil prices and growth.

From a methodological perspective, to the best of the author’s knowledge, the study is the first attempt to use the PVAR system GMM and such a large sample group of 184 economies in the post-COVID-19 era to examine the impacts of oil prices on countries’ growth while controlling for other crucial variables, which is noteworthy. Two, using the World Bank categorisation of countries according to income groups, the study adds another layer of contribution to the literature by decomposing the 184 sample economies into four income groups: high-income, low-income, upper-middle-income and lower-middle-income groups to investigate the potential for asymmetric effects of oil prices on growth, the first of its kind in the post-COVID-19 period.