The Impact of Renewable Energy Sources and Economic Growth on the Environmental Quality in Nigeria

The Impact of Renewable Energy Sources and Economic Growth on the Environmental Quality in Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Renewable Energy Sources and Economic Growth on the Environmental Quality in Nigeria Abigail Eruore Onakposeha, Dr. Babatunde Onasanya, Olaoluwa Simon Yaya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5529773/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Renewable energy sources (such as solar, wind, geothermal, and hydropower) has the potential to drive economic growth without damaging environmental quality. Many researchers have looked at renewable energy consumption as a whole with little reference to separating each of the types of renewable energy that are more economical and environmentally peculiar to Nigeria. This study was therefore designed to consider each renewable energy source as it affects environmental quality. It explores the impact of renewable energy sources and economic growth on the environmental quality in Nigeria using a time series dataset spanning from 2000 to 2022. The study finds that as per capita GDP increases by one unit, the ecological footprint per person rises by 2.59×10 -4 units and a one-unit increase in per capita squared GDP results in a decrease of 4.21 ×10 -8 units in the ecological footprint per person. Also, raising solar energy by one unit reduces the ecological footprint per person by 2.07 ×10 -3 units. On the other hand, a one-unit rise in hydro energy increases the ecological footprint per person by 2.5 ×10 -5 units. Finally, an increase of one unit in HCD lowers the ecological footprint per person by 4.99 units. Hence, the impact of economic growth on the environment initially worsens but diminishes as the economy grows further. Solar energy source doesn't seem to have an impact on the environment, while using hydro-energy and human capital development have an impact on the environment. Environmental Economics Renewable Resources Renewable Energy Economic Growth Environmental Quality Solar Hydro Human Capital Development Nigeria 1. Introduction Globally, environmental concerns have received a lot of attention among researchers and scholars. Concerns about climate change are now prevalent, as a result of excessive greenhouse gases emissions, which are dominated by CO 2 . Anthropogenic sources which include deforestation and, the burning of fossil fuels (coal, petroleum, and natural gas) are the major contributors to greenhouse gases which in turn bring about global warming. A study conducted by (Ojonugwa 2021) demonstrated that the decline of the environment is a result of growth in the economy, particularly in developing economies as they strive to improve their economies. In the context of EKC theories, some researchers have looked at the link between environmental deterioration and growth in the economy (Iorember et al. 2020 ; Usman 2021 ; Yuzhao et al. 2022) the findings of this research have been conflicting; nonetheless, some have concluded that there is an inverse U-shaped association that is consistent with the EKC theory between environmental deterioration and growth of the economy, other findings found no proof of an EKC relationship. In particular, a reverse U-shaped connection says that environmental deterioration rises with income, whereas a U-shaped relationship suggests that environmental pollution falls with income. Lastly, an N-shaped relationship indicates that there is little chance of the original EKC theory to persist over time since, above a certain income threshold, income growth may once more result in a positive association between environmental degradation and the growth of the economy (See Fried and Getzner 2003 ). Concerns to raise the proportion of renewable energy sources in the global energy mix are growing (Bhattacharya et al. 2016 ). Economic growth is frequently linked with environmental degradation; The utilization of renewable energy sources is acknowledged as a means to alleviate the adverse environmental consequences typically associated with economic growth. Nigeria's economy is expanding quickly and uses a lot of renewable energy, be that as it may, the nation also has to deal with a multitude of environmental issues. To discover potential policy interventions that can help promote economic growth while also safeguarding the environment this study is very vital. Much attention has been given to renewable energy consumption as a whole with little reference to separating each of the types of renewable energy that are more economical and environmentally peculiar to Nigeria (see Asongu et al. 2019 ; Yuzhao et al. 2022; Ojonugwa 2021; Salih and Necati 2021; Iorember et al. 2020 ; Iorember et al. 2019 ; and Ibrahim and Sagir 2021) hence it can be said that this component represents a gap. This study is therefore designed to separate each renewable energy source as it affects environmental quality. The link between environmental well-being and human capital in Nigeria and Africa, in general, has received minimal attention about the role human capital plays in environmental sustainability. Nigeria's fast population growth points to both increased environmental strain and public spending on human capital (health and education). The knowledge, skills, and capacities of a people are referred to as human capital. It plays a vital role in the growth and progress of the economy, with significant potential for environmental conservation. A well-educated and skilled population is more inclined to adopt and utilize renewable energy technologies, as well as support environmental policies. When examining the interplay between energy, the environment, and economic development in Nigeria, it is imperative to factor in human capital for various reasons. Firstly, Nigeria's large and youthful population presents a substantial opportunity for human capital development. Secondly, Nigeria’s economy, a country that is fast developing, is mostly reliant on fossil fuels. This is why it's critical to figure out how to lower environmental pollution and switch to a more sustainable energy mix. Third, Nigeria is dealing with a diverse range of environmental issues, including water pollution, climate change, and pollution in the air, developing human capital can be essential to overcoming these obstacles. Research conducted in other countries has also examined this relationship; these studies include (Mahmood et al. 2019 ; Bano et al. 2018 ; Ahmed and Wang 2019 ; Yang et al. 2017; Ahmed et al. 2020; and Shujah-ur et al. 2019). All of these studies concur that developing human capital is crucial to environmental sustainability because it reduces emissions from fossil fuels. This research will examine the connection between these variables in Nigeria to better understand the potential for renewable energy to help enhance the country's environmental quality. The need to achieve sustainable development on a worldwide scale has prompted a heightened emphasis on the intricate connection between the utilization of renewable energy, economic growth, and environmental well-being. The Environmental Kuznets Curve (EKC) theory suggests that environmental degradation tends to worsen as economies grow but starts to improve after reaching a certain income threshold. Although this idea has been discussed and examined extensively in a variety of settings, its application is still up for debate. Nigeria, an African country that is developing quickly, has seen significant economic growth as well as an increase in its reliance on renewable energy sources as part of its energy transition plan. It does, however, also confront serious environmental issues, including biodiversity loss, air and water pollution, and deforestation. Evaluating whether the EKC hypothesis is valid in the Nigerian context and whether the growing use of renewable energy sources is consistent with a possible reversal of environmental degradation is therefore crucial. To this end, the study addresses the following research questions; What impact does each of the renewable energy sources have on Nigeria's environmental quality? How does human capital development affect environmental quality in Nigeria? Is the EKC hypothesis valid in Nigeria? 2. Brief Empirical Review Using developing economies in Sub-Saharan Africa as a case study, (Imran, 2018) investigated the effects of growth in the economy, urbanization, and the use of non-renewable energy sources, solid fuels, and clean energy on environmental degradation. According to the findings, the use of fossil fuels and solid fuel for cooking as well as the growth of metropolitan regions both greatly increase CO 2 emissions and promote air pollution. The findings also show that the connection between CO 2 emissions and per capita economic development is shaped like an inverted U. This relationship demonstrates that middle-class and lower-class economies in Sub-Saharan Africa have an environmental Kuznets curve (EKC). Additionally, the results show that using renewable energy options reduces direct household exposure to harmful gasses and controls carbon emissions, both of which enhance air quality. In a study conducted by (Asongu et al. 2019 ), they explored the connection between using renewable energy and environmental degradation in Sub-Saharan Africa. They found that the use of renewable energy consistently leads to a reduction in carbon dioxide (CO 2 ) emissions, as indicated by both estimation methods. Additionally, when comparing countries with higher CO 2 emissions to those with lower emissions, the negative environmental impact is less pronounced in the former. The study conducted by (Abubakar and Dan 2020) examined the impact of energy use on Nigeria’s environmental quality, using CO 2 emissions as evidence. Results indicated that whereas fuel wood usage has a long-term potential to increase CO 2 emissions, charcoal consumption has a long-term tendency to decrease CO 2 emissions. From 2001 to 2018, (Syed et al. 2020) looked at the relationship between renewable energy use and environmental quality and international trade in Nordic countries. The results showed that in Nordic nations, renewable energy has a high and favorable correlation with international trade. Additionally, the data show that environmental quality improves with the consumption of renewable energy. (Iorember et al. 2020 ) examined the nexus between Nigeria’s environmental quality and renewable energy consumption: the impact of broad-based financial growth. The findings show that while financial development harms the environment, while renewable energy usage enhances environmental quality. Additionally, the findings support Nigeria’s reverse U-shaped connection between environmental deterioration and economic growth. According to (Ojonugwa 2021), the research included structural breaks to ascertain the role it plays in the link between economic development, and renewable energy usage on the environmental quality in Nigeria. Based on the findings, environmental deterioration is slowed down by renewable energy, but it is accelerated by economic expansion. In the presence of structural fractures, the data further support the EKC concept in Nigeria. 3. Methods 3.1 Theoretical Framework The objective of the study is to analyze the impact each renewable energy source and economic growth have on the environmental quality in Nigeria. Hence this study adopts the Environmental Kuznets Curve (EKC) Theory propounded by (Grossman and Krueger 1995), as it provides an avenue for linking renewable energy consumption, economic growth, and environmental quality. According to the EKC theory, a nation's environmental quality first declines as its economy expands but subsequently improves as it becomes affluent. This is so that a nation has more money to devote to environmental preservation when its economy expands. However, a nation may prioritize economic development over environmental conservation in the early phases of its economic expansion. 3.2 Data Description and Model Specification In order to construct the empirical model for the research, it is evident from the literature that multiple investigations have examined the relationship between environmental pollution and economic growth in the framework of the EKC hypothesis. The body of research takes the stance that, in many nations, the vigorous pursuit of economic growth and development—a tactic used to reduce poverty—is to blame for the degradation of environmental quality. The demand for energy rises when economic growth policies are implemented, which may be detrimental to the environment's quality. In this study, Nigeria's scenario is thoroughly evaluated by integrating renewable energy sources into the environmental quality equation through the use of the EKC model. To this extent, the functional form of the EKC model with incorporation of each of the various renewable energy sources is expressed as follows: EFP = f (GDP, GDP 2 , SOLAR, HYDRO, BIOFUEL, HCD) …………………....... (1) where the EFP measures environmental quality through ecological footprint, GDP measures per capita income, solar energy, hydro energy and biofuel energy measures each of the renewable energy sources. The GDP squared is obtained from taking the squared term of the income per capita to determine whether the EKC is an inverted U-shape or simply a U-shape. Transforming the Eq. (1) into linear expression provides the empirical model for this study, which is given as follows: EFP t = α 0 + α G GDP t + α G 2 GDP 2 t + α S SOLAR t + α H HYDRO t + α HD HCD t + ε t …... (2) From Eq. (2), it is clear that the EFP denotes the measure of environmental quality. GDP and GDP squared are per capita income level and its squared term and ε is the stochastic or error term that is invariably assumed to have possessed a zero mean. Renewable energy (RE) is divided into three sources which include solar, hydro and biofuel and Human Capital is denoted by HDI. 3.3 Data Sources The data used in the study is secondary data. This means it has already been collected through primary sources and made readily available for use. It is a time series data with an annual frequency which spans from 2000 to 2022. The data used were sourced from reliable sources such as Global Footprint Network (GFN), World Development Indicator (WDI), Penn World Table, and International Energy Agency (IEA). Table 1 provides full details of variables and their sources. Table 1 Variables and their sources Variable Acronym Unit of Measurement Source Environmental quality (ecological footprint) EFP The ecological footprint index, including built-up land, grazing land, cropland, forestland, carbon footprint, and fishing grounds, is measured in global hectares (gha) per person. Global Footprint Network (GFN 2018) Economic growth GDP Gross domestic product per capita (constant 2010 US $ ) World Development indicator (World Bank 2018) Solar Energy Source SOLAR Gigawatt hours (equivalent to one million kilowatt hours) per capita International Energy Agency (IEA 2020) Hydro Energy Source HYDRO Gigawatt hours (equivalent to one million kilowatt hours) per capita International Energy Agency (IEA 2020) Human capital development HCD Index of human capital, based on years of schooling and returns to education Penn World Table (PWT 9.1 2019) Source: Authors’ Compilation 4. Results and Discussion 4.1 Pre-estimation Results In this study, the unit root analysis employed two distinct methodologies: The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. This was done to confirm that all the variables under consideration exhibit integration. A variable is considered integrated when it lacks a unit root issue, meaning its mean and variance do not exhibit a time-dependent relationship. The findings in Table 2 reveal that all variables are indeed integrated at the first order [1(1)]. This result holds true for both the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests and is statistically significant at the 1% and 5% levels. Both unit root test reports the stationarity of the variables in their level and first difference forms. Table 2 Unit Root Stationary Test ADF PP Variables Level(0) First Difference(1) Level(0) First Difference(1) EFP 0.102569 (0.9585) -4.523304*** (0.0020) 0.000866 (0.9490) -4.523795*** (0.0020) GDP -1.830354 (0.3569) -3.271515** (0.0297) 1.810573 (0.3658) -3.271515** (0.0297) GDP 2 -1.932909 (0.3119) -3.304022** (0.0278) -1.575689 (0.4777) -3.329712** (0.0264) SOLAR 0.427458 (0.9795) -4.892780*** (0.0009) 0.669412 (0.9884) -4.892780*** (0.0009) HYDRO -1.755385 (0.3913) -5.130601*** (0.0005) -1.737974 (0.3994) -5.139133*** (0.0005) HCD -0.899423 (0.7679) -3.714089** (0.0117) -1.542470 (0.4940) -3.764661** (0.0105) Key: * indicates p < 10%, ** indicates p < 5%, *** indicates p < 1% Source: Researchers’ Compilation using E Views. Table 3 displays the results of the Johansen cointegration test for each variable, which we did prior to estimating the long-run result. The Johansen test permits more than one cointegrating association, in contrast to the Engle-Granger test (Johansen 1991 ). Errors that are passed over to the subsequent step can be avoided by using the test to determine the cointegration of multiple time series. We set K 0 to zero to see if the null hypothesis would be rejected when performing the trace test to check for cointegration in the sample. We can conclude that there is a cointegration relationship in the sample if it is rejected. Consequently, in order to verify if there is a cointegration connection in the sample, the null hypothesis needs to be rejected. We may move further with the long-run regression analysis as the Johansen cointegration test result indicated that the models are cointegrated. Table 3 Cointegration Test Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.963250 163.4497 95.75366 0.0000 At most 1 * 0.905749 94.07355 69.81889 0.0002 At most 2 0.629600 44.47595 47.85613 0.1003 At most 3 0.496189 23.61935 29.79707 0.2170 At most 4 0.325045 9.222725 15.49471 0.3452 At most 5 0.045023 0.967426 3.841466 0.3253 Source: Researchers’ Compilation using E Views. 4.2 FMOLS Regression Result Table 4 reveals the results of the FMOLS coefficients of the environmental quality function. The results show that in the FMOLS equation, the per capita GDP is positive and significant while the squared term of per capita GDP is negative and significant. This implies that a 1-unit increase in per capita GDP would cause the ecological footprint per person to rise by 2.59 X 10 − 4 units. Also, a 1-unit increase in per capita squared GDP would lead the ecological footprint per person to fall by 4.21 X 10 − 8 units. These results indicate that the relationship between per capita GDP and ecological footprint per person follows the pattern of an inverted U-shape, confirming the environmental Kuznets curve hypothesis for Nigeria. (Usman et al. 2019) for India, (Usman et al. 2020c ) for South Africa, (Usman et al. 2019) for Singapore, and (Ali et al. 2021 ) for Nigeria all reached similar conclusions as this one. The effect of solar energy sources on the ecological footprint in Nigeria is negative and not statistically significant. Specifically, a 1-unit increase in solar energy reduces the ecological footprint per person by 2.07 X 10 − 3 units. The findings broadly agree with those of (Parkman 2020 ) and (Destek and Aslan 2020 ), who conclude that solar energy has a negative but not statistically significant impact on India's efforts to decrease emissions. The effect of hydro-energy sources on the ecological footprint in Nigeria is positive and statistically significant. Specifically, a 1-unit increase in hydro energy increases the ecological footprint per person by 2.5 X 10 − 5 units. The finding is consistent with (Andrei et al. 2019) who found that the ecosystem services value decreased with the construction of the hydropower plant in Ecuador. Finally, the effect of HDI on the ecological footprint in Nigeria is negative and significant. Specifically, a 1-unit increase in HDI reduces the ecological footprint per person by 4.99 units. This result is in line with research by (Wang et al. 2019 ), which shows that there is a negative correlation between ecological footprint and human capital. Table 4 Results of FMOLS long-run coefficients Variable Coefficient Std. Error t-Statistic Prob. GDP 0.000259*** 7.42E-05 3.492275 0.0030 GDP 2 -4.21E-08** 1.70E-08 -2.474301 0.0249 SOLAR -0.002069 0.001341 -1.542234 0.1426 HYDRO 2.50E-05** 8.91E-06 2.802630 0.0128 HCD -4.990827*** 1.060214 -4.707379 0.0002 C 3.016109*** 0.443213 6.805100 0.0000 Key: * indicates p < 10%, ** indicates p < 5%, *** indicates p < 1% Source: Researchers’ Compilation using E Views. Table 5 displayed the Granger causality between the study's variables. Granger causality is the idea that one variable's past value can be used to predict another variable's future value. The null hypothesis states that variable a does not granger cause variable b. If there is a significant probability value between these two variables, the null hypothesis is rejected; if otherwise, it is not rejected. GDP and GDP 2 . All the relationships with significant values are uni-variate. This means that the previous value of GDP and its squared term (GDP 2 ) can be used to predict the change in ecological footprint per person. This finding is consistent with (Usman et al. 2019) and (Usman et al. 2020c ). HCD granger causes EFP, this means that the previous value of HCD can be used to predict the change in ecological footprint per person. This finding is consistent with (Danish et al. 2019). Lastly, EFP granger causes hydro, this means that the previous value of ecological footprint per person can be used to predict the change in hydro. All the relationships with significant values are uni-variate. Table 5 Granger Causality Analysis Null Hypothesis: Obs F-Statistic Prob. GDP does not Granger Cause EFP 21 9.05861*** 0.0023 EFP does not Granger Cause GDP 0.54633 0.5895 GDP 2 does not Granger Cause EFP 21 6.69405*** 0.0077 EFP does not Granger Cause GDP 2 0.70426 0.5092 SOLAR does not Granger Cause EFP 21 2.13172 0.1511 EFP does not Granger Cause SOLAR 1.73521 0.2079 HYDRO does not Granger Cause EFP 21 0.53134 0.5978 EFP does not Granger Cause HYDRO 3.29203* 0.0635 HCD does not Granger Cause EFP 21 9.01094*** 0.0024 EFP does not Granger Cause HCD 1.97317 0.1714 Key: * indicates p < 10%, ** indicates p < 5%, *** indicates p < 1% Source: Researchers’ Compilation using E Views. 5. Conclusion and Policy Recommendation The study investigates the impact of economic growth and various renewable energy sources on environmental quality in Nigeria over the period from 2000 to 2022. Its primary objectives are to assess the environmental effects of specific renewable energy sources, namely solar energy and hydropower, to evaluate the role of human capital development in environmental quality, and to test the validity of the Environmental Kuznets curve (EKC) hypothesis for Nigeria. To achieve these goals, the study employs econometric techniques, particularly Fully Modified Ordinary Least Squares (FMOLS) regression, following preliminary tests for stationarity and cointegration, including the Augmented Dickey-Fuller Test (ADF) and Phillips-Perron (PP) tests, as well as the Johansen cointegration tests. The empirical findings indicate that economic growth, as measured by per capita GDP, has a positive and significant impact on environmental degradation, while the effect of the squared term of per capita GDP is negative and significant. This suggests an inverted U-shaped relationship between economic growth and environmental degradation, validating the Environmental Kuznets curve hypothesis for Nigeria. In other words, as the economy grows, environmental quality initially worsens before improving. Additionally, the results reveal that solar energy has a negative effect on the environmental degradation indicator, implying that an increase in the use of these renewable energy sources reduces environmental degradation. This highlights the positive impact of solar energy in generating electricity without harming the environment. Conversely, hydropower exhibits a positive and significant impact, indicating that increased hydropower consumption puts upward pressure on environmental degradation. This suggests that sustainable investment in hydropower projects is essential to mitigate their environmental impact. Lastly, the study finds that the relationship between human capital development and environmental quality is negative and significant. This underscores the importance of continued government investment in human capital development through education and training programs relevant to the green economy. With respect to the research findings on the impact of each renewable energy sources and economic growth on environmental quality in Nigeria, the study proffers the following recommendations; Allocate greater resources to develop and implement renewable energy technologies: The government should offer financial incentives and other forms of assistance. This could involve loan guarantees, tax incentives, and subsidies. Promote eco-conscious and sustainable economic development; The government has the capacity to promote economic growth that is both environmentally friendly and sustainable. This could involve funding initiatives for green infrastructure, renewable energy, and educational and training materials and Invest in human capital; To create the information and abilities required for a green economy, the government can fund educational and training initiatives. Programs about energy efficiency, renewable energy, and environmental protection may fall under this category. Declarations Ethics approval and consent to participate: Not Applicable Consent for publication: Not Applicable Availability of data and materials: The data used in this study are available from the corresponding author on reasonable request Competing interests: The authors declare that they have no competing interests Funding: There is no funding received for this study Authors' contributions: AEO conceptualized the study, wrote sections 1 and 3, and discuss the findings. BO presented the literature review (section 2). OSY wrote the conclusion section and conducted the editorial work. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5529773","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383010531,"identity":"3297407a-a3cf-42f3-b4d5-59bbb458ffe9","order_by":0,"name":"Abigail Eruore Onakposeha","email":"data:image/png;base64,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","orcid":"","institution":"University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"Abigail","middleName":"Eruore","lastName":"Onakposeha","suffix":""},{"id":383010532,"identity":"2a56d256-5ea2-4851-ab2e-4f3e7a82bf9a","order_by":1,"name":"Dr. Babatunde Onasanya","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"Dr.","firstName":"Babatunde","middleName":"","lastName":"Onasanya","suffix":""},{"id":383010533,"identity":"6c609404-e69a-4fa8-acd4-a2e60dbe45ef","order_by":2,"name":"Olaoluwa Simon Yaya","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Olaoluwa","middleName":"Simon","lastName":"Yaya","suffix":""}],"badges":[],"createdAt":"2024-11-26 17:14:42","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5529773/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5529773/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78872000,"identity":"705bdbc0-b0f6-438e-856a-1224288742e0","added_by":"auto","created_at":"2025-03-20 06:11:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":808427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5529773/v1/b10a3dac-b5b4-4433-bc1e-8880e17d0851.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe Impact of Renewable Energy Sources and Economic Growth on the Environmental Quality in Nigeria\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, environmental concerns have received a lot of attention among researchers and scholars. Concerns about climate change are now prevalent, as a result of excessive greenhouse gases emissions, which are dominated by CO\u003csub\u003e2\u003c/sub\u003e. Anthropogenic sources which include deforestation and, the burning of fossil fuels (coal, petroleum, and natural gas) are the major contributors to greenhouse gases which in turn bring about global warming. A study conducted by (Ojonugwa 2021) demonstrated that the decline of the environment is a result of growth in the economy, particularly in developing economies as they strive to improve their economies. In the context of EKC theories, some researchers have looked at the link between environmental deterioration and growth in the economy (Iorember et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Usman \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yuzhao et al. 2022) the findings of this research have been conflicting; nonetheless, some have concluded that there is an inverse U-shaped association that is consistent with the EKC theory between environmental deterioration and growth of the economy, other findings found no proof of an EKC relationship. In particular, a reverse U-shaped connection says that environmental deterioration rises with income, whereas a U-shaped relationship suggests that environmental pollution falls with income. Lastly, an N-shaped relationship indicates that there is little chance of the original EKC theory to persist over time since, above a certain income threshold, income growth may once more result in a positive association between environmental degradation and the growth of the economy (See Fried and Getzner \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Concerns to raise the proportion of renewable energy sources in the global energy mix are growing (Bhattacharya et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEconomic growth is frequently linked with environmental degradation; The utilization of renewable energy sources is acknowledged as a means to alleviate the adverse environmental consequences typically associated with economic growth. Nigeria's economy is expanding quickly and uses a lot of renewable energy, be that as it may, the nation also has to deal with a multitude of environmental issues. To discover potential policy interventions that can help promote economic growth while also safeguarding the environment this study is very vital. Much attention has been given to renewable energy consumption as a whole with little reference to separating each of the types of renewable energy that are more economical and environmentally peculiar to Nigeria (see Asongu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yuzhao et al. 2022; Ojonugwa 2021; Salih and Necati 2021; Iorember et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Iorember et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; and Ibrahim and Sagir 2021) hence it can be said that this component represents a gap. This study is therefore designed to separate each renewable energy source as it affects environmental quality.\u003c/p\u003e \u003cp\u003eThe link between environmental well-being and human capital in Nigeria and Africa, in general, has received minimal attention about the role human capital plays in environmental sustainability. Nigeria's fast population growth points to both increased environmental strain and public spending on human capital (health and education). The knowledge, skills, and capacities of a people are referred to as human capital. It plays a vital role in the growth and progress of the economy, with significant potential for environmental conservation. A well-educated and skilled population is more inclined to adopt and utilize renewable energy technologies, as well as support environmental policies. When examining the interplay between energy, the environment, and economic development in Nigeria, it is imperative to factor in human capital for various reasons. Firstly, Nigeria's large and youthful population presents a substantial opportunity for human capital development. Secondly, Nigeria\u0026rsquo;s economy, a country that is fast developing, is mostly reliant on fossil fuels. This is why it's critical to figure out how to lower environmental pollution and switch to a more sustainable energy mix. Third, Nigeria is dealing with a diverse range of environmental issues, including water pollution, climate change, and pollution in the air, developing human capital can be essential to overcoming these obstacles. Research conducted in other countries has also examined this relationship; these studies include (Mahmood et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bano et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ahmed and Wang \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al. 2017; Ahmed et al. 2020; and Shujah-ur et al. 2019). All of these studies concur that developing human capital is crucial to environmental sustainability because it reduces emissions from fossil fuels. This research will examine the connection between these variables in Nigeria to better understand the potential for renewable energy to help enhance the country's environmental quality.\u003c/p\u003e \u003cp\u003eThe need to achieve sustainable development on a worldwide scale has prompted a heightened emphasis on the intricate connection between the utilization of renewable energy, economic growth, and environmental well-being. The Environmental Kuznets Curve (EKC) theory suggests that environmental degradation tends to worsen as economies grow but starts to improve after reaching a certain income threshold. Although this idea has been discussed and examined extensively in a variety of settings, its application is still up for debate.\u003c/p\u003e \u003cp\u003eNigeria, an African country that is developing quickly, has seen significant economic growth as well as an increase in its reliance on renewable energy sources as part of its energy transition plan. It does, however, also confront serious environmental issues, including biodiversity loss, air and water pollution, and deforestation. Evaluating whether the EKC hypothesis is valid in the Nigerian context and whether the growing use of renewable energy sources is consistent with a possible reversal of environmental degradation is therefore crucial. To this end, the study addresses the following research questions; What impact does each of the renewable energy sources have on Nigeria's environmental quality? How does human capital development affect environmental quality in Nigeria? Is the EKC hypothesis valid in Nigeria?\u003c/p\u003e"},{"header":"2. Brief Empirical Review","content":"\u003cp\u003eUsing developing economies in Sub-Saharan Africa as a case study, (Imran, 2018) investigated the effects of growth in the economy, urbanization, and the use of non-renewable energy sources, solid fuels, and clean energy on environmental degradation. According to the findings, the use of fossil fuels and solid fuel for cooking as well as the growth of metropolitan regions both greatly increase CO\u003csub\u003e2\u003c/sub\u003e emissions and promote air pollution. The findings also show that the connection between CO\u003csub\u003e2\u003c/sub\u003e emissions and per capita economic development is shaped like an inverted U. This relationship demonstrates that middle-class and lower-class economies in Sub-Saharan Africa have an environmental Kuznets curve (EKC). Additionally, the results show that using renewable energy options reduces direct household exposure to harmful gasses and controls carbon emissions, both of which enhance air quality. In a study conducted by (Asongu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), they explored the connection between using renewable energy and environmental degradation in Sub-Saharan Africa. They found that the use of renewable energy consistently leads to a reduction in carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) emissions, as indicated by both estimation methods. Additionally, when comparing countries with higher CO\u003csub\u003e2\u003c/sub\u003e emissions to those with lower emissions, the negative environmental impact is less pronounced in the former. The study conducted by (Abubakar and Dan 2020) examined the impact of energy use on Nigeria\u0026rsquo;s environmental quality, using CO\u003csub\u003e2\u003c/sub\u003e emissions as evidence. Results indicated that whereas fuel wood usage has a long-term potential to increase CO\u003csub\u003e2\u003c/sub\u003e emissions, charcoal consumption has a long-term tendency to decrease CO\u003csub\u003e2\u003c/sub\u003e emissions. From 2001 to 2018, (Syed et al. 2020) looked at the relationship between renewable energy use and environmental quality and international trade in Nordic countries. The results showed that in Nordic nations, renewable energy has a high and favorable correlation with international trade. Additionally, the data show that environmental quality improves with the consumption of renewable energy. (Iorember et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) examined the nexus between Nigeria\u0026rsquo;s environmental quality and renewable energy consumption: the impact of broad-based financial growth. The findings show that while financial development harms the environment, while renewable energy usage enhances environmental quality. Additionally, the findings support Nigeria\u0026rsquo;s reverse U-shaped connection between environmental deterioration and economic growth.\u003c/p\u003e \u003cp\u003eAccording to (Ojonugwa 2021), the research included structural breaks to ascertain the role it plays in the link between economic development, and renewable energy usage on the environmental quality in Nigeria. Based on the findings, environmental deterioration is slowed down by renewable energy, but it is accelerated by economic expansion. In the presence of structural fractures, the data further support the EKC concept in Nigeria.\u003c/p\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Theoretical Framework\u003c/h2\u003e \u003cp\u003eThe objective of the study is to analyze the impact each renewable energy source and economic growth have on the environmental quality in Nigeria. Hence this study adopts the Environmental Kuznets Curve (EKC) Theory propounded by (Grossman and Krueger 1995), as it provides an avenue for linking renewable energy consumption, economic growth, and environmental quality.\u003c/p\u003e \u003cp\u003eAccording to the EKC theory, a nation's environmental quality first declines as its economy expands but subsequently improves as it becomes affluent. This is so that a nation has more money to devote to environmental preservation when its economy expands. However, a nation may prioritize economic development over environmental conservation in the early phases of its economic expansion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data Description and Model Specification\u003c/h2\u003e \u003cp\u003eIn order to construct the empirical model for the research, it is evident from the literature that multiple investigations have examined the relationship between environmental pollution and economic growth in the framework of the EKC hypothesis. The body of research takes the stance that, in many nations, the vigorous pursuit of economic growth and development\u0026mdash;a tactic used to reduce poverty\u0026mdash;is to blame for the degradation of environmental quality. The demand for energy rises when economic growth policies are implemented, which may be detrimental to the environment's quality. In this study, Nigeria's scenario is thoroughly evaluated by integrating renewable energy sources into the environmental quality equation through the use of the EKC model. To this extent, the functional form of the EKC model with incorporation of each of the various renewable energy sources is expressed as follows:\u003c/p\u003e \u003cp\u003eEFP\u0026thinsp;=\u0026thinsp;f (GDP, GDP\u003csup\u003e2\u003c/sup\u003e, SOLAR, HYDRO, BIOFUEL, HCD) \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;....... (1)\u003c/p\u003e \u003cp\u003ewhere the EFP measures environmental quality through ecological footprint, GDP measures per capita income, solar energy, hydro energy and biofuel energy measures each of the renewable energy sources. The GDP squared is obtained from taking the squared term of the income per capita to determine whether the EKC is an inverted U-shape or simply a U-shape.\u003c/p\u003e \u003cp\u003eTransforming the Eq.\u0026nbsp;(1) into linear expression provides the empirical model for this study, which is given as follows:\u003c/p\u003e \u003cp\u003eEFP\u003csub\u003et\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;α\u003csub\u003e0 +\u003c/sub\u003e α\u003csub\u003eG\u003c/sub\u003eGDP\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003eG\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003eGDP\u003csup\u003e2\u003c/sup\u003e\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003eS\u003c/sub\u003eSOLAR\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003eH\u003c/sub\u003eHYDRO\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003eHD\u003c/sub\u003eHCD\u003csub\u003et +\u003c/sub\u003e ε\u003csub\u003et\u003c/sub\u003e\u0026hellip;... (2)\u003c/p\u003e \u003cp\u003eFrom Eq.\u0026nbsp;(2), it is clear that the EFP denotes the measure of environmental quality. GDP and GDP squared are per capita income level and its squared term and ε is the stochastic or error term that is invariably assumed to have possessed a zero mean. Renewable energy (RE) is divided into three sources which include solar, hydro and biofuel and Human Capital is denoted by HDI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data Sources\u003c/h2\u003e \u003cp\u003eThe data used in the study is secondary data. This means it has already been collected through primary sources and made readily available for use. It is a time series data with an annual frequency which spans from 2000 to 2022. The data used were sourced from reliable sources such as Global Footprint Network (GFN), World Development Indicator (WDI), Penn World Table, and International Energy Agency (IEA). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides full details of variables and their sources.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables and their sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcronym\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit of Measurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental quality (ecological footprint)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe ecological footprint index, including built-up land, grazing land, cropland, forestland, carbon footprint, and fishing grounds, is measured in global hectares (gha) per person.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlobal Footprint Network (GFN 2018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGross domestic product per capita (constant 2010 US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Development indicator (World Bank 2018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolar Energy Source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOLAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGigawatt hours (equivalent to one million kilowatt hours) per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternational Energy Agency (IEA 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydro Energy Source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHYDRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGigawatt hours (equivalent to one million kilowatt hours) per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternational Energy Agency (IEA 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman capital development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex of human capital, based on years of schooling and returns to education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePenn World Table (PWT 9.1 2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource: Authors\u0026rsquo; Compilation\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Pre-estimation Results\u003c/h2\u003e \u003cp\u003eIn this study, the unit root analysis employed two distinct methodologies: The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. This was done to confirm that all the variables under consideration exhibit integration. A variable is considered integrated when it lacks a unit root issue, meaning its mean and variance do not exhibit a time-dependent relationship. The findings in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveal that all variables are indeed integrated at the first order [1(1)]. This result holds true for both the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests and is statistically significant at the 1% and 5% levels. Both unit root test reports the stationarity of the variables in their level and first difference forms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnit Root Stationary Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFirst Difference(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFirst Difference(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.102569 (0.9585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.523304*** (0.0020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000866 (0.9490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.523795*** (0.0020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.830354 (0.3569)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.271515** (0.0297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.810573 (0.3658)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.271515** (0.0297)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.932909 (0.3119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.304022** (0.0278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.575689 (0.4777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.329712** (0.0264)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOLAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.427458 (0.9795)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.892780*** (0.0009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.669412 (0.9884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.892780*** (0.0009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHYDRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.755385 (0.3913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.130601*** (0.0005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.737974 (0.3994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.139133*** (0.0005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.899423 (0.7679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.714089** (0.0117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.542470 (0.4940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.764661** (0.0105)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eKey: * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;10%, ** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;5%, *** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;1%\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource: Researchers\u0026rsquo; Compilation using E Views.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the results of the Johansen cointegration test for each variable, which we did prior to estimating the long-run result. The Johansen test permits more than one cointegrating association, in contrast to the Engle-Granger test (Johansen \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Errors that are passed over to the subsequent step can be avoided by using the test to determine the cointegration of multiple time series. We set K\u003csub\u003e0\u003c/sub\u003e to zero to see if the null hypothesis would be rejected when performing the trace test to check for cointegration in the sample. We can conclude that there is a cointegration relationship in the sample if it is rejected. Consequently, in order to verify if there is a cointegration connection in the sample, the null hypothesis needs to be rejected. We may move further with the long-run regression analysis as the Johansen cointegration test result indicated that the models are cointegrated.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCointegration Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eUnrestricted Cointegration Rank Test (Trace)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of CE(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCritical Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.963250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163.4497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.75366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.905749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.07355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.81889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.629600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.47595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.85613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.496189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.61935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.79707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.325045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.222725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.49471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.045023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.967426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.841466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource: Researchers\u0026rsquo; Compilation using E Views.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 FMOLS Regression Result\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals the results of the FMOLS coefficients of the environmental quality function. The results show that in the FMOLS equation, the per capita GDP is positive and significant while the squared term of per capita GDP is negative and significant. This implies that a 1-unit increase in per capita GDP would cause the ecological footprint per person to rise by 2.59 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e units. Also, a 1-unit increase in per capita squared GDP would lead the ecological footprint per person to fall by 4.21 X 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e units. These results indicate that the relationship between per capita GDP and ecological footprint per person follows the pattern of an inverted U-shape, confirming the environmental Kuznets curve hypothesis for Nigeria. (Usman et al. 2019) for India, (Usman et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020c\u003c/span\u003e) for South Africa, (Usman et al. 2019) for Singapore, and (Ali et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Nigeria all reached similar conclusions as this one.\u003c/p\u003e \u003cp\u003eThe effect of solar energy sources on the ecological footprint in Nigeria is negative and not statistically significant. Specifically, a 1-unit increase in solar energy reduces the ecological footprint per person by 2.07 X 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e units. The findings broadly agree with those of (Parkman \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and (Destek and Aslan \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who conclude that solar energy has a negative but not statistically significant impact on India's efforts to decrease emissions.\u003c/p\u003e \u003cp\u003eThe effect of hydro-energy sources on the ecological footprint in Nigeria is positive and statistically significant. Specifically, a 1-unit increase in hydro energy increases the ecological footprint per person by 2.5 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e units. The finding is consistent with (Andrei et al. 2019) who found that the ecosystem services value decreased with the construction of the hydropower plant in Ecuador.\u003c/p\u003e \u003cp\u003eFinally, the effect of HDI on the ecological footprint in Nigeria is negative and significant. Specifically, a 1-unit increase in HDI reduces the ecological footprint per person by 4.99 units. This result is in line with research by (Wang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which shows that there is a negative correlation between ecological footprint and human capital.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of FMOLS long-run coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000259***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.42E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.492275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.21E-08**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.474301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOLAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.542234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHYDRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.50E-05**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.91E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.802630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.990827***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.060214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.707379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.016109***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.443213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.805100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eKey: * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;10%, ** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;5%, *** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;1%\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource: Researchers\u0026rsquo; Compilation using E Views.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displayed the Granger causality between the study's variables. Granger causality is the idea that one variable's past value can be used to predict another variable's future value. The null hypothesis states that variable a does not granger cause variable b. If there is a significant probability value between these two variables, the null hypothesis is rejected; if otherwise, it is not rejected. GDP and GDP\u003csup\u003e2\u003c/sup\u003e. All the relationships with significant values are uni-variate. This means that the previous value of GDP and its squared term (GDP\u003csup\u003e2\u003c/sup\u003e) can be used to predict the change in ecological footprint per person. This finding is consistent with (Usman et al. 2019) and (Usman et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020c\u003c/span\u003e). HCD granger causes EFP, this means that the previous value of HCD can be used to predict the change in ecological footprint per person. This finding is consistent with (Danish et al. 2019). Lastly, EFP granger causes hydro, this means that the previous value of ecological footprint per person can be used to predict the change in hydro. All the relationships with significant values are uni-variate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGranger Causality Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNull Hypothesis:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP does not Granger Cause EFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.05861***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEFP does not Granger Cause GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003csup\u003e2\u003c/sup\u003e does not Granger Cause EFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.69405***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEFP does not Granger Cause GDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOLAR does not Granger Cause EFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEFP does not Granger Cause SOLAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHYDRO does not Granger Cause EFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEFP does not Granger Cause HYDRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.29203*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCD does not Granger Cause EFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.01094***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEFP does not Granger Cause HCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eKey: * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;10%, ** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;5%, *** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;1%\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource: Researchers\u0026rsquo; Compilation using E Views.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and Policy Recommendation","content":"\u003cp\u003eThe study investigates the impact of economic growth and various renewable energy sources on environmental quality in Nigeria over the period from 2000 to 2022. Its primary objectives are to assess the environmental effects of specific renewable energy sources, namely solar energy and hydropower, to evaluate the role of human capital development in environmental quality, and to test the validity of the Environmental Kuznets curve (EKC) hypothesis for Nigeria.\u003c/p\u003e \u003cp\u003eTo achieve these goals, the study employs econometric techniques, particularly Fully Modified Ordinary Least Squares (FMOLS) regression, following preliminary tests for stationarity and cointegration, including the Augmented Dickey-Fuller Test (ADF) and Phillips-Perron (PP) tests, as well as the Johansen cointegration tests. The empirical findings indicate that economic growth, as measured by per capita GDP, has a positive and significant impact on environmental degradation, while the effect of the squared term of per capita GDP is negative and significant. This suggests an inverted U-shaped relationship between economic growth and environmental degradation, validating the Environmental Kuznets curve hypothesis for Nigeria. In other words, as the economy grows, environmental quality initially worsens before improving. Additionally, the results reveal that solar energy has a negative effect on the environmental degradation indicator, implying that an increase in the use of these renewable energy sources reduces environmental degradation. This highlights the positive impact of solar energy in generating electricity without harming the environment. Conversely, hydropower exhibits a positive and significant impact, indicating that increased hydropower consumption puts upward pressure on environmental degradation. This suggests that sustainable investment in hydropower projects is essential to mitigate their environmental impact. Lastly, the study finds that the relationship between human capital development and environmental quality is negative and significant. This underscores the importance of continued government investment in human capital development through education and training programs relevant to the green economy. With respect to the research findings on the impact of each renewable energy sources and economic growth on environmental quality in Nigeria, the study proffers the following recommendations; Allocate greater resources to develop and implement renewable energy technologies: The government should offer financial incentives and other forms of assistance. This could involve loan guarantees, tax incentives, and subsidies. Promote eco-conscious and sustainable economic development; The government has the capacity to promote economic growth that is both environmentally friendly and sustainable. This could involve funding initiatives for green infrastructure, renewable energy, and educational and training materials and Invest in human capital; To create the information and abilities required for a green economy, the government can fund educational and training initiatives. Programs about energy efficiency, renewable energy, and environmental protection may fall under this category.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics\u0026nbsp;approval\u0026nbsp;and\u0026nbsp;consent\u0026nbsp;to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u0026nbsp;for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability\u0026nbsp;of data\u0026nbsp;and\u0026nbsp;materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;data used\u0026nbsp;in this\u0026nbsp;study\u0026nbsp;are\u0026nbsp;available from\u0026nbsp;the corresponding\u0026nbsp;author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;authors declare\u0026nbsp;that they\u0026nbsp;have\u0026nbsp;no\u0026nbsp;competing\u0026nbsp;interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere\u0026nbsp;is no funding\u0026nbsp;received for\u0026nbsp;this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAEO\u0026nbsp;conceptualized\u0026nbsp;the study,\u0026nbsp;wrote\u0026nbsp;sections\u0026nbsp;1\u0026nbsp;and\u0026nbsp;3,\u0026nbsp;and\u0026nbsp;discuss\u0026nbsp;the findings.\u003c/p\u003e\n\u003cp\u003eBO\u0026nbsp;presented\u0026nbsp;the\u0026nbsp;literature\u0026nbsp;review\u0026nbsp;(section\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;OSY\u0026nbsp;wrote the conclusion section and conducted the editorial work.\u003c/p\u003e\n\u003cp\u003eAEO\u0026nbsp;analyzed\u0026nbsp;and\u0026nbsp;interpret\u0026nbsp;the\u0026nbsp;results.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed, Z., \u0026amp; Wang, Z. (2019). Investigating the impact of human capital on the ecological footprint in India: an empirical analysis. Environmental Science and Pollution Research, 26, 26782-26796.\u003c/li\u003e\n\u003cli\u003eAli A, Usman M, Usman O. \u0026amp; Sarkodie S.A (2021) Modeling the Effects of Agricultural Innovation and Biocapacity on Carbon Dioxide Emissions in an Agrarian-Based Economy: Evidence From the Dynamic ARDL Simulations. Front. Energy Res. 8:592061.\u003c/li\u003e\n\u003cli\u003eAsongu, S. A., Iheonu, C. O., \u0026amp; Odo, K. O. (2019). The conditional relationship between renewable energy and environmental quality in sub-Saharan Africa. Environmental Science and Pollution Research, 26(36), 36993-37000.\u003c/li\u003e\n\u003cli\u003eBano, S., Zhao, Y., Ahmad, A., Wang, S., \u0026amp; Liu, Y. (2018). Identifying the impacts of human capital on carbon emissions in Pakistan. Journal of Cleaner Production, 183, 1082-1092.\u003c/li\u003e\n\u003cli\u003eBhattacharya, M., Paramati, S. R., Ozturk, I., \u0026amp; Bhattacharya, S. (2016). The effect of renewable energy consumption on economic growth: Evidence from top 38 countries. Applied energy, 162, 733-741.\u003c/li\u003e\n\u003cli\u003eBhattacharya, M., Paramati, S. R., Ozturk, I., \u0026amp; Bhattacharya, S. (2016). The effect of renewable energy consumption on economic growth: Evidence from top 38 countries. Applied energy, 162, 733-741.\u003c/li\u003e\n\u003cli\u003eBriones-Hidrovo, A., Uche, J., \u0026amp; Mart\u0026iacute;nez-Gracia, A. (2021). Hydropower and environmental sustainability: A holistic assessment using multiple biophysical indicators. Ecological Indicators, 127, 107748.\u003c/li\u003e\n\u003cli\u003eDestek, M. A., \u0026amp; Aslan, A. (2020). Disaggregated renewable energy consumption and environmental pollution nexus in G-7 countries. Renewable energy, 151, 1298-1306.\u003c/li\u003e\n\u003cli\u003eFried, L.B., \u0026amp; Getzner, M. (2003). 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Frontiers in Energy Research, 10, 958839.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Ibadan","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Renewable Energy, Economic Growth, Environmental Quality, Solar, Hydro, Human Capital Development, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-5529773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5529773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRenewable energy sources (such as solar, wind, geothermal, and hydropower) has the potential to drive economic growth without damaging environmental quality. Many researchers have looked at renewable energy consumption as a whole with little reference to separating each of the types of renewable energy that are more economical and environmentally peculiar to Nigeria. This study was therefore designed to consider each renewable energy source as it affects environmental quality. It explores the impact of renewable energy sources and economic growth on the environmental quality in Nigeria using a time series dataset spanning from 2000 to 2022. The study finds that as per capita GDP increases by one unit, the ecological footprint per person rises by 2.59×10\u003csup\u003e-4\u0026nbsp;\u003c/sup\u003eunits and a one-unit increase in per capita squared GDP results in a decrease of 4.21 ×10\u003csup\u003e-8\u003c/sup\u003e\u0026nbsp;units in the ecological footprint per person. Also, raising solar energy by one unit reduces the ecological footprint per person by 2.07 ×10\u003csup\u003e-3\u003c/sup\u003e\u0026nbsp;units. On the other hand, a one-unit rise in hydro energy increases the ecological footprint per person by 2.5 ×10\u003csup\u003e-5\u0026nbsp;\u003c/sup\u003eunits. Finally, an increase of one unit in HCD lowers the ecological footprint per person by 4.99 units. Hence, the impact of economic growth on the environment initially worsens but diminishes as the economy grows further. Solar energy source doesn't seem to have an impact on the environment, while using hydro-energy and human capital development have an impact on the environment.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"The Impact of Renewable Energy Sources and Economic Growth on the Environmental Quality in Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 06:03:20","doi":"10.21203/rs.3.rs-5529773/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"697856a8-b58f-488a-81dd-62b479902067","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40795663,"name":"Environmental Economics"},{"id":40795664,"name":"Renewable Resources"}],"tags":[],"updatedAt":"2025-03-20T06:03:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-20 06:03:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5529773","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5529773","identity":"rs-5529773","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}