Environmental consequences of energy commodities and economic growth: Evidence from Nigeria

This study explored the relationship between energy commodities, economic growth, and Nigeria's carbon dioxide (CO ₂ ) emissions spanning from 1981 to 2021. Employing the Vector Error Correction Mechanism (VECM) process, the study revealed a negative correlation between fossil fuel consumption, economic growth, and carbon dioxide emissions. Long-term elasticities indicate that carbon dioxide emissions would rise by 24 and 211% if both fossil fuel consumption and economic growth decreased by 1%, contradicting the Environmental Kuznets Curve (EKC) theory in Nigeria. Nevertheless, a positive correlation was observed between carbon dioxide emissions and the total annual population. As per the error correction model (ECM = -2.64441), two years are required for carbon dioxide emissions to return to long-term equilibrium, with 26.4% of a shock in the variable resolved within a year. Upon closer examination of the impulse response function, it is evident that GDPPC and FFC will exert a negative short-and long-term impact on CO ₂ emissions. The study proposes that Nigeria's government should implement a comprehensive strategy to bolster investments in renewable energy. This encompasses creating a stable policy environment, establishing ambitious targets for renewable energy capacity, providing financial incentives, and introducing feed-in tariffs, given that the country's consumption of fossil fuels has not yet reached a point where emissions are increasing.


INTRODUCTION
Energy commodities, encompassing nuclear, chemical, mechanical, thermal, radiation, and electrical energy, contribute to economic growth by enhancing productivity and employment.They exist in various forms-liquids, solids, and gases-yet their environmental impact is intricate, particularly with the combustion of fossil fuels contributing to global warming.The production, transportation, and consumption of energy almost invariably result in significant environmental consequences.
The consumption of fossil fuels can lead to localized air pollution and climate change (Han et al., 2019).Recent research by the WEF (2022) indicates that certain pollutants related to fossil fuels actually have a cooling effect.According to Bölük and Mert (2015), natural gas is less aggressive than oil, accounting for only half of the CO₂ emissions compared to coal.On average, the combustion of oil (in the form of petroleum) releases approximately 33% less carbon dioxide (CO₂) per unit of energy produced compared to the combustion of coal.
In many emerging nations like Nigeria, fossil fuels remain the primary source of energy (Sugiawan and Managi, 2019).Despite their numerous benefits, such as providing thermal power plants with more precise operational control and monitoring (Vincent and Ezaal, 2022), these systems face various challenges that have been extensively studied.Robinson et al. (2007) note that Nigeria is not an exception to the escalating environmental concerns.Okafor and Joe-Uzoegbu (2010) emphasize the environmental impact of urbanization in Nigeria, where rural communities rely on traditional biomass for energy, resulting in greenhouse gas emissions.This imbalance contributes to global warming and environmental degradation, with Nigeria experiencing some of the highest CO₂ emissions worldwide.
Elevated carbon dioxide emissions are primarily associated with economic growth, as proposed by the Environmental Kuznets (1955) Curve (EKC) hypothesis and supported by studies such as those conducted by Han et al. (2018), Acheampong (2018), Abbas et al. (2019), andEsmaeili et al. (2023).The EKC theory posits that income contributes to environmental degradation in the early stages of development but diminishes once certain income levels are reached.However, the EKC exhibits diverse shapes, suggesting different policy implications.The validity of this hypothesis is debated due to variations in methodology, independent variables, examined sectors, and modeling.Few empirical studies focus on Nigeria, and no recent dataset has been employed to analyze Nigerian CO₂ emissions, unlike the studies by Chuku (2011), Ogundipe (2013), Alege and Ogundipe (2013), and Okon (2021).To our knowledge, as of the time of writing, no paper concentrating on Nigerian CO₂ emissions has utilized a more recent dataset.
Nigeria's economy remains susceptible to the risks associated with climate change due to the country's escalating energy consumption and the ensuing CO₂ pollution.Figure 1 illustrates the shift in energy consumption from negative to positive after 1995, resulting in a 7.4% increase in CO₂ emissions in 1996 compared to the negative rates of 24.1% and 6.7% in 1994 and 1995, respectively.Concurrently, due to the rising energy consumption, GDP per capita rose from 18.9% in 1995 to 27.03% in 1996, and the overall economy expanded from -0.1% in 1995 to 4.2% in 1996.The year 2010 marked the most significant change in CO₂ emissions (46%) and the highest GDP per capita (19.3%) during the research period, with an 8% expansion in the economy.Conversely, the lowest changes occurred in 1989, with a -0.35% change in energy consumption and a -41.7% change in CO₂ emissions.Although both CO₂ emissions and GDP deviated from their 1981 values, the growth rate of CO₂ emissions exceeded that of GDP.This suggests limited evidence of absolute decarbonization, indicating that the nation's CO₂ emissions were not proportional to economic growth.Figure 1 demonstrates that from 1981 to 2021, Nigeria's economy did not follow a low-carbon trajectory.Policymakers need to comprehend the directional and causal relationship between energy commodities, economic growth, and the environment.
The increase in CO₂ emissions over the past 70 years is also attributed to the expansion of the human population.A growing population results in increased demands for commodities, energy, and food, leading to higher emissions from transportation, industry, and agriculture.However, to minimize emissions per person, additional measures such as enhancing energy efficiency, transitioning to renewable sources, and altering consumption habits must be implemented alongside population policies (The Conversation, 2023).Globally, CO₂ emissions are distributed unevenly, with high-and upper-middle-income countries, housing slightly less than half of the world's population, responsible for over 80% of global CO₂ emissions.According to Our World in Data (2023), the average person in high-income countries emits over ten times as much CO₂ as the average person in lowincome countries.
This study addresses several gaps in existing literature.Firstly, it focuses on Nigeria from 1981 to 2021, as data before the 1980s are incomplete.Secondly, it employs the Vector Error Correction Model (VECM) to explore longitudinal cointegration and causal links between the environment, energy commodities, and economic growth, providing fresh empirical data for the ongoing discussion about their relationship.

LITERATURE REVIEW
The links between energy consumption, CO₂ emissions, and economic growth are the subject of three broad genres of literature.The first discusses whether the relationship between economic growth and CO₂ emissions is consistent with the environmental Kuznets curve (EKC) theory.According to this theory, some pollutants and percapita income have an inverted U-shaped connection (Grossman and Krueger, 1995).The Environmental Kuznets Curve (EKC) is a relationship between income change and environmental quality, based on Kuznets' work.It suggests that rapid industrialization leads to increased pollution and resource use, putting pressure on the environment.As income increases, people value the environment more, leading to a decline in pollution levels.The EKC hypothesis reveals how environmental quality changes as a country's fortunes change, with an inverted U-shaped curve when pollution indicators are plotted against income per capita (Dinda, 2004).Richer consumers put more pressure on lawmakers to enact environmental laws and regulations, in addition to being prepared to spend more money on eco-friendly goods.The majority of the examples where emissions have decreased while income has increased can be attributed to institutional reforms at the local and national levels, including environmental laws and market-based incentives aimed at halting environmental degradation.
In their study, Özokcu and Özdemir (2017) verified the "inverted U shape theory."However, Friedl and Getzner (2003) hypothesize a long-term link that takes the form of an N or another shape rather than an inverted U between CO₂ emissions and per-capita income.Although He and Richard (2010) and Agras and Chapman (1999) maintain that there is no correlation between CO₂ emissions and economic growth in their non-existence theory, the primary issue with these early investigations on the EKC hypothesis is that they may be biased by missing Ihugba et al. 3 variables.This happens when one or more independent variables that correlate with one or more of the included independent variables and have an impact on the dependent variable are excluded from a statistical model (Tong et al., 2020).
Recently, the Granger causality test, an econometric technique particularly well-suited for time series and panel data analyses, was proposed to examine the connection between economic growth and carbon emissions.For example, Hossain (2012) discovered that in newly industrialized nations, there was unidirectional short-run causality between economic growth and carbon dioxide emissions, as well as between urbanization and economic growth.Wang et al. (2016) discovered that economic growth was a Granger cause of CO₂ emissions in China between 1995and 2012, and Hamit-Haggar (2012) found a unidirectional causality relationship between the economy and greenhouse gas emissions in both the short and long runs in their investigation of the Canadian industrial sector.In contrast to Omri (2013), who discovered only a one-way Granger causation linking CO₂ emissions to economic growth in some European, Central Asian, Latin American, and Caribbean countries, Salahuddin and Gow (2014) showed a two-way Granger causal association between the two components.
Abubakar and Cudjoe (2021) estimate the short-run and long-run impacts of energy consumption on Nigeria's environment through total CO₂ emissions using error correction models and normalized estimations.Results show that GDP has a significant long-run tendency to reduce total CO₂ emissions in Nigeria, confirming the Kuznets curve hypothesis for climate.The research also supports the suggestion that environmental destruction increases with per capita income during early economic development stages and decreases with an increase after reaching a plateau.Rafindadi (2016) modeled the relationships between economic development, energy use, and emissions.The model had collinearity issues because the study takes into account CO₂ emissions as a function of income, income squared, and income cubed in addition to other explanatory variables like energy consumption.
In his paper, Okon (2021) used the auto-regressive distributed lag approach to investigate the applicability of the Environmental Kuznets Curve in Nigeria from 1970 to 2018.According to the bounds test, there exists an equilibrium relationship over a long period of time between the gross domestic product per capita, the square of the GDP per capita, waste, combustible renewable energy, alternative and nuclear energy, adjusted savings, or net forest depletion.However, neither short-run nor long-run results are consistent with the Environmental Kuznets Curve hypothesis, nor there is no evidence of an inverse U-shaped link between growth and fluorinated greenhouse gas emissions in Nigeria.Omisakin (2009) tested the Environmental Kuznets Curve (EKC) hypothesis in Nigeria, finding no long-term causal relationship between

METHODOLOGY
Granger causality, a linear regression model, and cointegration tests will all be used to examine the collected data.Using the unit root test, the initial step will be to determine the stationarity and order of integration among the study variables.Using cointegration tests, the second phase will look at the long-term relationship between the research variables.In the third, regression analysis will be used to examine how the independent study variables affect CO₂ emissions.The Granger causality test will be used in the fourth step to determine the causal relationship between the research variables.

Data and sources
The Central Bank of Nigeria's (CBN) statistical bulletins and the World Bank 2023 Development Indicators (WDI) provided the data used in the study's empirical analysis.Table 1 lists the variables' names, meanings, and measurements.

Model specification
Based on the empirical literature in energy economics, it makes sense to write the long-term relationship between CO₂ emissions, energy commodities, and economic growth in the form of a linear logarithmic quadratic.This will allow us to test the EKC hypothesis in the following way (Equation 1): In this case, POP stands for population, GFCF for gross fixed capital formation, GDPPC for gross domestic product per capita, FFC for fossil fuel energy consumption, and C02 for carbon dioxide emissions per capita.Equation 2 can be expressed as follows in the natural log form for C02, GDPPC, GFCF, and POP as well as in the econometric model:

Stationarity test
Since it can affect a series' behavior, stationarity is a significant phenomenon.Regressing X on Y in Equation (3) will result in spurious or gibberish regression if X and Y are two non-stationary series (Yule, 1926).
The series is considered non-stationary if it has a unit root.The series is stationary if it doesn't have a unit root.The purpose of the stationarity test is to determine if an autoregressive model has a unit root or not.To ascertain the sequence of the variables' integration, the unit root test is helpful.To verify if the provided series is stationary, the augmented Dickey-Fuller test (ADF) and Phillips-Perron test (PP) have been employed.

Co-integration test
To determine whether there is a co-integration relationship between the two variables' non-stationary series, the Johansen-Juselius test is used.We can determine whether there is co-integration between two non-stationary series using the Johansen-Juselius co-integration procedure.This indicates that 0<rank (π) = r <n, which is the maximum rank that the matrix π can have.In terms of the vector or matrix of adjustment parameters  and the vector or matrix of cointegrating vectors ′, π can be expressed as =′, where (r) is the number of co-integration vectors and (n) is the number of variables.
Based on a likelihood ratio test (LR), this procedure uses the Trace test and the Maximum Eigenvalues test (λ) to calculate the number of co-integration vectors between variables.The definition of a trace test is:

 
In contrast to the alternative hypothesis, which states that there are r co-integration vectors, the null hypothesis states that there are ≤r co-integration vectors.
The Maximum Eigenvalues test The null hypothesis that the number of co integration vectors = r against the alternative those they .

Granger-causality
The results of the stationarity and co-integration tests will determine the Granger-Causality test's application in the following ways: The following vector auto-regression (VAR) should be estimated in order to perform the conventional Granger-Causality test to determine whether the series (FFC), (LGDPPC), (LGFCF), (LPOP), and (LCO₂) are stationary.

Vector error correction model (VECM)
The conventional VECM is written compactly as (Equation 12): where 1 − t ECT = OLS residual with a lag derived from the long-run cointegrating formula (Equation 13): And express as (Equation 14):  = coefficient of the ECT and the speed at which changes to X and R cause Y to stabilize.The specific VECM for this study is as follows (Equations 15 to 19): This model was selected because empirical studies show that when economic variables show individual cointegration, or a strong longterm relationship, the VECM performs well for model estimation.
Another advantage is its capacity to integrate the short-run dynamic and long-run equilibrium models into a single, efficient system.It also guarantees accuracy, conceptual rigor, and data integrity (Abubakar and Cudjoe, 2021).

RESULTS AND FINDING
The empirical estimation result and a suitable justification are presented in this section to support the study's argument.

Stationarity test
The study initiated by examining the stationarity of relevant variables using tests detailed in the methodology section.The Augmented Dickey-Fuller (ADF) and Phillips and Perron (PP) unit root tests were employed to determine if the variables are stationary.Table 2 presents compelling evidence that all our variables are integrated at order one (that is, I(1)).The data reveals that for each variable, at least one of the tests does not reject the null hypothesis of the unit root at levels, indicating non-stationarity.In contrast, it is found that every variable in the first difference is stationary.The subsequent step involves confirming whether our variables of interest exhibit a long-run relationship since all the variables in our model are integrated of order one, according to at least one of the tests employed.

Endogeneity analysis
In order to ascertain whether variables are exogenous or endogenous, Endogeneity analysis is necessary.To verify it, apply the paired Granger causality test.Secondly, CO₂ emissions in Nigeria, a proxy for the environment, do not have any feedback from gross domestic product per capita, a proxy for economic growth, and instead Granger-cause it.Third, gross fixed capital formation is a proxy for investment without feedback and population growth, Granger-cause CO₂ emissions in Nigeria.Fourth, gross fixed capital formation, a stand-in for investment that lacks feedback, Granger-causes gross domestic product per capita (a proxy for economic growth).

Lag selection
The Vector Error Correction Model (VECM), the Phillips and Perron (PP), the Augmented Dickey-Fuller (ADF), and the co-integration tests are sensitive to the number of lags when they are run.Thus, the Schwarz Information Criterion (SIC) and Akaike Information Criterion (AIC) were used to determine the actual amount of lags used.Table 4 displays the proper lag length for each variable.Table 4 shows that the AIC value at lag 3 is the lowest and is likewise lower than the SIC value at lag 1.To estimate Equation (1), the model (Lag 3) is selected as a result.Below is the cointegration result.

Cointegration test
The relevant hypothesis is that there is no long-run relationship in order to ascertain whether the variables are cointegrated over the long term, such as: Hypothesis: λ1 = λ2 = λ3 = λ4 = 0 (no long-term association exists).Hypothesis 1: λ1 ≠ λ2 ≠ λ3 ≠ λ4 ≠ 0 (a long-term relationship exists) The next step is to run a cointegration test after confirming that all variables are integrated to order one and that I(1) cannot be refused.Johansen (1988) and Johansen and Juselius (1990) suggested the multivariate cointegration technique, which is used with multivariate time series to find stable long-term links between carbon dioxide emissions, GDP per capita, gross fixed capital formation, energy use from fossil fuels, and population.Because the cointegration vectors will be used for the subsequent vector error correction model (VECM), it should be emphasized that the cointegration test is conducted before the VECM.The Johansen test, for instance, rejects the existence of one or fewer cointegrating relations but fails to reject the existence of at most two in model 1.This suggests that there are two cointegrating equations in model 1.
Using trace test statistics, the null hypothesis is rejected because the probability value is less than 5% (P-value = 0.00) and the trace statistic value is greater than the critical value (115.2717>69.81889).This suggests the existence of at least one cointegrating vector.A second evaluation states that since the trace statistic value is higher than the essential values, we reject the null hypothesis for asterisks ranked one through two.Every associated probability value is less than five percent.In summary, the results indicate that two equations are cointegrated to order one (1) at the 0.05 critical level, and there is at least one cointegrating vector.
Based on the Max-Eigen results, the null hypothesis that there are no cointegrating equations is likewise rejected.This is because the probability value is less than 5% (Pvalue = 0.00) and the Max-Eigen Statistic is bigger than the important value (46.24334>33.87687).This suggests the existence of at least one cointegrating vector.Based on the trace statistical test and the Max-Eigen test, the series are cointegrated to the same order (1), as seen in Table 5.This study also takes advantage of a series that has a long-standing relationship.This study will estimate the VECM using trace value statistics since it offers a more accurate alternative hypothesis that specifies the number of cointegrating vectors.Consequently, one may contend that there is a long-term relationship between the variables and that both their short-and long-term dynamics can be found using the VECM model.

Vector Error Correction Model (VECM) Estimation
Using the same variables, two distinct vector autoregression models (VAR and VEC) were made to determine which one more accurately captured the relationship between Nigeria's economic growth, energy commodities, and environmental factors in the real world.Despite not being as structural as the VAR, the VEC model functioned well as a limited substitute.Meanwhile, as Table 5 illustrates, the cointegration relationship between the variables made the VAR ineffective.The optimum model to apply in this situation is the Vector Error Correction Model (VECM).Table 6 displays the outcomes of the vector error correction model (VECM) for the cointegrated series' first, second, and third differences.It also includes the error-correction terms from Equation 20.The results are displayed in two sections: the first section displays the cointegrating equations, and the second section displays the outcomes of the vector error correction models.Table 7 displays the regression's result.
The target equations D(LCO₂), D(FFC), and D(LGFCF) have error correction terms that are negative (-0.26), -0.62), and (-0.29), respectively, according to Table 6 above, but D(LGDPPC) and D(LPOP) have positive (0.01) and (0.01) error correction terms, respectively.You can see that the VEC model can explain about 69% of the changes in the variables that you can depend on, 43% of the changes in the target variable D(LCO₂), and 48% of the changes in the D(FFC), D(LGDPPC), D(LGFCF), and D(LPOP) equations.This suggests that all five models fit the data.
Using the VECM approach, VAR generated and computed a simultaneous equation in Table 6.On the other hand, the simultaneous equation computed under VAR using the VECM technique only yields coefficients, standard errors, and t-statistics; probability values are absent.Therefore, in order to evaluate the relationship between the environment, energy commodities, and economic growth in Nigeria, the simultaneous equation  must be evaluated.This is due to the fact that within-group designs and two samples are the ideal settings for the tstatistic's application.This makes the simultaneous model of t-statistic-based result interpretation insufficient.Second, t-statistics are inappropriate for sample sizes greater than or equal to 30 (n ≥ 30).The independent variables are not homogeneous; they have variations for both groups (Engle and Granger, 1987).The impact of the explanatory factors on Nigeria's carbon dioxide emission is estimated using the simultaneous equation in the study using ordinary least squares (OLS).
The error correction term (ECT) in Table 7 indicates the    rate of correction of the disequilibrium between the longrun and short-run estimations.The value indicates that only about 26% of errors generated in the previous period are corrected in the current period for the equation.With a p-value of 0.06 at a 5% confidence level and a standard error of 0.133009, this value is significant.

Model checking
The null hypothesis (H0) is accepted if the probability is more than 5%, indicating no serial correlation in the longrun model in Table 8.The normality test shows a kurtosis of 2.81 and skewness of 0.36, indicating normal distribution.The heteroscedasticity test shows continuous variance, indicating continuous variance.The stability test shows the Cusum of squares plots do not pass the 5% critical line, indicating the model is stable and suitable for economic study.

Test for normality
A normal model is indicated by residual skewness and kurtosis, and confirmed by JB test (Figure 2).

Test for stability
The Figures 3 and 4 show the results of the stability tests   (CUSUM and CUSUMSQ).They show that the estimates, variance, residuals, and square residual are stable because they are all within the 5% critical boundaries for both the CUSUM and the CUSUMSQ.Both the CUSUM and CUSUMSQ tests accept parameter stability as one of their null assumptions (Tables 9 and 10).

Simultaneous equation short-run simulation and analysis
The results of the short-run test are presented in Table 11.The Chi-square joint statistics probability values show that, aside from LGFCF, there is a short-run relationship between the explanatory variables and the independent variable according to our findings in Table 11.If the p-value of the chi-square test for (FFC) fossil fuel energy consumption, (LGDPPC) gross domestic product per capita, and (LPOP) annual total population is less than 0.05, the null hypotheses (0): β5=0 will be rejected, therefore they cause LCO₂ in the short run, while (LGFCF) gross fixed capital formation as a proxy for investment does not cause LCO₂ in the short run.The VECM systems Granger causality tests results dos not conform to the Pairwise Granger causality tests except for LGDPPC and LPOP.The next step is to conduct exante forecasting involving impulse response and variance decomposition tests.

Impulse response function
According to Table 12, Nigeria's carbon dioxide emissions forecast are on the positive side, with sporadic variations brought on by innovations and shocks.The findings demonstrate that (FFC) fossil fuel energy consumption, (LGDPPC) gross domestic product per capita, and (LGFCF) gross fixed capital formation will all contribute to explaining the country's increased carbon dioxide emissions.A one-standard deviation positive own shock will result in a short-term change from 0.14 to 0.07 and a long-term increase at a decreasing rate to 0.037.Second, projections indicate that the energy consumption of fossil fuels (FFC) has a short-term negative impact on carbon dioxide emissions (-0.01) and a long-term positive impact (0.01).This indicates that FFC has a long-term beneficial effect on carbon dioxide emissions.Third, the simulation shows that in the short run, carbon dioxide emissions will rise by 0.015 in response to a onepositive standard deviation shock from (LGDPPC) gross domestic product per capita.The shocks will ultimately be negative (-0.021).Fourth, innovations for (LGFCF) gross  fixed capital formation result in higher carbon dioxide emissions over a five-year period.Simulations show that carbon dioxide emissions rise by 0.034 in the short term and 0.032 in the long term for every standard deviation increase in LGFCF.Accordingly, the amount invested has a significant impact on carbon dioxide emissions.Fifth, projections indicate that, despite both short-and long-term declines, Nigeria's annual population will not be a cause for concern when it comes to carbon dioxide emissions.

Variance decomposition
To predict the error variance effects for each endogenous variable in a system, variance decomposition is used.Any change in time in a simple linear equation corresponds to a change in the dependent variable (Wickremasinghe 2011).This study's forecast consists of three time periods: short-term (two years), medium-term (five years), and long-term (ten years), all based on the Monte Carlo method and Cholesky's ordering.LCO₂, FFC, LGDPPC, LGFCF, and LPOP are the outcomes of the variance decomposition forecast for endogenous variables.
In the short run, impulses, innovations, or shocks to carbon dioxide emissions account for 85.1% of fluctuations in carbon dioxide emissions.However, carbon dioxide emissions own shock fluctuations continuously increase to 140.2% in the long run.Meanwhile, shocks to fossil fuel energy consumption account for 0.1% of fluctuations in carbon dioxide emissions in the short run.The fluctuations in carbon dioxide emissions due to fossil fuel energy consumption increase in the long run to 1.5%.In the short run, shocks to gross domestic product per capita account for 0.3%, gross fixed capital formation accounts for 1.6%, and annual population accounts for 12.9%.In the long run, shocks to gross domestic product per capita increase to 5.9%, gross fixed capital formation increases to 9.6%, and the annual population accounts for 92.7%.Shocks to carbon dioxide emissions will account for the highest fluctuations in Nigeria's carbon dioxide emissions, followed by its own shock (Table 13).

DISCUSSION
The primary objective of this paper was to investigate the relationship between CO₂ emissions, energy commodities, and economic growth between 1981 and 2021 in Nigeria.The vector error correction model (VECM) method was utilized to estimate Equation 2 using annual data.Both the Phillips and Perron (1988) and Dickey and Fuller (1981) Augmented Dickey-Fuller (ADF) unit root-testing methods are used to check the time series properties of the variables in Equation 1.In Equation 1, all the series seem to have a unit root in their levels, but their first differences show that they are stationary.
The analysis of the data collected has revealed some notable findings that make this study a significant contribution to knowledge in the area of carbon dioxide emissions in Nigeria.Firstly, The findings does not support the Kuznets Curve (EKC) hypothesis for climate change by demonstrating that economic growth as measured by GDP per capita has a significant negative long-run tendency to reduce total CO₂ emissions in Nigeria.The adjustment term (-2.11356) in the second year from the estimated result in Table 7 is statistically significant.The findings indicate that when income rises, emissions fall, and vice versa.The World Bank (2018), divides Nigeria's income distribution into five quintiles.In 2018, the second 20% of the population held an income share of 11.60%, while the third 20% of the population held an income share of 16.20 percent.22.70 percent of the population, or the fourth 20%, had an income share.The richest 20% of the population owned 42.40% of the total income.Nigeria's 2022 Gini coefficient for nations with high levels of wealth inequality was 35.1%.Nigeria is ranked 100th out of 163 countries worldwide and 11th in West Africa with this score (Harmon, 2023).
This can be as a result of changes in consumption patterns, energy efficiency, technology, or income inequality.The findings can also be attributed to institutional reforms at the local and national levels, including environmental laws and market-based incentives aimed at halting environmental degradation not necessarily increase in income.The finding of the negative effect of income on CO₂ emissions agrees with the findings of Friedl and Getzner (2003), He and Richard (2010), Agras and Chapman (1999) and Alege and Ogundipe (2013) but disagrees with the findings of Dinda (2004), Özokcu and Özdemir (2017), Abubakar and Cudjoe (2021) and Okon (2021).The pairwise Granger causality test also indicates that CO₂ emissions in Nigeria, a proxy for the environment, do not have any feedback from gross domestic product per capita, a proxy for economic growth, and instead Granger-cause it.This finding is line with the study of Omisakin (2009), Omri (2013) and Wang et al. (2016).Salahuddin and Gow (2014) findings do not agree with our pairwise Granger causality test result.
Secondly, this study found that, in the long run, fossil fuel consumption had a negative effect on CO₂ emissions.The results show that fossil fuel use and carbon dioxide emissions are inversely correlated; a decrease in fossil fuel use corresponds to a decrease in atmospheric carbon dioxide emissions.Even when it is the biggest cause of air pollution in developed countries, It impacts positively in the current year but negatively after a year of fossil fuel consumption.The findings suggest that the quantity of fossil fuel used for energy production, transport, or industrial processes is still low in the country.In 2014, the nation's share of global energy consumption from fossil fuels was 18.9%, lower than the 79.4% global average for the same year.The pairwise Granger causality test indicates that fossil fuel consumption does not cause CO₂ emissions in Nigeria.The findings of the negative effect of fossil fuel consumption on CO₂ emissions do not agree with the findings of Abubakar and Cudjoe (2021).
Thirdly, investments proxied by gross fixed capital formation have the long-term possibility of reducing total CO₂ emissions if increased, and finally, the total annual population has a significant positive effect on total CO₂ emissions in the long run.This signifies that an increase in total annual population has a possibility of rising Nigerian total CO₂ emissions in the future.Also, the implication of the finding is that increasing total annual population threatening Nigeria's effort to meet the global goal for O2 emission reduction as outlined in the 2015 Paris Climate Change Conference.This finding agrees that the increase in CO₂ emissions during the past 70 years has also been attributed to the expansion of the human population.

Conclusion
This paper empirically analyzes the dynamic relationships between CO₂ emissions, energy commodities, and economic growth in Nigeria, using CO₂ emissions as a proxy for the environment.The long-run relationship, with CO₂ emissions as the dependent variable, is examined to test the short-run and long-run elasticities of CO₂ emissions with respect to explanatory variables.
Contrary to the typical positive correlation between income and emissions, indicating higher emissions per capita in wealthier nations, our findings reveal the opposite trend.However, this relationship is not constant, suggesting that emissions rise at varying rates based on income levels.In high-income countries, consumptionbased emissions tend to exceed production-based emissions, while the reverse is observed in low-income countries.This implies that high-income countries are net importers of emissions, while low-income countries are net exporters.
The significant impact of fossil fuel usage on the environment is acknowledged in our study.Surprisingly, our findings indicate a significantly negative impact on the environment of Nigeria, as fossil fuel usage influences the amount of CO₂ emissions.This relationship mirrors the social and economic development of the nation.Given the high prices and limited supply of fossil energy in Nigeria, insufficient to meet the demands of its over 200 million inhabitants and expanding economy, there is a pressing need for a substantial increase in energy efficiency.Additionally, the creation of new energy consumption structures, particularly those based on affordable renewable sources like solar energy, is essential for sustainable growth over time.
Furthermore, our empirical results challenge the Environmental Kuznets Curve (EKC) theory of climate change, which posits that higher income can lower a nation's environmental pollution once a certain threshold is reached.

RECOMMENDATIONS AND POLICY CONSEQUENCES
The study's conclusions lead to the recommendation that, to mitigate environmental degradation in Nigeria, governments should support initiatives educating and training rural residents to use fewer non-renewable energy sources.Despite nonrenewable resources being widely utilized for fuel, industrial production, and residential energy consumption in Nigeria without currently causing substantial environmental harm, the suggestion is for the nation to prioritize energy sources causing minimal environmental damage.Policymakers, serious about preventing long-term environmental damage, should enact policies promoting the use of environmentally friendly machinery, vehicles, utilities, and equipment.
Considering fossil fuel consumption has not yet reached a point where CO₂ emissions are increasing, Nigerian policymakers should focus more on adopting renewable energy sources to reduce emissions from other sources.All energy-related investments and developments in the country should prioritize renewable energy and include it as a key performance indicator in investment appraisal considerations.
While concerns about economic growth are valid, this study suggests a bi-directional causal relationship between GDPPC and CO₂ in Nigeria.Policymakers should consider both factors when making decisions, emphasizing the need for a comprehensive strategy to boost renewable energy investments.This includes creating a stable policy environment, setting ambitious targets for renewable energy capacity, providing financial incentives, and implementing feed-in tariffs.The government should invest in research and development, workforce development, and public-private partnerships, encouraging private sector participation.Risk mitigation instruments should be introduced to reduce perceived risks associated with renewable energy projects.Infrastructure development should incorporate grid integration and energy storage.Sustainable finance initiatives, such as green bonds, public investment, community engagement, and awareness campaigns, should be established.International support can be leveraged through climate finance, technology transfer, and streamlined permitting processes.Performance monitoring should be instituted to ensure projects meet their objectives, attracting investors and accelerating project development.
The study's general conclusions propose that, to reduce poverty and lower CO₂ emissions in Nigeria, the government should directly deliver goods and services, including free medical services, subsidized housing, and education.Implementing negative income taxes to supplement the earnings of the poor and providing a guaranteed income are additional measures suggested.

Figure 1 .
Figure 1.Carbon dioxide emissions, energy consumption and economic growth.

Table 1 .
Variables measurement and sources of data.

Table 2 .
ADF and PP unit root test results.

Table 4 .
VAR Lag order selection criteria.

Table 6 .
Result of vector error correction model.

Table 11 .
Wald tests and short-run test.