Driving forces of CO2 Emissions based on impulse response function and variance decomposition: A case of the main African countries

Abstract

Background: The need to understand the causes of CO2 emissions has prompted the formulation of strategies to prevent global warming. Therefore, the purpose of the study was to determine the input variable that is the most influential in contributing to CO2 emissions and at the same time to forecast the effect of a shock in macroeconomic variables on CO2 emissions for 6 leading African countries over the period of 1970 to 2019. Methods: In this study, the statistical methods of impulse response function and variance decomposition techniques of analysis were used. Results: A one-standard-deviation rise in economic growth leads to an increase in CO2 emissions. A shifts in the square of economic growth increased CO2 emissions, the shock was smaller than that of economic growth. This confirms the theory of environmental Kuznets curve (EKC) in Africa. A shocks to FDI had a positive influence on CO2 emissions. A one standard deviation shock in financial development had an instantaneous positive impact on CO2 emissions. FDI had a greater effect than other factors in explaining CO2 emissions over the short and medium term. In the long run, economic growth contributes the most to CO2 emissions among the explanatory variables. Conclusion: The findings of the study can be used as a reference for international organizations and environmental policymakers in forecasting climate change and assisting in policy decision-making. Africa must boost economic growth through industrial, agricultural, and energy usage patterns and integrate innovation, research, and technology advances into their developmental agenda to fulfil sustainable development goals while lowering CO2 emissions and their consequences.

Exploring the Impact of the Greening of the Agri-Food Sector on Economic Growth: An Empirical Approach in the BVAR Framework for the EU

“…similar vein, variance decomposition or in other words 'forecast error variance decom sition is a specific tool that may adequately and precisely interpret the relations betw variables described by the model estimated. This methodology will amplify impuls sponse analysis since it further quantifies the contribution rates of all variables to the pact on the dependent variable [18,19]. The model's evaluation was based on forecast accuracy performance for the cla VAR and BVAR specifications, respectively, with the assistance of the following ind the root mean square error (RMSE) and the mean absolute error (MAE) [17].…”

“…This means that the greening of the agri-food sector cannot provide steadily increasing growth and therefore that more steps need to be taken for ecoefficiency to become an achievable objective in EU in Figure 3. The figures constructed were based on the Bayesian methodology of Gibbs sampling while 1000 iterations were implemented to acquire the results [18]. GDP is increasing with a declining trend for a time period of twenty years while emissions are increasing with a declining trend in the first decade, though then the slope of the curve begins to change and increases.…”

“…In a similar vein, variance decomposition or in other words 'forecast error variance decomposition is a specific tool that may adequately and precisely interpret the relations between variables described by the model estimated. This methodology will amplify impulse response analysis since it further quantifies the contribution rates of all variables to the impact on the dependent variable [18,19].…”