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It is highly important to clarify the determinants of China's energy consumption for the smooth implementation of energy conservation. For this purpose, this paper discusses the contribution of China's different economic sectors to decoupling progress. Then, it uses an extended logarithmic mean Divisia index (LMDI) method to decompose China's energy consumption into seven factors. The major findings indicate that during the 2000–2016 period, (1) the decoupling status between China's economy and energy consumption were determined largely by the secondary industry; (2) the positive contribution of three sectors to the decoupling process was ranked as follows: primary industry>tertiary industry>secondary industry; (3) China's energy consumption showed a growing trend, and the expansion of per capita fixed asset investment was the main factor responsible for the energy consumption increase, especially in some provinces whose investment structure was skewed towards energy-intensive industries. Additionally, population growth had mild positive impacts on the energy consumption increase; (4) declines in energy intensity and investment efficiency were two major contributors to energy-saving. However, economic structure, energy structure, and spatial structure adjustment failed to reduce energy consumption but had overall positive impacts on the energy consumption increase. Finally, several policy recommendations related to reducing energy consumption are proposed. • The empirical analysis was conducted at both national and provincial levels. • Decoupling status was determined largely by the secondary industry. • Per capita fixed asset investment was dominant for energy consumption increase. • The decline in energy intensity and investment efficiency contributed to energy-saving. [ABSTRACT FROM AUTHOR]

China has advocated and developed renewable electricity to protect the environment and achieve sustainable development. However, non-renewable electricity continues to dominate. Here, we investigate the influencing factors of non-renewable and renewable electricity production in China and compare the influencing factors of non-renewable and renewable electricity production. Based on a time series analysis from 2006 to 2020, we discuss the long-term and short-term relationships between renewable electricity production, non-renewable electricity production, economic growth, aging, and electricity production capacity through the cointegration test and vector error correction model. The empirical results indicate that economic growth and electricity production capacity have a long-run relationship with renewable and non-renewable electricity production, whereas aging only has a long-run relationship with renewable electricity production. Furthermore, a short-term causal relationship exists between economic growth, aging, electricity production capacity, and renewable and non-renewable electricity production. These findings provide policy implications. • Compare factors affecting non-renewable and renewable electricity production. • Economic development and aging have inconsistent impacts on electricity production. • Installed capacity has a positive effect on electricity production. • The VECM outperforms both the GM (1,1) and ARIMA models in prediction. • China's electricity energy transition and dual-carbon goals must be promoted further. [ABSTRACT FROM AUTHOR]

The importance of energy in modern life is self-evident. Forecasting future energy consumption can help governments and businesses formulate reasonable energy supply and demand policies to ensure energy security and economic development. To this end, a novel adaptive fractional grey model with fractional derivative was established. Firstly, a novel fractional cumulative operator is proposed that operates in a fractional-order domain and has the potential to alternate between giving priority to new or old information. This method facilitates the effective utilization of data when working with a limited number of samples. Secondly, the model's adaptability and flexibility were improved through the introduction of a nonlinear term in the whitening equation; and the fractional derivative was introduced into the whitening equation to solve the problem of poor adaptability of existing integer-order derivative to nonlinearity and volatility. To enhance the model's performance, the study utilized the Grey Wolf Optimization (GWO) algorithm to optimize the model parameters. Furthermore, the robustness of the proposed model was verified using Monte Carlo simulations and probability density analysis; and the experimental results indicated that the proposed model exhibits better robustness. Finally, three actual cases of China's total energy consumption, total crude oil consumption and domestic heat consumption are predicted. • A novel structure adaptive fractional derivative grey model is proposed. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other models. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of China's economy has significantly stimulated demand for electricity consumption. After China's economy entered the phase of "New normal" with moderately lower growth rates, the growth of electricity consumption has also slowed down, and there seemed to be a trend of gradual decoupling between these two variables. To investigate whether and how electricity consumption changed along with economic growth, 100 prefecture-level cities' dynamic panel data are used in this study to examine the relationship between the decoupling indicator of electricity consumption and economic growth. To control for the potential endogeneity, the first-order difference Generalized Method of Movements (GMM) estimator is employed. The empirical results indicate that the relationships between the decoupling indicator of electricity consumption and real GDP per capita differs across different regions. For the whole nation and the eastern region, there is a significant "inverted N-shaped" relationship between the two factors, and decoupling indicators are still rising along with economic development. In contrast, in the central and western regions, there is a significant "N-shaped" relationship between the two factors. Besides, other macroeconomic factors, including industrial structure, FDI and financial investment in technology, may also affect the decoupling indicator of electricity consumption. • Relationship between the decoupling indicator of electricity consumption and economic growth in China is investigated. • A dynamic panel data consisting of 100 cities for the period of 2000–2015 is utilized. • Generalized Method of Moments (GMM) estimator is employed to control for potential endogeneity. • Nexus of electricity consumption's decoupling indicator and GDP per capita differ across regions. • Other macroeconomic factors like industrial structure, FDI and technology investment also affect the decoupling effect. [ABSTRACT FROM AUTHOR]

The objective of this research is to examine the effect of economic growth, energy consumption and CO2 emissions on each other for a panel of 68 countries. I used panel data from 68 countries over the period 2001–2017 for developed, emerging and the Middle East and North Africa (MENA) countries. I applied seemingly unrelated regression (SUR) and dynamic models estimated through means of the generalized method of moments (GMM) and System generalized method of moments (Sys GMM) for data analysis. The estimated empirical results indicates that economic growth increase with increase in energy consumption in developed and emerging countries while declined in MENA countries; CO2 emissions increase in all countries due to increase in energy consumption. Energy consumption increase in all countries but economic growth increase in all countries except MENA due to increase in CO 2 emissions. The CO 2 emissions increase while the energy consumption decrease in developed and MENA countries but energy consumption increase and CO 2 emissions decrease in emerging countries due to increase in economic growth. Based on the estimated results it is recommended that the policy maker in these countries should adopt such strategies that should focus to adopt environmentally friendly technologies to decrease the CO2 emissions. • Energy consumptions boost the economic growth in developed & emerging countries. • Energy consumptions negatively effect on economic growth in MENA countries. • CO 2 emissions boost the economic growth in developed & MENA countries. • Economic growth increases the energy consumption in emerging countries. • Economic growth increases the CO 2 emission in developed and MENA countries. [ABSTRACT FROM AUTHOR]

Introduction of the fractional order accumulation has made significant contributions to the development of forecasting methods, and fractional grey models play a key role in such new methods. However, the fractional grey models may also be inaccurate in some cases as they do not consider the time delayed effect. To further improve the applicability of the existing fractional grey models, a novel fractional grey model called the fractional time delayed grey model is proposed in this paper. The essence of the fractional time delayed term is discussed, revealing that the fractional time delayed term is essentially a function between the polynomial functions with integer order, which can be more flexible to improve the modelling accuracy. The cutting-edge Grey Wolf Optimizer is introduced to find the optimal value of fractional order. Detailed modelling procedures, including the computational steps and the intelligent optimization algorithm, have been clearly presented. Four real world case studies are used to validate the effectiveness of the proposed model, in comparison with 8 existing grey models. Finally the proposed model is applied to forecast the coal and natural gas consumption of Chongqing China, the results show that the proposed model significantly outperforms the other 8 existing grey models. • A novel fractional time delayed grey model is proposed. • Essence of fractional time delayed term is revealed. • Grey Wolf Optimizer is used to find the optimal fractional order. • The proposed model outperforms 8 models in 6 real world cases. • The new model is efficient in energy forecasting for complex energy systems. [ABSTRACT FROM AUTHOR]

The effect of financial development on carbon emissions (CO 2) has been examined mostly without taking into account the mediating function of energy consumption and how to integrate this effect into a national energy modeling system. This study will address three topics against this background. The first is whether empirical research should study and prioritize the direct or indirect impacts of financial development on CO 2 through the mediation of energy consumption. The second is which empirical models should be utilized to assess these impacts. Third is how to include these impacts into a national energy modeling system at the level of policy formulation. The article utilizes the U.S. National Energy Modeling System for 1990M1 – 2021M2 using the bootstrap manifest structural equation modeling technique. The article has three findings. First, empirical study should concentrate more on the indirect rather than direct effects of financial development on CO 2. Second, both types of impacts should be incorporated into a national energy modeling system via the distinct (indirect) effects of the components of aggregate financial development, namely financial market development (FMD) and financial institutional development (FID). Third, these effects should be investigated by at least six empirical models that estimate the FMD's and FID's effects on CO 2 (i) via the single mediation of sectoral (nonrenewable and renewable) energy consumption, (ii) via the serial mediation of sectoral energy consumption and economic growth, (iii) via the serial mediation of aggregate energy consumption and growth, and (iv) via the serial mediation of energy consumption in one single sector and output in this sector. • Investigates the mediating roles of renewable and non-renewable energy between financial development and carbon emissions. • Explains these mediating roles based on a processual system approach. • Introducing 14 linear and nonlinear structural equation models for the empirical estimation of these roles. • Demonstrates how to integrate the financial system into a national energy modeling system. [ABSTRACT FROM AUTHOR]

This study investigates the dynamic relationship between rural GDP, rural energy consumption and rural investment based on a provincial panel data set covering the 1995–2010 period. The vector error correction model (VECM) and fully modified ordinary least squares (FMOLS) are utilized to examine the short- and long-run causal relationship between the variables of interest. Then, the impulse response function (IRF) and variance analysis are used to examine the dynamic impact of the process and the contribution of relevant factors. The estimation results indicate that there is two-way Granger causality between rural GDP and rural investment and unilateral Granger causality extending from rural energy consumption to rural GDP and investment in the long term. Furthermore, the results also verify the existence of unidirectional causal relationship from rural GDP to rural investment and bilateral causal relationship between rural GDP and rural energy consumption in the short run. Hence, it is feasible for China to promote economic development through hastening the optimization and upgrading of its rural industrial structure and encouraging and promoting the use of green energy. Additionally, rural economic growth is helpful for stimulating rural investment and energy consumption, which in turn benefits economic development in rural areas. [ABSTRACT FROM AUTHOR]

This study investigates the effect of financial structure on renewable energy consumption in G20 countries, further categorized into BRICS, G7, developed, and developing countries. It also examines the economic growth channel through which financial structure affects renewable energy consumption and the causality relationships between the two. Using feasible generalized least squares and fixed-effects models, the study finds that financial structure significantly increases renewable energy consumption in all sample groups except BRICS, where it decreases consumption. The study also finds that financial structure indirectly promotes renewable energy consumption through economic growth, with threshold values of economic growth varying by sample group. Specifically, the economic growth threshold beyond which financial structure will impact renewable energy consumption is 8.574 (logarithm) in the full sample, 9.078 (logarithm) in the BRICS, 10.477 (logarithm) in the G7, 10.557 (logarithm) in developed countries, and 8.732 (logarithm) in developing countries. These correspond to $5292 billion in the full sample, $8760 billion in the BRICS, $3,5490 billion in the G7, $3,8446 billion in developed countries, and $6198 billion in developing countries. Finally, the study reveals an uni-directional causality from renewable energy consumption to financial structure in the full sample while the reverse exists in the other sample groups. • Estimate the impact of financial structure on renewable energy consumption using G20 countries. • Examine the economic growth channel and the causality relationships between the two variables. • Financial structure significantly increases renewable energy consumption. • Financial structure indirectly improves renewable energy consumption via economic growth. • There exists an uni-directional causality from renewable energy consumption to financial structure. [ABSTRACT FROM AUTHOR]

Mineral resources can contribute to economic, energy, and climate goals by expanding critical minerals used in advanced technology. However, mineral complexity influences the sustainability of achieving these goals. In this study, we investigate the mineral complexity and fossil fuel impact on economic growth, examining 21 emerging Asian economies from 2000 to 2018 using the panel cross-sectional autoregressive distributed lag and vector autoregressive causality. The findings indicate that mineral complexity impedes economic growth in the long and short run. In contrast, foreign direct investment and capital are positively associated with long-term economic growth. The empirical outcomes showed a unidirectional causality between fossil fuels and economic growth, suggesting that fossil fuels are essential for sustainable economic growth. This study also investigates a unidirectional causality between economic growth and the exogenous variables, running from mineral complexity, foreign direct investment, and capital to economic growth. The findings suggest that mineral complexity, including supporting factors, contributes to sustainable economic growth. The findings illustrate the significance of understanding the critical minerals, global value chain, and capital for sustainable growth policies. • Examine the role of mineral complexity in technological advancement. • Technological advancement, economic growth, and mineral resources nexus. • Minerals-based batteries are currently used in modern technologies as alternate power. • Role of foreign investment and capital on emerging economies. [ABSTRACT FROM AUTHOR]

The unbalanced development among energy price, energy consumption, and economic growth will destroy the stability of the energy market. The nonlinear differential equations among several factors are established based on the actual situation of energy market changes and the causal relationship between energy price and economic growth in an economic stage and discuss the relationship among the factors. The nonlinear grey prediction model group of energy price-energy consumption-economic growth is established by using grey difference information. The least-square method is utilized to give the parameter estimation formula of the prediction model group, and the approximate time response formula of the model is obtained by recursive relation transformation. Finally, the new models are applied to the prediction of coal price and coal consumption in China, and the effectiveness of the two models analyzes the same data set. It shows that the prediction results of coal price and coal consumption are better than the other three classical grey prediction models, and it can effectively predict the coal price and consumption in China from 2021 to 2025. The prediction results can provide adequate information for China's energy system and have theoretical and practical significance for the stability of the energy system. • The highlights of this article are as follows: • The novel energy price and energy consumption grey models are established. • The energy price and energy consumption are analyzed. • The grey models show theirs high precision and practicability. • Energy price and energy consumption have been successfully predicted. [ABSTRACT FROM AUTHOR]

This paper explores the relationships between the prices of crude oil, corn and ethanol over the period from January 1986 to August 2015 using a Vector Autoregressive Model and Vector Error Correction Model. The structural breaks are endogenously determined using these variables and then the overall period is divided into three sub-periods. A long-run causal relationship among these three prices is found and depends on the level of the crude oil prices. The empirical results show that the corn price is driven by ethanol prices, but that the price of corn did not influence ethanol prices until 2005. However, there is a unidirectional causality that runs from crude oil prices to ethanol prices throughout the period. The empirical results indicate that a 1% increase in ethanol consumption could have reduced the crude oil price by 6.08% in the case of the U.S. during the period from 1987 to 2011, which implies that the development of ethanol production is one way of controlling inflation in relation to crude oil prices. [ABSTRACT FROM AUTHOR]

Based on data availability, this study investigates the impact of structural change on energy consumption (EC) in Iran from 1976 to 2017. Therefore, the economic complexity index (ECI) was incorporated into the energy demand besides real GDP (RGDP) and real energy price index (REPI), which is constructed based on Iran's leading energy carriers. Multiple econometrics techniques are applied to deliver a more reliable and accurate analysis, such as the autoregressive distributed lag model, error correction model, Bayer and Hanck cointegration test, and three cointegration regressions: fully-modified ordinary least square (OLS), dynamic OLS, and canonical cointegrating regression, and finally Breitung-Candelon Spectral Granger. In the short and long-run, RGDP and ECI increase (rebound effect) EC while REPI reduces it. Furthermore, the causality results show that RGDP, ECI, and REPI are linked to EC in the short, medium, and long run. The study's results have implications for policymakers to manage Iran's energy consumption. They highlighted the importance of raising public environmental awareness, diversifying the energy mix, and continuing to implement the law on targeted subsidy. • The impact of the structural change on energy demand in Iran was investigated. • The ARDL, ECM, Byer and Hank, Breitung-Candelon Spectral Granger were conducted. • The robustness was checked by applying FMOLS, DOLS, and CCR. • The economic complexity index and the eoncomic growth are positively linked to energy consumption. • Energy price is reversely linked to energy demand. [ABSTRACT FROM AUTHOR]

This study aims to investigate the threshold effect of GDP on the causality between GDP and energy consumption (EC), using panel data of 26 OECD countries over the period 1971–2014. Threshold regression technique is employed to explore whether there exists a threshold of GDP in the relationship between GDP and energy consumption and Granger causality testing procedure based on panel VECM is applied to test the causality across GDP regimes. The empirical results highlight the existence of GDP threshold in which the effects of GDP on energy consumption and the direction of the energy-growth causality depend on the initial value of GDP. When real GDP per capita is less than US$ 48,170, there exists unidirectional causality from EC to GDP in both the short and long run. However, when GDP exceeds US$ 48,170 per capita, there is no causality relationship in the short run but GDP is found to Granger cause EC in the long run. The findings urge for the attention of policymakers to take the initiative in implementing energy conservation policies when the economy reaches certain high levels of development and to switch energy policies appropriately in different periods of economic growth, to foster sustainable development. • We examine the GDP-dependent causality between energy use and GDP in 26 OECD countries. • We use threshold regression technique and causality testing based on panel VECM. • There exists a single threshold effect of GDP with the critical value is 48,170 USD. • In the first regime, there are causality relationships running from energy to GDP. • When GDP exceeds the threshold, there is unidirectional causality from GDP to energy. [ABSTRACT FROM AUTHOR]

This paper examines the dynamic causal relationship between renewable energy prices and economic growth in the cases of Norway, New Zealand, and two Canadian provinces. A Markov-switching vector autoregression (MS-VAR) model is used with model parameters being governed by a hidden Markov chain, allowing the causal relationship to vary amongst different states of the economy. A unidirectional causal link, running from economic growth to renewable energy is found in the case of New Zealand, Alberta, and Ontario. The causality emerges in the cases of New Zealand and Ontario, during periods of high economic growth, whilst it materializes in the cases of Alberta during periods of low economic growth. Our empirical results document the existence of a time-dependent causal relationship between renewable-energy prices and economic growth. • Causality link between renewable-energy (RE) prices & economic growth (EG) is probed. • The cases of Norway, New Zealand (NZ) and two Canadian provinces are presented. • Oil prices & interest rate are control variates in a Markov-switching VAR model. • There is a unidirectional causality (EG to RE) at high EG period for NZ & Ontario. • A unidirectional causality is found from EG to RE at low EG period for Alberta. [ABSTRACT FROM AUTHOR]

This paper examines the dynamic causal relationship between renewable energy prices and economic growth in the cases of Norway, New Zealand, and two Canadian provinces. A Markov-switching vector autoregression (MS-VAR) model is used with model parameters being governed by a hidden Markov chain, allowing the causal relationship to vary amongst different states of the economy. A unidirectional causal link, running from economic growth to renewable energy is found in the case of New Zealand, Alberta, and Ontario. The causality emerges in the cases of New Zealand and Ontario, during periods of high economic growth, whilst it materializes in the cases of Alberta during periods of low economic growth. Our empirical results document the existence of a time-dependent causal relationship between renewable-energy prices and economic growth. • Causality link between renewable-energy (RE) prices & economic growth (EG) is probed. • The cases of Norway, New Zealand (NZ) and two Canadian provinces are presented. • Oil prices & interest rate are control variates in a Markov-switching VAR model. • There is a unidirectional causality (EG to RE) at high EG period for NZ & Ontario. • A unidirectional causality is found from EG to RE at low EG period for Alberta. [ABSTRACT FROM AUTHOR]

The effective utilization of wind energy is a positive development trend in renewable energy that provides an effective impetus to alleviate global warming. However, the complex volatility and extreme suddenness of wind speeds make it difficult to accurately predict wind speeds. To overcome this challenge, a novel short-term wind speed prediction model based on double decomposition, piecewise error correction, Elman neural network and the autoregressive integrated moving average model is proposed. In this model, the fuzzy entropy is applied to judge the complexity of each subseries generated by variational mode decomposition. To address error correction, a piecewise error correction method that needs to extract only a part of the error sequence as a training set is proposed. Three experiments utilizing 6 Datasets and 14 compared models are conducted to verify the effectiveness of the proposed model. The results show that (i) the hybrid strategy based on double decomposition and piecewise error correction can improve the final prediction accuracy of different single models; (ii) the proposed hybrid model can effectively handle both linear and nonlinear problems; (iii) the root mean squared error of the proposed model for Dataset1-6 in seven-step forecasting are 0.1243, 0.1444, 0.0233, 0.1015, 0.1846 and 0.2513, respectively. • The double decomposition strategy is applied to wind speed prediction. • A novel piecewise error correction method has been developed. • Fuzzy entropy is used to judge the complexity of subsequences. • The proposed model is verified using 6 wind speed datasets in multi-step forecasting. [ABSTRACT FROM AUTHOR]

Many empirical studies have examined the causal relationship between electricity consumption and economic growth. Given the significant role of China in global economic growth and electricity use, this study analyzes the multiple causal mechanisms underlying the electricity consumption-economic growth nexus in China using a structural equation model and comparing the nexus with national-level data from 1978 to 2019. This study contributes to the understanding of the direct and indirect causal mechanisms between the electricity consumption-economic growth nexus. We find: (1) the nexus has strengthened with time, and the direct and indirect effects from electricity consumption to economic growth have also increased with time; (2) indirect influences of electricity consumption on economic growth are stronger than the direct influence; and (3) increasing ecological awareness brought about by economic growth increases the demand for electricity generation from clean energy sources. Therefore, the Chinese government should formulate policies to facilitate high-quality electricity production to meet the increasing demand from economic growth. • SEM tests the causality between electric consumption and economic growth in China. • Causal mechanisms between electricity consumption and economic growth are compared. • The direct effect of electricity consumption on economic growth increases. • The effect of electricity development on ecological awareness increases with time. [ABSTRACT FROM AUTHOR]

The debate on the energy-growth nexus has been revisited widely in the research literature focusing on the causal relationship between energy consumption and Gross Domestic Product. The research stream has not resulted in optimal policy recommendations since the results are ambiguous and mixed. In this study, a wavelet approach with fuzzy regression analysis describes the fuzzy causal relationship between energy consumption and Gross Domestic Product for different timescales of 61 countries between 1990 and 2014. The results allow for policy-making according to the fuzziness description of the data and different timescales. A focused analysis of each country is conducted according to the associated continent and Human Development Index classification to contrast the results. • Energy consumption-economic growth nexus differs by country. • Four theories about the energy consumption-economic growth nexus are tested. • Wavelet transform with fuzzy regression captures differences in energy consumption-economic growth by country. • Index of Confidence developed to test fuzzy regression model improvement. [ABSTRACT FROM AUTHOR]

Abstract: The relationships between industrial structure and the respective growth paths of oil consumption of the primary, secondary, and tertiary industries in China are examined. Based on the results, the degrees of grey correlations between total oil consumption and output values of the primary and secondary industries in China have declined, whereas that between Chinese oil consumption and output values of tertiary industries has increased. Using the VECM (vector-error correction model), the long and short run relationships between oil consumption and the three types of industries are identified. From 1978 to 2009, the long-run elasticity of total oil consumption with respect to the output value of tertiary industries ranked first, followed by the secondary industries. The negative effects of the primary industries on total oil consumption result from the "crowding out effects" of the two other industries. Although the error correction coefficients are small, the short-term impacts of the inhibition of oil consumption are limited, and China's oil consumption growth shows strong continuity and inertia. Causal tests and cointegration show that output values of the three types of industries Granger cause total oil consumption in the long run but not in the short run. [Copyright &y& Elsevier]

Abstract: This study examines the cointegration and causal relationship between economic growth, carbon dioxide (CO2) emissions and energy consumption in selected Association of Southeast Asian Nations (ASEAN) countries for the period 1971–2009. The recently developed Autoregressive Distributed Lag (ARDL) methodology and Granger causality test based on Vector Error-Correction Model (VECM) were used to conduct the analysis. There was cointegration relationship between variables in all the countries under the study with statistically significant positive relationship between carbon emissions and energy consumption in both the short and long-run. The long-run elasticities of energy consumption with respect to carbon emissions are higher than the short-run elasticities. This implies that carbon emissions level is found to increase in respect to energy consumption over time in the selected ASEAN countries. A significant non-linear relationship between carbon emissions and economic growth was supported in Singapore and Thailand for the long-run which supports the Environmental Kuznets Curve (EKC) hypothesis. The Granger causality results suggested a bi-directional Granger causality between energy consumption and CO2 emissions in all the five ASEAN countries. This implies that carbon emissions and energy consumption are highly interrelated to each other. All the variables are found to be stable suggesting that all the estimated models are stable over the study period. [Copyright &y& Elsevier]

The importance of manufacturing as a main determinant of economic growth has been explored extensively in the literature considering manufacturing as the engine of economic growth. In our study, a new database covering 117 countries for 1995–2017 containing four indicators, namely manufacturing value added share in GDP, growth of GDP, manufacturing energy intensity, and UNIDO's Competitive Industrial Performance score is constructed. Relying on this dataset and the Granger causality tests the paper investigates the effect of economic growth, energy intensity and the competitiveness index on manufacturing value added. Applying adequate econometric methods the effect of this set of variables on the relative importance of manufacturing in the economy as a whole measured by the share of MVA in GDP (MVAsh) is analyzed. An inverted U shape is verified with different turning points between static and dynamic analyses in the full sample, as well as in industrialized countries and emerging and developing countries separately, with turning points all within samples. Energy intensity has negative effect in most of the cases while CIP has positive effect with high magnitudes. The rate of adjustment is high. • We estimate the adjustment rate and main indicators effect on manufacturing sector. • Static and dynamic panel specifications are proposed modelling MVA share and growth. • We explore energy intensity effect on selected indicators of industrial development. • We use for first time in such specifications manufacturing energy intensity. • UNIDO's Competitive Industrial Performance score is used for the first time. [ABSTRACT FROM AUTHOR]

Using a panel data analysis of a newly developed sample of monthly data by state for January 2005 to December 2019, we estimate a series of error correction models for US residential electricity demand postulated to move with electricity price, natural gas price, income, and weather. Our key findings are as follows. First, the short-run own-price elasticity estimate is not statistically different from zero (p -value > 0.8). Second, the long-run own- and cross-price elasticity estimates are −0.054 (p -value = 0.000) and 0.019 (p -value = 0.000) under the double-log specification, smaller in size than the long-run own- and cross-price elasticity estimates of −0.120 (p -value = 0.000) and 0.069 (p -value = 0.000) under the linear demand specification. Third, price elasticity estimates have been shrinking in size over time. Fourth, erroneously ignoring the panel data's cross-sectional dependence tends to more than double the long-run price elasticity estimates. Fifth, mismatching the timing of price information's availability and consumption decision leads to anomalous price elasticity estimates. Finally, our new empirics' key takeaway of low price-responsiveness supports continuation of energy efficiency standards and demand-side management programs. • Estimate residential electricity demand's price responsiveness in the US. • Adopt dynamic panel data modelling with cross-section dependence. • Document low price responsiveness that shrinks over time. • Offer empirical support for non-price-based demand side management. [ABSTRACT FROM AUTHOR]

Energy plays an essential role in economic growth. It is expected that the electricity consumption projections in Egypt will exceed electricity generation capabilities for the upcoming years. Therefore, this research aims to examine the determinants of electricity consumption in Egypt. In this quantitative research, the secondary data, energy consumption, gross domestic product, total population, urban population, rural population, industry value-added GDP, inflation consumer price, and final consumption expenditure have been collected from 1997 to 2018. There is a long-run causality between electricity consumption and gross domestic product. Urbanization, industry value-added GDP, inflation consumer price, final consumption expenditure, and electricity consumption show the regression analysis's short-run causality. In this, the unit root test was run to know if the data is stationary or non-stationary. Johansen co-integration and vector error correction model (VECM) also used to find the result. The results revealed that as the gross domestic product increases, the electricity consumption would also increase, and short-run causality between electricity consumption and an increase in population. This study is limited to Egypt and merely focused on energy. Future studies can use this research to investigate renewable energy consumption from the perspective of other countries. • Egypt facing an energy crisis after 2003 and in 2012 there was the worst condition. • GDP is the most important determinant of energy consumption in Egypt. • There is a short-term causality between electricity consumption and determinants. • Egypt's urban population is increasing rapidly and triggers energy consumption. • Government needs to invest more in renewable energy instead of natural resources. [ABSTRACT FROM AUTHOR]

This study explores the dynamic relationship of energy consumption, international trade and foreign direct investment (FDI) with economic growth for a panel of BRICS and ASEAN countries over the period of 1990–2017 within a multivariate framework. The panel co-integration test, panel quantile regression method, impulse response function and heterogeneous panel causality test are used for empirical investigation. The results reveal the existence of a long-run equilibrium relationship among the variables. In the long run, the effects of energy consumption, international trade, capital and FDI are found to be positive and significant on the economic growth of these countries. The heterogeneous panel causality test results indicate bidirectional causality between energy consumption and economic growth, labour force and economic growth, international trade and energy consumption, and labour force and international trade. A unidirectional causality from economic growth to international trade and FDI, and from international trade and energy consumption to FDI is also identified. The implications of these results are also discussed. • A dynamic link of energy use, international trade and economic growth is explored. • Panel data of BRICS and ASEAN countries are considered for this study. • Energy use and international trade positively affect economic growth. • Effect of energy is three times more than that of trade on economic growth. • A bidirectional causality between energy use and economic growth is found. [ABSTRACT FROM AUTHOR]

The main scope of this research was to develop and apply new methodology for energy prediction during a long-term period, based on the logarithmic relationship between electricity per capita and GDP per capita. This relationship was proven to be valid until 1989. It contains changeable energy and financial indicators and a dynamic rate of growth K, in the form of hyperbolic curves. With a range of slopes from 0.1 to 6, it enables a new economic classification of countries. Further research of this method, called IKoMet, has made the application of this relationship for future periods possible through the deduction of new energy and financial indicators. It was first applied to Italy, by the determination of indicators for a prediction after 1990. The results show that energy growth in the first period was more than ten times faster in comparison to the following period, while financial growth was almost twice as large in the first period. This procedure was also successfully applied for Switzerland, thus it can be concluded that IKoMet could generally be used for long-term energy prediction. • Electricity and economic growth are related according to a new relationship. • The methodology, called IKoMet, enables accurate predictions for national energy strategies. • The development of new energy and financial indicators enable long-term forecasting. • All countries are economically classified according to a new hyperbolic constant. • A new ratio of growth between the gross domestic product and energy is developed. [ABSTRACT FROM AUTHOR]

The relationship between energy consumption and economic growth has deservedly received much attention in the literature due to its significance to sustainable development, particularly in countries such as China with rapid economic growth. However, few studies have explored the spatial heterogeneous nature of this relationship within countries where there are spatial variations in terms the level and speed of economic development. This study analyzes how industrial GDP and employment are related to industrial electricity consumption at prefecture city level across China using Multiscale Geographically Weighted Regression (MGWR). It also examines the role of foreign direct investment (FDI) and research/development (RD) in mediating the relationship between economic growth and electricity consumption. The results show that, during the study period, industrial electricity consumption became increasingly related to industrial GDP while the influence of employment diminished. However, this relationship varied over space and time with clear differences between the cities in the developed east coast and the rest of the country. Also, the FDI and RD improved efficiency in electricity consumption especially in less developed central and western regions during the study period. • Identify the spatial heterogeneity of energy-economy relationship across China. • MGWR models are used. • Results on spatial variations correspond to the knowledge of regional gaps in China. • Results on temporal effects examine the predictability of energy consumption. [ABSTRACT FROM AUTHOR]

Accessibility to modern energy services is imperative in resolving numerous present-day global development issues which affect people's socio-economic and physical well-being, plus, the potentiality of meeting the global goals of lessening carbon emissions. The absence of access, termed energy poverty in developing countries, has several key aspects: inaccessibility to electricity on demand, is one aspect. However, inaccessibility to modern (or clean) cooking fuels is another critical aspect. A cause and effect of households' continuous heavy reliance on traditional (or solid) fuels. Despite over a third of the global population having this issue, this aspect of energy poverty continues to be a severe, yet overlooked development issue: particularly in developing countries. A review of literature shows that there are currently no empirical studies which quantify the sole relationship between the utilization of traditional energy fuels for cooking and/or heating and economic development. Thus, in this paper, we advance existing literature in multiple-folds. Firstly, we provide a review in an absent area of literature. Secondly, using the data from 46 sub-Saharan African countries, we provide empirical evidence of the impacts of the continued utilization of traditional fuels on economic development (employing Gross Domestic Product (GDP) per Capita as variable). Following the establishment of a negative causal relationship running from traditional fuels use (solid) to GDP per capita, we provide an insight into policy implications. Finally, we comment on potential policy strategies. • Lack of access to clean cooking fuels is an overlooked issue in developing countries. • Impact of solid cooking fuels on economic growth was analysed for sub-Saharan Africa. • Results show unidirectional causality between solid cooking fuels and GDP per capita. • Use of solid cooking fuels is observed to negatively influence economic development. • Reducing use of solid cooking fuels is vital for economic growth of these countries. [ABSTRACT FROM AUTHOR]

This paper examines the effect of remittance income on energy consumption and the economy between 1976 and 2019 in the four highest remittance recipient nations of South Asia. The detailed analysis explores long-run and directional relationships utilizing stationary tests, the panel cointegration test, dynamic ordinary least square (DOLS), fully modified ordinary least square (FMOLS), and Granger causality tests using a Vector Error Correction Model (VECM). The study reveals the presence of long-run relationships among remittances, energy consumption, GDP and urbanization. Cointegrating regression identifies that remittance income, economic growth and urbanization individually have a positive and significant effect on energy consumption in the long-run. Results showed that a 1% increase in remittance income results in a 0.045% increase in energy consumption. Among nations analyzed, this effect was found to be strongly dominant in case of Bangladesh and Pakistan. The statistically significant and negative coefficient of the error correction term confirms long-run causality from remittance, GDP, and urbanization toward energy consumption. Variance decomposition analysis demonstrated that energy usage is heavily impacted by remittance inflows among studied variables. Policy implications of this study suggest that higher remittance inflows will increase energy consumption in South Asian countries, engendering positive economic growth and sustainable development. Image 1 • The effects of remittance on energy consumption studied for South Asian countries. • Novel statistical approaches applied to explore the causality and direction between remittance income and energy use. • Remittance income provides a potential stimulus toward national investments and ensures reliable and clean energy sources. • It is evidence that 1% raise in remittance income results in a 0.045% increase in energy consumption in the long run. • Policy makers can formulate strategic plans for migrant workers to ensure affordable clean energy. [ABSTRACT FROM AUTHOR]

Despite the importance of energy efficiency (EE) in promoting economic growth (EG), the empirical evidence about the growth effect of EE is quite thin. This research intends to examine the heterogeneous impacts of EE, renewable energy consumption (REC), and other factors on EG of BRICS countries for 1990–2014. The empirical results unveil that EE in BRICS countries is an important source for EG. The findings of fixed-effect panel quantile regression analysis clearly explain that the effects of all the selected components of EG are heterogeneous along the quantiles. The effect of EE is significantly positive across all the quantiles, but the positive effect is more robust at 50th and 60th quantiles of EG. REC significantly decreases the EG in BRICS economies, but the negative influence is more robust at the upper quantiles of EG (0.60–0.90). Moreover, the results obtained from Dumitrescu-Hurlin (D-H) heterogeneous panel causality test approve the feedback hypothesis between EE and EG in BRICS countries. The findings also provide the bidirectional causal relationship between REC and EG. Furthermore, a causal association is observed from EE to REC. It suggests that EE is also beneficial to enhance REC in BRICS countries. The study suggests that more prolific use of energy can stimulate EG in BRICS countries by improvement in EE and renewable energy (RE). • Heterogeneous impact of energy efficiency and renewable energy use on the economic growth of BRICS countries. • Fixed effect panel quantile regression analysis is used to capture the heterogeneous effects. • Energy efficiency is an important source of economic growth in BRICS countries. • The feedback hypothesis exists between energy efficiency and economic growth. [ABSTRACT FROM AUTHOR]

It is a well-documented phenomenon that coal combustion is a significant cause of air pollution, while few documents have revealed the process. Based on the panel dataset covering 30 Chinese provinces (excluding Tibet) in China during the period 2000–2012, this study investigates the actual contribution of centralized and scattered coal consumption to PM 2.5 concentrations through a panel cointegration model and panel vector error-correction model (PVECM). The empirical results indicate that both scattered and centralized coal consumption have significant positive correlations with PM 2.5 concentrations in the long term. Specifically, the PM 2.5 pollution caused by scattered coal combustion is approximately 1.38 times that of the same amount of centralized coal combustion. Furthermore, in the short-term, centralized coal consumption has a significant positive relationship with PM 2.5 pollution, while scattered coal has no significant effect. Therefore, in the short term, the consumption of centralized coal should be reduced, whereas in the long term, scattered coal control policies should be the primary focus of the government. • Coal consumption is the main reason for PM 2.5 pollution in China. • Scattered coal should be the focus of the coal control policy. • A panel cointegration model with FMOLS estimator is used. [ABSTRACT FROM AUTHOR]

Despite the importance of energy efficiency (EE) in promoting economic growth (EG), the empirical evidence about the growth effect of EE is quite thin. This research intends to examine the heterogeneous impacts of EE, renewable energy consumption (REC), and other factors on EG of BRICS countries for 1990–2014. The empirical results unveil that EE in BRICS countries is an important source for EG. The findings of fixed-effect panel quantile regression analysis clearly explain that the effects of all the selected components of EG are heterogeneous along the quantiles. The effect of EE is significantly positive across all the quantiles, but the positive effect is more robust at 50th and 60th quantiles of EG. REC significantly decreases the EG in BRICS economies, but the negative influence is more robust at the upper quantiles of EG (0.60–0.90). Moreover, the results obtained from Dumitrescu-Hurlin (D-H) heterogeneous panel causality test approve the feedback hypothesis between EE and EG in BRICS countries. The findings also provide the bidirectional causal relationship between REC and EG. Furthermore, a causal association is observed from EE to REC. It suggests that EE is also beneficial to enhance REC in BRICS countries. The study suggests that more prolific use of energy can stimulate EG in BRICS countries by improvement in EE and renewable energy (RE). • Heterogeneous impact of energy efficiency and renewable energy use on the economic growth of BRICS countries. • Fixed effect panel quantile regression analysis is used to capture the heterogeneous effects. • Energy efficiency is an important source of economic growth in BRICS countries. • The feedback hypothesis exists between energy efficiency and economic growth. [ABSTRACT FROM AUTHOR]

Planning process focusing on Energy-Environment-Economy nexus heavily relies upon the results of causality studies for policy and decision guidance. These studies are mostly aimed to determine the true direction of the causal-path among key variables such as energy consumption, economic growth and GHGs emissions. However, there are some weak points attached to already available methods and their variants such as Granger type causality tests and Peter and Clark (PC) algorithms based on Graph theoretic approach. Swanson and Granger (1997) [89] used the vector autoregressive (VAR) residuals in the PC algorithms to find the causal ordering in time series. However, these residuals carry only contemporaneous information about cross-variable effect and remove the past information. This study modified the original PC algorithms based on modified R-residuals test to analyze the causal relationship between energy consumption, economic growth and GHGs emissions in Pakistan. The results reveal that, unidirectional causality is running from economic growth towards CO 2 emissions. However, no causality is running from energy consumption towards economic growth and vice-versa. This is due to the fact that, a significant portion of energy is consumed in residential and transportation sectors, both of which does not contribute to economic growth in long-run. [ABSTRACT FROM AUTHOR]

This article investigates the impact of export product diversification, extensive margin and intensive margin on emerging economies energy demand covering the period from 1971 to 2014. The study contributes to energy economics by unveiling the interaction between export diversification and energy demand of 10 newly industries countries (NICs). Owing to the growth prospect and trade volume of these nations, it is necessary to assess the various facades of export growth on the energy demand. In this pursuit, we have considered the export product diversification index in its aggregate and disaggregated forms (i.e. extensive margin and intensive margin) in this study. The empirical estimation has been carried out based on GMM, FGLS, FMOLS, and DOLS techniques. The empirical results demonstrate that export diversification, extensive margin, and intensive margin help to reduce the overall energy demand in NICs. Further, the empirical outcomes identify that economic growth, urbanization, and natural resources increase energy consumption. The study discusses fruitful policy implications regarding the exports diversification and energy demand nexus for emerging economies. • The impact of export product diversification on energy demand is investigated. • 10 NICs over 1971–2014 are analyzed. • Results show that export product diversification reduces energy consumption. • Extensive and intensive margins also help to reduce overall energy demand. [ABSTRACT FROM AUTHOR]

Our work provides a comprehensive investigation of the coupling between the efficiency of economic growth and the efficiency of carbon emissions reduction in China, and makes a distinction between the coupling over the long-run and the short-run. Our estimates suggested significant evidence of coupling between economic growth and efficiency in carbon emissions reduction in the long-run, but China's economic growth still increased carbon emissions at the aggregate level. In the short-run, coupling effects were different across provinces that were at different stages of economic development. Provinces with low- and high-stages of development saw positive short-run coupling, but provinces at the intermediate stage of development saw the contrary, which resulted in an obvious "U" shaped relationship between short-run coupling effects and real GDP per capita. Finally, our findings provide an economic rationale for differentiated low-carbon policies. • Coupling Economic Growth with Carbon Emissions Reduction at the Efficiency Level. • Making a distinction between the coupling in the long-run and that in the short-run. • Over the period 1997–2017, we saw evidence of the long-run coupling. • The Short-run coupling varies across different provinces. [ABSTRACT FROM AUTHOR]

Forecasting energy consumption of economic activities is important for China to cope with climate change and maintain sustainable economic growth. This study proposes a novel temporal input-output approach that not only forecast the energy consumption of various economic sectors, but also reflects the spread process of inter-sectoral ripple effect of energy consumption from a temporal perspective. Using China's 2015 annual input-output table and data of nearly 3500 listed companies in China's A-share market, we plot the ripple time curves depicting actual energy consumption resulting from China's three main final demands, i.e., Investment, Export, and Consumption. The results show that the manufacturing sector, construction sector and electric power/thermal sector have critical impact on the China's energy consumption. Among all final demands, the Export demand and Investment demand are the main factors causing energy consumption. This study improves the input-output approach from the perspective of time in order to make timely and effective forecast for the China's energy consumption based on real-time data about final demands. • A novel temporal input-output approach is proposed to forecast the indirect energy consumption from a time perspective. • Energy consumption and ripple times at all consumption stages in the industrial chain are calculated. • A polynomial function of time is developed to reflect the intersectoral ripple effects of energy consumption. • The ripple effects of export, investment and consumption on the consumption of various energy sources are examined. [ABSTRACT FROM AUTHOR]

Researchers and academia have not studied the heterogeneous effect of eco-innovation and human capital on the varying sources of energy. Where this study is the first that attempts to investigate the heterogeneous effect of eco-innovation and human capital along with energy price, financial development, research & development expenditure on the total energy consumption (TEC), non-renewable energy consumption (NREC) and renewable energy consumption (REC) by employing Westerlund and Edgerton's panel cointegration and Augmented Mean Group (AMG) for finding the short and long-run estimates by using data of G-7 countries from 1995 to 2017. This study also uses different macroeconomic variables to conduct sensitivity analysis and to examine their impact on TEC, NREC, and REC. The empirical findings confirm a negative association of human capital, eco-innovation, energy price, and research & development expenditures with TEC and NREC. Whereas financial development has found to be positively associated with TEC and NREC. Moreover, human capital, eco-innovation, energy price, and research & development expenditures enhance REC, while financial development reduces REC. Based on the findings of this study, we recommend investment in human capital development and the formulation of regulation & policies in the financial sector for encouraging the use of eco-innovation. • The study examines heterogeneous effect of eco-innovation and human capital on non-renewable, renewable, and total energy consumption. • The study examines the sensitivity of related macroeconomic variables on varying sources of energy consumption. • The study employs Westerlund and Edgerton's panel cointegration and augmented mean group for short- and long-run estimation results. • The study finds a negative association of human capital and eco-innovation on both non-renewable and total energy consumption. [ABSTRACT FROM AUTHOR]

As global energy consumption continues to increase, an increasing attention is also being drawn to the need to embrace cleaner energy despite ensuring energy security and efficiency in production. This study examines the relationship between economic growth and electricity consumption in sub-Sahara African economies between 1971 and 2017. It employs the System Generalized Methods-of-Moments (System GMM) techniques so as to address the issues of endogeneity in the data generating process. We also examine whether the impact of electricity consumption differs by the level of energy intensity, by employing an advanced dynamic panel threshold regression model to ascertain the degree of threshold level of energy intensity and the potential of threshold asymmetric of energy consumption on economic growth. Our results show significant positive relationship between electricity consumption and growth, including a threshold level of energy intensity which stood at 0.48%. This suggests that energy consumption beyond 0.48% will reduce growth, while consumption below this level will stimulate growth. • Relationship between economic growth and electricity consumption in African economies examined. • Bi-directional relationship exists between electricity consumption and economic growth in Africa economies. • Energy efficiency threshold in Africa is 48%. • Energy consumption beyond 0.48% will reduce Africa's growth. [ABSTRACT FROM AUTHOR]

What impact will increasing renewable energy consumption have on economic growth? What is the direction or magnitude of the impact and the determinants behind it? Existing research on the relationship between renewable energy and economic growth focuses on the linear relationship, whereas ignoring the non-linear relationship between them. In this study, the nonlinear relationship between renewable energy and economic growth in OECD countries was investigated by developing panel threshold regression models. Non-renewable energy intensity, urbanization level and per capita income are used as threshold variables to explore the internal mechanism of renewable energy for economic development. Three panel threshold models are developed based on these three threshold variables. The results show that the effect of renewable energy consumption on economic growth is positive, which indicates that increased renewable energy consumpiotn contributes to economic growth. In addition, this positive relationship changes as the threshold value changes, which means that the role of increasing renewable energy consumption to promote economic development is nonlinear. In other words, if the EU countries increase their renewable energy consumption by more than a certain amount (threshold value), the role of renewable energy consumption in promoting economic development is more significant. • Analyzing renewable energy and economic in OECD countries. • The fixed-effect model and non-linear threshold model are employed. • Three factors were used as threshold variables to explore the mechanism. • Renewable energy has a threshold effect on economic growth. [ABSTRACT FROM AUTHOR]

The use of non-renewable resources emits a high quantity of CO 2 into environment, leading to a greenhouse effect, to reduce CO 2 emissions all countries have shifted to use renewable energy sources. Therefore, this study re-examines the effect of renewable energy consumption on economic growth across 38 renewable-energy-consuming countries from 1990 to 2018. The dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS) and heterogeneous non-causality approaches are applied. The empirical analysis confirms the presence of a long-run relationship between renewable energy consumption and economic growth. Further, we noted that renewable energy, non-renewable energy, capital and labor have positive impact on economic growth, particularly, renewable energy consumption has a positive impact on economic growth for 58% of the sample countries. The empirical results suggest that international cooperation agencies, energy organizers, governments, and associated bodies must act together in increasing renewable energy investment for low carbon growth in most of these economies. • This study evaluates the impact of sustainable and non-sustainable energy sources. • It employs DOLS and heterogeneous non-causality approaches. • The study estimated a time-series analysis of long-run output elasticities. • Renewable energy has a positive and significant impact on economic growth. • Cooperation agencies must act together in increasing renewable energy investment. [ABSTRACT FROM AUTHOR]

This paper makes an attempt to introduce energy consumption variable into economic growth model as an input factor in energy economic analysis by taking into consideration China's marine economic zones as the research object. In order to avoid the model setting errors, a semi-parametric linear model comprising of the parametric part to elucidate the impact of capital stock and human capital on the economic growth, and the non-parametric part to explain the impact of energy consumption on the economic growth, is constructed. This paper makes an empirical study on the northern, eastern and southern marine economic zones, and describes in detail the fitting results, monotonicity, convexity of the curve and characteristics of the dynamic changes. By analyzing the concavity and convexity of the fitting curve, the basic form of the energy consumption factors in the production function can be defined fuzzily, and the second derivative shows positive and negative changes under the dynamic time point. Finally, the paper discusses energy economy from production factor input and production factor efficiency. • Designs a semi-parametric partial linear model. • Model indicators include energy, Labor, Capital stock and Human Capital. • Uses first-order and second derivatives to analyze monotonicity and convexity. [ABSTRACT FROM AUTHOR]

Natural gas increasingly has become an alternative low-carbon energy source for governments to modify the energy mix and fulfill the commitments that mitigate greenhouse gas emissions. Predicting natural gas consumption therefore is becoming crucial in such situations. In order to obtain accurate forecasts of natural gas consumption, this study has designed a novel discrete grey model considering nonlinearity and fluctuation, which can overcome the inherent drawbacks of the traditional discrete grey model and its optimized variants. Besides, to further enhance the forecasting performance of this proposed model, the Cultural Algorithm (CA) is employed to optimally determine the emerging parameters of this model. Subsequently, two empirical examples are provided for verifying the efficacy and reliability of the new model by comparing with other existing grey models and statistical models. Lastly, based on the original observations from 2005 to 2017, the novel model is built for predicting the total natural gas demand in Jiangsu province in China. The results indicate that the new model is much superior to other competitors, offering more accurate and reliable performances in the aspect of lower errors in both in-sample and out-of-sample forecasts. Then, based on the above projections, several main reasons for low gas consumption and reasonable suggestions are put forward for Jiangsu's government, which has high potential to boost gas demand in the coming future. • A novel discrete grey model is developed for predicting Jiangsu's natural gas consumption. • The Cultural Algorithm is employed for effectively determining the emerging coefficients. • Comparative studies illustrate the superiority of the new model over other seven benchmark models. • The future natural gas consumptions are predicted by using the novel model from 2018 to 2025. [ABSTRACT FROM AUTHOR]

We employ STIRPAT (stochastic impact of regression on population, affluence, and technology) model to investigate the impact of natural resource depletion on energy use and carbon dioxide emissions for a panel of 56 "Belt & Road Initiative" (BRI) countries over 1990–2014. We utilize the Augmented Mean Group (AMG) panel estimator and Common Correlated Effects Mean Group (CCEMG) estimator for estimating our model. The results indicate that increasing natural resource depletion increases CO 2 emissions and energy use, which shows that a 1% increase in natural resource depletion for our sample of BRI countries will increase CO 2 emissions and energy use by 0.0286% and 0.0117%, respectively. To check the causal relationship among our variables, we employ the VECM Granger causality test. The causality results underscore feedback hypothetical links among CO 2 emissions, energy use, economic growth, natural resource depletion, urbanization, and trade openness in the long run. Our results show a significant cross-sectional dependence and heterogeneity. Image 1 • There exists significant cross-sectional dependence and heterogeneity. • The robust panel data estimator Common Correlated Effect Mean Group (CCEMG) is used. • Natural resource depletion positively and significantly influences CO 2 emissions and energy use. • Different causality links lie among the underlying variables. [ABSTRACT FROM AUTHOR]

This paper analyzes data from 18 Asian countries, spanning from 1980 to 2017 to determine panel long-run causality between income growth, transport energy consumption and environmental quality. The empirical methodology of this paper considers structural breaks and cross-sectional dependence issues. In this way, our study becomes one of the few energy-GDP studies that address cross-sectional dependence in the unit root, cointegration, causality and elasticity estimation. We attempt to fill the gap in energy literature by using robust estimates in the case of Asian countries. After these methodological advances, a bi-directional long-run Granger causality between transport energy consumption, environment and GDP growth is found. The common correlated effects mean group (CMG) is used to obtain long-run elasticities. It is revealed that a 1% increase in transport energy consumption and GDP growth deteriorate environmental quality by about 0.57% and 0.46% respectively in Asian countries. Furthermore, the magnitude of elasticities varies from country to country. The empirical findings add new dimensions in energy-efficient technologies in the transport sector that positively affect economic growth without compromising environmental quality. • Causality between transport energy, economic growth and environmental quality is analyzed. • [8] panel cointegration and [7] causality techniques are applied. • Structural breaks are observed in long-run. • Evidence show negative impact of growth and transport energy on environmental quality. • Bidirectional Granger causality is observed between selected variables in the long run. [ABSTRACT FROM AUTHOR]

This study introduces a method to systematically project retailed gasoline prices in China and provides projection results. Based on oil taxation and pricing mechanisms in China, this study establishes the statistical relationship between the retailed gasoline prices in China and international crude oil prices using error correction models (ECM), time-series models. Detailed comparison among asymmetric ECM, threshold ECM, and threshold asymmetric ECM are provided. By adopting the ECMs, the study projects the retailed gasoline prices from 2019 to 2050 based on the international crude oil prices from the Annual Energy Outlook provided by the U.S. Energy Information Administration. The results show that the asymmetric responsiveness and the threshold effect exist under current Chinese oil pricing policy with a lag of one month or even shorter in gasoline price adjustment. Moreover, the analysis informs one that, in the short-run, the retailed gasoline prices in China are more sensitive to international crude oil price increases; while in the long-run, the retailed gasoline prices are more sensitive to international crude oil price decreases. Compared with the national average gasoline price in China of 6.04 CNY/Liter in 2017, the national average gasoline price in China is projected to be about 7.64 CNY/Liter (2017 dollar) by 2050. • The refined oil products pricing policies in China are investigated. • Three variants of error correction models are created and analyzed in scenarios. • Fluctuation of the retailed gasoline price has asymmetry and threshold effects. • Retailed gasoline price in 2050 is estimated at 7.64 CNY/Liter (2017 price level). [ABSTRACT FROM AUTHOR]

The remarkable prediction performance of electricity consumption has always assumed particular importance for electric power utility planning and economic development. On account of the complexity and uncertainty of the electricity system, this paper establishes a self-adaptive grey fractional weighted model to predict Jiangsu's electricity consumption, which efficiently enhances the prediction quality of electricity consumption. This newly constructed grey model introduces the fractional weighted coefficients to design a novel initial condition. Compared with the old one in the conventional grey models, the newly optimized initial condition has a flexible structure, which has advantages in capturing the dynamic characteristics of the electricity consumption observations. In addition, to further promote the forecasting precision, the adjustable fractional weighted coefficients and corresponding time parameter of the initial condition are estimated by utilizing the Particle Swarm Algorithm (PSO). Furthermore, five competing models are employed to forecast Jiangsu's electricity consumption in China, which certifies the validity of the established model. Experimental results illustrate that the newly designed model has significant advantages over other five competing models. According to the forecasted results, electricity consumption in Jiangsu Province is expected to reach 6778 billion kilowatt-hours in 2020, while the growth rate will fall down by 1.11%. Therefore, several proposals are made for decision-makers. • A self-adaptive fractional weighted grey model is developed for electricity demand forecast. • The novel initial condition having dynamic fractional order weighted coefficients is proposed. • The PSO algorithm is applied to estimate the weighted coefficients and time parameter. • Jiangsu's electricity consumption is predicted from 2018 to 2020. [ABSTRACT FROM AUTHOR]

This paper carries out an econometric examination of nonlinear unit root and nonlinear causality in the natural gas-economic growth nexus in Nigeria. The techniques employed include the Kapetanios-Shin-Shell (KSS) nonlinear unit root test, Kruse nonlinear unit root test, Brock-Dechert-Scheinkman (BDS) nonlinearity test, Nonlinear Ordinary Least Squares (NOLS) model, and, the Hatemi-J asymmetric causality test. The results revealed that natural gas consumption and economic growth series follow nonlinear trend process as indicated by the KSS and Kruse tests, and that the relationship between natural gas consumption and economic growth in Nigeria is nonlinear as confirmed by the BDS and NOLS tests, where an increase in natural gas consumption increases economic growth. The asymmetric causality test revealed evidence of bidirectional causality between the positive impact of natural gas consumption and economic growth, and, unidirectional causality running from economic growth to negative impact of natural gas consumption. For further research, the paper suggests extending the discussions on the trend process of energy consumption to the Organization of Petroleum Exporting Countries (OPEC) or to other regional energy organizations using panel data techniques. • Nonlinear Unit Root and Nonlinear Causality in Natural Gas- Economic Growth Nexus. • Nonlinear Unit Root Tests, Nonlinearity Tests, and Nonlinear Causality Test. • Natural Gas Consumption and Economic Growth Series Follow Nonlinear Trend Process. • Natural gas Consumption and Economic Growth Relationship is Nonlinear. • Natural Gas Consumption bolsters Economic Growth. [ABSTRACT FROM AUTHOR]

This study contributes to the extant literature on the nexus among energy consumption, agriculture, capital and economic growth in Pakistan. We use time series data from 1971 to 2014 and employ the Non-linear Autoregressive Distributed Lag (NARDL) model. The NARDL testing results affirms asymmetric co-integration among the variables. Asymmetric causality is noted between positive shocks in energy consumption and economic growth running from energy consumption to economic growth. A feedback effect is found between agriculture and economic growth for positive shocks. A unidirectional nexus is noted between capital and economic growth for both positive and negative shocks. Similarly, the results of a Granger causality test indicate symmetric causality between energy consumption, agriculture, capital and economic growth. This research suggests that policymakers should revisit their policies regarding agriculture and energy sectors by attracting foreigner investors to build new hydropower dams to both affirm the availability of energy to the industrial sector and control the scarcity of water. • Nonlinear nexus examine among energy, agriculture, capital, and economic growth. • Asymmetric nexus observed for Pakistan over the span of 1971–2014. • We examined asymmetric co-integration among the variables. • Asymmetric growth nexus noted between energy consumption and economic growth. [ABSTRACT FROM AUTHOR]

Reliable energy consumption prediction plays a vital role in formulating government's energy policies. In this study, a novel multiple decomposition-ensemble method based on error compensation is proposed to stably predict energy consumption. Firstly, trend decomposition is used to decompose the energy consumption into trend-subseries and error-subseries. In view of error compensation is a feasible approach for improving the prediction accuracy, the error-subseries that mentioned above are further divided into one low-frequency approximation error-subseries and several high-frequency detailed error-subseries by wavelet transform. Depending on their different dynamic changing characteristics, this study uses a Linear Regression Model to predict the trend-subseries, employs a Triple Exponential Smoothing Model to estimate the low-frequency approximation error-subseries, and uses an Auto Regression model to find the high-frequency detailed error-subseries. Finally, the overall energy consumption is the summation of these subseries predictions. Using the energy consumption data from China in 2007–2016, empirical study is carried out. The proposed multiple decomposition-ensemble method based on error compensation achieves the highest performance, which is compared with other six models (three single models, two traditional decomposition-ensemble models, and combination model). The proposed model is also validated by the U.S. data. Forecasts indicate that the energy demand of China will increase to 4.957343 billion Tons of standard Coal Equivalent in 2021, implying that China should speed up its transition to an energy-efficiency economy. • A novel prediction model based on multiple decomposition-ensemble method is built. • Error compensation is a feasible approach for improving the prediction accuracy. • Multiple decomposition-ensemble method improves the accuracy of error compensation. • Energy consumptions of China and U.S. are forecasted to demonstrate these methods. • The proposed model achieves the highest performance compared with other six models. [ABSTRACT FROM AUTHOR]

A recently developed bootstrap autoregressive-distributed lag test with structural breaks has been used to test the cointegration and causality regarding major economies worldwide, in order to investigate the relationship between electricity and economic growth during the period 1971–2014. Although a long-run cointegrating relationship between the time series of real GDP and electricity consumption per capita cannot be sufficiently detected, the results of this research show the contingency of causality. In India, the past histories of electricity consumption serve as a predictive indicator influencing the present states of economic growth. Positive and negative feedback exists in the United States and China, respectively. The results of the growth hypothesis support the government in India budgeting an enormous amount of money for its infrastructural development in the fiscal year 2017–2018. The government in the United States continues to investigate modern methods of developing the supply of electricity, such as renewable energy technologies. After the economic growth in China, Chinese citizens live in better conditions and garner a higher level of culture. Thus, they may pursue lower-energy-consuming products to strive for a better environment. Based on empirical analysis, certain policy and managerial implications have been addressed by decision-makers at the macroeconomic and microeconomic levels. • Bootstrap ARDL test with structural breaks uses to test the cointegration. • No cointegration between real GDP and electricity consumption in China, the United States, and India. • Positive and negative feedback exists in the United States and China. • Positive unidirectional causality running from electricity consumption to economic growth in India. [ABSTRACT FROM AUTHOR]