http://hdl.handle.net/123456789/1050 2026-04-08T02:41:50Z 2026-04-08T02:41:50Z Obeng, Samuel http://hdl.handle.net/123456789/12222 2025-06-09T12:04:34Z 2025-02-01T00:00:00Z

Public Debt, Green Commitment and Environmental Quality in Sub-Saharan Africa Obeng, Samuel Knowing the extent to which the fiscal state of a nation exerts pressure on the environment has become crucial in Sub-Saharan Africa. This study examines the effect of public debt on environmental quality, paying particular attention to the threshold at which the effect becomes nonlinear and the mediating role of natural resource extraction. It also examines how the effect of public debt is diluted when nations commit to regional green agreements. Employing panel data from 2007 to 2020 in 37 Sub-Saharan African countries, the system generalised method of moments (GMM) and dynamic panel threshold estimation techniques are utilised to achieve the study's objectives. The results reveal that public debt degrades environmental quality and that resource extraction mediates this relationship in the sub-region. However, public debt exhibits a non-linear relationship with a threshold value of 51.5%, below which is not harmful to the environment. Additionally, this study demonstrates that green commitment reduces the negative effects of debt on the environment. The study recommends that sub-Saharan African countries should manage public debt carefully to avoid exceeding the threshold value of 51.5 (% of GDP), where debt begins to be detrimental to the environment. Moreover, countries are encouraged to ratify regional environmental agreements such as the Africa Convention on the Conservation of Nature and Natural Resources. xiv, 104p:, ill.

2025-02-01T00:00:00Z Amofa, Sarpong George http://hdl.handle.net/123456789/12185 2025-06-05T11:47:21Z 2024-07-01T00:00:00Z

Building a Predictive Model on Maternal Mortality Using Machine Learning: Comparison of Different Modelling Techniques Amofa, Sarpong George Maternal mortality remains a critical public health issue in Ghana, influenced by complex socio-economic, health-related, and contextual factors. This study aimed to identify the most effective predictive modeling technique for maternal mortality and elucidate key risk factors. We developed and evaluated three predictive models: Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) using data from the Ghana Maternal Health Survey (GMHS) 2017, which included 1,240 deceased women with detailed demographic, socio-economic, and health-related information. XGBoost emerged as the most robust and reliable model, achieving the highest average KFold score (0.8774), test accuracy (0.90), F1 score (0.47), and Jaccard score (0.82), indicating superior predictive performance. Significant predictors identified included place of death, marital status, blood pressure, traditional medication use, fever, season of death, and age at death. These findings highlight the need for targeted interventions to improve healthcare access, integrate traditional medicine into formal healthcare systems, and address socio-cultural barriers. Policy efforts should focus on enhancing healthcare access in rural areas, promoting gender equality in health decision-making, and targeting high-risk groups, particularly younger women and those with hypertension. Future research should include additional variables, conduct longitudinal studies, explore advanced machine learning techniques, and evaluate community-based interventions and policy impacts to further improve maternal health outcomes in Ghana. xv, 115p:, ill.

2024-07-01T00:00:00Z Gyan, Samuel Ansu http://hdl.handle.net/123456789/12088 2025-06-02T12:56:57Z 2024-07-01T00:00:00Z

The Preference of Cash for Transactions Among Mobile Money Users in Kenya: A Machine Learning Analysis Gyan, Samuel Ansu Digital finance platforms like Mobile Money have been a game changer in many African economies, gaining widespread uptake and improving the narrative of financial inclusion. Research shows that in Kenya, where the Mobile Money market is the largest in Africa, hundreds of thousands of lives have been improved through access to the service. At the same time, there exists evidence suggesting a rather broad preference of cash for retail transactions. Using open data from a field survey conducted in 2019, I empirically test the level of cash preference among Mobile Money users in Kenya and predict same with 11 user experience variables on 818 users in a simple classification model. Results indicate that cash preference is invariably high even among Mobile Money users, and that it is associated with negative user experiences. I recommend that the Kenyan government and service providers work together to improve infrastructural security, among other factors, to mitigate the rate of negative user sentiments that could lead to a decline in the usage of the service. x 92p:, ill

2024-07-01T00:00:00Z Mensah, Benedict Amoako http://hdl.handle.net/123456789/12012 2025-05-28T10:31:34Z 2024-07-01T00:00:00Z

Predictive Model on Digital Financial Inclusion and Sustainable Development in Sub-Saharan Africa Mensah, Benedict Amoako This study examined the nexus between digital financial inclusion (DFI) and sustainable development in Sub-Saharan Africa (SSA). The research aimed at analysing trends in DFI and exploring its impact on economic, social, environmental, and overall sustainability across 28 SSA economies with complete data from 2009 to 2023. Using three key indicators the study assessed DFI's role in promoting sustainable development. The two-step System Generalized Methods of Moment (GMM) approach is employed to address endogeneity and improve the accuracy of panel data estimations. Key findings reveal significant disparities in DFI across the region, with notable progress in some countries but ongoing challenges in ensuring equitable access, especially in rural and underserved areas. Moreover, the study provides strong evidence that higher levels of DFI positively influence all the dimensions of sustainable development. These findings emphasize the role of digital financial services in promoting sustainable development by enhancing economic participation, reducing poverty, and fostering social inclusion and environmental sustainability. The study recommends that governments in SSA integrate DFI into economic growth strategies, promote public-private partnerships to expand digital infrastructure, and enhance financial literacy and inclusivity, especially among marginalized populations. Future research could expand this analysis to other emerging economies and examine country-specific dynamics to inform more tailored policy interventions. xii 103p:, ill

2024-07-01T00:00:00Z