A blessing or a curse? Climate change and political economy effects on the agricultural sector: empirical evidence from Sub-Saharan Africa

Sub-Saharan Africa (SSA) is particularly vulnerable to climate variability and change, facing documented cycles of drought, flooding, rising temperatures and uneven rainfall distribution (Stavi et al., 2022). These climatic factors significantly impact agricultural productivity, which is crucial to many developing economies. While climate change is a global issue, its effects are most pronounced in low-income countries with limited capacity to adapt. This is particularly so for SSA countries, where agriculture is a significant source of livelihood for more than 60% of the rural population (FAO, 2020; World Bank, 2020).

Agriculture in SSA is not only vital for household consumption and income but also a significant contributor to national GDPs and food security (UNCTAD, 2021; FAO, 2021). It provides essential raw materials for industries and employment opportunities, making it central to both local and global food security (World Bank, 2019; UNDP, 2019). However, agriculture in SSA is highly sensitive to climate change due to its reliance on unpredictable environmental conditions. Numerous studies indicate that climate change has led to reduced cereal crop yields in the region, contributing to food insecurity (Ahsan et al., 2020a, 2020b; Kumar et al., 2023a, 2023b).

The interrelationship between agriculture and climate change is a two-way process: although climate change escalates risks in agriculture, the latter, by itself, could be the reason for environmental degradation. Given that most of SSA depends on rain-fed agriculture, it is certainly very vulnerable to changes in rainfall and temperature patterns. Kumar et al. (2023a, 2023b) note that much as cereal production has gone up in SSA over recent years, this growth is tempered by challenges emanating from climate change. As of 2022, SSA’s total cereal production reached 176.4 million metric tons, with projections suggesting a rise in global cereal trade shares (World Bank, 2023). However, this positive trend is overshadowed by the increasing frequency of extreme weather events that disrupt crop cycles and lower productivity (FAO and ECA, 2018).

Water scarcity further exacerbates these problems. While the region has gained 12% more cultivated land in the last five decades, the lack of access to irrigation, especially for rain-fed agriculture, has continued to retard productivity growth. A mere 3% of SSA’s farmland is irrigated, and this has made the region highly vulnerable to climatic shocks (FAO, 2022). The average cereal yield in SSA remains low, at about 1,546.4 kg per hectare, compared to higher yields in other regions (Ssozi et al., 2019). Cereal crop production in SSA is important for both food security and economic growth. By 2030, the region is expected to experience significant population growth, further increasing demand for cereal crops. Many countries in SSA, such as Ethiopia, rely heavily on cereals, with these crops constituting the majority of grain production (Liliane and Charles, 2020). Despite the importance of cereals, SSA faces a growing food deficit, with large portions of its population experiencing hunger and undernourishment, especially in Eastern Africa, where 125 million people are affected (OECD-FAO, 2021). Climate change poses serious challenges to agriculture in SSA, primarily through rising temperatures and erratic rainfall, which reduce crop yields. However, the severity and nature of these impacts vary across the region, shaped by differences in ecological conditions and political economy factors. Weak institutions, corruption and poor policy coordination exacerbate vulnerabilities, while countries with stronger governance structures are better able to adapt through climate-smart agriculture (CSA) and innovation. In some well-governed contexts, climate change may even offer opportunities to enhance resilience and attract investment, the effects of climate change are uneven, with the most severe consequences concentrated in areas with fragile institutions and limited adaptive capacity. The study explores how political instability, policy uncertainty and weak property rights affect agricultural investment and productivity. These factors can deter technology adoption and long-term planning, further exacerbating climate change’s negative effects on agriculture. The specific objectives for the study:

• to assess the influence of temperature and precipitation on cereal crop productivity in SSA;

• to evaluate the impact of CO₂ emissions on agricultural productivity in SSA; and

• to investigate the impact of political stability in influencing agricultural productivity in SSA.

This study addresses the complexities of climate change’s impact on agricultural productivity in SSA. It contributes empirical evidence and insights into the key factors influencing productivity, complementing existing research. The findings aim to guide policymakers in creating effective strategies to improve agricultural output. This study is structured into five main sections: an introduction that outlines the research context and objectives; a literature review that situates the study within existing scholarly work; a methodology section detailing data and research design; a presentation and analysis of the findings; and a conclusion that offers policy recommendations informed by the study’s results.

This literature review covers key concepts, stylized facts and theoretical and empirical studies focusing on climate change and its political economy effects on agriculture in SSA.

SSA is among the most vulnerable regions to climate change, primarily because of its heavy dependence on rain-fed agriculture and low adaptive capacity among rural populations. Changes in temperature and precipitation have been shown to significantly impact crop yields, food security and livelihoods. Lobell et al. (2011) found that these changes severely affect agricultural productivity. According to Thornton et al. (2014), crop yield decline is expected to reach 50% by 2050, especially in water-scarce areas. CSA, including water management and drought-resistant crops, can improve resilience but requires significant investment and technical support (Sushil et al., 2017).

Ethiopia’s nationally determined contribution (NDC) focuses on sustainable land management and climate-resilient crops, while Kenya emphasizes climate-smart practices and improved irrigation. However, challenges in implementing these policies exist, primarily due to insufficient funding, weak institutional capacity and coordination problems (Bennett and Fenton, 2020a, 2020b; Harvey et al., 2019a, 2019b). Overcoming these barriers is crucial for successful NDC execution.

The political economy of agriculture heavily influences climate change policies in SSA. Political elites, multinational corporations and local actors shape agricultural outcomes, often balancing economic growth with sustainability (Hazell et al., 2010). Deininger et al. (2011) highlight that insecure land tenure systems prevent long-term investments in sustainable agriculture, reducing farmers’ resilience to climate change.

Looking at Figure 1, we can see how nature-based strategies to cut down CO₂ emissions like planting more forests or increasing soil carbon storage are connected to the political and economic realities of farming in SSA. These climate-friendly efforts are often driven by global goals but end up interacting with local politics, institutions and economies in complex ways, sometimes creating tough choices. While these approaches can help the environment, they might also limit how much land local farmers can access or pull resources away from other important adaptation projects. This can make farming development and food security even more challenging in the region. Many studies suggest that using these strategies could boost agricultural production in SSA while also helping farmers adapt to changing climate conditions (Lal, 2004a, 2004b; Rockström et al., 2009; Nkonya, Mirzabaev, and von Braun, 2016). Still, Lal (2004a, 2004b) points out that making these benefits happen is not simple; there are often obstacles like weak institutions, insecure land rights, poor infrastructure, limited access to finance and technology, and policies that do not work well. Experts like Collier and Dercon (2014), De Janvry and Sadoulet (2007) and Toulmin (2009) argue that unless we tackle these deep-rooted political and economic problems, the full promise of climate-smart farming would not be realized, even if the biophysical aspects are promising (Lal, 2010; FAO, 2017). In other words, expanding these practices is not just about the science it is about fixing governance and institutional issues that hold things back.

Figure 2 displays different technological options that can help SSA agriculture adapt to and mitigate the impacts of climate change. These options are based on global models like those from the IPCC and include empirical research such as Lal’s (2004a, 2004b) studies on carbon capture and Pretty et al.’s (2011) work on sustainable farming practices. Technologies like conservation farming and agroforestry stand out as especially suitable for the region because they tend to be affordable and make good use of land. Still, the authors point out that factors like poor infrastructure, limited access to finance and weak governance considerably influence whether these technologies are adopted. This perspective aligns with broader research by scholars such as Acemoglu and Robinson (2012) and Scoones et al. (2005), who emphasize how political and institutional issues can make or break technological efforts in agriculture.

Figure 3 shows how changing rainfall patterns and more frequent extreme weather events are putting a heavy strain on farming in SSA. Researchers like Schlenker and Lobell (2010), Lobell et al. (2011) and Thornton et al. (2014) have all emphasized this trend. Even though agriculture does contribute to CO₂ emissions mainly through land use changes and livestock farming its vulnerability is made worse by weak institutions and ineffective policies, as noted by Sachs (2005), Dercon (2009a, 2009b) and Barrett et al. (2014). The report points out that emissions come directly from farming activities like plowing, which can produce up to 15.2 kg of carbon per hectare; irrigation, which can emit between 150 and 200 kg per hectare; and grain drying, adding another 60–70 kg per hectare. There are also big indirect emissions related to soil erosion, land conversion and burning biomass amounting to 1.1 petagrams of carbon each year from soil erosion, 0.88 petagrams from land clearing, and between 2 and 5 petagrams from burning plant material. Using fertilizers and pesticides adds to this, with nitrogen fertilizers releasing about 1.3 kg of carbon per kg used and herbicides releasing roughly 6.3 kgC per kg. All these factors combined threaten both food security and the region’s ability to adapt to climate change, making it a real challenge for the future.

As illustrated in Figure 4, agriculture in SSA faces increasing threats from climate change, making it important to develop targeted strategies that can keep food production on track. In the near term, introducing crop varieties that can withstand tough weather conditions, using more sustainable fertilizers, and implementing water-efficient irrigation systems can help minimize crop losses and boost resilience, based on insights from FAO (2013) and Lipper et al. (2014). Looking further ahead, smarter farming practices like recycling water, using fertilizers that maximize nutrient use, and enhancing irrigation techniques are key to tackling climate risks and water shortages, aligning with the ideas of Bryan et al. (2013) and Sivakumar et al. (2011). Over the long haul, the focus shifts to adaptive management, building resilient infrastructure and establishing closed-loop farming systems that support sustainability. That said, challenges like weak institutions and a lack of political commitment emphasized by Acemoglu and Robinson (2012) and Scoones et al. (2005) still stand in the way of widespread adoption of these important innovations.

Figure 5 shows how sudden climate shocks, like extreme weather or changes in rainfall patterns, can seriously shake up the physical environment and economy of agriculture in SSA. Rising global temperatures speed up processes that break down soil, such as erosion, the loss of organic carbon and increased soil moisture, all of which make it harder for soil to support healthy crops. These shifts mess with nutrient cycles, cut down photosynthesis and hamper seedling growth, finally leading to much lower crop yields. Mendelsohn and his colleagues (2006) point out that while weather changes directly affect farm productivity, how bad these impacts get really depends on the strength of local institutions. Weak governance, poor infrastructure, insecure land rights and ineffective policies make farms more vulnerable, as Barrett et al. (2014) and Dercon (2009a, 2009b) emphasize. On the flip side, research from Sachs (2005) and Bryan et al. (2013) shows that good governance, along with investments in climate-proof infrastructure and supportive policies, can help shield agriculture from these climate threats. So, it is really the combination of environmental shocks and weak institutions that makes farming in the region especially risky.

Figure 6 offers a detailed look at the social factors behind food insecurity in SSA, showing how these issues are made worse by climate change and longstanding structural inequalities in the political and economic systems. The region’s population is skyrocketing – expected to hit nearly 9.8 billion by 2050 and over 11 billion by 2100 which puts even more strain on its already fragile food infrastructure. This fast population growth, along with climate challenges like desertification, loss of biodiversity and land degradation, leads to widespread hunger, affecting more than 21 million people who are currently food insecure. At the same time, activities like industrial development, deforestation, mining, and poorly planned urban expansion such as building roads and houses are damaging ecosystems and reducing agricultural productivity. The figure emphasizes how these interconnected issues block progress toward important Sustainable Development Goals (SDGs), including ending poverty (SDG 1), eliminating hunger (SDG 2), promoting health and well-being (SDG 3), ensuring access to clean water and sanitation (SDG 6), expanding clean energy options (SDG 7), encouraging responsible consumption and production (SDG 12), taking urgent climate action (SDG 13) and safeguarding land ecosystems (SDG 15). All these widespread challenges show that tackling food insecurity is not simple it calls for integrated policies that address both environmental risks and deep-rooted social and economic barriers.

Vulnerability theory examines how populations are affected by climate change due to exposure, sensitivity and limited adaptive capacity. SSA’s dependence on rain-fed agriculture makes it highly vulnerable. Makuvano et al. (2024) show that smallholder farmers in the Democratic Republic of the Congo, for instance, adapt based on education and farm size, emphasizing the need for tailored interventions.

Political economy theory examines how political and economic dynamics influence policy outcomes. A 2024 study stresses the need for integrating environmental risks into economic planning to mitigate the impacts on agriculture and forestry sectors, demonstrating the importance of governance in climate adaptation.

In SSA, fostering sustainable agricultural practices is crucial for ensuring long-term agricultural resilience. According to Antwi-Agyei et al. (2022a, 2022b), incorporating indigenous knowledge and sustainable farming methods plays a vital role in enhancing adaptation to environmental changes and promoting sustainable livelihoods within the region.

Resilience theory focuses on a system’s ability to withstand shocks. Bahta and Lombard (2023a, 2023b) emphasize that households with higher social vulnerability are less resilient to agricultural droughts, highlighting the importance of supporting vulnerable groups through resources and policy interventions.

Adaptation theory explores how systems adjust to climate change. Iams et al. (2020) show that access to water, health services and social networks in Ghana significantly influence farmers’ capacity to adapt to climate variability. Targeted interventions are necessary to enhance resilience.

Empirical evidence has shown that climate change has already had devastating impacts on agriculture in SSA, and future scenarios are not promising. According to the Intergovernmental Panel on Climate Change, IPCC (2014), temperature increase in the year 2050 is expected to reach 1.5°C–2°C, accompanied by changes in rainfall patterns. This will particularly affect rain-fed farming systems, which are highly vulnerable to changing climate conditions. Other studies, such as Lobell et al. (2011), have shown that these climatic changes have already resulted in reduced crop yields, especially in staple crops like maize, wheat and sorghum.

Further empirical evidence from FAO (2020) shows that SSA is highly vulnerable to extreme weather conditions. For example, the 2017 drought in Ethiopia resulted in a 30% reduction in crop yield, while a flood disaster in 2019 led to a loss of 20% in maize production. In Kenya, crop yield has declined by 10%–15% between 2015 and 2019 due to shifting rainfall patterns and drought. These are many other variations in weather that place enormous pressure on agricultural productivity.

The other concern is desertification. According to Boko et al. (2007), land degradation has been intensifying in semi-arid regions like the Sahel, increasing the rate of desertification by 1%–3% every year. With all these added temperature increases and fluctuations in rainfall, this increased amount of desertified land complicates agricultural activities.

In the process, smallholder farmers, who are dominant in agriculture across SSA, are the hardest hit. A study by Ochieng et al. (2018) shows that 65% of the smallholders in Kenya lack access to information on climate change, while 70% are unable to finance the adaptive technologies. Besides, there is a high level of vulnerability to climate-induced risks, such as crop failures and water scarcity, arising from limited crop insurance services.

Implementation of NDCs: mixed results. While countries like Ethiopia and Kenya have made remarkable progress in the integration of climate adaptation strategies, broader challenges remain. As Harvey et al. (2019a, 2019b) showed, 40% of NDC-related agricultural projects faced funding shortfalls, while 50% had been delayed due to institutional weaknesses and political instability. Many SSA countries also depend on international assistance for climate adaptation, but such assistance has often been inadequate and irregular in recent times, as Bennett and Fenton (2020a, 2020b) asserted.

Political economy factors also play an important role in complicating NDCs. Various studies, such as Omotayo and Oladipo (2019) and Deininger et al. (2011), find corruption and land tenure conflict as key challenges to effective agricultural adaptation. Corruption in the oil and agricultural sectors is said to weaken sustainable agriculture policies in Nigeria, while insecure land rights in Ghana have implications for limited benefits from land reforms.

Finally, the influence of external agents such as the World Bank and multinational corporations: Holt-Giménez and Shattuck (2011) argue that these institutions favor large-scale agricultural investments that focus on the interests of multinational corporations at the expense of small-scale farmers. As confirmed by a report from Oxfam (2017), while there has been some incorporation of local approaches to adaptation within the NDCs, often external interests prevail, leaving vulnerable populations with fewer resources to adapt.

Data utilized for this research was sourced from the World Bank’s World Development Indicators, Worldwide Governance Indicators (2024) and FAOStat database (2024). The study was concerned with examining the impact of climate change on the productivity of cereal crops in SSA. The study made use of data for a total of 27 SSA countries based on data availability from 1996 to 2024. Temperature, rainfall and carbon dioxide emissions variables were used in the estimation of climate change and its effect on crop yields.

Table 1 lists the main variables that are used to analyze how political-economic factors and climate change affect agriculture in SSA. Together with agricultural measures like grain output, fertilizer use and arable land, it also incorporates environmental indicators like temperature, precipitation and CO₂ emissions. Political stability, GDP per capita, trade openness, foreign direct investment and other economic and governance aspects are also taken into account. Measurements are standardized in terms of kilos per hectare and percentage of GDP, and the FAO and World Bank are the main sources of the data.

SSA is increasingly vulnerable to the negative effects of climate change. This is mainly because many of its communities rely heavily on rain-fed agriculture and have limited resources to adapt. As the climate becomes more unpredictable temperatures rising, rainfall becoming erratic and extreme weather events happening more often the pressure on farming and food security in the region keeps growing. These climate-related challenges do not hit all areas equally; some parts are more prone to droughts, floods and land deterioration. Because of this, climate change risks are likely to make hunger worse and slow down efforts to achieve long-term development goals. For this study, we focus on 27 SSA countries selected based on their geographic diversity and heavy dependence on agriculture. These include Angola, Benin, Burkina Faso, Cameroon, Central African Republic, Republic of Congo, Côte d‘Ivoire, Democratic Republic of Congo, Ghana, Guinea, Kenya, Madagascar, Mali, Mauritius, Mozambique, Niger, Nigeria, Senegal, Sierra Leone, Tanzania, Togo, South Africa, Tunisia, Uganda, Zambia and Zimbabwe. Their locations are shown on the map below and are described in more detail in Table 2.

Figure 7 presents a map of SSA depicting the dominant farming systems, based on data from Dixon et al. (2001) and updated by Garrity et al. (2012a, 2012b). The shapefiles were published by the Food and Agriculture Organization of the United Nations (Auricht and Dixon, 2015a, 2015b). The map includes both natural and human factors contributing to desertification. Among the most significant human activities driving desertification is overgrazing, which destroys vegetation cover and leads to the erosion of fertile topsoil. This process is further accelerated by climate change, particularly rising temperatures. Higher temperatures disrupt established precipitation patterns and intensify evaporation, exacerbating desertification.

Many studies use the Cobb–Douglas production function to calculate the impact of climate change on agricultural output, incorporating climate variables like temperature and rainfall (Zhang et al., 2017). This research adopts the framework from Bond et al. (2014a, 2014b) and Dell et al. (2015) to estimate climate change effects on economic growth, a standard model in economic analysis model as:

In equation (1), Y_it denotes the gross domestic product (GDP) of country i in year t, while L signifies labor supply, which is derived from the total population and reflects human capital. The variable A represents technological advancement or labor productivity, while C accounts for climate factors, specifically temperature and precipitation, affecting country i in year t. This formulation aims to capture the interaction between economic output, labor dynamics, technological progress and environmental conditions in shaping economic performance. The growth of an economy is indirectly affected by the state of its institutions (Acemoglu et al., 2001; Dell et al., 2016a, 2016b). For instance, labor productivity is affected by climate factors (C_it) and unobserved country-specific institutional factors and captured by y_i. This influence is represented as:

Equation (2) encompasses both the direct and indirect effects of climate change on agriculture. It demonstrates how variations in temperature and precipitation affect crop yields and livestock productivity (Greenstone and Deschênes, 2016a, 2016b, 2016c; Schlenker and Roberts, 2019), while also highlighting the influence of climate on labor productivity, which in turn affects overall economic growth (Dell et al., 2012; Kjellstrom et al., 2019):

Therefore, a simplified model without the lagged effect ⁸ can be based on equation (3) is:

where Y_it is the per capita output, η represents the overall intercept, y_i denotes the country-specific fixed effects and δ_t is the time-specific fixed effect. The direct impact of climate change on economic growth appears through α, and indirect appears through β (i.e. the effect of growth) (Bond et al., 2012a, 2012b; Dell et al., 2016a, 2016b).

As highlighted in both this section and the theoretical framework, temperature and precipitation, along with variations in precipitation, are key indicators of climate change that influence economic growth at both the aggregate and sectoral levels (Kimball and Idso, 2018). In equations (1)–(3), climate change variables are explained as a vector C_it, which includes temperature and precipitation. Therefore, consider the following functional form of the empirical model to estimate the effects of climate change on economic growth:

where Y is the gross domestic product growth per capita of country i at time t, TEMP and AnnPreMz denote average annual temperature and precipitation, respectively. CVPM represents the coefficient of variations in precipitation and captures the annual variability in precipitation for each country, X is the vector of other macroeconomic variables such as population and human capital (in terms of gross national percentage of secondary school enrollments), and ε_it is a random error.

The seemingly unrelated regression (SUR) model offers greater asymptotic efficiency compared to single-equation models when dealing with multiple interconnected equations. To estimate the effects at the sector level, the SUR model is represented in vector form as follows:

where: $Yit=(Y1,itY2,it⋮YM,it)$ is the M × 1 vector of sector-specific value-added GDP per capita outcome variables for country i at time $t,η=(η1η2⋮ηM)$ is the M × 1 dimension vector of intercepts, y_i is the M × 1 vector of country-specific fixed effects for country i, δ_t is the M × 1 vector of year-specific fixed effects for year t, B is the M × k matrix of coefficients for theclimate change and control variables in X_it, X_it is the k × 1 vector of climate change and control variables for country i at time t, and $ϵit=(ϵ1,itϵ2,it⋮ϵM,it)$ is M × 1 vector of error terms.

Including year and country dummies in economic studies, particularly in panel data models like the SUR model, is crucial for controlling unobserved heterogeneity across time and countries (Greene, 2016; Wooldridge, 2017). These dummies capture global trends, isolate country-specific factors such as culture, institutions and history, and help address omitted variable bias. They also improve estimate reliability and efficiency by managing cross-sectional dependence, as noted by Driscoll and Hsiao (2022).

By including year and country dummies, the model presents as follows:

where: D_country,i is a J × 1 vector of country dummy variables for country i, Γ is the M × J matrix of coefficients for the country dummy variables, D_year,t is a K × 1 vector of year dummy variables for year t, and Δ is the M × K matrix of coefficients for the year dummy variables. The system of equations in the SUR framework was stacked for each sector’s outcome variable and can be represented in the following matrix form for all outcome variables:

Drawing on the work of Moon and Perron (2018) and Zellner (1962), this study explores the effects of climate change on economic growth across various sectors, including agriculture, industry, manufacturing, and services. Employing the SUR model, it tests the hypothesis that climate change does not have a significant negative impact on growth, while also tackling issues of endogeneity and efficiency (Hill et al., 2018). Therefore, a series of equations was established to analyze the impact of climate change at the sector level as follows:

The equations capture the economic impact of climate change across key sectors agriculture, industry, manufacturing and services by examining how temperature, annual rainfall and rainfall variability affect sectoral output. Agriculture emerges as the most climate-sensitive sector, where higher temperatures and irregular rainfall patterns can significantly reduce productivity. In contrast, industry and manufacturing are less directly affected but still face economic risks through increased energy costs, disrupted supply chains and infrastructure stress. The service sector experiences more indirect impacts, such as reduced labor productivity and urban disruptions. To achieve this goal, some additional notation is required. Let the 1-sized vector of the deterministic variables from Equation Y_t = [1, DU, Dλ, T, λ], and let the OLS-estimated coefficient vector be:

with variance $σε2(λ)[∑t=λ0(1−λ0)TXt ′Xt]−1$⁠. Let also the estimated and the true parameter vector respectively be defined as $β^(λ)≡[μ^1,τ^1,μ^2,τ^2]$ and $β*≡[μ1*,τ1*,μ2*,τ2*]$⁠, such that the scaling matrix of the rates of convergence of $β^(λ)$ with respect to β* is given by ϒ_t = diag[T^1/2, T^3/2, T^1/2, T^3/2].

Then, by generating Δy_t according to equation (11) we have, for 0 < λ < 1:

whereby, for W(r) a standard Brownian motion in the plane r ∈ [0, 1], the following limit expressions ensue:

And:

From equation (11) the limit distribution of the coefficient vector is the same as that reported by Perron and Zhu for Model Ib (2020, p. 81), while its asymptotic t statistics are computed as follows:

where Ω_T(λ) = σ²I₄(Θ_T(λ))⁻¹ and I₄ is the 4x4 identity matrix. The theoretical t statistics of the level break t_T(λ, L) and of the trend break t_T(λ, T) are thus:

The empirical critical values of the t statistics are obtained by Monte Carlo simulation. For select magnitudes of λ running from 0.10 to 0.90, and for a reasonable sample size (T = 200), the 1%, 5% and 10% finite-sample critical values.

The L-sized vector of sample moments for the trimmed time interval is:

where the coefficient vector $β^$ and the first-stage residuals $e^t$ stem from a (possibly) consistent TSLS estimation of equation (11). The sample means of the above are:

with the orthogonality property that $E[g(β^t)]≡0$⁠.

Let also the ensuing L × L weight matrix be:

such that $β^GMM(λ)=argminβ∈B(g¯(β^,λ)W(β^,λ)−1g¯(β^,λ))$⁠.

Computation of the partial first derivatives of the sample moments yields the L × K Jacobian matrix:

where z_t, x_t, respectively, are the Lth and the Kth element of vectors Z_t and X_t. Finally, the efficient GMM estimator, by letting $Z′y=∑t=λ0T(1−λ0)TTztΔyt$ is:

where, specifically:

whose asymptotic normality property is:

The dynamic panel data model is preferred over static linear regression methods, such as ordinary least squares (OLS), because OLS cannot address unobservable country-specific effects and potential endogeneity issues in the regressors (Roodman, 2021; Liu and Liang, 2022). Static models like OLS fail to capture unobserved heterogeneity and cannot resolve endogeneity problems when including the lagged dependent variable in the model (Cheng et al., 2023). While fixed- and random-effects models can handle heterogeneity, they are insufficient in addressing endogeneity arising from dynamic relationships (Han and Kim, 2023). This approach is advantageous because it mitigates omitted variable bias and endogeneity by first differencing the variables, thereby improving model validity (Liu and Liang, 2022). The system GMM method is regarded as superior to fixed- and random-effects models because it accounts for unobserved country-specific heterogeneity while capturing endogeneity in the regressors (Roodman, 2021). Moreover, dynamism is incorporated into the model by including the one-year lagged value of the dependent variable, which allows for partial adjustment toward long-run equilibrium (Choi and Okui, 2023). Recent research has refined GMM techniques, enhancing their robustness in dynamic panel data modeling and improving their application in complex data sets (Cheng et al., 2023; Liu and Liang, 2022). The model is specified as:

The model specified in this study includes several key components. The dependent variable, Y_it, represents the log of crop yield for country i at time t. The variable CC_it captures the climate change indicators, which are the primary factors under investigation in this study. The term X_it refers to the control variables, which account for other factors that might influence agricultural productivity. In addition, μ_i represents the unobserved country-specific effects, capturing any country-level heterogeneity that could influence the outcome. A time-specific effect is accounted for by the dummy variable θ_t, which allows for the inclusion of year-specific variations in the model. Finally, ϵit ϵi the error term, which represents the unobservable factors or random disturbances affecting the relationship between the variables at any given time. This structure ensures that both country- and time-specific factors are taken into account, providing a more robust estimate of the effects of climate change on crop productivity.

The descriptive statistics in Figure 8 present climate change, coupled with the political economy, shapes vulnerability and resilience in the agricultural sector across SSA, although at several country levels. Underpinning this are uneven levels of political stability that shape governments’ ability to pursue agricultural policies and attract investment across the region.

From Figure 8, most NDCs in SSA are driven predominantly by political stability, trade openness, infrastructure development and climate variability. For instance, political instability in countries such as the Central African Republic and Burkina Faso has led to the inability of the pursuance of agricultural policies, resulting in poor infrastructure and, finally, low food security. On the other hand, stable countries like Ghana are very apt to undertake climate-smart agricultural policies that improve productivity.

Trade openness is another determinant of agricultural growth. The more open countries have wider access to the world markets and improved technologies, while the less open countries face significant barriers to import agricultural inputs and export produce, hence their response to climate change will be limited.

Climate variability is yet another crucial challenge that continues to confront. According to the Intergovernmental Panel on Climate Change, because of climate change, the agricultural productivity of Africa has already declined by 34% since 1961-more than in any other region. Maize and wheat yields in SSA have already fallen by 5.8% and 2.3%, respectively, between 1974 and 2008. In the no-adaptation scenario, West African maize yields may shrink by 9% at 1.5°C warming and 41% at 4°C warming. Excessive temperatures and erratic rainfall patterns only heighten the risk to agriculture.

Other binding constraints to adaptation in SSA agriculture include inadequate infrastructure, such as reduced irrigation and generally weak transportation networks. In this regard, investment in climate-resilient infrastructure is greatly needed alongside land reforms and climate-smart agricultural practices. It is here that sustainable land use, efficient irrigation systems and resilience at the seed variety through effective NDC policy should effectively reduce climate risks while raising food security and economic stability in SSA.

Before proceeding with further statistical modeling and regression analysis in the context of climate change and political economy effects on the agricultural sector in SSA, it is of essence to check that the variables involved in the empirical model are suitably treated for their statistical properties. One of the first tests that goes into preparing this is the test for the stationarity of data. A stationary time series is one whose statistical properties are time-invariant. In econometrics, a nonstationary data series can result in misleading conclusions, especially in regression analysis, owing to spurious relationships that may arise.

First of all, we carry out the panel unit root test of Levin et al. (2002) to check for stationarity of the variables, which tests whether variables contain unit roots-meaning, nonstationarities. The null hypothesis of this test (H0) is that all the panels, that is, countries-contain unit roots, meaning the series at each country is nonstationary. The alternative hypothesis, which is that all series are stationary or, in other words, the time series for each country has a constant mean and variance over time, can be expressed as H1:

From the result of the unit root test for all the variables in the empirical model, one may reject H0. That is, the series contain no unit roots; they are level stationary and hence need no differencing to make them stationary. Rejection of the null hypothesis suggests that the modeled relationship in the empirical analysis is not spurious, and further regression analysis can be reliably conducted.

In this respect, the Levin-Lin-Chu test is especially appealing, as it allowed us to model both individual effects-and therefore time-invariant characteristics, such as political stability or the extent of arable land-for each country, as well as time-specific effects, common across countries in any one time, such as global climate conditions or international trade policies. This allows a more precise examination of the dynamic relationships between climate change, political economy, and agriculture across the diverse panel of SSA countries. The model can be written as:

where αi is the individual effect, λt is the time effect, xit is a vector of explanatory variables, and β is a vector of coefficients for the explanatory variables, as shown in Table 3 below. To eliminate the individual and time effects (λt), we take deviations from individual means and time means, transforming the model into a multivariate regression model that can be estimated by ordinary least squares (OLS). This allows the coefficients β to be consistently estimated. The key assumptions are that the αi and λt are fixed parameters, and the idiosyncratic error ϵit has a conditional mean of zero. This implies that cereal yield, political stability, temperature, trade openness, FDI inflows, GDP per capita, precipitation, arable land, fertilizer usage and CO₂ emissions are stationary over the sample period. Consequently, the data do not contain a unit root.

The outcome of the Levin-Lin-Chu unit root test is presented in Table 3 and confirms that all these variables like cereal yield, political stability, temperature, rainfall, trade openness, FDI inflow, GDP per capita, arable land, fertilizer use and CO₂ emissions are stationary at the usual significance levels, with and without time trends (p < 0.1, 0.05 or 0.01). This implies that these variables do not exhibit unit roots and are mean-reverting over the long run, validating their suitability for panel regression analysis without differencing. Therefore, empirical models based on these variables are less prone to spurious outcomes, enhancing the robustness of inferences on climate change and political economy effects on agriculture in SSA (Levin et al., Lin, and Chu, 2002).

The panel cointegration test outcomes in Table 4 provide us with strong evidence of a long-run equilibrium relationship between climate change, political economy and agriculture in SSA. Within-dimension tests, under the assumption of common autoregressive dynamics across countries, report the Panel v-Statistic, Panel rho-Statistic and Panel ADF-Statistic at the 1% level, indicating a strong cointegrating relationship for the panel. Although the Panel PP-Statistic is not significant, all the indicators show that the variables are trending together in the long run. Between-dimension tests, which allow for heterogeneous country-specific dynamics, also show cointegration, with the Group PP-Statistic and Group ADF-Statistic being significant at the 5% and 1% levels, respectively. The Kao (1999) residual-based test also shows these findings with a significant ADF t-statistic. Overall, the cointegration findings for the panel indicate that climate change and political economy variables are systematically related to agricultural performance in the region, emphasizing the necessity of long-term overall policy initiatives.

Table 5 presents the results of the system GMM model for the short-run analysis. At the 1% level of significance, the lagged value of cereal yield has a positive and statistically significant impact on current-year productivity. This suggests that past cereal yield affects current crop productivity, supporting the use of dynamic panel data models.

From Table 5, using a GMM regression framework, it is shown that increases in cereal yield (coefficient = 0.391, p < 0.01), GDP per capita (0.127, p < 0.01), temperature (0.075, p < 0.01), precipitation (0.271, p = 0.05) and fertilizer use (0.012, p < 0.01) are all positively and significantly associated with improvements in the agricultural sector. These findings suggest that productivity gains, moderate climatic variability and input intensification are crucial for short-term agricultural development. On the other hand, political instability (−0.031, p < 0.05), trade openness (−0.153, p < 0.01) and CO₂ emissions (−0.092, p < 0.01) negatively affect agricultural outcomes, implying that governance challenges, environmental degradation and exposure to external markets may pose risks to agricultural resilience. Meanwhile, foreign direct investment (FDI) inflows (0.0701, p = 0.009) and arable land availability (−0.098, p = 0.627) show statistically insignificant effects, indicating a limited role in shaping short-term agricultural dynamics in the region.

Long-run results: The long-run results, as presented in Table 6, show that both temperature and carbon dioxide emissions have a significant negative relationship with cereal yield in the long run. Precipitation, however, does not have a significant effect on cereal yield in the long run, contrary to the empirical findings of previous studies (Chandio et al., 2020), which indicated that precipitation had a positive impact on crop productivity over longer periods.

The long-run GMM regression results indicate that political economy and climate change controls both have statistically significant negative effects on agriculture in SSA (Table 6). Surprisingly, political stability also has a statistically significant negative coefficient of (−0.512, p < 0.01), suggesting that political stability might lead to structural shifts away from agriculture toward service or industrial sectors. Climate determinants, such as temperature and CO₂ emissions, also show strong negative correlations with crop production (−0.388 and −0.530, respectively, both p < 0.01), indicating the industry’s vulnerability to rising temperatures and pollution. Economic indicators like GDP per capita (−0.359, p < 0.01) and trade openness (−0.611, p < 0.01) are also negatively related to agriculture, suggesting that economic development and increased international exposure may harm the region’s traditional agricultural industries. Fertilizer application is negatively related as well (−0.462, p < 0.01), possibly indicating inefficiencies or environmental overuse. Precipitation, typically a significant input, was statistically significant in this model (p = 0.001), possibly due to spatial heterogeneity or adaptive management.

Table 7 denotes the Arellano–Bond diagnostic for first-order serial correlation in the dynamic panel data model used to evaluate the influence of climate and political-economic factors potentially on agriculture. The testing clearly discovers the presence of significant first-order autocorrelation (p = 0.002), which is more likely to emerge in the differenced equations resulting from the model transformation in GMM estimation. The first-order aspect is unchanged, so the test for the second-order autocorrelation with p = 0.125 can be considered to have no significant results as the model does not have an error that is due to serial correlation. This conclusion also shows that the lagged explanatory variables are valid instruments, which confirms the robustness of the GMM estimation employed in the research. Thus, the results presented in the paper regarding the impact of climate change, political stability and economic factors on agricultural productivity in SSA reflect both statistical and methodological accuracy.

Table 8 presents solid empirical evidence on how temperature, rainfall and various political and economic factors influence agricultural value production in SSA, using both pooled cross-section and pseudo-panel approaches. The effects of temperature differ depending on the model setup. In the pooled analysis, higher temperatures greatly reduce agricultural output in Model 1 but show a positive effect in Models 2 and 3, emphasizing the sensitivity to model choice. The pseudo-panel results reveal even stronger temperature effects: Model 1 indicates a strong negative link (−0.066), while Models 2 and 3 display large positive coefficients (3.474 and 2.411) possibly capturing long-term adaptations or variations among different cohorts. Precipitation effects are statistically noteworthy but small and switch signs, pointing to nonlinear relationships. Political stability shows a very clear positive impact across all models, with the pseudo-panel estimates suggesting roughly double the effect, emphasizing its essential role in agricultural resilience. Higher GDP per capita is closely tied to increased output in the pooled data but falls short of significance in the pseudo-panel, which might be due to reverse causality or unaccounted differences. Trade openness consistently shows a negative relationship, hinting that greater foreign market access could hurt local agricultural production, perhaps because of price swings or increased competition. CO₂ emissions are positively linked to output, likely serving as a stand-in for input use or farming intensification. Fertilizer use has huge positive effects in restricted models but loses significance and turns negative in more flexible ones, which might reflect overuse or diminishing returns once other factors are considered.

Figure 9 illustrates the varying impacts of temperature change on the yields of four major crops: maize, rice, wheat and soy. Maize is the crop most negatively affected by increased temperatures, experiencing significant yield declines due to heat stress, especially during pollination. Rice also sees yield declines, although less severe than maize, as temperature increases impact grain filling. Wheat yields decrease as temperatures rise, with varying impacts depending on growing seasons and altitude; highland areas are expected to lose productivity. Soy is the least sensitive crop to temperature change, showing small increases in yields with moderate temperature rises due to its heat tolerance and nitrogen-fixing capabilities.

Results are presented on key evidence related to climate change, political economy and agricultural productivity in SSA, with direct implications for Nationally Determined Contributions. The findings indicate that, over the long term, both temperature increases and CO2 emissions have a detrimental impact on cereal crop yields. This, in particular, means that for the long-run series, a 1% rise in temperature is associated with a 0.388 decline in cereal yields, while a 1% rise in CO₂ emissions is associated with a 0.53 decline in yields, at the 1% significance level. This indicates how temperature rise and CO₂ emission greatly affect agriculture in SSA; therefore, NDCs should focus their strategies on the mitigation of climate change by reducing emissions and stabilizing temperature trends.

In contrast, this study finds that the other important climate change indicator-precipitation-does not significantly affect the long-run cereal yields, contrasting with findings by Chandio et al. (2020) that precipitation influences long-term productivity positively. This hints at the complexity of climate impacts and further puts forth the need to consider multiple climate variables in the design of NDCs and agricultural policies.

The study further outlines the crucial role played by political stability in determining agricultural productivity. The World Bank (2019) indicated that for every 1% increase in political stability, agricultural productivity increases by 0.5% in SSA. This is further and more importantly consolidated by the fact that, according to the deductions made in this study, agricultural productivity is hurt by political instability, with agricultural productivity decreasing 0.512% for a 1% increase in political instability. These are observed in both the short and long runs, hence inferring that the negative shocks that political instability places on agricultural performance are lasting.

The other finding is that FDI influences agricultural productivity positively. From the analyses, it is seen that in developing countries, for a 1% increase in inflows of FDI, agricultural productivity increases by 0.701. It confirms that NDCs need not only to emphasize climate adaptation and mitigation but also to create a favorable political environment which would contribute to attracting FDI into the agricultural sector. These will bring capital, technology and expertise to mitigate the adverse impacts of climate change on productivity.

Clearly, the most critical insight into agricultural productivity in SSA – where agriculture remains a key contributor to the livelihoods and economic growth of most economies – has been the interlinkages that exist between climate change and political economy factors. Agricultural economics need theoretical models that take into account how environmental factors such as temperature, precipitation and CO₂ emissions interact in complex ways with political-economic factors like political stability, quality of governance and FDI.

These findings highlight how serious the effect of climate change has been on agricultural output and the yield of cereal crops. In empirical evidence, increased temperature and CO₂ emission rates have become detrimental to long-term agricultural productivity in SSA. A 1% increase in temperature leads to a decrease in cereal yields of 0.407, whereas a 1% increase in CO₂ emission results in productivity falling by 0.56. These results align with environmental economics theories, which highlight agriculture’s vulnerability to climate change due to its reliance on environmental conditions for crop production (Ahsan et al., 2020a, 2020b; Kumar et al., 2023a, 2023b).

However, the political economy theoretical perspective has argued that agricultural productivity improves when there is political stability. Stable governance tends to sustain improved investment climates and FDI in countries, leading to successful implementations of agricultural development strategies. According to the present study, in general, SSA’s political instability affects agricultural productivity adversely, implying a 1% increase in political instability contributes negatively to 0.391 decline in yield. Contrary to this received wisdom that political stability always engendered agricultural growth, and disruptiveness associated with instability, the perceived instability is instrumental in affecting investment and climate adaptation strategies.

The study adds to the integration of aspects of climate change and political economy in agricultural economics by reinforcing that while important, environmental factors, political stability and good governance are equally imperative in developing resilience and adaptive capacity in SSA agriculture.

The findings from this study on the impacts of climate change and political economy factors on agricultural productivity in SSA offer significant practical implications for policymaking, agricultural practices and international cooperation. As SSA faces unique challenges from both environmental changes and political-economic factors, addressing these issues through targeted interventions and sustainable practices is crucial for improving agricultural productivity.

A key implication of this could be the integration of climate change adaptation strategies into agricultural policies. The increased temperature and CO₂ emissions that eventually cause negative impacts on crop productivity urge the development of climate-resilient farming systems. This can include the introduction of drought-tolerant varieties of crops, improving irrigation infrastructure and improvement in weather forecasting systems to reduce vulnerability to climate-related shocks.

Political stability and good governance are essential partners for attaining agriculture development, an aspect pursued similarly in the paper. Also, political instability will reduce agricultural outputs by up to 0.496% given a rise of 1% in its magnitude. Therefore, ensuring political stability deserves to become SSA governments’ chief priority. Undoubtedly, stable politics engender not just domestic investment in agriculture but even foreign investments, thus providing these sectors with key resources and vital infrastructure necessary to spur progress across the economy as a whole.

Another set of crucial players to enhance the yield of agricultural sectors are foreign direct investments. FDI can bring in urgently needed capital, technology and expertise to upgrade the agricultural system. Improvement in infrastructure, attractive investment incentives and transparency and predictability encourage more FDI. International cooperation may facilitate the transfer of technologies, improvement of techniques of production and efficiencies within food supply chains.

This study offers some important insights into how climate change, political economy and agriculture are interconnected in SSA. That said, there are a few limitations worth noting. For starters, data collection remains a challenge, especially in rural areas where impacts of climate and farming practices are not as thoroughly recorded. Gaps or inconsistencies in the data can weaken the strength of the study’s conclusions. Besides, since the research combines results across the entire SSA region, it might oversimplify the diverse climatic, economic and political realities of individual countries. Conducting more focused studies at the national or local level could help address this. Another point to consider is the difficulty in establishing clear cause-and-effect relationships. Because the study is based on observation rather than experimental data, it’s hard to definitively say what causes what when it comes to climate change and agricultural or economic outcomes. Finally, the study mainly looks at short-term effects, leaving the longer-term impacts of climate change on agriculture and political economies across SSA still relatively underexplored.

Looking ahead, future research could benefit from more targeted country-level case studies to better account for regional differences in climate, economy and politics. Exploring how technological innovations like digital agriculture and climate-smart farming might help mitigate these issues could also provide some useful insights. Long-term studies that track changes over time would be critical in understanding how climate change continues to shape the region’s agriculture and economies. Comparing SSA with other regions facing similar challenges, such as South Asia or Latin America, could offer valuable lessons and best practices. Finally, future work should pay closer attention to governance and institutional factors – like land rights, agricultural policies and institutional capacity as these play a big role in shaping climate adaptation efforts and agricultural resilience, all essential for sustainable development.

The current study deals with the impacts of climate change on cereal crop productivity in 27 SSA countries by using panel data from 1996 to 2024 sourced from the World Bank and FAO databases. Among these climate indicators are temperature, precipitation and CO₂ emissions, plus control variables. In addition, unobserved heterogeneity and endogeneity are considered through the use of a dynamic model: the system GMM. The results highlight the complex interdependence of climate variables and agricultural productivity in the region. In the short run, a rise in temperature has a small positive effect on crop yield, as a 1% rise in temperature leads to a 0.388 increase in yield. Similarly, productivity benefits from increased precipitation. However, the long-term effects are even more alarming: for every 1% increase in temperature, there is a corresponding 0.407 decrease in yields, while for every 1% increase in CO₂ emissions, productivity falls by 0.530. These results highlight that NDCs should focus on mitigation strategies addressing temperature rise and CO₂ emissions to ensure food security in SSA.

The study further points out that the increase in CO₂ emissions is detrimental to productivity. In this regard, a 1% increase in such emissions causes a 0.085 decline in yields for the short run and a 0.56 decline in the long run. This calls for the inclusion of carbon reduction strategies in NDCs with a focus on SSA agriculture.

Besides, political stability plays a complementary role in agricultural productivity, increasing it by 0.512 with every 1% increase in the level of political stability. Other economic variables include GDP growth and fertilizer consumption. On the other hand, trade openness has a negative impact on domestic agricultural production, implying that NDCs should focus on strengthening their respective industries. The study calls for climate-smart agricultural strategies that consider political stability, economic growth and climate mitigation as interlinked pathways to securing a resilient agricultural sector in SSA.

The study concludes that climate change impacts the agricultural sector in SSA overwhelmingly both as blessing (an opportunity) and curse (threat). For example, rising temperatures and erratic rainfall have reduced crop yields in countries like Niger and Malawi, worsening food insecurity. However, in regions like Ethiopia and Rwanda, investments in climate-resilient farming and irrigation have improved productivity. The political economy strongly influences these outcomes where institutions are weak, adaptation remains limited, but where governance supports innovation and investment, agriculture can become more resilient.

To improve cereal crop productivity in the study area, the following policy recommendations are suggested:

• Water resource management: Governments should implement policies to enhance the management of water resources, including rainfall harvesting and efficient irrigation systems, to boost cereal crop production. Climate-smart agricultural practices and financial support for smallholder farmers should also be promoted.

• Climate-smart agriculture: SSA countries should adopt environmental policies aimed at reducing regional temperatures, promote high-temperature and drought-resistant crops, invest in research and development and provide incentives for transitioning to CSA.

• Promote adoption of precision agriculture technologies: Governments and development agencies should invest in scaling up access to Precision Agriculture tools such as GPS-based soil mapping, remote sensing and variable-rate input application to help farmers adapt to climate variability and optimize resource use.

• Green economy: Governments should prioritize green economy strategies that address CO₂ emissions and contribute to mitigating global warming while fostering sustainable economic growth.

• Political stability and governance: Stable governance and political stability are critical for creating an enabling environment for agricultural productivity. Governments should implement policies to reduce political instability and encourage investments in the agricultural sector, including subsidies for inputs and trade policies that promote market access.

In a nutshell, our research emphasizes important policy ideas. To make optimal agriculture productivities in SSA, policies need to focus on both climate resilience and good political governance. As climate change poses growing threats to farmers’ livelihoods, governments should invest in ways to help them adapt like building irrigation systems, promote adoption of precision agriculture technologies, promoting drought-tolerant seeds and offering weather-based insurance schemes. But just technical fixes are not enough. Strong institutions and transparent governance are just as essential to make sure assistance reaches those who need it the most. Promoting Anti-Patronage Policies, tackling corruption, boosting transparency and designing support programs based on genuine needs rather than political ties will help create a fairer, more efficient agriculture sector. When these approaches are combined, they can improve food security, support rural growth and strengthen long-term resilience throughout the region.

Nzabirinda Etienne would like to express deep appreciation to all my PhD lecturers who taught me in-person classes from the School of Business, Economics, and Law at the University of Gothenburg: Prof Yonas Alem, Prof Ola Olsson, Prof Ann-Sofie Isaksson, Prof Anika Lindskog, Prof Dick Durevall, Prof Joe Vecci and Meseret Abebe. Their standards have made me truly better at what he can do.

I’m also indebted to my PhD local lecturers at the University of Rwanda who taught me in-person classes: Dr Joseph Nkurunziza, Dr Mugenzi Martin and Prof François Niragire, whose immense contributions have enriched my learning experience.

I would further like to acknowledge my regional PhD lecturer, Prof Ibrahim Mukisa of Makerere University, Uganda, who taught me in-person classes.

I would also like to acknowledge my Africa-based PhD lecturer, Prof Tendai Gwatidzo of the University of the Witwatersrand, Johannesburg, South Africa, who taught me in-person classes and provided impactful learning.

I further acknowledge world-class PhD lecturer, Prof Salvatore Di Falco of the University of Geneva, Switzerland, who taught me in class and made top-level contributions to my learning.

I also expresse my gratitude to Dr Aimable Nsabimana, a Development Economist and Research Fellow at UNU-WIDER, who taught me advanced impact evaluation in July 2022.

I would like to thank the management of Institute for Climate Change and Adaptation at the University of Nairobi, and most especially Prof Daniel Olago and Prof Marc Zolver for teaching me climate change and adaptation.

My gratitude also extends to Prof Tilo Halaszovich and Sonja Mattfeld from ICN Business School, France, for teaching me advanced research methodology during the PhD Research Colloquium.

In addition, I acknowledge with thanks Prof Harald von Korflesch, Dr Kornelia van der Beek and researchers at Universität Koblenz, as well as Nyiringango Pascal and Materne Mateso Lumiere for providing insightful comments on my research papers during the KeyComp4Practice@UR project.

I also appreciates Dr Abdurrahman Gümrah and the IBSCO team for accepting my research paper for presentation at the International Business and Society Conference on 01–02 August 2024 in Cappadocia, Türkiye, and i would like to express my sincere gratitude to Chief Editor Professor Walter Leal Filho of Hamburg University of Applied Sciences, Germany, Editor Dr Gustavo Nagy of Universidad de la República, Uruguay, Handling Manuscript Editor Prof Johannes M. Luetz of the University of the Sunshine Coast, Australia, Journal Editorial Office Dr Tejaswi Gaikwad of Emerald Publishing, Supplier Project Manager Dr Vidhi Tyagi of Emerald Publishing, Publisher Dr Paul Kidd, and all the world-class anonymous reviewers for their invaluable contributions to the review of my manuscript submitted to the International Journal of Climate Change Strategies and Management.

Funding: The authors declare that no funds, grants or other support were received during the preparation of this manuscript.

Conflict of interest statement: The author declares no conflict of interest regarding the publication of this manuscript.

Author contributions statement: Nzabirinda Etienne led the study design and was responsible for drafting and revising the manuscript. Nkurunziza Joseph supervised, PhD, and Mugenzi Martin, PhD also did the supervision. All authors have read and approved the final manuscript and agreed on the order in which their names appeared.