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Oil and gas are key energy sources in the Gulf Cooperation Council (GCC) region. The present study examines the asymmetrical environmental effects of these energy sources and also tests the environmental Kuznets curve (EKC) from 1975 to 2019. In the long run, the EKC is corroborated in Kuwait and Saudi Arabia. But the EKC is not validated in the GCC Panel. Increasing oil consumption raises carbon dioxide (CO 2 ) emissions in all investigated GCC countries, and decreasing oil consumption reduces CO 2 emissions in Kuwait, Oman, Saudi Arabia, and the United Arab Emirates (UAE). The effect of oil consumption is found asymmetrical in Qatar and symmetrical in the rest of GCC countries. Increasing natural gas consumption (NGC) carries a positive effect in all investigated GCC countries, and decreasing NGC reduces emissions in Oman, Qatar, and the UAE. Moreover, NGC's effects are asymmetrically in all GCC countries except Qatar. In the panel estimates, both increasing and decreasing oil and NGC have positive effects on CO 2 emissions. The long-run effect of oil consumption on CO 2 emissions is larger than the effect of NGC in most GCC economies and panel results. In the short run, increasing and decreasing oil consumption and NGC have a positive effect on emissions in all investigated economies except Saudi Arabia. In the long run, coefficients of decreasing oil consumption are found significantly greater than coefficients of increasing NGC in Kuwait, Oman, Saudi Arabia, the UAE, and the whole GCC. This finding corroborates that increasing CO 2 emissions with increasing NGC is lower than decreasing CO 2 emissions with decreasing oil consumption. Hence, we recommend these countries switch from oil consumption to NGC to reduce overall CO 2 emissions., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Environmental degradation stemming from the combustion of conventional energy sources is not only a major factor behind climate change but it also poses an adverse impact on human health. Undoubtedly, fossil fuels are major drivers of economic growth; however, their detrimental environmental impacts are of global concern. In the literature, there is no comprehensive empirical evidence on the linkage between the use of different energy sources and carbon dioxide emissions in the context of Russia, a nation that is ranked third in the list of the top carbon dioxide-emitting global countries. Hence, this paper aims to scrutinize the relationships between oil consumption, natural gas consumption, coal consumption, and carbon dioxide emissions controlling economic growth for Russia over the 1990-2016 period. The findings from the econometric analysis indicate that carbon dioxide emissions in Russia have long-run associations with economic growth and consumption of oil, gas, and coal. The long-run elasticity estimates reveal that economic growth is not directly harming Russia's environmental quality. However, higher oil, gas, and coal consumption degrades environmental quality by boosting the level of carbon dioxide emissions in Russia. In addition, the results from the Granger causality analysis confirm the existence of both long and short-term causal connections among the variables of concern. In line with these findings, several policy recommendations to address the environmental challenges in Russia are put forward., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The world is stepping forward to a carbon-neutral economic system in response to the rising issues caused by climate change. Fossil fuel combustion is the primary source of increased carbon emissions, energy mix adjustment is critical to climate change mitigation and the carbon neutrality goal. This study investigates the different responses to energy sources' economic growth and environmental sustainability using balanced panel data from 34 Organization for Economic Cooperation and Development (OECD) countries during 1995–2019. This study uses oil, natural gas, and renewable energy to represent traditional, emerging alternative fossil, and green energy sources, respectively. Results show that renewable energy, oil, and natural gas all impose impacts on economic growth, however, renewable energy contributes more than oil and natural gas. Furthermore, there is a significant inverse relationship between the amount of renewable energy produced and carbon dioxide (CO2) emissions. While both natural gas and oil have a positive effect on CO2 emissions, the effect of natural gas is much smaller than that of oil. Furthermore, the causality investigation reveals that renewable energy, oil, and natural gas all show impacts on carbon emissions but do not contribute to economic growth. These findings suggest that increasing investment in renewable energy, with natural gas playing the role of a transitional replacement for oil, will contribute to the "carbon neutrality" process of these countries. [ABSTRACT FROM AUTHOR]

The paper examines the linkage between oil price, climate variability and Food Inflation (FI) in India using linear ARDL model for the period January 2001 to December 2021. The study uses monthly time series data on CPI (general index, food price index, Money Supply (M3)), oil price and trade openness (volume of Export+Import to % GDP) and temperature change retrieved from the online data base of Reserve Bank of India, FAOSTAT, MOSPI and Ministry of Petroleum and Natural Gas. Since the variables are in different units, natural logarithm values are used in model estimation. The study found that a positive correlation between FI and study variables. The ARDL Model coefficient values show that Money Supply (MS) positively impacts FI, whereas lag MS negatively affects FI. Oil price has a positive impact on FI and trade openness coefficient is positive but not significant. Temperature Change (TC) has a negative impact on FI. The ARDL bound test result indicates that there is cointegration between FI and the study variables. The ARDL-VEC coefficient values indicate that oil price and TC have significant causal relationship with FI. To tackle high FI, there is a need of government and policymakers’ intervention for oil consumption diversification from crude oil to natural gas. The RBI should tighten the monetary policy and repo rate in order to lower FI. [ABSTRACT FROM AUTHOR]

This paper examines for the first time the relationship between CO2 emissions and the consumption of renewable and non-renewable energy in Uzbekistan, spanning the period 1985–2020. The analysis uses the Autoregressive Distributed Lags (ARDL) model to estimate the long-run dynamic multipliers and short-run elasticity coefficients of energy consumption variables. Economic factors, such as GDP, are excluded in the analysis as they may cause multicollinearity problems. The empirical results document that in the short- and long-run, hydropower (renewable) energy consumption negatively impacts CO2 emissions per capita, showing a unidirectional causal effect. As regards with non-renewable energy consumption, natural gas and oil energy consumption have a positive impact on CO2 emissions per capita both in the short and long run. Coal consumption positively impacts CO2 emissions in the short run, while it is negative in the long run. Policy measures to enhance the collection of energy from additional renewable energy sources, in particular hydropower, should be taken into account in order to increase the share of renewable energy, and thus, to compensate non-renewable energy consumption which is the main contributor to CO2 emissions. Moreover, solar and wind energy should be explicitly taken into consideration as an additional renewable energy source, which has the lack of attention by policymakers. Furthermore, policy actions, such as the involvement of the private sector into renewable energy projects and the implementation of effective carbon tax policies, could be further options to reduce CO2 emissions. [ABSTRACT FROM AUTHOR]

This study investigates the time–frequency nexus of carbon dioxide (CO2) emissions with economic growth, nonrenewable (i.e., coal, natural gas, and oil), and renewable (i.e., hydro and geothermal) energy consumption. In this context, BRICS countries (namely, Brazil, Russian Federation, India, China, and South Africa), which are leading emerging countries, are included, and quarterly data from 1990/Q1 to 2019/Q4 is used. The study employs the wavelet coherence (WC) approach to explore the co-movement between the variables at different frequencies. The empirical results show that (i) there is a strong and positive co-movement between CO2 emission and economic growth; however, it is weak for Russia and South Africa in the medium and long-term; (ii) coal energy consumption is strongly and positively co-moved with CO2 emission for all BRICS countries; (iii) natural gas energy consumption is strongly and positively co-moved with CO2 emissions in Brazil, India, and China; however, it is weakly and positively co-moved in Russia and South Africa; (iv) oil energy consumption is strongly and positively co-moved with CO2 emissions in Brazil, India, and China; however, it changes a bit for Russia and South Africa; (v) hydro energy consumption is weakly and positively co-moved with CO2 emissions in general, whereas country-based results vary; (vi) geothermal energy consumption is also similar to hydro energy consumption. Thus, the WC results highlight the strong co-movement of economic growth and nonrenewable energy consumption with CO2 emissions, whereas renewable energy consumption has a relatively lower co-movement. Based on the results, policy implications are also discussed for BRICS countries. [ABSTRACT FROM AUTHOR]

Residential sector is one of the energy-consuming districts of countries that causes CO2 emission in large extent. In this regard, this sector must be considered in energy policy making related to the reduction of emission of CO2 and other greenhouse gases. In the present work, CO2 emission related to the residential sector of three countries, including Indonesia, Thailand, and Vietnam in Southeast Asia, are discussed and modeled by employing Group Method of Data Handling (GMDH) and Multilayer Perceptron (MLP) neural networks as powerful intelligent methods. Prior to modeling, data related to the energy consumption of these countries are represented, discussed, and analyzed. Subsequently, to propose a model, electricity, natural gas, coal, and oil products consumptions are applied as inputs, and CO2 emission is considered as the model’s output. The obtained R² values for the generated models based on MLP and GMDH are 0.9987 and 0.9985, respectively. Furthermore, values of the Average Absolute Relative Deviation (AARD) of the regressions using the mentioned techniques are around 4.56% and 5.53%, respectively. These values reveal significant exactness of the models proposed in this article; however, making use of MLP with the optimal architecture would lead to higher accuracy. [ABSTRACT FROM AUTHOR]

Natural gas has returned to prominence in the agenda of European countries since the beginning of the invasion of Ukraine by Russia in 2022. However, natural gas is a fossil source with severe environmental implications. This paper aims to verify the impact of natural gas on carbon dioxide (CO2) emissions for a European panel from 1993 to 2018 for sixteen countries. An Autoregressive Distributed Lag (ARDL) model in the form of an unrestricted error correction model was used to identify the short-run impacts, the long-run elasticities, and the speed of adjustment of the model. The results indicate that in the short-run, natural gas has a negligible impact on CO2 emissions when faced with oil consumption (6.7 times less), whereas the consumption of renewables and hydroelectric energy proved to be able to decrease the CO2 emissions both in the short- and long-run. The elasticity of oil consumption is lower than the unit, indicating that efficiency gains have been achieved during the process of the energy transition to clean energy sources. If economies use non-renewable energy, governments must continue to prefer natural gas to oil. Renewables and hydroelectric consumption must be used to revert the path of CO2 emissions. Given the unstable scenario that has been caused by the War in Eastern Europe, politicians should focus on accelerating the transition from fossil to renewable energies. [ABSTRACT FROM AUTHOR]

Primary fuels, i.e., crude oil, natural gas, and power coal, dominate the total global demand for primary energy. Among them, crude oil plays a particularly important role due to the universality of applications and the practical lack of substitutes in transport. Crude oil is also one of the main sources of primary energy in Poland and accounts for around 30% of the energy consumed. Poland covers only 3% of its needs from domestic deposits. The rest is imported from Russia, Saudi Arabia, Nigeria, Great Britain, Kazakhstan, and Norway. Due to such a high import of raw material, Poland must anticipate future demand. On the one hand, this article aims to analyze the current (2020) and future (2040) crude oil consumption on the Polish market. The study analyzes the geopolitical and economic foundations of the functioning of the energy raw-materials market, the crude oil supply, the structure of Poland's energy mix, and assumptions about the energy policy until 2040. On the other hand, conclusions from the research were used to build a model of crude oil consumption for the internal market. It has been also shown that the consumption of crude oil on the Polish market is a nonlinear phenomenon with a small set of statistical data, which makes it difficult to build an accurate model. This paper proposes a new model based on artificial neural networks that includes long-term memory (LSTM). The accuracy of the constructed model was assessed using the MSE, Theil, and Janus coefficients. The results show that LSTM models can be used to forecast crude oil consumption, and they cope with the nonstationary and nonlinear time series. Many important contemporary problems posed in the field of energy economy are also discussed, and it is proposed to solve them with the use of modern machine-learning tools. [ABSTRACT FROM AUTHOR]

Investigating the energy intensity convergence in Iran, as a country with very high energy intensity in the world, is important in order to assess whether the government's energy policies were effective to reduce energy intensity and its externality effects. This research investigated the convergence of total energy intensity and energy carriers' consumption intensity in Iran and whether the energy intensity in the provinces with high energy intensity has been converged to the provinces with low energy intensity. To that end, the Markov chain method was used to investigate the convergence of total energy intensity and energy carriers' consumption intensity in Iranian provinces from 2002 to 2016. The results indicated that there was club convergence in total energy, natural gas, electricity, and oil consumption intensity. Therefore, provinces with similar levels of energy intensity (e.g., high energy intensity and low energy intensity) converged to a unique steady state. [ABSTRACT FROM AUTHOR]

Purpose: In spite of the drive toward environmental sustainability and the attainment of sustainable development goals (SDGs), coal, oil and natural gas energy utilization has remained the Turkey's largest energy mix. In view of this concern, this study examined the role of coal and oil energy utilization in environmental sustainability drive of Turkey from the framework of sustainable development vis-à-vis income expansion over an extended period of 1965–2017. Design/methodology/approach: In this regard, the authors employ carbon emission as an environmental and dependent variable while the Gross Domestic Product per capita (GDPC), coal and oil energy consumption are the explanatory variables employed in the study. Findings: The study found that both energy mixes (coal and oil) have a detrimental impact on the environment in both the short and long run, but oil consumption exerts a less severe impact as compared to coal energy. In addition, sustainable development via income growth is not feasible because the income–environmental degradation relationship follows a U-shaped pattern (invalidating the Environmental Kuznets curve, EKC hypothesis) especially when coal and oil remained the major source of lubrication to the economy. At least the EKC hypothesis is unattainable in Turkey as long as the country's major energy mix or primary energy (coal and oil) is in use, thus the application of other socioeconomic, macroeconomic policies might be essential. Research limitations/implications: Considering the lingering energy challenge associated with Turkey, this novel insight further presented useful policy perspectives to the government and stakeholders in the country's energy sector. Originality/value: This evidence (the U-shaped relationship) is further ascertained when the aggregate primary energy is employed. Thus, this study provides a novel insight that attaining a sustainable economic growth in Turkey remained a herculean task as long as a more aggressive energy transition approach is not encouraged. [ABSTRACT FROM AUTHOR]

Pakistan is experiencing increasing CO2 emissions in contrast with other regions of the world. The country is also facing the problems of low economic growth, energy poverty, and environmental pollution. The objective of the study is to analyze economic growth of Pakistan and obtain some alternate sources of production for sustainable environment. Time series data of Pakistan from 1985 to 2018 is used. In order to estimate direct and substitution effect among energy and non-energy factors on economic growth, translog functional form is used. The presence of multicollinearity among explanatory variables approves to employ ridge regression. Capital per worker has the highest elasticity (0.1531) among all variables followed by consumption of oil (0.0571), natural gas (0.0333), technology (0.0329), and hydroelectricity (0.02). Average output elasticity for oil, natural gas, hydroelectricity, capital per worker, and technology are 0.4474, 0.3127, 0.433, 1.0037, and 0.2309, respectively. Technical progress of variables is ranked as capital per worker, oil consumption, natural gas consumption, hydroelectricity, and technology. Relatively lower but efficient substitution between oil and natural gas provides opportunity to save huge foreign exchange on import of oil. Investment on capital per worker in transport, power, fertilizer, and industrial sectors can reduce the demand of oil and natural gas which would eventually lower carbon dioxide emissions in the country. Increasing 10% investment on capital/worker would mitigate 208.283 million tons of carbon dioxide. Energy inputs are substitutes; therefore, mega hydropower projects and small renewable projects may be launched to cope with energy poverty and environmentally sustainable challenges. [ABSTRACT FROM AUTHOR]

We examine the stationarity properties of primary energy consumption for "Fragile Five" countries and assesses whether shocks on coal, natural gas, and oil consumption are permanent or temporary. The findings suggest that shocks on coal consumption are temporary for Brazil, South Africa, and Turkey, while are permanent for India and Indonesia. Moreover, the effect of shocks on natural gas consumption is temporary for five fragile countries except for South Africa. Finally, it is found that shocks on oil consumption are temporary for only India and Indonesia. [ABSTRACT FROM AUTHOR]

Energy performances as the essence in the modern economy globally, where the natural gas, the nuclear energy and the oil apprehend the most if its area. These factors are the crucial element on the real GDP of the states. This study investigates the impact of degenerating the natural gas, oil consumption, and the nuclear energy as the depletion in the use of these factors will help the nation to achieve a better real GDP. For this purpose, we taken 10 ASEAN countries in the analysis that includes Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam. The independent variables are nuclear energy, oil consumption, and natural gas and their effect is seen on the real GDP. The total electricity consumption is taken as a control variable. The data is collected over a period of 28 years as it is a time series analysis and for this, we have used panel Autoregressive distributed time lag (ARDL) model and Augmented Dickey Fuller (ADF) technique along with unit root tests and the co-integration tests among the variables. The results indicate that oil consumption, nuclear energy, significantly impact the fuel prices and the energy consumption in these countries. The research gives useful directions for the policymakers and the government as how to cope up with the prices of these non-renewable resources. [ABSTRACT FROM AUTHOR]

Energy demand is rising day by day, driven mainly by the development of countries. At the same time, uneven economic growth in countries is the prime cause of inequality in energy consumption. Keeping in view the worth of energy in the growth process, this study quantifies the impact of energy inequalities and trade on environmental quality over the period 1995–2018 for 57 countries. The Theil approach is used to quantify inter-and intra-regional disparities in five energy sources; oil, coal, natural gas hydroelectricity, and renewable energy. The results show that North America has the highest oil consumption inequality between the regions while East Asia & Pacific has the highest index value within the regions. Coal consumption inequality is declining in North America, but not in East Asia and the Pacific. Europe & Central Asia, and North America have the highest inequalities in natural gas consumption between the regions. Inequality is shrinking in hydropower consumption between the regions, however, such trend has not loomed within the regions. Europe & Central Asia and East Asia & Pacific have major renewable consumption inequalities within the regions. Generally, there is a decreasing temporal trend in energy consumption inequalities of all energy sources. The GMM technique is applied to investigate the impact of energy inequalities and trade openness on environmental quality. The results reveal that energy inequalities degrade environmental quality. Moreover, trade has a positive impact on environmental quality. However, democratic countries can be advantageous to improve the environmental quality. The study implies that countries should take actions to reduce energy inequalities within and between the regions. Specialization in production through trade can also be an option for improvement in the environment. [ABSTRACT FROM AUTHOR]

This paper investigates the interaction between energy consumption and oil shocks in the U.S. from 1974 to 2018 using monthly data. Its contributions rely on the double disaggregation of energy consumption and oil shocks in a time-varying context. Oil shocks are disaggregated into oil supply, oil demand and aggregated demand shocks following the method of Kilian (2009). Energy consumption is disaggregated according to the production source in distinguishing between renewable and non-renewable energy consumption (hydropower, geothermal, wood, waste, coal, natural gas and petroleum). The impulse response function results show that renewable energy consumption responds the most to aggregate demand and oil supply shocks while for non-renewable energy consumption, it is oil demand shocks. The dynamic connectedness results show that oil supply and demand shocks spillover the most to hydropower consumption while aggregate demand shocks spillover the less. However, these relationships change over time and recommend the flexibility of energy policies. [ABSTRACT FROM AUTHOR]

Improvement in productivity is one of the Oil and Gas industry's biggest challenges. About 60% of crude Oil still lay trapped in the reservoir even after primary and secondary recovery process have been completed, hence the need for a method that further improves recovery. In this study, flooding experiment was conducted at laboratory condition to investigate the use of locally sourced alkaline in improving recovery on sand samples. The efficiency of the local alkaline (palm bunch ash) was compared with other conventional alkaline (NaOH, KOH, and Na2CO3). Eight cores (A1 - A$ and C1 - C4) were prepared, saturated with formation brine of 30, 000ppm salinity; crude (light and medium) were then flowed through sand samples. The samples were individually flooded with brine for secondary recovery process where an average of 55% of initial Oil in place was recovered. Each sample was flooded with different alkaline (local and conventional) for the tertiary recovery stage. The result obtained during flooding on samples saturated with light crude showed that KOH recovered 74%, NaOH recovered 66%, and palm bunch ash recovered an average of 64% while Na2CO3recovered 59%. It is recommended that the further studies to investigate the impact of the palm bunch ash should be carried out using heavy crude under reservoir conditions. These studies if successful will help and increase the use of local content which is relatively cheaper and this will in turn increase the local content drive of the Nigerian government in the oil and gas industry. [ABSTRACT FROM AUTHOR]

Natural gas transmission networks (GTNs) are widely used to transmit natural gas over long distances while the efficient management of GTNs requires as accurate as possible information about the demand of the customers. However, the demand of the users can only be predicted with a limited accuracy. Accordingly, the uncertainties resulting from the demand prediction errors should be addressed to increase the robustness of the daily planning of GTNs. An optimization algorithm based on chance-constrained programming technique is developed to consider the uncertainty of the forecasted future demands, whose solution is the set point of the compressor stations discharge pressure. An important prerequisite of chance-constrained programming formulation is the mean and variance of the objective function and constraints. Accordingly, unscented transform is used as the uncertainty propagation tool. Then, the methodology is applied to a long distance transmission pipeline with 8 compressor stations to illustrate the effectiveness of the proposed approach where the end user load is characterized by a Gaussian random variable with known mean and variance. The results of the optimization based on the proposed algorithm are compared to the deterministic ones where an optimum pressure buffer can be obtained based on the mean and the variance value of the customer demand which is required for the safe operation of the network. Pressure buffer is also affected by the probability assigned for the constraint satisfaction. The results of the algorithm are compared to deterministic approach in terms of power consumption and it is concluded that to gain robust solutions to overcome demand uncertainties, extra power usage is essential. Also, it is shown that demand uncertainty handling requires a specific strategy rather than operating the system based on the expected condition. [ABSTRACT FROM AUTHOR]