One of the most important components of human capital is health, which is seen as a long-lasting characteristic that has a favorable correlation with labor productivity. Grossman (34) offered the basic theoretical framework in the literature for modeling health. The suggested health production function is regarded as one of the most important instruments for examining the health status of a nation. The classic production function, which illustrates the relationship between socioeconomic input and output, is comparable to the health production function. Reduced morbidity or LE is used to assess the dependent variables of the health production function; on the other hand, income, education, health care, the environment, health and medical expenditures, genetic characteristics, etc. are the inputs (7, 35).

The Preston (36) Curve describes the positive relationship between a country’s income level and its average LE. At lower income levels, small increases in income lead to significant gains in LE due to better nutrition, healthcare, and living conditions. However, as income rises, the curve flattens, meaning additional income brings smaller improvements in LE. This theory highlights how economic growth contributes to health outcomes, but with diminishing returns at higher income levels. This theory is also validated by Rahman et al. (37) in 31 of the world’s most polluted countries and Alhassan et al. (38) in Developing Countries.

Human health is impacted by political, social, and economic instability and upheavals (riots, civil wars, and conflicts) due to the devastation caused by various weapons, as well as the pressures and punishments imposed by the social, political, and economic structures. People discuss their health requirements throughout this era, as well as the ensuing malnutrition, drought, and diseases caused by these disturbances, as well as the misallocation of financial resources to military purposes (39, 40). Changes in death trends and an increase in suicides may result from economic stress and insecurity (41, 42). Donors’ technical and financial assistance as well as peacekeeping efforts during various unrests and instabilities can have an operational impact on the health sector (43). According to Majeed and Ozturk (6) categories into which climate change influences health into three categories such as direct, indirect, and delayed consequences. The deaths and injuries brought on by extreme weather events like floods and cyclones are among the direct or major effect. The infectious illnesses brought on by climate change are among the indirect or secondary impacts. Disruption to social and health services is one of the delayed or tertiary consequences, which develops gradually (44, 45).

Hossain et al. (46) analyzed the connection between CO2-E and LE in Bangladesh for the period from 1974 to 2024 by using the ARDL approach. They found that environmental deterioration has a major detrimental effect on life expectancy over the long term. Ali and Audi (47) analyzed CO2-E, income inequality and LE in Pakistan from 1980 to 2015 by using the ARDL approach. The study’s findings indicated that Pakistan’s life expectancy is significantly impacted negatively by both environmental deterioration and income disparity. Das and Debanth (48) analyzed Impact of CO2-E on LE in India from 1991 to 2018 by using the ARDL methods. According to the study finding CO2-E has negatively affect LE. Using the ARDL model, Osabohien et al. (49) examined the relationship between life expectancy and carbon emissions in Nigeria from 1980 to 2017. The results indicated that life expectancy is negatively impacted by carbon emissions. This result suggested that life expectancy can be lowered by 0.35% on average due to carbon emissions. Azam et al. (8) investigated the connection between CO2-E and LE in Pakistan for the period from 1975 to 2020 by utilizing the ARDL approaches. They concluded that, management authorities must control carbon emissions to increase life expectancy, which is a major factor in determining economic growth. Azam and Adeleye (50) investigated the association between CO2-E sources and LE in in Asia and the Pacific region from 2005 to 2010. They found that CO2-E has negative effect on LE. Guo et al. (51) analyzed SAARC countries from 1990 to 2022 using the panel ARDL model to capture both short- and long-run effects, complemented by the method of moments quantile regression (MMQR) to explore heterogeneity across quantiles. Their results revealed that CO₂ emissions reduce life expectancy and increase infant mortality, while renewable energy, urbanization, GDP, and industrialization improve health outcomes. To ensure robustness, they applied FMOLS and DOLS estimators, which further validated the main findings.

Previous studies examining the relationship between geopolitical risks and LE have employed various proxies for geopolitical risks, such as war, terrorism, and political instability. Plümper and Neumayer (26) examined the Armed Conflict’s Impact on the Gender Difference in Life Expectancy. This paper offers the first thorough examination of how armed conflict affects women’s life expectancy in comparison to men’s. We find that, on average, women are more negatively impacted by interstate and civil conflicts than males are over the course of the conflict. Women usually live longer than males do during times of calm. Therefore, the difference in life expectancy between men and women tends to close during armed conflict. We also discover that, in comparison to previous civil wars, ethnic conflicts and battles fought in “failed” governments cause significantly greater harm to women. Mahmud (27) analyzed the nexus between terrorism and human development in Iraq, they found that terrorism has negative effect on human development. Feyzabadi et al. (28) examine the nexus between peace and LE among world countries from 2007 to 2012 by using the fixed effect. They found that peace index had negative effect on LE. Wesemann et al. (29) analyzed the relationship between terrorist attacks and mental health based on Quantitative Studies. They found that the prevalence rates of major depression, anxiety disorders, and post-traumatic stress disorder ranged from 1.3 to 25.8%, 1.7 to 14%, and 1.3 to 16.5%, respectively. The World Trade Center attacks were associated with the greatest prevalence rates. Gender, lack of emergency services training, peritraumatic dissociation, physical closeness to the incident, and social isolation were all listed as risk factors.

Chukwuma (52) examined the geopolitical dimensions of health and biosecurity in relation to gain-of-function research. The study highlighted widespread global concerns about how emerging and reemerging infectious diseases spread, as well as the geopolitical implications tied to the nature of gain-of-function research. These issues have become increasingly important for both national and international health governance, as infectious diseases are clearly geopolitical in nature. The research emphasized that health diplomacy now plays a critical role in addressing current and future outbreaks, pandemics, and microbiome variations linked to gain-of-function studies. Towne (53) used logistic regression to assess geopolitical disparities in US health care access from 2011 to 2015. Research, practice, and policy related to connecting people to essential medical care are informed by the conclusion that geopolitical factors state Medicaid expansion or not may influence whether or not an individual reports forgoing medical treatment. Paramati et al. (25) investigated the impact of geopolitical risk on CO₂ emissions and public health risk across 17 countries during 1990–2018. Using generalized quantile regression and panel corrected standard errors, their results confirmed that geopolitical risk amplifies carbon emissions, which subsequently escalates health risks. Their robust methodology strengthens the evidence that political instability indirectly undermines public health through environmental degradation.

Badmus et al. (54) explored the role of IQ in Health Expenditure in Africa by using the GMM technique. They discovered that the crowding-out impact on government health expenditures in Africa is only reversed when an interaction term including military spending and institutional quality is included; private and out-of-pocket health expenditures remain unaffected. In a similar vein, the interaction term’s positive impact on health outcomes is limited to higher life expectancy and lower rates of maternal death. Sharma (55) analyzed IQ and health outcomes in European Union countries from 2000 to 2018 by using the GMM technique. The results suggest a positive association between the quality of economic institutions and health outcomes. Hadipour (56) analyzed the IQ and health outcomes nexus for 158 countries from 2001 to 2020 by using the GMM methods. The findings showed that while institutional quality increased life expectancy, it decreased newborn mortality rates. Similar correlations were found between newborn mortality rates and life expectancy and factors including GDP, mean years of schooling, total health spending, and urbanization rate. Nica et al. (57) analyzed IQ and LE nexus in Eastern Europe from 1990 to 2021 by using the CS- ARDL methods. They found that IQ encourage to surge the LE. Ejuvbekpokpo (35) analyzed the impact of IQ on human development in SSA countries for the period from 2005 to 2013 by using the GMM methods. The findings demonstrate a relationship between poor levels of human development and institutional quality. However, the findings indicate that, in the SSA nations, institutional quality is critical to human development. Uddin et al. (12) examined the connection between IQ and LE in SAARC economies from 2002 to 2020 by using the CS-ARDL methods. They found that IQ rise the LE. In the context of developed economies, Vatamanu et al. (58) explored the relationship between renewable energy, institutional quality, and life expectancy in 27 EU countries from 2000 to 2020. Employing cointegration analysis, factor analysis, panel FMOLS tests, they found that renewable energy consumption and institutional quality both significantly enhance life expectancy. Their findings highlight that strong governance institutions amplify the effectiveness of renewable energy adoption in sustaining longevity and improving overall well-being.

Segbefia et al. (59) examined the effects of renewable energy, TI, CO2-E, and human capital on LE in NAFTA economies. Utilizing the CS-ARDL model and panel data from 1990 to 2020, the study focuses on the United States, Mexico, and Canada. Findings indicate that human capital, renewable energy, technological innovation, and economic growth positively affect LE, while carbon emissions have a negative impact. Causality tests reveal a unidirectional relationship between human capital, economic growth, technological innovation, and LE, and a bidirectional relationship between carbon emissions, renewable energy, and LE. The study recommends stringent air quality regulations and cleaner technologies to enhance LE. Shaari et al. (60) investigated the impact of green technology, CO2-E, and health expenditures on LE in ASEAN-5 countries by using the FMOLS and DOLS estimators and CS-ARDL, the study focuses on ASEAN-5 (Thailand, Philippines, Singapore, Malaysia, and Indonesia). Findings indicate that green technology significantly enhances LE, while CO2 emissions negatively affect it. FMOLS and DOLS results highlight that health expenditure improves LE in Thailand and the Philippines. Economic expansion positively influences LE in Thailand, the Philippines, and Singapore. The study recommends prioritizing healthcare investments, promoting clean energy, and implementing incentives for green technology to enhance economic resilience and public health. Muradov et al. (61) analyzed the relationship between air pollution and LE in the United States, considering medical innovation, health expenditures, economic complexity, and government effectiveness. Using cointegration analysis and data from 1995 to 2019, the study finds that medical innovation positively affects LE, while CO2 emissions, economic complexity, and government effectiveness negatively impact it. Health expenditures are found to be ineffective in improving LE. The study suggests that policymakers should focus on reducing air pollution and improving the efficiency of economic and health expenditures to maximize their benefits for LE.

This research aims to examine the impact of environmental degradation, GPR, institutional quality and technological innovation on LE. The model employed in this study is derived from earlier research conducted by literatures:

In Equation 1, LE, CO2-E, GPR, IQ, TI and GDP represent life expectancy, Carbon emission, geopolitical risk, institutional quality, technological innovation and gross domestic product, respectively. Where, e it denote the error term. LE is a dependent variable while CO2-E, GPR, IQ, TI and GDP are independent variable. To overcome the problem of data sharpness and Hetrocedasticity, we convert all the variables into natural logarithm except IQ.

This study take four BRIC economies from 1993 to 2022, by using the six variables such as LE, CO2-E, GPR, IQ, TI and GDP. The data has been taken from World development indicator (WDI), policy uncertainty and world governance indicator (WGI). Table 1, shows the variable measurement. Technological innovation is measured using total patent applications, following the approach of Segbefia et al. (59). The IQ index was constructed based on six governance indicators commonly used in the literature (24). These indicators include (1) Control of Corruption (CC), (2) Government Effectiveness (GE), (3) Political Stability and Absence of Violence/Terrorism (PS), (4) Regulatory Quality, (5) Rule of Law, and (6) Voice and Accountability. Each indicator is measured on a scale ranging from −2.5 to +2.5, where lower values reflect weaker institutional performance and higher values reflect stronger institutional quality. Since these indicators are highly correlated, the IQI is derived using Principal Component Analysis (PCA). PCA reduces dimensionality by extracting components that capture the maximum variation across variables. The eigenvalues of the covariance matrix represent the explained variance of each component, and in most cases, the first principal component explains the largest share of variation. This first component is therefore used to construct the index. A key advantage of PCA is that the weights assigned to each indicator are determined objectively by the data, rather than arbitrarily (7).

Table 2 shows the descriptive statistics; the mean value of LE, CO2-E, GPR, IQ, TI and GDP are 71.026, 5.525, 156.595, −0.004, 175923.800 and 6100.025, respectively. The Standard deviation of the LE, CO2-E, GPR, IQ, TI and GDP are 3.993, 4.461, 120.493, 1.738, 379741.500 and 3300.240, respectively. The LE is positively correlated with all variables, especially TI, 0.58 and GDP (0.56), but weakly with CO2 emissions (0.10).

The empirical analysis begins by examining CSD in the dataset, as addressing CSD is crucial for ensuring the reliability of panel estimations. The results of the CSD tests are presented in Table 3, where the null hypothesis (𝐻0) of cross-country independence is tested against the alternative hypothesis that cross-sectional units are interdependent. Based on the statistical significance of the p-values at the 1% level, the null hypothesis is strongly rejected across all tests, confirming the presence of CSD in the model residuals. This finding indicates that economic, environmental, or policy-related shocks in one BRIC country tend to have spillover effects on others, highlighting the interconnectedness of these economies.

To examine the stationarity of the variables, this study applied the Levin-Lin-Chu (LLC) test and the CIPS test. The results of these tests are presented in Table 4. According to the LLC test, only LE is stationary at the level, whereas the CIPS test indicates that GPR is stationary at the level. All other variables become stationary after first differencing, suggesting the presence of both I(0) and I(1) processes in the dataset. The Kao residual cointegration test confirms that at the 5% significance level, the null hypothesis of no cointegration is rejected. This indicates the presence of a long-run relationship among the variables in the model.

Table 5 shows the result of panel CS-ARDL. In the long run and short run, IQ, TI and GDP have a positive effect on LE, while CO2-E, and GPR negatively impacts the LE. The coefficients of CO2-E are −0.638 and −0.625, showing that a 1% increase in CO2-E reduces the LE by 0.638 and 0.625% in the long and short run. The reason for the negative sign. In the BRIC context, however, the adverse health effects of environmental degradation dominate because of Rapid industrial growth, high pollution intensity, and insufficient environmental regulations, exposing populations to higher levels of air pollution, respiratory diseases, and other health risks, outweighing the potential benefits of development. The outcome is consistent with a recent investigation conducted by Wang et al. (68), Wu et al. (71), Uddin et al. (12), Kolasa-Więcek & Suszanowicz (69), and Ebenstein et al. (70). According to Uddin et al. (12) research on SAARC economies, and found that CO2-E deters the LE. Pakistan’s LE has decreased due to CO2-E, according to Azam et al. (8) analysis. After looking at Pakistan’s annual statistics, Wang et al. (68) came to the conclusion that energy usage shortens life expectancy by causing environmental degradation. A recent research conducted by Wu et al. (71) looked at China’s urban population data between 2013 and 2017. According to the study, life expectancy may be raised by reducing air pollution. Between 1992 and 2016, air pollution had a detrimental impact on life expectancy in 20 European countries, according to Kolasa-Więcek & Suszanowicz (69). Air pollution reduces life expectancy, according to research done by Hill et al. (72), who looked at data from all 49 states in the US. Ebenstein et al. (70) conducted a study which found that life expectancy in Chinese cities was negatively impacted by air pollution at the city level. According to Younus et al. (73) the degradation of the environment causes serious diseases, poor health, and a shortened life span.

The estimated coefficient of −0.286 and −0.157, showing that a 1% increase in GPR reduce the LE by 0.286 and 0.157% in the long and short runs. Thus, GPR involves political unrest, economic sanctions, and wars, can have a negative influence on LE and health in a number of ways such as (i) Direct violence: Political unrest and armed conflicts can result in direct violence that kills and injures people. (ii) Indirect violence: Conflict-related food shortages, housing displacement, and limited access to medical treatment can all have a negative impact on people’s health and longevity (iii) Disease outbreaks: Public health systems can be disrupted by political unrest and wars, which raises the danger of disease outbreaks and limits access to treatments and vaccinations (iv) Mental health: Prolonged exposure to stress, trauma, and violence as a result of geopolitical hazards can have a detrimental impact on mental health and increase the likelihood of mental illnesses including anxiety and depression. (v) Healthcare access: Geopolitical threats have the potential to restrict access to healthcare services, such as screening, treatment, and preventative care, which can have a negative impact on health and shorten life expectancy. The finding consistent with the line of Mahmud (27) and Wesemann et al. (29).

The estimated coefficient of IQ is 0.739 and 0.057, indicating that a 1 unit increase in institutional quality surges the LE by 0.739 and 0.057% in the long and short run. The reason of the positive sign in that Institutional quality improves LE by ensuring better governance, reducing corruption, and enhancing public service efficiency, leading to improved healthcare and social welfare. Strong institutions promote economic stability and equitable resource distribution, which supports access to clean water, sanitation, and medical care. The outcomes consistent with the line of Uddin et al. (7). Uddin et al. (7) examined the connection between IQ and LE in SAARC economies from 2002 to 2020 and they found that IQ rise the LE.

The coefficient of TI is also positive effect on LE, it shows that 1% surge the TI leads to raise the LE by 0.166 and 0.001% in long run and short run. Thus, TI enhances healthcare through advanced medical treatments, improved disease prevention, and better healthcare infrastructure, leading to longer life expectancy. The finding is consistent with the finding of Shaari et al. (60) and Muradov et al. (61). Shaari et al. (60) investigated the impact of green technology on LE in ASEAN-5 countries by Findings indicate that green technology significantly enhances LE. Muradov et al. (61) analyzed the relationship between air pollution and LE in the US, considering medical innovation, health expenditures, economic complexity, and government effectiveness. Using cointegration analysis and data from 1995 to 2019, the study finds that medical innovation positively affects LE.

The coefficient of GDP shows a positive effect on life expectancy (LE), indicating that a 1% increase in GDP raises LE by 0.011% in the long run. This positive relationship supports the existence of the Preston Curve, which links higher income to longer life expectancy. Similar findings were reported by Alhassan et al. (38), who showed that GDP per capita significantly enhances life expectancy across all income levels in developing economies. Higher GDP increases LE by funding better healthcare, nutrition, and living standards, reducing disease and mortality rates. It also supports infrastructure, education, and public services, creating healthier and safer environments for people. The finding is consistent with the finding of Azam et al. (8) and found that GDP raised the LE.

The ECM coefficient is −0.728, it means that 72.8% of any short-term deviation from the long-run equilibrium in LE is corrected each period (yearly). This implies a relatively fast adjustment process, suggesting that factors influencing LE quickly return to a stable long-run relationship over time.

For the robustness analysis (see Table 6), this study employs the FMOLS and DOLS estimators. Both methods confirm the same sign, although the coefficient magnitudes differ, which is attributed to the distinct estimation procedures. The empirical findings reveal that CO2-E and GPR have a significant negative impact on Life Expectancy LE. On the other hand, Institutional Quality, TI, and GDP positively influence LE. The Panel Causality Test shows in Table 7 that the Unidirectional causality was exposed from GPR to LE, IQ to LE, CO2-E to GPR, TI to CO2-E, GDP to GPR, TI to IQ, and GDP to IQ. The panel causality test reveals a unidirectional causality from GPR to LE, indicating that GPR significantly influences life expectancy. Higher geopolitical risks undermine stability, disrupt health systems, and negatively affect longevity. However, changes in LE do not influence geopolitical risk. The causality test results reveal a unidirectional causal relationship from IQ to LE. This implies that improvements in governance, rule of law, and institutional effectiveness lead to better health outcomes and longer life spans. However, the reverse causality from LE to IQ was not established, suggesting that health improvements alone do not directly strengthen institutions. Therefore, institutional reforms play a driving role in enhancing LE. While bi-directional causality exists between CO2-E and LE, LE and TI, GDP and CO2-E, IQ and GPR, TI and GPR. The panel causality results indicate a bidirectional causality between CO2-E and LE. This means rising CO2-E adversely affects health outcomes and longevity, while improvements in life expectancy, through economic growth and higher energy use, can also drive up emissions. The feedback relationship highlights the environmental health nexus.