Natural Gas Energy Consumption Research Articles

Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning

Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is proposed for natural gas consumption forecasting. Firstly, to obtain the potential data characteristics, a nonlinear concave and convex transformation-based data dimension enhancement method is designed. Then, with the aid of a stacking ensemble learning framework, the multiscale autoregressive integrated moving average (ARIMA) and high-order fuzzy cognitive map (HFCM) methods are chosen as the base learner models, while the meta learner model is constructed via a well-designed deep neural network with long short-term memory (LSTM) cells. Finally, with the natural gas energy consumption data of national and 30 provinces (where the data of Xizang are unavailable) of China from 2000 to 2019, the numerical results show the proposed algorithm has a better forecasting performance in accuracy, robustness to noise, and sensitivity to data variations than the seven compared traditional and ensemble methods, and the corresponding model applicability rate could achieve more than 90%.

Disaggregated Impact of Non-Renewable Energy Consumption on the Environmental Sustainability of the United States: A Novel Dynamic ARDL Approach

While there is a vast body of literature on environmental sustainability, the disaggregated impact of major non-renewable energy (NRE) consumption on the environmental sustainability of the United States (U.S.) is understudied, particularly in terms of using a load capacity factor (LCF) perspective. In this study, the above research gap is addressed using a dynamic autoregressive distributed lag (DYNARDL) model to analyze the heterogeneous impact of NRE consumption on the environmental sustainability of the U.S. from 1961 to 2022. Given the U.S.’s heavy reliance on energy consumption from NRE sources, this analysis provides an in-depth examination of the long-term effects of this energy consumption on the environment. Based on the analysis of the DYNARDL model, it is found that an increase of one unit of coal, natural gas, and petroleum energy consumption reduces environmental sustainability by 0.007, 0.006, and 0.008 units in the short-run and 0.006, 0.004, and 0.005 units in the long-run, respectively. However, one unit of nuclear energy consumption increases environmental sustainability by 0.007 units in the long-run. The kernel-based regularized system (KRLS) result reveals that coal and petroleum energy consumption have a significantly negative causal link with environmental sustainability, while nuclear energy consumption demonstrates a significant positive causal relationship. The research suggests the expansion of the use of nuclear energy by gradually reducing the utilization of coal and petroleum-based forms of energy, then natural gas, to improve environmental sustainability in the U.S., while considering the social and economic implications of efforts aimed at shifting away from the use of fossil fuels.

Forecast of Commodity Energy Consumption and Structure of Rural Households in Sichuan Province

With the growth in energy consumption, problems such as environmental pollution and resource depletion are becoming more and more prominent, and the sustainable use of energy resources and sustainable economic and social development can be achieved only through scientific forecasting and effective management. Forecasting the changing trend of energy consumption structure can guide the government and society to take corresponding measures for environmental protection and sustainable development, and reduce the negative impact of energy consumption on the environment. Based on the current development situation of commodity energy in rural households in Sichuan Province and related consumption data, this paper uses the grey prediction model and Markov model to predict the consumption and consumption structure of commodity energy in rural households in Sichuan Province. It is found that among the four types of commodity energy consumed by rural households in Sichuan Province, the consumption of electricity shows exponential and growth, and it is expected that the consumption of electricity by rural households in Sichuan Province from 2022 to 2025 will be 3,316,166, 3,582,203, 3,886,622, and 4,216,240 tonnes of standard coal, respectively. Meanwhile, using Markov chain to predict the consumption structure, it is found that under the no-planning constraint scenario, the share of energy consumption of electricity and natural gas is increasing, while the share of energy consumption of coal and liquefied petroleum gas (LPG) will be decreasing, and the shares of energy consumption of coal, electricity, natural gas, and LPG will be 1.97 per cent, 75.82 per cent, 26.36 per cent, and 0.02 per cent, respectively, by 2030.

Analyzing India's coal, natural gas, and biomass energy consumption: Evidence from a Fourier technique to promote sustainable development

AbstractInalignment with the United Nations Sustainable Development Goals (UN‐SDGs),which pinpoint vital aspects such as SDG‐7 and SDG‐8, this study delves intothe impact of biomass energy on economic growth in the Indian context. It alsoconsiders the roles of fossil fuels, specifically natural gas and coal. Toaccomplish this, a comprehensive analysis is initiated, making use of annualdata spanning from 1975 to 2022. A unique Fourier ARDL test serves as a centralanalytical tool alongside the Fourier Toda Yamamoto test. The empirical resultsunveil the presence of co‐integration among the variables scrutinized. Moreover,it becomes evident that coal and biomass make positive contributions toeconomic growth, whereas natural gas appears to exert a negative influence oneconomic growth. To validate these findings, a Fourier‐based Toda‐Yamamoto testis applied, demonstrating that natural gas, coal, and biomass possesspredictive capabilities concerning economic growth. These discoveries carrysubstantial implications for comprehending the factors influencing economicgrowth in India.

Retracted: Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center

Retracted: Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center

Facing the challenge of alternative energy sources: The scenario of European Union countries based on economic and environmental analysis

This study evaluates the potential impacts of natural gas (NGC), renewable energy (RCC), and nuclear energy consumption (NCC) from an economic and environmental perspective for four European Union countries (Czechia, Finland, Hungary, and Germany), which have a reliance on Russia as well as use renewable and nuclear energy. In this context, quantile-based approaches are used; NGC, RCC, and NCC are considered as explanatory variables; and data between 1993/Q1 and 2021/Q4 is used. The findings show that (i) NGC has generally an accelerating impact on the economy and environment in all countries; (ii) RCC has a stimulating impact on the economy in Finland at all quantiles and in Czechia at higher quantiles. Also, it has a curbing impact on CO2 emissions in Czechia at all quantiles and in Hungary and Germany at the higher quantiles; (iii) NCC has an increasing impact on the economy in Hungary and Germany at higher quantiles. Besides, it has a limiting impact on CO2 emissions in Hungary at all quantiles, in Finland at higher quantiles, and in Czechia at middle quantiles; (iv) NGC, RCC, and NCC have a causal impact on both GDP and CO2 emissions. The outcomes imply that the impact of the alternatives on GDP and CO2 emissions varies by country, alternatives, and quantiles. RCC is the most suitable option for Czechia, NCC is the most suitable option for Hungary, and there is a trade-off for Finland and Germany.

The Impact of Renewable Energy Consumption on Economic Growth in Bangladesh: Evidence from ARDL and VECM Analyses

In pursuit of achieving developed country status by 2041, Bangladesh is committed to comprehensive socioeconomic development—a goal intrinsically tied to the critical task of securing a reliable, uninterrupted energy supply while optimizing utilization of available energy sources. This study used 1980–2018 annual data to examine the implications of energy transition and causal relationships among economic growth, renewable energy, and natural gas consumption in Bangladesh. A rigorous two-step process investigated the causal correlations among variables. The autoregressive distributive lag (ARDL) model was used to scrutinize long-term relationships, while a vector error correction (VEC) model was used to ascertain the directionality of these causal relationships. The outcomes of the bound tests conclusively revealed the presence of a long-run equilibrium relationship among the variables. Causality analyses indicated a unidirectional causal relationship from renewable energy consumption to economic growth in the long run and from natural gas consumption to economic growth in the short run. A bidirectional causal relationship was found between natural gas and renewable energy consumption in the long run. These findings underscore the potential of energy conservation strategies to catalyze economic growth and suggest an avenue for Bangladesh to achieve its ambitious socioeconomic development goals.

Role of natural gas and nuclear energy consumption in fostering environmental sustainability in India

This paper investigates the role of nuclear energy in promoting ecological sustainability in India, focusing on three ecological indicators: ecological footprint (EF), CO2 emissions (CO2), and load capacity factor (LF). In addition to nuclear energy, the study considers the influence of gas consumption and other drivers of ecological sustainability using data spanning from 1970 to 2018. The analysis also takes into account the impact of the 2008 global financial crisis on the model, employing the autoregressive distributed lag (ARDL) and frequency domain causality approaches to assess the relationships. Unlike previous studies, this research evaluates both the Environmental Kuznets Curve (EKC) and load capacity curve (LCC) hypotheses. The ARDL results support the validity of both the EKC and LCC hypotheses in the Indian context. Furthermore, the findings reveal that nuclear energy and human capital contribute positively to ecological quality, while gas consumption and economic growth have a negative impact on ecological sustainability. The study also highlights the increasing effect of the 2008 global financial crisis on ecological sustainability. Additionally, the causality analysis demonstrates that nuclear energy, human capital, gas consumption, and economic growth can serve as predictors of long-term ecological sustainability in India. Based on these findings, the research presents policy recommendations that can guide efforts towards achieving SDGs 7 and 13.

NATURAL GAS ENERGY CONSUMPTION-WEALTH NEXUS: UNVEILING A NON-QUANTITATIVE WELFARE MEASURE

This paper examined the type of relationship existing between wealth accumulation ( and /or human development), and natural gas energy consumption (NGC) in Nigeria using the Human Development Index (HDI1 and HDI2) as proxies for human development and wealth accumulation. We employed the theoretical pedestal of natural gas energy consumption-growth nexus in our empirical review, and econometric model specification, and in evaluating the resultant estimates of this relationship. Annual time series data for the selected indicators were extracted for several years. Our results indicated that in the long-run, natural gas energy consumption does not facilitate human development and wealth accumulation vice versa in Nigeria. Furthermore, our results suggest that natural gas energy is consumed or utilized in Nigeria for non-wealth accumulation and non-human development purposes. We recommend that gas energy should be consumed and/or redirected to a more purposeful wealth and human development facilitating consumption.
 Keywords: Natural Gas Energy, Growth, Nigeria, ARDL Bounds Tests.
 JEL Classification Code:C50,Q32,Q400,Q430

Effects of possible changes in natural gas, nuclear, and coal energy consumption on CO2 emissions: Evidence from France under Russia’s gas supply cuts by dynamic ARDL simulations approach

The study explores the influences of potential changes in energy consumption on carbon dioxide (CO2) emissions, focusing on disaggregated energy consumption sources. In this manner, the study considers France as the leading nuclear energy-consuming country in Europe, includes yearly data between 1970 and 2021, and performs the dynamic autoregressive distributed lag (DYNARDL) model. In addition, the Kernel-based regularized least squares (KRLS) is used for robustness check. The results reveal that (i) cointegration exists between the disaggregated energy consumption indicators and CO2 emissions; (ii) nuclear, natural gas, oil, and coal energy have a statistically significant effect on CO2 emissions, while renewable energy is not statistically significant; (iii) nuclear power has a decreasing effect on CO2 emissions; (iv) positive (i.e., increasing) shocks to nuclear reduce CO2 emissions, even if they are 300 % in the case of counterfactual shocks; (v) any positive (i.e., increasing) shocks to coal have a drastically increasing effect on CO2 emissions, even if they are 25 % in the case of counterfactual shocks; (vi) the KRLS approach confirms the robustness of the results. Thus, this study suggests that France should continue to rely on nuclear power for electricity generation and that French policymakers should reduce electricity exports to European Union countries to provide an alternative against the Russian natural gas shock by preventing a reduction in energy supply.

Long-run impact of coal usage decline on CO2 emissions and economic growth: Evidence from disaggregated energy consumption perspective for China and India by dynamic ARDL simulations

This study examines the long-run impact of coal usage decline from a disaggregated energy consumption perspective by considering the role of coal usage on carbon dioxide (CO2) emissions and economic growth. For this purpose, the study focuses on China and India as the leading coal-user countries; considers coal, oil, natural gas, and renewable energy consumption as explanatory variables; uses yearly data from 1990 to 2021, and applies a novel dynamic autoregressive distributed lag (DYNARDL) simulations. Also, the Kernel-based regularized least squares (KRLS) approach is performed for robustness checks. The outcomes show that (i) there is cointegration between coal usage and CO2 emissions, and between coal usage and economic growth in the long run; (ii) coal usage is the most important indicator among explanatory variables; (iii) coal usage has a statistically significant impact on CO2 emissions in both China and India; (iv) however, coal usage is not significantly effective on economic growth for both China and India based on DYNARDL simulations; (v) negative counterfactual shocks in coal usage have a decreasing impact on CO2 emissions; (vi) KRLS results show that coal usage has a causal impact on CO2 emissions and the results of the DYNARDL simulations are robust. Hence, counterfactual shocks present important insights about the future impacts of coal usage decline on both CO2 emissions and economic growth. Based on the empirical results, policy implications, such as decreasing the amount of coal usage on and increasing the amount of alternative energy sources in the total energy mix, are also proposed.

Investigating the association among CO2 emissions, renewable and non-renewable energy consumption in Uzbekistan: an ARDL approach.

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.

Natural Gas Consumption and Price Stability in Top Gas-Producing Countries in Africa

This study investigated the effects of natural gas consumption on price level in top gas producing countries in Africa namely; Algeria, Angola, Egypt, Gabon, Libya and Nigeria. Natural gas consumption was measured in terms of natural gas used/utilized in the countries domestic economy, price of natural gas and motor gasoline energy consumption of the countries while price level was captured in term of consumer price index (CPI) also of the countries of interest. The Panel Autoregressive and Distributed Lag (PARDL) was used to analyse data sourced after descriptive statistics and panel unit root tests. The results revealed that natural gas consumption had positive and substantial influence on consumer price index or inflation. Natural gas price had negative but insignificant effect on price level while motor gasoline energy consumption had negative and substantial effect on price level (consumer price index) in the top-gas producing African countries. The result also shows that a long run relationship existed between natural gas consumption and price level in the top-gas producing countries in Africa. Based on these results/findings, the study recommended policies that will enhance a stable natural price and domestic refining and consumption of motor gasoline energy in order to stabilize price level in Africa especially the gas producing countries.

Role of hydroelectricity and natural gas consumption on environmental sustainability in the United States: Evidence from novel time-frequency approaches

This study analyzes time- and frequency-varying impacts of hydroelectricity energy consumption, natural gas energy consumption, and economic growth on environmental sustainability proxied by carbon dioxide (CO2) emissions in the United States of America (the US) for the period 1965/Q1 to 2020/Q4. This study is the first of its kind to contribute to the current literature by analyzing dynamic relationships among these variables in the short-, medium-, and long-term at different time frequencies in the framework of a multivariate correlation, hence providing a more comprehensive picture about the impacts of these effective factors on CO2 emissions. To meet the objectives of the study, Wavelet local multiple local (WLMC), which is a recent novel methodology developed by Polanco-Martínez et al. (2020), is applied. Moreover, the Wavelet coherence (WTC) approach is used for robustness check. The outcomes provide fresh insights into the long-term dynamic correlations among hydroelectricity energy consumption, natural gas energy consumption, economic growth, and CO2 emissions. The study discovers a robust positive co-movement between natural gas energy consumption and CO2 emissions and a negative correlation between hydro energy consumption and CO2 emissions that is the most intense on the long-term frequencies. Furthermore, economic growth causes CO2 emissions, which is evidenced by a positive relationship between both factors at short- and long-term time-frequencies. Supported by the outcomes of the study, the authors urge to suggest crucial insights and policy points for the US policymakers to shift from fossil energy to renewable energy sources to meet Sustainable Development Goals (SDGs), especially SDG-7 and SDG-13, since they induce lower emissions.

OXIDATION PROCESSES OF HIGH-ALLOYED ALLOYS OF WORKING LAYER AND THE INTERPHASE SURFACES OF BIMETALLIC PAIRS

Methods of improving the quality of regulation of technological parameters by combining various intelligent control algorithms in one automation system, which helps to reduce energy costs by 10-13%, are analyzed. It has been established that heating and ventilation systems have the highest energy consumption for indoor buildings (on average, more than 4,000 m3 of natural gas and almost 1,000 kWh of electricity are consumed per day for heating and ventilation in an industrial greenhouse. Correlation analysis of links between external disturbances and energy costs that ensure compliance with the technology of plant production, confirmed the hypothesis of conditions of uncertainty in the operation of industrial greenhouses are formed by random disturbances, incomplete information about the biological component, with linear correlation coefficients not exceeding r<0.35. both for forecasting energy costs and for the formation of energy efficient management strategies. Based on the use of fuzzy logic methods and genetic algorithm, models for finding and using optimal parameters of PI controller settings adapted to changes in the operating conditions of the automation system have been developed and studied. This provides better regulation in conditions of uncertainty, the time of regulation, over-regulation is reduced by two to three times. To create an energy-efficient microclimate management system in industrial greenhouses, operating in conditions of uncertainty, a neural network model for predicting the energy consumption of natural gas and electricity has been developed. The input parameters of the neural network forecasting model are: the value of external and internal air temperatures of the greenhouse, the value of relative humidity, the solar radiation absorbed by the greenhouse and the level of carbon dioxide in the greenhouse. The outputs of the forecasting model are the values of natural gas and electricity costs. The structural and functional scheme of the temperature and humidity control automation system in industrial greenhouses has been improved by combining intelligent algorithms for stabilizing the operation of technological equipment at the lower management level and optimizing energy costs by forecasting them at the upper level. The introduction of such a system saves up to 13% on natural gas for heating and up to 10% on electricity.

Industrial restructuring, energy consumption and economic growth: Evidence from China

Under the downward pressure of the economy, China's extensive economic growth that relies on factor input is difficult to sustain. Scientific analysis of the industrial restructuring and energy consumption has become the key to realizing China's economic growth from high-speed to high-quality. Based on the fully-modified least square method and panel vector error correction model, this paper explores the equilibrium relationship and Granger causality among the industrial restructuring, energy consumption and economic growth at aggregated and disaggregated levels. The results show that: (1) The effect of industrial structure rationalization on economic growth is higher than that of industrial structure upgrading. When emphasizing industrial structure upgrade, the more reliable industrial policy to achieve China's sustainable economic growth is to pay attention to the industrial structure rationalization, especially in western China. (2) The impacts of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption; more energy policies should be designed to encourage clean energy development.

Retracted] Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center

Energy consumption is the key factor leading to high operating costs in data center. In this paper, considering the data center service level under the premise of using electricity and gas as energy supply of energy supply and energy consumption from two angles, the data center energy scheduling model is built. The lower model is calculation scheduling model for the data center, and the upper model is data center energy supply scheduling model. Then the particle swarm algorithm is used to simulate the schedule. The results show that using natural gas as supplementary energy supply can effectively reduce the overall energy consumption of data center in considering the service delay level of data center.

Rule of Law, Corruption Control, Governance, and Economic Growth in Managing Renewable and Nonrenewable Energy Consumption in South Asia.

Strong governance is vital for developing environmental policies to promote renewable energy consumption and discourage nonrenewable energy sources. The present research explores the effect of economic growth and different governance indicators on renewable and nonrenewable energy consumption in Pakistan, India, Bangladesh, and Sri Lanka using data from 1996 to 2019. For this purpose, the study uses different econometric techniques to find the long-term effects of the rule of law, regulatory quality, corruption control, government effectiveness, political stability, voice and accountability, and economic growth on oil, natural gas, coal, hydroelectricity, and renewable energy consumption. The results show that economic growth has a positive impact on all investigated renewable and nonrenewable energy sources. Additionally, regulatory quality measures also increase all types of renewable and nonrenewable energy consumption. Except for natural gas, the impact of the rule of law is negative, and government effectiveness positively affects all energy sources. Control of corruption has a positive effect on natural gas consumption. Political stability has a negative effect on nonrenewable energy sources and a positive impact on renewable energy sources. The magnitudes of the effects of economic growth and most governance indicators are found to be larger on nonrenewable sources than renewable sources. The testing of the energy consumption and governance nexus is scant in global literature and is missing in South Asian literature. Hence, the study results contribute to how South Asian economies can be more sustainable in energy use by enhancing governance indicators in the economies. Particularly, the results imply that these countries should focus on improving the rule of law, corruption control, governance, regulatory quality, political stability, and economic growth to help maintain a sustainable balance of renewable and nonrenewable energy sources. Moreover, this issue needs further attention in developing countries, as governance indicators would play an effective role in promoting sustainable energy.

METHODOLOGY OF DEVELOPMENT OF INTELLECTUAL ENERGY EFFICIENT SYSTEM OF CONTROL OF TEMPERATURE-HUMIDITY REGIME IN INDUSTRIAL HEAT

Methods of improving the quality of regulation of technological parameters by combining various intelligent control algorithms in one automation system, which helps to reduce energy costs by 10-13%, are analyzed. It has been established that heating and ventilation systems have the highest energy consumption for indoor buildings (on average, more than 4,000 m3 of natural gas and almost 1,000 kWh of electricity are consumed per day for heating and ventilation in an industrial greenhouse. Correlation analysis of links between external disturbances and energy costs that ensure compliance with the technology of plant production, confirmed the hypothesis of conditions of uncertainty in the operation of industrial greenhouses are formed by random disturbances, incomplete information about the biological component, with linear correlation coefficients not exceeding r<0.35. both for forecasting energy costs and for the formation of energy efficient management strategies. Based on the use of fuzzy logic methods and genetic algorithm, models for finding and using optimal parameters of PI controller settings adapted to changes in the operating conditions of the automation system have been developed and studied. This provides better regulation in conditions of uncertainty, the time of regulation, over-regulation is reduced by two to three times. To create an energy-efficient microclimate management system in industrial greenhouses, operating in conditions of uncertainty, a neural network model for predicting the energy consumption of natural gas and electricity has been developed. The input parameters of the neural network forecasting model are: the value of external and internal air temperatures of the greenhouse, the value of relative humidity, the solar radiation absorbed by the greenhouse and the level of carbon dioxide in the greenhouse. The outputs of the forecasting model are the values of natural gas and electricity costs. The structural and functional scheme of the temperature and humidity control automation system in industrial greenhouses has been improved by combining intelligent algorithms for stabilizing the operation of technological equipment at the lower management level and optimizing energy costs by forecasting them at the upper level. The introduction of such a system saves up to 13% on natural gas for heating and up to 10% on electricity.

Numerical Simulation of New Anode Baking Furnace

The quality of baked anode is directly determined by the uniformity of temperature field in the baking furnace flue-wall. The temperature difference between the upstream and downstream of the traditional furnace is large and the temperature distribution in the pit is uneven, and the natural gas energy consumption is high. In this work, the flow field in the flue and the temperature homogeneity in the pit of newly developed anode baking furnace are mainly presented. The industrial test in a carbon plant showed that a uniform temperature distribution is achieved and the quality of the baked anode is improved greatly with the natural gas saving of 10.4 m3 per ton of carbon produced.