Exploring the nexus between climate finance, rural–urban disparities, and rural brain drain in Somalia: the mediating role of climate resilience

Rural brain drain poses a major development challenge in fragile, climate-vulnerable contexts where climate shocks, economic disparities, and weak governance converge. This study investigates how climate-sensitive development financing (CSDF), climate resilience (CR), and rural-urban disparity (RUD) interact to influence rural brain retention (RBR) in Southwest Somalia, a conflict- and drought-prone region. Using data from 118 rural households, the study applies Partial Least Squares Structural Equation Modeling (PLS-SEM) supported by confirmatory factor analysis (CFA) to test a reflective four-factor model. The model examines both direct and mediated effects between CSDF, CR, RUD, and RBR. The results reveal that climate resilience is central to rural brain retention, acting as a key pathway through which climate-sensitive financing strengthens local adaptive capacity. Conversely, rural-urban disparities undermine resilience and exacerbate skilled outmigration. The findings highlight the importance of integrated, context-sensitive strategies that enhance resilience and improve opportunities in rural areas. Integrating climate-sensitive financing into rural development agendas can enhance adaptive capacity and reduce skilled outmigration. Development partners should prioritize concessional funding, youth-led entrepreneurship, and climate-smart infrastructure while addressing service and opportunity gaps between rural and urban areas. This is among the first empirical studies to model rural brain retention-rather than migration-linking it to climate finance, resilience, and structural disparities in a fragile context. The study advances migration and adaptation theory by positioning climate resilience as a mediator between financial investments, disparities, and human capital retention. It operationalizes CSDF as a measurable construct and demonstrates the utility of advanced SEM techniques in data-scarce, high-risk environments.