Comparisons of inverse modelling approaches for predicting energy consumption, economic growth and CO2 emissions: a case study for low-income countries

Abstract

This study examines the causal relationships between CO2 emissions, energy consumption and economic growth in low-income countries for the period 1995-2013. The artificial neural network (ANN) model is applied to predict three variables during 2014-2025. Our empirical results show that there is bidirectional causality between economic growth and CO2 emissions, as well as energy consumption and economic growth, and there is unidirectional causal relationship running from energy consumption to CO2 emissions. In order to reduce emissions and to avoid a negative effect on the economic growth, low-income countries should adopt the dual strategy of increasing investment in energy infrastructure and stepping up energy conservation policies to increase energy efficiency and reduce wastage of energy. Comparison with dynamic simultaneous-equation model shows that the ANN model performs better for predictions.