African green growth initiatives have a positive impact on agricultural productivity but not on fisheries

Abstract

Sustainable agri-food system transformation in Africa is critical for addressing the persistent challenges of hunger and malnutrition while promoting sustainability. Here, we investigate how green growth initiatives intersect with the agri-food system transformation in the African Continental Free Trade Area context. We combine World Development Indicators, International Financial Statistics, and OECD data, and we use the Prais-Winsten regression model to analyze the relationship between green growth initiatives and Sustainable Development Goal 2- Zero Hunger indicators across 46 African countries. We show that green growth initiatives positively impact agricultural productivity and cereal yield but negatively affect total fisheries production. Low-income countries, including Malawi, Uganda, Mozambique, and Sierra Leone experience mixed effects, while upper-middle-income countries such as South Africa, Botswana, Namibia, and Mauritius face more challenges. Economic growth, renewable energy, and financial development significantly influence agri-food system sustainability, but challenges like carbon dioxide emissions and deforestation hinder progress. Policymakers should prioritize pro-poor green technology investments, enhance finance accessibility, and implement environmental mitigation measures while fostering trade openness and regional cooperation.

Introduction

Sustainable agri-food system transformation has received considerable attention in recent years. It has become a subject of high policy relevance due to its potential to rescue and accelerate progress towards Sustainable Development Goals (SDG)¹. This is amidst multiple concerns ranging from diet quality and nutrition, health outcomes, social, economic, and environmental impacts of food supply chains, and the food environments^2,3,4. Food systems and green growth are closely linked, with interactions that span the entire food supply chain and the broader food environment. Agri-food systems are increasingly shaped by biophysical and environmental drivers such as climate change, natural assets, and ecosystem services. Conversely, unsustainable agri-food systems lead to the depletion of natural assets, environmental degradation, climate change, and impact the ecosystem services crucial for our well-being. Therefore, green growth and agri-food system transformation create opportunities for Africa to build sustainable, resilient, and equitable food systems, and exert a positive impact on the socio-economic and environmental facets of the global sustainable development goals (SDG2)¹. The High-Level Panel of Experts posits that “sustainable food systems ensure food and nutrition security for all in such a way that the economic, social, and environmental bases of future generations are not compromised”. Likewise, the Organization for Economic Cooperation and Development OECD define green growth as “fostering economic growth and development while ensuring that natural assets continue to provide the resources and environmental services on which our well-being relies”³.

Africa’s food system remains fragile and highly vulnerable to both exogenous and endogenous shocks, including environmental and socio-economic upheavals. Hunger and malnutrition are persistent challenges, and the continent is not on track to meet the food security and nutrition targets of SDG 2, despite numerous efforts to tackle the triple burden of malnutrition (undernutrition, micronutrient deficiencies, and overweight/obesity). In 2019, ~256 million Africans, or about 20% of the population, were undernourished, with sub-Saharan Africa accounting for 239 million of these individuals and Northern Africa for 17 million⁵. In 2020, undernourished Africans were ~281.6 million, an increase of 25.6 million since 2019 and 89.1 million since 2014, with significant regional variations⁶. Eastern Africa accounted for ~44.4% of the continent’s undernourished population, Western Africa 26.7%, Central Africa 20.3%, Northern Africa 6.2%, and 2.4% in Southern Africa⁶. Additionally, 452 million Africans faced moderate food insecurity, while severe food insecurity experienced 346.4 million. It is worth noting that conflict, displacement, climatic hazards, and economic shocks are the primary determinants of food insecurity in Africa^6,7.

Green growth initiatives are essential for the transformation of African agri-food systems to eradicate hunger and malnutrition, which is a central pillar of the AU’s Agenda 2063. In this light, questions have arisen about how African food systems can be inclusive, resilient, and sustainable amidst climatic and economic shocks. While it is widely acknowledged that sustainable transformation of agri-food systems can enable African countries to attain food security, high-quality diets, and better nutrition, the definition and approach to food system sustainability remain contentious and heavily debated^8,9. The African Continental Free Trade Area (AfCFTA) holds the potential to facilitate intra-Africa trade in agri-food products by developing agricultural value chains, stabilizing food prices, and enhancing food security¹⁰. This platform is crucial for transforming agri-food systems, especially considering the improved intra-Africa agricultural trade noted over the past two decades, facilitated by proximity to markets and regional trade agreements within economic communities^10,11.

African countries are prioritizing sustainable agriculture in their development goals to achieve a more environmentally friendly and sustainable future. For instance, Rwanda’s Green Growth and Climate Resilience Strategy integrates sustainable agriculture, demonstrating how economic development and environmental conservation can coexist¹². Similarly, Kenya’s Vision 2030 and the Greening Kenya Initiative emphasize sustainable agriculture, promoting climate-smart farming practices and resource efficiency to align growth with ecological responsibility¹³. Uganda also focuses on a resilient and sustainable agricultural future through climate-smart agriculture, agro-industrialization, and productivity enhancement, as highlighted in its National Development Plan (NDP)¹⁴. Meanwhile, Ethiopia’s Green Economy Strategy aims to balance environmental sustainability with agricultural development by emphasizing eco-friendly techniques and organic farming methods, reflecting the country’s commitment to environmentally friendly agriculture¹⁵.

In Nigeria, the National Agricultural Technology and Innovation Policy (NATIP) addresses sustainable agriculture challenges by prioritizing food security, resource efficiency, and technology adoption, reflecting a commitment to environmental sustainability and economic growth¹⁶. Similarly, Mali’s Agricultural Intensification Program employs sustainable farming methods that improve food security and climate resilience, using climate-smart practices and efficient water use¹⁷. Togo is moving towards agricultural sustainability through organic farming and agroecology to enhance rural farmers’ welfare and boost the health of soil and biodiversity^18,19. Senegal is pioneering sustainable urban agriculture to integrate food production into urban areas, increasing local food production and reducing the ecological impact of urban living²⁰. Likewise, Malawi’s National Resilience Strategy underlines resilience in climate-smart agriculture (climate adaptation) while ensuring food security and local economic empowerment through sustainable farming practices, effective resource management, and community involvement²¹. Mozambique’s Green Economy Roadmap outlines a shift to a sustainable economic model, including renewable energy, eco-friendly tourism, and sustainable forestry, with a focus on social inclusion to reduce poverty and improve living standards²². Sierra Leone’s SDG Localization Strategy integrates national development and green growth concepts, aligning with global SDGs to promote environmental sustainability, poverty alleviation, and social inclusion, ensuring local contexts and communities are considered in sustainable development projects²³.

Zambia’s 7th National Development Plan (7NDP) emphasizes environmental management through climate adaptation, sustainable resource use, and biodiversity preservation, with a focus on inclusivity to ensure marginalized populations benefit from conservation efforts²⁴. Botswana’s NDP reflects a commitment to sustainable agriculture by incorporating sustainable farming practices, water management, and biodiversity conservation²⁵. In Mauritius, the Smart Cities for Sustainable Urban Development project sets a standard by integrating energy efficiency, waste management, and green infrastructure, showcasing a vision of urban development that aligns with environmental sustainability²⁶. Similarly, most Southern African countries, including South Africa, Namibia, Zimbabwe, and Angola, have NDPs outlining strategies for inclusive and sustainable agricultural development²⁷. Similarly, Cameroon’s Agricultural Sector Development Strategy focuses on agroecology, sustainable land use, balancing environmental preservation with agricultural transformation, and rural development²⁸. The Democratic Republic of the Congo’s National Agriculture Investment Plan promotes climate-smart agriculture, agroforestry, and sustainable land use to harmonize agricultural expansion with environmental care²⁹. In North Africa, the Green Morocco Plan envisions a productive and climate-resilient agricultural sector through sustainable agriculture, water management, and rural development^30,31. Similarly, Algeria’s National Agricultural and Rural Renewal Program aims to enhance rural communities and preserve natural landscapes by emphasizing modernization, agroecological practices, and water efficiency³².

There is insufficient focus on inclusivity and equity within green growth initiatives and their implications for smallholder farmers and marginalized communities³³. Although various national strategies emphasize sustainable agriculture, they often overlook how these initiatives can disproportionately affect different segments of the population³⁴. Current literature does not adequately address whether these policies equitably distribute benefits or exacerbate existing inequalities. This study critically analyzes these aspects, ensuring that discussions around green growth consider social equity and access for disadvantaged groups. We identify key policy recommendations to align green growth initiatives with trade agreements, focusing on inclusivity and equity for smallholder farmers. This involves analyzing existing policies and suggesting modifications that promote sustainable practices while ensuring equitable access to resources. Moreover, the potential role of digital tools and innovations in transforming agri-food systems towards sustainability has been highlighted in some studies³⁵. However, there is a lack of focused evidence on how these tools can specifically support green growth initiatives within the AfCFTA framework. We investigate how technology adoption can enhance sustainable agricultural practices and facilitate cross-border trade, thereby contributing to both economic growth and environmental sustainability. By examining the role of digital tools in improving agricultural productivity and sustainability, we highlight innovative solutions that can support both green growth objectives and economic integration efforts under the AfCFTA.

Results and discussion

Preliminary analyses of the variables

Table 1 presents the descriptive statistics and pairwise correlation (using untransformed values) to investigate the normality and variability of the variables. The results show that the mean of SDG indicators proxied by SDG index, agriculture, forestry, and fishing, value added per worker, total fisheries production (TFP) (metric tons), aquaculture production (AQP) (metric tons), cereal yield (kg per hectare) are −0.05, US$2207.1, 180,194, 23,508, and 1448 kg, respectively. Moreover, Algeria boasted the highest agriculture, forestry, and fishing value added per worker of US$20,599 in 2022, while Mauritius was the highest producer of cereals with the value of 10,138.54 kg per hectare in 2022. In addition, the analysis demonstrates average green growth index obtained from environmental and resource productivity is −10.8%.

Table 1 Descriptive characteristics of the variables

Also, the means of renewable energy, non-renewable energy, annual population growth, financial development index, and trade openness is 63.5%, 26.3%, 2.4%, 13.9% and 63.4%, respectively. However, Burundi stands out as the most affected area in Africa regarding deforestation rate, with net forest depletion (% of GNI) at 41.35% in 2003; while Libya is most affected concerning CO_2(g) emissions with 9.986 metric tons per capita in 2013. A look at other model series reveals that they are consistent since their mean values lie entirely between the minimum and maximum values. Moreover, the findings from the lower section of the correlation matrix in Table 1 demonstrate that the variables align with anticipated values. For instance, none of the pairs among the independent variables has a correlation value higher than 65%. Further examination of the correlation analysis shows that the highest associations were between the SDG2 index and its indicators (73.9%, 61.6%, 70.7% and 65%, respectively), which does not pose any problem since the models are estimated separately. Overall, it is not expected that the issue of multicollinearity will arise in this study’s models.

A further set of pre-estimation checks before engaging the econometric analyzes includes cross-sectional dependence (CSD), autocorrelation, stationarity, and cointegration tests. Failure to adjust for CSD may lead to skewed estimates³⁶, due to continuous dependence among economies. Table 2 presents the three CSD tests (Pesaran, Friedman, and Frees) showing sufficient evidence to reject the null hypothesis of cross-sectional independence among the countries. The analysis presents CSD among countries since the null hypothesis of cross-sectional independence is rejected at a 1% (***p < 0.01) statistical level of significance in the three tests. This implies that any shocks to initiatives focused on promoting green growth and the transformation of agricultural and food systems towards sustainability in one African country may be easily transmitted to others.

Table 2 CSD, panel unit root, autocorrelation and cointegration tests

In addition, the result of the Wooldridge test for autocorrelation in the panel of Africa in Table 2 presents strong evidence to reject the null hypothesis of no first-order autocorrelation at a 1% significance level. Thus, we conclude that there is no contemporaneous correlation of errors across panels. Furthermore, evidence from the cointegration test shows that the variables are cointegrated across some panels, thus rejecting the null hypothesis of no cointegration at the 5% level³⁷. In summary, the evidence of panel cointegration and no serial correlation requires an appropriate econometric technique to deal with it, and this necessitates the adoption of the Prais–Winsten regression model with panel-corrected standard errors (PCSE), which assumes that disturbances are heteroscedastic and contemporaneously serially correlated^38,39.

To address econometric problems associated with time-series and cross-sectional data, including CSD, serial correlation in the data, as well as cointegration among the variables, we estimate Eq. (2) by employing the Prais–Winsten regression model with PCSE, which also controls for heteroscedasticity and serial correlation for the panels of African and low-income countries, lower-middle income countries, and upper-middle income countries following the 2023 World Bank income classification. To confirm the consistency of the results, we deploy Driscoll–Kraay Standard Errors techniques for robustness checks⁴⁰.

Effect of green growth initiatives on sustainable agri-food systems transformation

To examine the effect of green growth initiatives on sustainable agri-food systems transformation, proxied by SDG2 indicators in Africa, we estimated 5 models. Model 1 was estimated with the SDG2 index as the dependent variable and the green growth index from OECD’s environmental and resource productivity as the main independent variable, as well as other control variables such as economic growth, carbon emission intensity, deforestation rate, trade openness, renewable and non-renewable energy, financial development index, and population growth. We also modeled different available indicators of SDG2 as separate dependent variables. The results of the Prais–Winsten regression model with PCSE, the baseline model, in Table 3 show that the green growth index (growth) positively affects SDG2 regarding Zero hunger. Further findings in columns 2 to 5 on specific indicators of SDG2 reveal that the green growth initiatives increase Agriculture, forestry, and fishing value added per worker (LAVA), Aquaculture production (LAQP), and Cereal yield (LCEY). However, green growth initiatives diminish total fisheries production (LTFP) in Africa. The economic implication of this is that green growth initiatives can affect sustainable development by leading to a more resilient, innovative, and competitive economy fuelled by sustained agricultural sufficiency, while also addressing environmental challenges. This finding is in line with some extant studies^41,42,43, which conclude that green growth initiatives are necessary to overcome potential challenges and ensure a smooth transition to a more sustainable future.

Table 3 Panel estimations for African countries

Moreover, economic growth, renewable energy, financial development, and population growth have significant positive effects on the SDG2 index in Africa. However, carbon emission intensity, deforestation rate, trade openness, and non-renewable energy have significant adverse effects on the sustainable transformation of agri-food systems in Africa. The findings support the conclusion that transition to renewable energy sources (for example, solar and wind) is a cornerstone of sustainability⁴⁴. Increased use of renewables reduces fossil fuel dependency, enhances climate change mitigation, and land degradation associated with resource extraction. However, reliance on non-renewable energy sources, like coal and oil, contributes to climate change and environmental degradation and thereby hinders sustainable agri-food systems transformation in Africa. The results of the robustness and consistency analysis from Driscoll–Kraay Standard Errors in Table 3 reveal similar findings.

Considering the African panels based on income classification, the results presented in Tables 4–6 show the findings for the panel of low-income countries, lower-middle-income countries, and upper-middle-income African countries, respectively. For instance, the findings in Table 4 show that green growth initiatives promote sustainable agri-food systems transformation in the panel of low-income countries in Africa by improving total fishery production, AQP, and total annual cereal yield. However, the green growth initiatives and environmental and resource productivity negatively affect agriculture, fishing, and forestry value added per worker (LAVA) in low-income countries. In addition, Table 5 shows that green growth initiatives have an adverse impact on sustainable agri-food systems transformation through their negative impact on the SDG2 index and total fishery production in the panel of lower-middle-income countries in Africa. More importantly, Table 6 reveals that green growth initiatives have a conspicuous negative impact on sustainable agri-food systems transformation, with adverse impact on the SDG2 index and agriculture, forestry, fishing, total fishery production, AQP, and total annual cereal yield in the panel of upper-middle income countries in Africa.

Table 4 Estimated results for the panel of low-income countries in Africa

Table 5 Estimated results for the panel of lower-middle income countries in Africa

Table 6 Estimated results for the panel of upper-middle income countries in Africa

These findings are in line with extant studies that increased environmental risks caused by green growth initiatives lead to more frequent and severe costs (transition and compliance costs) for farmers and fishers, reduced productivity, market access challenges, land use restrictions, limited economic diversification, and supply chain disruptions^42,43. These events can result in substantial economic losses, damage to infrastructure, and disruptions to communities, thereby reducing agricultural values and productivity per worker within the UN SDG framework. The economic implication of this is that while green growth initiatives generally aim to promote sustainability and reduce environmental impacts, there could be negative effects on specific sectors such as agriculture, forestry, and fishing^45,46,47. Implementing green growth initiatives may require changes in farming and fishing practices, including the adoption of sustainable and eco-friendly approaches. However, these changes entail some costs for farmers and fishers, potentially affecting their profitability and short-run value added in the panel of low-income countries.

Also, the impact of trade openness, renewable and non-renewable energy, financial development, and population increase varies depending on the individual panel being analyzed. For instance, speedy population growth can contribute to increased demand for agricultural products, creating opportunities for farmers and the agri-food industries. This is in line with the findings that population growth stimulates economic growth and supports rural livelihoods, leading to an improved sustainable agri-food systems transformation as revealed from the findings from the panels of low-income countries and lower-middle income countries^48,49 in Tables 4 and 5. Conversely, expeditious population growth can exacerbate pressure on natural resources, including land, water, and biodiversity. Over-exploitation of these resources results in environmental degradation, biodiversity loss, and soil erosion, thereby reducing sustainable agri-food systems transformation, as revealed in the panel of upper-middle-income countries in Table 6. Therefore, population growth can have both positive and negative effects on sustainable agri-food systems. The impact depends on how well African countries handle and adjust to the evolving demographic landscape.

Furthermore, financial development can have both positive and negative effects on sustainable agriculture and food systems transformation in Africa. According to prior research, the impact largely depends on how financial systems are structured, regulated, and integrated into broader economic and agricultural policies⁴⁴. The findings in Tables 5 and 6 show that financial development has a positive impact on sustainable agri-food systems transformation in the panels of lower-middle-income countries and upper-middle-income countries. Implying that improved financial development can provide farmers, agricultural enterprises, and related businesses with better access to capital^44,49. This can facilitate investments in modern technologies, equipment, and infrastructure that could increase agricultural productivity and sustainability. However, findings from Table 4 reveal that financial development hinders advancements in sustainable agri-food systems in low-income African countries. This evidence supports the conclusion of some extant studies that while access to credit is critical for agricultural development, excessive or poorly managed credit can lead to financial instability for farmers and negatively impact the economic viability of farming operations^50,51. Trade openness poses an adverse impact on sustainable agri-food systems transformation in upper-middle-income countries. This indicates that openness to international trade can result in increased resource extraction and diminished agricultural productivity. However, trade openness can provide opportunities for green technology transfer, through which developing countries benefit from access to environmentally friendly technologies.

Conclusion

The analysis conducted on green growth initiatives and sustainable agri-food systems transformation in Africa reveals several important findings. Firstly, green growth initiatives have a positive impact on some tenets of sustainable development, particularly in promoting resilience and innovation within the agricultural sector. However, the effects vary from one SDG2 indicator to another. While green growth initiatives enhance agriculture, forestry, and fishery value added per worker and cereal yield, they may negatively impact TFP. Additionally, factors such as economic growth, renewable energy, financial development, and population growth also influence SDG2 indicators positively, whereas carbon emission intensity, deforestation rate, trade openness, and non-renewable energy have negative effects.

In West Africa, the agrifood systems face challenges such as conflicts, climate shocks, and fragile economies. The governments’ willingness to tackle terrorism and regional insecurity will play a crucial role in advancing sustainable agrifood systems and building climate resilience. In the Southern African Development Community (SADC), significant progress in agro-industrialization and renewable energy investment has been made. Continued efforts to harness natural resources, particularly focusing on water conservation, soil fertility, seed development, pest management, and climate-smart agriculture, are essential for sustainable agrifood systems. North Africa’s comparative advantage lies in renewable energy development, which should be leveraged to improve food productivity. A strong focus on water conservation and soil management is critical, given the region’s vulnerability to land degradation and climate shocks. The East African region, blessed with abundant natural resources, faces the challenge of climate change, particularly carbon emissions from livestock. Increased investment in renewable energy and climate-smart crop-livestock systems will be crucial to reduce emissions while ensuring sustainable agrifood systems. Given Africa’s diverse economic landscapes, regional disparities must be considered when recommending policies.

To balance trade-offs between agricultural productivity and fisheries sustainability, policymakers should adopt integrated land-water-fisheries management approaches that prioritize cross-sectoral collaboration and context-specific strategies. For upper-middle-income countries like South Africa and Mauritius, this involves deploying advanced technologies to mitigate fisheries declines while maintaining agricultural gains. Low-income countries such as Malawi and Uganda require targeted support to enhance agricultural productivity without exacerbating pressures on aquatic ecosystems. Financial and technical incentives such as subsidies for agroecology, sustainable fishing practices, and renewable energy adoption can align sectoral goals. Successful models like Rwanda’s Green Growth Strategy and Kenya’s climate-smart farming demonstrate how inclusive, adaptive policies can harmonize environmental and economic objectives. Moreover, systemic challenges, including limited access to green financing, weak institutional frameworks, and population pressures, must be addressed through strengthened regulatory systems, specialized green investment funds, and incentives for pro-poor technologies. Governments should prioritize capacity-building for smallholders, women, and marginalized groups while phasing out non-renewable energy reliance. Continuous monitoring of green growth initiatives and regional knowledge-sharing platforms is critical for refining strategies. Future research should evaluate long-term impacts on urban agriculture, livestock, and biodiversity, alongside comparative studies to identify best practices for equitable, sustainable agri-food systems transformation across Africa.

Methods

Model specification and data

Using appropriate standard errors makes accurate statistical inferences in the analysis that involves time-series and cross-sectional data with potential correlation and heteroscedasticity across panels^38,39,40. Thus, the Prais–Winsten regression model with PCSE, which controls for heteroscedasticity and serial correlation, is adopted in the study. In line with extant studies linking green growth and SDG-2 indicators (agricultural productivity, crop yields, agricultural output per capita, livelihoods, and economic growth)^{41–44,52,53}. Specifying the functional model:

$${Y}_{{it}}={\beta }_{0}+{\beta }_{1}{X}_{{it}}+{\beta }_{2}{Z}_{{it}}+{\varepsilon }_{{it}}$$

(1)

Where ${Y}_{{it}}$ is Sustainable Development Goal-2 (SDG2) indicators at time, $t$ and country, ${i;}$ and ${X}_{{it}}$ represents the green growth index (ggrowth) generated via Principal Component Analysis (PCA) at time, $t$ and country, $i,$, while ${Z}_{{it}}$ is the vector of other important control variables such as economic growth (gdpgr), carbon emissions intensity (cei), deforestation (der), trade openness (trad), renewable energy (ren), non-renewable energy (nren), financial development (fdx), and population growth (pop), at time, $t$ and firm $i.$ ${\varepsilon }_{{it}}$ is the country-specific disturbance term at time, $t.$ Explicitly, Eq. (1) is therefore expressed as:

$${{sdg}2i}_{{it}}= {\beta }_{0}+{\beta }_{1}{{g\,growth}}_{{it}}+{\beta }_{2}{{gdpgr}}_{{it}}+{\beta }_{3}{{cei}}_{{it}}+{\beta }_{4}{{der}}_{{it}}+{\beta }_{5}{{trad}}_{{it}}\\ + {\beta }_{6}{{ren}}_{{it}}+{\beta }_{6}{{nren}}_{{it}}+{\beta }_{7}{{fdx}}_{{it}}+{\beta }_{8}{{pop}}_{{it}}+{\varepsilon }_{{it}}$$

(2)

Moreover, the Sustainable Development Goal-2 indicators include the Sustainable Development Goal-2 index (SDG2I) obtained via PCA, Agriculture, forestry, and fishing, value added per worker (AVA), TFP, AQP, and Cereal yield (CEY). Equation (2) is analyzed for the panel of African countries as well as the panels of low-income countries, lower-middle-income countries, and upper-middle-income countries as per the 2023 World Bank income classification⁵⁴.

Table 7 provides a comprehensive overview of various data measurements, their respective sources related to SDG-2 indicators, green growth indicators, control variables, and the sources of data for each measurement. The data covers agricultural productivity, CO_2(g) productivity, energy productivity, non-energy material productivity, deforestation, renewable energy, carbon emission, trade openness, financial development, population growth, and economic growth. Data sources include; World Development Indicators (WDI), IMF’s International Financial Statistics (IFS) and Organization for Economic Co-operation and Development (OECD). The table serves as a valuable resource for understanding and examining sustainable development and economic performance variables. The study employed MS Excel 365 and STATA version 18 for data cleaning and formal analysis (descriptive and econometric analysis), respectively.

Table 7 Data description/measurements and sources

The study population consists of all 55 African countries. However, the sample for the study comprises 46 African countries, given data availability. These countries include; Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic (CAR), Chad, Comoros, Congo Democratic Republic, Congo Republic, Cote d’Ivoire, Egypt, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia, and Zimbabwe. Using the 2023 World Bank income classification, the study partitions the empirical analyzes as depicted in Table 8.

Table 8 World Bank income classification of African Countries