The Causal Relation between Energy Consumption, Carbon Dioxide Emissions, and Macroeconomic Variables in Somalia

Abstract

This study investigated the relationship between energy consumption, carbon dioxide emissions, and macroeconomic variables in Somalia with data spanning from 1990 to 2019 using ARDL model. The study found a negative long-run relationship between carbon dioxide emissions and energy consumption in Somalia, suggesting that improving access to clean energy can reduce the gradual rise of carbon dioxide emissions. The study also found that rising industrial value-added had a significant positive impact on energy consumption. Furthermore, findings from Cholesky's variance decomposition showed that 13.13% of future fluctuations in energy consumption are due to shocks in carbon dioxide emission, 33.63% of future fluctuations in carbon dioxide emissions are due to shocks in energy consumption, 40.63% of future fluctuations in industrialization are due to shocks in energy consumption and 41.23% of future fluctuations in population are due to shocks in energy consumption. There was evidence of a bidirectional causality between: energy consumption and population. The study suggests adding renewable energy technologies to the energy portfolio. This would help reduce reliance on unstable energy sources and reduce the chance that changes in commodity prices will interrupt the energy supply, which eventually would help reduce the effects of climate change.

On the Determinants of Unemployment in Somalia: What is the Role of External Debt?

“…According to the cointegration theory [25] non-stationary variables can be linearly combined to generate stationary variables, indicating that these variables have a cointegration link. These standard approaches, however, have been criticized for being highly unreliable in small samples, inconsistent with different order integrated variables, resulting in significantly misleading results, and biased against rejecting the null hypothesis (no-co-integration), necessitating an adjustment for critical values [26][27][28]. As a consequence, autoregressive distributed lag (ARDL) bounds testing was utilized to increase test power [29][30][31][32][33].…”