Environmental degradation in emerging-market economies of Africa: evaluating impacts of human capital development, international trade, renewable energy consumption, and urbanization

This research explores the impact of human capital development, international trade, financial development, renewable energy consumption, and urbanization on environmental degradation in emerging-market economies in Africa. The study adopts a quantitative approach using panel data from 8 African countries between 1991 and 2021. The study adopted the method of Mean Group Dynamic Least Squares and Method of moments quantile regression methods to estimate the empirical relationship between the variables of interest. The findings indicate that urbanization, energy consumption, economic growth, and human capital development have significant and positive effects on environmental degradation, while financial development, renewable energy consumption, manufacturing activities, and international trade have a significant negative effect on environmental degradation. The study concludes that policymakers in emerging-market economies in Africa need to promote financial development and renewable energy consumption while simultaneously addressing the negative impacts of urbanization on the environment to achieve sustainable economic growth.

1 Introduction

Emerging-market economies in Africa have experienced significant economic growth (GDP) recently, resulting in increased financial development, urbanization (UB), and demand for energy. However, this has also contributed to an increase in pollution and environmental degradation and deterioration. This study aims to analyze the impact of financial development, energy consumption (ENC), and UB on environmental deterioration in these economies. It will examine how these factors affect the environment and explore potential policies and strategies to mitigate the adverse impact of economic growth on the environment. The findings of this study could have significance for stakeholders and policymakers working to seek a balance between economic-growth and environmental sustainability in developing market economies in Africa.

The current levels of CO2 emissions are a significant environmental issue since they contribute to ecological imbalances such as global warming, with all related impact from rising sea levels, ice caps melting and biodiversity loss. To tackle this issue, global cooperation is necessary, and governments should take a leading role in implementing policies to reduce greenhouse gas emissions. Sustainable development is crucial to economic growth and social welfare, but it must be based on a balance between economic, social, and environmental aspects.

It is essential to educate the public on climate change in order to create awareness and change behaviors to reduce environmental impact and adopt practices that promote sustainable development. Strengthening human capital (HCD) is crucial for sustained economic growth, according to the World Development Report (2019). Enhancing human capital is a crucial policy to put into action since it raises awareness of environmental issues and promotes energy efficiency by changing how people use and consume energy. Fostering renewable energy (RE) is another strategic action that could help in lowering CO2 emissions, but it must be undertaken gradually to guarantee that the long-term sustainability of the energy transition. On the other hand, urbanization and its associated infrastructure are significant contributors to global CO2 emissions, and policies to reduce the impact of urbanization on the environment should be implemented urgently. Blue and green infrastructure, for example, is seen as a sustainable and long-term solution to mitigate the effects of urbanization on the ecosystem. Therefore, these policies can include promoting public transport, creating green spaces and permeable sidewalks, enhancing parks, and promoting constructed wetlands. Addressing climate change related issues requires cooperation from all sectors to implement policies that promote sustainable development while reducing global CO2 emissions. Education, improving human capital, and encouraging RE are specific policies that can support the transition towards more sustainable development practices.

The high-energy consumption in urban areas is due to the increasing demand for transportation, sanitation, infrastructure development, sewages, and housing. To manage and reduce CO2 emissions and energy consumption, there is a need to analyze the correlation between RE, ENC, GDP, industrialization, trade openness (TOP), HCD, and environmental impact in emerging market economies. These economies have experienced significant transformation in the current century, including strong GDP, high per capita income, industrialization, and UB. While previous studies have examined the impact of RE, nonrenewable energy (NRE), governance quality, and HCP on GDP and environmental characteristics, few have taken into account the interaction between HCD and the key variables, and their conditional and nonlinear effects on the correlation between RE, NRE, GDP, and environmental quality. Consequently, this investigation aims to study the relationship between, HCD, energy sources, GDP, and CO2 emissions for sustainable development in newly emerging market economies. The study add up to the current understanding of the literature on sustainable development and environmental protection, mainly through the analysis of the conditional impact of HCD on RE, and CO2 emissions, GDP nexus. Additionally, the study examines the nonlinear effect of HCD on environmental quality and the impacts of UB, TOP, and industrialization on sustainable development. Finally, the study utilizes two econometric techniques to generate robust and consistent estimates of the relationship between the variables under investigation. Our primary focus is on the emerging African market economies, driven by their vibrant GDP and abundant RE resources, as well as the environmental challenges resulting from the rapid urbanization needed to support their expanding populations. Consequently, the findings of this study carry policy implications that can significantly contribute to the sustainable development of these nations and the global environment’s betterment. The subsequent sections of this study are structured as follows: Section 2 offers the literature review, while Section 3 outlines the materials and methods used. It is also dedicated to presenting and discussing our empirical results, and in Section 4, we conclude the research article by offering policy recommendations and suggesting others for future research.

2 Literature review

Different research articles around energy, especially RE, NRE and CO2 emissions, have been deeply discussed through the recent literature review. However, our study introduces a novel dimension to this discussion by examining the moderating influence of HCD on the relationship between these variables, which has not been explored in prior research. Previous empirical studies have primarily concentrated on elucidating the relationship between energy consumption and CO2 emissions (Mohsin et al., 2021; Bilan et al., 2019; Ibrahim and Ajide, 2021; Radoine et al., 2022). There is a research gap in the existing literature in terms of considering newly emerging market economies in the analysis and exploring the conditional and nonlinear effects of HCD on the interplay between RE, NRE, GDP, and CO2 emissions.

Shahnazi and Shabani (2021) focused their analysis on the European Union, while Mahalik et al. (2021) concentrated on BRICS countries. Additionally, these previous studies often did not adequately address cross-dependence analysis in their methodologies. They primarily utilized various methods such as FMOLS and Markov switching regression models (Feng, 2022), dynamic fixed effect and GMM (Muhammad et al., 2021), PMG-ARDL (Berkun et al., 2019), ARDL (Pata, 2018), and DOLS (Dogan and Seker, 2016), and finally Timmons et al. (2014) suggested that higher prices of RE can lead to a greater reliance on fossil fuels. Therefore, it is recommended that policymakers implement measures to reduce the cost of RE and promote the development of environmentally friendly technologies. Chen et al. (2019) found a negative association between RE, TOP, and CO2 emissions. Dong et al. (2018) applied the Kuznets Curve hypothesis and revealed that RE has a negative impact on carbon emissions. Shahnazi and Shabani (2021) found that RE mitigated CO2 emissions during the period from 2000 to 2017. In the article of De Souza Mendonça et al., 2020, a study involving 50 largest countries, they identified that growth and population growth were contributors to CO2 emissions, while RE was not. Furthermore, there is evidence, as suggested by Pata (2018), of an inverted U-shaped relationship between CO2 emissions and GDP per capita. Pata’s study also indicated that RE had no significant impact on CO2 emissions, while UB was linked to environmental degradation. According to Awosusi et al. (2022), globalization and the adoption of RE can help mitigate CO2 emissions in the case of Colombia country. In the research article of Adebayo and Kirikkaleli (2021), using quarterly data, they found that in Japan the RE use effectively reduced CO2 emissions.

On the other hand, RE consumption, UB, and secondary education were associated with reduced carbon emissions. Oke et al. (2021) while analyzing 51 African countries over the same time have demonstrated similar findings. Substantial evidence for the EKC hypothesis was offered by Sarkodie and Ozturk (2020), who noted that energy consumption has a positive effect on CO2 emissions whereas RE has a negative effect on CO2 emissions. In a panel of G-7 nations from 1991 to 2016, Raza and Shah (2018) investigated the role of RE consumption in the context of the EKC hypothesis. Their study employed various regression models to establish evidence of cointegration and suggested that the development of RE in G7 countries was a significant factor in long-term decarbonization policy. When trade indicators were integrated with RE use and GDP, the results lent support the G-7 countries validity of the EKC hypothesis. The relationship between RE and CO2 emissions has been the subject of numerous studies, including those by Lau et al. (2019), Cai et al. (2018), Zoundi (2017), and Ito (2017), but many of these studies omitted the important role of HCD, which can have significant effects on environmental quality. In their research, Szetela et al. (2022) explored the connection between RE and CO2 emissions in major natural resource-dependent countries from 2000 to 2015, emphasizing the role of governance. They used Ordinary Least Squares Fixed effects in their study. Panel data were analyzed using Generalized Least Squares methods and two-step GMM estimators, and it was found that RE significantly reduced per capita CO2 emissions.

Furthermore, the relationship between CO2 emissions and Gross Domestic Product per capita was found to be U-shaped. Qi et al. (2014) investigated RE targets in China and found that a 1.8% reduction in CO2 emissions could be achieved from 2010 to 2020. Inglesi-Lotz and Dogan (2018) demonstrated a long-term association between GDP, RE, NRE, and CO2 emissions in a study that focused on the top 10 electricity-producing nations in Sub-Saharan Africa from 1980 to 2011. Their findings also suggested causality running from RE to CO2 emissions and from CO2 emissions to TOP. Bajja et al. (2023) analyze the determinants of environmental quality in urban Morocco in the context of important factors, such as ENC, UB, manufacturing, and financial development. The study draws on time series data covering the period from 1971 to 2019. Fatima et al. (2021) have made a significant contribution to the existing body of research by delving into the intricate relationship between GDP, RE, and CO2 emissions, utilizing comprehensive global panel data. Employing a diverse set of econometric methodologies, their study demonstrates that GDP plays a pivotal role in moderating the association between RE usage and CO2 emissions. Concurrently, the study reveals that GDP exerts an influence on NRE consumption, consequently leading to an increase in CO2 emissions. In West Africa, Radoine et al. (2024) analysed annual data from 1991 to 2018 of urban population growth, gross domestic product, energy consumption, and greenhouse gas emissions. The study revealed variation across the selected countries in terms of rate of urbanization, productivity, and energy consumption. In the realm of the HCD-CO2 emissions link, there is a paucity of studies. Desha et al. (2015) posit that HCD, nurtured through education, not only enhances RE consumption but also facilitates the production of environmentally less polluting goods, as suggested by Hartman and Kwon (2005). This notion has been further been substantiated by Bano et al. (2018), contending that an improvement in HCD can effectively reduce CO2 without compromising GDP. In the corporate sphere, highly educated employees tend to leverage innovative technologies for cleaner production processes and actively engage in environmental management and compliance efforts, a point also underscored by Dasgupta et al. (2000). In MENA region, Bajja et al. (2024) assert that HCD significantly contributes to environmental degradation.

Exploring 11 European Union countries, they uncover, in Bayar et al. (2022), a negative association between HCD and carbon dioxide emissions in Croatia, Hungary, the Czech Republic, and Slovenia. In contrast, this relationship exhibits a positive impact in Lithuania and Latvia. Yao et al. (2020) emphasize that advanced HCD, tied to years of schooling, exerts a negative influence on CO2 emissions. Interestingly, they discern a shift in this relationship from positive to negative after the 1950s. Li and colleagues (2022), in their analysis of the HCD -CO2 emissions nexus, ascertain that an increase in education levels leads to a reduction in CO2 emissions, while conversely, a decrease in education levels is associated with an increase in CO2 emissions.

3 Material and methods

In this article, we examine on the impacts of specific factors including GDP as a measure of economic growth, manufacturing value-added (MVA), urbanization rate (UB), financial development (FD), renewable energy use (RE), energy consumption (ENC), international trade (TR), human capital (HCD) on CO2 emissions. The conceptual framework illustrating these factors is shown in Figure 1, while the research steps followed in this study are detailed in Figure 2. The focus of our work was on 8 african countries including Morocco, Egypt, Tunisia, Cote d'lvoire, Ghana, Nigeria, South Africa, and Kenya. We collected annual data spanning from 1991 to 2021 for our analysis. Although we aimed to utilize more recent and extensive data, we faced limitations as comprehensive and consistent data on CO2 emissions were only available until 2021.

Figure 1

Figure 1. Conceptual framework. Source: authors constructed

Figure 2

Figure 2. Steps of research.

Figure 3 through 11 provide trends in each of the main factors affecting CO₂ emissions across the study period, including urbanization rates (Figure 3), financial development (Figure 4), CO₂ emissions (Figure 5), GDP levels (Figure 6), energy consumption (Figure 7), manufacturing value-added (Figure 8), international trade (Figure 9), human capital development (Figure 10), and renewable energy use (Figure 11). The data utilized in this study were sourced from the World Development Indicators (WDI), provided by the World Bank. Information specifically pertaining to RE was gathered from the IEA website. To estimate carbon emissions, the study will employ the following functions.

$CO2=ƒ\left(MVA;UB;FD;RE,ENC,TR\right)(1)$

$CO2=ƒ\left(GDP;HCD;UB,TR\right)(2)$

Figure 3

Figure 3. Urbanization rate levels from 1991 to 2021.

Figure 4

Figure 4. Financial development levels from 1991 to 2021.

Figure 5

Figure 5. CO2 emissions levels from 1991 to 2021.

Figure 6

Figure 6. Gross Domestic Product levels from 1991 to 2021.

Figure 7

Figure 7. Energy consumption levels from 1991 to 2021.

Figure 8

Figure 8. Manufacturing value-added levels from 1991 to 2021.

Figure 9

Figure 9. International trade levels from 1991 to 2021.

Figure 10

Figure 10. Human capital investment levels from 1991 to 2021.

Figure 11

Figure 11. Renewable energy levels from 1991 to 2021.

In Equations 1, 2, the variable CO2 represents carbon emissions measured in metric tons. GDP represents the per capita gross domestic product in current US dollars, which serves as a proxy for economic growth. MVA denotes manufacturing value-added as a percentage of total GDP. UB represents the urbanization rate, expressed as a percentage of the total population. HCD, measures the development of a country in terms of income, education, and health. FD refers to financial development, measured as a percentage of GDP and is represented by the proxy domestic credit issued to the Private sector (see Appendix 1 for more detail).

All the variables used in this model are transformed into logarithmic form, as shown in Equations 3, 4, to mitigate the influence of extreme values in the data and to observe the elasticity of variables. This transformation helps to ensure a more stable and balanced analysis.

$LNC{O}_{it}={\beta }_0+{\beta }_{1t}LNMV{A}_{it}+{\beta }_{2t}{{LNURB}_{it}+\beta }_{3t}{LNFD}_{it}+{\beta }_{4t}L{NRE}_{it}+{\beta }_{5t}L{NENC}_{it}+{\beta }_{6t}L{NTR}_{it}+{\epsilon }_{it}(3)$

$LNC{O}_{it}={\alpha }_0+{\alpha }_{1t}\mathit{LN}{GDP}_{it}+{\alpha }_{2t}{LNURB}_{it}+{\alpha }_{3t}{LNHCD}_{it}+{\alpha }_{4t}L{NTR}_{it}+u_{it}(4)$

whereas i represents the number of countries; t indicates the time period; ε and u represent the error term; and ${\beta }_1,{\beta }_2,{\beta }_3,{\beta }_4,{\beta }_5,{\beta }_6,\mathrm{a}\mathrm{n}\mathrm{d}{\alpha }_1,{\alpha }_2,{\alpha }_3,{\alpha }_4$ are the parameters of manufacturing value-added, UB, FD, economic growth, RE use, ENC, HCD, and international trade, respectively.

4 Results and discussion

After the models are defined, Table 1 indicates the descriptive statistics for the eight countries over the 31-year time periods (1990–2021). Accordingly, the mean values of the series in Table 1 are 1.80 for CO2, $4,118.03 for GDP, 16.04% for MVA, 47.7% for UB, 0.57 for HCD, 41.80% for FD, 42.82% for RE, 813.71 for ENC and 59.07% for TR while the median values of the series are 0.91 for CO2, $2914.51 for GDP, 15.50% for MVA, 47.14% for UB, 0.57 for HCD, 26.48% for FD, 31.62% for RE, 585.15 for ENC and 55.46% for TR. It is seen that there are high differences between the maximum and minimum values of the series, except for HCD and UB. For this reason, logarithmic transformation of all variables was made in order to avoid the problem of heteroscedasticity and to measure the elasticity values of the variables. Figure 3 presents the scatterplot matrix representation of the data.

Table 1

Table 1. Descriptive Statistics of selected variables.

Table 2 presents the correlation analysis results, while Figure 12 provides a visual representation of the findings. Correlation analysis results indicate the direction of the relationship between variables. If there is a high correlation between variables, this also indicates that a multicollinearity problem may arise. As with the VIF test results in Table 3, correlation analysis results proved that there was no multicollinearity problem. According to the VIF test results in Table 3, it is determined that the use of Models 1 and 2 is appropriate because the variables and Mean VIF values are less than 5. Moreover, the correlation analysis results showed that there was a positive relationship between GDP, MVA, UB, HCD, FD, ENC and CO2, while there was a negative relationship between RE and CO2.

Table 2

Table 2. Correlation analysis results.

Figure 12

Figure 12. Graphical visualization of the correlation matrix.

Table 3

Table 3. Variance inflation factor (VIF) test results.

Technological developments after the Second World War and the end of the bipolar world with the collapse of the Soviet Union after 1989 were two fundamental facts that accelerated globalization. Although protectionism came to the fore for some countries from time to time after this period, the integration process of countries with each other has reached an advanced stage with globalization. The results of the cross-sectional dependence (Breusch-Pagan LM, Pesaran scaled LM, Bias-corrected scaled LM, Pesaran CD) test in Table 4 revealed that the countries examined are related to each other. At the same time, the cross-sectional dependence test results in Table 4 (Panel A and B) proved that the the existence of cross-sectional dependence for models and variables was detected. In other words, we found that there is cross-section dependence for each models and variables. In this regard, we should use second generation test methods that can be used under cross-section dependence. Additionally, Table 4 (Panel C) revealed that the heterogeneous panel models are valid for Model 1 and Model 2.

Table 4

Table 4. Cross-section dependence and slope homogeneity tests results.

In the continuation of the analysis, the cross-sectional ADF (CADF) unit root test developed by Pesaran (2007) was used. This unit root test, which can be applied under cross-section dependence and heterogeneity, is considered among the second-generation unit root tests. Table 5 presents the results obtained considering the constant model. Accordingly, the variables became stationary by taking their first difference. A parallel result was also obtained for the model with trend and constant. For this reason, it could not be added to the table separately.

Table 5

Table 5. Panel CADF unit root test results.

After reporting the panel unit root test results, we used the second-generation panel cointegration tests proposed by Westerlund (2008) to determine whether there is a long-term cointegration relationship between the variables. The cointegration results in Table 6 revealed that there is a long-term relationship between the variables.

Table 6

Table 6. Durbin-Hausman cointegration test results.

For long-term parameter estimates, we applied the mean group dynamic least squares (DOLSMG) developed by Pedroni (2001). As Models 1 and 2 are established in full logarithmic form, the coefficients can be interpreted as percentage changes. For Model 1, the DOLSMG estimator showed that a 1% increase in LNMVA would reduce LNCO2 by 1.4%, while a 1% increase in LNUB raises LNCO2 by 1.3%. Additionally, a 1% increase in LNRE reduces LNCO2 by 0.5%, while a 1% increase in LNTR decreases LNCO2 by 1.4%. However, a 1% increase in LNENC rises LNCO2 by 2.7%. On the other hand, DOLSMG results for Model 2 prove that a 1% increase in LNUB raises LNCO2 by 3.1%, while a 1% increase in LNHCD raises LNCO2 by 2.86% (see Table 7). The results are visually displayed in Figure 13.

Table 7

Table 7. The results of the mean group dynamic least squares (DOLSMG).

Figure 13

Figure 13. Long-run coefficient results for entire panel.

Long-term country results include differences in all panel results presented in Table 8. For example, a 1% increase in LNMVA in Morocco reduces LNCO2 by 1.22%. In parallel with this result, a 1% increase in LNMVA reduces LNCO2 in Tunisia by 0.44%, LNCO2 in Egypt by 2.48%, LNCO2 in Cote d’lvoire by 6.35%, LNCO2 in GHANA by 0.40%, LNCO2 in Nigeria by 1.18% and LNCO2 in South Africa by 0.40%. Contrary to these results, it was found that a 1% increase in LNMVA in Kenya leads to a 1.02% rise in LNCO2. Another result was that an increase in LNFD decreased LNCO2 in Cote d’lvoire, South Africa and Kenya, while an increase in LNFD was found to raise LNCO2 in Ghana and Nigeria. While increasing the use of LNRE was found to reduce CO2 emissions in countries other than Ghana and Kenya, increasing LNENC was found to rise CO2 emissions in all countries. Looking at the results by level of urbanization, increases in LNUB were found to raise LNCO2 in countries other than Egypt and Côte d’lvoire. In addition, increases in LNTR reduce LNCO2 in Ghana and South Africa, but increase LNCO2 in Nigeria and Kenya. Finally, increases in LNHCD were found to raise LNCO2 in Morocco, Ghana, Nigeria and Kenya, while lowering LNCO2 in South Africa.

Table 8

Table 8. Country-specific long run coefficients results.

The results of the MMQR tests are shown in Table 9. First, contradictions were found between the MMQR results and the DOLMG results for MVA. Accordingly, according to the MMQR results, it was concluded that the increase in LNMVA leads to an increment in LNCO2 based on different quantiles (0.10, 0.25, 0.50, 0.75, 0.90) for model 1. In addition, the increase in LNFD was found to contribute to the increment in LNCO2 based on different quantile levels. Considering the results in which DOLMG and MMQR are compatible, it was observed that the increase in the use of LNRE decreased LNCO2, while the increase in LNUB raised LNCO2. It was also found that increase in LNENC leds to rise in LNCO2. MMQR results for model 2 observed that increases in LNUB and LNGDP raised LNCO2 based on different quantile levels. On the other hand, an increase in LNHCD was found to raise LNCO2, while an increase in LNTR was found to lower LNCO2 at different quantile levels. The MMQR results are visually presented in Figure 14. These results are in line with the DOLSMG results.

Table 9

Table 9. MMQR test results.

Figure 14

Figure 14. MMQR test results.

In the final stage of the analysis, we applied the causality analysis developed by Dumitrescu-Hurlin (2012), which can be used for heterogeneous panels. The null hypothesis assumes no causal relationship between two variables, and results where the null hypothesis was rejected are reported in Table 10. According to our results (see Table 10), while a unidirectional causality relationship was detected from LNUB, LNHCD and LNRE to LNCO2, a unidirectional causality relationship was found from LNCO2 to LNGDP and LNENC.

Table 10

Table 10. Dumitrescu Hurlin causality tests results.

5 Conclusion

In conclusion, this research sheds light on the complex interplay of human capital development, international trade, financial development, renewable energy consumption, and urbanization in the context of environmental degradation in emerging-market economies in Africa. The findings emphasize the nuanced nature of these relationships, with financial development and renewable energy consumption revealing both positive and negative impacts on environmental degradation.

The empirical results conclude that urbanization, energy consumption, economic growth and human capital development have significant and positive effects on environmental degradation, while financial development, renewable energy consumption, manufacturing activities and international trade have a significant negative effect on environmental degradation. As the African continent continues to experience economic growth and urbanization, it is paramount for policymakers to recognize the dual nature of these forces and their environmental consequences. Encouraging financial development and promoting renewable energy consumption can serve as positive steps towards mitigating environmental degradation. Simultaneously, addressing the adverse effects of urbanization on the environment is vital to ensure a sustainable path for economic growth in these emerging-market economies.

This study underscores the necessity for a comprehensive and well-balanced approach in shaping environmental policies in African emerging-market economies, one that harnesses the potential of human capital, international trade, and financial development, while also acknowledging the vital role of renewable energy and urbanization management in achieving a sustainable and environmentally friendly economic future for the continent. The study makes a valuable contribution to the discussion on environmental degradation in Africa, particularly by exploring the interplay between human capital development, international trade, renewable energy consumption, and urbanization. However, it has certain limitations, mainly due to its focus on only eight parameters. Future research could expand by incorporating additional variables and exploring their impact on various environmental indicators, leading to a more comprehensive understanding of urban sustainability in Africa. Additionally, analyzing the data within the context of the Environmental Kuznets Curve could shed light on the link between economic development and environmental quality. That said, the lack of some necessary datasets poses a challenge that future studies should address.

Data availability statement

The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author.

Author contributions

SB: Investigation, Methodology, Supervision, Writing–original draft, Writing–review and editing. RE-B: Methodology, Writing–review and editing. AÇ: Conceptualization, Methodology, Software, Writing–review and editing. ZA: Supervision, Validation, Writing–review and editing. HR: Funding acquisition, Project administration, Writing–review and editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research article received financial assistance from the research Project “Sustainable, Resilient and Smart African Cities,” a collaboration between Mohammed VI Polytechnic University-Morocco and École Polytechnique Fédérale de Lausanne-Switzerland (AS22).

Acknowledgments

During the preparation of this work, the author(s) utilized ChatGPT (GPT-4, developed by OpenAI) to enhance language clarity and readability. The author(s) reviewed and edited the content after using this tool and assume full responsibility for the final version and accuracy of this publication.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbreviations

GDP, Gross Domestic Product; UB, Urbanization; ENC, Energy Consumption; CO2, Carbon Dioxide; HCD, Human Capital; RE, Renewable Energy; TOP, Trade Openness; NRE, Nonrenewable Energy; FD, Financial Development; FMOLS, Fully Modified Ordinary Least Squares; GMM, Generalized Method of Moment; DOLS, Dynamic Ordinary Least Squares; PMG-ARDL, Pooled Mean Group–Autoregressive Distributed Lag; EKC, Environmental Kuznets Curve; MVA, Manufacturing Value-Added; VIF, Variance Inflation Factor; MMQR, Method of Moments Quantile Regression; DOLSMG, The Mean Group Dynamic Least Squares; IEA, International Energy Agency.

References

Adebayo, T. S., and Kirikkaleli, D. (2021). Impact of renewable energy consumption, globalization, and technological innovation on environmental degradation in Japan: application of wavelet tools. Environ 23, 16057–16082. doi:10.1007/s10668-021-01322-2

CrossRef Full Text | Google Scholar

Awosusi, A. A., Mata, M. N., Ahmed, Z., Coelho, M. F., Altuntaş, M., Martins, J. M., et al. (2022). How do renewable energy, economic growth and natural resources rent affect environmental sustainability in a globalized economy? Evidence from Colombia based on the gradual shift causality approach. Front. Energy Res. 9, 739721. doi:10.3389/fenrg.2021.739721

CrossRef Full Text | Google Scholar

Bajja, S., Radoine, H., Abbas, S., Dakyaga, F., and Chenal, J. (2023). Determinants of urban environmental quality in Morocco: the roles of energy consumption, urbanization, manufacturing, and financial development in achieving SDG 13. Front. Environ. Sci. 11, 1174439. doi:10.3389/fenvs.2023.1174439

CrossRef Full Text | Google Scholar

Bajja, S., Radoine, H., Celik, A., Dakyaga, F., and Damrah, S. (2024). Human capital and manufacturing activities under environmentally-driven urbanization in the MENA region. Front. Environ. Sci. 11, 1322789. doi:10.3389/fenvs.2023.1322789

CrossRef Full Text | Google Scholar

Bano, S., Zhao, Y., Ahmad, A., Wang, S., and Liu, Y. (2018). Identifying the impacts of human capital on carbon emissions in Pakistan. J. Clean. Prod. 183, 1082–1092. doi:10.1016/j.jclepro.2018.02.008

CrossRef Full Text | Google Scholar

Bayar, Y., Smirnov, V., Danilina, M., and Kabanova, N. (2022). Impact of institutions and human capital on CO2 emissions in E.U. Transition economies. Sustainability 14, 353. doi:10.3390/su14010353

CrossRef Full Text | Google Scholar

Berkun, F. V., Emir, F., and Sarkodie, S. A. (2019). Another look at the relationship between energy consumption, carbon dioxide emissions, and economic growth in South Africa. Sci. Total Environ. 655, 759–765. doi:10.1016/j.scitotenv.2018.11.271

PubMed Abstract | CrossRef Full Text | Google Scholar

Bilan, Y., Streimikiene, D., Vasylieva, T., Lyulyov, O., Pimonenko, T., and Pavlyk, A. (2019). Linking between renewable energy, CO2 emissions, and economic growth: challenges for candidates and potential candidates for the EU membership. U. Membsh. Sustain. 11 (6), 1528. doi:10.3390/su11061528

CrossRef Full Text | Google Scholar

Cai, Y., Sam, C. Y., and Chang, T. (2018). Nexus between clean energy consumption, economic growth and CO2 emissions. J. Clean. Prod. 182, 1001–1011. doi:10.1016/j.jclepro.2018.02.035

CrossRef Full Text | Google Scholar

Chen, Y., Wang, Z., and Zhong, Z. (2019). CO2 emissions, economic growth, renewable and non-renewable energy production and foreign trade in China. Renew. Energy 131, 208–216. doi:10.1016/j.renene.2018.07.047

CrossRef Full Text | Google Scholar

Dasgupta, S., Hettige, H., and Wheeler, D. (2000). What improves environmental compliance? Evidence from Mexican industry. J. Environ. Econ. Manage. 39, 39–66. doi:10.1006/jeem.1999.1090

CrossRef Full Text | Google Scholar

Desha, C., Robinson, D., and Sproul, A. (2015). Working in partnership to develop engineering capability in energy efficiency. J. Clean. Prod. 106, 283–291. doi:10.1016/j.jclepro.2014.03.099

CrossRef Full Text | Google Scholar

De Souza Mendonça, A. K., Silva, S. A. D., Pereira, L. Z., Bornia, A. C., and Andrade, D. F. D. (2020). An overview of environmental policies for mitigation and adaptation to climate change and application of multilevel regression analysis to investigate the CO2 emissions over the years of 1970 to 2018 in all Brazilian states. Sustainability 12 (21), 9175. doi:10.3390/su12219175

CrossRef Full Text | Google Scholar

Dogan, E., and Seker, F. (2016). Determinants of CO2 emissions in the European Union: the role of renewable and nonrenewable energy. Renew. Energy 94 (C), 429–439. doi:10.1016/j.renene.2016.03.078

CrossRef Full Text | Google Scholar

Dong, K., Sun, R., Jiang, H., and Zeng, X. (2018). CO2 emissions, economic growth, and the environmental Kuznets curve in China: what roles can nuclear energy and renewable energy play? J. Clean. Prod. 196, 51–63. doi:10.1016/j.jclepro.2018.05.271

CrossRef Full Text | Google Scholar

Dumitrescu, E. I., and Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Econ. Model. 29 (4), 1450–1460. doi:10.1016/j.econmod.2012.02.014

CrossRef Full Text | Google Scholar

Fatima, T., Shahzad, U., and Cui, L. (2021). Renewable and nonrenewable energy consumption, trade and CO2 emissions in high emitter countries: does the income level matter? J. Environ. Plan. Manag. 64 (7), 1227–1251. doi:10.1080/09640568.2020.1816532

CrossRef Full Text | Google Scholar

Feng, H. (2022). The impact of renewable energy on carbon neutrality for the sustainable environment: role of green finance and technology innovations. Front. Environ. Sci. 10. doi:10.3389/fenvs.2022.924857

CrossRef Full Text | Google Scholar

Hartman, R., and Kwon, O.-S. (2005). Sustainable growth and the environmental Kuznets curve. J. Econ. Dyn. Control 29 (10), 1701–1736. doi:10.1016/j.jedc.2004.10.001

CrossRef Full Text | Google Scholar

Ibrahim, R. L., and Ajide, K. B. (2021). The dynamic heterogeneous impacts of nonrenewable energy, trade openness, total natural resource rents, financial development and regulatory quality on environmental quality: evidence from BRICS economies. Resour. Policy 74, 102251. doi:10.1016/j.resourpol.2021.102251

CrossRef Full Text | Google Scholar

Inglesi-Lotz, R., and 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 Βig 10 electricity generators. Renew. Energy 123, 36–43. doi:10.1016/j.renene.2018.02.041

CrossRef Full Text | Google Scholar

Ito, K. (2017). CO2 emissions, renewable and non-renewable energy consumption, and economic growth: evidence from panel data for developing countries. Int. Econ. 151, 1–6. doi:10.1016/j.inteco.2017.02.001

CrossRef Full Text | Google Scholar

Lau, L. S., Choong, C. K., Ng, C. F., Liew, F. M., and Ching, S. L. (2019). Is nuclear energy clean? Revisit of environmental Kuznets curve hypothesis in OECD countries. Econ. Model. 77, 12–20. doi:10.1016/j.econmod.2018.09.015

CrossRef Full Text | Google Scholar

Mahalik, M. K., Mallick, H., and Padhan, H. (2021). Do educational levels influence the environmental quality? The role of renewable and nonrenewable energy demand in selected BRICS countries with a new policy perspective. Renew. Energy 164 (C), 419–432. doi:10.1016/j.renene.2020.09.090

CrossRef Full Text | Google Scholar

Mohsin, M., Kamran, H. W., Atif Nawaz, M., Sajjad Hussain, M., and Dahri, A. S. (2021). Assessing the impact of transition from nonrenewable to renewable energy consumption on economic growth-environmental nexus from developing asian economies. J. Environ. Manage. 284, 111999. doi:10.1016/j.jenvman.2021.111999

PubMed Abstract | CrossRef Full Text | Google Scholar

Muhammad, B., Khan, M. K., Khan, M. I., and Khan, S. (2021). Impact of foreign direct investment, natural resources, renewable energy consumption, and economic growth on environmental degradation: evidence from BRICS, developing, developed and global countries. Environ. Sci. Pollut. Res. 28 (17), 21789–21798. doi:10.1007/s11356-020-12084-1

CrossRef Full Text | Google Scholar

Oke, D. M., Ibrahim, R. L., and Bokana, K. G. (2021). Can renewable energy deliver African quests for sustainable development? J. Dev. Areas 55 (1). doi:10.1353/jda.2021.0022

CrossRef Full Text | Google Scholar

Pata, U. K. (2018). The effect of urbanization and industrialization on carbon emissions in Turkey: evidence from ARDL bounds testing procedure. Environ. Sci. Pollut. Res. 25 (8), 7740–7747. doi:10.1007/s11356-017-1088-6

CrossRef Full Text | Google Scholar

Pata, U. K. (2018). Renewable energy consumption, urbanization, financial development, income and CO2 emissions in Turkey: testing EKC hypothesis with structural breaks. J. Clean. Prod. 187, 770–779. doi:10.1016/j.jclepro.2018.03.236

CrossRef Full Text | Google Scholar

Pedroni, P. (2001). Purchasing power parity tests in cointegrated panels. Rev. Econ. Statistics 83 (4), 727–731. doi:10.1162/003465301753237803

CrossRef Full Text | Google Scholar

Pesaran, M. H. (2007). A simple panel unit root test in the presence of crosssection dependence. J. Appl. Econ. 22 (2), 265–312. doi:10.1002/jae.951

CrossRef Full Text | Google Scholar

Qi, T., Zhang, X., and Karplus, V. J. (2014). The energy and CO2 emissions impact of renewable energy development in China. Energy Policy 68, 60–69. doi:10.1016/j.enpol.2013.12.035

CrossRef Full Text | Google Scholar

Radoine, H., Bajja, S., Chenal, J., and Ahmed, Z. (2022). Impact of urbanization and economic growth on environmental quality in western africa: do manufacturing activities and renewable energy matter? Front. Environ. Sci. 10, 1012007. doi:10.3389/fenvs.2022.1012007

CrossRef Full Text | Google Scholar

Radoine, H., Bajja, S., Dakyaga, F., Çelik, A., Kamana, A., Yakubu, H., et al. (2024). Modelling the dynamics of urbanization for urban sustainability in West Africa. J. Urban Manag. doi:10.1016/j.jum.2024.06.004

CrossRef Full Text | Google Scholar

Raza, S. A., and Shah, N. (2018). Testing environmental Kuznets curve hypothesis in G7 countries: the role of renewable energy consumption and trade. Environ. Sci. Pollut. Res. 25, 26965–26977. doi:10.1007/s11356-018-2673-z

CrossRef Full Text | Google Scholar

Sarkodie, S. A., and Ozturk, I. (2020). Investigating the environmental Kuznets curve hypothesis in Kenya: a multivariate analysis. Renew. Sustain. Energy Rev. 117 (C), 109481. doi:10.1016/j.rser.2019.109481

CrossRef Full Text | Google Scholar

Shahnazi, R., and Shabani, Z. D. (2021). The effects of renewable energy, spatial spillover of CO2 emissions and economic freedom on CO2 emissions in the EU. Renew. Energy 169, 293–307. doi:10.1016/j.renene.2021.01.016

CrossRef Full Text | Google Scholar

Szetela, B., Majewska, A., Jamroz, P., Djalilov, B., and Salahodjaev, R. (2022). Renewable energy and CO2 emissions in top natural resource rents depending countries: the role of governance. Front. Energy Res. 10, 872941. doi:10.3389/fenrg.2022.872941

CrossRef Full Text | Google Scholar

Westerlund, J. (2008). Panel cointegration tests of the Fisher effect. J. Appl. Econ. 23 (2), 193–233. doi:10.1002/jae.967

CrossRef Full Text | Google Scholar

Yao, Y., Ivanovski, K., Inekwe, J., and Smyth, R. (2020). Human capital and CO2 emissions in the long run. Energy Econ. 91, 104907. doi:10.1016/j.eneco.2020.104907

CrossRef Full Text | Google Scholar

Zoundi, Z. (2017). CO2 emissions, renewable energy and the Environmental Kuznets Curve, a panel cointegration approach. Renew. Sustain. Energy Rev. 72, 1067–1075. doi:10.1016/j.rser.2016.10.018

CrossRef Full Text | Google Scholar

Appendix 1

TABLE A1

TABLE A1. Definition and Data source of variables.