2. Literature Review
Udemba (2020) used Nigeria as a case study to examine the interconnection between GDPR and ECOL. Using Nigeria is motivated by the fact that the nation’s economy mostly depends on fossil fuels. According to the results of the Granger Causality (GC) and Autoregressive Distributed Lag (ARDL) methods, the scale effect theory is confirmed. This means that GDPR and ECOL are growing at the same rate. Likewise, using the same methodology for the Nigerian economy, Dada et al. (2022) found that while GDPR contributes to the degradation of the environment, HUMC and URB reduce ECOL. According to Akadiri et al. (2025), concerns over Nigeria’s environmental sustainability have been raised by the country’s drive for economic expansion, especially in light of the consequences of resource reliance, urbanization, and foreign direct investment (FDI). Results show that environmental quality continuously deteriorates by economic expansion across all quantiles, with the impacts getting worse with time. Urbanization and FDI both have detrimental ecological effects, especially at medium and high quantiles. Obekpa et al. (2025) argued that the total ECOL increases because of population growth and economic expansion.
Nathaniel (2021) argued that in addition to being the most developed and urbanized nation in Africa, South Africa is the biggest emitter of CO2. Long–term results indicate that although HUMC supports environmental sustainability, urbanization and economic expansion raise ECOL. In addition, the relationship between urbanization and HUMC lessens environmental deterioration. The methods used include ARDL, FMOLS, DOLS, and CCR. In Brazil and China, employing the QARDL approach, Tiwari et al. (2022) established that GDPR increases ECOL, while HUMC reduces it. Furthermore, adopting the dynamic ARDL and KRLS methods, Zhou et al. (2022) found a contradictory result regarding HUMC and that is, HUMC increases ECOL in Pakistan. In addition, GDPR drives ECOL, while urbanization undermines it. In GCC economies, Amer et al. (2022) showed how urbanization decreases ECOL and how GDPR & HUMC increase ECOL using the FGLS technique. In G–11 economies, Mehmood (2024) opined that urbanization and GDPR increase ECOL.
In recent studies, Degirmenci et al. (2025) established that URB contributes to ecological sustainability in Brazil, while it harms the environment in China. It was also found that HUMC spurs the sustainability of the environment. In BRICS economies, Liu et al. (2025) argued that the availability of natural resources and growth in the economy both increase ECOL. In addition, the study suggests a causal feedback link between HUMC, URB, and ECOL. Tunio et al. (2025) confirmed that economic expansion exacerbates environmental deterioration over time while reducing it in the short run. Also, URB has been identified as a major contributor to long-term environmental deterioration, highlighting the necessity of focused governmental responses. According to the study of Mohamed et al. (2025), GDPR and URB increase ECOL for the Somalian economy.
Conclusively, the literature, shows that GDPR (Economic Growth) leads to ecological degradation, while the impact of URB (Urbanization) and HUMC (Human Capital) on the environment can be both positive and negative. Therefore, further research is required due to the inconsistent outcomes.
Table 1: Literature Review Summary.
|
Author(s)
|
Year
|
Country(ies)
|
Methods
|
Outcome
|
|
Udemba (2020)
|
1981–2018
|
Nigeria
|
ARDL & Granger Causality
|
GDPR (+)
|
|
Nathaniel (2021)
|
1970–2016
|
South Africa
|
ARDL, FMOLS, DOLS & CCR
|
URB (+), GDPR (+), HUMC (–)
|
|
Zhou et al. (2022)
|
1980–2018
|
Pakistan
|
Dynamic ARDL & KRLS
|
HUMC (+), GDPR (+), URB (–)
|
|
Dada et al. (2022)
|
1970–2017
|
Nigeria
|
ARDL & Granger Causality
|
URB (–), HUMC (–), GDPR (+)
|
|
Tiwari et al. (2022)
|
1971Q1–2017Q4
|
Brazil & China
|
QARDL
|
GDPR (+), HUMC (–)
|
|
Amer et al. (2022)
|
1995–2017
|
GCC
|
FGLS
|
URB (–), GDPR (+), HUMC (+)
|
|
Mehmood (2024)
|
1990–2020
|
G–11
|
CS–ARDL
|
URB (+), GDPR (+)
|
|
Akadiri et al. (2025)
|
1970–2022
|
Nigeria
|
Wavelets Quantile Regression
|
URB (+), GDPR (+)
|
|
Obekpa et al. (2025)
|
1981–2020
|
Nigeria
|
Quantile Regression & KRLS
|
GDPR (+)
|
|
Degirmenci et al. (2025)
|
1991–2019
|
E–7
|
AMG
|
URB (+), URB (–), HUMC (–)
|
|
Liu et al. (2025)
|
1992–2016
|
BRICS
|
AMG, PMG, CCEMG, FMOLS & DOLS
|
GDPR (+)
|
|
Tunio et al. (2025)
|
1970–2022
|
34 High-Income European and Asian
|
VECM
|
GDPR (+), URB (+)
|
|
Mohamed et al. (2025)
|
1990–2020
|
Somalia
|
ARDL, FMOLS & CCR
|
GDPR (+), URB (+)
|
Note: (+) denotes positive association; (–) denotes negative association.
3. Data, Theoretical Framework, and Methodology
3.1 Data and Sources
The data employed in this research (1961–2020) and their sources are as follows: Ecological Footprint (global hectares per capita) (ECOL), which is the dependent variable, is used as a proxy for environmental progress, and its source is the g. The independent variables include Urban Population (% of total population) (UPOP), a proxy for urbanization, Index of Human Capital Per Person (HUMC), and Gross Domestic Product (constant 2015 US$) (GDPR), which is a proxy for economic expansion. UPOP and GDPR data sources are (the World Bank, 2024), while the HUMC data source is from (Feenstra et al., 2015). It is important to note that the data period stopped in 2020 because of limited data availability.
3.2 Theoretical Framework
3.2.1 Theories of Urbanization
The theoretical framework that links urbanization to ecological quality includes the Ecological Modernization Theory (EMT), Environmental Transition Theory (ETT), and Compact City Theory (CTT). According to the EMT, emissions increase as urban areas become more developed and then decrease after a specific degree of modernization is reached (Mol & Spaargaren, 2000). Examining urbanization from another perspective, ETT proposes that the impact of urbanization differs at different phases of development (Jacobi et al., 2010). Lastly, CCT asserts that urbanization can positively contribute to ecological progress due to an improvement in public infrastructure, such as hospitals, schools, water and electricity supply, and public transportation (Burton, 2000; Sadorsky, 2014).
3.2.2 Environmental Kuznets Curve
The EKC hypothesis, posited by Grossman & Krueger (1991), argues that economic growth can be grouped into three (3) phases. The initial phase is the scale effect, which is related to the early stages of economic growth and is marked by high competitiveness. During this stage, governments are more concerned with economic performance than with ecological performance. This stage is primarily linked to developing countries that want to be like industrialized nations, and to accomplish this, competitive economic growth is implemented, which has a negative impact on the environment. The technological expansion effect stage is the second phase. The shift from the scale effect to a more enlightened stage is what defines this stage when people will begin to understand how their activities affect the environment and their health and will begin to advocate for a high–quality environment. The final stage is the composite effect stage. At this point, a complete understanding of the necessity of excellent environmental quality and clean economic operations is observed and implemented. Most economic operations will be more sophisticated, with cutting–edge technology that will ensure both economic and environmental sustainability. Service sectors and R&D activities will drive economic progress.
3.3 Methodology
The Quantile ADF (QADF) test proposed by Adebayo & Özkan (2024) was carried out as a preliminary assessment to test for stationarity conditions. Once the stationarity results have been obtained, we proceed to adopt the next method, which is the Wavelet Quantile Correlation (WQC) proposed by (Kumar & Padakandla, 2022). According to previous empirical research, the connections between variables can change over time. Nonetheless, the classic Quantile Regression (QR) technique does not account for the potential of separate impacts of the independent variable on the conditional quantiles of the dependent variable over several temporal dimensions. To solve this restriction, the WQC technique was adopted. In addition, for results confirmation and robustness check, the Quantile on Quantile Granger Causality (QQGC) proposed by Adebayo & Özkan (2024) was adopted. Compared to the Granger Causality (GC) and Quantile Granger Causality (QGC) approaches, QQGC allows for a more detailed study of causation by examining causality from the quantiles of an independent variable to the quantiles of a dependent variable.
4. Results and Discussions
4.1 Descriptive Statistics
The first preliminary investigation carried out is the descriptive statistics. We found an unequal distribution in the dataset for the majority of the variables since their mean values surpassed the values of the standard deviation. In addition, ECOL and UPOP are skewed to the left (negatively skewed), while GDPR and HUMC are skewed to the right (positively skewed). Aside from ECOL, all other variables exhibit non–normal distribution because the p-values are less than 5%.
Table 2: Descriptive Statistics.
|
|
ECOL
|
GDPR
|
HUMC
|
UPOP
|
|
Mean
|
0.001261
|
2.810756
|
0.034534
|
0.363637
|
|
Median
|
0.002163
|
2.797440
|
0.022652
|
0.369119
|
|
Maximum
|
0.023713
|
2.926920
|
0.073959
|
0.429534
|
|
Minimum
|
–0.026550
|
2.700024
|
0.015279
|
0.297930
|
|
Std. Dev.
|
0.010096
|
0.065298
|
0.020197
|
0.040755
|
|
Skewness
|
–0.106260
|
0.351242
|
0.698096
|
–0.054668
|
|
Kurtosis
|
2.718179
|
2.194047
|
1.928865
|
1.686894
|
|
Jarque–Bera
|
1.245875
|
11.43042
|
30.96683
|
17.36201
|
|
Probability
|
0.536366
|
0.003295*
|
0.000000*
|
0.000170*
|
* denotes < p–value of 1%
4.2 Nonlinearity Test
To examine the series' nonlinear patterns, Broock et al. (1996) nonlinearity test is adopted. As shown in Table 3, the results of nonlinearity tests confirm the presence of nonlinearity in each variable under examination, demonstrating that the quantile-based technique is appropriate for this study.
Table 3: Result of the Nonlinearity Test.
|
|
ECOL
|
GDPR
|
HUMC
|
UPOP
|
|
DIM2
|
0.176495*
|
0.202384*
|
0.205072*
|
0.208327*
|
|
DIM3
|
0.291713*
|
0.342204*
|
0.346645*
|
0.353975*
|
|
DIM4
|
0.365823*
|
0.439496*
|
0.445041*
|
0.456302*
|
|
DIM5
|
0.412253*
|
0.507715*
|
0.513889*
|
0.528617*
|
|
DIM6
|
0.440993*
|
0.555877*
|
0.562402*
|
0.580003*
|
* denotes < p–value of 1%.
4.3 Stationary Test
The QADF test, proposed by Adebayo & Özkan (2024), was performed to investigate the data’s stationarity over different quantiles. Unlike traditional ADF and PP tests, which presume consistent distributional features across the whole range, the QADF test takes into account probable distributional changes at different points within the distribution. This method adds to a more thorough knowledge of the data characteristics (Adebayo & Özkan, 2024). Figure 1 shows that all the variables are stationary at the level.
Figure 1. Quantile ADF Unit Root Test.
4.4 Wavelet Quantile Correlation (WQC)
The WQC is a reliable method for dealing with difficulties involving patterns that are tail–dependent. This approach fully investigates the relationship between variables, taking into account both frequency scales and quantile dependency. Figures 2, 3 & 4 depict the asymmetric behaviour between GDPR, HUMC, UPOP, and ECOL over various periods and quantiles. The Y–axis displays the distinct periods, namely short, medium, and long term, while the X–axis displays the various quantiles (0.1–0.9).
The outcome of the WQC analysis shows that the relationship between GDPR, HUMC, UPOP, and ECOL in the short and medium term is negative. However, in the long run, the association is mainly positive, although, between the quantiles 0.1 and 0.5, there exists a negative link.
The negative short-run and positive long-term association between GDPR and ECOL can be justified. Nigeria’s short–term ecological footprint reduction with initial GDP growth may be explained by a move away from traditional, highly resource-intensive practices and toward somewhat more efficient, albeit still basic, technologies and consumption patterns linked to the country's early phases of industrialization and urbanization. This outcome corresponds with the study of Wang et al. (2022) for 166 economies. However, Nigeria's dominant resource extraction (particularly oil), weak environmental regulations and enforcement, rapid population growth that increases demand for consumption, and an economic development trajectory that has historically placed expansion above environmental sustainability are likely the main causes of the country's overwhelming long-term trend of growing ecological footprint. This corresponds to the studies of (Dada et al., 2022; Udemba, 2020). It is important to note that the EKC is not confirmed in this research. This is because the short and medium-term results are negative, while the long-run result is positive.
Secondly, the relationship between HUMC and ECOL in the short and medium term is negative, and this corresponds to the study of (Dada et al., 2022; Nathaniel, 2021; Tiwari et al., 2022). However, in the long run, the association is mainly positive. This means the approach of using human capital to solve the environmental crisis in Nigeria is not sustainable. The outcome of this research is justifiable. As human capital develops, which involves the development of skills, knowledge, education, and experience, it should lead to better choices, which should contribute to ecological quality. However, this is not always the case in the real world. Human capital could develop, and yet people can still live below the poverty line. Therefore, their actions will be centred on making use of the resources around them to survive, and this includes the use of fossils. This positive association in the long run corresponds to the studies of (Akadiri et al., 2025; Amer et al., 2022; Mehmood, 2024; Zhou et al., 2022).
Lastly, the negative short-run association (Amer et al., 2022; Dada et al., 2022) between UPOP and ECOL in Nigeria could be attributed to initial efficiency gains from urbanization, such as improved infrastructure use, shared public services, and more compact living, which reduces per capita resource use. However, a long-term positive link is predicted to emerge as urban populations grow, increasing energy use, transportation use, waste output, and pressure on natural resources. Over time, rapid urban growth can outrun sustainable planning and infrastructure development, resulting in a bigger environmental impact. This aligns with the studies of (Akadiri et al., 2025; Mehmood, 2024; Nathaniel, 2021).
Figure 2. The impact of GDPR on ECOL.
Figure 3. The impact of HUMC on ECOL.
Figure 4. The impact of UPOP on ECOL.
4.5 Quantile–on–Quantile Granger Causality Analysis (QQGC)
The QQGC results in Figures 5, 6, & 7 revealed that UPOP, HUMC, and GDPR all have a substantial impact on ECOL across all quantiles. This can be seen in the orange and red colours with asterisks.
|
Figure 5. Causality from GDPR to ECOL.
|
Figure 6. Causality from HUMC to ECOL.
|
|
Figure 7. Causality from UPOP to ECOL.
|
|
|
|
5. Conclusion and Recommendations
This study investigates how urbanization, human capital, and economic growth affect ecological footprint from 1961–2020 using the Wavelet Quantile Correlation (WQC), and the Quantile–on–Quantile Granger Causality analysis (QQGC) methods. The WQC outcomes show that urban population, human capital, and economic growth reduce the ecological footprint in the short and medium term, while in the long term, the findings are predominantly positive. However, within the quantiles 0.1 and 0.5, there exists a negative link. In addition, the QQGC confirms the WQC across all quantiles.
The study recommends the following: (1) To strike a balance between HUMC and ECOL, and guarantee long–term environmental preservation, it is essential to incorporate environmental education into all educational levels, encouraging green innovation, and coordinating human capital development with sustainability objectives. (2) To manage urban growth without adding to ecological pressure, boosting the use of renewable energy, investing in green infrastructure, enforcing environmental legislation, and promoting sustainable urban planning is crucial. (3) Decoupling economic growth from ecological decline also entails the adoption of clean energy sources, mixed with stringent environmental policies to discourage the use of unclean energy.
The study has some limitations, such as, it only focuses on Nigeria. Other studies can use a group of other countries or individual countries and investigate similar research questions. In addition, other econometric methods can be adopted for more robust findings.
Acknowledgements
It is important to note that the data period ends in 2020 due to limited data availability.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Conflicts of Interest
The authors declare no conflicts of interest.
Data availability statement
The authors confirm that the data supporting the findings of this study are available within the article, and data is available upon request.
Institutional Review Board Statement
Not applicable.
CRediT author statement
Oluwatoyin Abidemi Somoye, Abraham Ayobamiji, and Toluwalope Seyi Akinwande all contributed equally to the writing – original draft preparation and writing – review & editing of the manuscript. All authors have read and approved the final version of the article.
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Abbreviations:
ADF:Augmented Dickey Fuller
AMG:Augmented Mean Group
ARDL:Autoregressive Distributed Lag
CCEMG:Common Correlated Effects Mean Group
CCR:Canonical Cointegrating Regression
CCT:Compact City Theory
DOLS:Dynamic Ordinary Least Squares
EKC:Environmental Kuznets Curve
EMT:Ecological Modernization Theory
ETT:Environmental Transition Theory
FGLS:Feasible Generalized Least Squares
FMOLS:Fully Modified Ordinary Least Squares
KRLS:Kernel–based Regularized Least Squares
NARDL:Non–linear ARDL
PMG:Pooled Mean Group
PP:Phillips Perron
QADF:Quantile Augmented Dickey Fuller
QARDL:Quantile Autoregressive Distributed Lag
JOURNAL OF CONTEMPORARY URBAN AFFAIRS, 9(1), 256–269/ 2025
