Discovering the effectiveness of climate finance for Somalia’s climate initiatives: a dual-modeling approach with multiple regression and support vector machine

Abstract

Introduction: This research investigates into the complex dynamics of climate finance in Somalia, a vulnerable region facing the dire consequences of climate change. The study aims to assess how financial inputs for climate-related projects align with the actual needs and identify critical factors that influence funding effectiveness. Methods: A dual-methodological approach was employed, integrating both multiple regression analysis and Support Vector Machine (SVM) techniques. This mixed-method analysis facilitates a robust examination of climate finance data to dissect the relationships and impacts of various determinants on funding effectiveness. Results: The results indicate that adaptation finance, robust governance, and the scale of financial interventions significantly enhance the effectiveness of climate finance flows. However, mitigation finance and aspects related to gender equality displayed less significant impacts. Notably, the study identifies a pervasive underfinancing of climate projects in Somalia, illustrating a significant gap between the needed and actual funds disbursed. Discussion: The findings underscore the need for enhanced governance frameworks and targeted large-scale financial interventions to optimize the allocation and impact of climate finance in vulnerable regions like Somalia. By quantifying the influence of adaptation finance and governance, this study contributes new insights to the literature on climate finance effectiveness and suggests practical strategies for policymakers and practitioners to improve climate resilience initiatives.