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This study aims to analyze the relationship between carbon dioxide (CO2) emissions, economic growth, and coal and oil consumption in Brazil, Russia, India, China, Turkey, and South Africa by using the bounds test approach autoregressive distributed lag (ARDL) over the period from 1969 to 2011. According to ARDL analysis results, it is determined short-run and long-run relationships among selected variables. Three long-run estimators: ARDL cointegration, dynamic ordinary least squares, and fully modified ordinary least squares are utilized to test the robustness of the estimation results. The Granger causality and the forecast error variance decomposition approaches indicate the evidence of a causal relation between variables. According to empirical results, there are the evidence of a uni-directional Granger causality from real gross domestic product (GDP) to carbon dioxide (CO2) emissions in analyzed countries, uni-directional causality from coal consumption to carbon dioxide (CO2) emissions, uni-directional causality from oil consumption to carbon dioxide (CO2) emissions in China, India, Turkey, and South Africa and bi-directional causality in Brazil and Russia. Meanwhile, there is bidirectional causality from GDP to coal consumption, from coal consumption to oil consumption for Brazil, Russia, China, Turkey, and South Africa. India's causality results reveal a bidirectional causality from GDP to coal consumption, oil consumption, and carbon dioxide (CO2) emissions, and the results of forecast error variance supported the results of the causality test.

In this study, we have investigated the co-integration and causality relationship between the biomass energy consumption and economic growth in the transition countries by the Panel Auto Regressive Distributed Lag (ARDL) method and Pedroni co-integration analysis for 1990-2011. Analysed countries are gathered under two groups. For Groups 1 and 2, Pedroni panel co-integration test and ARDL bound test results show that biomass energy consumption and economic growth are co-integrated. Fully modified ordinary least squares results suggested that biomass energy consumption has positive effect on the economic growth.

Under the influence of the COVID-19 pandemic and the concurrent oil conflict between Russia and Saudi Arabia, oil prices have exhibited unusual and sudden changes. For this reason, the volatilities of the West Texas Intermediate (WTI), Brent and Dubai crude daily oil price data between 29 May 2006 and 31 March 2020 are analysed. Firstly, the presence of chaotic and nonlinear behaviour in the oil prices during the pandemic and the concurrent conflict is investigated by using the Shanon Entropy and Lyapunov exponent tests. The tests show that the oil prices exhibit chaotic behavior. Additionally, the current paper proposes a new hybrid modelling technique derived from the LSTARGARCH (Logistic Smooth Transition Autoregressive Generalised Autoregressive Conditional Heteroskedasticity) model and LSTM (long-short term memory) method to analyse the volatility of oil prices. In the proposed LSTARGARCHLSTM method, GARCH modelling is applied to the crude oil prices in two regimes, where regime transitions are governed with an LSTAR-type smooth transition in both the conditional mean and the conditional variance. Separating the data into two regimes allows the efficient LSTM forecaster to adapt to and exploit the different statistical characteristics and ARCH and GARCH effects in each of the two regimes and yield better prediction performance over the case of its application to all the data. A comparison of our proposed method with the GARCH and LSTARGARCH methods for crude oil price data reveals that our proposed method achieves improved forecasting performance over the others in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) in the face of the chaotic structure of oil prices.

This paper examines the long-run and the causal relationship among CO emissions, militarization, economic growth, and energy consumption for USA for the period 1960-2013. Using the bound test approach to cointegration, a short-run as well as a long-run relationship among the variables with a positive and a statistically significant relationship between CO emissions and militarization was found. To determine the causal link, MWALD and Rao's F tests were applied. According to Rao's F tests, the evidence of a unidirectional causality running from militarization to CO emissions, from energy consumption to CO emissions, and from militarization to energy consumption all without a feedback was found. Further, the results determined that 26% of the forecast-error variance of CO emissions was explained by the forecast error variance of militarization and 60% by energy consumption.