The Human Development Index, which accounts for both net foreign income and the total value of goods and services generated domestically, illustrates how income becomes less significant as Gross National Income (GNI) rises by using the logarithm of income. South Africa ranks 109th out of 189 countries in the Human Development Index (HDI) within the Brazil, Russia, India, China and South Africa (BRICS) economic bloc, raising long-term sustainability concerns. The study explores the relationship between economic, demography, policy indicators and human development in South Africa. South Africa’s unique status as a developing country within the BRICS economic group, alongside its lengthy history of racial discrimination, calls for a sophisticated approach to understanding Human Development. Existing research considered economic, demography, policy indicators independently; the gap of understanding their interconnection and long-term effects in the South African contexts exists. The study addresses the gap by using Autoregressive-Distributed Lag (ARDL) approach to investigate the short-term and the long-term relationship between economic, demography, policy indicators and human development in South Africa. By discovering these links, the study hopes to provide useful insights for policymakers seeking to promote sustainable human development in South Africa. The findings indicate that growth in GDP is a key factor in the HDI since it shows that there are more financial resources available for human development. By discovering these links, the study hopes to provide useful insights for policymakers seeking to promote sustainable human development in South Africa.

The failure to achieve sustainable development in South Africa is due to the inability to deliver quality and adequate health services that would lead to the achievement of sustainable human security. As we live in an era of digital technology, Machine Learning (ML) has not yet permeated the healthcare sector in South Africa. Its effects on promoting quality health services for sustainable human security have not attracted much academic attention in South Africa and across the African continent. Hospitals still face numerous challenges that have hindered achieving adequate health services. For this reason, the healthcare sector in South Africa continues to suffer from numerous challenges, including inadequate finances, poor governance, long waiting times, shortages of medical staff, and poor medical record keeping. These challenges have affected health services provision and thus pose threats to the achievement of sustainable security. The paper found that ML technology enables adequate health services that alleviate disease burden and thus lead to sustainable human security. It speeds up medical treatment, enabling medical workers to deliver health services accurately and reducing the financial cost of medical treatments. ML assists in the prevention of pandemic outbreaks and as well as monitoring their potential epidemic outbreaks. It protects and keeps medical records and makes them readily available when patients visit any hospital. The paper used a qualitative research design that used an exploratory approach to collect and analyse data.

The success of a city’s entrepreneurial ecosystem (EE) depends on a combination of interconnected factors that foster innovation, collaboration and growth. Urban planning, infrastructure management and an entrepreneurial culture are essential factors for the success of cities’ Entrepreneurial Ecosystems (EEs). Land use and infrastructure management create opportunities for growth and industry expansion. EEs are local, social, business, institutional and cultural stakeholders that encourage and enhance the formation and growth of new businesses, which are supported by enabling infrastructure. The objective of this study was to investigate how urban planning affects EEs in the metropolitan region, Nelson Mandela Bay (NMB), South Africa. NMB is known for poor land use management, which hinders the management of diverse spatial needs, as well as bureaucratic processes for land rezoning for commercial activity. In order to better understand the fundamental issues, a qualitative case study was conducted. The data were collected from fifteen economic development role players from NMB using semi-structured interviews combined with secondary data from the NMB Integrated Development Plan (IDP). The data analysis included thematic analysis using Atlas.ti and Claude 2.0. In order to validate the findings, qualitative data were cross-referenced with secondary sources from the NMB IDP. The key themes that emerged effect the NMB metropole’s management of infrastructure to support the EE. These include, Land use issues, Poor oversight by metropolitan leadership, Lack of infrastructure maintenance and pushing out potential investment and economic growth. The results highlight that the NMB metropole fails to prioritise land use and infrastructure challenges, impacting the NMB metropolitan area’s economic development and worsening inequality among different groups. The findings from this study add to the current research on cities’ EEs and The Right to the City Theory, which supports the UN Sustainable Development Goals 8, 9 and 11.

One of the most frequently debated subjects in international forums is economic growth, which is regarded as a global priority. Consequently, researchers have turned their attention from conventional economic growth at a single average coefficient to divisible economic growth at levels of its value. Although the existing literature has discussed several determinants of economic growth, our article contributes to examining the sources of economic growth in African countries during the generations of reforms from 1990 to 2019 and in the context of economic vulnerability. The variables used in the analysis are gross domestic product, trade openness, financial development, and economic vulnerability. The study uses a quantile regression econometric model to examine these variables at different stages of reform. Quantile regression (QR) estimates for quantiles 0.05 to 0.95 showed mixed results: financial development is favorable to African economic growth at all quantile levels. However, economic vulnerability is a major impediment to economic growth at all quantile levels. In addition, it was found that a high degree of trade openness has a detrimental effect on African economic growth from quantile 0.5 of the dependent variable. Finally, another important result proves that financial development is a remedy for decision-makers against economic vulnerability.

Cassava’s adaptability to different agroecological conditions, high yield, as well as its ability to thrive under harsh climatic conditions, makes it an essential food security crop. In South Africa, the cassava value chain is currently uncoordinated and underdeveloped, with a couple of smallholder farmers growing the crop for household consumption and as a source of income. Other farmers regard it as a secondary crop and hardly any producers grow it for industrial purposes. Hence, this study sought to analyze the determinants of household participation in the cassava value chain in South Africa. The study employed the multivariate probit model to analyze the determinants of household participation in the cassava value chain in South Africa, using a primary dataset collected through a simple sample method from smallholder farmers in KwaZulu-Natal, Mpumalanga, and Limpopo provinces. Results show that livestock ownership has a positive and significant effect on the likelihood of farmers participating in the value chain by growing cassava for household food consumption. Also, findings reveal that hiring labour in cassava production and an increase in the yield during the previous season increases the probability of farmers’ interest in selling cassava tubers along the value chain. Hence, the positive and statistically significant influence of hiring labour during cassava production in driving the farmers’ interest in selling cassava tubers and cuttings implies that the development of the cassava value chain presents great opportunities for creating jobs (employment) in the country. Also, policy interventions that ensure land tenure security and empower farmers to increase their cassava yields are bound to encourage further participation in the value chain with an interest in selling fresh tubers, among other derived products to generate income. Lastly, programmes that empower and encourage youth participation in the cassava value chain can increase the number of farmers interested in selling cassava products.

The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.