The impact of climate change on economic growth in Somalia: using random forest and Bayesian approach

Climate change has emerged as a critical global challenge, significantly impacting economic stability, particularly in developing nations. Somalia, a fragile state with an agriculture-based economy, remains highly vulnerable to climate-induced shocks such as rising temperatures, erratic rainfall patterns, and increasing CO2 emissions. This study examines the relationship between climate change and Somalia’s economic growth by analyzing the effects of temperature, rainfall, and CO2 emissions on GDP. Using Random Forest Regression and Bayesian Regression, this study provides a robust empirical framework for assessing climate-economic interactions. The findings reveal that temperature has a significant negative impact on GDP, while rainfall exhibits a positive effect, reinforcing its role in sustaining Somalia’s agricultural productivity. Additionally, CO2 emissions negatively influence GDP, highlighting the economic consequences of environmental degradation. The study further demonstrates that machine learning models, particularly Random Forest Regression, outperform Bayesian Regression in predictive accuracy, capturing non-linear relationships among climate variables and GDP. The study underscores the importance of climate-sensitive economic policies, recommending that governments invest in climate-resilient agricultural practices, enhance environmental regulations, and promote sustainable energy solutions. These findings hold significant implications for regulators, investors, and international organizations in shaping economic strategies that foster long-term resilience against climate change. This study contributes to the growing body of climate-economic literature by applying advanced statistical techniques to a fragile economy, offering a comprehensive, data-driven perspective on climate-related economic vulnerabilities in Somalia.