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 $FGM\left(1,1\right)$ with traditional ones, like standard $GM\left(1,1\right)$ and $ARIMA\left(1,1,1\right)$ 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 $FGM\left(1,1\right)$ for a specific range of order parameters and the $ARIMA\left(1,1,1\right)$ 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.

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 research aims to examine the vulnerability and resilience of road transport enterprises in Poland to a crisis caused by the COVID-19 pandemic. In theory, we refer to the Schumpeterian perspective of creative destruction. In the empirical analysis, survey data on 500 transport companies randomly selected from the database were used. We estimated partial proportional odds models to show the factors responsible for the enterprises' vulnerability and resilience to unforeseen shock. The perspective refers to the total sample size and the division into two subgroups: micro and small and medium enterprises. To justify the results, we calculated a set of statistical indicators and tests. These models enable separating enterprises according to the vulnerability level. Transport enterprises occurred significantly vulnerable to the COVID-19 crisis, particularly the demand shock. The only factor that influenced resilience was the decrease in fuel prices, which allowed a cost reduction. The crisis showed that government aid was helpful in the short run, particularly for micro and small enterprises. The medium-sized enterprises were more resilient than micro and small ones. We formulated several recommendations to help transport enterprises to adjust in the medium term.