• Energy Research

Due to industrialization, urbanization, and rapid population increases, the worldwide energy demand is increasing daily. The need for energy meets limitations, and searching for new energy sources is crucial, particularly for European countries. Energy crises occur temporarily due to different circumstances and cause oil price fluctuations. The present study aims to identify circular economy (CE) determinants that conditionally increase/decrease energy use to remain sustainable. It focuses on the linkage between energy consumption and circular economy in the European Union (EU) member states. In the econometric panel model specifications, two alternative endogenous variables are considered, i.e., total energy consumption and energy consumption from renewable sources. The results demonstrate that the selected CE indicators decreased the former and increased the latter. The data covered the period from 2010–2019. The significance of this study relies upon identifying the current level of CE implementation in the EU countries in the context of reducing total energy consumption and increasing the share of energy from renewable sources.

The paper aims to identify the most likely factors that determine the demand for energy consumption from renewable sources (renewable energy consumption—REC) in European countries. Although in Europe, a high environmental awareness is omnipresent, countries differ in scope and share of REC due to historical energetic policies and dependencies, investments into renewable and traditional energetic sectors, R&D development, structural changes required by energetic policy change, and many other factors. The study refers to a set of macroeconomic, institutional, and social factors affecting energetic renewable policy and REC in selected European countries in two points of time: i.e., before and after the Paris Agreement. The Bayesian Average Classical Estimates (BACE) is applied to indicate the most likely factors affecting REC in 2015 and 2018. The comparison of the results reveals that the Gross Domestic Product (GDP) level, nuclear and hydro energy consumption were the determinants significant in both analyzed years. Furthermore, it became clear that in 2015, the REC depended strongly on the energy consumption structure, while in 2018, the foreign direct investment and trade openness played their role in increasing renewable energy consumption. The direction of changes is gradual and positive. It complies with the Sustainable Development Goals (SDGs).

Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to predict energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the predictive ability of the FGM1,1 with traditional ones, like standard GM1,1 and ARIMA1,1,1 models. Moreover, it illustrates the view of BRICS’s nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM1,1 for a specific range of order parameters and the ARIMA1,1,1 model and the usefulness of both approaches for energy consumption efficient forecasting.

This study aims to identify the factors most likely to affect renewable energy consumption (REC) across mostly homogenous country groups worldwide. Classifying countries into a relatively homogenous group is taken from their economic and social development level measured with the Human Development Index. We delimited highly, medium-, and low-developed countries and checked whether the sets of determinants for using renewable energy sources are the same. We constructed a panel dataset as a basis for the panel Bayesian model averaging (panel BMA) as a factor selection method. The most likely factors were found and compared between the groups of countries. Then, the panel fixed-effects models for each country group were estimated. The results allowed us to confirm that CO2 per capita emissions, terms of trade, GDP, foreign direct investment, crude oil price, and energy consumption from alternative sources are the most critical drivers of REC in group I. The most important factors in group II are CO2 per capita, labor force, forest area, and gas and coal consumption. In the third group, REC consumption differs from that of the more advanced groups and strongly depends on foreign direct investment inflow. The results allow the formulation of policy recommendations on a global scale.

Abstract A recent survey of energy-growth literature has highlighted the potential trade-off between bivariate models that suffer from omitted variable bias, and the danger of over-parameterization of multivariate models in the individual country setting (Narayan and Smyth [2] ). This is a serious limitation when the interest is in drawing policy implications for specific countries with short times series of available data. The maximum entropy bootstrap approach was used to re-examine the nature of causal relationship between energy consumption and economic growth for Pakistan where the available time series data was only from 1971 to 2011. Unlike the techniques used in much of the earlier literature, this approach does not rely on asymptotic methods and, therefore, leads to robust inference even in small samples. Moreover, the approach can be applied in the presence of non-stationarity of any type, and structural breaks, without requiring data transformation for to achieving stationarity, and is not sensitive to specification errors such as those in lag length selection. The empirical findings, based on both the bivariate as well as the multivariate frameworks, supported the conservation hypothesis, implying the existence of a unidirectional causality from economic growth to energy consumption.

The issue of examining the stationarity of per capita energy consumption (PCEC) series is central in energy literature. Most of the existing studies examine this issue by employing unit root tests based on the time domain only providing a partial picture of the problem at hand due to not including the information related to the frequency domain. In contrast to existing studies, the present study uses a recently projected wavelet-based unit root testing scheme-Fourier wavelet KSS (FWKSS) introduced by Aydin and Pata. Energy 207:118245 (2020) which has several advantages over the existing wavelet unit root tests such as the ones proposed by Fan and Gençay (2010). Specifically, the FWKSS takes care of any possible smooth structural break in the data series under examination. This study re-examines the stationarity of PCEC across the globe covering for the first time a total of 133 countries and including all income groups constituting 49 high-, 76 middle- and 8 low-income countries. The selection of countries is made based on the availability of at least 25 years of annual time series data. The empirical results based on latest available data reveal that among 133 chosen countries, 67 countries are found to have non-stationary PCEC including 20 high-, 43 middle- and 4 low-income countries while the remaining 66 countries are found to have stationary PCEC including 29 high-, 33 middle- and 4 low-income countries. PCEC is found to be stationary in 79 of them including 35 high-, 40 middle- and 4 low-income countries. Some relevant policy implications are discussed as well.

Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to forecast energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the forecasting ability of the FGM(1,1) with traditional ones, like standard GM(1,1) and ARIMA(1,1,1) models. Also, it illustrates the view of BRICS's nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM(1,1) for a specific range of order parameters and the ARIMA(1,1,1) model and the usefulness of both approaches for energy consumption efficient forecasting.