Climate change, financial development, and health outcomes in sub Saharan Africa

Abstract

This study examines the relationship between climate change and health outcomes in Africa. It further explores the role of financial development in moderating the relationship between climate change and health outcomes. It applies the dynamic system of the generalized method of moments estimation to a panel dataset of 43 African countries over the period 2000–2023. The key findings confirm that higher CO₂ emissions significantly reduce life expectancy and increase mortality rates, whereas financial development has a positive effect on life expectancy but negatively affects mortality rates, indicating that an increase in financial development mitigates the negative impact of carbon emissions on health outcomes. This paper provides empirical evidence of the fundamental role of financial development in the nexus between climate change and health outcomes. While governments are encouraged to increase investments in climate-resilient healthcare infrastructure, clean water systems, sustainable energy, and disease monitoring, financial sector players are advised to prioritize the creation of innovative financial tools related to climate and health, such as climate risk insurance, weather-indexed insurance, and health insurance, which can help reduce the impact of health problems caused by climate change on people, governments and the economy as a whole.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-026-02894-z.

Introduction

The Sustainable Development Goals (SDGs) highlight the importance of achieving specific health targets to increase human capital and achieve sustainable development. However, to an extent, the effects of climate change seem to inhibit the attainment of these goals [1–3]. Climate change directly contributes to catastrophes such as heatwaves, tropical storms, and floods, which are becoming more widespread, frequent, and intense. Available statistics indicate that approximately 3.6 billion people live in places vulnerable to climate change [4]. Moyo et al. [5] reported that since the beginning of 2022, extreme weather events in Africa have endangered approximately 19 million people and killed at least 4,000 people. Furthermore, the World Health Organization (WHO) [6], projects that climate change will increase annual mortality by 250,000 due to malnutrition, malaria, diarrhoea, and heat exhaustion between 2030 and 2050. By 2030, the direct costs of health damage, excluding expenses in areas that determine health, such as agriculture and water and sanitation, are projected to range from $2–$4 billion annually. Developing countries with poor health infrastructure will be the most vulnerable if there are no support systems in place.

The debate in recent literature clearly shows that climate change has emerged as one of the most significant threats to health outcomes, with far-reaching implications for human well-being and sustainable development [7]. Increasing climate change, including increase in temperatures, changing precipitation patterns and the rising frequency of extreme weather events have intensified environmental risks, leading to direct and indirect expect on health outcomes [8, 9]. Reducing greenhouse gas emissions through improved food, energy, and transportation choices can have a significant positive impact on health, especially by lowering air pollution [7, 10, 11]. From the theoretical perspective of the health production function, climate change negatively affects the production of health by increasing the exposure to climate-sensitive diseases, increasing food insecurity, and exacerbating heat-related illness [12, 13]. Empirical evidence increasingly shows that these climate-related stressors contribute to higher mortality rates and reduced life expectancy, particularly in vulnerable regions such as Sub-Saharan Africa [8, 9, 14, 15].

A growing body of literature has examined the relationship between climate change and health outcomes in Africa, highlighting significant adverse effects across different dimensions (see [16–18]). More recent empirical contributions provide cross-country evidence on the climate–health nexus [19, 20], while others extend the discussion to sector-specific linkages such as renewable energy, food security, and health expenditure [21–23]. However, the direct climate-health nexus empirically remains scanty from the SSA context. Beyond direct effects, climate change also operates indirectly through financial systems [24]. Guo et al. [24] reported that financial development plays a crucial role in energy consumption and greenhouse gas emissions, thus, could moderate the relationship between climate change and health outcomes. This relationship can be grounded in the climate change vulnerability theory, which stipulates that climate change increases exposures to environmental risks, but the extent to which these risks translate into adverse health outcomes depends largely on a society’s adaptive capacity (see [9]). Financial development enhances adaptive capacity by improving access to credit, insurance, and investment capital, thereby enabling households and governments to respond more effectively to climate-related shocks [25]. On one hand, finance has the potential to propel the advancement of a low-carbon economy through direct investments and credits allocated to green projects. Indirectly, it can also facilitate the mobilization of funds for green and low-carbon innovative projects [26]. It has also been argued that the economic development benefits of financial development could also increase industrialization, impacting energy consumption and carbon emissions. [27, 28].

On the other hand, financial development provides opportunities and critical resources for improvements in health infrastructure and health outcomes. Shi et al. [29], Rana et al. [30], Bialowolski et al. [31], and Claessens and Feijen [32] explore several channels through which financial development can affect the health outcomes of a nation. According to the income effect channel, financial development stimulates industrialization, which further creates new economic activities that propel the employment and per capita incomes of households. This increase in income provides opportunities for people to access better health care, nutrition, and housing, which ultimately has positive impacts on health outcomes [33–35]. Financial development also improves access to credit, savings, income growth, and risk management [36] and, in turn, helps households access health care and better health outcomes. Moreover, through the lens of gender equality, advancements in financial development and financial inclusion improve women’s empowerment [37]. In line with Pratley [38], it is believed that women who are empowered have a greater ability to spend more on the health of their children. It further argued that the opposite could also be true. That is, if financial development does not enhance financial access and inclusion but increases the requirements for certain groups to be able to access finance, then such actions may lead to a reduction in health care expenditures for these groups and hence deterioration of health outcomes. The supposition of the education channel is that financial development enhances literacy rates through investments in educational infrastructure, which helps people with more access to education make more informed decisions about their health [39–41]. Moreover, financial development tends to increase the health of the populace through improvements in health infrastructure as more investments in hospitals and healthcare facilities are made. Accordingly, with advancements in financial development, governments and private sector investors can access capital from financial markets for healthcare investments, leading to improved quality healthcare, delivery, and outcomes [25, 42, 43].

In this study, we propose that financial development could have a significant indirect effect on the relationship between climate change and health outcomes in sub-Saharan Africa. Accordingly, this research contributes to the empirical literature in several ways. First, the study provides empirical evidence on the impact of climate change on health outcomes, specifically in the sub-Saharan African region, to address the paucity of empirical studies on the subject matter. Indeed, while the contribution of sub-Saharan Africa to global greenhouse gas emissions is under 10%, it is the most susceptible region to the effects of climate change due to its poor infrastructure, high dependency on rain-fed agriculture, and large share of agriculture in GDP, among other factors. Statistics from the Food and Agriculture Organization (FAO) of the United Nations indicate that the number of undernourished people has markedly increased since 2012 in drought-stricken sub-Saharan African countries (see [44]). In the area of health, the WHO) report (2020) also indicated that approximately 93% of global malaria deaths occurred in Africa in 2017 [11]. This spike has been attributed in part to warmer temperatures and increased rainfall, which increase habitat suitability for biting insects and the transmission of vector-borne diseases.

Moreover, sub-Saharan Africa, relative to other regions, has lagged behind in terms of health outcome metrics. In Africa bears over 20% of the global burden of disease. Insight is needed to empirically unravel the linkages between climate change and health outcomes in this region. Despite the pertinence of climate change to health outcomes in the sub-Saharan region, the literature can no longer be observed as limited to a few isolated contributions such as the work by Ramin and McMichael [45]. Interestingly, recent studies in the literature have highlighted the nexus between climate change and health in SSA [19, 46, 47]. For instance, the existing literature identifies several important determinants of health outcomes, including healthcare expenditure, institutional quality, energy consumption, and environmental degradation (see [48–52]). However, the literature remains fragmented and still exhibits significant gaps. While existing studies are unevenly distributed across economies and research areas, others have focused on relatively small number of SSA economies with rigorous causal and mechanism-based empirical work still limited in the literature (see [5, 53, 54]). The existing literature remains fragmented and largely focuses on direct effects or sector-specific interactions, with limited attention to the underlying transmission mechanisms through which climate change affects health outcomes. In particular, the role of financial development as a critical enabling and conditioning factor in this nexus has received little empirical consideration.

In contrast, this study addresses this gap by examining the role of financial development in the climate change-health nexus in Sub-Saharan Africa. It employs real data from 2000 to 2023 to analyse the impact of climate change on health outcomes in the region. The motivation stems from the fact that financial systems play a central role in mobilizing resources for healthcare investment, facilitating adaptation to climate shocks, and enhancing resilience through insurance, credit access, and infrastructure financing. However, empirical evidence integrating financial development into the climate-health relationship remains limited and less developed. Unlike existing studies, which predominantly focus on direct effects of climate change or isolated determinants of health, this study introduces financial development as a moderating channel that conditions the impact of climate change on health outcomes. This study contributes to the literature by providing a more comprehensive understanding of the underlying transmission mechanism (i.e., the role of financial development) that explains the nexus between climate change and health outcomes. The findings of this study are expected to benefit policymakers, financial institutions, and development agencies by providing evidence that the health outcomes of climate change may vary across countries with different levels of financial sector development. In particular, the study offers policy-relevant insights into the design of climate-resilient healthcare financing strategies and the role of financial markets in supporting sustainable health outcomes in vulnerable regions such as Sub-Saharan Africa,

We present the other sections of this paper in the following order: In Section “Literature review”, we discuss the theoretical framework, the empirical literature review, and the development of the hypotheses. In Section “Data and methodology”, the data and methods used to achieve the research objectives are presented. Section “Results and discussion” presents the results and discusses the findings. In Section “Conclusion and recommendations”, the conclusion and implications for policy are presented.

Literature review

Climate change and health outcomes

The impact of climate change on health continues to gain traction because of the increasing incidence of heatwaves, tropical storms, and floods, even in the empirical literature. Ayejoto et al. [55] reviewed the consequences of air pollution and climate change for human health and well-being in Africa. Within the Health Production Function Theory, climate change constitutes a critical negative environmental input that disrupts the production of health by increasing exposure to climate-sensitive diseases, exacerbating food insecurity, and intensifying heat-related illnesses (see [12, 13]). A growing body of empirical evidence indicates that climate-related stressors significantly increase mortality rates and reduce life expectancy, with the effects being particularly pronounced in vulnerable regions such as Sub-Saharan Africa (see [8, 9, 14, 15]). This review stresses that climate change exacerbates air pollution and the latent long-term consequences for public health in Africa. It also highlights the unequal effects of air pollution on vulnerable groups. Using a review approach, Moyo et al. [5], Matandirotya et al. [56], and Imane et al. [57] also examined the health effects of climate change in Africa. This study revealed that climate change-induced natural catastrophes and extreme weather events may make it more difficult for people to obtain sanitary facilities, clean water, and medical treatment, increasing their susceptibility to various diseases.

Paavola [58], investigated the health impacts of climate change as well as the associated health and social inequalities in the UK. This study advances the notion that climate change has differential impacts on different groups of people because of variations in their exposure, sensitivity, and adaptive capacity. Moreover, age, preexisting medical conditions, and social deprivation are critical factors that determine people’s vulnerability to the health impacts of climate change. Rocque et al. [59] conducted a systematic review on the health impact of climate change on key health outcomes, including mortality. The preponderance of studies reviewed generally indicated worsening human health effects of climate change, even though some studies called for more research in this area. Nilsson et al. [60] also provided a narrative review of the impacts of weather, climate, and climate change on health in sub-Saharan Africa and South Asia. The review argues that health risks are disproportionately distributed worldwide and within countries as well as across population groups. Accordingly, existing health challenges and inequalities are likely to be exacerbated by climate change. Arafat et al. [48] examine the relationships among energy consumption, environmental degradation, and financial development and their effects on health outcomes in Pakistan. The findings of the study indicate a one-directional causality from energy consumption and environmental degradation to health outcomes. More recent studies shows that the climate-health nexus remains negative even in a broad cross-country setting and after controlling for socioeconomic conditions. For instance, Arisco et al. [61] show that extreme heat and low precipitation are associated with excess deaths from climate-sensitive causes in emerging economies. While Brimicombe et al. [14] found that ambient heat exposure raises mortality risks among children under five across African countries, RRoy [15], found that a 1 °C increase in annual average temperature reduces life expectancy at birth by less than six months, while a rise in a composite climate-change index also significantly lowers life expectancy.

Thus, these studies establish a clear empirical pattern: climate stressors such as rising temperature, drought, low precipitation, and broader climate-change exposure worsen survival and longevity outcomes. At the same time, the newer literature is still somewhat fragmented. While much of it focuses on children, neonatal mortality, or single-country case studies, fewer studies examine broader macro-health indicators for Sub-Saharan Africa in a unified framework. The gaps in the literature give a strong basis for empirical studies to test the relationship using regional panel data and broader health indicators such as life expectancy and infant mortality. On that basis, we state Hypothesis 1 as follows:

Hypothesis 1:

Climate change negatively impacts health outcomes.

Financial development and health outcomes

The concept of financial development and health outcomes can be grounded in the climate change vulnerability theory, which conceptualizes vulnerability as a function of exposure, sensitivity, and adaptive capacity. Through efficient resource mobilization and allocation, financial systems support healthcare infrastructure, facilitate climate-resilient investments, and promote risk-sharing mechanisms, which collectively reduce the health impacts of climate change [39]. Conversely, weak financial systems constrain adaptive responses and may amplify the adverse health effects of climate variability, particularly in vulnerable regions such as Sub-Saharan Africa [9]. In 2006 and 2007, Claessens and Feijen conducted two studies that sought to examine the impact of financial development on health outcomes (undernourishment/hunger) through agricultural productivity and on the achievement of the millennium development goals. Since this seminal work, several other researchers have sought to explore this phenomenon. Employing panel regression methods and a sample of 49 countries, Claessens and Feijen [62] show that a deeper financial sector leads to higher agricultural productivity, which further leads to lower undernourishment. Claessens and Feijen [32] assessed the role of financial sector development in various countries on key Millennium development themes, including health. The findings of the study provide evidence that financial sector development has positive impacts on health outcomes. This finding was also corroborated in Bhatta [63] for a sample of high-income OECD countries and low-income Asian countries, with the coefficient being higher for developing countries than for developed countries. Rewilak [64], further advanced the subject using micro and macro datasets. The evidence presented in the study indicates a negative relationship between financial deepening and health outcomes, proxied by mortality rates. The study by Arafat et al. [48] focused on Pakistan and revealed a bidirectional causality between financial development and health outcomes. It also shows that financial development has a positive association with health outcomes. Furthermore, Shi et al. [29] examined the association between financial development and health outcomes for a sample of Asian economies between 1991 and 2019. They present findings that financial development has a positive effect on life expectancy. In sub-Saharan Africa, Chireshe and Ocran [49] examined the relationship between financial development and health outcomes via data from 1995 - 2014. The findings reveal that financial development leads to better health outcomes. Specifically, they find that financial development reduces the mortality rate and increases life expectancy.

Moreover, financial development has been shown to enhance access to healthcare services, reduce mortality rates, and improve life expectancy, particularly in developing economies [65]. These effects occur through multiple channels, including improved access to credit, increased insurance coverage, and enhanced government capacity to finance healthcare systems [66]. Moreover, financial development has been found to reduce out-of-pocket healthcare expenditure and improve healthcare utilization, thereby contributing to better population health outcomes (Rjoub et al., 2024).

Recently, Chagoma et al. [67] synthesize a systematic review on the impact of official development assistant for health (DAH) on health outcomes in low-and middle-income countries, highlighting both the positive and negative effect, and identifying key mechanisms through which aids affect health outcome. By observing different methodological approaches, they explain with a weight of evidence that DAH, generally, has a positive impact, particularly in countries with higher governance standards and better economic conditions. However, despite these advancements, literature remains limited in several respects. First, most studies focus primarily on the direct effects of financial development on health outcomes, with limited attention to its role as a moderating factor [66]. Second, existing studies largely overlook the interaction between financial development and climate-related risks, particularly in vulnerable regions such as Sub-Saharan Africa. Third, there is insufficient empirical evidence on the mechanisms through which financial development influences health outcomes under environmental stress conditions. Consequently, this study extends the literature by examining both the direct effect of financial development on health outcomes and its moderating role in the climate change–health nexus. This leads us to formulate the following hypothesis:

Hypothesis 2:

Financial development positively impacts health outcomes.

Climate change, financial development and health outcomes

The interplay between financial development and climate change significantly influences health outcomes, particularly in vulnerable regions such as sub-Saharan Africa. Accordingly, from the perspective of the climate change vulnerability theory, financial development acts as a moderating factor that conditions the relationship between climate change and health outcomes, such that higher levels of financial development mitigate the negative health effects of climate-related risks. Financial systems can mitigate climate-health risks by funding resilient health infrastructure and early warning systems [68], as evidenced by the $7.1 billion in global climate-health financing reported in 2022 [69]. However, financing gaps persist, with only 4.9% of adaptation funds committed to health activities in Africa between 2009 and 2019 [70].

Significant risks counterbalance the benefits of financial development. Debt-driven adaptation creates unsustainable burdens, particularly for Africa. Unequal access to financial resources may widen existing health disparities [71], leaving marginalized groups disproportionately exposed to climate health risks. Perhaps most paradoxically, financial systems that continue funding high-emission industries may inadvertently accelerate the very climate changes that threaten health outcomes. These contradictions are particularly acute in sub-Saharan Africa, where despite contributing minimally to global emissions, countries face some of the most severe climate health consequences with limited financial resources to respond.

Financial mechanisms demonstrate dual potential in climate–health dynamics. While innovative tools such as climate bonds could generate protective financing, current flows remain inadequate and are often misaligned. The effectiveness of financial systems depends on overcoming short-term profit motives and ensuring equitable access to avoid exacerbating existing health disparities. Importantly, financial development operates as a moderating mechanism, influencing the magnitude and direction of the impact of climate change on health outcomes. In countries with more developed financial systems, the adverse effects of climate change on health are expected to be attenuated due to improved resource mobilization, healthcare financing, and risk-sharing mechanisms. Conversely, in financially underdeveloped contexts, climate shocks are likely to translate more strongly into poor health outcomes. This conditional relationship underscores the critical role of financial development in shaping the climate–health nexus.

Clearly, the critical role of financial development makes it indispensable in addressing the interconnected challenges of climate change and health. Hence, given the scarcity of empirical evidence on the nexus between financial development, climate change, and health outcomes in sub-Saharan Africa, we hypothesize that

Hypothesis 3:

Financial development moderates the relationship between climate change and health outcomes.

Data and methodology

The objective of this study is to examine the linkages between climate change, financial development, and health outcomes in sub-Saharan Africa. The study employed panel data on 43 African countries from 2000—2023 (see Supplementary Table 4). The criteria for including countries were mainly based on data availability. The objective of this study is to examine the linkages between climate change, financial development, and health outcomes in Sub-Saharan Africa. The study employs panel data for 43 countries over the period 2000–2023. The selection of countries is guided not only by data availability but also by the region’s unique structural and vulnerability characteristics. Sub-Saharan Africa is widely recognized as one of the most climate-vulnerable regions globally, with high exposure to climate shocks such as droughts, floods, and temperature variability, which have significant implications for public health outcomes. At the same time, the region is characterized by relatively underdeveloped financial systems, limited healthcare infrastructure, and high disease burden, making it an appropriate context for examining the role of financial development in mitigating climate-related health risks.

Furthermore, the countries included in the sample represent a broad cross-section of SSA economies, capturing heterogeneity in financial sector development, institutional quality, and health system capacity. This variation is essential for identifying differential effects and strengthening the empirical validity of the analysis. The study period (2000–2023) is also particularly relevant, as it encompasses major policy shifts related to financial sector reforms, climate adaptation strategies, and global health initiatives, including the Millennium Development Goals and the Sustainable Development Goals.

Given that some variables lack data for some countries or times, the panel is unbalanced. Year trends, however, were included to account for missing data. Consequently, the selected sample provides a suitable empirical setting for analyzing the dynamic interactions between climate change, financial development, and health outcomes. Given data limitations across countries and time, the panel is unbalanced; however, this does not bias the estimates, as the system GMM estimator accommodates unbalanced panels. Time (year) effects are included to control for common shocks and unobserved temporal dynamics. Importantly, the model specification is not ad hoc but is grounded in established theoretical frameworks.

The baseline model is informed by the health production function, which posits that health outcomes are determined by a combination of socioeconomic, environmental, and institutional factors. Within this framework, climate change enters as an environmental risk factor that directly affects health through channels such as disease transmission, food insecurity, and heat stress. Financial development is incorporated as a facilitating mechanism that enhances resource allocation, supports healthcare investment, and strengthens adaptive capacity. This is further supported by endogenous growth theory, which emphasizes the role of financial systems in mobilizing capital for human capital development, including health.

Accordingly, the empirical model specifies health outcomes as a function of climate change, financial development, and relevant control variables. This specification allows for the examination of both direct effects and the underlying transmission mechanisms linking climate change to health outcomes within the Sub-Saharan African context.

The baseline model, which is represented below, is what we use:

1

Model specification and estimation technique

From the baseline equation, we estimate the effects of climate change and financial development on health outcomes by following the methods of Rana et al. [30], Chandio et al. [72], Hakimi and Inglesi-Lotz [73], and Shafiei et al. [74]. We employ a dynamic panel model estimated using the system Generalized Method of Moments (system GMM) to examine the relationship between climate change, financial development, and health outcomes. The choice of this estimator is motivated by several econometric considerations. First, health outcomes such as life expectancy and infant mortality are inherently persistent, as current health conditions depend on past outcomes due to structural and institutional factors. Incorporating a lagged dependent variable captures this dynamic adjustment but renders conventional estimators such as pooled OLS and fixed effects biased and inconsistent.

Second, the model addresses endogeneity arising from reverse causality, omitted variables, and simultaneity. For instance, while financial development influences health outcomes, improvements in health may also enhance financial sector performance. The system GMM estimator mitigates these concerns by using internal instruments based on lagged values of the variables.

Third, given the “large N, small T” structure of the panel, system GMM effectively controls for unobserved heterogeneity, heteroskedasticity, and autocorrelation. Compared to difference GMM, it improves efficiency by combining level and differenced equations, thereby reducing weak instrument problems.

Estimation validity is confirmed using the Arellano–Bond test for serial correlation and the Hansen test of over-identifying restrictions. Our model is expressed as follows:

2

where subscript i denotes the cross-sectional dimension, ranging from 1 to 43 countries; t denotes the time series dimension, ranging from 1 to 24 years; α denotes the constant term; HO represents the dependent variable proxied by life expectancy and mortality rate; denotes the coefficient of the lagged health outcome variable; is the coefficient of climate change measured by carbon dioxide emissions (CO₂); is the coefficient of financial development; represents the coefficient of a vector of control variables; : k = 1,…,N represents regression parameters for vector X (control variables) to be estimated; is the country fixed effect; and is the time fixed effect t; and is an idiosyncratic error term, which controls for the unit-specific residual in the model for the i^thcountry at period t. The control variables are gross domestic product per capita, primary school enrolment rate, health expenditure, urban population, government effectiveness, and immunization coverage.

Description and measurement of the variables

First, the dependent variable in focus (health outcomes) has generally been measured by (1) life expectancy and (2) infant mortality, according to the preponderance of the empirical literature (see [29, 48]). These indicators are selected because they capture both the overall health status of the population (life expectancy) and vulnerability among the most sensitive population group (infant mortality), thereby providing a comprehensive assessment of health outcomes. Compared to alternative measures such as morbidity rates, disease-specific indicators, or self-reported health status, life expectancy and infant mortality offer several advantages. First, they are widely available and consistently measured across countries and over time, making them particularly suitable for panel data analysis in Sub-Saharan Africa. Second, they are less prone to measurement error and reporting bias, which are common in survey-based or disease-specific data. Third, these indicators are highly responsive to changes in socioeconomic conditions, environmental quality, and healthcare access, making them appropriate for capturing the multidimensional effects of climate change and financial development on health outcomes.

Life expectancy at birth estimates how long a newborn would live if the mortality rates at the time of birth remained constant throughout their life. The infant mortality rate is the number of deaths of infants under one year of age per 1,000 live births each year. Given the sensitivity of these variables to socioeconomic factors and the quality of healthcare provision, they correctly capture the health status of the populace.

Climate change is proxied by carbon dioxide (CO₂) emissions. Carbon dioxide emissions are those stemming from the energy, agriculture, waste, and industrial sectors, excluding land use, land-use change, and forestry (LULUCF). CO₂ emissions have been employed in similar studies, including Kinda [75], and Ceesay et al. [76]. In this context, CO₂ emissions are employed as a comprehensive proxy for climate change because they capture the broader environmental degradation and atmospheric conditions that directly influence health outcomes through multiple pathways, including air pollution, temperature changes, and disease transmission. While there are other proxies for climate change such as total annual temperature, average annual temperature, precipitation index, we employ CO₂ emissions because it is closely linked to industrialization, energy consumption and urbanization and enhances comparability with previous studies. Moreover, CO2 emissions are widely available for most of the African countries and consistently measured over long periods. Furthermore, interacting financial development with CO₂ emissions therefore allows us to assess whether financial systems can mitigate or amplify the health effects of climate-related environmental stressors, which aligns more closely with our research objective.

The inclusion of explanatory variables employed in this study is mainly informed by the empirical literature. Financial development has been noted be a key determinant of health outcomes. Several proxies of financial development have been employed in empirical analysis. In this study, we focus on a proxy for financial depth. Accordingly, we use domestic credit to the private sector (% of GDP). This measure captures the extent to which financial institutions mobilize and allocate resources to the private sector, thereby reflecting the availability of financing for productive and welfare-enhancing investments. Within the climate change vulnerability framework, financial depth enhances adaptive capacity, which determines how climate-related risks translate into health outcomes. Specifically, increased domestic credit to the private sector facilitates access to funds for households, firms, and healthcare providers, enabling investments in climate-resilient infrastructure, improved healthcare services, and preventive health measures. Previous studies in which financial development has been employed as an explanatory variable of health outcomes include Claessens and Feijen [32] and Chireshe and Ocran [49]. Through these channels, financial depth reduces vulnerability and mitigates the adverse effects of climate change on health outcomes. This perspective is consistent with the broader financial development literature, which emphasizes the role of financial systems in resource mobilization and risk management [32, 49] and supports its inclusion as both a direct determinant and a moderating factor in the climate–health nexus.

The gross domestic product (GDP) per capita is employed to measure economic growth. In Claessens and Feijen [32], income plays a key role in the health status of an economy. The study posits that the impact of income on health outcomes tends to be greater in low-income countries than in high-income countries. That is, higher incomes provide opportunities for households to obtain nutritional food requirements, quality health care, and other human capital-related investments [77, 78].

We measure education using the primary school enrollment rate. This measures the total number of students enrolled in primary education, regardless of age, compared with the population of children in the official age range for primary education, which aims to provide foundational skills in subjects such as reading, writing, and math. There is empirical evidence that suggests that households with higher educational attainment tend to have comparatively better health status [79, 80].

Health expenditure captures the percentage of a country’s GDP spent on healthcare goods and services each year, excluding capital health expenditures such as machinery, buildings, IT, and emergency vaccines. The expectation is that more investments in health should improve health outcomes. Several studies, including Sultana et al. [81] and Vărzaru [82], have employed this variable., exploring the impact of health investments on health outcomes.

Urbanization refers to individuals residing in areas classified as urban areas by each country’s national statistical office. As urbanization rates increase, living standards improve, and this rise in living standards has a positive effect on public health [83]. Additionally, urban areas typically have better hospitals, clinics, and specialists.

The quality of institutions is measured by how effective the government is, which reflects how people view the quality of public services, the professionalism and independence of civil servants, how well policies are designed and carried out, and how reliably the government follows its own rules. This multidimensional metric evaluates both the competence of bureaucratic institutions and their ability to operate without undue political interference. Government effectiveness is measured as a continuous index ranging from −2.5 to +2.5, with higher values indicating more effective government institutions. Hadipour [52] reported a negative relationship between institutional quality and infant mortality rates and a positive relationship with life expectancy. Enhancing government effectiveness helps create merit-based and democratic structures, build infrastructure, create effective tax and subsidy systems, allocate funds optimally, promote public education, and expand access to primary health care.

Immunization coverage is measured by the percentage of children aged 12–23 months who have received at least one dose of the measles vaccine, indicating the level of immunization coverage against measles. Shattock et al. [84] and Yakubu and Shehu [85], asserted that vaccination has led to a reduction in infant mortality.

Interaction effect of climate change and financial development

We specify a model that introduces the interactive effect of climate change and financial development on health outcomes in sub-Saharan Africa, as presented in Eq. (3) below.

3

In Eq. 3, denotes the coefficient of the interaction term. All other variables are the same as previously stated. Equation 3 explains the interaction effect of climate change and financial development on health outcomes. It also explains the role that financial development plays in the relationship between climate change and health outcomes. For proper interpretation, we compute the marginal effects of climate change on health outcomes conditioned on financial development by following Brambor et al. [86]. We anticipate that financial development will change the impact by either reducing or reversing the negative effects of climate change on health outcomes. This study explains that financial development dampens or magnifies the effect of climate change on health outcomes.

Estimation techniques

We resort to the system generalized method of moments (GMM), an instrumental variable approach, to estimate the dynamic panel data model. System GMM is preferred because the model is dynamic, health outcomes depend on their past values, and conventional estimators such as OLS and fixed effects become biased and inconsistent in the presence of a lagged dependent variable. Moreover, system GMM effectively addresses endogeneity arising from reverse causality and omitted variables by using internal instruments, while also being suitable for large N, small T panels. The GMM estimator produces reliable estimates for the models and helps in resolving the endogeneity problem. Moreover, the robustness of the GMM estimator is enhanced by its ability to account for heteroscedasticity and autocorrelation. With this approach, we may choose parameters to minimize the correlation between a set of instruments and the disturbance term, aiming for a correlation that is almost zero [87, 88]. The presence of heterogeneity and heteroscedasticity among variables is a necessary assumption for estimating the GMM. As a result, after the equations are estimated with the GMM, heterogeneity and heteroscedasticity are not needed. Consequently, we did not perform these tests. The GMM utilizes lagged levels as instruments for first-differenced variables. Importantly, earlier studies by Blundell and Bond [88] and Arellano and Bover [89] show that lagged levels can sometimes be ineffective instruments for first-differenced variables. These researchers expanded the GMM estimator by including both lagged levels and lagged differences to overcome this drawback. Accordingly, several researchers employ this extended estimator, often known as the system GMM, to solve the problem of weak instruments [87, 89]. System GMM estimations use the Hansen test, also known as the J statistic, to evaluate the validity of the overidentifying restrictions. Specifically, it tests the null hypothesis that the instruments are valid, meaning that they are uncorrelated with the error term and are correctly excluded from the estimated equation. A failure to reject the null hypothesis implies that the instruments are likely exogenous and appropriate for the model. The residuals are also subjected to Arellano‒Bond AR(1) and AR(2) autocorrelation tests. The AR (1) and AR (2) tests check if there is an autocorrelation in the residuals, usually starting with the assumption that there is no pattern, and the results help evaluate whether the model’s estimates are reliable.

Results and discussion

Table 1 presents the descriptive statistics of the variables employed in the study. The average life expectancy across the sample is 59.077 years, with a standard deviation of 6.686 years. This value indicates significant variation in health outcomes across countries. The lowest recorded life expectancy is 41.957 years, whereas the highest is 77.237 years. In terms of the mortality rate, there is substantial variation across countries. The minimum number of deaths is 10.6, whereas other rates are more than twelve times higher (127.7). The standard deviation of 23.03 suggests significant dispersion around the mean of 55.94, reflecting wide disparities in health outcomes across the region. The average CO₂ emissions per capita are 17.16 Mt, but with a very large standard deviation of 67.3 Mt. The minimum value is 0.055 Mt, whereas the maximum is 487.909 Mt, indicating substantial differences between countries with high industrialization and those with minimal emissions. The average domestic credit provided by financial institutions is 20.897%, with a standard deviation of 21.972%. The values range from 0.383% to 142.422%, showing considerable differences in financial development across observations.

Table 1.

Descriptive statistics

Variables	Obs	Mean	Std. Dev.	Min	Max	Skew.	Kurt.
(1) LifeExpectancy	1032	59.077	6.686	41.957	77.237	0.102	3.099
(2) MortalityRate	989	55.938	23.028	10.6	127.7	0.221	2.623
(3) CO2	1032	17.16	67.3	0.055	487.909	5.876	37.327
(4) DCredit	1032	20.897	21.972	0.383	142.422	2.878	12.237
(5) GDPCapita (000)	1032	2.201	2.874	0.253	19.482	2.739	11.563
(6) PrimaryEnrollment	1028	99.38	21.95	31.847	156.614	−0.142	2.9
(7) HealthExp	936	4.999	2.132	1.107	16.968	1.167	5.159
(8) UrbanPopulation	1032	40.189	17.667	8.246	91.029	0.362	2.57
(9) Government	945	−0.719	0.601	−1.88	1.15	0.667	3.179
(10) Immunization	1031	73.027	17.58	16	99	−0.512	2.519

Note: 1 = Life expectancy at birth, total (years), 2 = Mortality rate, infant (per 1,000 live births), 3 = Carbon dioxide (CO2) emissions (total) excluding LULUCF (Mt CO2e), 4 = Domestic credit to private sector (% of GDP), 5 = GDP per capita (constant 2015 US$), 6 = School enrollment, primary (% gross), 7 = Current health expenditure (% of GDP), 8 = Urban population (% of total population), 9 = Government effectiveness, 10 = Immunization, measles (% of children aged 12–23 months)

Supplementary Table 2 presents the results of the correlation coefficient for the explanatory variables. In general, the results of the correlation matrix do not imply that multicollinearity should be a concern in our sample (as confirmed by a VIF below the threshold of 10) (see Supplementary Table 3). Consequently, we do not include them together in the regressions.

The effects of climate change and financial development on health outcomes

The discussion of the results begins with the diagnostics to ensure that the coefficients are reliable. The Hansen test indicates that the instruments are valid and that the models are correctly specified. Additionally, the AR (1) test is significant, indicating the presence of first-order autocorrelation, which is expected in dynamic panel models. The AR (2) test is not significant, indicating no second-order autocorrelation, which supports the validity of the GMM estimator. The chi-square statistics confirm the joint significance of the regressors in all the models. Additionally, the number of instruments is less than that of countries, thereby preventing issues such as overfitting.

In Table 2, we find a negative effect of carbon dioxide emissions on life expectancy (see Model 1), indicating that higher CO₂ emissions reduce longevity. This finding supports the health production function, which posits that climate change undermines health outcomes. This result aligns with broader evidence linking CO₂ emissions to environmental degradation and public health risks. High emissions exacerbate air pollution, which is directly associated with respiratory illnesses (e.g., asthma, chronic obstructive pulmonary disease) and cardiovascular conditions, which shorten life spans [55]. Furthermore, climate change intensifies systemic health threats, such as extreme weather events, heatwaves, and expanded habitats for disease vectors such as mosquitoes, disproportionately affecting vulnerable populations such as children, elderly individuals, and low-income communities [58]. While the immediate effect of CO₂ appears modest, cumulative emissions over time could compound health burdens, particularly in regions such as sub-Saharan Africa, where adaptive capacity is limited.

Table 2.

Climate Change and Health Outcomes Proxied by Life Expectancy

VARIABLES	Model 1	Model 2	Model 3
LifeExpectancyt-1	0.8392***	0.9094***	0.8772***
	(0.0358)	(0.0814)	(0.0407)
CO2	−0.0044**		−0.0156***
	(0.0022)		(0.0056)
DCredit		0.0302*	0.0204**
		(0.0163)	(0.0088)
CO2*DCredit			0.0001*
			(0.0001)
HealthExp	0.0099	−0.0503	−0.1725**
	(0.0684)	(0.0982)	(0.0859)
Immunization	0.0312***	0.0418**	0.0193***
	(0.0071)	(0.0183)	(0.0069)
PrimaryEnrollment	0.0077	0.0212*	0.0088*
	(0.0055)	(0.0119)	(0.0048)
GDPCapita	0.6212**	−0.0432	0.0577
	(0.2852)	(0.0640)	(0.0635)
Government	−1.0565**	1.5865	−0.1906
	(0.4743)	(1.0081)	(0.1951)
UrbanPopulation	0.0067	−0.0567	−0.0055
	(0.0307)	(0.0650)	(0.0283)
Constant	4.6326***	3.8574	5.8194***
	(1.0326)	(3.1055)	(1.1901)
Observations	850	850	850
Number of Countries	43	43	43
Number of Instruments	21	27	27
AR (1)	−2.51	−2.63	−2.42
p Value	0.0120	0.00847	0.0154
AR (2)	−0.81	−0.60	−0.94
p Value	0.417	0.550	0.347
Hansen’s Test	15.02	12.98	23.53
p Value	0.241	0.793	0.100
Chi-squared	300765	35303	1.653e+06
Chi-squared p value	0	0	0
Marginal Effect			−0.0135***

Standard errors in parentheses

*** p < 0.01, ** p < 0.05, * p < 0.1

From a contextual perspective, the magnitude of the effect, although modest in the short run, is economically meaningful when considered cumulatively. Persistent increases in emissions can lead to long-term deterioration in health outcomes through rising incidence of respiratory and cardiovascular diseases, heat-related mortality, and the spread of vector-borne diseases. These effects are particularly severe in Sub-Saharan Africa due to limited adaptive capacity, weak healthcare infrastructure, and high baseline vulnerability.

The findings also suggest that climate change is not only an environmental issue but a critical public health challenge with implications for human capital development and economic productivity. A decline in life expectancy reduces labor productivity and increases healthcare expenditure, thereby constraining long-term economic growth. Consequently, policies aimed at reducing emissions and strengthening adaptive capacity, particularly through improved healthcare systems and climate-resilient investments, are essential for mitigating the adverse health impacts of climate change in the region.

Immunization coverage has a strong positive effect on life expectancy. This result reflects the critical role of vaccines in preventing infectious diseases [90] and increasing life expectancy. Herd immunity further amplifies these benefits, protecting unvaccinated populations and fostering broader public health resilience. Such evidence underscores the importance of sustained investment in vaccination programs, particularly in countries with fragmented healthcare systems. The GDP per capita has a positive and significant coefficient, suggesting that economic growth enhances life expectancy [91]. Higher incomes enable households to access nutritious food, quality healthcare, and improved sanitation, while governments gain the fiscal capacity to invest in public health infrastructure, disease prevention, and medical research. Government effectiveness, a proxy for institutional quality, shows an unexpected negative coefficient. However, effective governance is typically linked to better health outcomes through policies such as healthcare funding and pollution regulation. The variable’s average score (−0.719 on a − 2.5–2.5 scale) indicates widespread institutional weakness across the region. In such contexts, even “effective” governments may lack the resources or political will to implement health-positive policies, or corruption might divert funds away from public services.

The role of financial development in the relationship between climate change and health outcomes

After introducing financial development (i.e., domestic credit to the private sector) into the model, we observe that domestic credit to the private sector has a positive effect on life expectancy (see Model 2), indicating that higher levels of financial development are associated with life expectancy. This finding supports Hypothesis 2, which opines that financial development positively impacts health outcomes. This finding is consistent with those of previous studies, such as those of Rana et al. [30] and Shi et al. [29], who assert that financial development can improve health outcomes by boosting economic activity, creating jobs, and increasing incomes, which enables individuals to afford better healthcare, nutrition, and living conditions. Aside from immunization, which has a positive effect on health outcomes, primary school enrollment also has a positive effect. This means that education improves health outcomes by increasing health literacy (e.g., hygiene and disease prevention), increasing future income for better healthcare access, and empowering individuals to make healthier choices.

In Model 3, we look at the interaction between CO₂ emissions and financial development and its effect on health outcomes. The interaction term (CO₂*DCredit) is positive, indicating that financial development moderates the negative impact of CO₂ emissions on life expectancy. This positive interaction shows that financial development helps reduce the harmful effects of climate change on health by providing capital, such as better health facilities and green technology, as well as improving household resilience. The coefficients for CO₂ emissions and financial development are both negative and positive, which is consistent with models 1 and 2. Health expenditure has a negative effect, which is counterintuitive and the reverse of expectations. Both immunization and primary enrollment positively affect health outcomes.

From a contextual perspective, this result is particularly relevant for Sub-Saharan Africa, where financial constraints often limit the ability of households and governments to respond effectively to climate-related health risks. A more developed financial sector can facilitate risk-sharing mechanisms, such as insurance and credit access, which enable households to cope with health shocks and maintain consumption during climate stress. At the macro level, it supports investments in healthcare infrastructure and climate-resilient technologies, thereby improving long-term health outcomes.

The negative coefficient on CO₂ emissions and the positive coefficient on financial development are consistent with earlier models, reinforcing the robustness of the findings. However, the negative effect of health expenditure, although counterintuitive, may reflect inefficiencies in healthcare spending, misallocation of resources, or the reactive nature of health expenditures in response to deteriorating health conditions. By contrast, the positive effects of immunization and primary school enrollment highlight the importance of preventive healthcare and human capital development in improving population health outcomes.

Consistent with Brambor et al. [86], there is a need to compute the overall impact of climate change on health outcomes when interacting with financial development. For instance, when Model 3 is used, the marginal effect of carbon emissions is −0.0135 [−0.0156 + (0.0001 * domestic credit to the private sector)] when domestic credit to the private sector has a mean of 20.897. This means that the negative impact of carbon dioxide emissions on life expectancy is reduced when domestic credit to the private sector increases. This means that carbon dioxide emissions generally harm life expectancies, mainly because of the environmental and health issues they cause, such as air pollution, respiratory diseases, and climate-related problems. However, the presence of higher levels of domestic credit to the private sector seems to mitigate or weaken this negative effect. In short, while carbon emissions are harmful, access to credit helps cushion the blow, allowing people and firms to better protect themselves and improve health outcomes, thus lessening the decrease in life expectancy caused by pollution.

In Table 3, we look at the effects of carbon emissions and financial development on health outcomes using the mortality rate as the dependent variable. In Model 4, CO₂ emissions have a positive effect on the mortality rate, supporting Hypothesis 1 that climate change exacerbates health risks. This aligns with evidence linking CO₂-driven air pollution to increased respiratory and cardiovascular mortality [55]. As expected, health expenditures have a negative effect on the mortality rate. Increasing government spending on health-related activities will result in a decrease in the number of deaths. Primary enrollment is negative, reinforcing education’s role in health literacy and preventive care.

Table 3.

Climate change and health outcomes according to mortality rate

VARIABLES	Model 4	Model 5	Model 6
MortalityRatet-1	0.9426***	0.9353***	0.9088***
	(0.0086)	(0.0125)	(0.0293)
CO2	0.0021*		0.0292**
	(0.0012)		(0.0146)
DCredit		−0.0299*	−0.0128
		(0.0157)	(0.0312)
CO2 *Dcredit			−0.0002***
			(0.0000)
HealthExp	−0.2161**	−0.3113**	−0.3097
	(0.0937)	(0.1419)	(0.1937)
Immunization	−0.0070	−0.0028	−0.0239
	(0.0088)	(0.0101)	(0.0190)
PrimaryEnrollment	−0.0115**	−0.0150**	−0.0147
	(0.0046)	(0.0070)	(0.0102)
GDPCapita	−0.0267	−0.1054	0.1565
	(0.1137)	(0.1578)	(0.1716)
Government	−0.3599	0.0086	−0.1670
	(0.4689)	(0.6641)	(0.5746)
UrbanPopulation	−0.0106	0.0012	−0.1085*
	(0.0094)	(0.0148)	(0.0645)
Constant	4.4351***	5.8038**	12.2874*
	(1.5681)	(2.7175)	(6.3712)
Observations	850	850	850
Number of Countries	43	43	43
Number of Instruments	22	21	30
AR (1)	−1.77	−1.74	−1.72
p Value	0.0768	0.0820	0.0863
AR (2)	−1.06	−1.18	−1.23
p Value	0.290	0.238	0.217
Hansen’s Test	13.46	13.50	18.74
p Value	0.413	0.334	0.473
Chi-squared	212366	115011	55522
Chi-squared p value	0	0	0
Marginal Effect			0.025***

Standard errors in parentheses

*** p < 0.01, ** p < 0.05, * p < 0.1

In Model 5, domestic credit to the private sector also negatively affects the mortality rate, along with health expenditures and primary enrollment. This is consistent with Hypothesis 2 and mirrors the findings of Rewilak [64], who reported that financial deepening negatively affects the mortality rate. Thus, financial deepening reduces the rate of mortality. In Model 6, the interaction term (CO₂ DCredit) has a negative coefficient. This result implies that financial development can mitigate the harmful effects of CO₂ emissions on health outcomes. In Model 6, the marginal effect of carbon emissions is 0.025 [0.0292 + (−0.0002 domestic credit to the private sector)] when domestic credit to the private sector has a mean of 20.897. This equation means that the positive impact of carbon emissions on the mortality rate is reduced when domestic credit to the private sector increases. Thus, as domestic credit increases, it weakens or dampens the harmful effect of carbon emissions on mortality. In other words, although emissions may still increase mortality, the magnitude of this increase is less when more credit is available. Access to credit acts as a buffer: it helps mitigate the deadly consequences of carbon emissions. Therefore, even if emissions rise, the mortality rate does not rise as sharply in countries with better access to private-sector credit. In other words, better access to credit, among other benefits, can help reduce the adverse health impacts of pollution. The urban population has a negative effect in this context, suggesting that urbanization can enhance living standards, which ultimately contributes to a reduction in mortality rates.

Conclusion and recommendations

This study sought to examine the implications of climate change for health outcomes in sub-Saharan Africa. This study further investigated the interactive effects of climate change and financial development on health outcomes. Financial development moderates CO₂’s negative health effects by funding adaptive infrastructure (e.g., healthcare facilities, clean energy) and providing household resilience tools (e.g., insurance, credit). The findings of this study provide important theoretical insights into the climate–health nexus by extending the health production function theory and the climate change vulnerability theory. Specifically, the results confirm that climate change acts as a negative environmental input that deteriorates health outcomes, while financial development enhances adaptive capacity and conditions this relationship. By demonstrating that financial development moderates the adverse effects of climate change on health, the study advances existing literature beyond direct effect models and provides empirical support for the role of financial systems as a key transmission channel in the climate–health relationship. This contributes to a more integrated theoretical understanding of how environmental risks interact with economic and institutional factors to shape health outcomes. From a practical perspective, the findings underscore the importance of strengthening financial systems as part of climate and health policy frameworks in Sub-Saharan Africa. Financial development enhances resilience by improving access to credit, insurance, and healthcare financing, thereby enabling households and governments to better cope with climate-related health shocks. This suggests that climate policy should not be pursued in isolation but rather integrated with financial sector development strategies. In particular, expanding access to financial services, promoting climate risk insurance, and supporting investments in climate-resilient healthcare infrastructure are critical for mitigating health vulnerabilities.

Furthermore, the results highlight the need for complementary investments in human capital, particularly in education and preventive healthcare, which reinforce resilience and improve long-term health outcomes. Strengthening governance and institutional quality is also essential to ensure that financial resources are efficiently allocated to health and climate adaptation priorities. Overall, the study emphasizes that coordinated policies linking finance, health, and climate adaptation are necessary to transform vulnerability into resilience and support sustainable development in the region.

Policymakers need to focus on including climate and health issues in financial development plans, which should involve budgeting for climate impacts, financing for climate risks, and promoting sustainable growth in the private sector. We must prioritize transparency and accountability in enhancing governance systems, utilizing frameworks such as the World Health Organization’s Health Inequality Monitor. While climate change poses severe health risks to sub-Saharan Africa, this study demonstrates that coordinated action on finance, education, and governance can turn vulnerability into resilience. By prioritizing integrated policies, the region can mitigate climate health threats and accelerate progress toward SDG 3 (Health) and SDG 13 (Climate Action).

Limitations and future directions of the study

Unlike other studies, this study is not without limitations. The study only considered the direct and indirect effect of climate change and health outcome conditioned on financial development from the Sub-Saharan African perspective. An examination of how renewable energy and other channels affect the climate-health nexus could be interesting for policymakers. Further, our study does not provide alternative measures of climate change and financial development. Separate analysis of alternative measures for climate change, including weather events, heat-related measures, etc., and financial development such as sustainable finance, green finance, etc., could provide interesting results in this context. In addition, the study is limited to only Sub-Saharan Africa. Future research is required to explore this study (including data extension) to other regions in the world to reveal how applicable this model fits the other part of the world.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

LA – conceptualization of the research topic, design of the work, preparation of final report. DOS – design of the work, analysis of data in Stata, and review of final report. JA – write-up of motivation of study, literature review, conclusion. EBA – interpretation of data and review of final report. JTK – collection of data, analysis of data in Stata, preparation of draft report.

Funding

Authors have received no funding from any institution for this research project, hence there is no funding to declare.

Data availability

The dataset supporting the findings of this study is openly available in the World Bank databank database at https://databank.worldbank.org/source/worlddevelopment-indicators.

Declarations

Ethical approval

Not applicable.

Competing interests

The authors declare no competing interests.