Environmental sustainability is a discourse that is becoming increasingly vital and will continue gaining momentum today and in the future. It expresses the urgency of being efficient in marshalling resources and, at the same time, protecting the natural ecosystems on which humanity depends. In its conventional sense, environmental sustainability (ES) refers to the effort to ensure that the current generation can fulfill its needs today and that future generations will have the capacity to fulfill theirs. This knowledge is grounded in the broader concept of sustainable development, which, in turn, requires incorporating environmental stewardship, social equity, and economic development. Natural resources and ecosystems play a central role in society’s welfare, and their assessment is directly linked to the community’s overall well-being (Ziaul & Wang, 2023).

Empirical studies make conceptually consistent explanations of the determinants of ES, and are deeply rooted in economic theory. The Human Capital Theory teaches us that the more knowledge a person is provided with, the better prepared he/she is to handle the environment. A highly educated population is thus better acquainted with sustainable practices and is willing to adopt new environmental protection strategies in a positive manner (Mourad, Wahid, Alkubise, and Najar, 2023). Neoclassical Environmental Kuznets Curve (EKC) theory argues that economic growth has not inevitably led to environmental degradation, as long as enlightened citizens steer countries toward cleaner technologies and a more sustainable path of development. Studies conducted by Duodu et al. show how education can influence attitudes and behaviour, with a view to facilitating civic participation in environmental campaigns, as explained in terms of behavioural economics (Ekins, 2011).

Environmental sustainability is affected both positively and negatively by the impact of trade openness. The Scale Effect means that, as industrial production and trade increase, pollution and resource consumption rise (Varghese, 2024). On the other hand, a change in the composition of the environment can be observed as the shift to cleaner industries or services producing what has been called the Environmental Composition Effect. Rehman, Bhatti, Kraus, and Ferreira (2020) contend that stringent environmental standards might complement trade, and as a result, a race to the top may mobilize in which high-income, environmentally neutral goods are swapped. There is an elegant correlation between ES and foreign direct investment (FDI). Duodu, Kwarteng, Oteng-Abayie, and Frimpong (2021) propose the Pollution Haven Theory, which suggests that multinational companies will set up in jurisdictions with weak environmental policies, thereby increasing pollution in the area. Conversely, the Pollution Halo Hypothesis states that FDI might also be beneficial to developing states by introducing new technology and improving environmental management systems (Gyimah, Fiati, Nwigwe, Vanessa, and Yao, 2023). Governance, in this respect, plays a critical role, since host-country regulations are the main drivers of the net effect of FDI on sustainability.

Urbanisation does give opportunities and challenges. The rapid urban development increases per capita pollution and resource exploitation, particularly in poorly planned cities (Bordun, Antonyak, Dumych, Hrytsyshyn, and Bilous, 2023). However, cities have economies of scale that help enhance social services and minimise waste in infrastructure (Bowles, Boetto, Jones, and McKinnon, 2016). City centres of higher learning and innovation encourage sustainability activities and nurture favourable laws and culture. Nonetheless, these objectives should be achieved through sustainable urban planning and policy frameworks (Ang, Choolani, and Poh, 2024). The four factors connected to education, trade, openness, and urbanisation interact in forming complicated systems to impact environmental sustainability. These dynamics are important to study to identify viable ways to support the provision of ecosystem services. The future governance should adopt technologies that can realise these strategies.

Education is a catalyst of growth and prosperity as it is an investment into individuals and the society in general. It has been known since long as an important factor of human development and economic growth. Over the recent years, the two-way communication between the environment and education has received the growing interest of scholars. Further elaboration of this relationship shows that it is a prerequisite to sustainability in practice, technology innovation, policy advocacy, diversify economy and exchange of best practice throughout the globe. The procedure highlights the immense ability of education to influence cognition as well as create a globally conscious society that is sustainably aware of everyone. However, there are two empirical studies, which present opposing results: the first one shows that education has a positive relationship with emissions and the other one indicates the opposite.

The quantitative data of the first group illustrate that education and CO 2 emissions have a positive correlation. Afolayan et al. (2020) have made the conclusion that a high level of education allows the economy to be active, which in turn pushes the size of the aggregate emissions. In the same fashion, both education and economic growth were found to have a positive impact on CO 2 emissions, as reported by Xu and Lan (2023). Continuous air pollution may be explained by the further use of fossil fuels. Suman (2021) suggested that the prevalence rates of greenhouses depend on the level of education and job opportunities on the regional level. Moreover, Zafar et al. (2022) hypothesized that education is also one of the impacts major causes of environmental degradation. The latter group highlights the adverse relationship between education and CO 2 emission. Leal Filho et al. (2019) noted that energy sustainability is a top priority agenda among universities in an effort to limit carbon emissions and lower energy use in buildings. These sustainability programs form a huge investment by many institutions of higher learning. As illustrated by Yin, Jiang, Lin, and Liu (2022), online education has a twofold effect on carbon emissions: to begin with, online classes reduce commuting-related emissions since most of them can be taken at home; to conclude, online education leads to emission through the use of devices and servers. Chen et al. (2022) argued that education enabled personal adaptation of the climate change and the creation of technologies that reduce the emission of greenhouse gases. According to Zafar et al. (2020), education has a positive effect on carbon emission reductions.

Two different dichotomies have been developed in the literature with respect to the nexus between foreign direct investment (FDI) and the greenhouse gas emissions. An extensive body of literature records a favorable relationship, that is, increased FDI inflows are correlated with emissions of carbon dioxide. According to Yuan Wang and Huang (2022a), a positive initial shock in short-term increases in augmentations in FDI can trigger an increase in CO 2. Expanding on the pollution haven hypothesis, Huang et al. (2022) argue that FDI is more concentrated in the jurisdictions where environmental regulations are relatively lenient, thus adding up to 525 percentage points of emissions by 2100, depending on geographic and institutional factors. According to Hao et al. (2021), this effect is explained by the emissions produced during the different levels of production and consumption. It is important to mention that the impact of the environment is considerably softened by the investor sphere, techno, and motivation. Also, Huang et al. (2022) emphasise the role of macro-variables namely economic development, financial development and urbanisation, and tourism in mediating the indirect impact of FDI on carbon emissions. The (quantitative) relationship was measured by Wang et al. (2022), and the coefficient estimates the result of a 1 percent increment of FDI resulting in a 0.03 percent increment in emissions; however, the error-correction coefficient has a negative value, which means that, in the short term, the system aims at returning the equilibrium situation to its original condition.

On the other hand, a substantial body of research suggests that FDI may lead to technological spillovers that reduce carbon production. To et al. (2019) showed that FDI contributes to the spread of clean technology innovations, thereby helping prevent emissions. They argued that host countries with higher education disproportionately benefit from the environmental returns of such inflows. Q. The same argument was supported by Wang et al. (2023), who reported that in better-quality countries, there is a higher likelihood that there would be a reduction in the level of CO 2 emission that can be linked to FDI. Hanh et al. (2020) also found that FDI tends to reduce pollution, primarily by providing environmentally friendly technologies that decrease energy consumption and the intensity of carbon emissions. As stated by Apergis, Pinar, and Unlu (2023) and Ren, Yuan, Ma, and Chen (2014), the potential of FDI to promote the proliferation of clean technology could be decisive in mitigating greenhouse gas emissions, and policy frameworks aimed at capitalising on positive externalities are thus critical.

Two research teams reported contrasting results: one team obtained a positive result, while the other obtained a negative result. The evidence from the former indicates a positive correlation between trade openness and carbon dioxide emissions. Jun et al. (2020) postulate that high levels of trade openness may strengthen the dominance of specific industries, thereby leading to high pollution levels. Chhabra, Giri, and Kumar (2023) reveal that the production of goods can serve as an indicator of carbon emissions. Additionally, the exchange of products and services can significantly impact the adoption of environmentally friendly technology, helping countries transition to more sustainable economic activities. Nonetheless, when goods are produced in consideration of contaminated products, environmental degradation occurs. There is even a possibility that trade openness can reduce carbon emissions in the wealthier nations under the conditions described by Wang and Zhang (2021).

Furthermore, Ali et al. (2020) found that liberal trade policies can predispose an environment to degradation because nations may loosen their regulations in pursuit of trade and investment. By liberalisation, energy-intensive industries will be forced to relocate to places with less stringent environmental regulations, thereby increasing carbon emissions into the atmosphere. Two distinct claims support this relocation trend, suggesting a common underlying causal mechanism. Another academic research stream introduces a negative dependent correlation between trade openness and carbon dioxide levels. Several reports argue that liberalised trade may reduce carbon emissions in developed countries. For example, Karedla, Mishra, and Patel (2021) found that CO2 emissions in India decreased significantly as the country opened up to trade. Accordingly, Lin and Umetsu note that carbon dioxide emissions are expected to decline in newly industrialised countries as they become more open. According to Chhabra, Giri, and Kumar (2022), increased trade facilitates the exchange of knowledge and technology, thereby improving environmental performance by reducing carbon dioxide emissions. Lastly, Thi, Tran, and Nguyen (2023) argue that emissions can be reduced by utilising renewable energy sources, increasing trade openness, and fostering innovation. A summary of the literature is presented in Table 1.

Figure 1 presents the theoretical foundation of the study, and Table 1 provides the summary of the literature survey.

The study uses annual panel data for Sub-Saharan African economies over 1990–2019. The operational definitions, transformations, and original data sources for environmental outcomes, education, FDI, trade openness, and urbanisation are reported in Table 2.

Data and methodology of the study

Model specification

The study aims to assess the effects of education on environmental sustainability in the top 50 polluted nations for the period 1990–2019. The generalised equation is as follows

Equation (1) has been extended to include three macro indicators, following the existing literature that focuses on environmental degradation. The revised Equation (2) is as follows:

ES, EDU, TO, FDI, and UR represent environmental sustainability, education, trade openness, foreign direct investment, and urbanisation, respectively. The logarithmic transformation of the variables in Eq. (2) can be displayed in the following regression form:

Where β0 is the intercept, β1, β2, β3, and β4 are the coefficients associated with the respective independent variables, and ε is the error term.

The first specification coefficient (β1) for the education variable (EDU) represents the marginal effect of a one-unit increase in educational attainment on CO2 emissions, holding other factors constant. Theoretically, 1 can be positive or negative, depending on the relationship between education and carbon emissions. Expecting that β1 should acquire a negative value, supposing that an individual is better informed about the environmental matters and readily accepts the idea of sustainability, it is possible to assume that the former variable will assume a negative value (Xu & Lan, 2023; Zafar et al., 2022). That is, as educational level increases, CO2 emissions will correspondingly decrease (Chen et al., 2022; Huang et al., 2022).

The second coefficient in the equation, 02, represents the exact effect of a unit change in trade openness (TO) on future variations in CO2 emissions, assuming all other factors remain constant. Variability may also be present in the sign of 2. Otherwise, with trade openness leading to increased international trade and transportation activity, a positive 2b, implying an increase in emissions, would be a sensible outcome (Chhabra et al., 2023; Thi et al., 2023). On the other hand, β2 might be negative if the diffusion of cleaner technologies and the decrease in dependence on carbon-intensive production are enhanced by increased openness (Q. Wang & Zhang, 2021; Jun et al., 2020).

The third coefficient, 03, represents the extent to which foreign direct investment (FDI) is influenced by CO2 emissions, holding all other factors constant. The 3(3) sign can vary across the orientation of FDI. Coefficient 3 = − may be, in fact, a negative value when FDI introduces ecologically friendly technology and sustainable methods of production (Yuan, Wang, and Huang, 2022a; Hanh et al., 2020; Huang et al., 2022). Instead, when FDI invests in highly polluting industries, one would expect a positive correlation (Hao et al., 2021; L. Wang et al., 2022).

The fourth coefficient, 04, represents the impact of a one-unit increase in urbanisation (UR) on CO2 emissions, holding the remaining variables constant. Urbanisation is expected to increase emissions as energy requirements and transportation activities grow, potentially providing a positive value of 4. Nevertheless, the urban growth, accompanied by the agglomeration of infrastructure and efficiency-related benefits, can lead to a negative 8. Finally, the actual values and size of these coefficients depend on the actual data set and contextual variables being researched. To properly quantify these effects and evaluate their statistical significance, an extensive dataset that can be carefully and thoroughly analysed through regression is essential. The existing policy regimes and sectoral practices dominant in the region of inquiry can also inform the orientations of the coefficients. The proxy measures of research variables are displayed in Table 2.

Estimation strategies.

To assess the empirical relationship between ED, EDU, TO, FDI, UR, and GCF, the present study implemented several econometric tools, including the cross-sectional dependency test following (Breusch & Pagan, 1980), (M Hashem Pesaran, 2004), (M. H. Pesaran, 2006), (M Hashem Pesaran, Ullah, & Yamagata, 2008). The following equation is used to derive the test statistics.

The Lagrange multiplier (CD_lm) is the scaled version of the LM test in Eq (6):

When N is larger than T, the CD lm estimation is subject to size issues. Therefore, M Hashem Pesaran (2006) proposed the following CD test, see Eq (7) & (8), which is suitable in a situation when N is more significant than T:

The stationary properties of the research variables are to be assessed using the framework proposed by M. Hashem Pesaran (2007).

Where Yit−1 and y¯t−1 The lagged-level average and first-difference operators are applied to each cross-section, and the CIPS unit root test is presented in Equation 11.

Where the parameter ∂i(N,T) explain the test statistics of CADF, which can be replaced in the following manner:

A cross-sectionally augmented version of the test statistics is provided below.

The error correction-based panel cointegration test has been performed to reveal the long-run cointegration in the empirical equation by following Westerlund (2007).

The results of the group test statistics can be derived using Equations 14 and 15.

The equation for coefficient estimation by executing the CUP-FM and CUP-BC is as follows:

In the following section, the empirical estimation has been extended with the inclusion of the asymmetric decomposition of EDU ( EDU+;EDU− ), TO ( TO+;TO− ) and FDI ( FDI+;FDI− ) and the asymmetric equation is as follows.

Where π+,π−, β+,β−,andγ+,γ−Stands for the long-run asymmetric coefficient of education, trade openness, and Foreign Direct investment. The decomposition of the explanatory variables can be derived as follows:

{POS(EDU)1,it=∑M=1WEDUM+=∑M=1WMAX(EDUM,0)NEG(EDU)1,it=∑M=1WEDUM−=∑M=1WMIN(EDUM,0):POS(TO)1,it=∑R=1tTOR+=∑R=1TMAX(TOM,0)NEG(TO)1,it=∑R=1tTOR−=∑R=1TMIN(TOM,0):POS(FDI)1,it=∑R=1tFDIR+=∑R=1TMAX(FDIM,0)NEG(FDI)1,it=∑R=1tFDIR−=∑R=1TMIN(FDIM,0)

The following equation documents the asymmetric coefficients in the long- and short-run assessments:

The error correction term in the above equation is as follows:

For cross-sectional unit i and period t, the dynamic SUR model for equation j can be expressed as

The findings from the cross-sectional dependency test ( Table 3) indicate a significant presence of cross-sectional dependence among the variables in the dataset. This means that the observations are not independent and may exhibit correlations or interdependence. The test results, including the LMBP, LMPS, LMadj, and CDPS statistics, indicate statistically significant values, suggesting heteroscedasticity and cross-sectional dependence. These findings underscore the importance of considering the interrelationships between variables when analysing data. Moreover, the test statistics of ∆ and Adj.∆ are statistically significant at the 1% level, suggesting heterogeneity in the research variables.

Panel unit root tests were conducted on the following variables: CO2, C-CO2, EDU, FDI, TO, and UR. Two specifications were used: one with a constant term and the other with both a constant term and a trend. CADF and CIPS test statistics were employed. The results in Table 4 indicate that, when the level specification includes a constant term, the variables exhibit unit roots, but the values are not statistically significant. However, when examining the first difference specification with a constant term, all variables exhibit statistically significant differences.

The study employed a panel cointegration test to document the long-run association between ES, EDU, TO, FDI, and UR by applying cointegration techniques familiarised by Westerlund (2007), Pedroni (2004), and Kao (1999). Table 5 displays the test statistics for different cointegration tests and shows that all tests reject the null of no cointegration. Thus, it is confirmed that there is a long-run association between the research variables.

In the DSUR model ( Table 6), the coefficients for EDU, FDI, TO, and UR are −0.10374, −0.12757, 0.0847, and 0.12987, respectively. These coefficients represent the expected change in carbon emissions when each independent variable changes by 1 unit, while all other variables remain constant. For instance, a one-unit increase in EDU correlates with a reduction of approximately 0.10374 units in carbon emissions, and a one-unit increase in FDI corresponds to a reduction of approximately 0.12757 units in carbon emissions. Conversely, a one-unit increase in TO is linked to a rise of roughly 0.0847 units in carbon emissions, and a one-unit increase in UR is associated with a rise of approximately 0.12987 units in carbon emissions.

The estimated values of the Explanatory variables EDU, FDI, TO, and UR in the CUP-HM model are −0.14077, −0.15352, 0.10451, and 0.12709, respectively. These numerical data suggest that a one-unit increase in education attainment (EDU) is associated with a decrease of approximately 0.14077 units in carbon emissions, and a unit increase in foreign direct investment (FDI) is also associated with a decline of approximately 0.15352 units in carbon emissions. Contrastingly, a one-unit increase in technological output (TO) is associated with a corresponding growth of approximately 0.10451 units in the number of emissions, and a one-unit increase in the rate of unemployment (UR) is also matched by a corresponding increase of nearly 0.12709 units in the number of emissions. These coefficient signs hence reflect the same trends in the DSUR model, but with significant differences in the magnitude of the effect. The regression estimates for the identical set of covariates in the CUP-BC model include coefficients of −0.10075 for EDU,- 0.07751 for FDI, 0.09972 for TO, and 0.12941 for UR. To this extent, a one-unit increase in education level is associated with a decrease of 0.10075 units in carbon emissions, whereas a one-unit increase in foreign direct investment is associated with a decrease of 0.07751 units. A one-unit change in technology output corresponds to a one-unit change in the increase in emissions of about 0.09972 units, and a one-unit change in the unemployment rate corresponds to an increase in emissions of about 0.12941 units. These patterns of coefficients reinforce the comparison with the previous DSUR and CUP-HM specifications, the difference between which lies solely in minor variations in the size of the estimated effects.

Under the DSUR, CUP-HM, and CUP-BC specifications, the estimated coefficients in Table 7 indicate how consumption-based carbon emissions depend on the explanatory variables EDU, FDI, TO, and UR. These values represent estimates of the change in consumption-based carbon emissions resulting from a one-unit change in each independent variable, assuming all other regressors remain constant. The regression coefficient for EDU in the DSUR specification is −0.15816, indicating that a one-unit increase in education level results in a decrease of approximately 0.15816 units in consumption-based carbon emissions. On the contrary, the FDI coefficient stands at 0.11508, indicating that a one-unit increase in foreign direct investment triggers a 0.11508-unit increase in consumption-based carbon emissions. The coefficient of TO is 0.13039, indicating that, on average, a 1-unit increase in trade openness causes a 0.13039-unit increase. Lastly, the UR coefficient value of 0.16056 indicates that a one-unit increase in urbanisation results in a corresponding increase in consumption-based carbon emissions of approximately 0.16056 units.

Directionally, the CUP-HM model produces almost identical associations, with only minor differences in magnitude. For example, the EDU coefficient (− 0.09699) indicates a smaller decrease in consumption-based carbon emissions per unit change in education than the DSUR coefficient. Its FDI coefficient, 0.1494, is higher than that of its counterpart in the DSUR, indicating a greater sensitivity of emissions to foreign direct investment. The trade openness and urbanisation coefficients are 0.16387 and 0.07819, respectively, with greater increases in consumption-based carbon emissions per unit rise in TO and UR in the CUP-HM framework. Similarly, the CUP-BC model showed relationships similar to those of the DSUR and CUP-HM models, but with slightly different magnitudes. The coefficient for EDU is −0.09002, indicating a minor decrease in consumption-based carbon emissions per one-unit increase in education, compared with the DSUR and CUP-HM models. The coefficient for FDI is 0.1194, suggesting a minor increase in consumption-based carbon emissions per unit of FDI. The coefficients for TO and UR are 0.17669 and 0.08383, respectively, indicating that a one-unit increase in trade openness and urbanisation results in larger increases in consumption-based carbon emissions compared to the other models.

Table 8 displayed the asymmetric coefficients. For education effects on ES, which is measured by CO₂ and consumption-based CO², the asymmetric coefficients ( EDULR+=−0.1079;EDULR−=−0.0965) and ( EDULR+=−0.0635;EDUsr−−0.0861) has revealed negative and statistically significant at a 1% level, indicating that the positive changes in education have beneficial effects in terms of environmental correction through the lessening of the injection of CO² in the ecosystem. Based on the research findings, it can be inferred that decreasing the quantity of carbon dioxide (CO₂) introduced into the ecosystem would have positive implications for environmental restoration. Based on the asymmetric coefficients, higher levels of education are associated with lower carbon dioxide emissions. Various studies support this conclusion by highlighting the impact of education on promoting long-term sustainability and reducing carbon emissions. The text includes references to sources (Kiehle, Kopsakangas-Savolainen, Hilli, & Pongrácz, 2023), (Gómez, Cadarso, & Monsalve, 2016).

Positive and negative innovations in trade openness are found to be positive and statistically significant, suggesting that an increase (decrease) in TO augments (controls) the ED, as supported by both models. In particular, a 10% positive (negative) change in TO will increase CO₂ and consumption-based CO² by 0.0925% (0.1263%) and 0.0815% (0.0765%), respectively. Empirical studies have consistently demonstrated a strong correlation between trade openness and its environmental impact, particularly in the regulation of carbon dioxide (CO2) emissions. Both positive and negative innovations in this field have had beneficial and statistically significant impacts. Based on the analysis, a 10% increase in trade openness results in a marginal increase in both CO² emissions and consumption-based CO² emissions. More precisely, there is a 0.0925% rise in CO² emissions and a 0.0815% increase in consumption-based CO², indicating that international trade openness has both positive and negative effects on carbon emissions (Sun, Attuquaye Clottey, Geng, Fang, & Clifford Kofi Amissah, 2019). However, the relationship between trade openness and carbon emissions is not uniformly valid but rather depends on a country’s economic development (Derindag, Maydybura, Kalra, Wong, & Chang, 2023; Zhang, Wang, Hua, Liao, & Peng, 2022).

The asymmetric coefficients of FDI in both models established a positive tie to environmental sustainability that is ( FDILR+=−0.0.069;FDILR−=−0.138) and ( FDILR+=−0.0781;FDIsr−−0.0667) and statistically significant at a 1% level, indicating inflows of FDI have an advise effect. In response to the negative shock to FDI, it will boost ecological improvement, and controlled FDI inflows are beneficial to environmental sustainability. The study reveals a positive relationship between FDI inflows and environmental sustainability, as indicated by the asymmetric FDI coefficients in both models. The coefficients showed that FDI was positively associated with CO2 emissions. However, the study also found that a negative FDI shock might lead to ecological improvements, indicating that moderate FDI inflows can be beneficial for environmental sustainability

Based on the Granger causality test results displayed in Table 9, a network of causal relationships emerges among the variables. Education (EDU) is found to Granger-cause carbon emissions (CO2), indicating a unidirectional causal influence from education to carbon emissions. Likewise, carbon emissions Granger-cause urbanisation (UR), suggesting a one-way causal linkage from CO2 to UR. Foreign direct investment (FDI) Granger-causes trade openness (TO), indicating a unidirectional causal impact from FDI to TO. Additionally, there are bidirectional causal relationships between CO2 and trade openness (TO), CO2 and FDI, EDU and TO, EDU and FDI, EDU and UR, TO and UR, and FDI and UR, indicating mutual causality between these pairs of variables. These causal relationships shed light on the complex interplay between education, economic factors, and urbanisation in influencing carbon emissions and environmental sustainability.

Table 10 reports the robustness estimates using total greenhouse-gas emissions as the dependent variable. The stability analysis supports the principal patterns identified with the carbon-dioxide measures: education is negatively associated with emissions, whereas trade openness and urbanisation are positively associated with emissions across the reported estimators. This result is consistent with the interpretation that human capital can facilitate cleaner practices while scale-related activity remains carbon intensive in the sample.

In Table 10, FDI is negatively associated with total greenhouse-gas emissions across the DSUR, CUP-FM, and CUP-BC specifications. This pattern is consistent with a pollution-halo interpretation in the alternative dependent-variable test, while the sign differences across other specifications underline the importance of emissions accounting and investment composition.

The coefficients have positive signs and values across all estimators (see Table 11), confirming the strength of both the education-emission and the foreign direct investment (FDI)-emission relationships. Education has a significant, negative long-term impact under both CO2 and consumption-based CO2 (C-CO2) specifications, suggesting that additional investment in education can increase technological literacy and environmental awareness while also reducing emissions. This is the strongest as it runs under the Common Correlated Effects Mean Group (CCEMG) estimator (0.231) [?] that accommodates unobserved common factors and reflects the significance of systemic learning spillovers. In contrast, FDI assumes a positive and significant coefficient in all models, indicating that foreign investment inflows are likely the cause of increased emissions, most likely due to industrial growth and energy-intensive production. The more influential effect in consumption-based CO2 ([?] +0.14) indicates that carbon is reduced only by domestic industries; at the same time, imported goods account for significant emissions from other countries. Urbanisation and openness to trade also constantly increase emissions, in line with the predominance of the scale effect in developing and highly emitting economies.

The negative error-correction value (−0.43) indicates that the model is adjusting rapidly toward its long-run equilibrium. Among estimators, the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) is a more dynamic adjustment model (due to adjustment paths), whereas the CCEMG allows cross-sectional heterogeneity, and the Augmented Mean Group (AMG) allows adjustment for unobserved common factors. The regulated outcomes indicate that the results are not peculiar to the specific species of estimations and represent structural relationships in the data. On the whole, the robustness analysis confirms that the nexus between education, FDI, trade, and urbanisation is consistent across methodological differences, which validates the policy implication that sustainable human-capital investment and green FDI strategies are key to decarbonization in high-emission economies.

Table 12 reports the System-GMM robustness check, which addresses potential endogeneity between the development variables and environmental outcomes. The significant lagged dependent-variable coefficients confirm emissions persistence. The reported AR(1), AR(2), and Hansen diagnostics support the specified instrument set: first-order serial correlation is expected, while the absence of second-order serial correlation and the Hansen test do not reject instrument validity.

Table 12 further shows that the FDI model has a positive coefficient for foreign investment, indicating that some investment inflows are associated with higher emissions after accounting for dynamics and endogeneity. The contrast with the alternative GHG specification reinforces the central theoretical point: FDI’s environmental effect depends on the type of investment, host-country conditions, and the emissions measure used. Trade openness and urbanisation remain emission-increasing in the System-GMM specifications, supporting green-conditional investment and trade policies.

In the case of the nexus of investment in education and environmental sustainability, the coefficient of education on environmental sustainability can be determined to be negatively related, which means that it has a deterministic influence on regulating the emission of CO2 in the environment, which in turn leads to environmental sustainability of sub-Saharan Africa (SSA). The literature of Guo et al. (2023), Thierry, Bruno Emmanuel, and Protus Biondeh (2022), and Asongu and Odhiambo (2021) points in the same direction, strongly arguing this point. The key to sustainable growth lies in communicating functional expertise and proven methods, which can be delivered through the educational platform (Yin et al., 2022).

The rising educational levels of people in sub-Saharan Africa will contribute to knowledge about implementing sustainable practices through positive examples (Ozbay and Duyar, 2022). The transfer of information is vital to spreading green practices across industries, such as the agricultural sector and power generation. In addition, education enhances economic diversification and the deindustrialisation of polluting economic activities. Those with higher knowledge and experience tend to take jobs in low-carbon industries. The education to a high level has a crucial role in developing environmentally conscious industries, which include the service sector, high-technology industry, and knowledge economy. The level of education an individual possesses directly determines their ability to promote strong environmental protection policies (Iorember, Jelilov, Usman, Isik, & Celik, 2021). Based on professional levels, better-educated practitioners can argue in favour of environmentally friendly policies and regulations at all levels of government. The advancements in reducing CO 2 emissions have been impressive and, to some extent, this achievement can be explained by the effective lobbying that led to the implementation of stringent environmental rules, the stimulation of environmentally friendly energy sources, and the establishment of ambitious emission-cutting objectives (Eyuboglu and Uzar, 2021; Piao and Managi, 2023).

The primary purpose of environmental education programmes is to educate students about the current environmental issues and to encourage them to lead more sustainable lives. Learners of formal education programs often engage in the following environment-protecting activities such as volunteerism, environmental awareness, and conservation (Thor & Karlsudd, 2020; Boca and Saracli, 2019; Jadhav, Jadhav, and Raut, 2014). Environmental sustainability education raises awareness of the need to conserve natural resources and influences potential action. By incorporating the idea of the economic, social, political, and ecological interdependence of urban and rural environments, education can enhance knowledge and motivation, thereby fostering the competence, ideas, and skills required to preserve and improve their natural environments (Liu, Gong, and Chen, 2018). Both formal and informal educational programs can alter the attitudes and practices of individuals, communities, and nations regarding the environment (Saracli, Yilmaz, and Arslan, 2014). Education for global competency may shape future generations into concerned, active participants in solving global social, political, economic, and environmental problems. Because of its importance in achieving sustainability goals, education is emphasized throughout the 2030 Agenda for Sustainable Development, which urges all nations to “ensure, by 2030, that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship, and appreciation of diversity and inclusion. Policies that support and promote sustainability education are crucial for integrating climate change and sustainable development into the educational system. These policies may foster a shared understanding of sustainability, establish guidelines for integrating sustainability concepts into educational programs, and support educators’ professional development (Abdullah, Zakaria, & Razman, 2018).

The impacts of trade openness to environmental sustainability have produced mixed results. In particular, when calculating with the data of the DSUR and CUP- FM the positive correlation is observed with the emissions of CO 2, which means that the liberalisation of trade has an adverse impact on the quality of the environment. On the other hand, estimated coefficients based on the CUP-BC model show negative association with CO 2 and that environmental sustainability may be improved through reducing carbon emissions. Trade openness has an overall positive correlation with the CO 2 emission level indicating that the liberalization process has had negative consequences on the quality of the environment. Increased production increases resource needs and the quantity of greenhouse gases and undermines the sustainability of the environment. The result is supported by a variety of empirical studies (Azimi, Rahman, and Nghiem, 2023; Copeland and Taylor, 2004; Nosheen, Iqbal, and Khan, 2021; Jeetoo and Chinyanga, 2023; Ullah et al., 2019), all of which highlight the harmful effects of globalisation on the ecosystem. Among the most obvious and direct negative implications of economic progression brought about by trade extension are high pollution rates and exhaustion of natural resources (Chhabra et al., 2023; Wang and Zhang, 2021). This implies that there are various moderating variables that affect the relationship between the environmental sustainability and trade openness. This led to the emergence of the heterogeneous results of the impact of trade openness coefficients on environmental sustainability with moderating factors including the DSUR and CUP-HM. However, there was a negative association of CO 2 emissions and coefficients that were generated using the CUP BC framework. This fact provides another fact that the reduction of carbon emissions is necessary to maintain the wellbeing of ecosystems. The result of a decreased production of the basic chemicals is that the consumption of natural resources will be low as well as the emission of green house gases hence improving the quality of air and water.

This paper shows that the damages of carbon dioxide emissions can be overcome by increased openness to trade in various ways. High trade rates are possible to mitigate carbon dioxide emissions as a higher proportion of actors learn to use the technologies that are less polluting and share and access more sustainable materials. This is possible since an increase in bilateral trade may help to increase the efficiency of production, thus reducing the impact on the environment (Rahman, Saidi, and Mbarek, 2020). Weaker trade barriers are associated with less CO 2 emission, mediated by a greater level of employment, moderated financial flows, and a more competitive business environment. The rationale on which it functions is the open market system that would motivate companies to use cleaner technology. In case the adoption of such technology results in an increase in efficiency and output, the economic cost of the environment is prone to decrease (Sun et al., 2019; Cherniwchan, Copeland, and Taylor, 2017). The reduction in carbon dioxide emission has been witnessed to correlate with trade liberalisation in high and upper-middle income economies. Individual nations that have developed technology and environmental management are able to reduce the harmful implications of their economies to the natural environment, hence acquiring the world at the technological level. These standards and technologies can be diffused with the help of trade liberalisation and other countries can reduce their environmental effects (Farhani, Chaibi, & Rault, 2014; Pao and Tsai, 2010).

Foreign direct investment (FDI) coefficient analysis between asymmetric and symmetric evaluations is statistically significant (positive correlation) with OC2, indicating that FDI flows are among the factors contributing to environmental degradation caused by ecological imbalance due to over-injection of CO2. The conclusions made by Yuan Wang and Huang (2022b) and Khan, Chen, Bibi, and Khan (2023) are also supported by the empirical findings. These findings corroborate a positive relationship between FDI inflows and CO2 emissions across the BRICS economies. Then, FDI inflows harm the environment by disrupting the ecological balance through the excess release of CO2. However, the literature suggests that FDI may have both positive and negative effects on carbon emissions, depending on contextual factors. Although FDI can spread cleaner technologies and resources, it can also increase CO2 emissions through so-called pollution havens and scale effects. The pollution haven effect refers to the tendency of companies to relocate their activities to jurisdictions with less stringent environmental laws, thereby avoiding the compliance costs associated with high-quality domestic regulations. The scale effect becomes evident when increased production levels attract proportional increases in energy consumption and, thus, CO2 emissions. Nevertheless, the correlation between FDI and carbon dioxide release is not one-way; it depends on a plethora of factors, such as the quality of institutions, the level of development, and regulations. In fact, FDI can help lower carbon emissions, especially in developed and upper-middle-income countries that are highly technologically advanced and have well-developed environmental laws.

In light of the environmental issues that Sub-Saharan Africa (SSA) faces and to harness the opportunities in education, trade openness, and foreign direct investment (FDI) to improve environmental sustainability, the following policy formulations are proposed. To begin with, there is an urgent need to strengthen educational programmes specifically aimed at fostering environmental awareness and adopting environmentally sustainable practices. The appropriate approach is to make sustainability a central idea in the mainstream curriculum at all levels, from primary education through to tertiary education. Introduction of better teacher training and the creation of instructional materials that outline the importance of individuals as environmental custodians should be given sufficient resources by policymakers. Moreover, allocating resources to adult training and education campaigns at the community level may significantly increase outreach and effectiveness. International development agencies must work closely with local budgetary mechanisms to secure the funding needed to implement these educational reforms. The expected result is an informed populace able to advance sustainable measures at both personal and organizational scales, thereby driving the eventual decrease in CO₂ levels and enhancing environmental standards for all.

Second, there should be increased advertising and the introduction of green investments to counter the environmental impacts of FDI and trade openness, which are the main determinants of economic growth. Governments should consider implementing FDI laws that will compel foreign investors to meet environmental performance standards. These would support a situation in which environmental considerations are systematically taken into account when making investment decisions. The only solution would be to require the integration of green technologies and environmentally friendly mechanisms as part of the investment permits. Implementation of these regulatory changes should be done through the consolidation of enforcement systems and the involvement of citizens, businesses, and non-bank financial institutions. Also, trade in environmentally friendly goods and services through interventions should be promoted to offset the environmental externalities arising from trade liberalization on global CO₂ emissions. Through these steps, policymakers will not only be able to improve economic growth but also minimize environmental degradation and develop a more balanced developmental paradigm. These advantages are expected to be better environmental standards, sustainable economic development and an increase in social welfare, which makes these reforms very appealing to the political establishment as well as to society.

The article acknowledges that its research has several shortcomings that can limit the richness of the insights and the applicability of the conclusions. Potential bias is also associated with the reliance on secondary data sources and the use of econometric models (DSUR, CUP-FM, CUP-BC, and NARDL), which may introduce inaccuracies in the data and underlying assumptions. In turn, these methodological limitations can affect both the internal and external validity of these results, particularly the accurate quantification of the subtle effects of education, FDI, and open trade on environmental sustainability. Furthermore, the research is limited to Sub-Saharan Africa; this area is of great importance, but the results may not generalize to other regions with unique socio-economic and environmental circumstances. Differences between locales in terms of culture, economic factors and policy issues imply that the associations are probably not generic. These conclusions, therefore, ought to be applied with care to other settings.

The study uses publicly accessible data from the World Development Indicators (World Bank), UNCTAD, and Our World in Data. The processed dataset and associated materials are available at: qamruzzaman, Md (2026). Reassessing the Education–FDI–Trade Nexus for a Sustainable Future – data set. Figshare. Dataset. https://doi.org/10.6084/m9.figshare.33070697. data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Not applicable.

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