Elom et al. Carbon Research (2024) 3:24 https://doi.org/10.1007/s44246-024-00102-7 ORIGINAL ARTICLE Open Access © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Achieving carbon neutrality in Africa is possible: the impact of education, employment, and renewable energy consumption on carbon emissions Chinyere Ori Elom1, Robert Ugochukwu Onyeneke2* , Daniel Adu Ankrah3, Eric Worlanyo Deffor4, Hayford Mensah Ayerakwa5 and Chidebe Chijioke Uwaleke6 Abstract This paper analysed the causal link between education, employment, renewable energy consumption and carbon emissions in Africa, where there is scant evidence. Relying on panel data obtained from the World Development Indicators for thirty-two African countries covering a period of 19 years, and five panel rigorous regression mod- els, we found that renewable energy, investment in education, school enrolment, net national income per capita, and employment had negative and significant effects on carbon emission, thus increasing these predictors would result in significant reduction in carbon emission in Africa. We identified a bidirectional causality between carbon emissions and net national income per capita, education expenditure and renewable energy consumption, and car- bon emissions and employment. Our findings suggest that investment in education, renewable energy, and employ- ment are relevant in mitigating carbon emissions in Africa. We recommend African governments to invest heavily in education, improve school enrolment, environmental education, renewable energy and employment provision to mitigate carbon emissions. Highlights • Achieving carbon neutrality in Africa is important. • This research investigated the impact of education, employment, and renewable energy consumption on carbon emissions in Africa. • This study utilized five panel regression models – fixed effect with Driscoll-Kraay standard errors, panel fixed effect model, random effect model, panel fully modified ordinary least square model, and panel canonical correlation analy- sis and panel data from 32 African countries. • The results shed new light on the emission reduction potential of renewable energy consumption, education expenditure, school enrolment, per capita income, and employment. • This article offers recommendations to attain carbon neutrality and environmental sustainability. Handling editor: Su Shiung Lam. *Correspondence: Robert Ugochukwu Onyeneke robertonyeneke@yahoo.com; robert.onyeneke@funai.edu.ng Full list of author information is available at the end of the article http://creativecommons.org/licenses/by/4.0/ http://crossmark.crossref.org/dialog/?doi=10.1007/s44246-024-00102-7&domain=pdf http://orcid.org/0000-0002-9242-901X Page 2 of 15Elom et al. Carbon Research (2024) 3:24 Keywords Carbon emissions, Net national income per capita, Education expenditure, Primary school enrolment, Renewable energy consumption, Employment, Panel data Graphical Abstract 1 Introduction Climate change manifested through carbon emissions remains the most prominent topic around the globe and there continues to be frantic efforts and advocacy towards the consumption of renewable energy, invest- ment in education, and the provision of environmentally friendly employment in mitigating carbon emissions, particularly in achieving global sustainable development goals (SDGs) such as quality education (Sustainable Development Goal 4), use of clean energy (Sustainable Development Goal 7), decent work and economic growth (Sustainable Development Goal 8), and climate action (Sustainable Development Goal 13). Despite attempts to reduce carbon emissions, global emissions appear to be increasing at an astronomical rate in the global north (Acheampong et al. 2021). Generally, sub-Saharan Africa (SSA) contributes less to global carbon emissions, but recent accounts show a rise in the region’s carbon emissions, raising policy concerns (Acheampong 2019). Neglect in addressing carbon emissions in the sub-region has dire economic consequences (Acheampong et  al. 2021). This article focuses on examining the link between education, employment, renewable energy consumption and carbon emissions in Africa, where there is a paucity of information concerning the dynamic causal impacts. The paper argues that investment in education, employ- ment, and renewable energy influences carbon emissions in sub-Saharan Africa (SSA). Even though empirical evidence on this argument is expected to abound, there appears to be  scant empirical  evidence specifically focused on SSA. Generally, the literature (Salahuddin et al. 2020; Aper- gis et al. 2018; Bhattacharya et al. 2017; Inglesi-Lotz and Dogan 2018; Acheampong et  al. 2019) establishes that renewable energy reduces carbon emissions. But there appears to be mixed findings. For instance, some litera- ture (Ghorbal et al. 2022; Ali and Kirikkaleli 2022; Mentel et  al. 2022; Adams and Nsiah 2019) argued that renew- able energy increases carbon emissions, while Al-Mulali et al. (2015) however showed that renewable energy has no effect on carbon emissions in Vietnam. Others (Amri 2017; Pata and Kartal 2023; Yazdi and Shakouri 2018a) argued that renewable energy consumption impact on carbon emissions remains insignificant. The literature points to mixed findings on the impact of renewable energy on carbon emissions, and hence there is a need to draw more understanding on the subject, particularly in SSA, where the topic is under-researched. The nexus between economic growth and carbon emissions appears mixed. Generally, some literature (Yusuf et  al. 2020; Salahuddin et  al. 2020; Adams and Nsiah 2019; Apergis et  al. 2018) establishes a positive impact between economic growth and carbon emis- sions. For instance, Adu and Denkyirah (2018) showed Page 3 of 15Elom et al. Carbon Research (2024) 3:24 that economic growth has significant positive impacts on carbon emissions in the short-run. Economic growth is embedded with employment, and this study considers segment of the population aged above 15 years who are gainfully employed. The proportion of individuals gain- fully employed obtain disposable income from various employment outlets. There will be differential impacts in areas of low economic opportunities and carbon emis- sions, where geographical spaces with more economic opportunities and higher incomes are likely to emit more carbon. For instance, Grunewald et al. (2017) established the link between income inequality and carbon emissions where they found that higher income inequalities lead to low carbon emissions in low and middle-income coun- tries, while Adeleye et al. (2021) observed that, per capita income positively impacts carbon emissions. Specifically, per capita income positively increases carbon emissions by 0.84% and 0.87% in Africa. El Montasser et al. (2018) examined income and carbon emissions relationships in 12 Middle East and North Africa (MENA) countries. However, literature that establishes the simultaneous impact of per capita income, education, employment, and renewable energy consumption on carbon emissions in Africa is scarce. Hence, the motivation for this study. The current paper finds few studies that examine the dynamic impact between education (proxied by primary school enrolment and adjusted savings on education expenditure) and carbon emissions in SSA. However, interrogating the impact of education on carbon emis- sions is important for the following reasons. First, we find that education is positively correlated with increased labour productivity and enhances economic devel- opment (Chansarn 2010; Razzak and Timmins 2010; Vladimirova and Le Blanc 2016). Low literacy has the tendency to reduce efficiency and productivity of labour, and eventually negatively impact economic growth. Sec- ond, education improves human capacity development that eventually improves productivity at the workplace. Indeed, some growth models (Pelinescu 2015; Mankiw et  al. 1992) indicate that education improves economic development. Specifically, an additional $1 investment in education will generate between $10 and $15 in eco- nomic growth (See Ahmmed and Uddin 2022). With a causal effect on how much an individual can earn based on the level of education (Ahmmed and Uddin 2022; Psacharopoulos and Patrinos 2018). Other studies pre- sent contrary findings on the impact of education on car- bon emissions. For instance, Sarwar et  al. (2021) found no significant impact of education on carbon emissions in the short-run, but found positive significant impacts in the long run. Bello et al. (2021) found two impacts of edu- cation on carbon emissions in Africa. First, the authors established that estimation from the pooled ordinary least square (OLS) regression indicated a negative impact between education and carbon emissions, while the ran- dom effects and system generalized method of moments (GMM) showed a positive impact between education and carbon emissions. Few studies (Umaroh 2019; Barro 2001) have examined the impact of education on carbon emissions but with mixed findings. For instance, Balaguer and Cantavella (2018) found that education has a posi- tive effect on environmental quality, Sapkota and Bastola (2017) found mixed results on the impact of education on carbon emissions, while others (Williamson 2017) showed no relationship between education and carbon emissions. This implies that educational policy reforms will influence an economy in the long term. However, studies that examine the impact of education on carbon emissions in Africa remain limited. Therefore, empirical evidence on the impact of educa- tion, employment, and renewable energy consumption on carbon emissions in Africa is largely scanty and the existing studies yielded mixed findings. Specifically, our paper contributes to the carbon emissions and sustain- able development literature through the identification of the causal pathways of the extent to which renewable energy, investment in education, primary school enrol- ment, net national income per capita, and employment impact on carbon emissions in Africa. First, we show the impact of education (proxied by net primary school enrolment) on carbon emissions, a pathway less studied for the African geographical space. Bello et  al. (2021) appears to be the closest in examined the impact of edu- cation on carbon emissions in Africa studying 46 African countries covering the period between 1996–2018 using four different estimators (fixed effects, random effects, pooled OLS, and system-GMM). Mahalik et  al. (2021) examined educational level on environmental quality in BRICS (Brazil, India, China, and South Africa) countries. The few literatures that examine the impact of education on carbon emissions present mixed conclusions that fails to focus on SSA with the exception of a recent study by Bello et  al. (2021). A distinct feature of our study is the inclusion of investment in education (adjusted savings -educational expenditure) and primary school enrolment as proxies. Thus, our study contributes to the burgeon- ing literature on the nexus between education and car- bon emissions in SSA. The main reason for highlighting education as a covariate in the carbon emissions nexus is because carbon emissions are closely associated with increasing human economic activities (Wang et al. 2017). Additionally, education and economic growth are highly correlated. Economists strongly believe that human capital plays a vital role in shaping a country’s long- term economic growth. Human capital is a by-product Page 4 of 15Elom et al. Carbon Research (2024) 3:24 of education expenditure, school enrolment, and skilled labour, which remain vital inputs to economic growth. Second, we find that few studies jointly examine the causal impact between renewable energy, education, employment, and carbon emissions. The literature (Matei 2017; Singh et  al. 2019), showed that recent studies on the nexus between renewable energy, education, employ- ment, and carbon emissions largely focus on non-SSA countries. Even more compelling are the contradictory findings (see Adewuyi and Awodumi 2017; Ito 2017; Lu 2017; Ozcan et  al. 2019) in the under-researched area. Indeed, Arminen and Menegaki (2019) indicated that given the limited studies and the contradictory find- ings, further research is needed to deepen knowledge and understanding of the dynamic relationship between renewable energy, economic growth proxied by employ- ment, net national income, education, and carbon emis- sions. Given the steady rise in carbon emissions in Africa, Antonakakis et  al. (2017) argue that establishing low- emissions pathways is necessary in mitigating the climate crisis. And rightly so, our study seeks to contribute to the extant literature in this direction. Finally, our study utilises five panel regression models – fixed effect with Driscoll-Kraay standard errors, panel fixed effect model, random effect model, panel fully modified ordinary least square model, and panel canonical correla- tion analysis model to examine the dynamic causal path- ways between education, employment, renewable energy, net national income per capita, and carbon emissions in Africa which remain rare. To the best of our knowledge, this is the first attempt to model the impacts of education, employment, renewable energy, and economic growth on carbon emissions using five different panel regression approaches as a way to ascertain consistency and robust- ness of the dataset. Our study contributes to the methodol- ogy on consistently estimating the determinants of carbon emissions using different approaches as a way to demon- strate consistency and robustness of panel datasets used. Our study contributes to the methodological estima- tion rigour. The remainder of the article proceeds as follows, the next section (Section  2) reviews the relevant extant lit- erature pertinent to the study. Section  3 presents the methodology underpinning the study. Section 4 presents the results, while Section  5 discusses the results within the body of relevant extant literature. A final section concludes and offers policy recommendations worthy of consideration. 2 Literature review 2.1 Education and carbon emissions The relationship that exists between education and carbon emissions may be complex and multifaceted. However, education plays an effective role in building social responsibility, promoting sustainable behaviour, reducing carbon emissions, and creating a more sustain- able future (Alkhateeb et  al. 2020; Gheraia et  al. 2023). Several studies have highlighted the positive relation- ship between education and carbon emissions reduction. Higher level of education is associated with demand for clean energy leading to lower individual and household carbon emissions (Versteijlen et  al. 2017; Cordero et  al. 2020). Balaguer and Cantavella (2018) argued that the human capital and educational systems of a country are critical to the energy resources, and that education can play a significant role in an economy on many fronts. The authors conclude that an expansion in the educational system can make up for carbon emissions that are associ- ated with income growth. A panel analysis of the Organi- zation for Economic Cooperation and Development (OECD) countries on the role of human capital on energy consumption concluded that up to 86% of clean energy consumption was associated with investments in human capital, especially higher education (Yao et  al. 2019). Similar conclusions on the role and benefits of education in reducing carbon emissions and achieving ecological footprints exist in the literature (Furqan and Mahmood 2020; Ma et al. 2019; Zafar et al. 2019; Zamil et al. 2019). This has led to a call to promote sustainability education in higher education institutions and the business com- munity to reduce greenhouse gas emissions (Kiehle et al. 2023; Liu et al. 2022a, b, c; Molthan-Hill et al. 2020). This includes advocacy for online education (Yin et  al. 2022; Liu et al. 2022a; Heller et al. 2021). Other studies have established the positive effect of education in the promotion of environmental sustain- ability and called for the promotion of investment in education as a way of improving the ecological quality (Zafar et al. 2020; Reimers 2021; Sinha et al. 2019, 2020). Education remains an important characteristic at both the individual and national levels (Zaman et al. 2021) as improvement in education levels aligns with the SDGs (Rasool et  al. 2020). One way of doing this is through adult education which has potential to make impact on improving renewable energy consumption and reducing carbon emissions (Hanmer and Klugman 2016). Angrist et al. (2023) also argued that educated individ- uals have the likelihood to understand the nuances of cli- mate science. They further argued that this requires the accumulation of human capital through increased educa- tional attainment as people with higher education were more likely to see climate change and carbon emission as existential  a threat to humanity. Leal Filho et  al. (2023) arrive at a similar conclusion where they recommend that climate literacy and climate education are needed to raise awareness among children on the implication Page 5 of 15Elom et al. Carbon Research (2024) 3:24 of climate change on the environment and human race. Unfortunately, education has been missing in most of the discourse on renewable energy emissions and climate change (Fagan and Huang 2019). This notwithstanding, increases in educational attainment could foster eco- nomic growth which may have its downsides including increases in carbon emissions (O’Neill et al. 2020). It must be noted that, despite the strong association between education and the reduction in carbon emis- sions, other studies have indicated the non-significant impact of education on the reduction of carbon emis- sions in the short-run, but positive and significant in the long-run suggesting that policies and reforms related to an educational system and reduction in carbon emission and climate change mitigation need long term effects to have its influence on an economy (Sarwar et al. 2021; Zhu et  al. 2021). Therefore, investments in education, par- ticularly in developing countries, may be associated with significant long-term implications for reducing carbon emissions while combating climate change. 2.2 Employment and carbon emissions The literature on carbon emissions and employment gen- eration suggests a strong correlation between employ- ment and carbon emissions (Li et al. 2021; Sun et al. 2022; Cui et  al. 2022a). A study by the International Labour Organization (ILO) suggests that achieving a low carbon economy could create up to 24 million jobs globally and reduce global carbon dioxide emissions by up to 70% by 2050 (ILO 2018). The OECD (2016) concludes that the renewable energy sector alone employed 8.1 million peo- ple globally in 2015. In a similar study, the International Renewable Energy Agency (IRENA) concludes that the renewable energy sector employed 10.3 million people globally in 2017, representing a 5.3% increase over the previous year (IRENA 2018). This notwithstanding, the European Trade Union Confederation cautions that while traditional jobs in industries such as coal mining and oil drilling should be replaced with jobs in clean energy technologies, the tran- sition to a low-carbon economy should be accompanied by proactive social policies to ensure that the existing workforce is not left behind (ETUC 2020). Using data from China, Bai et al. (2021) cautions that transitioning to low emission sectors should be done in a way that does not have adverse impact on the econ- omy. They argue that small changes in key emission sec- tors can affect economic growth and possibly lead to job losses. However, the promotion of the service sector which is labour intensive creates an opportunity for eco- nomic growth and stable jobs, and a reduction in carbon emissions, especially in contexts that promote employee participation in reduction of carbon emissions (Markey et al. 2019). The review thus suggests transition to a low- carbon economy has the potential to create millions of new jobs globally, particularly in the renewable energy sector. Therefore, it is crucial for governments to imple- ment policies that encourage investment in low-carbon industries and green sectors to reduce carbon emissions and stimulate economic growth as countries with higher levels of renewable energy investment have higher levels of employment in the renewable energy sector. 2.3 Renewable energy consumption and carbon emissions Renewable energy consumption which is the use of renewable energy sources such as wind, solar, hydro, and biofuels for electricity generation, heating, and transportation has been promoted to mitigate climate change problems under various schemes, including the Paris Agreement and the Kyoto Protocol (Kwakwa 2021; Nguyen and Kakinaka 2019). Reaching the sus- tainable development goals (SDGs) requires closing of the gap between carbon emissions and economic devel- opment (Swain and Karimu 2020; Saidi and Omri 2020) as renewable energy has been associated with promot- ing economic growth and mitigating carbon emissions (Sadorsky 2012; Omri et  al. 2015; Gozgor et  al. 2018). Several studies have established a positive correlation exists between renewable energy consumption, carbon emission, and economic growth. A study by Ozturk and Al-Mulali (2015) examined the natural gas consumption and economic growth nexus using panel data from Gulf Cooperation Council (GCC) countries and concluded that natural gas energy consumption affects GCC coun- tries economic growth positively. Jin and Kim (2018) also concluded from their analysis of panel data that renew- able energy, but not nuclear, contributes to reduction in carbon emissions, and ultimately to the promotion of economic growth. 2.4 National income and carbon emissions Governments all over the world are confronted with the need to invest to transform their economies to create the needed jobs (Wang et  al. 2016). The quest for economic growth has led to increased energy consumption and to greater carbon emissions. The relationship between income and carbon emissions may however not always be linear. Rather, it may follow an inverted U-shaped curve, otherwise referred to as Environmental Kuznets Curve (EKC). The EKC is anchored on the assumption that, as income increases, environmental degradation will increase initially, and decrease. That is, there is a tendency to see a positive relationship between pollution and national income, but a negative relationship is observed at high income levels (Liu 2005). Zafar et  al. (2022) concludes from their studies on the determinants of carbon emission Page 6 of 15Elom et al. Carbon Research (2024) 3:24 that economic growth is conducive to environmental degradation. Factors such as remittances, export diver- sification, renewable energy consumption contributed to the reduction in carbon emission. Similar findings are observed by Hailemariam et al. (2020) where an increase in top income inequality is found to be positively corre- lated with carbon emissions, but a nonlinear association between economic growth and carbon emission. Over- all, the relationship between national income and carbon emissions remains complex and varies across geographies. However, economic growth is associated with increased carbon emissions. Addressing this relationship is impor- tant to enhance sustainable economic development while mitigating the impacts of climate change. 3 Methodology 3.1 Data source The paper focused on Africa, where educational attain- ment/enrolment and expenditure, employment, renew- able energy consumption, and national income are low. Panel data for a period of 19  years (2000–2018) and obtained from the World Development Indicators were used (see Table 1 for details). The time-period, countries, and variables (renewable energy consumption, carbon emissions, education expenditure, employment level, school enrolment, and net national income per capita) chosen were informed by data availability. The countries studied are presented in Table 2. 3.2 Analytical technique We applied different panel regression models to analyse the impacts of education (proxied by education expendi- ture, and school enrolment), employment, and covariates (renewable energy consumption, and net national income per capita) on carbon emissions in Africa. Five panel regression models – fixed effect with Driscoll-Kraay stand- ard errors, panel fixed effect model, random effect model, panel fully modified ordinary least square model, and panel canonical correlation analysis model were employed. We used different panel regression models to check the robust- ness and consistency of the estimates generated by each model. The implicit form of our models is stated as follows: Where; Y = Carbon emissions (kt) X1 = Adjusted net national income per capita (cur- rent US$) X2 = Adjusted savings: education expenditure (cur- rent US$) X3 = School enrolment, primary (% gross) X4 = Renewable energy consumption (% of total final energy consumption) X5 = Employment to population ratio, 15 + , total (%) (modeled ILO estimate) We converted the observed values of the variables to their natural logarithmic values as a traditional way to control for possible heteroskedasticity in the dataset. The panel regression model with the logarithm is pre- sented thus: Where; i:1, 2, 3, …, 32 countries; t:2000, 2001, 2002, …, 2018 year; ln denotes natural logarithm; ε is the error term. Furthermore, β1 , β2 , β3 , β4 , and β5 define the esti- mated percentage change in carbon emissions caused by a one percent change in net national income per capita, education expenditure, school enrolment, renewable energy consumption, and employment level, respectively, while all other factors  are constant. We further carried out Granger causality test to determine the causal rela- tionships existing among the variables. We used STATA 17 software to analyse the data. 4 Results Descriptive statistics for the variables used in the study are presented in Table 3. The table presents the distri- bution of the respective variables (minimum and maxi- mum), the mean values, standard deviations, skewness and kurtosis. A total of 608 data points was used rep- resenting a balanced panel data set. The mean value for adjusted net national income per capita (current (1)Y = f (X1,X2,X3,X4,X5) (2) lnYit = β0 + β1lnX1it + β2lnX2it + β3lnX3it + β4 lnX4it + β5lnX5it + εit Table 1 List of variables Data Source: https:// datab ank. world bank. org/ source/ world- devel opment- indic ators# Variable Data source Adjusted net national income per capita (current US$) World Development Indicators (World Bank 2023) Adjusted savings: education expenditure (current US$) World Development Indicators (World Bank 2023) School enrolment, primary (% gross) World Development Indicators (World Bank 2023) Renewable energy consumption (% of total final energy consumption) World Development Indicators (World Bank 2023) Employment to population ratio, 15+, total (%) (modeled ILO estimate) World Development Indicators (World Bank 2023) CO2 emissions (kt) World Development Indicators (World Bank 2023) https://databank.worldbank.org/source/world-development-indicators# Page 7 of 15Elom et al. Carbon Research (2024) 3:24 US$) was 1408.27, while the minimum value was 41.53 and the maximum was 11,114.26. The mean value for CO2 emission (kt) was 29,831.56, the minimum value was 447,927.20 and the maximum was 29,831.56. 4.1 Preliminary results Table 3. 4.2 Multicollinearity test In Table  4, the data series on the predictors were subjected to a variance inflation factor (VIF test) to test for multicollinearity. The result shows that the Variance Inflation Factor (VIF) for all the variables were < 5 signifying the absence of multicollinearity. Previous studies (Chidiebere-Mark et  al. 2022; Eme- nekwe et  al. 2022; Onyeneke et  al. 2023a, b, c, d) have used 5 as the cut-off point for determination of multicollinearity. 4.3 Cross section dependence test A test of cross-sectional dependence formed our first step in the estimation strategy for this paper, since the presence of cross sectional dependence can bias the result while estimating a panel model. We used the cross- sectional dependence (CD) test to establish cross-section dependence in the data set. To reject the null hypothesis of no cross sectional dependency the p-values should be lower than 1%. From Table  5, we observed probability values for the computed CD were lower than 1%, hence the null hypothesis (absence of cross-sectional depend- encies) is rejected and the subsistence of cross-sectional Table 2 List of countries studied Code Country 1 Algeria 2 Benin 3 Burkina Faso 4 Burundi 5 Cabo Verde 6 Cameroon 7 Djibouti 8 Egypt 9 Eswatini 10 Ethiopia 11 Gambia 12 Ghana 13 Guinea 14 Lesotho 15 Madagascar 16 Malawi 17 Mali 18 Mauritania 19 Mauritius 20 Morocco 21 Mozambique 22 Namibia 23 Niger 24 Nigeria 25 Rwanda 26 Senegal 27 South Africa 28 Tanzania 29 Togo 30 Tunisia 31 Uganda 32 Zambia Table 3 Descriptive statistics Variable N Minimum Maximum Mean Std. deviation Adjusted net national income per capita (current US$) 608 47.53 11,114.26 1408.27 1607.42 Adjusted savings: education expenditure (current US$) 608 7,469,764.66 26,500,000,000.00 1,586,543,450.37 3,566,452,636.81 School enrolment, primary (% gross) 608 32.36 149.32 101.75 22.85 Renewable energy consumption (% of total final energy consumption) 608 0.06 96.04 58.38 30.62 Employment to population ratio, 15+, total (%) (modeled ILO estimate) 608 23.74 85.87 58.13 15.57 CO2 emissions (kt) 608 152.80 447,927.20 29,831.56 78,101.27 Table 4 Multicollinearity test result using the variance inflation factor (VIF) Independent variable VIF lnX1 2.57 lnX2 1.87 lnX3 1.12 lnX4 2.13 lnX5 2.24 Page 8 of 15Elom et al. Carbon Research (2024) 3:24 dependencies between the panels is found. The results indicate that the predictors X1, X2, X3, X4 and X5 and the dependent variable Y in one of the countries is likely to influence those of other African countries. 4.4 Unit test of the variables The unit root test results for the data with their respec- tive p-values are presented in Table 6. The test was con- ducted using both Pesaran’s Cross-sectional Augmented Dickey Fuller (CADF) test and Im-Pesaran-Shin Unit root test approaches for panel and times series. The pre- dictors were tested at levels and first difference. Under the Pesaran’s CADF test, we found that the predic- tors (net national income per capita (lnX1); education expenditure (lnX2); school enrolment, primary (lnX3) were stationary at level; while all the variables were stationary at first difference.  For the Im–Pesaran–Shin unit-root test, none of the variables was stationary at level, while all of them were stationary at first differ- ence. Based on the results, the study rejected the null hypotheses that all panels contain unit roots and the time series data are not stationary (i.e. series have a unit root). The test findings indicate that the predictors are I (1) and the maximum integration level is specified as 1. The variables used for this paper showed mixed nature of stationarity properties, being stationary at the level and first difference (Zaman et al. 2021). 4.5 Cointegration tests The test for Co-integration between the dependent vari- able (CO2 emission) and the independent variables are presented in Table 7. The null hypothesis of no cointegra- tion was tested against the alternative using the Pedroni test (Philips-Perron t statistic). Other studies have used the ARDL bound test for cointegration based on F-test to check for cointegration (Narayan 2004). From Table 7, we confirm that a long-run relationship among the stud- ied variables exists. Based on the p-values (critical val- ues at 1% level of significance) the null hypothesis of no cointegration is rejected. Therefore, there is cointegra- tion relationship between dependent variable and the predictors (net national income, education expenditure, school enrolment, renewable energy consumption and employment). 4.6 Empirical results from panel regression models Although technological innovations are making life easy and comfortable, they come with some environmental challenges such as high emission of greenhouse gases into the atmosphere. These gases have led to changes in weather patterns, global warming with resultant effect on soil (flora and fauna), ocean life and the health of human (Wang et  al. 2021; Rahman et  al. 2020). Having established cointegration relationship among the vari- ables for the study, different panel models were estimated to establish the effect of education, employment, renew- able energy consumption on carbon emission in Africa. Results from the respective panel regression models are presented in Table 8. Net income per capita had a negative and significant effect on CO2 emissions as presented in Table 8. The vari- able was not significant in the second model even though Table 5 Cross section dependence test Variable CD-test P-value lnY 79.39 0.000 lnX1 82.28 0.000 lnX2 88.06 0.000 lnX3 29.34 0.000 lnX4 51.27 0.000 lnX5 9.13 0.000 Table 6 Unit root test results a Indicate significance at 1% level Variables H0 = All panels contain unit roots H0 = series have a unit roots Pesaran’s CADF test Result Im–Pesaran–Shin unit-root test Result Decision Level p-value 1st diff. p-value Level p-value 1st diff. p-value Net national income per capita (lnX1) -2.51a 0.000 -3.07a 0.000 I(0) 1.18 0.880 -9.98a 0.000 I(1) Reject H0 Education expenditure (lnX2) -2.29a 0.000 -2.98a 0.000 I(0) 3.09 0.999 -10.03a 0.000 I(1) Reject H0 School enrolment, primary (lnX3) -2.20a 0.010 -2.65a 0.000 I(0) 1.67 0.950 -4.72a 0.000 I(1) Reject H0 Renewable energy consumption (lnX4) -1.75 0.460 -2.51a 0.000 I(1) 1.02 0.850 -5.96a 0.000 I(1) Reject H0 Employment (lnX5) -1.94 0.120 2.19a 0.010 I(1) 0.28 0.610 -3.65a 0.000 I(1) Reject H0 CO2 emissions (lnY) -1.94 0.120 -2.91a 0.000 I(1) 0.07 0.530 -7.23a 0.000 I(1) Reject H0 Page 9 of 15Elom et al. Carbon Research (2024) 3:24 the direction of the effect was negative. In the other mod- els net income per capita was highly significant at 1% level of significance. From a theory perspective, education expenditure is hypothesized to influence environmental quality lead- ing to economic growth and development (Duarte et al. 2012; Cui et  al. 2022b). The a priori expectation is that expenditure on education targeted at creating awareness of environmental issues will significantly minimize envi- ronmental degradation through different transmission channels (Dedeoğlu et al. 2021; Zaman et al. 2021). The estimated models, presented in Table 8, show that educa- tion expenditure had positive effect on CO2 emission in Africa. This relationship was significant at 1% level of sig- nificance for all the models. This suggests that in Africa increasing education expenditure would increase the CO2 emission. In terms of school enrolment (primary), the variable yielded significant and negative effect on carbon emis- sions. The variable was negative and significant at 1% level of significance in the first panel data presented in Table 8. Renewable energy was found to have a negative and significant effect on carbon emission from the panel data analysis presented in Table 8. An increase in the use of renewable energy is expected to result in significant reduction in CO2 emission in Africa. Employment had a negative but not significant effect on CO2 emission under the fixed effect model with Driscoll-Kraay standard errors model, the variable, however, had negative but significant effect on CO2 emis- sions in Africa for the other models. The relationship was highly significant at 1% level of significance. This suggests that increasing employment (particularly green jobs) will help in reducing CO2 emission by in the environment. The result of the study is in line with other studies indi- cating that increasing employment (particularly green jobs) levels has implications for CO2 emission in Africa. Africa as a continent has the largest number of youth as well as high levels of youth unemployment, striking a bal- ance between creating employment with minimal CO2 emission would be a critical and important policy action. From the empirical literature, there is mixed results regarding the relationship between (un)employment and the environment (Liu and Feng 2022). 4.7 Granger test for panel causality Granger test for panel causality was conducted using Dumitrescu and Hurlin (2012), the results and significant levels are presented in Table 9. The test Granger checks the relationship between the predictors in the model and the dependent variable. We identified bidirectional cau- sality between the dependent variable CO2 emission and net national income per capita (lnX1), education expend- iture (lnX2), renewable energy consumption. The results also show bidirectional causality between CO2 emis- sions and employment. In other words, carbon emissions Granger causes employment and employment Granger causes carbon emissions (bidirectional causality identi- fied). In terms of relationship between Carbon emissions variable and school enrolment, the results show that CO2 emission does not Granger cause school enrolment, but school enrolment Granger causes carbon emissions, hence a unidirectional causality was identified. The cau- sality test to establish the relationship among each of the predictors is also presented in Table 9. There is bidirec- tional causality among all the predictors. For example, the study data showed that net national income per cap- ita does Granger cause education expenditure and educa- tion expenditure does Granger cause net national income Table 7 Cointegration test H0: No cointegration Ha: All panels are cointegrated Pedroni test for cointegration Test Statistic p-value Modified Phillips–Perron t 6.33 0.000 Phillips–Perron t -8.14 0.000 Augmented Dickey–Fuller t -6.90 0.000 Table 8 Panel regression estimates of the impact of education, employment, renewable energy consumption, and national income on carbon emissions a and b denote statistical significance at 1% and 5% levels, respectively Variable Fixed effect with Driscoll- Kraay standard errors Fixed effect with ordinary standard errors Random effect Panel fully modified ordinary least square Panel canonical cointegrating regressions lnX1 -0.16a (-2.62) -0.08 (-1.38) -0.21b (-3.95) -0.14b (-8.39) -0.13b (-4.98) lnX2 -1.10b (-91.33) -0.44b (-10.62) -0.55b (-14.31) -0.29b (-49.65) -0.28b (-28.64) lnX3 -0.63b (-3.75) 0.08 (1.02) -0.01 (-0.07) -0.14b (-6.76) -0.15b (-7.37) lnX4 -0.21b (-16.64) -0.34b (-6.17) -0.37b (-7.16) -3.77b (-193.89) -3.76b (-119.76) lnX5 -0.04 (-0.25) -1.05b (-4.51) -0.82b (-3.91) -0.96b (-33.56) -0.93b (-19.89) Page 10 of 15Elom et al. Carbon Research (2024) 3:24 per capita (bidirectional causality identified). Details are presented in Table 9. 5 Discussion Economic growth is expected to influence the emis- sion of carbon into the environment through the path- way of industrialization. For instance, Xin et  al. (2023) using Gross Domestic Product (GDP) as a proxy for income and economic growth found that increased out- put growth contributes to carbon emissions in China. We assumed that higher income per capita should translate to higher income levels of people living in the countries. As income per capita increases, people with high income will have more opportunities to fulfil their desires and maintain a good lifestyle that improves the environment (Mulderij et al. 2021). Also, higher income per capita tends to promote better environmental aware- ness and consciousness, for instance, higher income groups tend to invest in more sustainable energy sources such as renewable energy, and hence income per capita is expected to lead to reduction in carbon emission. In this paper net income per capita was used as a proxy for eco- nomic growth, and the results shows that net income per capita negatively influences carbon emission in Africa. In other words, higher net income per capita in Africa would decrease the levels of carbon emission. Struc- tural transformation of economies in Africa is propelled by the service than industry. Hence countries in Africa emit less carbon into the environment. Xin et al. (2023) confirmed the findings of the current study. Our findings Table 9 Dumitrescu and Hurlin (D-H) Granger non-causality test results Ho one variable does not Granger cause the other variable for at least one panel variable a  and b denote statistical significance at 1%, 5%, and 10% levels Hypothesis Z-bar Z-bar tilde Remark lnY → lnX1 4.62a 3.07a Carbon emissions Granger causes net national income per capita and net national income per capita Granger causes carbon emissions (bidirectional causality identified).lnX1 → lnY 4.62a 3.08a lnY → lnX2 7.48a 5.28a Carbon emissions Granger causes education expenditure and education expenditure Granger causes carbon emissions (bidirectional causality identified).lnX2 → lnY 5.50a 3.75a lnY → lnX3 1.39 0.59 Carbon emissions variable does not Granger cause school enrolment and school enrolment Granger causes carbon emissions (unidirectional causality identified).lnX3 → lnY 10.03a 7.24a lnY → lnX4 2.72a 1.62 Carbon emissions Granger causes renewable energy consumption and renewable energy consumption Granger causes carbon emissions (bidirectional causality identified).lnX4 → lnY 10.25a 7.40a lnY → lnX5 6.04a 4.17a Carbon emissions Granger causes employment and employment Granger causes carbon emissions (bidi- rectional causality identified).lnX5 → lnY 13.23a 9.69a lnX1 → lnX2 9.64a 6.93a Net national income per capita does Granger-cause education expenditure and education expenditure does Granger-cause net national income per capita (bidirectional causality identified).lnX2 → lnX1 6.84a 4.78a lnX1 → lnX3 10.84a 7.85a Net national income per capita does Granger-cause school enrolment and school enrolment does Granger-cause net national income per capita (bidirectional causality identified).lnX3 → lnX1 9.59a 6.89a lnX1 → lnX4 2.72a 1.62 Net national income per capita does Granger-cause renewable energy consumption and renewable energy consumption does Granger-cause net national income per capita (bidirectional causality identi- fied). lnX4 → lnX1 5.4739a 3.7324a lnX1 → lnX5 3.9815a 2.5859a Net national income per capita does Granger-cause employment and employment does Granger-cause net national income per capita (bidirectional causality identified).lnX5 → lnX1 28.2825a 21.2544a lnX2 → lnX3 12.0853a 8.8114a Education expenditure Granger causes school enrolment and school enrolment Granger causes education expenditure (bidirectional causality identified).lnX3 → lnX2 9.0628a 6.4894a lnX2 → lnX4 3.9501a 2.5618b Education expenditure Granger causes renewable energy consumption and renewable energy consump- tion Granger causes education expenditure (bidirectional causality identified).lnX4 → lnX2 5.5854a 3.8180a lnX2 → lnX5 4.4827a 2.9709a Education expenditure Granger causes employment and employment Granger causes education expendi- ture (bidirectional causality identified).lnX5 → lnX2 25.4694a 19.0933a lnX3 → lnX4 9.4301a 6.7716a School enrolment does Granger cause renewable energy consumption and renewable energy consump- tion does Granger-cause school enrolment (bidirectional causality identified).lnX4 → lnX3 4.0286a 2.6221a lnX3 → lnX5 10.0982a 7.2849a School enrolment does Granger cause employment and employment does Granger-cause school enrol- ment (bidirectional causality identified).lnX5 → lnX3 32.4613a 24.4646a lnX4 → lnX5 8.4410a 6.0118a Renewable energy consumption does Granger cause employment and employment does Granger-cause renewable energy consumption (bidirectional causality identified).lnX5 → lnX4 22.9613a 17.1665a Page 11 of 15Elom et al. Carbon Research (2024) 3:24 confirmed the study by Acheampong et  al. (2021) who found that economic growth (proxied by GDP) reduces carbon dioxide emission significantly in the British ex- colonies but negligibly in other SSA countries. However, Grunewald et  al. (2017) found that higher inequality is likely to lower carbon emissions for low income and mid- dle income economies while a higher income inequality increases carbon emissions in upper middle income and high-income economies. Education expenditure has been used as a proxy for human capital in studies that looked at the relationship between human capital development and carbon emis- sion. Human capital contributes to the rising environ- mental sustainability by controlling carbon emission and sustainable growth. It is therefore, expected that increas- ing expenditure on environmental education should cre- ate awareness and lead to significant reduction in carbon emissions. Hence a significant negative a priori rela- tionship was expected between education and carbon emissions. This paper also found a negative relationship between education and carbon emissions, implying that expenditure on education significantly decreased car- bon emissions in Africa. The finding deviates from most empirical studies that looked at the relationship between education expenditure and carbon emission. High invest- ments/budget in education with a commensurate cur- riculum that teaches environmental issues results in high level of environmental knowledge and the consequence of degradation on the environment. This finding supports the finding of Zaman et al. (2021), who found a negative effect between education expenditure and carbon dioxide emission in China. Another study by Liu et  al. (2022b) found a negative association between education and carbon dioxide emission. Similarly, Li and Ullah (2022) reported that an increase in education significantly con- trols CO2 emissions, while a decline in educational attain- ment thus amplifies carbon dioxide emission in BRICS economies. Zafar et  al. (2022) reported robust findings between education and environmental deprivation. The findings for school enrolment showed a significant negative effect on carbon emissions implying that school enrolment decreased carbon emissions on the continent. High enrolment in primary schools with a commensurate curriculum that covers environmental issues would likely result in high level of environmental knowledge and the implication on sustainable environmental management. Such people would be concerned about the environment and are likely to attach values to it (Mahalik et al. 2021). From the perspective of Xin et al. (2023) policy on envi- ronmental education should be considered at early levels of education such as the primary level. The current paper sought to address the relationship between consumption of renewable energy and carbon emission reduction. Renewable energy consumption sig- nificantly decreased carbon emissions in Africa. This sub- stantiates the findings that posit that increasing renewable energy help improve environmental quality and reduces carbon emissions (Waheed et al. 2018; Bilgili et al. 2016; Danish et  al. 2017; Wang et  al. 2016). Furthermore, the finding supports a study conducted in Brazil, Russia, India, China, and South Africa (BRICS) countries by Dong et  al. (2017), where increasing renewable energy con- sumption mitigates CO2 emissions in the environment. The current paper also confirms the work of Jin and Kim (2018) who established a long-run relationship between carbon dioxide emission, renewable energy consumption and nuclear energy consumption. We also found that the current results are consistent with the findings of Chidie- bere-Mark et al. (2022), Adams and Acheampong (2019), Yazdi and Beygi (2018) who established that renewable energy reduces carbon emission in Africa. This however contradicts the work of Hu et  al. (2018) and Yazdi and Shakouri (2018b) who found that renewable energy con- sumption increased carbon emission. The result of this study shows that employment has a negative and significant effect on carbon emissions. This implies that employment decreased carbon emissions in Africa. The result of the study is in line with other studies indicating that increasing employment levels (especially green and climate compatible jobs) have implications for carbon dioxide emission in Africa (Liu and Feng 2022). Although the nexus between unemployment and the environment is found inconclusive (Mulderij et al. 2021), a few studies have established a relationship between unemployment and carbon emission. Xin et  al. (2023) established that unemployment significantly increases carbon dioxide emission in the long-run. Based on the results of the current paper, we strongly note that struc- tural changes in Africa and the creation of sustainable employment will lead to reduction in carbon emission. From the empirical literature, employment was found to be negatively related to carbon emission in Africa, this suggesting that all things being equal higher levels of employment will result in the significant reduction in CO2 emissions on the continent. 6 Conclusions Despite attempts to reduce carbon emissions, global emissions appear to be increasing at an astronomical rate. Mitigating climate change through reduction in emission of carbon becomes challenging given the need for countries to grow their economies, improve human capital development (through education) and provide jobs. Striking a balance between economic growth, edu- cation, employment, and renewable energy use and emis- sion of carbon into the environment remains high on Page 12 of 15Elom et al. Carbon Research (2024) 3:24 the global policy agenda. The global sustainable devel- opment goals, advocacy for the consumption of renew- able energy, investment in education, and the provision of environmentally friendly employment avenues are all priorities in the global policy goals. This study therefore examined the dynamic causal link between education (proxied by adjusted savings in education expenditure, primary school enrolment), employment, renewable energy consumption and car- bon emissions in Africa, where there is scant evidence. Our study relies on panel data obtained from the World Development Indicators for thirty-two African countries covering a period of 19 years (2000–2018), making use of five panel rigorous regression models – fixed effect with Driscoll-Kraay standard errors, panel fixed effect model, random effect model, panel fully modified ordinary least square model, and panel canonical correlation analysis model to establish causality of carbon emissions. The results showed a positive impact of education expenditure on carbon emissions. Renewable energy reduced carbon emissions in Africa suggesting the need to promote renewable energy consumption in Africa. We conclude that policy initiative targeted at increasing the consumption of renewable energy as part of total energy mix will help mitigate carbon emission in the environment. Employment significantly reduced carbon emissions in Africa. African governments are encouraged to target increasing avenues of creating decent and environmen- tally friendly employment to help mitigate carbon emis- sions. Similarly, net income per capita decreased carbon emissions on the continent. There is a bidirectional cau- sality between carbon emissions and net national income per capita, education expenditure and renewable energy consumption, and carbon emissions and employment; However, carbon emissions do not Granger cause school enrolment, but school enrolment Granger causes carbon emissions, thus a unidirectional causality. The findings sug- gest that renewable energy and employment are relevant for mitigating carbon emissions in Africa. Fortunately, Africa abounds with renewable energy resources which currently remain under-utilized and exploited. We recommend Afri- can governments to invest heavily in renewable energy and provide environmentally friendly employment options that mitigate carbon emissions. Investments in education and the improvement in primary school enrolment have posi- tive dividends on carbon emissions reduction; hence there is the need to turn attention to improvements in invest- ment in education and primary school enrolment in Africa. Abbreviations BRICS Brazil, Russia, India, China, and South Africa CO2 Carbon dioxide CD Cross-sectional dependence CADF Cross-sectional augmented dickey fuller D-H Dumitrescu and Hurlin DOLS Dynamic ordinary least squares EKC Environmental Kuznets curve FMOLS Fully modified ordinary least squares GMM Generalized method of moments GDP Gross domestic product GCC Gulf Cooperation Council ILO International Labour Organisation IRENA International Renewable Energy Agency MENA Middle East and North Africa OLS Ordinary least square OECD Organization for Economic Cooperation and Development SSA Sub-Saharan Africa SDGs Sustainable development goals VIF Variance inflation factor Authors’ contributions Conceptualization, Chinyere Ori Elom, Robert Ugochukwu Onyeneke and Chidebe Chijioke Uwaleke; Formal analysis, Robert Ugochukwu Onyeneke; Investigation, Chinyere Ori Elom, Daniel Adu Ankrah, Eric Worlanyo Deffor, Hayford Mensah Ayerakwa and Chidebe Chijioke Uwaleke; Literature Search and Review, Chinyere Ori Elom, Daniel Adu Ankrah, and Hayford Mensah Ayerakwa; Methodology, Robert Ugochukwu Onyeneke; Supervision, Chinyere Ori Elom, Daniel Adu Ankrah, Eric Worlanyo Deffor, and Hayford Mensah Ayerakwa; Validation, Chinyere Ori Elom and Robert Ugochukwu Onyeneke; Visualization, Chinyere Ori Elom, Robert Ugochukwu Onyeneke, Daniel Adu Ankrah and Eric Worlanyo Deffor; Writing—original draft, Chinyere Ori Elom, Robert Ugochukwu Onyeneke, Daniel Adu Ankrah, Eric Worlanyo Deffor, Hayford Mensah Ayerakwa and Chidebe Chijioke Uwaleke; Writing—review and editing, Chinyere Ori Elom, Robert Ugochukwu Onyeneke, Daniel Adu Ankrah, Eric Worlanyo Deffor, Hayford Mensah Ayerakwa and Chidebe Chijioke Uwaleke. All authors have read and agreed to the published version of the manuscript. Funding The authors received no funding for the study. Availability of data and materials The data used for this study are openly available. The data can be downloaded from the World Development Indicators website. https:// datab ank. world bank. org/ source/ world- devel opment- indic ators#. Declarations Competing interests The authors declare no known conflict of interest. Author details 1 Department of Educational Foundations, Alex Ekwueme Federal University Ndufu-Alike, Abakaliki, Nigeria. 2 Department of Agriculture, Alex Ekwueme Federal University Ndufu-Alike, Abakaliki, Nigeria. 3 Department of Agricul- tural Extension, University of Ghana, Legon, Accra, Ghana. 4 Ghana Institute of Management and Public Administration, Accra, Ghana. 5 University of Ghana Learning Centres, School of Continuing and Distance Education, University of Ghana, Accra, Ghana. 6 Department of Science Education, Alex Ekwueme Federal University Ndufu-Alike, Abakaliki, Nigeria. Received: 17 November 2023 Revised: 11 January 2024 Accepted: 14 January 2024 References Acheampong AO (2019) Modelling for insight: does financial development improve environmental quality? Energy Econ 83:156–179. https:// doi. org/ 10. 1016/j. eneco. 2019. 06. 025 Acheampong AO, Adams S, Boateng E (2019) Do globalization and renewable energy contribute to carbon emissions mitigation in sub-Saharan Africa? Sci Total Environ 677:436–446. https:// doi. org/ 10. 1016/j. scito tenv. 2019. 04. 353 https://databank.worldbank.org/source/world-development-indicators# https://databank.worldbank.org/source/world-development-indicators# https://doi.org/10.1016/j.eneco.2019.06.025 https://doi.org/10.1016/j.eneco.2019.06.025 https://doi.org/10.1016/j.scitotenv.2019.04.353 Page 13 of 15Elom et al. Carbon Research (2024) 3:24 Acheampong AO, Dzator J, Savage DA (2021) Renewable energy, CO2 emis- sions and economic growth in sub-Saharan Africa: does institutional quality matter? J Policy Model 43(5):1070–1093. https:// doi. org/ 10. 1016/j. jpolm od. 2021. 03. 011 Adams S, Acheampong AO (2019) Reducing carbon emissions: the role of renewable energy and democracy. J Clean Prod 240:118245 Adams S, Nsiah C (2019) Reducing carbon dioxide emissions: does renewable energy matter? Sci Total Environ 693:133288. https:// doi. org/ 10. 1016/j. scito tenv. 2019. 07. 094 Adeleye BN, Osabohien R, Lawal AI, De Alwis T (2021) Energy use and the role of per capita income on carbon emissions in African countries. PLoS One 16(11):e0259488. https:// doi. org/ 10. 1371/ journ al. pone. 02594 88 Adewuyi AO, Awodumi OB (2017) Renewable and non-renewable energy- growth-emissions linkages: review of emerging trends with policy implications. Renew Sust Energ Rev 69:275–291. https:// doi. org/ 10. 1016/j. rser. 2016. 11. 178 Adu DT, Denkyirah EK (2018) Economic growth and environmental pollution in West Africa: testing the environmental Kuznets curve hypothesis. Kasetsart J Soc Sci 40(2):281–288. https:// so04. tci- thaijo. org/ index. php/ kjss/ artic le/ view/ 242142 Ahmmed S, Uddin M (2022) Exploring the disparities in learning outcomes among the primary school students of Bangladesh. Int J Educ Dev 93:102644. https:// doi. org/ 10. 1016/j. ijedu dev. 2022. 102644 Ali M, Kirikkaleli D (2022) The asymmetric effect of renewable energy and trade on consumption-based CO2 emissions: the case of Italy. Integr Environ Asses Manag 18(3):784–795. https:// doi. org/ 10. 1002/ ieam. 4516 Alkhateeb TTY, Mahmood H, Altamimi NN, Furqan M (2020) Role of education and economic growth on the CO2 emissions in Saudi Arabia. Entrepreneurship Sustain Issues 8(2):195–209. https:// doi. org/ 10. 9770/ jesi. 2020.8. 2(12) Al-Mulali U, Saboori B, Ozturk I (2015) Investigating the environmental Kuznets curve hypothesis in Vietnam. Energy Policy 76:123e131. https:// doi. org/ 10. 1016/j. enpol. 2014. 11. 019 Amri F (2017) Carbon dioxide emissions, output, and energy consumption categories in Algeria. Environ Sci Pollut Res 24:14567–14578. https:// doi. org/ 10. 1007/ s11356- 017- 8984-7 Angrist N, Winseck K, Patrinos HA, Graff Zivin JS (2023) Human capital and climate change. NBER Working Paper No. 31000, Cambridge, Massa- chusetts. https:// www. nber. org/ system/ files/ worki ng_ papers/ w31000/ w31000. pdf. Accessed 20 Jan 2024 Antonakakis N, Chatziantoniou I, Filis G (2017) Energy consumption, CO2 emissions, and economic growth: an ethical dilemma. Renew Sustain Energy Rev 68:808–824. https:// doi. org/ 10. 1016/j. rser. 2016. 09. 105 Apergis N, Ben Jebli M, Ben YS (2018) Does renewable energy consumption and health expenditures decrease carbon dioxide emissions? Evidence for sub-Saharan Africa countries. Renewable Energy 127:1011–1016. https:// doi. org/ 10. 1016/j. renene. 2018. 05. 043 Arminen H, Menegaki AN (2019) Corruption, climate and the energy-environ- ment-growth nexus. Energy Econ 80:621–634. https:// doi. org/ 10. 1016/j. eneco. 2019. 02. 009 Bai S, Zhang B, Ning Y, Wang Y (2021) Comprehensive analysis of carbon emissions, economic growth, and employment from the perspective of industrial restructuring: a case study of China. Environ Sci Pollut Res 28:50767–50789. https:// doi. org/ 10. 1007/ s11356- 021- 14040-z Balaguer J, Cantavella M (2018) The role of education in the environmental Kuznets curve. evidence from Australian data. Energy Econ 70:289–296. https:// doi. org/ 10. 1016/j. eneco. 2018. 01. 021 Barro R (2001) Education and economic growth. In: Helliwell JF (ed) The contri- bution of human and social capital to sustained economic growth and well-being. OECD, Paris Bello AA, Agabo T, Adedoyin FF (2021) The anthropogenic consequences of energy consumption in sub-Saharan Africa: is there a role for education? Environ Chall 5:100234. https:// doi. org/ 10. 1016/j. envc. 2021. 100234 Bhattacharya M, Churchill SA, Paramati SR (2017) The dynamic impact of renewable energy and institutions on economic output and CO2 emissions across regions. Renew Energ 111:157–167. https:// doi. org/ 10. 1016/j. renene. 2017. 03. 102 Bilgili F, Koçak E, Bulut Ü (2016) The dynamic impact of renewable energy consumption on CO2 emissions: a revisited environmental Kuznets curve approach. Renew Sust Energ Rev 54:838–845. https:// doi. org/ 10. 1016/j. rser. 2015. 10. 080 Chansarn S (2010) Labor productivity growth, education, health and techno- logical progress: a cross-country analysis. Econ Anal Policy 40:249–261. https:// doi. org/ 10. 1016/ S0313- 5926(10) 50027-4 Chidiebere-Mark NM, Onyeneke RU, Uhuegbulem IJ, Ankrah DA, Onyeneke LU, Anukam BN, Chijioke-Okere MO (2022) Agricultural production, renewable energy consumption, foreign direct investment, and carbon emissions: new evidence from Africa. Atmosphere 13(12):1981. https:// doi. org/ 10. 3390/ atmos 13121 981 Cordero EC, Centeno D, Todd AM (2020) The role of climate change education on individual lifetime carbon emissions. PLoS One 15(2):e0206266. https:// doi. org/ 10. 1371/ journ al. pone. 02062 66 Cui Y, Wang G, Irfan M, Wu D, Cao J (2022a) The effect of green finance and unemployment rate on carbon emissions in China. Front Environ Sci 10:887341. https:// doi. org/ 10. 3389/ fenvs. 2022. 887341 Cui L, Weng S, Nadeem AM, Rafique MZ, Shahzad U (2022b) Exploring the role of renewable energy, urbanization and structural change for environ- mental sustainability: comparative analysis for practical implications. Renew Energ 184:215–224. https:// doi. org/ 10. 1016/j. renene. 2021. 11. 075 Danish ZB, Wang B, Wang Z (2017) Role of renewable energy and non-renew- able energy consumption on EKC: evidence from Pakistan. J Clean Prod 156:855–864 Dedeoğlu M, Koçak E, Uucak ZŞ (2021) The impact of immigration on human capital and carbon dioxide emissions in the USA: an empirical inves- tigation. Air Qual Atmos Health 14:705–714. https:// doi. org/ 10. 1007/ s11869- 020- 00973-w Dong K, Sun R, Hochman G (2017) Do natural gas and renewable energy con- sumption lead to less CO2 emission? empirical evidence from a panel of BRICS countries. Energy 141:1466–1478. https:// doi. org/ 10. 1016/j. energy. 2017. 11. 092 Duarte R, Mainar A, Sánchez-Chóliz J (2012) Social groups and CO2 emissions in Spanish households. Energy Policy 44:441–450. https:// doi. org/ 10. 1016/j. enpol. 2012. 02. 020 Dumitrescu E-I, Hurlin C (2012) Testing for Granger non-causality in heteroge- neous panels. Econ Model 29(4):1450–1460. https:// doi. org/ 10. 1016/j. econm od. 2012. 02. 014 El Montasser G, Ajmi AN, Nguyen DK (2018) Carbon emissions-income relationships with structural breaks: the case of the Middle Eastern and North African countries. Environ Sci Pollut Res 25(3):2869–2878. https:// doi. org/ 10. 1007/ s11356- 017- 0725-4 Emenekwe CC, Onyeneke RU, Nwajiuba CU (2022) Financial development and carbon emissions in sub-Saharan Africa. Environ Sci Pollut Res 29:19624–19641. https:// doi. org/ 10. 1007/ s11356- 021- 17161-7 European Trade Union Confederation (2020) Adaptation to climate change and the world of work: a guide for trade unions. https:// www. etuc. org/ sites/ defau lt/ files/ publi cation/ file/ 2020- 08/ ETUC- adapt ation- clima te- guide_ EN. pdf Fagan M, Huang C (2019) A look at how people around the world view climate change. United States of America. Retrieved from https:// polic ycomm ons. net/ artif acts/ 616841/ a- look- at- how- people- around- the- world- view- clima te- change/ 15975 36/ on 11 Jan 2024. CID: 20.500.12592/79f840 Furqan M, Mahmood H (2020) Does education reduce homicide? a panel data analysis of Asian region. Qual Quant 54(4):1197–1209 Gheraia Z, Abid M, Abdelli H, Alenezi F (2023) Does education sector improve environmental quality in Saudi Arabia? Int J Energy Econ Policy 13(3):592–603. https:// www. econj ourna ls. com/ index. php/ ijeep/ artic le/ view/ 13772/ 7349 Ghorbal S, Farhani S, Youssef SB (2022) Do renewable energy and national pat- ents impact the environmental sustainability of Tunisia? Environ Sci Pol- lut Res 29:25248–25262. https:// doi. org/ 10. 1007/ s11356- 021- 17628-7 Gozgor G, Lau CKM, Lu Z (2018) Energy consumption and economic growth: new evidence from the OECD countries. Energy 153:27–34. https:// doi. org/ 10. 1016/j. energy. 2018. 03. 158 Grunewald N, Klasen S, Martínez-Zarzoso I, Muris C (2017) The trade-off between income inequality and carbon dioxide emissions. Ecol Econ 142:249–256. https:// doi. org/ 10. 1016/j. ecole con. 2017. 06. 034 Hailemariam A, Dzhumashev R, Shahbaz M (2020) Carbon emissions, income inequality and economic development. Empir Econ 59:1139–1159. https:// doi. org/ 10. 1007/ s00181- 019- 01664-x https://doi.org/10.1016/j.jpolmod.2021.03.011 https://doi.org/10.1016/j.jpolmod.2021.03.011 https://doi.org/10.1016/j.scitotenv.2019.07.094 https://doi.org/10.1016/j.scitotenv.2019.07.094 https://doi.org/10.1371/journal.pone.0259488 https://doi.org/10.1016/j.rser.2016.11.178 https://doi.org/10.1016/j.rser.2016.11.178 https://so04.tci-thaijo.org/index.php/kjss/article/view/242142 https://so04.tci-thaijo.org/index.php/kjss/article/view/242142 https://doi.org/10.1016/j.ijedudev.2022.102644 https://doi.org/10.1002/ieam.4516 https://doi.org/10.9770/jesi.2020.8.2(12) https://doi.org/10.1016/j.enpol.2014.11.019 https://doi.org/10.1016/j.enpol.2014.11.019 https://doi.org/10.1007/s11356-017-8984-7 https://doi.org/10.1007/s11356-017-8984-7 https://www.nber.org/system/files/working_papers/w31000/w31000.pdf https://www.nber.org/system/files/working_papers/w31000/w31000.pdf https://doi.org/10.1016/j.rser.2016.09.105 https://doi.org/10.1016/j.renene.2018.05.043 https://doi.org/10.1016/j.eneco.2019.02.009 https://doi.org/10.1016/j.eneco.2019.02.009 https://doi.org/10.1007/s11356-021-14040-z https://doi.org/10.1016/j.eneco.2018.01.021 https://doi.org/10.1016/j.envc.2021.100234 https://doi.org/10.1016/j.renene.2017.03.102 https://doi.org/10.1016/j.renene.2017.03.102 https://doi.org/10.1016/j.rser.2015.10.080 https://doi.org/10.1016/j.rser.2015.10.080 https://doi.org/10.1016/S0313-5926(10)50027-4 https://doi.org/10.3390/atmos13121981 https://doi.org/10.3390/atmos13121981 https://doi.org/10.1371/journal.pone.0206266 https://doi.org/10.3389/fenvs.2022.887341 https://doi.org/10.1016/j.renene.2021.11.075 https://doi.org/10.1016/j.renene.2021.11.075 https://doi.org/10.1007/s11869-020-00973-w https://doi.org/10.1007/s11869-020-00973-w https://doi.org/10.1016/j.energy.2017.11.092 https://doi.org/10.1016/j.energy.2017.11.092 https://doi.org/10.1016/j.enpol.2012.02.020 https://doi.org/10.1016/j.enpol.2012.02.020 https://doi.org/10.1016/j.econmod.2012.02.014 https://doi.org/10.1016/j.econmod.2012.02.014 https://doi.org/10.1007/s11356-017-0725-4 https://doi.org/10.1007/s11356-017-0725-4 https://doi.org/10.1007/s11356-021-17161-7 https://www.etuc.org/sites/default/files/publication/file/2020-08/ETUC-adaptation-climate-guide_EN.pdf https://www.etuc.org/sites/default/files/publication/file/2020-08/ETUC-adaptation-climate-guide_EN.pdf https://www.etuc.org/sites/default/files/publication/file/2020-08/ETUC-adaptation-climate-guide_EN.pdf https://policycommons.net/artifacts/616841/a-look-at-how-people-around-the-world-view-climate-change/1597536/ https://policycommons.net/artifacts/616841/a-look-at-how-people-around-the-world-view-climate-change/1597536/ https://policycommons.net/artifacts/616841/a-look-at-how-people-around-the-world-view-climate-change/1597536/ https://www.econjournals.com/index.php/ijeep/article/view/13772/7349 https://www.econjournals.com/index.php/ijeep/article/view/13772/7349 https://doi.org/10.1007/s11356-021-17628-7 https://doi.org/10.1016/j.energy.2018.03.158 https://doi.org/10.1016/j.energy.2018.03.158 https://doi.org/10.1016/j.ecolecon.2017.06.034 https://doi.org/10.1007/s00181-019-01664-x Page 14 of 15Elom et al. Carbon Research (2024) 3:24 Hanmer L, Klugman J (2016) Exploring women’s agency and empowerment in developing countries: where do we stand? Fem Econ 22(1):237–263. https:// doi. org/ 10. 1080/ 13545 701. 2015. 10910 87 Heller RF, Sun YY, Guo Z, Malik A (2021) Impact on carbon emissions of online study for a cohort of overseas students: a retrospective cohort study. F1000Res 10:849. https:// doi. org/ 10. 12688/ f1000 resea rch. 55156.5 Hu H, Xie N, Fang D, Zhang X (2018) The role of renewable energy consump- tion and commercial services trade in carbon dioxide reduction: evidence from 25 developing countries. Appl Energy 211:1229–1244. https:// doi. org/ 10. 1016/j. apene rgy. 2017. 12. 019 Inglesi-Lotz R, Dogan E (2018) The role of renewable versus non-renewable energy to the level of CO2 emissions a panel analysis of sub-Saharan Africa’s Big 10 electricity generators. Renew Energ 123:36–43. https:// doi. org/ 10. 1016/j. renene. 2018. 02. 041 International Labour Organization (2018) World employment social outlook: trends 2018. ILO, Geneva International Renewable Energy Agency (2018) Renewable energy and jobs – annual review 2018 Ito K (2017) CO2 emissions, renewable and non-renewable energy consump- tion, and economic growth: evidence from panel data for developing countries. Int Econ 151:1–6. https:// doi. org/ 10. 1016/j. inteco. 2017. 02. 001 Jin T, Kim J (2018) What is better for mitigating carbon emissions–renewable energy or nuclear energy? a panel data analysis. Renew Sust Energ Rev 91:464–471. https:// doi. org/ 10. 1016/j. rser. 2018. 04. 022 Kiehle J, Kopsakangas-Savolainen M, Hilli M, Pongrácz E (2023) Carbon foot- print at institutions of higher education: the case of the University of Oulu. J Environ Manage 329:117056. https:// doi. org/ 10. 1016/j. jenvm an. 2022. 117056 Kwakwa PA (2021) What determines renewable energy consumption? startling evidence from Ghana. Int J Energy Sect Manag 15(1):101–118. https:// doi. org/ 10. 1108/ IJESM- 12- 2019- 0019 Leal Filho W, Balasubramanian M, Abeldaño Zuñiga RA, Sierra J (2023) The effects of climate change on children’s education attainment. Sustain- ability 15(7):6320. https:// doi. org/ 10. 3390/ su150 76320 Li H, Zhang B, Wen L, Li Z (2021) On the relationship between the energy conservation and emissions reduction policy and employment adjust- ment by manufacturing firms: microdata from China. J Clean Prod 297:126652. https:// doi. org/ 10. 1016/j. jclep ro. 2021. 126652 Liu X (2005) Explaining the relationship between CO2 emissions and national income—the role of energy consumption. Econ Lett 87(3):325–328. https:// doi. org/ 10. 1016/j. econl et. 2004. 09. 015 Li X, Ullah S (2022) Caring for the environment: how CO2 emissions respond to human capital in BRICS economies? Environ Sci Pollut Res 29:18036– 18046. https:// doi. org/ 10. 1007/ s11356- 021- 17025-0 Liu YQ, Feng C (2022) The effects of nurturing pressure and unemployment on carbon emissions: cross-country evidence. Environ Sci Pollut Res 29:52013–52032. https:// doi. org/ 10. 1007/ s11356- 022- 19515-1 Liu J, Tian J, Lyu W, Yu Y (2022a) The impact of COVID-19 on reducing carbon emissions: from the angle of international student mobility. Appl Energy 317:119136. https:// doi. org/ 10. 1016/j. apene rgy. 2022. 119136 Liu N, Hong C, Sohail MT (2022b) Does financial inclusion and education limit CO2 emissions in China? A new perspective. Environ Sci Pollut Res 29:18452–18459. https:// doi. org/ 10. 1007/ s11356- 021- 17032-1 Liu Z, Lan J, Chien F, Sadiq M, Nawaz MA (2022c) Role of tourism development in environmental degradation: a step towards emission reduction. J Environ Manage 303:114078. https:// doi. org/ 10. 1016/j. jenvm an. 2021. 114078 Lu W-C (2017) Renewable energy, carbon emissions, and economic growth in 24 Asian countries: evidence from panel cointegration analysis. Environ Sci Pollut Res 24:26006–26015. https:// doi. org/ 10. 1007/ s11356- 017- 0259-9 Ma S, Dai J, Wen H (2019) The influence of trade openness on the level of human capital in China: on the basis of environmental regulation. J Clean Prod 225:340–349. https:// doi. org/ 10. 1016/j. jclep ro. 2019. 03. 238 Mahalik MK, Mallick H, Padhan H (2021) Do educational levels influence the environmental quality? The role of renewable and non-renewable energy demand in selected BRICS countries with a new policy perspective. Renew Energ 164:419–432. https:// doi. org/ 10. 1016/j. renene. 2020. 09. 090 Mankiw NG, Romer D, Weil DN (1992) A contribution to the empirics of economic growth. Q J Econ 107(2):407–437. https:// doi. org/ 10. 2307/ 21184 77 Markey R, McIvor J, O’Brien M, Wright CF (2019) Reducing carbon emissions through employee participation: evidence from Australia. Ind Relat J 50(1):57–83. https:// doi. org/ 10. 1111/ irj. 12238 Matei I (2017) Is there a link between renewable energy consumption and economic growth? a dynamic panel investigation for the OECD coun- tries. Rev Econ Polit 127(6):985–1012. https:// doi. org/ 10. 3917/ redp. 276. 0985 Mentel U, Wolanin E, Eshov M, Salahodjaev R (2022) Industrialization and CO2 emissions in Sub-Saharan Africa: the mitigating role of renewable elec- tricity. Energies 15(3):946. https:// doi. org/ 10. 3390/ en150 30946 Molthan-Hill P, Robinson ZP, Hope A, Dharmasasmita A, McManus E (2020) Reducing carbon emissions in business through responsible manage- ment education: influence at the micro-, meso-and macro-levels. Int J Manag Educ 18(1):100328. https:// doi. org/ 10. 1016/j. ijme. 2019. 100328 Mulderij LS, Hernandez JI, Mouter N, Verkooijen KT, Wagemakers A (2021) Citizen preferences regarding the public funding of projects promoting a healthy body weight among people with a low income. Soc Sci Med 280:114015. https:// doi. org/ 10. 1016/j. socsc imed. 2021. 114015 Narayan P (2004) Reformulating critical values for the bounds F-statistics approach to cointegration: an application to the tourism demand model for Fiji (Vol. 2, No. 04). Monash University, Australia Nguyen KH, Kakinaka M (2019) Renewable energy consumption, carbon emis- sions, and development stages: some evidence from panel cointegra- tion analysis. Renew Energ 132:1049–1057. https:// doi. org/ 10. 1016/j. renene. 2018. 08. 069 O’Neill BC, Jiang L, Samir KC, Fuchs R, Pachauri S, Laidlaw EK, Zhang T, Zhou W, Ren X (2020) The effect of education on determinants of cli- mate change risks. Nat Sustain 3:520–528. https:// doi. org/ 10. 1038/ s41893- 020- 0512-y Omri A, Daly S, Rault C, Chaibi A (2015) Financial development, environmen- tal quality, trade and economic growth: what causes what in MENA countries? Energy Econ 48:242–252. https:// doi. org/ 10. 1016/j. eneco. 2015. 01. 008 Onyeneke RU, Chidiebere-Mark NM, Ankrah DA, Onyeneke LU (2023a) Deter- minants of access to clean fuels and technologies for cooking in Africa: a panel autoregressive distributed lag approach. Environ Prog Sustain Energy 42(3):e14147. https:// doi. org/ 10. 1002/ ep. 14147 Onyeneke RU, Ankrah DA, Atta-Ankomah R, Agyarko FF, Onyeneke CJ, Nejad JG (2023b) Information and communication technologies and agricul- tural production: new evidence from Africa. Appl Sci 13(6):3918. https:// doi. org/ 10. 3390/ app13 063918 Onyeneke RU, Osuji EE, Anugwa IQ, Chidiebere-Mark NM (2023c) Impacts of biocapacity, climate change, food vulnerability, readiness and adap- tive capacity on cereal crops yield: Evidence from Africa. Environ Dev Sustain. https:// doi. org/ 10. 1007/ s10668- 023- 03615-0 Onyeneke RU, Agyarko FF, Onyeneke CJ, Osuji EE, Ibeneme PA, Esfahani IJ (2023d) How does climate change affect tomato and okra production? evidence from Nigeria. Plants 12(19):3477. https:// doi. org/ 10. 3390/ plant s1219 3477 Organization for Economic Co-operation and Development (2016) Green growth and sustainable development. OECD, Paris Ozcan B, Tzeremes PG, Tzeremes NG (2019) Energy consumption, economic growth and environmental degradation in OECD countries. Econ Model 83:203–213. https:// doi. org/ 10. 1016/j. econm od. 2019. 04. 010 Ozturk I, Al-Mulali U (2015) Natural gas consumption and economic growth nexus: panel data analysis for GCC countries. Renew Sustain Energ Rev 51:998–1003. https:// doi. org/ 10. 1016/j. rser. 2015. 07. 005 Pata UK, Kartal MT (2023) Impact of nuclear and renewable energy sources on environment quality: testing the EKC and LCC hypotheses for South Korea. Nucl Eng Technol 55(2):587–594. https:// doi. org/ 10. 1016/j. net. 2022. 10. 027 Pelinescu E (2015) The impact of human capital on economic growth. Procedia Econ and Financ 22:184–190. https:// doi. org/ 10. 1016/ S2212- 5671(15) 00258-0 Psacharopoulos G, Patrinos HA (2018) Returns to investment in education: a decennial review of the global literature. Policy Research Working Paper 8402. The World Bank. https:// doi. org/ 10. 1596/ 1813- 9450- 8402 Rahman MM, Saidi K, Mbarek MB (2020) Economic growth in South Asia: the role of CO2 emissions, population density and trade openness. Heliyon 6(5):e03903. https:// doi. org/ 10. 1016/j. heliy on. 2020. e03903 https://doi.org/10.1080/13545701.2015.1091087 https://doi.org/10.12688/f1000research.55156.5 https://doi.org/10.1016/j.apenergy.2017.12.019 https://doi.org/10.1016/j.renene.2018.02.041 https://doi.org/10.1016/j.renene.2018.02.041 https://doi.org/10.1016/j.inteco.2017.02.001 https://doi.org/10.1016/j.rser.2018.04.022 https://doi.org/10.1016/j.jenvman.2022.117056 https://doi.org/10.1016/j.jenvman.2022.117056 https://doi.org/10.1108/IJESM-12-2019-0019 https://doi.org/10.1108/IJESM-12-2019-0019 https://doi.org/10.3390/su15076320 https://doi.org/10.1016/j.jclepro.2021.126652 https://doi.org/10.1016/j.econlet.2004.09.015 https://doi.org/10.1007/s11356-021-17025-0 https://doi.org/10.1007/s11356-022-19515-1 https://doi.org/10.1016/j.apenergy.2022.119136 https://doi.org/10.1007/s11356-021-17032-1 https://doi.org/10.1016/j.jenvman.2021.114078 https://doi.org/10.1007/s11356-017-0259-9 https://doi.org/10.1007/s11356-017-0259-9 https://doi.org/10.1016/j.jclepro.2019.03.238 https://doi.org/10.1016/j.renene.2020.09.090 https://doi.org/10.2307/2118477 https://doi.org/10.2307/2118477 https://doi.org/10.1111/irj.12238 https://doi.org/10.3917/redp.276.0985 https://doi.org/10.3917/redp.276.0985 https://doi.org/10.3390/en15030946 https://doi.org/10.1016/j.ijme.2019.100328 https://doi.org/10.1016/j.socscimed.2021.114015 https://doi.org/10.1016/j.renene.2018.08.069 https://doi.org/10.1016/j.renene.2018.08.069 https://doi.org/10.1038/s41893-020-0512-y https://doi.org/10.1038/s41893-020-0512-y https://doi.org/10.1016/j.eneco.2015.01.008 https://doi.org/10.1016/j.eneco.2015.01.008 https://doi.org/10.1002/ep.14147 https://doi.org/10.3390/app13063918 https://doi.org/10.3390/app13063918 https://doi.org/10.1007/s10668-023-03615-0 https://doi.org/10.3390/plants12193477 https://doi.org/10.3390/plants12193477 https://doi.org/10.1016/j.econmod.2019.04.010 https://doi.org/10.1016/j.rser.2015.07.005 https://doi.org/10.1016/j.net.2022.10.027 https://doi.org/10.1016/j.net.2022.10.027 https://doi.org/10.1016/S2212-5671(15)00258-0 https://doi.org/10.1016/S2212-5671(15)00258-0 https://doi.org/10.1596/1813-9450-8402 https://doi.org/10.1016/j.heliyon.2020.e03903 Page 15 of 15Elom et al. Carbon Research (2024) 3:24 Rasool SF, Wang M, Zhang Y, Samma M (2020) Sustainable work performance: the roles of workplace violence and occupational stress. Int J Environ Res Publ Health 17(3):912. https:// doi. org/ 10. 3390/ ijerp h1703 0912 Razzak WA, Timmins J (2010) Education and labour productivity in New Zealand. Appl Econ Lett 17:169–173. https:// doi. org/ 10. 1080/ 13504 85070 17199 42 Reimers FM (2021) The role of universities building an ecosystem of climate change education. In: Reimers FM (ed) Education and climate change. International explorations in outdoor and environmental education. Springer, Cham Sadorsky P (2012) Energy consumption, output and trade in South America. Energy Econ 34(2):476–488. https:// doi. org/ 10. 1016/j. eneco. 2011. 12. 008 Saidi K, Omri A (2020) The impact of renewable energy on carbon emissions and economic growth in 15 major renewable energy-consuming countries. Environ Res 186:109567. https:// doi. org/ 10. 1016/j. envres. 2020. 109567 Salahuddin M, Habib MA, Al-Mulali U, Ozturk I, Marshall M, Ali MI (2020) Renewable energy and environmental quality: a second-generation panel evidence from the Sub Saharan Africa (SSA) countries. Environ Res 191:110094. https:// doi. org/ 10. 1016/j. envres. 2020. 110094 Sapkota P, Bastola U (2017) Foreign direct investment, income, and environ- mental pollution in developing countries: panel data analysis of Latin America. Energy Econ 64:206–212. https:// doi. org/ 10. 1016/j. eneco. 2017. 04. 001 Sarwar S, Streimikiene D, Waheed R, Mighri Z (2021) Revisiting the empiri- cal relationship among the main targets of sustainable develop- ment: growth, education, health and carbon emissions. Sustain Dev 29(2):419–440. https:// doi. org/ 10. 1002/ sd. 2156 Singh N, Nyuur R, Richmond B (2019) Renewable energy development as a driver of economic growth: evidence from multivariate panel data analysis. Sustainability 11(8):2418. https:// doi. org/ 10. 3390/ su110 82418 Sinha A, Gupta M, Shahbaz M, Sengupta T (2019) Impact of corruption in public sector on environmental quality: implications for sustainability in BRICS and next 11 countries. J Clean Prod 232:1379–1393. https:// doi. org/ 10. 1016/j. jclep ro. 2019. 06. 066 Sinha A, Sengupta T, Alvarado R (2020) Interplay between technological innovation and environmental quality: formulating the SDG policies for next 11 economies. J Clean Prod 242:118549. https:// doi. org/ 10. 1016/j. jclep ro. 2019. 118549 Sun D, Liu Y-Y, Yang X-W, Lyu L-Q, Yuan J-H (2022) Economic and employment effects of China’s power transition based on input-output and scenario simulation. Adv Clim Chang Res 13(5):721–728. https:// doi. org/ 10. 1016/j. accre. 2022. 09. 001 Swain RB, Karimu A (2020) Renewable electricity and sustainable development goals in the EU. World Dev 125:104693. https:// doi. org/ 10. 1016/j. world dev. 2019. 104693 Umaroh R (2019) Does education reduce CO2 emmisions? empirical evidence of the environmental Kuznets curve in Indonesia. J Rev Glob Econ 8:662–671 Versteijlen M, Salgado FP, Groesbeek MJ, Counotte A (2017) Pros and cons of online education as a measure to reduce carbon emissions in higher education in the Netherlands. Curr Opin Environ Sustain 28:80–89. https:// doi. org/ 10. 1016/j. cosust. 2017. 09. 004 Vladimirova K, Le Blanc D (2016) Exploring links between education and sustainable development goals through the lens of UN flagship reports. Sustain Dev 24:254–271. https:// doi. org/ 10. 1002/ sd. 1626 Waheed R, Chang D, Sarwar S, Chen W (2018) Forest, agriculture, renewable energy, and CO2 emission. J Clean Prod 172:4231–4238. https:// doi. org/ 10. 1016/j. jclep ro. 2017. 10. 287 Wang Q, Zeng YE, Wu BW (2016) Exploring the relationship between urbaniza- tion, energy consumption, and CO2 emissions in different provinces of China. Renew Sust Energ Rev 54:1563–1579. https:// doi. org/ 10. 1016/j. rser. 2015. 10. 090 Wang G, Yang K, Yang Y, Zhang S (2017) Coupling natural and human pro- cesses to simulate changes in the water environment in the Dianchi lake basin, China. Geosystem Eng 20(4):207–215. https:// doi. org/ 10. 1080/ 12269 328. 2016. 12707 77 Wang WZ, Liu LC, Liao H, Wei YM (2021) Impacts of urbanization on carbon emissions: an empirical analysis from OECD countries. Energy Policy 151:112171. https:// doi. org/ 10. 1016/j. enpol. 2021. 112171 Williamson C (2017) Emission, education, and politics: an empirical study of the carbon dioxide and methane environmental Kuznets curve. Park Place Econ 25(1):9. https:// digit alcom mons. iwu. edu/ parkp lace/ vol25/ iss1/9/ World Bank (2023) World development indicators (WDI). https:// datab ank. world bank. org/ source/ world- devel opment- indic ators#. Accessed 11 May 2023 Xin Y, Yang S, Rasheed MF (2023) Exploring the impacts of education and unemployment on CO2 emissions. Econ Res-Ekon Istraz 36(2):2110139. https:// doi. org/ 10. 1080/ 13316 77X. 2022. 21101 39 Yao Y, Ivanovski K, Inekwe J, Smyth, R (2019) Human capital and energy consumption: evidence from OECD Countries. Energ Econ 84:104534. https:// doi. org/ 10. 1016/j. eneco. 2019. 104534 Yazdi SK, Beygi EG (2018) The dynamic impact of renewable energy consump- tion and financial development on CO2 emissions: for selected African countries. Energ Sources Part B 13(1):13–20. https:// doi. org/ 10. 1080/ 15567 249. 2017. 13773 19 Yazdi S, Shakouri B (2018a) The effect of renewable energy and urbanization on CO2 emissions: a panel data. Energy Sources B Econ Plan Policy 13(2):121–127. https:// doi. org/ 10. 1080/ 15567 249. 2017. 14006 07 Yazdi SK, Shakouri B (2018b) The renewable energy, CO2 emissions, and economic growth: VAR model. Energy Sources B Econ Plan Policy 13(1):53–59. https:// doi. org/ 10. 1080/ 15567 249. 2017. 14034 99 Yin Z, Jiang X, Lin S, Liu J (2022) The impact of online education on carbon emissions in the context of the COVID-19 pandemic–taking Chinese universities as examples. Appl Energy 314:118875. https:// doi. org/ 10. 1016/j. apene rgy. 2022. 118875 Yusuf AM, Abubakar AB, Mamman SO (2020) Relationship between green- house gas emission, energy consumption, and economic growth: evidence from some selected oil-producing African countries. Environ Sci Pollut Res 27:15815–15823. https:// doi. org/ 10. 1007/ s11356- 020- 08065-z Zafar MW, Zaidi SAH, Khan NR, Mirza FM, Hou F, Kirmani SAA (2019) The impact of natural resources, human capital, and foreign direct investment on the ecological footprint: the case of the United States. Resour Policy 63:101428. https:// doi. org/ 10. 1016/j. resou rpol. 2019. 101428 Zafar MW, Shahbaz M, Sinha A, Sengupta T, Qin Q (2020) How renewable energy consumption contribute to environmental quality? The role of education in OECD countries. J Clean Prod 268:122149. https:// doi. org/ 10. 1016/j. jclep ro. 2020. 122149 Zafar MW, Saleem MM, Destek MA, Caglar AE (2022) The dynamic linkage between remittances, export diversification, education, renewable energy consumption, economic growth, and CO2 emissions in top remittance-receiving countries. Sustain Dev 30(1):165–175. https:// doi. org/ 10. 1002/ sd. 2236 Zaman Q, Wang Z, Zaman S, Rasool SF (2021) Investigating the nexus between education expenditure, female employers, renewable energy consumption and CO2 emission: evidence from China. J Clean Prod 312:127824. https:// doi. org/ 10. 1016/j. jclep ro. 2021. 127824 Zamil AM, Furqan M, Mahmood H (2019) Trade openness and CO2 emissions nexus in Oman. Entrep Sustain Issues 7(2):1319–1329. https:// doi. org/ 10. 9770/ jesi. 2019.7. 2(36) Zhu TT, Peng HR, Zhang YJ, Liu JY (2021) Does higher education development facilitate carbon emissions reduction in China. Appl Econ 53(47):5490– 5502. https:// doi. org/ 10. 1080/ 00036 846. 2021. 19236 41 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- lished maps and institutional affiliations. https://doi.org/10.3390/ijerph17030912 https://doi.org/10.1080/13504850701719942 https://doi.org/10.1080/13504850701719942 https://doi.org/10.1016/j.eneco.2011.12.008 https://doi.org/10.1016/j.envres.2020.109567 https://doi.org/10.1016/j.envres.2020.109567 https://doi.org/10.1016/j.envres.2020.110094 https://doi.org/10.1016/j.eneco.2017.04.001 https://doi.org/10.1016/j.eneco.2017.04.001 https://doi.org/10.1002/sd.2156 https://doi.org/10.3390/su11082418 https://doi.org/10.1016/j.jclepro.2019.06.066 https://doi.org/10.1016/j.jclepro.2019.06.066 https://doi.org/10.1016/j.jclepro.2019.118549 https://doi.org/10.1016/j.jclepro.2019.118549 https://doi.org/10.1016/j.accre.2022.09.001 https://doi.org/10.1016/j.accre.2022.09.001 https://doi.org/10.1016/j.worlddev.2019.104693 https://doi.org/10.1016/j.worlddev.2019.104693 https://doi.org/10.1016/j.cosust.2017.09.004 https://doi.org/10.1002/sd.1626 https://doi.org/10.1016/j.jclepro.2017.10.287 https://doi.org/10.1016/j.jclepro.2017.10.287 https://doi.org/10.1016/j.rser.2015.10.090 https://doi.org/10.1016/j.rser.2015.10.090 https://doi.org/10.1080/12269328.2016.1270777 https://doi.org/10.1080/12269328.2016.1270777 https://doi.org/10.1016/j.enpol.2021.112171 https://digitalcommons.iwu.edu/parkplace/vol25/iss1/9/ https://digitalcommons.iwu.edu/parkplace/vol25/iss1/9/ https://databank.worldbank.org/source/world-development-indicators# https://databank.worldbank.org/source/world-development-indicators# https://doi.org/10.1080/1331677X.2022.2110139 https://doi.org/10.1016/j.eneco.2019.104534 https://doi.org/10.1080/15567249.2017.1377319 https://doi.org/10.1080/15567249.2017.1377319 https://doi.org/10.1080/15567249.2017.1400607 https://doi.org/10.1080/15567249.2017.1403499 https://doi.org/10.1016/j.apenergy.2022.118875 https://doi.org/10.1016/j.apenergy.2022.118875 https://doi.org/10.1007/s11356-020-08065-z https://doi.org/10.1007/s11356-020-08065-z https://doi.org/10.1016/j.resourpol.2019.101428 https://doi.org/10.1016/j.jclepro.2020.122149 https://doi.org/10.1016/j.jclepro.2020.122149 https://doi.org/10.1002/sd.2236 https://doi.org/10.1002/sd.2236 https://doi.org/10.1016/j.jclepro.2021.127824 https://doi.org/10.9770/jesi.2019.7.2(36) https://doi.org/10.9770/jesi.2019.7.2(36) https://doi.org/10.1080/00036846.2021.1923641 Achieving carbon neutrality in Africa is possible: the impact of education, employment, and renewable energy consumption on carbon emissions Abstract Highlights 1 Introduction 2 Literature review 2.1 Education and carbon emissions 2.2 Employment and carbon emissions 2.3 Renewable energy consumption and carbon emissions 2.4 National income and carbon emissions 3 Methodology 3.1 Data source 3.2 Analytical technique 4 Results 4.1 Preliminary results 4.2 Multicollinearity test 4.3 Cross section dependence test 4.4 Unit test of the variables 4.5 Cointegration tests 4.6 Empirical results from panel regression models 4.7 Granger test for panel causality 5 Discussion 6 Conclusions References