Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia.

Extensive research has been conducted on poverty in developing countries using conventional regression analysis, which has limited prediction capability. This study aims to address this gap by applying advanced machine learning (ML) methods to predict poverty in Somalia. Utilizing data from the first-ever 2020 Somalia Demographic and Health Survey (SDHS), a cross-sectional study design is considered.

Applying Climate Compatible Development and economic valuation to coastal management: A case study of Kenya's mangrove forests.

Mangrove forests are under global pressure. Habitat destruction and degradation persist despite longstanding recognition of the important ecological functions of mangroves. Hence new approaches are needed to help stakeholders and policy-makers achieve sound management that is informed by the best science.